pax_global_header00006660000000000000000000000064142046171260014515gustar00rootroot0000000000000052 comment=0652e955c400bc56df5661db3298a06854c7cce8 pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/000077500000000000000000000000001420461712600206715ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/.github/000077500000000000000000000000001420461712600222315ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/.github/ISSUE_TEMPLATE/000077500000000000000000000000001420461712600244145ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/.github/ISSUE_TEMPLATE/bug_report.md000066400000000000000000000012241420461712600271050ustar00rootroot00000000000000--- name: Bug report about: Create a report to help us improve title: "[BUG]" labels: '' assignees: '' --- **Describe the bug** A clear and concise description of what the bug is, including what you were expecting to happen and what actually happened. Please report the version of pomegranate that you are using and the operating system. Also, please make sure that you have upgraded to the latest version of pomegranate before submitting the bug report. **To Reproduce** Please provide a snippet of code that can reproduce this error. It is much easier for us to track down bugs and fix them if we have an example script that fails until we're successful. pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/.github/workflows/000077500000000000000000000000001420461712600242665ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/.github/workflows/python-package.yml000066400000000000000000000031151420461712600277230ustar00rootroot00000000000000# This workflow will install Python dependencies, run tests and lint with a variety of Python versions # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions name: build on: push: branches: [ master ] pull_request: branches: [ master ] jobs: build: name: ${{ matrix.os }} Python ${{ matrix.python-version }} runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-latest, windows-latest, macOS-latest] python-version: ['3.7', '3.8', '3.9'] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.os }} ${{ matrix.python-version }} uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Setup Miniconda uses: conda-incubator/setup-miniconda@v2.1.1 with: python-version: ${{ matrix.python-version }} channels: conda-forge - name: Install dependencies run: | pip install flake8 pytest conda install --file requirements.txt pip install -r dev-requirements.txt python setup.py install - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Test with pytest run: python setup.py test pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/.github/workflows/wheels.yml000066400000000000000000000025611420461712600263040ustar00rootroot00000000000000# This is a basic workflow to help you get started with Actions name: Wheels on: workflow_dispatch jobs: build_wheels: name: Build wheels for ${{ matrix.os }} runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-latest, windows-latest, macOS-latest] steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 name: Install Python with: python-version: '3.8' - name: Setup Miniconda uses: conda-incubator/setup-miniconda@v2.1.1 with: python-version: ${{ matrix.python-version }} channels: conda-forge - name: Install dependencies run: | python -m pip install --upgrade pip conda install --file requirements.txt pip install -r dev-requirements.txt python setup.py install - name: Install cibuildwheel run: python -m pip install cibuildwheel > 1.7.2 - name: Build wheels run: python -m cibuildwheel --output-dir wheelhouse env: CIBW_SKIP: "cp27-* cp35-* cp36-* cp310-* pp*" # skip Python 2.7 wheels CIBW_BEFORE_BUILD: "rm -rf build/" - name: Upload wheels run: | python -m pip install twine python -m twine upload -u ${{ secrets.PYPI_USERNAME }} -p ${{ secrets.PYPI_PASSWORD }} ./wheelhouse/*.whl pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/.gitignore000066400000000000000000000001641420461712600226620ustar00rootroot00000000000000*.pyc *.c .ipynb_checkpoints *~ .DS_Store build *.so .idea/ .vscode/ dist/ .eggs/ *.egg-info/ *.pyd .python-version pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/LICENSE000066400000000000000000000020731420461712600217000ustar00rootroot00000000000000The MIT License (MIT) Copyright (c) 2014 Jacob Schreiber Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/MANIFEST000066400000000000000000000015331420461712600220240ustar00rootroot00000000000000# file GENERATED by distutils, do NOT edit CHANGES.txt requirements.txt setup.py pomegranate/BayesClassifier.c pomegranate/BayesClassifier.pyx pomegranate/BayesianNetwork.c pomegranate/BayesianNetwork.pyx pomegranate/FactorGraph.c pomegranate/FactorGraph.pyx pomegranate/MarkovChain.c pomegranate/MarkovChain.pyx pomegranate/NaiveBayes.c pomegranate/NaiveBayes.pyx pomegranate/__init__.py pomegranate/__init__.pyc pomegranate/base.c pomegranate/base.pxd pomegranate/base.pyx pomegranate/bayes.c pomegranate/bayes.pxd pomegranate/bayes.pyx pomegranate/distributions.c pomegranate/distributions.pxd pomegranate/distributions.pyx pomegranate/gmm.c pomegranate/gmm.pyx pomegranate/hmm.c pomegranate/hmm.pyx pomegranate/kmeans.c pomegranate/kmeans.pyx pomegranate/parallel.c pomegranate/parallel.pyx pomegranate/utils.c pomegranate/utils.pxd pomegranate/utils.pyx pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/MANIFEST.in000066400000000000000000000001371420461712600224300ustar00rootroot00000000000000include *.txt include LICENSE graft tests recursive-include pomegranate *.py *.c *.pxd *.pyx pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/Makefile000066400000000000000000000053241420461712600223350ustar00rootroot00000000000000PACKAGE_NAME=pomegranate PY2_ENV=py2.7 PY3_ENV=py3.6 default: echo no default .PHONY: install test bigclean nbtest nbclean .PHONY: bigbuild py2build py3build .PHONY: biginstall py2install py3install .PHONY: biguninstall py2uninstall py3uninstall .PHONY: bigtest py2test py3test .PHONY: bignbtest py2nbtest py3nbtest install: python setup.py install test: python setup.py test bigclean: nbclean rm -rf build rm -rf dist rm -rf .eggs rm -rf $(PACKAGE_NAME).egg-info find . -name '*.pyc' -print | xargs rm find . -name '*.so' -print | xargs rm ifndef NO_REMOVE_CFILES find . -name '*.c' -print | xargs rm endif # Add python dependencies ifdef PY2_ENV bigbuild: py2build biginstall: py2install biguninstall: py2uninstall bigtest: py2test bignbtest: py2test endif ifdef PY3_ENV bigbuild: py3build biginstall: py3install biguninstall: py3uninstall bigtest: py3test # Don't know how to override the kernelspec and the notebooks are python2 only anyway # bignbtest: py3test endif py2build: (source activate $(PY2_ENV) ; python setup.py build ; python setup.py build_ext --inplace ) py3build: (source activate $(PY3_ENV) ; python setup.py build ; python setup.py build_ext --inplace ) py2install: (source activate $(PY2_ENV) ; python setup.py install ) py3install: (source activate $(PY3_ENV) ; python setup.py install ) py3uninstall: rm -rf ~/miniconda2/envs/$(PY3_ENV)/lib/python3.6/site-packages/$(PACKAGE_NAME)* py2uninstall: rm -rf ~/miniconda2/envs/$(PY2_ENV)/lib/python2.7/site-packages/$(PACKAGE_NAME)* py3test: (source activate $(PY3_ENV) ; python setup.py test ) py2test: (source activate $(PY2_ENV) ; python setup.py test ) ## Notebook tests PYTHON_NOTEBOOKS= examples/*.ipynb tutorials/*.ipynb benchmarks/*.ipynb EXECUTE_TIMEOUT=600 ALLOW_ERRORS= # Allow errors if you want to check as many cells as possible #ALLOW_ERRORS=--allow-errors # Required conda package installs: # CONDA_INSTALL_BASE_PACKAGES=cython scipy scikit-learn pandas joblib nose networkx=1.11 # CONDA_INSTALL_NOTEBOOK_PACKAGES=jupyter jupyter_contrib_nbextensions jupyter_nbextensions_configurator seaborn xlrd pygraphviz pillow nbtest: for nb in $(PYTHON_NOTEBOOKS) ; do time jupyter nbconvert $$nb --execute --ExecutePreprocessor.timeout=$(EXECUTE_TIMEOUT) --to html $(ALLOW_ERRORS) ; done py2nbtest: (source activate $(PY2_ENV) ; for nb in $(PYTHON_NOTEBOOKS) ; do time jupyter nbconvert $$nb --execute --ExecutePreprocessor.timeout=$(EXECUTE_TIMEOUT) --to html $(ALLOW_ERRORS) ; done ) py3nbtest: (source activate $(PY3_ENV) ; for nb in $(PYTHON_NOTEBOOKS) ; do time jupyter nbconvert $$nb --execute --ExecutePreprocessor.timeout=$(EXECUTE_TIMEOUT) --to html $(ALLOW_ERRORS) ; done ) nbclean: for nb in $(PYTHON_NOTEBOOKS) ; do rm -f $${nb%%.ipynb}.html ; done pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/README.md000066400000000000000000000126421420461712600221550ustar00rootroot00000000000000 [![Downloads](https://pepy.tech/badge/pomegranate)](https://pepy.tech/project/pomegranate)![build](https://github.com/jmschrei/pomegranate/workflows/build/badge.svg) [![Documentation Status](https://readthedocs.org/projects/pomegranate/badge/?version=latest)](http://pomegranate.readthedocs.io/en/latest/?badge=latest) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jmschrei/pomegranate/master) Please consider citing the [**JMLR-MLOSS Manuscript**](http://jmlr.org/papers/volume18/17-636/17-636.pdf) if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic models in Python that is implemented in Cython for speed. A primary focus of pomegranate is to merge the easy-to-use API of scikit-learn with the modularity of probabilistic modeling to allow users to specify complicated models without needing to worry about implementation details. The models implemented here are built from the ground up with big data processing in mind and so natively support features like multi-threaded parallelism and out-of-core processing. Click on the binder badge above to interactively play with the tutorials! ### Installation pomegranate is pip-installable using `pip install pomegranate` and conda-installable using `conda install pomegranate`. If neither work, more detailed installation instructions can be found [here](http://pomegranate.readthedocs.io/en/latest/install.html). If you get an error involving `pomegranate/base.c`, try installing with `pip install --no-cache-dir pomegranate`. If you get an error involving `pomegranate/distributions/NeuralNetworkWrapper.c: No such file or directory`, try installing Cython first and then re-installing. A few packages are optional to use pomegranate but necessary for some specific functionality. For example, pandas is needed to run the tests involving I/O, matplotlib and pygraphviz are needed for plotting capabilities, and cupy is needed for GPU acceleration. ### Models * [Probability Distributions](http://pomegranate.readthedocs.io/en/latest/Distributions.html) * [General Mixture Models](http://pomegranate.readthedocs.io/en/latest/GeneralMixtureModel.html) * [Hidden Markov Models](http://pomegranate.readthedocs.io/en/latest/HiddenMarkovModel.html) * [Naive Bayes and Bayes Classifiers](http://pomegranate.readthedocs.io/en/latest/NaiveBayes.html) * [Markov Chains](http://pomegranate.readthedocs.io/en/latest/MarkovChain.html) * [Discrete Bayesian Networks](http://pomegranate.readthedocs.io/en/latest/BayesianNetwork.html) * [Discrete Markov Networks](https://pomegranate.readthedocs.io/en/latest/MarkovNetwork.html) The discrete Bayesian networks also support novel work on structure learning in the presence of constraints through a constraint graph. These constraints can dramatically speed up structure learning through the use of loose general prior knowledge, and can frequently make the exact learning task take only polynomial time instead of exponential time. See the [PeerJ manuscript](https://peerj.com/articles/cs-122/) for the theory and the [pomegranate tutorial](https://github.com/jmschrei/pomegranate/blob/master/tutorials/B_Model_Tutorial_4b_Bayesian_Network_Structure_Learning.ipynb) for the practical usage! To support the above algorithms, it has efficient implementations of the following: * Kmeans/Kmeans++/Kmeans|| * Factor Graphs ### Features * [sklearn-like API](https://pomegranate.readthedocs.io/en/latest/api.html) * [Multi-threaded Training](http://pomegranate.readthedocs.io/en/latest/parallelism.html) * [BLAS/GPU Acceleration](http://pomegranate.readthedocs.io/en/latest/gpu.html) * [Out-of-Core Learning](http://pomegranate.readthedocs.io/en/latest/ooc.html) * [Data Generators and IO](https://pomegranate.readthedocs.io/en/latest/io.html) * [Semi-supervised Learning](http://pomegranate.readthedocs.io/en/latest/semisupervised.html) * [Missing Value Support](http://pomegranate.readthedocs.io/en/latest/nan.html) * [Customized Callbacks](http://pomegranate.readthedocs.io/en/latest/callbacks.html) Please take a look at the [tutorials folder](https://github.com/jmschrei/pomegranate/tree/master/tutorials), which includes several tutorials on how to effectively use pomegranate! See [the website](http://pomegranate.readthedocs.org/en/latest/) for extensive documentation, API references, and FAQs about each of the models and supported features. No good project is done alone, and so I'd like to thank all the previous contributors to YAHMM, and all the current contributors to pomegranate, including the graduate students who share my office I annoy on a regular basis by bouncing ideas off of. ### Dependencies pomegranate requires: ``` - Cython (only if building from source) - NumPy - SciPy - NetworkX - joblib ``` To run the tests, you also must have `nose` installed. ## Contributing If you would like to contribute a feature then fork the master branch (fork the release if you are fixing a bug). Be sure to run the tests before changing any code. You'll need to have [nosetests](https://github.com/nose-devs/nose) installed. The following command will run all the tests: ``` python setup.py test ``` Let us know what you want to do just in case we're already working on an implementation of something similar. This way we can avoid any needless duplication of effort. Also, please don't forget to add tests for any new functions. pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/benchmarks/000077500000000000000000000000001420461712600230065ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/benchmarks/benchmark_distributions.py000066400000000000000000000114651420461712600303030ustar00rootroot00000000000000# benchmark_distributions.py # Contact: Jacob Schreiber ( jmschreiber91@gmail.com ) """ Benchmark the distribution module, printing out the time it takes to do log probability and training calculations. """ from pomegranate import * import random import numpy import time numpy.random.seed(0) random.seed(0) def print_benchmark( distribution, duration ): """Formatted print.""" print( "{:25}: {:.4}s".format( distribution.__class__.__name__, duration ) ) def bench_log_probability( distribution, n=10000000, symbol=5 ): """Bench a log probability distribution.""" tic = time.time() for i in range(n): logp = distribution.log_probability( symbol ) return time.time() - tic def bench_from_sample( distribution, sample, n=1000 ): """Bench the training of a probability distribution.""" tic = time.time() for i in range(n): distribution.summarize( sample ) return time.time() - tic def benchmark_distribution_log_probabilities(): """Run log probability benchmarks.""" distributions = [ UniformDistribution( 0, 17 ), NormalDistribution( 7, 1 ), LogNormalDistribution( 7, 1 ), ExponentialDistribution( 7 ), GammaDistribution( 7, 3 ), GaussianKernelDensity([0, 1, 4, 3, 2, 0.5, 2, 1, 2]), UniformKernelDensity([0, 1, 4, 3, 2, 0.5, 2, 1, 2]), TriangleKernelDensity([0, 1, 4, 3, 2, 0.5, 2, 1, 2]), MixtureDistribution( [UniformDistribution( 5, 2 ), NormalDistribution( 7, 1 ), NormalDistribution( 3, 0.5 )] ) ] for distribution in distributions: print_benchmark( distribution, bench_log_probability( distribution ) ) distribution = DiscreteDistribution({'A': 0.2, 'B': 0.27, 'C': 0.3, 'D': 0.23}) print_benchmark( distribution, bench_log_probability( distribution ) ) distribution = IndependentComponentsDistribution([ NormalDistribution( 5, 1 ), NormalDistribution( 8, 0.5), NormalDistribution( 2, 0.1), NormalDistribution( 13, 0.1), NormalDistribution( 0.5, 0.01) ]) print_benchmark( distribution, bench_log_probability( distribution, symbol=(5,4,3,2,1) ) ) mu = np.random.randn(4) cov = np.random.randn(4, 4) / 10 cov = np.abs( cov.dot( cov.T ) ) + np.eye( 4 ) distribution = MultivariateGaussianDistribution( mu, cov ) print_benchmark( distribution, bench_log_probability( distribution, n=100000, symbol=(1,2,3,4) ) ) def benchmark_distribution_train(): """Run training benchmarks.""" distributions = [ UniformDistribution( 0, 17 ), NormalDistribution( 7, 1 ), LogNormalDistribution( 7, 1 ), ExponentialDistribution( 7 ), GammaDistribution( 7, 3 ), GaussianKernelDensity([0, 1, 4, 3, 2, 0.5, 2, 1, 2]), UniformKernelDensity([0, 1, 4, 3, 2, 0.5, 2, 1, 2]), TriangleKernelDensity([0, 1, 4, 3, 2, 0.5, 2, 1, 2]), MixtureDistribution( [UniformDistribution( 5, 2 ), NormalDistribution( 7, 1 ), NormalDistribution( 3, 0.5 )] ) ] sample = np.random.randn(10000) for distribution in distributions: print_benchmark( distribution, bench_from_sample( distribution, sample ) ) sample = ['A']*2500 + ['B']*3000 + ['C']*3500 + ['D']*1000 distribution = DiscreteDistribution({'A': 0.2, 'B': 0.27, 'C': 0.3, 'D': 0.23}) print_benchmark( distribution, bench_from_sample( distribution, sample ) ) sample = np.random.randn(10000, 5) distribution = IndependentComponentsDistribution([ NormalDistribution( 5, 1 ), NormalDistribution( 8, 0.5), NormalDistribution( 2, 0.1), NormalDistribution( 13, 0.1), NormalDistribution( 0.5, 0.01) ]) print_benchmark( distribution, bench_from_sample( distribution, sample ) ) sample = np.random.randn(10000, 4) mu = np.random.randn(4) cov = np.random.randn(4, 4) / 10 cov = np.abs( cov.dot( cov.T ) ) + np.eye( 4 ) distribution = MultivariateGaussianDistribution( mu, cov ) print_benchmark( distribution, bench_from_sample( distribution, sample ) ) print( "DISTRIBUTION BENCHMARKS" ) print( "-----------------------" ) print() print( "LOG PROBABILITY (N=10,000,000 iterations, N=100,000 FOR MVG)" ) benchmark_distribution_log_probabilities() print() print( "TRAINING (N=1,000 ITERATIONS, BATCHES=10,000 ITEMS)" ) benchmark_distribution_train() pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/benchmarks/benchmark_hmm.py000066400000000000000000000136261420461712600261630ustar00rootroot00000000000000# benchmark_hmm.py # Contact: Jacob Schreiber ( jmschreiber91@gmail.com ) """ Benchmark the HMM module, including multithreading. """ from pomegranate import * import numpy as np import time import random np.random.seed(0) random.seed(0) def global_alignment( match_distributions, insert_distribution ): """Create a global alignment HMM from match distributions. """ model = HiddenMarkovModel() i0 = State( insert_distribution, name="i0" ) model.add_state(i0) model.add_transition( i0, i0, 0.3 ) model.add_transition( model.start, i0, 0.3 ) last_match, last_insert, last_delete = model.start, i0, None for i, distribution in enumerate( match_distributions ): match = State( distribution, name="m{}".format(i+1) ) insert = State( insert_distribution, name="i{}".format(i+1) ) delete = State( None, name="d{}".format(i+1) ) model.add_states([match, insert, delete]) model.add_transition( last_match, match, 0.5 ) model.add_transition( last_match, delete, 0.1 ) model.add_transition( last_insert, match, 0.5 ) model.add_transition( last_insert, delete, 0.2 ) if last_delete is not None: model.add_transition( last_delete, match, 0.7 ) model.add_transition( last_delete, delete, 0.2 ) model.add_transition( insert, insert, 0.3 ) model.add_transition( match, insert, 0.1 ) model.add_transition( delete, insert, 0.1 ) last_match, last_insert, last_delete = match, insert, delete model.add_transition( last_match, model.end, 0.6 ) model.add_transition( last_insert, model.end, 0.7 ) model.add_transition( last_delete, model.end, 0.9 ) model.bake() return model def benchmark_forward( model, sample ): tic = time.time() for i in range(25000): logp = model.forward( sample )[-1, model.end_index] print("{:16}: time: {:5.5}, logp: {:5.5}".format( "FORWARD", time.time() - tic, logp )) def benchmark_backward( model, sample ): tic = time.time() for i in range(25000): logp = model.backward( sample )[0, model.start_index] print("{:16}: time: {:5.5}, logp: {:5.5}".format( "BACKWARD", time.time() - tic, logp )) def benchmark_forward_backward( model, sample ): tic = time.time() for i in range(25000): model.forward_backward( sample ) print("{:16}: time: {:5.5}".format( "FORWARD-BACKWARD", time.time() - tic )) def benchmark_viterbi( model, sample ): tic = time.time() for i in range(25000): logp, path = model.viterbi( sample ) print("{:16}: time: {:5.5}, logp: {:5.5}".format( "VITERBI", time.time() - tic, logp )) def benchmark_training( model, samples, n_jobs ): tic = time.time() improvement = model.fit( samples, max_iterations=10, verbose=False, n_jobs=n_jobs , return_history=True) print("{:16}: time: {:5.5}, improvement: {:5.5} ({} jobs)".format( "BW TRAINING", time.time() - tic, improvement[1].total_improvement[0], n_jobs )) def main(): n = 15 print("HIDDEN MARKOV MODEL BENCHMARKS") print("gaussian emissions") sigma = 3 means = np.random.randn(n)*20 gaussian_dists = [ NormalDistribution(mean, 1) for mean in means ] gaussian_model = global_alignment( gaussian_dists, NormalDistribution(0, 10) ) gaussian_sample = np.random.randn(n)*sigma + means gaussian_batch = np.random.randn(50, n)*sigma + means benchmark_forward( gaussian_model, gaussian_sample ) benchmark_backward( gaussian_model, gaussian_sample ) benchmark_viterbi( gaussian_model, gaussian_sample ) benchmark_forward_backward( gaussian_model, gaussian_sample ) benchmark_training( gaussian_model, gaussian_batch, 1 ) # Reset the model gaussian_dists = [ NormalDistribution(mean, 1) for mean in means ] gaussian_model = global_alignment( gaussian_dists, NormalDistribution(0, 10) ) benchmark_training( gaussian_model, gaussian_batch, 4 ) print print("multivariate gaussian emissions") m = 10 means = np.random.randn(n, m) * 5 + np.arange(m) * 3 mgd = MultivariateGaussianDistribution multivariate_gaussian_dists = [ mgd( mean, np.eye(m) ) for mean in means ] multivariate_gaussian_model = global_alignment( multivariate_gaussian_dists, mgd( np.zeros(m), np.eye(m)*3 ) ) multivariate_gaussian_sample = np.random.randn(n, m)*sigma + means multivariate_gaussian_batch = np.random.randn(500, n, m)*sigma + means benchmark_forward( multivariate_gaussian_model, multivariate_gaussian_sample ) benchmark_backward( multivariate_gaussian_model, multivariate_gaussian_sample ) benchmark_viterbi( multivariate_gaussian_model, multivariate_gaussian_sample ) benchmark_forward_backward( multivariate_gaussian_model, multivariate_gaussian_sample ) benchmark_training( multivariate_gaussian_model, multivariate_gaussian_batch, 1 ) # Reset the model multivariate_gaussian_dists = [ mgd( mean, np.eye(m) ) for mean in means ] multivariate_gaussian_model = global_alignment( multivariate_gaussian_dists, mgd( np.zeros(m), np.eye(m)*3 ) ) benchmark_training( multivariate_gaussian_model, multivariate_gaussian_batch, 4 ) print print("discrete distribution") probs = np.abs( np.random.randn(n, 4) * 20 ).T probs = (probs / probs.sum(axis=0)).T discrete_dists = [ DiscreteDistribution({ char: prob for char, prob in zip('ACGT', row)}) for row in probs ] discrete_model = global_alignment( discrete_dists, DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})) discrete_sample = list('ACGTAGCTACGACATCAGAC') discrete_batch = np.array([ np.random.choice(list('ACGT'), size=200, p=row) for row in probs ]).T benchmark_forward( discrete_model, discrete_sample ) benchmark_backward( discrete_model, discrete_sample ) benchmark_viterbi( discrete_model, discrete_sample ) benchmark_forward_backward( discrete_model, discrete_sample ) benchmark_training( discrete_model, discrete_batch, 1 ) # Reset the model discrete_dists = [ DiscreteDistribution({ char: prob for char, prob in zip('ACGT', row)}) for row in probs ] discrete_model = global_alignment( discrete_dists, DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})) benchmark_training( discrete_model, discrete_batch, 4 ) if __name__ == '__main__': main() pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/benchmarks/pomegranate_vs_hmmlearn.ipynb000066400000000000000000006045101420461712600307540ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pomegranate / hmmlearn comparison\n", "\n", "hmmlearn is a Python module for hidden markov models with a scikit-learn like API. It was originally present in scikit-learn until its removal due to structural learning not meshing well with the API of many other classical machine learning algorithms. Here is a table highlighting some of the similarities and differences between the two packages.\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Featurepomegranatehmmlearn
Graph Structure
Silent States✓
Optional Explicit End State✓
Sparse Implementation✓
Arbitrary Emissions Allowed on States✓
Discrete/Gaussian/GMM Emissions✓✓
Large Library of Other Emissions✓
Build Model from Matrices✓✓
Build Model Node-by-Node✓
Serialize to JSON✓
Serialize using Pickle/Joblib✓
Algorithms
Priors✓
Sampling✓✓
Log Probability Scoring✓✓
Forward-Backward Emissions✓✓
Forward-Backward Transitions✓
Viterbi Decoding✓✓
MAP Decoding✓✓
Baum-Welch Training✓✓
Viterbi Training✓
Labeled Training✓
Tied Emissions✓
Tied Transitions✓
Emission Inertia✓
Transition Inertia✓
Emission Freezing✓✓
Transition Freezing✓✓
Multi-threaded Training✓Coming Soon
\n", "

\n", "\n", "Just because the two features are implemented doesn't speak to how fast they are. Below we investigate how fast the two packages are in different settings the two have implemented. \n", "\n", "## Fully Connected Graphs with Multivariate Gaussian Emissions\n", "\n", "Lets look at the sample scoring method, viterbi, and Baum-Welch training for fully connected graphs with multivariate Gaussian emisisons. A fully connected graph is one where all states have connections to all other states. This is a case which pomegranate is expected to do poorly due to its sparse implementation, and hmmlearn should shine due to its vectorized implementations." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import hmmlearn, pomegranate, time, seaborn\n", "from hmmlearn.hmm import *\n", "from pomegranate import *\n", "seaborn.set_style('whitegrid')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both hmmlearn and pomegranate are under active development. Here are the current versions of the two packages." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hmmlearn version 0.2.0\n", "pomegranate version 0.4.0\n" ] } ], "source": [ "print \"hmmlearn version {}\".format(hmmlearn.__version__)\n", "print \"pomegranate version {}\".format(pomegranate.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first should have a function which will randomly generate transition matrices and emissions for the hidden markov model, and randomly generate sequences which fit the model." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def initialize_components(n_components, n_dims, n_seqs):\n", " \"\"\"\n", " Initialize a transition matrix for a model with a fixed number of components,\n", " for Gaussian emissions with a certain number of dimensions, and a data set\n", " with a certain number of sequences.\n", " \"\"\"\n", " \n", " transmat = numpy.abs(numpy.random.randn(n_components, n_components))\n", " transmat = (transmat.T / transmat.sum( axis=1 )).T\n", "\n", " start_probs = numpy.abs( numpy.random.randn(n_components) )\n", " start_probs /= start_probs.sum()\n", "\n", " means = numpy.random.randn(n_components, n_dims)\n", " covars = numpy.ones((n_components, n_dims))\n", " \n", " seqs = numpy.zeros((n_seqs, n_components, n_dims))\n", " for i in range(n_seqs):\n", " seqs[i] = means + numpy.random.randn(n_components, n_dims)\n", " \n", " return transmat, start_probs, means, covars, seqs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets create the model in hmmlearn. It's fairly straight forward, only some attributes need to be overridden with the known structure and emissions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def hmmlearn_model(transmat, start_probs, means, covars):\n", " \"\"\"Return a hmmlearn model.\"\"\"\n", "\n", " model = GaussianHMM(n_components=transmat.shape[0], covariance_type='diag', n_iter=1, tol=1e-8)\n", " model.startprob_ = start_probs\n", " model.transmat_ = transmat\n", " model.means_ = means\n", " model._covars_ = covars\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets create the model in pomegranate. Also fairly straightforward. The biggest difference is creating explicit distribution objects rather than passing in vectors, and passing everything into a function instead of overriding attributes. This is done because each state in the graph can be a different distribution and many distributions are supported." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def pomegranate_model(transmat, start_probs, means, covars):\n", " \"\"\"Return a pomegranate model.\"\"\"\n", " \n", " states = [ MultivariateGaussianDistribution( means[i], numpy.eye(means.shape[1]) ) for i in range(transmat.shape[0]) ]\n", " model = HiddenMarkovModel.from_matrix(transmat, states, start_probs, merge='None')\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets now compare some algorithm times." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def evaluate_models(n_dims, n_seqs):\n", " hllp, plp = [], []\n", " hlv, pv = [], []\n", " hlm, pm = [], []\n", " hls, ps = [], []\n", " hlt, pt = [], []\n", "\n", " for i in range(10, 112, 10):\n", " transmat, start_probs, means, covars, seqs = initialize_components(i, n_dims, n_seqs)\n", " model = hmmlearn_model(transmat, start_probs, means, covars)\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.score(seq)\n", " hllp.append( time.time() - tic )\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.predict(seq)\n", " hlv.append( time.time() - tic )\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.predict_proba(seq)\n", " hlm.append( time.time() - tic ) \n", " \n", " tic = time.time()\n", " model.fit(seqs.reshape(n_seqs*i, n_dims), lengths=[i]*n_seqs)\n", " hlt.append( time.time() - tic )\n", "\n", " model = pomegranate_model(transmat, start_probs, means, covars)\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.log_probability(seq)\n", " plp.append( time.time() - tic )\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.predict(seq)\n", " pv.append( time.time() - tic )\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.predict_proba(seq)\n", " pm.append( time.time() - tic ) \n", " \n", " tic = time.time()\n", " model.fit(seqs, max_iterations=1, verbose=False)\n", " pt.append( time.time() - tic )\n", "\n", " plt.figure( figsize=(12, 8))\n", " plt.xlabel(\"# Components\", fontsize=12 )\n", " plt.ylabel(\"pomegranate is x times faster\", fontsize=12 )\n", " plt.plot( numpy.array(hllp) / numpy.array(plp), label=\"Log Probability\")\n", " plt.plot( numpy.array(hlv) / numpy.array(pv), label=\"Viterbi\")\n", " plt.plot( numpy.array(hlm) / numpy.array(pm), label=\"Maximum A Posteriori\")\n", " plt.plot( numpy.array(hlt) / numpy.array(pt), label=\"Training\")\n", " plt.xticks( xrange(11), xrange(10, 112, 10), fontsize=12 )\n", " plt.yticks( fontsize=12 )\n", " plt.legend( fontsize=12 )" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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mkn0xx9bliIiIiIids7tADRAW7EtObh57DqbauhQRERERsXN2GagLjs/TedQi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\n49FHH8X77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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "evaluate_models(10, 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like in this case pomegranate and hmmlearn are approximately the same for large (>30 components) dense graphs for the forward algorithm (log probability), MAP, and training. However, hmmlearn is significantly faster in terms of calculating the Viterbi path, while pomegranate is faster for smaller (<30 components) graphs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sparse Graphs with Multivariate Gaussian Emissions\n", "\n", "pomegranate is based off of a sparse implementations and so excels in graphs which are sparse. Lets try a model architecture where each hidden state only has transitions to itself and the next state, but running the same algorithms as last time." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def initialize_components(n_components, n_dims, n_seqs):\n", " \"\"\"\n", " Initialize a transition matrix for a model with a fixed number of components,\n", " for Gaussian emissions with a certain number of dimensions, and a data set\n", " with a certain number of sequences.\n", " \"\"\"\n", " \n", " transmat = numpy.zeros((n_components, n_components))\n", " transmat[-1, -1] = 1\n", " for i in range(n_components-1):\n", " transmat[i, i] = 1\n", " transmat[i, i+1] = 1\n", " transmat[ transmat < 0 ] = 0\n", " transmat = (transmat.T / transmat.sum( axis=1 )).T\n", "\n", " start_probs = numpy.abs( numpy.random.randn(n_components) )\n", " start_probs /= start_probs.sum()\n", "\n", " means = numpy.random.randn(n_components, n_dims)\n", " covars = numpy.ones((n_components, n_dims))\n", " \n", " seqs = numpy.zeros((n_seqs, n_components, n_dims))\n", " for i in range(n_seqs):\n", " seqs[i] = means + numpy.random.randn(n_components, n_dims)\n", " \n", " return transmat, start_probs, means, covars, seqs" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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5OEXn4hSdi1PaYpDXJm0kRUVFLF68mLy8PKAheH19PTfccAMrVqygsrKS8vJy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cnlklBc2SiH658K69Biscvbk2JC5RMaz86M5v9HP7NUVODQyqdgraxC3OxfIPPOO3hVSCIaVBi2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\nOs6oBvB0+AV50TMa7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "evaluate_models(10, 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sparse Graph with Discrete Emissions\n", "\n", "Lets also compare MultinomialHMM to a pomegranate HMM with discrete emisisons for completeness." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def initialize_components(n_components, n_seqs):\n", " \"\"\"\n", " Initialize a transition matrix for a model with a fixed number of components,\n", " for Gaussian emissions with a certain number of dimensions, and a data set\n", " with a certain number of sequences.\n", " \"\"\"\n", " \n", " transmat = numpy.zeros((n_components, n_components))\n", " transmat[-1, -1] = 1\n", " for i in range(n_components-1):\n", " transmat[i, i] = 1\n", " transmat[i, i+1] = 1\n", " transmat[ transmat < 0 ] = 0\n", " transmat = (transmat.T / transmat.sum( axis=1 )).T\n", " \n", " start_probs = numpy.abs( numpy.random.randn(n_components) )\n", " start_probs /= start_probs.sum()\n", "\n", " dists = numpy.abs(numpy.random.randn(n_components, 4))\n", " dists = (dists.T / dists.T.sum(axis=0)).T\n", " \n", " seqs = numpy.random.randint(0, 4, (n_seqs, n_components*2, 1))\n", " return transmat, start_probs, dists, seqs\n", "\n", "def hmmlearn_model(transmat, start_probs, dists):\n", " \"\"\"Return a hmmlearn model.\"\"\"\n", "\n", " model = MultinomialHMM(n_components=transmat.shape[0], n_iter=1, tol=1e-8)\n", " model.startprob_ = start_probs\n", " model.transmat_ = transmat\n", " model.emissionprob_ = dists\n", " return model\n", "\n", "def pomegranate_model(transmat, start_probs, dists):\n", " \"\"\"Return a pomegranate model.\"\"\"\n", " \n", " states = [ DiscreteDistribution({ 'A': d[0],\n", " 'C': d[1],\n", " 'G': d[2], \n", " 'T': d[3] }) for d in dists ]\n", " model = HiddenMarkovModel.from_matrix(transmat, states, start_probs, merge='None')\n", " return model\n", "\n", "def evaluate_models(n_seqs):\n", " hllp, plp = [], []\n", " hlv, pv = [], []\n", " hlm, pm = [], []\n", " hls, ps = [], []\n", " hlt, pt = [], []\n", "\n", " dna = 'ACGT'\n", " \n", " for i in range(10, 112, 10):\n", " transmat, start_probs, dists, seqs = initialize_components(i, n_seqs)\n", " model = hmmlearn_model(transmat, start_probs, dists)\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.score(seq)\n", " hllp.append( time.time() - tic )\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.predict(seq)\n", " hlv.append( time.time() - tic )\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.predict_proba(seq)\n", " hlm.append( time.time() - tic ) \n", " \n", " tic = time.time()\n", " model.fit(seqs.reshape(n_seqs*i*2, 1), lengths=[i*2]*n_seqs)\n", " hlt.append( time.time() - tic )\n", "\n", " model = pomegranate_model(transmat, start_probs, dists)\n", " seqs = [[dna[i] for i in seq] for seq in seqs]\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.log_probability(seq)\n", " plp.append( time.time() - tic )\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.predict(seq)\n", " pv.append( time.time() - tic )\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.predict_proba(seq)\n", " pm.append( time.time() - tic ) \n", " \n", " tic = time.time()\n", " model.fit(seqs, max_iterations=1, verbose=False)\n", " pt.append( time.time() - tic )\n", "\n", " plt.figure( figsize=(12, 8))\n", " plt.xlabel(\"# Components\", fontsize=12 )\n", " plt.ylabel(\"pomegranate is x times faster\", fontsize=12 )\n", " plt.plot( numpy.array(hllp) / numpy.array(plp), label=\"Log Probability\")\n", " plt.plot( numpy.array(hlv) / numpy.array(pv), label=\"Viterbi\")\n", " plt.plot( numpy.array(hlm) / numpy.array(pm), label=\"Maximum A Posteriori\")\n", " plt.plot( numpy.array(hlt) / numpy.array(pt), label=\"Training\")\n", " plt.xticks( xrange(11), xrange(10, 112, 10), fontsize=12 )\n", " plt.yticks( fontsize=12 )\n", " plt.legend( fontsize=12 )" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jmschr/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:77: DeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n" ] }, { "data": { "image/png": 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AVxJTGTd9G+evXKd3+5o806u+9mYXAoVtERERkRImNj6FcZ9v42JcCo92eogn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\nAIA5qSF4eG0mkmMC3FwZ0fQjiiJOFVRj76dFsNsEzF+WiJV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6ZDBYM1QTEU17Exa2bTYb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AOA/moBACUlJZg5cyZGjRqFCRMm4MSJEwD0V4vMzEzt8eB8bPTu3RvV1dW6qwUA7Ny5E2PHjsXY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\nK1fe9X0hI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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "evaluate_models(50)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/benchmarks/pomegranate_vs_libpgm.ipynb000066400000000000000000003204351420461712600304240ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pomegranate / libpgm comparison\n", "\n", "authors: Jacob Schreiber (jmschreiber91@gmail.com)\n", "\n", "libpgm is a python package for creating and using Bayesian networks. I was unable to figure out how to use libpgm to do inference properly without raising errors, but I was able to get structure learning working. libpgm uses constraints for structure learning, a process which is not probabilistic, but can be asymptoptically more efficient (between O(n^2) and O(n^3) as opposed to exponential). To my knowledge, they do not have exact structure learning implemented, likely due to the super-exponential nature of the naive algorithm.\n", "\n", "pomegranate has both the exact structure learning problem, and the Chow-Liu tree approximation, implemented. The exact structure learning problem uses an efficient dynamic programming solution to reduce the complexity from super-exponential to exponential in time with the number of variables. The Chow-Liu tree approximation finds the best tree which spans all variables.\n", "\n", "Lets compare the structure learning task in pomegranate versus the structure learning task in libpgm for different numbers of variables to compare these speed of the two packages." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import seaborn, time\n", "seaborn.set_style('whitegrid')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets first compare the two packages based on number of variables." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pomegranate import BayesianNetwork\n", "from libpgm.pgmlearner import PGMLearner\n", "\n", "libpgm_time = []\n", "pomegranate_time = []\n", "pomegranate_cl_time = []\n", "\n", "for i in range(2, 15):\n", " tic = time.time()\n", " X = numpy.random.randint(2, size=(10000, i))\n", " model = BayesianNetwork.from_samples(X, algorithm='exact')\n", " pomegranate_time.append(time.time() - tic)\n", "\n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X, algorithm='chow-liu')\n", " pomegranate_cl_time.append(time.time() - tic)\n", "\n", " X = [{j : X[i, j] for j in range(X.shape[1])} for i in range(X.shape[0])]\n", " learner = PGMLearner()\n", "\n", " tic = time.time()\n", " model = learner.discrete_constraint_estimatestruct(X)\n", " libpgm_time.append(time.time() - tic)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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FIdDWtlglSyAJ02vnSVPmTExMWLJkCdu3b8fZ2ZlRo0YxePBgevfuXYQRCiGE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c1FcnIyFixY8FIZHRwccPr0aaSnp2PRokVyx/T09FBYWIinT5+iuLgY33zzDQYNGtRmyZwiQ4YM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\nERERKcCCiYiIiIiISIH/A16W6ZK6bkohAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 6))\n", "plt.title(\"Bayesian Network Structure Learning Time\", fontsize=16)\n", "plt.xlabel(\"Number of Variables\", fontsize=14)\n", "plt.ylabel(\"Time (s)\", fontsize=14)\n", "plt.plot(range(2, 15), libpgm_time, c='c', label=\"libpgm\")\n", "plt.plot(range(2, 15), pomegranate_time, c='m', label=\"pomegranate exact\")\n", "plt.plot(range(2, 15), pomegranate_cl_time, c='r', label=\"pomegranate chow liu\")\n", "plt.legend(loc=2, fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see many expected results from this graph. libpgm implements a quadratic time algorithm, and it appears that the growth is roughly quadratic. The exact algorithm in pomegranate is an exponential time algorithm, and so while it does have an efficient implementation that causes the it to be much faster than libpgm's algorithm for small numbers of variables, eventually it will become slower. Lastly, the chow-liu tree algorithm is far faster than both other algorithms because it is finding the best tree approximation. While it is a quadratic time algorithm, it is also a simpler one.\n", "\n", "Lets now compare the speed on different numbers of samples." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "libpgm_time = []\n", "pomegranate_time = []\n", "pomegranate_cl_time = []\n", "\n", "x = 10, 25, 100, 250, 1000, 2500, 10000, 25000, 100000, 250000, 1000000\n", "for i in x:\n", " tic = time.time()\n", " X = numpy.random.randint(2, size=(i, 10))\n", " model = BayesianNetwork.from_samples(X, algorithm='exact')\n", " pomegranate_time.append(time.time() - tic)\n", "\n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X, algorithm='chow-liu')\n", " pomegranate_cl_time.append(time.time() - tic)\n", "\n", " X = [{j : X[i, j] for j in range(X.shape[1])} for i in range(X.shape[0])]\n", " learner = PGMLearner()\n", "\n", " tic = time.time()\n", " model = learner.discrete_constraint_estimatestruct(X)\n", " libpgm_time.append(time.time() - tic)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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5coRff/2VVq1aERUVxZUrV2jfvr26fsuWLXTq1IlatWqxePFiGjRogJOTExqN\nhitXrhAREUHv3r3Ztm0bZ8+eVacxCiGEEEII8TAk5OSwUK9nSVISafn5aIBXbWwY5uhIFxsbjB7j\naov/liRnj6FbKyhqNBr1tUaj4Y033iAqKoqRI0dSq1YtZs+eTfXq1dX2PXv2ZNy4cZw4cYJnnnmG\nuXPnAlC7dm2mTZvGzJkzmTt3Lr6+vvTs2ZP9+/f/rThKey2EEEIIIQTcKPCxMyODML2erenpKECN\nSpUY5+TEEK2WehYW5R1ihSDJ2WPoxIkT6s8//PCD+vOKFSvu+966desSGRlZ6rpXX32V7t27q68n\nTZqEnZ0dAJ6eniW2e3scAGvXrr1/8EIIIYQQ4qmRnp/P0sREwvV6zuXmAtDS2pphjo70rlUL8ye8\nwMeDkmoOAoCcnBy8vLxYsWIFRUVFnDhxgp07d9KuXbvyDk0IIYQQQjxmfr96lUEnT1J7/37GnjuH\n3mAgyN6eQ+7uRLu7E2hvL4lZKWTk7Clyr2mHFhYWzJs3j1mzZjFnzhyqV6/OgAED6NmzZxlGKIQQ\nQgghHlc5hYVEpqSwQK/nUFYWAA0sLBiq1fKmvT02t1UGF3eS5Owpcvs0xNt5e3sTFRVVRtEIIYQQ\nQognQXxODl/p9XyTmEhGQQFGwGs1ajDc0ZGO1as/1QU+HpQkZ0IIIYQQQogHUqgobEtPJ0yvZ0dG\nBgC1TEyYUKcOwVotdc3NyznCx5MkZ0IIIYQQQoi/JdVgYEliIl/p9fyVlwdA6ypVGOboSM9atTAz\nkpIW/4YkZ0IIIYQQQoi7UhSF6KtXCdPr+S4lBYOiYGlkxNsODgzVamlubV3eIT4xJDkTQgghhBBC\n3OFaYSFrkpMJ0+s5nJ0NwAsWFgxzdGSgvT1VK0kq8bDJERVCCCGEEEKoTl+/Trhez9LERK4UFmIM\nvF6zJsMcHfGpVu2eFcDFvyPJmRBCCCGEEE+5gqIittws8PG/y5cBsDMx4Z3atXnbwYHaUuCjTEhy\nJoQQQgghxFMq2WDg68REFur1XLxZ4OPFqlUZ5uiIX82amEqBjzIlyZl4opw8eZL09HRat25d3qH8\nbVlZWWzfvp3evXuXdyhCCCGEeAooisK+K1dYoNezLjWVfEXBytiYoVotw7RaXKysyjvEp5YkZ+KJ\nsnbtWkxMTB6r5Oy3334jMjJSkjMhhBBCPFLZBQVEpKQQptMRe+0aAI0sLRnm6EiAnR1VpMBHuZNx\nyseMTqeEg17hAAAgAElEQVTD2dmZXbt24evrS9OmTenXrx8pKSlqm8OHD9OnTx/c3d1p06YNn332\nGQUFBQCsX7+ebt268f3339OmTRtatGjB0qVL2b9/P126dMHNzY1JkyapfRkMBqZOnYqPjw+urq70\n79+fkydPqutjY2Pp0qULrq6uBAcHExkZiZeXV4lYV69ejZeXFxs2bABgxYoVdO7cGVdXVzp37sy6\ndevU/kJDQxk6dChLliyhTZs2eHp6Mn36dHV9ZmYm7777Lq1bt6ZFixYMHDiQhIQEAD755BMiIiJY\nuXIlHTp0AODq1auMHTuWtm3b4ubmRnBwMDqd7q7H9+DBg+qxa9u2Lf/9738ByMvLo1OnTqxcuVJt\nGx4eTrdu3cjPzwdgzpw5+Pj4EBQUhK+vLz/++KPatqioiNmzZ9O2bVs8PT0ZOXIk6enpbN26lTFj\nxnDixAmaNWvGX3/99XcuAyGEEEKIv+3EtWuMPHMG7f79DDl9mrjr1+lVqxY/Nm/OsRYtGO7oKIlZ\nRaE84Q4dOlTeITxUly5dUl544QVlwIABSlJSkpKVlaUEBQUp//nPfxRFUZT09HSlefPmyvLlyxWD\nwaCcPXtW8fHxUebNm6coiqJERUUprq6uyn//+1/FYDAoS5YsUZo0aaKMGTNGyc7OVqKjo5UXXnhB\nOX78uKIoijJ16lTF399fSU5OVvLy8pS5c+cq7du3VwoKCpS8vDzF29tbmTZtmpKXl6f88ssvSuvW\nrRUvL68SsY4cOVLJzs5WFEVRfv/9d6Vx48bKiRMnFEVRlL179yoNGzZUEhISFEVRlPnz5yteXl5K\neHi4YjAYlB9//FF54YUXlFOnTimKoigTJ05UAgMDlevXryt5eXnKe++9p/Tp00c9PgMGDFCmT5+u\nvh46dKgyfPhw5cqVK8q1a9eUiRMnKv7+/qUe26SkJMXV1VWJiopSioqK1GMXGRmpKIqiREdHK56e\nnkp6erqi1+sVV1dX5ciRI4qiKMqGDRuUVq1aKTqdTjl06JCyatUqpXnz5kpWVpaiKIqydOlSpVOn\nTsqlS5eUnJwcZfjw4UpwcLC6zz179vw3l4V4ij1pf+NExSbXmyhLcr39O4bCQmVtSory0uHDCnv3\nKuzdq2j37VMmJyQoutzc8g6vQqoI15ykyLeJHxtPyvcp92/4ENn2sqX+zPoP9J6+fftiZ2cHQFBQ\nEMHBweTl5bF582ZsbW0JDAwEoH79+vTt25d169YxcuRIAHJzcwkODsbExIT27dszY8YM/Pz8qFy5\nMi1btsTCwoLz58/TsGFDoqKimD17Nra2tgCMGDGCVatWER0djYWFBRkZGQwdOhRTU1PatGlD27Zt\nS4wYAWrfAB4eHkRHR2N1cy5z+/btsbCwIC4ujmeeeUZ9T3BwMBqNhnbt2mFubk58fDzPP/88n3zy\nCYWFhZjfrBjUqVMnQkJCSj1GGRkZ7Nmzhy1btlClShUAQkJC8Pb25vz58yW2B7BlyxaeffZZ/Pz8\n1GMXEBBAVFQUvXv3pmXLlnTu3JmZM2eSm5vLG2+8QdOmTQF47bXX6NChA1ZWViQmJvLqq68yZcoU\n4uPjadasGevXr8ff3x9HR0cAPvjgA+Li4h7onAshhBBC3E9iXh6LEhNZpNejNxgAeKlaNYY7OvJa\njRqYSIGPCk2Ss8dUvXr11J+1Wi2FhYWkpaVx6dIl6tcvmejVrVu3xFQ+a2trNbkxMzMDUJOv4mUG\ng4H09HSuXbvGyJEj1edZKIpCUVERiYmJWFtbY2FhQbVq1dT3Nm3a9I7kzMHBQf25oKCA0NBQdu7c\nSUZGBoqikJ+fj+HmH4/i9rc+P8Pc3Jy8m9WDzp8/z/Tp0zl69Cg5OTkUFRVRWFhY6jG6ePEiAD17\n9lSXKYpCpUqVSExMvCM5u3DhAnFxcTRr1qxE+5o1a6qvx40bR9euXTE2Nmbbtm3q8mvXrjFt2jR+\n/vlnrly5gkajQaPRqPt14cIFateuXWIfbz0uQgghhBD/lKIo/HzlCgt0OtanpVGgKFQxNmakoyND\ntVoa3vySXFR8kpzdpv7M+g88ilUebk1IFEUBKJEM3O7WZMeolG9MSltWnMBFRETQpEmTO9Zv374d\nExOT+/ZT6ZY5zKGhoWzbto3w8HAaN24MgKen5337gBv7GRwcjJubG9u3b8fGxoYffviBESNGlNre\nzMwMjUbD3r17sbGxKbXNrczNzWnTpg2LFi26a5vLly+Tl5eHoihkZGSoI2GTJ0/m5MmTREREkJaW\nxgsvvICHh0eJfSoqKrpvDEIIIYQQf9fVggJWJicTptMRd/06AE0qV2a4oyP9bW2xkvvIHjsyrvmY\nunDhgvqzTqfD2NiYmjVrUqdOHc6dO1eibXx8PHXq1LlrX3d7yruVlRXVq1cvUQCkeHsANWrUICsr\ni+zsbHXdkSNH7tn30aNHeemll9TE7OLFi1y9evWusd0qLS0NvV5PQECAmmwdO3bsru1r166NkZER\np06dUpcpikJiYmKp7evUqcOZM2dKLMvIyFBH7QAmTZrEgAED6NWrV4nCKUePHsXX15e6deuqr2/l\n5OSkFi4B0Ov1LFu27D57LIQQQghxp2PZ2Qw7fRrH/fsZceYMZ3Jy6Gtryy/Nm3PEw4NgrVYSs8eU\nJGePqcjISFJTU7ly5QrLli2jbdu2mJqa0rVrV5KSkli1ahUFBQWcPHmSNWvWlJjad7vikbfS9O3b\nl6+++oozZ85QWFhIZGQkPXr0IDs7GxcXFywtLVm4cCEGg4F9+/YRHR19z76dnJw4deoUOTk5JCQk\nMH36dOzt7UlOTr7vPtvY2GBpacnhw4cxGAzs2rWLQ4cOAajVKs3Nzbl06RJZWVlYWVnRrVs3Zs2a\nhV6vJy8vj3nz5hEYGFjqPvv6+pKdnc38+fPJzc1Fr9fz1ltvqSNp69atQ6fTERwczPDhwzl37pxa\ngdLJyYljx46Rn59PQkICa9aswczMTN2vnj178u233xIfH09OTg5ffvklv/32G3BjhC8tLY3MzMy7\njnwKIYQQ4ulmKCoiMiWFdocP0+TQIcL1eqpXqsTUevW44OXF6kaNaFOt2l2/dBePB0nOHlPdu3cn\nKCiIF198kdzcXKZMmQLcuJdpwYIFbNy4ES8vL0aNGkVgYCBvvvnmXfu6/Zf41tdDhgzBx8eHwMBA\nWrRowYYNG1i8eDFWVlZYWloyd+5ctm7dSqtWrVi3bh1BQUElpiXe3veQIUMwMjLC29ub9957j7ff\nfpvevXsTHh7Od999d8/4jI2NmTJlCt988w3e3t7s3r2b+fPn07BhQ7p168aVK1d4/fXX2bdvHy+/\n/DIFBQV8+OGHNGjQgO7du9O2bVtiY2NZuHBhqX+4qlSpQnh4OD/99BNeXl707dsXT09Phg0bRnp6\nOjNmzOCjjz7C1NQUS0tLJk6cyBdffEFGRgYhISGcP38eT09PVq5cSUhICN27d+ejjz7i559/JiAg\ngF69ejFgwADat29Pfn4+06ZNA6Bjx45oNBpeeumlO0bchBBCCPF0u5Sby6SEBOpGR9MnLo6fr1zh\n5erV2eDiwrmWLfmgbl3sb9YQEI8/jXKvYZMnQExMDO7u7uUdxkOj0+no2LEjmzdvpkGDBuUdjnof\nVXFCtnDhQnbu3ElUVFR5hlWunrRrTlRscr2JsiTXmyhLT/P1pigKezIzWaDTsSktjUKgqrExgxwc\nGKrV8rylZXmH+ESqCNecTEZ9DFWkfPqVV17Bx8eH9957D71ez7p163j11VfLOywhhBBCiMdOZn4+\nK24W+DiVkwOAq5UVwx0d6WNrS2Vj43KOUDxqkpw9hirSXOI5c+bw2Wef4enpiZWVFZ06dWLIkCHl\nHZYQQgghxGPjSHY2C3Q6IpKTuV5UhKlGwwA7O4ZrtbSsUqVCffYTj5YkZ48ZR0dHTpw4Ud5hqBo1\nakRERER5hyGEEEII8VjJKypiXWoqC3Q6frtZubqumRlDHR0JsrenlqlpOUcoyoMkZ0IIIYQQQpSR\nC7m5fKXX83ViIqn5+QB0sbFhuFbLKzVqYCyjZE81Sc6EEEIIIYR4hIoUhf9dvkyYTseW9HSKAJtK\nlQhxcmKIVkt9C4vyDlFUEJKcCSGEEEII8Qhczs9naVIS4Xo9Z28W+PCwtma4Vou/rS0WUuBD3EaS\nMyGEEEIIIR6imKwswnQ61qSkkFNUhJlGw5v29gzTamlRpUp5hycqMEnOhBBCCCGE+JdyCwv5LjWV\nMJ2OA1lZADxrbs5QrZZBDg7UMDEp5wjF40CSMyGEEEIIIf6hhJwcvtLrWZKYSHpBARqgW40aDNNq\n6Wxjg5EU+BAPwKi8AxDicXDw4EGcnZ3JuTlfvKyEhobSs2dPADZu3Ej79u3LdPtCCCGEuFORorAt\nPZ1usbHUP3CAGRcvAvC+kxPxLVuyuUkTXqlRQxIz8cBk5Ew8UU6ePEl6ejqtW7d+6H2X1wMgi7fb\nvXt3unfvXi4xCCGEEALS8/P5JjGRcL2ehNxcALyqVGGYVkuvWrUwlwIf4l+S5Ew8UdauXYuJickj\nSc6EEEII8XQ6ePUqYTod36akkKcoWBgZMdjenmGOjrhZW5d3eOIJItMaHzM6nQ5nZ2d27dqFr68v\nTZs2pV+/fqSkpKhtDh8+TJ8+fXB3d6dNmzZ89tlnFBQUALB+/Xq6devG999/T5s2bWjRogVLly5l\n//79dOnSBTc3NyZNmqT2ZTAYmDp1Kj4+Pri6utK/f39Onjypro+NjaVLly64uroSHBxMZGQkXl5e\nJWJdvXo1Xl5ebNiwAYAVK1bQuXNnXF1d6dy5M+vWrVP7Cw0NZejQoSxZsoQ2bdrg6enJ9OnT1fWZ\nmZm8++67tG7dmhYtWjBw4EASEhIA+OSTT4iIiGDlypV06NABgKtXrzJ27Fjatm2Lm5sbwcHB6HS6\nux7fffv24efnh6urK6+99ho//fRTifV//vknvr6+NGnShKCgIK5evaqu27NnD35+fgwaNIiXXnqJ\nsLAw4EbCeOuIV2xsLM7OzuzYsUNdNmLECObPn3/XuACioqLUY3vgwIE7pllOmDCBUaNG3bMPIYQQ\nQvw9OYWFLE1MpEVMDC3/+IPlycnUMTdnTv366Fq14mtnZ0nMxEMnydljauXKlXz99df89ttvWFhY\n8MEHHwCQkZFBUFAQXbt2JTo6muXLl7Nnzx7Cw8PV9+r1evR6PXv37mXo0KHMmTOHtWvXsm7dOsLD\nw/nuu++Ii4sDYObMmRw7doxvv/2WAwcO0LJlS4YOHUphYSEGg4GhQ4fSrl07Dhw4QEBAAPPnz79j\n+l90dDQ//PADPXr04NChQ8yYMYO5c+dy+PBhJkyYwEcffcT58+fV9n/++Sf5+fns3buXmTNnsnTp\nUk6fPq3Gk5GRwe7du9m3bx+1atVi4sSJwI3kzMPDg8DAQH744QcAxo8fT05ODlu3buXXX3+lZs2a\nvPfee6Ue0+TkZEaMGMFbb73FoUOHCA4OZtSoUSQnJwOgKAqbN29mzZo17Ny5kzNnzvDtt98CcPr0\naUaOHKkmlnPmzGH58uVqQnX27FmuXbsG3Lh/7dlnnyUmJkbddkxMDN7e3vc85xqNRj22t/4shBBC\niIfn7PXrhJw9i+P+/QSdOsUfWVn0qFmTXU2bctLTk9FOTlSXyoviEZFpjbcbOxa+/75st9mrF8yc\n+UBv6du3L3Z2dgAEBQURHBxMXl4emzdvxtbWlsDAQADq169P3759WbduHSNHjgQgNzeX4OBgTExM\naN++PTNmzMDPz4/KlSvTsmVLLCwsOH/+PA0bNiQqKorZs2dja2sL3BjhWbVqFdHR0VhYWJCRkcHQ\noUMxNTWlTZs2tG3blh9//LFErMV9A3h4eBAdHY2VlRUA7du3x8LCgri4OJ555hn1PcHBwWg0Gtq1\na4e5uTnx8fE8//zzfPLJJxQWFmJubg5Ap06dCAkJKfUYZWRksGfPHrZs2UKVm88UCQkJwdvbm/Pn\nz5fYHsD27dupXbs2Xbt2BeDVV1/F2NgY45vzxzUaDYMGDcLKygorKys8PDw4e/YsAOvWraNly5Z0\n6tSJmJgYmjdvTteuXdm2bRuvv/469vb2HDlyBG9vb37//Xf69etHVFQUAPHx8eTl5dGsWbMHuAKE\nEEII8bAUKgpb09MJ0+nYefkyALYmJnxQpw5va7XUufm5Q4hHrVyTs2nTprFixQp1mtzBgwf58ssv\nOXv2LHZ2dgQGBtKnTx+1fUREBBEREaSkpNCgQQNCQkLw8PAor/DLVb169dSftVothYWFpKWlcenS\nJerXr1+ibd26dUtM5bO2tlaTGzMzMwA1+SpeZjAYSE9P59q1a4wcOVIdpVEUhaKiIhITE7G2tsbC\nwoJq1aqp723atOkdyZmDg4P6c0FBAaGhoezcuZOMjAwURSE/Px+DwVCi/a2jQubm5uTl5QFw/vx5\npk+fztGjR8nJyaGoqIjCwsJSj9HFm5WTiqsdFsdfqVIlEhMT70jOLl68iKOjY4llXbp0AeDcuXMA\nJdabm5tz/fp19b2lHffo6GgAvLy8+OOPP2jVqhVHjhxhzpw5hIeHc+3aNWJiYvDw8KBSJfmuRAgh\nhChLKQYDSxIT+Uqv58LNzxptqlZlmFbL67VqYWYkk8xE2Sq3T4MnTpxg06ZN6ofw1NRUhg4dyrhx\n4/Dz8+P48eO89dZb1K5dmzZt2vDjjz8yZ84cFi1ahIuLC+vXr2fIkCHs2rULGxubhxfYzJkPPIpV\nHm5NSBRFAW6M7Nya5Nzq1mTHqJQ/NKUtK07gIiIiaNKkyR3rt2/fjsltw/ql9XNr0hEaGsq2bdsI\nDw+ncePGAHh6et63D7ixn8HBwbi5ubF9+3ZsbGz44YcfGDFiRKntzczM0Gg07N27929dIxqNRj2W\n92pTmvsddy8vL9avX8+JEyeoXbs2lpaWNGnShD/++INDhw7RqlWr+8Z3P3dLUoUQQgjx/xRFYf/N\nAh/fp6ZiUBQqGxkR7ODAUEdHmt2c3SNEeSiXrwMUReGTTz4hKChIXbZp0yZq166Nv78/pqamuLq6\n0r17d/Wenm+//RY/Pz/c3NwwNTXF398fBwcHtmzZUh67UO4uXLig/qzT6TA2NqZmzZrUqVNHHeUp\nFh8fT506de7a190SDisrK6pXr16iAEjx9gBq1KhBVlYW2dnZ6rojR47cs++jR4/y0ksvqYnZxYsX\nSxTVuJe0tDT0ej0BAQFqsnXs2LG7tq9duzZGRkacOnVKXaYoComJiaW2d3JyUouLFIuMjLzjeJam\ntON+7tw59bh7eXnx559/sn//fnW019XVlZiYGGJiYh44OSse8by1IMit14QQQgghSrpWWMhivR63\nmBhaHz5MREoKz1pYMK9BA3Te3nz1wguSmIlyVy7J2Zo1a7CwsODVV19Vl8XFxdGoUaMS7Ro1asTR\no0eBGx/Ciz/Ql7b+aRMZGUlqaipXrlxh2bJltG3bFlNTU7p27UpSUhKrVq2ioKCAkydPsmbNmhJT\n+253r9Givn378tVXX3HmzBkKCwuJjIykR48eZGdn4+LigqWlJQsXLsRgMLBv3z51Gt/d+nZycuLU\nqVPk5OSQkJDA9OnTsbe3V4tu3IuNjQ2WlpYcPnwYg8HArl27OHToEIBardLc3JxLly6RlZWFlZUV\n3bp1Y9asWej1evLy8pg3bx6BgYGl7nO3bt1ISUlhzZo15Ofns3v3br744gt1BPFex6lHjx4cOHCA\n3bt3U1RUxKFDh9i6dat63GvVqoW9vT1r164tkZzt3r2bnJwcnJ2d77v/t6pduzbGxsbs3LmTwsJC\ntm7dKsmZEEIIUYpT168z+swZHH/7jbdPn+ZodjY9a9bkh2bNiGvRgpG1a1NVbi0QFUSZJ2dpaWmE\nhYUxefLkEsszMzOpWrVqiWVVq1bl8s2bMjMzM9WiDqWtf9p0796doKAgXnzxRXJzc5kyZQpw436t\nBQsWsHHjRry8vBg1ahSBgYG8+eabd+3r9tGtW18PGTIEHx8fAgMDadGiBRs2bGDx4sVYWVlhaWnJ\n3Llz2bp1K61atWLdunUEBQWVmJZ4e99DhgzByMgIb29v3nvvPd5++2169+6tVom8V3zGxsZMmTKF\nb775Bm9vb3bv3s38+fNp2LAh3bp148qVK7z++uvs27ePl19+mYKCAj788EMaNGhA9+7dadu2LbGx\nsSxcuLDU0cIaNWqwdOlSVq9ejaenJ/Pnz2fevHlotdpS9+VWTZs25fPPP2fevHm89dZbTJ48mY8+\n+oiXX35ZbePl5cX58+dxc3NT33P+/Pl/NKWxRo0ahISEEBoaipeXF4cPH5YHVAshhBA3FRQVsT41\nlZePHMH54EHm6nRYGBszqW5dznt5sdbFBZ/q1aXysahwNMr9brJ5yEJCQqhbty4jR45Ep9PRsWNH\nTpw4weDBg2nQoAETJkxQ2+7evZsxY8YQGxuLi4sLc+fOVZ9fBTcKipw7d46vv/76rtuLiYnB3d39\nke5TWSo+Zps3b6ZBgwblHQ5FRUXA/98ntnDhQnbu3KlWInwaPWnXnKjY5HoTZUmuN1GW/sn1lpSX\nx9eJiSxMTOTSzQIf7apWZZijI341a2IiBT7EPVSEv3FlOoa7f/9+jh49yrRp04CS08SqV69OZmZm\nifaZmZnUqFEDuDGl7V7r7+XW50k97lJTU1EUhePHj3PlypXyDocxY8bg7u5Onz59SEtLIyIiAm9v\n7yfqmP8TT/v+i7Il15soS3K9ibL0d643RVH4s7CQ7/Pz2VNQQAFgCfQyMaGniQkNiorg4kVib1Zx\nFqIiK9PkbNOmTaSkpPDiiy8CN36ZFEWhVatWDBo0iA0bNpRoHxsbqz77ycXFhWPHjpW4dyo2NlZ9\nnte9lHcG/DDpdDo0Gg2NGzeuECNn4eHhfPbZZwwZMgQrKys6derE2LFj1YIVT6OK8K2LeHrI9SbK\nklxvoizd73rLKiggIjmZML2eozcLZDW2tGSYoyMBdnZYy31k4gFVhC+fyvSqnThxIqNHj1ZfJyUl\n4e/vz8aNGykoKODrr79m9erVvPHGGxw+fJgtW7awePFiAPr3788777yDr68vLi4urF69mqtXr+Lr\n61uWu1DuHB0dOXHiRHmHoWrUqBERERHlHYYQQgghnhJx164RrtezPCmJrMJCKmk09K5Vi+GOjrSt\nWlXuIxOPtTJNzqytrbG2tlZfFxQUoNFo1AcgL1y4kClTpjB9+nTs7OyYPHmy+o1J69atGT9+PCEh\nIaSnp+Ps7MzixYtL9CeEEEIIIZ48+UVFbExLY4Fez483b3NxNDVlrJMT/3FwwOEpnrEjnizlOt57\n+yiQq6vrPQtJ9OrVi169epVFaEIIIYQQopzp8/JYnJjIIr0evcEAQIdq1Rjm6MhrNWpQSQp8iCeM\nTMYVQgghhBAVhqIo/JSZydScHH7cv59CoIqxMe84OjJUq8W5cuXyDlGIR0aSMyGEEEIIUe6uFhSw\nMjmZMJ2OuOvXAWhauTLDHR3pZ2uLlRT4EE8BucqFEEIIIUS5OZqdTZhez8qkJK4VFWGi0dDP1haf\nrCyCPDykwId4qkhyJoQQQgghypShqIio1FTC9Hp+ufncViczMyZqtQx2cMDO1JSYmBhJzMRTR5Iz\nIYQQQghRJi7m5rIoMZHFej3J+fkAdKpenWGOjrxqYyMFPsRTT5IzIYQQQgjxyCiKwg+XLxOm17Mx\nLY0ioFqlSrxbuzZDtVqes7Qs7xCFqDAkORNCCCGEEA9dZn4+y28W+DidkwOAq5UVwx0d6Wtri6Wx\ncTlHKETFI8mZEEIIIYR4aP7MyiJMryciOZnrRUWYajQE2Nkx3NERT2truY9MiHuQ5EwIIYQQQvwr\neUVFrE1NZYFOx/6rVwF4xtycIVotQfb21DI1LecIhXg8SHImhBBCCCH+kb9yc/lKr2dJYiKp+flo\ngFdsbBju6EgXGxuMZZRMiAciyZkQQgghhPjbihSF/12+zAKdjq3p6RQBNpUqEeLkxBCtlvoWFuUd\nohCPLUnOhBBCCCHEfWXk57M0KYlwnY743FwAPK2tGeboSO9atbCQAh9C/GuSnAkhhBBCiLuKycpi\ngU7HmpQUcouKMDcyYpC9PcO0WjyqVCnv8IR4okhyJoQQQgghSsgtLCQyNZUwnY6DWVkA1Dc3Z6ij\nI4Ps7bExMSnnCIV4MklyJoQQQgghADiXk8NXej3fJCaSXlCABvCtUYNhWi2dbGwwkgIfQjxSkpwJ\nIYQQQjzFChWFHRkZhOl0bM/IQAFqmpgwvk4dgh0ceEYKfAhRZiQ5E0IIIYR4CqUZDHyTlMRXej0J\nNwt8tKpShWFaLb1sbTEzMirnCIV4+khyJoQQQgjxlFAUhYNZWYTpdESmpJCnKFgYGfEfBweGabW4\nWluXd4hCPNUkORNCCCGEeMJdLyzk25QUwnQ6YrKzAXjewoJhjo4MtLOjmhT4EKJCkORMCCGEEOIJ\ndeb6dcL1epYmJZFZUIAR0KNmTYZrtfhUry4FPoSoYCQ5E0IIIYR4ghQqClvT01mg07Hr8mUA7ExM\n+LBuXd52cMDJ3LycIxRC3I0kZ0IIIYQQT4AUg4GvExNZqNdzIS8PgDZVqzJcq+X1WrUwlQIfQlR4\nkpwJIYQQQjymFEVh/9WrLNDp+D41lXxFobKREUO0/8fencdHVd/7H3/NTDLZE7In5wTQW7UYqSih\niNpK0XvVuhYQsVXRXqtAgtqq9aptvbXWLg+09rYSwL0LSl17+WFLW2292ooiURFEtIpWmDNZScie\n2b6/PwiRAAkBJjNZ3s/HYx4k55zJfAYOk3nP+X4/X4uFlsXx6enxLlFEDoLCmYiIiMgw0xYOs6Km\nhkqfjw1tbQAcm5pKuWVxeVERWQl6iycyHOl/roiIiMgwsaWtjaWOw6PV1TSHw3iAi/LzKbcsvjRm\nDKOesTEAACAASURBVC41+BAZ1hTORERERIawUCTCqoYGKn0+XmhqAqDY6+VbJSVcbVnYSUlxrlBE\nokXhTERERGQI8nd19TT48AUCAHxpzBgqLIsL8/JIVIMPkRFH4UxERERkiDDG8PLOnVT6fDxdX0/I\nGDI8Hiosi3LbpjQtLd4lisggUjgTERERibOWUIjf1tRQ6Ths6m7wMTEtjQrL4tLCQjLU4ENkVND/\ndBEREZE4eaetjaU+H7+uqaElHCbB5WJufj4Vts0XsrLU4ENklFE4ExEREYmhYCTC7+vrqXQcXuxu\n8FGSlMTNY8fyjeJiitTgQ2TUUjgTERERiQFfVxcPOA73+/34uxt8nDFmDBW2zfm5uSSowYfIqKdw\nJiIiIjJIjDG82NREpePwbF0dYSDL4+F622aBZTFBDT5EZA8KZyIiIiJRtjMU4jfV1VQ6Du+2twMw\nKS2NCtvma4WFpHk8ca5QRIYihTMRERGRKHm7tZVKn4/f1tTQFongdbm4tKCActvm5MxMNfgQkX4p\nnImIiIgchkAkwtN1dVQ6Dn/fuROAcUlJfMeyuKq4mAKvN84VishwoXAmIiIicgg+6ezkfsfhAb+f\n2mAQgLOysym3bc7NzcWjq2QicpAUzkREREQGKGIMLzQ2Uuk4rKqvJwKMSUjghpISFlgWR6emxrtE\nERnGFM5EREREDqAxGORX1dUsdRze7+gAYHJ6OhW2zSUFBaSqwYeIRIHCmYiIiEgf3mxpodJxWFFT\nQ0ckQpLLxbzCQipsm89nZKjBh4hElcKZiIiIyB46w2GeqqtjiePwanMzAEcmJ7PQsvh6URF5avAh\nIoNE4UxEREQE+Lijg+V+Pw/6/dQHg7iAc3NyKLdtzsrJUYMPERl0CmciIiIyakWM4c87drDEcXiu\noQED5CYkcPPYscy3LP4tJSXeJYrIKKJwJiIiIqPOjmCQR6qrWerz8WFnJwAnZWRQbttcnJ9Pshp8\niEgcKJyJiIjIqLG+uZkljsPK2lo6IxGS3W7+s6iIctumLCMj3uWJyCincCYiIiIjWkc4zBN1dSzx\n+Xi9pQWAo1JSWGhZXFlURE5iYpwrFBHZReFMRERERqStHR0sdRwe9vvZEQrhBi7IzaXctvmP7Gzc\navAhIkOMwpmIiIiMGGFjWLNjB0t8Ptbs2IEB8hMTuXXcOOZbFuOTk+NdoohInxTOREREZNirDwR4\nqLqaZY7Dx90NPk7JzKTctrkoP58ktzvOFYqIHJjCmYiIiAxLxhhea26m0nF4oraWLmNIdbu5uriY\ncsviBDX4EJFhRuFMREREhpX2cJjHa2tZ4vPxZmsrAMekpFBu21xRWMgYNfgQkWFK4UxERESGhffb\n21nmODxSXU1Td4OPmXl5VNg2p48Zg0sNPkRkmFM4ExERkSErFInwXHeDj780NgJQmJjI98aP5+ri\nYsaqwYeIjCAKZyIiIjLk1AQCPOT3s8xx2NbVBcBpWVmU2zYz8/LwqsGHiIxACmciIiIyJBhjeKW5\nmSU+H0/V1RE0hnSPh4WWxULL4nPp6fEuUURkUCmciYiISFy1hkKsqK2l0ufj7bY2AEpTUym3bS4v\nLCQzQW9XRGR0iPmr3VtvvcU999zD5s2bSUlJ4aSTTuLWW28lLy+PdevWcc899/DBBx9QWFjIvHnz\nuOSSS3ruu2LFClasWEFtbS1HHXUUN910E1OmTIn1UxAREZEo2NLWRqXj8KvqaprDYRJcLubk51Nu\nWUxXgw8RGYViOmC7ubmZq666irPOOot169axatUqamtr+f73v099fT0LFy5k1qxZrF27lrvuuou7\n776bv//97wC8+OKL3Hvvvfzwhz/klVdeYebMmSxYsIAdO3bE8imIiIjIYQhFIjxdV8cZb73Fsa+/\nzi99PtI9Hr5/xBH8a9o0njjuOL6Una1gJiKjUkzDWSAQ4Lvf/S6XXXYZHo+HnJwczjzzTLZs2cKq\nVasoKSlh7ty5eL1eTjzxRC688EJWrlwJwMqVK5k5cyaTJ0/G6/Uyd+5ciouLWb16dSyfgoiIiBwC\nf1cXP/j4Y4549VUueucd/trUxIwxY3iytJSPp03jv484AispKd5liojEVUyHNebl5TFz5sye7z/8\n8EOeffZZzj33XN555x1KS0t7HV9aWsrzzz8PwKZNmzj77LP32b9x48bBL1xEREQOmjGGl3bupNLn\n45n6ekLGkOHxsMi2WWhZlKalxbtEEZEhJS4zbN977z1mz56NMYY5c+Zw/fXXc/XVV3P00Uf3Oi4r\nK4vG7jVNmpqayMzM3Gf/1q1bY1a3iIiIHFhLKMRvamqo9Pl4p70dgM+lpVFuWVxWWEi6GnyIiOxX\nXF4dP/vZz7Jp0yY++ugjbr/9dm644QZg1yds/TnQfhEREYmfd9raqPT5+HVNDa3hMIkuF5cUFFBh\nWZyalaV5ZCIiBxDXj66OPPJIbrzxRi655BJOPvlkmpqaeu1vamoiNzcXgJycnH7396eqqip6RYsM\ngM45iSWdbxJLe59vIWP4WyjEk8Egb4TDABS6XFzm9fKVxETyOjrgww95Ix7FyrCn1zcZbWIaztas\nWcP999/PM88807PN5XLhcrmYPn06TzzxRK/j3377bSZNmgTAxIkT2bRpE7Nnz+61f968eQd83LKy\nsig9A5EDq6qq0jknMaPzTWJpz/PN19XF/Y7D/X4/1YEAAP+enU2FZXFebi4J7pj2HJMRSK9vEmtD\n4cOAmL5yTp48mW3btrF06VK6urpoaGjgvvvuo6ysjAsvvJD6+noee+wxAoEAr732GqtXr+byyy8H\n4NJLL2XVqlW88cYbBAIBHn30UZqbmzn//PNj+RRERERGLWMMf21s5KJNmxi/di0/+Ne/6AiH+WZJ\nCVumTuUvkybxlfx8BTMRkUMU0ytnBQUFPPTQQ/z4xz9m+fLlpKenc9JJJ3HXXXeRnZ3N8uXLufPO\nO/npT39KYWEhd9xxR88nJqeeeiq33HILN910Ew0NDUyYMIEHHniAjIyMWD4FERGRUWdnKMSvq6v5\nWXs7H2/YAMAJ6elUWBZfLSwkzeOJc4UiIiNDzOecHX/88Tz++OP73XfiiSf2GvK4tzlz5jBnzpzB\nKk1ERET2sKG1lUqfj9/W1NAeiZAIXFZYSLllMS0zUw0+RESiTL1sRUREpEdXJMLTdXVU+nz8o7kZ\ngPFJSSywLKbU1vLvxx4b5wpFREYuhTMRERHhk85OljsOD/r91AaDAJydk0O5ZXFObi4el4uq+vo4\nVykiMrIpnImIiIxSEWN4vrGRSp+P/9fQQATITkjgxpISFlgWR6WmxrtEEZFRReFMRERklGkMBnm0\nupqljsM/OzoAKEtPp8K2uaSggBQ1+BARiYsBhbNAIMCGDRt4//33aWxsxBhDTk4OxxxzDJMmTcLr\n9Q52nSIiInKY3mhpodLn47HaWjoiEZJcLq4oLKTCtvl8Zma8yxMRGfX6DWe1tbU88MADPPXUUwQC\nAYqKisjOzsblcrFjxw6qq6vxer3MmTOHb3zjGxQUFMSqbhERERmAznCYJ+vqqHQcXu1u8PFvycks\ntCy+XlxMbmJinCsUEZHd+gxnf/rTn7j99tuZPHkyP//5zykrKyM9Pb3XMW1tbaxfv54nnniC888/\nnx/84AecddZZg160iIiI9O+jjg6WOw4PVVdTHwziAs7NyaHCtjkrJwe32uCLiAw5fYazX/ziFzz8\n8MMcd9xxfd45LS2N6dOnM336dDZv3szNN9+scCYiIhInEWP4044dVDoOzzU0YIDchAT+a+xY5lsW\nR6akxLtEERHpR5/h7Nlnn+01lywSieB2u3u+3rJlC8XFxWRnZwNQWlra7wLSIiIiMjgagkEe8ftZ\n6jhs7ewE4KSMDCpsmzn5+SSrwYeIyLDQZzjbM5i9+uqr3Hzzzbz00kuEQiEuu+wy3nrrLbxeL7/8\n5S+ZPn36PvcRERGRwfV6czOVjsPK2lo6IxFS3G6uKipioW1TlpER7/JEROQgDahb4+LFi7n22msB\neO6559i+fTt//etfeeutt/jFL37RE85ERERkcHWEw/yutpYljsP6lhYAjkpJodyyuLKoiGw1+BAR\nGbYGFM4++ugjLrroIgBefPFFzjnnHCzLori4mO9973uDWqCIiIjAhx0dLHMcHvb72REK4QYuzM2l\n3Lb59+xsNfgQERkBBhTOkpOTaW5uJikpiVdeeYWf//znALS2tuLSLwMREZFBETaGPzY0sMRxWLNj\nBwD5iYncNm4c11gW45OT41yhiIhE04DC2fTp07niiivweDxkZ2czbdo0urq6uOuuuygrKxvsGkVE\nREaVukCAh/x+ljkO/+rqAuDUzEwqbJtZ+fkkdTfoEhGRkWVA4ey///u/efTRR2lpaeFrX/saLpeL\nSCRCXV0dP/rRjwa7RhERkRHPGMOr3Q0+nqitJWAMqW431xQXU27bTNprrVERERl5+gxn//d//9fT\n6CM5OZkFCxb02p+SksJDDz3Ua9tLL73EaaedNghlioiIjExt4TCP19RQ6Ti82doKwITUVMoti3lF\nRWQlDOhzVBERGQH6fMW/6667eP7551m4cCGWZfX7Q/x+P5WVlaxbt07hTEREZADeb29nqePwiN/P\nznAYDzArL48K22bGmDGa0y0iMgr1Gc6eeeYZ7rjjDs466yxOOeUUpk2bxjHHHENWVhYul4umpib+\n+c9/8uqrr/KPf/yDc845h6effjqWtYuIiAwroUiE1Q0NVDoOf2lsBKDI6+X6khKuLi6mRA0+RERG\ntT7DWXp6OosXL2b+/PmsXLmSJ554go8++qjXMUceeSSnnnoqv//97/nMZz4z6MWKiIgMRzWBAA/6\n/Sx3HLZ1N/g4LSuLCtvmK3l5eNXgQ0REGEBDkKOOOorvfve7AIRCIXbu3AlAVlYWCRoHLyIisl/G\nGP6xcydLHIen6+oIGkO6x0O5ZbHQspioBh8iIrKXg0pXCQkJ5ObmDlYtIiIiw15rKMSK2loqfT7e\nbmsDoDQ1lQrb5rLCQjL1waaIiPRBvyFERESi4N22Niodh19VV9MSDpPgcnFxfj7lts1p3fO1RURE\n+qNwJiIicoiCkQirGhpY4vPxt6YmACyvl5vGjuUbxcVYSUlxrlBERIYThTMREZGD5O/q4n6/n/sd\nBycQAOD0MWMot20uyM0lUQ0+RETkEAw4nDU3N7NmzRr8fj/XX389AB9//DFHHHHEYNUmIiIyZBhj\neGnnTpb4fDxbX0/IGDI9Hq61bRZaFsempcW7RBERGeYGFM7Wrl1LRUUFJSUlfPTRR1x//fX4fD5m\nzpzJvffey5e+9KVBLlNERCQ+mkMhflNTQ6XPx+b2dgCOT0uj3La5tKCAdDX4EBGRKBnQb5TFixdz\n6623MmfOHI4//ngAbNvm7rvv5n/+538UzkREZMTZ1NpKpePwm5oaWsNhEl0uvlpQQIVtc0pmphp8\niIhI1A0onG3dupVZs2YB9PplNGPGDG666abBqUxERCTGApEIz9bXU+nz8VL3up5jk5K4ddw4riou\nptDrjXOFIiIykg0onBUUFLB9+3bGjx/fa/ubb75JRkbGoBQmIiISK9s7O7nf7+cBv5/q7gYfZ2Zn\nU27bnJuTQ4IafIiISAwMKJxdcMEFXHPNNcybN49IJMKaNWvYsmULjz/+OPPmzRvsGkVERKLOGMNf\nm5qo9Pn43/p6wsCYhAS+VVLCAsvimNTUeJcoIiKjzIDCWUVFBenp6Tz++OO4XC5uv/12xo0bx803\n38zs2bMHu0YREZGoaQoG+XV3g4/3OjoAODE9nQrb5qsFBaR6PHGuUERERqsBhTOXy8WVV17JlVde\nOcjliIiIDI4Nra0s8flYUVNDeySC1+Xi8sJCyi2Lk9TgQ0REhoABhbNQKMTf/vY3Pv74Y7q6uvbZ\nv2jRoqgXJiIicri6IhGerqtjic/HK83NAIxPSmKhbfOfRUXkq8GHiIgMIQMKZ9dffz0vvfQSRxxx\nBN69fpG5XC6FMxERGVI+6exkmePwoN9PXTCIC/hyTg7llsWXc3Px6CqZiIgMQQMKZ6+88gqrVq3i\nyCOPHOx6REREDknEGP7S2Eilz8fqhgYiQE5CAjeNHcsCy+IzKSnxLlFERKRfAwpnRxxxBFlZWYNd\ni4iIyEFrDAZ5pLqapY7DB90NPj6fkUG5ZTG3oIAUNfgQEZFhYkDh7Mc//jG33HILZ5xxBgUFBbj3\nWu9l+vTpg1KciIhIX6paWqj0+Xi8tpaOSIRkt5sri4ootyw+n5kZ7/JEREQO2oDC2e9//3teeukl\nXnrppX32uVwu3n333agXJiIisrfOcJgn6uqo9Pl4raUFgH9LTmahZfH14mJyExPjXKGIiMihG1A4\n+93vfsfPf/5zTj/99H0agoiIiAy2jzo6WOY4POT30xAK4QLOy82lwrI4MycHtxp8iIjICDCgcJaT\nk8OMGTMUzEREJGYixrBmxw4qfT7+sGMHBshLTOSWceOYX1zMEWrwISIiI8yAwtl3v/tdFi9ezFe/\n+lWKior2mXOWol+QIiISJQ3BIA/7/Sx1HD7q7ATg5MxMyi2Li/LzSVaDDxERGaEGFM5uuOEGOjs7\nWbFixX73a86ZiIgcrnXNzVT6fKysraXLGFLcbr5RXMxCy2JyRka8yxMRERl0Awpny5cvH+w6RERk\nFOoIh1lZW0ul47C+u8HH0SkplFsWVxQVka0GHyIiMooMKJxNnTp1sOsQEZFR5IP2dpY5Dg9XV9MY\nCuEGvpKXR7llcUZ2thp8iIjIqNRnOPva177GY489BsDs2bNx9fOL8qmnnop+ZSIiMqKEjeEPDQ0s\n8fn4U2MjAAWJiXxn3DjmWxZjk5PjXKGIiEh89RnOvvjFL/Z8PWPGjJgUIyIiI09tIMBDfj/LHIdP\nuroA+EJWFuWWxez8fLx7NZkSEREZrfoMZwsXLmT9+vVMmTKFRYsWxbImEREZ5owxrO1u8PFkXR0B\nY0hzu5lfXEy5bXN8enq8SxQRERly+p1zdtVVV7Fhw4ZY1SIiIsNcWzjMYzU1VDoOb7W2AjAhNZVy\ny2JeURFZCQOa6iwiIjIq9ftb0hgTqzpERGQYe6+9naU+H49WV7MzHMYDzM7Lo8K2+dKYMf3OWxYR\nEZFd+g1n+mUqIiJ9CUUi/L+GBiodh+e7G3wUeb1cX1LCNZaFnZQU5wpFRESGl37DWVdXF8cee+wB\nf4gWoRYRGT2qu7p40O9nud/P9u4GH9Ozsqiwbb6Sl0eiGnyIiIgckn7DWUJCAvfdd1+sahERkSHK\nGMPfd+6k0nF4uq6OoDGkezxUWBYLbZvj0tLiXaKIiMiw128483g8fOlLX4pRKSIiMtS0hEKs6G7w\nsbGtDYDjUlOpsG0uKywkQw0+REREokYNQUREZB+b29pY6jj8qrqalnCYBJeLufn5lNs2X8zK0pxk\nERGRQdBvOLvwwgtjVYeIiMRZMBLhf+vrWeI4vNjUBIDt9fLtsWO5uriYIjX4EBERGVT9hrM777wz\nVnWIiEicOF1dPOD3c7/j4AQCAJwxZgzlts0FubkkqMGHiIhITMR8soDjOPzkJz/h9ddfx+VyMXXq\nVG677TYKCgpYt24d99xzDx988AGFhYXMmzePSy65pOe+K1asYMWKFdTW1nLUUUdx0003MWXKlFg/\nBRGRYc8Yw/81NbHEcXi2ro4wkOnxcJ1ts9CymKAGHyIiIjEX849DFyxYQEpKCi+88AKrV6+mqamJ\n22+/nfr6ehYuXMisWbNYu3Ytd911F3fffTd///vfAXjxxRe59957+eEPf8grr7zCzJkzWbBgATt2\n7Ij1UxARGbaaQyGW+HxMfP11ZmzYwFN1dRyXlsbyY47BOeUU/ufooxXMRERE4iSm4aylpYXPfe5z\n3HTTTaSmppKTk8PFF1/M+vXrWbVqFSUlJcydOxev18uJJ57IhRdeyMqVKwFYuXIlM2fOZPLkyXi9\nXubOnUtxcTGrV6+O5VMQERmWNra2svD997FeeYVF//wn/+zo4GsFBfzjxBN5a8oUrrEs0jyeeJcp\nIiIyqsV0WGNGRgZ33XVXr22O41BYWMg777xDaWlpr32lpaU8//zzAGzatImzzz57n/0bN24c3KJF\nRIapQCTCM3V1VDoOL+/cCcC4pCS+Y1lcVVxMgdcb5wpFRERkT3FdoGbr1q0sW7aMO+64g2eeeYaj\njz661/6srCwaGxsBaGpqIjMzc5/9W7dujVm9IiLDwbbOTu73+3nAcagJBgE4MzubCtvm3NxcPGqD\nLyIiMiTFLZxt3LiRBQsWcNVVV3HuuefyzDPPHHBdNa27JiKyf8YYXmhspNJx+N/6eiLAmIQEbigp\nYYFlcXRqarxLFBERkQOISzh7+eWX+da3vsW3v/1t5s6dC0B2djZN3evq7NbU1ERubi4AOTk5/e7v\nT1VVVZQqFxkYnXMSKy3GcNPatTwZCPBJ9wdYE9xu5ni9nJWQQPLOnTTv3InOSIkWvb5JLOl8k9Em\n5uFsw4YN3HjjjSxevJgZM2b0bJ84cSJPPvlkr2PffvttJk2a1LN/06ZNzJ49u9f+efPmHfAxy8rK\nolS9yIFVVVXpnJNB91ZLC5WOw2/8fjqBJJeLeYWFlNs2UzMycGnoogwCvb5JLOl8k1gbCh8GxLRb\nYzgc5jvf+Q7XXnttr2AGcMEFF1BXV8djjz1GIBDgtddeY/Xq1Vx++eUAXHrppaxatYo33niDQCDA\no48+SnNzM+eff34sn4KISNx0RSKsqKnhlDfe4MSqKh7w+8lxufjpv/0b208+mV8deywnZWYqmImI\niAxTMb1y9uabb/Lhhx9y9913s3jxYlwuF8YYXC4Xa9asYfny5dx555389Kc/pbCwkDvuuKPnE5NT\nTz2VW265hZtuuomGhgYmTJjAAw88QEZGRiyfgohIzP2rs5NljsNDfj91wSAu4JycHMptm/yPPmLq\nuHHxLlFERESiIKbhbMqUKbz77rt97i8uLuaZZ57pc/+cOXOYM2fOYJQmIjKkRIzhL42NLPH5eK6h\ngQiQk5DAt8eOZb5l8ZmUFACqPv44rnWKiIhI9MS1lb6IiPS2Ixjkkepqlvp8fNjZCcDUjAwqbJs5\n+fmkaKFoERGREUvhTERkCKhqaWGJz8fjtbV0RiIku918vaiIcstiyl5rPIqIiMjIpHAmIhInneEw\nv6uro9LnY11LCwCfSU6m3La5sqiInMTEOFcoIiIisaRwJiISY1s7OljmODzs99MQCuECzs/NpcK2\n+Y/sbNzqtigiIjIqKZyJiMRA2BjW7NhBpc/HH3fswAD5iYncOm4c1xQXc0R3gw8REREZvRTOREQG\nUX0gwMPV1SxzHD7qbvBxcmYmFbbNRfn5JLljutykiIiIDGEKZyIiUWaMYV1LC5U+H7+rraXLGFLd\nbq4uLmahZXGi1mcUERGR/VA4ExGJkvZwmJW1tVT6fFS1tgJwTEoK5bbNFYWFjFGDDxEREemHwpmI\nyGH6Z3s7yxyHR6qraQyFcAMz8/IotyzOyM7GpQYfIiIiMgAKZyIihyBsDM81NFDp8/GnxkYAChMT\n+e748VxTXMzY5OQ4VygiIiLDjcKZiMhBqA0EeNDvZ7nj8ElXFwBfzMqi3LKYlZ+PVw0+RERE5BAp\nnImIHIAxhrXNzSzx+Xiyro6gMaS53SywLBZaFsenp8e7RBERERkBFM5ERPrQFg6zoqaGSp+PDW1t\nABybmkqFbXN5YSGZCXoJFRERkejROwsRkb1saWtjqePwaHU1zeEwHuCi/HwqLIvpY8aowYeIiIgM\nCoUzEREgFImwqrvBxwtNTQAUe73cMHYsVxcXYyUlxblCERERGekUzkRkVPN3dfU0+PAFAgDMGDOG\ncsviwrw8EtXgQ0RERGJE4UxERh1jDC/v3Emlz8fT9fWEjCHD42GRbbPQsihNS4t3iSIiIjIKKZyJ\nyKjREgrx25oaKh2HTd0NPiampVFhWVxaWEiGGnyIiIhIHOmdiIiMeO+0tbHU5+PXNTW0hMMkuFxc\nUlBAuWXxhawsNfgQERGRIUHhTERGpGAkwu/r66l0HF7sbvBRkpTEzWPH8o3iYorU4ENERESGGIUz\nERlRfF1dPOA43O/34+9u8PHv2dmUWxbn5+aSoAYfIiIiMkQpnInIsGeM4cWmJiodh2fr6ggDWR4P\n19s2C22bz6amxrtEERERkQNSOBORYWtnKMRvqqupdBzebW8HYFJaGhW2zdcKC0nzeOJcoYiIiMjA\nKZyJyLCzsbWVJT4fv62poS0SwetycWlBARW2zbTMTDX4EBERkWFJ4UxEhoVAJMLTdXVUOg5/37kT\ngHFJSXzHsriquJgCrzfOFYqIiIgcHoUzERnSPuns5H7H4QG/n9pgEICzsrOpsG3Oyc3Fo6tkIiIi\nMkIonInIkBMxhhcaG6l0HFbV1xMBshMSuLGkhAWWxVFq8CEiIiIjkMKZiAwZjcEgv6quZqnj8H5H\nBwBl6elU2DZzCwpIVYMPERERGcEUzkQk7t5saaHScVhRU0NHJEKSy8UVhYWU2zZTMzPjXZ6IiIhI\nTCiciUhcdIbDPFVXxxLH4dXmZgCOTE5moWXx9aIi8tTgQ0REREYZhTMRiamPOzpY7vfzoN9PfTCI\nCzg3J4dy2+bsnBzcavAhIiIio5TCmYgMuogx/HnHDpY4Ds81NGCA3IQEbh47lgWWxZEpKfEuUURE\nRCTuFM5EZNDsCAZ5pLqapT4fH3Z2AnBSRgblts3F+fkkq8GHiIiISA+FMxGJuvXNzSxxHFbW1tIZ\niZDsdvOfRUWU2zZlGRnxLk9ERERkSFI4E5Go6AiHeaKujiU+H6+3tABwVEoKCy2LK4uKyElMjHOF\nIiIiIkObwpmIHJatHR0sdRwe9vvZEQrhBi7IzaXCtvn37Gw1+BAREREZIIUzETloYWNYs2MHBfwH\nxQAAIABJREFUS3w+1uzYgQHyExO5ddw45lsW45OT412iiIiIyLCjcCYiA1YfCPBQdTXLHIePuxt8\nnJKZSYVtMzs/nyS3O84VioiIiAxfCmci0i9jDK81N1PpODxRW0uXMaS63VxTXMxCy+IENfgQERER\niQqFMxHZr/ZwmMdra6n0+XijtRWAz6akUG7bzCssZIwafIiIiIhElcKZiPTyfns7yxyHR6qraQqF\n8ACz8vIot21OHzMGlxp8iIiIiAwKhTMRIRSJ8Fx3g4+/NDYCUJiYyPfGj+ea4mJK1OBDREREZNAp\nnImMYjWBAA/5/SxzHLZ1dQFwWlYW5bbNzLw8vGrwISIiIhIzCmcio4wxhleam1ni8/FUXR1BY0j3\neFhoWZRbFhPT0+NdooiIiMiopHAmMkq0hkKs6G7w8XZbGwClqamU2zaXFxaSmaCXAxEREZF40rsx\nkRHu/fZ2Kn0+HqmupjkcJsHlYk5+PhW2zWlZWWrwISIiIjJEKJyJjEBhY/hDQwP3+Xz8ubvBR7HX\nyw1jx3J1cTFWUlKcKxQRERGRvSmciYwgDcEgD/n9LHUcPu7sBHY1+Fhk23wlL49ENfgQERERGbIU\nzkRGgKqWFpb4fDxeW0tnJEKq28384mIqbJvPqcGHiIiIyLCgcCYyTHVFIjxVV8d9Ph+vNjcDcHRK\nCuWWxZVFRYxJTIxzhSIiIiJyMBTORIaZbZ2dLHcc7vf7qQsGcQHn5eayyLb5j+xs3GrwISIiIjIs\nKZyJDAPGGF5sauI+n4//ra8nDGQnJPDtsWNZaFkcmZIS7xJFRERE5DApnIkMYS2hEL+tqeE+n4/N\n7e0AnJieziLb5pKCAlI9njhXKCIiIiLRonAmMgRtaWuj0nF4tLqalnCYRJeLrxUUsMi2mZaZqbXJ\nREREREYghTORISJsDKu71yZ7vnttMtvr5eaxY7nasij0euNcoYiIiIgMJoUzkTirDwR40O9nmePw\nr64uAL40ZgyLbJsLcnO1NpmIiIjIKBHzd33vvfce5513HmeccUav7evWrWPu3LmUlZVxzjnnsHLl\nyl77V6xYwTnnnMOUKVO45JJLWL9+fSzLFom69c3NXPnuu5SsXcutH31EfTDIAsti45Qp/O2EE5id\nn69gJiIiIjKKxPTK2R//+Ed+/OMfM2nSJDZv3tyzvb6+noULF3LzzTczc+ZM3nnnHa6++mpKSkr4\nwhe+wIsvvsi9997L/fffz8SJE3n22WdZsGABf/7zn8nJyYnlUxA5LJ3hME92r022rqUFgGNSUqiw\nba4oKiIrQRezRUREREarmH4s39HRwRNPPMG0adN6bV+1ahUlJSXMnTsXr9fLiSeeyIUXXthz9Wzl\nypXMnDmTyZMn4/V6mTt3LsXFxaxevTqW5Yscsk86O7lt61bGvfoq87Zs4fWWFi7IzeXPxx/Pu1On\ncl1JiYKZiIiIyCgX03eDs2bN2u/2d955h9LS0l7bSktLef755wHYtGkTZ5999j77N27cODiFikSB\nMYa/NjWxpHttsgiQk5DAf40dywLL4gitTSYiIiIiexgSH9U3NTVx9NFH99qWlZVFY3fHuqamJjIz\nM/fZv3Xr1pjVKDJQbcawxOdjic/Hu91rk01OT+da22ZuQQEpWptMRERERPZjSIQz2HWV4XD2i8Tb\nu21tLPH5eLS1lbZ//hOvy8VlhYVUWBYnaW0yERERETmAIRHOsrOzaWpq6rWtqamJ3NxcAHJycvrd\nfyBVVVXRKVRkLyFjeDkU4slgkHXhMACFLhfzEhP5SmIiue3t8MEHvBHnOmVk02ucxJLON4klnW8y\n2gyJcDZx4kSefPLJXtvefvttJk2a1LN/06ZNzJ49u9f+efPmDejnl5WVRa9YEaCue22ypY7Dtu61\nyWZ0r01m/+tfnDRlSpwrlNGiqqpKr3ESMzrfJJZ0vkmsDYUPA+KyiNLeQxQvuOAC6urqeOyxxwgE\nArz22musXr2ayy+/HIBLL72UVatW8cYbbxAIBHj00Udpbm7m/PPPj0f5Moqta25mXvfaZLd99BE7\ngkHKLYtNn/88fz3hBGbl55Og4YsiIiIicghieuXs7LPPxu/3Ew6HCYfDHH/88bhcLtasWcPy5cu5\n8847+elPf0phYSF33HFHz6clp556Krfccgs33XQTDQ0NTJgwgQceeICMjIxYli+jVGc4zO/q6lji\n8/F699pkn+1em2ye1iYTERERkSiJ6bvKNWvW9LmvuLiYZ555ps/9c+bMYc6cOYNRlsh+/auzk2WO\nwwOOQ0MohBu4MDeXRbbNGdnZavAhIiIiIlGlj/xF9mCM4YXGRu7z+fh/DQ1EgNyEBG4ZN44FlsX4\n5OR4lygiIiIiI5TCmQjQHArxq+pqlvh8vNfRAcCUjAyutW0uzs8nWWuTiYiIiMggUziTUe2d7rXJ\nflNTQ2s4jNfl4vLCQhbZNlP3WvhcRERERGQwKZzJqBOKRFjV0MB9Ph9/614/b2xSEreNG8c3iovJ\n93rjXKGIiIiIjEYKZzJq1AYCPOD3s8xx2N69NtkZ3WuTnZebS4I7LitLiIiIiIgACmcywhljeK25\nmft8Pp6sqyNgDOkeD4tsm3LL4ti0tHiXKCIiIiICKJzJCNURDrOytpYlPh9Vra0AHJuaSoVtc3lh\nIZlam0xEREREhhi9Q5UR5eOODpY6Dg/6/ezoXptsZl4ei2ybGWPGaG0yERERERmyFM5k2IsYw/Pd\na5OtbmjAAHmJidw2bhzzLYtxWptMRERERIYBhTMZtnaGQjxaXU2lz8f73WuTnZSRQYVtM0drk4mI\niIjIMKNwJsPOptZWljgOv6mupi0SIcnl4orCQipsm89rbTIRERERGaYUzmRYCEYi/G99Pff5fPzf\nzp0AjEtK4nu2zVVFReRpbTIRERERGeYUzmRIq+7q6lmbzAkEAPiP7Gwqutcm86jBh4iIiIiMEApn\nMuQYY1jb3MyS7rXJgsaQ4fFwbffaZBO0NpmIiIiIjEAKZzJkdITDPF5by30+H292r01WmprKItvm\nssJCMrQ2mYiIiIiMYHq3K3G3tXttsof8fhpDITzA7O61yaZrbTIRERERGSUUziQuIsbw5x07uM/n\n4w87dmCAgsREvtO9NtlYrU0mIiIiIqOMwpnEVFMwyCPV1VQ6Dh90r002LTOTRbbNRfn5JLndca5Q\nRERERCQ+FM4kJt5ubWWJz8dva2po716b7OtFRVTYNmUZGfEuT0REREQk7hTOZNAEIxGe7V6b7OXu\ntcmOSE5moWVxVXExuYmJca5QRERERGToUDiTqPPvsTaZv3ttsjOzs1lk25yjtclERERERPZL4Uyi\nwhjDP3buZInj8FRdHSFjyPR4uN62KbdtjklNjXeJIiIiIiJDmsKZHJb2cJjHamq4z+djQ1sbABPT\n0qiwLC4rLCRda5OJiIiIiAyI3jnLIfmwo4NKn4+Hq6tp6l6b7KL8fBbZNqdlZWltMhERERGRg6Rw\nJgMWMYY/da9N9sfutckKExP53vjxXFNcTInWJhMREREROWQKZ3JAjbvXJvP5+LCzE4BTutcmm52f\nj1drk4mIiIiIHDaFM+nThtZW7vP5WFFTQ0ckQrLbzVXda5OdqLXJRERERGSYiwQihFvDmLCJdymA\nwpnsZffaZL/0+fh799pkRyYnU25Z/GdxMTlam0xEREREYsQYgwkYwm3hnlukbVeg2mfbIRxjQp+G\nsoz18b/4oHAmANQFAtzv97PU58PXvTbZ2Tk5LLJtzs7J0dpkIiIiIrJfxhgiXZH9B6Q9vg+39hGi\nDnAM4SgU6QJPmgd3mhtPmofEvEQ8aZ6ebQljEminPQoPdHgUzka5N1pa+KXPx+M1NXQZQ4bHw3W2\nTYXWJhMREREZMfYJUK0HuOq0R1g64DHRClBuegWmxPzeAcqT7un5fs+gtc/36ftucye7D9hNvKqq\nKgpP4vAonI1Cu4cu/mL7dv7R3AzAMSkpLLJtrigqIlNrk4mIiIjEnDGGSGek/zA0kFDVxzFEolDk\nHgHKk+4hsSCx38DkSe8nRO0VvNxJBw5QI53ehY8i+xu6+OWcHK6zbc7MycE9yv8ziIiIiBzIngFq\nIMP0Dmp+VHuUApRnrwBVmNjr+36vOh3gGAWowaVwNgpo6KKIiIiMJsYYIh2R/YehAQ7T6y94EY3G\nfrsDVLoHT4YHb5H3oIbp9XeMy+tSgBqmFM5GKA1dFBERkaGsJ0D1MQQvuDGI/y3/oc2Pao9EJUC5\nElw94SchMwFP8UHMfTrAMQpQsj96hz7CaOiiiIiIRIuJ9L4CdVDD9A4UqgYQoN7jvQPW6Epw9cxr\nShiTQJKddMhzn/Y+xu11R+lvUmRgFM5GCA1dFBERGZ1MxBBuP7xhen0Fr0h7NCZAgSvR1atleVJJ\n0gGH6fkafBxZeuQB50cpQMlIonA2jGnoooiIyPCw3wA1kLlPAzgmagHK6+oJPAk5CSSN3U+AOsi5\nTz1tzBMPPkDVVdVRVFYUlecmMlzo3fswVBcI8IDfT6WGLoqIiESNiZjDG6bXzzGRjugHqMTcRJLG\nJfV5Relg14Y6lAAlItGlcDaMaOiiiIiMdia86wrUQbcoH8D8qKgFqKQ9AlReIknj9whQA2lj3s/8\nKHeCApTISKZwNsTtb+ji0SkpXKuhiyIiMkSZsOk3DA1k7lNfx0Q6oxyg0j0k5ieSfETy4bUx3x2o\nUhWgRIYdYyASndeWw6V39kOUhi6KiMhg6glQB9GivPOjTrakbTng/CjTFY1FoMCd7O4JP94C70EP\n0+srVClAicSRMdDVBYHArj/7ux3omGju93jgtdfi/bejcDbUaOiiiIjsFglFoj73afftUANUNdW9\nvncnu3uG4HmLvAc9TK/P+VGpHlwefRApclgikUMLLYMZjILB2P89eL2QlPTpzeuFjIze29LTY1/X\nfiicDQEauigiMnxFgpEDX3U6xPlRJhClK1Ap7p7w0xOgDmHu03v/eo/Pff5znx6jACXyqVAo/sFn\n7/2hUOz/HvYMPElJkJICY8bsG472Pm6w9nu9MNARZ1VVg/t3MwB61x9HGrooIhIb+w1QA2ljPoD5\nUVELUKmfhiKv5d1/YDqIuU8921I9uNzR+X3i8XpI+UxKVH6WyCEz5tMgNJSuCMV6zpLbvW8oyciA\n3NzYBZ+9bwkJAw9Csl8KZ3HwZksLv9DQRRGRXiKBSP9XlA5jbSgTHKQAdZhznwYjQIlElTGfhpHD\nDTUH+TNKd+7c9UZ/f/tNdP5PD1hCwr6hJCsrtsFn7/0aWTUi6V81RnYPXfylz8ffd+4ENHRRRIaf\nfQLUAFuUD2R+VNQC1B7hJzE78fDnPu3eluJWgJLBFYn0HV7idUWoe2RPTCUmQlISiR4PpKXtCiK7\n/4zHFSGvd1ezCJEYUCIYZH0NXbzWtjlLQxdFJMqMMZjA4bcx7yt4mVAUApSr9xWoxJzEgxum11+o\nUoCSgQqH4x989t4fj0YJ+wslmZnxuyLk9e4argdsqKqirKws9n8nInGkcDZI9jd08VrbZpGGLoqM\nevsEqEMYprf71lbXxmvmtV7HEY5CkS56hZ/E3L0C1EDmPvXVxjzFjUsfTI0ufc0PimcwCkfjP8pB\ncLn2DSRpaZCTE9uhcHveEhM1P0hkiFE4iyINXRQZOYwxRLoOs415P6EqWgGKFAhlhHYtpJuXeHDD\n9PoJVe5kBahhyZhdV18GKfgc4ffvChQHe/9YN0rwePYNJRkZkJcXvytCapQgIgOgtBAFGrooEh+9\nAtRBtigfyPwoovF+0k2v8JOY/2mAGsjcp/6OcSe7eeONNzTsJ172bJQQz6Fwe28bRLn725iQsG8g\nyc6ObfDZ+6b5QSIyTCmcHYa9hy6ma+iiyD6MMUQ6978O1EDmPh3omKgGqO4rS4kFiX0HpoHMfdrj\nGHeSrkBFxd4LqQ6FOULxXkh199fp6TELPm+/9x7Hf/7zvfd3zw8SEZHDp3B2kDR0UUaingB1GHOf\n+jymPUoBykOv8JNYmHjYc58UoPqwZ6OEoXJFaKgtpBqPK0IHs5DqIAnu3AkFBXG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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 6))\n", "plt.title(\"Bayesian Network Structure Learning Time\", fontsize=16)\n", "plt.xlabel(\"Number of Samples\", fontsize=14)\n", "plt.ylabel(\"Time (s)\", fontsize=14)\n", "plt.plot(x, libpgm_time, c='c', label=\"libpgm\")\n", "plt.plot(x, pomegranate_time, c='m', label=\"pomegranate exact\")\n", "plt.plot(x, pomegranate_cl_time, c='r', label=\"pomegranate chow liu\")\n", "plt.legend(loc=2, fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like when fitting for the same number of variables that pomegranate is able to scale much better than libpgm is. This could mean that for large datasets, pomegranate is much faster than libpgm even though it has a worse theoretical notation.\n", "\n", "However, it's also important to note that this isn't exactly a fair comparison because they are two different algorithms. libpgm is implementing the constraint based structure learning algorithm which is quadratic in time, and pomegranate is using an exact structure learning algorithm which is exponential in time. However, this benchmark should lay out some practical speed differences when deciding which package to use." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/benchmarks/pomegranate_vs_sklearn_gmm.ipynb000066400000000000000000021512241420461712600314510ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pomegranate / sklearn GMM comparison\n", "\n", "authors:
\n", "Nicholas Farn (nicholasfarn@gmail.com)
\n", "Jacob Schreiber (jmschreiber91@gmail.com)\n", "\n", "sklearn is a very popular machine learning package for Python which implements a wide variety of classical machine learning algorithms. In this notebook we benchmark the GMM implementations in pomegranate and compare it to the implementation in sklearn. In sklearn, GMM refers exclusively to Gaussian mixture models, while in pomegranate it refers to General mixture models, as it is flexible enough to allow any combination of distributions or models to be used as components.\n", "\n", "However, a simpler version of the GMM is kmeans clustering. Both pomegranate and sklearn implement these, so lets take a look at those first." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import seaborn, time\n", "seaborn.set_style('whitegrid')\n", "\n", "from sklearn.mixture import GMM\n", "from sklearn.cluster import KMeans\n", "from pomegranate import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets create some functions to evaluate models efficiently. We can start with fitting and predicting against an increasing data size. The data we compare against are Gaussian blobs which are 3 standard deviations away from each other to allow for some, but not a lot of, overlap." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def create_dataset(n_samples, n_dim, n_classes):\n", " \"\"\"Create a random dataset with n_samples in each class.\"\"\"\n", " \n", " X = numpy.concatenate([numpy.random.randn(n_samples, n_dim) + i*3 for i in range(n_classes)])\n", " y = numpy.concatenate([numpy.zeros(n_samples) + i for i in range(n_classes)])\n", " return X, y\n", "\n", "def plot( fit, predict, skl_error, pom_error, sizes, xlabel ):\n", " \"\"\"Plot the results.\"\"\"\n", " \n", " idx = numpy.arange(fit.shape[1])\n", " \n", " plt.figure( figsize=(14, 4))\n", " plt.plot( fit.mean(axis=0), c='c', label=\"Fitting\")\n", " plt.plot( predict.mean(axis=0), c='m', label=\"Prediction\")\n", " plt.plot( [0, fit.shape[1]], [1, 1], c='k', label=\"Baseline\" )\n", " \n", " plt.fill_between( idx, fit.min(axis=0), fit.max(axis=0), color='c', alpha=0.3 )\n", " plt.fill_between( idx, predict.min(axis=0), predict.max(axis=0), color='m', alpha=0.3 )\n", " \n", " plt.xticks(idx, sizes, rotation=65, fontsize=14)\n", " plt.xlabel('{}'.format(xlabel), fontsize=14)\n", " plt.ylabel('pomegranate is x times faster', fontsize=14)\n", " plt.legend(fontsize=12, loc=4)\n", " plt.show()\n", " \n", " \n", " plt.figure( figsize=(14, 4))\n", " plt.plot( 1 - skl_error.mean(axis=0), alpha=0.5, c='c', label=\"sklearn accuracy\" )\n", " plt.plot( 1 - pom_error.mean(axis=0), alpha=0.5, c='m', label=\"pomegranate accuracy\" )\n", " \n", " plt.fill_between( idx, 1-skl_error.min(axis=0), 1-skl_error.max(axis=0), color='c', alpha=0.3 )\n", " plt.fill_between( idx, 1-pom_error.min(axis=0), 1-pom_error.max(axis=0), color='m', alpha=0.3 )\n", " \n", " plt.xticks( idx, sizes, rotation=65, fontsize=14)\n", " plt.xlabel( '{}'.format(xlabel), fontsize=14)\n", " plt.ylabel('Accuracy', fontsize=14)\n", " plt.legend(fontsize=14) \n", " plt.show()\n", " \n", "def evaluate_kmeans():\n", " sizes = numpy.around( numpy.exp( numpy.arange(8, 16) ) ).astype('int')\n", " n, m = sizes.shape[0], 20\n", " \n", " skl_predict, pom_predict = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_fit, pom_fit = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_error, pom_error = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "\n", " for i in range(m):\n", " for j, size in enumerate(sizes):\n", " X, y = create_dataset( size, 1, 2 )\n", "\n", " pom = Kmeans(2)\n", " skl = KMeans(2, max_iter=1, n_init=1, precompute_distances=True, init=X[:2].copy())\n", "\n", " # bench fit times\n", " tic = time.time()\n", " skl.fit( X )\n", " skl_fit[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom.fit( X, max_iterations=1 )\n", " pom_fit[i, j] = time.time() - tic\n", "\n", " # bench predict times\n", " tic = time.time()\n", " skl_predictions = skl.predict( X )\n", " skl_predict[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom_predictions = pom.predict( X )\n", " pom_predict[i, j] = time.time() - tic\n", "\n", " # check number wrong\n", " skl_e = (y != skl_predictions).mean()\n", " pom_e = (y != pom_predictions).mean()\n", "\n", " skl_error[i, j] = min(skl_e, 1-skl_e)\n", " pom_error[i, j] = min(pom_e, 1-pom_e)\n", " \n", " fit = skl_fit / pom_fit\n", " predict = skl_predict / pom_predict\n", " \n", " plot(fit, predict, skl_error, pom_error, sizes, \"samples per component\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we see the fit and predict speeds for the two algorithms on increasing dataset sizes. pomegranate is always faster than sklearn for both prediction and fitting steps. These numbers fluctuate a bit every run and so running it multiple times (20 in this case) and reporting mean and standard deviations is ideal. Since these are unsupervised algorithms, accuracy is kind of a weird metric, but it refers to assigning cluster labels correctly corresponding to the underlying distribution which generated the data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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pFB+tri53KUIMsN1xeC+bpTWfJ+372L2Bp4RBPWIYuFqzMZPhnXSa6ZEIe1dU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EpLSzl16hRw9+/X1tYWgDp16gB3ezLXrl1LdnY2b775ps7irK3mzp3L2bNnee2114C7ic+1a9fQ\naDQ4ODhgZGRE06ZNadasGf7+/jqOVuia9PwI8RT57TpVZ8+eZeTIkcyaNYtu3bqpq17v2rULW1tb\n2rVrp+OIa5dhw4bh4uLC5MmT2bJlC0uXLsXLywtnZ2d27NhBdnY2S5Yswc/PT+ZGPKL4+HhWrVqF\nt7c306dPR6PRkJKSgr29vTr0LS0tjcGDB+Pr68vChQt1HHHtUlpayj/+8Q8yMzPx9PSkoKAAPz8/\ngoKCsLGx4ciRI3h6enLw4EFOnz5NVFSUrkOutXJzc5kzZw4///wzdnZ2GBgYUFFRQa9evRg8eDD1\n69cHYPjw4SQnJ3PkyBEdR1y7ZGVl8dJLL5GUlES9evX46KOPiI+Px8zMjKqqKry8vIiKisLa2lqq\n5wlAkh8hnhoPsk7VqFGj5B/sA8rKyqJXr1589913ODk5ERISQmRkJL179wbuLrA5c+ZMiouLWbZs\nmY6jrf26dOnCtGnT6NOnD99//z2rV6+msLCQkpISOnTowNtvv42zszPbtm3Dy8urxtw2cX9SU1MZ\nO3YswcHB1K1bl4MHD3L79m2aN2/Ohg0bOHHihNozIR7e+PHjMTY25t133yU/P59z586RnJxMUlIS\nRkZGDBo0iMDAQM6fP09ubq7Mw3xAGzduZMeOHaxdu5adO3cyf/583nvvPQAyMzNJTEzE2dmZqVOn\nSvIjADB4V0o9CfFU0NfX5+rVq2zatImAgAAsLCzQ09PD2tqauLg4+vXrh6mpKe+88w7dunWjS5cu\nug65Vjl79izbtm0jKSmJixcvYmRkRGhoqDpUyNDQEFtbW7755hu8vLxkfZ9HcO7cOfbv38+MGTMo\nLS0lIiKCiIgIevfujY+PD6dOnWL//v107doVT09P9cm5uH+Koqh/oxkZGYwZM4Y2bdpgb2/Ppk2b\nqFOnDpcvX8bJyUkW2nwEd+7c4eOPP2bGjBlYWVlhYWHBc889h5ubGy4uLhQUFJCUlISHhwctW7ZU\ni06I+2dhYUFCQgIBAQEcPXqUTp06ERQUhJOTE+3atcPY2JjVq1fTqVMnmVcsACl1LcRT5X7WqTp+\n/LisU/UQfHx8OHfuHK+88gq7du0iMTGR8+fP1zinUaNG6g2jeDiKouDk5ISdnR2JiYlcunSJrl27\nMnjwYHr27MlLL73EuHHjyM7OlnK1D0mr1arl7vv27UtKSgoLFy7E2dkZT09PiouLGTRoEDdv3kSj\n0Uji8wgURcHR0ZEdO3bU2G9ubo63tzcTJ05ET0+PNWvWIANxHpxWq8XGxgZ7e3tef/11zp49S35+\nvnpcX1+fl156CRcXFy5evKjDSMWTRHp+hHjKtG7dusaTcFNTU06cOEFmZibbt2/Hzc2NXr166TDC\n2s3Ly4uhQ4fi6emJt7c3BgYGZGdnc/78ed5//328vb3VNSXEg9PT08PQ0JDLly8TExNDVVUVlZWV\n9OzZE319fbRaLba2tpSUlJCWliZt/YA0Gg2GhoZqW9atW5eOHTuybt06evbsybp166hTpw7Tp0/H\n09NTEvlHZGRkxJ07d9i8eTMVFRU0bNiwxveziYkJjo6OJCQkEBQUpK6zJO6Pnp4eBgYG9O3bl6Ki\nIo4fP87evXupqqrC0dERc3NzDh8+zMqVK3n33XelsJIAJPkR4pnQokUL5s2bx6lTp4iNjZV/AI+B\ng4MDBgYGFBYWsnjxYpYsWYK/vz9jx46VRXofg+effx5LS0sOHDjA4cOHKS0tVdepunr1Ku+//z6B\ngYE1qsCJvxYZGcmOHTto1aqVWjjC0tKSjIwMvv76a3bs2ME777yDvb29OqRTPLjq4jJwd321wsJC\nEhISOHfuHGVlZQBYW1sD8PHHH2NkZET//v11Fm9tdOfOHbKzszlx4gQWFhb4+Piow72PHTvGypUr\nWbNmDSdPniQoKEgelAiVFDwQ4hkh61T9d2i1Wm7fvk1+fj7NmzfXdTi12q1btzAzM1O3S0tLOXr0\nKImJiezbt4/r16/TtGlTDAwMcHBw4NNPP9VhtLWPRqNh8uTJfPvttwC4uLgwfvx4dYL9q6++ioOD\nA4sWLapx8y4eTm5uLmfPnsXY2Bg/Pz9+/vlnVqxYweXLl7G3t6eyspLbt28DsHjxYina8YCmTJnC\n4cOHadCgAQ0bNmTGjBk0a9aMkpISUlNTKSws5MaNG/j4+GBvby+9akIlyY8QzwhFUdBqtfIPQDyx\nxo8fT3JyMsOGDePVV19V/1aLioq4evUqOTk5pKam0qFDB1xdXaVn4iHk5eXxwQcf0K9fPy5evMjy\n5ctp2LAhU6dOZfbs2Xz00Ue0a9dOXetHPJwffviB2NhYcnJysLW1xd3dnWnTpgFw6tQpfvrpJ0xN\nTdFqtfTu3ZsmTZroOOLaJS4ujoSEBN59911yc3P56quv0Gq1LFu2DBMTE12HJ55wkvwIIYTQOY1G\nw4gRI6iqquLGjRtotVoCAwMJDw+nXr166nnl5eUUFxfTuHFjHUZbO1X35ixdupTExETi4uIoLy/n\nm2++4cMPP6SyspKoqCjpHX4Munfvzttvv42rqyunT59m4cKFjBw5Ul0gWTyagQMHEhkZSZ8+fQC4\ndOkSY8eOZeHChbi4uKjnJSUlSWVTcQ95rCOEEELnjI2NcXFxwcbGhjlz5hAQEMB3331HcHAwMTEx\nXLlyBYDw8HA2b96s42hrp+phbKNHj6Z169asWrUKS0tLIiIiaNy4MS+//DLLli3j888/13Gktduh\nQ4cwMTEhMDCQFi1aEBwcTGRkJAcOHKCyspLy8nIADh48SFpamo6jrX00Gg3NmzenqKgIuDv0+Lnn\nnsPa2pq9e/eq53322WcsWrRIR1GKJ5kkP0IIIZ4IXbp0oVOnTnh4eDBy5EjeffddBg4cyLFjx4iI\niGDMmDGkpaXx5ptv6jrUWkur1QIwaNAgkpKSyMvL49ChQ+jr6zNnzhyOHz9OeHi4jqOs3Ro0aED9\n+vXJyclR93Xu3Jlz585RWFioDsuaMGFCjXPE/TE2NqZFixasXr2azMxMdf+AAQNISEhQt1evXk1U\nVJQuQhRPOENdByCEEEIA+Pv7q2t0WFhY4OXlRZs2bejatSuXLl1iypQpDBs2TKrpPYLqeTxt2rSh\nW7duTJw4kYyMDIYOHQogRQ4egyZNmlBRUUFcXByTJk1CURRatmxJ06ZN2b59O0OGDCE+Pp769evT\ntWtXXYdb65SUlBAQEICfnx+Ojo7q/nbt2lFWVkZBQQH79+/H1NRULeYhxK9J8iOEEOKJUV3+t1qd\nOnVwd3enSZMmGBkZMWzYMB1F9vQZNWoU2dnZlJaWEhISoutwnhoNGjRgxYoVXL9+HT09PXXx0p49\ne7J7926GDBnC8uXLeeutt3Qcae2zYsUKDh48SEZGBh4eHsTExKgVIps3b46npycJCQls2bJF2lf8\nIUl+hBBCPLGqJ+mvWLGCLl26YGlpqeuQnipTpkwhMzOzRlEJ8XB+XardysqKBg0aAP/pTevevTt7\n9uxhxYoV3Lp1i+DgYJ3FWhudPn2aL7/8kjFjxmBsbMzKlSvJysri6tWrpKenExgYyIgRI+jfvz+2\ntrYMGDBA1yGLJ5RUexNCCPHEKyoqQlEUSX7EE2v8+PGcPHmSYcOGERoaqpZqr6qqQlEUDA0N+eCD\nD4iNjWXatGlS+e0BjR49mhYtWvDPf/4TgJkzZ5Kbm0tOTg63bt0iNzeXWbNmUVFRgY2NjQx5E39I\nen6EEEI88aqfogvxJNJoNBQUFGBnZ8e6dev4/PPPCQoKYvDgwTXWowoLC6N+/fqS+DwgjUaDnp4e\nbdq0Ufft3buXXr16MW3aNOzs7Fi8eDHr169n9erVsgaY+FNS7U0IIYQQ4hH8Xqn2nTt3EhQUxPz5\n89WqbtHR0Wg0Gh1HW/sYGxvTuHFj1q5dS1VVFSUlJXh5efGvf/0LOzs7tFotYWFhVFRUcP78eV2H\nK55wMuxNCCGEEOIR7d+/n9zcXEJDQykpKeHixYscOnSIvXv3UlBQgKurK0lJSRw5ckQqFj6EW7du\n8dNPP+Hj44O5uTkajaZGO+bl5fHyyy9z4MABaV/xpyT5EUIIIYR4DPLz82tULCwrK+PChQs1SrVP\nmDBBhxHWblqtVi3XXu3UqVPcunWLZcuW0bx5c2bOnKmj6ERtIcmPEEIIIcR/UX5+Pj169GDfvn1S\ntOMxysrKYujQody8eZPQ0FAiIyNlvo/4S5L8CCGEEEL8F1SXap89ezZXrlxh+fLlug7pqaLRaCgp\nKUGj0WBnZ6frcEQtIcmPEEIIIcR/kZRqF+LJIcmPEEIIIYQQ4pkgpa6FEEIIIYQQzwRJfoQQQggh\nhBDPBEl+hBBCCCGEEM8ESX6EEEL8qfDwcObPn6/rMIQQQohHJsmPEEII8RRbs2YNGo1G12EIIcQT\nQZIfIYQQ4ilVUFBATEwMFRUVug5FCCGeCJL8CCHEE2rVqlV0796d9u3b06NHD+Li4tRjZ86cITw8\nnE6dOtG5c2cmTZpEaWkpANnZ2bRq1Yo9e/bQr18/2rdvz7hx48jKyiIsLAwPDw/Cw8MpLCwEIDo6\nmujoaGbPnk2HDh3w9vZm7dq1fxjX+vXr1ev27t2bnTt3qsf27dtHUFAQnp6e+Pj4MGPGjD/sdWjV\nqhVbtmwhNDQUd3d3AgMDSU1NVY+fP3+eN954g06dOuHt7c306dMpLy8HYOvWrfTp04cFCxbg6elJ\nVlbWPde/c+cO06dPx9vbG29vb6Kjo7l9+zZwd3HEmJgYAgICcHd359VXX+Xo0aPqa7t3784XX3xB\nREQE7du3Z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vLaGAJCIiIiJ9xtwFz9O0vp54Yj2z5z3JniPHEAyEOGDU12hcV8Nrbz2N49is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\nu+94+eWXJTlyMH9/f+Lj4+nevTs7d+5k7dq1pKen88ADDxAVFWVfGJ0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "evaluate_kmeans()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The lines show the mean of the plotted value, and the bounds show the minimum and maximum value. We can see that kmeans looks liks it's between 5-10x faster per fitting iteration than sklearn is, and around 3-4x faster when it comes to prediction! To confirm that the two are getting the same results, we plot the unsupervised 'accuracy' for the two, and see that they are identical. \n", "\n", "Lets now look at Gaussian datasets with two components when running Gaussian Mixture Models. We'll look at how many times faster pomegranate is, which means that values > 1 show pomegranate is faster and < 1 show pomegranate is slower. Lets also look at the accuracy of both algorithms. Accuracy should be roughly the same, but different, since both have different random initialization points. The measured the time is the time it takes to do a single iteration of EM." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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QxeMsjULRknicha576MHIGKxfpcHTclArJuYo1G4PK2/Qp6ekGIYQdaaiIXVE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\n796sWrWKj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FpIhrdLtO1UJ7PyPbFboeicN/u2TDjsgOOd1f2LGlRCJbtbEQM1OIJaw+VZzE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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def evaluate_models():\n", " sizes = numpy.around( numpy.exp( numpy.arange(8, 16) ) ).astype('int')\n", " n, m = sizes.shape[0], 20\n", " \n", " skl_predict, pom_predict = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_fit, pom_fit = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_error, pom_error = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "\n", " for i in range(m):\n", " for j, size in enumerate(sizes):\n", " X, y = create_dataset( size, 1, 2 )\n", "\n", " pom = GeneralMixtureModel( NormalDistribution, n_components=2 )\n", " skl = GMM( n_components=3, n_iter=1 )\n", " \n", " # bench fit times\n", " tic = time.time()\n", " skl.fit( X )\n", " skl_fit[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom.fit( X, max_iterations=1 )\n", " pom_fit[i, j] = time.time() - tic\n", "\n", " # bench predict times\n", " tic = time.time()\n", " skl_predictions = skl.predict( X )\n", " skl_predict[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom_predictions = pom.predict( X )\n", " pom_predict[i, j] = time.time() - tic\n", "\n", " # check number wrong\n", " skl_e = (y != skl_predictions).mean()\n", " pom_e = (y != pom_predictions).mean()\n", "\n", " skl_error[i, j] = min(skl_e, 1-skl_e)\n", " pom_error[i, j] = min(pom_e, 1-pom_e)\n", " \n", " fit = skl_fit / pom_fit\n", " predict = skl_predict / pom_predict\n", " plot(fit, predict, skl_error, pom_error, sizes, \"samples per component\")\n", "\n", "evaluate_models()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Lets also see how well it scales as we add more components where there are 10,000 total data points. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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kjz1tJcNzFWF0zPexhZiWCnIzjYMg92KOlw8GJBMmSxybZc7sVv2qIuxRPZVe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\n7ty/n9ZIhHVhCfHTk8npD8UTArU8Bq0W1pYCeLrmBsMKTbf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N+11kV+hdyk5hcq0O7mjv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\nEEIIIYQQ8gIVSIQQQgghhBDyA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CZ6wgJjfXiAlYY6iJoyGWerD5XCzWrA7YX1k2l68r5rLee4oxpaX5DuXbG1Cx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\nu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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def evaluate_models():\n", " sizes = numpy.arange(2, 21, dtype='int')\n", " n, m = sizes.shape[0], 20\n", " \n", " skl_predict, pom_predict = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_fit, pom_fit = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_error, pom_error = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "\n", " for i in range(m):\n", " for j, size in enumerate(sizes): \n", " X, y = create_dataset(10000 / size, 1, size )\n", "\n", " pom = GeneralMixtureModel( NormalDistribution, n_components=size )\n", " skl = GMM( n_components=size, n_iter=1 )\n", " \n", " # bench fit times\n", " tic = time.time()\n", " skl.fit( X )\n", " skl_fit[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom.fit( X, max_iterations=1 )\n", " pom_fit[i, j] = time.time() - tic\n", "\n", " # bench predict times\n", " tic = time.time()\n", " skl_predictions = skl.predict( X )\n", " skl_predict[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom_predictions = pom.predict( X )\n", " pom_predict[i, j] = time.time() - tic\n", "\n", " # check number wrong\n", " skl_e = (y != skl_predictions).mean()\n", " pom_e = (y != pom_predictions).mean()\n", "\n", " skl_error[i, j] = min(skl_e, 1-skl_e)\n", " pom_error[i, j] = min(pom_e, 1-pom_e)\n", " \n", " fit = skl_fit / pom_fit\n", " predict = skl_predict / pom_predict\n", " plot(fit, predict, skl_error, pom_error, sizes, \"number of components\")\n", "\n", "evaluate_models()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like pomegranate can be much faster than sklearn at this task--around 10x faster for the fitting step. The reason that the accuracies vary is because a different random initialization is used for each model.\n", "\n", "Now lets take a look at Multivariate Gaussian models. For sklearn the initialization is exactly the same, but for pomegranate the MultivariateGaussianDistribution object must be passed in instead of the NormalDistribution object." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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MM+WrTvomy2Dey0Kmb6yrKYadd5JJnqirY20yGfqiuyIiIiLSd4QKPyeccALz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tzIrVu3tP2DBg1i79692vbGjRuJiIgwRIiiijMxdABCCCEEgK+vr7ZGh7W1NR4eHnTo0AEfHx9S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\n2p45cybz5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LZK8Msygx1aCm/BP9kHUevoioc4QjIBsy/BP9yJ2VC2Ow0eXxPlqeBm9fb4wjJEpN8ZgV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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def evaluate_models():\n", " sizes = numpy.around( numpy.exp( numpy.arange(8, 16) ) ).astype('int')\n", " n, m = sizes.shape[0], 20\n", " \n", " skl_predict, pom_predict = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_fit, pom_fit = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_error, pom_error = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " \n", " for i in range(m):\n", " for j, size in enumerate(sizes):\n", " X, y = create_dataset( size, 5, 2 )\n", " \n", " pom = GeneralMixtureModel(MultivariateGaussianDistribution, n_components=2)\n", " skl = GMM( n_components=2, n_iter=1 )\n", " \n", " # bench fit times\n", " tic = time.time()\n", " skl.fit( X )\n", " skl_fit[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom.fit( X, max_iterations=1 )\n", " pom_fit[i, j] = time.time() - tic\n", "\n", " # bench predict times\n", " tic = time.time()\n", " skl_predictions = skl.predict( X )\n", " skl_predict[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom_predictions = pom.predict( X )\n", " pom_predict[i, j] = time.time() - tic\n", " \n", " # check number wrong\n", " skl_e = (y != skl_predictions).mean()\n", " pom_e = (y != pom_predictions).mean()\n", "\n", " skl_error[i, j] = min(skl_e, 1-skl_e)\n", " pom_error[i, j] = min(pom_e, 1-pom_e)\n", " \n", " fit = skl_fit / pom_fit\n", " predict = skl_predict / pom_predict\n", " plot(fit, predict, skl_error, pom_error, sizes, \"samples per component\")\n", "\n", "evaluate_models()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like pomegranate is faster in the fitting step for multivariate Gaussian mixture models and roughly the same speed for making predictions. The accuracy seems to be pretty much overlapping for these at near perfect.\n", "\n", "Lets see how the two models scale when changing the dimensionality of the data between 2 and 20 dimensions." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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//jgPP/ww55xzDh6Ph3vuuYfZs2cXO9/wYYBqy6PlkthHVXSfDSQGVKPHwxWN\njXy2vp4nOztZ0tlJOJ9nQ3bXbYcaUGsYvcXQzsWRTDbrm6YU6zIZRrvdjJRpe0IIIYQoMwUPPFi4\ncCG///3vmTlzJlu2bOGhhx6iqamJlpaWYuYbXnSF2maiLUxiH+MHTQqgYhhhGHy+oYFZySQTW1qI\nmyZbcjk25XJs6Xnb3PPnsmSSZcnkLq8R0vXuYsjj+VBx1OB2E9L1YT1YwaUUr8bjfMIwcMnqmRBC\nCCHKSEHFzxVXXMFrr73GjTfeyKmnnoppmjzwwAOce+65XHDBBVxzzTXFzjl8aAoVtdDnJbCODYBr\n+N5EHyhBl4ugy8Wkil23HGZt+0PF0I5/rkqnWZlO7/IxPk37UDG0458jDAN9GBRGtuPwSizGzKqq\nUkcRQgghhOhVUPHj9Xp56qmnqK6u7v4gl4vLL7+cT3ziE9x8881FDTgsaQoyNvpLCayZFeCTLVal\n4tE0xnq9jPV6d3mf6Ths7aMo2pLL0ZbNsjaz6xALl1KM7Gs7ncfDSGPoHHqrlKI9n+f9dJqxPl+p\n4wghhBBCAAUWP9///vf7fLylpYXf/e53AxpI9FAKTAd9fhJrhh+CUgCVG5dSjPJ4GOXxMG2n9zmO\nQ1fPdrrNOxRF2/+5LZHY5fUUUK0UMzZtYlYoxOSKCrRBvErkUoplyST10iMlhBBCiDIhh5yWOwf0\nBUms/6iAKvl2DRZKKWoMgxrD4JA+Dv5MWNaHiqHN2SxbcjlaUymeCod5Khym1jCYFQoxKxSixesd\nlH1EClgci3F8z6qxEEIIIUQpyd30IKEvTGJNr4D6obM1ajgL6DoTfT4m7rQl7N3Vq0mOHMn8aJSF\nsRh/3raNP2/bxii3m1mhEMeFQozpYwteOYuYJiuTSSb3UQQKIYQQQhxIUvwMFppCfzWFNbUCGqUA\nGqp0pTgyGOTIYJAv2zavJRLMj0Z5JRbjsa1beWzrVsZ4PL0rQqMHwThpXSlWplKMcrsJDaG+JiGE\nEEIMPgUXP9FolFAoBEAikWDhwoU0NzczefLkooUrZ20/byPwogWnOgfuTB6XQn89hZ3z4owv/5te\nsX/cmsYxlZUcU1lJxrZ5NR5nfiTC0kSCRzs6eLSjg4leL7Oqqji2spI6t7vUkXdLV4rFsRgn1dQM\n6j4mIYQQQgxuBRU/f//737npppt47bXXSKfTnH322XR0dJDP5/nOd77DGWecUeycZaf9kXaqX7Yw\n13eS+2KAKMxsAAAgAElEQVQNuA/QeSaGQnsrg51zcD4yuLY/iX3n1bTe1Z6kZbE4FmN+NMobiQSr\nt2zhV1u2MLmiguNCIT5WWUl1Ga6wpG2bZYkE04LBUkcRQgghxDBVUPHzk5/8hHvvvReAv/zlL1iW\nxYIFC3jrrbe49dZbh2Xxc9ifDmPezIW4FqZQ7Sapr9Wi1xygXYSGQnsvh5O1sY/Y9WwaMbT5dZ2P\nV1fz8epqYqbJgp5CaEUyycpUioc2b+Ywv59ZoRAzKiupdJXH7lZNKdZlMox2uxk5CLbrCSGEEGLo\nKeiuaNOmTRx33HEAzJs3j1NOOQWfz8dRRx1FW1tbUQOWK/dIN4FrPHjneon8K0LV/+3A8/UGsmMN\nkrZFznbQlSrejjgD1MY8Wj6JfWTFgdt6J8pKpcvFJ2pq+ERNDeF8npdjMeZHIryZTPJmMsnPN21i\nWiDArFCI/6ispKLEI6ddSrEkHudkw8ClHaDVUiGEEEKIHgUVP4FAgPb2dtxuNwsXLuTSSy8FoLOz\nE3cZ9xkUmzIUjV9rxDPGQ/tv20nf0kbT1U2EZobIWTZhM0/cskhaNmnbBsdB1wawSNEVqsNEW5TE\n/g9/9+GoYtiqMQxOGzGC00aMoCOX49/RKPOjUZYkEixJJDA2bWJ6MMhxoRAfDQbxlKj4sByHV2Ix\nZlZVleT6QgghhBi+Cip+Tj31VM4991w0TeOggw5i6tSpJJNJrrvuOmbNmlXsjGVNKUXd2XV4Gj1s\nvGcjG763gewFWerOq6PB46Gh53m24xA1TaKmScKySdkWpu3gUqr7MJR9pSlUxEKfn8D6WGAgPqUB\nYToOltO9+uXTNBzHKXWkYaXe7easujrOqqujLZtlfk8htCgWY1EshlfTODoYZFYoxJGBAMYBLISU\nUrTn87yfTjN2p1HfQgghhBDFVFDxc91113HIIYcQj8c55ZRTADAMg8bGRq699tqiBhwsKo+pZPyd\n41n/nfV0PNpBdkOWxisb0TzdN5WaUlQbxoca0dOWRWfeJGVZJCyLjG2jKdX/BRxNQdpGfymBGm0P\n4Ge1ZzsWOBW6ToWmdb/pOiGXixqXC4+moZSiYs0aEj0fMxgP6xzMGj0ePlNfz/l1dby/vRCKRJgX\njTIvGsXfM1XuuFCIIwIB9APw/XEpxbJkknq3G1+Jt+IJIYQQYvgoqPhRSnHaaad96DG32823v/3t\nooQarHzjfbR8v4X3v/s+0XlRcltyjLlxDEZN35O3fLpO0w43fpbtEM7nidsWyZ7tcrbj4CqkGlIK\nTIfAqw5U56FhYKZ95W0by3EweoqaHQucKpeLasMoaPuUX9OYUVPDvEiEhGXJuOMSUEoxzutlnNfL\nhfX1rM5kmB+J8O9olOcjEZ6PRKjUdWb2HKZ6SEVFUb9PClgUi3FCdXXRriGEEEIIsaPdFj/HH388\nL774IgDHHHPMHn9bv3DhwgEPNli5ql2M/854Nv1kE5F/RWj9eitjvzkWX8vet/fomqLO46au558d\nxyFhWoTN7tWhQgYpKBv0JSmo1rEme2DE3ougnG3jAMaOKzi6jl/TqHK5qDIM3AOwLcrQNE6ormZx\nLMaWbFYa3ktIKcUkn49JPh+XNDSwMpViXjTKy9EoT4fDPB0OU+NycWzPeG2tSNsWo6bJymSSyX5/\nUV5fCCGEEGJHuy1+rrrqqt6/X3/99QckzFChubXuQQjNHtofbmfNN9bQdFX3IIT+UEoRNFwEjQ++\nTXnLprNnkELKskn1NUjBUJCw0RemcEa4sA72kKvsLjTc21dutq/k6Do1Lhchl+uA9H1oSjEjFGJF\nIsF76XR3z5MoKU0pDvH7OcTv54ujRrE8mWR+NMqCaJQnOzt5srOTKbrOt4uwZVFXipWpFKPcbkJl\neDaREEIIIYaW3RY/n/70p3v/fuaZZx6QMEOJUoq6c+rwNO0wCOHCLHXn1u3XDaShazTofQ9SSFrd\nhZACPJqGx6vhSSvcr+SpbvRRN6USb1V5TOc7LBDAr+ssSyQOSI+JKIyuFFMDAaYGAlw2ahRvJBL8\ncetWlqXTPNnZyadra4tyzcWxGCfV1Mh2SCGEEEIUVcn3HT355JOcfvrpnHXWWb3b7IaS7YMQjFqD\njkc62HjPRuzswA0l2D5IYZzPx6EBP4e5XHy0spIjAgE+4q9gnM/H6KAPXwziL0SJLopipawBu/7+\nGO/zMaOyEpkDV54MTeOjlZV8c8wYgsCvt2zh3VSqKNdK2zbLEomivLYQQgghxHYlLX66urr4yU9+\nwu9+9zt+/vOf8/zzz5cyTtH4xvuY8IMJ+D7iI/pSlLXfXEu+K3/AcyhDkd+ap/PpTmKvxrCypS+C\nRno8nFBVhcz7Kl/VhsEFbjc2cPeGDcRNc8CvoSnF2nSa9mx2wF9bCCGEEGK7khY/CxcuZMaMGQQC\nAerr67nttttKGaeojGqD8bePJ3R8iPSqNK3XtJJuTZcki2Zo5LbkCP8jTPy1OHb+wI3H7kvQ5eKk\n6mp8moYt5wGVpYN0nfPr6ujI57mvra0o5zYZmsaSeBzTLu3PoxBCCCGGLuX04y5m3bp1bN68mRkz\nZgDd08j2p3/lwQcfZM2aNUQiEWKxGFdeeWXva/dl6dKl+3ytYjBfM6GfO3Ucx8F83sT8uwlucM9x\nox9RunUPx3HAATVaobfoKL10PRe24/CmZRFxHOn9KEO24/DzXI7Vts2nDYPZroIm5feL4zhUKcWU\nIry2EEIIIYaX6dOn7/JYQXcYbW1tXHPNNSxbtgyXy8Xy5cvZvHkzn/vc53jggQeYMGHCPoeKRCL8\n+Mc/ZtOmTXzuc5/jX//61x4Lqr4+iVJZ/NpiWia29P8DJ0FsSowN92wg9+sc9RfW7/cghO1aV7fu\nUybHcWAL+Fp8+A/2o/p90uqeLV26tKDv3UeBZYkErek0RpELoNWtrUxs2YfvXxGVYyboznXQxInc\nnM/z1dZWnjJNZjU3c1BFxYBfy3QcRgQCjPPteTx8oT9TB1o55pJMhSvHXJKpcOWYqxwzQXnmkkyF\nK9dc5WR3iyYFbXv79re/TUtLCwsWLOi9QW9oaODUU0/l9ttv3+dQI0aMYNq0abhcLsaMGYPf7ycc\nDu/z6w0mlTMqmXDnhA8PQsiVbruPUgqlFOnVaTr/3kny3SSOXZotaFMCAaYEAliyBa7sVBsG1zQ1\nYTkOd23YQMIa+L4xl1K8mUySLsJrCyGEEGJ4K6j4efXVV/nmN79JdXV1b/GjlOKyyy5j+fLl+3zx\nY489lkWLFmHbNl1dXaRSKaqH0Wnvvgk7DUK4sTSDEHakNAUOpN5JEX46TKo1VZT+jr1p8fk4prIS\n6f4oP1MCgQ/6fzZuLMrPhwIWxWID/rpCCCGEGN4KKn78fj9mHxOeOjs79+vGZ+TIkZx88smcd955\nfPGLX+Smm25COwAHbZaTnQchrLlmDek1pRmEsCOlKxzLIflmkvAzYTLvZw54hgaPh+NDIen/KUPn\n19dzuN/P4nicJzs7i3KNqGmyMpksymsLIYQQYngqqNI45phjuPHGG1m9ejUA4XCYhQsXcuWVV/Lx\nj398vwJ85jOfYe7cucydO5cTTzxxv15rsNLcGk1XNTHyopHkt+VZc/0aYgvL47feyqVw8g6x12J0\nPttJpu3AFkEhw+Ckqio8MgmurOhKcU1TEyFd5zft7awqwvk/ulKsTKWI5ku7GiqEEEKIoaOg4ufm\nm2/Gtm1OPfVUstksH/vYx/g//+f/MHHiRG666aZiZxwWlFLUnVvHmBvGALD+u+vpeKyjJFvO+qK5\nNJysQ/yVOOEXwuTacwfs2h5d56TqamoMQ/qAykiNYXBNc3NR+390pVgUi0nhK4QQQogBUdC0t8rK\nSn76058SDofZsGEDHo+HpqYmAoFAn9vhxL7bPghh/XfW0/FIB9kNWRqvbERzl8d2QOVS2Emb6MtR\nXCNc+A/z4x7hLvp1NaU4NhTijUSCdZkMLtkKVxam9vT//GHrVn60cSM3jBkzIFMLd5SxbZYlEkwL\nBgf0dYUQQggx/BR0R719O1pNTQ1Tpkxh8uTJBAIB4vE4xx57bFEDDke9gxAO6hmE8M3SD0LYmTIU\nVswi8lKEyPwI+Ujx8ymlmBYMclhFBaasBJSN7f0/i+Jx/lqE/h9NKdam07RnswP+2kIIIYQYXva4\n8vPyyy/z73//m/b2du66665d3r9x40bysh+/KIxqg/F3jKft/jaiL0VZc80axtw0Bt+EPZ99cqBp\nhoYZMYm8EMHd4MZ/uB9XsLgHVE7y+/HrOq/G4zIMoQxs7//56urV/Lq9nckVFQN+/o+haSyJxznZ\nMHANs6EoQgghhBg4e7yLGDFiBPl8HsuyWL58+S5v6XSa73znOwcq67CjuTWarm6i/sL6DwYhLCqP\nQQg7U4Yi35mn67kuooujWKnintEy2uvluKoqpPQpDwei/8dyHF6Nxwf8dYUQQggxfOzxV/STJ0/m\npptuwjRNbr311j6fE41Gi5FL9FBKUX9ePd5mLxvu2cD6O9Yz8qKR1J5TO+C9FQNBuRT5jjzhZ8J4\nmjz4j/Cje/SiXKvaMDixupr50SgZyyrLr0d/WI6D6TjoQNq2sQGd7kM/B8PnNjUQ4Ly6Ov5YpP4f\npRSbcznWpdOM85XXCqgQQgghBoeC9iftrvDp6Ojg1FNP5ZVXXhnITKIPlTMqmfC9Cbx/+/u0P9xO\nZkOGxivKZxDCzpRLkduSI9uWxTvGi5MrTo+OT9c5sbqalyMRwqaJPgiKhB2ZjoMG1BsGozweRrtc\nHF1bi+M4ZG2bhGURNU0ytk3Gtsn2PJ62bbK2jWnbKKUwlCqLLYCfqa/n7VSqt//n9NraAX19Qyne\nTCapdxd/yIYQQgghhp6Cip+1a9dy44038tZbb+3S43PwwQcXJZjYla/FR8sPWlh/+3qiL0bJbc4x\n9saxuKqL22OzP5SuyGzMYL5jsnXLVjSPhubT0LwayqvQPBq6T8dV7UKv0PepmNOVYlZVFa/F46zP\nZst+Epxp2+iaxkjDYLTHQ6PH01u4dPb8qZTCq+t4dZ3aPdzo52ybpGURMc3ugsiyyDoOmZ4CKWfb\n5HuGQ7iUKnpxuHP/z8EVFUwa4P4fBSyOxagc0FcVQgghxHBQ8MpPY2Mjn//857n66qu57777WLFi\nBUuWLOH+++8vdkaxg50HIbRe08qYm8fgG1++24CUUii36j4w1XKwEhZW4oOeEMd2cPLdN+hKV2je\nngLJ010g6V4dzat1F0h+Hc3YtUBSSjG9shJ/Msk7qVTZFUB528ajadS73TR5PDS43QOyJcytabg1\njWrD2O1zzJ5CqCufJ9WzYpSxbTI9RVKu5zGH7gJpf792NYbB1c3N3LpuHXdu2MC9EycS0Ad262PU\nNOm0LKYP6KsKIYQQYqgrqPh5++23efnll3G73WiaxoknnsiJJ57Is88+yx133NHnJDhRPNsHIXia\nPXQ80sHa69fSdHUTlccMzt+FK02hPB/ccDumgxW3sOK7KZBcPQXSzm8+jZYqNxU+jdfSiZJvgcs5\nDr6eFZ4xXi+1hlGS3h2XphHUNIKu3f/rbvUUQlHTJG6aZHsKonTPNrv+HjI6bcf+n7Y2bmhuHtDP\nXVeKVttmSSzGlEAAQybACSGEEKIABRU/brcb27YB8Pl8hMNhampqOP7447nxxhuLGlD0bfsgBE+T\nh40/3Mj673YPQnCmDM3zb3YpkPIOVr6PAinn4FUwBZt3nDSOW4FHgVfD8SjwaTghDXzFGcKQtW0C\nus5It5uxHg81g6Q3RVcKv67j13XweHZ5f1bXyTkORj8KmN7+n1iMv4XDnDZixEBGRlOKTdksbdks\nE30+Dvb7y6Lvqdx05HKsME0OtSy8A7wCJ4QQQgw2BRU/Rx99NJdddhk///nPOfzww7njjju46KKL\neP3116kY4P38on9CM0O4R7q7ByH8th19qk7sUzG8470Y9aVZaSgVpSmUt/vzDaIxxfLzdiJJPm6D\nsrrHYtsO5B1AEQybKLI4Lbve7PdHzrapdLkYaRiM9fkI7WGFZbAapWnk3W46crmCf6Z27P/51ZYt\nTPb5Br7/RykU8F46zbpslkMqKhgvk+AACOdyLE8m6TRNwsCz4TBHBYOM9npLHU0IIYQomYLu0m65\n5RbuvvtudF3n+uuv50tf+hJ/+9vfqKio4Lbbbit2RrEXvhYfLd9vYf0d60m/kWb9G+sB0Pwa3rFe\nvON3eBvjRfMMjy1CHl1jSjDA28kkCctGU4DWsxIEKBTauxnYmMea6oVQYUWL4zjkHIdqw2CkYTDe\n68U/BAuenX20spJnOjvpzwk+B6L/B7oLLdtxeCMeZ3U6zZRAYNhOhIuZJssSCbbmchia1rtap5Ri\nUSzGhHyeKYHAsPrFiBBCCLFdQXdsVVVV3H777QBMmjSJ559/nm3btlFTU4Mu2yjKglFjMP6741n9\n9GqqM9Vk1mXIrM2QWpki9Xbqgydq4Bnt6S6Exn1QFLlqXEPyZkhTikP9flan03Tmze4CaEe6goyN\n/u8kTrMb+zAvuz6pu+DJOw4jDIN6t5vxXi++YfazryvF0ZWVzI9G+zUUYVogwLl1dTxWpP6fHbk0\njYxt83I0Sq1hMDUQ2GOv01CSNE3eTCbZksvhUqrPPihD01iXydBpmsysrBx2P8NCCCFEwXcFq1at\norW1lWw2u8v7zjjjjAENNVhoEzVIAmXSZqMZGvrBOnUT63ofs7M2mfXdhVDv27oM2Y1ZovM/OKBW\nD+ofrA71FEWeZk+fk9UGG6UUkyoq8GaybMxk0PsobnApVFsOvT2PdZgPRhnYjoMJ1BoGDYbBeJ8P\n9zBvrK91u5no89GaTvdroMRn6+t5O5lkUSzGU+Ewpw5w/8/OXEoRMU3+GQ7T7PUyxe/HPURv9DOW\nxZvJJBszGQxN22thqitFyrL4ZzjM9GCQRtkGJ4QQYhgpqPi58847+dWvfoXX68W70/8olVLDt/ip\n1Kj+WDXR+VGsuIXSy2/lRPNoVEyqoGLSB70WjuOQb8/3rg5l1mZIr0uTfDNJ8s3kBx+sg6epe5XI\nN97XWxS5qgbnb9KbvR68StGayfS1uAOawraAJUkqR3sZdXSICSE/rmFe8OzsML+fLbkcmZ4hKIXQ\nleKa5ma+tno1/7tlCx+pqGDS/vTm2A7elTZaLoPd4obdnA9laBqbdxiKcMgQGoqQt23eTCZZn8ns\ndqVnT5RSvBKLMS6fZ6psgxNCCDFMFHQX+6c//YkHHniA2bNnFzvPoKO7dao/Xk18SZzshizKKP8b\nCKUU7gY37gb3h8ZjWymruyDaoSjKvJ8h+36W6IsfrBK5ql0f2jLnHe/F0+gpy+JvZ3UeNx5N4930\nB1sB7Z6Vuypdp9owqKs00HIK5mXJHqLhmuQvUdrypJRiRmUlz3V19Wv1Z4RhcHVTE7e+/z53rV/P\nD/e1/ydroS9K4263UXoWvTWLU+fCbjZglAE7ZVJKoQOt6TTvD4GhCKZt81YyybpsFuU4+3Uuk0vT\neD+TYVs+z8dCISqG6OqYEEIIsV3Bo65nzpxZ7CyDllKKyo9WkqpOkVyeRLnKvwjoi16h4z/Ej/+Q\nD272HcshtyXXu11ue1GUeD1B4vVE7/OUofCM8XyoKPKN96EHyu9mqtJwcYQWYKvjENJd1BgGtUYf\nPU8apN5KkV2fJTg9iFG1+4NEh5uAy8URgQDLEon+9f8Eg739P/e3tfGN/vb/REz0xT2Fq6a6Cx0D\nVMRC7zRhRQZnlIE9wQ3+D//saTsMRXivZyjCyEE0FMF2HN5OJlmTTgPdn8/Ohd6+0JUiY9s819XF\ntECAZtkGJ4QQYggrqPj5/Oc/zy9/+Uu+9KUvydaIPaiYWIEr5CK2OFY2fUD7S+kKT6MHT6OH0LGh\n3sfNuPnhFaJ1GbLrs2RaMx/6eKPWwDvBi9lkYo4wcVWXx5Y5j67xEZeLFv+eRy8rXWGnbCL/iuAd\n5yUwJYDqc8/c8DPB52NTNks4n+/Xfxe29/8s7G//z4Ys+vJM95CKvugKHFCb8ujrclClYzcZOGPd\nHxpi4dI0srbNgp6hCFMCASrLeCiC7Ti8l0rxXjqN5ThF27angCXxOB25HEcGg/LfeiGEEENSQf/H\nX7JkCW+88Qa/+c1vGDVqFNpOe8vnzp1blHCDkbvOTfXHq4n+O4qVtobsjbIr6CJweIDA4YHexxzT\nIbspu8twhfgrcXgFVj6xEv/hfkKzQlTOqMQVLN8bzp0plyKzPkNucw7/VD/e0fLbcYCjg0Ge7erq\nV63f7/4fx0FbkUG9n4NCt5W6FaRstJUZeCeLM9KFPd4NNR/8zG0fivBcOEyT18vUMhuK4DgOa9Jp\n3k2nydk2ulJF71dyKcWGbJZwzzS44TDCXQghxPBS0P/ZDj30UA499NBiZxky9Aqd6pOqiS6MkuvI\nobmGR8O8cim8Y7rPEmKH9rD8tjxrnlyDa6WL5LIkyWVJNv1sE8FpQUKzQgT/I4heUT43nbujNIVj\nOcQXxcmOzBI4KoDuKf/cxeTWdY4KBlkQjfar4b7g/h/LQVucRHVZhRc+O+r55YPaZqJvyoNfwxlt\nYLd4el/P0DS2ZLP8I5ejxesti6EI72cyvJNMkrZtXEr1q7dqf/Vug4tEOFK2wQkhhBhiCip+rrji\nit2+77HHHhuwMEOJ0hRVH6si+XaS1LupQdsHNBCMWgPXcS5a/ruFXHuO6L+jROdHiS+JE18SR7kV\nwaN6CqGjgmV/CKsyFPlwnq5nuvAd7MM/zAciNHg8jPV62ZjN9qto2LH/58dtbVy/c/9Pwuru78nb\nMBD//rgV5B3Uuiz66p4hCWPc0NDd76XoGYqQyXCw38+EEgxFaMtkeCuZJGlZuAoYW11MGt3b4Np7\ntsGVuiAUQgghBkLBexrWrVvH22+/TS6X632svb2dn/3sZ5x33nlFCTcU+A/xo4d0EksS3XcTw5x7\npJu6s+uoO7uu+6yhf0eJzosSWxAjtiCG5tMIHh0kdFyIwNRAeZ8zpHYYiHBUECM0fAciTAsG2ZbP\nk3P61+z22fp63komWbBz/8+WPPrr6e5/Zwb6pnvHIQnbUuBWOA3dQxI0v44NLIvHaU2nOdzvp8Hj\nGdjr96E9l2NFIkHUsjCUKpvx6i6laNthG1xAtsEJIYQY5AoedX3zzTfj8/lIpVIEg0FisRgNDQ1c\neumlxc446HkbvbgqXUTmR3BMRxqJe3iaPNR/pp668+vIrssSmR/pLoZe6n7T/BqVMyqpmlWF/wh/\nWY7S7h2I8MLwHoigKcUxlZW8EIn0a7VCV4qv79T/c9BGhbYqOzCrPXvjUmDvMCShWsduNnA1u8nY\nNgtiMWoNg6lFGorQmcuxPJkkbJoYSmGU4X8bNKXI2jbPd3UxNRBg7CAeEy6EEEIU9OvFBx98kJ/+\n9KcsXboUwzB45ZVX+Oc//8lhhx3GscceW+yMQ4Ir6KLmv2pwVblwzCEyCm6AKKXwjvfS8LkGDnrg\nICb8YAIjPj0CzasReS7CulvWsfKSlWz6+SaSbyVx7PL7+m0fiBB+JkxmU2bvHzAEhQyDgysqMPtx\n+Cl80P9jOQ53rX6f1HuZA1P47MytIGmjvZVBfzqO9loKI2oRNU2e6+rilViMrGUNyKWi+TzzIxFe\nikSI96z2lDtNKZYmErwai2H3c4VPCCGEKBcFFT8dHR0cf/zxAL2rFs3NzVxzzTXceuut+x0ik8lw\n0kkn8fjjj+/3a5UzzaVRNasK73ivFEC7oZSiYlIFo74wio/88iOM/+54aj5VA0D472HW3rCWd7/w\nLpt/uZnUeymcMroJU5rCMR3ii+NEF0SxsgNzozyYTPb7qTb6v/1vmruC81Ih2pXJj+o6cUo5K15X\noIPaaqLPT6L/K457VZb2ZJZ/hMMsTyT2+eY/aZosiEZ5PhIhYpr9GhJRDgyl2JTN8lw4TNw0Sx1H\nCCGE6LeC/s9bX1/PypUrAaipqeGtt94CoKGhgbVr1+53iJ/97GeEQqG9P3EIUEoRnBokMC2AY5XP\njXs5UprCf6if0ZeNZvKvJzPu/46j6qQq7IxN5186WXPNGlZ9aRXtD7eTWZspm0JIuRT5zu6BCMn3\nkqWOc8AdU1nZv+9FOI/+YpILuio5LOvhZV+av/kTe/+4A8GtIOegrcmiPxPDeDXF2rUx/tHZ2XvY\naCEylsXiWIxnwmE68/lBsdKzO5pSZB2H57u6WNePr4EQQghRDgraxH7BBRdwzjnnsGjRIk4++WS+\n/OUvc8IJJ/Duu+9y8MEH71eA1tZWVq9e3buyNFz4xvlwhVxEX44OmQNRi0npisC0AIFpAewv2yRe\nT3RPjFscZ+v/28rW/7cVT7OH0KwQoVkhPI3Fb1Lfe2hIrkgOu4EIXl1nWjDIq/H4Xm/y1fos2oru\ng0t1FNeFR3Bl/RYeCnUxOedhUt59gFLvhaZAA9Vl4eowwZNhRUOK1ZO8HFEX2u1QhJxl8WYyyYZs\nFpdSg26lZ090pXg9kaA9l+OjlZUyDU4IIcSgoJwCf0W7ZMkSjjrqKEzT5Mc//jHLly+nubmZyy67\njIaGhn0OcOmll3LzzTfzxBNP0NjYyFlnnbXb5y5dunSfr1Ou7LyN9boFKYZlo/z+cnIO9ts25usm\n9js29OzEUY0KfZqOPlVHqyn9DadjOt2ZJunD5vu8wjTpdHYz4MNx8L7n4Nlk4+w0yOKtSpt7J5nU\nZuHmdwwqrPL9einTIVsJngbFhEYXQVf3WUWm49BqWWxynJ6BdeX7Oewv23FwA4frOsEhVNwJIYQY\n/KZPn77LYwWt/DzxxBOcccYZ3R/gcvG1r31tQAI98cQTTJ06lebm5oI/pq9PolSWLl06IHmc/3D4\n/+y9e5QcZZ3//3qep6rvc78mkwyEBEIEg5ggKCKCXNXdlQOiHEHE3fPV7Kqoh58uuquuiAfioiss\nCE/clH0AACAASURBVMdVQfSoQPCOUSR4AblGjEAukIRLbpNk7n3vqnqe3x9V3dNzSyaTmckk1Ouc\nPvVUdXXX0z093fWuz+fz/qTXpim+WkRMppFjFVs2b2HhooUHPaepZlrn9TrgEvBynl9v8+cB0s+k\ncX/l4v7KJX58nPoz6qk9vRa7cSj6MtPvldEG2S1JnpQkNnfsxpFT9ZmaaiYzrzcYw297ehhV+eQa\n5JNZhPFg3ujP+xxgR6afe2oG+cnxFtf2NuN34RnOrl1dzJkz+QsvU0rGkN4E0c4E24ovE33jIlLA\n4lkiejZv2cKihdP7We8zhs5UasL9kY6kz/p0E85p4szGec3GOcHsnFc4p4kzW+c1mxgvaDIh8XPD\nDTdw7rnnkkxObTPHP/zhD2zbto0//OEPdHV1EYlEaG9v5y1vecuUHme2I4Sgdnktufoc2Wezr+mG\nqAeDSijqz6qn/qx63EGXwccHGfjzANlns+Q35tn1f7tInpik7ow6at9SO+PzqzZEKLYVSS1LoaJq\nxucxUygheFNtLY8MDKDKIiAdNC51tW8sMA6XD9bxfKTIo/E8v05meHe2ZoZmPUmUQAF9O/IMvuox\ntzuNabIwLQozL7LP13qkYAnB3zIZdheLvKmubuhvHhISEhISMouYkPj5xCc+wbXXXstFF13EnDlz\nsEb0u1i0aNGkDv4///M/lfEtt9xCR0fHa074VJNYlMCqsxh8fPBQT+Wwx6r1rcUbz2vE6XMYfHRI\nCGWfzbLz9p3IYyW97+il5tQa7IaZq8cpGyL0ru4leUKSxKLEjB17pmmORFgUj7M5n0d1uai/Taxx\naXX9z7fr+lg8m+p/9oMIGvOKHhex14FnC9CgMI0Wer4NqSNX8NpCsMdx+F1vL2+praVuEs5/ISEh\nISEh08mExM+Xv/xlAH73u99VtgkhMEE+/4YNG6Zndq9BIi0RGt7RwMAjA3h57zVTHzKd2A02Te9u\noundTZT2lhh8ZJD+P/dT2FRg56ad8C1ILE5Q++Zaak6rITpnZswShBRkns1QeKVwRBsinJBM0vXc\nIKWNeTiAtM5mbXFNXxNfaN7LDY3d3LynnaQ5zGpKAqMEMhqRKaFeLEJKYpoUus2GNmu/QvBwQwqB\nawwP9/fz+lSKhWFT1JApoqQ1Oc9j0HXJaU1Ra0rGUAzGRWPY5LoM9vVRoxT1lkV7JEJSqSO67i4k\nJOTAmJD4eeihh6Z7Hnz84x+f9mMcLqiEouGcBgYeH6C0u4S0DrMTvllMpCVC80XNNF/UzItPvkjD\nrgYGHxsktyFHbmOOru91ETs6Rs1pNdS+uZbY0bFp/dGUlkTnNH0P9RE/Jj4rG7geDEYbBp8cZPEO\nyd+tsSp39s2yYpxL07XcUzPIzfW9/Htfk1//k9dY3QbazeElHqICHIPoclHbHRAC06wwzRZmng2R\nI+d/XQnB34M0uFPDNLiQcTDGUDKGvOfR77oUykImEDPFqvVy+2RLiH1+njKeR8bz2Fks8kwmQ0QI\naiyLWqWosyzaIhFqQkEUEvKaZULip6OjY7rnETICIQX1b6knuz5LbmPuoI0QQkYjGyXNb2qm+Z+a\ncftdBp8cZPDxQbJ/y1L4cYG9P96L3WZTe1ottW+uJbE4gZim2g1pSwqvFHBfdsmmssQWxFCxwzs9\nyst7fgQz5xGPWBxlYrxcKHCgwcxy/c8jiRwP7ba58KcG66E0R+UMel4X7luTuKcnoeEwe7+C2j7R\n5yF6XHi+AHUS02ShO2yon9DX86zGEoJux+F3PT2cVlc3qQa4IYcnJhAuOa0ZcF3yWuNoTUFrCsZU\nxmVRI/DTJsezTJ+MTbwQgljwfFnPIxsIonWZDFZZEElJjWUxJxKh1rJCQRRyRLF48WI6OztRauj3\nsaOjg+985ztceeWVfOYzn+GEE07gnnvu4dJLLwVg3bp1RKNRjj/+eH7wgx/Q3d09ZUZns4UJ/bqe\ndtpp434hSClpa2vjzDPP5KMf/SjRcfpdhEyO5OuSqDpF5unMBFvShkwGq36oRsjLeWTWZhh8fJD0\n02l6ft5Dz897sOotak6tofa0WpJLk0h7av8gQgqEFuRezJF9PotVb2G32ETnR4k0Hh71LmVK3SW/\nds0MWbi3RyP0Og4Zz+NAQkAKwefW1/H3NXs4+7cD2A6YWkl2CSRedIj8uB/7J/3o18dwz0jiLYsf\nfhEUKSAC5A1iu4PaUoKY8E0TWi3MXPuwNU0QQuACf+zv54REgmOn2DgnZGbwjMEzBtcYSlrTrTVb\nczkKI9LOyuNSIGokvggeT9REZtgeXQhBNJhLzvPIeR5djsNz2SxKCGqUolYpaoKUuTrLCntYhRzW\n3H333WO2pLnrrrsA8DyPlStXVsTPqlWrWLZsGccffzyXX375jM51ppiQ+PnUpz7FLbfcwhlnnMHS\npUuRUrJu3Toef/xxPvzhD5PNZlm1ahXpdJr/+I//mO45v+aIdcSwai0GHhlAOzq8MjXNqISqNEvV\njia7LsvgY4MMPjlI32/76PttHzIhqVnuC6HUshQqPnVRByEEIirQeU3x1SKFzQVkXGK1WETnRonO\nic7qWrD8S3ky6zJjRskWJxL8NZOecF9f8VIJ+1eDzHkyx1wDO+bC6ksk/3jyHAa79zIn1YL1eA71\nSBb19wLq7wVMQuCdmsA9I4U+NnJ4pcWViQpfOHa7iN0OrMtDo0I3WZhOG6bw8zZTKCF4Ppdjj+Nw\nau30uy3q4ES9fLJejjI4wbbqk3kXKuMNrku+vx8ZpFZJ/BN4JXxHPxFsV/j1TVIIIkJgCUFESiLB\ntpH7zCSm6jU6gSApBILEg8p9lfch2DZqe9V7aBjqxy2AlzyPYjY77u/RTIuagyUazDevNXmt2e04\nrM9mEVBJmauxLNpsmwbbDgVRyGHP2WefzcqVK7n55ptJp9NccMEFfPCDH+TnP/85a9asobe3l0wm\nQ1dXF9dffz1XXHEFZ599Nr/73e/Yvn07p5xyCjfddBNCCO6//35uuukmmpqa+NCHPsS1117Lpk2b\nDvVLHJcJiZ81a9Zwww038Na3vrWy7X3vex+PPvooq1at4utf/zoXXnghl19+eSh+pgmrxqLh3AYG\n/jKA2+OGdtgzhLR9kVOzvIa53lxyG3IMPu6nxw38aYCBPw0gbEHqDSlqT6ul5tQarNqpTVcSEYHx\nDE6XQ2l7ibRIY7fYRFojxI6OTXkEarIYY8j8LUPhpcK4aZpKChbF42zM5lDjCThjkM8XsX81iHqu\nAIA+yqb0D7X88h0lflKf5uVcH1d2G6hRuOfW4J5bg9jhYD2SRT2SxXrYv+k2C/etSby3JjEth2ka\nmRKggLRGDhZhUxFqgvS4dgtaDh/TBCUEPYEbXEzrYffpEZGFygn7iBPyarEycnv1ybrG/0wi/Fqz\nsoDZ30lrFuhz3Qm/JhMcSwcCofyqqo8iYZggGktAqap9RgquLZ6HnU77rxFGvc6RwkWPECuV186B\nN9wti7uRWEIc8RfiygKuEHwW9zgOG7JZoEoQKUWLbdMUiYR1bSEA/H9btnDvnj3Teoz3trby/il6\nrq9+9aucd955rF69GoDf/OY3XHLJJfzTP/0Tt9xyy7B916xZw/e+9z201pxzzjn89a9/ZeHChfzX\nf/0X9957L4sWLeKaa66ZoplNHxM6G3jyySdHvQEAp5xyCp/4xCcAmDt3LplMZmpnFzIMaUka3tZA\n5u8Z8lvyoQCaYYQSJE9MkjwxSfs/t1PYWvCF0GODpJ9Kk34qDbdC8oSkL4ROqyHSMrXpauW/udvr\n4nQ7ZP6ewW60sVtsYkfHsFKH5gRfu5qBRwZw+9391qc12Dat0QjdJWf4Obs2qKfzWL8cRL1UAsB7\nXRTnH2rRJ8ZACD6QTfBcvMQjiRxz2xSXCk08cIAzHTbO++px3luHfL6A9ecs6uk8kVUDsGoAb0nU\nT4s7JQHx2SEYDxghIAqUDGKXg3q1BBaYRgvTYvk9hWZ5faAQAg9Y63kUenqGRRnAP2E/ELEy1vPb\nM3gSKgKBMtETXw8gECkTZYcxxIvFCe1rCXHYiOHDkbIgKmrNXq3Z6zhsyuUwQpCUkrogQtRs27SE\ngihkFnDFFVcMq/lZvnw5X/nKVyb1XBdccAGxmN+k/eijj2bXrl1kMhmOPvpojjvuOAAuu+wyfv3r\nXx/8xKeRCZ0ptbW1cdNNN7FixQrq6+sByGQy3HHHHdTV1aG15qabbmLJkiXTOtkQn9TSlF8H9MzY\nqUUh048QgvjCOPGFcdo+0EZxZ9FPjXt8sNJLaNe3dxFbFPMNE06rJTo/OqVXSoUUiIjAy3h4GY/c\nphwqqbBbbaIdUSKtkRm5MusMOgw8MoBxzYQ/j8fEYgw6Lg4GHIN6JIv960Fkl4sR4J4Sx313LXrh\n8BrC6v4/98z3uIftxLWgyVM0akWjp/zxaYqmN8VpTieY/xeXhj/lsDcUURuKmLv68E6J4741hX5d\nlAN2YJhN2CNME54rQJ3yo0LzLZjiKORUYgmBZwwiGFvhSWLIYUrZjMExhm7Xpdt1eSGXQwNJpdjl\nush0mriUpAIL7oRSYercEcrXFi7kawsXTvtx1q5dO6H9xqv5mQypVKoyVkrheR6Dg4PU1dVVtre1\ntU3JsaaTCf0yrly5khUrVvD973+feDyObduk02kSiQTf/OY3AT8U9o1vfGNaJxsyRPyouF8H9OgA\nEy6gCJk2onOjtFzcQsvFLTg9DoNPDAmhwuYCe36wh0hHpOIcF18Un/K6HRmRGMdQ2lGi8HIBaUns\nVptIe4TY/Ni0COXCjkLFjONAhJYQgmNNlE2/2kNk9SBiQGMUuGcmcd5V6xf4j0Oztriup5V7xR4K\nNRF6pEev8thuj5Gq1AgcBVwGC3cI3vVbwRkPGpofyWE9kiPXJNj9thi5tyVItkdp8BT2ARtyzxIq\npgkasb2E2lqEuMA0WlhFDQvM4S30QkIOI8qCyDWGAWB7ELkr12GV0wnjUhKruiWVosGySCo1KYe7\nkJCZJpVKkcvlKut7pjnlbyqYkPhZunQpDz/8MM899xx79+5Fa01TUxMnnngiiYTfnf63v/3ttE40\nZDR2g03DeQ0M/HkAL+2FUaBZgt1k0/TOJpre2YSX8Ug/lfZT4/6apntVN92rurGaLGpP9YVQ8oTk\nlKcwluuAnL0Opa4Smb9msJts7FY/PW4qDBoyz2fIv3Dg6ZdOn0PPL3ro/U0v0ZzGxATOu2pwL6jB\nNEwsUrHIifChXRZz5rQOPS+GPuXRKz16lC+IepVXEUe9rR7f/ZDHzVcZTnwOzv8tvP0PhgU/zcNP\n86xfAj89H54+UxBNWDQFkaRKNEkHS09RrxVqtoukiAAPxF6XxHaN6ktjWhSmzfZ7CoVCKCRkxlEj\nehSVDRbKlPsegR8RjUlJXCliZaEUuNHVWxZRKY/4uquQ6ce2bbTWZDIZUqkUlmWRTqcn/PgTTjiB\nTZs28corrzB//nzuu+++aZzt1DDhnAilFPl8nnQ6zSWXXAIQ1vjMAlRE0XB2A+m1aYqvTiwnPGTm\nUClF/Vn11J9Vjy5qMs9kKjVCvQ/00vtALyqlqDnFb6pqaqc+jCeCgnl30MUZcHwb7TrLT4+bhI22\n0YaBJwYodR1YA97iziLdP+um/6F+jGNQdYq2K9rY/bYoTvxAX9VobAStnkWrZ4Gzj3mg6W3x6LnS\n4y8fcEk8XWD+w0WOX+fxug3g3Gp4/M0OD1zg8NAp4I3xLSkN1FeLo6pxvtGjOZbDAmwjsBBYBiwj\nsEeNBRbBugmK3qdDVAWfAdHrIfa68GwhEEJBnVB44SQkZFZQbcUNfiqd47oMVu3jBK59EnxhJCWx\nKqGUlJIG2yahVFhzFLJfWlpaWLZsGWeddRZ33HEH55xzDl/72tfYtm3bsDS38WhtbeXTn/40H/zg\nB2lubub9738/P/3pT2dg5pNnQuJn48aNrFixgmw2Sy6X45JLLmHHjh285z3v4dvf/jZveMMbpnue\nIftACEHt8lpy9TnMljAHbrYio7JS/2NcQ/b5bKVOqP/hfvof7gcbth67lcTiBPHFcRKLE9hNU9cY\nsmKjXZicjbaX9/xIY96bsPDJb8mzd9VeBv8yCBrsdpuWi1qoP7seGZXUe5pnMpkD6v1zMESRzPEk\nc7zgfX1jCt4IxT4X9WgO689ZzviTwxl/ArdOsuetUTafHeGVhcKPKAWRpB7l8artsDlSGn6ABoDu\nSc9PBeKoLIrK4mlISAXrY4inymOqhJZCYLTHcbEc7a5Fu7BIGDGsuWqln9D8SKX5akhIyOzElpLy\nr4JnjN/Atep+L2giKwL79ZiUlfS6eHBrsO2KyUjIkcu+7KbXrFlTGf/whz+sjN/4xjfygQ98YNT+\nd99997jrH/rQh7jqqqsAePHFF6mdgXYGB8OExM91113HRRddxMc+9rGK0Ono6OCaa67hxhtv5Ec/\n+tG0TjJkYiQWJVBvVMTaYhjXDN0cg/GCZfV2z4Dxw+zCEgglZnX/mCMJYQlSJ6VInZRizv+bQ/7F\nPIOPD9Lzlx5yG3Lk1g/lz9rNdkUIJRYniC2MIaeoieeYNtrNNpG20Tbapb1B41LY7+fEGEP271n2\nrtpL9m/+z3LsmBgtF7dQ+5baYSmatpIcE4+zOZ8/pJlYpsHCfXct7rtqkC+VUH/OYj2WY+6v88z9\ndR7daeOekcR9Sy3U+WmDBkNGGHqV66fWSY+9gwMk62txhcHB4ApwMbhi9Ngpj4P9nKpx+fFe8JiM\n1KMeP2FqoFqQ1XqSds+i3bVo8yzaSxbtLynaN1m01EUQrRFM5+x3jgsJCRmNEmKYu1e58WwZE9Qd\nbQ16WrXaNgtiMWLq8OsfFnLocV2Xt7/97dx6662cdNJJPPDAA7M+KDIh8bN+/Xq+973vIUfkl15y\nySXceOON0za5kANH1ktSJ+4/TAn4gsg16JJGFzRe3sOUzGjh5I6xzTNoR/tNLcoXjxShgJoEQoqK\nsMmenuXouUeT35wnvylPblOO3KYcg48OMvhoIDwsQWxBzBdEx/kRokj7wTu7VWy0+1ycnuE22t5L\nHgPbBvZbV2Y8w+Djg+xdtZfCZr9HT3JpkuaLm0m9ITXuHJsjNr2uQ5/jHnqXXiHQx0TRx0RxPtCA\neibvN1H9W57ID/uxf9SPPimGe0YK7+Q4Nbakxo1wVOC3sKs7wxy7ZtqnafD73ZTFkxMIJq88DkSS\nIwwvD/RSbEnRpVy6LJcu5bLVLvHCyKgVfkpfS1rR/jeLdmXTFo/S1hqjPR6lLRKhRqmwziAk5DBG\nVDXmHXRdBhyH57NZvw2BbXN0LEaNNXudIkNmF5Zl8cUvfpHPfvazGGNoaWnh+uuvP9TT2icT+nQ3\nNDTQ399Pa2vrsO1bt24lGo2O86iQ2Y5QfrRHRmVwZfjAKYsg4xi8vIfO++NhAskFsVegkgpd0Oii\nxrjGdwizj/xGeQeKSihSS1Oklvoi1hiDs9shtylH/gVfEBW2Fsi/mKeXXv8xtWpYqlz82DgqMfmr\neCNttPUrGnHc+H8n7Wj61/TT/dNuSjtLIKD2LbU0X9xM4tjEhI65KB7nGTft90GZLVgC75SE3xto\n0MN6LOcLoWcKqGcKmKTEPS2Bd0YSvTAyo/1VBAIbPx0O2KfrY2O/ZE58eBqCxtArPbosl12Wy+4q\nYdRluayLF1lHEbwM7Bp6XEJK2iIR2iIR2m2b9mDcFonQZtuhQ1VIyGFGuc4o53m87HlsyuVIKkWb\nbTMvGqUlMjNtE0ImjgmaGc8Wzj33XM4999xDPY0JMyHxc/bZZ/OJT3yCFStWYIzh2WefZePGjdx+\n++28+93vnu45hsxihArC6zGwasb/OCmpaFjWUFnXJY2X8XAHXF80FTWmYNBFjc77kSjj+f/Ywn5t\nR5OEEETaI0TaI9Sf6ffZ0iVNYWthmCCqNFoFEBDtjPpC6DhfEEXnRSftCDje++9lPXpX99Lzix7c\nPhdhCRrOa6D5Pc1E5x3YhREpBMfFEzyfy81OI7JahXt+De75NYhtJaxHsqhHc9gPZbAfyqDnWLhv\nTZKMa+RAAZOSmKSCGjkr62gkgmZt0VyyOHF0AIiC0OxR3jBB1KUcupTLTq/IS4XCqMcIoMmyhsRR\n1bLdtqm3rIM6iTLG4AElrXGMGVoGNQ6lEePKMti3ejze4/PFIsmXXkIGDVYlVJZqxPr+luPtLw7w\n+fZ6HplMhogQ2FISCa7cR6TEFoKolFgjXMRCQiZLVEpcY9hRKvFyoYAtJa22zZxolHnRaNifaAZx\ntabfdelxHL+2S2uynkfO83C15pRDPcHDlAmJn8985jN87Wtf49Of/jSlUon3vve9NDQ0cNlll/HR\nj350uucYcgQiIxLZKLEbxy/m147Gy3m4/S46pytRI10YGhsnEEhBzdJrBRmRJI5PkDh+KKri9DmV\nVLn8C3nyL+bpe6WPvt/1+Y+Jy4oQKqfMWXWTS22otqvWOY2MS5ovaqbpH5sOyqChxraYE4mwq1Sa\nnQIowMyP4FwWwbm0HvlcAevPWdTaPJF7B/BbyQ3vc2BiApOUkJKYGhUIo/K6PzYpXyj5YwlJeUjt\nqGNG0ulKOt3Rf0+DoV9qurTD7lrNrhrNrpTHbu3Q5Tisz+V4vqrvQ5mIEBUxpEolUtu3H5B4cYxB\nj3rWaSCb3f8+M83LL+93F0sI7CphNFIsVcZj3FctpOyq/aNV+1XWhWDQGDxjQsF1hFOO5O5xHHaW\nSqxNp2mxbdoiEY6OxcJI7xRgjKGgNXtLJQYDYVMWOQWtEUBkxPtsSzkz34VHKBM684lEInz+85/n\nc5/7HD09PcRisQnZ34WEHAzSlsg6iV23D4Hk+pEit9/Fy/lpd5UoUlkslTSYQCDNwivwU4XdYGOf\nZlN7mp/eZDxD4dXCkCDalCe7Lkt23dCJXaQ9MpQqtzg+yuRgJOPZVTde2IhKTU2xbGcsSr/nDivQ\nnbUogT4pTumkOGQ16tk86Vf6qZNJyGhERiMyHiKj/fUuF/nKPny4qzACSPhCyKQCoZTyRdLw9UA4\nlfeLiWlPvxMIGrSiAcWSQWAQKBpfvLVYFOcr9sYMXaUSXaUSux2H3eVxqcS2oOEj/f2jnltC5US8\nfFKeGnHyXllWnbRXn8BHRpzADzvhH2Ncvdy8ZQvHHHMMGt81SwN6AktvvPvH2deMs99Yx+zq7qau\nsRHHGIojolbFEWKxWCUWc1pTct2KgJxqrPXrabZt2oKT4dZgWU6BPNhIX8jswgr+ln1BJOLvmQxN\ntk1LYJiQCOuE9olnDIPBe5fxvGGRHNcY7DGit9FQXE4LE/6krl+/npdffplSaXR+xHve854pnVRI\nyESRlkTWyH2m3BltfIE04OKm3Ur0iC4/guLlPL8GSbHPE//DDaEE8QVx4gviNF7QCICX8ci9mBsm\niAb+OMDAHwf8x9iC+ML4MEFkN9vobZpXV73K4GNj21VP6byF4Ph4gr9lMofe/OBASEq805IMHJUm\nMad+/P0cEwgjD5HViLQeEkpZb2g9G2xLe4huFzHBYiijCISQ8qNINZLGOhd5ZhG9IDJ90aSogJJB\n7HCIbS0xPyWZ12qhO+LQPvz/M+N5/G3rVo7p7BwlRA51JEEFgmk2sXlggEUjam4PFG0MbhBZK42I\nrJXM8FTAYevj3Lc3nSYfibDHcViXzY4ZLYsIQWsghIYtg/GRYp7hBKlJ/Z7nL0fceotFOnbsoNGy\naLRtGiyLRsuiIRCI1mH4Hsjg4kLa8xh0XTbkctRaFq22TWc0SmPkwPrHHUkUtaa7VGKgKoqTcd1K\nM9uIGF7rbAXmEyEzx4TEzxe+8AXuuece4vH4KIMDIUQofkJmNUL6ZgsqqYgy9Pm1lEXjMl8U6KLG\n6XNwB/wUOy/v+al2uSByhG8Lfbj/UKuUoubkGmpO9h0ujDGUdpbIvTAkiHIv5MhtzNFDj/+YWoU3\n6FGkOK5d9VQTVZKjYzFeKhRmdfrbpLAFNChMg9qXR8FwjIGi8SNI6bIwCoRSdowIU0YjBjzETgdh\ngtZDa3ajGxTe8jjesgT6+Oj01SJFBTi+EFJbS5AUmBYb3WFDk0VKKdqlZO5sMczRhiDMAt7sKSKe\nSsonqxGAKbA03rxlC4sWLgT8k73dpRJ7gghfeVkeby+O3YA7HtSSlKNFlXGwTBxC6+VSWdCMc+ur\nGmcnEKV+vq9vzO0CqFWKhipRNFIglccjU59mC2XDhKLWbCsW2VIoEJeSlsAwof0INEzQxpDxPPaW\nSmQ8j5zWlWiOG6SDjhQ0YRRn9jAh8fOrX/2Ku+66i1NPPXW65xMSckiQUUm0PUq0ffTJmHY1XtrD\n6XXwskFqXc5Pt/PyHujAlOEwrDkSQhDtiBLtiNJwlm9IoYua/OahyFB+Sx7Tbuj8QCfJNyRn7Ees\nLRqh13FIe96MNUCdtQgBMYGJSWjep7HbcLSBrGbg8d20bIminsljP5jBfjCDSUq8k+O+GHp9DKY4\nglchKnzHx10O6pWS/zpaLCxHw9zALl/7rpD+TSNcCPLIyvlhCM2QQDFUtlPeXhYtJhjrqvsZvm/l\nuQy+sDRD47o9Hqorg2lS6Dk2NKgZdfE7HIlKSWcsRmcsNub9Oc+rpD6OJZJeGUccpQLHsdYqQVQd\nQYod4MlkUethomVft9x+BI0AapSiybZZaFnUV90aRqxvf+UVGufNo9d16XVd+hyHvhHj3YG5wL5I\nSkmjbfuiKBBG5XFZMDVY1iEVjeBHNjzjp71uKxRQQtAaOEN2xuOHPLo7EcqNYgtas1trNmSzvtFA\nleGAYXQUR80y45HFixfT2dmJUgpjDKlUimuuuYY3v/nNB/W8t912G6+++io33HADV155JZ/5zGc4\n4YQTxt3/nnvu4dJLLwWY0P7TzYTET2trKyeeeOJ0zyUkZFYiLYlskNgNYxR+a4OX83B6HN8S/hmA\n+wAAIABJREFUOjBm8HL+2LjmsKs1klFJ8oQkyROSlW1bNm8htWjm6/yOSyR4JpMOCzsnixRQo8id\nKCmd2wSuQW4sotbmUE/nsR7JYj2SxUQF3tIY3rIE3slx32xhOogIX3zsdklu06idaSpSTgq/4EdU\n36bp/0YAqnplaGxsAXmN2K5RLxXBEphGC9NkYTosiIWNIA+UhFIsUIoFY4gjYwxpzxs3alSOJIxF\nnVKjokZ7XJen9uwZFaUZqEo7Gg+JL2hagnS08cRMvWVRZ1kTPsntE4L5sRjz97Nf3vOGRJHr0hsI\no5HjbeOIxTJxKStCqFoUNVYJpkZ78sY0B0I5hbTbddntOPwtmx1WJzTdjVVNVbpnNojSldM4Ha1x\n8KN85X3KqaCeMRj8b4eXPQ8vnx/2vLM1CjcWd999N+3tvhXP2rVrWbFiBatXr6axsXFKnv+uu+7a\n5/2e57Fy5cqK+Nnf/jPBhMTPF7/4Rb7whS9w0UUX0draihzxR1+0aNG0TC4kZLYjpMBKWVipsf+V\nvILvVucOuJWIUbUxAxwZ6XTThZKCRfE4G7I51Mj8N2382xGXFzeNWAJ9Ygx9YgznCoN8qYR6Oo96\nOof1VB7rqTxGgV4SC9Lj4piG6SliNpYfzZrVYb2gBlD0eYheF543UKMwjQrdakGbHX7+DhIhBLWW\nRa1lsSgeH3W/MYZ+zxsdNQqMNLYWCrww4sSUPUNuixKosyzaI5FRAmbkrVapQ3rVPq4UcaX2mw5a\nCiJY4wmk3mC8q1TaZ5S4UQhO2r6dJYkEr0skpt3GuvzeDrgu/UFj1XrLojUSYcEEGqvqoN6sEKSY\nFapETNnUo3rdDfb3g7pDqWj7+70dWYMzmyI5B8uyZcvo7OzkmWeeYfHixbz//e/nne98J+vXr+cH\nP/gBa9eu5atf/SqDg4M0NDRw0003MX/+fAqFAv/+7//OunXr6Ojo4Jhjjqk859lnn83KlStZvnw5\nP/vZz/jWt74FwNKlS7n++uv5l3/5F9LpNBdccAHf/va3ufLKKyv7/+Y3v+HWW2/FdV1aW1v5yle+\nQmdnJ7fccgt9fX3s3r2bjRs30tDQwG233Taq3+hkmdCv2saNG3nwwQf59a9/XdkmhMAYgxCCDRs2\nTMlkQkKONFRModrV2Ol0TlU6Xd7D5A3s9KNJuL4oCoF626YtGmFPoYT0gFqJabDIpSS6Mw4FjSj6\nNTGUdLA0iJLxU6ck/jddeJI6HCnQC6PohVGc99X79TlP+xEh9VwB9VwB7uzDWxTBW57AWx7HtM/M\n1eJZiRBDpg5dLmq7AxSgwf886nk21IRRoalGCFGJXByfGN0w2TOmkja2p1Riz549HN/RURE0NUod\ncX1pIlWNhveFZ4xvuBCk15UjSn2Ow17HYUMmw8P9/TwcuC7WKMWSRMIXQ8kki6bRyrpcJ5TXmlcK\nBV4MGqvudV28wcHhIqZKzJSjMRPta3UoIjTRL3Vh/WJgWo/h/mMdxQ9O8rGuSyT47PT397NkyRI+\n97nPkclkWLFiBd/4xjc4/fTT+dWvfsXVV1/N/fffz6pVq+ju7ubBBx8knU5z8cUX86Y3vWnY827f\nvp0bb7yRn/3sZ7S2tvLxj3+c73//+3z1q1/lvPPOY/Xq1cP237lzJ//5n//JqlWrOOqoo/jud7/L\nF77wBe68804AVq9ezb333svcuXP56Ec/yqpVq1ixYsXkXvQIJiR+brvtNj75yU/y9re/fZThQUhI\nyOSQ9uheR5ayaH5jM+6AS3FH0Y8a9bnogn7NRYiMZzCewaqxOK69ln47R7HdqhTpu1skZr7/BT7m\n1U3jiyDKjmpF7Quigva3lwITgVJQC6LwvxFfQ+9xNabDxu2ow/2nOkS3i1rrR4TkxiJqcwl+3I+e\nZ+Mti+OeksAcZb9m3ytgyCwirRHpEurFIsTLKXIKMy8yK5vbHmkoIWi2bZptmxOSSTb39rIobMUB\n+O9Nk23TZNswRlTthc2bic2bx/pcjg25HOuzWZ5Mp3ky7TfLtoXg2Hic1wVi6PhEgtQ0palFpMQx\nhm6gfgxX4dlWS3O48sc//pHu7m7e+MY30tfXh+M4nHvuuYCfEtfW1sbpp58OwLvf/W6+9KUvsXPn\nTp5++mnOPfdcLMuioaGBs846i+wIh8dHH32Uk08+mba2NgBuuukmlFJ0dXWNOZdHH32UU089laOO\nOgqA9773vXzta1/DdV0Ali9fTkdHBwBLlixh165dU/Y+TEj8RKNRrrjiCuwZyhENCXktI4TArrex\n64f+39xsIIZ6fTHkZTxfDB1B0QyjDcbxxY5qVESaI0TnRSv246c6CR7u72fCP73lK/VRiQlSm8cV\nScXAfjrt+WKoLJKcIIpUMP7YA2x8oXQE/xCbZgv3/Brc82sg7aH+mg8iQnnsnzvYPx9EN6tKREgf\nFw0ja9GgnqnbRexx4NkC1AUpcu02NIXGCSGzCylExajigqD+o8dxKkJofS7HxlyO9bkcdHcjgM5o\nlNclk74gSiRoeQ1bWo9H8UvtFL/UPv0H2rp1QrtdccUVFcODjo4Ovv3tb5NMJunr60MpVenbOTg4\nyLZt27jgggsqj41EIvT29jIwMEBNTU1le21t7Sjx09fXR21tbWV9f8GSkfvX1NRgjKEvcEasPp5S\nCs+bYL+HCTAh8XP11Vdz22238ZGPfITYOG4uk2XlypWsXbsW13X5yEc+wnnnnTelzx8SciRgJS2s\n44b+XXVRU9hR8I0W+jzcQdc3VjiMHOeMMZiiQaUUVpOF3WQTmx9DRsZOU6izbV6XSPB8Nos1lakM\ngZMaMYlptsbPkddDIom0i1Pwo0nKgagriJbAKhn6Swa3oFGHmdHFuNQovDNTeGemoKBRfy/46XHP\n5LFXp7FXpzG1vnOcuzyBPjHm23m/lpECIvjGCTs06uUq44TGwDghHqbIhcw+mmybt9bV8da6OsB3\n6tsUCKD1uRybcjleKRb5TW8vAM3B9/LrEgmWJJN0RqNhhGaWUW14sC9aW1s55phjuP/++0fdV1tb\nSzqICAL0Bn//ahoaGnjmmWcq65lMhsI+HAybmpqG7T8wMICUkoaGhv3O9WCZkPi566672LlzJ3fc\ncQc1NTWjDA8ee+yxSR388ccf58UXX+QnP/kJfX19XHTRRaH4CQmZADIqSRyTgKDmUDua4s5iRQw5\nA46/3yxq2mqMwZQMKqGwGn2xE50XRR3ASeBxySRdjsNAEBafrnm6xuDhB3hiUhJTipgticUE0QZJ\nXErqbZuUUqNyyp9+spsTju/k5d1ZdvcV6M85qIJBBhElkQ+iSBhfJBxOEZOYxHtTAu9NCd857vmC\n7xr31xzWH7NYf8xiYgLvpDjeKXG8pXFIzJ7P4CFjpHHC+tA4IeTwIKEUJ9fUcHJwFd41hq35fEUM\nbchm+dPAAH8a8GtcklJyfFXd0LHxeNjf5jDhpJNOYu/evaxbt46TTjqJbdu2cfPNN7Ny5Ure8IY3\nsGbNGi6//HIGBgb405/+NKr9zZlnnsl///d/s337djo6OvjiF7/Isccey0UXXYTWmkwmU4kyAZx+\n+unccMMNbNu2jfnz5/PjH/+Y008/HWs/xhdTwYSO8M///M/TcvBTTjmFpUuXAr6qzOfzeJ6HOsQe\n9SEhhxvSlsSPihM/ys/rNtpQ2l2itKeE1+/h9DkYz4wbVZkudFEj4xKrwcJutIl2RrESB/fFdmpN\nDb8b46rTRHEDcSPx+5NEpfQFjvRFTVxKapSi1rKISnnAdVZCCeK1EZbURlgCuFrzcqHAzmKRbtdF\nAVIDOQ8xoKFgEAU93LihYMCd5QLJEuiT4uiT4jhXNSA3lyqGCdYTOawnchgL9Akx3OUJvDfGoS78\nbi+nY5qiwdvpoF8tIhHoBoVstLDm29h1kYrjVFRKBoAGy6r0GHGNwQ5rIEIOAZYQHJdIcFwiwXvw\nLxbtLJV8MZTNsiGXY20mw9pMprL/oliMJUGq3JJEgtoZOLkNOXBisRg333wz1113HdlsFtu2ufrq\nqxFCcOmll/L0009zzjnnMHfuXM4555xhkSCA9vZ2vvzlL3PllVeilOL1r389V111FbZts2zZMs46\n6yzuuOOOYft/5Stf4V//9V9xHId58+Zx3XXXzchrndAn8KKLLpqWgyulSATuLffddx9ve9vbQuET\nEjIFCCmIzokSnePn3BpjcHodSrtKuH2BiUJJI6e4saUu+s9pNQZiZ14Uq2Zqf+iiSvGGmho2mtEJ\nauXGdCI4aSyLmqiUxIUgplRF2MSknBEXKEtKFiUSLEokcANnox3FIj0SRMq31h0z1c41kA8EUq4s\njLRff1QMXO3coEFn5BALJCnQx0XRx0VxLqtHbHN8EfR0DrWugFpXwHwX9HFR30J7+WjXrlEYM9S8\n1AVcg3CDsRe8dhdE1RhvnH1cM/p5PH888jHNwsWaP+ibFjRZmEaFaVAwRkqpMb79rodBIBD4dRS+\nXpVYAhQiKNb2i7YtBJb0l2XxXb5fDAj0Ho1KGKwmhd1qE5sXQ1kWy+rrK8cteB49jsNA0GwxqzW5\nYKzxGy8eaQ5nIbMTIQQd0Sgd0SjnBulKfa7LhkAIrc/leCGfZ2M+z0+Dx8yPRiv22q9LJmmz7deU\nmc9MsmnTpnHvmzdvHuvXrx+27eSTT+a+++4btW8ikeB///d/x3yeNWvWVMYXXnghF1544ah9fvjD\nH465//nnn8/5558/av+Pf/zj+1w/WIQxY5xBjMB1Xb71rW/xwAMPsGPHDoQQdHZ2cvHFF/OhD33o\noCfx+9//njvuuIPvfve7wwqcRrJ27dqDPlZISIiPzmjMXoMZNJAGUzBgc0A/QsY1oEDUCqgD2SqR\nqZmJLr3guqSBqBBE8X0I4kJQE2yb7VfFXWPo0pq9xtAXRKIO+ITVNci8QWZB5UGWgJJBlvyxKBpE\n0NfRHKI6HKvHkHxOk3xeE3vZIIJfnFIzvqDwDMIF4Q3dKI/3++s0MxgBuhZ0vUDXC6gHUy8Q9QLV\nILDqBSrlR2qmUnQY7Ys0kRKBXTsg8GXWqIawYDAUMGQE5I2hKKEEFAUUMRgZlGNVCzkJJrghwajg\nuVRwPCn8+wiOI/33g5HPEwL4F5qCj/CwP4/G/456LZ7kF43hVa3ZqjUvac0rWlPdprUW/Ca4UtIu\nBHEhiAOxYDnbv8sPFfOFYEEYMNgvy5YtG7VtQpdkb7zxRtasWcNll11WsaTbsmUL3/ve9/A876DS\n4v785z9z++2383//93/7FD5lxnoRh4q1a9fOqvnA7JwTzM55hXMajptzKe0o4fQ6Q45ytu8ot2Xz\nFhYuWoh2NEIJ7AYbq9Ei2hHFqrcOzQ/6LPz7weT+hp4xvJLPs7NUYm+phJhK8eYYtv59M4sS8xH9\nLvRp0LpShzLtzAFO9ANU+QEPtTaICG0uIC2BUdI/K4wJ3xraAqOE3wTVwjcKUENjgrGpjIcew4jH\nGAVa+evCEli2RAZLZQssS2LbEssWxG1F1FZsX/8Kc+LtON3O0G1vsNzmwCtjKzJtC0yTjWyxsZtt\n7PKyaqySkztR2bJ5CwvnL5zUYyvzM4acp0l7LgWtKXqaojEUtYfrGV/LlBvOaoAgqmhgWCPaQJF2\nde2mvbMdahWm1q9fMq32Ibf33rxlC4sWHtx7Bf7/ZHVfGQnYUmILgS0ElpREgtREu/oWpM4mg3pA\nWwie+etfWXzSSWwrFukN+u2UjCFyiE/qp+q9mggnVI09Y3i5UKikyq3P5Vjnuqwbx80rJiWJ4D1N\nKkWyapyQklR5e7CeVIpU1Tgygcam+2Im36cDobh166z8DZxNjBc0mZD4+c1vfsNdd93Fwqo//rnn\nnsvb3/52rr766kmLn3Q6zcqVK7nzzjuprwrph4SEzDxWwsI6tspRruQ7yrk9LnRB7OgYkfYIdnOY\nojDVKCE4JpHgmEQCzxheDWqE9jpO5f5JYwt0vUQvDJw6jYE+D7nLRQy40K99J7uZiAzVKbyzU3hn\np9i1q4s5cw7cDlYHqWblk9KIFFjCP8m0ALu8LkWlhssWEjXBtEArrahdVDvmfcY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lAAAg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\n8yGTyTBu3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7nCQJVL+nGlo9fV6Je+hEaXBQgkTKRmsI1oFLa9Qgs5QgkeBSNAUiQmdIibti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\nR6d8jgsvvBCrVq1Ce3s7GGO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YhBBCzuSHdkAheBWH3ujvk+oFtXi/9KUvIZlM4sknn8Rb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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "numpy.set_printoptions(suppress=True, linewidth=200)\n", "\n", "def evaluate_models():\n", " sizes = numpy.arange(2, 21).astype('int')\n", " n, m = sizes.shape[0], 20\n", " \n", " skl_predict, pom_predict = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_fit, pom_fit = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_error, pom_error = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " \n", " for i in range(m):\n", " for j, size in enumerate(sizes):\n", " X, y = create_dataset(50000, size, 2)\n", " \n", " pom = GeneralMixtureModel(MultivariateGaussianDistribution, n_components=2)\n", " skl = GMM( n_components=2, n_iter=1 )\n", " \n", " # bench fit times\n", " tic = time.time()\n", " skl.fit( X )\n", " skl_fit[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom.fit( X, max_iterations=1 )\n", " pom_fit[i, j] = time.time() - tic\n", "\n", " # bench predict times\n", " tic = time.time()\n", " skl_predictions = skl.predict( X )\n", " skl_predict[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom_predictions = pom.predict( X )\n", " pom_predict[i, j] = time.time() - tic\n", " \n", " # check number wrong\n", " skl_e = (y != skl_predictions).mean()\n", " pom_e = (y != pom_predictions).mean()\n", "\n", " skl_error[i, j] = min(skl_e, 1-skl_e)\n", " pom_error[i, j] = min(pom_e, 1-pom_e)\n", " \n", " fit = skl_fit / pom_fit\n", " predict = skl_predict / pom_predict\n", " plot(fit, predict, skl_error, pom_error, sizes, \"dimensions\")\n", "\n", "evaluate_models()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "It seems as though both fitting and prediction using pomegranate scale less well than sklearn to more dimensions, but that it is stil faster. \n", "\n", "This notebook tested a main portion of the overlap between sklearn and pomegranate, but both offer clustering options which aren't displayed here. pomegranate allows any distribution or mixture of distributions, univariate or multivariate, and even some more complex models to be used as a component in the mixture model. The out of core API which pomegranate offers also extends to GMMs, allowing them to be trained using exact EM updates on data which can't fit in memory. In contrast, sklearn offers suppoort for both dirichlet process GMMs (DPGMMs) and variational Bayes GMMs (VBGMMs) as well, which are robust clustering models.\n", "\n", "We hope this has been useful to you! If you're interested in using pomegranate, you can get it using `pip install pomegranate` or by checking out the github repo." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 } pomegranate_vs_sklearn_naive_bayes.ipynb000066400000000000000000016527411420461712600331100ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/benchmarks{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pomegranate / sklearn Naive Bayes comparison\n", "\n", "authors:
\n", "Nicholas Farn (nicholasfarn@gmail.com)
\n", "Jacob Schreiber (jmschreiber91@gmail.com)\n", "\n", "sklearn is a very popular machine learning package for Python which implements a wide variety of classical machine learning algorithms. In this notebook we benchmark the Naive Bayes implementations in pomegranate and compare it to the implementation in sklearn." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import seaborn, time\n", "seaborn.set_style('whitegrid')\n", "\n", "from sklearn.naive_bayes import GaussianNB\n", "from pomegranate import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets first define a function which will create a dataset to train on. We want to be able to test a range of datasets, from very small to very large, to see which implementation is faster. We also want a function which will take in the models and evaluate them. Lets define both of those now." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def create_dataset(n_samples, n_dim, n_classes):\n", " \"\"\"Create a random dataset with n_samples in each class.\"\"\"\n", " \n", " X = numpy.concatenate([numpy.random.randn(n_samples, n_dim) + i for i in range(n_classes)])\n", " y = numpy.concatenate([numpy.zeros(n_samples) + i for i in range(n_classes)])\n", " return X, y\n", "\n", "def plot(fit, predict, skl_error, pom_error, sizes, xlabel):\n", " \"\"\"Plot the results.\"\"\"\n", " \n", " idx = numpy.arange(fit.shape[1])\n", " \n", " plt.figure(figsize=(14, 4))\n", " plt.plot(fit.mean(axis=0), c='c', label=\"Fitting\")\n", " plt.plot(predict.mean(axis=0), c='m', label=\"Prediction\")\n", " plt.plot([0, fit.shape[1]], [1, 1], c='k', label=\"Baseline\")\n", " \n", " plt.fill_between(idx, fit.min(axis=0), fit.max(axis=0), color='c', alpha=0.3)\n", " plt.fill_between(idx, predict.min(axis=0), predict.max(axis=0), color='m', alpha=0.3)\n", " \n", " plt.xticks(idx, sizes, rotation=65, fontsize=14)\n", " plt.xlabel('{}'.format(xlabel), fontsize=14)\n", " plt.ylabel('pomegranate is x times faster', fontsize=14)\n", " plt.legend(fontsize=12, loc=4)\n", " plt.show()\n", " \n", " \n", " plt.figure(figsize=(14, 4))\n", " plt.plot(1 - skl_error.mean(axis=0), alpha=0.5, c='c', label=\"sklearn accuracy\")\n", " plt.plot(1 - pom_error.mean(axis=0), alpha=0.5, c='m', label=\"pomegranate accuracy\")\n", " \n", " plt.fill_between(idx, 1-skl_error.min(axis=0), 1-skl_error.max(axis=0), color='c', alpha=0.3)\n", " plt.fill_between(idx, 1-pom_error.min(axis=0), 1-pom_error.max(axis=0), color='m', alpha=0.3)\n", " \n", " plt.xticks(idx, sizes, rotation=65, fontsize=14)\n", " plt.xlabel('{}'.format(xlabel), fontsize=14)\n", " plt.ylabel('Accuracy', fontsize=14)\n", " plt.legend(fontsize=14) \n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets look first at single dimension Gaussian datasets. We'll look at how many times faster pomegranate is, which means that values > 1 show pomegranate is faster and < 1 show pomegranate is slower. Lets also look at the accuracy of both algorithms. They should have the same accuracy since they implement the same algorithm." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sizes = numpy.around(numpy.exp(numpy.arange(8, 16))).astype('int')\n", "n, m = sizes.shape[0], 20\n", "\n", "skl_predict, pom_predict = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "skl_fit, pom_fit = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "skl_error, pom_error = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "\n", "for i in range(m):\n", " for j, size in enumerate(sizes):\n", " X, y = create_dataset(size, 1, 2)\n", "\n", " # bench fit times\n", " tic = time.time()\n", " skl = GaussianNB()\n", " skl.fit(X, y)\n", " skl_fit[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom = NaiveBayes.from_samples(NormalDistribution, X, y)\n", " pom_fit[i, j] = time.time() - tic\n", "\n", " # bench predict times\n", " tic = time.time()\n", " skl_predictions = skl.predict(X)\n", " skl_predict[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom_predictions = pom.predict(X)\n", " pom_predict[i, j] = time.time() - tic\n", "\n", " # check number wrong\n", " skl_e = (y != skl_predictions).mean()\n", " pom_e = (y != pom_predictions).mean()\n", "\n", " skl_error[i, j] = min(skl_e, 1-skl_e)\n", " pom_error[i, j] = min(pom_e, 1-pom_e)\n", "\n", "fit = skl_fit / pom_fit\n", "predict = skl_predict / pom_predict\n", "\n", "plot(fit, predict, skl_error, pom_error, sizes, \"samples per component\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks as if pomegranate is approximately the same speed for training small models but that the prediction time can be a lot faster in pomegranate than in sklearn.\n", "\n", "Now let's take a look at how speeds change as we increase the number of classes that need to be predicted rather than phrasing all of the comparisons on binary classification." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sizes = numpy.arange(2, 21).astype('int')\n", "n, m = sizes.shape[0], 20\n", "\n", "skl_predict, pom_predict = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "skl_fit, pom_fit = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "skl_error, pom_error = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "\n", "for i in range(m):\n", " for j, size in enumerate(sizes):\n", " X, y = create_dataset(50000 // size, 1, size)\n", "\n", " # bench fit times\n", " tic = time.time()\n", " skl = GaussianNB()\n", " skl.fit(X, y)\n", " skl_fit[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom = NaiveBayes.from_samples(NormalDistribution, X, y)\n", " pom_fit[i, j] = time.time() - tic\n", "\n", " # bench predict times\n", " tic = time.time()\n", " skl_predictions = skl.predict(X)\n", " skl_predict[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom_predictions = pom.predict(X)\n", " pom_predict[i, j] = time.time() - tic\n", "\n", " # check number wrong\n", " skl_e = (y != skl_predictions).mean()\n", " pom_e = (y != pom_predictions).mean()\n", "\n", " skl_error[i, j] = min(skl_e, 1-skl_e)\n", " pom_error[i, j] = min(pom_e, 1-pom_e)\n", "\n", "fit = skl_fit / pom_fit\n", "predict = skl_predict / pom_predict\n", "\n", "plot(fit, predict, skl_error, pom_error, sizes, \"number of classes\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like, again, pomegranate is around the same speed as sklearn for fitting models, but that it is consistently much faster to make predictions." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.57 ms ± 38.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n", "672 µs ± 11.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "X, y = create_dataset(50000, 1, 2)\n", "skl = GaussianNB()\n", "skl.fit(X, y)\n", "\n", "pom = NaiveBayes.from_samples(NormalDistribution, X, y)\n", "\n", "%timeit skl.predict(X)\n", "%timeit pom.predict(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This does show that pomegranate is faster at making predictions but that both are so fast that potentially it doesn't really matter.\n", "\n", "While it's good to start off by looking at naive Bayes' models defined on single features, the more common setting is one where you have many features. Let's look take a look at the relative speeds on larger number of examples when there are 5 features rather than a single one.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sizes = numpy.around(numpy.exp(numpy.arange(8, 16))).astype('int')\n", "n, m = sizes.shape[0], 20\n", "\n", "skl_predict, pom_predict = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "skl_fit, pom_fit = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "skl_error, pom_error = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "\n", "for i in range(m):\n", " for j, size in enumerate(sizes):\n", " X, y = create_dataset(size, 5, 2)\n", "\n", " # bench fit times\n", " tic = time.time()\n", " skl = GaussianNB()\n", " skl.fit(X, y)\n", " skl_fit[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom = NaiveBayes.from_samples(NormalDistribution, X, y)\n", " pom_fit[i, j] = time.time() - tic\n", "\n", " # bench predict times\n", " tic = time.time()\n", " skl_predictions = skl.predict(X)\n", " skl_predict[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom_predictions = pom.predict(X)\n", " pom_predict[i, j] = time.time() - tic\n", "\n", " # check number wrong\n", " skl_e = (y != skl_predictions).mean()\n", " pom_e = (y != pom_predictions).mean()\n", "\n", " skl_error[i, j] = min(skl_e, 1-skl_e)\n", " pom_error[i, j] = min(pom_e, 1-pom_e)\n", "\n", "fit = skl_fit / pom_fit\n", "predict = skl_predict / pom_predict\n", "\n", "plot(fit, predict, skl_error, pom_error, sizes, \"samples per component\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like pomegranate can be around twice as fast at fitting multivariate Gaussian Naive Bayes models than sklearn when there is more than one feature.\n", "\n", "Finally lets show an increasing number of dimensions with a fixed set of 10 classes and 50,000 samples per class." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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uhFWYKqE+k8d9SfO7q60MBvn06tV8Y3iYH46N8eTMDB8aHGSFT9pf95kmB4pFbkunuS6VIuqzvZSEEEIIIcTxYakhgU8CnwO2U2ls8CiVkaL5Hzurn8VR8DoDqI/PwkRr1vKYqsof9/XxkZNOYrJc5gPbtnHn2BieT9bvdBsGs67LLek0E6VSq+MIIYQQQojj0KIFkW3bXwa6gPVURnZeAZx3yMe51c/iaAQUCDW/wcKhzonF+Pwpp3BGJMJ/7N/P/929mymfNFxIVdtw3zw6ykix2OI0QgghhBDieLPkPCTbtieBTZZlvR34uW3bhebEOnF4yQDqUBF3VxFvZfMaLBwqEQjwkZNO4o6xMb5+4ADv3bqV9/f3c7YPGi7EAwFmHIdbRke5LpViwCfT+oQQQgghxPJX0yp627a/IcVQ47ipANpDOSi4Lc2hKgrXd3TwmdWriWkaf79rF1/Zv5+S29pcAFFNIx4IcPvYGFtzuVbHEUIIIYQQx4nWtxUTEFKh6KE+NdvqJACcHAzy2dWruTaZ5LaxMT64fTu78/lWxyKsaXTpOnePj/PUzEyr4wghhBBCiOOAFEQ+4XVqqE/mYdwfa3dMVeVP+vr48IoVjJVKfGDbNu7yQcMFU1XpMwx+PjnJI5lMy/MIIYQQQojlTQoiv9AUvLBKYEMWXP/c5J/b1sa/rlnD6ZEIX9q/n0/u3k2mxQ0XdFVlwDR5NJPhF1NTOFIUCSGEEEKIo3TUBZFlWXo9gwggoaHsL6Hs9Fc3tYSu89GTTuIPe3p4bGaG923dyqYWT1nTqhu4Pp3N8uOJCV+scxJCCCGEEMtPTQWRZVnvsyzr9fO+/gowa1mWbVmW1bB0JyC3I4C2IQt5f93gq4rCqzs6+PSqVUQ0jY/s3MlXW9xwQVUUBgyDHbOz3DU+Tt5xWpZFCCGEEEIsT7WOEL0PGAWwLOsS4HeANwObgM80JtoJKqhCEdQnjr3Bwqzn4dZ5OtmqUIjPrl7NK5JJbh0b40PbtzNUaF0DQkVR6DNNRopFbk+nmfHJ/klCCCGEEGJ5qLUg6gd2Vh+/Cvgf27Z/APw9cEH9Y53YvG4N9Zk8Svrob+5HSyWyrstQoVD3xgOmqvKuvj7+bsUKRksl3r91K3ePj7e0wUGPYZB1HG5Np5kslVqWQwghhBBCLC+1FkQZoLP6+Ergx9XHJUB2yaw3VcGLqmgPHXmDBc/z2FcokAgEuDYYZF04zN5iY9YknV9tuHBaOMwX9u3jH1vccKHDMHCBm0ZHGW3Q9yyEEEIIIY4vtRZE9wD/WV07tAb43+rzpwM7GhHshNeuoYyWUbbXPh3N9Tz2FAqcHApxXSpFRFW5JB7nJNNkf4OmtSV1nb9fuZJ39PTwq2rDhSda2HAhEQgQUlVuGR1lyAd7JwkhhBBCCH+rtSB6N/AA0AH8tm3b49Xnzwa+24hgy9HsxhmUQv2mjbkdGtqGHOQO37igXC2GXhKN8vJEAkOtvLUBVeXlySRdhsFIg6aSqYrCazo6+NSqVYSrDRe+NjzcsoYLsUCA9kCA28bG2JrLtSSDEEIIIYRYHgK1HGTbdgZ47wLPf7TuiZaxfe/YSl/ChZd4oCjHfkJTBcdFfWIW97ciix5WcF0OFItc0t7Oi6JRlEOubagqVyeT3J5OM1YqkdIb0zF9dSjEv6xezVf27+eWdJqnZmb4wOAgA6bZkOstJaxpdCsKPxofJ++6nBGNNj2DEEIIIYTwv5r3IbIsq9uyrA9alvVFy7I6qs9daFnWyY2Lt7wk39dLdJuL9ots3c7pdWmoW/IoIwuvzZlxHNKlEq9IpTgzFntBMTQnpGlcl0qhKQoTDVznY6oqf9rfz9+uWMGBUok/37qVe1rUcMFUVXoNg59OTvKrTKalTR+EEEIIIYQ/1boP0XrABt4C/CHQVn3pSuCTjYm2/MTf3kVuUMH47wmU8ToVHaqCF1PRHpoB5/k39BPlMlnH4TUdHawKhQ57qmggwKtSKcqu2/DmBxe0tfH5NWtYGw7zb/v28f/27CHbgil0uqoyaJpsyGT45dQUjhRFQgghhBBinlpHiD4N3GDb9kuA+avzfwRcWPdUy5SiKgxfq0MZjK+MQ71uvts0lLSDsvU3P/qRYhEN+O3OTnqOYEpaXNd5VUcHWdcl1+CNTFO6zsdWruRt3d08Oj3NP05P80y2fqNntdIUhUHT5KmZGX48MdHSzWSFEEIIIYS/1FoQrQe+scDz+4HuWk5gWdZXLcsasSzr6UVeb7cs64eWZT1hWdYzlmW9vcZsvlJKKpTeGEd7Io92f/1u/t0ujcAjObwZp9JWW9d5bWcn8aNYD9RpGLwqlWK8XCbf4OJAVRRe19nJP69aRUBR+LsdO/jeyEjTR2pURWHANNkxO8v/jo9TkJEiIYQQQghB7QXRLJBY4Pm1wEiN5/g6cM0Sr78b2Gzb9lnAZcBnLMsyajy3r5SvjOKsNStT58bqNDXNUHE9j7FHpjg5FOKVqRRhTTvq0/WaJq9IJhkpFik2YcRkTSjEX8diXNLezndGRvjwjh2km7yBqqIo9Jkmw8UiP83nW7pnkhBCCCGE8IdaC6LbgI9aljU3N8uzLGsl8E/ATbWcwLbt+4HxJQ7xgJhlWQoQrR67PO9YVYXiO5Pg1m/qnOvBaLvH6l0KFxciB9tqH4uVoRBXJBLsLxYpN2HEJKgofGBwkPf397M1n+fPtm7l4Uym4dc9VK9hMAv8YGSEXbOzTb++EEIIIYTwD6WWzluWZbUBdwFnAhFgmMpUuQeAa23brmluWLWIusO27TMWeC0G3E5l1CkGvNG27TsPd85NmzZ5ZgvaOi8kPVrmqTuniPZV8kTvL5G8ucjY7xpkLzj6VtdlzyPjuqwJBOidVVACCsGrgyja0q298/k8wWDwsOffUirxWKlEj6qi1qNd+CKKxSKGURn0O+A4fC2XY4/jcKlh8NpQCL2B114oi6vrjDsOp+s6Z+g6gSZe/1C1vlfN4Kcs4K88fsoC/srjpyzgrzx+ygL+yuOnLOCvPH7KAv7K46cs4K88fsriN7lcbuP69evPWei1I9mH6CLLsi6nshmrCjxm2/Z99YvJ1cAm4HJgNXCvZVm/qF57UaZpsm7dujrGOHr7YjNs1p6ioyNVeeI1Hs7mEZK3FQn/VgdeqqYf9/MUXJcZx+Hl0ejB5gn5PXniXpzouqX31tmyZUtNP5u1nkf/9DS/mp6m3zAaVhTtGRpicGAAgEHgLNflGwcOcPvYGLtVlQ81cc+iuSyO57G/WORZw+DliQTtgSN/j+qh1veqGfyUBfyVx09ZwF95/JQF/JXHT1nAX3n8lAX8lcdPWcBfefyUBfyVx09Z/Gbjxo2LvnZE865s2/6Jbduftm37n+tcDAG8HbjZtm3Ptu2twA4qo0XLl6pQ/ONUZercfx351Lms4zDruFzQ1va8TnJGt0HmoQzlmfrMKFQUhXNjMc6IRNhbLDZtvx5dVfmj3l4+vGIFY9U9i+6bmGjqfkFatdnCdLnMD0ZG2CFT6IQQQgghTig1/zrcsqyXAC8DujikkLJt+y/rkGU3cAXwC8uyugEL2F6H87aU1xWg9MY4xjcn0H6exbls6VGdOZlSmYCq8NL2dqKB5zdPUA0VVMhsyJB8ebIuOVVF4aL2dgquy47ZWfqaOA3x3LY2bgiF+OzQEJ/fu5cnZmZ4V1/fMTWNOFIpXSfvutw5NsaLo1HOb2tDr8M6LSGEEEII4W81FUSWZf0l8P+AXcABKg0Q5tT063zLsr5LpXtch2VZQ8BHAR3Atu0vAZ8Avm5Z1lOAAvyVbdvp2r4Nfyu/PIr2aA7j2xPkXxRceuqcB+PlEm0BnfXRKKa28E253qWTs3NE1kUw++tTvGiKwsvicQquy3CxSI/RvCZ/KV3n4ytXctPoKN8ZGcHO5fjQ4CCnhMNNyxCsbuL6VDbLcLHIyxOJo2prLoQQQgghlo9aR4j+HHiXbdv/cbQXsm37TYd5fR9w1dGe39dUheI7UwT/Zj/Gf41T+MtOWGCdjufBWKlEr2HwomiUgLr4Wh5FUQgkAkz8fIKu3+lCDdRnNENXVa5MJLhjbIzRYpHOJhZFmqLwO11dnBGJ8JmhIf5y+3Z+v6eHV6dSDW32MN/cfkVjpRI/GB3l8nicNU0syoQQQgghRHPVehetAj9uZJDjx8IDZl5XgNLvxtGeyqP97IVN+VwP0qUSK4NBzootXQzNCcQCOFMO2afrtwEsQFDTeEUqRVBVGW/yXkEAp0Ui3LBmDee1tfG14WE+tmsXE03eMyil6yQCAf53fJxfTE5SasJeTUIIIYQQovlqLYi+SKXpgViClgjghBTILnzzXL4iirPOxPj2BEr6Nzf4ZddjrFTitEiYdZHwEY2G6N06mYczlDP1LRgimsYrOzrwgKkWbGAa1TT+enCQd/X18Uw2y59t3crjMzNNzRBUVVaYJpuzWW4ZHWWiBcWhEEIIIYRorFoLoo8BZ1uWtcmyrG9ZlvXV+R+NDLicKLrK1NkB1Aln4Y5y1alzeL/pOldwXSadMmfHopwcCqEc4dQwVVdRAgqZh+q/wWlbIMCrOjqYrbb+bjZFUXhFMslnVq8mpml8dOdOvjE83JRNZOeoikKfaZJ3XX4wMsJzuVxTu+AJIYQQQojGqrUg+iSV9T0lIAF0HvIhqgrdCu5KA2V84QLi4NS5p/M4P5lm1nH5rbY2eo+hq5veqZN7Lkd+KH/U51hMStd5VSrFZLnMbAuKIoCTgkE+u3o1VycS3JRO89fbtzNcLDY1Q0LXSek6Pxof5/6pKYoyhU4IIYQQ4rhQa1OFPwXebNv29xsZ5rigKDjnhtFvmsQrexB44YhP+Yoo3sNZIt+dYt0FHcfcyUxRFPSUzuTPJul6YxeqXt920T2myXXJJD8cG6NbUTBb0I7aVFXe3d/PWdEo/753L+/fupV39/dzcXt7UzMMmia/zmbZXyhwVTJJUrrQCSGEEEIsa7Xe2c4CjzcyyHGlTcN5SQhlZOG1NxNOmeI7O9CAiS/ur8sULC2q4Uw7ZJ+qb4OFOStCIa5KJhkuFlvaYOCi9nY+t2YNK0yTT+3Zw7/u3Uu+iXnmptAVPY8fjIxgZ7MyhU4IIYQQYhmrtSD6F+D9lmU1p/fxccA9PQRhFXK/uVn3PEgXS3TqBueuTtDzth6ym7JM/GiiLtfUu3Uyj2QoTzWmCcIp4TCXxuPsLRZxWlgEdBsG/7BqFW/o7OS+iQk+sG0bO/L1ny64lEQgQKeuc+/EBD+bnKQgU+iEEEIIIZalWqfMXQxcAlxnWdZmKmuJDrJt+/p6B1v2dAXnt8IE7p3BDau41T2GTg4GWVvtJJe8JknmwQzDXxsmenYUo+vY9vxRdRXFUJh6cIrkNck6fSPP96JolILr8nAmw4BpNm1/oEMFFIW3dndzZiTCZ4eG+OC2bbyjp4drk8kjbkxxtIxqF7rncjmGi0WuSiZJyRQ6IYQQQohlpdYRojRwM/ATYBgYO+RDLMAbNHAHDZx0acG22oqq0P/efgD2/uveuky90jt0ZrfNkt/duBGT9bEYZ0WjDBUKLZ8udlY0yg1r1nBmJMJ/7N/PP+7ezXQT24QrikKvaeJUp9BtkSl0QgghhBDLSk0jRLZtyx5ER0NRmF5v4tyc4yU97fSFgi84xOg26HlbD/u+uI+JH00c88iOoijoHZUGC96LG3NjrigKL21vJ++6PJfLMRB84ffVTPFAgA+fdBK3j43xzQMH+LNt2/iLgQFOj0SalqE9ECCkqvx4YoK9hQIXx+MtaT4hhBBCCCGOjNyxNdBkucx0DC64pJvU5OLTuBLXJIicFWH4a8MUDxx7O2ktouHOupTsxm0kqioKl8XjrAgG2VcoNOw6R5LnNR0d/POqVeiKwt/t2MH3RkaautZpbgrd9nyeG0dGGG1ya3AhhBBCCHHkFh0hsizrSeBS27YnLMt6Clj0ztK27TMbEW45Gy0W0VWV13V2Ek9pHHjuAE7OQQtrLzhWUSpT57a+dyt7/20vKz++8pjXwRjdBuXHyxQuLGD2H/0eR0sJqCovTya5c2yMkVKJLh+sn1kTCvEvq1fzpX37+M7ICE/OzPCBwVeIdpEAACAASURBVEE6mpRNURR6DYNMucyNo6NcEo9zWjjctHVNQgghhBDiyCw1QnQTUJj3eKkPMc++QoF2Xed1nZ0kdR3VVGm/pJ3y2OJrW4wug56395B9IsvE3cfedU4JKKjtKqO3jpLd0phW3FDZm+cVySQRVWW81LgRqSMR1jQ+MDjI+/v72ZrP82dbt/JwJtPUDG2BAN26zs8mJrhvYoJ8iza1FUIIIYQQS1t0hMi27Y/Ne/z3TUlzHJj1PE4KBrk8kcCYt4YkdHKI3ECO0lgJPbXwaEXi6gRTD0wx/PVq17nuY+s6p4QUjG6DiR9PUJ4s03Z+G4pa/5GKkKZxXSrFLaOjTJbLxAO1Ni9srMsTCaxwmE/t2cMnd+/mlckkb+vped770kh6dSPXHfk8w8UiVyeTdBnH9p4KIYQQQoj6qunO0LKsn1iWFV/g+TbLsn5S/1jLU0TTOM8wuDKZfMFNt6IotF/Ujptz8ZyFZx/OTZ2Datc599jXv6iGijlgMv3YNBP3TeAWG7NfTiwQ4FUdHRRdt6ld3g6n3zT51KpVXJ9Kccf4OB/avp2hJq55mptCpwI3jo7y1MwMrg+60HmeR95xGCuV2JPPszmb5YGpKX6YTvPD2VnsbONGFYUQQggh/KTWX+VfBiz0q+0glT2KBJWC6DRdR1tkvYie1ImeHWXmiRnMvoXX9cxNndv3hX2M3z1O6trUMedSNAVz0GR25yzl28skr0kSiNZ/FCeh61zf0cEto6NoikJYe+F6qVbQVZU/6u3lrEiEG/bu5c+3buX1wSBv8LxF36t6a6t2obt/cpJ9xSKXtLcTavDPx/U8co5D1nXJOQ6ZcpmxcpnxUomJcvkFDSdMVcVUFAKKwj0TE4yXy5zf1tayvaaEEEIIIZphybtiy7LOnvflmZZljc/7WgOuBvY2ItjxKvriKLktOZxZBy208A1x4uoEUw9OceDrB4idHcPoOfZpVoqiYPaaFNNFRm8cJXVdCqOz/tO3Og2D6zo6uC2dRlMUX7WePretjRtCIT47NMR3sll+sXUrb+7q4readNM/N4Vudz7P/xQKXJVM0mMeW8OLkuuSnVf0TJRKB4ue6UPWLalAUFUxVZXOJQr3SUWhzzR5bHqaqXKZlyUSvnofhRBCCCHq6XDDBL+i0l3OA+5Z4PVZ4L31DnU804Ia7Re3M/6jcbQVCxdEB7vOvWcre/91Lys/sbJua3+MDoNypszoTaMkr0kSWhmqy3nn6zdNrkkm+d/xcXp1Hd1HN9MpXecTK1dy+44d3Os4/NOePZwcDPKmri7Oj8Ua3g1OURR6DIPpcpmbRke5sL2dM6PRRY/3PI98tejJVacjjpfLjJVKTJbL5N3nT4HUq0VoUFWJadpRfz+aolSKt0KBW0dHeUUqRZtP1oYJIYQQQtTT4e5wTgYUYDtwHjA677UiMGLbdk3tsyzL+irwyuqfOWORYy4DPgfoQNq27UtrOfdyE1oVwuwzKU2U0BMLN1gwOg163tHDvn+v39S5OYG2AKqhMnbHGO0XtRM9K1r3QmBVKMTl8Tj3TUwwYJoEfDTtSlUU1hsGr+rv5xdTU3xvZIR/2L2bNcEgb+7uZn20/j+PQ8UCAYKqygOZDPsKBRKuy3ChQM51mayO8IyXy0yWy89bc6RS2e/IVFXaNI1UA9uJz61/GiuVuHFkhGtTqWMe0RJCCCGE8JslCyLbtndVH9bjV/xfB/4N+OZCL1abNnwBuMa27d2WZXXV4Zq+pKgK7Re3M/L9EQJtARRt4ZvvxFUJMg9m6jp1bo4aVDH7TaZ+OYWTcWh7aRtqoL4jOesiEfKuywNTUwz48EZaq24ue3F7Oz+bnOR7IyN8fNcuTg2FeEtXFy9ucGGkVzdy3V8q8XA+T286fTDX3HqeriWmtjVLSteZcRxuTqe5Ih7HikRamkcIIYQQop6aNpfJtu37gfElDnkzcLNt27urx480JViLGB0G0RdHKY0svnePoij0vacPlPp1nXve+QOVZgvZZ7KM/+84Tr7+e+W8OBplfSzG3mIRzwfd1RaiKQpXJBJ88dRTeXdfH+PlMh/dtYu/2bGDJ2dmGn79Ll2nW9PoN036TZMewyARCBDWtJYXQ3Oimka3rnPPxAQPZzK+6JQnhBBCCFEPSjNvUi3LWgncsdCUOcuy5qbKnQ7EgBts215wNGm+TZs2eaaPRh/y+TzBYLCmY728x+yds6gRFcVc/Ma38ECB2e/MEvqdEOaltX+vxWIRo8Z9b5y0gxJWMC8xUWP1rZNdz+PRUoktuRw9wSCmD27yl/rZlDyPh4pF7s7nmfI8Tg0EuC4YZE0D19AcyXvVaEtlcT2PA47DYCDA+YaB0YT38kj+m2o0P2UBf+XxUxbwVx4/ZQF/5fFTFvBXHj9lAX/l8VMW8FceP2Xxm1wut3H9+vXnLPSan1ZJB4D1wBVACHjIsqwNtm0/u9QfMk2TdevWNSNfTbZs2XJEeXKRHBP3TWAOLl7oeG/02LVlF9nbsgy+fLDmqXNDe4YYGByoLcgglCZKeE96pK5LYfbWt8hc63nc/dRTpDs7yToOKV1vaVvuPUNDDA4s/rNZBbzBdbl7fJwb02n+ZWaGl0SjvLmrCyscbnqeZjpclhWex3CpxK81rSnNFo70v6lG8lMW8FceP2UBf+XxUxbwVx4/ZQF/5fFTFvBXHj9lAX/l8VMWv9m4ceOir/mn/RcMAXfbtp21bTsN3A+c1eJMDRdaE8LoNihPLL6Z6dzUOUVTGPr8UN2nzs3REzpaRCN9S5rcs7m6nltTFFbpOm/p7ubKZJKS57GnUGDGqf80vXoxVJXrOzr4z1NP5e09PWybneVD27fz8V272Do72+p4LTPXbGHWdblxZIThJm50K4QQQghRbzUVRJZlnbfEa2+sU5bbgIstywpYlhUGzge21OncvjXXYMGZcZYsdOa6zuWezjF+11JLsY6NFtXQu3XG7xkn86tM3YuvgKpySjjMm7q7uSaRQAH25PNkyosXhK1mqiqvrRZGb+3u5te5HB/Yto1/2LWLHfl8q+O1TErXCWkaN6fTPJurbwEthBBCCNEstc51+YVlWZ8APmnbtgdgWVaUSle41wPfP9wJLMv6LnAZ0GFZ1hDwUSprhrBt+0u2bW+xLOtu4EnABf7Ltu2nj/D7WZaMLoPIiyLktuQwehefDpe4stJ1bvgbw0TXR+s+rW2OaqiYAybTD09TniwTvzSOqtd3MFFTFFaFw6wMhRgqFHgkk2FPPk8sECDu0/1uQprGGzo7uS6Z5PaxMW5Lp9mwdSsXtrXxpq4uVpyAc3ajmoahKNwzPs5Eucy5sVhTNrkVQgghhKiXWu88Xw18DbjKsqzfAwaA/wYyVPYnOizbtt9UwzGfAj5VY6bjSmx9jJydwy24qObCxYeiKPS/p5/n3vMcez+/l5M/eXLdNmx9wbU0BWPQYHbbLE7GIXl1Ei1S/zU/qqKwIhhk0DTZVyzyaCbD7kKBmKoSDwQavh/Q0QhrGr/b1cUrUyluS6e5fWyMBzMZLmlv541dXb5sMd5IhqoyYJr8KpNhslTiskQC00eb8QohhBBCLKWmuxbbtu8GzqRSAD0F/BS4FTjPtu1nGhfvxKGFNdovbF+yDTeA3qHT+4e95J5p7NQ5qBRgZp9JabLEyE0jlMaWznas1+o3TV7d0cHrOzpI6jpDhQJjpZJv23VHNY23dHfzn6eeyus6OtiQyfCe557jc0ND7D/B1tVoisKgabKrUOC20VFfT4EUQgghhJjvSH6NGwVSQBFQgFnAvyvil6HwqWH0Tp3y1NI3k/GXx4mujzL8jWEK+xt/4210GiiqwuhNo8zubmwzAUVR6DFNXtnRwRu6uugxDIaKRUaLRd/ufdMWCPAHPT38p2VxfSrFL6emeNdzz/Fve/cyUiy2Ol7TzDVbyEmzBSGEEEIsI7U2Vfg9YBOwD7CAK4G3ABssyzqlcfFOLIpWbbCQWbrBgqIo9L+7HyWgsPfz9d+wdSGB9gBaXGPsh2PMPD3TlFGbTsPgmlSKN3Z1cVIwyL5ikQPFIo5PC6N4IMA7env58qmncl0qxU8mJ/mT557jC/v2kS41bnTNb6TZghBCCCGWk1pHiL4EfNC27dfbtj1u2/bPqbTE3gY83rB0JyCzxyR8WpjS6BFMnbuzsVPn5mghDaPXYPJnk0w9MIXnNKcwSek6VySTvKmri1PDYfb7vDBK6jrvrBZGVyYS3DcxwR8/+yxf3reP8ROkMIpqGt26zj3j4zycyfh2dE8IIYQQotaC6Gzbtv9z/hO2bU/Ztv27wJ/UP9aJre3cNvDALbpLHhe/Ik70nOrUuX3NmZ6k6irmoMnMEzOM/WgMJ9+8WZNxXeeSeJy3dHdzWiTCcLHI/mKRsk9vtjt0nXf19fHFU07h8nicu8bH+eNnn+Ur+/czeQKssZnfbOHe8XEK7tJ/n4UQQgghWqHWpgrPLvHaf9cvjgDQIhrtL22neGDp9ScHp87pzZs6B5W9k4IrghT3Fknflqacae7NfVsgwEvb23lrTw8vjkYZKZXYVyhQ8ukNd7dh8J7+fr54yilc1N7OD8fGeKdt843h4eO++YA0WxBCCCGE30lvXJ8Krw2jJ/XDFht6Sqf3j3rJbc4xdsdYk9JVGD0G7qzLyI0jhy3eGiGiaZzX1sbvd3dzblsb4+UyewsFij4tjHpNk/cPDPDvp5zCBW1t3JxO885nn+W/Dxwg7Ti+7aZ3rKTZghBCCCH8TAoin1I0hfilccqT5cOO/MQvjxM7J8aBbx5o2tS5OXpKRwtpjN4ySm5raxbQhzSNs2Mxfq+7m5e2tTFVLjNUKJD3aWHUb5r8xeAg/7pmDWdHo/xgdJSPTk/zh7bNZ/bs4Ufj4wwVCsddgTTXbOEWabYghBBCCB+pdWNW0QJmr0l4bZj8jjxGt7HocYqi0Pfuvt9s2PoPjduwdSFaVEPRFcbvHsd5qUP0JdGWbKga1DTOjMVYG4mwdXaWRzIZ0qUSyUCAsFb/TWWP1YpgkL9asYJ9hQI/HRpin2HwZDbLz6emAEgEApweDnNGJMLpkQiDponqw41qj0RU0zAUhXvGx5kolzk3FvP99zRdLjNeLbI3zM6yf2KCteEwPYbh++xCCCGEODwpiHyu7fw28tvyuCUXVV98QE9P6fS+s5e9n9vL2A/H6Hh1RxNTgmqqmP0mUw9OUZ4q035R+5J5G8lQVU6LRDglFGL77CyPTE8zViiQDASI+LAw6jNNLjFNBgcG8DyPfcUiT2ezPJ3N8kw2yy8zGQBimsbpkQhnVIukk4JBtGV4Qz6/2cJUucyl8Tim6o/Bas/zyDgOY6USu/N5dhcK5JxK4xBDUVAUhV35PHYuR1BVOSMSYXUoRFLXW5xcCCGEEEerpoLIsqzTAMe2bbv69ZXAHwDPAP9s27Zs0NoggWiA2AUxMg9kMAfMJY+NvyxO5oEMB751gNi5sSYl/A0loGCuMMn9OoeTcUhcmUALt64A0VUVKxJhTTjMztlZHs5k2JPPEw/49/cAiqLQb5r0myZXJ5N4nseBUolnqgXS09ksG6oFUqRa+J1RLZJWhULLpkCaa7awM59ncnSUa1Ip2lrwvriex2S5XCmACgV25/MHp1qGVJWYppGYlyuvKHRUi5+i6/L49DSPTk/Tqeu8KBJhRTDoy9FIIYQQQiyu1juQrwA3ALZlWQPAbcDPgHcDbcDfNCSdACByWoTs01nK02UCscXfMkVR6PvT6tS5G/ai/2nzf2utKArmgElppMToLaOkrk2hJ1r723NNUVgdDnNyKMTufJ5HpqfZ7zikHMf3N6+KotBjGPQYBlckEgCMFos8k8sdHEF6dHoaqNzAr62OHp0RibAmGET3ycjLQuaaLYyVStw4MsK1qRQ95tJF/7FyPI+JUonR6gjQUKFAqbpWK6SqtGvawYLncAxVpbead8Zx+NnkJB6wMhjk9EiEXsPw9c9fCCGEEBW1FkTrgMeqj98APGzb9rWWZb0M+BpSEDWUGlBJXJogfWu6sl5niVEAPaXT+8e97P2XvQR/GoS3NS/n83J06ZQny4zeWCmKzP7G3ujWQlUUVoZCrAgGSR44wG7HIes4dBqLr8/yo07D4DLD4LJ4HIDxUonN1QLp6WyWbx04AFSmeK0LhyvT7CIRTg2FMHx4g57SdWYch1vSaa5IJDg1HK7buUuuy0S5zEixyM58nn3F4sFNYiOaRlLXCdRhVC2qaUQ1DdfzGC0WuSOfx1AUTotEWBMK0anrLVlXJ4QQQojDq7Ug0oC5vspXAHdVH28DuusdSryQ2W8SOjVEfnceo2vpG/j4ZZWpc9M3T7Nzx046XttB5KxI02/IAvEATs5h9NZREpcnmnrtpaiKQn8gwAVdXfx0cpLd+Tx9plmXG+NWSOo6F7W3c1F7OwBT5TLPZLMHR5G+OzKCB+iKwqmh0MECaW04TNAnBVK9mi0UXZexUulgAbS/WPlnS1EUoqpKl643dFqhqigkdJ0ElWJsczbLppkZ2jWNF0UinBwKEfPxlE0hhBDiRFTr/5mfBt5lWdYdVAqiuRGhfiDdiGDihdrOb2N2++xhGywoisLgBwfZ/u3t5O/Ps/MjOwmeHKTjdR20X9SOojWxA11YQ+lVmLhvgmKyiLvaRTV8chMeCHBtKsUTMzM8NDVFUteJ+nwKXS3aqxvXvrRaIM04DpvnmjTkctw4OsoPRkfRgFPmFUjrwuGWTiE8mmYLecdhrFxmuFBgRz5PulQCKvsJRDWN3hZ2gtNVle7q6OOs4/BgJsMvMxkGDIPTIxEGgkHfNJMQQgghTmS1FkR/BdwKfBD4hm3bT1Wfvx54pBHBxAsF2gK0nddGZsPhGyyoQZXgVUH6fr+PqZ9Nkb4lzdBnhjjwrQOkrk9VGh6EmnPzq+oq5qBJ+fEyB753gMTlCYIDwaZc+3A0ReHsWIxew+Ce8XEOOA5dx9n0pmh1A9vz2toAyDkOW+atQbo1neamdBoVWBUKscJ1uSCTYV04THuTRzPmmi3smJ1lqlzm6mTyea9nHYfxUol9hQI7CwXGqwVQQFGIaRp9huHL9y6kafRr2sEudvdMTKBVR+yscJhuaeEthBBCtExNdzu2bd9vWVYn0Gbb9sS8l/4DkB0WmyhyRoTsM1mcGQcteviCRtVVElcmiF8RZ/pX06RvTjP8X8OMfm+U5LVJUtelCCQaf9OraApat4aqq6RvTRM+LUz7Be0t7UI3X69p8oauLu6fnGTb7OxxvSA+rGmsj8VYH6t0Iiy4Lr+etwbp/kKBn+zeDUC/YXBaJMJp4TDrwmF6m1BwKIpCn2kyVipx08gIPeUy41NT7JydZbraAltXFKKaRr9PC6DFKIpCeyBAeyCA43lsn51lSy5HSFV5USTCqlCIhLTwPq54nsek61JwXRkRFEIIn6r5TrjaWnvikOd21juQWJqqq8QvjZO+PY0aUWu+GVRUhbbz2mg7r43cr3Okb0kz+j+jpG9JE39ZnI7XdBx21KketKiGGlbJb8uT35Enfmmc0OqQL25qw5rGVckkz2Sz/GJqinZNa0kr6GYzVZWzolHOikYB2L5nD4VUis3ZLFtyOR7KZLh3ovKffjwQOFgcnRaJsKqBeyHNNVvYUCxyUjZLWyBwXL0fmqIcbOhRcF02zszw8PQ0XfNaeIeOgymcJ6rJUold+TxPZ7PY+Tybh4c5p7pxtBRGQgjhL4veXViWdTvwe7ZtZ6qPF2Xb9vWHu5BlWV8FXgmM2LZ9xhLHnQtsAN5o2/aNhzvvicgcMAmtClHYX8DoPPIOaeG1YVb8zQoK+wqkb00z+eNJJu6dIHZ+jM7XdhJeV78uXwtRVAWjx8DNu4zfPU5wVZD4RXECba2/2VUVhRdFo/RUp9DtLxbpOc6m0B2OriisqhY9UNmrZ6hQYHMux5Zcjs3ZLA9W90IKqipWKHSwQLJCobrexEc1jW5Na2knwOlymX3FIvsKBQ4UClxdKtV9FMdUVXqr3+OM4/DTyUkATg4GOa3awjsgN9G+N1Mus6dQ4OlslnSpVGmyoWn0aBrxQICHMhkenZ6uFEbhMEEpeIUQwheWugMdA7x5j4/V14F/A7652AGWZWnAPwE/qsP1jluKotD+0nYOfOcAXtlDCRzdzbrZZ9L/p/10v7mbsTvHGL9rnO0bthNeF6bjtR3EzouhqA3syBVUCZ4UpDhc5MB3D9B+YTuRdZGmNn1YTKdh8PrOTh6YmuLXs7P06LovW1Y3g6oorAgGWREMck11Tc9YqXSwONqcy/GD0VHc0dHKOqRgkHXVaXanhcPLYgrYrOOwr1hkf7HI3kLhYAG0r1g8OE1vzvdsmxdFIlwaj3NBW1vdG3HMb+E9XCyy/ZAW3h0nWIHud7OOw95CgWey2YNdDdsDAQYO2VPLUFX6TZOi6/JwJsOvpqdZH4uxTgojIYRouUULItu2377Q46NVXYe08jCHvRe4CTj3WK93vAu0B4idF2P6keljnuoWiAfofks3na/vZOK+CdK3ptn9D7sx+g06XttB/LJ4QzvDGZ0Gbsll8v5JZu1Z4pfF0VOtv4kOahqXJxIMmCY/nZwkrGkkjqMpW8cidUir75zjYOdybK5+/Gh8nB+OVX6P0mMYB4uj0yKRlq37Kbku+4vF5xU7c5/Hy+Xnf3+BAH2myUvb2ugzTfoNgz7TZO/wMFuDQX4+NcXn9+7lC/v2cU4sxqXt7ZwTi9V1KpSqKCR1nWQ1+zPZLI/PzBDXNM6MRjkp6I/GJCeiouuyr1BgSy7HrnweD2irsamHoar0mSYl1+WReYXRaVIYCSFEyyie5x3+qDqpFkR3LDRlzrKsfuA7wOXAV6rHHXbK3KZNmzyzwbvbH4l8Pk+wSTcqXskjf1ceRVdQQi/8n3CxWMQ4iqlGnuNR2lSicG8BZ4+DElMwLzMxLjFQw0d/w1dLHnfKxZv1CJwRQF+ro+iNuXE+0vdp0nV5qFAg43l0qGrdO4Id7XvVCPXI4ngeexyHreUy2x2HbeUyM9V/a6KKwupAgFWaxupAgEFNW3IPqCPJ43ge467LSPVj1HEOPh53Xeb/axdVFLpUlS5No1NVn/fYXCTPXBbP89jlOPyqVGJjsUjG8wgCZ+k65xgGViDQsLVVec8j47q4QLRcJmqaGIBJZTNeQ1EIKgoalXVKASpd+DR43mMN6lqYNvPfvlrUO0/Z80i7LjvLZXY7Dq7nEVJVoopy2H8Plvo7XKr+ndWA03Sd1YEAwQb/wsBP75WfsoC/8vgpC/grj5+ygL/y+CmL3+RyuY3r168/Z6HX/PTr7s8Bf2XbtmNZVs1/yDRN1q1b17hUR2jLli1NzTPbNsvYnWMEB1/4l39ozxADgwNHd+KV4L3aI/tklvTNaWZ+OEPx3iKJqxKkrk8ddnPYhdSUZxC8skfxQBHtaY3EyxKYvfUveI/mfTrHddmQyfDEzAydhlHXTU33DA0xOHCU71Wd1SvLSuDi6mPP89hbLB5s1LA5l+OJfB6o3MRbc40awmHWHrIf0qF5XM9jvFxmb6FwcIrb3OcDpRLleb/kCasqfYbB6aZJX3WUp98w6DXNo5rqNj/Liur353hepTvf1BQPTk3xcDZLu6ZxYXs7l8bjrA01pmmI53lsHRoi3tODU82R9TymPA8XUKjMeT545WoGz/PwqOzVpFeLv6CqYqgqpqoSqj5nqiqharEaUBT06udDH2uKgqIoTf+373Dqkcf1PEaKRbZVuwGWqkXQWUdY8B7uv6lVVEYBR0slxhWF9dEo6yKRhu0L5of3Kl+danj/s8+yqrubAdMkpeskAoGWdvj0w89mjp+ygL/y+CkL+CuPn7L4zcaNGxd9zU8F0TnA96rFUAdwrWVZZdu2b21tLH8LnhQkuDJIaaSE3lHfaWaKohA9K0r0rCj5HXnSt6YZu3OMsTvGaL+4nY7XdhBaFarrNQGUgILZb1KeLjN68yjRM6PEzo2hBVs7nURXVS6Ox+k3DH48OYmuKKSWwfoYP1AUhQHTZMA0uaq6Dmlibh1S9ePG0VFcKjfqJwWDB4ujA4UCPx0erkxxKxbZXyhQmFf0GIpCr2GwIhjkgnlT3HpNk7imNXx6nqYoB7v0/UlvLxtnZvj55CT3TUxw1/g4XbrOxe3tXBKPs9I065ZHqY4ExY5yGqfneQcLqbLnUSiXmax+Pffhzr9e5aLP+/NzzxuqSnp2lp1jY3QbBslAgKimEQsEll1HNc/zSJdK/5+9N49u5DzvdJ+vvqrCzp1sks1uqbslQZK1Wl4VW4qcieMlsWJn1XXkm+tkjk8SO55s53piTzzjTDLxOIkziZ3xvU5yHU8SOzOJYzuTxEu8yZtktTZraaHVbfXCZjd3Yiug1u/+UQUQZJPdIBskwe56eHAAVBWAj0Ch8P3qfd/fy4l6naerVWpBgCkEA1s8UTdaUukOl8scrlS4I5vlxi0URtuNHQSctW2etSxORCdEitGy47UaEO5PQ4bB/mSSPaZJf7QvxXVzMTExW0nXCKJCoXCgcTufz3+MMGUuFkMXoWGwMPO3MyhfbZkhQfJAkolfmWDkZ0aY/+w8i19YpPi1IpnbMgy/cZjMbZmO/2DpOR2ZkVhHLGrHavTd00fyquSO/zAeTKcZNE3+dXGRSdtmzDS3LD3qcqbfMLizt5c7ozqkmu9ztFYLBVK1ypeWlvinhQUAZK3GaBThuTWTYbwR8TFNBg2ja5qaGprGy3p6eFlPD5bv81CpxAPFIv8QNb/dn0hwVySORnc4RVK0pNNdCg1hVRSCeddl0rbxlWpGqNJSMmIYjERCKafr5KTsOpOSxRYRVPZ9dGDAMLb9pEdDGHlK8Ui5zKOVCrdHwiizC4VRo3bvqGVxvF4nCAIyUjIWNSMONI1+w6A/2l4phRUEPFGpNKO9xIAdFwAAIABJREFUyciQYl8URerb4SjSlUjR85j0fa4Jgvi9j7ks2TZBlM/nPwF8PzCUz+cngfcCBkChUPjIdo3jcsToN8jdkaP8aJnE+NbWU5nDJmM/N8bIT42w8PkF5j87z4n3niB5IMnQm4bo/b7eTbverYXQBOaYiW/5zP/TPOlr0/Tc2YOe3Vkt36vr3Ds4yOFymUfKZYYM47I5i7tTpKRc0Q/JV4pT9ToLMzPctn//rhOdaSm5p7+fe/r7KXoe3ywWeaBY5K9mZvirmRnyqRR39fXxip6eXeHEtx4NYWU2ms6uWu8EAXOuy2nbxlMKje4RSmXP41Rkkz3vuuhC0B81zt1p9KhBsacUj1UqPFapcFs2ywt2gTDygoBzjsPRWo1jloVPmL66xzAu+j0WQpCRcsX/6K6KIgGMGAb7oijSgK6TiaNIHUdFTpePVyo8X69ztl5nanqal/X0cCiVilsBxFxWtH3Uz+fzNwNvAw4Bby0UCmfz+fyPAicLhcJjF3t8oVC4r93XKhQKP9vutjEh2VuzVJ+u4ls+Mr31P5YyKxn+sWEG3zBI8atF5j49x+QfTDL98WkG7x2k/wf7kanOjUOmJdo+jfqpOvWTdXrv6iV9bXpLbcEvhq5pvKy3l/EoWlT1/R3tl3O5IYXgQCqFLuWuE0Or6dV1Xjc4yOsGB5lxHL4eiaOPnj3Ln589yy2ZDHf19fHynp6un+xuFDOqT7qQUPKjSIAitB1vCiXDICclOSk7dlbaWmWTrRF+Pvu6yJynFT1KCfWU4olKhccrFW7JZLg5m+2qfcVXimnH4VitRsGy8KJ6q5EORNANTaNf05pRpEApakHA45VKc99Jahr7kkkmov2mX9fjCfsmcYOAk/U6h8tlFlyXjJRMmCboOilN40uLizxcLnNnTw9Xp1JdE6GPCWvzjrguY65L3y4+0bYTtCWI8vn8q4HPAv9C6ALXKBw5BPws8KNbMbiY9tFMjd67eln83OK2CKLm6xoa/T/YT98P9FE+XGbuU3Oc+7NzzH5yloHXDjDwwwMY/Z35UgohMPeYBHbA4r8uUnu2Ru9dvR17/s2yP5XiJw2DrywtcbpeZyyRuOQ0pJjLl5Gox9WPDQ9zql7ngWKRB5aW+OMzZ/jvkY33XVtg491tXEgozboup1al3mWlZHiTQqlRu7LaJnunLOA3gy4Eo5Ew+m61yhOVCrdks9ycyZDdoYhWq+nEs5aFrRRJIRgyjC09BmrrRJHO1Os8Z1nNZY0o0mijFqkLIn/dTDVqn/BYuYyjFH26zr5VbmUpKdknJZbv87nFRQbKZe7s7WVfB+sjYzaOGwQcqVb5TrlMwXF4ke/HgmiDtHt0+G3gVwuFwp/m8/lyy/KvAr/W8VHFbIrUgRTWhIU77257Hx+hCXpe0kPPS3qwnrWY+4c5Zv9ulrlPz9F3Tx9DPzrUsdfSEhrJ/UnceZeZT87Q8/IeMjdl0PSdmzxmdZ3XDw7yeKXCt4tFBgyj4w07Yy4/9ieT/EwyyZtHRjhaq/FAscjXi0W+XSqR0jRe3tPDXb293JrN7vooWbusJ5TsIGDGdTlZr69w0ctJyZBhhAX4LULJU4rT9TpHLYtjjdoVXW/WruxWGhEjXymejITRrdksN2UymzbY2AiBUsy6Ls9Hznu1ICARpRruZG3JWlEka1UUKaVpTMRRpPOYdRyeqlZ51rLQCE0tLpa6mpaStJSUPY9/nJ9n1DRDU5tddJLhcsBXimOWxbdLJWpBwIhhkN7pQe1S2j16vgD45zWWLwADnRtOzKUghKD3Fb3MfHIG1bd9/aVWk74+zf5/vx97ymb+0/MsfmmRxS8uYr7SJHhHgJbozA+QMWgQuAGlb5ewChb99/Rvyg68U2hC8MJcjjHT5IsLC0z7PiOGEf84xFwUEVmP59Np3jo6ypPVKl9bWuLBUokvLy3RKyWviMwYtsrGu9tJRLbgfasm/auFUoPpWo2R+XlSbdau7DZkizB6KhJGN2cy3JLNdlwYtTrvPVOtYgUBeuS8N9SlZ6E1IchKueLElLNOFOmqZLIZebxS8JXijG3zSLnMWdsmqWmbMgjKRfV/Rc/j07Oz7EsmeWlPD3vi9PEtJVCKk/U63ywWKXkew5HBUMzmafeouQjsBU6sWv5CYLKTA4q5NIwBg+wLs1SeqOz0UEiMJxj/xXFG/o8RZv/XLPP/OM/xk8fZ9xv7SO7vTNMwzdBITCTwih4z/2uG3Atz5O7IoZk7d9ZvLJHgx0dGeGBpieO1GmOmGbvyxLSNFILbslluy2b5hSBo2nh/cXGRf1pl4x0oRc33CQijJUFkla2i6yDqObTi9lrLVq1f8Rws9y5qrlvjdslxSDrOttfRrSWUlFJ4msZEl9YFdRIZpdL5SvGMZfFktcpNkTDquQRhpKJ+XycjEdRw3us3jF0rHBrRx9VRpEdbokiLtRqLxSL7EwmGTfOyS1ut+z7H63UOl0pUg4BeKc9Li9sMvZEZyaLr8vczMxxIpXhxLsdQLIw6ilKKKcfhm8Uic47DQJQWGnPptHu0/BvgA/l8/icJf//0fD5/N/D7wP+3VYOL2RzZ27JYRyzU4s5FiVrR+3TG/u0Y9X116n9d5/ivHmfs58fo/6H+jp3p1nt1ZFZS+W4F6zmL/lf1k5zYuYNEWkpePTDA09UqXy8W6ZXykiYnMVcmZpQ29/LIxvvBUomvtdh4A1As7uwgW/jo0aMM6noz2nV9Os2hZHLbLbaFELs6LW4ztAqjZ1uE0a0bFEaLrsupyH686PvILnLe6zRrRZHqQvBstcp3KxWEEIwaBtek0806pN26Xy25blMwK6UY3CJh2x/Zok87Dv9zdpbrUinuyOV2tZNmtzDtODxYLDJp22vWd8VcGu0e4d4DfAw4SZi6/Ux0/TfA72zJyGI2jUxKel/Zy9RfTu30UFZgvMBg4o8nOPNHZ5j60ykqj1UYf/s4eq4zP7RCChLjCfyKz9yn50jfmKb3Zb3bajLRiiYEN2ezjJomX1hY4KzjMBqn0MVskrSUvKq/n1f197PkeTxUKjG5sEB/by+aEAjCfU6Dlbej6xW3W9YJQLbc1tbZXkTL1nut42fPUszlKFgWz1oW3yqVgLDm5WAyGYqkVIp8Oh2nkm4hUgj2RMKoYFk8Xa1yYySM1hM1Rc/jdL3O05bFguuiCUG/lFdEhG01phDNKGegFFXf5+tLS0AYjTyQTHIglWJ4F7RaCFpss0/W6xhCMLLFhhcQHjcGDYP+qHXC0VqNmzIZbrvEqOWVyoLr8nC5zHHLIisl+2MhtCW0tWcWCgUXeHM+n/8PhGlyGvBYoVB4bisHF7N5UgdTaHs17EkboQlkr0Rmdv7gbfQbXPXeq5j/zDznPn6O2jtrTPzaBJkXZDr2GjIr0dIa9eN16s/X6bu7j9Shnau7GDZNfnxkhG8uLXGkVmO0jYLVmJgL0afr/NDAAKcti33Dwzs9HAACXWff4CA/MjgIhJGGQmTB/Kxl8fmFBf4xSkvq1/WmOMqn01ybSl12qUk7TaswOho1O74hmpRC2IPpdGQ/Pue6CMLP5UoUQeuhCdGskYHQyetEvc6zloUiNB+4JpVib9Qwtlvq1Nwg4Pl6nUfKZRY9j6ym7YijohaJyyDaB5+uVrktm+WWLrOM71ZKnsdjlQpPVSqhIUjs5LeltGu7/VvA7xcKhe8B32tZngJ+o1AovG+LxhezSYQmSNyVYGTPCPXJOrVCDXvSBgEyJ5FZuWM9fIQmGHrjEOmb0kx+YJLn3/08Iz81wvBPDiNkZ8YkNIE5ahLUAxY+t0DyYJK+V/Sh9+zM2amEpnFPfz97Ewm+urRESkr64zNlXY+nFG4Q4CqFqxRzvk/CcdCFaF5ky3XMMv2GwcsMg5f19ADhe3myXm8KpEKtxoPl0LRUAw40okjpNNenUozGblUdoSGMAqV4LhJGVq1GenoaQVj7EYug9jA0jcFIuKuo/ujhUomHCCOhVyWTHEylGDGMbXH8W03F8yhYFo9XKjhK0d8l/bW0FnH+ZLXKk9UqL4yaDKdiYXQelu83e44ZQjCRSOzaVM3dRLvf2PcCHwGsVcvT0bpYEHUhQgiMQQNj0CB3aw6v7GGftUNxdMZGKYVMSfQ+vWNCZCOkr01z6I8Ocfb/OcvMJ2aoPFFh4tcmMIc7V4SpJTWSVyVxzjlMf2Ka3u/rJXND56JRG0EIQT6TYThyoZuybUZ3uQXwbmS1yHGVwov63QgAIVBRJMPUNLJS0hfVGWQNg/2ZDFYQUAsCar5PLQgoRs/ZoGEJ3XpfrhJRRnTdSFe73NGF4FAqxaFUitdFUaSi53HUsng2iiR9ZWmJf15YAKBXyqZAyqdSXJtKdf3kSUUNQ4ueR9H3KUXXc7bNnfU6+3fwDK/WIoyOCbGrejB1I2JVLyRfKc7aNsdqNSDcf69Np5lIJBgyjC0z1lGRDfqTlQpHa7UwKrPDFujr0XBG9JTikXKZxyoVXpzLcUMmE0eICR0zn65WORydKNqM61/M5mlXEK3+fW9wO6H1dswuQM/p6DmdzHUZ/LqPO+1SO16jdryG8hTCFOh9+rY6tMm0ZOJXJsjcluHsR85y/J3HGX/7OL13ru5CcmmYwyaBG7D0wBK1Qo1gPLj4g7aIAcPgTcPDPFgq8USlEtuTdgC/IW4ioeO0iJzVrBY5OV0nK2XoViZE07UsoWnn/RgdOXeOG3rX3jcDpXBahJbTMhY7CLAi8WS1CKmS72MHQXOcjevWg22rkGoKKrgsmv/26jov7unhxVEUyVeKU7ZNwbKakaTvtESRrmqpRbo+nd7ynidKKaoNgeN5lHz/vNurl7lqbTObTxw7Rq+UvCCT4ZZMhpsymR1pZqkJQUqIWAx1GCkE/YbRdLCr+T6PVyocLpfRgH3JJAejJrG9un7J77+vFJO2zeFymenINnt8l5xg04VgPJHACQIeLJV4tFzmpb29XJdKdaWQ22rcIOBorcaDxSKeUgxvoYCOWZ8LCqKoCauKLt/L5/MrfqeBJGHkKGaXIZMSeZUkeVWS3rt6cWdc6ifqWEct3JoLMnJu2yZDgv57+knn00z+/iSnf+801ddUGf250Y71LILQoju5L4m76FL/5zrnvncujKANGRj9BlpGQ2YlMr316YSGpvHKvj72miZfWlpiJkrFAs6bxDd+OM9bvuqaVdudt3718lU/nI17NaWwfP+i/8Olehi2+/hqELDgus1IDkRjj6ygFWFKYjZy8stFIiejaSSlvKjI6RSaECSlZKPlrkH0f7UKKDcIcKJlrSLKCgLqLUJq2vfRbBtdCHJSktK0XT3RlUKERevJJK8ZCFvclT2Po7VamGZnWTywtMTnoihSTkryqRTXRY5216VSFyx0D5Si4vuUfJ8lz2tGcIott1uvS77f3OdWk9I0eqSkV9cZNAwOJJNN6+HG8oa75JmzZ1no6WmmCzUMJ3ql5OZMhpuzWW7OZOKozWVESspmRDNQioXIvQ9Cg5RDqRT7EwlGNmjtXfN9jtVqPFoud9Q2eycwNY29iQR2EPDA0hKHSyVe3tvLoVTqioiM+EpxvFbj28UiVhAwbBhxpGwHuViE6O2E86S/AN4NtPq7OsCJQqHw7S0aW8w2oekaifEEifEEPS/rwZ13sSdtrIKFfWZV3dEWHqQS4wkOvP8AM381w9w/zFF9phr2LLqqswd7o99AG9XQEhruXPi/Km950iOEQO/X0Qd0jEEDvV9HZkJTCi3V2QnnwXSaIdPki3NzXJXNhhP8lol+64XV66I+MA0a/WCA5nIVPqh5v7F+9fM2etAopTAA4xL+x7Xen4082+rHZzWN/clkKHSk3FaRsx1oQmAKsWGjDaUUjxWLjAwOMmXbPG/bnHGcZnpez2UgkCBs/HhHLscduRwQ7quTtt2sQypYFo/MzKAI97P9iQTXpdPYloV/6tSKCE456tm0FhlNoycSMSOGwbWpFD1S0qfrzeWtYmcjn5cjJS/s7+ff9PejlGLadUNxVKnwZLXKNyKB1K/r3JTJhCIpk9nyCFjM9qAJ0RTLEKZGFRrW3sCoaXJNKsVYIrGutfei6/JMtcpTlgVKMbCL+0GtJhEZBtR8n39dWOBhXeflPT1cnUrtiojXRgki971vFYss+T5Dun7ZfJa7mQsKokKh8JcA+Xz+eeBbkdtczGWM0ATmsIk5bJK7PYdX9LCnQnHknHFAgJbW0Hu2pu5IMzRG/69RMrdlOPPBMxz/teOMvnWUgdcOdHRiIIRAS4SiiFUZUCpQKEfhTDnUn6+H6YRaWFcipAiF0oCBMWKg5yKxlJWbjmb16Do3GgY3RGlDO82R+XluGBnZ6WE0OTI7yw39/Rff8ApDRKlPE8kkE8kkLyFsujjveaFAqtdXCKSclKQvA4GkCcH+ZJL9ySSvjpZVfJ/nIoH0rGXxYKlEEAT0EabljScS3JBOrxA0PbpOXxTB6ZFy21JURNQvaNQ0+cFIIJ11HJ6KokdPRr3LAAZ0nZuj9LqbMxnGYoF0WZDQtKa1t4qilt8slVBKYWoaB5NJrk4msZTijG3zeLnMyXodU9PY00Vudp0mJSX7pKTq+3xucZGBcpk7e3t3JLV0K2h8179dKjHtOF1jehET0q7t9tcat/P5/Chgrlp/qsPjiukS9F4dvVcnc0MG3/Jxph2soxb1k3UIWK47Mjo7mcjdnuOaP76GyT+a5OxHzlJ5vMLed+ztWM+iCyE0gUgKtOT5/5PyFYEdUD8Vphc2T0sr0EwNfUDHHDbRB8N6LS2jhZGlDr8/MTFrkZSSvVKyN5HgxT09TYF0LhJIU1FapgbNtMLLYaKRlZLbczluj6JIAKcnJ9k3MbGDo2oPEdVTjCcSvHpgoNmJviGOvlut8rVIIA2uiiDFTny7H9Gw9o7uN6y9j1gW52o19szNkb3CLJcbZhVlz+Mf5+cZNU1e3tOzq08IzDoOD5VKnLTtMM0xFkJdR7u22z3AnwA/ySoxFNHd1j8xHUGmJakDKVIHUgRugDMdRlBqz9VwbRd0MPqMNYXEZtD7dK76rauY/+w80x+f5tg7j7HvV/eRuWlnXOIgbP4q03LN2irlKYJagHXUQjkqdCqLxJJMS/RBven6J7OymYa3Ew5/MVcGrQLpjp4e7CBg3nU55zicqNXOE0hpTbssU1R2E0II9iYS7E0keE0kkM5EAumpapXHK5WmQBoyjKY4uikSSDG7mxXW3lf4xLnRA6roeXx6bo6JRIKX9vTsKiOiRdflcLnMc7UaGU1j/xX8eXY77Z5u/wPgVuBHgU8BbwX2Au8Efm1rhhbTzWiGRnIiSXIiSe/39eLOudRP17GetXDnXRCg94QRkks5oyM0wdCPDpG5KcPp3z/N8+95nuGfHGbkp0a6TkgIXYRCJ3u+WAqcAG/Jw5l2UI5CtFg76z06dsWmuFgMo21pDZmSaEkNLaVtq+tfzOVNQtOa0YgX5nI4rQKpXuec46AIBVI2OksbC6SdRUR9SCYSCV4bCaRJ225GkB4tl/nK0hIAI4axIoI0sosmjjEx69Gov1p0Xf5+ZoYDqRR9wc45xbZDudFUtVolKQQTuzi6daXQriB6LXBfoVD4ej6f94FHCoXC3+bz+bPA24C/27IRxnQ9QhOYIybmiEnuhblw4j/lUH22ijPlhBGSrEQFm/clS12T4tAHw55Fs5+cpfpENexZNLI7fvA1c21ho1RYrxQsBlSfraJctZyGB2Faoi6QORmmL/boyL4wQqWlloWTMGMb3ZiNY2oaY4kEY4kEt+dyuJFAml4lkAShQMrGAmnHEUKwL5lkXzLJ6wYHUUpxukUgPVwu8+VIIO1ZJZCGL1EgNeztnSDAjlwR7RaHRLvFHbH1urGtEwTYLS6KdhBQq9UYO3OGgaiwfFDX6TcMBqJJ8OVaLxOzcfoNgz5d55zj8O16nWfOnm0a7CQjg52kECQ1jVTUSsGIWhU0r1ct6/TvZs33+W7UVFUKwXjcS2jX0K4g6gNORreLwCBwDPg28GdbMK6YXYoQAqM/tLHOvCCDX/XDZrDP1QiOBThpB2PI2NRBSKYkE/9uguztWab+dIpj7zzG3rfvpff7OtuzaDsRQiASAi2nrduQVvkK5SrcBRfnnEPgBMv2cFFKntAEskcicxKjzwhvZ1uiTMnQQCIWTTEXwtA0RhMJRhMJbo0E0oLnMW3bnLRtpmw7FEhCkNU0MlLGP/Y7jGgxmXj94GDoYGXbPBXVH32nXOZLkUAajQSSqNVInj27LGoigeJGgmW1oGlcr2dB3g4aoQBvOCo2rt0g4FSpRMn3z7Pi14C+SCgNtF6vWtYTC/UrBiEEQ4ZBTUoGdR1fKfzImGLJ85r3faXwCU1Y1KqedK37mRGJKLNFVKWinnQpKUlq2gUFlR6JKkcpHi+Xm33TRkxzR3rFVXyfpS6PnnUr7Qqi48BB4BRwBPjpfD7/HeBNtNmYNZ/P/wXww8BMoVC4aY31bwb+7+huBfiFQqHwRJvji+lSZEaSviZN+po05wbOkVxKYh2z0PvCaMdm6Lu7j3Q+zenfP83p95+m8kMVxn5+rKM9i7oJIQVCrm3y0KDhjOctebizLoEdiqbW1DyhRSl9PVG0qU9vWok3hdNl+h7GbA5D09hjmuwxTW7J5fAigTQTRZCmHKdp5V4OAtwgiBsK7jCaEFwduZT9cCSQTtbrzQjSg+Uydd8n4boYkTBpnEk3o4lgzjAwouWrBcyFrhvP03icET2fGU0q16JhfuEpxZLnseC6LLRcz0fXM67Ls5ZFaY0eaRKaUaX1RNNA1KMsPil0+SCFuOQTMg3x5ClF2fNYWrWsVTytFlWN31dT05is1RgplRjZxqaqDQv/I9Uqz1gWRyyLU7aNBN4ei6IN0+6M9GPALcBXgd8D/jdhjyKNsI6o3ef4EPDxddY/D9xdKBQW8/n8a4H/F3hpm88dswvQejQGXjpA5uYMxW8UsU/ZGCObM2EwR00O/t5Bpv96mrlPzWE9Y7Hv1/eRPLA7G9RdKhdyxmuggjDS5JU83Dk3jDQFgABBdBZNE9SWapQqJVKHUhgDcW+EmGV0TWPENBkxTW7KZvGCgEXPY9Z1+frsLEu+j+26aIS7li4EqZYzrzHbjyYEB1IpDqRSvGFoCOhOBz49OvM/dJF+LG60zy2sIZ4WXJepKDpWWUM46UJcVDRdShQsZvfREFWbTSZt9AOsRk1mtxJPKZ6v13mmWuVIJIAWPQ8I+6jl02nu6u0lWS7Hx9tN0K7t9gdbbn85n89fD7wIeK5QKDzZ5nM8kM/nr77A+m+13H0Q6K6jdUzHSIwnGP7xYWrHaix9cwm1oDBHTIS+sTM9QheM/p+jZG/NMvnBSY7/etSz6HWd7Vl0uSC0KD3vAlEgFShEVVB+rEz5cBlj2CB7a5bEvgQyGZtJxqxEj/qpDJsmIpHghrEx6r5Pxfcp+34olhyHWddlznWbZ1QFNIVS8jKx/o7ZHowWUX4h7IZwWkM0LXgep2ybxysVrFVn0nXgOtflhnSaGzMZbkinycor99jnBgHH63WO1Wos2jbXlkoMGgZDUY3XlZ6qKIRAwpakDlu+z7OR8DliWRQsCzsS7COGwS3R/nljJsP+RKL5WRy2rI6P5UpAqG08GxIJov+9Vsrcqu1+Hbi+UCj8/MWe8/HHH1eJLrIxrNfrJJPdEaXoprHA2uNRjsItuHjPeAhdIPo3V+QYlAOs/2HhPe2h36yT/pk0Wnb9ib/jOJhd5MDUTeNpHUtgBahyaB8uD0j0AzraoIbQtu9HsJv2424aC3TXeC42FlcpLKWoKkUlCFiILuXoN6hh3mAKERZKwyVNtrr1O9UNdNN4dnostlIUg4CiUiwFAScchxNKccr3aUilcU3joK5zSNc5JCUD2yTit/u9UUoxGwSc8H1OeB4nfJ9J3+f8WFuIBPo0jT4h6Ne08Lam0d9yPyfEloimnd5vVtOJ8SwGAcc9r3mZCoLmcXFCSg5KySFd56Cu03+BCNCZep1XZbOMX8FCfj0sy3rkjjvueNFa69ou4sjn8y8FfgAYIUyVa1IoFH75kka48nXuAX4OeEU72ycSCW644YZOvfwlc+TIka4ZTzeNBS4wnlvBK3mUHiphHbXQe8JmsBtF/a5i/h/nmf7YNNb7LSZ+dYLsLdk1t508PcnEvu4JQnbTeNYai/IV3qJH8FSAzEoyt2RIHUxtug5sI3TTftxNY4HuGs9mx+IrRTWKKJWj9LtZx2HO85r1SQpItLhHtVOs3E1pYd00Fuiu8XTTWGB5PHYQcNSymrUZj1oW34j6dg3q+ooI0tXJ5JZECLb6vSl7HkdrtfBiWRyt1ShHqYZJTeOaZJJ702muS6W4Lp1m5uxZEsPDzEUR3znXZT66nvQ8nnAc3FUn2SUwaBhhVCm6DOr68u3IOW6j71+37jft4ke1fUda9rE51wXC9z6fSvHKaB+7LpUivQFxM33iBNdecw37u+RkWTfxyCOPrLuu3casvw78V0JnuSlWmnR0LMSUz+dvIXSte22hUJjv1PPGdD96j87AD7bUF5220Yd0ZKr9g4AQgqE3DJF5Qdiz6MR/OMHwTwwz8tMjG07Hi1mJkAJjKMztD+oBpYdKlL5dwtxrkr0lS2JvAs2Ic5ZjNo4Ugh5dp0fXIZHg+mi5UgorCCh7HhXfZ851Q7HkujhRmpNiuU4ppWmYcd58TIdIaBo3Z7PcnA1PqvlKcapeb05en6lW+UapBITpn9en09wQXfLpNMku2xfdIOCEbTeFz1HL4kwk8ASwL5Hgpbkc10Xj359InCdSaprGvlSKQ6nUmq+hlKIUfVfnW0WT5zHnuhyr1XioVMJZQzQNNERTJJZaBdTQJkVTN1EPAgot6W/PWha16Dg2oOvcmE5z49DQlgrsmAvT7unddwK/XCgUPrRzyn15AAAgAElEQVRVA8nn8/sJm77eXygUjm7V68R0N4nRBMNvGqZ2vEbxm0W8RW/D9UWpQykO/eEhzn70LLP/c5bKdyvs+7V9mHu6J7y+m9GSGonxBEop/JLPwr8sIAxB+oY06evSm7ZVj4lpRQhBJmoOC3BNy7p6FFGqRHVKM47TFEyNOqUZ3yfpOE2r3IZNbjzRiNkMssWc4vWDgwDMOs6yQLIsPjEz02xsfCiVCqNIkUjqv4hZRCdRSjHjuk3hU6jV+F6t1hQifbpOPpXiVf39XJdKce0GIxDrIYRoNlG9kGgqt4im2RbBNOe6fK9e5zvl8nmiSSMUTUO6zqBhYNbrDE9PN+sQEy01iY1eRE0L7Wj5VvQdWo8F110R/flerdbwMGJ/IsH39/U1BfSIcem/mYFSLHoeVhCQ0zR69a3P3rjcaPcd6wH++VJeKJ/PfwL4fmAon89PAu8FDIBCofAR4LcI+xv9aT6fB/AKhcKaeX4xlzdCE6SvTZO8Kkn1qSql75QQusAYNtquXZEpycQvT5C9LepZ9O+OsfeX9tL7it3bs6jbEEKE9t29OspTWEcsqk9U0Qd0srdkSV6VRGbiHOaYzpOUkqSUDAMHWpa7QdAUSk/Mz7Mnk6ESBFR9n6rvU/J93OisrBACWmx1NVghnBo9Rq70ovGY9Rk2Te42Te7u6wPCHjCFxiS4WuVzCwt8dj5Mdhk1zTAKEKVB7TXNjk3OLd/nuVqNQiP6U6uxFLmPmUJwKJXitQMD5KP0t+EOTMA3i2iJCB+8iGiad13mIrHUGnE6Ua8z7zg4s7NsxFxag/ME1ArRFAmpNUXWWts3lgnBlO/z9MJCGAGqVjkXpb+ZQnBdKsWPDw83o4edNOmwo2baAXAomeSmbJbFYjEWRJug3XfsE8BrgD/d7AsVCoX7LrL+54GLmijEXDlopkbuhTlS16YoPVyi9mwNmZPofe1/0fvu6iN9XZrTf3Ca0//1NJXHKoz927EtHPWVidBFMwLnWz5LX1+Cr0HyYJLMCzIkxhIIGU8sY7YWQ9MY0DQGDIOqYXBD7/knQPxGI9KWJqR2QzQFARXfp+J51IKAahA065gEQBR9aqTqtQqn1iaNMVcmWSm5I5fjjlwOWHZoa0ySD5fLfDlqkpuTshlBujGT4VAy2Vb/mkbqXqFFAE1GDZMB9pomt2ez5KO6n6uTyR1pEHoptIqmA+tsc3pykom9e3Gi73N91cUOAupKUfN9bKWay2qNdS2Xqu+z4LorH78Zw7FymV4puTGT4XWDg9yYTnOgzc91IzRSE0u+T0rTeElPD9ekUuQiEVTcZZ93t9DuzPI08J/y+fz3Ad8F3NaVhULhDzs9sJiYBnpOZ+BVAzg3ORS/UaR+uo4xaCDT7Z1lMUdNDv6Xg8x8YobZv5ul+kwV86dN1LiKJ+lbgExLZFqiAoVzzqH+vTpaQiNzc4bUNSmM/ri3UczOIYUgLWXbKUJuEDQnXXbL7UoUdWpcl32/WRPQEE+NCNS076Pb9gqxJFou4eZi+bGr1iHEeds3t2l53IXWtW7jx712tgUjqi26Pp3mjUNDKKU44zgr+sh8p1wGwkjCNalUUyBdn04DMO+6FKK0t6OWxbFarTlZz0kZFt/39pJPpbj2CrMIFw1HSk2jp8PPHTSE1gUEVy0IGHEcXqIUGd8nKeVySq7nQakUXjpI47u7N2p4LAGxtMRkyzau63LkyJGOvu5uIpPJMDExgbZBIdquIPp5oALcGV1aUUAsiGK2HHPEZOhHh6idqFH8elhfZIwYbRXzC12w5/49ZG7JMPnBSSp/UOGZ//YM5l6TxN4EiYnosi+BOW5uyMwhZm2EJsLGrgMQOAGVxyuUHy5j7DFCI4a4t1HMLsDQNAxo1jJdCKVUUzC1RqCenp9nX08PCpqXQKlm9CmILmstby6LbhPdbixj1eNUy+NWPLblOYtBwBnbbgqlRsSrkRJkXqGpgqrlvd8KhBBMJBJMJBK8emAAgEXP40iLQPqHuTn+bm4OAWSEoBJFlHQhOJhM8uqBgdD1LZVitINpdzEr0YQgJSVrJ/WFJGo1RoKAsfFxkJLEFtmA+4RCqLV/m36ByX6tViO1Tjri5U4QBJw5c4a5uTlGRkY29Nh2G7OuF7WMidlWhCZIH0yT3BfWF5UfLiOkQB/S26ovyt6a5Zo/voaTnztJxspgT9rUT9QpPViiNRnZGDIwJ8xloRRd9H49/gHaBJqpYY6GPxZe2WPxXxcRmiCVT5G+Ph0aZ2xjb6OYmK2g9Yx1K3XD4IYojaobOLK4yKGxMSzfxwoCrMicYsHzWPI8pl2XIJqANftDRbUSiV3q5udF4tRtuVYsR9EagnHG98m5LgPbZILQr+vc2dvLnVF6Zz2y+z5iWRxfWuLmwUGuS6U4uAWpVzGXRn+1ytj4OIlUCsd1L/6ADaAIRVBAKIZ7pMTUtCvyRMVG0DSNPXv2cPLkya0RRDEx3YZmaORuD+uLKo9UqD5dRctqbaVj6T06iZcnGN032lwWuAHOWQd70m5enEmHpS8tEdSWlZKW0VZGlKKLOboxJ7wrGT2no+d0lK+ofa+GdcRa7m10YHt6G8XEXOmYkbDpW2OdUopaJJSsKD1w0fNYcF2Wol5RrcgWIZjQtG118vOVWiFynCBounnBcl+QhKbRIyV9hkFOSvp0vXm2vbVg/uGlJU5oGlO2zahpbvsENKlp3JLNcks2y2nHYV/kaLfT1Hyfad/HcJzm+7Za+F9paJ6H2eFePwHLaXGN/TKuTdwYhmHgRaYiG2EjjVmvA34c2A+siAsWCoW3bviVY2I6gJ7V6bu7j/SNaYrfivoXDept1xc10AyN5P4kyf0rD25KKbx5b4VQsidtKo9XWPry0vKGEhJjifOjSnsTsdPaOggpMIfDQ0lQDyg9WKL0rRKJiQSZmzNxb6OYTdGwg/fnfFSg4sjjJhAXqbPyovqJaiSYip7HouexGFkou0GwwsXPbBFLRpvpeEEkdBoix1GqOVFsjerIyOq5LyrC75GSrK6f5w7Wrkjr0TTeODzMt4pFnqpWGTPNXRkR6ySzjkMAvNgwGM/lmHUcZiPHt4bNfSOdKyUliStoAt+p/9OLokFSCLJRNChuEbA5NvuZtNuY9fXA3wOPAXcADwOHgATw9U29ckxMBzGHTYbeMET9ZJ3i14vYCzbGnvbqiy6EEGFDUmPIIHtbdsU63/Kxz9jYp5cjSvYZm/LD5TDpN0If0M8TSYl9CfTBOP2uQWtvI6/oNXsbZW7M4Hs+3oSHTMvYBCNmTfy6jzvrYp+yqR2v4Vd97LM206emyd2RI3UghZa4sie1nUTXNHKa1nS1Wo3dEl2yfJ9F12UhSsmb97xwAt3SL0qzbWBlVEcTgkz0GqOmSa+uk5PyPKGzFWlkpqZxd18fo6bJV5eWyEkZNg7eZkRdgadgh7IPPKWYsm32JZPc09fHZLG4IvWz1eZ+qaUf2JTrrki3bFhVJ+OUrxUowvdYsfwebWevpJiVtPsNfx/wnwqFwn/J5/Nl4H5gCvgfwLe3anAxMRtBCEHq6hSJvQmqR6qUHwrde4yR9vsXbQSZlqSvTZO+Nr1iufJCdzV70l4hmJa+ukRgtaTfpbSmqUNd1pkemUamJFpKCy9pbcV9mZJoaQ1hXr4HzNW9jarPVLEnbaafmQZo2q4bAwb6gI7MhI52MiPjCe8VhPIV7oKLPRUKIHc6TOESCYHeo6P360glEbpg6StLFL9eJHNzhvT1aYy+2OVwq2lEg/rXWBesSsd7Yn6e6/r6SK0SO+YOTwyFEFyfyTBoGHx+YYGzjsPodvTvqQWIsy7acza9TzvoI4sE1yVQBxOoQRm6F24D5Sjq9/LeXm7NZteMVrTa3O9vWe4rRcX3KXseJd9nJoooNWvTIjHcKpSupGhIwyRBA9LRfr9d///U1BSvf/3rOXz4MLJNR8LDhw/z7ne/m89//vNbPLqdpV1BlAf+NrrtAulCoVDP5/PvA/6J2GUupovQDI3cLTnSh9KUHy1TebKCTEuMwe2ZCAldNKNBrSil8Ja85YjSmVA0WUcs3KLLrD3b3gtorBBIqwVT83YkqlbfXyG6klrXphQ1ehtJR5LYG0aPlBO+h860g3Ki6gABBKFxg+yTTbGk94Spk1pGC6NLXfp/roUKFIETENQDlK0I7IDADvDLPl7Zwy/7+BWf2qkai+cWMcdNjH4DvVe/bIWhV/JwZkIb9/rJOsoLbfNlTmLuXdttq2EBH7gBle9WqDxaIXl1kuytWcyx2MhjJ9CEICNl07XPMgyuz2R2eFTrM2ya/PjwMF8rFjlWqzFump3v62MHiHMe2nN1tNNu6BCY1fBGBKpfoh214ak69EqCG5ME+wzIbk0qtlKKc65LUgh+bHiY0UTi4g9aRSONsdEc9Mbo8w2UalrVlzyPOc9j2raZc128FhMPM3I8TEm563oorUdrNMgQgqyub7nof9WrXsXc3NwK4fO5z32Oxx57rHn//vvv5w1veAM/8RM/0VyWz+f5whe+wFVXXQXAi170osteDEH7gqgMNIorzgLXAE9Fj1/rJFBMzI4jM5K+V/aRviFN6Vsl6qei/kU7VNMjhMDoNzD6Q9vpViZPT7J3716CekBQCy++5S/frvkEVrDmfb8WbufNeyvut9vCuymOIuHkGi5Th6aW0/smEugDO5/eJ4RAJMS6E37lh8KhfrpO8Fz0/zd+YWmJLg0aYQQhI5sRpu0SEYEbipzAXhY6ftUPBU7Zx6t4+BV/2chjVWW4kCKMEBoCzQijhfVTdayC1XwNvVfHHDdJjCfQ+3T0Pn1X1mIFdoAz62CfjtLgymEeqpbRMIaNDaVPaoZGYiwU1c6cw9xn5pC9ktyLcqSujtPpYi5MUkp+sL+fPYbBN4tFBgzj0vv9OAFi2kMcs5GnHFQAKqMRjOrQEOpzAnSBGo6marUA7aEq2oOgxg2CG5KoUR3Mzuy/ThBw1nG4Pp3mFb29JDvc00gTgpyuk9N1xhIJ8tFypVTTvKPsecy5LrPRxQ4CNCEIlMIQIqxT2kU1XQE0xV47ltmd5iMf+Qh33rm6W07MWrQriB4CXgE8QxgR+oN8Pn8r8EbilLmYLsccMhn8kUHqp+oUv1HEPmOj3O5rTCg00Tyjfak0oimBtSyQ1hVWqwSYM7uGu14qdNdbbRphjpldM9kWcv33b93oUoRmaMh+GUZZGtGlTBhxu1h0SQWqKXIaQsevhRGchtDxK+FFBVG6CGrZ/kqE0S1hhEJH5mTb9u7CjHo9tRDUA+rP17GOWE1BaAwY4ec3aqL36cheiaZ3x+fWoJkGd9amfryOM+2ACv/HRhrlpdI4KUF/WAO49OUlinqRzC0ZMtdnOvIaMZcnmhDclsuxxzT53MICNd9neKN9Z1yFmHERxxzkCTsUQWmNYKRFBF2IlIZKaaAUFH3kl8oIXeAfSqAOmaFw2mTUc9F1sYKAf9PfTz6d3tYTYKIlarjHNLmmZV09anpc8X3mI5E04zjM+D4yqj1r0Oi9JQmtqqUQ511LOmeEcCEaJgndZpk9OTnJD/zAD/D000/zJ3/yJxw+fJjHH3+c3/3d3+WNb3wjhUIBgHvvvRchBL/zO7/D4OAgv/Ebv8EDDzwAhJGnN7/5zXz6059mamqKV77ylbz//e8nEUUTP/rRj/KXf/mXALzzne/kPe95z4qIU7fS7tH/V4HGKe3/COSAHwOORutiYroaIQSpq8L6IutZi3OfOod9xg4nWz2XX5pRazRF79/YJG/y9CR7J/biLbS4650Jr62nLIpfLS5vrIUNcxMTLWIpiirJHrnjUaUGm4outSBzYSqes+RQXCjilbymyAnsoPkasNzPpCFwGpEcY2RjUY3NoiXDVMjm/6YUQS3AOmpRebLSXG4MrRRJeo++7aYVzTS451vS4LQoDW58a5tOtqbTVb9bpfJIheSBKJ1uNE6ni1mbsUSCnxwZ4UuLi5y2bcZN88L1H55CzHqI4zba8w7CVai0aF8ErYUQ0CtRvRLlKbTnbcSz9TDCdGOS4CoTeto7sRYoxVnHYUDX+eGhoW3rv9QuSSlJSskwcKCl2egTi4sc3LOn6UToRrbrrlLUfJ9a5ITYMPhworomOwhaz0VFN5ad8hQ0hdN6omo9/mZmhr+eDutdNUIR3emjyFvHxnjL6OjFN2yDX/mVX+HRRx9dM2XuM5/5TFPAPPTQQ+c99l/+5V/4sz/7MxKJBPfddx+f+tSnuO+++3jggQf42Mc+xsc+9jEmJib4rd/6rY6MdTu46Ewpn8/rwPWEUSIKhYIF/MIWjysmZkvQdI3sTVlSforBgUHsyagoe84FFdXm9MiuiXrsFEIIjEEDY9Age+sqd72ajzPlrBBKzqRD5buVFZEXmZPLUaW9q3o2dZlbXDvRJXfBxT/hY9lWKHZ0gd6nd33/KSHO/98aUa3qkSqVxysgwgilPhQ5Iu5JIHtlKJI6KAwCO8Cdc6mfrodpcKWWNLhBY0feS80ImwYrpXBmHOY+PYfsk8vudB1KR4q5fMhIyesHBzlcLvNwqcSIYZBqTS/zIxH0vIN2zEZ4CpUQqEGJ6vSxTxeoIT2c5NcDtEdqaIctGNHxb0iixg1Irr0PW77PrOtyezbLS3p6dlXjVzNKv9soSim8SDh5a4gpJxJStSCgHgTUIhFVV4qS7+MEy2fL9kSPAcJGv5ENvICOC6HN8ku/9EvNGqKXvOQl/OZv/mZHnvf+++9nz549ANxzzz0cOXIECIXSm970Jq699loA3v72t/PZz362I6+51Vx0byoUCl4+n/8UoSia3/ohxcRsPcIUJPclSe5L0vvyXryyhzvrUj9Zp/58HdcJXatkViKzsd1zKzIlSR1KkTqUWrFcBSq0Pm6JKjlnHCqPVFj61+WeTUIXmGNm02FvRc+mLSoUvhRao0tav7Zt5hxbyVrpmSoIUyyr361S8ZZFkrkn/KzM4SjdLtd+5E8FoZB0zjnUjoVucCpQCCNKg5vonhQ1IaL0w4GWdDojdKeL0+k6j1IK5SlUXRF4QdelcF4MKQQv7elh1DD4wuIilucztCQQJx2052yEo1CmQA1I1HYJ/aSGGotS6ioB8msVhAb+wQTqmgRqRIfot2zWcVDAjwwOclUqdeHnvYwQQmAIwWaP4qqlP9aJYpE+PRSjtu/zi3v38ksTE50cbntjCqLvkheZ8VgBgQyF24c//OEVNUSTk5Mdec3h4eHm7VQqxczMDAAzMzPcdNNNzXVjY2Mdeb3toN0j/BOERgontm4oMTE7h57T0XM6qYMp1N1RrUnkaGWfsVG+Ai3cTstoXZMK1k00J897THJ35Fas8ytRz6YzK3s2VQ5XUN5yVEnv089LvfM1HzWuYlG6xQhNNE8ANFC+wqt4OI864XeAZUGbGE9gDBuhSGoxKmmeXHi+Tu1EDeWGdVNaTsMY3RoL/E5zXjpdqztdnE4Xppi6QTgJc5cnY83brsKvh+mkQa3FKbEeEDhBKILccMJWO1vj7ENnkRmJPqA3I9MN0xMtrXVtxF4FirGy5PUnUhx+eIaK5dGTNFD9EmXs4D4iBOQkKidRgUKbdBDP2aikhnu9ydQY7B3LcE9fH9kd6K+0mxFCYAqBSZgS14iq+WxPbRKE3z/lh9+zwA7CY3PU9EloYrmYagcYGRlhOkobBDh79uzODGQTtPtN+I+ERgrvBR4Bqq0rC4XCQofHFROzYwgtPFNsDBhkrs8QeEFYT3M2TK9zpqJib0MgeyQy1X1RjW5DZiXpfJp0flXPJl/hTDvLUaXIjrz0zVLTVQzgGf2ZcBK+N9GMLJnj4XU31Spdbggp0HN6WDUaofzIPv6M3ay10kwN27U59+i55TS4tIYxsDNpcJ3ickynU4FC1cIGyMo7X9gEzkoR49dCYaPq0TpnZY1ds3aO5UlZ2Fk1FM9CE6E7oi7ClgFJDZEJbwshkEFYL6bcVaYnjd0mCB1D9f7IIXJQR8+GJ6ZkZvvTm5VSuPMu9RN1qk9XCawAYQhePN7HUa/OiXqdftn+5GrL0QRqIIxi1Goe9ccq3PZUkqsnfMQtNv5+0REjn5itQSkFAcvfVVs1T04hIufRSxDfQ0NDnD59es1lmzFBeM1rXsO73/1u7r33XsbHx/nwhz+86bFtN+1+Z/8puv4UK3Vnswatk4OKiekmNF3DHDExR0xyt+aW6yAmwzoIe9IOncKScf3RRhFSkBhPkBhPwEtWrvNKoanDuSfPkallsKfCCFP5cHlFVElmZVMcmXvDyEVDMF1uZhndgJBR89Oe5Z+PwA0IjgVoA1pXpcF1ivXS6bK3ZElfn17xXnQDylehnbvlE1QD3EUXd97FW/DwSz61qRrT49MtD2h5cGOSFYkZZCRsTIFMSnTZ2boyiNJSTbGuwAycAK/s4cyuEktR3acxYKwUS2kNme3csVgphbfohSLomWp4skYnbKPQ4vJ4o0rTb+g8UamQUhrpDttWbxoFS56HYQpeeu0gfYaOX/FZ+soSCEhenSRzYyY8Zu6y1MXLDaWi6I8XOcW2noDQwhO2wuzc9+8tb3kL73rXu/jEJz7Bvffey3ve8x7e/va38653vYt6vc773vc+BgcH236+u+++m/vvv5+3vOUtCCH4xV/8RT7zmc9gbtSRcQdo9yh+z5aOIiZmF6ElQgvqxN4EvS/txa/6OLPOcv1RPao/SsswehGnem0KvUdHv1EnkUswum/ZVUf54dn6hrGDM+VgT9lUnqzgfcVb8RzGsNEUS61RpY32sYm5MJqhoWVXuttdrqxo9vp4hfLhcuhOd1uUTrdN0crACwiqUR+rqo+76OLNe6H5R0t0FUJB03AfNMYMpC/DkxC7BM3UQrGUO39d4K4SSw3WEEsyK5dT8dqI7rlLYV1p9ekqftEPTwb0r28BL4RgPJEgJ3UeLZdYcj36dH1HK+y9QLHoe0yYCW5MpzFk+H830mNVoJpOj8IUZG7IkLo2hTFkxJH3baBZS9cqgBq7sRaenNhsuPHLX/7yecsmJiaa1toAt99++3lNV++77z7uu+++FcsalttrPe873vGOFfff9ra38ba3vQ2A48ePo2naipqjbqWtt7lQKHxtqwcSE7NbkRlJKpMidXUKdVeYitL4gbFPhfVHjRoKmblwT5uYiyOkIDGWIDGWOK9WKagH2FP2sgveVJiCt/TVJQJrOc9HGMt1MCtS8CYSGzINiLly0QwNc8wMJ5TTDrP/MIvRb5C7I0fy6mRH0ukCNxQ8QTXAq3ih4Jl38RY9fMtfESkRZmT8kdCuqH1YM6Iao3XEkl/1ceddlBO6gInIYllLtoil/tAsRGYkQSmg8lQF62kLd8ENa+v6JImJ9gVkTpfc2dPLU5bFlG0zoOvIHTjuVz0fWwXclskynlhbrAttuTdX4AZUnwmdJ2WfJHtLluTVSfRsd0VAdzOrDRCUu9yTrplausu/ul/84he5++67qdVqfOADH+Cee+5B3wW1am2NMJ/P37XOKgXUgeMXqyPK5/N/AfwwMFMoFG5aY70A/hvwOsACfrZQKDzazvhiYroFIQRGn4HRZ5C5LtNsNulMO2H90Vkn3E5G9UfbkLvdyEFGrbodqGbxZfN2AEExwE27LU/Q8jyty1YVbiqlVqberC7sXOsgr9ZZ3oI/7eMknfCsburChhZaUiN1MEXq4CoHPKXwi8vGDs6ZMKpkT66Tgrd3pVhKjIeCaS2UUuCznObgL6c84Ie5380fQV+Bx4ptmttGeeKty5uPbywLWp7XV9ScGnN758KzvTmJnosmdtH9OAq29Qht2aLer/osfmkRYQiyt2ZJ5y+eThfYy1Eev+zjLYaix11ww+bILR+hMKNIT2bj/cWuRDRDA4M13SubYmnBDXuJRcei+lSd4lgRvV/fkAhajSE1bstm6NclT1ctcrokuU221oGCRc+lR+q8ONtDVm/vd0YzNMw94XHOt3yK3yxS/EaRxN4EmZszYf1KoOKTehvgPAOExm9NZIBwKfU/3conP/lJ3vWudyGl5MUvfjHvfe97d3pIbdHuEfWrrOirDqvuB/l8/rPA/YVCocrafAz4EPDxdda/Frg2urwU+O/RdUzMrkVIgTkcWhZnb8oSOGH9kT1lUz9ex560USj8OR9bt5uiZE2RIEAgUKi1rxuCRYUHWhWsKryMipqFHhU5y7A+QNO15ToBKdCURvLqZPijJ6KiaUHYaS5qsCA0sby8ZZ0Qy8XSTceb1qYMrc9Hy/M1RI5gxfZCCBaPLJLJZLBPh5GfxnYy074luhBhzyC9TyfzgsyKdc0UvDPOCrFU+e75KXgiKyhTXil6VjVx3XIkzc8vqAecU+fW3bRRSyF7wvdKz+lNsdQUTj0SPauvWNZtQqrRODeoR5dGkX90+7x1tsJWNpWbKyT2J9AH9G2JmDTSsQI3oPJYhfLDZZKHkvgpH3fExa/6oQvfXFjT4y/5YYpMoxpXsBzl6ZGXhcV7t7KeWJJ+2D+tEwghuDqVolfXebRcoe579BlbK2TtIKDk+RxKJrk2nd50ZKqRGqpUaHYx/y/zWOcsph6cCvdTXQM9fB8bfdmajaijptTCiOrCGtut+u1p3taWb69et5vE1woDBCcIo5KrDRA6WP/Trfz5n//5Tg9hU7T7zXw98AHgd4gatBKKlX8PvJdwSvBB4PeAd6z1BIVC4YF8Pn/1BV7jXuDjhUJBAQ/m8/m+fD4/VigUdo1n38c//nE+9KEPkU6nL77xNmBZVteMBbprPN0yFhWEZ45qpRrJdPJ8cdB6Hd0WiPPXiYus3wDd8t40WDGeIKydaJ5ts1VTCDbcrOjUSdgxUHtU6LblhK/l1T2kLtcUbq3L1hJ46wrB9QTjes/fgl23MQ1zzQhVc5mjUOdWLfMVF6JZTN+YlOhrTFRWLXNcJ+ot3YAAACAASURBVBxLsBxxVIFavr/GNUHLJOJC226Wv1v+f5q9pBJhLY2WCCduW5qeosLJkV23VxQVC000C6R3Ij3Gtm0Sie6oIeqmscDWjSdQUPV9XBWgC62tj911XQyjfVHsRcfCrJQYWyAkVrw3ja+lanEYXLV8zfXr0Di5t+a6luNf4ztjOy1juci/KpoH0gtvt9Hv4m//4W8jVPigIAjC12kJFaz4Ld5GAj9AK2ldIyYHBwcZGhra6WFclHYF0X8G3lkoFL7Usux7+Xx+Fnh/oVC4I5/P+8CfsI4gaoO9QKv332S07IKCyLbtZofcnWZqaoogCLAsa6eHAtBVY4HuGk83jQVApRS2sKM7rLzeZrrtvVlzPAJIAgmWJ/qOgjorJvtNgXQpvwta9FpJEDmB0s7/YNb7Id8UG+ghoZTC8aKomQasY+Qjor8Vr9EQGz7hJVh+Lxu3m+LJidZdJBrm4V14g5WDWhlF1Foueku0sXW9WBYTK6KWq7cjFM7CFeF+4YT7h1f2YGnlGIQhwASRiM7emnReKOng0pKGGonBnUIFCtu2d24ALXTTWGBrx2MoCFDUlIdEXPTcjVIK13UvslX4dfZQGAhSQhAEPlvxH+zIZ9V6PGy5DoIA27/IWLb4N1QFisAPmq+lxMq08naE4P/f3pnHOVWf+/99kswGDCMwsiN1fcQWve7Xfa2KcqFS9eq1WDcU3LeqtQuiVhHQ2lpaF7y2Wttq/blQl7pULdaWqsh1gz4VN3aQTYTZMkl+f3xPMplMZiYDk+QM87xfr7xycs7JOZ+cfM/yfL/Pki9SBloAiEaj1NbWFnyfHbUNcjWI9gCWZZm/zF8G8D4wMMs6uZLtn2u3KZWVlTFixIit2G3nMWLECMaOHRsYPQsXLgyMFgiWniBpgWDpCZIW6LieWJ2Lw2hY3UD94noaVjYVFQ2V++l4tzAd99IlSxk6rPCVyFuj0HoS8QTxGpfVK7bJxbwkX+tXrafPgD6pERiv3LnLJLObpUZmykN4pfl3hWnt2MQ2xahfUk/dkjrql9S719J6oqubHjxTxWeHlTV/DSnbomQJ3bXdJBoTLvV3bTz1Sv8cq42xYc0G+gzs49wNe4Sb6gv18N/LC9vTXYhj80V9A/M3bSLseVS2Ed+zZs1aqqvbTnlcG4uxKRZnj549+Fp5eV5dQ4PUjoOgZWWflew6bFcAog1RSkqD4eLasLmBiuqKbl12oqSkJOtzw7x581r9Tq4G0QLgByJyvqrWA4hIGXCDvwxgGNC6M3v7LPW3kWQosHwrtmcYRjckXB4mPCjsstDtVUki7nzgG9c3uofgpfVE1/gPvyGIVLq6JUFxLwgyXshLpevNpHZJLf2G5V6voliEe4XpMaIHPUY0dwuN1cZoWNpA3VKXHbJ+aT11n9Wxce7GZnVASvuXUraDbyANLXPTQ8q2ieKWiYSf+jeL4RKviTf/3IaRk1w3Ec2te3xFW44gHinjKNwjzVDKeG82nWFYhcrbTsZSaLYvK+XQSBX/t2kza6NR+kZK6LA8v7ZQacjjkKqqvMcmGca2Tq5n0EXAn4BlIvIBbuRmJO42MdpfZyfgl1uhZTZwiYj8ARef9GVXih8yDCOYeCFXVLOkbwkVO7vsc7EafxRpVQN1S+poWNmQGo8OVfijSJ2QNtnoOoQrwlTsWkHFrs0zFMYb4i6Ne8ao0qZ3NjXLTlhSXdJyRGlYGZHK3G6z6QHZiaiflcqPl0vVKUkuy5xOmxePxrOvn/a+ef1mPuXTrEZNrq58oYpQ6hWucBkgS7YvoazCGYfNlqd9Dlf4Ror/ecXKFQzsN5B4TbxZMdnY5ljLeTV+zaU1UTfPX96u5hBZjakWhlWPMA21DWxcsdGNaFakjXAmRzdLvE4xrnqEwxxYWYnW1PBpXR19IhEiOXbKtFZbqKMkEn5iktrWjd3UvLo4tfW1rB2+lpK+JUT6RlxNpu0iVozc2CbItQ7RP0VkR+A7gODc234PPJLMKqeqrWWPA0BEfg8cCVSLyFJcMoYS/7v3AM/hUm4vwqXdPmcLfo9hGEa7JDMolQ0po3KfShIxN4rUsNZlm6tbXEe01o0ieWE3KhLqYTf9oNAs4UKiKb4pPUFDfKPLOueVbd0DbKjUZV0s/1o5VVQ1aYglaFjh6l3VLa5Lud5t/vPmZgVCI9tFSFQlWFSyKKsBk/45H/EG6RnAvIh7xcIx4r3jzojpV9KqsZI0dEI90qZ9I6GzRlS9Da7Dgr5b9v1EwiU+SaYuTxpJKWMq07Dyp6Oro9TV1LnlGYbgYha3vsMQzY2kpOGU7b287ZdX7rFbeRlVkRDvRzdTFgnTM9zGSGMiweaaRhprY+wZqqBfY4iGulrqso3aJT/XtWHs1MVzbnNeqUcimmBFomU/dbh3mEifiDOU+kSaT/dt+rxNunAlM8MWKVaoGCxdupRjjjmGDz/8kEgkwvnnn89JJ53EySef3KHtLF++nJNOOom3336bcFvtvkDkPMbqGz73bumOVPWMdpYngIu3dPuGYRhbihduqiXTczeXlrtxk3Oza1jpjyKtaCC2OkZ9OCOQd2tuhMl0y9nInJ8lc2BstUvX3uyBPyNDHWRkuKP58lQmpFYyF7bImJe2jfRliQbX29zCWMnMLJc2LzNy1PP8TFMeeAkve1BygtSDfbMH/TI/zW/E9eKHoiGI41K1e03fbfZAuhXpxb2w51zmhpbR+z97N8mLJ4h+EU2NJNUtqWPT0k1EekVSmlMpiDMMlWaf01MZR0LN18s2XdJy24TJagwGIf6is/A8z8WrlYe2OE15IuG7CW6OseKzFfTfrj+xulhTavdsr3rfqPDfkyNX6fPTDeP2GAokSj3i5a4tUx5iADFKGpdDXQKvNg71CXr4m/zSf7V6XEq95kZtRYhIVYTQwJbGbeZ6STfD1Dz/XFny2RIGVg6kcV0j0fVRGte5a2T6dN2SOhrXN7rkLBmEeoTaNJyS06Ee+XdxTMQzXEP9Ech4Te7vPX/fk7pEXWqbsVCs+bmc+fKzchaKo48+mjVr1hAOh6moqOCII47ghz/8IT179mz/yx1g1qxZOeu55ZZbOPjggwEYPHgw8+fP71QtW0POBpGIjMIZLDsBx6vqEhE5H/g0I/ucYRhGlyfSK0KkV4TyYeX03r838WicDW9uoHqn6qyZANPrQLUoUJu5nA6uk5wdb1qeSCRYu2gtVTtVNTM2kssy5yUNkfSCvC0K9KanwM72vaSWeMb346Rqbnglflru9Af5Eq9lrZL0tN2hLNNptUlazM/hYWnlwpX0H9GfRCzRVPTUrwHUsLqB6JqoG5XxjVKvxEs9HG6NC5AX8igdUErpgFIq96sEti0DZFvE87zUqHG4LkzFsIr2v5QDqfpZScOpNePKfzXWxln7VT01tTHKGnDf61VKrBzqS6GqspTtK8sI98hixCQ/+204H7XE0juOKmj9GCXiCXe+JQ2n9Y3Njah1jdRoDY3rG7MajV6p5wykPmnGUp8mN72SPiXEv4pT79U3uVPWNrlVJg3U1HtN2nL/PV6bg2+oR5PLp+9SGa4MUzqwlFBFCMoh0jcCIYhFY4QSoVT5g3h9PKtRiEfrxlKEpulO+vvuueceDj74YFatWsV5553Hr371K6655prU8kTCla4IFahocJDJySASkTOBe4BZwDH4rm44W/dawAwiwzC2aUIlIUJ9Q5QNCk7NlJJQCb1G9Cq2DADWLVzHgBEDii2jBV7YI9I7QqR3hDKa/rtEwmXMi22KEdsUo2FtA9Evoq5oam1aquOQS9QRqghttfud0b3wwk2GVq4MTiRYXFfPhzWbqduwgd7bbQfAXr16sX1ZK3n1A4YX8ohURYhURSjfsbzV9RKJBPHN8WYjTI3rG4mui6am6z6vo/H/GonXtDRgNrKx1W2HykMpIybUw02X9HXuoekxY229t+caurLXSmcQ4TqEWmSZ8+uQperDpbnJ0ugM3kSsFVfZCM2MpWxGVEeMpgEDBnDYYYfx0UcfMX78ePbZZx/++c9/smDBAv70pz/Rt29fbrvtNubMmYPneYwbN47LLruMcDhMLBZjxowZPPHEE/Tq1Ytzzz232bbHjx/PmDFjOPXUUwF47LHHePDBB1m5ciWDBg1i+vTp/PrXv2b58uVMnDiRcDjMRRddxKhRo5q53q1atYrJkyfzzjvvUFVVxYQJEzjttNMAuPvuu1m0aBFlZWW89NJLDB48mKlTpzJy5MjcD0I75DpCdC0wQVX/4I8KJZkL3NRpagzDMAyjAHielwqsZwCphBsA8fomQym6IUp0tW8orY22dL/z40Xy0SNvdD88z2N4RTlVkQgvb9hAVUmEkT17Uh6AGIvOxvPSslYOa3vdeH28yVBa18iaJWvoN7hfU0bBCv/dT+BRjPNx9R9Ws+q3qzr+xfTR+ETG6Dxpn4HqMdX0O8nP5hnKPtqUrZj1ihUrmDNnDt/85jeZN28eTz/9NPfffz877rgjiUSCyy+/nOrqal588UVqa2u58MILGTRoEKeffjqPPfYYr776Kk899RQVFRVcemnr5Uaff/557r77bmbOnMnIkSNZvHgxkUiE6dOnM2/evGYuc0uXLm323auvvppddtmF119/nU8++YRzzjmHYcOGcdBBBwHwyiuv8Itf/ILbbruNu+66i5tvvpnHHnus48e7FXI1iHYF/pFl/iagd5b5hmEYhtElCZU5I6ekXwnlw5t6uBOxRMpQavyykYY1zvUuujrqHkKS7nelXlOsUidk4Eq6MmZ9YGrrc9x/oErWjvQflOJr4zSUNdBmnBm0jDXLmNciLi25HXJYx59ONPjuRZn73RI64Rk4vbBzMdmuJML+JaXsUFlpo5K4c7JsUFlqhP6rJV+x3bDtiqyqk8h2npHlc8IlaSnpX9Ji1CnTRS8x0LXjiy++mHA4TGVlJUcccQQTJ05kwoQJnHzyyey6q6uhtGbNGubMmcPbb79NeXk5PXr04Oyzz+bRRx/l9NNP5/nnn+e73/0ugwYNAuDCCy/kzTffzPpTHn/8cc4//3z23HNPAIYPH57TIVixYgXz5s3jnnvuSdUXPfXUU3n66adTBtG+++7LEUccAcDYsWP5zW9+k9O2cyVXg2g5sBvwecb8w4GPO1WRYRiGYQQQL9zkClQ2pIyeuODkpPtdsmBtdG3aqFJ9NJWMw8NPFJG+Tbym2LHkPM9rHk/mJ0dIj61KxViFXOwBHoQioebzw03rhiIh99AVcbW3eg73A6sz48wSackw0uPh0pNhZPRcZ8acNYtjgxbLmmUGzGYQtUWW2LtWV82MycuB+MY49cvqsy5Lxsa1SHyRJ4Ml7JmLZlei/+n96X96/+IJ8F30onXRVLuZOXNmakQmnaRxAy7bW2NjI4ceemhqXjweT62zevXqZusPHjy4VQkrVqxghx126LD01atXU1VVRa9eTS7ggwcP5oMPPkh9rq6uTk2Xl5dTX19PY2MjkUjn1ODKdSv3AT9Pc5cbJiKHAdOAGztFiWEYhmF0QZq53wHs0rQsVhdjwzsb2H6X7ZsSQoRwwdUhr9l7q8s7+aG4pLKEqhFV7a9YANYtXMfAEQOLLSPFhoUbGLzbYOINfqa4ej8xQpobZeq1MdYUnJ8cCEwaXyEXd9giqYgZOEa+8FxyGBpyWDWtHQ4cOJDS0lLmzp2b1bjYfvvtWbGiKd16+nQmgwYNYvHiNtLWt0L//v358ssv2bRpU8ooWrFiBQMGFC4uNdc6RNNEpAp4CSgHXgXqgRmqOjOP+gzDMAyjyxIuDxPqE6K0f9cIiDf8ZAgVYcIV7cftJOIJ4g3NDad4vZ/dbFOcxk2NqelYbcwZTF7GyKD/INvMgIq0bTil3Cih466UW/AZz6X5byhv6FR3UKP49O/fn0MOOYSpU6dyxRVX0KNHD5YuXcrKlSs54IADGDVqFA8//DBHHXUUFRUV3Hfffa1u65RTTmHq1Knsu+++fP3rX0/FEA0ZMoTq6mqWLFmS9XuDBg1i77335s477+S6667j008/5fHHH2f69On5+tkt6Egdoh+IyE+APXB9IQtUdVPelBmGYRiGYQQYL+QRLg+7ruJ2SCQSKRfB5CtRnyBW49LCxzb7I081/vTqGPWhVuqeZbhRptI5e2kjURE/XX3Ia+lKmWUeIQiFndtlKmA/me7eg7X/XkvPPj2dS+i6KI11janfBTQrRtueQWcEi2nTpjFjxgxOPPFENm/ezLBhw5gwYQIAp512Gp999hljx46lZ8+enHfeecydOzfrdkaNGsWGDRu4+uqrWb16NUOGDGHatGkMGTKECy64gFtuuYXp06czadIkjj/++GbfvfPOO5k8eTKHHXYYvXv35tJLL+WQQw7J+29P0iHHO1WtAd7OkxbDMAzDMIxtEs9zRVdDZe2PrCQSCb5870sG7T4oFXSfcq3MgxtlLpQ0Nne1jEd9N0LfkGtc6xKNNK5tdHFhIVKjS82MJcvIWBBeeeWVrPMffvjhFvMqKyuZMmUKU6ZMabEsEolwww03cMMNN6TmnXnmma1u74wzzuCMM85osZ1jjz2WY489ttk8VU1NDxw4kHvvvTer5szMdkOHDm323c4g1zpEZcBFwFFAf5q8ZQFQ1QM6VZVhGIZhGEY3xfM8l60wB+OpWIRKQoT6hCjpU9JiWdJtMFkUOZmRsXFtI/FoPJWREY8mF7wukL4+Vbw6WT/Iz+yWLdW10bXIdYTofmA08DSwgA7lbDEMwzAMwzC6C6nU9X2dsZTMyAgu0Uh8szOYGr9sdNkY10WJfhF1hkbSWAqRMpTaK5LaHol4oimzYcyfjjVNJ5elG2pJMjNBhkpdkWYg5bpIo8uY6EW8jCEDo6uQq0E0Bhirqn/NpxjDMAzDMAxj2yVcHiZcHqakX/ORpUQiQbyuyQ2v8ctGGtc2upilVa7WVzIlfWxNjIbShqaRGh/PS0ttn951H3KGTKg0hFfuCsImjTavzNUNC5eHmwqcJrMCZnv57orrF66nZDv3G6KlUSKhCLFNMWcYlWSp62UEmlwNotXAmnwKMQzDMAzDMLonnpeW3W/75suStb6Sbnhr31lL5Y6VzpgpDbUwYEIloeZGTAFc8UJlTke8zukEPw220SXI1SC6AbhVRM5W1fX5FGQYhmEYhmEYSTJrfZXUlVA5orLIqlrihTzCPdzoU2xzjHhdvKl4shFocv2LXgQuAFaLyEogmr5QVXfqbGGGYRiGYRiG0dXwwh6R3hHiFfEmNzqLLwo0uRpEDwFfB+4CVmFJFQzDMAzDMAyjVUIlIbztPJd1b1MMGrH4ooCSq0H0TeBoVf1nPsUYhmEYhmEYxraC57nivaHSEPFaP77Is/iioJHr4N1ioL7dtQzDMAzDMAzDaIYXcnFQJf1KCJWFSDQkUnWMtgWeeOKJZgVZ9957b5YsWVJERR0j1xGiK4FpInKRqi7a0p2JyAnAz4AwMEtVp2Ys3wH4DbCdv871qvrclu7PMAzDMAzDMIJCs/iir/IXX3T00UezZs0awuEwkUiEvffemylTpjBo0KDO3VErzJ8/vyD76SxyPfx/BI4EVERqRGRj+iuXDYhIGJgJjAL2AM4QkT0yVvsh8Jiq7g2cDvwyR32GYRiGYRiG0SUIlYSI9IkQ7h12hWMbEp0eoX/PPfcwf/58/va3v9GvXz9uvvnmzt3BNkSuI0SXdMK+DgAWqeonACLyB2AssCBtnQTQ25+uApZ3wn4NwzAMwzAMI1BkjS8K4UaMOpGysjJOOOEEbr31VgBee+017rrrLhYvXkxlZSWnnHIKl156KQD19fX84Ac/4PXXXycWizF8+HDuvfdeqqur+eqrr7jtttuYM2cOnucxbtw4LrvsMsLhcIt9iggvvvgiw4cP5/rrr6eiooJly5bx1ltvscsuu3DHHXewww47APDxxx9zyy238OGHH9KnTx8uv/xyTjzxxE49Bu2Rk0Gkqr/phH0NAdKdCZcCB2ascyPwoohcCvQEjm1vo/X19SxcuLAT5HUOdXV1gdETJC0QLD1B0gLB0hMkLRAsPUHSAsHSEyQtECw9QdICwdITJC0QLD1B0gLB0hMELdFolNraWsAVjp01axYPPfTQ1m00AcQhEU+4xAtec8No/BnjOfO0M9vZRIL6+nri8Tj19fXU1tZSW1vL7Nmz+cY3vkFtbS2hUIibbrqJnXfemUWLFjFx4kR22mknjj76aB5//HG+/PJLnn/+eUpLS1FVEokEtbW1XHPNNfTr14/Zs2dTW1vLpZdeSnV1NaeccgoNDQ3E4/HUMQH3P9XW1tLY2MgzzzzDzJkzueOOO/jRj37EjBkzuP3226mtreWcc85h0qRJ/PznP+ejjz5i0qRJDBs2jF122WWLDmM0Gu1w+8i5VJSIlAFn4tzdEsCHwO9VNddkC9nM3czBwTOAX6vqHSJyEPCwiHxDVeOtbbSsrIwRI0bkKCH/LFy4MDB6gqQFgqUnSFogWHqCpAWCpSdIWiBYeoKkBYKlJ0haIFh6gqQFgqUnSFogWHqCoGXhwoVUVFQAUFtbS2lpKaFQJwQChYEEJGK+C13a03M4EqaktKTNrzdEGygrKyMUCnHVVVcRDoepqamhb9++PPDAA1RUVHD44Yen1t9rr70YPXo07777LieddBIVFRV89dVXrF69mt133519990XgDVr1vDGG2/w9ttvU15eDsC5557Lo48+yvjx41O/P3lMAMrLy6moqCASiXDcccdxwAEHAHDyySdz2223UVFRwauvvsrQoUNTCRn22Wcfjj/+eP76178ycuTILTqEJSUlWdvHvHnzWv1OTgaRH+vzZ5w72/v+7AnAFBE5QVVzMcOWAsPSPg+lpUvcecAJAKr6DxEpB6qB1bnoNAzDMAzDMIxCc9ZZZ3HWWWd12vYSiYSrX/SVS0XnRTpev2jmzJkcfPDBxGIx/vKXvzB+/HieffZZli9fzowZM/joo4+IRqM0NDRwwgknADB27FhWrlzJVVddxcaNGxkzZgxXXnkly5cvp7GxkUMPPTS1/Xg8nnOShurq6tR0eXk5NTU1ACxbtoz33nuP/fbbL7U8FosxZsyYjv3YrSTXEaKfAfOB8aq6EUBEegO/xRVrPT6HbbwF7CoiOwLLcEkT/idjncXAMcCvRWQEUA58kaNGwzAMwzAMw+jypMcXxWpixGviWxxfFA6HOe644/jxj3/MvHnzmDFjBt/5zneYNWsWZWVl/OQnP2H9+vWAG1255JJLuOSSS1i6dCkXXHABO+64I0cccQSlpaXMnTuXSCRnB7N2GTRoEPvvvz8PPvhgp21zS8h1bO8Q4IakMQTgT/8AOLTVb6Whqo245AwvAAtx2eQ+FJGbRCRpBl4NTBCRd4HfA2eraifn3DAMwzAMwzCM4OOFPCK9IkT6RgiVbFn9okQiwcsvv8zGjRvZeeed2bx5M1VVVZSVlfHee+/xzDPPpNadO3cuqkosFqNXr15EIhHC4TD9+/fnkEMOYerUqWzatIl4PM7ixYt58803t+r3HXnkkXz22Wc89dRTRKNRotEo7733Hh9//PFWbbej5Gri1eFqA2VS5S/LCb+m0HMZ836cNr0AZ3wZhmEYhmEYhgGEIiG83h6haCjn+kUTJ05MZYAbMmQIU6dOZdddd2Xy5Mncfvvt3HTTTRxwwAGMGjWKjRvdmMeaNWuYPHkyq1atokePHpx44okp97Vp06YxY8YMTjzxRDZv3sywYcOYMGHCVv2uXr168cADDzB16lSmTp1KIpFARPj+97+/VdvtKLkaRH8C7heRCcBcf95BwL3A7HwIMwzDMAzDMAzD4XkeXqmH19cjXhcntikGsezxRa+88kqr2znhhBNSMUOZjB49mtGjR2ddVllZyZQpU5gyZUqLZePGjWPcuHGpz6qamp46dWqzdQ888EDmzJmT+rzTTjtx3333taq3EOTqMnc58BHwOm5EqA74K/Bv4Ir8SDMMwzAMwzAMIx3P8whXhCnpV0KoIkQimiDRaBEmW0OudYg2AGNFZFdgd5wdukBVF+VTnGEYhmEYhmEYLUnGF8XL3WhRoiHRsqCNkRMdShOhqh+JyAp/elN+JBmGYRiGYRiGkQuhSAivyiPRkCC6Ptrh9NxGxwqzXgFcBQzxPy8H7gTuskxwhmEYhmEYhlEcPM/DK/MIVYUIlXZCgdhuRq6FWacBFwDTgX/4sw8CfgwMAq7NizrDMAzDMAzDCCDxeJxQyIyPIJFIbNkYTa4jROcD56vq42nzXhERxWWaM4PIMAzDMAzD6Bb07NmTZcuWMWDAgC1+CDc6l0Qiwdq1aykvL+/wdzsSQ/ReK/PMNDYMwzAMwzC6DUOHDmXNmjV8/vnn1NbWUlJSUmxJAESj0cBoKQbl5eUMHTq0w9/L1SB6CLgYl347nUnAwx3eq2EYhmEYhmF0UUKhEP3796d///4sXLiQESNGFFsSQKC0dCVyNYjKgP8RkeNpKsx6IDAYeEREfp5cUVUv61yJhmEYhmEYhmEY+SFXg2h34B1/erj/vtJ/pZuh5kRpGIZhGIZhGEaXIdfCrEflW4hhGIZhGIZhGEahsYQIhmEYhmEYhmF0W7yunipw3rx5XwCfF1uHYRiGYRiGYRiBZfi+++67fbYFXd4gMgzDMAzDMAzD2FLMZc4wDMMwDMMwjG6LGUSGYRiGYRiGYXRbzCAyDMMwDMMwDKPbYgaRYRiGYRiGYRjdFjOIDMMwDMMwDMPotphBZBiGYRiGYRhGt8UMIsMwDMMwDMMwui1mEG0lImLHsIshIl6xNSQJkhajbZLnerH/s2Lv3+gYQfq/gqTFaJsg/VdB0gLB0hMkLcbWYQ/zW4GIlKpqvNg6spF+khbrhE3uN2gXDFUNRDViEekbFC0QjDaTvu8Atpu4/56A4ugLWpsBNT+h/gAAF4xJREFUazftEZT/K2htJ4DtJlDPQ0H5r4LWbsCOTS4E7TrYFfASiUD+l10CEZkD3KGqTxdbSzZEpERVo2mfvUKevP4JWamqGwu1z7YQkT2BC4EaYC0wX1VfKJKWEmA+MFpVPyv0f9OWrmK2meQ+yWg3SR1F0hOIdhPUNgPWblrRE4h242sJZNsRkd4Z/1eo0J2MviFUparr0+ZZuyF47caOjZFPzCDaQkRkX2AuMFBV1/rz9gTKgaiqzi+Srq8BY4Djgc+BZcBLqvpmEbRMBnZR1fGF3ncWLZcBFwOb/Fn1uP/qY+A3wHOFvBGLyE+AE1V1b/9zJbA/UAusB7RQF9cgtRlfT1dqN8+raqxAWgLTZvz9fw1rN61psetN61r2AM4AjgFWA18Aj6nqS4XYfxY904F9VPWYYuw/Q4u1m9a12LHJTdc3gP8C9gHmAR8CH6jqp4XW0tWIFFtAF+Z24BFVXSsi+wEXAWfjLu4LROR5VZ1WBF1PAlFgCbAHMBI4WUTeBO5X1fkF7IW7FvhO8oOIHAX0BTYC/1bVzwvYq3Ij8D1VfcDXshdwHHAU8AOgAvhjAXQkuQQ4z9cyHrgA+E/cxfQt4GfA7AJpCVKbgZbt5migD83bTaH03Ehw2k2Q2gxYu2mLGwlOu4FgtZ0ncG3mfaAUGALMEpEFwAxV/UuBdCS5ADgHUr3+Y4D+QBx4s8Cdmzdi7aY1bsSOTZv4x+QxoAFQ4DIgBrwpIo8DT6lqrY1mZcdGiLYAEdkR1yuxnapuFJFXgS+Bmbjei3OBM4HzVPX3BdQ1CbgSOFxVV/rzDgFOAg7DPRhMVNUlBdByBXCRqu4mIn1xBuQ4oAzXezIXuEJVlxVAy3/iepCOUdWlGct2B24ATgOOVtW/F0DPRcCNqtpfRMqA94DngZ8Cg4AfAscCh6nqW3nWEpg24+/b2k12LYFpM74eazetawlMu/H3GZi2IyIXAN8D9lfVDf683XFtZjTOELm8UKOMGe2mGvgF7iG7FliH6+C8VlXfLoAWazeta7Fjk5uuPwOLgeuSLqAi8m3gUmBv4Feqen2h9HQ1AhVE2IUY57/f4A/jDgYuVtWXVPUfqjoB+DtweIF17QH8PfmAAqCqb6jqDbgelJ2BR0WkogBargNm+dNX+fuepKq9cL0WBwK/E5HSAmj5FAjjLgqIiCciYQBV/ZeqngW8DRxUAC3gHhrrReRK4LfAR7ib7ueqOldVRwPLgd0LoCVIbQas3bRGkNoMWLtpiyC1GwhW2xkMvJc0hiB1TO7HPUTWAQ/5Rm0h+D7uQRvgcmB74FRVHYL7/6qAB0WkVwG0WLtpHTs27SAivYGBwBuquj55rVPV/6eqRwITgQtF5JpCaepqmEG0ZTwJ/AjnL3or8BrOFxoRSbohvgzsKIXNXPMPYKzfU5t50ZiDG96tAnbIpwg/vqoCOFJEfoQLgpyqqo/5Wp4Ergeqge3yqcXf3yrcTe9MEbkYCGnLuI9PcT63eUVc4Pe9uDb0bZzR/JiqNvjLI34bmkdhLqZzgW8Vu834+w5iu3mIIrcbv83MIjhtBgJyrfH3HcR2E6TrzX0Ep+28A/yXiJyedq8EQFXfx7meR4Fd8i1EXCxTPXCGiNyLc3mamnTZU9XXcAZTGGco5ZWgXG8gePeptHPqLBG5hOIfm/txx+Y04EiKfD32XeA24s6v0wFUtUFEwv4IFr630h9w18kKsSx0LbAYoi1AVT8BfiIiD+L8jVcDjf6yRn+1w4H3C+SvnuRVnF/23SJynbog1fSLxqfAAKB3nnV8BFyBc4M4A/gAWJBc6J+IHwIeLqZodZ71oKo3i0g5MAO4RETuxBmtJcBw4EScm2O+dSSA2SLyJ9yF9CDc/5Jc3igi2+GO3S/zrQfXZt4DZorI94rYZgAW4dywDsP9Fx9S/HZzkz/KcQdwsYj8lAK3G7/NPCkiTwFH4/zUP0lbXug2A64TKAjXGnDH4irgEIJ1vSklGNebp0VkNu6edAhFvN6o6mwR+S0wGeglIk8C67QpnmE9LqYoXAAtC0TkDJxr00nAv0g7Nn5n5me456S8jhCJH9uWdr0JQrtJ3qeOxo2wFvM+lTynanCd0JeLyAzgRYpzbJ4CnhKXSOt4XExccnkxjk3y/HkGeMS/V1yjqotofl1+EZgGNKjFELXAYog6AXH1iJK9A6XAEbjeg6+p6poCaUimCd0euAc4Gfgrzif6A5ouGKNUVQqkqSfuZjMQ14OSntb0e8B4Vd2zEFrS9rsXLhjyVNwD0iqgB/Ckql5aSC1pmtLbzwBgEnCKqn6jQPvvg+vxGodrMzNxD7sFbzO+njLcTWYQRWg3vk/6MbiMaf/25+0JXA2MwrWXFbhRiby2G1/LN4EXVVXT5vdS1U3+dMHaTEZb3R7Xi/wt4HXgbgrcbkSkTFXr/en0683j6mf/9JcVot1U40bFVqhqjT9vT5yR/20gQQGvN76ePsCypB5/fo80fQVpOyKyD+6e9GOcUToVl8jgQ1zP/we4UZijgINU9ev50pKm50RVvcU3QPbFtZuX0935RORaXLsZmU89/r6GqR9z59+nLsXdp6DA9ynfGByqqovT5qWfa4VqNyXAXsB+uPv1IpzRPBaYgDNUC3ItTtOzN669hHAdL6+lGxfFuIdnaDwaZ/QMAP4C/Ao3cnQkzrNpgapOLLSuroCNEHUCyQcEn/2Bm4B7CmUM+RoS/vsXInIq7sHgIuARXHBoGe6kmFBATZtxvZNexgXjUJxby+R87l9EqoBTcD0k81X1XVV9V0QuxAX2Hgr0w7mNfZ5PLa3pgRbt579wQ97fy7MWAZLpif+gqqeIyPG4/+U3uOQgJbhh/7y3GV/Pd3HH5nG/J7lZRjAROYwCtBtcdqCRwCEiMg9nGL0nIucDO/mvgbg4wXy3m6SWg3wtr6jLelUrIgN8V5KxFKDN+Lzs91o/rapfAONE5Dhchs2CtxvgJRH5qao+WezrDe7B4/9wHVI1AKr6HnCO7wJ1PM5g+gcFuN5k0+NrShpDHoVrO7fikhUsVNXfARPEpbu+GmckfYEzlF7Gz/hWCD0i8omv529Z2s3RODe6H+dTiIj8N/A/QD9xCZp+4t8bzheRy3HHbTsKc73J1PMKcLuqbk4zhgrZbu7GGUSDcImrdsIZRX/AGUm7A0Mp3DmVqWdXQEXkEVzCgs24Y/PfuKyXBcXvsHpFRE7DjZaPwnVWfYVLFPI2Lt7SyIKNEOUBccFtMf/kyPe+huCGtF8HFmc8QIaASpyrxCpcz8CmrBvKv5bkCFZ/4Ce40bNv5lHLbriRjpG4nrUa4BxVfT7zxlcIWtFztqr+OcuNeISqLsyjlq/jHl7LcT1sJb6u9bje/fW4XrA64F1Vrc2Xllb0lAJ7+cb9cHXpkitwo51DVPWEPGqJAHfifMP/ibsBr8CN+J4J1KnqsfnafztadvS1PI17YElpyXeb8feRrfbaSFzvbCMu8+aBuJvv/xWg3WTTszeuo69RXdrvKlzmp8F5bjf7AHNwoxvvi6sFMhbXfuar6i/yte8O6BmDi82Zr6p3p60r6aOPedCyP/AGflwVLunGL7R5Id+DgZXA0owOokLouQGYqarRZCeMiPQDbgaqVfW0PGr5D1wa8tdx192TcYbzV4Co6iv52neOer7l69ns6/lL2rr5bjf74FxzT1DVv4vICOB3uJHWZEr0i/K1/xz07O7r8fBTW+Pc1GpEZLekd0EBdH0Nd26fgDMKPwOeVdUP/HY8GJcEZznwz3yfX10ZM4i6OOJ8sMfietYew90EP8ts9OJXks+nMZBFy199LdGM9cqB8nTXhDxoeRqX+neKqi4SFzS7r6rul7aOB3hagDivXPT464U1z4U+ReQZYA0uLXKdiDyP8y0+F9iAc1/5Xj6N53b0POfrOR83uvlvXFD8OqC/pmU2y5OeoTiXnmuB3XDH5T/86adwhu3czHZdQC17AeJruRt4qxA3ORF5GffAera42msX40b1VuPiL55W1Z/mW0cbeprVgsMVzZ3mdwxt74+m5UvLLNw17TsichIuGH8ozpA9FNd2z1PVNwvRIdOGnrm42Ia1uA6iefnU4Wt5GVikqhNF5H9x3guTVPXZfO+7A3omqupzWdbto2kuu3nQ8izOxfJ8//P9uE6hw3EdUlHgKlV9rkDtpi09tbiOj8tV9YV86vD3/VOgSlXPTd4XReRsnGv3SzhPnJ+qi7sqxH2zNT0np+m5U1VvyaeOLLrm01QPrh+ug7MH8Decp9KHhdTTlbEsc10YERkI7Inr4doM/BznIneRiOzpGx6IyLdwPc3k64Laipbf4QLRR2ZomZ5nY2gY7iHkNnVBheDqkgwVkdPTVj0QyHsKylz1iMhBOBeSfGrZATgYuAV3EQXXg3Qazoh9Cee2Vwj3q9b0DMG5HPwBZ1wfB1yqLug438aQp67ORS1wq6q+pi6l6wKcQbsz7lz6UT51tKNlYZqWu3DpivOtZUfc6O9l/qzpuPiU43EPKB8Bt4lI3oOa29DT19fzLZxbzY0icqbfbvJpDHm4joQV/qybgD/jrofn4h6Y1vnTebsG56jnPF/PegrjCrszLnZhqj9rIq7T7iEROctfJywFysbahp6HxRXXRERC/ugseTaG+gJfA/43bfbeOAPkMtzo1Wc4t71CtJv29HzH1zMpnzrSWAvs5BulSWNnDLDEH+G8CeeCTr6NoXb0LPVHgG/G3UcLhrh6cD2BMar6bXXpta/F1UTaG5juP38YOWAGUddmBK5n9gVVPRl3w/sAd2I+CnxPXFrcO3E3wGJpeSxDS96MIZ8jcb6yqaBqdZkBfw+cknbzvR/nUphvctVzXwH0HIczMtb6PVz/gXOXu0hVb1bVm3AuWd+QwtRsaUvPLb6eP/l6yvItJu2h41pgDxEZ438ejXug/S4uLiNvriJB1ELbtdf+rq722t9whn8hyLUWXN71+P/TJ7g05Pvj4qheVNWN6mIv3sSlU/66iAzuZnpmALNV9TMRifgjmdfgsltOFpH9VTVWiFH6HPTc6OuJa1O22HwSw41mThKRHUTkGFza6OvU1Y6ZCzwMDBeRQiS1CZqeN3DPFReIyOEichWus2Omv3wepBKXFIKg6YG268HdgHPZLWQ9uC6NGURdm7dxcRWLAVR1kaqeg4s1eBbXq/MsLttIvodxO6Ll5jxreQPnYxwG1wPpz38Kd4Ev942z3cj/cQmanhdwIy91/udq4Efq4i2SSVbexCUNKESvW0f0FOIhJem2uB54ADhcXIaypbiEBgtUdYKqPtLNtASt9lrQ9MzCGaYX4s7zzAKRn+Di8/Ke8jtgeu7FueulSlL4D28TcCOds32XvkIRGD2q+iXOi+LruJidK3GJj9JHwVfiromftNjAtq/nVdy5/QPgOVySgBtUNZlSP4S7L+Q1djKoenwCUw9uW8BiiLYh/Bu/lxzOFVdd+1Nglqp+vztpkaYkDsn3EC748X2ahrbXqep5+dYSRD0Z2jKzuT0HqKpeWWgtQdLju4EmXY0mqup9/v+WyLf7SlC1+CMKydprT2rzZCDPAR+qakHcLYOmR0R2BR7EuYBuxLllvYxzabkNl2iikEHggdKTpiuZuGBn3Kj4jsDRqvpZobUEQY+4hBeCMz5m45KA/B7nkvoA8GmB202g9PiaDsYZ+Ov9/2oH4Le487tQLnyB0yMig3DeQL1wI3kvZSwfjKszOEpV3yqUrq6KGUTbINJUgfhwnCvAAHUpcrutlrSb3vdxRRx74+osFOu4BEZPmpFWigu4fgLYWQuYNj6oesRlEvoWLjg1366eXUlL0WuvBVWPuBoyd+NivBpwo+IPAdeqqyZfUIKmJ0PbMNwI8WjNY7xOV9Hju3/+AHgLl1J6NXBSsY5N0PT4mkpxbsOn4Nxki9puiqVHAlh7sqtjBtE2jIj8F3CgquY96LqraBGRPXAXi9+qC1AvKkHS4/d63YrLoHZ9MbUESY8UIINRrgRJSxLfXWMG8Iaq5j1JSVfR44/qDcO5gy4sUFxKV9KT7BSq8t21ikpQ9IjILjhXx6XAn1Xzl9q6K+oBEJFKoKfmOclOrhRbj+8tkKw9eQLNa0/e7LvPGe1gBtE2jBQwrXQX0zIAVyeqKCMgmQRJj39hj2laZftiEjQ9RnakgLXXciFoegzDMDoDCVDtyW0NM4gMwzAMwzAMI+BIgGpPbmuYQWQYhmEYhmEYAcZ3e30DVzLkQFzdtQ9x9SdfAf6trrj5t4BjVPXSoontgljabcMwDMMwDMMINkGqPbnNYSNEhmEYhmEYhhFg/JjaQ4E3VXVt2vxq4Hpcse4S/9Un043OaBsziAzDMAzDMAyjC1Hseo/bGmYQGYZhGIZhGEYXJCj1Hrs6ZhAZhmEYhmEYRhcmKPUeuyqRYgswDMMwDMMwDGOreAZ4ttgiuio2QmQYhmEYhmEYRrfF0m4bhmEYhmEYhtFtMYPIMAzDMAzDMIxuixlEhmEYhmEYhmF0W8wgMgzDMDoNEXlGRH7tT78mIr8osqR2EZEjRSThFzg0DMMwuhmWZc4wDMPIF+OAaLFF5MDfgUHA2vZWNAzDMLY9zCAyDMMw8oKqriu2hlxQ1QZgZbF1GIZhGMXBDCLDMAxjixCRHsAvgVOAzcDPMpa/Bnygqpf4nz8D/hfYCfg2sAG4BngBuAcYDawALlbVF9O2swcwHVeJvRb4C3Clqq70l/8aqAZeAq4FegBP+dup8dc5HJgGfAOIAf8CzlPVD0TkSFyF9+1VdY2//jhgCrAbsNrXd6uqJtJ+yyxgGHAGsBH4mapOT9N9IXA1sAPwFfAOcJKqNnbgMBuGYRh5xmKIDMMwjC1lBvBNnHFzDLA3zmhpiyuAN4F9gMeA3wC/A54D/gOYA/xWRMoBRGSQP+8D4ADgWKAXMFtE0u9hh+GMnWOB/wZOBi73txEBngb+BuwFHIgz3mLZBIrIvsAfgSeAkcD1wPeBSzJWvRJ43/8ttwPTROQgfxv7ATNxRpX4uv7czrExDMMwioCNEBmGYRgdRkR6AecB56rqC/68c4Cl7Xz1BVX9pb/+ZOAqYJGqPuTPuxk4F2fcvA1MAt5V1evS9n0WsA7YD2dcgRuhmeSPviwUkT/ijLTbgN7AdsCfVPVjf/1/taHxKuCvqjrZ//xvEdkVuA64O229F1U1mTTibhG5zN/nP3CjQpuB2ar6FfA58G47x8YwDMMoAjZCZBiGYWwJOwOluId/AFR1E27EpC3ey1i/JuM7q/z3/v77vsDhIrIp+QKWpGlIsiDDFW15cht+LNOvgRdE5FkRuUpEhrWhcQTwRsa8vwFDRKR3tt+SuU+c+97nwKci8oiIfFdEKtvYp2EYhlEkzCAyDMMwtgRvC7+XmXUukTEv4b+H0t6fxbnTpb92BZ5pZ7upe5yqnoNzlZsDjMGN+hzfikYvTUcm6fNb3ac/KrQPcBqwGOdy9y8RGdzKdg3DMIwiYS5zhmEYxpawCGcQ/CfwCYCI9MS5un3cxvc6yjs4o+JzVd2qFN6q+i7Obe12EXke+C4uoUMmC4BDM+YdCiz1DZ1c99cIvAK84rsHrsYljrhvC+QbhmEYecIMIsMwDKPDqOomEXkAZ1x8gXMX+zEQ7uRdzQQmAI+KyO3AF7gsdacBV+dioIjIjsCFwGxgmf/9PYFftfKVO4C3RORGXMKH/XHZ4m7IVbSIjMa59M3BxTsdBVQCC3PdhmEYhlEYzGXOMAzD2FKuwaWrftJ//wBnAHQaqrocOASI47K0fYgzkur9Vy7U4NJn/xH4Ny6z3SO4zHDZ9vkOcCoue94HwFT/9Yts67fCBuBbwMu4BA7XAOer6usd2IZhGIZRALxEojU3acMwDMMwDMMwjG0bGyEyDMMwDMMwDKPbYgaRYRiGYRiGYRjdFjOIDMMwDMMwDMPotphBZBiGYRiGYRhGt8UMIsMwDMMwDMMwui1mEBmGYRiGYRiG0W0xg8gwDMMwDMMwjG6LGUSGYRiGYRiGYXRbzCAyDMMwDMMwDKPb8v8BmWyZshJ2OUwAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sizes = numpy.arange(5, 101, 5).astype('int')\n", "n, m = sizes.shape[0], 20\n", "\n", "skl_predict, pom_predict = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "skl_fit, pom_fit = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "skl_error, pom_error = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "\n", "for i in range(m):\n", " for j, size in enumerate(sizes):\n", " X, y = create_dataset(50000, size, 2)\n", "\n", " # bench fit times\n", " tic = time.time()\n", " skl = GaussianNB()\n", " skl.fit(X, y)\n", " skl_fit[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom = NaiveBayes.from_samples(NormalDistribution, X, y)\n", " pom_fit[i, j] = time.time() - tic\n", "\n", " # bench predict times\n", " tic = time.time()\n", " skl_predictions = skl.predict(X)\n", " skl_predict[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom_predictions = pom.predict(X)\n", " pom_predict[i, j] = time.time() - tic\n", "\n", " # check number wrong\n", " skl_e = (y != skl_predictions).mean()\n", " pom_e = (y != pom_predictions).mean()\n", "\n", " skl_error[i, j] = min(skl_e, 1-skl_e)\n", " pom_error[i, j] = min(pom_e, 1-pom_e)\n", "\n", "fit = skl_fit / pom_fit\n", "predict = skl_predict / pom_predict\n", "\n", "plot(fit, predict, skl_error, pom_error, sizes, \"dimensions\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like pomegranate is consistently faster than sklearn at fitting the model but conveges to be approximately the same speed at making predictions in the high dimensional setting. Their accuracies remain identical indicating that the two are learning the same model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Out of Core Training\n", "\n", "Lastly, both pomegranate and sklearn allow for out of core training by fitting on chunks of a dataset. pomegranate does this by calculating summary statistics on the dataset which are enough to allow for exact parameter updates to be done. sklearn implements this using the `model.partial_fit(X, y)` API call, whereas pomegranate uses `model.summarize(X, y)` followed by `model.from_summaries()` to update the internal parameters. \n", "\n", "Lets compare how long each method takes to train on 25 batches of increasing sizes and the accuracy of both methods." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sizes = numpy.around( numpy.exp( numpy.arange(8, 16) ) ).astype('int')\n", "n, m = sizes.shape[0], 20\n", "\n", "skl_time, pom_time = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "skl_error, pom_error = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "\n", "for i in range(m):\n", " for j, size in enumerate(sizes):\n", " skl = GaussianNB()\n", " pom = NaiveBayes([IndependentComponentsDistribution([NormalDistribution(0, 1) for i in range(5)]),\n", " IndependentComponentsDistribution([NormalDistribution(0, 1) for i in range(5)])])\n", " \n", " for l in range(5):\n", " X, y = create_dataset(size, 5, 2)\n", "\n", " tic = time.time()\n", " skl.partial_fit(X, y, classes=[0, 1])\n", " skl_time[i, j] += time.time() - tic\n", "\n", " tic = time.time()\n", " pom.summarize( X, y )\n", " pom_time[i, j] += time.time() - tic\n", "\n", " tic = time.time()\n", " pom.from_summaries()\n", " pom_time[i, j] += time.time() - tic\n", "\n", " skl_predictions = skl.predict( X )\n", " pom_predictions = pom.predict( X )\n", "\n", " skl_error[i, j] = ( y != skl_predictions ).mean()\n", " pom_error[i, j] = ( y != pom_predictions ).mean()\n", "\n", "fit = skl_time / pom_time\n", "idx = numpy.arange(fit.shape[1])\n", "\n", "plt.figure( figsize=(14, 4))\n", "plt.plot( fit.mean(axis=0), c='c', label=\"Fitting\")\n", "plt.plot( [0, fit.shape[1]], [1, 1], c='k', label=\"Baseline\" )\n", "plt.fill_between( idx, fit.min(axis=0), fit.max(axis=0), color='c', alpha=0.3 )\n", "\n", "plt.xticks(idx, sizes, rotation=65, fontsize=14)\n", "plt.xlabel('{}'.format(xlabel), fontsize=14)\n", "plt.ylabel('pomegranate is x times faster', fontsize=14)\n", "plt.legend(fontsize=12, loc=4)\n", "plt.show()\n", "\n", "plt.figure( figsize=(14, 4))\n", "plt.plot( 1 - skl_error.mean(axis=0), alpha=0.5, c='c', label=\"sklearn accuracy\" )\n", "plt.plot( 1 - pom_error.mean(axis=0), alpha=0.5, c='m', label=\"pomegranate accuracy\" )\n", "\n", "plt.fill_between( idx, 1-skl_error.min(axis=0), 1-skl_error.max(axis=0), color='c', alpha=0.3 )\n", "plt.fill_between( idx, 1-pom_error.min(axis=0), 1-pom_error.max(axis=0), color='m', alpha=0.3 )\n", "\n", "plt.xticks( idx, sizes, rotation=65, fontsize=14)\n", "plt.xlabel('Batch Size', fontsize=14)\n", "plt.ylabel('Accuracy', fontsize=14)\n", "plt.legend(fontsize=14) \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pomegranate seems to be much faster at doing out-of-core training. The out of core API of calculating sufficient statistics using `summarize` and then updating the model parameters using `from_summaries` extends to all models in pomegranate. \n", "\n", "In this notebook we compared an intersection of the features that pomegranate and sklearn offer. pomegranate allows you to use Naive Bayes with any distribution or model object which has an exposed `log_probability` and `fit` method. This allows you to do things such as compare hidden Markov models to each other, or compare a hidden Markov model to a Markov Chain to see which one models the data better. \n", "\n", "We hope this has been useful to you! If you're interested in using pomegranate, you can get it using `pip install pomegranate` or by checking out the github repo." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/binder/000077500000000000000000000000001420461712600221345ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/binder/requirements.txt000066400000000000000000000000521420461712600254150ustar00rootroot00000000000000pomegranate watermark matplotlib seaborn pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/dev-requirements.txt000066400000000000000000000002151420461712600247270ustar00rootroot00000000000000wheel pandas cython >= 0.22.1 nose numpy >= 1.20.0 scipy >= 0.17.0 sphinx >= 4.2.0 sphinx-rtd-theme >= 0.2.0, < 0.3.0 networkx >= 2.4 pyyaml pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/000077500000000000000000000000001420461712600216215ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/BayesianNetwork.rst000066400000000000000000000233011420461712600254570ustar00rootroot00000000000000.. _bayesiannetwork: Bayesian Networks ================= - `IPython Notebook Tutorial `_ - `IPython Notebook Structure Learning Tutorial `_ `Bayesian networks `_ are a probabilistic model that are especially good at inference given incomplete data. Much like a hidden Markov model, they consist of a directed graphical model (though Bayesian networks must also be acyclic) and a set of probability distributions. The edges encode dependency statements between the variables, where the lack of an edge between any pair of variables indicates a conditional independence. Each node encodes a probability distribution, where root nodes encode univariate probability distributions and inner/leaf nodes encode conditional probability distributions. Bayesian networks are exceptionally flexible when doing inference, as any subset of variables can be observed, and inference done over all other variables, without needing to define these groups in advance. In fact, the set of observed variables can change from one sample to the next without needing to modify the underlying algorithm at all. Currently, pomegranate only supports discrete Bayesian networks, meaning that the values must be categories, i.e. 'apples' and 'oranges', or 1 and 2, where 1 and 2 refer to categories, not numbers, and so 2 is not explicitly 'bigger' than 1. Initialization -------------- Bayesian networks can be initialized in two ways, depending on whether the underlying graphical structure is known or not: (1) the graphical structure can be built one node at a time with pre-initialized distributions set for each node, or (2) both the graphical structure and distributions can be learned directly from data. This mirrors the other models that are implemented in pomegranate. However, typically expectation maximization is used to fit the parameters of the distribution, and so initialization (such as through k-means) is typically fast whereas fitting is slow. For Bayesian networks, the opposite is the case. Fitting can be done quickly by just summing counts through the data, while initialization is hard as it requires an exponential time search through all possible DAGs to identify the optimal graph. More is discussed in the tutorials above and in the fitting section below. Let's take a look at initializing a Bayesian network in the first manner by quickly implementing the `Monty Hall problem `_. The Monty Hall problem arose from the gameshow *Let's Make a Deal*, where a guest had to choose which one of three doors had a prize behind it. The twist was that after the guest chose, the host, originally Monty Hall, would then open one of the doors the guest did not pick and ask if the guest wanted to switch which door they had picked. Initial inspection may lead you to believe that if there are only two doors left, there is a 50-50 chance of you picking the right one, and so there is no advantage one way or the other. However, it has been proven both through simulations and analytically that there is in fact a 66% chance of getting the prize if the guest switches their door, regardless of the door they initially went with. Our network will have three nodes, one for the guest, one for the prize, and one for the door Monty chooses to open. The door the guest initially chooses and the door the prize is behind are uniform random processes across the three doors, but the door which Monty opens is dependent on both the door the guest chooses (it cannot be the door the guest chooses), and the door the prize is behind (it cannot be the door with the prize behind it). .. code-block:: python from pomegranate import * guest = DiscreteDistribution({'A': 1./3, 'B': 1./3, 'C': 1./3}) prize = DiscreteDistribution({'A': 1./3, 'B': 1./3, 'C': 1./3}) monty = ConditionalProbabilityTable( [['A', 'A', 'A', 0.0], ['A', 'A', 'B', 0.5], ['A', 'A', 'C', 0.5], ['A', 'B', 'A', 0.0], ['A', 'B', 'B', 0.0], ['A', 'B', 'C', 1.0], ['A', 'C', 'A', 0.0], ['A', 'C', 'B', 1.0], ['A', 'C', 'C', 0.0], ['B', 'A', 'A', 0.0], ['B', 'A', 'B', 0.0], ['B', 'A', 'C', 1.0], ['B', 'B', 'A', 0.5], ['B', 'B', 'B', 0.0], ['B', 'B', 'C', 0.5], ['B', 'C', 'A', 1.0], ['B', 'C', 'B', 0.0], ['B', 'C', 'C', 0.0], ['C', 'A', 'A', 0.0], ['C', 'A', 'B', 1.0], ['C', 'A', 'C', 0.0], ['C', 'B', 'A', 1.0], ['C', 'B', 'B', 0.0], ['C', 'B', 'C', 0.0], ['C', 'C', 'A', 0.5], ['C', 'C', 'B', 0.5], ['C', 'C', 'C', 0.0]], [guest, prize]) s1 = Node(guest, name="guest") s2 = Node(prize, name="prize") s3 = Node(monty, name="monty") model = BayesianNetwork("Monty Hall Problem") model.add_states(s1, s2, s3) model.add_edge(s1, s3) model.add_edge(s2, s3) model.bake() .. NOTE:: The objects 'state' and 'node' are really the same thing and can be used interchangeable. The only difference is the name, as hidden Markov models use 'state' in the literature frequently whereas Bayesian networks use 'node' frequently. The conditional distribution must be explicitly spelled out in this example, followed by a list of the parents in the same order as the columns take in the table that is provided (e.g. the columns in the table correspond to guest, prize, monty, probability.) However, one can also initialize a Bayesian network based completely on data. As mentioned before, the exact version of this algorithm takes exponential time with the number of variables and typically can't be done on more than ~25 variables. This is because there are a super-exponential number of directed acyclic graphs that one could define over a set of variables, but fortunately one can use dynamic programming in order to reduce this complexity down to "simply exponential." The implementation of the exact algorithm actually goes further than the original dynamic programming algorithm by implementing an A* search to somewhat reduce computational time but drastically reduce required memory, sometimes by an order of magnitude. .. code-block:: python from pomegranate import * import numpy X = numpy.load('data.npy') model = BayesianNetwork.from_samples(X, algorithm='exact') The exact algorithm is not the default, though. The default is a novel greedy algorithm that greedily chooses a topological ordering of the variables, but optimally identifies the best parents for each variable given this ordering. It is significantly faster and more memory efficient than the exact algorithm and produces far better estimates than using a Chow-Liu tree. This is set to the default to avoid locking up the computers of users that unintentionally tell their computers to do a near-impossible task. Probability ----------- You can calculate the probability of a sample under a Bayesian network as the product of the probability of each variable given its parents, if it has any. This can be expressed as :math:`P = \prod\limits_{i=1}^{d} P(D_{i}|Pa_{i})` for a sample with $d$ dimensions. For example, in the Monty Hal problem, the probability of a show is the probability of the guest choosing the respective door, times the probability of the prize being behind a given door, times the probability of Monty opening a given door given the previous two values. For example, using the manually initialized network above: .. code-block:: python >>> print(model.probability([['A', 'A', 'A'], ['A', 'A', 'B'], ['C', 'C', 'B']])) [ 0. 0.05555556 0.05555556] Prediction ---------- Bayesian networks are frequently used to infer/impute the value of missing variables given the observed values. In other models, typically there is either a single or fixed set of missing variables, such as latent factors, that need to be imputed, and so returning a fixed vector or matrix as the predictions makes sense. However, in the case of Bayesian networks, we can make no such assumptions, and so when data is passed in for prediction it should be in the format as a matrix with ``None`` in the missing variables that need to be inferred. The return is thus a filled in matrix where the Nones have been replaced with the imputed values. For example: .. code-block:: python >>> print(model.predict([['A', 'B', None], ['A', 'C', None], ['C', 'B', None]])) [['A' 'B' 'C'] ['A' 'C' 'B'] ['C' 'B' 'A']] In this example, the final column is the one that is always missing, but a more complex example is as follows: .. code-block:: python >>> print(model.predict([['A', 'B', None], ['A', None, 'C'], [None, 'B', 'A']])) [['A' 'B' 'C'] ['A' 'B' 'C'] ['C' 'B' 'A']] Fitting ------- Fitting a Bayesian network to data is a fairly simple process. Essentially, for each variable, you need consider only that column of data and the columns corresponding to that variables parents. If it is a univariate distribution, then the maximum likelihood estimate is just the count of each symbol divided by the number of samples in the data. If it is a multivariate distribution, it ends up being the probability of each symbol in the variable of interest given the combination of symbols in the parents. For example, consider a binary dataset with two variables, X and Y, where X is a parent of Y. First, we would go through the dataset and calculate P(X=0) and P(X=1). Then, we would calculate P(Y=0|X=0), P(Y=1|X=0), P(Y=0|X=1), and P(Y=1|X=1). Those values encode all of the parameters of the Bayesian network. API Reference ------------- .. automodule:: pomegranate.BayesianNetwork :members: :inherited-members: pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/CODE_OF_CONDUCT.rst000066400000000000000000000076161420461712600246420ustar00rootroot00000000000000====== Code of Conduct ====== Our Pledge ---------- In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation. 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Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. Scope ----- This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. Enforcement ----------- Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at jmschreiber91@gmail.com. Because the project team currently consists of only one member, that member shall investigate within one week whether a violation of the code of conduct occurred and what the appropriate response is. That member shall then contact the original reporter and any other affected parties to explain the response and note feedback for the record. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Should you wish to file a report anonymously you should fill out a report at https://goo.gl/forms/aQtlDdrhZf4Y8flk2. If your report involves any members of the project team, if you feel uncomfortable making a report to the project team for any reason, or you feel that the issue has not been adequately handled, you are encouraged to send `your report `_ to conduct@numfocus.org where it will be independently reviewed by the `NumFOCUS team `_. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. Attribution ----------- This Code of Conduct is adapted from the `Contributor Covenant homepage `_, `version 1\.4 `_. For answers to common questions about this code of conduct, see https://www.contributor-covenant.org/faq. pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/Distributions.rst000066400000000000000000000134011420461712600252140ustar00rootroot00000000000000.. _distributions: Probability Distributions ========================= `IPython Notebook Tutorial `_ While probability distributions are frequently used as components of more complex models such as mixtures and hidden Markov models, they can also be used by themselves. Many data science tasks require fitting a distribution to data or generating samples under a distribution. pomegranate has a large library of both univariate and multivariate distributions which can be used with an intuitive interface. **Univariate Distributions** .. currentmodule:: pomegranate.distributions .. autosummary:: UniformDistribution BernoulliDistribution NormalDistribution LogNormalDistribution ExponentialDistribution PoissonDistribution BetaDistribution GammaDistribution DiscreteDistribution **Kernel Densities** .. autosummary:: GaussianKernelDensity UniformKernelDensity TriangleKernelDensity **Multivariate Distributions** .. autosummary:: IndependentComponentsDistribution MultivariateGaussianDistribution DirichletDistribution ConditionalProbabilityTable JointProbabilityTable While there are a large variety of univariate distributions, multivariate distributions can be made from univariate distributions by using ```IndependentComponentsDistribution``` with the assumption that each column of data is independent from the other columns (instead of being related by a covariance matrix, like in multivariate gaussians). Here is an example: .. code-block:: python d1 = NormalDistribution(5, 2) d2 = LogNormalDistribution(1, 0.3) d3 = ExponentialDistribution(4) d = IndependentComponentsDistribution([d1, d2, d3]) Use MultivariateGaussianDistribution when you want the full correlation matrix within the feature vector. When you want a strict diagonal correlation (i.e no correlation or "independent"), this is achieved using IndependentComponentsDistribution with NormalDistribution for each feature. There is no implementation of spherical or other variations of correlation. Initialization -------------- Initializing a distribution is simple and done just by passing in the distribution parameters. For example, the parameters of a normal distribution are the mean (mu) and the standard deviation (sigma). We can initialize it as follows: .. code-block:: python from pomegranate import * a = NormalDistribution(5, 2) However, frequently we don't know the parameters of the distribution beforehand or would like to directly fit this distribution to some data. We can do this through the `from_samples` class method. .. code-block:: python b = NormalDistribution.from_samples([3, 4, 5, 6, 7]) If we want to fit the model to weighted samples, we can just pass in an array of the relative weights of each sample as well. .. code-block:: python b = NormalDistribution.from_samples([3, 4, 5, 6, 7], weights=[0.5, 1, 1.5, 1, 0.5]) Probability ----------- Distributions are typically used to calculate the probability of some sample. This can be done using either the `probability` or `log_probability` methods. .. code-block:: python >>> a = NormalDistribution(5, 2) >>> a.log_probability(8) -2.737085713764219 >>> a.probability(8) 0.064758797832971712 >>> b = NormalDistribution.from_samples([3, 4, 5, 6, 7], weights=[0.5, 1, 1.5, 1, 0.5]) >>> b.log_probability(8) -4.437779569430167 These methods work for univariate distributions, kernel densities, and multivariate distributions all the same. For a multivariate distribution you'll have to pass in an array for the full sample. .. code-block:: python >>> d1 = NormalDistribution(5, 2) >>> d2 = LogNormalDistribution(1, 0.3) >>> d3 = ExponentialDistribution(4) >>> d = IndependentComponentsDistribution([d1, d2, d3]) >>> >>> X = [6.2, 0.4, 0.9] >>> d.log_probability(X) -23.205411733352875 Fitting ------- We may wish to fit the distribution to new data, either overriding the previous parameters completely or moving the parameters to match the dataset more closely through inertia. Distributions are updated using maximum likelihood estimates (MLE). Kernel densities will either discard previous points or downweight them if inertia is used. .. code-block:: python d = NormalDistribution(5, 2) d.fit([1, 5, 7, 3, 2, 4, 3, 5, 7, 8, 2, 4, 6, 7, 2, 4, 5, 1, 3, 2, 1]) d { "frozen" :false, "class" :"Distribution", "parameters" :[ 3.9047619047619047, 2.13596776114341 ], "name" :"NormalDistribution" } Training can be done on weighted samples by passing an array of weights in along with the data for any of the training functions, like the following: .. code-block:: python d = NormalDistribution(5, 2) d.fit([1, 5, 7, 3, 2, 4], weights=[0.5, 0.75, 1, 1.25, 1.8, 0.33]) d { "frozen" :false, "class" :"Distribution", "parameters" :[ 3.538188277087034, 1.954149818564894 ], "name" :"NormalDistribution" } Training can also be done with inertia, where the new value will be some percentage the old value and some percentage the new value, used like `d.from_samples([5,7,8], inertia=0.5)` to indicate a 50-50 split between old and new values. API Reference ------------- .. automodule:: pomegranate.distributions :members: BernoulliDistribution,BetaDistribution,ConditionalProbabilityTable,DirichletDistribution,DiscreteDistribution,ExponentialDistribution,GammaDistribution,IndependentComponentsDistribution,JointProbabilityTable,KernelDensities,LogNormalDistribution,MultivariateGaussianDistribution,NormalDistribution,PoissonDistribution,UniformDistribution pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/FactorGraph.rst000066400000000000000000000001771420461712600245600ustar00rootroot00000000000000.. _factorgraph: Factor Graphs ============= API Reference ------------- .. automodule:: pomegranate.FactorGraph :members: pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/GeneralMixtureModel.rst000066400000000000000000000140361420461712600262730ustar00rootroot00000000000000.. _generalmixturemodel: General Mixture Models ====================== `IPython Notebook Tutorial `_ General Mixture models (GMMs) are an unsupervised probabilistic model composed of multiple distributions (commonly referred to as components) and corresponding weights. This allows you to model more complex distributions corresponding to a singular underlying phenomena. For a full tutorial on what a mixture model is and how to use them, see the above tutorial. Initialization -------------- General Mixture Models can be initialized in two ways depending on if you know the initial parameters of the model or not: (1) passing in a list of pre-initialized distributions, or (2) running the ``from_samples`` class method on data. The initial parameters can be either a pre-specified model that is ready to be used for prediction, or the initialization for expectation-maximization. Otherwise, if the second initialization option is chosen, then k-means is used to initialize the distributions. The distributions passed for each component don't have to be the same type, and if an ``IndependentComponentDistribution`` object is passed in, then the dimensions don't need to be modeled by the same distribution. Here is an example of a traditional multivariate Gaussian mixture where we pass in pre-initialized distributions. We can also pass in the weight of each component, which serves as the prior probability of a sample belonging to that component when doing predictions. .. code-block:: python >>> from pomegranate import * >>> d1 = MultivariateGaussianDistribution([1, 6, 3], [[1, 0, 0], [0, 1, 0], [0, 0, 1]]) >>> d2 = MultivariateGaussianDistribution([2, 8, 4], [[1, 0, 0], [0, 1, 0], [0, 0, 2]]) >>> d3 = MultivariateGaussianDistribution([0, 4, 8], [[2, 0, 0], [0, 3, 0], [0, 0, 1]]) >>> model = GeneralMixtureModel([d1, d2, d3], weights=[0.25, 0.60, 0.15]) Alternatively, if we want to model each dimension differently, then we can replace the multivariate Gaussian distributions with ``IndependentComponentsDistribution`` objects. .. code-block:: python >>> from pomegranate import * >>> d1 = IndependentComponentsDistribution([NormalDistribution(5, 2), ExponentialDistribution(1), LogNormalDistribution(0.4, 0.1)]) >>> d2 = IndependentComponentsDistribution([NormalDistribution(3, 1), ExponentialDistribution(2), LogNormalDistribution(0.8, 0.2)]) >>> model = GeneralMixtureModel([d1, d2], weights=[0.66, 0.34]) If we do not know the parameters of our distributions beforehand and want to learn them entirely from data, then we can use the ``from_samples`` class method. This method will run k-means to initialize the components, using the returned clusters to initialize all parameters of the distributions, i.e. both mean and covariances for multivariate Gaussian distributions. Afterwards, expectation-maximization is used to refine the parameters of the model, iterating until convergence. .. code-block:: python >>> from pomegranate import * >>> model = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, n_components=3, X=X) If we want to model each dimension using a different distribution, then we can pass in a list of callables and they will be initialized using k-means as well. .. code-block:: python >>> from pomegranate import * >>> model = GeneralMixtureModel.from_samples([NormalDistribution, ExponentialDistribution, LogNormalDistribution], n_components=5, X=X) Probability --------------- The probability of a point is the sum of its probability under each of the components, multiplied by the weight of each component c, :math:`P = \sum\limits_{i \in M} P(D|M_{i})P(M_{i})`. The ``probability`` method returns the probability of each sample under the entire mixture, and the ``log_probability`` method returns the log of that value. Prediction ---------- The common prediction tasks involve predicting which component a new point falls under. This is done using Bayes rule :math:`P(M|D) = \frac{P(D|M)P(M)}{P(D)}` to determine the posterior probability :math:`P(M|D)` as opposed to simply the likelihood :math:`P(D|M)`. Bayes rule indicates that it isn't simply the likelihood function which makes this prediction but the likelihood function multiplied by the probability that that distribution generated the sample. For example, if you have a distribution which has 100x as many samples fall under it, you would naively think that there is a ~99% chance that any random point would be drawn from it. Your belief would then be updated based on how well the point fit each distribution, but the proportion of points generated by each sample is important as well. We can get the component label assignments using ``model.predict(data)``, which will return an array of indexes corresponding to the maximally likely component. If what we want is the full matrix of :math:`P(M|D)`, then we can use ``model.predict_proba(data)``, which will return a matrix with each row being a sample, each column being a component, and each cell being the probability that that model generated that data. If we want log probabilities instead we can use ``model.predict_log_proba(data)`` instead. Fitting ------- Training GMMs faces the classic chicken-and-egg problem that most unsupervised learning algorithms face. If we knew which component a sample belonged to, we could use MLE estimates to update the component. And if we knew the parameters of the components we could predict which sample belonged to which component. This problem is solved using expectation-maximization, which iterates between the two until convergence. In essence, an initialization point is chosen which usually is not a very good start, but through successive iteration steps, the parameters converge to a good ending. These models are fit using ``model.fit(data)``. A maximum number of iterations can be specified as well as a stopping threshold for the improvement ratio. See the API reference for full documentation. API Reference ------------- .. automodule:: pomegranate.gmm :members: :inherited-members: pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/HiddenMarkovModel.rst000066400000000000000000000254621420461712600257200ustar00rootroot00000000000000.. _hiddenmarkovmodel: Hidden Markov Models ==================== - `IPython Notebook Tutorial `_ - `IPython Notebook Sequence Alignment Tutorial `_ `Hidden Markov models `_ (HMMs) are a structured probabilistic model that forms a probability distribution of sequences, as opposed to individual symbols. It is similar to a Bayesian network in that it has a directed graphical structure where nodes represent probability distributions, but unlike Bayesian networks in that the edges represent transitions and encode transition probabilities, whereas in Bayesian networks edges encode dependence statements. A HMM can be thought of as a general mixture model plus a transition matrix, where each component in the general Mixture model corresponds to a node in the hidden Markov model, and the transition matrix informs the probability that adjacent symbols in the sequence transition from being generated from one component to another. A strength of HMMs is that they can model variable length sequences whereas other models typically require a fixed feature set. They are extensively used in the fields of natural language processing to model speech, bioinformatics to model biosequences, and robotics to model movement. The HMM implementation in pomegranate is based off of the implementation in its predecessor, Yet Another Hidden Markov Model (YAHMM). To convert a script that used YAHMM to a script using pomegranate, you only need to change calls to the ``Model`` class to call ``HiddenMarkovModel``. For example, a script that previously looked like the following: .. code-block:: python from yahmm import * model = Model() would now be written as .. code-block:: python from pomegranate import * model = HiddenMarkovModel() and the remaining method calls should be identical. Initialization -------------- Hidden Markov models can be initialized in one of two ways depending on if you know the initial parameters of the model, either (1) by defining both the distributions and the graphical structure manually, or (2) running the ``from_samples`` method to learn both the structure and distributions directly from data. The first initialization method can be used either to specify a pre-defined model that is ready to make predictions, or as the initialization to a training algorithm such as Baum-Welch. It is flexible enough to allow sparse transition matrices and any type of distribution on each node, i.e. normal distributions on several nodes, but a mixture of normals on some nodes modeling more complex phenomena. The second initialization method is less flexible, in that currently each node must have the same distribution type, and that it will only learn dense graphs. Similar to mixture models, this initialization method starts with k-means to initialize the distributions and a uniform probability transition matrix before running Baum-Welch. If you are initializing the parameters manually, you can do so either by passing in a list of distributions and a transition matrix, or by building the model line-by-line. Let's first take a look at building the model from a list of distributions and a transition matrix. .. code-block:: python from pomegranate import * dists = [NormalDistribution(5, 1), NormalDistribution(1, 7), NormalDistribution(8,2)] trans_mat = numpy.array([[0.7, 0.3, 0.0], [0.0, 0.8, 0.2], [0.0, 0.0, 0.9]]) starts = numpy.array([1.0, 0.0, 0.0]) ends = numpy.array([0.0, 0.0, 0.1]) model = HiddenMarkovModel.from_matrix(trans_mat, dists, starts, ends) Next, let's take a look at building the same model line by line. .. code-block:: python from pomegranate import * s1 = State(NormalDistribution(5, 1)) s2 = State(NormalDistribution(1, 7)) s3 = State(NormalDistribution(8, 2)) model = HiddenMarkovModel() model.add_states(s1, s2, s3) model.add_transition(model.start, s1, 1.0) model.add_transition(s1, s1, 0.7) model.add_transition(s1, s2, 0.3) model.add_transition(s2, s2, 0.8) model.add_transition(s2, s3, 0.2) model.add_transition(s3, s3, 0.9) model.add_transition(s3, model.end, 0.1) model.bake() Initially it may seem that the first method is far easier due to it being fewer lines of code. However, when building large sparse models defining a full transition matrix can be cumbersome, especially when it is mostly 0s. Models built in this manner must be explicitly "baked" at the end. This finalizes the model topology and creates the internal sparse matrix which makes up the model. This step also automatically normalizes all transitions to make sure they sum to 1.0, stores information about tied distributions, edges, pseudocounts, and merges unnecessary silent states in the model for computational efficiency. This can cause the `bake` step to take a little bit of time. If you want to reduce this overhead and are sure you specified the model correctly you can pass in `merge="None"` to the bake step to avoid model checking. The second way to initialize models is to use the ``from_samples`` class method. The call is identical to initializing a mixture model. .. code-block:: python >>> from pomegranate import * >>> model = HiddenMarkovModel.from_samples(NormalDistribution, n_components=5, X=X) Much like a mixture model, all arguments present in the ``fit`` step can also be passed in to this method. Also like a mixture model, it is initialized by running k-means on the concatenation of all data, ignoring that the symbols are part of a structured sequence. The clusters returned are used to initialize all parameters of the distributions, i.e. both mean and covariances for multivariate Gaussian distributions. The transition matrix is initialized as uniform random probabilities. After the components (distributions on the nodes) are initialized, the given training algorithm is used to refine the parameters of the distributions and learn the appropriate transition probabilities. Log Probability --------------- There are two common forms of the log probability which are used. The first is the log probability of the most likely path the sequence can take through the model, called the Viterbi probability. This can be calculated using ``model.viterbi(sequence)``. However, this is :math:`P(D|S_{ML}, S_{ML}, S_{ML})` not :math:`P(D|M)`. In order to get :math:`P(D|M)` we have to sum over all possible paths instead of just the single most likely path. This can be calculated using ``model.log_probability(sequence)`` and uses the forward algorithm internally. On that note, the full forward matrix can be returned using ``model.forward(sequence)`` and the full backward matrix can be returned using ``model.backward(sequence)``, while the full forward-backward emission and transition matrices can be returned using ``model.forward_backward(sequence)``. Prediction ---------- A common prediction technique is calculating the Viterbi path, which is the most likely sequence of states that generated the sequence given the full model. This is solved using a simple dynamic programming algorithm similar to sequence alignment in bioinformatics. This can be called using ``model.viterbi(sequence)``. A sklearn wrapper can be called using ``model.predict(sequence, algorithm='viterbi')``. Another prediction technique is called maximum a posteriori or forward-backward, which uses the forward and backward algorithms to calculate the most likely state per observation in the sequence given the entire remaining alignment. Much like the forward algorithm can calculate the sum-of-all-paths probability instead of the most likely single path, the forward-backward algorithm calculates the best sum-of-all-paths state assignment instead of calculating the single best path. This can be called using ``model.predict(sequence, algorithm='map')`` and the raw normalized probability matrices can be called using ``model.predict_proba(sequence)``. Fitting ------- A simple fitting algorithm for hidden Markov models is called Viterbi training. In this method, each observation is tagged with the most likely state to generate it using the Viterbi algorithm. The distributions (emissions) of each states are then updated using MLE estimates on the observations which were generated from them, and the transition matrix is updated by looking at pairs of adjacent state taggings. This can be done using ``model.fit(sequence, algorithm='viterbi')``. However, this is not the best way to do training and much like the other sections there is a way of doing training using sum-of-all-paths probabilities instead of maximally likely path. This is called Baum-Welch or forward-backward training. Instead of using hard assignments based on the Viterbi path, observations are given weights equal to the probability of them having been generated by that state. Weighted MLE can then be done to update the distributions, and the soft transition matrix can give a more precise probability estimate. This is the default training algorithm, and can be called using either ``model.fit(sequences)`` or explicitly using ``model.fit(sequences, algorithm='baum-welch')``. pomegranate also supports labeled training of hidden Markov models. This setting is where one has state labels for each observation and wishes to derive the transition matrix and observations given those labels. The emissions simply become MLE estimates of the data partitioned by the labels and the transition matrix is calculated directly from the adjacency of labels. This option can be specified using ``model.fit(sequences, labels=labels, state_names=state_names)`` where ``labels`` has the same shape as ``sequences`` and ``state_names`` has the set of all possible labels. .. note:: The sequence of labels can include hidden states! However, a consequence of this is that each sequence of labels must begin with the start state because that is where each sequence begins with being aligned to the model. For instance, for the sequence of observations ``[1, 5, 6, 2]`` the corresponding labels would be ``['None-start', 'a', 'b', 'b', 'a']`` because the default name of a model is ``None`` and the name of the start state is ``{name}-start``. Likewise, you will need to add the end state label at the end of each sequence if you want an explicit end state, making the labels ``['None-start', 'a', 'b', 'b', 'a', 'None-end']``. There are a number of optional parameters that provide more control over the training process, including the use of distribution or edge inertia, freezing certain states, tying distributions or edges, and using pseudocounts. See the tutorial linked to at the top of this page for full details on each of these options. API Reference ------------- .. automodule:: pomegranate.hmm :members: :inherited-members: pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/Makefile000066400000000000000000000167741420461712600233000ustar00rootroot00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = _build # User-friendly check for sphinx-build ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) $(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) endif # Internal variables. 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The XML files are in $(BUILDDIR)/xml." .PHONY: pseudoxml pseudoxml: $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml @echo @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/MarkovChain.rst000066400000000000000000000060501420461712600245560ustar00rootroot00000000000000.. _markovchain: Markov Chains ============= `IPython Notebook Tutorial `_ Markov chains are form of structured model over sequences. They represent the probability of each character in the sequence as a conditional probability of the last k symbols. For example, a 3rd order Markov chain would have each symbol depend on the last three symbols. A 0th order Markov chain is a naive predictor where each symbol is independent of all other symbols. Currently pomegranate only supports discrete emission Markov chains where each symbol is a discrete symbol versus a continuous number (like 'A' 'B' 'C' instead of 17.32 or 19.65). Initialization -------------- Markov chains can almost be represented by a single conditional probability table (CPT), except that the probability of the first k elements (for a k-th order Markov chain) cannot be appropriately represented except by using special characters. Due to this pomegranate takes in a series of k+1 distributions representing the first k elements. For example for a second order Markov chain: .. code-block:: python from pomegranate import * d1 = DiscreteDistribution({'A': 0.25, 'B': 0.75}) d2 = ConditionalProbabilityTable([['A', 'A', 0.1], ['A', 'B', 0.9], ['B', 'A', 0.6], ['B', 'B', 0.4]], [d1]) d3 = ConditionalProbabilityTable([['A', 'A', 'A', 0.4], ['A', 'A', 'B', 0.6], ['A', 'B', 'A', 0.8], ['A', 'B', 'B', 0.2], ['B', 'A', 'A', 0.9], ['B', 'A', 'B', 0.1], ['B', 'B', 'A', 0.2], ['B', 'B', 'B', 0.8]], [d1, d2]) model = MarkovChain([d1, d2, d3]) Probability ----------- The probability of a sequence under the Markov chain is just the probability of the first character under the first distribution times the probability of the second character under the second distribution and so forth until you go past the (k+1)th character, which remains evaluated under the (k+1)th distribution. We can calculate the probability or log probability in the same manner as any of the other models. Given the model shown before: .. code-block:: python >>> model.log_probability(['A', 'B', 'B', 'B']) -3.324236340526027 >>> model.log_probability(['A', 'A', 'A', 'A']) -5.521460917862246 Fitting ------- Markov chains are not very complicated to train. For each sequence the appropriate symbols are sent to the appropriate distributions and maximum likelihood estimates are used to update the parameters of the distributions. There are no latent factors to train and so no expectation maximization or iterative algorithms are needed to train anything. API Reference ------------- .. automodule:: pomegranate.MarkovChain :members: :inherited-members: pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/MarkovNetwork.rst000066400000000000000000000127361420461712600251750ustar00rootroot00000000000000.. _markovnetwork: Markov Networks =============== - `IPython Notebook Tutorial `_ `Markov networks `_ (sometimes called Markov random fields) are probabilistic models that are typically represented using an undirected graph. Each of the nodes in the graph represents a variable in the data and each of the edges represent an associate. Unlike Bayesian networks which have directed edges and clear directions of causality, Markov networks have undirected edges and only encode associations. Currently, pomegranate only supports discrete Markov networks, meaning that the values must be categories, i.e. 'apples' and 'oranges', or 1 and 2, where 1 and 2 refer to categories, not numbers, and so 2 is not explicitly 'bigger' than 1. Initialization -------------- Markov networks can be initialized in two ways, depending on whether the underlying graphical structure is known or not: (1) a list of the joint probabilities tables can be passed into the initialization, with one table per clique in the graph, or (2) both the graphical structure and distributions can be learned directly from data. This mirrors the other models that are implemented in pomegranate. However, because finding the optimal Markov network requires enumerating a number of potential graphs that is exponential with the number of dimensions in the data, it can be fairly time intensive to find the exact network. Let's see an example of creating a Markov network with three cliques in it. .. code-block:: python from pomegranate import * d1 = JointProbabilityTable([ [0, 0, 0.1], [0, 1, 0.2], [1, 0, 0.4], [1, 1, 0.3]], [0, 1]) d2 = JointProbabilityTable([ [0, 0, 0, 0.05], [0, 0, 1, 0.15], [0, 1, 0, 0.07], [0, 1, 1, 0.03], [1, 0, 0, 0.12], [1, 0, 1, 0.18], [1, 1, 0, 0.10], [1, 1, 1, 0.30]], [1, 2, 3]) d3 = JointProbabilityTable([ [0, 0, 0, 0.08], [0, 0, 1, 0.12], [0, 1, 0, 0.11], [0, 1, 1, 0.19], [1, 0, 0, 0.04], [1, 0, 1, 0.06], [1, 1, 0, 0.23], [1, 1, 1, 0.17]], [2, 3, 4]) model = MarkovNetwork([d1, d2, d3]) model.bake() That was fairly simple. Each `JointProbabilityTable` object just had to include the table of all values that the variables can take as well as a list of variable indexes that are included in the table, in the order from left to right that they appear. For example, in d1, the first column of the table corresponds to the first column of data in a data matrix and the second column in the table corresponds to the second column in a data matrix. One can also initialize a Markov network based completely on data. Currently, the only algorithm that pomegranate supports for this is the Chow-Liu tree-building algorithm. This algorithm first calculates the mutual information between all pairs of variables and then determines the maximum spanning tree through it. This process generally captures the strongest dependencies in the data set. However, because it requires all variables to have at least one connection, it can lead to instances where variables are incorrectly associated with each other. Overall, it generally performs well and it fairly fast to calculate. .. code-block:: python from pomegranate import * import numpy X = numpy.random.randint(2, size=(100, 6)) model = MarkovNetwork.from_samples(X) Probability ----------- The probability of an example under a Markov network is more difficult to calculate than under a Bayesian network. With a Bayesian network, one can simply multiply the probabilities of each variable given its parents to get a probability of the entire example. However, repeating this process for a Markov network (by plugging in the values of each clique and multiplying across all cliques) results in a value called the "unnormalized" probability. This value is called "unnormalized" because the sum of this value across all combinations of values that the variables in an example can take does not sum to 1. The normalization of an "unnormalized" probability requires the calculation of a partition function. This function (frequently abbreviated `Z`) is just the sum of the probability of all combinations of values that the variables can take. After calculation, one can just divide the unnormalized probability by this value to get the normalized probability. The only problem is that the calculation of the partition function requires the summation over a number of examples that grows exponentially with the number of dimensions. You can read more about this in the tutorial. If you have a small number of variables (<30) it shouldn't be a problem to calculate the partition function and then normalized probabilities. .. code-block:: python >>> print(model.probability([1, 0, 1, 0, 1])) -4.429966143312331 Prediction ---------- Markov networks can be used to predict the value of missing variables given the observed values in a process called "inference." In other predictive models there are typically a single or fixed set of missing values that need to be predicted, commonly referred to as the labels. However, in the case of Markov (or Bayesian) networks, the missing values can be any variables and the inference process will use all of the available data to impute those missing values. For example: .. code-block:: python >>> print(model.predict([[None, 0, None, 1, None]])) [[1, 0, 0, 1, 1]] API Reference ------------- .. automodule:: pomegranate.MarkovNetwork :members: :inherited-members: pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/NaiveBayes.rst000066400000000000000000000217141420461712600244060ustar00rootroot00000000000000.. _naivebayes: Bayes Classifiers and Naive Bayes ================================= `IPython Notebook Tutorial `_ Bayes classifiers are simple probabilistic classification models based off of Bayes theorem. See the above tutorial for a full primer on how they work, and what the distinction between a naive Bayes classifier and a Bayes classifier is. Essentially, each class is modeled by a probability distribution and classifications are made according to what distribution fits the data the best. They are a supervised version of general mixture models, in that the ``predict``, ``predict_proba``, and ``predict_log_proba`` methods return the same values for the same underlying distributions, but that instead of using expectation-maximization to fit to new data they can use the provided labels directly. Initialization -------------- Bayes classifiers and naive Bayes can both be initialized in one of two ways depending on if you know the parameters of the model beforehand or not, (1) passing in a list of pre-initialized distributions to the model, or (2) using the ``from_samples`` class method to initialize the model directly from data. For naive Bayes models on multivariate data, the pre-initialized distributions must be a list of ``IndependentComponentDistribution`` objects since each dimension is modeled independently from the others. For Bayes classifiers on multivariate data a list of any type of multivariate distribution can be provided. For univariate data the two models produce identical results, and can be passed in a list of univariate distributions. For example: .. code-block:: python from pomegranate import * d1 = IndependentComponentsDistribution([NormalDistribution(5, 2), NormalDistribution(6, 1), NormalDistribution(9, 1)]) d2 = IndependentComponentsDistribution([NormalDistribution(2, 1), NormalDistribution(8, 1), NormalDistribution(5, 1)]) d3 = IndependentComponentsDistribution([NormalDistribution(3, 1), NormalDistribution(5, 3), NormalDistribution(4, 1)]) model = NaiveBayes([d1, d2, d3]) would create a three class naive Bayes classifier that modeled data with three dimensions. Alternatively, we can initialize a Bayes classifier in the following manner .. code-block:: python from pomegranate import * d1 = MultivariateGaussianDistribution([5, 6, 9], [[2, 0, 0], [0, 1, 0], [0, 0, 1]]) d2 = MultivariateGaussianDistribution([2, 8, 5], [[1, 0, 0], [0, 1, 0], [0, 0, 1]]) d3 = MultivariateGaussianDistribution([3, 5, 4], [[1, 0, 0], [0, 3, 0], [0, 0, 1]]) model = BayesClassifier([d1, d2, d3]) The two examples above functionally create the same model, as the Bayes classifier uses multivariate Gaussian distributions with the same means and a diagonal covariance matrix containing only the variances. However, if we were to fit these models to data later on, the Bayes classifier would learn a full covariance matrix while the naive Bayes would only learn the diagonal. If we instead wish to initialize our model directly onto data, we use the ``from_samples`` class method. .. code-block:: python from pomegranate import * import numpy X = numpy.load('data.npy') y = numpy.load('labels.npy') model = NaiveBayes.from_samples(NormalDistribution, X, y) This would create a naive Bayes model directly from the data with normal distributions modeling each of the dimensions, and a number of components equal to the number of classes in ``y``. Alternatively if we wanted to create a model with different distributions for each dimension we can do the following: .. code-block:: python >>> model = NaiveBayes.from_samples([NormalDistribution, ExponentialDistribution], X, y) This assumes that your data is two dimensional and that you want to model the first distribution as a normal distribution and the second dimension as an exponential distribution. We can do pretty much the same thing with Bayes classifiers, except passing in a more complex model. .. code-block:: python >>> model = BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y) One can use much more complex models than just a multivariate Gaussian with a full covariance matrix when using a Bayes classifier. Specifically, you can also have your distributions be general mixture models, hidden Markov models, and Bayesian networks. For example: .. code-block:: python >>> model = BayesClassifier.from_samples(BayesianNetwork, X, y) That would require that the data is only discrete valued currently, and the structure learning task may be too long if not set appropriately. However, it is possible. Currently, one cannot simply put in GeneralMixtureModel or HiddenMarkovModel despite them having a ``from_samples`` method because there is a great deal of flexibility in terms of the structure or emission distributions. The easiest way to set up one of these more complex models is to build each of the components separately and then feed them into the Bayes classifier method using the first initialization method. .. code-block:: python >>> d1 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, n_components=5, X=X[y==0]) >>> d2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, n_components=5, X=X[y==1]) >>> model = BayesClassifier([d1, d2]) Prediction ---------- Bayes classifiers and naive Bayes supports the same three prediction methods that the other models support, ``predict``, ``predict_proba``, and ``predict_log_proba``. These methods return the most likely class given the data (argmax_m P(M|D)), the probability of each class given the data (P(M|D)), and the log probability of each class given the data (log P(M|D)). It is best to always pass in a 2D matrix even for univariate data, where it would have a shape of (n, 1). The ``predict`` method takes in samples and returns the most likely class given the data. .. code-block:: python from pomegranate import * model = NaiveBayes([NormalDistribution(5, 2), UniformDistribution(0, 10), ExponentialDistribution(1.0)]) model.predict( np.array([[0], [1], [2], [3], [4]])) [2, 2, 2, 0, 0] Calling ``predict_proba`` on five samples for a Naive Bayes with univariate components would look like the following. .. code-block:: python from pomegranate import * model = NaiveBayes([NormalDistribution(5, 2), UniformDistribution(0, 10), ExponentialDistribution(1)]) model.predict_proba(np.array([[0], [1], [2], [3], [4]])) [[ 0.00790443 0.09019051 0.90190506] [ 0.05455011 0.20207126 0.74337863] [ 0.21579499 0.33322883 0.45097618] [ 0.44681566 0.36931382 0.18387052] [ 0.59804205 0.33973357 0.06222437]] Multivariate models work the same way. .. code-block:: python from pomegranate import * d1 = MultivariateGaussianDistribution([5, 5], [[1, 0], [0, 1]]) d2 = IndependentComponentsDistribution([NormalDistribution(5, 2), NormalDistribution(5, 2)]) model = BayesClassifier([d1, d2]) clf.predict_proba(np.array([[0, 4], [1, 3], [2, 2], [3, 1], [4, 0]])) array([[ 0.00023312, 0.99976688], [ 0.00220745, 0.99779255], [ 0.00466169, 0.99533831], [ 0.00220745, 0.99779255], [ 0.00023312, 0.99976688]]) ``predict_log_proba`` works the same way, returning the log probabilities instead of the probabilities. Fitting ------- Both naive Bayes and Bayes classifiers also have a ``fit`` method that updates the parameters of the model based on new data. The major difference between these methods and the others presented is that these are supervised methods and so need to be passed labels in addition to data. This change propagates also to the ``summarize`` method, where labels are provided as well. .. code-block:: python from pomegranate import * d1 = MultivariateGaussianDistribution([5, 5], [[1, 0], [0, 1]]) d2 = IndependentComponentsDistribution(NormalDistribution(5, 2), NormalDistribution(5, 2)]) model = BayesClassifier([d1, d2]) X = np.array([[6.0, 5.0], [3.5, 4.0], [7.5, 1.5], [7.0, 7.0 ]]) y = np.array([0, 0, 1, 1]) model.fit(X, y) As we can see, there are four samples, with the first two samples labeled as class 0 and the last two samples labeled as class 1. Keep in mind that the training samples must match the input requirements for the models used. So if using a univariate distribution, then each sample must contain one item. A bivariate distribution, two. For hidden markov models, the sample can be a list of observations of any length. An example using hidden markov models would be the following. .. code-block:: python d1 = HiddenMarkovModel... d2 = HiddenMarkovModel... d3 = HiddenMarkovModel... model = BayesClassifier([d1, d2, d3]) X = np.array([list('HHHHHTHTHTTTTH'), list('HHTHHTTHHHHHTH'), list('TH'), list('HHHHT')]) y = np.array([2, 2, 1, 0]) model.fit(X, y) API Reference ------------- .. automodule:: pomegranate.NaiveBayes :members: :inherited-members: .. automodule:: pomegranate.BayesClassifier :members: :inherited-members: pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/_templates/000077500000000000000000000000001420461712600237565ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/_templates/class.rst000066400000000000000000000002441420461712600256150ustar00rootroot00000000000000{{ fullname }} {{ underline }} .. currentmodule:: {{ module }} .. autoclass:: {{ objname }} {% block methods %} .. automethod:: __init__ {% endblock %} pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/api.rst000066400000000000000000000104741420461712600231320ustar00rootroot00000000000000======= The API ======= pomegranate has a minimal core API that is made possible because all models are treated as a probability distribution regardless of complexity. Regardless of whether it's a simple probability distribution, or a hidden Markov model that uses a different probability distribution on each feature, these methods can be used. Each model documentation page has an API reference showing the full set of methods and parameters for each method, but generally all models have the following methods and parameters for the methods. .. code-block:: python >>> model.probability(X) This method will take in either a single sample and return its probability, or a set of samples and return the probability of each one, given the model. .. code-block:: python >>> model.log_probability(X) The same as above but returns the log of the probability. This is helpful for numeric stability. .. code-block:: python >>> model.fit(X, weights=None, inertia=0.0) This will fit the model to the given data with optional weights. If called on a mixture model or a hidden Markov model this runs expectation-maximization to perform iterative updates, otherwise it uses maximum likelihood estimates. The shape of data should be (n, d) where n is the number of samples and d is the dimensionality, with weights being a vector of non-negative numbers of size (n,) when passed in. The inertia shows the proportion of the prior weight to use, defaulting to ignoring the prior values. .. code-block:: python >>> model.summarize(X, weights=None) This is the first step of the two step out-of-core learning API. It will take in a data set and optional weights and extract the sufficient statistics that allow for an exact update, adding to the cached values. If this is the first time that summarize is called then it will store the extracted values, if it's not the first time then the extracted values are added to those that have already been cached. .. code-block:: python >>> model.from_summaries(inertia=0.0) This is the second step in the out-of-core learning API. It will used the extracted and aggregated sufficient statistics to derive exact parameter updates for the model. Afterwards it will reset the stored values. .. code-block:: python >>> model.clear_summaries() This method clears whatever summaries are left on the model without updating the parameters. .. code-block:: python >>> Model.from_samples(X, weights=None) This method will initialize a model to a data set. In the case of a simple distribution it will simply extract the parameters from the case. In the more complicated case of a Bayesian network it will jointly find the best structure and the best parameters given that structure. In the case of a hidden Markov model it will first find clusters and then learn a dense transition matrix. Compositional Methods --------------------- These methods are available for the compositional models, i.e., mixture models, hidden Markov models, Bayesian networks, naive Bayes classifiers, and Bayes' classifiers. These methods perform inference on the data. In the case of Bayesian networks it will use the forward-backward algorithm to make predictions on all variables for which values are not provided. For all other models, this will return the model component that yields the highest posterior P(M|D) for some sample. This value is calculated using Bayes' rule, where the likelihood of each sample given each component multiplied by the prior of that component is normalized by the likelihood of that sample given all components multiplied by the prior of those components. .. code-block:: python >>> model.predict(X) This will return the most likely value for the data. In the case of Bayesian networks this is the most likely value that the variable takes given the structure of the network and the other observed values. In the other cases it is the model component that most likely explains this sample, such as the mixture component that a sample most likely falls under, or the class that is being predicted by a Bayes' classifier. .. code-block:: python >>> model.predict_proba(X) This returns the matrix of posterior probabilities P(M|D) directly. The predict method is simply running argmax over this matrix. .. code-block:: python >>> model.predict_log_proba(X) This returns the matrix of log posterior probabilities for numerical stability. pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/callbacks.rst000066400000000000000000000106301420461712600242720ustar00rootroot00000000000000.. _callbacks: Callbacks ========= - `IPython Notebook Tutorial `_ Callback refer to functions that should be executing during the training procedure. These functions can be executed either at the start of training, the end of each epoch, or at the end of training. They mirror in style the callbacks from keras, and so are passed in using the `callbacks` keyword in `fit` and `from_sample` methods. In pomegranate, a callback is an object that inherits from the `pomegranate.callbacks.Callback` object and has the following three methods implemented or inherited: * `on_training_begin(self)` : What should happen when training begins. * `on_epoch_end(self, logs)` : What should happen at the end of an epoch. The model will pass a dictionary of logs to each callback with each call that includes summary information about the training. The logs file is described more in depth below. * `on_training_end(self, logs)` : What should happen when training ends. The final set of logs is passed in as well. The log dictionary that is returned has the following entries: - `epoch` : `int`, the iteration or epoch that the model is currently on - `improvement` : `float`, the improvement since the latest iteration in the training set log probability - `total_improvement` : `float`, the total improvement seen in the training set log probability since the beginning of training - `log_probability` : `float`, the log probability of the training set after this round of training - `last_log_probability` : `float`, the log probability of the training set before this round of training - `duration` : `float`, the time in seconds that this epoch took - `epoch_start_time` : the time according to `time.time()` that this epoch began - `epoch_end_time`: the time according to `time.time()` that this epoch eded - `n_seen_batches` : `int`, the number of batches that have been seen by the model, only useful for mini-batching - `learning_rate` : The learning rate. This is undefined except when a decaying learning rate is set. The following callbacks are built in to pomegranate: 1. ``History()``: This will keep track of the above values in respective lists, e.g., `history.epochs` and `history.improvements`. This callback is automatically run by all models, and is returned when `return_history=True` is passed in. .. code-block:: python from pomegranate.callbacks import History from pomegranate import * model = HiddenMarkovModel.from_samples(X) # No history returned model, history = HiddenMarkovModel.from_samples(X, return_history=True) 2. ``ModelCheckpoint(name=None, verbose=True)``: This callback will save the model parameters to a file named `{name}.{epoch}.json` at the end of each epoch. By default the name is the name of the model, but that can be overriden with the name passed in to the callback object. The verbosity flag indicates if it should print a message to the screen indicating that a file was saved, and where to, at the end of each epoch. .. code-block:: python >>> from pomegranate.callbacks import ModelCheckpoint >>> from pomegranate import * >>> HiddenMarkovModel.from_samples(X, callbacks=[ModelCheckpoint()]) 3. ``CSVLogger(filename, separator=',', append=False)``: This callback will save the statistics from the logs dictionary to rows in a file at the end of each epoch. The filename specifies where to save the logs to, the separator is the symbol to separate values, and append indicates whether to save to the end of a file or to overwrite it, if it currently exists. .. code-block:: python >>> from pomegranate.callbacks import CSVLogger, ModelCheckpoint >>> from pomegranate import * >>> HiddenMarkovModel.from_samples(X, callbacks=[CSVLogger('model.logs'), ModelCheckpoint()]) 4. ``LambdaCallback(on_training_begin=None, on_training_end=None, on_epoch_end=None)``: A convenient wrapper that allows you to pass functions in that get executed at the appropriate points. The function `on_epoch_end` and `on_training_end` should accept a single argument, the dictionary of logs, as described above. .. code-block:: python >>> from pomegranate.callbacks import LambdaCheckpoint >>> from pomegranate import * >>> >>> def on_training_end(logs): >>> print("Total Improvement: {:4.4}".format(logs['total_improvement'])) >>> >>> HiddenMarkovModel.from_samples(X, callbacks=[LambdaCheckpoint(on_training_end=on_training_end)]) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/conf.py000066400000000000000000000233411420461712600231230ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # pomegranate documentation build configuration file, created by # sphinx-quickstart on Sun Oct 30 18:10:26 2016. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os import subprocess #import mock #MOCK_MODULES = ['numpy', 'scipy', 'joblib', 'networkx', 'cython'] #for mod_name in MOCK_MODULES: # sys.modules[mod_name] = mock.Mock() subprocess.call('pip install numpydoc', shell=True) # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.insert(0, os.path.abspath('../..')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'numpydoc' ] autosummary_generate = True numpydoc_show_class_members = False # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'pomegranate' copyright = u'2016-2018, Jacob Schreiber' author = u'Jacob Schreiber' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # import pomegranate # The short X.Y version. # If you really want the short one use the next line. # version = '.'.join(pomegranate.__version__.split('.')[0:2]) # Use this version if you want the full version string in the document version = pomegranate.__version__ # The full version, including alpha/beta/rc tags. release = pomegranate.__version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = True # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'sphinx_rtd_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' #html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # Now only 'ja' uses this config value #html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. #html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = 'pomegranatedoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', # Latex figure (float) alignment #'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'pomegranate.tex', u'pomegranate Documentation', u'Jacob Schreiber', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'pomegranate', u'pomegranate Documentation', [author], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'pomegranate', u'pomegranate Documentation', author, 'pomegranate', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/faq.rst000066400000000000000000000201171420461712600231230ustar00rootroot00000000000000.. _faq: FAQ === **Can I create a usable model if I already know the parameters I want, but don't have data to fit to?** Yes! pomegranate has two ways of initializing models, either by starting off with pre-initialized distributions or by using the ``Model.from_samples`` class method. In the case where you have a model that you'd like to use you can create the model manually and use it to make predictions without the need to fit it to data. **How do I create a model directly from data?** pomegranate attempts to closely follow the scikit-learn API. However, a major area in which it diverges is in the initialization of models directly from data. Typically in scikit-learn one would create an estimator and then call the ``fit`` function on the training data. In pomegranate one would use the ``Model.from_samples`` class method, such as ``BayesianNetwork.from_samples(X)``, to learn a model directly from data. **My data set has missing values. Can I use pomegranate?** Yes! pomegranate v0.9.0 merged missing value support. This means that you can learn models and run inference on data sets that have missing values just as easily as if they were fully observed. Indicate that a value is missing using either `numpy.nan` for numeric data sets or `'nan'` in string data sets. **What is the difference between ``fit`` and ``from_samples``?** The ``fit`` method trains an initialized model, whereas the ``from_samples`` class method will first initialize the model and then train it. These are separated out because frequently a person already knows a good initialization, such as the structure of the Bayesian network but maybe not the parameters, and wants to fine-tune that initialization instead of learning everything directly from data. This also simplifies the backend by allowing the ``fit`` function to assume that the model is initialized instead of having to check to see if it is initialized, and if not then initialize it. This is particularly useful in structured models such as Bayesian networks or hidden Markov models where the ``Model.from_samples`` task is really structure learning + parameter learning, because it allows the ``fit`` function to be solely parameter learning. **How can I use pomegranate for semi-supervised learning?** When using one of the supervised models (such as naive Bayes or Bayes classifiers) simply pass in the label -1 for samples that you do not have a label for. **How can I use out-of-core learning in pomegranate?** Once a model has been initialized the ``summarize`` method can be used on arbitrarily sized chunks of the data to reduce them into their sufficient statistics. These sufficient statistics are additive, meaning that if they are calculated for all chunks of a dataset and then added together they can yield exact updates. Once all chunks have been summarized then ``from_summaries`` is called to update the parameters of the model based on these added sufficient statistics. Out-of-core computing is supported by allowing the user to load up chunks of data from memory, summarize it, discard it, and move on to the next chunk. **Does pomegranate support parallelization?** Yes! pomegranate supports parallelized model fitting and model predictions, both in a data-parallel manner. Since the backend is written in cython the global interpreter lock (GIL) can be released and multi-threaded training can be supported via joblib. This means that parallelization is utilized time isn't spent piping data from one process to another nor are multiple copies of the model made. **Does pomegranate support GPUs?** Currently pomegranate does not support GPUs. **Does pomegranate support distributed computing?** Currently pomegranate is not set up for a distributed environment, though the pieces are currently there to make this possible. **How can I cite pomegranate?** The research paper that presents pomegranate is: *Schreiber, J. (2018). Pomegranate: fast and flexible probabilistic modeling in python. Journal of Machine Learning Research, 18(164), 1-6.* which can be downloaded from `JML`_ or from `arXiv`_. .. _jml: http://www.jmlr.org/papers/volume18/17-636/17-636.pdf .. _arxiv: https://arxiv.org/abs/1711.00137 The paper can be cited as: :: @article{schreiber2018pomegranate, title={Pomegranate: fast and flexible probabilistic modeling in python}, author={Schreiber, Jacob}, journal={Journal of Machine Learning Research}, volume={18}, number={164}, pages={1--6}, year={2018} } Alternatively, the GitHub repository can be cited as: :: @misc{Schreiber2016, author = {Jacob Schreiber}, title = {pomegranate}, year = {2016}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/jmschrei/pomegranate}}, commit = {enter commit that you used} } **How does pomegranate compare to other packages?** A comparison of the features between pomegranate and others in the python ecosystem can be seen in the following two plots. .. image:: logo/pomegranate_comparison.png The plot on the left shows model stacks which are currently supported by pomegranate. The rows show each model, and the columns show which models those can fit in. Dark blue shows model stacks which currently are supported, and light blue shows model stacks which are currently being worked on and should be available soon. For example, all models use basic distributions as their main component. However, general mixture models (GMMs) can be fit into both Naive Bayes classifiers and hidden Markov models (HMMs). Conversely, HMMs can be fit into GMMs to form mixtures of HMMs. Soon pomegranate will support models like a mixture of Bayesian networks. The plot on the right shows features compared to other packages in the python ecosystem. Dark red indicates features which no other package supports (to my knowledge!) and orange shows areas where pomegranate has an expanded feature set compared to other packages. For example, both pomegranate and sklearn support Gaussian naive Bayes classifiers. However, pomegranate supports naive Bayes of arbitrary distributions and combinations of distributions, such as one feature being Gaussian, one being log normal, and one being exponential (useful to classify things like ionic current segments or audio segments). pomegranate also extends naive Bayes past its "naivity" to allow for features to be dependent on each other, and allows input to be more complex things like hidden Markov models and Bayesian networks. There's no rule that each of the inputs to naive Bayes has to be the same type though, allowing you to do things like compare a markov chain to a HMM. No other package supports a HMM Naive Bayes! Packages like hmmlearn support the GMM-HMM, but for them GMM strictly means Gaussian mixture model, whereas in pomegranate it ~can~ be a Gaussian mixture model, but it can also be an arbitrary mixture model of any types of distributions. Lastly, no other package supports mixtures of HMMs despite their prominent use in things like audio decoding and biological sequence analysis. Models can be stacked more than once, though. For example, a "naive" Bayes classifier can be used to compare multiple mixtures of HMMs to each other, or compare a HMM with GMM emissions to one without GMM emissions. You can also create mixtures of HMMs with GMM emissions, and so the most stacking currently supported is a "naive" Bayes classifier of mixtures of HMMs with GMM emissions, or four levels of stacking. **How can pomegranate be faster than numpy?** pomegranate has been shown to be faster than numpy at updating univariate and multivariate gaussians. One of the reasons is because when you use numpy you have to use ``numpy.mean(X)`` and ``numpy.cov(X)`` which requires two full passes of the data. pomegranate uses additive sufficient statistics to reduce a dataset down to a fixed set of numbers which can be used to get an exact update. This allows pomegranate to calculate both mean and covariance in a single pass of the dataset. In addition, one of the reasons that numpy is so fast is its use of BLAS. pomegranate also uses BLAS, but uses the cython level calls to BLAS so that the data doesn't have to pass between cython and python multiple times. pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/gpu.rst000066400000000000000000000034211420461712600231460ustar00rootroot00000000000000.. _gpu: GPU Usage ========= pomegranate has GPU accelerated matrix multiplications to speed up all operations involving multivariate Gaussian distributions and all models that use them. This has led to an approximately 4x speedup for multivariate Gaussian mixture models and HMMs compared to using BLAS only. This speedup seems to scale better with dimensionality, with higher dimensional models seeing a larger speedup than smaller dimensional ones. By default, pomegranate will activate GPU acceleration if it can import cupy, otherwise it will default to BLAS. You can check whether pomegranate is using GPU acceleration with this built-in function: .. code-block:: python >>> import pomegranate >>> print(pomegranate.utils.is_gpu_enabled()) If you'd like to deactivate GPU acceleration you can use the following command: .. code-block:: python >>> pomegranate.utils.disable_gpu() Likewise, if you'd like to activate GPU acceleration you can use the following command: .. code-block:: python >>> pomegranate.utils.enable_gpu() FAQ --- Q. Why cupy and not Theano? A. pomegranate only needs to do matrix multiplications using a GPU. While Theano supports an impressive range of more complex operations, it did not have a simple interface to support a matrix-matrix multiplication in the same manner that cupy does. Q. Why am I not seeing a large speedup with my GPU? A. There is a cost to transferring data to and from a GPU. It is possible that the GPU isn't fast enough, or that there isn't enough data to utilize the massively parallel aspect of a GPU for your dataset. Q. Does pomegranate work using my type of GPU? A. The supported GPUs will be better documented on the cupy package. Q. Is multi-GPU supported? A. Currently, no. In theory it should be possible, though. pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/index.rst000066400000000000000000000071051420461712600234650ustar00rootroot00000000000000.. Introduction documentation master file, created by sphinx-quickstart on Sun Oct 30 18:10:26 2016. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. .. image:: logo/pomegranate-logo.png :width: 300px | .. image:: https://travis-ci.org/jmschrei/pomegranate.svg?branch=master :target: https://travis-ci.org/jmschrei/pomegranate .. image:: https://ci.appveyor.com/api/projects/status/github/jmschrei/pomegranate?svg=True :target: https://ci.appveyor.com/project/JacobSchreiber/pomegranate/branch/master .. image:: https://readthedocs.org/projects/pomegranate/badge/?version=latest :target: http://pomegranate.readthedocs.io/en/latest/?badge=latest | Home ==== pomegranate is a Python package that implements fast and flexible probabilistic models ranging from individual probability distributions to compositional models such as Bayesian networks and hidden Markov models. The core philosophy behind pomegranate is that all probabilistic models can be viewed as a probability distribution in that they all yield probability estimates for samples and can be updated given samples and their associated weights. The primary consequence of this view is that the components that are implemented in pomegranate can be stacked more flexibly than other packages. For example, one can build a Gaussian mixture model just as easily as building an exponential or log normal mixture model. But that's not all! One can create a Bayes classifier that uses different types of distributions on each features, perhaps modeling time-associated features using an exponential distribution and counts using a Poisson distribution. Lastly, since these compositional models themselves can be viewed as probability distributions, one can build a mixture of Bayesian networks or a hidden Markov model Bayes' classifier that makes predictions over sequences. In addition to a variety of probability distributions and models, pomegranate has a variety of built-in features that are implemented for all of the models. These include different training strategies such as semi-supervised learning, learning with missing values, and mini-batch learning. It also includes support for massive data supports with out-of-core learning, multi-threaded parallelism, and GPU support. Thank You ========= No good project is done alone, and so I'd like to thank all the previous contributors to YAHMM, all the current contributors to pomegranate, and the many graduate students whom I have pestered with ideas and questions. Contributions ============= Contributions are eagerly accepted! If you would like to contribute a feature then fork the master branch and be sure to run the tests before changing any code. Let us know what you want to do on the issue tracker just in case we're already working on an implementation of something similar. Also, please don't forget to add tests for any new functions. Please review the `Code of Conduct `_ before contributing. .. toctree:: :maxdepth: 1 :hidden: :caption: Getting Started self install.rst CODE_OF_CONDUCT.rst faq.rst whats_new.rst .. toctree:: :maxdepth: 1 :hidden: :caption: Features api.rst ooc.rst io.rst semisupervised.rst parallelism.rst gpu.rst nan.rst callbacks.rst .. toctree:: :maxdepth: 1 :hidden: :caption: Models Distributions.rst GeneralMixtureModel.rst HiddenMarkovModel.rst NaiveBayes.rst MarkovChain.rst BayesianNetwork.rst MarkovNetwork.rst FactorGraph.rst pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/install.rst000066400000000000000000000061241420461712600240240ustar00rootroot00000000000000.. _install: Installation ============ The easiest way to get pomegranate is through pip using the command .. code-block:: bash pip install pomegranate This should install all the dependencies in addition to the package. You can also get pomegranate through conda using the command .. code-block:: bash conda install pomegranate This version may not be as up to date as the pip version though. Lastly, you can get the bleeding edge from GitHub using the following commands: .. code-block:: bash git clone https://github.com/jmschrei/pomegranate cd pomegranate python setup.py install On Windows machines you may need to download a C++ compiler if you wish to build from source yourself. For Python 2 this `minimal version of Visual Studio 2008 works well `_. For Python 3 `this version of the Visual Studio build tools `_ has been reported to work. The requirements for pomegranate can be found in the requirements.txt file in the repository, and include numpy, scipy, networkx (v2.0 and above), joblib, cupy (if using a GPU), and cython (if building from source or on an Ubuntu machine). FAQ --- Q. I'm on a Windows machine and I'm still encountering problems. What should I do? A. If those do not work, it has been suggested that https://wiki.python.org/moin/WindowsCompilers may provide more information. Note that your compiler version must fit your python version. Run python --version to tell which python version you use. Don't forget to select the appropriate Windows version API you'd like to use. If you get an error message "ValueError: Unknown MS Compiler version 1900" remove your Python's Lib/distutils/distutil.cfg and retry. See http://stackoverflow.com/questions/34135280/valueerror-unknown-ms-compiler-version-1900 for details. Q. I've been getting the following error: ```ModuleNotFoundError: No module named 'pomegranate.utils'.``` A. A reported solution is to uninstall and reinstall without cached files using the following: .. code-block:: bash pip uninstall pomegranate pip install pomegranate --no-cache-dir If that doesn't work for you, you may need to downgrade your version of numpy to 1.11.3 and try the above again. Q. I've been getting the following error: ```MarkovChain.so: unknown file type, first eight bytes: 0x7F 0x45 0x4C 0x46 0x02 0x01 0x01 0x00.``` A. This can be fixed by removing the .so files from the pomegranate installation or by building pomegranate from source. Q. I'm encountering some other error when I try to install pomegranate. A. pomegranate has had some weird linker issues, particularly when users try to upgrade from an older version. In the following order, try: 1. Uninstalling pomegranate using pip and reinstalling it with the option --no-cache-dir, like in the above question. 2. Removing all pomegranate files on your computer manually, including egg and cache files that cython may have left in your site-packages folder 3. Reinstalling the Anaconda distribution (usually only necessary in issues where libgfortran is not linking properly) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/io.rst000066400000000000000000000032551420461712600227670ustar00rootroot00000000000000.. _io: Data Generators and IO ====================== - `IPython Notebook Tutorial `_ The main way that data is fed into most Python machine learning models is formatted as numpy arrays. However, there are some cases where this is not convenient. The first case is when the data doesn't fit into memory. This case was dealt with a little bit in the Out of Core documentation page. The second case is when the data lives in some other format, such as a CSV file or some type of data base, and one doesn't want to create an entire copy of the data formatted as a numpy array. Fortunately, pomegranate supports the use of data generators as input rather than only taking in numpy arrays. Data generators are objects that wrap data sets and yield batches of data in a manner that is specified by the user. Once the generator is exhausted the epoch is ended. The default data generator is to yield contiguous chunks of examples of a certain batch size until the entire data set has been seen, finish the epoch, and then start over. The strength of data generators is that they allow the user to have a much greater degree of control over the training process than hardcoding a few training schemes. By specifying how exactly a batch is generated from the data set (and the preprocessing that might go into converting examples for use by the model) and exactly when an epoch ends, users can do a wide variety of out-of-core and mini-batch training schemes without anything needed to be built-in to pomegranate. 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¼V«UeÊ”Ñ{ｧ}ûöÉÛÛ[ÎÎÎJNNÖÂ… U¿~}Û›K Ëýœÿ&Mš¨eË–Ú¼y³^}õUêâÅ‹úæ›oäëëk{Cg®ñãÇkРAŠˆˆÐÀU«V->|X‹-RÕªUõÌ3Ïê±̇ F#ƒ‘ÁP<1¨ ‹Å’g’m‹Åb»’üçö\]ºtQ… ¥Ù³gëÒ¥K*W®œ¼½½õå—_ÚæGº›ˆˆeeeiýúõz÷ÝwÕ A½÷Þ{zòÉ'uõêUMž<_“ßßmßùm¿Ÿó?}útMž`_ As of version 0.9.0, pomegranate supports missing values for almost all methods. This means that models can be fit to data sets that have missing values in them, inference can be done on samples that have missing values, and even structure learning can be done in the presence of missing values. Currently, this support exists in the form of calculating sufficient statistics with respect to only the variables that are present in a sample and ignoring the missing values, in contrast to imputing the missing values and using those for the estimation. Missing value support was added in a manner that requires the least user thought. All one has to do is add ``numpy.nan`` to mark an entry as missing for numeric data sets, or the string ``'nan'`` for string data sets. pomegranate will automatically handle missing values appropriately. The functions have been written in such a way to minimize the overhead of missing value support, by only acting differently when a missing value is found. However, it may take some models longer to do calculations in the presence of missing values than on dense data. For example, when calculating the log probability of a sample under a multivariate Gaussian distribution one can typically use BLAS or a GPU since a dot product is taken between the data and the inverse covariance matrix. Unfortunately, since missing data can occur in any of the columns, a new inverse covariance matrix has to be calculated for each sample and BLAS cannot be utilized at all. As an example, when fitting a ``NormalDistribution`` to a vector of data, the parameters are estimated simply by ignoring the missing values. A data set with 100 observations and 50 missing values would produce the same model as a data set comprised simply of the 100 observations. This comes into play when fitting multivariate models, like an ``IndependentComponentsDistribution``, because each distribution is fit to only the observations for their specific feature. This means that samples where some values are missing can still be utilized in the dimensions where they are observed. This can lead to more robust estimates that by imputing the missing values using the mean or median of the column. Here is an example of fitting a univariate distribution to data sets with missing values: .. code-block:: python >>> import numpy >>> from pomegranate import * >>> >>> X = numpy.random.randn(100) >>> X[75:] = numpy.nan >>> >>> NormalDistribution.from_samples(X) { "frozen" :false, "class" :"Distribution", "parameters" :[ -0.0007138484812874587, 1.0288813172046551 ], "name" :"NormalDistribution" } >>> NormalDistribution.from_samples(X[:75]) { "frozen" :false, "class" :"Distribution", "parameters" :[ -0.0007138484812874587, 1.0288813172046551 ], "name" :"NormalDistribution" } Multivariate Gaussian distributions take a slightly more complex approach. The means of each column are computed using the available data, but the covariance is calculated using sufficient statistics calculated from pairs of variables that exist in a sample. For example, if the sample was (2.0, 1.7, numpy.nan), then sufficient statistics would be calculated for the variance of the first and second variables as well as the covariance between the two, but nothing would be updated about the third variable. All univariate distributions return a probability of 1 for missing data. This is done to support inference algorithms in more complex models. For example, when running the forward algorithm in a hidden Markov model in the presence of missing data, one would simply ignore the emission probability for the steps where the symbol is missing. This means that when getting to the step when a missing symbol is being aligned to each of the states, the cost is simply the transition probability to that state, instead of the transition probability multiplied by the likelihood of that symbol under that states' distribution (or, equivalently, having a likelihood of 1.) Under a Bayesian network, the probability of a sample is just the product of probabilities under distributions where the sample is fully observed. See the tutorial for more examples of missing value support in pomegranate! FAQ --- Q. How do I indicate that a value is missing in a data set? A. If it is a numeric data set, indicate that a value is missing using ``numpy.nan``. If it is strings (such as 'A', 'B', etc...) use the string ``'nan'``. If your strings are stored in a numpy array, make sure that the full string 'nan' is present. numpy arrays have a tendancy to truncate longer strings if they're defined over shorter strings (like an array containing 'A' and 'B' might truncate 'nan' to be 'n'). Q. Are all algorithms supported? A. Almost all! The only known non-supported function is Chow-Liu tree building. You can fit a Gaussian Mixture Model, run k-means clustering, decode a sequence using the Viterbi algorithm for a hidden Markov model, and learn the structure of a Bayesian network on data sets with missing values now! Q. It is much slower to fit models using multivariate Gaussian distributions to missing data. Why? A. When calculating the log probability of a point with missing values, a new inverse covariance matrix needs to be calculated over the subset of variables that are observed. This is a double whammy for speed because you need to (1) invert a matrix once per sample, and (2) cannot use BLAS for the calculation since there is no fixed sized covariance matrix to operate with. Q. Performance on data sets without missing values appears to be worse now. What should I do? A. Please report it on the GitHub issue tracker or email me. I have tried to minimize overhead in as many places as I can, but I have not run speed tests on all cases. Please include a sample script, and the amount of time it took. pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/ooc.rst000066400000000000000000000104751420461712600231420ustar00rootroot00000000000000.. _ooc: Out of Core Learning ==================== - `IPython Notebook Tutorial `_ Sometimes datasets which we'd like to train on can't fit in memory but we'd still like to get an exact update. pomegranate supports out of core training to allow this, by allowing models to summarize batches of data into sufficient statistics and then later on using these sufficient statistics to get an exact update for model parameters. These are done through the methods ```model.summarize``` and ```model.from_summaries```. Let's see an example of using it to update a normal distribution. .. code-block:: python >>> from pomegranate import * >>> import numpy >>> >>> a = NormalDistribution(1, 1) >>> b = NormalDistribution(1, 1) >>> X = numpy.random.normal(3, 5, size=(5000,)) >>> >>> a.fit(X) >>> a { "frozen" :false, "class" :"Distribution", "parameters" :[ 3.012692830297519, 4.972082359070984 ], "name" :"NormalDistribution" } >>> for i in range(5): >>> b.summarize(X[i*1000:(i+1)*1000]) >>> b.from_summaries() >>> b { "frozen" :false, "class" :"Distribution", "parameters" :[ 3.01269283029752, 4.972082359070983 ], "name" :"NormalDistribution" } This is a simple example with a simple distribution, but all models and model stacks support this type of learning. Lets next look at a simple Bayesian network. .. code-block::python >>> from pomegranate import * >>> import numpy >>> >>> d1 = DiscreteDistribution({0: 0.25, 1: 0.75}) >>> d2 = DiscreteDistribution({0: 0.45, 1: 0.55}) >>> d3 = ConditionalProbabilityTable([[0, 0, 0, 0.02], [0, 0, 1, 0.98], [0, 1, 0, 0.15], [0, 1, 1, 0.85], [1, 0, 0, 0.33], [1, 0, 1, 0.67], [1, 1, 0, 0.89], [1, 1, 1, 0.11]], [d1, d2]) >>> >>> d4 = ConditionalProbabilityTable([[0, 0, 0.4], [0, 1, 0.6], [1, 0, 0.3], [1, 1, 0.7]], [d3]) >>> >>> s1 = State(d1, name="s1") >>> s2 = State(d2, name="s2") >>> s3 = State(d3, name="s3") >>> s4 = State(d4, name="s4") >>> >>> model = BayesianNetwork() >>> model.add_nodes(s1, s2, s3, s4) >>> model.add_edge(s1, s3) >>> model.add_edge(s2, s3) >>> model.add_edge(s3, s4) >>> model.bake() >>> model2 = model.copy() >>> >>> X = numpy.random.randint(2, size=(10000, 4)) >>> print(model.states[0].distribution.equals(model2.states[0].distribution)) True >>> model.fit(X) >>> print(model.states[0].distribution.equals(model2.states[0].distribution)) False >>> model2.summarize(X[:2500]) >>> model2.summarize(X[2500:5000]) >>> model2.summarize(X[5000:7500]) >>> model2.summarize(X[7500:]) >>> model2.from_summaries() >>> >>> print(model.states[0].distribution.equals(model2.states[0].distribution)) True We can see that before fitting to any data, the distribution in one of the states is equal for both. After fitting the first distribution they become different as would be expected. After fitting the second one through summarize the distributions become equal again, showing that it is recovering an exact update. FAQ --- Q. How many examples should I summarize at a time? A. You should summarize the largest amount of data that fits in memory. The larger the block of data, the more efficient the calculations can be, particularly if GPU computing is being used. Q. Can I still do multi-threading / use a GPU with out-of-core learning? A. Absolutely. You will have to call joblib yourself if you use the formulation above but the computational aspects of the call to summarize have the GIL released and so multi-threading can be used. Q. Does out of core learning give exact or approximate updates? A. It gives exact updates as long as the total set of examples that are summarized is the same. Sufficient statistics are collected for each of the batches and are equal to the sufficient statistics that one would get from the full dataset. However, the initialization step is done on only a single batch. This may cause the final models to differ due simply to the different initializations. If one has pre-defined initializations and simply calls `fit`, then the exact same model will be yielded. pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/parallelism.rst000066400000000000000000000076151420461712600246710ustar00rootroot00000000000000.. _parallelism: Parallelism =========== pomegranate supports multi-threaded parallelism through the joblib library. Typically, python applications use multi-processing in order to get around the Global Interpreter Lock (GIL) that prevents multiple threads from running in the same Python process. However, since pomegranate does most of its computation using only C level primitives, it can release the GIL and enable multiple threads to work at the same time. The main difference that a user will notice is that it is more memory efficient, because instead of copying the data across multiple processes that each have their own memory allocated, each thread in pomegranate can operate on the same single memory allocation. Using parallelism in pomegranate is as simple as specifying the `n_jobs` parameter in any of the methods-- both fitting and prediction methods! For example: .. code-block:: python import pomegranate, numpy X = numpy.random.randn(1000, 1) # No parallelism model = GeneralMixtureModel.from_samples(NormalDistribution, 3, X) # Some parallelism model = GeneralMixtureModel.from_samples(NormalDistribution, 3, X, n_jobs=2) # Maximum parallelism model = GeneralMixtureModel.from_samples(NormalDistribution, 3, X, n_jobs=-1) If you instead have a fit model and you're just looking to speed up prediction time, you need only pass the n_jobs parameter in to those methods as well. .. code-block:: python model = X = numpy.random.randn(1000, 1) # No parallelism y = model.predict_proba(X) # Some parallelism y = model.predict_proba(X, n_jobs=2) # Maximum parallelism y = model.predict_proba(X, n_jobs=-1) FAQ --- Q. What models support parallelism? A. All models should support parallel fitting. All models (except for HMMs) support parallel predictions natively through the `n_jobs` parameter. Basic distributions do not support parallelism as they typically take a negligible amount of time to do anything with. Q. How can I parallelize something that doesn't have built-in parallelism? A. You can easily write a parallelized prediction wrapper for any model using multiprocessing. It would likely look like the following: .. code-block:: python from joblib import Parallel, delayed from pomegranate import BayesianNetwork def parallel_predict(name, X): """Load up a pomegranate model and predict a subset of X""" model = BayesianNetwork.from_json(name) return model.predict(X) X_train, X_test = numpy.load("train.data"), numpy.load("test.data") model = BayesianNetwork.from_samples(X_train) with open("model.json", "w") as outfile: outfile.write(model.to_json()) n = len(X_test) starts, ends = [i*n/4 for i in range(4)], [(i+1)*n/4 for i in range(4)] y_pred = Parallel(n_jobs=4)( delayed(parallel_predict)( X_test[start:end]) for start, end in zip(starts, ends)) Q. What is the difference between multiprocessing and multithreading? A. Multiprocessing involves creating a whole new Python process and passing the relevant data over to it. Multithreading involves creating multiple threads within the same Python process that all have access to the same memory. Multithreading is frequently more efficient because it doesn't involve copying potentially large amounts of data between different Python processes. Q. Why don't all modules use multithreading? A. Python has the Global Interpreter Lock (GIL) enabled which prevents more than one thread to execute per processes. The work-around is multiprocessing, which simply creates multiple processes that each have one thread working. When one uses Cython, they can disable to GIL when using only C-level primitives. Since most of the compute-intensive tasks involve only C-level primitives, multithreading is a natural choice for pomegranate. In situations where the size of the data is small and the cost of transferring it from one process to another is negligible, then multithreading can simply make things more complicated. pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/semisupervised.rst000066400000000000000000000123051420461712600254230ustar00rootroot00000000000000.. _semisupervised.rst Semi-Supervised Learning ======================== Semi-supervised learning is a branch of machine learning that deals with training sets that are only partially labeled. These types of datasets are common in the world. For example, consider that one may have a few hundred images that are properly labeled as being various food items. They may wish to augment this dataset with the hundreds of thousands of unlabeled pictures of food floating around the internet, but not wish to incur the cost of having to hand label them. Unfortunately, many machine learning methods are not able to handle both labeled and unlabeled data together and so frequently either the unlabeled data is tossed out in favor of supervised learning, or the labeled data is only used to identify the meaning of clusters learned by unsupervised techniques on the unlabeled data. Probabilistic modeling offers an intuitive way of incorporating both labeled and unlabeled data into the training process through the expectation-maximization algorithm. Essentially, one will initialize the model on the labeled data, calculate the sufficient statistics of the unlabeled data and labeled data separately, and then add them together. This process can be thought of as vanilla EM on the unlabeled data except that at each iteration the sufficient statistics from the labeled data (MLE estimates) are added. pomegranate follows the same convention as scikit-learn when it comes to partially labeled datasets. The label vector `y` is still of an equal length to the data matrix `X`, with labeled samples given the appropriate integer label, but unlabeled samples are given the label `-1`. While `np.nan` may be a more intuitive choice for missing labels, it isn't used because `np.nan` is a double and the `y` vector is integers. When doing semi-supervised learning with hidden Markov models, however, one would pass in a list of labels for each labeled sequence, or `None` for each unlabeled sequence, instead of `-1` to indicate an unlabeled sequence. All models that support labeled data support semi-supervised learning, including naive Bayes classifiers, general Bayes classifiers, and hidden Markov models. Semi-supervised learning can be done with all extensions of these models natively, including on mixture model Bayes classifiers, mixed-distribution naive Bayes classifiers, using multi-threaded parallelism, and utilizing a GPU. Below is a simple example. Notice that there is no difference in the `from_samples` call, the presence of -1 in the label vector is enough. .. code-block:: python import numpy from sklearn.datasets import make_blobs from sklearn.model_selection import train_test_split from pomegranate import NaiveBayes, NormalDistribution n, d, m = 50000, 5, 10 X, y = make_blobs(n, d, m, cluster_std=10) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1) n_unlabeled = int(X_train.shape[0] * 0.999) idxs = numpy.random.choice(X_train.shape[0], size=n_unlabeled) y_train[idxs] = -1 model = NaiveBayes.from_samples(NormalDistribution, X_train, y_train, verbose=True) While HMMs can theoretically be trained on sequences of data that are only partially labeled, currently semi-supervised learning for HMMs means that some sequences are fully labeled, and some sequences have no labels at all. This means that instead of passing in a normal label vector as a list of lists such as `[[model.start, s1, s2, model.end], [model.start, s1, s1, model.end]]`, one would pass in a list of mixed list/None types, with lists defining the labels for labeled sequences, and None specifying that a sequence is unlabeled. For example, if the second sequence was unlabeled, one would pass in `[[model.start, s1, s2, model.end], None]` instead. FAQ --- Q. What ratio of unlabeled / labeled data is typically best? A. It's hard to say. However, semi-supervised learning works best when the underlying distributions are **more complicated than the labeled data captures**. If your data is simple Gaussian blobs, not many samples are needed and adding in unlabeled samples likely will not help. However, if the true underlying distributions are some complex mixture of components but your labeled data looks like a simple blob, semi-supervised learning can help significantly. Q. If this uses EM, what's the difference between semi-supervised learning and a mixture model? A. Semi-supervised learning is a middle ground between unsupervised learning and supervised learning. As such, it adds together the sufficient statistics from unsupervised learning (using the EM algorithm) and supervised learning (using MLE) to get the complete model. An immediate benefit of this is that since there is a supervised initialization, the learned components will always align with the intended classes instead of being randomly assigning class values. Q. Can parallelism be used with semi-supervised learning? A. Yes. All aspects of pomegranate that can be used with naive Bayes classifiers or general Bayes classifiers can be used in the context of semi-supervised learning in the same way one would do so in supervised learning. One need only set the `n_jobs` parameters as normal. Literally the only difference for the user that the label vector now contains many `-1` values. pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/docs/whats_new.rst000066400000000000000000000645331420461712600243650ustar00rootroot00000000000000.. currentmodule:: pomegranate =============== Release History =============== Version 0.14.8 ============== Highlights ---------- - Fix an issue with saving and loading BayesModels Version 0.14.6 ============== Highlights ---------- - Adds ICD support for features having both discrete and continuous distributions. Thanks @lmcinnes! Version 0.14.6 ============== Highlights ---------- - Determinism added to k-means initializations through a `random_state` parameter - Determinism added to `HiddenMarkovModel.from_samples` by passing its `random_state` parameter to k-means - Fixed an issue where the JSON of an HMM would be updated after a call to `fit` but not a call to `from_summaries` - Fixed an issue where independent component distributions would not be created correctly for HMM states when also passing in labels - Separated out the initialization of distributions in `HiddenMarkovModel.from_samples` and the extraction or labeling of unlabeled examples - Updated the NetworkX requirement to be at least 2.4. - Force GMM models to respect its `frozen` attribute in the `from_summaries` method. Version 0.14.0 ============== Highlights ---------- - A variety of minor bug fixes and enhancements - Installations should be cleaner due to a transition from TravisCI/appveyor to GitHub actions. BayesModels ----------- - These models should now be able to fit to IndependentComponentDistributions. Version 0.13.1 ============== Highlights ---------- - A variety of minor bug fixes and speed improvements - Bayesian networks now support sampling using a Gibbs sampler or rejection sampling. Thanks @pascal-schetelat - HMMs now have the option to disable rechecking the inputs at each iteration, which can dramatically speed up training for small models. General ------- - pomegranate will now use `numpy.asarray` instead of `numpy.array` to avoid re-copying arrays. BayesianNetwork --------------- - An error will now appropriately be raised when passing in constraints and selecting the Chow-Liu algorithm. - Bayesian networks will now use 32-bit floats instead of 64-bit floats internally, leading to lower memory models. Thanks @alexhenrie Version 0.13.0 ============== Highlights ---------- - A variety of minor bug fixes and speed improvements - You can now pass in key sets to each model to define distributions over, even if that symbol doesn't occur in the training set - The returns from `from_sample` uses class distributions instead of predefined ones, allowing for inheritance of distributions - Checks added to ensure that input arrays are C ordered instead of transposed Distributions ------------- - JSON dtypes are checked to be numpy types instead of assumed to be - __eq__ and __mul__ sped up for DiscreteDistributions BayesianNetwork --------------- - Fixed bug with constraint graphs when a node has both self loop and parent constraints MarkovChains ------------ - Fixed an issue with sampling length. Version 0.12.1 ============== Highlights ---------- - A variety of minor bug fixes. Version 0.12.0 ============== Highlights ---------- - MarkovNetwork models have been added in and include both inference and structure learning. - Support for Python 2 has been deprecated. - Markov network, data generator, and callback tutorials have been added in - A robust `from_json` method has been added in to __init__.py that can deserialize JSONs from any pomegranate model. MarkovNetwork ------------- - MarkovNetwork models have been added in as a new probabilistic model. - Loopy belief propagation inference has been added in using the FactorGraph backend - Structure learning has been added in using Chow-Liu trees BayesianNetwork --------------- - Chow-Liu tree building has been sped up slightly, courtesy of @alexhenrie - Chow-Liu tree building was further sped up by almost an order of magnitude - Constraint Graphs no longer fail when passing in graphs with self loops, courtesy of @alexhenrie BayesClassifier --------------- - Updated the `from_samples` method to accept BayesianNetwork as an emission. This will build one Bayesian network for each class and use them as the emissions. Distributions ------------- - Added a warning to DiscreteDistribution when the user passes in an empty dictionary. - Fixed the sampling procedure for JointProbabilityTables. - GammaDistributions should have their shape issue resolved - The documentation for BetaDistributions has been updated to specify that it is a Beta-Bernoulli distribution. io --- - New file added, io.py, that contains data generators that can be operated on - Added DataGenerator, DataFrameGenerator, and a BaseGenerator class to inherit from HiddenMarkovModel ----------------- - Added RandomState parameter to `from_samples` to account for randomness when building discrete models. Misc ---- - Unneccessary calls to memset have been removed, courtesy of @alexhenrie - Checking for missing values has been slightly refactored to be cleaner, courtesy of @mareksmid-lucid - Include the LICENSE file in MANIFEST.in and simplify a bit, courtesy of @toddrme2178 - Added in a robust from_json method that can be used to deserialize a JSON for any pomegranate model. docs ---- - Added io.rst to briefly describe data generators - Added MarkovNetwork.rst to describe Markov networks - Added links to tutorials that did not have tutorials linked to them. Tutorials --------- - Added in a tutorial notebook for Markov networks - Added in a tutorial notebook for data generators - Added in a tutorial notebook for callbacks CI -- - Removed unit tests for Py2.7 from AppVeyor and Travis - Added unit tests for Py3.8 to AppVeyor and Travis Version 0.11.2 ============== Highlights ---------- - Faster BSNL, particularly when there is missing data, courtesy of @alexhenrie - GPU acceleration should be fixed BayesianNetwork --------------- - A speed improvement by making `isnan` an inline function, courtesy of @alexhenrie - A speed improvement by changing the manner that parent sets are iterated, courtesy of @alexhenrie Utils ----- - The `enable_gpu` call has been moved to the bottom of the GPU checking code and so should not crash anymore. Version 0.11.1 ============== Highlights ---------- - Added speed improvements to Bayesian network structure learning when missing data is present. BayesianNetwork --------------- - By default duplicates get merged in a data set so that there are fewer rows with larger weights, dramatically improving speed. However, because `np.nan != np.nan`, rows with missing values don't get merged. This fix changes `np.nan` to `None` so that the rows get merged appropriately. - A few misc changes that sometimes improve speed. - Changed the probability calculation when a node is being scored given a single row. Previously it would return 0, meaning that sometimes it will return the densest graph possible erroneously. This may change your networks in edge cases, but will reduce their complexity. Version 0.11.0 ============== Highlights ---------- - Allowed for user specified custom distributions by implementing a Python fallback option if the distribution object doesn't inherit from the base distribution class. - Fixed an issue with GammaDistribution update - Removed deterministic seed being set in hmm.bake - Made pomegranate compatible with NetworkX v2.0 and above - NeuralHMMs and Neural Mixture Models are now possible through the custom distributions - Many new tutorials Distributions ------------- - Fixed an error in GammaDistribution's cython level update step where sufficient statistics were incorrectly collected from a data set. This will only affect GammaDistributions that are used as part of a composition model rather than stand-alone ones. - Added in support for custom distributions. This is done by checking whether a distribution is inherited from the base pomegranate distribution object. If not, it will use the python methods. - Added in examples of using custom distributions, including neural networks, with pomegranate models. - Made NormalDistribution.blank and LogNormalDistribution.blank return distributions with a standard deviation of 1, to avoid DivisionByZero errors. - Added in a NeuralNetworkWrapper distribution that should handle wrapping a neural network correctly for use in pomegranate. This assumes a keras-like API. HiddenMarkovModel ----------------- - Removed a deterministic seed being set in hmm.bake. These lines were set because it was thought that there was some randomness in either the internal state generation of the topological sort. However, it appears that this is not necessary, and so it has been removed. - Fixed a bug where semi-supervised learning would not work because of an undefined variable. - Added in support for networkx v2.0 and above using their new API. Tutorials --------- - Revamped the tutorials in the tutorials folder, greatly expanding their scope - Added in new tutorials about custom distributions and neural probabilistic models Version 0.10.0 ============== Highlights ---------- - Broke distributions into their own files and placed them in their own folder - Fixed Bayesian network failing in call to np.isnan when fitting to character data - Added in callbacks to all models in the style of keras, with built-ins being History, ModelCheckpoint, and CVLogger. History is calculated for each model. Use `return_history=True` to gt the model and the history object that contains training. - Added top-level Makefile for convenience in development to build/test/clean/install/uninstall with multiple conda environments. - Added top-level rebuildconda for convenience in development to create or re-create a conda development environment for a given python version, defaulting to 2.7. Changelog --------- Callbacks --------- - Added in a callbacks module, and the use of callbacks in all iterative training procedures. Callbacks are called at the beginning of training, at the end of each epoch, and at the end of the training procedure, using the respective functions. See the documentation page for more details. Distributions ------------- - Broke the distributions.pyx into a folder where each distribution has its own file. This will speed up compilation when the code is modified. - Added in a `dtype` attribute to DiscreteDistribution, ConditionalProbabilityTable, and JointProbabilityTable, to prevent automatic casting of keys as floats when converting to and from jsons - For MultivariateGaussianDistributions, added in an epsilon when performing a ridge adjustment on a non-positive semidefinite matrix to hopefully completely fix this issue. - NormalDistribution update should now check to see if the weights are below an epsilon, rather than equal to 0, resolving some stability issues. - Fixed an issue with BernoulliDistribution where it would raise a ZeroDivisionError when `from_summaries` was called with no observations. - Fixed an issue where an IndependentComponentsDistribution would print upon calls to `log_probability` HiddenMarkovModel ----------------- - Changed the output to be the fit model, like in scikit-learn, instead of the total improvement, to allow for chaining - Added in callback functionality to both the `fit` and `from_samples` methods - Added in the `return_history` parameter to both the `fit` and `from_samples` methods, which will return the history callback as well as the fit model - Resolved an issue in the `summary` method where default weights were assigned to the wrong variable when not passed in. - Resolved an issue where printing an empty model resulted in an error. GeneralMixtureModel ------------------- - Changed the output to be the fit model, like in scikit-learn, instead of the total improvement, to allow for chaining - Added in callback functionality to both the `fit` and `from_samples` methods - Added in the `return_history` parameter to both the `fit` and `from_samples` methods, which will return the history callback as well as the fit model NaiveBayes ---------- - Added in callback functionality to both the `fit` and `from_samples` methods that will be used only in semi-supervised learning - Added in the `return_history` parameter to both the `fit` and `from_samples` methods, which will return the history callback as well as the fit model that will be used only in semi-supervised learning BayesClassifier --------------- - Added in callback functionality to both the `fit` and `from_samples` methods that will be used only in semi-supervised learning - Added in the `return_history` parameter to both the `fit` and `from_samples` methods, which will return the history callback as well as the fit model that will be used only in semi-supervised learning BayesianNetwork --------------- - Modified the built keymap to be a numpy array of objects to prevent casting of all keys as the type of the first column. Makefile --------------- - There is a new top-level "convenience" Makefile for development to make it easy to develop with two conda environments. The default is for two conda environments, py2.7 and py3.6, but those could be overridden at run time with, for example, `make PY3_ENV=py3.6.2 biginstall`. Targets exist for `install, test, bigclean, and nbtest` along with variations of each that first activate either one or both conda environments. For example, `make biginstall` will install for both `py2.7` and `py3.6` environments. When developing pomegranate, one frequently wants to do a fully clean build, wipe out all installed targets, and replace them. This can be done with `make bigclean biguninstall biginstall`. In addition, there is a target `nbtest` for testing all of the jupyter notebooks to ensure that the cells run. See the Makefile for a list of additional conda packages to install for this to work. The default is to stop on first error but you can run `make ALLOW_ERRORS=--allow-errors nbtest` to run all cells and then inspect the html output manually for errors. - There is a new top-level "convenience" rebuildconda script which will remove and create a conda environment for development. Be careful using it that the environment you want to rebuild is the right one. You can list environments with `conda info --envs`. The default is to rebuild the `2.7` environment with name `py2.7`. With this, you can create an alternative environment, test it out, and remove it as in `./rebuildconda 2.7.9 ; make PY2_ENV=py2.7.9 bigclean py2build py2test py2install nbtest ; source deactivate ; conda env remove --name py2.7.9`. Version 0.9.0 ============= Highlights ---------- - Missing value support has been added in for all models except factor graphs. This is done by included the string `nan` in string datasets, or `numpy.nan` in numeric datasets. Model fitting and inference is supported for all models for this. The technique is to not collect sufficient statistics from missing data, not to impute the missing values. - The unit testing suite has been greatly expanded, from around 140 tests to around 370 tests. Changelog --------- HiddenMarkovModel ----------------- - The documentation has been fixed so that states are defined as `State(NormalDistribution(0, 1))` instead of incorrectly as `State(Distribution(NormalDistribution(0, 1)))` - Fixed a bug in `from_samples` that was causing a TypeError if `name` was not specified when using `DiscreteDistribution` with custom labels. - Expanded the number of unit tests to include missing value support and be more comprehensive Distributions ------------- - Multivariate Gaussian distributions have had their parameter updates simplified. This doesn't lead to a significant change in speed, just less code. - Fixed an issue where Poisson Distributions had an overflow issue caused when calculating large factorials by moving the log inside the product. - Fixed an issue where Poisson Distributions were not correctly calculating the probability of 0 counts. - Fixed an issue where Exponential Distribution would fail when fed integer 0-mode data. - Fixed an issue where IndependentComponentDistribution would have incorrect per-dimension weights after serialization. - Added in missing value support for fitting and log probability calculations for all univariate distributions, ICD, MGD, and CPTs through calculating sufficient statistics only on data that exists. The only distributions that currently do not support missing values are JointProbabilityTables and DirichletDistributions. - Fixed an issue with multivariate Gaussian distributions where the covariance matrix is no longer invertible with enough missing data by subtracting the smallest eigenvalue from the diagonal K-Means ------- - Added in missing value support for k-means clustering by ignoring dimensions that are missing in the data. Can now fit and predict on missing data. - Added in missing value support for all initialization strategies - Added in a suite of unit tests - Added in the `distance` method that returns the distance between each point and each centroid GeneralMixtureModel ------------------- - Added in missing value support for mixture models through updates to the distributions - Fixed an issue where passing in a list of distributions to `from_samples` along with a number of components did not produce a mixture of IndependentComponentsDistribution objects - Expanded the unit test suite and added tests for missing value support BayesianNetwork --------------- - Vectorized the `predict_proba` method to take either a single sample or a list of samples - Changed the output of `predict_proba` to be individual symbols instead of a distribution where one symbol has a probability of 1 when fed in as known prior knowledge. - Added in an n_jobs parameter to parallelize the prediction of samples. This does not speed up a single sample, only a batch of samples. - Factored out `_check_input` into a function that be used independently - Added unit tests to check each of the above functions extensively - Missing value support added for the `log_probability`, `fit`, and `from_samples` methods. Chow-Liu trees are not supported for missing values, but using a constraint graph still works. Version 0.8.1 ============= Highlights ---------- This will serve as a log for the changes added for the release of version 0.8.1. - Univariate offsets have been added to allow for distributions to be fit to a column of data rather than a vector of numbers. This stops the copying of data that had to be done previously. Changelog --------- Base ---- - Parameters `column_idx` and `d` have been added to the `_summarize` method that all models expose. This is only useful for univariate distributions and models that fit univariate distributions and can be ignored by other models. The `column_idx` parameter specifies which column in a data matrix the distribution should be fit to, essentially serving as an offset. `d` refers to the number of dimensions that the data matrix has. This means that a univariate distribution will fit to all samples `i` such that `i*d + column_idx` in a pointer array. Multivariate distributions and models using those can ignore this. - A convenience function `to_yaml` was added to `State` and `Model` classes. `YAML` is a superset of `JSON` that can be 4 to 5 times more compact. You need the `yaml` package installed to use it. Distributions ------------- - The `summarize` method has been moved from most individual distributions to the `Distribution` base object, as has the `fit` method. - `min_std` has been moved from the `from_summaries` method and the `fit` method to the `__init__` method for the `NormalDistribution` and `LogNormalDistribution` objects. NaiveBayes ---------- - Moved the `fit` and `summarize` methods to `BayesModel` due to their similarity with BayesClassifier BayesClassifier --------------- - Moved the `fit` and `summarize` methods to `BayesModel` due to their similarity to NaiveBayes GeneralMixtureModel ------------------- - Fixed a bug where `n_jobs` was ignored in the `from_samples` method because `batch_size` was reset for the k-means initialization HiddenMarkovModel ----------------- - The default name of a HiddenMarkovModel has been changed from "None" to "HiddenMarkovModel" Version 0.8.0 ============= Highlights ---------- This will serve as a log for the changes added for the release of version 0.8.0. Changelog --------- k-means ....... - k-means has been changed from using iterative computation to using the alternate formulation of euclidean distance, from ||a - b||^{2} to using ||a||^{2} + ||b||^{2} - 2||a \cdot b||. This allows for the centroid norms to be cached, significantly speeding up computation, and for dgemm to be used to solve the matrix matrix multiplication. Initial attempts to add in GPU support appeared unsuccessful, but in theory it should be something that can be added in. - k-means has been refactored to more natively support an out-of-core learning goal, by allowing for data to initially be cast as numpy memorymaps and not coercing them to arrays midway through. Hidden Markov Models .................... - Allowed labels for labeled training to take in string names of the states instead of the state objects themselves. - Added in `state_names` and `names` parameters to the `from_samples` method to allow for more control over the creation of the model. - Added in semi-supervised learning to the `fit` step that can be activated by passing in a list of labels where sequences that have no labels have a None value. This allows for training to occur where some sequences are fully labeled and others have no labels, not for training to occur on partially labeled sequences. - Supervised initialization followed by semi-supervised learning added in to the `from_samples` method similarly to other methods. One should do this by passing in string labels for state names, always starting with -start, where model_name is the `name` parameter passed into the `from_samples` method. Sequences that do not have labels should have a None instead of a list of corresponding labels. While semi-supervised learning using the `fit` method can support arbitrary transitions amongst silent states, the `from_samples` method does not produce silent states, and so other than the start and end states, all states should be symbol emitting states. If using semi-supervised learning, one must also pass in a list of the state names using the `state_names` parameter that has been added in. - Fixed bug in supervised learning where it would not initialize correctly due to an error in the semi-supervised learning implementation. - Fixed bug where model could not be plotted without pygraphviz due to an incorrect call to networkx.draw. General Mixture Models ...................... - Changed the initialization step to be done on the first batch of data instead of the entire dataset. If the entire dataset fits in memory this does not change anything. However, this allows for out-of-core updates to be done automatically instead of immediately trying to load the entire dataset into memory. This does mean that out-of-core updates will have a different initialization now, but then yield exact updates after that. - Fixed bug where passing in a 1D array would cause an error by recasting all 1D arrays as 2D arrays. Bayesian Networks ................. - Added in a reduce_dataset parameter to the `from_samples` method that will take in a dataset and create a new dataset that is the unique set of samples, weighted by their weighted occurrence in the dataset. Essentially, it takes a dataset that may have repeating members, and produces a new dataset that is entirely unique members. This produces an identically scoring Bayesian network as before, but all structure learning algorithms can be significantly sped up. This speed up is proportional to the redundancy of the dataset, so large datasets on a smallish (< 12) number of variables will see massive speed gains (sometimes even 2-3 orders of magnitude!) whereas past that it may not be beneficial. The redundancy of the dataset (and thus the speedup) can be estimated as n_samples / n_possibilities, where n_samples is the number of samples in the dataset and n_possibilities is the product of the number of unique keys per variable, or 2**d for binary data with d variables. It can be calculated exactly as n_samples / n_unique_samples, as many datasets are biased towards repeating elements. - Fixed a premature optimization where the parents were stripped from conditional probability tables when saving the Bayesian Network to a json, causing an error in serialization. The premature optimization is that in theory pomegranate is set up to handle cyclic Bayesian networks and serializing that without first stripping parents would cause an infinite file size. However, a future PR that enabled cyclic Bayesian networks will account for this error. Naive Bayes ........... - Fixed documentation of `from_samples` to actually refer to the naive Bayes model. - Added in semi-supervised learning through the EM algorithm for samples that are labeled with -1. Bayes Classifier ................ - Fixed documentation of `from_samples` to actually refer to the Bayes classifier model. - Added in semi-supervised learning through the EM algorithm for samples that are labeled with -1. Distributions ............. - Multivariate Gaussian Distributions can now use GPUs for both log probability and summarization calculations, speeding up both tasks ~4x for any models that use them. This is added in through CuPy. Out Of Core ........... - The parameter "batch_size" has been added to HMMs, GMMs, and k-means models for built-in out-of-core calculations. Pass in a numpy memory map instead of an array and set the batch size for exact updates (sans initialization). Minibatching ............ - The parameter "batches_per_epoch" has been added to HMMs, GMMs, and k-means models for build-in minibatching support. This specifies the number of batches (as defined by "batch_size") to summarize before calculating new parameter updates. - The parameter "lr_decay" has been added to HMMs and GMMs that specifies the decay in the learning rate over time. Models may not converge otherwise when doing minibatching. Parallelization ............... - `n_jobs` has been added to all models for both fitting and prediction steps. This allows users to make parallelized predictions with their model without having to do anything more complicated than setting a larger number of jobs. Tutorials ......... - Removed the PyData 2016 Chicago Tutorial due to it's similarity to tutorials_0_pomegranate_overview. pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples/000077500000000000000000000000001420461712600225075ustar00rootroot00000000000000bayes_classifier_hmm_cheating_coin_toss.ipynb000066400000000000000000000761661420461712600336050ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Bayes Classifier with Hidden Markov Model emissions Coin Toss\n", "\n", "author: Nicholas Farn [nicholasfarn@gmail.com],\n", " Jacob Schreiber [jmschreiber91@gmail.com]\n", " \n", "In this example we will be using a simple Hidden Markov Model to demonstrate how it can be used in a Bayes classifier. In this particular example we have a couple friends who enjoy playing a game of chance. Someone tosses a coin and whenever it lands on heads, the tosser gains \\$1, otherwise he pays \\$1. However some of our friends are prone to using rigged coins in order to come out ahead in the long run.\n", "\n", "Players fall into 3 categories, non-cheaters, smart cheaters, and dumb cheaters. Non-cheaters, as the name implies, do not use rigged coins. Smart cheaters make sure to switch between rigged and unrigged coins in order to keep the deception going. Dumb cheaters only use rigged coins." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "from pomegranate import *\n", "import numpy as np\n", "%pylab inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start by creating our hmm's. Since this is a simple example, we will only have 2 states, rigged and unrigged, with discrete distributions." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "rigged = State(DiscreteDistribution({'H': 0.8, 'T': 0.2}), name=\"rigged\")\n", "unrigged = State(DiscreteDistribution({'H': 0.5, 'T': 0.5}), name=\"unrigged\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create our non-cheater and dumb cheater since their hmm's consist of a single state." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "non_cheater = HiddenMarkovModel(name=\"non-cheater\")\n", "non_cheater.add_state(unrigged)\n", "\n", "dumb_cheater = HiddenMarkovModel(name=\"dumb-cheater\")\n", "dumb_cheater.add_state(rigged)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now add our starting states to our models." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "non_cheater.start = unrigged\n", "dumb_cheater.start = rigged" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also add the transitions to our non-cheater and dumb cheater." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "non_cheater.add_transition(unrigged, unrigged, 1)\n", "dumb_cheater.add_transition(rigged, rigged, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we bake our models to finalize the structure." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "non_cheater.bake()\n", "dumb_cheater.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can create our smart cheater." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "smart_cheater = HiddenMarkovModel(name=\"smart-cheater\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The structure of our smart cheater hmm should look like the following." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's add in our starting transitions. Smart cheaters start out half the time with an unrigged coin, half the time without." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "smart_cheater.add_transition(smart_cheater.start, unrigged, 0.5)\n", "smart_cheater.add_transition(smart_cheater.start, rigged, 0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can add our transition matrix." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "smart_cheater.add_transition(rigged, rigged, 0.5)\n", "smart_cheater.add_transition(rigged, unrigged, 0.5)\n", "smart_cheater.add_transition(unrigged, rigged, 0.5)\n", "smart_cheater.add_transition(unrigged, unrigged, 0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we can bake the model to finalize its structure." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "smart_cheater.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can view the structures of all our hmm's in the images below." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.title(\"smart cheater hmm\")\n", "smart_cheater.plot()\n", "\n", "plt.title(\"dumb cheater hmm\")\n", "dumb_cheater.plot()\n", "\n", "plt.title(\"non-cheater hmm\")\n", "non_cheater.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can finally create our Naive Bayes classifier. We'll let 0 stand for a non-cheater, 1 stand for a smart cheater, and 2 stand for a dumb cheater." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "players = BayesClassifier([non_cheater, smart_cheater, dumb_cheater])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create a sequence of coin tosses to test our classifier on." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "data = np.array([list('HHHHHTHTHTTTTHHHTHHTTHHHHHTH'),\n", " list('HHHHHHHTHHHHTTHHHHHHHTTHHHHH'),\n", " list('THTHTHTHTHTHTTHHTHHHHTTHHHTT')])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now check the log probability of each string of data occurring under each model." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For sequence 1, 21.4% non-cheater, 68.0% smart cheater, 10.6% dumb cheater.\n", "For sequence 2, 0.173% non-cheater, 12.1% smart cheater, 87.7% dumb cheater.\n", "For sequence 3, 66.5% non-cheater, 33.0% smart cheater, 0.515% dumb cheater.\n" ] } ], "source": [ "probs = players.predict_proba(data)\n", "\n", "for i in range(len(probs)):\n", " print(\"For sequence {}, {:.3}% non-cheater, {:.3}% smart cheater, {:.3}% dumb cheater.\".format(i+1, 100*probs[i][0], 100*probs[i][1], 100*probs[i][2]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we can classify whether a non-cheater (0), smart cheater (1), or dumb cheater (2) created the string of data." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sequence 1 is a smart cheater\n", "Sequence 2 is a dumb cheater\n", "Sequence 3 is a non-cheater\n" ] } ], "source": [ "output = players.predict(data)\n", "\n", "for i in range(len(output)):\n", " print(\"Sequence {} is a {}\".format(i+1, \"non-cheater\" if output[i] == 0 else \"smart cheater\" if output[i] == 1 else \"dumb cheater\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also train our hmm's through Naive Bayes. Let's used the following data, all marked as known games for smart cheaters." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "X = np.array([list('HHHHHTHTHTTTTH'),\n", " list('HHTHHTTHHHHHTH')])\n", "\n", "y = np.array([1, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's fit our Bayes classifier to our data." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"BayesClassifier\",\n", " \"models\" : [\n", " {\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"name\" : \"non-cheater\",\n", " \"start\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"H\" : 0.49218749999999994,\n", " \"T\" : 0.5078125000000001\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : 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\"name\" : \"rigged\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"dumb-cheater-end\",\n", " \"weight\" : 1.0\n", " }\n", " ],\n", " \"end_index\" : 1,\n", " \"start_index\" : 0,\n", " \"silent_index\" : 1,\n", " \"edges\" : [\n", " [\n", " 0,\n", " 0,\n", " 1.0,\n", " 1,\n", " null\n", " ]\n", " ],\n", " \"distribution ties\" : []\n", " }\n", " ],\n", " \"weights\" : [\n", " -Infinity,\n", " 0.0,\n", " -Infinity\n", " ]\n", "}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "players.fit(X, y)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples/bayesnet_asia.ipynb000066400000000000000000000753241420461712600263740ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Asia Bayesian Network." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "authors:
\n", "Jacob Schreiber [jmschreiber91@gmail.com]
\n", "Nicholas Farn [nicholasfarn@gmail.com]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example shows how to create an Asia Bayesian Network. A description of the network can be found here." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "from pomegranate import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create the distributions starting with Asia and the conditional probability for tuberculosis." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "asia = DiscreteDistribution( { 'True' : 0.5, 'False' : 0.5 } )\n", "\n", "tuberculosis = ConditionalProbabilityTable(\n", " [[ 'True', 'True', 0.2 ],\n", " [ 'True', 'False', 0.8 ],\n", " [ 'False', 'True', 0.01 ],\n", " [ 'False', 'False', 0.99 ]], [asia])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Then the distribution for smoking along with the conditional probability of lung cancer and bronchitis." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "smoking = DiscreteDistribution( { 'True' : 0.5, 'False' : 0.5 } )\n", "\n", "lung = ConditionalProbabilityTable(\n", " [[ 'True', 'True', 0.75 ],\n", " [ 'True', 'False', 0.25 ],\n", " [ 'False', 'True', 0.02 ],\n", " [ 'False', 'False', 0.98 ]], [smoking] )\n", "\n", "bronchitis = ConditionalProbabilityTable(\n", " [[ 'True', 'True', 0.92 ],\n", " [ 'True', 'False', 0.08 ],\n", " [ 'False', 'True', 0.03 ],\n", " [ 'False', 'False', 0.97 ]], [smoking] )\n", "\n", "tuberculosis_or_cancer = ConditionalProbabilityTable(\n", " [[ 'True', 'True', 'True', 1.0 ],\n", " [ 'True', 'True', 'False', 0.0 ],\n", " [ 'True', 'False', 'True', 1.0 ],\n", " [ 'True', 'False', 'False', 0.0 ],\n", " [ 'False', 'True', 'True', 1.0 ],\n", " [ 'False', 'True', 'False', 0.0 ],\n", " [ 'False', 'False', 'True', 0.0 ],\n", " [ 'False', 'False', 'False', 1.0 ]], [tuberculosis, lung] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then the probability of an x-ray, which is dependent on whether one has tuberculosis or cancer." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "xray = ConditionalProbabilityTable(\n", " [[ 'True', 'True', 0.885 ],\n", " [ 'True', 'False', 0.115 ],\n", " [ 'False', 'True', 0.04 ],\n", " [ 'False', 'False', 0.96 ]], [tuberculosis_or_cancer] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally the probability of dyspnea, which is dependent on whether one has bronchitis and tuberculosis or cancer." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "dyspnea = ConditionalProbabilityTable(\n", " [[ 'True', 'True', 'True', 0.96 ],\n", " [ 'True', 'True', 'False', 0.04 ],\n", " [ 'True', 'False', 'True', 0.89 ],\n", " [ 'True', 'False', 'False', 0.11 ],\n", " [ 'False', 'True', 'True', 0.96 ],\n", " [ 'False', 'True', 'False', 0.04 ],\n", " [ 'False', 'False', 'True', 0.89 ],\n", " [ 'False', 'False', 'False', 0.11 ]], [tuberculosis_or_cancer, bronchitis])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create the states for our bayesian network." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "s0 = State( asia, name=\"asia\" )\n", "s1 = State( tuberculosis, name=\"tuberculosis\" )\n", "s2 = State( smoking, name=\"smoker\" )\n", "s3 = State( lung, name=\"cancer\" )\n", "s4 = State( bronchitis, name=\"bronchitis\" )\n", "s5 = State( tuberculosis_or_cancer, name=\"TvC\" )\n", "s6 = State( xray, name=\"xray\" )\n", "s7 = State( dyspnea, name='dyspnea' )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we can create our bayesian network. We do this by creating an instance of BayesianNetwork, then adding the states." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "network = BayesianNetwork( \"asia\" )\n", "network.add_nodes(s0, s1, s2, s3, s4, s5, s6, s7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we add the edges to our network." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "network.add_edge( s0, s1 )\n", "network.add_edge( s1, s5 )\n", "network.add_edge( s2, s3 )\n", "network.add_edge( s2, s4 )\n", "network.add_edge( s3, s5 )\n", "network.add_edge( s5, s6 )\n", "network.add_edge( s5, s7 )\n", "network.add_edge( s4, s7 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We finish by baking our network to finalize its structure." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "network.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the resulting structure:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "network.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's test out our network on a person who has tuberculosis, is not a smoker, and has bronchitis." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "asia\t\t{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"True\" :0.9523809523809521,\n", " \"False\" :0.04761904761904782\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "tuberculosis\t\tTrue\n", "smoker\t\tFalse\n", "cancer\t\t{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"False\" :0.9799999999999995,\n", " \"True\" :0.020000000000000438\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "bronchitis\t\tTrue\n", "TvC\t\t{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"False\" :0.0,\n", " \"True\" :1.0\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "xray\t\t{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"False\" :0.11500000000000017,\n", " \"True\" :0.8849999999999999\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "dyspnea\t\t{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"False\" :0.040000000000000216,\n", " \"True\" :0.9599999999999997\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "observations = { 'tuberculosis' : 'True', 'smoker' : 'False', 'bronchitis' : 'True' }\n", "beliefs = map( str, network.predict_proba( observations ) )\n", "print(\"\\n\".join( \"{}\\t\\t{}\".format( state.name, belief ) for state, belief in zip( network.states, beliefs ) ))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples/bayesnet_huge_monty_hall.ipynb000066400000000000000000001324631420461712600306330ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Huge Monty Hall Bayesian Network" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "authors:
\n", "Jacob Schreiber [jmschreiber91@gmail.com]
\n", "Nicholas Farn [nicholasfarn@gmail.com]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets expand the Bayesian network for the monty hall problem in order to make sure that training with all types of wild types works properly." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import math\n", "from pomegranate import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll create the discrete distribution for our friend first." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "friend = DiscreteDistribution( { True: 0.5, False: 0.5 } )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The emissions for our guest are completely random." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "guest = ConditionalProbabilityTable(\n", "\t[[ True, 'A', 0.50 ],\n", "\t [ True, 'B', 0.25 ],\n", "\t [ True, 'C', 0.25 ],\n", "\t [ False, 'A', 0.0 ],\n", "\t [ False, 'B', 0.7 ],\n", "\t [ False, 'C', 0.3 ]], [friend] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then the distribution for the remaining cars." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "remaining = DiscreteDistribution( { 0: 0.1, 1: 0.7, 2: 0.2, } )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The probability of whether the prize is randomized is dependent on the number of remaining cars." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "randomize = ConditionalProbabilityTable(\n", "\t[[ 0, True , 0.05 ],\n", " [ 0, False, 0.95 ],\n", " [ 1, True , 0.8 ],\n", " [ 1, False, 0.2 ],\n", " [ 2, True , 0.5 ],\n", " [ 2, False, 0.5 ]], [remaining] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the conditional probability table for the prize. This is dependent on the guest's friend and whether or not it is randomized." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "prize = ConditionalProbabilityTable(\n", "\t[[ True, True, 'A', 0.3 ],\n", "\t [ True, True, 'B', 0.4 ],\n", "\t [ True, True, 'C', 0.3 ],\n", "\t [ True, False, 'A', 0.2 ],\n", "\t [ True, False, 'B', 0.4 ],\n", "\t [ True, False, 'C', 0.4 ],\n", "\t [ False, True, 'A', 0.1 ],\n", "\t [ False, True, 'B', 0.9 ],\n", "\t [ False, True, 'C', 0.0 ],\n", "\t [ False, False, 'A', 0.0 ],\n", "\t [ False, False, 'B', 0.4 ],\n", "\t [ False, False, 'C', 0.6]], [randomize, friend] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we can create the conditional probability table for our Monty. This is dependent on the guest and the prize." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "monty = ConditionalProbabilityTable(\n", "\t[[ 'A', 'A', 'A', 0.0 ],\n", "\t [ 'A', 'A', 'B', 0.5 ],\n", "\t [ 'A', 'A', 'C', 0.5 ],\n", "\t [ 'A', 'B', 'A', 0.0 ],\n", "\t [ 'A', 'B', 'B', 0.0 ],\n", "\t [ 'A', 'B', 'C', 1.0 ],\n", "\t [ 'A', 'C', 'A', 0.0 ],\n", "\t [ 'A', 'C', 'B', 1.0 ],\n", "\t [ 'A', 'C', 'C', 0.0 ],\n", "\t [ 'B', 'A', 'A', 0.0 ],\n", "\t [ 'B', 'A', 'B', 0.0 ],\n", "\t [ 'B', 'A', 'C', 1.0 ],\n", "\t [ 'B', 'B', 'A', 0.5 ],\n", "\t [ 'B', 'B', 'B', 0.0 ],\n", "\t [ 'B', 'B', 'C', 0.5 ],\n", "\t [ 'B', 'C', 'A', 1.0 ],\n", "\t [ 'B', 'C', 'B', 0.0 ],\n", "\t [ 'B', 'C', 'C', 0.0 ],\n", "\t [ 'C', 'A', 'A', 0.0 ],\n", "\t [ 'C', 'A', 'B', 1.0 ],\n", "\t [ 'C', 'A', 'C', 0.0 ],\n", "\t [ 'C', 'B', 'A', 1.0 ],\n", "\t [ 'C', 'B', 'B', 0.0 ],\n", "\t [ 'C', 'B', 'C', 0.0 ],\n", "\t [ 'C', 'C', 'A', 0.5 ],\n", "\t [ 'C', 'C', 'B', 0.5 ],\n", "\t [ 'C', 'C', 'C', 0.0 ]], [guest, prize] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can create the states for our bayesian network." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "s0 = State( friend, name=\"friend\")\n", "s1 = State( guest, name=\"guest\" )\n", "s2 = State( prize, name=\"prize\" )\n", "s3 = State( monty, name=\"monty\" )\n", "s4 = State( remaining, name=\"remaining\" )\n", "s5 = State( randomize, name=\"randomize\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll create our bayesian network with an instance of BayesianNetwork, then add the possible states." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "network = BayesianNetwork( \"test\" )\n", "network.add_states(s0, s1, s2, s3, s4, s5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then the possible transitions." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "network.add_transition( s0, s1 )\n", "network.add_transition( s1, s3 )\n", "network.add_transition( s2, s3 )\n", "network.add_transition( s4, s5 )\n", "network.add_transition( s5, s2 )\n", "network.add_transition( s0, s2 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With a \"bake\" to finalize the structure of our network." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "network.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create our network from the following data." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"BayesianNetwork\",\n", " \"name\" : \"test\",\n", " \"structure\" : [\n", " [],\n", " [\n", " 0\n", " ],\n", " [\n", " 5,\n", " 0\n", " ],\n", " [\n", " 1,\n", " 2\n", " ],\n", " [],\n", " [\n", " 4\n", " ]\n", " ],\n", " 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" \"parameters\" : [\n", " {\n", " \"0\" : 0.25,\n", " \"1\" : 0.4166666666666667,\n", " \"2\" : 0.3333333333333333\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"remaining\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"ConditionalProbabilityTable\",\n", " \"table\" : [\n", " [\n", " \"0\",\n", " \"True\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"0\",\n", " \"False\",\n", " \"0.6666666666666666\"\n", " ],\n", " [\n", " \"1\",\n", " \"True\",\n", " \"0.2\"\n", " ],\n", " [\n", " \"1\",\n", " \"False\",\n", " \"0.8\"\n", " ],\n", " [\n", " \"2\",\n", " \"True\",\n", " \"0.5\"\n", " ],\n", " [\n", " \"2\",\n", " \"False\",\n", " \"0.5\"\n", " ]\n", " ],\n", " \"dtypes\" : [\n", " \"int\",\n", " \"bool\",\n", " \"float\"\n", " ],\n", " \"parents\" : [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"int\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"0\" : 0.25,\n", " \"1\" : 0.4166666666666667,\n", " \"2\" : 0.3333333333333333\n", " }\n", " ],\n", " \"frozen\" : false\n", " }\n", " ]\n", " },\n", " \"name\" : \"randomize\",\n", " \"weight\" : 1.0\n", " }\n", " ]\n", "}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = [[ True, 'A', 'A', 'C', 1, True ],\n", "\t\t[ True, 'A', 'A', 'C', 0, True ],\n", "\t\t[ False, 'A', 'A', 'B', 1, False ],\n", "\t\t[ False, 'A', 'A', 'A', 2, False ],\n", "\t\t[ False, 'A', 'A', 'C', 1, False ],\n", "\t\t[ False, 'B', 'B', 'B', 2, False ],\n", "\t\t[ False, 'B', 'B', 'C', 0, False ],\n", "\t\t[ True, 'C', 'C', 'A', 2, True ],\n", "\t\t[ True, 'C', 'C', 'C', 1, False ],\n", "\t\t[ True, 'C', 'C', 'C', 0, False ],\n", "\t\t[ True, 'C', 'C', 'C', 2, True ],\n", "\t\t[ True, 'C', 'B', 'A', 1, False ]]\n", "\n", "network.fit( data )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see the results below. Lets look at the distribution for our Friend first." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"bool\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"True\" :0.5833333333333334,\n", " \"False\" :0.4166666666666667\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "print(friend)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then our Guest." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\tB\t0.0\n", "True\tC\t0.7142857142857143\n", "True\tA\t0.2857142857142857\n", "False\tB\t0.4\n", "False\tC\t0.0\n", "False\tA\t0.6\n" ] } ], "source": [ "print(guest)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the remaining cars." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"int\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"0\" :0.25,\n", " \"1\" :0.4166666666666667,\n", " \"2\" :0.3333333333333333\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "print(remaining)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the probability the prize is randomized." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\tFalse\t0.6666666666666666\n", "0\tTrue\t0.3333333333333333\n", "1\tFalse\t0.8\n", "1\tTrue\t0.2\n", "2\tFalse\t0.5\n", "2\tTrue\t0.5\n" ] } ], "source": [ "print(randomize)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the distribution of the Prize." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\tTrue\tB\t0.3333333333333333\n", "False\tTrue\tC\t0.6666666666666666\n", "False\tTrue\tA\t0.0\n", "False\tFalse\tB\t0.4\n", "False\tFalse\tC\t0.0\n", "False\tFalse\tA\t0.6\n", "True\tTrue\tB\t0.0\n", "True\tTrue\tC\t0.5\n", "True\tTrue\tA\t0.5\n", "True\tFalse\tB\t0.3333333333333333\n", "True\tFalse\tC\t0.3333333333333333\n", "True\tFalse\tA\t0.3333333333333333\n" ] } ], "source": [ "print(prize)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally our Monty." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "B\tB\tB\t0.5\n", "B\tB\tC\t0.5\n", "B\tB\tA\t0.0\n", "B\tC\tB\t0.3333333333333333\n", "B\tC\tC\t0.3333333333333333\n", "B\tC\tA\t0.3333333333333333\n", "B\tA\tB\t0.3333333333333333\n", "B\tA\tC\t0.3333333333333333\n", "B\tA\tA\t0.3333333333333333\n", "C\tB\tB\t0.0\n", "C\tB\tC\t0.0\n", "C\tB\tA\t1.0\n", "C\tC\tB\t0.0\n", "C\tC\tC\t0.75\n", "C\tC\tA\t0.25\n", "C\tA\tB\t0.3333333333333333\n", "C\tA\tC\t0.3333333333333333\n", "C\tA\tA\t0.3333333333333333\n", "A\tB\tB\t0.3333333333333333\n", "A\tB\tC\t0.3333333333333333\n", "A\tB\tA\t0.3333333333333333\n", "A\tC\tB\t0.3333333333333333\n", "A\tC\tC\t0.3333333333333333\n", "A\tC\tA\t0.3333333333333333\n", "A\tA\tB\t0.2\n", "A\tA\tC\t0.6\n", "A\tA\tA\t0.2\n" ] } ], "source": [ "print(monty)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples/bayesnet_monty_hall_classic.ipynb000066400000000000000000000574051420461712600313260ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Classic Monty Hall Bayesian Network" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "authors:
\n", "Jacob Schreiber [jmschreiber91@gmail.com]
\n", "Nicholas Farn [nicholasfarn@gmail.com]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets test out the Bayesian Network framework to produce the Monty Hall problem, but modified a little. The Monty Hall problem is basically a game show where a guest chooses one of three doors to open, with an unknown one having a prize behind it. Monty then opens another non-chosen door without a prize behind it, and asks the guest if they would like to change their answer. Many people were surprised to find that if the guest changed their answer, there was a 66% chance of success as opposed to a 50% as might be expected if there were two doors.\n", "\n", "This can be modelled as a Bayesian network with three nodes-- guest, prize, and Monty, each over the domain of door 'A', 'B', 'C'. Monty is dependent on both guest and prize, in that it can't be either of them. Lets extend this a little bit to say the guest has an untrustworthy friend whose answer he will not go with." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import math\n", "from pomegranate import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create the distributions for the guest and the prize. Note that both distributions are independent of one another." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "guest = DiscreteDistribution( { 'A': 1./3, 'B': 1./3, 'C': 1./3 } )\n", "prize = DiscreteDistribution( { 'A': 1./3, 'B': 1./3, 'C': 1./3 } )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create the conditional probability table for our Monty. The table is dependent on both the guest and the prize." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "monty = ConditionalProbabilityTable(\n", "\t[[ 'A', 'A', 'A', 0.0 ],\n", "\t [ 'A', 'A', 'B', 0.5 ],\n", "\t [ 'A', 'A', 'C', 0.5 ],\n", "\t [ 'A', 'B', 'A', 0.0 ],\n", "\t [ 'A', 'B', 'B', 0.0 ],\n", "\t [ 'A', 'B', 'C', 1.0 ],\n", "\t [ 'A', 'C', 'A', 0.0 ],\n", "\t [ 'A', 'C', 'B', 1.0 ],\n", "\t [ 'A', 'C', 'C', 0.0 ],\n", "\t [ 'B', 'A', 'A', 0.0 ],\n", "\t [ 'B', 'A', 'B', 0.0 ],\n", "\t [ 'B', 'A', 'C', 1.0 ],\n", "\t [ 'B', 'B', 'A', 0.5 ],\n", "\t [ 'B', 'B', 'B', 0.0 ],\n", "\t [ 'B', 'B', 'C', 0.5 ],\n", "\t [ 'B', 'C', 'A', 1.0 ],\n", "\t [ 'B', 'C', 'B', 0.0 ],\n", "\t [ 'B', 'C', 'C', 0.0 ],\n", "\t [ 'C', 'A', 'A', 0.0 ],\n", "\t [ 'C', 'A', 'B', 1.0 ],\n", "\t [ 'C', 'A', 'C', 0.0 ],\n", "\t [ 'C', 'B', 'A', 1.0 ],\n", "\t [ 'C', 'B', 'B', 0.0 ],\n", "\t [ 'C', 'B', 'C', 0.0 ],\n", "\t [ 'C', 'C', 'A', 0.5 ],\n", "\t [ 'C', 'C', 'B', 0.5 ],\n", "\t [ 'C', 'C', 'C', 0.0 ]], [guest, prize] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets create the states for the bayesian network." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "s1 = State( guest, name=\"guest\" )\n", "s2 = State( prize, name=\"prize\" )\n", "s3 = State( monty, name=\"monty\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then the bayesian network itself, adding the states in after." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "network = BayesianNetwork( \"test\" )\n", "network.add_states( s1, s2, s3 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then the transitions." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "network.add_transition( s1, s3 )\n", "network.add_transition( s2, s3 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With a \"bake\" to finalize the structure of our network." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "network.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can check the possible states in our network." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "guest\tprize\tmonty\n" ] } ], "source": [ "print(\"\\t\".join([ state.name for state in network.states ]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can see what happens to our network when our Guest chooses 'A'." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "guest\tA\n", "prize\t{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.3333333333333333,\n", " \"B\" :0.3333333333333333,\n", " \"C\" :0.3333333333333333\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "monty\t{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.0,\n", " \"C\" :0.49999999999999983,\n", " \"B\" :0.49999999999999983\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "observations = { 'guest' : 'A' }\n", "beliefs = map( str, network.predict_proba( observations ) )\n", "print(\"\\n\".join( \"{}\\t{}\".format( state.name, belief ) for state, belief in zip( network.states, beliefs ) ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now our host chooses 'B'. (note that prize goes to 66% if you switch)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "guest\tA\n", "prize\t{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.3333333333333334,\n", " \"B\" :0.0,\n", " \"C\" :0.6666666666666664\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "monty\tB\n" ] } ], "source": [ "observations = { 'guest' : 'A', 'monty' : 'B' }\n", "beliefs = map( str, network.predict_proba( observations ) )\n", "print(\"\\n\".join( \"{}\\t{}\".format( state.name, belief ) for state, belief in zip( network.states, beliefs ) ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also see what happens if our host simply chooses 'B'." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "guest\t{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.49999999999999983,\n", " \"B\" :0.0,\n", " \"C\" :0.49999999999999983\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "prize\t{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.49999999999999983,\n", " \"B\" :0.0,\n", " \"C\" :0.49999999999999983\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "monty\tB\n" ] } ], "source": [ "observations = { 'monty' : 'B' }\n", "beliefs = map( str, network.predict_proba( observations ) )\n", "print(\"\\n\".join( \"{}\\t{}\".format( state.name, belief ) for state, belief in zip( network.states, beliefs ) ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's train our network on the following set of data." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"BayesianNetwork\",\n", " \"name\" : \"test\",\n", " \"structure\" : [\n", " [],\n", " [],\n", " [\n", " 0,\n", " 1\n", " ]\n", " ],\n", " \"states\" : [\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.4166666666666667,\n", " \"B\" : 0.16666666666666666,\n", " \"C\" : 0.4166666666666667\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"guest\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.4166666666666667,\n", " \"B\" : 0.25,\n", " \"C\" : 0.3333333333333333\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"prize\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"ConditionalProbabilityTable\",\n", " \"table\" : [\n", " [\n", " \"A\",\n", " \"A\",\n", " \"A\",\n", " \"0.2\"\n", " ],\n", " [\n", " \"A\",\n", " \"A\",\n", " \"B\",\n", " \"0.2\"\n", " ],\n", " [\n", " \"A\",\n", " \"A\",\n", " \"C\",\n", " \"0.6\"\n", " ],\n", " [\n", " \"A\",\n", " \"B\",\n", " \"A\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"A\",\n", " \"B\",\n", " \"B\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"A\",\n", " \"B\",\n", " \"C\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"A\",\n", " \"C\",\n", " \"A\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"A\",\n", " \"C\",\n", " \"B\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"A\",\n", " \"C\",\n", " \"C\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"B\",\n", " \"A\",\n", " \"A\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"B\",\n", " \"A\",\n", " \"B\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"B\",\n", " \"A\",\n", " \"C\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"B\",\n", " \"B\",\n", " \"A\",\n", " \"0.0\"\n", " ],\n", " [\n", " \"B\",\n", " \"B\",\n", " \"B\",\n", " \"0.5\"\n", " ],\n", " [\n", " \"B\",\n", " \"B\",\n", " \"C\",\n", " \"0.5\"\n", " ],\n", " [\n", " \"B\",\n", " \"C\",\n", " \"A\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"B\",\n", " \"C\",\n", " \"B\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"B\",\n", " \"C\",\n", " \"C\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"C\",\n", " \"A\",\n", " \"A\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"C\",\n", " \"A\",\n", " \"B\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"C\",\n", " \"A\",\n", " \"C\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"C\",\n", " \"B\",\n", " \"A\",\n", " \"1.0\"\n", " ],\n", " [\n", " \"C\",\n", " \"B\",\n", " \"B\",\n", " \"0.0\"\n", " ],\n", " [\n", " \"C\",\n", " \"B\",\n", " \"C\",\n", " \"0.0\"\n", " ],\n", " [\n", " \"C\",\n", " \"C\",\n", " \"A\",\n", " \"0.25\"\n", " ],\n", " [\n", " \"C\",\n", " \"C\",\n", " \"B\",\n", " \"0.0\"\n", " ],\n", " [\n", " \"C\",\n", " \"C\",\n", " \"C\",\n", " \"0.75\"\n", " ]\n", " ],\n", " \"dtypes\" : [\n", " \"str\",\n", " \"str\",\n", " \"str\",\n", " \"float\"\n", " ],\n", " \"parents\" : [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.4166666666666667,\n", " \"B\" : 0.16666666666666666,\n", " \"C\" : 0.4166666666666667\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.4166666666666667,\n", " \"B\" : 0.25,\n", " \"C\" : 0.3333333333333333\n", " }\n", " ],\n", " \"frozen\" : false\n", " }\n", " ]\n", " },\n", " \"name\" : \"monty\",\n", " \"weight\" : 1.0\n", " }\n", " ]\n", "}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = [[ 'A', 'A', 'C' ],\n", "\t\t[ 'A', 'A', 'C' ],\n", "\t\t[ 'A', 'A', 'B' ],\n", "\t\t[ 'A', 'A', 'A' ],\n", "\t\t[ 'A', 'A', 'C' ],\n", "\t\t[ 'B', 'B', 'B' ],\n", "\t\t[ 'B', 'B', 'C' ],\n", "\t\t[ 'C', 'C', 'A' ],\n", "\t\t[ 'C', 'C', 'C' ],\n", "\t\t[ 'C', 'C', 'C' ],\n", "\t\t[ 'C', 'C', 'C' ],\n", "\t\t[ 'C', 'B', 'A' ]]\n", "\n", "network.fit( data )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see the results! Starting with the Monty." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\tA\tA\t0.2\n", "A\tA\tC\t0.6\n", "A\tA\tB\t0.2\n", "A\tB\tA\t0.3333333333333333\n", "A\tB\tC\t0.3333333333333333\n", "A\tB\tB\t0.3333333333333333\n", "A\tC\tA\t0.3333333333333333\n", "A\tC\tC\t0.3333333333333333\n", "A\tC\tB\t0.3333333333333333\n", "B\tA\tA\t0.3333333333333333\n", "B\tA\tC\t0.3333333333333333\n", "B\tA\tB\t0.3333333333333333\n", "B\tB\tA\t0.0\n", "B\tB\tC\t0.5\n", "B\tB\tB\t0.5\n", "B\tC\tA\t0.3333333333333333\n", "B\tC\tC\t0.3333333333333333\n", "B\tC\tB\t0.3333333333333333\n", "C\tA\tA\t0.3333333333333333\n", "C\tA\tC\t0.3333333333333333\n", "C\tA\tB\t0.3333333333333333\n", "C\tB\tA\t1.0\n", "C\tB\tC\t0.0\n", "C\tB\tB\t0.0\n", "C\tC\tA\t0.25\n", "C\tC\tC\t0.75\n", "C\tC\tB\t0.0\n" ] } ], "source": [ "print(monty)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then our Prize." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.4166666666666667,\n", " \"B\" :0.25,\n", " \"C\" :0.3333333333333333\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "print(prize)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally our Guest." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.4166666666666667,\n", " \"B\" :0.16666666666666666,\n", " \"C\" :0.4166666666666667\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "print(guest)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples/bayesnet_monty_hall_train.ipynb000066400000000000000000000434251420461712600310170ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Training a Monty Hall Bayesian Network" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "authors:
\n", "Jacob Schreiber [jmschreiber91@gmail.com]
\n", "Nicholas Farn [nicholasfarn@gmail.com]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets test out the Bayesian Network framework to produce the Monty Hall problem, but modified a little. The Monty Hall problem is basically a game show where a guest chooses one of three doors to open, with an unknown one having a prize behind it. Monty then opens another non-chosen door without a prize behind it, and asks the guest if they would like to change their answer. Many people were surprised to find that if the guest changed their answer, there was a 66% chance of success as opposed to a 50% as might be expected if there were two doors.\n", "\n", "This can be modelled as a Bayesian network with three nodes-- guest, prize, and Monty, each over the domain of door 'A', 'B', 'C'. Monty is dependent on both guest and prize, in that it can't be either of them. Lets extend this a little bit to say the guest has an untrustworthy friend whose answer he will not go with." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import math\n", "from pomegranate import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create the distributions for the guest and the prize. Note that both distributions are independent of one another." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "guest = DiscreteDistribution( { 'A': 1./3, 'B': 1./3, 'C': 1./3 } )\n", "prize = DiscreteDistribution( { 'A': 1./3, 'B': 1./3, 'C': 1./3 } )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create the conditional probability table for our Monty. The table is dependent on both the guest and the prize." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "monty = ConditionalProbabilityTable(\n", "\t[[ 'A', 'A', 'A', 0.0 ],\n", "\t [ 'A', 'A', 'B', 0.5 ],\n", "\t [ 'A', 'A', 'C', 0.5 ],\n", "\t [ 'A', 'B', 'A', 0.0 ],\n", "\t [ 'A', 'B', 'B', 0.0 ],\n", "\t [ 'A', 'B', 'C', 1.0 ],\n", "\t [ 'A', 'C', 'A', 0.0 ],\n", "\t [ 'A', 'C', 'B', 1.0 ],\n", "\t [ 'A', 'C', 'C', 0.0 ],\n", "\t [ 'B', 'A', 'A', 0.0 ],\n", "\t [ 'B', 'A', 'B', 0.0 ],\n", "\t [ 'B', 'A', 'C', 1.0 ],\n", "\t [ 'B', 'B', 'A', 0.5 ],\n", "\t [ 'B', 'B', 'B', 0.0 ],\n", "\t [ 'B', 'B', 'C', 0.5 ],\n", "\t [ 'B', 'C', 'A', 1.0 ],\n", "\t [ 'B', 'C', 'B', 0.0 ],\n", "\t [ 'B', 'C', 'C', 0.0 ],\n", "\t [ 'C', 'A', 'A', 0.0 ],\n", "\t [ 'C', 'A', 'B', 1.0 ],\n", "\t [ 'C', 'A', 'C', 0.0 ],\n", "\t [ 'C', 'B', 'A', 1.0 ],\n", "\t [ 'C', 'B', 'B', 0.0 ],\n", "\t [ 'C', 'B', 'C', 0.0 ],\n", "\t [ 'C', 'C', 'A', 0.5 ],\n", "\t [ 'C', 'C', 'B', 0.5 ],\n", "\t [ 'C', 'C', 'C', 0.0 ]], [guest, prize] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets create the states for the bayesian network." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "s1 = State( guest, name=\"guest\" )\n", "s2 = State( prize, name=\"prize\" )\n", "s3 = State( monty, name=\"monty\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then the bayesian network itself, adding the states in after." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "network = BayesianNetwork( \"test\" )\n", "network.add_states( s1, s2, s3 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then the transitions." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "network.add_transition( s1, s3 )\n", "network.add_transition( s2, s3 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With a \"bake\" to finalize the structure of our network." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "network.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can train our network on the following data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"BayesianNetwork\",\n", " \"name\" : \"test\",\n", " \"structure\" : [\n", " [],\n", " [],\n", " [\n", " 0,\n", " 1\n", " ]\n", " ],\n", " \"states\" : [\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.4166666666666667,\n", " \"B\" : 0.16666666666666666,\n", " \"C\" : 0.4166666666666667\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"guest\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.4166666666666667,\n", " \"B\" : 0.25,\n", " \"C\" : 0.3333333333333333\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"prize\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"ConditionalProbabilityTable\",\n", " \"table\" : [\n", " [\n", " \"A\",\n", " \"A\",\n", " \"A\",\n", " \"1.0\"\n", " ],\n", " [\n", " \"A\",\n", " \"A\",\n", " \"B\",\n", " \"0.0\"\n", " ],\n", " [\n", " \"A\",\n", " \"A\",\n", " \"C\",\n", " \"0.0\"\n", " ],\n", " [\n", " \"A\",\n", " \"B\",\n", " \"A\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"A\",\n", " \"B\",\n", " \"B\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"A\",\n", " \"B\",\n", " \"C\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"A\",\n", " \"C\",\n", " \"A\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"A\",\n", " \"C\",\n", " \"B\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"A\",\n", " \"C\",\n", " \"C\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"B\",\n", " \"A\",\n", " \"A\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"B\",\n", " \"A\",\n", " \"B\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"B\",\n", " \"A\",\n", " \"C\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"B\",\n", " \"B\",\n", " \"A\",\n", " \"0.0\"\n", " ],\n", " [\n", " \"B\",\n", " \"B\",\n", " \"B\",\n", " \"0.5\"\n", " ],\n", " [\n", " \"B\",\n", " \"B\",\n", " \"C\",\n", " \"0.5\"\n", " ],\n", " [\n", " \"B\",\n", " \"C\",\n", " \"A\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"B\",\n", " \"C\",\n", " \"B\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"B\",\n", " \"C\",\n", " \"C\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"C\",\n", " \"A\",\n", " \"A\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"C\",\n", " \"A\",\n", " \"B\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"C\",\n", " \"A\",\n", " \"C\",\n", " \"0.3333333333333333\"\n", " ],\n", " [\n", " \"C\",\n", " \"B\",\n", " \"A\",\n", " \"1.0\"\n", " ],\n", " [\n", " \"C\",\n", " \"B\",\n", " \"B\",\n", " \"0.0\"\n", " ],\n", " [\n", " \"C\",\n", " \"B\",\n", " \"C\",\n", " \"0.0\"\n", " ],\n", " [\n", " \"C\",\n", " \"C\",\n", " \"A\",\n", " \"0.25\"\n", " ],\n", " [\n", " \"C\",\n", " \"C\",\n", " \"B\",\n", " \"0.0\"\n", " ],\n", " [\n", " \"C\",\n", " \"C\",\n", " \"C\",\n", " \"0.75\"\n", " ]\n", " ],\n", " \"dtypes\" : [\n", " \"str\",\n", " \"str\",\n", " \"str\",\n", " \"float\"\n", " ],\n", " \"parents\" : [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.4166666666666667,\n", " \"B\" : 0.16666666666666666,\n", " \"C\" : 0.4166666666666667\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.4166666666666667,\n", " \"B\" : 0.25,\n", " \"C\" : 0.3333333333333333\n", " }\n", " ],\n", " \"frozen\" : false\n", " }\n", " ]\n", " },\n", " \"name\" : \"monty\",\n", " \"weight\" : 1.0\n", " }\n", " ]\n", "}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = [[ 'A', 'A', 'A' ],\n", "\t\t[ 'A', 'A', 'A' ],\n", "\t\t[ 'A', 'A', 'A' ],\n", "\t\t[ 'A', 'A', 'A' ],\n", "\t\t[ 'A', 'A', 'A' ],\n", "\t\t[ 'B', 'B', 'B' ],\n", "\t\t[ 'B', 'B', 'C' ],\n", "\t\t[ 'C', 'C', 'A' ],\n", "\t\t[ 'C', 'C', 'C' ],\n", "\t\t[ 'C', 'C', 'C' ],\n", "\t\t[ 'C', 'C', 'C' ],\n", "\t\t[ 'C', 'B', 'A' ]]\n", "\n", "network.fit( data )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's see what happens when our Guest says 'A' and the Prize is 'A'." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "guest\tA\n", "prize\tA\n", "monty\t{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :1.0,\n", " \"B\" :0.0,\n", " \"C\" :0.0\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "observations = { 'guest' : 'A', 'prize' : 'A' }\n", "beliefs = map( str, network.predict_proba( observations ) )\n", "print(\"\\n\".join( \"{}\\t{}\".format( state.name, belief ) for state, belief in zip( network.states, beliefs ) ))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples/distributions_conditionalupdate.ipynb000066400000000000000000000124401420461712600322430ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Conditional Distributions Update" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "authors:
\n", "Jacob Schreiber [jmschreiber91@gmail.com]
\n", "Nicholas Farn [nicholasfarn@gmail.com]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example shows the implementation of the classic Monty Hall problem." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pomegranate import *\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets create the distributions for the guest's choice and prize's location. They are both discrete distributions and are independent of one another." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "guest = DiscreteDistribution( { 'A': 1./3, 'B': 1./3, 'C': 1./3 } )\n", "prize = DiscreteDistribution( { 'A': 1./3, 'B': 1./3, 'C': 1./3 } )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll create a conditional probability table for the Monty Hall problem. The results of the Monty Hall problem is dependent on both the guest and the prize." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "monty = ConditionalProbabilityTable(\n", "\t[[ 'A', 'A', 'A', 0.0 ],\n", "\t [ 'A', 'A', 'B', 0.5 ],\n", "\t [ 'A', 'A', 'C', 0.5 ],\n", "\t [ 'A', 'B', 'A', 0.0 ],\n", "\t [ 'A', 'B', 'B', 0.0 ],\n", "\t [ 'A', 'B', 'C', 1.0 ],\n", "\t [ 'A', 'C', 'A', 0.0 ],\n", "\t [ 'A', 'C', 'B', 1.0 ],\n", "\t [ 'A', 'C', 'C', 0.0 ],\n", "\t [ 'B', 'A', 'A', 0.0 ],\n", "\t [ 'B', 'A', 'B', 0.0 ],\n", "\t [ 'B', 'A', 'C', 1.0 ],\n", "\t [ 'B', 'B', 'A', 0.5 ],\n", "\t [ 'B', 'B', 'B', 0.0 ],\n", "\t [ 'B', 'B', 'C', 0.5 ],\n", "\t [ 'B', 'C', 'A', 1.0 ],\n", "\t [ 'B', 'C', 'B', 0.0 ],\n", "\t [ 'B', 'C', 'C', 0.0 ],\n", "\t [ 'C', 'A', 'A', 0.0 ],\n", "\t [ 'C', 'A', 'B', 1.0 ],\n", "\t [ 'C', 'A', 'C', 0.0 ],\n", "\t [ 'C', 'B', 'A', 1.0 ],\n", "\t [ 'C', 'B', 'B', 0.0 ],\n", "\t [ 'C', 'B', 'C', 0.0 ],\n", "\t [ 'C', 'C', 'A', 0.5 ],\n", "\t [ 'C', 'C', 'B', 0.5 ],\n", "\t [ 'C', 'C', 'C', 0.0 ]], [guest, prize] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create some sample data to train our model." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data = [[ 'A', 'A', 'C' ],\n", "\t\t[ 'A', 'A', 'C' ],\n", "\t\t[ 'A', 'A', 'B' ],\n", "\t\t[ 'A', 'A', 'A' ],\n", "\t\t[ 'A', 'A', 'C' ],\n", "\t\t[ 'B', 'B', 'B' ],\n", "\t\t[ 'B', 'B', 'C' ],\n", "\t\t[ 'C', 'C', 'A' ],\n", "\t\t[ 'C', 'C', 'C' ],\n", "\t\t[ 'C', 'C', 'C' ],\n", "\t\t[ 'C', 'C', 'C' ],\n", "\t\t[ 'C', 'B', 'A' ]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then train our model and see the results." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\tA\tA\t0.3333333333333333\n", "A\tA\tB\t0.3333333333333333\n", "A\tA\tC\t0.3333333333333333\n", "A\tB\tA\t0.3333333333333333\n", "A\tB\tB\t0.3333333333333333\n", "A\tB\tC\t0.3333333333333333\n", "A\tC\tA\t0.3333333333333333\n", "A\tC\tB\t0.3333333333333333\n", "A\tC\tC\t0.3333333333333333\n", "B\tA\tA\t0.3333333333333333\n", "B\tA\tB\t0.3333333333333333\n", "B\tA\tC\t0.3333333333333333\n", "B\tB\tA\t0.0\n", "B\tB\tB\t0.25\n", "B\tB\tC\t0.75\n", "B\tC\tA\t0.3333333333333333\n", "B\tC\tB\t0.3333333333333333\n", "B\tC\tC\t0.3333333333333333\n", "C\tA\tA\t0.3333333333333333\n", "C\tA\tB\t0.3333333333333333\n", "C\tA\tC\t0.3333333333333333\n", "C\tB\tA\t1.0\n", "C\tB\tB\t0.0\n", "C\tB\tC\t0.0\n", "C\tC\tA\t0.7\n", "C\tC\tB\t0.0\n", "C\tC\tC\t0.3\n" ] } ], "source": [ "monty.fit( data, weights=[1, 1, 3, 3, 1, 1, 3, 7, 1, 1, 1, 1] )\n", "print(monty)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples/distributions_test_table.ipynb000066400000000000000000000136531420461712600306720ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Distribution Test Tables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example demonstrates how to create some conditional probability tables and a bayesian network." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pomegranate import *\n", "import math" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First let's define some conditional probability tables." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "c_table = [[0, 0, 0, 0.6],\n", " [0, 0, 1, 0.4],\n", " [0, 1, 0, 0.7],\n", " [0, 1, 1, 0.3],\n", " [1, 0, 0, 0.2],\n", " [1, 0, 1, 0.8],\n", " [1, 1, 0, 0.9],\n", " [1, 1, 1, 0.1]]\n", "\n", "d_table = [[ 0, 0, 0.5 ],\n", " [ 0, 1, 0.5 ],\n", " [ 1, 0, 0.3 ],\n", " [ 1, 1, 0.7 ]]\n", "\n", "f_table = [[ 0, 0, 0, 0.8 ],\n", " [ 0, 0, 1, 0.2 ],\n", " [ 0, 1, 0, 0.3 ],\n", " [ 0, 1, 1, 0.7 ],\n", " [ 1, 0, 0, 0.6 ],\n", " [ 1, 0, 1, 0.4 ],\n", " [ 1, 1, 0, 0.9 ],\n", " [ 1, 1, 1, 0.1 ]]\n", "\n", "e_table = [[ 0, 0, 0.7 ],\n", " [ 0, 1, 0.3 ],\n", " [ 1, 0, 0.2 ],\n", " [ 1, 1, 0.8 ]]\n", "\n", "g_table = [[ 0, 0, 0, 0.34 ],\n", " [ 0, 0, 1, 0.66 ],\n", " [ 0, 1, 0, 0.83 ],\n", " [ 0, 1, 1, 0.17 ],\n", " [ 1, 0, 0, 0.77 ],\n", " [ 1, 0, 1, 0.23 ],\n", " [ 1, 1, 0, 0.12 ],\n", " [ 1, 1, 1, 0.88 ]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then let's convert them into distribution objects." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "a = DiscreteDistribution({ 0: 0.5, 1: 0.5 })\n", "b = DiscreteDistribution({ 0: 0.7, 1: 0.3 })\n", "e = ConditionalProbabilityTable( e_table, [b] )\n", "c = ConditionalProbabilityTable( c_table, [a,b] )\n", "d = ConditionalProbabilityTable( d_table, [c] )\n", "f = ConditionalProbabilityTable( f_table, [c,e] )\n", "g = ConditionalProbabilityTable( g_table, [c,e] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we can convert these distributions into states." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "a_s = State( a, \"a\" )\n", "b_s = State( b, \"b\" )\n", "c_s = State( c, \"c\" )\n", "d_s = State( d, \"d\" )\n", "e_s = State( e, \"e\" )\n", "f_s = State( f, \"f\" )\n", "g_s = State( g, \"g\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have our states created, we can finally start making our bayesian network." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "model = BayesianNetwork( \"derp\" )\n", "model.add_nodes( a_s, b_s, c_s, d_s, e_s, f_s, g_s )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we define the edges." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "model.add_edge( a_s, c_s )\n", "model.add_edge( b_s, c_s )\n", "model.add_edge( c_s, d_s )\n", "model.add_edge( c_s, f_s )\n", "model.add_edge( b_s, e_s )\n", "model.add_edge( e_s, f_s )\n", "model.add_edge( c_s, g_s )\n", "model.add_edge( e_s, g_s )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We finish by baking the network to finalize its structure." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can check on the structure of our bayesian network." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a : {0: 0.5, 1: 0.5}\n", "b : {0: 0.6999999999999997, 1: 0.3000000000000003}\n", "c : {0: 0.52, 1: 0.4799999999999999}\n", "d : {0: 0.4040000000000001, 1: 0.596}\n", "e : {0: 0.5499999999999998, 1: 0.45000000000000034}\n", "f : {0: 0.6518, 1: 0.34820000000000007}\n", "g : {0: 0.52066, 1: 0.4793399999999999}\n" ] } ], "source": [ "print(\"\\n\".join( \"{:10.10} : {}\".format( state.name, belief.parameters[0] ) for state, belief in zip( model.states, model.predict_proba({}, max_iterations=100) ) ))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples/hmm_example.ipynb000066400000000000000000000256221420461712600260550ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hidden Markov Model Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "authors:
\n", "Jacob Schreiber [jmschreiber91@gmail.com]
\n", "Nicholas Farn [nicholasfarn@gmail.com]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A simple example highlighting how to build a model using states, add\n", "transitions, and then run the algorithms, including showing how training\n", "on a sequence improves the probability of the sequence." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import random\n", "from pomegranate import *\n", "\n", "random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we will create the states of the model, one uniform and one normal." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "state1 = State( UniformDistribution(0.0, 1.0), name=\"uniform\" )\n", "state2 = State( NormalDistribution(0, 2), name=\"normal\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will then create the model by creating a HiddenMarkovModel instance. Then we will add the states." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "model = HiddenMarkovModel( name=\"ExampleModel\" )\n", "model.add_state( state1 )\n", "model.add_state( state2 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll add the start states to the model." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "model.add_transition( model.start, state1, 0.5 )\n", "model.add_transition( model.start, state2, 0.5 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the transition matrix." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "model.add_transition( state1, state1, 0.4 )\n", "model.add_transition( state1, state2, 0.4 )\n", "model.add_transition( state2, state2, 0.4 )\n", "model.add_transition( state2, state1, 0.4 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally the ending states to the model." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "model.add_transition( state1, model.end, 0.2 )\n", "model.add_transition( state2, model.end, 0.2 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To finalize the model, we \"bake\" it." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "New we'll create a sample sequence using our model." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.42128476]\n" ] } ], "source": [ "sequence = model.sample()\n", "print(sequence)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll feed the sequence through a forward algorithm with our model." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-3.9368559131089986\n" ] } ], "source": [ "print(model.forward( sequence )[ len(sequence), model.end_index ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we'll do the same, except with a backwards algorithm." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-3.9368559131089986\n" ] } ], "source": [ "print(model.backward( sequence )[0,model.start_index])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we'll feed the sequence again, through a forward-backward algorithm." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0. 0. 0. 1.]\n", " [0. 0. 0. 0.]\n", " [1. 0. 0. 0.]\n", " [0. 0. 0. 0.]]\n", "[[ 0. -inf]]\n" ] } ], "source": [ "trans, ems = model.forward_backward( sequence )\n", "print(trans)\n", "print(ems)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we'll train our model with our example sequence." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"name\" : \"ExampleModel\",\n", " \"start\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"ExampleModel-start\",\n", " \"weight\" : 1.0\n", " },\n", " \"end\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"ExampleModel-end\",\n", " \"weight\" : 1.0\n", " },\n", " \"states\" : [\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " NaN,\n", " NaN\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"normal\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"UniformDistribution\",\n", " \"parameters\" : [\n", " 0.0,\n", " 1.0\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"uniform\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"ExampleModel-start\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"ExampleModel-end\",\n", " \"weight\" : 1.0\n", " }\n", " ],\n", " \"end_index\" : 3,\n", " \"start_index\" : 2,\n", " \"silent_index\" : 2,\n", " \"edges\" : [\n", " [\n", " 2,\n", " 1,\n", " 0.0,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 2,\n", " 0,\n", " 1.0,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 1,\n", " 1,\n", " 0.4,\n", " 0.4,\n", " null\n", " ],\n", " [\n", " 1,\n", " 0,\n", " 0.4,\n", " 0.4,\n", " null\n", " ],\n", " [\n", " 1,\n", " 3,\n", " 0.20000000000000004,\n", " 0.2,\n", " null\n", " ],\n", " [\n", " 0,\n", " 0,\n", " 0.0,\n", " 0.4,\n", " null\n", " ],\n", " [\n", " 0,\n", " 1,\n", " 0.0,\n", " 0.4,\n", " null\n", " ],\n", " [\n", " 0,\n", " 3,\n", " 1.0,\n", " 0.2,\n", " null\n", " ]\n", " ],\n", " \"distribution ties\" : []\n", "}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit( [ sequence ] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then repeat the algorithms we fed the sequence through before on our improved model." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Forward\n", "nan\n", "\n", "Backward\n", "nan\n", "\n", "[[nan nan 0. nan]\n", " [nan nan 0. nan]\n", " [nan nan 0. 0.]\n", " [ 0. 0. 0. 0.]]\n", "[[nan nan]]\n" ] } ], "source": [ "print(\"Forward\")\n", "print(model.forward( sequence )[ len(sequence), model.end_index ])\n", "print()\n", "print(\"Backward\")\n", "print(model.backward( sequence )[ 0,model.start_index ])\n", "print()\n", "trans, ems = model.forward_backward( sequence )\n", "print(trans)\n", "print(ems)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples/hmm_infinite.ipynb000066400000000000000000000254501420461712600262260ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Infinite Hidden Markov Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "authors:
\n", "Jacob Schreiber [jmschreiber91@gmail.com]
\n", "Nicholas Farn [nicholasfarn@gmail.com]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example shows how to use pomegranate to sample from an infinite HMM. The premise is that you have an HMM which does not have transitions to the end state, and so can continue on forever. This is done by not adding transitions to the end state. If you bake a model with no transitions to the end state, you get an infinite model, with no extra work! This change is passed on to all the algorithms." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pomegranate import *\n", "import itertools as it\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we define the possible states in the model. In this case we make them all have normal distributions." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "s1 = State( NormalDistribution( 5, 2 ), name=\"S1\" )\n", "s2 = State( NormalDistribution( 15, 2 ), name=\"S2\" )\n", "s3 = State( NormalDistribution( 25, 2 ), name=\"S3\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then create the HMM object, naming it, logically, \"infinite\"." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "model = HiddenMarkovModel( \"infinite\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then add the possible transition, making sure not to add an end state. Thus with no end state, the model is infinite!" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "model.add_transition( model.start, s1, 0.7 )\n", "model.add_transition( model.start, s2, 0.2 )\n", "model.add_transition( model.start, s3, 0.1 )\n", "model.add_transition( s1, s1, 0.6 )\n", "model.add_transition( s1, s2, 0.1 )\n", "model.add_transition( s1, s3, 0.3 )\n", "model.add_transition( s2, s1, 0.4 )\n", "model.add_transition( s2, s2, 0.4 )\n", "model.add_transition( s2, s3, 0.2 )\n", "model.add_transition( s3, s1, 0.05 )\n", "model.add_transition( s3, s2, 0.15 )\n", "model.add_transition( s3, s3, 0.8 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we \"bake\" the model, finalizing the model." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can check whether or not our model is infinite." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Not implemented: print model.is_infinite()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets the possible states in the model." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "States\n", "S1\n", "S2\n", "S3\n", "infinite-start\n", "infinite-end\n" ] } ], "source": [ "print(\"States\")\n", "print(\"\\n\".join( state.name for state in model.states ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets test out our model by feeding it a sequence of values. We feed our sequence of values first through a forward algorithm in our HMM." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Forward\n", "[[ -inf -inf -inf 0. -inf]\n", " [ -1.97376066 -16.22652363 -54.91967081 -inf -inf]\n", " [ -4.14167156 -16.93342888 -51.83481875 -inf -inf]\n", " [-51.86583105 -18.40758124 -7.05897823 -inf -inf]\n", " [-65.74670193 -25.1481525 -8.97420455 -inf -inf]\n", " [-24.39327178 -12.54465999 -25.12068379 -inf -inf]\n", " [-72.85427528 -31.60428336 -16.04740908 -inf -inf]\n", " [-35.50647567 -19.65786431 -28.2338883 -inf -inf]\n", " [-22.21746699 -33.46742001 -70.40988373 -inf -inf]\n", " [-24.34161966 -38.38333577 -74.53476684 -inf -inf]]\n" ] } ], "source": [ "sequence = [ 4.8, 5.6, 24.1, 25.8, 14.3, 26.5, 15.9, 5.5, 5.1 ]\n", "\n", "print(\"Forward\")\n", "print(model.forward( sequence ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That looks good as well. Now lets feed our sequence into the model through a backwards algorithm." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Backward\n", "[[-24.49576972 -24.90123357 -26.9806721 -24.34161886 -inf]\n", " [-22.36785863 -22.77331446 -24.85273373 -22.21370663 -inf]\n", " [-20.19994915 -20.60539952 -19.21912118 -21.29854671 -inf]\n", " [-18.26347263 -18.6689355 -17.28264358 -19.36208268 -inf]\n", " [-15.77270345 -14.38657639 -15.36741456 -15.07964315 -inf]\n", " [-11.39151868 -11.7969834 -10.41068947 -12.49013058 -inf]\n", " [ -8.69968233 -7.31340196 -8.29421024 -8.00654767 -inf]\n", " [ -4.27832043 -4.68377831 -6.76320251 -4.12416872 -inf]\n", " [ -2.12416054 -2.52962166 -4.60905363 -1.97000929 -inf]\n", " [ 0. 0. 0. -inf -inf]]\n" ] } ], "source": [ "print(\"Backward\")\n", "print(model.backward( sequence ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Continuing on we now feed the sequence in through a forward-backward algorithm." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Forward-Backward\n", "[[1.99998844e+00 5.59973220e-06 1.00000592e+00 0.00000000e+00\n", " 0.00000000e+00]\n", " [9.99976663e-01 9.85835713e-06 9.99979797e-01 0.00000000e+00\n", " 0.00000000e+00]\n", " [3.44897138e-05 1.99995123e+00 1.00004800e+00 0.00000000e+00\n", " 0.00000000e+00]\n", " [9.99999569e-01 4.30542744e-07 8.44725590e-25 0.00000000e+00\n", " 0.00000000e+00]\n", " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", " 0.00000000e+00]]\n", "[[-4.30542826e-07 -1.46582192e+01 -5.54307857e+01]\n", " [-1.85577414e-06 -1.31972095e+01 -4.67123211e+01]\n", " [-4.57876848e+01 -1.27348979e+01 -2.94648857e-06]\n", " [-5.71777865e+01 -1.51931100e+01 -2.52183224e-07]\n", " [-1.14431716e+01 -2.45377600e-05 -1.11897544e+01]\n", " [-5.72123388e+01 -1.45760665e+01 -4.67406625e-07]\n", " [-1.54431772e+01 -2.37681755e-05 -1.06554720e+01]\n", " [-8.67193612e-06 -1.16554228e+01 -5.06773185e+01]\n", " [-7.97553827e-07 -1.40417169e+01 -5.01931480e+01]]\n" ] } ], "source": [ "print(\"Forward-Backward\")\n", "trans, emissions = model.forward_backward( sequence )\n", "print(trans)\n", "print(emissions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we feed the sequence through a Viterbi algorithm to find the most probable sequence of states." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Viterbi\n", "Prob: -24.341682585068853\n", "infinite-start\n", "S1\n", "S1\n", "S3\n", "S3\n", "S2\n", "S3\n", "S2\n", "S1\n", "S1\n", "\n", "MAP\n", "Prob: -6.372782082308959e-05\n", "S1\n", "S1\n", "S3\n", "S3\n", "S2\n", "S3\n", "S2\n", "S1\n", "S1\n" ] } ], "source": [ "print(\"Viterbi\")\n", "prob, states = model.viterbi( sequence )\n", "print(\"Prob: {}\".format( prob ))\n", "print(\"\\n\".join( state[1].name for state in states ))\n", "print()\n", "print(\"MAP\")\n", "prob, states = model.maximum_a_posteriori( sequence )\n", "print(\"Prob: {}\".format( prob ))\n", "print(\"\\n\".join( state[1].name for state in states ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we try and reproduce the transition matrix from 100,000 samples." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"Should produce a matrix close to the following: \")\n", "print(\" [ [ 0.60, 0.10, 0.30 ] \")\n", "print(\" [ 0.40, 0.40, 0.20 ] \")\n", "print(\" [ 0.05, 0.15, 0.80 ] ] \")\n", "print()\n", "print(\"Transition Matrix From 100000 Samples:\")\n", "sample, path = model.sample( 100000, path=True )\n", "trans = np.zeros((3,3))\n", "\n", "for state, n_state in it.izip( path[1:-2], path[2:-1] ):\n", "\tstate_name = int( state.name[1:] )-1\n", "\tn_state_name = int( n_state.name[1:] )-1\n", "\ttrans[ state_name, n_state_name ] += 1\n", "\n", "trans = (trans.T / trans.sum( axis=1 )).T\n", "print(trans)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples/hmm_rainy_sunny.ipynb000066400000000000000000000147011420461712600267740ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Rainy or Sunny Hidden Markov Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "authors:
\n", "Jacob Schreiber [jmschreiber91@gmail.com]
\n", "Nicholas Farn [nicholasfarn@gmail.com]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is an example of a sunny-rainy hidden markov model using yahmm. The example is drawn from the Wikipedia article on Hidden Markov Models describing what Bob likes to do on rainy or sunny days." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pomegranate import *\n", "import random\n", "import math\n", "\n", "random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first create a `HiddenMarkovModel` object, and name it \"Rainy-Sunny\"." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "model = HiddenMarkovModel( name=\"Rainy-Sunny\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then create the two possible states of the model, \"rainy\" and \"sunny\". We make them both discrete distributions, with the possibilities of Bob either walking, shopping, or cleaning." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "rainy = State( DiscreteDistribution({ 'walk': 0.1, 'shop': 0.4, 'clean': 0.5 }), name='Rainy' )\n", "sunny = State( DiscreteDistribution({ 'walk': 0.6, 'shop': 0.3, 'clean': 0.1 }), name='Sunny' )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then add the transitions probabilities, starting with the probability the model starts as sunny or rainy." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "model.add_transition( model.start, rainy, 0.6 )\n", "model.add_transition( model.start, sunny, 0.4 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then add the transition matrix. We make sure to subtract 0.05 from each probability to add to the probability of exiting the hmm." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "model.add_transition( rainy, rainy, 0.65 )\n", "model.add_transition( rainy, sunny, 0.25 )\n", "model.add_transition( sunny, rainy, 0.35 )\n", "model.add_transition( sunny, sunny, 0.55 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Last, we add transitions to mark the end of the model." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "model.add_transition( rainy, model.end, 0.1 )\n", "model.add_transition( sunny, model.end, 0.1 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we \"bake\" the model, finalizing its structure." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "model.bake( verbose=True )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets check on Bob each hour and see what he is doing! In other words lets create a sequence of observations." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "sequence = [ 'walk', 'shop', 'clean', 'clean', 'clean', 'walk', 'clean' ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets check the probability of observing this sequence." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.854526990800002e-05\n" ] } ], "source": [ "print(math.e**model.forward( sequence )[ len(sequence), model.end_index ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then the probability that Bob will be cleaning a step 3 in this sequence." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9120990704186626\n" ] } ], "source": [ "print(math.e**model.forward_backward( sequence )[1][ 2, model.states.index( rainy ) ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The probability of the sequence occurring given it is Sunny at step 4 in the sequence." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0004459435000000002\n" ] } ], "source": [ "print(math.e**model.backward( sequence )[ 3, model.states.index( sunny ) ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally the probable series of states given the above sequence." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sunny Rainy Rainy Rainy Rainy Sunny Rainy\n" ] } ], "source": [ "print(\" \".join( state.name for i, state in model.maximum_a_posteriori( sequence )[1] ))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples/hmm_tied_states.ipynb000066400000000000000000000775541420461712600267450ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tied States Hidden Markov Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "authors:
\n", "Jacob Schreiber [jmchreiber91@gmail.com],
\n", "Nicholas Farn [nicholasfarn@gmail.com]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An example of using tied states to represent the same distribution across multiple states. This example is a toy example derived from biology, where we will look at DNA sequences.\n", "\n", "The fake structure we will pretend exists is:\n", "```\n", "start -> background -> CG island -> background -> poly-T region\n", "```\n", "DNA is comprised of four nucleotides, A, C, G, and T. Lets say that in the background sequence, all of these occur at the same frequency. In the CG island, the nucleotides C and G occur more frequently. In the poly T region, T occurs most frequently.\n", "\n", "We need the graph structure, because we fake know that the sequence must return to the background distribution between the CG island and the poly-T region. However, we also fake know that both background distributions need to be the same." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pomegranate import *\n", "import random\n", "import numpy as np\n", "\n", "random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets start off with an example without tied states and see what happens." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "untiedmodel = HiddenMarkovModel( \"No Tied States\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we'll define the four states." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "background_one = State( DiscreteDistribution({'A': 0.25, 'C':0.25, 'G': 0.25, 'T':0.25 }), name=\"B1\" )\n", "CG_island = State( DiscreteDistribution({'A': 0.1, 'C':0.4, 'G': 0.4, 'T':0.1 }), name=\"CG\" )\n", "background_two = State( DiscreteDistribution({'A': 0.25, 'C':0.25, 'G': 0.25, 'T':0.25 }), name=\"B2\" )\n", "poly_T = State( DiscreteDistribution({'A': 0.1, 'C':0.1, 'G': 0.1, 'T':0.7 }), name=\"PT\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then add the starting transitions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "untiedmodel.add_transition( untiedmodel.start, background_one, 1. )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The transition matrix." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "untiedmodel.add_transition( background_one, background_one, 0.9 )\n", "untiedmodel.add_transition( background_one, CG_island, 0.1 )\n", "untiedmodel.add_transition( CG_island, CG_island, 0.8 )\n", "untiedmodel.add_transition( CG_island, background_two, 0.2 )\n", "untiedmodel.add_transition( background_two, background_two, 0.8 )\n", "untiedmodel.add_transition( background_two, poly_T, 0.2 )\n", "untiedmodel.add_transition( poly_T, poly_T, 0.7 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally the ending transitions." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "untiedmodel.add_transition( poly_T, untiedmodel.end, 0.3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finishing with the method \"bake\" to finalize the structure of our model." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "untiedmodel.bake( verbose=True )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's define the following sequences. Keep in mind training must by done on a list of lists, not on a string in order to allow strings of any length." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "sequences = [ numpy.array(list(\"TAGCACATCGCAGCGCATCACGCGCGCTAGCATATAAGCACGATCAGCACGACTGTTTTT\")),\n", "\t numpy.array(list(\"TAGAATCGCTACATAGACGCGCGCTCGCCGCGCTCGATAAGCTACGAACACGATTTTTTA\")),\n", "\t numpy.array(list(\"GATAGCTACGACTACGCGACTCACGCGCGCGCTCCGCATCAGACACGAATATAGATAAGATATTTTTT\")) ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets check our distributions before training our model." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "B1: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.25,\n", " \"C\" :0.25,\n", " \"G\" :0.25,\n", " \"T\" :0.25\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "B2: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.25,\n", " \"C\" :0.25,\n", " \"G\" :0.25,\n", " \"T\" :0.25\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "CG: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.1,\n", " \"C\" :0.4,\n", " \"G\" :0.4,\n", " \"T\" :0.1\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "PT: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.1,\n", " \"C\" :0.1,\n", " \"G\" :0.1,\n", " \"T\" :0.7\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "result = \"\\n\".join( \"{}: {}\".format( state.name, state.distribution )\n", "\tfor state in untiedmodel.states if not state.is_silent() )\n", "\n", "print(result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets train our model." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"name\" : \"No Tied States\",\n", " \"start\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"No Tied States-start\",\n", " \"weight\" : 1.0\n", " },\n", " \"end\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"No Tied States-end\",\n", " \"weight\" : 1.0\n", " },\n", " \"states\" : [\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.31639790507943677,\n", " \"C\" : 0.2920431428628597,\n", " \"G\" : 0.21191370125516387,\n", " \"T\" : 0.1796452508025395\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"B1\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.4178729606164158,\n", " \"C\" : 0.22068400964255355,\n", " \"G\" : 0.19558677075666792,\n", " \"T\" : 0.16585625898436263\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"B2\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.061102410527201965,\n", " \"C\" : 0.5058121809386029,\n", " \"G\" : 0.3384433263771247,\n", " \"T\" : 0.09464208215707043\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"CG\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.09055188417721106,\n", " \"C\" : 4.501540436607585e-09,\n", " \"G\" : 0.01138728389704469,\n", " \"T\" : 0.8980608274242039\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"PT\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"No Tied States-start\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"No Tied States-end\",\n", " \"weight\" : 1.0\n", " }\n", " ],\n", " \"end_index\" : 5,\n", " \"start_index\" : 4,\n", " \"silent_index\" : 4,\n", " \"edges\" : [\n", " [\n", " 4,\n", " 0,\n", " 1.0,\n", " 1.0,\n", " null\n", " ],\n", " [\n", " 0,\n", " 0,\n", " 0.9489674911696251,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 0,\n", " 2,\n", " 0.05103250883037496,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 2,\n", " 2,\n", " 0.9256192712029668,\n", " 0.8,\n", " null\n", " ],\n", " [\n", " 2,\n", " 1,\n", " 0.07438072879703318,\n", " 0.2,\n", " null\n", " ],\n", " [\n", " 1,\n", " 1,\n", " 0.9570959686922387,\n", " 0.8,\n", " null\n", " ],\n", " [\n", " 1,\n", " 3,\n", " 0.04290403130776134,\n", " 0.2,\n", " null\n", " ],\n", " [\n", " 3,\n", " 3,\n", " 0.8417505801851788,\n", " 0.7,\n", " null\n", " ],\n", " [\n", " 3,\n", " 5,\n", " 0.15824941981482132,\n", " 0.3,\n", " null\n", " ]\n", " ],\n", " \"distribution ties\" : []\n", "}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "untiedmodel.fit( sequences, stop_threshold=0.01 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And check our new distributions after training." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "B1: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.31639790507943677,\n", " \"C\" :0.2920431428628597,\n", " \"G\" :0.21191370125516387,\n", " \"T\" :0.1796452508025395\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "B2: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.4178729606164158,\n", " \"C\" :0.22068400964255355,\n", " \"G\" :0.19558677075666792,\n", " \"T\" :0.16585625898436263\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "CG: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.061102410527201965,\n", " \"C\" :0.5058121809386029,\n", " \"G\" :0.3384433263771247,\n", " \"T\" :0.09464208215707043\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "PT: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.09055188417721106,\n", " \"C\" :4.501540436607585e-09,\n", " \"G\" :0.01138728389704469,\n", " \"T\" :0.8980608274242039\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "result = \"\\n\".join( \"{}: {}\".format( state.name, state.distribution )\n", "\tfor state in untiedmodel.states if not state.is_silent() )\n", " \n", "print(result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can try our example with tied states." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "tiedmodel = HiddenMarkovModel( \"Tied States\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets redefine the four states." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "background = DiscreteDistribution({'A': 0.25, 'C':0.25, 'G': 0.25, 'T':0.25 })\n", "\n", "background_one = State( background, name=\"B1\" )\n", "CG_island = State( DiscreteDistribution({'A': 0.1, \n", "\t'C':0.4, 'G': 0.4, 'T':0.1 }), name=\"CG\" )\n", "background_two = State( background, name=\"B2\" )\n", "poly_T = State( DiscreteDistribution({'A': 0.1, \n", "\t'C':0.1, 'G': 0.1, 'T':0.7 }), name=\"PT\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then add the starting transitions." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "tiedmodel.add_transition( tiedmodel.start, background_one, 1. );" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then the tranisiton matrix." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "tiedmodel.add_transition( background_one, background_one, 0.9 )\n", "tiedmodel.add_transition( background_one, CG_island, 0.1 )\n", "tiedmodel.add_transition( CG_island, CG_island, 0.8 )\n", "tiedmodel.add_transition( CG_island, background_two, 0.2 )\n", "tiedmodel.add_transition( background_two, background_two, 0.8 )\n", "tiedmodel.add_transition( background_two, poly_T, 0.2 )\n", "tiedmodel.add_transition( poly_T, poly_T, 0.7 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally adding the ending transitions." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "tiedmodel.add_transition( poly_T, tiedmodel.end, 0.3 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We \"bake\" the model to finalize its structure." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "tiedmodel.bake( verbose=True )" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Now let's use the following sequences to train our model." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "sequences = [ numpy.array(list(\"TAGCACATCGCAGCGCATCACGCGCGCTAGCATATAAGCACGATCAGCACGACTGTTTTT\")),\n", "\t\t\t numpy.array(list(\"TAGAATCGCTACATAGACGCGCGCTCGCCGCGCTCGATAAGCTACGAACACGATTTTTTA\")),\n", "\t\t\t numpy.array(list(\"GATAGCTACGACTACGCGACTCACGCGCGCGCTCCGCATCAGACACGAATATAGATAAGATATTTTTT\")) ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But before that let's check the distributions in our model." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "B1: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.25,\n", " \"C\" :0.25,\n", " \"G\" :0.25,\n", " \"T\" :0.25\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "B2: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.25,\n", " \"C\" :0.25,\n", " \"G\" :0.25,\n", " \"T\" :0.25\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "CG: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.1,\n", " \"C\" :0.4,\n", " \"G\" :0.4,\n", " \"T\" :0.1\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "PT: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.1,\n", " \"C\" :0.1,\n", " \"G\" :0.1,\n", " \"T\" :0.7\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "result = \"\\n\".join( \"{}: {}\".format( state.name, state.distribution ) \n", "\tfor state in tiedmodel.states if not state.is_silent() )\n", "\n", "print(result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's train our model." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"name\" : \"Tied States\",\n", " \"start\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"Tied States-start\",\n", " \"weight\" : 1.0\n", " },\n", " \"end\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"Tied States-end\",\n", " \"weight\" : 1.0\n", " },\n", " \"states\" : [\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.3825505694630474,\n", " \"C\" : 0.23803901803476255,\n", " \"G\" : 0.20043821449422436,\n", " \"T\" : 0.1789721980079656\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"B1\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : 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\"C\" : 1.4280989721539988e-17,\n", " \"G\" : 0.0060865515333644584,\n", " \"T\" : 0.9009639984757188\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"PT\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"Tied States-start\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"Tied States-end\",\n", " \"weight\" : 1.0\n", " }\n", " ],\n", " \"end_index\" : 5,\n", " \"start_index\" : 4,\n", " \"silent_index\" : 4,\n", " \"edges\" : [\n", " [\n", " 4,\n", " 0,\n", " 1.0,\n", " 1.0,\n", " null\n", " ],\n", " [\n", " 0,\n", " 0,\n", " 0.9331385232071054,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 0,\n", " 2,\n", " 0.06686147679289454,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 2,\n", " 2,\n", " 0.9433476039763057,\n", " 0.8,\n", " null\n", " ],\n", " [\n", " 2,\n", " 1,\n", " 0.05665239602369436,\n", " 0.2,\n", " null\n", " ],\n", " [\n", " 1,\n", " 1,\n", " 0.9580129624347591,\n", " 0.8,\n", " null\n", " ],\n", " [\n", " 1,\n", " 3,\n", " 0.04198703756524091,\n", " 0.2,\n", " null\n", " ],\n", " [\n", " 3,\n", " 3,\n", " 0.8397947514093073,\n", " 0.7,\n", " null\n", " ],\n", " [\n", " 3,\n", " 5,\n", " 0.16020524859069266,\n", " 0.3,\n", " null\n", " ]\n", " ],\n", " \"distribution ties\" : [\n", " [\n", " 0,\n", " 1\n", " ],\n", " [\n", " 1,\n", " 0\n", " ]\n", " ]\n", "}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiedmodel.fit( sequences, stop_threshold=0.01 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's check our new distributions." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "B1: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.3825505694630474,\n", " \"C\" :0.23803901803476255,\n", " \"G\" :0.20043821449422436,\n", " \"T\" :0.1789721980079656\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "B2: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.3825505694630474,\n", " \"C\" :0.23803901803476255,\n", " \"G\" :0.20043821449422436,\n", " \"T\" :0.1789721980079656\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "CG: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.10879783006206004,\n", " \"C\" :0.4779841622676619,\n", " \"G\" :0.3129317572182933,\n", " \"T\" :0.10028625045198475\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n", "PT: {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.09294944999091662,\n", " \"C\" :1.4280989721539988e-17,\n", " \"G\" :0.0060865515333644584,\n", " \"T\" :0.9009639984757188\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "result = \"\\n\".join( \"{}: {}\".format( state.name, state.distribution ) \n", "\tfor state in tiedmodel.states if not state.is_silent() )\n", "\n", "print(result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that states B1 and B2 are the same after training with tied states, not so without tied states." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } naivebayes_hmm_cheating_coin_toss.ipynb000066400000000000000000000761731420461712600324220ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Naive Bayes Hidden Markov Coin Toss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "author: Nicholas Farn [nicholasfarn@gmail.com]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we will be using a simple Hidden Markov Model to demonstrate how it can be used in a Naive Bayes classifier. In this particular example we have a couple friends who enjoy playing a game of chance. Someone tosses a coin and whenever it lands on heads, the tosser gains \\$1, otherwise he pays \\$1. However some of our friends are prone to using rigged coins in order to come out ahead in the long run.\n", "\n", "Players fall into 3 categories, non-cheaters, smart cheaters, and dumb cheaters. Non-cheaters, as the name implies, do not use rigged coins. Smart cheaters make sure to switch between rigged and unrigged coins in order to keep the deception going. Dumb cheaters only use rigged coins." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "from pomegranate import *\n", "import numpy as np\n", "%pylab inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start by creating our hmm's. Since this is a simple example, we will only have 2 states, rigged and unrigged, with discrete distributions." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "rigged = State( DiscreteDistribution({'H': 0.8, 'T': 0.2}), name=\"rigged\" )\n", "unrigged = State( DiscreteDistribution({'H': 0.5, 'T': 0.5}), name=\"unrigged\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create our non-cheater and dumb cheater since their hmm's consist of a single state." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "non_cheater = HiddenMarkovModel( name=\"non-cheater\" )\n", "non_cheater.add_state( unrigged )\n", "\n", "dumb_cheater = HiddenMarkovModel( name=\"dumb-cheater\" )\n", "dumb_cheater.add_state( rigged )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now add our starting states to our models." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "non_cheater.start = unrigged\n", "dumb_cheater.start = rigged" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also add the transitions to our non-cheater and dumb cheater." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "non_cheater.add_transition( unrigged, unrigged, 1 )\n", "dumb_cheater.add_transition( rigged, rigged, 1 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we bake our models to finalize the structure." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "non_cheater.bake()\n", "dumb_cheater.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can create our smart cheater." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "smart_cheater = HiddenMarkovModel( name=\"smart-cheater\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The structure of our smart cheater hmm should look like the following." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's add in our starting transitions. Smart cheaters start out half the time with an unrigged coin, half the time without." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "smart_cheater.add_transition( smart_cheater.start, unrigged, 0.5 )\n", "smart_cheater.add_transition( smart_cheater.start, rigged, 0.5 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can add our transition matrix." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "smart_cheater.add_transition( rigged, rigged, 0.5 )\n", "smart_cheater.add_transition( rigged, unrigged, 0.5 )\n", "smart_cheater.add_transition( unrigged, rigged, 0.5 )\n", "smart_cheater.add_transition( unrigged, unrigged, 0.5 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we can bake the model to finalize its structure." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "smart_cheater.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can view the structures of all our hmm's in the images below." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.title(\"smart cheater hmm\")\n", "smart_cheater.plot()\n", "\n", "plt.title(\"dumb cheater hmm\")\n", "dumb_cheater.plot()\n", "\n", "plt.title(\"non-cheater hmm\")\n", "non_cheater.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can finally create our Naive Bayes classifier. We'll let 0 stand for a non-cheater, 1 stand for a smart cheater, and 2 stand for a dumb cheater." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "players = NaiveBayes([ non_cheater, smart_cheater, dumb_cheater ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create a sequence of coin tosses to test our classifier on." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "data = np.array([list( 'HHHHHTHTHTTTTHHHTHHTTHHHHHTH' ),\n", " list( 'HHHHHHHTHHHHTTHHHHHHHTTHHHHH' ),\n", " list( 'THTHTHTHTHTHTTHHTHHHHTTHHHTT' )])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now check the log probability of each string of data occurring under each model." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For sequence 1, 21.4% non-cheater, 68.0% smart cheater, 10.6% dumb cheater.\n", "For sequence 2, 0.173% non-cheater, 12.1% smart cheater, 87.7% dumb cheater.\n", "For sequence 3, 66.5% non-cheater, 33.0% smart cheater, 0.515% dumb cheater.\n" ] } ], "source": [ "probs = players.predict_proba( data )\n", "\n", "for i in range(len(probs)):\n", " print(\"For sequence {}, {:.3}% non-cheater, {:.3}% smart cheater, {:.3}% dumb cheater.\".format( i+1, 100*probs[i][0], 100*probs[i][1], 100*probs[i][2]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we can classify whether a non-cheater (0), smart cheater (1), or dumb cheater (2) created the string of data." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sequence 1 is a smart cheater\n", "Sequence 2 is a dumb cheater\n", "Sequence 3 is a non-cheater\n" ] } ], "source": [ "output = players.predict( data )\n", "\n", "for i in range(len(output)):\n", " print(\"Sequence {} is a {}\".format( i+1, \"non-cheater\" if output[i] == 0 else \"smart cheater\" if output[i] == 1 else \"dumb cheater\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also train our hmm's through Naive Bayes. Let's used the following data, all marked as known games for smart cheaters." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "X = np.array([list( 'HHHHHTHTHTTTTH' ),\n", " list( 'HHTHHTTHHHHHTH' )])\n", "\n", "y = np.array([ 1, 1 ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's fit our NaiveBayes classifier to our data." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"NaiveBayes\",\n", " \"models\" : [\n", " {\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"name\" : \"non-cheater\",\n", " \"start\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"H\" : 0.49218749999999994,\n", " \"T\" : 0.5078125000000001\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : 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\"name\" : \"rigged\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"dumb-cheater-end\",\n", " \"weight\" : 1.0\n", " }\n", " ],\n", " \"end_index\" : 1,\n", " \"start_index\" : 0,\n", " \"silent_index\" : 1,\n", " \"edges\" : [\n", " [\n", " 0,\n", " 0,\n", " 1.0,\n", " 1,\n", " null\n", " ]\n", " ],\n", " \"distribution ties\" : []\n", " }\n", " ],\n", " \"weights\" : [\n", " 0.0,\n", " 1.0,\n", " 0.0\n", " ]\n", "}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "players.fit( X, y )" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } naivebayes_multivariate_male_female.ipynb000066400000000000000000000117541420461712600327260ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Naive Bayes Male or Female Multivariate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "author: Nicholas Farn [nicholasfarn@gmail.com]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example shows how to create a Multivariate Guassian Naive Bayes Classifier using pomegranate. In this example we will use a set od data measuring a person's height (feet), weight (lbs), and foot size (inches) in order to classify them as male or female. This example is drawn from the example in the Wikipedia article on Naive Bayes Classifiers." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "from pomegranate import *\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Since we are simply using two Multivariate Gaussian Distributions, our Naive Bayes model is very simple to initialize." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = NaiveBayes( MultivariateGaussianDistribution, n_components=2 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course currently our model is unitialized and needs data in order to be able to classify people as male or female. So let's create the data. For multivariate distributions, the training data set has to be specified as a list of lists with each entry being a single case for the data set. We will specify males as a 0 and females with a 1." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = np.array([[ 6, 180, 12 ],\n", " [ 5.92, 190, 11 ],\n", " [ 5.58, 170, 12 ],\n", " [ 5.92, 165, 10 ],\n", " [ 6, 160, 9 ],\n", " [ 5, 100, 6 ],\n", " [ 5.5, 100, 8 ],\n", " [ 5.42, 130, 7 ],\n", " [ 5.75, 150, 9 ],\n", " [ 5.5, 140, 8 ]])\n", "\n", "y = np.array([ 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can fit our Naive Bayes model to the set of data." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model.fit( X, y )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's test our model on the following sample." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = np.array([[ 5.75, 130, 8 ]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First the probability of the data occurring under each model." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Height 5.75, weight 130.0, and foot size 8.0 is 27.1% male, 72.9% female.\n" ] } ], "source": [ "for sample, probs in zip( data, model.predict_proba( data ) ):\n", " print \"Height {}, weight {}, and foot size {} is {:.3}% male, {:.3}% female.\".format( sample[0], sample[1], sample[2], 100*probs[0], 100*probs[1] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the probability that the sample is a female is significantly larger than the probability that it is male. Logically when we classify the data as either male (0) or female (1) we get the output: female." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Person with height 5.75, weight 130.0, and foot size 8.0 is female\n" ] } ], "source": [ "for sample, result in zip( data, model.predict( data ) ):\n", " print \"Person with height {}, weight {}, and foot size {} is {}\".format( sample[0], sample[1], sample[2], \"female\" if result else \"male\" )" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/examples/naivebayes_simple_male_female.ipynb000066400000000000000000000473071420461712600315730ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Naive Bayes Simple Male or Female" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "author: Nicholas Farn [nicholasfarn@gmail.com]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example shows how to create a simple Gaussian Naive Bayes Classifier using pomegranate. In this example we will be given a set of data measuring a person's height (feet) and try to classify them as male or female. This example is a simplification drawn from the example in the Wikipedia article on Naive Bayes Classifiers." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "from pomegranate import *\n", "import seaborn\n", "seaborn.set_style('whitegrid')\n", "%pylab inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we'll create the distributions for our model. In this case we'll assume that height, weight, and foot size are normally distributed. We'll fit our distribution to a set of data for males and females." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "male = NormalDistribution.from_samples([ 6.0, 5.92, 5.58, 5.92, 6.08, 5.83 ])\n", "female = NormalDistribution.from_samples([ 5.0, 5.5, 5.42, 5.75, 5.17, 5.0 ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check on the parameters for our male and female height distributions." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Male distribution has mu = 5.89 and sigma = 0.158\n", "Female distribution has mu = 5.31 and sigma = 0.275\n" ] } ], "source": [ "male.plot( n=100000, edgecolor='c', color='c', bins=50, label='Male' )\n", "female.plot( n=100000, edgecolor='g', color='g', bins=50, label='Female' )\n", "plt.legend( fontsize=14 )\n", "plt.ylabel('Count')\n", "plt.xlabel('Height (ft)')\n", "plt.show()\n", "\n", "print(\"Male distribution has mu = {:.3} and sigma = {:.3}\".format( *male.parameters ))\n", "print(\"Female distribution has mu = {:.3} and sigma = {:.3}\".format( *female.parameters ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Everything seems to look good so let's create our Naive Bayes Classifier." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "clf = NaiveBayes([ male, female ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at how our classifier calls people of various heights. We can either look at a probabilistic measurement of the sample being male or female, or a hard call prediction. Lets take a look at both." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Height [5.], 4.3856389353194124e-05 chance male and 99.99995614361063 chance female\n", "Height [6.], 97.02328468247669 chance male and 2.976715317523313 chance female\n", "Height [4.92], 3.2247855094331937e-06 chance male and 99.99999677521448 chance female\n", "Height [5.5], 9.788762297712132 chance male and 90.21123770228787 chance female\n" ] } ], "source": [ "data = np.array([[5.0], [6.0], [4.92], [5.5]])\n", "\n", "for sample, probability in zip( data, clf.predict_proba(data) ):\n", " print(\"Height {}, {} chance male and {} chance female\".format( sample, 100*probability[0], 100*probability[1]))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Person with height [5.] is female.\n", "Person with height [6.] is male.\n", "Person with height [4.92] is female.\n", "Person with height [5.5] is female.\n" ] } ], "source": [ "for sample, result in zip( data, clf.predict( data )):\n", " print(\"Person with height {} is {}.\".format( sample, \"female\" if result else \"male\" ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These results look good. We can also train a our classifier with a set of data. This is done by creating a set of observations along with a set with the corresponding correct classification." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"NaiveBayes\",\n", " \"models\" : [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 176.25,\n", " 9.60143218483576\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 132.5,\n", " 20.463381929681123\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " \"weights\" : [\n", " 0.5,\n", " 0.5\n", " ]\n", "}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = np.array([[180], [190], [170], [165], [100], [150], [130], [150]])\n", "y = np.array([ 0, 0, 0, 0, 1, 1, 1, 1 ])\n", "\n", "clf.fit( X, y )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case we fitted the normal distributions to fit a set of data with male an female weights (lbs). Let's check the results with the following data set." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "data = np.array([[130], [200], [100], [162], [145]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's enter it into our classifier." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Person with weight [130] is female.\n", "Person with weight [200] is male.\n", "Person with weight [100] is female.\n", "Person with weight [162] is male.\n", "Person with weight [145] is female.\n" ] } ], "source": [ "for sample, result in zip( data, clf.predict( data )):\n", " print(\"Person with weight {} is {}.\".format( sample, \"female\" if result else \"male\" ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Everything looks good from here. In this tutorial we created a simple Naive Bayes Classifier with normal distributions. It is possible to create a classifier with more complex distributions, or even with a Hidden Markov Model." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/000077500000000000000000000000001420461712600231735ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/BayesClassifier.pyx000066400000000000000000000212351420461712600270100ustar00rootroot00000000000000#cython: boundscheck=False #cython: cdivision=True # BayesClassifier.pyx # Contact: Jacob Schreiber ( jmschreiber91@gmail.com ) import numpy cimport numpy from .bayes cimport BayesModel from distributions import Distribution from .gmm import GeneralMixtureModel from .hmm import HiddenMarkovModel from .BayesianNetwork import BayesianNetwork from .io import BaseGenerator from .io import DataGenerator DEF NEGINF = float("-inf") DEF INF = float("inf") cdef class BayesClassifier(BayesModel): """A Bayes classifier, a more general form of a naive Bayes classifier. A Bayes classifier, like a naive Bayes classifier, uses Bayes' rule in order to calculate the posterior probability of the classes, which are used for the predictions. However, a naive Bayes classifier assumes that each of the features are independent of each other and so can be modelled as independent distributions. A generalization of that, the Bayes classifier, allows for an arbitrary covariance between the features. This allows for more complicated components to be used, up to and including even HMMs to form a classifier over sequences, or mixtures to form a classifier with complex emissions. Parameters ---------- models : list A list of initialized distribution objects to use as the components in the model. weights : list or numpy.ndarray or None, default None The prior probabilities of the components. If None is passed in then defaults to the uniformly distributed priors. Attributes ---------- models : list The model objects, either initialized by the user or fit to data. weights : numpy.ndarray The prior probability of each component of the model. Examples -------- >>> from pomegranate import * >>> >>> d1 = NormalDistribution(3, 2) >>> d2 = NormalDistribution(5, 1.5) >>> >>> clf = BayesClassifier([d1, d2]) >>> clf.predict_proba([[6]]) array([[ 0.2331767, 0.7668233]]) >>> X = [[0], [2], [0], [1], [0], [5], [6], [5], [7], [6]] >>> y = [0, 0, 0, 0, 0, 1, 1, 0, 1, 1] >>> clf.fit(X, y) >>> clf.predict_proba([[6]]) array([[ 0.01973451, 0.98026549]]) """ def __init__(self, distributions, weights=None): super(self.__class__, self).__init__(distributions, weights) def __reduce__(self): return self.__class__, (self.distributions, self.weights) def to_dict(self): if self.d == 0: raise ValueError("must fit components to the data before prediction") return { 'class' : 'BayesClassifier', 'models' : [ model.to_dict() for model in self.distributions ], 'weights' : self.weights.tolist() } @classmethod def from_dict(cls, d): models = list() for j in d['models']: if j['class'] == 'Distribution': models.append(Distribution.from_dict(j)) elif j['class'] == 'GeneralMixtureModel': models.append(GeneralMixtureModel.from_dict(j)) elif j['class'] == 'HiddenMarkovModel': models.append(HiddenMarkovModel.from_dict(j)) elif j['class'] == 'BayesianNetwork': models.append(BayesianNetwork.from_dict(j)) nb = cls( models, numpy.array(d['weights'])) return nb @classmethod def from_samples(cls, distributions, X, y=None, weights=None, inertia=0.0, pseudocount=0.0, stop_threshold=0.1, max_iterations=1e8, callbacks=[], return_history=False, keys=None, verbose=False, n_jobs=1, **kwargs): """Create a Bayes classifier directly from the given dataset. This will initialize the distributions using maximum likelihood estimates derived by partitioning the dataset using the label vector. If any labels are missing, the model will be trained using EM in a semi-supervised setting. A homogeneous model can be defined by passing in a single distribution callable as the first parameter and specifying the number of components, while a heterogeneous model can be defined by passing in a list of callables of the appropriate type. A Bayes classifier is a superset of the naive Bayes classifier in that the math is identical, but the distributions used do not have to be independent for each feature. Simply put, one can create a multivariate Gaussian Bayes classifier with a full covariance matrix, but a Gaussian naive Bayes would require a diagonal covariance matrix. Parameters ---------- distributions : array-like, shape (n_components,) or callable The components of the model. This should either be a single callable if all components will be the same distribution, or an array of callables, one for each feature. X : array-like, shape (n_samples, n_dimensions) This is the data to train on. Each row is a sample, and each column is a dimension to train on. y : array-like, shape (n_samples,) The labels for each sample. The labels should be integers between 0 and k-1 for a problem with k classes, or -1 if the label is not known for that sample. weights : array-like, shape (n_samples,), optional The initial weights of each sample in the matrix. If nothing is passed in then each sample is assumed to be the same weight. Default is None. inertia : double, optional Inertia used for the training the distributions. pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Default is 0. stop_threshold : double, optional, positive The threshold at which EM will terminate for the improvement of the model. If the model does not improve its fit of the data by a log probability of 0.1 then terminate. Only required if doing semisupervised learning. Default is 0.1. max_iterations : int, optional, positive The maximum number of iterations to run EM for. If this limit is hit then it will terminate training, regardless of how well the model is improving per iteration. Only required if doing semisupervised learning. Default is 1e8. callbacks : list, optional A list of callback objects that describe functionality that should be undertaken over the course of training. return_history : bool, optional Whether to return the history during training as well as the model. keys : list A list of sets where each set is the keys present in that column. If there are d columns in the data set then this list should have d sets and each set should have at least two keys in it. verbose : bool, optional Whether or not to print out improvement information over iterations. Only required if doing semisupervised learning. Default is False. n_jobs : int, optional The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. **kwargs : dict, optional Any arguments to pass into the `from_samples` methods of other objects that are being created such as BayesianNetworks or HMMs. Returns ------- model : BayesClassifier The fit Bayes classifier model. """ if isinstance(distributions, (list, numpy.ndarray, tuple)): for distribution in distributions: if not callable(distribution): raise ValueError("must pass in class constructors, not initiated distributions (e.g. NormalDistribution)") if not isinstance(X, BaseGenerator): if y is None: raise ValueError("Must pass in both X and y as arrays or a data generator for X.") batch_size = len(X) // n_jobs + len(X) % n_jobs data_generator = DataGenerator(X, weights, y, batch_size=batch_size) else: data_generator = X n, d = data_generator.shape n_components = len(data_generator.classes) - (-1 in data_generator.classes) if callable(distributions): if distributions in (BayesianNetwork, HiddenMarkovModel): batches = [batch for batch in data_generator.batches()] X = numpy.concatenate([batch[0] for batch in batches]) y = numpy.concatenate([batch[1] for batch in batches]) weights = numpy.concatenate([batch[2] for batch in batches]) labels = numpy.unique(y) distributions = [distributions.from_samples(X[y == label], weights=weights, keys=keys, pseudocount=pseudocount) for label in labels] return cls(distributions) elif d > 1: distributions = [distributions.blank(d) for i in range(n_components)] else: distributions = [distribution.blank() for i in range(n_components)] else: distributions = [distribution.blank() for distribution in distributions] model = cls(distributions) _, history = model.fit(X=data_generator, weights=weights, inertia=inertia, pseudocount=pseudocount, stop_threshold=stop_threshold, max_iterations=max_iterations, callbacks=callbacks, return_history=True, verbose=verbose, n_jobs=n_jobs) if return_history: return model, history return model pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/BayesianNetwork.pyx000066400000000000000000002666561420461712600270670ustar00rootroot00000000000000# BayesianNetwork.pyx # Contact: Jacob Schreiber ( jmschreiber91@gmail.com ) from libc.stdlib cimport srand import itertools as it import time import networkx as nx import numpy cimport numpy import os import random from joblib import Parallel from joblib import delayed from libc.stdlib cimport calloc from libc.stdlib cimport free from libc.stdlib cimport malloc from libc.string cimport memset from .base cimport GraphModel from .base cimport Model from .base cimport State from distributions import Distribution from distributions.distributions cimport MultivariateDistribution from distributions.DiscreteDistribution cimport DiscreteDistribution from distributions.ConditionalProbabilityTable cimport ConditionalProbabilityTable from .FactorGraph import FactorGraph from .utils cimport _log from .utils cimport _log2 from .utils cimport isnan from .utils import PriorityQueue from .utils import parallelize_function from .utils import _check_nan from .utils cimport choose_one #from .utils import choose_one from .utils import check_random_state from .io import BaseGenerator from .io import DataGenerator #from libcpp.list cimport list as cpplist from libc.math cimport exp as cexp cimport cython from collections import defaultdict DEF INF = float("inf") DEF NEGINF = float("-inf") nan = numpy.nan def _check_input(X, model): """Ensure that the keys in the sample are valid keys. Go through each variable in the sample and make sure that the observed symbol is a valid key according to the model. Raise an error if the symbol is not a key valid key according to the model. Parameters ---------- X : dict or array-like The observed sample. states : list A list of states ordered by the columns in the sample. Returns ------- None """ indices = {state.name: state.distribution for state in model.states} if isinstance(X, dict): for name, value in X.items(): if isinstance(value, Distribution): if set(value.keys()) != set(indices[name].keys()): raise ValueError("State '{}' does not match with keys provided." .format(name)) continue if name not in indices: raise ValueError("Model does not contain a state named '{}'" .format(name)) if value not in indices[name].keys(): raise ValueError("State '{}' does not have key '{}'" .format(name, value)) elif isinstance(X, (numpy.ndarray, list)) and isinstance(X[0], dict): for x in X: for name, value in x.items(): if isinstance(value, Distribution): if set(value.keys()) != set(indices[name].keys()): raise ValueError("State '{}' does not match with keys provided." .format(name)) continue if name not in indices: raise ValueError("Model does not contain a state named '{}'" .format(name)) if value not in indices[name].keys(): raise ValueError("State '{}' does not have key '{}'" .format(name, value)) elif isinstance(X, (numpy.ndarray, list)) and isinstance(X[0], (numpy.ndarray, list)): for x in X: if len(x) != len(indices): raise ValueError("Sample does not have the same number of dimensions" \ " as the model {} {}".format(x, len(indices))) for i in range(len(x)): if isinstance(x[i], Distribution): if set(x[i].keys()) != set(model.states[i].distribution.keys()): raise ValueError("State '{}' does not match with keys provided." .format(model.states[i].name)) continue if _check_nan(x[i]): continue if x[i] not in model.states[i].distribution.keys(): raise ValueError("State '{}' does not have key '{}'" .format(model.states[i].name, x[i])) X = numpy.array(X, ndmin=2, dtype=object) else: raise ValueError("X must be a 2D array of shape (n_samples, n_variables) or " \ "a list of lists or a list of dictionaries.") return X cdef class BayesianNetwork(GraphModel): """A Bayesian Network Model. A Bayesian network is a directed graph where nodes represent variables, edges represent conditional dependencies of the children on their parents, and the lack of an edge represents a conditional independence. Parameters ---------- name : str, optional The name of the model. Default is None Attributes ---------- states : list, shape (n_states,) A list of all the state objects in the model graph : networkx.DiGraph The underlying graph object. Example ------- >>> from pomegranate import * >>> d1 = DiscreteDistribution({'A': 0.2, 'B': 0.8}) >>> d2 = ConditionalProbabilityTable([['A', 'A', 0.1], ['A', 'B', 0.9], ['B', 'A', 0.4], ['B', 'B', 0.6]], [d1]) >>> s1 = Node( d1, name="s1" ) >>> s2 = Node( d2, name="s2" ) >>> model = BayesianNetwork() >>> model.add_nodes(s1, s2) >>> model.add_edge(s1, s2) >>> model.bake() >>> print(model.log_probability([['A', 'B']])) -1.71479842809 >>> print(model.predict_proba({'s2' : 'A'})) [{ "class" :"Distribution", "dtype" :"str", "name" :"DiscreteDistribution", "parameters" :[ { "A" :0.05882352941176483, "B" :0.9411764705882352 } ], "frozen" :false } 'A'] >>> print(model.predict([[None, 'A']])) [array(['B', 'A'], dtype=object)] """ cdef public list idxs cdef public numpy.ndarray keymap cdef int* parent_count cdef int* parent_idxs cdef numpy.ndarray distributions cdef void** distributions_ptr @property def structure( self ): structure = [() for i in range(self.d)] indices = { distribution : i for i, distribution in enumerate(self.distributions) } for i, state in enumerate(self.states): d = state.distribution if isinstance(d, MultivariateDistribution): structure[i] = tuple(indices[parent] for parent in d.parents) return tuple(structure) def __dealloc__( self ): free(self.parent_count) free(self.parent_idxs) def plot(self, filename=None): """Draw this model's graph using pygraphviz and matplotlib. If no filename, it uses pygraphviz to write a temporary png file, and matplotlib to `imshow()` it. If using jupyter or IPython, enable `%matplotlib inline` and this will immediately display your graph. Otherwise, per usual matplotlib convention, you have to issue a `plt.show()` or `matplotlib.pyplot.show()` to open a window with the image. TODO: Use svg or pdf for original image. Jupyter and IPython can render SVG directly, e.g. `from IPython.display import SVG` and `SVG(filename=...)`. Parameters ---------- filename : str, optional Filename for saving the .pdf graph. Default is None Returns ------- None """ try: import tempfile import pygraphviz import matplotlib.pyplot as plt import matplotlib.image except ImportError: pygraphviz = None if pygraphviz is not None: G = pygraphviz.AGraph(directed=True) for state in self.states: G.add_node(state.name, color='red') for parent, child in self.edges: G.add_edge(parent.name, child.name) if filename is None: with tempfile.NamedTemporaryFile() as tf: G.draw(tf.name, format='png', prog='dot') img = matplotlib.image.imread(tf.name) plt.imshow(img) plt.axis('off') else: G.draw(filename, format='pdf', prog='dot') else: raise ValueError("must have matplotlib and pygraphviz installed for visualization") def bake(self): """Finalize the topology of the model. Assign a numerical index to every state and create the underlying arrays corresponding to the states and edges between the states. This method must be called before any of the probability-calculating methods. This includes converting conditional probability tables into joint probability tables and creating a list of both marginal and table nodes. Parameters ---------- None Returns ------- None """ self.d = len(self.states) # Initialize the factor graph self.graph = FactorGraph( self.name+'-fg' ) # Create two mappings, where edges which previously went to a # conditional distribution now go to a factor, and those which left # a conditional distribution now go to a marginal f_mapping, m_mapping = {}, {} d_mapping = {} fa_mapping = {} # Go through each state and add in the state if it is a marginal # distribution, otherwise add in the appropriate marginal and # conditional distribution as separate nodes. for i, state in enumerate( self.states ): # For every state (ones with conditional distributions or those # encoding marginals) we need to create a marginal node in the # underlying factor graph. keys = state.distribution.keys() d = DiscreteDistribution({ key: 1./len(keys) for key in keys }) m = State( d, state.name ) # Add the state to the factor graph self.graph.add_node( m ) # Now we need to copy the distribution from the node into the # factor node. This could be the conditional table, or the # marginal. f = State( state.distribution.copy(), state.name+'-joint' ) if isinstance( state.distribution, ConditionalProbabilityTable ): fa_mapping[f.distribution] = m.distribution self.graph.add_node( f ) self.graph.add_edge( m, f ) f_mapping[state] = f m_mapping[state] = m d_mapping[state.distribution] = d for a, b in self.edges: self.graph.add_edge(m_mapping[a], f_mapping[b]) # Now go back and redirect parent pointers to the appropriate # objects. for state in self.graph.states: d = state.distribution if isinstance( d, ConditionalProbabilityTable ): dist = fa_mapping[d] d.parents = [ d_mapping[parent] for parent in d.parents ] d.parameters[1] = d.parents state.distribution = d.joint() state.distribution.parameters[1].append( dist ) # Finalize the factor graph structure self.graph.bake() indices = {state.distribution : i for i, state in enumerate(self.states)} n, self.idxs = 0, [] self.keymap = numpy.array([state.distribution.keys() for state in self.states], dtype=object) for i, state in enumerate(self.states): d = state.distribution if isinstance(d, MultivariateDistribution): idxs = tuple(indices[parent] for parent in d.parents) + (i,) self.idxs.append(idxs) d.bake(tuple(it.product(*[self.keymap[idx] for idx in idxs]))) n += len(idxs) else: self.idxs.append(i) d.bake(tuple(self.keymap[i])) n += 1 self.keymap = numpy.array([{key: i for i, key in enumerate(keys)} for keys in self.keymap]) self.distributions = numpy.array([state.distribution for state in self.states]) self.distributions_ptr = self.distributions.data self.parent_count = calloc(self.d+1, sizeof(int)) self.parent_idxs = calloc(n, sizeof(int)) j = 0 for i, state in enumerate(self.states): distribution = state.distribution if isinstance(distribution, ConditionalProbabilityTable): for k, parent in enumerate(distribution.parents): distribution.column_idxs[k] = indices[parent] distribution.column_idxs[k+1] = i distribution.n_columns = len(self.states) if isinstance(distribution, MultivariateDistribution): self.parent_count[i+1] = len(distribution.parents) + 1 for parent in distribution.parents: self.parent_idxs[j] = indices[parent] j += 1 self.parent_idxs[j] = i j += 1 else: self.parent_count[i+1] = 1 self.parent_idxs[j] = i j += 1 if i > 0: self.parent_count[i+1] += self.parent_count[i] def log_probability(self, X, check_input=True, n_jobs=1): """Return the log probability of samples under the Bayesian network. The log probability is just the sum of the log probabilities under each of the components. The log probability of a sample under the graph A -> B is just P(A)*P(B|A). This will return a vector of log probabilities, one for each sample. Parameters ---------- X : array-like, shape (n_samples, n_dim) The sample is a vector of points where each dimension represents the same variable as added to the graph originally. It doesn't matter what the connections between these variables are, just that they are all ordered the same. check_input : bool, optional Check to make sure that the observed symbol is a valid symbol for that distribution to produce. Default is True. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. Returns ------- logp : numpy.ndarray or double The log probability of the samples if many, or the single log probability. """ if self.d == 0: raise ValueError("must bake model before computing probability") n = len(X) if n_jobs > 1 or isinstance(X, BaseGenerator): batch_size = n // n_jobs + n % n_jobs if not isinstance(X, BaseGenerator): data_generator = DataGenerator(X, batch_size=batch_size) else: data_generator = X fn = '.pomegranate.tmp' with open(fn, 'w') as outfile: outfile.write(self.to_json()) with Parallel(n_jobs=n_jobs, backend='threading') as parallel: f = delayed(parallelize_function) logp_array = parallel(f(batch[0], BayesianNetwork, 'log_probability', fn) for batch in data_generator.batches()) os.remove(fn) return numpy.concatenate(logp_array) elif check_input: X = _check_input(X, self) logp = numpy.zeros(n, dtype='float64') for i in range(n): for j, state in enumerate(self.states): logp[i] += state.distribution.log_probability(X[i, self.idxs[j]]) return logp if n > 1 else logp[0] cdef void _log_probability( self, double* symbol, double* log_probability, int n ) nogil: cdef int i, j, l, li, k cdef double logp cdef double* sym = malloc(self.d*sizeof(double)) memset(log_probability, 0, n*sizeof(double)) for i in range(n): for j in range(self.d): memset(sym, 0, self.d*sizeof(double)) logp = 0.0 for l in range(self.parent_count[j], self.parent_count[j+1]): li = self.parent_idxs[l] k = l - self.parent_count[j] sym[k] = symbol[i*self.d + li] ( self.distributions_ptr[j])._log_probability(sym, &logp, 1) log_probability[i] += logp free(sym) def marginal(self): """Return the marginal probabilities of each variable in the graph. This is equivalent to a pass of belief propagation on a graph where no data has been given. This will calculate the probability of each variable being in each possible emission when nothing is known. Parameters ---------- None Returns ------- marginals : array-like, shape (n_nodes) An array of univariate distribution objects showing the marginal probabilities of that variable. """ if self.d == 0: raise ValueError("must bake model before computing marginal") return self.graph.marginal() def predict(self, X, max_iterations=100, check_input=True, n_jobs=1): """Predict missing values of a data matrix using MLE. Impute the missing values of a data matrix using the maximally likely predictions according to the forward-backward algorithm. Run each sample through the algorithm (predict_proba) and replace missing values with the maximally likely predicted emission. Parameters ---------- X : array-like, shape (n_samples, n_nodes) Data matrix to impute. Missing values must be either None (if lists) or np.nan (if numpy.ndarray). Will fill in these values with the maximally likely ones. max_iterations : int, optional Number of iterations to run loopy belief propagation for. Default is 100. check_input : bool, optional Check to make sure that the observed symbol is a valid symbol for that distribution to produce. Default is True. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. Returns ------- y_hat : numpy.ndarray, shape (n_samples, n_nodes) This is the data matrix with the missing values imputed. """ if self.d == 0: raise ValueError("must bake model before using impute") y_hat = self.predict_proba(X, max_iterations=max_iterations, check_input=check_input, n_jobs=n_jobs) for i in range(len(y_hat)): for j in range(len(y_hat[i])): if isinstance(y_hat[i][j], Distribution): y_hat[i][j] = y_hat[i][j].mle() return y_hat def predict_proba(self, X, max_iterations=100, check_input=True, n_jobs=1): """Returns the probabilities of each variable in the graph given evidence. This calculates the marginal probability distributions for each state given the evidence provided through loopy belief propagation. Loopy belief propagation is an approximate algorithm which is exact for certain graph structures. Parameters ---------- X : dict or array-like, shape <= n_nodes The evidence supplied to the graph. This can either be a dictionary with keys being state names and values being the observed values (either the emissions or a distribution over the emissions) or an array with the values being ordered according to the nodes incorporation in the graph (the order fed into .add_states/add_nodes) and None for variables which are unknown. It can also be vectorized, so a list of dictionaries can be passed in where each dictionary is a single sample, or a list of lists where each list is a single sample, both formatted as mentioned before. max_iterations : int, optional The number of iterations with which to do loopy belief propagation. Usually requires only 1. Default is 100. check_input : bool, optional Check to make sure that the observed symbol is a valid symbol for that distribution to produce. Default is True. n_jobs : int, optional The number of threads to use when parallelizing the job. This parameter is passed directly into joblib. Default is 1, indicating no parallelism. Returns ------- y_hat : array-like, shape (n_samples, n_nodes) An array of univariate distribution objects showing the probabilities of each variable. """ if self.d == 0: raise ValueError("must bake model before using forward-backward algorithm") n = len(X) if n_jobs > 1 or isinstance(X, BaseGenerator): batch_size = n // n_jobs + n % n_jobs if not isinstance(X, BaseGenerator): data_generator = DataGenerator(X, batch_size=batch_size) else: data_generator = X fn = '.pomegranate.tmp' with open(fn, 'w') as outfile: outfile.write(self.to_json()) with Parallel(n_jobs=n_jobs, backend='threading') as parallel: f = delayed(parallelize_function) logp_array = parallel(f(batch[0], BayesianNetwork, 'predict_proba', fn) for batch in data_generator.batches()) os.remove(fn) return numpy.concatenate(logp_array) elif check_input and not isinstance(X, dict): X = _check_input(X, self) if isinstance(X, dict): return self.graph.predict_proba(X, max_iterations) elif isinstance(X, (list, numpy.ndarray)) and not isinstance(X[0], (list, numpy.ndarray, dict)): data = {} for state, val in zip(self.states, X): if not _check_nan(val): data[state.name] = val return self.graph.predict_proba(data, max_iterations) else: y_hat = [] for x in X: y_ = self.predict_proba(x, max_iterations=max_iterations, check_input=False, n_jobs=1) y_hat.append(y_) return y_hat def fit(self, X, weights=None, inertia=0.0, pseudocount=0.0, verbose=False, n_jobs=1): """Fit the model to data using MLE estimates. Fit the model to the data by updating each of the components of the model, which are univariate or multivariate distributions. This uses a simple MLE estimate to update the distributions according to their summarize or fit methods. This is a wrapper for the summarize and from_summaries methods. Parameters ---------- X : array-like or generator, shape (n_samples, n_nodes) The data to train on, where each row is a sample and each column corresponds to the associated variable. weights : array-like, shape (n_nodes), optional The weight of each sample as a positive double. Default is None. inertia : double, optional The inertia for updating the distributions, passed along to the distribution method. Default is 0.0. pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Only effects hidden Markov models defined over discrete distributions. Default is 0. verbose : bool, optional Whether or not to print out improvement information over iterations. Only required if doing semisupervised learning. Default is False. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. Returns ------- self : BayesianNetwork The fit Bayesian network object with updated model parameters. """ training_start_time = time.time() batch_size = len(X) // n_jobs + len(X) % n_jobs if not isinstance(X, BaseGenerator): data_generator = DataGenerator(numpy.asarray(X, dtype=object), weights, batch_size=batch_size) else: data_generator = X with Parallel(n_jobs=n_jobs, backend='threading') as parallel: f = delayed(self.summarize) parallel(f(*batch) for batch in data_generator.batches()) self.from_summaries(inertia, pseudocount) self.bake() if verbose: total_time_spent = time.time() - training_start_time print("Total Time (s): {:.4f}".format(total_time_spent)) return self def sample(self, n=1, evidences=[{}], algorithm='rejection',random_state=None,**kwargs): """Sample the network, optionally given some evidences Use rejection to condition on non marginal nodes Parameters ---------- n : int, optional The number of samples to generate. Defaults to 1. evidences : list of dict, optional Evidence to set constant while samples are generated. algorithm: : str, one of 'gibbs', 'rejection' optional. default 'rejection' Rejection sampling successively sample each node given its parents evidence. When evidences are given on non-root nodes, only draws that are compatible with evidence nodes are not rejected. Rejection sampling is a good option when evidences nodes are not far from the root nodes or when given evidence is likely. Rare evidences lead to a high rate of rejected samples, thus to significant slow down of the sampling. Gibbs sampling scheme is a Markov Chain Monte Carlo (MCMC) technique designed to speed up the sampling. It builds conditional probability of state transition of each nodes given its neighbours in its markov blanket. Works well with a lot of evidences in the network, even when they are far from the root nodes. Drawback : convergence is only guaranteed when there is a non null probability path between states. If the posterior consists of isolated islands of high probability, Gibbs sampling will stay stuck in one the island and will never transition to the others. Successive samples will have high correlation. min_prob : float <1 optional. If algorithm == "rejection" stop iterations when Sum P(X|Evidence) < min_prob. generated samples for a given evidence will be incomplete (>> network.sample(evidence = [{'HLML': '2'},{'HLML': '2','TYPL':'1'},{'NBPI': '02','TYPL':'1'}]) """ if algorithm == "rejection": return self._rejection(n=n,evidences=evidences,random_state=random_state,**kwargs) if algorithm == "gibbs": return self._gibbs( n=n, evidences=evidences,seed=random_state, **kwargs) def _rejection(self, n=1, evidences=[{}],min_prob=0.01,random_state=None): """Sample the network, optionally given some evidences Use rejection to condition on non marginal nodes Parameters ---------- n : int, optional The number of samples to generate. Defaults to 1. evidences : list of dict, optional Evidence to set constant while samples are generated. min_prob : stop iterations when Sum P(X|Evidence) < min_prob. generated samples for a given evidence will be incomplete ( n/min_prob: # raise if P(X|Evidence) < 1% raise Exception('Maximum iteration limit. Make sure the state configuration hinted at by evidence is reasonably reachable for this network or lower min_prob') # Rejection sampling # If the predicted value is not the one given in evidence, we start over until we reach the expected number of samples by evidence j, node = iter_.__next__() name = node.name if node.distribution.name == "DiscreteDistribution": if name in evidence : val = evidence[name] else : val = node.distribution.sample(random_state=random_state) else : val = node.distribution.sample(args,random_state=random_state) # rejection sampling if node.distribution.name != "DiscreteDistribution" and (name in evidence): if evidence[name] != val: # make sure we start with the first node in the topoplogical order [iter_.__next__() for i in range(self.d - j - 1)] args = {node_dict[k]:v for k,v in evidence.items()} state_dict = evidence.copy() continue else: state_dict[name] = val args[node_dict[name]] = val if (j + 1) == self.d: samples.append(state_dict) args = {node_dict[k]:v for k,v in evidence.items()} state_dict = evidence.copy() count += 1 # make sure we start with the first node in the topoplogical order [iter_.__next__() for i in range(self.d - j - 1)] keys = node_dict.keys() return numpy.array([[r[k] for k in keys ] for i,r in enumerate(samples)]) def _gibbs(self, int n, list evidences=[], dict initial_state ={}, int burnin=10,seed=1, scan_order='random', double pseudocount=0): """ Draw samples from the bayesian network given evidences. Evidences can be given for any nodes (root or not). This will return sample of size for each of the given evidence in Node sampling order is shuffled each iteration Parameters ---------- n : int The number of sample to draw for each evidence each sample as a positive double. Default is None. evidences : list [{:}] The data to train on, where each row is a sample and each column corresponds to the associated variable. Default [{}] initial_state : dict, optional Initial state used by the sampler. Default is to use the maximum joint-probability values, calculated with self.predict(). The default should be optimal. scan_order: str, optional ['topological','random',] scan order or the gibbs sampler. Indicate in which order nodes are sampled. Default : 'topological'. burnin : int, optional Number of sample to discard at the begining of the sampling. Default is 0. seed : seed to be applied to srand pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Only effects hidden Markov models defined over discrete distributions. Default is 0. Returns ------- array : samples (n*len(evidences),n_state) sample drawn from the bayesian network """ if seed is None: seed = round(time.time()) srand(seed) cdef int n_step, n_state, i,j,k, step, n_cpd, n_mod, node_pos, idx, col_n, e cdef double p, s cdef str val cdef dict col_dict, col_dict_inv, graph_dict cdef list modalities, cardinalities, cols, col_idxs, probs, cpds_, node_idx, samples n_step = burnin+n n_state = len(self.states) cdef numpy.ndarray[numpy.double_t, ndim=1,mode='c'] current_state = numpy.empty([n_state],dtype=numpy.float64) cdef numpy.ndarray[numpy.double_t, ndim=2,mode='c'] all_states = numpy.empty([n*len(evidences),n_state],dtype=numpy.float64) cdef numpy.ndarray[numpy.double_t,ndim=1,mode='c'] prob, prob_tmp, state_subset, proba cdef double [:] current_state_view = current_state cdef double [:,:] all_states_view = all_states col_dict = {i:state.name for i,state in enumerate(self.states) } col_dict_inv = {state.name:i for i,state in enumerate(self.states) } graph_dict = {state.name:state for i,state in enumerate(self.graph.states) } # building graph of the model G = nx.DiGraph() for state in self.states: G.add_node(state.name) for parent, child in self.edges: G.add_edge(parent.name, child.name) topo_order = [col_dict_inv[node_name] for node_name in nx.topological_sort(G)] if initial_state == {} : # initial_state = {state.name: state.distribution.keys()[0] for state in self.states} # Optimal initial state in_st_nan = numpy.empty( (1, len(self.states)) ) in_st_nan[:] = numpy. nan in_st_pred = self.predict(in_st_nan) initial_state = {state.name: in_st_pred[0][i] for i, state in enumerate(self.states)} modalities = [] modalities_int = [] modalities_dict = [] cpds = defaultdict(list) node_idx_dict = defaultdict(list) col_idxs = [] columns_idxs_dict = defaultdict(list) columns_idxs = [] state_names = {i:state.name for i,state in enumerate(self.states)} for i,state in enumerate(self.states): d = state.distribution modalities_dict.append({mod : c for c,mod in enumerate(d.keys())} ) if isinstance(state.distribution,MultivariateDistribution): cols = [col_dict[idx] for idx in d.column_idxs ] col_idxs.append(d.column_idxs) for col in cols : cpds[col].append(d) node_idx_dict[col].append(numpy.where(d.column_idxs==col_dict_inv[col])[0][0]) columns_idxs_dict[col].append(d.column_idxs) else : cols = [state.name] col_idxs.append([i]) for col in cols : cpds[col].append(d) node_idx_dict[col].append(0) columns_idxs_dict[col].append([i]) cardinalities = [len(m.keys()) for m in modalities_dict] prob = numpy.zeros(numpy.max(cardinalities)) prob_tmp = numpy.zeros(numpy.max(cardinalities)) state_subset = numpy.zeros(n_state) cdef double [:] prob_view = prob cdef double [:] prob_tmp_view = prob_tmp cdef double [:] state_subset_view = state_subset cpds_ = [cpds[state.name] for state in self.states ] node_idx = [node_idx_dict[state.name] for state in self.states ] columns_idxs = [columns_idxs_dict[state.name] for state in self.states ] samples = [] state_order = list(range(n_state)) if scan_order == 'topological': state_order = topo_order scan_order_is_random = scan_order == 'random' if scan_order_is_random: random.seed(seed) for e,evidence in enumerate(evidences) : for i,state in enumerate(self.states): if state.name in evidence: mod_i = modalities_dict[i] ev_for_name = evidence[state.name] if isinstance(list(mod_i.keys())[0], str): current_state[i] = mod_i[ev_for_name] else: current_state[i] = mod_i[int(ev_for_name)] else : current_state[i] = modalities_dict[i][initial_state[state.name]] pass for step in range(n_step): if scan_order_is_random: random.shuffle(state_order) for i in state_order: if col_dict[i] in evidence: #all_states_view[e*(n_step)+step+1,i] = mod_i = modalities_dict[i] if isinstance(list(mod_i.keys())[0], str): current_state_view[i] = mod_i[evidence[col_dict[i]]] else: current_state_view[i] = mod_i[int(evidence[col_dict[i]])] continue cardinality = cardinalities[i] column_idxs = columns_idxs[i] for k,cpd in enumerate(cpds_[i]) : col_len = len(column_idxs[k]) node_pos = node_idx[i][k] for col_n,idx in enumerate(column_idxs[k]): state_subset_view[col_n] = current_state_view[idx] if col_len !=1 : self.cpd_prod(cpd, state_subset[:col_n+1], prob,prob_tmp,cardinality,node_pos) else : self.cpd_prod_marginal(cpd, state_subset[:col_n+1], prob,prob_tmp,cardinality,node_pos) # normalizing log_prob to avoid probabilities smaller than double precision prob[:] -= prob[:cardinality].max() prob[:] = numpy.exp(prob[:]) prob[:] = prob[:]/prob[:cardinality].sum() current_state_view[i] = choose_one(prob_view[:cardinality], cardinality) prob_view[:] = 0. if step >= burnin: all_states_view[e*(n)+step-burnin,:] = current_state_view[:] # convert back int to code modalities_type = type(list(modalities_dict[0].keys())[0]) decoded_states = numpy.empty_like(all_states.astype(modalities_type)) for i,modality_dict in enumerate(modalities_dict): to_values,from_values = list(zip(*modality_dict.items())) sort_idx = numpy.argsort(from_values) idx_ = numpy.searchsorted(from_values,all_states[:,i],sorter = sort_idx) decoded_states[:,i]= numpy.array(to_values)[sort_idx][idx_] return decoded_states @cython.boundscheck(False) @cython.wraparound(False) @cython.nonecheck(False) cdef void cpd_prod(self,ConditionalProbabilityTable cpd, numpy.ndarray[numpy.double_t, ndim=1,mode="c"] state_subset, numpy.ndarray[numpy.double_t, ndim=1,mode='c'] prob, numpy.ndarray[numpy.double_t, ndim=1,mode='c'] prob_tmp, int cardinality,int node_pos): cdef int j for j in range(cardinality): state_subset[node_pos] = j cpd._log_probability(&state_subset[0],&prob_tmp[0],1) if prob_tmp[0] != -numpy.inf: prob[j] += prob_tmp[0] else : # default probability of unobserved event prob[j] += -20 @cython.boundscheck(False) @cython.wraparound(False) @cython.nonecheck(False) cdef void cpd_prod_marginal(self,DiscreteDistribution d, numpy.ndarray[numpy.double_t, ndim=1,mode="c"] state_subset, numpy.ndarray[numpy.double_t, ndim=1,mode='c'] prob, numpy.ndarray[numpy.double_t, ndim=1,mode='c'] prob_tmp, int cardinality,int node_pos): cdef int j for j in range(cardinality): state_subset[node_pos] = j d._log_probability(&state_subset[0],&prob_tmp[0],1) if prob_tmp[0] != -numpy.inf: prob[j] += prob_tmp[0] else : # default probability of unobserved event prob[j] += -20 #----------------------------------------------------------------------------------------------- def summarize(self, X, weights=None): """Summarize a batch of data and store the sufficient statistics. This will partition the dataset into columns which belong to their appropriate distribution. If the distribution has parents, then multiple columns are sent to the distribution. This relies mostly on the summarize function of the underlying distribution. Parameters ---------- X : array-like, shape (n_samples, n_nodes) The data to train on, where each row is a sample and each column corresponds to the associated variable. weights : array-like, shape (n_nodes), optional The weight of each sample as a positive double. Default is None. Returns ------- None """ cdef numpy.ndarray X_subset cdef numpy.ndarray weights_ndarray cdef double* X_subset_ptr cdef double* weights_ptr cdef int i, n, d if self.d == 0: raise ValueError("must bake model before summarizing data") indices = {state.distribution: i for i, state in enumerate(self.states)} n, d = len(X), len(X[0]) cdef double* X_int = malloc(n * d * sizeof(double)) for i in range(n): for j in range(d): if _check_nan(X[i][j]): X_int[i * d + j] = nan else: X_int[i * d + j] = self.keymap[j][X[i][j]] if weights is None: weights_ndarray = numpy.ones(n, dtype='float64') else: weights_ndarray = numpy.asarray(weights, dtype='float64') weights_ptr = weights_ndarray.data # Go through each state and pass in the appropriate data for the # update to the states for i, state in enumerate(self.states): if isinstance(state.distribution, ConditionalProbabilityTable): with nogil: ( self.distributions_ptr[i])._summarize(X_int, weights_ptr, n, 0, 1) else: state.distribution.summarize([x[i] for x in X], weights) free(X_int) def from_summaries(self, inertia=0.0, pseudocount=0.0): """Use MLE on the stored sufficient statistics to train the model. This uses MLE estimates on the stored sufficient statistics to train the model. Parameters ---------- inertia : double, optional The inertia for updating the distributions, passed along to the distribution method. Default is 0.0. pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Default is 0. Returns ------- None """ for state in self.states: state.distribution.from_summaries(inertia, pseudocount) self.bake() def to_dict(self): states = [ state.copy() for state in self.states ] return { 'class' : 'BayesianNetwork', 'name' : self.name, 'structure' : self.structure, 'states' : [ state.to_dict() for state in states ] } @classmethod def from_dict(cls, d): # Make a new generic Bayesian Network model = cls(str(d['name'])) states = [State.from_dict(j) for j in d['states']] structure = d['structure'] for state, parents in zip(states, structure): if len(parents) > 0: state.distribution.parents = [states[parent].distribution for parent in parents] state.distribution.parameters[1] = state.distribution.parents state.distribution.m = len(parents) model.add_states(*states) for i, parents in enumerate(structure): for parent in parents: model.add_edge(states[parent], states[i]) model.bake() return model @classmethod def from_structure(cls, X, structure, weights=None, pseudocount=0.0, name=None, state_names=None, keys=None): """Return a Bayesian network from a predefined structure. Pass in the structure of the network as a tuple of tuples and get a fit network in return. The tuple should contain n tuples, with one for each node in the graph. Each inner tuple should be of the parents for that node. For example, a three node graph where both node 0 and 1 have node 2 as a parent would be specified as ((2,), (2,), ()). Parameters ---------- X : array-like, shape (n_samples, n_nodes) The data to fit the structure too, where each row is a sample and each column corresponds to the associated variable. structure : tuple of tuples The parents for each node in the graph. If a node has no parents, then do not specify any parents. weights : array-like, shape (n_nodes), optional The weight of each sample as a positive double. Default is None. pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Default is 0. name : str, optional The name of the model. Default is None. state_names : array-like, shape (n_nodes), optional A list of meaningful names to be applied to nodes keys : list A list of sets where each set is the keys present in that column. If there are d columns in the data set then this list should have d sets and each set should have at least two keys in it. Returns ------- model : BayesianNetwork A Bayesian network with the specified structure. """ if isinstance(X, BaseGenerator): batches = [batch for batch in X.batches()] X = numpy.concatenate([batch[0] for batch in batches]) weights = numpy.concatenate([batch[1] for batch in batches]) else: X = numpy.asarray(X) if weights is None: weights = numpy.ones(X.shape[0], dtype='float64') else: weights = numpy.asarray(weights, dtype='float64') d = len(structure) nodes = [None for i in range(d)] for i, parents in enumerate(structure): if len(parents) == 0: keys_ = None if keys is None else keys[i] nodes[i] = DiscreteDistribution.from_samples(X[:,i], weights=weights, pseudocount=pseudocount, keys=keys_) while True: for i, parents in enumerate(structure): if nodes[i] is None: for parent in parents: if nodes[parent] is None: break else: keys_ = None if keys is None else [keys[j] for j in parents] + [keys[i]] nodes[i] = ConditionalProbabilityTable.from_samples(X[:,parents+(i,)], parents=[nodes[parent] for parent in parents], weights=weights, pseudocount=pseudocount, keys=keys_) break else: break if state_names is not None: states = [State(node, name=node_name) for node, node_name in zip(nodes,state_names)] else: states = [State(node, name=str(i)) for i, node in enumerate(nodes)] model = cls(name=name) model.add_nodes(*states) for i, parents in enumerate(structure): for parent in parents: model.add_edge(states[parent], states[i]) model.bake() return model @classmethod def from_samples(cls, X, weights=None, algorithm='greedy', max_parents=-1, penalty=None, root=0, constraint_graph=None, include_edges=None, exclude_edges=None, pseudocount=0.0, state_names=None, name=None, reduce_dataset=True, keys=None, low_memory=None, n_jobs=1): """Learn the structure of the network from data. There are currently two types of approaches implemented. The first, the Chow-Liu algorithm, finds a tree-like structure from symmetric mutual-information scores given a root node (the `root` parameter). The second type searches through structures and returns the structure that maximizes the following objective function: P(D|M) + penalty * |M| where P(D|M) is the probability of the data given the found model, penalty is a user-specified parameters, and |M| is the number of parameters in the model. When this penalty is log2(|D|) / 2 (the default) where |D| is the weight sum of the examples, this is equivalent to the minimum description length (MDL). There are currently three ways that the learned structure can be controlled. The first is to increase the penalty term to increase sparsity. The second is to pass in a specified list of edges that must exist (`include_edges`) or cannot exist (`exclude_edges`). Lastly, a constraint graph can be specified where each node in the graph is a set of variables being modeled and the edges in the graph indicate which sets of variables can be parents to which other sets of variables (and where a self-loop is the normal structure learning step). Parameters ---------- X : array-like or generator, shape (n_samples, n_nodes) The data to fit the structure too, where each row is a sample and each column corresponds to the associated variable. weights : array-like, shape (n_samples), optional The weight of each sample as a positive double. Default is None. algorithm : str, one of 'chow-liu', 'greedy', 'exact', 'exact-dp' optional The algorithm to use for learning the Bayesian network. Default is 'greedy' that greedily attempts to find the best structure, and frequently can identify the optimal structure. 'exact' uses DP/A* to find the optimal Bayesian network, and 'exact-dp' tries to find the shortest path on the entire order lattice, which is more memory and computationally expensive. 'exact' and 'exact-dp' should give identical results, with 'exact-dp' remaining an option mostly for debugging reasons. 'chow-liu' will return the optimal tree-like structure for the Bayesian network, which is a very fast approximation but not always the best network. max_parents : int, optional The maximum number of parents a node can have. If used, this means using the k-learn procedure. Can drastically speed up algorithms. If -1, no max on parents. Default is -1. penalty : float or None, optional The weighting of the model complexity term in the objective function. Increasing this value will encourage sparsity whereas setting the value to 0 will result in an unregularized structure. Default is log2(|D|) / 2 where |D| is the sum of the weights of the data. root : int, optional For algorithms which require a single root ('chow-liu'), this is the root for which all edges point away from. User may specify which column to use as the root. Default is the first column. constraint_graph : networkx.DiGraph or None, optional A directed graph showing valid parent sets for each variable. Each node is a set of variables, and edges represent which variables can be valid parents of those variables. The naive structure learning task is just all variables in a single node with a self edge, meaning that you know nothing about include_edges : list or None, optional A list of (parent, child) tuples that are edges which must be present in the found structure. Default is None. exclude_edges : list or None, optional A list of (parent, child) tuples that are edges which cannot be present in the found structure. Default is None. pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Default is 0. state_names : array-like, shape (n_nodes), optional A list of meaningful names to be applied to nodes name : str, optional The name of the model. Default is None. reduce_dataset : bool, optional Given the discrete nature of these datasets, frequently a user will pass in a dataset that has many identical samples. It is time consuming to go through these redundant samples and a far more efficient use of time to simply calculate a new dataset comprised of the subset of unique observed samples weighted by the number of times they occur in the dataset. This typically will speed up all algorithms, including when using a constraint graph. Default is True. keys : list, optional A list of sets where each set is the keys present in that column. If there are d columns in the data set then this list should have d sets and each set should have at least two keys in it. Default is None. low_memory : bool or None, optional Whether to use a low-memory version of the search algorithm. This option only affects algorithm="greedy" and algorithm="exact". Although the low-memory version of both the greedy and exact algorithms will use less memory, it will also significantly slow down the exact algorithm. However, setting this to True will also significantly speed up the greedy algorithm. Setting this value to None will enable it when algorithm="greedy" and disable it otherwise. Default is None. n_jobs : int, optional The number of threads to use when learning the structure of the network. If a constraint graph is provided, this will parallelize the tasks as directed by the constraint graph. If one is not provided it will parallelize the building of the parent graphs. Both cases will provide large speed gains. Returns ------- model : BayesianNetwork The learned BayesianNetwork. """ if algorithm == 'chow-liu' and include_edges is not None: raise ValueError("Cannot use the Chow-Liu algorithm with inclusion constraints.") if algorithm == 'chow-liu' and exclude_edges is not None: raise ValueError("Cannot use the Chow-Liu algorithm with exclusion constraints.") include_edges = include_edges or [] exclude_edges = exclude_edges or [] if constraint_graph is not None: if len(include_edges) > 0: raise ValueError("Cannot use both a constraint graph and " / "forced edge inclusions.") if low_memory is None: low_memory = algorithm == 'greedy' if isinstance(X, BaseGenerator): batches = [batch for batch in X.batches()] X = numpy.concatenate([batch[0] for batch in batches]) weights = numpy.concatenate([batch[1] for batch in batches]) else: X = numpy.asarray(X) if weights is None: weights = numpy.ones(X.shape[0], dtype='float64') else: weights = numpy.asarray(weights, dtype='float64') n, d = X.shape keys = keys or [set([x for x in X[:,i] if not _check_nan(x)]) for i in range(d)] keymap = numpy.array([{key: i for i, key in enumerate(keys[j])} for j in range(d)]) key_count = numpy.array([len(keymap[i]) for i in range(d)], dtype='int32') if reduce_dataset: X_count = {} for x, weight in zip(X, weights): # Convert NaN to None because two tuples containing # (1.0, 2.0, 3.0, nan) are not considered equal, but two tuples # containing (1.0, 2.0, 3.0, None) are considered equal x = tuple(None if _check_nan(xn) else xn for xn in x) if x in X_count: X_count[x] += weight else: X_count[x] = weight weights = numpy.array(list(X_count.values()), dtype='float64') X = numpy.array(list(X_count.keys()), dtype=X.dtype) n, d = X.shape X_int = numpy.empty((n, d), dtype='float32') for i in range(n): for j in range(d): if _check_nan(X[i, j]): X_int[i, j] = nan else: X_int[i, j] = keymap[j][X[i, j]] w_sum = weights.sum() if max_parents == -1 or max_parents > _log2(2*w_sum / _log2(w_sum)): max_parents = int(_log2(2*w_sum / _log2(w_sum))) if penalty is None: penalty = -1 if algorithm == 'chow-liu': if numpy.any(numpy.isnan(X_int)): raise ValueError("Chow-Liu tree learning does not current support missing values") structure = discrete_chow_liu_tree(X_int, weights, key_count, pseudocount=pseudocount, root=root) elif algorithm == 'exact' and constraint_graph is not None: structure = discrete_exact_with_constraints(X=X_int, weights=weights, key_count=key_count, include_edges=include_edges, exclude_edges=exclude_edges, pseudocount=pseudocount, penalty=penalty, max_parents=max_parents, constraint_graph=constraint_graph, low_memory=low_memory, n_jobs=n_jobs) elif algorithm == 'exact': structure = discrete_exact_a_star(X=X_int, weights=weights, key_count=key_count, include_edges=include_edges, exclude_edges=exclude_edges, pseudocount=pseudocount, penalty=penalty, max_parents=max_parents, low_memory=low_memory, n_jobs=n_jobs) elif algorithm == 'greedy': structure = discrete_greedy(X=X_int, weights=weights, key_count=key_count, include_edges=include_edges, exclude_edges=exclude_edges, pseudocount=pseudocount, penalty=penalty, max_parents=max_parents, low_memory=low_memory, n_jobs=n_jobs) elif algorithm == 'exact-dp': structure = discrete_exact_dp(X=X_int, weights=weights, key_count=key_count, include_edges=include_edges, exclude_edges=exclude_edges, pseudocount=pseudocount, penalty=penalty, max_parents=max_parents, n_jobs=n_jobs) else: raise ValueError("Invalid algorithm type passed in. Must be one of 'chow-liu', 'exact', 'exact-dp', 'greedy'") return cls.from_structure(X, structure=structure, weights=weights, pseudocount=pseudocount, name=name, state_names=state_names, keys=keys) cdef class ParentGraph(object): """ Generate a parent graph for a single variable over its parents. This will generate the parent graph for a single parents given the data. A parent graph is the dynamically generated best parent set and respective score for each combination of parent variables. For example, if we are generating a parent graph for x1 over x2, x3, and x4, we may calculate that having x2 as a parent is better than x2,x3 and so store the value of x2 in the node for x2,x3. Parameters ---------- X : numpy.ndarray, shape=(n, d) The data to fit the structure too, where each row is a sample and each column corresponds to the associated variable. weights : numpy.ndarray, shape=(n,) The weight of each sample as a positive double. Default is None. key_count : numpy.ndarray, shape=(d,) The number of unique keys in each column. include_parents : tuple A set of parents that this node must have. exclude_parents : tuple A set of parents that this node cannot have. pseudocount : double A pseudocount to add to each possibility. penalty : float or None, optional The weighting of the model complexity term in the objective function. Increasing this value will encourage sparsity whereas setting the value to 0 will result in an unregularized structure. Default is log2(|D|) / 2 where |D| is the sum of the weights of the data. max_parents : int The maximum number of parents a node can have. If used, this means using the k-learn procedure. Can drastically speed up algorithms. If -1, no max on parents. Default is -1. low_memory : bool or None, optional Whether to use a low-memory version of the search algorithm. This option only affects algorithm="greedy" and algorithm="exact". Although the low-memory version of both the greedy and exact algorithms will use less memory, it will also significantly slow down the exact algorithm. However, setting this to True will also significantly speed up the greedy algorithm. Setting this value to None will enable it when algorithm="greedy" and disable it otherwise. Default is None. Returns ------- structure : tuple, shape=(d,) The parents for each variable in this SCC """ cdef int i, n, d, max_parents cdef double pseudocount cdef dict values cdef numpy.ndarray X cdef numpy.ndarray weights cdef numpy.ndarray key_count cdef set include_parents cdef set exclude_parents cdef int* m cdef int* parents cdef double penalty cdef bint low_memory def __init__(self, X, weights, key_count, i, include_edges=[], exclude_edges=[], pseudocount=0.0, penalty=-1, max_parents=-1, low_memory=False): self.X = X self.weights = weights self.key_count = key_count self.i = i self.pseudocount = pseudocount self.max_parents = max_parents self.values = {} self.n = X.shape[0] self.d = X.shape[1] self.include_parents = set([parent for parent, child in include_edges if child == i]) self.exclude_parents = set([parent for parent, child in exclude_edges if child == i]) self.m = malloc((self.d+2)*sizeof(int)) self.parents = malloc(self.d*sizeof(int)) self.penalty = penalty self.low_memory = low_memory def __len__(self): return len(self.values) def __dealloc__(self): free(self.m) free(self.parents) def calculate_value(self, value): cdef int k, parent, l = len(value) cdef float* X = self.X.data cdef int* key_count = self.key_count.data cdef int* m = self.m cdef int* parents = self.parents cdef double* weights = self.weights.data cdef double score m[0] = 1 for k, parent in enumerate(value): m[k+1] = m[k] * key_count[parent] parents[k] = parent parents[l] = self.i m[l+1] = m[l] * key_count[self.i] m[l+2] = m[l] * (key_count[self.i] - 1) with nogil: score = discrete_score_node(X, weights, m, parents, self.n, l+1, self.d, self.pseudocount, self.penalty) return score def __getitem__(self, value): if value in self.values: return self.values[value] best_parents, best_score = (), NEGINF max_parents= max(self.max_parents,len(self.include_parents)) if len(value) <= max_parents: for parent in value: if parent in self.exclude_parents: break else: for parent in self.include_parents: if parent not in value: break else: best_parents, best_score = value, self.calculate_value( value) if self.low_memory: if len(value) > 0: max_parents = min(max_parents, len(value) - 1) for parent_subset in it.combinations(value, max_parents): parents, score = self[parent_subset] if score > best_score: best_score = score best_parents = parents else: for i in range(len(value)): parent_subset = value[:i] + value[i+1:] parents, score = self[parent_subset] if score > best_score: best_score = score best_parents = parents self.values[value] = (best_parents, best_score) return self.values[value] def discrete_chow_liu_tree(numpy.ndarray X_ndarray, numpy.ndarray weights_ndarray, numpy.ndarray key_count_ndarray, double pseudocount, int root): cdef int i, j, k, l, lj, lk, Xj, Xk, xj, xk cdef int n = X_ndarray.shape[0], d = X_ndarray.shape[1] cdef int max_keys = key_count_ndarray.max() cdef float* X = X_ndarray.data cdef double* weights = weights_ndarray.data cdef int* key_count = key_count_ndarray.data cdef double* mutual_info = calloc(d * d, sizeof(double)) cdef double* marg_j = malloc(max_keys*sizeof(double)) cdef double* marg_k = malloc(max_keys*sizeof(double)) cdef double* joint_count = malloc(max_keys**2*sizeof(double)) for j in range(d): for k in range(j): if j == k: continue lj = key_count[j] lk = key_count[k] for i in range(max_keys): marg_j[i] = pseudocount marg_k[i] = pseudocount for l in range(max_keys): joint_count[i*max_keys + l] = pseudocount for i in range(n): Xj = X[i*d + j] Xk = X[i*d + k] joint_count[Xj * lk + Xk] += weights[i] marg_j[Xj] += weights[i] marg_k[Xk] += weights[i] for xj in range(lj): for xk in range(lk): if joint_count[xj*lk+xk] > 0: mutual_info[j*d + k] -= joint_count[xj*lk+xk] * _log( joint_count[xj*lk+xk] / (marg_j[xj] * marg_k[xk])) mutual_info[k*d + j] = mutual_info[j*d + k] cdef int x, y, min_x, min_y cdef double min_score, score structure = [[] for i in range(d)] visited = [root] unvisited = list(range(d)) unvisited.remove(root) for i in range(d-1): min_score = float("inf") min_x = -1 min_y = -1 for x in visited: for y in unvisited: score = mutual_info[x*d + y] if score < min_score: min_score = score min_x = x min_y = y structure[min_y].append(min_x) visited.append(min_y) unvisited.remove(min_y) free(mutual_info) free(marg_j) free(marg_k) free(joint_count) return tuple(tuple(x) for x in structure) def discrete_exact_dp(X, weights, key_count, include_edges, exclude_edges, pseudocount, penalty, max_parents, n_jobs): """ Find the optimal graph over a set of variables with no other knowledge. This is the naive dynamic programming structure learning task where the optimal graph is identified from a set of variables using an order graph and parent graphs. This can be used either when no constraint graph is provided or for a SCC which is made up of a node containing a self-loop. This is a reference implementation that uses the naive shortest path algorithm over the entire order graph. The 'exact' option uses the A* path in order to avoid considering the full order graph. Parameters ---------- X : numpy.ndarray, shape=(n, d) The data to fit the structure too, where each row is a sample and each column corresponds to the associated variable. weights : numpy.ndarray, shape=(n,) The weight of each sample as a positive double. Default is None. key_count : numpy.ndarray, shape=(d,) The number of unique keys in each column. include_edges : list or None A set of (parent, child) tuples where each tuple is an edge that must exist in the found structure. exclude_edges : list or None A set of (parent, child) tuples where each tuple is an edge that cannot exist in the found structure. penalty : float or None, optional The weighting of the model complexity term in the objective function. Increasing this value will encourage sparsity whereas setting the value to 0 will result in an unregularized structure. Default is log2(|D|) / 2 where |D| is the sum of the weights of the data. max_parents : int The maximum number of parents a node can have. If used, this means using the k-learn procedure. Can drastically speed up algorithms. If -1, no max on parents. Default is -1. n_jobs : int The number of threads to use when learning the structure of the network. This parallelizes the creation of the parent graphs. Returns ------- structure : tuple, shape=(d,) The parents for each variable in this SCC """ cdef int i, n = X.shape[0], d = X.shape[1] cdef list parent_graphs = [] parent_graphs = Parallel(n_jobs=n_jobs, backend='threading')( delayed(generate_parent_graph)(X, weights, key_count, i, include_edges, exclude_edges, pseudocount, penalty, max_parents) for i in range(d)) order_graph = nx.DiGraph() for i in range(d+1): for subset in it.combinations(range(d), i): order_graph.add_node(subset) for variable in subset: parent = tuple(v for v in subset if v != variable) structure, weight = parent_graphs[variable][parent] weight = -weight if weight < 0 else 0 order_graph.add_edge(parent, subset, weight=weight, structure=structure) path = nx.shortest_path(order_graph, source=(), target=tuple(range(d)), weight='weight') score, structure = 0, list( None for i in range(d) ) for u, v in zip(path[:-1], path[1:]): idx = list(set(v) - set(u))[0] parents = order_graph.get_edge_data(u, v)['structure'] structure[idx] = parents score -= order_graph.get_edge_data(u, v)['weight'] return tuple(structure) def discrete_exact_a_star(X, weights, key_count, include_edges, exclude_edges, pseudocount, penalty, max_parents, low_memory, n_jobs): """ Find the optimal graph over a set of variables with no other knowledge. This is the naive dynamic programming structure learning task where the optimal graph is identified from a set of variables using an order graph and parent graphs. This can be used either when no constraint graph is provided or for a SCC which is made up of a node containing a self-loop. It uses DP/A* in order to find the optimal graph without considering all possible topological sorts. A greedy version of the algorithm can be used that massively reduces both the computational and memory cost while frequently producing the optimal graph. Parameters ---------- X : numpy.ndarray, shape=(n, d) The data to fit the structure too, where each row is a sample and each column corresponds to the associated variable. weights : numpy.ndarray, shape=(n,) The weight of each sample as a positive double. Default is None. key_count : numpy.ndarray, shape=(d,) The number of unique keys in each column. include_edges : list or None A list of (parent, child) tuples where each tuple corresponds to an edge that must exist in the found structure. exclude_edges : list or None A list of (parent, child) tuples where each tuple corresponds to an edge that cannot exist in the found structure. pseudocount : double A pseudocount to add to each possibility. penalty : float or None, optional The weighting of the model complexity term in the objective function. Increasing this value will encourage sparsity whereas setting the value to 0 will result in an unregularized structure. Default is log2(|D|) / 2 where |D| is the sum of the weights of the data. max_parents : int The maximum number of parents a node can have. If used, this means using the k-learn procedure. Can drastically speed up algorithms. If -1, no max on parents. Default is -1. low_memory : bool or None, optional Whether to use a low-memory version of the search algorithm. This option only affects algorithm="greedy" and algorithm="exact". Although the low-memory version of both the greedy and exact algorithms will use less memory, it will also significantly slow down the exact algorithm. However, setting this to True will also significantly speed up the greedy algorithm. Setting this value to None will enable it when algorithm="greedy" and disable it otherwise. Default is None. n_jobs : int The number of threads to use when learning the structure of the network. This parallelizes the creation of the parent graphs. Returns ------- structure : tuple, shape=(d,) The parents for each variable in this SCC """ cdef int i, n = X.shape[0], d = X.shape[1] parent_graphs = [ParentGraph(X=X, weights=weights, key_count=key_count, include_edges=include_edges, exclude_edges=exclude_edges, i=i, pseudocount=pseudocount, penalty=penalty, max_parents=max_parents, low_memory=low_memory) for i in range(d)] other_variables = {} for i in range(d): other_variables[i] = tuple(j for j in range(d) if j != i) o = PriorityQueue() closed = set() h = sum(parent_graphs[i][other_variables[i]][1] for i in range(d)) o.push(((), h, [() for i in range(d)]), 0) while not o.empty(): weight, (variables, g, structure) = o.pop() if variables in closed: continue else: closed.add(variables) if len(variables) == d: return tuple(structure) out_set = tuple(i for i in range(d) if i not in variables) for i in out_set: pg = parent_graphs[i] parents, c = pg[variables] e = g - c f = weight - c + pg[other_variables[i]][1] local_structure = structure[:] local_structure[i] = parents new_variables = tuple(sorted(variables + (i,))) entry = (new_variables, e, local_structure) prev_entry = o.get(new_variables) if prev_entry is not None: if prev_entry[0] > f: o.delete(new_variables) o.push(entry, f) else: o.push(entry, f) def discrete_greedy(X, weights, key_count, include_edges, exclude_edges, pseudocount, penalty, max_parents, low_memory, n_jobs): """Find the optimal graph over a set of variables with no other knowledge. Parameters ---------- X : numpy.ndarray, shape=(n, d) The data to fit the structure too, where each row is a sample and each column corresponds to the associated variable. weights : numpy.ndarray, shape=(n,) The weight of each sample as a positive double. Default is None. key_count : numpy.ndarray, shape=(d,) The number of unique keys in each column. include_edges : list or None A list of (parent, child) tuples where each tuple corresponds to an edge that must exist in the found structure. exclude_edges : list or None A list of (parent, child) tuples where each tuple corresponds to an edge that cannot exist in the found structure. pseudocount : double A pseudocount to add to each possibility. penalty : float or None, optional The weighting of the model complexity term in the objective function. Increasing this value will encourage sparsity whereas setting the value to 0 will result in an unregularized structure. Default is log2(|D|) / 2 where |D| is the sum of the weights of the data. max_parents : int The maximum number of parents a node can have. If used, this means using the k-learn procedure. Can drastically speed up algorithms. If -1, no max on parents. Default is -1. low_memory : bool or None, optional Whether to use a low-memory version of the search algorithm. This option only affects algorithm="greedy" and algorithm="exact". Although the low-memory version of both the greedy and exact algorithms will use less memory, it will also significantly slow down the exact algorithm. However, setting this to True will also significantly speed up the greedy algorithm. Setting this value to None will enable it when algorithm="greedy" and disable it otherwise. Default is None. n_jobs : int The number of threads to use when learning the structure of the network. This parallelizes the creation of the parent graphs. Returns ------- structure : tuple, shape=(d,) The parents for each variable in this SCC """ cdef int i, n = X.shape[0], d = X.shape[1] cdef list parent_graphs = [] parent_graphs = [ParentGraph(X=X, weights=weights, key_count=key_count, include_edges=include_edges, exclude_edges=exclude_edges, i=i, pseudocount=pseudocount, penalty=penalty, max_parents=max_parents, low_memory=low_memory) for i in range(d)] structure, seen_variables, unseen_variables = [() for i in range(d)], (), set(range(d)) for i in range(d): best_score = NEGINF best_variable = -1 best_parents = None for j in unseen_variables: parents, score = parent_graphs[j][seen_variables] if score > best_score or (score == NEGINF and best_score == NEGINF): best_score = score best_variable = j best_parents = parents structure[best_variable] = best_parents seen_variables = tuple(sorted(seen_variables + (best_variable,))) unseen_variables.remove(best_variable) parent_graphs[best_variable] = None #free memory return tuple(structure) def discrete_exact_with_constraints(X, weights, key_count, include_edges, exclude_edges, pseudocount, penalty, max_parents, constraint_graph, low_memory, n_jobs): """This returns the optimal Bayesian network given a set of constraints. This function controls the process of learning the Bayesian network by taking in a constraint graph, identifying the strongly connected components (SCCs) and solving each one using the appropriate algorithm. This is mostly an internal function. Parameters ---------- X : numpy.ndarray, shape=(n, d) The data to fit the structure too, where each row is a sample and each column corresponds to the associated variable. weights : numpy.ndarray, shape=(n,) The weight of each sample as a positive double. Default is None. key_count : numpy.ndarray, shape=(d,) The number of unique keys in each column. include_edges : list or None A set of (parent, child) tuples where each tuple is an edge that must exist in the found structure. exclude_edges : list or None A set of (parent, child) tuples where each tuple is an edge that cannot exist in the found structure. pseudocount : double A pseudocount to add to each possibility. penalty : float or None, optional The weighting of the model complexity term in the objective function. Increasing this value will encourage sparsity whereas setting the value to 0 will result in an unregularized structure. Default is log2(|D|) / 2 where |D| is the sum of the weights of the data. max_parents : int The maximum number of parents a node can have. If used, this means using the k-learn procedure. Can drastically speed up algorithms. If -1, no max on parents. Default is -1. constraint_graph : networkx.DiGraph A directed graph showing valid parent sets for each variable. Each node is a set of variables, and edges represent which variables can be valid parents of those variables. The naive structure learning task is just all variables in a single node with a self edge, meaning that you know nothing about low_memory : bool or None, optional Whether to use a low-memory version of the search algorithm. This option only affects algorithm="greedy" and algorithm="exact". Although the low-memory version of both the greedy and exact algorithms will use less memory, it will also significantly slow down the exact algorithm. However, setting this to True will also significantly speed up the greedy algorithm. Setting this value to None will enable it when algorithm="greedy" and disable it otherwise. Default is None. n_jobs : int The number of threads to use when learning the structure of the network. This parallelized both the creation of the parent graphs for each variable and the solving of the SCCs. -1 means use all available resources. Default is 1, meaning no parallelism. Returns ------- structure : tuple, shape=(d,) The parents for each variable in the network. """ parent_sets = {node : tuple() for node in constraint_graph.nodes()} for parents, children in constraint_graph.edges(): parent_sets[children] += parents tasks = [] for component in nx.strongly_connected_components(constraint_graph): component = list(component) if len(component) == 1: children = component[0] parents = tuple(sorted(parent_sets[children])) if children == parents: task = (0, parents, children) tasks.append(task) elif set(children).issubset(set(parents)): task = (1, parents, children) tasks.append(task) else: if len(parents) > 0: for child in children: task = (2, parents, child) tasks.append(task) else: parents = [parent_sets[children] for children in component] task = (3, parents, component) tasks.append(task) with Parallel(n_jobs=n_jobs, backend='threading') as parallel: local_structures = parallel(delayed(discrete_exact_with_constraints_task)( X, weights, key_count, include_edges, exclude_edges, pseudocount, penalty, max_parents, task, low_memory, n_jobs) for task in tasks) structure = [[] for i in range(X.shape[1])] for local_structure in local_structures: for i in range(X.shape[1]): structure[i] += list(local_structure[i]) return tuple(tuple(node) for node in structure) def discrete_exact_with_constraints_task(X, weights, key_count, include_edges, exclude_edges, pseudocount, penalty, max_parents, task, low_memory, n_jobs): """This is a wrapper for the function to be parallelized by joblib. This function takes in a single task as an id and a set of parents and children and calls the appropriate function. This is mostly a wrapper for joblib to parallelize. Parameters ---------- X : numpy.ndarray, shape=(n, d) The data to fit the structure too, where each row is a sample and each column corresponds to the associated variable. weights : numpy.ndarray, shape=(n,) The weight of each sample as a positive double. Default is None. key_count : numpy.ndarray, shape=(d,) The number of unique keys in each column. include_edges : list or None A set of (parent, child) tuples where each tuple is an edge that must exist in the found structure. exclude_edges : list or None A set of (parent, child) tuples where each tuple is an edge that cannot exist in the found structure. pseudocount : double A pseudocount to add to each possibility. penalty : float or None, optional The weighting of the model complexity term in the objective function. Increasing this value will encourage sparsity whereas setting the value to 0 will result in an unregularized structure. Default is log2(|D|) / 2 where |D| is the sum of the weights of the data. max_parents : int The maximum number of parents a node can have. If used, this means using the k-learn procedure. Can drastically speed up algorithms. If -1, no max on parents. Default is -1. task : tuple A 3-tuple containing the id, the set of parents and the set of children to learn a component of the Bayesian network over. The cases represent a SCC of the following: 0 - Self loop and no parents 1 - Self loop and parents 2 - Parents and no self loop 3 - Multiple nodes low_memory : bool or None, optional Whether to use a low-memory version of the search algorithm. This option only affects algorithm="greedy" and algorithm="exact". Although the low-memory version of both the greedy and exact algorithms will use less memory, it will also significantly slow down the exact algorithm. However, setting this to True will also significantly speed up the greedy algorithm. Setting this value to None will enable it when algorithm="greedy" and disable it otherwise. Default is None. n_jobs : int The number of threads to use when learning the structure of the network. This parallelizes the creation of the parent graphs for each task or the finding of best parents in case 2. Returns ------- structure : tuple, shape=(d,) The parents for each variable in this SCC """ d = X.shape[1] structure = [() for i in range(d)] case, parents, children = task if case == 0: parents = list(parents) include_edges = [(parents.index(parent), parents.index(child)) for parent, child in include_edges if parent in parents and child in parents] exclude_edges = [(parents.index(parent), parents.index(child)) for parent, child in exclude_edges if parent in parents and child in parents] local_structure = discrete_exact_a_star(X[:,parents].copy(), weights, key_count[list(parents)], include_edges=include_edges, exclude_edges=exclude_edges, pseudocount=pseudocount, penalty=penalty, max_parents=max_parents, low_memory=low_memory, n_jobs=n_jobs) for i, parent in enumerate(parents): structure[parent] = tuple([parents[k] for k in local_structure[i]]) elif case == 1: structure = discrete_exact_slap(X, weights, task, key_count, include_edges=include_edges, exclude_edges=exclude_edges, pseudocount=pseudocount, penalty=penalty, max_parents=max_parents, n_jobs=n_jobs) elif case == 2: exclude_parents = set([parent for parent, child in exclude_edges if child == children]) parents = tuple(parent for parent in parents if parent not in exclude_parents) logp, local_structure = discrete_find_best_parents(X, weights, key_count, pseudocount, penalty, max_parents, parents, children) structure[children] = local_structure elif case == 3: structure = discrete_exact_component(X, weights, task, key_count, include_edges=include_edges, exclude_edges=exclude_edges, pseudocount=pseudocount, max_parents=max_parents, penalty=penalty, n_jobs=n_jobs) return tuple(structure) def discrete_exact_slap(X, weights, task, key_count, include_edges, exclude_edges, pseudocount, penalty, max_parents, n_jobs): """ Find the optimal graph in a node with a Self Loop And Parents (SLAP). Instead of just performing exact BNSL over the set of all parents and removing the offending edges there are efficiencies that can be gained by considering the structure. In particular, parents not coming from the main node do not need to be considered in the order graph but simply added to each entry after creation of the order graph. This is because those variables occur earlier in the topological ordering but it doesn't matter how they occur otherwise. Parent graphs must be defined over all variables however. Parameters ---------- X : numpy.ndarray, shape=(n, d) The data to fit the structure too, where each row is a sample and each column corresponds to the associated variable. weights : numpy.ndarray, shape=(n,) The weight of each sample as a positive double. Default is None. key_count : numpy.ndarray, shape=(d,) The number of unique keys in each column. include_edges : list or None A list of (parent, child) tuples where each tuple corresponds to an edge that must exist in the found structure. exclude_edges : list or None A list of (parent, child) tuples where each tuple corresponds to an edge that cannot exist in the found structure. pseudocount : double A pseudocount to add to each possibility. penalty : float or None, optional The weighting of the model complexity term in the objective function. Increasing this value will encourage sparsity whereas setting the value to 0 will result in an unregularized structure. Default is log2(|D|) / 2 where |D| is the sum of the weights of the data. max_parents : int The maximum number of parents a node can have. If used, this means using the k-learn procedure. Can drastically speed up algorithms. If -1, no max on parents. Default is -1. n_jobs : int The number of threads to use when learning the structure of the network. This parallelizes the creation of the parent graphs. Returns ------- structure : tuple, shape=(d,) The parents for each variable in this SCC """ cdef tuple parents = task[1], children = task[2] cdef tuple outside_parents = tuple(i for i in parents if i not in children) cdef int i, n = X.shape[0], d = X.shape[1] cdef list parent_graphs = [None for i in range(max(parents)+1)] graphs = Parallel(n_jobs=n_jobs, backend='threading')( delayed(generate_parent_graph)(X, weights, key_count, i, include_edges, exclude_edges, pseudocount, penalty, max_parents) for i in children) for i, child in enumerate(children): parent_graphs[child] = graphs[i] order_graph = nx.DiGraph() for i in range(d+1): for subset in it.combinations(children, i): subset_and_outside = tuple(sorted(tuple(set(subset + outside_parents)))) order_graph.add_node(subset_and_outside) for variable in subset: parent = tuple(v for v in subset if v != variable) parent += outside_parents parent = tuple(sorted(tuple(set(parent)))) structure, weight = parent_graphs[variable][parent] weight = -weight if weight < 0 else 0 order_graph.add_edge(parent, subset_and_outside, weight=weight, structure=structure) path = nx.shortest_path(order_graph, source=outside_parents, target=parents, weight='weight') score, structure = 0, list(() for i in range(d)) for u, v in zip(path[:-1], path[1:]): idx = list(set(v) - set(u))[0] parents = order_graph.get_edge_data(u, v)['structure'] structure[idx] = parents score -= order_graph.get_edge_data(u, v)['weight'] return tuple(structure) def discrete_exact_component(X, weights, task, key_count, include_edges, exclude_edges, pseudocount, penalty, max_parents, n_jobs): """Find the optimal graph over a multi-node component of the constaint graph. The general algorithm in this case is to begin with each variable and add all possible single children for that entry recursively until completion. This will result in a far sparser order graph than before. In addition, one can eliminate entries from the parent graphs that contain invalid parents as they are a fast of computational time. Parameters ---------- X : numpy.ndarray, shape=(n, d) The data to fit the structure too, where each row is a sample and each column corresponds to the associated variable. weights : numpy.ndarray, shape=(n,) The weight of each sample as a positive double. Default is None. include_edges : list or None A list of (parent, child) tuples where each tuple corresponds to an edge that must exist in the found structure. exclude_edges : list or None A list of (parent, child) tuples where each tuple corresponds to an edge that cannot exist in the found structure. key_count : numpy.ndarray, shape=(d,) The number of unique keys in each column. pseudocount : double A pseudocount to add to each possibility. penalty : float or None, optional The weighting of the model complexity term in the objective function. Increasing this value will encourage sparsity whereas setting the value to 0 will result in an unregularized structure. Default is log2(|D|) / 2 where |D| is the sum of the weights of the data. max_parents : int The maximum number of parents a node can have. If used, this means using the k-learn procedure. Can drastically speed up algorithms. If -1, no max on parents. Default is -1. n_jobs : int The number of threads to use when learning the structure of the network. This parallelizes the creation of the parent graphs. Returns ------- structure : tuple, shape=(d,) The parents for each variable in this SCC """ cdef int i, n = X.shape[0], d = X.shape[1] variable_set = set() for parents in task[1]: variable_set = variable_set.union(parents) for children in task[2]: variable_set = variable_set.union(children) parent_sets = {variable: () for variable in variable_set} child_sets = {variable: () for variable in variable_set} for parents, children in zip(task[1], task[2]): for child in children: parent_sets[child] += parents for parent in parents: child_sets[parent] += children graphs = Parallel(n_jobs=n_jobs, backend='threading')( delayed(generate_parent_graph)(X, weights, key_count, child, include_edges, exclude_edges, pseudocount, penalty, max_parents, parents) for child, parents in parent_sets.items()) parent_graphs = [None for i in range(d)] for (child, _), graph in zip(parent_sets.items(), graphs): parent_graphs[child] = graph last_layer = [] last_layer_children = [] order_graph = nx.DiGraph() order_graph.add_node(()) for variable in variable_set: order_graph.add_node((variable,)) structure, weight = parent_graphs[variable][()] weight = -weight if weight < 0 else 0 order_graph.add_edge((), (variable,), weight=weight, structure=structure) last_layer.append((variable,)) last_layer_children.append(set(child_sets[variable])) layer = [] layer_children = [] seen_entries = {(variable,): 1 for variable in variable_set} for i in range(len(variable_set)-1): for parent_entry, child_set in zip(last_layer, last_layer_children): for child in child_set: parent_set = parent_sets[child] entry = tuple(sorted(parent_entry + (child,))) filtered_entry = tuple(variable for variable in parent_entry if variable in parent_set) structure, weight = parent_graphs[child][filtered_entry] weight = -weight if weight < 0 else 0 order_graph.add_edge(parent_entry, entry, weight=round(weight, 4), structure=structure) new_child_set = child_set - set([child]) for grandchild in child_sets[child]: if grandchild not in entry: new_child_set.add(grandchild) if entry not in seen_entries: seen_entries[entry] = 1 layer.append(entry) layer_children.append(new_child_set) last_layer = layer last_layer_children = layer_children layer = [] layer_children = [] path = nx.shortest_path(order_graph, source=(), target=tuple(sorted(variable_set)), weight='weight') score, structure = 0, list(() for i in range(d)) for u, v in zip(path[:-1], path[1:]): idx = list(set(v) - set(u))[0] parents = order_graph.get_edge_data(u, v)['structure'] structure[idx] = parents score -= order_graph.get_edge_data(u, v)['weight'] return tuple(structure) def generate_parent_graph(numpy.ndarray X_ndarray, numpy.ndarray weights_ndarray, numpy.ndarray key_count_ndarray, int i, list include_edges, list exclude_edges, double pseudocount, double penalty, int max_parents, tuple parent_set=()): """ Generate a parent graph for a single variable over its parents. This will generate the parent graph for a single parents given the data. A parent graph is the dynamically generated best parent set and respective score for each combination of parent variables. For example, if we are generating a parent graph for x1 over x2, x3, and x4, we may calculate that having x2 as a parent is better than x2,x3 and so store the value of x2 in the node for x2,x3. Parameters ---------- X : numpy.ndarray, shape=(n, d) The data to fit the structure too, where each row is a sample and each column corresponds to the associated variable. weights : numpy.ndarray, shape=(n,) The weight of each sample as a positive double. Default is None. key_count : numpy.ndarray, shape=(d,) The number of unique keys in each column. i : int The column index to build the parent graph for. include_edges : list or None A list of (parent, child) tuples where each tuple corresponds to an edge that must exist in the found structure. exclude_edges : list or None A list of (parent, child) tuples where each tuple corresponds to an edge that cannot exist in the found structure. pseudocount : double A pseudocount to add to each possibility. penalty : float or None, optional The weighting of the model complexity term in the objective function. Increasing this value will encourage sparsity whereas setting the value to 0 will result in an unregularized structure. Default is log2(|D|) / 2 where |D| is the sum of the weights of the data. max_parents : int The maximum number of parents a node can have. If used, this means using the k-learn procedure. Can drastically speed up algorithms. If -1, no max on parents. Default is -1. parent_set : tuple, default () The variables which are possible parents for this variable. If nothing is passed in then it defaults to all other variables, as one would expect in the naive case. This allows for cases where we want to build a parent graph over only a subset of the variables. Returns ------- structure : tuple, shape=(d,) The parents for each variable in this SCC """ cdef int j, k, variable, l cdef int n = X_ndarray.shape[0], d = X_ndarray.shape[1] cdef float* X = X_ndarray.data cdef int* key_count = key_count_ndarray.data cdef int* m = malloc((d+2)*sizeof(int)) cdef int* parents = malloc(d*sizeof(int)) cdef double* weights = weights_ndarray.data cdef dict parent_graph = {} cdef double best_score, score include_parents = set([parent for parent, child in include_edges if child == i]) exclude_parents = set([parent for parent, child in exclude_edges if child == i]) if parent_set == (): parent_set = tuple(set(range(d)) - set([i])) cdef int n_parents = len(parent_set) m[0] = 1 for j in range(n_parents+1): for subset in it.combinations(parent_set, j): subset_ = set(subset) best_structure = () best_score = NEGINF if j <= max(max_parents, len(include_parents)): for parent in include_parents: if parent not in subset_: break else: for k, variable in enumerate(subset): if variable in exclude_parents: break m[k+1] = m[k] * key_count_ndarray[variable] parents[k] = variable else: best_structure = subset parents[j] = i m[j+1] = m[j] * key_count[i] m[j+2] = m[j] * (key_count[i] - 1) with nogil: best_score = discrete_score_node(X, weights, m, parents, n, j+1, d, pseudocount, penalty) for k, variable in enumerate(subset): parent_subset = tuple(l for l in subset if l != variable) structure, score = parent_graph[parent_subset] if score > best_score: best_score = score best_structure = structure parent_graph[subset] = (best_structure, best_score) free(m) free(parents) return parent_graph cdef discrete_find_best_parents(numpy.ndarray X_ndarray, numpy.ndarray weights_ndarray, numpy.ndarray key_count_ndarray, double pseudocount, double penalty, int max_parents, tuple parent_set, int i): cdef int j, k cdef int n = X_ndarray.shape[0], l = X_ndarray.shape[1] cdef float* X = X_ndarray.data cdef int* key_count = key_count_ndarray.data cdef int* m = malloc((l+2)*sizeof(int)) cdef int* combs = malloc(l*sizeof(int)) cdef double* weights = weights_ndarray.data cdef double best_score = NEGINF, score cdef tuple best_parents, parents m[0] = 1 for k in range(min(max_parents, len(parent_set))+1): for parents in it.combinations(parent_set, k): for j in range(k): combs[j] = parents[j] m[j+1] = m[j] * key_count[combs[j]] combs[k] = i m[k+1] = m[k] * key_count[i] m[k+2] = m[k] * (key_count[i] - 1) with nogil: score = discrete_score_node(X, weights, m, combs, n, k+1, l, pseudocount, penalty) if score > best_score: best_score = score best_parents = parents free(m) free(combs) return best_score, best_parents cdef double discrete_score_node(float* X, double* weights, int* m, int* parents, int n, int d, int l, double pseudocount, double penalty) nogil: cdef int i, j, k, idx cdef double w_sum = 0 cdef double logp = 0 cdef double count, marginal_count cdef double* counts = calloc(m[d], sizeof(double)) cdef double* marginal_counts = calloc(m[d-1], sizeof(double)) cdef float* row for i in range(n): idx = 0 row = X+i*l for j in range(d-1): k = parents[j] if isnan(row[k]): break idx += row[k] * m[j] else: k = parents[d-1] if isnan(row[k]): continue marginal_counts[idx] += weights[i] idx += row[k] * m[d-1] counts[idx] += weights[i] for i in range(m[d]): w_sum += counts[i] count = pseudocount + counts[i] marginal_count = pseudocount * (m[d] / m[d-1]) + marginal_counts[i%m[d-1]] if count > 0: logp += count * _log2(count / marginal_count) if w_sum > 1: if penalty == -1: logp -= _log2(w_sum) / 2 * m[d+1] else: logp -= penalty * m[d+1] else: logp = NEGINF free(counts) free(marginal_counts) return logp pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/FactorGraph.pyx000066400000000000000000000323561420461712600261460ustar00rootroot00000000000000# FactorGraph.pyx # Contact: Jacob Schreiber (jmschreiber91@gmail.com) cimport numpy import numpy from .base cimport GraphModel from .base cimport State from distributions.distributions cimport Distribution from distributions.distributions cimport MultivariateDistribution cdef class FactorGraph(GraphModel): """A Factor Graph model. A bipartite graph where conditional probability tables are on one side, and marginals for each of the variables involved are on the other side. Parameters ---------- name : str, optional The name of the model. Default is None. """ cdef numpy.ndarray transitions, edge_count, marginals def __init__(self, name=None): """ Make a new graphical model. Name is an optional string used to name the model when output. Name may not contain spaces or newlines. """ self.name = name or str(id(self)) self.states = [] self.edges = [] def plot(self, **kwargs): """Draw this model's graph using NetworkX and matplotlib. Note that this relies on networkx's built-in graphing capabilities (and not Graphviz) and thus can't draw self-loops. See networkx.draw_networkx() for the keywords you can pass in. Parameters ---------- **kwargs : any The arguments to pass into networkx.draw_networkx() Returns ------- None """ try: import pygraphviz import tempfile import matplotlib import matplotlib.pyplot as plt except: pygraphviz = None if pygraphviz is not None: G = pygraphviz.AGraph(directed=True) for state in self.states: G.add_node(state.name, color='red') for parent, child in self.edges: G.add_edge(self.states[parent].name, self.states[child].name) with tempfile.NamedTemporaryFile() as tf: G.draw(tf.name, format='png', prog='dot') img = matplotlib.image.imread(tf.name) plt.imshow(img) plt.axis('off') else: raise ValueError("must have pygraphviz installed for visualization") def bake(self): """Finalize the topology of the model. Assign a numerical index to every state and create the underlying arrays corresponding to the states and edges between the states. This method must be called before any of the probability-calculating methods. This is the same as the HMM bake, except that at the end it sets current state information. Parameters ---------- None Returns ------- None """ n, m = len(self.states), len(self.edges) # Initialize the arrays self.marginals = numpy.empty(n, dtype=numpy.bool_) # We need a good way to get transition probabilities by state index that # isn't N^2 to build or store. So we will need a reverse of the above # mapping. It's awkward but asymptotically fine. indices = {self.states[i]: i for i in range(n)} self.edges = [(indices[a], indices[b]) for a, b in self.edges] # We need a new array for an undirected model which will store all # edges involving this state. There is no direction, and so it will # be a single array of twice the length of the number of edges, # since each edge belongs to two nodes. self.transitions = numpy.full(m*2, -1, dtype=numpy.int32) self.edge_count = numpy.zeros(n+1, dtype=numpy.int32) # Go through each node and classify it as either a marginal node or a # factor node. for i, node in enumerate(self.states): self.marginals[i] = ( not isinstance(node.distribution, MultivariateDistribution) and not node.name.endswith('-joint') ) # Now we need to find a way of storing edges for a state in a manner # that can be called in the cythonized methods below. This is basically # an inversion of the graph. We will do this by having two lists, one # list size number of nodes + 1, and one list size number of edges. # The node size list will store the beginning and end values in the # edge list that point to that node. The edge list will be ordered in # such a manner that all edges pointing to the same node are grouped # together. This will allow us to run the algorithms in time # nodes*edges instead of nodes*nodes. for a, b in self.edges: # Increment the total number of edges going to node b. self.edge_count[b+1] += 1 # Increment the total number of edges leaving node a. self.edge_count[a+1] += 1 # Take the cumulative sum so that we can associate array indices with # in or out transitions self.edge_count = numpy.cumsum(self.edge_count, dtype=numpy.int32) # Now we go through the edges again in order to both fill in the # transition probability matrix, and also to store the indices sorted # by the end-node. for a, b in self.edges: # Put the edge in the dict. Its weight is log-probability start = self.edge_count[b] # Start at the beginning of the section marked off for node b. # If another node is already there, keep walking down the list # until you find a -1 meaning a node hasn't been put there yet. while self.transitions[start] != -1: if start == self.edge_count[b+1]: break start += 1 # Store transition info in an array where the edge_count shows # the mapping stuff. self.transitions[start] = a # Now do the same for out edges start = self.edge_count[a] while self.transitions[start] != -1: if start == self.edge_count[a+1]: break start += 1 self.transitions[start] = b def marginal(self): """Return the marginal probabilities of each variable in the graph. This is equivalent to a pass of belief propagation on a graph where no data has been given. This will calculate the probability of each variable being in each possible emission when nothing is known. Parameters ---------- None Returns ------- marginals : array-like, shape (n_nodes) An array of univariate distribution objects showing the marginal probabilities of that variable. """ return self.predict_proba({}) def predict_proba(self, data, max_iterations=10, verbose=False): """Returns the probabilities of each variable in the graph given evidence. This calculates the marginal probability distributions for each state given the evidence provided through loopy belief propagation. Loopy belief propagation is an approximate algorithm which is exact for certain graph structures. Parameters ---------- data : dict or array-like, shape <= n_nodes, optional The evidence supplied to the graph. This can either be a dictionary with keys being state names and values being the observed values (either the emissions or a distribution over the emissions) or an array with the values being ordered according to the nodes incorporation in the graph (the order fed into .add_states/add_nodes) and None for variables which are unknown. If nothing is fed in then calculate the marginal of the graph. max_iterations : int, optional The number of iterations with which to do loopy belief propagation. Usually requires only 1. check_input : bool, optional Check to make sure that the observed symbol is a valid symbol for that distribution to produce. Returns ------- probabilities : array-like, shape (n_nodes) An array of univariate distribution objects showing the probabilities of each variable. """ n, m = len(self.states), len(self.transitions) # Save our original distributions so that we don't permanently overwrite # them as we do belief propagation. distributions = numpy.empty(n, dtype=Distribution) # Clamp values down to evidence if we have observed them for i, state in enumerate(self.states): if state.name in data: val = data[state.name] if isinstance(val, Distribution): distributions[i] = val else: distributions[i] = state.distribution.clamp(val) else: distributions[i] = state.distribution # Create a buffer for each marginal node for messages coming into the # node and messages leaving the node. out_messages = numpy.empty(m, dtype=Distribution) in_messages = numpy.empty(m, dtype=Distribution) # Explicitly calculate the distributions at each round so we can test # for convergence. prior_distributions = distributions.copy() current_distributions = numpy.empty(m, dtype=Distribution) # Go through and initialize messages from the states to be whatever # we set the marginal to be. For edges which are encoded as leaving # a marginal, set it to that marginal, otherwise follow the edge from # the factor to the marginal and set it to the marginal. for i, state in enumerate(self.states): # Go through and set edges which are encoded as leaving the # marginal distributions as the marginal distribution if self.marginals[i] == 1: for k in range(self.edge_count[i], self.edge_count[i+1]): out_messages[k] = distributions[i] # Otherwise follow the edge, then set the message to be # the marginal on the other side. else: for k in range(self.edge_count[i], self.edge_count[i+1]): kl = self.transitions[k] out_messages[k] = distributions[kl] in_messages[k] = distributions[kl] # We're going to iterate two steps here: # (1) send messages from variable nodes to factor nodes, containing # evidence and beliefs about the marginals # (2) send messages from the factors to the variables, containing # the factors belief about each marginal. # This is the flooding message schedule for loopy belief propagation. cdef bint done iteration = 0 while True: # We have now updated all of the messages leaving the marginal node, # now we have to update all the messages going to the marginal node. for i, state in enumerate(self.states): # Now we ignore the marginal nodes if self.marginals[i] == 1: continue # We need to calculate the new in messages for the marginals. # This involves taking in all messages from all edges except the # message from the marginal we are trying to send a message to. for k in range(self.edge_count[i], self.edge_count[i+1]): ki = self.transitions[k] # We can simply calculate this by turning the CPT into a # joint probability table using the other messages, and # then summing out those variables. d = {} for l in range(self.edge_count[i], self.edge_count[i+1]): # Don't take in messages from the marginal we are trying # to send a message to. if k == l: continue li = self.transitions[l] d[self.states[li].distribution] = out_messages[l] for l in range(self.edge_count[ki], self.edge_count[ki+1]): li = self.transitions[l] if li == i: in_messages[l] = state.distribution.marginal(neighbor_values=d) break # Calculate the current estimates on the marginals to compare to the # last iteration, so that we can stop if we reach convergence. done = 1 for i in range(n): if self.marginals[i] == 0: continue current_distributions[i] = distributions[i] # Multiply the factors together by the original marginal to # calculate the new estimate of the marginal for k in range(self.edge_count[i], self.edge_count[i+1]): current_distributions[i] *= in_messages[k] if done and not current_distributions[i].equals(prior_distributions[i]): done = 0 # If we have converged, then we're done! if done == 1: break # Increment our iteration calculator iteration += 1 if iteration >= max_iterations: break # Set this list of distributions to the prior observations of the # marginals prior_distributions = current_distributions.copy() # UPDATE MESSAGES LEAVING THE MARGINAL NODES for i, state in enumerate(self.states): # Ignore factor nodes for now if self.marginals[i] == 0: continue # We are trying to calculate a new message for each edge leaving # this marginal node. So we start by looping over each edge, and # for each edge loop over all other edges and multiply the factors # together. for k in range(self.edge_count[i], self.edge_count[i+1]): ki = self.transitions[k] # Start off by weighting by the distribution at this factor-- # keep in mind that this is a uniform distribution unless evidence # is provided by the user, at which point it is clamped to a # specific value, acting as a filter. message = distributions[i] for l in range(self.edge_count[i], self.edge_count[i+1]): # Don't include the previous message received from here if k == l: continue # Update the out message by multiplying the factors # together. message *= in_messages[l] for l in range(self.edge_count[ki], self.edge_count[ki+1]): li = self.transitions[l] if li == i: out_messages[l] = message break y_hat = numpy.empty(n, dtype=Distribution) j = 0 for i, state in enumerate(self.states): if self.marginals[i]: if state.name in data: y_hat[j] = data[state.name] else: # We've already computed the current belief about the # marginals, so we can just return that. y_hat[j] = current_distributions[i] j += 1 y_hat.resize(j) return y_hat def to_dict(self): return { "class": "FactorGraph", "name": self.name, "states": [state.to_dict() for state in self.states], "edges": self.edges } @classmethod def from_dict(cls, d): model = cls(str(d["name"])) states = [State.from_dict(j) for j in d['states']] model.add_states(*states) for node1, node2 in d["edges"]: model.add_edge(states[node1], states[node2]) model.bake() return model pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/MarkovChain.pyx000066400000000000000000000214201420461712600261360ustar00rootroot00000000000000#cython: boundscheck=False #cython: cdivision=True # MarkovChain.pyx # Contact: Jacob Schreiber import numpy import json import itertools as it from distributions import Distribution from distributions import DiscreteDistribution from distributions import ConditionalProbabilityTable cdef class MarkovChain(object): """A Markov Chain. Implemented as a series of conditional distributions, the Markov chain models P(X_i | X_i-1...X_i-k) for a k-th order Markov network. The conditional dependencies are directly on the emissions, and not on a hidden state as in a hidden Markov model. Parameters ---------- distributions : list, shape (k+1) A list of the conditional distributions which make up the markov chain. Begins with P(X_i), then P(X_i | X_i-1). For a k-th order markov chain you must put in k+1 distributions. Attributes ---------- distributions : list, shape (k+1) The distributions which make up the chain. Examples -------- >>> from pomegranate import * >>> d1 = DiscreteDistribution({'A': 0.25, 'B': 0.75}) >>> d2 = ConditionalProbabilityTable([['A', 'A', 0.33], ['B', 'A', 0.67], ['A', 'B', 0.82], ['B', 'B', 0.18]], [d1]) >>> mc = MarkovChain([d1, d2]) >>> mc.log_probability(list('ABBAABABABAABABA')) -8.9119890701808213 """ cdef int k cdef public list distributions def __init__(self, distributions): self.k = len(distributions) - 1 self.distributions = distributions def __reduce__(self): return self.__class__, (self.distributions,) def log_probability(self, sequence): """Calculate the log probability of the sequence under the model. This calculates the first slices of increasing size under the corresponding first few components of the model until size k is reached, at which all slices are evaluated under the final component. Parameters ---------- sequence : array-like An array of observations Returns ------- logp : double The log probability of the sequence under the model. """ n, k = len(sequence), self.k l = min(k, n) logp = 0.0 for i in range(l): key = sequence[0] if i == 0 else tuple(sequence[:i+1]) logp += self.distributions[i].log_probability(key) for j in range(n-l): key = tuple(sequence[j:j+k+1]) if k > 0 else sequence[j] logp += self.distributions[-1].log_probability(key) return logp def sample(self, length): """Create a random sample from the model. Parameters ---------- length : int or Distribution Give either the length of the sample you want to generate, or a distribution object which will be randomly sampled for the length. Continuous distributions will have their sample rounded to the nearest integer, minimum 1. Returns ------- sequence : array-like, shape = (length,) A sequence randomly generated from the markov chain. """ if isinstance(length, Distribution): length = int(length.sample()) length = max(length, 1) sequence = [ self.distributions[0].sample() ] if length == 1: return sequence for j, distribution in enumerate(self.distributions[1:]): parents = {self.distributions[l] : sequence[l] for l in range(j+1)} sequence.append(distribution.sample(parents)) if len(sequence) == length: return sequence for l in range(length - len(sequence)): parents = {self.distributions[k] : sequence[l+k+1] for k in range(self.k)} sequence.append(self.distributions[-1].sample(parents)) return sequence def fit(self, sequences, weights=None, inertia=0.0): """Fit the model to new data using MLE. The underlying distributions are fed in their appropriate points and weights and are updated. Parameters ---------- sequences : array-like, shape (n_samples, variable) This is the data to train on. Each row is a sample which contains a sequence of variable length weights : array-like, shape (n_samples,), optional The initial weights of each sample. If nothing is passed in then each sample is assumed to be the same weight. Default is None. inertia : double, optional The weight of the previous parameters of the model. The new parameters will roughly be old_param*inertia + new_param*(1-inertia), so an inertia of 0 means ignore the old parameters, whereas an inertia of 1 means ignore the new parameters. Default is 0.0. Returns ------- None """ self.summarize(sequences, weights) self.from_summaries(inertia) def summarize(self, sequences, weights=None): """Summarize a batch of data and store sufficient statistics. This will summarize the sequences into sufficient statistics stored in each distribution. Parameters ---------- sequences : array-like, shape (n_samples, variable) This is the data to train on. Each row is a sample which contains a sequence of variable length weights : array-like, shape (n_samples,), optional The initial weights of each sample. If nothing is passed in then each sample is assumed to be the same weight. Default is None. Returns ------- None """ if weights is None: weights = numpy.ones(len(sequences), dtype='float64') else: weights = numpy.asarray(weights) n = max( map(len, sequences) ) for i in range(self.k): if i == 0: symbols = [ sequence[0] for sequence in sequences ] else: symbols = [ sequence[:i+1] for sequence in sequences if len(sequence) > i ] self.distributions[i].summarize(symbols, weights) for j in range(n-self.k): if self.k == 0: symbols = [ sequence[j] for sequence in sequences if len(sequence) > j+self.k ] else: symbols = [ sequence[j:j+self.k+1] for sequence in sequences if len(sequence) > j+self.k ] self.distributions[-1].summarize(symbols, weights) def from_summaries(self, inertia=0.0): """Fit the model to the collected sufficient statistics. Fit the parameters of the model to the sufficient statistics gathered during the summarize calls. This should return an exact update. Parameters ---------- inertia : double, optional The weight of the previous parameters of the model. The new parameters will roughly be old_param*inertia + new_param * (1-inertia), so an inertia of 0 means ignore the old parameters, whereas an inertia of 1 means ignore the new parameters. Default is 0.0. Returns ------- None """ for i in range(self.k+1): self.distributions[i].from_summaries(inertia=inertia) def to_json(self, separators=(',', ' : '), indent=4): """Serialize the model to a JSON. Parameters ---------- separators : tuple, optional The two separators to pass to the json.dumps function for formatting. Default is (',', ' : '). indent : int, optional The indentation to use at each level. Passed to json.dumps for formatting. Default is 4. Returns ------- json : str A properly formatted JSON object. """ model = { 'class' : 'MarkovChain', 'distributions' : [ d.to_dict() for d in self.distributions ] } return json.dumps(model, separators=separators, indent=indent) @classmethod def from_json(cls, s): """Read in a serialized model and return the appropriate classifier. Parameters ---------- s : str A JSON formatted string containing the file. Returns ------- model : object A properly initialized and baked model. """ d = json.loads(s) distributions = [ Distribution.from_dict(j) for j in d['distributions'] ] model = cls(distributions) return model @classmethod def from_samples(cls, X, weights=None, k=1): """Learn the Markov chain from data. Takes in the memory of the chain (k) and learns the initial distribution and probability tables associated with the proper parameters. Parameters ---------- X : array-like, list or numpy.array The data to fit the structure too as a list of sequences of variable length. Since the data will be of variable length, there is no set form weights : array-like, shape (n_nodes), optional The weight of each sample as a positive double. Default is None. k : int, optional The number of samples back to condition on in the model. Default is 1. Returns ------- model : MarkovChain The learned markov chain model. """ symbols = set() for seq in X: for symbol in seq: symbols.add(symbol) n = len(symbols) d = DiscreteDistribution({ symbol: 1./n for symbol in symbols }) distributions = [d] for i in range(1, k+1): table = [] for key in it.product(symbols, repeat=i+1): table.append( list(key) + [1./n] ) d = ConditionalProbabilityTable(table, distributions[:]) distributions.append(d) model = cls(distributions) model.fit(X) return model pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/MarkovNetwork.pyx000066400000000000000000000654471420461712600265660ustar00rootroot00000000000000import itertools import time import numpy cimport numpy from scipy.special import logsumexp from joblib import Parallel from joblib import delayed from libc.stdlib cimport calloc from libc.stdlib cimport free from libc.stdlib cimport malloc from libc.string cimport memset from .base cimport Model from .base import State from distributions import Distribution from distributions.DiscreteDistribution cimport DiscreteDistribution from distributions.JointProbabilityTable cimport JointProbabilityTable from .FactorGraph import FactorGraph from .utils cimport _log from .utils cimport isnan from .utils import _check_nan nan = numpy.nan cdef class MarkovNetwork(Model): """A Markov Network Model. A Markov network is an undirected graph where nodes represent variables, edges represent associations between the variables, and the lack of an edge represents a conditional independence. Parameters ---------- distributions : list, tuple, or numpy.ndarray A collection of joint probability distributions that represent the name : str, optional The name of the model. Default is None """ cdef object graph cdef list idxs cdef public list keys_ cdef public numpy.ndarray keymap cdef int* parent_count cdef int* parent_idxs cdef public numpy.ndarray distributions cdef void** distributions_ptr cdef public float partition def __init__(self, distributions, name=None): self.distributions = numpy.array(distributions) if len(self.distributions) == 0: raise ValueError("Must pass in at least one distribution to initialize a Markov Network.") self.name = 'MarkovNetwork' self.d = len(set(numpy.concatenate([d.parents for d in distributions]))) @property def structure(self): return tuple(tuple(d.parents) for d in self.distributions) def __dealloc__(self): free(self.parent_count) free(self.parent_idxs) def bake(self, calculate_partition=True): """Finalize the topology of the underlying factor graph model. Assign a numerical index to every clique and create the underlying factor graph model. This method must be called before any of the probability-calculating or inference methods because the probability calculating methods rely on the partition function and the inference methods rely on the factor graph. Parameters ---------- calculate_partition : bool, optional Whether to calculate the partition function. This is not necessary if the goal is simply to perform inference, but is required if the goal is to calculate the probability of examples under the model. Returns ------- None """ # Initialize the factor graph self.graph = FactorGraph(self.name+'-fg') self.idxs = [] self.keys_ = [None for i in range(self.d)] marginal_nodes = numpy.empty(self.d, dtype=object) factor_nodes = {} n = 0 # Determine all marginal nodes and their distributions for i, d in enumerate(self.distributions): keys = numpy.array(d.keys(), dtype=object) for j, parent in enumerate(d.parents): keys_ = numpy.unique(keys[:,j]) self.keys_[parent] = keys_ d_ = DiscreteDistribution({key: 1. / len(keys_) for key in keys_}) m = State(d_, str(parent)+"-marginal") marginal_nodes[parent] = m # Add each marginal node to the graph for i in range(self.d): self.graph.add_node(marginal_nodes[i]) # Add each factor node to the graph with respective edges for i, d in enumerate(self.distributions): f = State(d.copy(), str(i)+"-joint") factor_nodes[i] = f self.graph.add_node(f) for j, parent in enumerate(d.parents): m = marginal_nodes[parent] self.graph.add_edge(m, f) idxs = tuple(d.parents) self.idxs.append(idxs) n += len(idxs) f.distribution.parents = [marginal_nodes[parent].distribution for parent in d.parents] d.n_columns = self.d # Finalize the factor graph structure self.graph.bake() #self.keymap = numpy.array([{key: i for i, key in enumerate(keys)} for keys in self.keymap]) self.parent_count = calloc(self.d+1, sizeof(int)) self.parent_idxs = calloc(n, sizeof(int)) self.partition = float("inf") if calculate_partition == True: X_ = list(itertools.product(*self.keys_)) self.partition = logsumexp(self.log_probability(X_, unnormalized=True)) def probability(self, X, n_jobs=1, unnormalized=False): """Return the probability of samples under the Markov network. This is just a wrapper that exponentiates the result from the log probability method. Parameters ---------- X : array-like, shape (n_samples, n_dim) The sample is a vector of points where each dimension represents the same variable as added to the graph originally. It doesn't matter what the connections between these variables are, just that they are all ordered the same. n_jobs : int, optional The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. unnormalized : bool, optional Whether to return the unnormalized or normalized probabilities. The normalized probabilities requires the partition function to be calculated. Returns ------- prob : numpy.ndarray or double The log probability of the samples if many, or the single log probability. """ return numpy.exp(self.log_probability(X, n_jobs=n_jobs, unnormalized=unnormalized)) def log_probability(self, X, n_jobs=1, unnormalized=False): """Return the log probability of samples under the Markov network. The log probability is just the sum of the log probabilities under each of the components minus the partition function. This method will return a vector of log probabilities, one for each sample. Parameters ---------- X : array-like, shape (n_samples, n_dim) The sample is a vector of points where each dimension represents the same variable as added to the graph originally. It doesn't matter what the connections between these variables are, just that they are all ordered the same. n_jobs : int, optional The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. unnormalized : bool, optional Whether to return the unnormalized or normalized probabilities. The normalized probabilities requires the partition function to be calculated. Returns ------- logp : numpy.ndarray or double The log probability of the samples if many, or the single log probability. """ if self.d == 0: raise ValueError("Must bake model before computing probability") if self.partition == float("inf") and unnormalized == False: raise ValueError("Must calculate partition before computing probability") X = numpy.array(X, ndmin=2, dtype=object) n, d = X.shape logp = numpy.zeros(n, dtype='float64') if unnormalized == False: logp -= self.partition for i in range(n): for j, d in enumerate(self.distributions): logp[i] += d.log_probability(X[i, self.idxs[j]]) return logp if n > 1 else logp[0] cdef void _log_probability(self, double* symbol, double* log_probability, int n) nogil: cdef int i, j, l, li, k cdef double logp cdef double* sym = malloc(self.d*sizeof(double)) memset(log_probability, 0, n*sizeof(double)) for i in range(n): for j in range(self.d): memset(sym, 0, self.d*sizeof(double)) logp = 0.0 for l in range(self.parent_count[j], self.parent_count[j+1]): li = self.parent_idxs[l] k = l - self.parent_count[j] sym[k] = symbol[i*self.d + li] ( self.distributions_ptr[j])._log_probability(sym, &logp, 1) log_probability[i] += logp log_probability[i] -= self.partition free(sym) def marginal(self): """Return the marginal probabilities of each variable in the graph. This is equivalent to a pass of belief propagation on a graph where no data has been given. This will calculate the probability of each variable being in each possible emission when nothing is known. Parameters ---------- None Returns ------- marginals : array-like, shape (n_nodes) An array of univariate distribution objects showing the marginal probabilities of that variable. """ if self.d == 0: raise ValueError("must bake model before computing marginal") return self.graph.marginal() def predict(self, X, max_iterations=100, n_jobs=1): """Predict missing values of a data matrix using MLE. Impute the missing values of a data matrix using the maximally likely predictions according to the loopy belief propagation (also known as the forward-backward) algorithm. Run each example through the algorithm (predict_proba) and replace missing values with the maximally likely predicted emission. Parameters ---------- X : array-like, shape (n_samples, n_nodes) Data matrix to impute. Missing values must be either None (if lists) or np.nan (if numpy.ndarray). Will fill in these values with the maximally likely ones. max_iterations : int, optional Number of iterations to run loopy belief propagation for. Default is 100. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. Returns ------- y_hat : numpy.ndarray, shape (n_samples, n_nodes) This is the data matrix with the missing values imputed. """ if self.d == 0: raise ValueError("Must bake model before calling predict.") y_hat = self.predict_proba(X, max_iterations=max_iterations, n_jobs=n_jobs) for i in range(len(y_hat)): for j in range(len(y_hat[i])): if isinstance(y_hat[i][j], Distribution): y_hat[i][j] = y_hat[i][j].mle() return y_hat def predict_proba(self, X, max_iterations=100, check_input=True, n_jobs=1): """Returns the probabilities of each variable in the graph given evidence. This calculates the marginal probability distributions for each state given the evidence provided through loopy belief propagation. Loopy belief propagation is an approximate algorithm which is exact for certain graph structures. Parameters ---------- X : dict or array-like, shape <= n_nodes The evidence supplied to the graph. This can either be a dictionary with keys being state names and values being the observed values (either the emissions or a distribution over the emissions) or an array with the values being ordered according to the nodes incorporation in the graph and None for variables which are unknown. It can also be vectorized, so a list of dictionaries can be passed in where each dictionary is a single sample, or a list of lists where each list is a single sample, both formatted as mentioned before. The preferred method is as an numpy array. max_iterations : int, optional The number of iterations with which to do loopy belief propagation. Usually requires only 1. Default is 100. check_input : bool, optional Check to make sure that the observed symbol is a valid symbol for that distribution to produce. Default is True. n_jobs : int, optional The number of threads to use when parallelizing the job. This parameter is passed directly into joblib. Default is 1, indicating no parallelism. Returns ------- y_hat : array-like, shape (n_samples, n_nodes) An array of univariate distribution objects showing the probabilities of each variable. """ if self.d == 0: raise ValueError("Must bake model before calling predict_proba") if isinstance(X, dict): return self.graph.predict_proba(X, max_iterations) elif isinstance(X, (list, numpy.ndarray)) and not isinstance(X[0], (list, numpy.ndarray, dict)): data = {} for i in range(self.d): if not _check_nan(X[i]): data[str(i)+"-marginal"] = X[i] return self.graph.predict_proba(data, max_iterations) else: y_hat = [] for x in X: y_ = self.predict_proba(x, max_iterations=max_iterations, check_input=False, n_jobs=1) y_hat.append(y_) return y_hat def fit(self, X, weights=None, inertia=0.0, pseudocount=0.0, verbose=False, calculate_partition=True, n_jobs=1): """Fit the model to data using MLE estimates. Fit the model to the data by updating each of the components of the model, which are univariate or multivariate distributions. This uses a simple MLE estimate to update the distributions according to their summarize or fit methods. This is a wrapper for the summarize and from_summaries methods. Parameters ---------- X : array-like, shape (n_samples, n_nodes) The data to train on, where each row is a sample and each column corresponds to the associated variable. weights : array-like, shape (n_nodes), optional The weight of each sample as a positive double. Default is None. inertia : double, optional The inertia for updating the distributions, passed along to the distribution method. Default is 0.0. pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Only effects hidden Markov models defined over discrete distributions. Default is 0. verbose : bool, optional Whether or not to print out improvement information over iterations. Only required if doing semisupervised learning. Default is False. calculate_partition : bool, optional Whether to calculate the partition function. This is not necessary if the goal is simply to perform inference, but is required if the goal is to calculate the probability of examples under the model. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. Returns ------- self : MarkovNetwork The fit Markov network object with updated model parameters. """ training_start_time = time.time() if weights is None: weights = numpy.ones(len(X), dtype='float64') else: weights = numpy.asarray(weights, dtype='float64') starts = [int(i*len(X)/n_jobs) for i in range(n_jobs)] ends = [int(i*len(X)/n_jobs) for i in range(1, n_jobs+1)] with Parallel(n_jobs=n_jobs, backend='threading') as parallel: parallel( delayed(self.summarize)( X[start:end], weights[start:end]) for start, end in zip(starts, ends)) self.from_summaries(inertia, pseudocount) self.bake(calculate_partition=calculate_partition) if verbose: total_time_spent = time.time() - training_start_time print("Total Time (s): {:.4f}".format(total_time_spent)) return self def summarize(self, X, weights=None): """Summarize a batch of data and store the sufficient statistics. This will partition the dataset into columns which belong to their appropriate distribution. If the distribution has parents, then multiple columns are sent to the distribution. This relies mostly on the summarize function of the underlying distribution. Parameters ---------- X : array-like, shape (n_samples, n_nodes) The data to train on, where each row is a sample and each column corresponds to the associated variable. weights : array-like, shape (n_nodes), optional The weight of each sample as a positive double. Default is None. Returns ------- None """ if self.d == 0: raise ValueError("must bake model before summarizing data") n, d = len(X), len(X[0]) if weights is None: weights = numpy.ones(n, dtype='float64') else: weights = numpy.asarray(weights, dtype='float64') for i, d in enumerate(self.distributions): d.summarize(X, weights) def from_summaries(self, inertia=0.0, pseudocount=0.0, calculate_partition=True): """Use MLE on the stored sufficient statistics to train the model. Parameters ---------- inertia : double, optional The inertia for updating the distributions, passed along to the distribution method. Default is 0.0. pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Default is 0. calculate_partition : bool, optional Whether to calculate the partition function. This is not necessary if the goal is simply to perform inference, but is required if the goal is to calculate the probability of examples under the model. Returns ------- None """ for d in self.distributions: d.from_summaries(inertia, pseudocount) self.bake(calculate_partition=calculate_partition) @classmethod def from_dict(cls, d): distributions = [] for j in d['distributions']: distribution = JointProbabilityTable.from_dict(j) distributions.append(distribution) model = cls(distributions, str(d['name'])) model.bake() return model @classmethod def from_structure(cls, X, structure, weights=None, pseudocount=0.0, name=None, calculate_partition=True): """Return a Markov network from a predefined structure. Pass in the structure of the network as a tuple of tuples and get a fit network in return. The tuple should contain n tuples, with one for each node in the graph. Each inner tuple should be of the parents for that node. For example, a three node graph where both node 0 and 1 have node 2 as a parent would be specified as ((2,), (2,), ()). Parameters ---------- X : array-like, shape (n_samples, n_nodes) The data to fit the structure too, where each row is a sample and each column corresponds to the associated variable. structure : tuple of tuples The parents for each node in the graph. If a node has no parents, then do not specify any parents. weights : array-like, shape (n_nodes), optional The weight of each sample as a positive double. Default is None. pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Default is 0. Returns ------- model : MarkovNetwork A Markov network with the specified structure. """ X = numpy.asarray(X) n, d = X.shape distributions = [] if weights is None: weights = numpy.ones(X.shape[0], dtype='float64') else: weights = numpy.asarray(weights, dtype='float64') for i, parents in enumerate(structure): distribution = JointProbabilityTable.from_samples(X[:, parents], parents=parents, weights=weights, pseudocount=pseudocount) distributions.append(distribution) model = cls(distributions) model.bake(calculate_partition=calculate_partition) return model @classmethod def from_samples(cls, X, weights=None, algorithm='chow-liu', max_parents=-1, pseudocount=0.0, name=None, reduce_dataset=True, calculate_partition=True, n_jobs=1): """Learn the structure of the network from data. Find the structure of the network from data using a Markov structure learning score. This currently enumerates all the exponential number of structures and finds the best according to the score. This allows weights on the different samples as well. The score that is optimized is the minimum description length (MDL). If not all states for a variable appear in the supplied data, this function can not gurantee that the returned Markov Network is optimal when 'exact' or 'exact-dp' is used. This is because the number of states for each node is derived only from the data provided, and the scoring function depends on the number of states of a variable. Parameters ---------- X : array-like, shape (n_samples, n_nodes) The data to fit the structure too, where each row is a sample and each column corresponds to the associated variable. weights : array-like, shape (n_nodes), optional The weight of each sample as a positive double. Default is None. algorithm : str, one of 'chow-liu', 'greedy', 'exact', 'exact-dp' optional The algorithm to use for learning the Bayesian network. Default is 'greedy' that greedily attempts to find the best structure, and frequently can identify the optimal structure. 'exact' uses DP/A* to find the optimal Bayesian network, and 'exact-dp' tries to find the shortest path on the entire order lattice, which is more memory and computationally expensive. 'exact' and 'exact-dp' should give identical results, with 'exact-dp' remaining an option mostly for debugging reasons. 'chow-liu' will return the optimal tree-like structure for the Bayesian network, which is a very fast approximation but not always the best network. max_parents : int, optional The maximum number of parents a node can have. If used, this means using the k-learn procedure. Can drastically speed up algorithms. If -1, no max on parents. Default is -1. root : int, optional For algorithms which require a single root ('chow-liu'), this is the root for which all edges point away from. User may specify which column to use as the root. Default is the first column. constraint_graph : networkx.DiGraph or None, optional A directed graph showing valid parent sets for each variable. Each node is a set of variables, and edges represent which variables can be valid parents of those variables. The naive structure learning task is just all variables in a single node with a self edge, meaning that you know nothing about pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Default is 0. name : str, optional The name of the model. Default is None. reduce_dataset : bool, optional Given the discrete nature of these datasets, frequently a user will pass in a dataset that has many identical samples. It is time consuming to go through these redundant samples and a far more efficient use of time to simply calculate a new dataset comprised of the subset of unique observed samples weighted by the number of times they occur in the dataset. This typically will speed up all algorithms, including when using a constraint graph. Default is True. n_jobs : int, optional The number of threads to use when learning the structure of the network. If a constraint graph is provided, this will parallelize the tasks as directed by the constraint graph. If one is not provided it will parallelize the building of the parent graphs. Both cases will provide large speed gains. Returns ------- model : MarkovNetwork The learned Markov Network. """ X = numpy.asarray(X) n, d = X.shape keys = [set([x for x in X[:,i] if not _check_nan(x)]) for i in range(d)] keymap = numpy.array([{key: i for i, key in enumerate(keys[j])} for j in range(d)]) key_count = numpy.array([len(keymap[i]) for i in range(d)], dtype='int32') if weights is None: weights = numpy.ones(X.shape[0], dtype='float64') else: weights = numpy.asarray(weights, dtype='float64') if reduce_dataset: X_count = {} for x, weight in zip(X, weights): # Convert NaN to None because two tuples containing # (1.0, 2.0, 3.0, nan) are not considered equal, but two tuples # containing (1.0, 2.0, 3.0, None) are considered equal x = tuple(None if isnan(xn) else xn for xn in x) if x in X_count: X_count[x] += weight else: X_count[x] = weight weights = numpy.array(list(X_count.values()), dtype='float64') X = numpy.array(list(X_count.keys()), dtype=X.dtype) n, d = X.shape X_int = numpy.zeros((n, d), dtype='float64') for i in range(n): for j in range(d): if _check_nan(X[i, j]): X_int[i, j] = nan else: X_int[i, j] = keymap[j][X[i, j]] w_sum = weights.sum() if max_parents == -1 or max_parents > _log(2*w_sum / _log(w_sum)): max_parents = int(_log(2*w_sum / _log(w_sum))) if algorithm == 'chow-liu': if numpy.any(numpy.isnan(X_int)): raise ValueError("Chow-Liu tree learning does not current support missing values") structure = discrete_chow_liu_tree(X_int, weights, key_count, pseudocount) else: raise ValueError("Invalid algorithm type passed in. Must be one of 'chow-liu', 'exact', 'exact-dp', 'greedy'") return cls.from_structure(X, structure=structure, weights=weights, pseudocount=pseudocount, name=name, calculate_partition=calculate_partition) def discrete_chow_liu_tree(numpy.ndarray X_ndarray, numpy.ndarray weights_ndarray, numpy.ndarray key_count_ndarray, double pseudocount): """Find the Chow-Liu tree that spans a data set. The Chow-Liu algorithm first calculates the mutual information between each pair of variables and then constructs a maximum spanning tree given that. This algorithm slightly from the one implemented for Bayesian networks because Bayesian networks are directed and need a node to be the root. In contrast, the structure here is undirected and so is a simple maximum spanning tree. """ cdef int i, j, k, l, lj, lk, Xj, Xk, xj, xk cdef int n = X_ndarray.shape[0], d = X_ndarray.shape[1] cdef int max_keys = key_count_ndarray.max() cdef double* X = X_ndarray.data cdef double* weights = weights_ndarray.data cdef int* key_count = key_count_ndarray.data cdef double* mutual_info = calloc(d * d, sizeof(double)) cdef double* marg_j = malloc(max_keys*sizeof(double)) cdef double* marg_k = malloc(max_keys*sizeof(double)) cdef double* joint_count = malloc(max_keys**2*sizeof(double)) for j in range(d): for k in range(j): if j == k: continue lj = key_count[j] lk = key_count[k] for i in range(max_keys): marg_j[i] = pseudocount marg_k[i] = pseudocount for l in range(max_keys): joint_count[i*max_keys + l] = pseudocount for i in range(n): Xj = X[i*d + j] Xk = X[i*d + k] joint_count[Xj * lk + Xk] += weights[i] marg_j[Xj] += weights[i] marg_k[Xk] += weights[i] for xj in range(lj): for xk in range(lk): if joint_count[xj*lk+xk] > 0: mutual_info[j*d + k] -= joint_count[xj*lk+xk] * _log( joint_count[xj*lk+xk] / (marg_j[xj] * marg_k[xk])) mutual_info[k*d + j] = mutual_info[j*d + k] structure = [] cdef int x, y, min_x, min_y cdef double min_score, score for x in range(d): min_score = float("inf") min_x = -1 min_y = -1 for y in range(d): if x == y: continue score = mutual_info[x*d + y] if score < min_score: min_score = score min_x = x min_y = y structure.append([min_x, min_y]) visited = [min_y, min_x] unvisited = list(range(d)) unvisited.remove(min_y) unvisited.remove(min_x) for i in range(d-2): min_score = float("inf") min_x = -1 min_y = -1 for x in visited: for y in unvisited: score = mutual_info[x*d + y] if score < min_score: min_score = score min_x = x min_y = y structure.append([min_x, min_y]) visited.append(min_y) unvisited.remove(min_y) free(mutual_info) free(marg_j) free(marg_k) free(joint_count) return tuple([tuple(neighbors) for neighbors in structure]) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/NaiveBayes.pyx000066400000000000000000000175121420461712600257710ustar00rootroot00000000000000#cython: boundscheck=False #cython: cdivision=True # NaiveBayes.pyx # Contact: Jacob Schreiber ( jmschreiber91@gmail.com ) import numpy cimport numpy from .bayes cimport BayesModel from distributions.distributions cimport Distribution from distributions import IndependentComponentsDistribution from distributions import MultivariateGaussianDistribution from distributions import DirichletDistribution from .gmm import GeneralMixtureModel from .io import BaseGenerator from .io import DataGenerator DEF NEGINF = float("-inf") DEF INF = float("inf") cdef class NaiveBayes(BayesModel): """A naive Bayes model, a supervised alternative to GMM. A naive Bayes classifier, that treats each dimension independently from each other. This is a simpler version of the Bayes Classifier, that can use any distribution with any covariance structure, including Bayesian networks and hidden Markov models. Parameters ---------- models : list A list of initialized distributions. weights : list or numpy.ndarray or None, default None The prior probabilities of the components. If None is passed in then defaults to the uniformly distributed priors. Attributes ---------- models : list The model objects, either initialized by the user or fit to data. weights : numpy.ndarray The prior probability of each component of the model. Examples -------- >>> from pomegranate import * >>> X = [0, 2, 0, 1, 0, 5, 6, 5, 7, 6] >>> y = [0, 0, 0, 0, 0, 1, 1, 0, 1, 1] >>> clf = NaiveBayes.from_samples(NormalDistribution, X, y) >>> clf.predict_proba([6]) array([[0.01973451, 0.98026549]]) >>> from pomegranate import * >>> clf = NaiveBayes([NormalDistribution(1, 2), NormalDistribution(0, 1)]) >>> clf.predict_log_proba([[0], [1], [2], [-1]]) array([[-1.1836569 , -0.36550972], [-0.79437677, -0.60122959], [-0.26751248, -1.4493653], [-1.09861229, -0.40546511]]) """ def __init__(self, distributions, weights=None): super(self.__class__, self).__init__(distributions, weights) def __reduce__(self): return self.__class__, (self.distributions, self.weights) def to_dict(self): if self.d == 0: raise ValueError("must fit components to the data before prediction") return { 'class' : 'NaiveBayes', 'models' : [model.to_dict() for model in self.distributions], 'weights' : numpy.exp(self.weights).tolist() } @classmethod def from_dict(cls, d): models = list() for j in d['models']: if j['class'] == 'Distribution': models.append(Distribution.from_dict(j)) elif j['class'] == 'GeneralMixtureModel': models.append(GeneralMixtureModel.from_dict(j)) nb = cls(models, numpy.array(d['weights'])) return nb @classmethod def from_samples(cls, distributions, X, y=None, weights=None, pseudocount=0.0, stop_threshold=0.1, max_iterations=1e8, callbacks=[], return_history=False, verbose=False, n_jobs=1): """Create a naive Bayes classifier directly from the given dataset. This will initialize the distributions using maximum likelihood estimates derived by partitioning the dataset using the label vector. If any labels are missing, the model will be trained using EM in a semi-supervised setting. A homogeneous model can be defined by passing in a single distribution callable as the first parameter and specifying the number of components, while a heterogeneous model can be defined by passing in a list of callables of the appropriate type. A naive Bayes classifier is a subrset of the Bayes classifier in that the math is identical, but the distributions are independent for each feature. Simply put, one can create a multivariate Gaussian Bayes classifier with a full covariance matrix, but a Gaussian naive Bayes would require a diagonal covariance matrix. Parameters ---------- distributions : array-like, shape (n_components,) or callable The components of the model. This should either be a single callable if all components will be the same distribution, or an array of callables, one for each feature. X : array-like or generator, shape (n_samples, n_dimensions) This is the data to train on. Each row is a sample, and each column is a dimension to train on. y : array-like, shape (n_samples,) The labels for each sample. The labels should be integers between 0 and k-1 for a problem with k classes, or -1 if the label is not known for that sample. weights : array-like, shape (n_samples,), optional The initial weights of each sample in the matrix. If nothing is passed in then each sample is assumed to be the same weight. Default is None. pseudocount : double, optional, positive A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Only effects mixture models defined over discrete distributions. Default is 0. stop_threshold : double, optional, positive The threshold at which EM will terminate for the improvement of the model. If the model does not improve its fit of the data by a log probability of 0.1 then terminate. Only required if doing semisupervised learning. Default is 0.1. max_iterations : int, optional, positive The maximum number of iterations to run EM for. If this limit is hit then it will terminate training, regardless of how well the model is improving per iteration. Only required if doing semisupervised learning. Default is 1e8. callbacks : list, optional A list of callback objects that describe functionality that should be undertaken over the course of training. return_history : bool, optional Whether to return the history during training as well as the model. verbose : bool, optional Whether or not to print out improvement information over iterations. Only required if doing semisupervised learning. Default is False. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. Default is 1. Returns ------- model : NaiveBayes The fit naive Bayes model. """ ICD = IndependentComponentsDistribution if distributions in (MultivariateGaussianDistribution, DirichletDistribution): raise ValueError("naive Bayes only supports independent features. Use BayesClassifier instead") elif isinstance(distributions, (list, numpy.ndarray, tuple)): for distribution in distributions: if not callable(distribution): raise ValueError("must pass in class constructors, not initiated distributions (i.e. NormalDistribution)") elif distribution in (MultivariateGaussianDistribution, DirichletDistribution): raise ValueError("naive Bayes only supported independent features. Use BayesClassifier instead") if not isinstance(X, BaseGenerator): if y is None: raise ValueError("Must pass in both X and y as arrays or a data generator for X.") batch_size = len(X) // n_jobs + len(X) % n_jobs data_generator = DataGenerator(X, weights, y, batch_size=batch_size) else: data_generator = X n_components = data_generator.classes n_components = len(n_components[n_components != -1]) n, d = data_generator.shape if callable(distributions): if d > 1: distributions = [ICD([distributions.blank() for j in range(d)]) for i in range(n_components)] else: distributions = [distributions.blank() for i in range(n_components)] else: if d > 1: distributions = [ICD([distribution.blank() for distribution in distributions]) for i in range(n_components)] else: distributions = [distribution.blank() for distribution in distributions] model = cls(distributions) _, history = model.fit(data_generator, pseudocount=pseudocount, stop_threshold=stop_threshold, max_iterations=max_iterations, verbose=verbose, callbacks=callbacks, return_history=True, n_jobs=n_jobs) if return_history: return model, history return model pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/__init__.py000066400000000000000000000037461420461712600253160ustar00rootroot00000000000000# __init__.py: pomegranate # Contact: Jacob Schreiber """ For detailed documentation and examples, see the README. """ import os from .base import * from .parallel import * from .distributions import * from .kmeans import Kmeans from .gmm import GeneralMixtureModel from .NaiveBayes import NaiveBayes from .BayesClassifier import BayesClassifier from .MarkovChain import MarkovChain from .hmm import HiddenMarkovModel from .BayesianNetwork import BayesianNetwork from .MarkovNetwork import MarkovNetwork from .FactorGraph import FactorGraph __version__ = '0.14.8' os.environ['OMP_NUM_THREADS'] = '1' os.environ['MKL_NUM_THREADS'] = '1' def from_json(s): """A robust loading method. This method can load an appropriately formatted JSON object from any model in pomegranate and return the appropriate object. This relies mostly on the 'class' attribute in the JSON. Parameters ---------- s : str Either the filename of a JSON object or a string that is JSON formatted, as produced by any of the `to_json` methods in pomegranate. """ try: d = json.loads(s) except: try: with open(s, 'r') as f: d = json.load(f) except: raise IOError("String must be properly formatted JSON or filename of properly formatted JSON.") if d['class'] == 'Distribution': return Distribution.from_json(s) elif d['class'] == 'GeneralMixtureModel': return GeneralMixtureModel.from_json(s) elif d['class'] == 'HiddenMarkovModel': return HiddenMarkovModel.from_json(s) elif d['class'] == 'NaiveBayes': return NaiveBayes.from_json(s) elif d['class'] == 'BayesClassifier': return BayesClassifier.from_json(s) elif d['class'] == 'BayesianNetwork': return BayesianNetwork.from_json(s) elif d['class'] == 'MarkovChain': return MarkovChain.from_json(s) elif d['class'] == 'MarkovNetwork': return MarkovNetwork.from_json(s) elif d['class'] == 'FactorGraph': return FactorGraph.from_json(s) else: raise ValueError("Must pass in an JSON with a valid model name.") pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/base.pxd000066400000000000000000000012501420461712600246200ustar00rootroot00000000000000# base.pxd # Contact: Jacob Schreiber (jmschreiber91@gmail.com) cimport numpy cdef class Model(object): cdef public str name cdef public int d cdef public bint frozen cdef public str model cdef void _log_probability(self, double* symbol, double* log_probability, int n) nogil cdef double _vl_log_probability(self, double* symbol, int n) nogil cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil cdef class GraphModel(Model): cdef public list states, edges cdef public object graph cdef int n_edges, n_states cdef class State(object): cdef public object distribution cdef public str name cdef public double weight pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/base.pyx000066400000000000000000000315321420461712600246530ustar00rootroot00000000000000# base.pyx # Contact: Jacob Schreiber ( jmschreiber91@gmail.com ) from .utils cimport * from .distributions import Distribution import json import numpy import uuid import yaml # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") cdef class Model(object): """The abstract building block for all distributions.""" def __cinit__(self): self.name = "Model" def __str__(self): return self.to_json() def __repr__(self): return self.to_json() def get_params(self, *args, **kwargs): return self.__getstate__() def set_params(self, state): self.__setstate__(state) def to_dict(self): """Serialize this object to a dictionary of parameters.""" raise NotImplementedError def to_json(self, separators=(',', ' : '), indent=4): """Serialize the model to JSON. Parameters ---------- separators : tuple, optional The two separators to pass to the json.dumps function for formatting. Default is (',', ' : '). indent : int, optional The indentation to use at each level. Passed to json.dumps for formatting. Default is 4. Returns ------- json : str A properly formatted JSON object. """ return json.dumps( self.to_dict(), separators=separators, indent=indent ) def to_yaml(self): """Serialize the model to YAML for compactness.""" return yaml.safe_dump(json.loads(self.to_json())) @classmethod def from_dict( cls, dictionary ): """Deserialize this object from a dictionary of parameters.""" raise NotImplementedError @classmethod def from_json( cls, s ): """Deserialize this object from its JSON representation. Parameters ---------- s : str A JSON formatted string containing the file. Returns ------- model : object A properly initialized and baked model. """ # Load a dictionary from a JSON formatted string try: d = json.loads(s) except: try: with open(s, 'r') as infile: d = json.load(infile) except: raise IOError("String must be properly formatted JSON or filename of properly formatted JSON.") return cls.from_dict(d) @classmethod def from_yaml( cls, yml ): """Deserialize this object from its YAML representation.""" return cls.from_dict(yaml.load(yml, Loader=yaml.SafeLoader)) def copy(self): """Return a deep copy of this distribution object. This object will not be tied to any other distribution or connected in any form. Parameters ---------- None Returns ------- distribution : Distribution A copy of the distribution with the same parameters. """ return self.__class__.from_json( self.to_json() ) def freeze(self): """Freeze the distribution, preventing updates from occurring.""" self.frozen = True def thaw(self): """Thaw the distribution, re-allowing updates to occur.""" self.frozen = False def copy(self): """Return a deep copy of this distribution object. This object will not be tied to any other distribution or connected in any form. Parameters ---------- None Returns ------- distribution : Distribution A copy of the distribution with the same parameters. """ return self.__class__(*self.parameters) def sample(self, n=None): """Return a random item sampled from this distribution. Parameters ---------- n : int or None, optional The number of samples to return. Default is None, which is to generate a single sample. Returns ------- sample : double or object Returns a sample from the distribution of a type in the support of the distribution. """ raise NotImplementedError def probability(self, symbol): """Return the probability of the given symbol under this distribution. Parameters ---------- symbol : object The symbol to calculate the probability of Returns ------- probability : double The probability of that point under the distribution. """ return numpy.exp(self.log_probability(symbol)) def log_probability(self, symbol): """Return the log probability of the given symbol under this distribution. Parameters ---------- symbol : double The symbol to calculate the log probability of (overridden for DiscreteDistributions) Returns ------- logp : double The log probability of that point under the distribution. """ raise NotImplementedError def score(self, X, y): """Return the accuracy of the model on a data set. Parameters ---------- X : numpy.ndarray, shape=(n, d) The values of the data set y : numpy.ndarray, shape=(n,) The labels of each value """ return (self.predict(X) == y).mean() def sample(self, n=None): """Return a random item sampled from this distribution. Parameters ---------- n : int or None, optional The number of samples to return. Default is None, which is to generate a single sample. Returns ------- sample : double or object Returns a sample from the distribution of a type in the support of the distribution. """ raise NotImplementedError def fit(self, items, weights=None, inertia=0.0): """Fit the distribution to new data using MLE estimates. Parameters ---------- items : array-like, shape (n_samples, n_dimensions) This is the data to train on. Each row is a sample, and each column is a dimension to train on. For univariate distributions an array is used, while for multivariate distributions a 2d matrix is used. weights : array-like, shape (n_samples,), optional The initial weights of each sample in the matrix. If nothing is passed in then each sample is assumed to be the same weight. Default is None. inertia : double, optional The weight of the previous parameters of the model. The new parameters will roughly be old_param * inertia + new_param * (1-inertia), so an inertia of 0 means ignore the old parameters, whereas an inertia of 1 means ignore the new parameters. Default is 0.0. Returns ------- None """ raise NotImplementedError def summarize(self, items, weights=None): """Summarize a batch of data into sufficient statistics for a later update. Parameters ---------- items : array-like, shape (n_samples, n_dimensions) This is the data to train on. Each row is a sample, and each column is a dimension to train on. For univariate distributions an array is used, while for multivariate distributions a 2d matrix is used. weights : array-like, shape (n_samples,), optional The initial weights of each sample in the matrix. If nothing is passed in then each sample is assumed to be the same weight. Default is None. Returns ------- None """ return NotImplementedError def from_summaries(self, inertia=0.0): """Fit the distribution to the stored sufficient statistics. Parameters ---------- inertia : double, optional The weight of the previous parameters of the model. The new parameters will roughly be old_param * inertia + new_param * (1-inertia), so an inertia of 0 means ignore the old parameters, whereas an inertia of 1 means ignore the new parameters. Default is 0.0. Returns ------- None """ return NotImplementedError def clear_summaries(self): """Clear the summary statistics stored in the object.""" return NotImplementedError cdef void _log_probability(self, double* symbol, double* log_probability, int n) nogil: pass cdef double _vl_log_probability(self, double* symbol, int n) nogil: return NEGINF cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil: pass cdef class GraphModel(Model): """Represents an generic graphical model.""" def __init__(self, name=None): """ Make a new graphical model. Name is an optional string used to name the model when output. Name may not contain spaces or newlines. """ # Save the name or make up a name. self.name = name or str(id(self)) self.states = [] self.edges = [] def __str__(self): """Represent this model with it's name and states.""" if self.states is not None: state_str = "".join(map(str, self.states)) else: state_str = "" return "{}:{}".format(self.name, state_str) def add_node(self, node): """Add a node to the graph.""" self.states.append(node) self.n_states += 1 def add_nodes(self, *nodes): """Add multiple states to the graph.""" for node in nodes: self.add_node(node) def add_state(self, state): """Another name for a node.""" self.add_node(state) def add_states(self, *states): """Another name for a node.""" for state in states: self.add_state(state) def add_edge(self, a, b): """ Add a transition from state a to state b which indicates that B is dependent on A in ways specified by the distribution. """ # Add the transition self.edges.append((a, b)) self.n_edges += 1 def add_transition(self, a, b): """Transitions and edges are the same.""" self.add_edge(a, b) def node_count(self): """Returns the number of nodes/states in the model""" return self.n_states def state_count(self): """Returns the number of states present in the model.""" return self.n_states def edge_count(self): """Returns the number of edges present in the model.""" return self.n_edges def dense_transition_matrix(self): """ Returns the dense transition matrix. Useful if the transitions of somewhat small models need to be analyzed. """ m = len(self.states) transition_log_probabilities = numpy.full((m, m), NEGINF) for i in range(m): for n in range( self.out_edge_count[i], self.out_edge_count[i+1] ): transition_log_probabilities[i, self.out_transitions[n]] = \ self.out_transition_log_probabilities[n] return transition_log_probabilities cdef class State(object): """ Represents a state in an HMM. Holds emission distribution, but not transition distribution, because that's stored in the graph edges. """ def __init__( self, distribution, name=None, weight=None ): """ Make a new State emitting from the given distribution. If distribution is None, this state does not emit anything. A name, if specified, will be the state's name when presented in output. Name may not contain spaces or newlines, and must be unique within a model. """ # Save the distribution self.distribution = distribution # Save the name self.name = name or str(uuid.uuid4()) # Save the weight, or default to the unit weight self.weight = weight or 1. def __reduce__(self): return self.__class__, (self.distribution, self.name, self.weight) def __str__(self): """ The string representation of a state is the json, so call that format. """ return self.to_json() def __repr__(self): """ The string representation of a state is the json, so call that format. """ return self.__str__() def tie( self, state ): """ Tie this state to another state by just setting the distribution of the other state to point to this states distribution. """ state.distribution = self.distribution def is_silent(self): """ Return True if this state is silent (distribution is None) and False otherwise. """ return self.distribution is None def tied_copy(self): """ Return a copy of this state where the distribution is tied to the distribution of this state. """ return State( distribution=self.distribution, name=self.name+'-tied' ) def copy( self ): """Return a hard copy of this state.""" return State( distribution=self.distribution.copy(), name=self.name ) def to_dict(self): """Convert this state to a dictionary of parameters.""" return { 'class' : 'State', 'distribution' : None if self.is_silent() else self.distribution.to_dict(), 'name' : self.name, 'weight' : self.weight } def to_json(self, separators=(',', ' : '), indent=4): """Convert this state to JSON format.""" return json.dumps(self.to_dict(), separators=separators, indent=indent) def to_yaml(self): """Convert this state to YAML format.""" return yaml.safe_dump(json.loads(self.to_json())) @classmethod def from_dict( cls, d ): """Read a State from a given dictionary of parameters.""" # If we're not decoding a state, we're decoding the wrong thing if d['class'] != 'State': raise IOError( "State object attempting to decode " "{} object".format( d['class'] ) ) # If this is a silent state, don't decode the distribution if d['distribution'] is None: return cls( None, str(d['name']), d['weight'] ) # Otherwise it has a distribution, so decode that name = str(d['name']) weight = d['weight'] from .gmm import GeneralMixtureModel c = d['distribution']['class'] dist = eval(c).from_dict( d['distribution'] ) return cls( dist, name, weight ) @classmethod def from_json( cls, s ): """Read a State from a given string formatted in JSON.""" # Load a dictionary from a JSON formatted string return cls.from_dict(json.loads(s)) @classmethod def from_yaml( cls, yml ): """Read a State from a given string formatted in YAML.""" return cls.from_dict(yaml.load(yml, Loader=yaml.SafeLoader)) # Create a convenient alias Node = State pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/bayes.pxd000066400000000000000000000011351420461712600250130ustar00rootroot00000000000000# gmm.pxd # Contact: Jacob Schreiber ( jmschreiber91@gmail.com ) cimport numpy from .base cimport Model cdef class BayesModel(Model): cdef public numpy.ndarray distributions cdef object distribution_callable cdef void** distributions_ptr cdef public numpy.ndarray weights cdef double* weights_ptr cdef public numpy.ndarray summaries cdef double* summaries_ptr cdef object keymap cdef public int n cdef public bint is_vl_ cdef public int cython cdef void _predict_log_proba(self, double* X, double* y, int n, int d) nogil cdef void _predict( self, double* X, int* y, int n, int d) nogil pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/bayes.pyx000066400000000000000000000756421420461712600250560ustar00rootroot00000000000000#cython: boundscheck=False #cython: cdivision=True # bayes.pyx # Contact: Jacob Schreiber ( jmschreiber91@gmail.com ) from libc.stdlib cimport calloc from libc.stdlib cimport free from libc.stdlib cimport malloc import time import numpy cimport numpy from .base cimport Model from distributions.distributions cimport Distribution from distributions import DiscreteDistribution from distributions import JointProbabilityTable from distributions import IndependentComponentsDistribution from .hmm import HiddenMarkovModel from .gmm import GeneralMixtureModel from .callbacks import History from .utils cimport _log from .utils cimport pair_lse from .utils cimport python_log_probability from .utils import _check_input from .utils import _convert from .utils import check_random_state from .io import BaseGenerator from .io import DataGenerator from joblib import Parallel from joblib import delayed DEF INF = float("inf") DEF NEGINF = float("-inf") cdef class BayesModel(Model): """A simple implementation of Bayes Rule as a base model. Bayes rule is foundational to many models. Here, it is used as a base class for naive Bayes, Bayes classifiers, and mixture models, that are all fundamentally similar and rely on bayes rule. Parameters ---------- distributions : array-like, shape (n_components,) The initialized components of the model. weights : array-like, optional, shape (n_components,) The prior probabilities corresponding to each component. Does not need to sum to one, but will be normalized to sum to one internally. Defaults to None. Attributes ---------- distributions : array-like, shape (n_components,) The component distribution objects. weights : array-like, shape (n_components,) The learned prior weight of each object d : int The number of dimensionals the model is built to consider. is_vl_ : bool Whether this model is built for variable length sequences or not. """ def __init__(self, distributions, weights=None): self.d = 0 self.is_vl_ = 0 self.cython = 1 self.n = len(distributions) if len(distributions) < 2: raise ValueError("must pass in at least two distributions") self.d = distributions[0].d for dist in distributions: if callable(dist): raise TypeError("must have initialized distributions in list") elif self.d != dist.d: raise TypeError("mis-matching dimensions between distributions in list") if not isinstance(dist, Distribution) and not isinstance(dist, Model): self.cython = 0 elif dist.model == 'HiddenMarkovModel': self.is_vl_ = 1 self.keymap = dist.keymap if weights is None: weights = numpy.ones_like(distributions, dtype='float64') / self.n else: weights = numpy.asarray(weights, dtype='float64') / sum(weights) self.weights = numpy.log(weights) self.weights_ptr = self.weights.data self.distributions = numpy.array(distributions) self.distributions_ptr = self.distributions.data self.summaries = numpy.zeros_like(weights, dtype='float64') self.summaries_ptr = self.summaries.data if self.is_vl_ == 0: if isinstance(distributions[0], JointProbabilityTable): self.cython = 0 else: self._build_keymap() def _build_keymap(self): """ Assemble keys of child distributions and bake encodings. """ dist = self.distributions[0] if isinstance(dist, DiscreteDistribution): keys = [] for distribution in self.distributions: keys.extend(distribution.keys()) keys.sort() self.keymap = [{key: i for i, key in enumerate(keys)}] for distribution in self.distributions: distribution.bake(tuple(keys)) elif isinstance(dist, IndependentComponentsDistribution): self.keymap = [{} for i in range(self.d)] keymap_tuples = [tuple() for i in range(self.d)] for distribution in self.distributions: for i in range(self.d): if isinstance(distribution[i], DiscreteDistribution): for key in distribution[i].keys(): if key not in self.keymap[i]: self.keymap[i][key] = len(self.keymap[i]) keymap_tuples[i] += (key,) for distribution in self.distributions: for i in range(self.d): d = distribution[i] if isinstance(d, DiscreteDistribution): d.bake(tuple(sorted(keymap_tuples[i]))) def __reduce__(self): return self.__class__, (self.distributions.tolist(), numpy.exp(self.weights)) def sample(self, n=1, random_state=None): """Generate a sample from the model. First, randomly select a component weighted by the prior probability, Then, use the sample method from that component to generate a sample. Parameters ---------- n : int, optional The number of samples to generate. Defaults to 1. random_state : int, numpy.random.RandomState, or None The random state used for generating samples. If set to none, a random seed will be used. If set to either an integer or a random seed, will produce deterministic outputs. Returns ------- sample : array-like or object A randomly generated sample from the model of the type modelled by the emissions. An integer if using most distributions, or an array if using multivariate ones, or a string for most discrete distributions. If n=1 return an object, if n>1 return an array of the samples. """ random_state = check_random_state(random_state) samples = [] random_seeds = random_state.randint(1000000, size=n) for i in range(n): d = random_state.choice(self.distributions, p=numpy.exp(self.weights)) samples.append(d.sample(random_state=random_seeds[i])) return numpy.array(samples) if n > 1 else samples[0] def log_probability(self, X, n_jobs=1, batch_size=None): """Calculate the log probability of a point under the distribution. The probability of a point is the sum of the probabilities of each distribution multiplied by the weights. Thus, the log probability is the sum of the log probability plus the log prior. This is the python interface. Parameters ---------- X : numpy.ndarray, shape=(n, d) or (n, m, d) The samples to calculate the log probability of. Each row is a sample and each column is a dimension. If emissions are HMMs then shape is (n, m, d) where m is variable length for each observation, and X becomes an array of n (m, d)-shaped arrays. n_jobs : int, optional The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. batch_size: int or None, optional The size of the batches to make predictions on. Passing in None means splitting the data set evenly among the number of jobs. Default is None. Returns ------- log_probability : double The log probability of the point under the distribution. """ cdef int i, j, n, d, m if self.is_vl_ or self.d == 1: n, d = len(X), self.d elif self.d > 1 and X.ndim == 1: raise ValueError("Must pass in an array with at least two dimensions.") else: n, d = X.shape if n_jobs > 1 or isinstance(X, BaseGenerator): if batch_size is None: batch_size = 1 if self.is_vl_ else n // n_jobs + n % n_jobs if not isinstance(X, BaseGenerator): data_generator = DataGenerator(X, batch_size=batch_size) else: data_generator = X with Parallel(n_jobs=n_jobs, backend='threading') as parallel: f = delayed(self.log_probability) logp_array = parallel(f(batch[0]) for batch in data_generator.batches()) return numpy.concatenate(logp_array) cdef numpy.ndarray logp_ndarray = numpy.empty(n) cdef double* logp = logp_ndarray.data cdef numpy.ndarray X_ndarray cdef double* X_ptr if not self.is_vl_: X_ndarray = _check_input(X, self.keymap) X_ptr = X_ndarray.data if d != self.d: raise ValueError("sample has {} dimensions but model has {} dimensions".format(d, self.d)) with nogil: if self.is_vl_: for i in range(n): with gil: X_ndarray = numpy.asarray(X[i]) X_ptr = X_ndarray.data logp[i] = self._vl_log_probability(X_ptr, n) else: self._log_probability(X_ptr, logp, n) return logp_ndarray cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i, j, d = self.d cdef double* logp = malloc(n * sizeof(double)) if self.cython == 1: ( self.distributions_ptr[0])._log_probability(X, log_probability, n) else: with gil: python_log_probability(self.distributions[0], X, log_probability, n) for i in range(n): log_probability[i] += self.weights_ptr[0] for j in range(1, self.n): if self.cython == 1: ( self.distributions_ptr[j])._log_probability(X, logp, n) else: with gil: python_log_probability(self.distributions[j], X, logp, n) for i in range(n): log_probability[i] = pair_lse(log_probability[i], logp[i] + self.weights_ptr[j]) free(logp) cdef double _vl_log_probability(self, double* X, int n) nogil: cdef int i cdef double log_probability_sum = NEGINF cdef double log_probability for i in range(self.n): log_probability = ( self.distributions_ptr[i])._vl_log_probability(X, n) + self.weights_ptr[i] log_probability_sum = pair_lse(log_probability_sum, log_probability) return log_probability_sum def predict_proba(self, X, n_jobs=1, batch_size=None): """Calculate the posterior P(M|D) for data. Calculate the probability of each item having been generated from each component in the model. This returns normalized probabilities such that each row should sum to 1. Since calculating the log probability is much faster, this is just a wrapper which exponentiates the log probability matrix. Parameters ---------- X : array-like, shape (n_samples, n_dimensions) The samples to do the prediction on. Each sample is a row and each column corresponds to a dimension in that sample. For univariate distributions, a single array may be passed in. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. batch_size: int or None, optional The size of the batches to make predictions on. Passing in None means splitting the data set evenly among the number of jobs. Default is None. Returns ------- probability : array-like, shape (n_samples, n_components) The normalized probability P(M|D) for each sample. This is the probability that the sample was generated from each component. """ return numpy.exp(self.predict_log_proba(X, n_jobs=n_jobs, batch_size=batch_size)) def predict_log_proba(self, X, n_jobs=1, batch_size=None): """Calculate the posterior log P(M|D) for data. Calculate the log probability of each item having been generated from each component in the model. This returns normalized log probabilities such that the probabilities should sum to 1 This is a sklearn wrapper for the original posterior function. Parameters ---------- X : array-like, shape (n_samples, n_dimensions) The samples to do the prediction on. Each sample is a row and each column corresponds to a dimension in that sample. For univariate distributions, a single array may be passed in. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. batch_size: int or None, optional The size of the batches to make predictions on. Passing in None means splitting the data set evenly among the number of jobs. Default is None. Returns ------- y : array-like, shape (n_samples, n_components) The normalized log probability log P(M|D) for each sample. This is the probability that the sample was generated from each component. """ cdef int i, n, d cdef numpy.ndarray X_ndarray cdef double* X_ptr cdef numpy.ndarray y cdef double* y_ptr if n_jobs > 1 or isinstance(X, BaseGenerator): if batch_size is None: batch_size = 1 if self.is_vl_ else len(X) // n_jobs + len(X) % n_jobs if not isinstance(X, BaseGenerator): data_generator = DataGenerator(X, batch_size=batch_size) else: data_generator = X with Parallel(n_jobs=n_jobs, backend='threading') as parallel: f = delayed(self.predict_log_proba) y_array = parallel(f(batch[0]) for batch in data_generator.batches()) return numpy.concatenate(y_array) if not self.is_vl_: X_ndarray = _check_input(X, self.keymap) X_ptr = X_ndarray.data n, d = X_ndarray.shape[0], X_ndarray.shape[1] if d != self.d: raise ValueError("sample only has {} dimensions but should have {} dimensions".format(d, self.d)) else: X_ndarray = X n, d = len(X_ndarray), self.d y = numpy.zeros((n, self.n), dtype='float64') y_ptr = y.data with nogil: if not self.is_vl_: self._predict_log_proba(X_ptr, y_ptr, n, d) else: for i in range(n): with gil: X_ndarray = _check_input(X[i], self.keymap) X_ptr = X_ndarray.data d = len(X_ndarray) self._predict_log_proba(X_ptr, y_ptr+i*self.n, 1, d) return y if self.is_vl_ else y.reshape(self.n, n).T cdef void _predict_log_proba(self, double* X, double* y, int n, int d) nogil: cdef double y_sum, logp cdef int i, j for j in range(self.n): if self.is_vl_: y[j] = ( self.distributions_ptr[j])._vl_log_probability(X, d) else: if self.cython == 1: ( self.distributions_ptr[j])._log_probability(X, y+j*n, n) else: with gil: python_log_probability(self.distributions[j], X, y+j*n, n) for i in range(n): y_sum = NEGINF for j in range(self.n): y[j*n + i] += self.weights_ptr[j] y_sum = pair_lse(y_sum, y[j*n + i]) for j in range(self.n): y[j*n + i] -= y_sum def predict(self, X, n_jobs=1, batch_size=None): """Predict the most likely component which generated each sample. Calculate the posterior P(M|D) for each sample and return the index of the component most likely to fit it. This corresponds to a simple argmax over the responsibility matrix. This is a sklearn wrapper for the maximum_a_posteriori method. Parameters ---------- X : array-like, shape (n_samples, n_dimensions) The samples to do the prediction on. Each sample is a row and each column corresponds to a dimension in that sample. For univariate distributions, a single array may be passed in. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. batch_size: int or None, optional The size of the batches to make predictions on. Passing in None means splitting the data set evenly among the number of jobs. Default is None. Returns ------- y : array-like, shape (n_samples,) The predicted component which fits the sample the best. """ cdef int i, n, d cdef numpy.ndarray X_ndarray cdef double* X_ptr cdef numpy.ndarray y cdef int* y_ptr if n_jobs > 1 or isinstance(X, BaseGenerator): if batch_size is None: batch_size = 1 if self.is_vl_ else len(X) // n_jobs + len(X) % n_jobs if not isinstance(X, BaseGenerator): data_generator = DataGenerator(X, batch_size=batch_size) else: data_generator = X with Parallel(n_jobs=n_jobs, backend='threading') as parallel: f = delayed(self.predict) y_array = parallel(f(batch[0]) for batch in data_generator.batches()) return numpy.concatenate(y_array) if not self.is_vl_: X_ndarray = _check_input(X, self.keymap) X_ptr = X_ndarray.data n, d = len(X_ndarray), len(X_ndarray[0]) if d != self.d: raise ValueError("sample only has {} dimensions but should have {} dimensions".format(d, self.d)) else: X_ndarray = X n, d = len(X_ndarray), self.d y = numpy.zeros(n, dtype='int32') y_ptr = y.data with nogil: if not self.is_vl_: self._predict(X_ptr, y_ptr, n, d) else: for i in range(n): with gil: X_ndarray = _check_input(X[i], self.keymap) X_ptr = X_ndarray.data d = len(X_ndarray) self._predict(X_ptr, y_ptr+i, 1, d) return y cdef void _predict( self, double* X, int* y, int n, int d) nogil: cdef int i, j cdef double max_logp, logp cdef double* r = calloc(n*self.n, sizeof(double)) for j in range(self.n): if self.is_vl_: r[j] = ( self.distributions_ptr[j])._vl_log_probability(X, d) else: if self.cython == 1: ( self.distributions_ptr[j])._log_probability(X, r+j*n, n) else: with gil: python_log_probability(self.distributions[j], X, r+j*n, n) for i in range(n): max_logp = NEGINF for j in range(self.n): logp = r[j*n + i] + self.weights_ptr[j] if logp > max_logp: max_logp = logp y[i] = j free(r) def fit(self, X, y=None, weights=None, inertia=0.0, pseudocount=0.0, stop_threshold=0.1, max_iterations=1e8, callbacks=[], return_history=False, verbose=False, n_jobs=1): """Fit the Bayes classifier to the data by passing data to its components. The fit step for a Bayes classifier with purely labeled data is a simple MLE update on the underlying distributions, grouped by the labels. However, in the semi-supervised the model is trained on a mixture of both labeled and unlabeled data, where the unlabeled data uses the label -1. In this setting, EM is used to train the model. The model is initialized using the labeled data and then sufficient statistics are gathered for both the labeled and unlabeled data, combined, and used to update the parameters. Parameters ---------- X : numpy.ndarray or list The dataset to operate on. For most models this is a numpy array with columns corresponding to features and rows corresponding to samples. For markov chains and HMMs this will be a list of variable length sequences. y : numpy.ndarray or list or None Data labels for supervised training algorithms. weights : array-like or None, shape (n_samples,), optional The initial weights of each sample in the matrix. If nothing is passed in then each sample is assumed to be the same weight. Default is None. inertia : double, optional Inertia used for the training the distributions. pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Default is 0. stop_threshold : double, optional, positive The threshold at which EM will terminate for the improvement of the model. If the model does not improve its fit of the data by a log probability of 0.1 then terminate. Only required if doing semisupervised learning. Default is 0.1. max_iterations : int, optional, positive The maximum number of iterations to run EM for. If this limit is hit then it will terminate training, regardless of how well the model is improving per iteration. Only required if doing semisupervised learning. Default is 1e8. callbacks : list, optional A list of callback objects that describe functionality that should be undertaken over the course of training. Only used for semi-supervised learning. return_history : bool, optional Whether to return the history during training as well as the model. Only used for semi-supervised learning. verbose : bool, optional Whether or not to print out improvement information over iterations. Only required if doing semisupervised learning. Default is False. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. Returns ------- self : object Returns the fitted model """ training_start_time = time.time() total_improvement = 0 if not isinstance(X, BaseGenerator): if y is None: raise ValueError("Must pass in both X and y as arrays or a data generator for X.") batch_size = X.shape[0] // n_jobs + X.shape[0] % n_jobs data_generator = DataGenerator(X, weights, y, batch_size=batch_size) else: data_generator = X n, d = data_generator.shape callbacks = [History()] + callbacks for callback in callbacks: callback.model = self callback.on_training_begin() with Parallel(n_jobs=n_jobs, backend='threading') as parallel: f = delayed(self.summarize) parallel(f(*batch) for batch in data_generator.batches()) self.from_summaries(inertia, pseudocount) self._build_keymap() semisupervised = -1 in data_generator.classes if semisupervised: initial_log_probability_sum = NEGINF iteration, improvement = 0, INF unsupervised = GeneralMixtureModel(self.distributions) f2 = delayed(unsupervised.summarize) while improvement > stop_threshold and iteration < max_iterations + 1: epoch_start_time = time.time() self.from_summaries(inertia, pseudocount) unsupervised.weights[:] = self.weights parallel(f(*batch) for batch in data_generator.labeled_batches()) unsupervised.summaries[:] = self.summaries log_probability_sum = sum(parallel(f2(*batch) for batch in data_generator.unlabeled_batches())) self.summaries[:] = unsupervised.summaries if iteration == 0: initial_log_probability_sum = log_probability_sum else: improvement = log_probability_sum - last_log_probability_sum epoch_end_time = time.time() time_spent = epoch_end_time - epoch_start_time if verbose: print("[{}] Improvement: {}\tTime (s): {:4.4}".format(iteration, improvement, time_spent)) total_improvement += improvement logs = { 'learning_rate': None, 'n_seen_batches' : None, 'epoch' : iteration, 'improvement' : improvement, 'total_improvement' : total_improvement, 'log_probability' : log_probability_sum, 'last_log_probability' : last_log_probability_sum, 'initial_log_probability' : initial_log_probability_sum, 'epoch_start_time' : epoch_start_time, 'epoch_end_time' : epoch_end_time, 'duration' : time_spent } for callback in callbacks: callback.on_epoch_end(logs) iteration += 1 last_log_probability_sum = log_probability_sum for callback in callbacks: callback.on_training_end(logs) self.clear_summaries() if verbose: print("Total Improvement: {}".format(total_improvement)) if verbose: total_time_spent = time.time() - training_start_time print("Total Time (s): {:.4f}".format(total_time_spent)) if return_history: history = callbacks[0] return self, history return self def summarize(self, X, y, weights=None): """Summarize data into stored sufficient statistics for out-of-core training. Parameters ---------- X : array-like, shape (n_samples, variable) Array of the samples, which can be either fixed size or variable depending on the underlying components. y : array-like, shape (n_samples,) Array of the known labels as integers weights : array-like, shape (n_samples,) optional Array of the weight of each sample, a positive float Returns ------- None """ X = _convert(X) y = _convert(y) if self.d > 0 and not isinstance( self.distributions[0], HiddenMarkovModel ): if X.ndim > 2: raise ValueError("input data has too many dimensions") elif X.ndim == 2 and self.d != X.shape[1]: raise ValueError("input data rows do not match model dimension") if weights is None: weights = numpy.ones(X.shape[0], dtype='float64') else: weights = numpy.asarray(weights, dtype='float64') if self.is_vl_: for i, distribution in enumerate(self.distributions): distribution.summarize(list(X[y==i]), weights[y==i]) else: for i, distribution in enumerate(self.distributions): distribution.summarize(X[y==i], weights[y==i]) for i in range(self.n): weight = weights[y==i].sum() self.summaries[i] += weight cdef double _summarize(self, double* X, double* weights, int n, int column_idx, int d) nogil: return -1 def from_summaries(self, inertia=0.0, pseudocount=0.0, **kwargs): """Fit the model to the collected sufficient statistics. Fit the parameters of the model to the sufficient statistics gathered during the summarize calls. This should return an exact update. Parameters ---------- inertia : double, optional The weight of the previous parameters of the model. The new parameters will roughly be old_param*inertia + new_param*(1-inertia), so an inertia of 0 means ignore the old parameters, whereas an inertia of 1 means ignore the new parameters. Default is 0.0. pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. If discrete data, will smooth both the prior probabilities of each component and the emissions of each component. Otherwise, will only smooth the prior probabilities of each component. Default is 0. Returns ------- None """ if self.frozen: return self if self.summaries.sum() == 0: return self summaries = self.summaries + pseudocount summaries /= summaries.sum() for i, distribution in enumerate(self.distributions): if isinstance(distribution, DiscreteDistribution): distribution.from_summaries(inertia, pseudocount) else: distribution.from_summaries(inertia, **kwargs) self.weights[i] = _log(summaries[i]) self.summaries[i] = 0. return self def clear_summaries(self): """Remove the stored sufficient statistics. Parameters ---------- None Returns ------- None """ self.summaries *= 0 for distribution in self.distributions: distribution.clear_summaries() pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/callbacks.py000066400000000000000000000102561420461712600254700ustar00rootroot00000000000000# callbacks.py # Authors: Jacob Schreiber class Callback(object): """An object that adds functionality during training. A callback is a function or group of functions that can be executed during the training process for any of pomegranate's models that have iterative training procedures. A callback can be called at three stages-- the beginning of training, at the end of each epoch (or iteration), and at the end of training. Users can define any functions that they wish in the corresponding functions. """ def __init__(self): self.model = None self.params = None def on_training_begin(self): """Functionality to add to the beginning of training. This method will be called at the beginning of each model's training procedure. """ pass def on_training_end(self, logs): """Functionality to add to the end of training. This method will be called at the end of each model's training procedure. """ pass def on_epoch_end(self, logs): """Functionality to add to the end of each epoch. This method will be called at the end of each epoch during the model's iterative training procedure. """ pass class ModelCheckpoint(Callback): """This will save the model to disk after each epoch.""" def __init__(self, name=None, verbose=True): self.model = None self.params = None self.name = None self.verbose = verbose def on_epoch_end(self, logs): """Save the model to disk at the end of each epoch.""" model = self.model.to_json() epoch = logs['epoch'] name = self.name if self.name is not None else self.model.name if self.verbose: print("[{}] Saving checkpoint to {}.{}.json".format(epoch, name, epoch)) with open('{}.{}.json'.format(name, epoch), 'w') as outfile: outfile.write(model) class History(Callback): """Keeps a history of the loss during training.""" def on_training_begin(self): self.total_improvement = [] self.improvements = [] self.log_probabilities = [] self.epoch_start_times = [] self.epoch_end_times = [] self.epoch_durations = [] self.epochs = [] self.learning_rates = [] self.n_seen_batches = [] self.initial_log_probablity = None def on_epoch_end(self, logs): """Save the files to the appropriate lists.""" self.total_improvement.append(logs['total_improvement']) self.improvements.append(logs['improvement']) self.log_probabilities.append(logs['log_probability']) self.epoch_start_times.append(logs['epoch_start_time']) self.epoch_end_times.append(logs['epoch_end_time']) self.epoch_durations.append(logs['duration']) self.epochs.append(logs['epoch']) self.learning_rates.append(logs['learning_rate']) self.n_seen_batches.append(logs['n_seen_batches']) self.initial_log_probability = logs['initial_log_probability'] class CSVLogger(Callback): """Logs results of training to a CSV file during training.""" def __init__(self, filename, separator=',', append=False): self.filename = filename self.separator = separator self.append = append self.file = None self.columns = ['epoch', 'duration', 'total_improvement', 'improvement', 'log_probability', 'last_log_probability', 'epoch_start_time', 'epoch_end_time', 'n_seen_batches', 'learning_rate'] def on_training_begin(self): if self.append == False: self.file = open(self.filename, 'w') self.file.write(self.separator.join(self.columns) + "\n") else: self.file = open(self.filename, 'a') def on_training_end(self, logs): self.file.close() def on_epoch_end(self, logs): self.file.write(self.separator.join(str(logs[col]) for col in self.columns) + "\n") class LambdaCallback(Callback): """A callback that takes in anonymous functions for any of the methods, for convenience.""" def __init__(self, on_training_begin=None, on_training_end=None, on_epoch_end=None): self.on_training_begin_ = on_training_begin self.on_training_end_ = on_training_end self.on_epoch_end_ = on_epoch_end def on_training_begin(self): if self.on_training_begin_ is not None: self.on_training_begin_() def on_training_end(self, logs): if self.on_training_end_ is not None: self.on_training_end_(logs) def on_epoch_end(self, logs): if self.on_epoch_end_ is not None: self.on_epoch_end_(logs) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions/000077500000000000000000000000001420461712600260755ustar00rootroot00000000000000BernoulliDistribution.pxd000066400000000000000000000003521420461712600330660ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions# BernoulliDistribution.pxd # Contact: Jacob Schreiber import numpy cimport numpy from .distributions cimport Distribution cdef class BernoulliDistribution(Distribution): cdef double p cdef double* logp BernoulliDistribution.pyx000066400000000000000000000043461420461712600331220ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions#!python #cython: boundscheck=False #cython: cdivision=True # BernoulliDistribution.pyx # Contact: Jacob Schreiber import numpy from libc.stdlib cimport malloc from libc.stdlib cimport free from ..utils cimport _log from ..utils cimport isnan from ..utils import check_random_state # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") cdef class BernoulliDistribution(Distribution): """A Bernoulli distribution describing the probability of a binary variable.""" property parameters: def __get__(self): return [self.p] def __set__(self, parameters): self.p = parameters[0] self.logp[0] = _log(1-self.p) self.logp[1] = _log(self.p) def __cinit__(self, p, frozen=False): self.p = p self.name = "BernoulliDistribution" self.frozen = frozen self.logp = malloc(2*sizeof(double)) self.logp[0] = _log(1-p) self.logp[1] = _log(p) self.summaries = [0.0, 0.0] def __dealloc__(self): free(self.logp) def __reduce__(self): """Serialize distribution for pickling.""" return self.__class__, (self.p, self.frozen) cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i for i in range(n): if isnan(X[i]): log_probability[i] = 0. else: log_probability[i] = self.logp[ X[i]] def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) return random_state.choice(2, p=[1-self.p, self.p], size=n) cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil: cdef int i cdef double w_sum = 0, x_sum = 0 cdef double item for i in range(n): item = items[i*d + column_idx] if isnan(item): continue w_sum += weights[i] if item == 1: x_sum += weights[i] with gil: self.summaries[0] += w_sum self.summaries[1] += x_sum def from_summaries(self, inertia=0.0): """Update the parameters of the distribution from the summaries.""" if self.summaries[0] < 1e-8 or self.frozen: return p = self.summaries[1] / self.summaries[0] self.p = self.p * inertia + p * (1-inertia) self.logp[0] = _log(1-p) self.logp[1] = _log(p) self.summaries = [0.0, 0.0] @classmethod def blank(cls): return cls(0) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions/BetaDistribution.pxd000066400000000000000000000003421420461712600320640ustar00rootroot00000000000000# BetaDistribution.pxd # Contact: Jacob Schreiber import numpy cimport numpy from .distributions cimport Distribution cdef class BetaDistribution(Distribution): cdef double alpha, beta, beta_norm pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions/BetaDistribution.pyx000066400000000000000000000054601420461712600321170ustar00rootroot00000000000000#!python #cython: boundscheck=False #cython: cdivision=True # BetaDistribution.pyx # Contact: Jacob Schreiber import numpy from ..utils cimport _log from ..utils cimport isnan from ..utils cimport lgamma from ..utils import check_random_state # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") cdef class BetaDistribution(Distribution): """A beta-bernoulli distribution. This object is a beta-bernoulli distribution. This means that it uses a beta distribution to model the distribution of values that the rate value can take rather than it being a single number. This should not be confused with a Beta distribution by itself. """ property parameters: def __get__(self): return [self.alpha, self.beta] def __set__(self, parameters): alpha, beta = parameters self.alpha, self.beta = alpha, beta self.beta_norm = lgamma(alpha+beta) - lgamma(alpha) - lgamma(beta) def __init__(self, alpha, beta, frozen=False): self.alpha = alpha self.beta = beta self.beta_norm = lgamma(alpha+beta) - lgamma(alpha) - lgamma(beta) self.summaries = [0, 0] self.name = "BetaDistribution" self.frozen = frozen def __reduce__(self): """Serialize distribution for pickling.""" return self.__class__, (self.alpha, self.beta, self.frozen) cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i cdef double alpha = self.alpha cdef double beta = self.beta cdef double beta_norm = self.beta_norm for i in range(n): if isnan(X[i]): log_probability[i] = 0. else: log_probability[i] = beta_norm + (alpha-1)*_log(X[i]) + \ (beta-1)*_log(1-X[i]) def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) return random_state.beta(self.alpha, self.beta, n) cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil: """Cython optimized function for summarizing some data.""" cdef int i cdef double alpha = 0, beta = 0 cdef double item for i in range(n): item = items[i*d + column_idx] if isnan(item): continue if item == 1: alpha += weights[i] else: beta += weights[i] with gil: self.summaries[0] += alpha self.summaries[1] += beta def from_summaries(self, inertia=0.0): """Use the summaries in order to update the distribution.""" if self.frozen == True: return alpha, beta = self.summaries self.alpha = self.alpha*inertia + alpha*(1-inertia) self.beta = self.beta*inertia + beta*(1-inertia) self.beta_norm = lgamma(self.alpha+self.beta) - lgamma(self.alpha) - lgamma(self.beta) self.summaries = [0, 0] def clear_summaries(self): """Clear the summary statistics stored in the object.""" self.summaries = [0, 0] @classmethod def blank(cls): return cls(0, 0) ConditionalProbabilityTable.pxd000066400000000000000000000011771420461712600341550ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions# ConditionalProbabilityTable.pxd # Contact: Jacob Schreiber import numpy cimport numpy from .distributions cimport MultivariateDistribution cdef class ConditionalProbabilityTable(MultivariateDistribution): cdef double* values cdef double* counts cdef double* marginal_counts cdef int n, k cdef public int n_columns cdef int* idxs cdef int* marginal_idxs cdef public numpy.ndarray column_idxs cdef int* column_idxs_ptr cdef public list parents, parameters, dtypes cdef public dict keymap cdef public dict marginal_keymap cdef public int m cdef void __summarize(self, items, double [:] weights) ConditionalProbabilityTable.pyx000066400000000000000000000247451420461712600342100ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions#!python #cython: boundscheck=False #cython: cdivision=True # ConditionalProbabilityTable.pyx # Contact: Jacob Schreiber from libc.stdlib cimport calloc from libc.stdlib cimport free from libc.stdlib cimport malloc from libc.string cimport memset from libc.math cimport exp as cexp from ..utils cimport _log from ..utils cimport isnan #from ..utils cimport choose_one from ..utils import _check_nan from ..utils import check_random_state import itertools as it import numpy from .JointProbabilityTable import JointProbabilityTable cdef class ConditionalProbabilityTable(MultivariateDistribution): """ A conditional probability table, which is dependent on values from at least one previous distribution but up to as many as you want to encode for. """ def __init__(self, table, parents=None, frozen=False): """ Take in the distribution represented as a list of lists, where each inner list represents a row. """ self.name = "ConditionalProbabilityTable" self.m = len(parents) if parents is not None else len(table[0])-2 self.n = len(table) self.k = len(set(row[-2] for row in table)) self.idxs = malloc((self.m+1)*sizeof(int)) self.marginal_idxs = malloc(self.m*sizeof(int)) self.values = malloc(self.n*sizeof(double)) self.counts = calloc(self.n, sizeof(double)) self.marginal_counts = calloc(self.n / self.k, sizeof(double)) self.column_idxs = numpy.arange(self.m+1, dtype='int32') self.column_idxs_ptr = self.column_idxs.data self.n_columns = self.m + 1 self.dtypes = [] for column in table[0]: dtype = str(type(column)).split()[-1].strip('>').strip("'") self.dtypes.append(dtype) self.idxs[0] = 1 self.idxs[1] = self.k for i in range(self.m-1): k = len(numpy.unique([row[self.m-i-1] for row in table])) self.idxs[i+2] = self.idxs[i+1] * k self.marginal_idxs[0] = 1 for i in range(self.m-1): k = len(numpy.unique([row[self.m-i-1] for row in table])) self.marginal_idxs[i+1] = self.marginal_idxs[i] * k self.keymap = {} for i, row in enumerate(table): self.keymap[tuple(row[:-1])] = i self.values[i] = _log(row[-1]) self.marginal_keymap = {} for i, row in enumerate(table[::self.k]): self.marginal_keymap[tuple(row[:-2])] = i self.parents = parents self.parameters = [table, self.parents] def __dealloc__(self): free(self.idxs) free(self.values) free(self.counts) free(self.marginal_idxs) free(self.marginal_counts) def __reduce__(self): """Serialize the distribution for pickle.""" return self.__class__, (self.parameters[0], self.parents, self.frozen) def __str__(self): return "\n".join( "\t".join(map(str, key + (cexp(self.values[idx]),))) for key, idx in self.keymap.items()) def __len__(self): return self.k def keys(self): """ Return the keys of the probability distribution which has parents, the child variable. """ return tuple(set(row[-1] for row in self.keymap.keys())) def bake(self, keys): """Order the inputs according to some external global ordering.""" keymap, values = [], [] for i, key in enumerate(keys): keymap.append((key, i)) idx = self.keymap[key] values.append(self.values[idx]) self.marginal_keymap = {} for i, row in enumerate(keys[::self.k]): self.marginal_keymap[tuple(row[:-1])] = i for i in range(len(keys)): self.values[i] = values[i] self.keymap = dict(keymap) def sample(self, parent_values=None, n=None, random_state=None): """Return a random sample from the conditional probability table.""" random_state = check_random_state(random_state) if parent_values is None: parent_values = {} for parent in self.parents: if parent not in parent_values: parent_values[parent] = parent.sample( random_state=random_state) sample_cands = [] sample_vals = [] for key, ind in self.keymap.items(): for j, parent in enumerate(self.parents): if parent_values[parent] != key[j]: break else: sample_cands.append(key[-1]) sample_vals.append(cexp(self.values[ind])) sample_vals /= numpy.sum(sample_vals) if n is None: sample_ind = numpy.where(random_state.multinomial(1, sample_vals))[0][0] return sample_cands[sample_ind] # Random choice if much faster larger value of n #elif n == 1: # return sample_cands[choose_one(sample_vals,len(sample_cands)-1)] elif n > 5: return random_state.choice(a=sample_cands,p=sample_vals,size=n) else: states = random_state.randint(1000000, size=n) return [self.sample(parent_values, n=None, random_state=state) for state in states] def log_probability(self, X): """ Return the log probability of a value, which is a tuple in proper ordering, like the training data. """ X = numpy.array(X, ndmin=2, dtype=object) log_probabilities = numpy.zeros(X.shape[0]) for i, x in enumerate(X): x = tuple(x) for x_ in x: if _check_nan(x_): break else: idx = self.keymap[x] log_probabilities[i] = self.values[idx] if X.shape[0] == 1: return log_probabilities[0] return log_probabilities cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i, j, idx for i in range(n): idx = 0 for j in range(self.m+1): if isnan(X[self.m-j]): log_probability[i] = 0. break idx += self.idxs[j] * X[self.m-j] else: log_probability[i] = self.values[idx] def joint(self, neighbor_values=None): """ This will turn a conditional probability table into a joint probability table. If the data is already a joint, it will likely mess up the data. It does so by scaling the parameters the probabilities by the parent distributions. """ neighbor_values = neighbor_values or self.parents+[None] if isinstance(neighbor_values, dict): neighbor_values = [neighbor_values.get(p, None) for p in self.parents + [self]] table, total = [], 0 for key, idx in self.keymap.items(): scaled_val = self.values[idx] for j, k in enumerate(key): if neighbor_values[j] is not None: scaled_val += neighbor_values[j].log_probability(k) scaled_val = cexp(scaled_val) total += scaled_val table.append(key + (scaled_val,)) table = [row[:-1] + (row[-1] / total if total > 0 else 1. / self.n,) for row in table] return JointProbabilityTable(table, self.parents) def marginal(self, neighbor_values=None): """ Calculate the marginal of the CPT. This involves normalizing to turn it into a joint probability table, and then summing over the desired value. """ # Convert from a dictionary to a list if necessary if isinstance(neighbor_values, dict): neighbor_values = [neighbor_values.get(d, None) for d in self.parents] # Get the index we're marginalizing over i = -1 if neighbor_values == None else neighbor_values.index(None) return self.joint(neighbor_values).marginal(i) def fit(self, items, weights=None, inertia=0.0, pseudocount=0.0): """Update the parameters of the table based on the data.""" self.summarize(items, weights) self.from_summaries(inertia, pseudocount) def summarize(self, items, weights=None): """Summarize the data into sufficient statistics to store.""" if len(items) == 0 or self.frozen == True: return if weights is None: weights = numpy.ones(len(items), dtype='float64') elif numpy.sum(weights) == 0: return else: weights = numpy.asarray(weights, dtype='float64') self.__summarize(items, weights) cdef void __summarize(self, items, double [:] weights): cdef int i, n = len(items) cdef tuple item for i in range(n): item = tuple(items[i]) for symbol in item: if _check_nan(symbol): break else: key = self.keymap[item] self.counts[key] += weights[i] key = self.marginal_keymap[item[:-1]] self.marginal_counts[key] += weights[i] cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil: cdef int i, j, idx, k cdef double* counts = calloc(self.n, sizeof(double)) cdef double* marginal_counts = calloc(self.n / self.k, sizeof(double)) for i in range(n): idx = 0 for j in range(self.m+1): k = i*self.n_columns + self.column_idxs_ptr[self.m-j] if isnan(items[k]): break idx += self.idxs[j] * items[k] else: counts[idx] += weights[i] idx = 0 for j in range(self.m): k = i*self.n_columns + self.column_idxs_ptr[self.m-1-j] idx += self.marginal_idxs[j] * items[k] marginal_counts[idx] += weights[i] with gil: for i in range(self.n / self.k): self.marginal_counts[i] += marginal_counts[i] for i in range(self.n): self.counts[i] += counts[i] free(counts) free(marginal_counts) def from_summaries(self, double inertia=0.0, double pseudocount=0.0): """Update the parameters of the distribution using sufficient statistics.""" cdef int i, k, idx w_sum = sum(self.counts[i] for i in range(self.n)) if w_sum < 1e-7: return with nogil: for i in range(self.n): k = i / self.k if self.marginal_counts[k] > 0: probability = ((self.counts[i] + pseudocount) / (self.marginal_counts[k] + pseudocount * self.k)) self.values[i] = _log(cexp(self.values[i])*inertia + probability*(1-inertia)) else: self.values[i] = -_log(self.k) for i in range(self.n): idx = self.keymap[tuple(self.parameters[0][i][:-1])] self.parameters[0][i][-1] = cexp(self.values[idx]) self.clear_summaries() def clear_summaries(self): """Clear the summary statistics stored in the object.""" with nogil: memset(self.counts, 0, self.n*sizeof(double)) memset(self.marginal_counts, 0, self.n*sizeof(double)/self.k) def to_dict(self): table = [list(key + tuple([cexp(self.values[i])])) for key, i in self.keymap.items()] table = [[str(item) for item in row] for row in table] return { 'class' : 'Distribution', 'name' : 'ConditionalProbabilityTable', 'table' : table, 'dtypes' : self.dtypes, 'parents' : [dist.to_dict() for dist in self.parents] } @classmethod def from_samples(cls, X, parents=None, weights=None, pseudocount=0.0, keys=None): """Learn the table from data.""" X = numpy.asarray(X) n, d = X.shape keys = keys or [numpy.unique(X[:,i]) for i in range(d)] for i in range(d): keys_ = [] for key in keys[i]: if _check_nan(key): continue keys_.append(key) keys[i] = keys_ table = [] for key in it.product(*keys): table.append(list(key) + [1./len(keys[-1]),]) d = ConditionalProbabilityTable(table, parents) d.fit(X, weights, pseudocount=pseudocount) return d DirichletDistribution.pxd000066400000000000000000000005651420461712600330500ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions# DirichletDistribution.pxd # Contact: Jacob Schreiber import numpy cimport numpy from .distributions cimport MultivariateDistribution cdef class DirichletDistribution(MultivariateDistribution): cdef public numpy.ndarray alphas cdef double* alphas_ptr cdef double beta_norm cdef numpy.ndarray summaries_ndarray cdef double* summaries_ptr DirichletDistribution.pyx000066400000000000000000000056011420461712600330710ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions#!python #cython: boundscheck=False #cython: cdivision=True # DirichletDistribution.pyx # Contact: Jacob Schreiber import numpy from ..utils cimport pair_lse from ..utils cimport _log from ..utils cimport lgamma from ..utils cimport isnan from ..utils import check_random_state cdef class DirichletDistribution(MultivariateDistribution): """A Dirichlet distribution, usually a prior for the multinomial distributions.""" property parameters: def __get__(self): return [self.alphas.tolist()] def __set__(self, alphas): self.alphas = numpy.array(alphas, dtype='float64') self.alphas_ptr = self.alphas.data self.beta_norm = lgamma(sum(alphas)) - sum([lgamma(alpha) for alpha in alphas]) def __init__(self, alphas, frozen=False): self.name = "DirichletDistribution" self.frozen = frozen self.d = len(alphas) self.alphas = numpy.array(alphas, dtype='float64') self.alphas_ptr = self.alphas.data self.beta_norm = lgamma(sum(alphas)) - sum([lgamma(alpha) for alpha in alphas]) self.summaries_ndarray = numpy.zeros(self.d, dtype='float64') self.summaries_ptr = self.summaries_ndarray.data cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i, j, d = self.d cdef double logp for i in range(n): log_probability[i] = self.beta_norm for j in range(d): log_probability[i] += self.alphas_ptr[j] * _log(X[i*d + j]) def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) return random_state.dirichlet(self.alphas, n) cdef double _summarize(self, double* X, double* weights, int n, int column_idx, int d) nogil: """Calculate sufficient statistics for a minibatch. The sufficient statistics for a dirichlet distribution is just the weighted count of the times each thing appears. """ cdef int i, j for i in range(n): for j in range(d): self.summaries_ptr[j] += X[i*d + j] * weights[i] def from_summaries(self, inertia=0.0, pseudocount=0.0): """Update the internal parameters of the distribution.""" if self.frozen == True: return self.summaries_ndarray += pseudocount alphas = self.summaries_ndarray * (1-inertia) + self.alphas * inertia self.alphas = alphas self.alphas_ptr = self.alphas.data self.beta_norm = lgamma(sum(alphas)) - sum([lgamma(alpha) for alpha in alphas]) self.summaries_ndarray *= 0 def clear_summaries(self): self.summaries_ndarray *= 0 def fit(self, X, weights=None, inertia=0.0, pseudocount=0.0): self.summarize(X, weights) self.from_summaries(inertia, pseudocount) @classmethod def from_samples(cls, X, weights=None, **kwargs): """Fit a distribution to some data without pre-specifying it.""" distribution = cls.blank(X.shape[1]) distribution.fit(X, weights, **kwargs) return distribution @classmethod def blank(cls, d=2): return cls(numpy.zeros(d)) DiscreteDistribution.pxd000066400000000000000000000007461420461712600327040ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions# DiscreteDistribution.pxd # Contact: Jacob Schreiber import numpy cimport numpy from .distributions cimport Distribution cdef class DiscreteDistribution(Distribution): cdef bint encoded_summary cdef int n cdef str dtype cdef dict dist, log_dist cdef tuple encoded_keys cdef double* encoded_counts cdef double* encoded_log_probability cdef public bint is_blank_ cdef double __probability(self, symbol) cdef double __log_probability(self, symbol) DiscreteDistribution.pyx000066400000000000000000000231771420461712600327340ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions#!python #cython: boundscheck=False #cython: cdivision=True # DiscreteDistribution.pyx # Contact: Jacob Schreiber import itertools as it import numpy import random from libc.stdlib cimport calloc from libc.stdlib cimport free from libc.stdlib cimport malloc from ..utils cimport _log from ..utils cimport isnan from ..utils import check_random_state from ..utils import _check_nan from libc.math cimport sqrt as csqrt # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") eps = numpy.finfo(numpy.float64).eps cdef class DiscreteDistribution(Distribution): """ A discrete distribution, made up of characters and their probabilities, assuming that these probabilities will sum to 1.0. """ property parameters: def __get__(self): return [self.dist] def __set__(self, parameters): d = parameters[0] self.dist = d self.log_dist = {key: _log(value) for key, value in d.items()} def __cinit__(self, dict characters = {}, bint frozen=False): """ Make a new discrete distribution with a dictionary of discrete characters and their probabilities, checking to see that these sum to 1.0. Each discrete character can be modelled as a Bernoulli distribution. """ self.name = "DiscreteDistribution" self.frozen = frozen self.is_blank_= True self.dtype = None if len(characters) > 0: self.is_blank_ = False self.dtype = self._get_dtype(characters) self.dist = characters.copy() self.log_dist = { key: _log(value) for key, value in characters.items() } self.summaries =[{ key: 0 for key in characters.keys() }, 0] def _get_dtype(self, characters: dict) -> str: """ Determine dtype from characters. """ return str(type(list(characters.keys())[0])).split()[-1].strip('>').strip("'") def __dealloc__(self): if self.encoded_keys is not None: free(self.encoded_counts) free(self.encoded_log_probability) def __reduce__(self): """Serialize the distribution for pickle.""" return self.__class__, (self.dist, self.frozen) def __len__(self): return len(self.dist) def __mul__(self, other): """Multiply this by another distribution sharing the same keys.""" self_keys = self.keys() other_keys = other.keys() distribution, total = {}, 0.0 if isinstance(other, DiscreteDistribution) and self_keys == other_keys: self_values = (self).dist.values() other_values = (other).dist.values() for key, x, y in zip(self_keys, self_values, other_values): if _check_nan(key): distribution[key] = (1 + eps) * (1 + eps) else: distribution[key] = (x + eps) * (y + eps) total += distribution[key] else: assert set(self_keys) == set(other_keys) self_items = (self).dist.items() for key, x in self_items: if _check_nan(key): x = 1. y = other.probability(key) distribution[key] = (x + eps) * (y + eps) total += distribution[key] for key in self_keys: distribution[key] /= total if distribution[key] <= eps / total: distribution[key] = 0.0 elif distribution[key] >= 1 - eps / total: distribution[key] = 1.0 return DiscreteDistribution(distribution) def equals(self, other): """Return if the keys and values are equal""" if not isinstance(other, DiscreteDistribution): return False self_keys = self.keys() other_keys = other.keys() if self_keys == other_keys: self_values = (self).log_dist.values() other_values = (other).log_dist.values() for key, self_prob, other_prob in zip(self_keys, self_values, other_values): if _check_nan(key): continue self_prob = round(self_prob, 12) other_prob = round(other_prob, 12) if self_prob != other_prob: return False elif set(self_keys) == set(other_keys): self_items = (self).log_dist.items() for key, self_prob in self_items: if _check_nan(key): self_prob = 0. else: self_prob = round(self_prob, 12) other_prob = round(other.log_probability(key), 12) if self_prob != other_prob: return False else: return False return True def clamp(self, key): """Return a distribution clamped to a particular value.""" return DiscreteDistribution({ k : 0. if k != key else 1. for k in self.keys() }) def keys(self): """Return the keys of the underlying dictionary.""" return tuple(self.dist.keys()) def items(self): """Return items of the underlying dictionary.""" return tuple(self.dist.items()) def values(self): """Return values of the underlying dictionary.""" return tuple(self.dist.values()) def mle(self): """Return the maximally likely key.""" max_key, max_value = None, 0 for key, value in self.items(): if value > max_value: max_key, max_value = key, value return max_key def bake(self, keys): """Encoding the distribution into integers.""" if keys is None: return n = len(keys) self.encoded_keys = keys free(self.encoded_counts) free(self.encoded_log_probability) self.encoded_counts = calloc(n, sizeof(double)) self.encoded_log_probability = malloc(n * sizeof(double)) self.n = n for i in range(n): key = keys[i] self.encoded_log_probability[i] = self.log_dist.get(key, NEGINF) def probability(self, X): """Return the prob of the X under this distribution.""" return self.__probability(X) cdef double __probability(self, X): if _check_nan(X): return 1. else: return self.dist.get(X, 0) def log_probability(self, X): """Return the log prob of the X under this distribution.""" return self.__log_probability(X) cdef double __log_probability(self, X): if _check_nan(X): return 0. else: return self.log_dist.get(X, NEGINF) cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i for i in range(n): if isnan(X[i]): log_probability[i] = 0. elif X[i] < 0 or X[i] > self.n: log_probability[i] = NEGINF else: log_probability[i] = self.encoded_log_probability[ X[i]] def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) keys = list(self.dist.keys()) probabilities = list(self.dist.values()) if n is None: return random_state.choice(keys, p=probabilities) else: return random_state.choice(keys, p=probabilities, size=n) def fit(self, items, weights=None, inertia=0.0, pseudocount=0.0): """ Set the parameters of this Distribution to maximize the likelihood of the given sample. Items holds some sort of sequence. If weights is specified, it holds a sequence of value to weight each item by. """ if self.frozen: return self self.summarize(items, weights) self.from_summaries(inertia, pseudocount) return self def summarize(self, items, weights=None): """Reduce a set of observations to sufficient statistics.""" if weights is None: weights = numpy.ones(len(items)) else: weights = numpy.asarray(weights) items = numpy.asarray(items).flatten() for i in range(len(items)): x = items[i] if _check_nan(x) == False: try: self.summaries[0][x] += weights[i] except KeyError: self.summaries[0][x] = weights[i] self.summaries[1] += weights[i] cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil: cdef int i cdef double item self.encoded_summary = 1 encoded_counts = calloc(self.n, sizeof(double)) for i in range(n): item = items[i*d + column_idx] if isnan(item): continue encoded_counts[ item] += weights[i] with gil: for i in range(self.n): self.encoded_counts[i] += encoded_counts[i] self.summaries[1] += encoded_counts[i] free(encoded_counts) def from_summaries(self, inertia=0.0, pseudocount=0.0): """Use the summaries in order to update the distribution.""" if self.summaries[1] == 0 or self.frozen == True: return if self.encoded_summary == 0: values = self.summaries[0].values() _sum = sum(values) + pseudocount * len(values) for key, value in self.summaries[0].items(): value += pseudocount try: self.dist[key] = self.dist[key]*inertia + (1-inertia)*(value / _sum) except KeyError: self.dist[key] = value / _sum self.log_dist[key] = _log(self.dist[key]) self.bake(self.encoded_keys) else: n = len(self.encoded_keys) for i in range(n): _sum = self.summaries[1] + pseudocount * n value = self.encoded_counts[i] + pseudocount key = self.encoded_keys[i] self.dist[key] = self.dist[key]*inertia + (1-inertia)*(value / _sum) self.log_dist[key] = _log(self.dist[key]) self.bake(self.encoded_keys) if self.is_blank_: self.dtype = self._get_dtype(self.dist) self.is_blank_ = False self.clear_summaries() def clear_summaries(self): """Clear the summary statistics stored in the object.""" self.summaries = [{ key: 0 for key in self.keys() }, 0] if self.encoded_summary == 1: for i in range(len(self.encoded_keys)): self.encoded_counts[i] = 0 def to_dict(self): return { 'class' : 'Distribution', 'dtype' : self.dtype, 'name' : self.name, 'parameters' : [{str(key): value for key, value in self.dist.items()}], 'frozen' : self.frozen } @classmethod def from_samples(cls, items, weights=None, pseudocount=0, keys=None): """Fit a distribution to some data without pre-specifying it.""" key_initials = {} if keys is not None: clean_keys = tuple(key for key in keys if not _check_nan(key)) # A priori equal probability. key_initials = {key: 1./len(clean_keys) for key in clean_keys} return cls(key_initials).fit(items, weights=weights, pseudocount=pseudocount) @classmethod def blank(cls): return cls() ExponentialDistribution.pxd000066400000000000000000000003451420461712600334230ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions# UniformDistribution.pxd # Contact: Jacob Schreiber import numpy cimport numpy from .distributions cimport Distribution cdef class ExponentialDistribution(Distribution): cdef double rate, log_rate ExponentialDistribution.pyx000066400000000000000000000047171420461712600334570ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions#!python #cython: boundscheck=False #cython: cdivision=True # ExponentialDistribution.pyx # Contact: Jacob Schreiber import numpy from ..utils cimport _log from ..utils cimport isnan from ..utils import check_random_state from libc.math cimport sqrt as csqrt # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") cdef class ExponentialDistribution(Distribution): """Represents an exponential distribution on non-negative floats.""" property parameters: def __get__(self): return [self.rate] def __set__(self, parameters): self.rate = parameters[0] def __init__(self, double rate, bint frozen=False): self.rate = rate self.summaries = [0, 0] self.name = "ExponentialDistribution" self.frozen = frozen self.log_rate = _log(rate) def __reduce__(self): """Serialize distribution for pickling.""" return self.__class__, (self.rate, self.frozen) cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i for i in range(n): if isnan(X[i]): log_probability[i] = 0. else: log_probability[i] = self.log_rate - self.rate * X[i] def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) return random_state.exponential(1. / self.parameters[0], n) cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil: """Cython function to get the MLE estimate for an exponential.""" cdef int i cdef double xw_sum = 0, w = 0 cdef double item # Calculate the average, which is the MLE mu estimate for i in range(n): item = items[i*d + column_idx] if isnan(item): continue xw_sum += item * weights[i] w += weights[i] with gil: self.summaries[0] += w self.summaries[1] += xw_sum def from_summaries(self, inertia=0.0): """ Takes in a series of summaries, represented as a mean, a variance, and a weight, and updates the underlying distribution. Notes on how to do this for a Gaussian distribution were taken from here: http://math.stackexchange.com/questions/453113/how-to-merge-two-gaussians """ if self.frozen == True or self.summaries[0] < 1e-7: return self.rate = (self.summaries[0] + 1e-7) / (self.summaries[1] + 1e-7) self.log_rate = _log(self.rate) self.summaries = [0, 0] def clear_summaries(self): """Clear the summary statistics stored in the object.""" self.summaries = [0, 0] @classmethod def blank(cls): return cls(1) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions/GammaDistribution.pxd000066400000000000000000000003311420461712600322310ustar00rootroot00000000000000# GammaDistribution.pxd # Contact: Jacob Schreiber import numpy cimport numpy from .distributions cimport Distribution cdef class GammaDistribution(Distribution): cdef double alpha, beta pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions/GammaDistribution.pyx000066400000000000000000000173701420461712600322710ustar00rootroot00000000000000#!python #cython: boundscheck=False #cython: cdivision=True # GammaDistribution.pyx # Contact: Jacob Schreiber import numpy import scipy import random from ..utils cimport _log from ..utils cimport isnan from ..utils cimport lgamma from ..utils import check_random_state from libc.math cimport sqrt as csqrt # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") cdef class GammaDistribution(Distribution): """ This distribution represents a gamma distribution, parameterized in the alpha/beta (shape/rate) parameterization. ML estimation for a gamma distribution, taking into account weights on the data, is nontrivial, and I was unable to find a good theoretical source for how to do it, so I have cobbled together a solution here from less-reputable sources. """ property parameters: def __get__(self): return [self.alpha, self.beta] def __set__(self, parameters): self.alpha, self.beta = parameters def __init__(self, double alpha, double beta, bint frozen=False): self.alpha = alpha self.beta = beta self.summaries = [0, 0, 0] self.name = "GammaDistribution" self.frozen = frozen def __reduce__(self): """Serialize distribution for pickling.""" return self.__class__, (self.alpha, self.beta, self.frozen) cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i cdef double alpha = self.alpha cdef double beta = self.beta for i in range(n): if isnan(X[i]): log_probability[i] = 0. else: log_probability[i] = (_log(beta) * alpha - lgamma(alpha) + _log(X[i]) * (alpha - 1) - beta * X[i]) def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) return random_state.gamma(self.parameters[0], 1.0 / self.parameters[1], n) def fit(self, items, weights=None, inertia=0.0, epsilon=1E-9, iteration_limit=1000, column_idx=0): """ Set the parameters of this Distribution to maximize the likelihood of the given sample. Items holds some sort of sequence. If weights is specified, it holds a sequence of value to weight each item by. In the Gamma case, likelihood maximization is necesarily numerical, and the extension to weighted values is not trivially obvious. The algorithm used here includes a Newton-Raphson step for shape parameter estimation, and analytical calculation of the rate parameter. The extension to weights is constructed using vital information found way down at the bottom of an Experts Exchange page. Newton-Raphson continues until the change in the parameter is less than epsilon, or until iteration_limit is reached See: http://en.wikipedia.org/wiki/Gamma_distribution http://www.experts-exchange.com/Other/Math_Science/Q_23943764.html """ self.summarize(items, weights, column_idx) self.from_summaries(inertia, epsilon, iteration_limit) def summarize(self, items, weights=None, column_idx=0): """ Take in a series of items and their weights and reduce it down to a summary statistic to be used in training later. """ if len(items) == 0: # No sample, so just ignore it and keep our old parameters. return # Make it be a numpy array items = numpy.array(items).reshape(len(items)) if weights is None: # Weight everything 1 if no weights specified weights = numpy.ones_like(items) else: # Force whatever we have to be a Numpy array weights = numpy.asarray(weights) if weights.sum() == 0: # Since negative weights are banned, we must have no data. # Don't change the parameters at all. return # Save the weighted average of the items, and the weighted average of # the log of the items. self.summaries[0] += items.dot(weights) self.summaries[1] += numpy.log(items).dot(weights) self.summaries[2] += weights.sum() cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil: cdef int i cdef double xw = 0, logxw = 0, w = 0 cdef double item for i in range(n): item = items[i*d + column_idx] if isnan(item): continue w += weights[i] xw += item * weights[i] logxw += _log(item) * weights[i] with gil: self.summaries[0] += xw self.summaries[1] += logxw self.summaries[2] += w def from_summaries(self, inertia=0.0, epsilon=1e-4, iteration_limit=100): """ Set the parameters of this Distribution to maximize the likelihood of the given sample given the summaries which have been stored. In the Gamma case, likelihood maximization is necesarily numerical, and the extension to weighted values is not trivially obvious. The algorithm used here includes a Newton-Raphson step for shape parameter estimation, and analytical calculation of the rate parameter. The extension to weights is constructed using vital information found way down at the bottom of an Experts Exchange page. Newton-Raphson continues until the change in the parameter is less than epsilon, or until iteration_limit is reached See: http://en.wikipedia.org/wiki/Gamma_distribution http://www.experts-exchange.com/Other/Math_Science/Q_23943764.html """ # If the distribution is frozen, don't bother with any calculation if self.summaries[2] < 1e-7 or self.frozen == True: return # First, do Newton-Raphson for shape parameter. # Calculate the sufficient statistic s, which is the log of the average # minus the average log. When computing the average log, we weight # outside the log function. (In retrospect, this is actually pretty # obvious.) statistic = _log(self.summaries[0] / self.summaries[2]) - \ self.summaries[1] / self.summaries[2] # Start our Newton-Raphson at what Wikipedia claims a 1969 paper claims # is a good approximation. # Really, start with new_shape set, and shape set to be far away from it shape = float("inf") if statistic != 0: # Not going to have a divide by 0 problem here, so use the good # estimate new_shape = (3 - statistic + csqrt((statistic - 3) ** 2 + 24 * statistic)) / (12 * statistic) if statistic == 0 or new_shape <= 0: # Try the current shape parameter new_shape = self.parameters[0] # Count the iterations we take iteration = 0 # Now do the update loop. # We need the digamma (gamma derivative over gamma) and trigamma # (digamma derivative) functions. Luckily, scipy.special.polygamma(0, x) # is the digamma function (0th derivative of the digamma), and # scipy.special.polygamma(1, x) is the trigamma function. while abs(shape - new_shape) > epsilon and iteration < iteration_limit: shape = new_shape new_shape = shape - (_log(shape) - scipy.special.polygamma(0, shape) - statistic) / (1.0 / shape - scipy.special.polygamma(1, shape)) #print(new_shape, scipy.special.polygamma(1, shape)) # Don't let shape escape from valid values if abs(new_shape) == float("inf") or new_shape == 0: # Hack the shape parameter so we don't stop the loop if we land # near it. shape = new_shape # Re-start at some random place. new_shape = random.random() iteration += 1 # Might as well grab the new value shape = new_shape # Now our iterative estimation of the shape parameter has converged. # Calculate the rate parameter rate = 1.0 / (1.0 / (shape * self.summaries[2]) * self.summaries[0]) # Get the previous parameters prior_shape, prior_rate = self.parameters # Calculate the new parameters, respecting inertia, with an inertia # of 0 being completely replacing the parameters, and an inertia of # 1 being to ignore new training data. self.alpha = prior_shape*inertia + shape*(1.0-inertia) self.beta = prior_rate*inertia + rate*(1.0-inertia) self.summaries = [0, 0, 0] def clear_summaries(self): """Clear the summary statistics stored in the object.""" self.summaries = [0, 0, 0] @classmethod def blank(cls): return cls(0, 0) IndependentComponentsDistribution.pxd000066400000000000000000000007321420461712600354400ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions# IndependentComponentsDistribution.pxd # Contact: Jacob Schreiber import numpy cimport numpy from ..base cimport Model from .distributions cimport Distribution from .distributions cimport MultivariateDistribution cdef class IndependentComponentsDistribution(MultivariateDistribution): cdef public numpy.ndarray distributions, weights cdef public int discrete cdef public int cython cdef double* weights_ptr cdef void** distributions_ptr IndependentComponentsDistribution.pyx000066400000000000000000000173301420461712600354670ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions#!python #cython: boundscheck=False #cython: cdivision=True # IndependentComponentsDistribution.pyx # Contact: Jacob Schreiber import numpy from libc.stdlib cimport calloc from libc.stdlib cimport free from libc.string cimport memset from ..utils cimport _log from ..utils cimport isnan from ..utils cimport python_log_probability from ..utils cimport python_summarize from ..utils import check_random_state from ..utils import weight_set from .DiscreteDistribution import DiscreteDistribution cimport numpy numpy.import_array() cdef extern from "numpy/ndarraytypes.h": void PyArray_ENABLEFLAGS(numpy.ndarray X, int flags) cdef class IndependentComponentsDistribution(MultivariateDistribution): """ Allows you to create a multivariate distribution, where each distribution is independent of the others. Distributions can be any type, such as having an exponential represent the duration of an event, and a normal represent the mean of that event. Observations must now be tuples of a length equal to the number of distributions passed in. s1 = IndependentComponentsDistribution([ExponentialDistribution(0.1), NormalDistribution(5, 2)]) s1.log_probability((5, 2)) """ property parameters: def __get__(self): return [self.distributions.tolist(), list(self.weights)] def __set__(self, parameters): self.distributions = numpy.asarray(parameters[0], dtype=numpy.object_) self.weights = parameters[1] def __cinit__(self, distributions=[], weights=None, frozen=False): """ Take in the distributions and appropriate weights. If no weights are provided, a uniform weight of 1/n is provided to each point. Weights are scaled so that they sum to 1. """ self.distributions = numpy.array(distributions) self.distributions_ptr = self.distributions.data self.d = len(distributions) self.discrete = isinstance(distributions[0], DiscreteDistribution) self.cython = 1 for dist in distributions: if not isinstance(dist, Distribution) and not isinstance(dist, Model): self.cython = 0 if weights is not None: self.weights = numpy.array(weights, dtype=numpy.float64) else: self.weights = numpy.ones(self.d, dtype=numpy.float64) self.weights_ptr = self.weights.data self.name = "IndependentComponentsDistribution" self.frozen = frozen def __getitem__(self, idx): """Return the distribution at idx dimension.""" return self.distributions[idx] def __reduce__(self): """Serialize the distribution for pickle.""" return self.__class__, (self.distributions, self.weights, self.frozen) def bake(self, keys): for i, distribution in enumerate(self.distributions): if isinstance(distribution, DiscreteDistribution): distribution.bake(keys[i]) def log_probability(self, X): """ What's the probability of a given tuple under this mixture? It's the product of the probabilities of each X in the tuple under their respective distribution, which is the sum of the log probabilities. """ cdef int i, j, n cdef numpy.ndarray X_ndarray cdef double* X_ptr cdef double logp cdef numpy.ndarray logp_array cdef double* logp_ptr if self.discrete: if not isinstance(X[0], (list, tuple, numpy.ndarray)) or len(X) == 1: n = 1 else: n = len(X) logp_array = numpy.zeros(n) for i in range(n): for j in range(self.d): logp_array[i] += self.distributions[j].log_probability(X[i][j]) * self.weights[j] if n == 1: return logp_array[0] else: return logp_array else: if isinstance(X[0], (int, float)) or len(X) == 1: n = 1 else: n = len(X) X_ndarray = numpy.asarray(X, dtype='float64') X_ptr = X_ndarray.data logp_array = numpy.empty(n, dtype='float64') logp_ptr = logp_array.data with nogil: self._log_probability(X_ptr, logp_ptr, n) if n == 1: return logp_array[0] else: return logp_array cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i, j cdef double logp memset(log_probability, 0, n*sizeof(double)) for i in range(n): for j in range(self.d): if self.cython == 1: ( self.distributions_ptr[j])._log_probability(X+i*self.d+j, &logp, 1) else: with gil: python_log_probability(self.distributions[j], X+i*self.d+j, &logp, 1) log_probability[i] += logp * self.weights_ptr[j] def sample(self, n=None, random_state=None): if n is None: return numpy.array([d.sample(random_state=random_state) for d in self.parameters[0]]) else: return numpy.array([d.sample(n, random_state=random_state) for d in self.parameters[0]]).T.copy() def fit(self, X, weights=None, inertia=0, pseudocount=0.0): """ Set the parameters of this Distribution to maximize the likelihood of the given sample. Items holds some sort of sequence. If weights is specified, it holds a sequence of value to weight each item by. """ if self.frozen: return self.summarize(X, weights) self.from_summaries(inertia, pseudocount) def summarize(self, X, weights=None): """ Take in an array of items and reduce it down to summary statistics. For a multivariate distribution, this involves just passing the appropriate data down to the appropriate distributions. """ X, weights = weight_set(X, weights) cdef double* X_ptr = ( X).data cdef double* weights_ptr = ( weights).data cdef int n = X.shape[0] cdef int d = X.shape[1] with nogil: self._summarize(X_ptr, weights_ptr, n, 0, d) cdef double _summarize(self, double* X, double* weights, int n, int column_idx, int d) nogil: cdef int i, j cdef numpy.npy_intp dim = n * self.d cdef numpy.npy_intp n_elements = n # Determine if the categories to summarize are still unknown in any of # the distributions. all_initialized = True with gil: for dist_i in self.distributions: if getattr(dist_i, 'is_blank_', False): all_initialized = False break if self.cython == 1 and all_initialized: for i in range(d): ( self.distributions_ptr[i])._summarize(X, weights, n, i, d) else: with gil: X_ndarray = numpy.PyArray_SimpleNewFromData(1, &dim, numpy.NPY_FLOAT64, X) X_ndarray = X_ndarray.reshape(n, self.d) w_ndarray = numpy.PyArray_SimpleNewFromData(1, &n_elements, numpy.NPY_FLOAT64, weights) for i in range(d): self.distributions[i].summarize(X_ndarray[:,i], w_ndarray) def from_summaries(self, inertia=0.0, pseudocount=0.0): """ Use the collected summary statistics in order to update the distributions. """ # If the distribution is frozen, don't bother with any calculation if self.frozen == True: return for d in self.parameters[0]: if isinstance(d, DiscreteDistribution): d.from_summaries(inertia, pseudocount) else: d.from_summaries(inertia) def clear_summaries(self): """Clear the summary statistics stored in the object.""" for d in self.parameters[0]: d.clear_summaries() def to_dict(self): return { 'class' : 'Distribution', 'name' : self.name, 'parameters' : [[dist.to_dict() for dist in self.parameters[0]], self.parameters[1]], 'frozen' : self.frozen } @classmethod def from_samples(cls, X, weights=None, distribution_weights=None, pseudocount=0.0, distributions=None): """Create a new independent components distribution from data.""" if distributions is None: raise ValueError("must pass in a list of distribution callables") X, weights = weight_set(X, weights) n, d = X.shape if callable(distributions): distributions = [distributions.from_samples(X[:,i], weights) for i in range(d)] else: distributions = [distributions[i].from_samples(X[:,i], weights) for i in range(d)] return cls(distributions, distribution_weights) JointProbabilityTable.pxd000066400000000000000000000007411420461712600327710ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions# JointProbabilityTable.pxd # Contact: Jacob Schreiber import numpy cimport numpy from .distributions cimport MultivariateDistribution cdef class JointProbabilityTable(MultivariateDistribution): cdef double* values cdef double* counts cdef double count cdef public int n, k, n_columns cdef int* idxs cdef public list parents, parameters, dtypes cdef public dict keymap cdef public int m cdef void __summarize(self, items, double [:] weights) JointProbabilityTable.pyx000066400000000000000000000175421420461712600330250ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions#!python #cython: boundscheck=False #cython: cdivision=True # JointProbabilityTable.pyx # Contact: Jacob Schreiber from libc.stdlib cimport calloc from libc.stdlib cimport free from libc.stdlib cimport malloc from libc.string cimport memset from libc.math cimport exp as cexp from ..utils cimport _log from ..utils cimport isnan from ..utils import check_random_state from ..utils import _check_nan import itertools as it import numpy from .DiscreteDistribution import DiscreteDistribution cdef class JointProbabilityTable(MultivariateDistribution): """ A joint probability table. The primary difference between this and the conditional table is that the final column sums to one here. The joint table can be thought of as the conditional probability table normalized by the marginals of each parent. """ def __cinit__(self, table, parents=None, frozen=False): """ Take in the distribution represented as a list of lists, where each inner list represents a row. """ self.name = "JointProbabilityTable" self.d = len(parents) if parents is not None else len(table[0]) - 1 self.m = len(parents) if parents is not None else len(table[0]) - 1 self.n = len(table) self.k = len(set(row[-2] for row in table)) self.idxs = malloc((self.m+1)*sizeof(int)) self.values = malloc(self.n*sizeof(double)) self.counts = calloc(self.n, sizeof(double)) self.n_columns = self.d self.dtypes = [] for column in table[0]: dtype = str(type(column)).split()[-1].strip('>').strip("'") self.dtypes.append(dtype) self.idxs[0] = 1 for i in range(self.m-1): self.idxs[i+1] = len(set(row[self.m-i-2] for row in table)) self.idxs[self.m] = 0 self.keymap = {} for i, row in enumerate(table): self.keymap[tuple(row[:-1])] = i self.values[i] = _log(row[-1]) self.parents = list(parents) if parents is not None else None self.parameters = [[list(row) for row in table], self.parents, self.keymap] def __dealloc__(self): free(self.idxs) free(self.values) free(self.counts) def __reduce__(self): return self.__class__, (self.parameters[0], self.parents, self.frozen) def __str__(self): return "\n".join( "\t".join(map(str, key + (cexp(self.values[idx]),))) for key, idx in self.keymap.items()) def __len__(self): return self.k def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) a = random_state.uniform(0, 1) values = numpy.cumsum(numpy.exp([self.values[i] for i in range(self.n)])) for i in range(self.n): if values[i] > a: return list(self.keymap.keys())[i] def bake(self, keys): """Order the inputs according to some external global ordering.""" keymap, values = [], [] for i, key in enumerate(keys): keymap.append((key, i)) idx = self.keymap[key] values.append(self.values[idx]) for i in range(len(keys)): self.values[i] = values[i] self.keymap = dict(keymap) def keys(self): return tuple(row for row in self.parameters[2].keys()) def log_probability(self, X): """ Return the log probability of a value, which is a tuple in proper ordering, like the training data. """ X = numpy.array(X, ndmin=2, dtype=object) log_probabilities = numpy.zeros(X.shape[0]) for i, x in enumerate(X): x = tuple(x) for x_ in x: if _check_nan(x_): break else: key = self.keymap[x] log_probabilities[i] = self.values[key] if X.shape[0] == 1: return log_probabilities[0] return log_probabilities cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i, j, idx for i in range(n): idx = 0 for j in range(self.m+1): if isnan(X[self.m-j]): log_probability[i] = 0. break idx += self.idxs[j] * X[self.m-j] else: log_probability[i] = self.values[idx] def marginal(self, wrt=-1, neighbor_values=None): """ Determine the marginal of this table with respect to the index of one variable. The parents are index 0..n-1 for n parents, and the final variable is either the appropriate value or -1. For example: table = A B C p(C) ... data ... table.marginal(0) gives the marginal wrt A table.marginal(1) gives the marginal wrt B table.marginal(2) gives the marginal wrt C table.marginal(-1) gives the marginal wrt C """ if isinstance(neighbor_values, dict): neighbor_values = [neighbor_values.get(d, None) for d in self.parents] if isinstance(neighbor_values, list): wrt = neighbor_values.index(None) # Determine the keys for the respective parent distribution d = {k: 0 for k in self.parents[wrt].keys()} total = 0.0 for key, idx in self.keymap.items(): logp = self.values[idx] if neighbor_values is not None: for j, k in enumerate(key): if j == wrt: continue logp += neighbor_values[j].log_probability(k) p = cexp(logp) d[key[wrt]] += p total += p for key, value in d.items(): d[key] = value / total if total > 0 else 1. / len(self.parents[wrt].keys()) return DiscreteDistribution(d) def summarize(self, items, weights=None): """Summarize the data into sufficient statistics to store.""" if len(items) == 0 or self.frozen == True: return if weights is None: weights = numpy.ones(len(items), dtype='float64') elif numpy.sum(weights) == 0: return else: weights = numpy.asarray(weights, dtype='float64') self.__summarize(items, weights) cdef void __summarize(self, items, double [:] weights): cdef int i, n = len(items) cdef tuple item for i in range(n): item = tuple(items[i]) if _check_nan(item): continue key = self.keymap[item] self.counts[key] += weights[i] self.count += weights[i] cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil: cdef int i, j, idx cdef double count = 0 cdef double* counts = calloc(self.n, sizeof(double)) for i in range(n): idx = 0 for j in range(self.m+1): if isnan(items[self.m-i]): break idx += self.idxs[i] * items[self.m-i] else: counts[idx] += weights[i] count += weights[i] with gil: self.count += count for i in range(self.n): self.counts[i] += counts[i] free(counts) def from_summaries(self, inertia=0.0, pseudocount=0.0): """Update the parameters of the table.""" cdef int i, k w_sum = sum(self.counts[i] for i in range(self.n)) if w_sum < 1e-7: return for i in range(self.n): probability = ((self.counts[i] + pseudocount) / (self.count + pseudocount * self.k)) self.values[i] = _log(cexp(self.values[i])*inertia + probability*(1-inertia)) for i in range(self.n): self.parameters[0][i][-1] = cexp(self.values[i]) self.clear_summaries() def clear_summaries(self): """Clear the summary statistics stored in the object.""" self.count = 0 memset(self.counts, 0, self.n*sizeof(double)) def fit(self, items, weights=None, inertia=0.0, pseudocount=0.0): """Update the parameters of the table based on the data.""" self.summarize(items, weights) self.from_summaries(inertia, pseudocount) def to_dict(self): table = [list(key + tuple([cexp(self.values[i])])) for key, i in self.keymap.items()] return { 'class' : 'Distribution', 'name' : 'JointProbabilityTable', 'table' : table, 'dtypes' : self.dtypes, 'parents' : [dist if isinstance(dist, int) else dist.to_dict() for dist in self.parameters[1]] } @classmethod def from_samples(cls, X, parents=None, weights=None, pseudocount=0.0, keys=None): """Learn the table from data.""" X = numpy.array(X) n, d = X.shape if parents is None: parents = list(range(X.shape[1])) keys = keys or [numpy.unique(X[:,i]).tolist() for i in range(d)] m = numpy.prod([len(k) for k in keys]) table = [] for key in it.product(*keys): table.append(list(key) + [1./m,]) d = cls(table, parents) d.fit(X, weights, pseudocount=pseudocount) return d pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions/KernelDensities.pxd000066400000000000000000000007431420461712600317060ustar00rootroot00000000000000# KernelDensities.pxd # Contact: Jacob Schreiber import numpy cimport numpy from .distributions cimport Distribution cdef class KernelDensity(Distribution): cdef numpy.ndarray points_ndarray, weights_ndarray cdef double* points cdef double* weights cdef int n cdef double bandwidth cdef class GaussianKernelDensity(KernelDensity): pass cdef class UniformKernelDensity(KernelDensity): pass cdef class TriangleKernelDensity(KernelDensity): pass pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions/KernelDensities.pyx000066400000000000000000000175711420461712600317420ustar00rootroot00000000000000#!python #cython: boundscheck=False #cython: cdivision=True # KernelDensities.pyx # Contact: Jacob Schreiber import numpy from ..utils cimport _log from ..utils cimport isnan from ..utils import check_random_state from libc.math cimport sqrt as csqrt from libc.math cimport fabs from libc.math cimport exp as cexp # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") DEF SQRT_2_PI = 2.50662827463 DEF LOG_2_PI = 1.83787706641 cdef class KernelDensity(Distribution): """An abstract kernel density, with shared properties and methods.""" property parameters: def __get__(self): return [self.points_ndarray.tolist(), self.bandwidth, self.weights_ndarray.tolist()] def __set__(self, parameters): self.points_ndarray = numpy.array(parameters[0]) self.points = self.points_ndarray.data self.bandwidth = parameters[1] self.weights_ndarray = numpy.array(parameters[2]) self.weights = self.weights_ndarray.data def __cinit__(self, points=[], bandwidth=1, weights=None, frozen=False): """ Take in points, bandwidth, and appropriate weights. If no weights are provided, a uniform weight of 1/n is provided to each point. Weights are scaled so that they sum to 1. """ points = numpy.asarray(points, dtype=numpy.float64) n = points.shape[0] if weights is not None: weights = numpy.asarray(weights, dtype=numpy.float64) / numpy.sum(weights) else: weights = numpy.ones(n, dtype=numpy.float64) / n self.n = n self.points_ndarray = points self.points = self.points_ndarray.data self.weights_ndarray = weights self.weights = self.weights_ndarray.data self.bandwidth = bandwidth self.summaries = [] self.name = "KernelDensity" self.frozen = frozen def __reduce__(self): """Serialize the distribution for pickle.""" return self.__class__, (self.points_ndarray, self.bandwidth, self.weights_ndarray, self.frozen) def fit(self, points, weights=None, inertia=0.0, column_idx=0): """Replace the points, allowing for inertia if specified.""" # If the distribution is frozen, don't bother with any calculation if self.frozen == True: return points = numpy.asarray(points, dtype=numpy.float64) n = points.shape[0] # Get the weights, or assign uniform weights if weights is not None: weights = numpy.asarray(weights, dtype=numpy.float64) / numpy.sum(weights) else: weights = numpy.ones(n, dtype=numpy.float64) / n # If no inertia, get rid of the previous points if inertia == 0.0: self.points_ndarray = points self.weights_ndarray = weights self.n = points.shape[0] # Otherwise adjust weights appropriately else: self.points_ndarray = numpy.concatenate((self.points_ndarray, points)) self.weights_ndarray = numpy.concatenate((self.weights_ndarray*inertia, weights*(1-inertia))) self.n = points.shape[0] self.points = self.points_ndarray.data self.weights = self.weights_ndarray.data def summarize(self, items, weights=None, column_idx=0): """Summarize a batch of data into sufficient statistics for a later update. Parameters ---------- items : array-like, shape (n_samples, n_dimensions) This is the data to train on. Each row is a sample, and each column is a dimension to train on. For univariate distributions an array is used, while for multivariate distributions a 2d matrix is used. weights : array-like, shape (n_samples,), optional The initial weights of each sample in the matrix. If nothing is passed in then each sample is assumed to be the same weight. Default is None. Returns ------- None """ # If no previously stored summaries, just store the incoming data if len(self.summaries) == 0: self.summaries = [items, weights] # Otherwise, append the items and weights else: prior_items, prior_weights = self.summaries items = numpy.concatenate([prior_items, items]) # If even one summary lacks weights, then weights can't be assigned # to any of the points. if weights is not None: weights = numpy.concatenate([prior_weights, weights]) self.summaries = [items, weights] @classmethod def blank(cls): return cls([]) cdef class GaussianKernelDensity(KernelDensity): """ A quick way of storing points to represent a Gaussian kernel density in one dimension. Takes in the points at initialization, and calculates the log of the sum of the Gaussian distance of the new point from every other point. """ def __cinit__(self, points=[], bandwidth=1, weights=None, frozen=False): self.name = "GaussianKernelDensity" cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef double mu, w, scalar = 1.0 / SQRT_2_PI, prob, b = self.bandwidth cdef int i, j for i in range(n): prob = 0.0 for j in range(self.n): mu = self.points[j] w = self.weights[j] prob += w * scalar * cexp(-0.5*((mu-X[i]) / b) ** 2) log_probability[i] = _log(prob) def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) sigma = self.parameters[1] if n is None: mu = random_state.choice(self.parameters[0], p=self.parameters[2]) return random_state.normal(mu, sigma) else: mus = random_state.choice(self.parameters[0], n, p=self.parameters[2]) samples = [random_state.normal(mu, sigma) for mu in mus] return numpy.array(samples) cdef class UniformKernelDensity(KernelDensity): """ A quick way of storing points to represent an Exponential kernel density in one dimension. Takes in points at initialization, and calculates the log of the sum of the Gaussian distances of the new point from every other point. """ def __cinit__(self, points=[], bandwidth=1, weights=None, frozen=False): self.name = "UniformKernelDensity" cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef double mu, w, scalar = 1.0 / SQRT_2_PI, prob, b = self.bandwidth cdef int i, j for i in range(n): if isnan(X[i]): log_probability[i] = 0.0 continue prob = 0.0 for j in range(self.n): mu = self.points[j] w = self.weights[j] if fabs(mu - X[i]) <= b: prob += w log_probability[i] = _log(prob) def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) band = self.parameters[1] if n is None: mu = random_state.choice(self.parameters[0], p=self.parameters[2]) return random_state.uniform(mu-band, mu+band) else: mus = random_state.choice(self.parameters[0], n, p=self.parameters[2]) samples = [random_state.uniform(mu-band, mu+band) for mu in mus] return numpy.array(samples) cdef class TriangleKernelDensity(KernelDensity): """ A quick way of storing points to represent an Exponential kernel density in one dimension. Takes in points at initialization, and calculates the log of the sum of the Gaussian distances of the new point from every other point. """ def __cinit__(self, points=[], bandwidth=1, weights=None, frozen=False): self.name = "TriangleKernelDensity" cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef double mu, w, scalar = 1.0 / SQRT_2_PI, prob cdef double hinge, b = self.bandwidth cdef int i, j for i in range(n): if isnan(X[i]): log_probability[i] = 0.0 continue prob = 0.0 for j in range(self.n): mu = self.points[j] w = self.weights[j] hinge = b - fabs(mu - X[i]) if hinge > 0: prob += hinge * w log_probability[i] = _log(prob) def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) band = self.parameters[1] if n is None: mu = random_state.choice(self.parameters[0], p=self.parameters[2]) return random_state.triangular(mu-band, mu, mu+band) else: mus = random_state.choice(self.parameters[0], n, p=self.parameters[2]) samples = [random_state.triangular(mu-band, mu, mu+band) for mu in mus] return numpy.array(samples) LogNormalDistribution.pxd000066400000000000000000000003501420461712600330230ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions# LogNormalDistribution.pxd # Contact: Jacob Schreiber import numpy cimport numpy from .distributions cimport Distribution cdef class LogNormalDistribution(Distribution): cdef double mu, sigma, min_std LogNormalDistribution.pyx000066400000000000000000000061611420461712600330560ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions#!python #cython: boundscheck=False #cython: cdivision=True # LogNormalDistribution.pyx # Contact: Jacob Schreiber import numpy from ..utils cimport _log from ..utils cimport isnan from ..utils import check_random_state from libc.math cimport sqrt as csqrt # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") DEF SQRT_2_PI = 2.50662827463 DEF LOG_2_PI = 1.83787706641 cdef class LogNormalDistribution(Distribution): """A lognormal distribution over non-negative floats. The parameters are the mu and sigma of the normal distribution, which is the the exponential of the log normal distribution. """ property parameters: def __get__(self): return [self.mu, self.sigma] def __set__(self, parameters): self.mu, self.sigma = parameters def __init__(self, double mu, double sigma, double min_std=0.0, frozen=False): self.mu = mu self.sigma = sigma self.summaries = [0, 0, 0] self.name = "LogNormalDistribution" self.frozen = frozen self.min_std = min_std def __reduce__(self): """Serialize distribution for pickling.""" return self.__class__, (self.mu, self.sigma, self.frozen) cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i for i in range(n): if isnan(X[i]): log_probability[i] = 0. else: log_probability[i] = -_log(X[i] * self.sigma * SQRT_2_PI) - 0.5\ * ((_log(X[i]) - self.mu) / self.sigma) ** 2 def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) return random_state.lognormal(self.mu, self.sigma, n) cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil: """Cython function to get the MLE estimate for a Gaussian.""" cdef int i cdef double x_sum = 0.0, x2_sum = 0.0, w_sum = 0.0 cdef double item, log_item for i in range(n): item = items[i*d + column_idx] if isnan(item): continue log_item = _log(item) w_sum += weights[i] x_sum += weights[i] * log_item x2_sum += weights[i] * log_item * log_item with gil: self.summaries[0] += w_sum self.summaries[1] += x_sum self.summaries[2] += x2_sum def from_summaries(self, inertia=0.0): """ Takes in a series of summaries, represented as a mean, a variance, and a weight, and updates the underlying distribution. Notes on how to do this for a Gaussian distribution were taken from here: http://math.stackexchange.com/questions/453113/how-to-merge-two-gaussians """ # If no summaries stored or the summary is frozen, don't do anything. if self.summaries[0] == 0 or self.frozen == True: return mu = self.summaries[1] / self.summaries[0] var = self.summaries[2] / self.summaries[0] - self.summaries[1] ** 2.0 / self.summaries[0] ** 2.0 sigma = csqrt(var) if sigma < self.min_std: sigma = self.min_std self.mu = self.mu*inertia + mu*(1-inertia) self.sigma = self.sigma*inertia + sigma*(1-inertia) self.summaries = [0, 0, 0] def clear_summaries(self): """Clear the summary statistics stored in the object.""" self.summaries = [0, 0, 0] @classmethod def blank(cls): return cls(0, 1) MultivariateGaussianDistribution.pxd000066400000000000000000000012441420461712600352750ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions# MultivariateGaussianDistribution.pxd # Contact: Jacob Schreiber import numpy cimport numpy from ..base cimport Model from .distributions cimport Distribution from .distributions cimport MultivariateDistribution cdef class MultivariateGaussianDistribution(MultivariateDistribution): cdef public numpy.ndarray mu, cov, inv_cov cdef double* _mu cdef double* _mu_new cdef double* _cov cdef double _log_det cdef double* column_sum cdef double* column_w_sum cdef double* pair_sum cdef double* pair_w_sum cdef double* chol_dot_mu cdef double* _inv_cov cdef double* _inv_dot_mu cdef double _log_probability_missing(self, double* X) nogil MultivariateGaussianDistribution.pyx000066400000000000000000000211561420461712600353260ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions#!python #cython: boundscheck=False #cython: cdivision=True # MultivariateGaussianDistribution.pyx # Contact: Jacob Schreiber import numpy import scipy from libc.stdlib cimport calloc from libc.stdlib cimport free from libc.string cimport memset from scipy.linalg.cython_blas cimport dgemm from ..utils cimport _log from ..utils cimport mdot from ..utils cimport ndarray_wrap_cpointer from ..utils cimport _is_gpu_enabled from ..utils cimport isnan from ..utils import check_random_state from libc.math cimport sqrt as csqrt # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") DEF SQRT_2_PI = 2.50662827463 DEF LOG_2_PI = 1.83787706641 eps = 1e-8 cdef class MultivariateGaussianDistribution(MultivariateDistribution): property parameters: def __get__(self): return [self.mu.tolist(), self.cov.tolist()] def __set__(self, parameters): self.mu = numpy.array(parameters[0]) self.cov = numpy.array(parameters[1]) def __cinit__(self, means=[], covariance=[], frozen=False): """ Take in the mean vector and the covariance matrix. """ self.name = "MultivariateGaussianDistribution" self.frozen = frozen self.mu = numpy.array(means, dtype='float64') self._mu = self.mu.data self.cov = numpy.array(covariance, dtype='float64') self._cov = self.cov.data _, self._log_det = numpy.linalg.slogdet(self.cov) if self.mu.shape[0] != self.cov.shape[0]: raise ValueError("mu shape is {} while covariance shape is {}".format(self.mu.shape[0], self.cov.shape[0])) if self.cov.shape[0] != self.cov.shape[1]: raise ValueError("covariance is not a square matrix, dimensions are ({}, {})".format(self.cov.shape[0], self.cov.shape[1])) if self._log_det == NEGINF: raise ValueError("covariance matrix is not invertible.") d = self.mu.shape[0] self.d = d self._inv_dot_mu = calloc(d, sizeof(double)) self._mu_new = calloc(d, sizeof(double)) chol = scipy.linalg.cholesky(self.cov, lower=True) self.inv_cov = scipy.linalg.solve_triangular(chol, numpy.eye(d), lower=True).T self._inv_cov = self.inv_cov.data mdot(self._mu, self._inv_cov, self._inv_dot_mu, 1, d, d) self.column_sum = calloc(d*d, sizeof(double)) self.column_w_sum = calloc(d, sizeof(double)) self.pair_sum = calloc(d*d, sizeof(double)) self.pair_w_sum = calloc(d*d, sizeof(double)) def __reduce__(self): """Serialize the distribution for pickle.""" return self.__class__, (self.mu, self.cov, self.frozen) def __dealloc__(self): free(self._inv_dot_mu) free(self._mu_new) free(self.column_sum) free(self.column_w_sum) free(self.pair_sum) free(self.pair_w_sum) cdef void _log_probability(self, double* X, double* logp, int n) nogil: cdef int i, j, d = self.d cdef double* dot if _is_gpu_enabled(): with gil: import cupy x = ndarray_wrap_cpointer(X, n*d).reshape(n, d) x1 = cupy.array(x) x2 = cupy.array(self.inv_cov) dot_ndarray = cupy.dot(x1, x2).get() dot = ( dot_ndarray).data else: dot = calloc(n*d, sizeof(double)) mdot(X, self._inv_cov, dot, n, d, d) for i in range(n): logp[i] = 0 for j in range(d): if isnan(X[i*d + j]): logp[i] = self._log_probability_missing(X+i*d) break else: logp[i] += (dot[i*d + j] - self._inv_dot_mu[j])**2 else: logp[i] = -0.5 * (d * LOG_2_PI + logp[i]) - 0.5 * self._log_det if not _is_gpu_enabled(): free(dot) cdef double _log_probability_missing(self, double* X) nogil: cdef double logp with gil: X_ndarray = ndarray_wrap_cpointer(X, self.d) avail = ~numpy.isnan(X_ndarray) if avail.sum() == 0: return 0 a = numpy.ix_(avail, avail) d1 = MultivariateGaussianDistribution(self.mu[avail], self.cov[a]) logp = d1.log_probability(X_ndarray[avail]) return logp def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) return random_state.multivariate_normal(self.parameters[0], self.parameters[1], n) cdef double _summarize(self, double* X, double* weights, int n, int column_idx, int d) nogil: """Calculate sufficient statistics for a minibatch. The sufficient statistics for a multivariate gaussian update is the sum of each column, and the sum of the outer products of the vectors. """ cdef int i, j, k cdef double x, w, sqrt_weight, w_sum = 0.0 cdef double* column_sum = calloc(d*d, sizeof(double)) cdef double* column_w_sum = calloc(d, sizeof(double)) cdef double* pair_sum cdef double* pair_w_sum = calloc(d*d, sizeof(double)) cdef double* y = calloc(n*d, sizeof(double)) cdef double alpha = 1 cdef double beta = 0 for i in range(n): w = weights[i] w_sum += w sqrt_weight = csqrt(w) for j in range(d): x = X[i*d + j] if isnan(x): y[i*d + j] = 0. for k in range(d): pair_w_sum[j*d + k] -= w if not isnan(X[i*d + k]): pair_w_sum[k*d + j] -= w column_sum[k*d + j] -= X[i*d + k] * w else: y[i*d + j] = x * sqrt_weight column_sum[j*d + j] += x * w column_w_sum[j] += w if _is_gpu_enabled(): with gil: import cupy x_ndarray = ndarray_wrap_cpointer(y, n*d).reshape(n, d) x_gpu = cupy.array(x_ndarray, copy=False) pair_sum_ndarray = cupy.dot(x_gpu.T, x_gpu).get() for j in range(d): self.column_w_sum[j] += column_w_sum[j] for k in range(d): self.pair_sum[j*d + k] += pair_sum_ndarray[j, k] self.pair_w_sum[j*d + k] += pair_w_sum[j*d + k] + w_sum self.column_sum[j*d + k] += column_sum[j*d + k] else: pair_sum = calloc(d*d, sizeof(double)) dgemm('N', 'T', &d, &d, &n, &alpha, y, &d, y, &d, &beta, pair_sum, &d) with gil: for j in range(d): self.column_w_sum[j] += column_w_sum[j] for k in range(d): self.pair_sum[j*d + k] += pair_sum[j*d + k] self.pair_w_sum[j*d + k] += pair_w_sum[j*d + k] + w_sum self.column_sum[j*d + k] += column_sum[j*d + k] free(pair_sum) free(column_sum) free(column_w_sum) free(pair_w_sum) free(y) def from_summaries(self, inertia=0.0, min_covar=1e-5): """ Set the parameters of this Distribution to maximize the likelihood of the given sample. Items holds some sort of sequence. If weights is specified, it holds a sequence of value to weight each item by. """ cdef int d = self.d, i, j, k cdef double* column_sum = self.column_sum cdef double pair_sum cdef double* mu = self._mu_new cdef double cov cdef numpy.ndarray chol cdef double w_sum = 0.0 for i in range(self.d): w_sum += self.column_w_sum[i] # If no summaries stored or the summary is frozen, don't do anything. if self.frozen == True or w_sum < 1e-7: return for i in range(d): mu[i] = self.column_sum[i*d + i] / self.column_w_sum[i] self._mu[i] = self._mu[i] * inertia + mu[i] * (1-inertia) for j in range(d): for k in range(d): x_jk = self.pair_sum[j*d + k] w_jk = self.pair_w_sum[j*d + k] if j == k: x_j = self.column_sum[j*d + j] x_k = self.column_sum[k*d + k] else: x_j = self.column_sum[j*d + j] + self.column_sum[j*d + k] x_k = self.column_sum[k*d + k] + self.column_sum[k*d + j] cov = (x_jk - x_j*x_k/w_jk) / w_jk if w_jk > 0.0 else 0 self._cov[j*d + k] = self._cov[j*d + k] * inertia + cov * (1-inertia) try: chol = scipy.linalg.cholesky(self.cov, lower=True) self.inv_cov = scipy.linalg.solve_triangular(chol, numpy.eye(d), lower=True).T except: if self.cov.sum() == 0: self.cov += numpy.eye(d) * min_covar else: min_eig = numpy.linalg.eig(self.cov)[0].min() self.cov -= numpy.eye(d) * (min_eig - eps) chol = scipy.linalg.cholesky(self.cov, lower=True) self.inv_cov = scipy.linalg.solve_triangular(chol, numpy.eye(d), lower=True).T _, self._log_det = numpy.linalg.slogdet(self.cov) self._inv_cov = self.inv_cov.data mdot(self._mu, self._inv_cov, self._inv_dot_mu, 1, d, d) self.clear_summaries() def clear_summaries(self): """Clear the summary statistics stored in the object.""" memset(self.column_sum, 0, self.d*self.d*sizeof(double)) memset(self.column_w_sum, 0, self.d*sizeof(double)) memset(self.pair_sum, 0, self.d*self.d*sizeof(double)) memset(self.pair_w_sum, 0, self.d*self.d*sizeof(double)) @classmethod def from_samples(cls, X, weights=None, **kwargs): """Fit a distribution to some data without pre-specifying it.""" distribution = cls.blank(X.shape[1]) distribution.fit(X, weights, **kwargs) return distribution @classmethod def blank(cls, d=2): mu = numpy.zeros(d) cov = numpy.eye(d) return cls(mu, cov) NeuralNetworkWrapper.py000066400000000000000000000126361420461712600325410ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions# NeuralNetworkWrapper.py # Contact: Jacob Schreiber import numpy class NeuralNetworkWrapper(): '''A wrapper for a neural network model for use in pomegranate. This wrapper will store a pointer to the model, as well as an indicator of what class it represents. It needs information about the number of dimensions of the input and the total number of classes in the input. It is currently built to work with keras models, but can be easily modified to work with the package of your choice. Training works in a somewhat hacky manner. Internally, pomegranate will scan over all components of the model, calling `summarize` for each one, and then `from_summaries` at the end. During the EM procedure, the samples and their associated weights are passed in to the `summarize` function. The associated weights are the responsibilities calculated during the EM algorithm. In theory, one could simply update the model using the samples and their corresponding weights. In practice, it's much better to reconstruct the whole responsibility matrix for the batch of data and then train on soft labels. The process for training is as follows. When pomegranate starts a round of optimization, this wrapper will store a pointer to the data set to the neural network model object. This data set is the same one passed to each NeuralNetworkWrapper, the only difference being the ~weights~. Thus, each successive call will store the weights that are passed in (the responsibilities from the EM algorithm) to an associated label matrix. The result is a single copy of the data batch and a corresponding matrix of soft labels. Keras allows us to train a classifier on soft labels, and this is the preferred strategy. Parameters ---------- model : object The neural network model being utilized. i : int The class that this distribution represents. n_dimensions : int The number of dimensions in the input. n_classes : int The total number of classes that the model can output. ''' def __init__(self, model, i, n_dimensions, n_classes): self.d = n_dimensions self.n_classes = n_classes self.model = model self.i = i self.model.X = [] self.model.y = [] self.model.w = [] def log_probability(self, X): ''' Return pseudo-log probabilities from the neural network. This method returns the log probability of the class that this wrapper represents given the model. Thus, it's not strictly a likelihood function, but rather, a posterior. However, because the HMM takes log probabilities, multiplies them with a prior, and then normalizes them, mathematically they work out to be equivalent. This method uses the `predict` function from the neural network, which should take in a single batch of data, and returns the posterior probability of each class given the network. Typically, this is calculated using a softmax function on the outputs. The output of this function should be a matrix of size (n, k), where n is the number of samples and k is the number of classes, where the sum over each sample is equal to 1. Parameters ---------- X : numpy.ndarray, shape=(n, d) The batch of data to calculate probabilities over. ''' return numpy.log(self.model.predict(X)[:,self.i]) def summarize(self, X, w): '''When shown a batch of data, store the data. This will store the batch of data, and associated weights, to the object. The actual update occurs when `from_summaries` is called. Parameters ---------- X : numpy.ndarray, shape=(n, d) The batch of data to be passed in. w : numpy.ndarray, shape=(n,) The associated weights. These can be uniform if unweighted. Returns ------- None ''' if self.i == 0: self.model.X = X.copy() self.model.y = numpy.zeros((X.shape[0], self.n_classes)) self.model.y[:, self.i] = w def from_summaries(self, inertia=0.0): '''Perform a single gradient update to the network. This will perform a single gradient update step, using the `train_on_batch` method from the network. This is already implemented for keras networks, but for other networks this method will have to be implemented. It should take in a single batch of data, along with associated sample weights, and update the model weights. Parameters ---------- inertia : double, optional This parameter is ignored for neural networks, but required for compatibility reasons. Returns ------- None ''' if self.i == 0: self.model.train_on_batch(self.model.X, self.model.y) self.clear_summaries() def clear_summaries(self): self.model.X = None self.model.y = None @classmethod def from_samples(self, X, weights): '''The training of this wrapper on data should be performed in the main model. This method should not be used directly to train the network. ''' return self NormalDistribution.pxd000066400000000000000000000004271420461712600323660ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions# NormalDistribution.pxd # Contact: Jacob Schreiber import numpy cimport numpy from .distributions cimport Distribution cdef class NormalDistribution(Distribution): cdef double mu, sigma, two_sigma_squared, log_sigma_sqrt_2_pi cdef object min_std NormalDistribution.pyx000066400000000000000000000060761420461712600324210ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions#!python #cython: boundscheck=False #cython: cdivision=True # NormalDistribution.pyx # Contact: Jacob Schreiber import numpy from ..utils cimport _log from ..utils cimport isnan from ..utils import check_random_state from libc.math cimport sqrt as csqrt # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") DEF SQRT_2_PI = 2.50662827463 DEF LOG_2_PI = 1.83787706641 cdef class NormalDistribution(Distribution): """A normal distribution based on a mean and standard deviation.""" property parameters: def __get__(self): return [self.mu, self.sigma] def __set__(self, parameters): self.mu, self.sigma = parameters def __init__(self, mean, std, frozen=False, min_std=0.0): self.mu = mean self.sigma = std self.name = "NormalDistribution" self.frozen = frozen self.summaries = [0, 0, 0] self.log_sigma_sqrt_2_pi = -_log(std * SQRT_2_PI) self.two_sigma_squared = 1. / (2 * std ** 2) self.min_std = min_std def __reduce__(self): """Serialize distribution for pickling.""" return self.__class__, (self.mu, self.sigma, self.frozen) cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i for i in range(n): if isnan(X[i]): log_probability[i] = 0. else: log_probability[i] = self.log_sigma_sqrt_2_pi - ((X[i] - self.mu) ** 2) *\ self.two_sigma_squared def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) return random_state.normal(self.mu, self.sigma, n) cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil: cdef int i, j cdef double x_sum = 0.0, x2_sum = 0.0, w_sum = 0.0 cdef double item for i in range(n): item = items[i*d + column_idx] if isnan(item): continue w_sum += weights[i] x_sum += weights[i] * item x2_sum += weights[i] * item * item with gil: self.summaries[0] += w_sum self.summaries[1] += x_sum self.summaries[2] += x2_sum def from_summaries(self, inertia=0.0): """ Takes in a series of summaries, represented as a mean, a variance, and a weight, and updates the underlying distribution. Notes on how to do this for a Gaussian distribution were taken from here: http://math.stackexchange.com/questions/453113/how-to-merge-two-gaussians """ # If no summaries stored or the summary is frozen, don't do anything. if self.summaries[0] < 1e-8 or self.frozen == True: return mu = self.summaries[1] / self.summaries[0] var = self.summaries[2] / self.summaries[0] - self.summaries[1] ** 2.0 / self.summaries[0] ** 2.0 sigma = csqrt(var) if sigma < self.min_std: sigma = self.min_std self.mu = self.mu*inertia + mu*(1-inertia) self.sigma = self.sigma*inertia + sigma*(1-inertia) self.summaries = [0, 0, 0] self.log_sigma_sqrt_2_pi = -_log(sigma * SQRT_2_PI) self.two_sigma_squared = 1. / (2 * sigma ** 2) if sigma > 0 else 0 def clear_summaries(self): """Clear the summary statistics stored in the object.""" self.summaries = [0, 0, 0] @classmethod def blank(cls): return cls(0, 1) PoissonDistribution.pxd000066400000000000000000000003311420461712600325620ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions# PoissonDistribution.pxd # Contact: Jacob Schreiber import numpy cimport numpy from .distributions cimport Distribution cdef class PoissonDistribution(Distribution): cdef double l, logl PoissonDistribution.pyx000066400000000000000000000053071420461712600326170ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions#!python #cython: boundscheck=False #cython: cdivision=True # PoissonDistribution.pyx # Contact: Jacob Schreiber import numpy from ..utils cimport _log from ..utils cimport isnan from ..utils cimport lgamma from ..utils import check_random_state from libc.math cimport sqrt as csqrt from .distributions cimport Distribution # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") cdef class PoissonDistribution(Distribution): """The probability of a number of events occuring in a fixed time window. A probability distribution which expresses the probability of a number of events occurring in a fixed time window. It assumes these events occur with at a known rate, and independently of each other. It can operate over both integer and float values. """ property parameters: def __get__(self): return [self.l] def __set__(self, parameters): self.l = parameters[0] def __init__(self, l, frozen=False): self.l = l self.logl = _log(l) self.name = "PoissonDistribution" self.summaries = [0, 0] self.frozen = frozen def __reduce__(self): """Serialize the distribution for pickle.""" return self.__class__, (self.l, self.frozen) cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i for i in range(n): if isnan(X[i]): log_probability[i] = 0. elif X[i] < 0 or self.l == 0: log_probability[i] = NEGINF else: log_probability[i] = X[i] * self.logl - self.l - lgamma(X[i]+1) def sample(self, n=None, random_state=None): random_state = check_random_state(random_state) return random_state.poisson(self.l, n) cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil: """Cython optimized function to calculate the summary statistics.""" cdef int i cdef double x_sum = 0.0, w_sum = 0.0 cdef double item for i in range(n): item = items[i*d + column_idx] if isnan(item): continue x_sum += item * weights[i] w_sum += weights[i] with gil: self.summaries[0] += x_sum self.summaries[1] += w_sum def from_summaries(self, inertia=0.0): """ Takes in a series of summaries, consisting of the minimum and maximum of a sample, and determine the global minimum and maximum. """ # If the distribution is frozen, don't bother with any calculation if self.frozen == True or self.summaries[0] < 1e-7: return x_sum, w_sum = self.summaries mu = x_sum / w_sum self.l = mu*(1-inertia) + self.l*inertia self.logl = _log(self.l) self.summaries = [0, 0] def clear_summaries(self): """Clear the summary statistics stored in the object.""" self.summaries = [0, 0] @classmethod def blank(cls): return cls(0) UniformDistribution.pxd000066400000000000000000000003421420461712600325510ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions# UniformDistribution.pxd # Contact: Jacob Schreiber import numpy cimport numpy from .distributions cimport Distribution cdef class UniformDistribution(Distribution): cdef double start, end, logp UniformDistribution.pyx000066400000000000000000000053441420461712600326050ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions#!python #cython: boundscheck=False #cython: cdivision=True # UniformDistribution.pyx # Contact: Jacob Schreiber import numpy from ..utils cimport _log from ..utils cimport isnan from ..utils import check_random_state # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") cdef class UniformDistribution(Distribution): """A uniform distribution between two values.""" property parameters: def __get__(self): return [self.start, self.end] def __set__(self, parameters): self.start, self.end = parameters def __init__(UniformDistribution self, double start, double end, bint frozen=False): self.start = start self.end = end self.summaries = [INF, NEGINF, 0] self.name = "UniformDistribution" self.frozen = frozen self.logp = -_log(end-start) def __reduce__(self): """Serialize distribution for pickling.""" return self.__class__, (self.start, self.end, self.frozen) cdef void _log_probability(self, double* X, double* log_probability, int n) nogil: cdef int i for i in range(n): if isnan(X[i]): log_probability[i] = 0. elif X[i] >= self.start and X[i] <= self.end: log_probability[i] = self.logp else: log_probability[i] = NEGINF def sample(self, n=None, random_state=None): """Sample from this uniform distribution and return the value sampled.""" random_state = check_random_state(random_state) return random_state.uniform(self.start, self.end, n) cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil: cdef int i cdef double minimum = INF, maximum = NEGINF cdef double item, weight = 0.0 for i in range(n): item = items[i*d + column_idx] if isnan(item): continue weight += weights[i] if weights[i] > 0: if item < minimum: minimum = item if item > maximum: maximum = item with gil: self.summaries[2] += weight if maximum > self.summaries[1]: self.summaries[1] = maximum if minimum < self.summaries[0]: self.summaries[0] = minimum def from_summaries(self, inertia=0.0): """ Takes in a series of summaries, consisting of the minimum and maximum of a sample, and determine the global minimum and maximum. """ # If the distribution is frozen, don't bother with any calculation if self.frozen == True or self.summaries[2] == 0: return minimum, maximum = self.summaries[:2] self.start = minimum*(1-inertia) + self.start*inertia self.end = maximum*(1-inertia) + self.end*inertia self.logp = -_log(self.end - self.start) self.summaries = [INF, NEGINF, 0] def clear_summaries(self): """Clear the summary statistics stored in the object.""" self.summaries = [INF, NEGINF, 0] @classmethod def blank(cls): return cls(0, 0) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions/__init__.py000066400000000000000000000022771420461712600302160ustar00rootroot00000000000000# distributions/__init__.py: pomegranate # Contact: Jacob Schreiber """ For detailed documentation and examples, see the README. """ from .distributions import * from .UniformDistribution import UniformDistribution from .BernoulliDistribution import BernoulliDistribution from .NormalDistribution import NormalDistribution from .LogNormalDistribution import LogNormalDistribution from .ExponentialDistribution import ExponentialDistribution from .BetaDistribution import BetaDistribution from .GammaDistribution import GammaDistribution from .DiscreteDistribution import DiscreteDistribution from .PoissonDistribution import PoissonDistribution from .KernelDensities import KernelDensity from .KernelDensities import UniformKernelDensity from .KernelDensities import GaussianKernelDensity from .KernelDensities import TriangleKernelDensity from .IndependentComponentsDistribution import IndependentComponentsDistribution from .MultivariateGaussianDistribution import MultivariateGaussianDistribution from .DirichletDistribution import DirichletDistribution from .ConditionalProbabilityTable import ConditionalProbabilityTable from .JointProbabilityTable import JointProbabilityTable pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions/distributions.pxd000066400000000000000000000004571420461712600315220ustar00rootroot00000000000000# distributions.pxd # Contact: Jacob Schreiber (jmschreiber91@gmail.com) cimport numpy from ..base cimport Model ctypedef numpy.npy_float64 DOUBLE_t ctypedef numpy.npy_intp SIZE_t cdef class Distribution(Model): cdef public list summaries cdef class MultivariateDistribution(Distribution): pass pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/distributions/distributions.pyx000066400000000000000000000234551420461712600315520ustar00rootroot00000000000000#!python #cython: boundscheck=False #cython: cdivision=True # distributions.pyx # Contact: Jacob Schreiber import numpy import sys from ..utils import weight_set from .UniformDistribution import UniformDistribution from .BernoulliDistribution import BernoulliDistribution from .NormalDistribution import NormalDistribution from .LogNormalDistribution import LogNormalDistribution from .ExponentialDistribution import ExponentialDistribution from .BetaDistribution import BetaDistribution from .GammaDistribution import GammaDistribution from .DiscreteDistribution import DiscreteDistribution from .PoissonDistribution import PoissonDistribution from .KernelDensities import KernelDensity from .KernelDensities import UniformKernelDensity from .KernelDensities import GaussianKernelDensity from .KernelDensities import TriangleKernelDensity from .IndependentComponentsDistribution import IndependentComponentsDistribution from .MultivariateGaussianDistribution import MultivariateGaussianDistribution from .DirichletDistribution import DirichletDistribution from .ConditionalProbabilityTable import ConditionalProbabilityTable from .JointProbabilityTable import JointProbabilityTable from .NeuralNetworkWrapper import NeuralNetworkWrapper cdef class Distribution(Model): """A probability distribution. Represents a probability distribution over the defined support. This is the base class which must be subclassed to specific probability distributions. All distributions have the below methods exposed. Parameters ---------- Varies on distribution. Attributes ---------- name : str The name of the type of distributioon. summaries : list Sufficient statistics to store the update. frozen : bool Whether or not the distribution will be updated during training. d : int The dimensionality of the data. Univariate distributions are all 1, while multivariate distributions are > 1. """ def __cinit__(self): self.name = "Distribution" self.frozen = False self.summaries = [] self.d = 1 def marginal(self, *args, **kwargs): """Return the marginal of the distribution. Parameters ---------- *args : optional Arguments to pass in to specific distributions **kwargs : optional Keyword arguments to pass in to specific distributions Returns ------- distribution : Distribution The marginal distribution. If this is a multivariate distribution then this method is filled in. Otherwise returns self. """ return self def copy(self): """Return a deep copy of this distribution object. This object will not be tied to any other distribution or connected in any form. Paramters --------- None Returns ------- distribution : Distribution A copy of the distribution with the same parameters. """ return self.__class__(*self.parameters) def log_probability(self, X): """Return the log probability of the given X under this distribution. Parameters ---------- X : double The X to calculate the log probability of (overridden for DiscreteDistributions) Returns ------- logp : double The log probability of that point under the distribution. """ cdef int i, n cdef double logp cdef numpy.ndarray logp_array cdef numpy.ndarray X_ndarray cdef double* X_ptr cdef double* logp_ptr n = 1 if isinstance(X, (int, float)) else len(X) logp_array = numpy.empty(n, dtype='float64') logp_ptr = logp_array.data X_ndarray = numpy.asarray(X, dtype='float64') X_ptr = X_ndarray.data self._log_probability(X_ptr, logp_ptr, n) if n == 1: return logp_array[0] else: return logp_array def fit(self, items, weights=None, inertia=0.0, column_idx=0): """ Set the parameters of this Distribution to maximize the likelihood of the given sample. Items holds some sort of sequence. If weights is specified, it holds a sequence of value to weight each item by. """ if self.frozen: return self.summarize(items, weights, column_idx) self.from_summaries(inertia) def summarize(self, items, weights=None, column_idx=0): """ Take in a series of items and their weights and reduce it down to a summary statistic to be used in training later. """ items, weights = weight_set(items, weights) if weights.sum() <= 0: return cdef double* items_p = ( items).data cdef double* weights_p = ( weights).data cdef int n = items.shape[0] cdef int d = 1 cdef int column_id = column_idx if items.ndim == 2: d = items.shape[1] with nogil: self._summarize(items_p, weights_p, n, column_id, d) cdef double _summarize(self, double* items, double* weights, int n, int column_idx, int d) nogil: pass def from_summaries(self, inertia=0.0): """Fit the distribution to the stored sufficient statistics. Parameters ---------- inertia : double, optional The weight of the previous parameters of the model. The new parameters will roughly be old_param*inertia + new_param*(1-inertia), so an inertia of 0 means ignore the old parameters, whereas an inertia of 1 means ignore the new parameters. Default is 0.0. Returns ------- None """ # If the distribution is frozen, don't bother with any calculation if self.frozen == True: return self.fit(*self.summaries, inertia=inertia) self.summaries = [] def clear_summaries(self): """Clear the summary statistics stored in the object. Parameters ---------- None Returns ------- None """ self.summaries = [] def plot(self, n=1000, **kwargs): """Plot the distribution by sampling from it. This function will plot a histogram of samples drawn from a distribution on the current open figure. Parameters ---------- n : int, optional The number of samples to draw from the distribution. Default is 1000. **kwargs : arguments, optional Arguments to pass to matplotlib's histogram function. Returns ------- None """ import matplotlib.pyplot as plt plt.hist(self.sample(n), **kwargs) def to_dict(self): return { 'class' : 'Distribution', 'name' : self.name, 'parameters' : self.parameters, 'frozen' : self.frozen } @classmethod def from_dict(cls, d): if ' ' in d['class'] or 'Distribution' not in d['class']: raise SyntaxError("Distribution object attempting to read invalid object.") if d['name'] == 'IndependentComponentsDistribution': d['parameters'][0] = [cls.from_dict(dist) for dist in d['parameters'][0]] return IndependentComponentsDistribution(d['parameters'][0], d['parameters'][1], d['frozen']) elif d['name'] == 'DiscreteDistribution': dp = d['parameters'][0] if d['dtype'] in ('str', 'unicode', 'numpy.string_'): dist = {str(key) : value for key, value in dp.items()} elif d['dtype'] == 'bool': dist = {key == 'True' : value for key, value in dp.items()} elif d['dtype'] == 'int': dist = {int(key) : value for key, value in dp.items()} elif d['dtype'] == 'float': dist = {float(key) : value for key, value in dp.items()} elif d['dtype'].startswith('numpy.'): dtype = d['dtype'][6:] dist = {numpy.array([key], dtype=dtype)[0]: value for key, value in dp.items()} else: dist = dp return DiscreteDistribution(dist, frozen=d['frozen']) elif 'Table' in d['name']: parents = [j if isinstance(j, int) else Distribution.from_dict(j) for j in d['parents']] table = [] for row in d['table']: table.append([]) for dtype, item in zip(d['dtypes'], row): if dtype in ('str', 'unicode', 'numpy.string_'): table[-1].append(str(item)) elif dtype == 'bool': table[-1].append(item == 'True') elif dtype == 'int': table[-1].append(int(item)) elif dtype == 'float': table[-1].append(float(item)) elif dtype.startswith('numpy.'): dtype = dtype[6:] table[-1].append(numpy.array([item], dtype=dtype)[0]) else: table[-1].append(item) if d['name'] == 'JointProbabilityTable': return JointProbabilityTable(table, parents) elif d['name'] == 'ConditionalProbabilityTable': return ConditionalProbabilityTable(table, parents) else: dist = eval("{}({}, frozen={})".format(d['name'], ','.join(map(str, d['parameters'])), d['frozen'])) return dist @classmethod def from_samples(cls, items, weights=None, **kwargs): """Fit a distribution to some data without pre-specifying it.""" distribution = cls.blank() distribution.fit(items, weights, **kwargs) return distribution cdef class MultivariateDistribution(Distribution): """ An object to easily identify multivariate distributions such as tables. """ def log_probability(self, X): """Return the log probability of the given X under this distribution. Parameters ---------- X : list or numpy.ndarray The point or points to calculate the log probability of. If one point is passed in, then it will return a single log probability. If a vector of points is passed in, then it will return a vector of log probabilities. Returns ------- logp : double or numpy.ndarray The log probability of that point under the distribution. If a single point is passed in, it will return a single double corresponding to that point. If a vector of points is passed in then it will return a numpy array of log probabilities for each point. """ cdef int i, n cdef numpy.ndarray logp_array cdef numpy.ndarray X_ndarray cdef double* X_ptr cdef double* logp_ptr if isinstance(X[0], (int, float)) or len(X) == 1: n = 1 else: n = len(X) X_ndarray = numpy.asarray(X, dtype='float64') X_ptr = X_ndarray.data logp_array = numpy.empty(n, dtype='float64') logp_ptr = logp_array.data with nogil: self._log_probability(X_ptr, logp_ptr, n) if n == 1: return logp_array[0] else: return logp_array pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/gmm.pyx000066400000000000000000000563211420461712600245240ustar00rootroot00000000000000#cython: boundscheck=False #cython: cdivision=True # gmm.pyx # Contact: Jacob Schreiber ( jmschreiber91@gmail.com ) from libc.stdlib cimport calloc from libc.stdlib cimport free from libc.stdlib cimport malloc from libc.math cimport exp as cexp import time import numpy cimport numpy from .base cimport Model from .kmeans import Kmeans from distributions.distributions cimport Distribution from distributions import DiscreteDistribution from distributions import IndependentComponentsDistribution from .bayes cimport BayesModel from .utils cimport pair_lse from .utils cimport python_log_probability from .utils cimport python_summarize from .utils import _check_input from .io import BaseGenerator from .io import DataGenerator from .callbacks import History from joblib import Parallel from joblib import delayed DEF NEGINF = float("-inf") DEF INF = float("inf") cdef class GeneralMixtureModel(BayesModel): """A General Mixture Model. This mixture model can be a mixture of any distribution as long as they are all of the same dimensionality. Any object can serve as a distribution as long as it has fit(X, weights), log_probability(X), and summarize(X, weights)/from_summaries() methods if out of core training is desired. Parameters ---------- distributions : array-like, shape (n_components,) The components of the model as initialized distributions. weights : array-like, optional, shape (n_components,) The prior probabilities corresponding to each component. Does not need to sum to one, but will be normalized to sum to one internally. Defaults to None. Attributes ---------- distributions : array-like, shape (n_components,) The component distribution objects. weights : array-like, shape (n_components,) The learned prior weight of each object Examples -------- >>> from pomegranate import * >>> >>> d1 = NormalDistribution(5, 2) >>> d2 = NormalDistribution(1, 1) >>> >>> clf = GeneralMixtureModel([d1, d2]) >>> clf.log_probability(5) -2.304562194038089 >>> clf.predict_proba([[5], [7], [1]]) array([[ 0.99932952, 0.00067048], [ 0.99999995, 0.00000005], [ 0.06337894, 0.93662106]]) >>> clf.fit([[1], [5], [7], [8], [2]]) >>> clf.predict_proba([[5], [7], [1]]) array([[ 1. , 0. ], [ 1. , 0. ], [ 0.00004383, 0.99995617]]) >>> clf.distributions array([ { "frozen" :false, "class" :"Distribution", "parameters" :[ 6.6571359101390755, 1.2639830514274502 ], "name" :"NormalDistribution" }, { "frozen" :false, "class" :"Distribution", "parameters" :[ 1.498707696758334, 0.4999983303277837 ], "name" :"NormalDistribution" }], dtype=object) """ def __init__(self, distributions, weights=None): super(self.__class__, self).__init__(distributions, weights) def __reduce__(self): return self.__class__, (self.distributions.tolist(), numpy.exp(self.weights)) def fit(self, X, weights=None, inertia=0.0, pseudocount=0.0, stop_threshold=0.1, max_iterations=1e8, batch_size=None, batches_per_epoch=None, lr_decay=0.0, callbacks=[], return_history=False, verbose=False, n_jobs=1): """Fit the model to new data using EM. This method fits the components of the model to new data using the EM method. It will iterate until either max iterations has been reached, or the stop threshold has been passed. Parameters ---------- X : array-like or generator, shape (n_samples, n_dimensions) This is the data to train on. Each row is a sample, and each column is a dimension to train on. weights : array-like, shape (n_samples,), optional The initial weights of each sample in the matrix. If nothing is passed in then each sample is assumed to be the same weight. Default is None. inertia : double, optional The weight of the previous parameters of the model. The new parameters will roughly be old_param*inertia + new_param*(1-inertia), so an inertia of 0 means ignore the old parameters, whereas an inertia of 1 means ignore the new parameters. Default is 0.0. pseudocount : double, optional, positive A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Only effects mixture models defined over discrete distributions. Default is 0. stop_threshold : double, optional, positive The threshold at which EM will terminate for the improvement of the model. If the model does not improve its fit of the data by a log probability of 0.1 then terminate. Default is 0.1. max_iterations : int, optional, positive The maximum number of iterations to run EM for. If this limit is hit then it will terminate training, regardless of how well the model is improving per iteration. Default is 1e8. batch_size : int or None, optional The number of samples in a batch to summarize on. This controls the size of the set sent to `summarize` and so does not make the update any less exact. This is useful when training on a memory map and cannot load all the data into memory. If set to None, batch_size is 1 / n_jobs. Default is None. batches_per_epoch : int or None, optional The number of batches in an epoch. This is the number of batches to summarize before calling `from_summaries` and updating the model parameters. This allows one to do minibatch updates by updating the model parameters before setting the full dataset. If set to None, uses the full dataset. Default is None. lr_decay : double, optional, positive The step size decay as a function of the number of iterations. Functionally, this sets the inertia to be (2+k)^{-lr_decay} where k is the number of iterations. This causes initial iterations to have more of an impact than later iterations, and is frequently used in minibatch learning. This value is suggested to be between 0.5 and 1. Default is 0, meaning no decay. callbacks : list, optional A list of callback objects that describe functionality that should be undertaken over the course of training. return_history : bool, optional Whether to return the history during training as well as the model. verbose : bool, optional Whether or not to print out improvement information over iterations. Default is False. n_jobs : int, optional The number of threads to use when parallelizing the job. This parameter is passed directly into joblib. Default is 1, indicating no parallelism. Returns ------- self : GeneralMixtureModel The fit mixture model. """ initial_log_probability_sum = NEGINF total_improvement, iteration, improvement = 0, 0, INF training_start_time = time.time() if not isinstance(X, BaseGenerator): data_generator = DataGenerator(X, weights, batch_size=batch_size, batches_per_epoch=batches_per_epoch) else: data_generator = X callbacks = [History()] + callbacks for callback in callbacks: callback.model = self callback.on_training_begin() with Parallel(n_jobs=n_jobs, backend='threading') as parallel: f = delayed(self.summarize) while improvement > stop_threshold and iteration < max_iterations + 1: epoch_start_time = time.time() step_size = 1 - ((1 - inertia) * (2 + iteration) ** -lr_decay) # Update parameters from the stored sufficient statistics self.from_summaries(step_size, pseudocount) # Calculate new sufficient statistics from the data log_probability_sum = sum(parallel(f(*batch) for batch in data_generator.batches())) if iteration == 0: initial_log_probability_sum = log_probability_sum else: epoch_end_time = time.time() time_spent = epoch_end_time - epoch_start_time improvement = log_probability_sum - last_log_probability_sum if verbose: print("[{}] Improvement: {}\tTime (s): {:.4}".format( iteration, improvement, time_spent)) total_improvement += improvement logs = { 'learning_rate': step_size, 'n_seen_batches' : None, 'epoch' : iteration, 'improvement' : improvement, 'total_improvement' : total_improvement, 'log_probability' : log_probability_sum, 'last_log_probability' : last_log_probability_sum, 'initial_log_probability' : initial_log_probability_sum, 'epoch_start_time' : epoch_start_time, 'epoch_end_time' : epoch_end_time, 'duration' : time_spent } for callback in callbacks: callback.on_epoch_end(logs) iteration += 1 last_log_probability_sum = log_probability_sum for callback in callbacks: callback.on_training_end(logs) self.clear_summaries() if verbose: total_time_spent = time.time() - training_start_time print("Total Improvement: {}".format(total_improvement)) print("Total Time (s): {:.4f}".format(total_time_spent)) history = callbacks[0] if return_history: return self, history return self def summarize(self, X, weights=None): """Summarize a batch of data and store sufficient statistics. This will run the expectation step of EM and store sufficient statistics in the appropriate distribution objects. The summarization can be thought of as a chunk of the E step, and the from_summaries method as the M step. Parameters ---------- X : array-like, shape (n_samples, n_dimensions) This is the data to train on. Each row is a sample, and each column is a dimension to train on. weights : array-like, shape (n_samples,), optional The initial weights of each sample in the matrix. If nothing is passed in then each sample is assumed to be the same weight. Default is None. Returns ------- logp : double The log probability of the data given the current model. This is used to speed up EM. """ cdef int i, n, d cdef numpy.ndarray X_ndarray cdef numpy.ndarray weights_ndarray cdef double log_probability if self.is_vl_: n, d = len(X), self.d elif self.d == 1: n, d = X.shape[0], 1 elif self.d > 1 and X.ndim == 1: n, d = 1, len(X) else: n, d = X.shape if weights is None: weights_ndarray = numpy.ones(n, dtype='float64') else: weights_ndarray = numpy.asarray(weights, dtype='float64') cdef double* X_ptr cdef double* weights_ptr = weights_ndarray.data if not self.is_vl_: X_ndarray = _check_input(X, self.keymap) X_ptr = X_ndarray.data with nogil: log_probability = self._summarize(X_ptr, weights_ptr, n, 0, self.d) else: log_probability = 0.0 for i in range(n): X_ndarray = _check_input(X[i], self.keymap) X_ptr = X_ndarray.data d = len(X_ndarray) with nogil: log_probability += self._summarize(X_ptr, weights_ptr+i, d, 0, self.d) return log_probability cdef double _summarize(self, double* X, double* weights, int n, int column_idx, int d) nogil: cdef double* r = malloc(self.n*n*sizeof(double)) cdef double* summaries = calloc(self.n, sizeof(double)) cdef int i, j cdef double total, logp, log_probability_sum = 0.0 for j in range(self.n): if self.cython == 0: with gil: python_log_probability(self.distributions[j], X, r+j*n, n) elif self.is_vl_: r[j*n] = ( self.distributions_ptr[j])._vl_log_probability(X, n) else: ( self.distributions_ptr[j])._log_probability(X, r+j*n, n) for i in range(n): total = NEGINF for j in range(self.n): r[j*n + i] += self.weights_ptr[j] total = pair_lse(total, r[j*n + i]) for j in range(self.n): r[j*n + i] = cexp(r[j*n + i] - total) * weights[i] summaries[j] += r[j*n + i] log_probability_sum += total * weights[i] if self.is_vl_: break for j in range(self.n): if self.cython == 0: with gil: python_summarize(self.distributions[j], X, r+j*n, n) else: ( self.distributions_ptr[j])._summarize(X, r+j*n, n, 0, d) with gil: for j in range(self.n): self.summaries_ptr[j] += summaries[j] free(r) free(summaries) return log_probability_sum def to_dict(self): return { 'class' : 'GeneralMixtureModel', 'distributions' : [ dist.to_dict() for dist in self.distributions ], 'weights' : numpy.exp(self.weights).tolist() } @classmethod def from_dict(cls, d): distributions = [ Distribution.from_dict(j) for j in d['distributions'] ] model = cls(distributions, numpy.array( d['weights'] )) return model @classmethod def from_samples(cls, distributions, n_components, X, weights=None, n_init=1, init='kmeans++', max_kmeans_iterations=1, inertia=0.0, pseudocount=0.0, stop_threshold=0.1, max_iterations=1e8, batch_size=None, batches_per_epoch=None, lr_decay=0.0, callbacks=[], return_history=False, verbose=False, n_jobs=1): """Create a mixture model directly from the given dataset. First, k-means will be run using the given initializations, in order to define initial clusters for the points. These clusters are used to initialize the distributions used. Then, EM is run to refine the parameters of these distributions. A homogeneous mixture can be defined by passing in a single distribution callable as the first parameter and specifying the number of components, while a heterogeneous mixture can be defined by passing in a list of callables of the appropriate type. Parameters ---------- distributions : array-like, shape (n_components,) or callable The components of the model. If array, corresponds to the initial distributions of the components. If callable, must also pass in the number of components and kmeans++ will be used to initialize them. n_components : int If a callable is passed into distributions then this is the number of components to initialize using the kmeans++ algorithm. X : array-like, shape (n_samples, n_dimensions) This is the data to train on. Each row is a sample, and each column is a dimension to train on. weights : array-like, shape (n_samples,), optional The initial weights of each sample in the matrix. If nothing is passed in then each sample is assumed to be the same weight. Default is None. n_init : int, optional The number of initializations of k-means to do before choosing the best. Default is 1. init : str, optional The initialization algorithm to use for the initial k-means clustering. Must be one of 'first-k', 'random', 'kmeans++', or 'kmeans||'. Default is 'kmeans++'. max_kmeans_iterations : int, optional The maximum number of iterations to run kmeans for in the initialization step. Default is 1. inertia : double, optional The weight of the previous parameters of the model. The new parameters will roughly be old_param*inertia + new_param*(1-inertia), so an inertia of 0 means ignore the old parameters, whereas an inertia of 1 means ignore the new parameters. Default is 0.0. pseudocount : double, optional, positive A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Only effects mixture models defined over discrete distributions. Default is 0. stop_threshold : double, optional, positive The threshold at which EM will terminate for the improvement of the model. If the model does not improve its fit of the data by a log probability of 0.1 then terminate. Default is 0.1. max_iterations : int, optional, positive The maximum number of iterations to run EM for. If this limit is hit then it will terminate training, regardless of how well the model is improving per iteration. Default is 1e8. batch_size : int or None, optional The number of samples in a batch to summarize on. This controls the size of the set sent to `summarize` and so does not make the update any less exact. This is useful when training on a memory map and cannot load all the data into memory. If set to None, batch_size is 1 / n_jobs. Default is None. batches_per_epoch : int or None, optional The number of batches in an epoch. This is the number of batches to summarize before calling `from_summaries` and updating the model parameters. This allows one to do minibatch updates by updating the model parameters before setting the full dataset. If set to None, uses the full dataset. Default is None. lr_decay : double, optional, positive The step size decay as a function of the number of iterations. Functionally, this sets the inertia to be (2+k)^{-lr_decay} where k is the number of iterations. This causes initial iterations to have more of an impact than later iterations, and is frequently used in minibatch learning. This value is suggested to be between 0.5 and 1. Default is 0, meaning no decay. callbacks : list, optional A list of callback objects that describe functionality that should be undertaken over the course of training. return_history : bool, optional Whether to return the history during training as well as the model. verbose : bool, optional Whether or not to print out improvement information over iterations. Default is False. n_jobs : int, optional The number of threads to use when parallelizing the job. This parameter is passed directly into joblib. Default is 1, indicating no parallelism. """ icd = False if not isinstance(X, BaseGenerator): data_generator = DataGenerator(X, weights, batch_size=batch_size, batches_per_epoch=batches_per_epoch) else: data_generator = X n, d = data_generator.shape if not callable(distributions) and not isinstance(distributions, list): raise ValueError("must either give initial distributions " "or constructor") if callable(distributions): d_ = distributions if d_ == DiscreteDistribution: raise ValueError("cannot fit a discrete GMM " "without pre-initialized distributions") if d == 1: distributions = [d_ for i in range(n_components)] elif d_.blank().d > 1: distributions = [d_ for i in range(n_components)] else: icd = True distributions = [d_ for i in range(d)] else: if d == len(distributions): icd = True elif d > 1: raise ValueError("must pass in a list with one distribution " "per dimension in multivariate data or multiple distributions " "for univariate data.") else: n_components = len(distributions) if n_components < 2: raise ValueError("must have at least two distributions " "for general mixture models") for dist in distributions: if not callable(dist): raise ValueError("must pass in uninitialized distributions") X_kmeans, weights_kmeans = next(data_generator.batches()) kmeans = Kmeans(n_components, init=init, n_init=n_init) kmeans.fit(X_kmeans, weights=weights_kmeans, max_iterations=max_kmeans_iterations, batch_size=batch_size, batches_per_epoch=batches_per_epoch, n_jobs=n_jobs) y = kmeans.predict(X_kmeans) if icd: distributions = [IndependentComponentsDistribution.from_samples( X_kmeans[y == i], weights_kmeans[y == i], distributions=distributions) for i in range(n_components)] else: distributions = [distribution.from_samples(X_kmeans[y == i], weights_kmeans[y == i]) for i, distribution in enumerate(distributions)] class_weights = numpy.array([(weights_kmeans * (y == i)).mean() for i in range(n_components)]) model = cls(distributions, class_weights) _, history = model.fit(X, weights, inertia=inertia, stop_threshold=stop_threshold, max_iterations=max_iterations, pseudocount=pseudocount, batch_size=batch_size, batches_per_epoch=batches_per_epoch, lr_decay=lr_decay, callbacks=callbacks, return_history=True, verbose=verbose, n_jobs=n_jobs) if return_history: return model, history return model pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/hmm.pyx000066400000000000000000003672431420461712600245350ustar00rootroot00000000000000#cython: boundscheck=False #cython: cdivision=True # hmm.pyx: Yet Another Hidden Markov Model library # Authors: Jacob Schreiber # Adam Novak from __future__ import print_function from libc.math cimport exp as cexp from operator import attrgetter import math import networkx import tempfile import warnings import time from .base cimport GraphModel from .base cimport Model from .base cimport State from distributions.distributions cimport Distribution from distributions.DiscreteDistribution cimport DiscreteDistribution from distributions.IndependentComponentsDistribution cimport IndependentComponentsDistribution from distributions.NeuralNetworkWrapper import NeuralNetworkWrapper from .kmeans import Kmeans from .callbacks import History from .utils cimport _log from .utils cimport pair_lse from .utils cimport python_log_probability from .utils cimport python_summarize from .utils import check_random_state from .utils import _check_nan from .io import BaseGenerator from .io import SequenceGenerator from libc.stdlib cimport calloc from libc.stdlib cimport free from libc.stdlib cimport malloc from libc.string cimport memset import numpy cimport numpy from joblib import Parallel from joblib import delayed # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") DEF SQRT_2_PI = 2.50662827463 def _check_input(sequence, model): n = len(sequence) if not isinstance(model, Distribution) and not isinstance(model, Model): return numpy.array(sequence, dtype=numpy.float64) if not model.discrete: sequence_ndarray = numpy.array(sequence, dtype=numpy.float64) elif model.multivariate and model.discrete: sequence_ndarray = numpy.empty((n, model.d), dtype=numpy.float64) for i in range(n): for j in range(model.d): symbol = sequence[i][j] keymap = model.keymap[j] if _check_nan(symbol): sequence_ndarray[i, j] = numpy.nan elif symbol in keymap: sequence_ndarray[i, j] = keymap[symbol] else: raise ValueError("Symbol '{}' is not defined in a distribution" .format(symbol)) else: sequence_ndarray = numpy.empty(n, dtype=numpy.float64) keymap = model.keymap[0] for i in range(n): symbol = sequence[i] if _check_nan(symbol): sequence_ndarray[i] = numpy.nan elif sequence[i] in keymap: sequence_ndarray[i] = keymap[symbol] else: raise ValueError("Symbol '{}' is not defined in a distribution" .format(symbol)) return sequence_ndarray def _initialize_distributions(X, distribution): """This function initializes the distribution to labeled data. At the beginning of training, the distributions in an HMM must be initialized. Because of the flexibility of how these distributions can be specified, the specific user inputs must be decoded into the precise probability distributions they intended, and these distributions must be fit to the data. This function takes in data that has been labeled, either through user-specified labels or through a clustering algorithm run on unlabeled data, and decodes the user input and fits that to the data using a MLE fit. Parameters ---------- X: list A list of data partitions, where each partition is the data associated with one component of the model. distribution: flexible The user input for the type of probability distribution to fit. Returns ------- distributons: list A list of distributions initialized to the given data. """ n_components = len(X) d = X[0].shape[1] distributions = [] if callable(distribution): for i in range(n_components): if d == 1: dist = distribution.from_samples(X[i]) elif distribution.blank().d > 1: dist = distribution.from_samples(X[i]) else: dist = IndependentComponentsDistribution.from_samples( X[i], distributions=distribution) distributions.append(dist) elif isinstance(distribution, list) and isinstance(distribution[0], NeuralNetworkWrapper): distributions = distribution elif isinstance(distribution, list): if len(distribution) == d: for i in range(n_components): dist = IndependentComponentsDistribution.from_samples( X[i], distributions=distribution) distributions.append(dist) elif len(distribution) == n_components: for i, dist in enumerate(distribution): dist = dist.from_samples(X[i]) distributions.append(dist) else: raise ValueError("When passing in a list of distributions, the " / "list must either have one distribution per dimension " / "(preferentially chosen) or one distribution per component.") else: raise ValueError("distribution must be either a callable (e.g., " / "NormalDistribution) or a list of callables.") return distributions cdef class HiddenMarkovModel(GraphModel): """A Hidden Markov Model A Hidden Markov Model (HMM) is a directed graphical model where nodes are hidden states which contain an observed emission distribution and edges contain the probability of transitioning from one hidden state to another. HMMs allow you to tag each observation in a variable length sequence with the most likely hidden state according to the model. Parameters ---------- name : str, optional The name of the model. Default is None. start : State, optional An optional state to force the model to start in. Default is None. end : State, optional An optional state to force the model to end in. Default is None. Attributes ---------- start : State A state object corresponding to the initial start of the model end : State A state object corresponding to the forced end of the model start_index : int The index of the start object in the state list end_index : int The index of the end object in the state list silent_start : int The index of the beginning of the silent states in the state list states : list The list of all states in the model, with silent states at the end Examples -------- >>> from pomegranate import * >>> d1 = DiscreteDistribution({'A' : 0.35, 'C' : 0.20, 'G' : 0.05, 'T' : 0.40}) >>> d2 = DiscreteDistribution({'A' : 0.25, 'C' : 0.25, 'G' : 0.25, 'T' : 0.25}) >>> d3 = DiscreteDistribution({'A' : 0.10, 'C' : 0.40, 'G' : 0.40, 'T' : 0.10}) >>> >>> s1 = State(d1, name="s1") >>> s2 = State(d2, name="s2") >>> s3 = State(d3, name="s3") >>> >>> model = HiddenMarkovModel('example') >>> model.add_states([s1, s2, s3]) >>> model.add_transition(model.start, s1, 0.90) >>> model.add_transition(model.start, s2, 0.10) >>> model.add_transition(s1, s1, 0.80) >>> model.add_transition(s1, s2, 0.20) >>> model.add_transition(s2, s2, 0.90) >>> model.add_transition(s2, s3, 0.10) >>> model.add_transition(s3, s3, 0.70) >>> model.add_transition(s3, model.end, 0.30) >>> model.bake() >>> >>> print(model.log_probability(list('ACGACTATTCGAT'))) -22.73896159971087 >>> print(", ".join(state.name for i, state in model.viterbi(list('ACGACTATTCGAT'))[1])) example-start, s1, s2, s2, s2, s2, s2, s2, s2, s2, s2, s2, s2, s3, example-end """ cdef public object start, end cdef public int start_index cdef public int end_index cdef public int silent_start cdef double* in_transition_pseudocounts cdef double* out_transition_pseudocounts cdef double [:] state_weights cdef public bint discrete cdef public bint multivariate cdef int summaries cdef int cython cdef int* tied_state_count cdef int* tied cdef int* tied_edge_group_size cdef int* tied_edges_starts cdef int* tied_edges_ends cdef double* in_transition_log_probabilities cdef double* out_transition_log_probabilities cdef double* expected_transitions cdef int* in_edge_count cdef int* in_transitions cdef int* out_edge_count cdef int* out_transitions cdef int finite, n_tied_edge_groups cdef public list keymap cdef object state_names cdef dict state_name_mapping cdef numpy.ndarray distributions cdef void** distributions_ptr def __init__(self, name=None, start=None, end=None): # Save the name or make up a name. self.name = str(name) or str(id(self)) self.model = "HiddenMarkovModel" # This holds a directed graph between states. Nodes in that graph are # State objects, so they're guaranteed never to conflict when composing # two distinct models self.graph = networkx.DiGraph() # Save the start and end or mae one up self.start = start or State(None, name=self.name + "-start") self.end = end or State(None, name=self.name + "-end") # Put start and end in the graph self.graph.add_node(self.start) self.graph.add_node(self.end) self.state_names = set() self.state_name_mapping = {} def __dealloc__(self): self.free_bake_buffers() def __getstate__(self): """Return model representation in a dictionary.""" state = { 'class' : 'HiddenMarkovModel', 'name' : self.name, 'start' : self.start, 'end' : self.end, 'states' : self.states, 'end_index' : self.end_index, 'start_index' : self.start_index, 'silent_index' : self.silent_start } indices = { state: i for i, state in enumerate(self.states)} # Get the number of groups of edges which are tied groups = [] n = self.n_tied_edge_groups-1 # Go through each group one at a time for i in range(n): # Create an empty list for that group groups.append([]) # Go through each edge in that group start, end = self.tied_edge_group_size[i], self.tied_edge_group_size[i+1] # Add each edge as a tuple of indices for j in range(start, end): groups[i].append((self.tied_edges_starts[j], self.tied_edges_ends[j])) # Now reverse this into a dictionary, such that each pair of edges points # to a label (a number in this case) d = { tup : i for i in range(n) for tup in groups[i] } # Get all the edges from the graph edges = [] for start, end, data in self.graph.edges(data=True): # If this edge is part of a group of tied edges, annotate this group # it is a part of s, e = indices[start], indices[end] prob, pseudocount = math.e**data['probability'], data['pseudocount'] edge = (s, e) edges.append((s, e, prob, pseudocount, d.get(edge, None))) state['edges'] = edges # Get distribution tie information ties = [] for i in range(self.silent_start): start, end = self.tied_state_count[i], self.tied_state_count[i+1] for j in range(start, end): ties.append((i, self.tied[j])) state['distribution ties'] = ties return state def __reduce__(self): return self.__class__, tuple(), self.__getstate__() def __setstate__(self, state): """Deserialize object for unpickling. Parameters ---------- state : The model state, (see `__reduce__()` documentation from the pickle protocol). """ self.name = state['name'] states = state['states'] for i, j in state['distribution ties']: # Tie appropriate states together states[i].tie(states[j]) # Add all the states to the model self.add_states(states) # Indicate appropriate start and end states self.start = states[state['start_index']] self.end = states[state['end_index']] # Add all the edges to the model for start, end, probability, pseudocount, group in state['edges']: self.add_transition(states[start], states[end], probability, pseudocount, group) # Bake the model self.bake(verbose=False) def free_bake_buffers(self): free(self.in_transition_pseudocounts) free(self.out_transition_pseudocounts) free(self.tied_state_count) free(self.tied) free(self.tied_edge_group_size) free(self.tied_edges_starts) free(self.tied_edges_ends) free(self.in_transition_log_probabilities) free(self.out_transition_log_probabilities) free(self.expected_transitions) free(self.in_edge_count) free(self.in_transitions) free(self.out_edge_count) free(self.out_transitions) def add_state(self, state): """Add a state to the given model. The state must not already be in the model, nor may it be part of any other model that will eventually be combined with this one. Parameters ---------- state : State A state object to be added to the model. Returns ------- None """ if state.name in self.state_names: raise ValueError("A state with name '{}' already exists".format(state.name)) self.graph.add_node(state) self.state_names.add(state.name) def add_states(self, *states): """Add multiple states to the model at the same time. Parameters ---------- states : list or generator Either a list of states which are entered sequentially, or just comma separated values, for example model.add_states(a, b, c, d). Returns ------- None """ for state in states: if isinstance(state, list): for s in state: self.add_state(s) else: self.add_state(state) def add_transition(self, a, b, probability, pseudocount=None, group=None): """Add a transition from state a to state b. Add a transition from state a to state b with the given (non-log) probability. Both states must be in the HMM already. self.start and self.end are valid arguments here. Probabilities will be normalized such that every node has edges summing to 1. leaving that node, but only when the model is baked. Psueodocounts are allowed as a way of using edge-specific pseudocounts for training. By specifying a group as a string, you can tie edges together by giving them the same group. This means that a transition across one edge in the group counts as a transition across all edges in terms of training. Parameters ---------- a : State The state that the edge originates from b : State The state that the edge goes to probability : double The probability of transitioning from state a to state b in [0, 1] pseudocount : double, optional The pseudocount to use for this specific edge if using edge pseudocounts for training. Defaults to the probability. Default is None. group : str, optional The name of the group of edges to tie together during training. If groups are used, then a transition across any one edge counts as a transition across all edges. Default is None. Returns ------- None """ pseudocount = pseudocount or probability self.graph.add_edge(a, b, probability=_log(probability), pseudocount=pseudocount, group=group) def add_transitions(self, a, b, probabilities, pseudocounts=None, groups=None): """Add many transitions at the same time, Parameters ---------- a : State or list Either a state or a list of states where the edges originate. b : State or list Either a state or a list of states where the edges go to. probabilities : list The probabilities associated with each transition. pseudocounts : list, optional The pseudocounts associated with each transition. Default is None. groups : list, optional The groups of each edge. Default is None. Returns ------- None Examples -------- >>> model.add_transitions([model.start, s1], [s1, model.end], [1., 1.]) >>> model.add_transitions([model.start, s1, s2, s3], s4, [0.2, 0.4, 0.3, 0.9]) >>> model.add_transitions(model.start, [s1, s2, s3], [0.6, 0.2, 0.05]) """ pseudocounts = pseudocounts or probabilities n = len(a) if isinstance(a, list) else len(b) if groups is None or isinstance(groups, str): groups = [groups] * n # Allow addition of many transitions from many states if isinstance(a, list) and isinstance(b, list): edges = zip(a, b, probabilities, pseudocounts, groups) for start, end, probability, pseudocount, group in edges: self.add_transition(start, end, probability, pseudocount, group) # Allow for multiple transitions to a specific state elif isinstance(a, list) and isinstance(b, State): edges = zip(a, probabilities, pseudocounts, groups) for start, probability, pseudocount, group in edges: self.add_transition(start, b, probability, pseudocount, group) # Allow for multiple transitions from a specific state elif isinstance(a, State) and isinstance(b, list): edges = zip(b, probabilities, pseudocounts, groups) for end, probability, pseudocount, group in edges: self.add_transition(a, end, probability, pseudocount, group) def dense_transition_matrix(self): """Returns the dense transition matrix. Parameters ---------- None Returns ------- matrix : numpy.ndarray, shape (n_states, n_states) A dense transition matrix, containing the log probability of transitioning from each state to each other state. """ m = len(self.states) transition_log_probabilities = numpy.full((m, m), NEGINF) for i in range(m): for n in range(self.out_edge_count[i], self.out_edge_count[i+1]): transition_log_probabilities[i, self.out_transitions[n]] = \ self.out_transition_log_probabilities[n] return numpy.exp(transition_log_probabilities) def copy(self): """Returns a deep copy of the HMM. Parameters ---------- None Returns ------- model : HiddenMarkovModel A deep copy of the model with entirely new objects. """ return self.__class__.from_dict(self.to_dict()) def freeze_distributions(self): """Freeze all the distributions in model. Upon training only edges will be updated. The parameters of distributions will not be affected. Parameters ---------- None Returns ------- None """ for state in self.states: if not state.is_silent(): state.distribution.freeze() def thaw_distributions(self): """Thaw all distributions in the model. Upon training distributions will be updated again. Parameters ---------- None Returns ------- None """ for state in self.states: if not state.is_silent(): state.distribution.thaw() def add_model(self, other): """Add the states and edges of another model to this model. Parameters ---------- other : HiddenMarkovModel The other model to add Returns ------- None """ self.graph = networkx.union(self.graph, other.graph) def concatenate(self, other, suffix='', prefix=''): """Concatenate this model to another model. Concatenate this model to another model in such a way that a single probability 1 edge is added between self.end and other.start. Rename all other states appropriately by adding a suffix or prefix if needed. Parameters ---------- other : HiddenMarkovModel The other model to concatenate suffix : str, optional Add the suffix to the end of all state names in the other model. Default is ''. prefix : str, optional Add the prefix to the beginning of all state names in the other model. Default is ''. Returns ------- None """ other.name = "{}{}{}".format(prefix, other.name, suffix) for state in other.states: state.name = "{}{}{}".format(prefix, state.name, suffix) self.graph = networkx.union(self.graph, other.graph) self.add_transition(self.end, other.start, 1.00) self.end = other.end def plot(self, precision=4, **kwargs): """Draw this model's graph using NetworkX and matplotlib. Note that this relies on networkx's built-in graphing capabilities (and not Graphviz) and thus can't draw self-loops. See networkx.draw_networkx() for the keywords you can pass in. Parameters ---------- precision : int, optional The precision with which to round edge probabilities. Default is 4. **kwargs : any The arguments to pass into networkx.draw_networkx() Returns ------- None """ try: import pygraphviz import matplotlib.pyplot as plt import matplotlib.image except ImportError: pygraphviz = None if pygraphviz is not None: G = pygraphviz.AGraph(directed=True) out_edges = self.out_edge_count for state in self.states: if state.is_silent(): color = 'grey' elif state.distribution.frozen: color = 'blue' else: color = 'red' G.add_node(state.name, color=color) for i, state in enumerate(self.states): for l in range(out_edges[i], out_edges[i+1]): li = self.out_transitions[l] p = cexp(self.out_transition_log_probabilities[l]) p = round(p, precision) G.add_edge(state.name, self.states[li].name, label=p) with tempfile.NamedTemporaryFile() as tf: G.draw(tf.name, format='png', prog='dot') img = matplotlib.image.imread(tf.name) plt.imshow(img) plt.axis('off') else: warnings.warn("Install pygraphviz for nicer visualizations") networkx.draw(self.graph, **kwargs) def bake(self, verbose=False, merge="all"): """Finalize the topology of the model. Finalize the topology of the model and assign a numerical index to every state. This method must be called before any of the probability- calculating methods. This fills in self.states (a list of all states in order) and self.transition_log_probabilities (log probabilities for transitions), as well as self.start_index and self.end_index, and self.silent_start (the index of the first silent state). Parameters ---------- verbose : bool, optional Return a log of changes made to the model during normalization or merging. Default is False. merge : "None", "Partial, "All" Merging has three options: "None": No modifications will be made to the model. "Partial": A silent state which only has a probability 1 transition to another silent state will be merged with that silent state. This means that if silent state "S1" has a single transition to silent state "S2", that all transitions to S1 will now go to S2, with the same probability as before, and S1 will be removed from the model. "All": A silent state with a probability 1 transition to any other state, silent or symbol emitting, will be merged in the manner described above. In addition, any orphan states will be removed from the model. An orphan state is a state which does not have any transitions to it OR does not have any transitions from it, except for the start and end of the model. This will iteratively remove orphan chains from the model. This is sometimes desirable, as all states should have both a transition in to get to that state, and a transition out, even if it is only to itself. If the state does not have either, the HMM will likely not work as intended. Default is 'All'. Returns ------- None """ if len(self.graph.nodes()) == 0: raise ValueError("Must add states to the model before baking it.") cdef int i self.free_bake_buffers() in_edge_count = numpy.zeros(len(self.graph.nodes()), dtype=numpy.int32) out_edge_count = numpy.zeros(len(self.graph.nodes()), dtype=numpy.int32) merge = merge.lower() if merge else None while merge == 'all': merge_count = 0 # Reindex the states based on ones which are still there prestates = list(self.graph.nodes) indices = { prestates[i]: i for i in range(len(prestates)) } # Go through all the edges, summing in and out edges for a, b in list(self.graph.edges()): out_edge_count[indices[a]] += 1 in_edge_count[indices[b]] += 1 # Go through each state, and if either in or out edges are 0, # remove the edge. for i in range(len(prestates)): if prestates[i] is self.start or prestates[i] is self.end: continue if in_edge_count[i] == 0: merge_count += 1 self.graph.remove_node(prestates[i]) if verbose: print("Orphan state {} removed due to no edges \ leading to it".format(prestates[i].name)) elif out_edge_count[i] == 0: merge_count += 1 self.graph.remove_node(prestates[i]) if verbose: print("Orphan state {} removed due to no edges \ leaving it".format(prestates[i].name)) if merge_count == 0: break # Go through the model checking to make sure out edges sum to 1. # Normalize them to 1 if this is not the case. if merge in ['all', 'partial']: for state in list(self.graph.nodes()): # Perform log sum exp on the edges to see if they properly sum to 1 out_edges = round(sum(numpy.e**x['probability'] for x in self.graph.adj[state].values()), 8) # The end state has no out edges, so will be 0 if out_edges != 1. and state != self.end: # Issue a notice if verbose is activated if verbose: print("{} : {} summed to {}, normalized to 1.0"\ .format(self.name, state.name, out_edges)) # Reweight the edges so that the probability (not logp) sums # to 1. for edge in self.graph.adj[state].values(): edge['probability'] = edge['probability'] - _log(out_edges) # Automatically merge adjacent silent states attached by a single edge # of 1.0 probability, as that adds nothing to the model. Traverse the # edges looking for 1.0 probability edges between silent states. while merge in ['all', 'partial']: # Repeatedly go through the model until no merges take place. merge_count = 0 for a, b, e in list(self.graph.edges(data=True)): # Since we may have removed a or b in a previous iteration, # a simple fix is to just check to see if it's still there if a not in list(self.graph.nodes()) or b not in list(self.graph.nodes()): continue if a == self.start or b == self.end: continue # If a silent state has a probability 1 transition out if e['probability'] == 0.0 and a.is_silent(): # Make sure the transition is an appropriate merger if merge=='all' or (merge=='partial' and b.is_silent()): # Go through every transition to that state for x, y, d in list(self.graph.edges(data=True)): # Make sure that the edge points to the current node if y is a: # Increment the edge counter merge_count += 1 # Remove the edge going to that node self.graph.remove_edge(x, y) pseudo = max(e['pseudocount'], d['pseudocount']) group = e['group'] if e['group'] == d['group'] else None # Add a new edge going to the new node self.graph.add_edge(x, b, probability=d['probability'], pseudocount=pseudo, group=group) # Log the event if verbose: print("{} : {} - {} merged".format( self.name, a, b)) # Remove the state now that all edges are removed self.graph.remove_node(a) if merge_count == 0: break if merge in ['all', 'partial']: # Detect whether or not there are loops of silent states by going # through every pair of edges, and ensure that there is not a cycle # of silent states. for a, b, e in list(self.graph.edges(data=True)): for x, y, d in list(self.graph.edges(data=True)): if a is y and b is x and a.is_silent() and b.is_silent(): print("Loop: {} - {}".format(a.name, b.name)) states = self.graph.nodes() n, m = len(states), len(self.graph.edges()) self.n_edges = m self.n_states = n silent_states, normal_states = [], [] for state in states: if state.is_silent(): silent_states.append(state) else: normal_states.append(state) normal_states = list(sorted(normal_states, key=attrgetter('name'))) silent_states = list(sorted(silent_states, key=attrgetter('name'))) silent_order = {state: i for i, state in enumerate(reversed(silent_states))} # We need the silent states to be in topological sort order: any # transition between silent states must be from a lower-numbered state # to a higher-numbered state. Since we ban loops of silent states, we # can get away with this. # Get the subgraph of all silent states silent_subgraph = self.graph.subgraph(silent_states) # Get the sorted silent states. Isn't it convenient how NetworkX has # exactly the algorithm we need? silent_states_sorted = list(networkx.lexicographical_topological_sort( silent_subgraph, silent_order.__getitem__)) # What's the index of the first silent state? self.silent_start = len(normal_states) # Save the master state ordering. Silent states are last and in # topological order, so when calculationg forward algorithm # probabilities we can just go down the list of states. self.states = normal_states + silent_states_sorted # We need a good way to get transition probabilities by state index that # isn't N^2 to build or store. So we will need a reverse of the above # mapping. It's awkward but asymptotically fine. indices = { self.states[i]: i for i in range(n) } # Create a sparse representation of the tied states in the model. This # is done in the same way of the transition, by having a vector of # counts, and a vector of the IDs that the state is tied to. self.tied_state_count = calloc(self.silent_start+1, sizeof(int)) for i in range(self.silent_start): for j in range(self.silent_start): if i == j: continue if self.states[i].distribution is self.states[j].distribution: self.tied_state_count[i+1] += 1 for i in range(1, self.silent_start+1): self.tied_state_count[i] += self.tied_state_count[i-1] self.tied = malloc(self.tied_state_count[self.silent_start]*sizeof(int)) for i in range(self.tied_state_count[self.silent_start]): self.tied[i] = -1 for i in range(self.silent_start): for j in range(self.silent_start): if i == j: continue if self.states[i].distribution is self.states[j].distribution: # Begin at the first index which belongs to state i... start = self.tied_state_count[i] # Find the first non -1 entry in order to put our index. while self.tied[start] != -1: start += 1 # Now that we've found a non -1 entry, put the index of the # state which this state is tied to in! self.tied[start] = j # Unpack the state weights self.state_weights = numpy.empty(self.silent_start) for i in range(self.silent_start): self.state_weights[i] = _log(self.states[i].weight) # This holds numpy array indexed [a, b] to transition log probabilities # from a to b, where a and b are state indices. It starts out saying all # transitions are impossible. self.in_transitions = malloc(m*sizeof(int)) self.in_edge_count = calloc(n+1, sizeof(int)) self.in_transition_pseudocounts = calloc(m, sizeof(double)) self.in_transition_log_probabilities = calloc(m, sizeof(double)) self.out_transitions = malloc(m*sizeof(int)) self.out_edge_count = calloc(n+1, sizeof(int)) self.out_transition_pseudocounts = calloc(m, sizeof(double)) self.out_transition_log_probabilities = calloc(m, sizeof(double)) self.expected_transitions = calloc(self.n_edges, sizeof(double)) memset(self.in_transitions, -1, m*sizeof(int)) memset(self.out_transitions, -1, m*sizeof(int)) # Now we need to find a way of storing in-edges for a state in a manner # that can be called in the cythonized methods below. This is basically # an inversion of the graph. We will do this by having two lists, one # list size number of nodes + 1, and one list size number of edges. # The node size list will store the beginning and end values in the # edge list that point to that node. The edge list will be ordered in # such a manner that all edges pointing to the same node are grouped # together. This will allow us to run the algorithms in time # nodes*edges instead of nodes*nodes. for a, b in self.graph.edges(): # Increment the total number of edges going to node b. self.in_edge_count[indices[b]+1] += 1 # Increment the total number of edges leaving node a. self.out_edge_count[indices[a]+1] += 1 # Determine if the model is infinite or not based on the number of edges # to the end state if self.in_edge_count[indices[self.end]+1] == 0: self.finite = 0 else: self.finite = 1 # Take the cumulative sum so that we can associate array indices with # in or out transitions for i in range(1, n+1): self.in_edge_count[i] += self.in_edge_count[i-1] self.out_edge_count[i] += self.out_edge_count[i-1] # We need to store the edge groups as name : set pairs. edge_groups = {} # Now we go through the edges again in order to both fill in the # transition probability matrix, and also to store the indices sorted # by the end-node. for a, b, data in self.graph.edges(data=True): # Put the edge in the dict. Its weight is log-probability start = self.in_edge_count[indices[b]] # Start at the beginning of the section marked off for node b. # If another node is already there, keep walking down the list # until you find a -1 meaning a node hasn't been put there yet. while self.in_transitions[start] != -1: if start == self.in_edge_count[indices[b]+1]: break start += 1 self.in_transition_log_probabilities[start] = data['probability'] self.in_transition_pseudocounts[start] = data['pseudocount'] # Store transition info in an array where the in_edge_count shows # the mapping stuff. self.in_transitions[start] = indices[a] # Now do the same for out edges start = self.out_edge_count[indices[a]] while self.out_transitions[start] != -1: if start == self.out_edge_count[indices[a]+1]: break start += 1 self.out_transition_log_probabilities[start] = data['probability'] self.out_transition_pseudocounts[start] = data['pseudocount'] self.out_transitions[start] = indices[b] # If this edge belongs to a group, we need to add it to the # dictionary. We only care about forward representations of # the edges. group = data['group'] if group != None: if group in edge_groups: edge_groups[group].append((indices[a], indices[b])) else: edge_groups[group] = [(indices[a], indices[b])] # We will organize the tied edges using three arrays. The first will be # the cumulative number of members in each group, to slice the later # arrays in the same manner as the transition arrays. The second will # be the index of the state the edge starts in. The third will be the # index of the state the edge ends in. This way, iterating across the # second and third lists in the slices indicated by the first list will # give all the edges in a group. total_grouped_edges = sum(map(len, edge_groups.values())) self.n_tied_edge_groups = len(edge_groups.keys())+1 self.tied_edge_group_size = malloc((len(edge_groups.keys())+1)* sizeof(int)) self.tied_edge_group_size[0] = 0 self.tied_edges_starts = calloc(total_grouped_edges, sizeof(int)) self.tied_edges_ends = calloc(total_grouped_edges, sizeof(int)) # Iterate across all the grouped edges and bin them appropriately. for i, (name, edges) in enumerate(edge_groups.items()): # Store the cumulative number of edges so far, which requires # adding the current number of edges (m) to the previous # number of edges (n) n = self.tied_edge_group_size[i] self.tied_edge_group_size[i+1] = n + len(edges) for j, (start, end) in enumerate(edges): self.tied_edges_starts[n+j] = start self.tied_edges_ends[n+j] = end for state in self.states: if not state.is_silent(): dist = state.distribution break self.d = dist.d self.multivariate = self.d > 1 if isinstance(dist, DiscreteDistribution): self.discrete = 1 states = self.states[:self.silent_start] keys = [] for state in states: keys.extend(state.distribution.keys()) self.keymap = [{ key: i for i, key in enumerate(set(keys)) }] for state in states: state.distribution.bake(tuple(set(keys))) if self.d > 1: keys = [[] for i in range(self.d)] self.keymap = [{} for i in range(self.d)] for state in self.states[:self.silent_start]: d = state.distribution if isinstance(d, IndependentComponentsDistribution): for i in range(self.d): di = d.distributions[i] if isinstance(di, DiscreteDistribution): self.discrete = 1 keys[i].extend(di.keys()) for i in range(self.d): keys[i] = tuple(set(keys[i])) self.keymap[i] = {key: j for j, key in enumerate(keys[i])} for state in self.states[:self.silent_start]: d = state.distribution if isinstance(d, IndependentComponentsDistribution): d.bake(keys) self.state_name_mapping = {state.name: i for i, state in enumerate(self.states)} self.distributions = numpy.empty(self.silent_start, dtype='object') for i in range(self.silent_start): self.distributions[i] = self.states[i].distribution if self.d != self.distributions[i].d: raise ValueError("mis-matching inputs for states") self.distributions_ptr = self.distributions.data self.cython = 1 for dist in self.distributions: if not isinstance(dist, Distribution) and not isinstance(dist, Model): self.cython = 0 # This holds the index of the start state try: self.start_index = indices[self.start] except KeyError: raise SyntaxError("Model.start has been deleted, leaving the \ model with no start. Please ensure it has a start.") # And the end state try: self.end_index = indices[self.end] except KeyError: raise SyntaxError("Model.end has been deleted, leaving the \ model with no end. Please ensure it has an end.") def sample(self, n=None, length=0, path=False, random_state=None): """Generate a sequence from the model. Returns the sequence generated, as a list of emitted items. The model must have been baked first in order to run this method. If a length is specified and the HMM is infinite (no edges to the end state), then that number of samples will be randomly generated. If the length is specified and the HMM is finite, the method will attempt to generate a prefix of that length. Currently it will force itself to not take an end transition unless that is the only path, making it not a true random sample on a finite model. WARNING: If the HMM has no explicit end state, must specify a length to use. Parameters ---------- n : int or None, optional The number of samples to generate. If None, return only one sample. length : int, optional Generate a sequence with a maximal length of this size. Used if you have no explicit end state. Default is 0. path : bool, optional Return the path of hidden states in addition to the emissions. If true will return a tuple of (sample, path). Default is False. random_state : int, numpy.random.RandomState, or None The random state used for generating samples. If set to none, a random seed will be used. If set to either an integer or a random seed, will produce deterministic outputs. Returns ------- sample : list or tuple If path is true, return a tuple of (sample, path), otherwise return just the samples. """ if self.d == 0: raise ValueError("must bake model before sampling") random_state = check_random_state(random_state) if n is None: return self._sample(length, path, random_state) else: return [self.sample(length=length, path=path, random_state=i) for i in random_state.randint(10000000, size=n)] cdef numpy.ndarray _sample(self, int length, int path, random_state): cdef int i, j, k, l, li, m=len(self.states) cdef double cumulative_probability cdef double [:,:] transition_probabilities = numpy.zeros((m,m)) cdef double [:] cum_probabilities = numpy.zeros(self.n_edges) cdef int* out_edges = self.out_edge_count for k in range(m): cumulative_probability = 0. for l in range(out_edges[k], out_edges[k+1]): cumulative_probability += cexp( self.out_transition_log_probabilities[l]) cum_probabilities[l] = cumulative_probability # This holds the numerical index of the state we are currently in. # Start in the start state i = self.start_index # Record the number of samples cdef int n = 0 # Define the list of emissions, and the path of hidden states taken cdef list emissions = [], sequence_path = [] cdef State state cdef double sample while i != self.end_index: # Get the object associated with this state state = self.states[i] # Add the state to the growing path sequence_path.append(state) if not state.is_silent(): # There's an emission distribution, so sample from it emissions.append(state.distribution.sample( random_state=random_state)) n += 1 # If we've reached the specified length, return the appropriate # values if length != 0 and n >= length: if path: return numpy.array([emissions, sequence_path]) return numpy.array(emissions) # What should we pick as our next state? # Generate a number between 0 and 1 to make a weighted decision # as to which state to jump to next. sample = random_state.uniform(0, 1) # Save the last state id we were in j = i # Find out which state we're supposed to go to by comparing the # random number to the list of cumulative probabilities for that # state, and then picking the selected state. for k in range(out_edges[i], out_edges[i+1]): if cum_probabilities[k] > sample: i = self.out_transitions[k] break # If the user specified a length, and we're not at that length, and # we're in an infinite HMM, we want to avoid going to the end state # if possible. If there is only a single probability 1 end to the # end state we can't avoid it, otherwise go somewhere else. if length != 0 and self.finite == 1 and i == self.end_index: # If there is only one transition... if len(range(out_edges[j], out_edges[j+1])) == 1: # ...and that transition goes to the end of the model... if self.out_transitions[out_edges[j]] == self.end_index: # ... then end the sampling, as nowhere else to go. break # Take the cumulative probability of not going to the end state cumulative_probability = 0. for k in range(out_edges[k], out_edges[k+1]): if self.out_transitions[k] != self.end_index: cumulative_probability += cum_probabilities[k] # Randomly select a number in that probability range sample = random_state.uniform(0, cumulative_probability) # Select the state is corresponds to for k in range(out_edges[i], out_edges[i+1]): if cum_probabilities[k] > sample: i = self.out_transitions[k] break # Done! Return either emissions, or emissions and path. if path: sequence_path.append(self.end) return numpy.array([emissions, sequence_path]) return numpy.array(emissions) cpdef double log_probability(self, sequence, check_input=True): """Calculate the log probability of a single sequence. If a path is provided, calculate the log probability of that sequence given the path. Parameters ---------- sequence : array-like Return the array of observations in a single sequence of data check_input : bool, optional Check to make sure that all emissions fall under the support of the emission distributions. Default is True. Returns ------- logp : double The log probability of the sequence """ if self.d == 0: raise ValueError("must bake model before computing probability") cdef numpy.ndarray sequence_ndarray cdef double* sequence_ptr cdef double log_probability cdef int n = len(sequence) cdef int mv = self.multivariate if check_input: sequence_ndarray = _check_input(sequence, self) else: sequence_ndarray = sequence sequence_ptr = sequence_ndarray.data with nogil: log_probability = self._vl_log_probability(sequence_ptr, n) return log_probability cdef double _vl_log_probability(self, double* sequence, int n) nogil: cdef double* f = self._forward(sequence, n, NULL) cdef double log_probability cdef int i, m = self.n_states if self.finite == 1: log_probability = f[n*m + self.end_index] else: log_probability = NEGINF for i in range(self.silent_start): log_probability = pair_lse(log_probability, f[n*m + i]) free(f) return log_probability cpdef numpy.ndarray forward(self, sequence): """Run the forward algorithm on the sequence. Calculate the probability of each observation being aligned to each state by going forward through a sequence. Returns the full forward matrix. Each index i, j corresponds to the sum-of-all-paths log probability of starting at the beginning of the sequence, and aligning observations to hidden states in such a manner that observation i was aligned to hidden state j. Uses row normalization to dynamically scale each row to prevent underflow errors. If the sequence is impossible, will return a matrix of nans. See also: - Silent state handling taken from p. 71 of "Biological Sequence Analysis" by Durbin et al., and works for anything which does not have loops of silent states. - Row normalization technique explained by http://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf on p. 14. Parameters ---------- sequence : array-like An array (or list) of observations. Returns ------- matrix : array-like, shape (len(sequence), n_states) The probability of aligning the sequences to states in a forward fashion. """ if self.d == 0: raise ValueError("must bake model before using forward algorithm") cdef numpy.ndarray sequence_ndarray cdef double* sequence_data cdef int n = len(sequence), m = len(self.states) cdef int mv = self.multivariate cdef void** distributions = self.distributions.data cdef numpy.ndarray f_ndarray = numpy.empty((n+1, m), dtype=numpy.float64) cdef double* f sequence_ndarray = _check_input(sequence, self) sequence_data = sequence_ndarray.data with nogil: f = self._forward(sequence_data, n, NULL) for i in range(n+1): for j in range(m): f_ndarray[i, j] = f[i*m + j] free(f) return f_ndarray cdef double* _forward(self, double* sequence, int n, double* emissions) nogil: cdef int i, k, ki, l, li cdef int p = self.silent_start, m = self.n_states cdef int dim = self.d cdef void** distributions = self.distributions_ptr cdef double log_probability cdef int* in_edges = self.in_edge_count cdef double* e = NULL cdef double* f = calloc(m*(n+1), sizeof(double)) # Either fill in a new emissions matrix, or use the one which has # been provided from a previous call. if emissions is NULL: e = malloc(n*self.silent_start*sizeof(double)) for l in range(self.silent_start): for i in range(n): if self.cython == 1: ( distributions[l])._log_probability(sequence+i*dim, e+l*n+i, 1) else: with gil: python_log_probability(self.distributions[l], sequence+i*dim, e+l*n+i, 1) e[l*n + i] += self.state_weights[l] else: e = emissions # We must start in the start state, having emitted 0 symbols for i in range(m): f[i] = NEGINF f[self.start_index] = 0. for l in range(self.silent_start, m): # Handle transitions between silent states before the first symbol # is emitted. No non-silent states have non-zero probability yet, so # we can ignore them. if l == self.start_index: # Start state log-probability is already right. Don't touch it. continue # This holds the log total transition probability in from # all current-step silent states that can have transitions into # this state. log_probability = NEGINF for k in range(in_edges[l], in_edges[l+1]): ki = self.in_transitions[k] if ki < self.silent_start or ki >= l: continue # For each current-step preceding silent state k log_probability = pair_lse(log_probability, f[ki] + self.in_transition_log_probabilities[k]) # Update the table entry f[l] = log_probability for i in range(n): for l in range(self.silent_start): # Do the recurrence for non-silent states l # This holds the log total transition probability in from # all previous states log_probability = NEGINF for k in range(in_edges[l], in_edges[l+1]): ki = self.in_transitions[k] # For each previous state k log_probability = pair_lse(log_probability, f[i*m + ki] + self.in_transition_log_probabilities[k]) # Now set the table entry for log probability of emitting # index+1 characters and ending in state l f[(i+1)*m + l] = log_probability + e[i + l*n] for l in range(self.silent_start, m): # Now do the first pass over the silent states # This holds the log total transition probability in from # all current-step non-silent states log_probability = NEGINF for k in range(in_edges[l], in_edges[l+1]): ki = self.in_transitions[k] if ki >= self.silent_start: continue # For each current-step non-silent state k log_probability = pair_lse(log_probability, f[(i+1)*m + ki] + self.in_transition_log_probabilities[k]) # Set the table entry to the partial result. f[(i+1)*m + l] = log_probability for l in range(self.silent_start, m): # Now the second pass through silent states, where we account # for transitions between silent states. # This holds the log total transition probability in from # all current-step silent states that can have transitions into # this state. log_probability = NEGINF for k in range(in_edges[l], in_edges[l+1]): ki = self.in_transitions[k] if ki < self.silent_start or ki >= l: continue # For each current-step preceding silent state k log_probability = pair_lse(log_probability, f[(i+1)*m + ki] + self.in_transition_log_probabilities[k]) # Add the previous partial result and update the table entry f[(i+1)*m + l] = pair_lse(f[(i+1)*m + l], log_probability) if emissions is NULL: free(e) return f cpdef numpy.ndarray backward(self, sequence): """Run the backward algorithm on the sequence. Calculate the probability of each observation being aligned to each state by going backward through a sequence. Returns the full backward matrix. Each index i, j corresponds to the sum-of-all-paths log probability of starting at the end of the sequence, and aligning observations to hidden states in such a manner that observation i was aligned to hidden state j. Uses row normalization to dynamically scale each row to prevent underflow errors. If the sequence is impossible, will return a matrix of nans. See also: - Silent state handling taken from p. 71 of "Biological Sequence Analysis" by Durbin et al., and works for anything which does not have loops of silent states. - Row normalization technique explained by http://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf on p. 14. Parameters ---------- sequence : array-like An array (or list) of observations. Returns ------- matrix : array-like, shape (len(sequence), n_states) The probability of aligning the sequences to states in a backward fashion. """ if self.d == 0: raise ValueError("must bake model before using backward algorithm") cdef numpy.ndarray sequence_ndarray cdef double* sequence_data cdef double* b cdef int n = len(sequence), m = len(self.states) cdef int mv = self.multivariate cdef numpy.ndarray b_ndarray = numpy.empty((n+1, m), dtype=numpy.float64) sequence_ndarray = _check_input(sequence, self) sequence_data = sequence_ndarray.data with nogil: b = self._backward(sequence_data, n, NULL) for i in range(n+1): for j in range(m): b_ndarray[i, j] = b[i*m + j] free(b) return b_ndarray cdef double* _backward(self, double* sequence, int n, double* emissions) nogil: cdef int i, ir, k, kr, l, li cdef int p = self.silent_start, m = self.n_states cdef int dim = self.d cdef void** distributions = self.distributions_ptr cdef double log_probability cdef int* out_edges = self.out_edge_count cdef double* e = NULL cdef double* b = calloc((n+1)*m, sizeof(double)) # Either fill in a new emissions matrix, or use the one which has # been provided from a previous call. if emissions is NULL: e = malloc(n*self.silent_start*sizeof(double)) for l in range(self.silent_start): for i in range(n): if self.cython == 1: ( distributions[l])._log_probability(sequence+i*dim, e+l*n+i, 1) else: with gil: python_log_probability(self.distributions[l], sequence+i*dim, e+l*n+i, 1) e[l*n + i] += self.state_weights[l] else: e = emissions # We must end in the end state, having emitted len(sequence) symbols if self.finite == 1: for i in range(m): b[n*m + i] = NEGINF b[n*m + self.end_index] = 0 else: for i in range(self.silent_start): b[n*m + i] = 0. for i in range(self.silent_start, m): b[n*m + i] = NEGINF for kr in range(m-self.silent_start): if self.finite == 0: break # Cython arrays cannot go backwards, so modify the loop to account # for this. k = m - kr - 1 # Do the silent states' dependencies on each other. # Doing it in reverse order ensures that anything we can # possibly transition to is already done. if k == self.end_index: # We already set the log-probability for this, so skip it continue # This holds the log total probability that we go to # current-step silent states and then continue from there to # finish the sequence. log_probability = NEGINF for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] if li < k+1: continue # For each possible current-step silent state we can go to, # take into account just transition probability log_probability = pair_lse(log_probability, b[n*m + li] + self.out_transition_log_probabilities[l]) # Now this is the probability of reaching the end state given we are # in this silent state. b[n*m + k] = log_probability for k in range(self.silent_start): if self.finite == 0: break # Do the non-silent states in the last step, which depend on # current-step silent states. # This holds the total accumulated log probability of going # to such states and continuing from there to the end. log_probability = NEGINF for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] if li < self.silent_start: continue # For each current-step silent state, add in the probability # of going from here to there and then continuing on to the # end of the sequence. log_probability = pair_lse(log_probability, b[n*m + li] + self.out_transition_log_probabilities[l]) # Now we have summed the probabilities of all the ways we can # get from here to the end, so we can fill in the table entry. b[n*m + k] = log_probability # Now that we're done with the base case, move on to the recurrence for ir in range(n): #if self.finite == 0 and ir == 0: # continue # Cython ranges cannot go backwards properly, redo to handle # it properly i = n - ir - 1 for kr in range(m-self.silent_start): k = m - kr - 1 # Do the silent states' dependency on subsequent non-silent # states, iterating backwards to match the order we use later. # This holds the log total probability that we go to some # subsequent state that emits the right thing, and then continue # from there to finish the sequence. log_probability = NEGINF for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] if li >= self.silent_start: continue # For each subsequent non-silent state l, take into account # transition and emission emission probability. log_probability = pair_lse(log_probability, b[(i+1)*m + li] + self.out_transition_log_probabilities[l] + e[i + li*n]) # We can't go from a silent state here to a silent state on the # next symbol, so we're done finding the probability assuming we # transition straight to a non-silent state. b[i*m + k] = log_probability for kr in range(m-self.silent_start): k = m - kr - 1 # Do the silent states' dependencies on each other. # Doing it in reverse order ensures that anything we can # possibly transition to is already done. # This holds the log total probability that we go to # current-step silent states and then continue from there to # finish the sequence. log_probability = NEGINF for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] if li < k+1: continue # For each possible current-step silent state we can go to, # take into account just transition probability log_probability = pair_lse(log_probability, b[i*m + li] + self.out_transition_log_probabilities[l]) # Now add this probability in with the probability accumulated # from transitions to subsequent non-silent states. b[i*m + k] = pair_lse(log_probability, b[i*m + k]) for k in range(self.silent_start): # Do the non-silent states in the current step, which depend on # subsequent non-silent states and current-step silent states. # This holds the total accumulated log probability of going # to such states and continuing from there to the end. log_probability = NEGINF for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] if li >= self.silent_start: continue # For each subsequent non-silent state l, take into account # transition and emission emission probability. log_probability = pair_lse(log_probability, b[(i+1)*m + li] + self.out_transition_log_probabilities[l] + e[i + li*n]) for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] if li < self.silent_start: continue # For each current-step silent state, add in the probability # of going from here to there and then continuing on to the # end of the sequence. log_probability = pair_lse(log_probability, b[i*m + li] + self.out_transition_log_probabilities[l]) # Now we have summed the probabilities of all the ways we can # get from here to the end, so we can fill in the table entry. b[i*m + k] = log_probability if emissions is NULL: free(e) return b def forward_backward(self, sequence): """Run the forward-backward algorithm on the sequence. This algorithm returns an emission matrix and a transition matrix. The emission matrix returns the normalized probability that each each state generated that emission given both the symbol and the entire sequence. The transition matrix returns the expected number of times that a transition is used. If the sequence is impossible, will return (None, None) See also: - Forward and backward algorithm implementations. A comprehensive description of the forward, backward, and forward-background algorithm is here: http://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm Parameters ---------- sequence : array-like An array (or list) of observations. Returns ------- emissions : array-like, shape (len(sequence), n_nonsilent_states) The normalized probabilities of each state generating each emission. transitions : array-like, shape (n_states, n_states) The expected number of transitions across each edge in the model. """ if self.d == 0: raise ValueError("must bake model before using forward-backward algorithm") cdef numpy.ndarray sequence_ndarray cdef double* sequence_data cdef int n = len(sequence), m = len(self.states) cdef int mv = self.multivariate cdef void** distributions = self.distributions.data sequence_ndarray = _check_input(sequence, self) sequence_data = sequence_ndarray.data return self._forward_backward(sequence_data, n) cdef tuple _forward_backward(self, double* sequence, int n): cdef int i, k, j, l, ki, li cdef int m=len(self.states) cdef int dim = self.d cdef double* e = malloc(n*self.silent_start*sizeof(double)) cdef double* f cdef double* b cdef void** distributions = self.distributions_ptr cdef numpy.ndarray expected_transitions_ndarray = numpy.zeros((m, m)) cdef double* expected_transitions = expected_transitions_ndarray.data cdef numpy.ndarray emission_weights_ndarray = numpy.zeros((n, self.silent_start)) cdef double* emission_weights = emission_weights_ndarray.data cdef double log_sequence_probability, log_probability cdef double log_transition_emission_probability_sum cdef int* out_edges = self.out_edge_count cdef int* tied_states = self.tied_state_count # Calculate the emissions table for l in range(self.silent_start): for i in range(n): if self.cython == 1: ( distributions[l])._log_probability(sequence+i*dim, e+l*n+i, 1) else: python_log_probability(self.distributions[l], sequence+i*dim, e+l*n+i, 1) e[l*n + i] += self.state_weights[l] f = self._forward(sequence, n, e) b = self._backward(sequence, n, e) if self.finite == 1: log_sequence_probability = f[n*m + self.end_index] else: log_sequence_probability = NEGINF for i in range(self.silent_start): log_sequence_probability = pair_lse( log_sequence_probability, f[n*m + i]) # Is the sequence impossible? If so, don't bother calculating any more. if log_sequence_probability == NEGINF: print("Warning: Sequence is impossible.") return (None, None) for k in range(m): # For each state we could have come from for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] if li >= self.silent_start: continue # For each state we could go to (and emit a character) # Sum up probabilities that we later normalize by # probability of sequence. log_transition_emission_probability_sum = NEGINF for i in range(n): # For each character in the sequence # Add probability that we start and get up to state k, # and go k->l, and emit the symbol from l, and go from l # to the end. log_transition_emission_probability_sum = pair_lse( log_transition_emission_probability_sum, f[i*m + k] + self.out_transition_log_probabilities[l] + e[i + li*n] + b[(i+1)*m + li]) # Now divide by probability of the sequence to make it given # this sequence, and add as this sequence's contribution to # the expected transitions matrix's k, l entry. expected_transitions[k*m + li] += cexp( log_transition_emission_probability_sum - log_sequence_probability) for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] if li < self.silent_start: continue # For each silent state we can go to on the same character # Sum up probabilities that we later normalize by # probability of sequence. log_transition_emission_probability_sum = NEGINF for i in range(n+1): # For each row in the forward DP table (where we can # have transitions to silent states) of which we have 1 # more than we have symbols... # Add probability that we start and get up to state k, # and go k->l, and go from l to the end. In this case, # we use forward and backward entries from the same DP # table row, since no character is being emitted. log_transition_emission_probability_sum = pair_lse( log_transition_emission_probability_sum, f[i*m + k] + self.out_transition_log_probabilities[l] + b[i*m + li]) # Now divide by probability of the sequence to make it given # this sequence, and add as this sequence's contribution to # the expected transitions matrix's k, l entry. expected_transitions[k*m + li] += cexp( log_transition_emission_probability_sum - log_sequence_probability) if k < self.silent_start: # Now think about emission probabilities from this state for i in range(n): # For each symbol that came out # What's the weight of this symbol for that state? # Probability that we emit index characters and then # transition to state l, and that from state l we # continue on to emit len(sequence) - (index + 1) # characters, divided by the probability of the # sequence under the model. # According to http://www1.icsi.berkeley.edu/Speech/ # docs/HTKBook/node7_mn.html, we really should divide by # sequence probability. emission_weights[i*self.silent_start + k] = f[(i+1)*m + k] + b[(i+1)*m + k] - \ log_sequence_probability free(e) free(b) free(f) return expected_transitions_ndarray, emission_weights_ndarray cpdef tuple viterbi(self, sequence): """Run the Viteri algorithm on the sequence. Run the Viterbi algorithm on the sequence given the model. This finds the ML path of hidden states given the sequence. Returns a tuple of the log probability of the ML path, or (-inf, None) if the sequence is impossible under the model. If a path is returned, it is a list of tuples of the form (sequence index, state object). This is fundamentally the same as the forward algorithm using max instead of sum, except the traceback is more complicated, because silent states in the current step can trace back to other silent states in the current step as well as states in the previous step. See also: - Viterbi implementation described well in the wikipedia article http://en.wikipedia.org/wiki/Viterbi_algorithm Parameters ---------- sequence : array-like An array (or list) of observations. Returns ------- logp : double The log probability of the sequence under the Viterbi path path : list of tuples Tuples of (state index, state object) of the states along the Viterbi path. """ if self.d == 0: raise ValueError("must bake model before using Viterbi algorithm") cdef numpy.ndarray sequence_ndarray cdef double* sequence_data cdef double logp cdef int n = len(sequence), m = len(self.states) cdef int mv = self.multivariate cdef void** distributions = self.distributions.data cdef int* path = calloc(n+m, sizeof(int)) cdef list vpath = [] sequence_ndarray = _check_input(sequence, self) sequence_data = sequence_ndarray.data logp = self._viterbi(sequence_data, path, n, m) for i in range(n+m): if path[i] == -1: break vpath.append((path[i], self.states[path[i]])) free(path) return logp, vpath if logp > NEGINF else None cdef double _viterbi(self, double* sequence, int* path, int n, int m) nogil: cdef int p = self.silent_start cdef int i, l, k, ki cdef int dim = self.d cdef void** distributions = self.distributions_ptr cdef int* tracebackx = calloc((n+1)*m, sizeof(int)) cdef int* tracebacky = calloc((n+1)*m, sizeof(int)) cdef double* v = calloc((n+1)*m, sizeof(double)) cdef double* e = malloc((n*self.silent_start)*sizeof(double)) cdef double state_log_probability cdef int end_index cdef double log_probability cdef int* in_edges = self.in_edge_count memset(path, -1, (n+m)*sizeof(int)) # Fill in the emission table for l in range(self.silent_start): for i in range(n): if self.cython == 1: ( distributions[l])._log_probability(sequence+i*dim, e+l*n+i, 1) else: with gil: python_log_probability(self.distributions[l], sequence+i*dim, e+l*n+i, 1) e[l*n + i] += self.state_weights[l] for i in range(m): v[i] = NEGINF v[self.start_index] = 0 for l in range(self.silent_start, m): # Handle transitions between silent states before the first symbol # is emitted. No non-silent states have non-zero probability yet, so # we can ignore them. if l == self.start_index: # Start state log-probability is already right. Don't touch it. continue for k in range(in_edges[l], in_edges[l+1]): ki = self.in_transitions[k] if ki < self.silent_start or ki >= l: continue # For each current-step preceding silent state k # This holds the log-probability coming that way state_log_probability = v[ki] + self.in_transition_log_probabilities[k] if state_log_probability > v[l]: v[l] = state_log_probability tracebackx[l] = 0 tracebacky[l] = ki for i in range(n): for l in range(self.silent_start): # Do the recurrence for non-silent states l # Start out saying the best likelihood we have is -inf v[(i+1)*m + l] = NEGINF for k in range(in_edges[l], in_edges[l+1]): ki = self.in_transitions[k] # For each previous state k # This holds the log-probability coming that way state_log_probability = v[i*m + ki] + \ self.in_transition_log_probabilities[k] + e[i + l*n] if state_log_probability > v[(i+1)*m + l]: v[(i+1)*m + l] = state_log_probability tracebackx[(i+1)*m + l] = i tracebacky[(i+1)*m + l] = ki for l in range(self.silent_start, m): # Now do the first pass over the silent states, finding the best # current-step non-silent state they could come from. # Start out saying the best likelihood we have is -inf v[(i+1)*m + l] = NEGINF for k in range(in_edges[l], in_edges[l+1]): ki = self.in_transitions[k] if ki >= self.silent_start: continue # For each current-step non-silent state k # This holds the log-probability coming that way state_log_probability = v[(i+1)*m + ki] + \ self.in_transition_log_probabilities[k] if state_log_probability > v[(i+1)*m + l]: v[(i+1)*m + l] = state_log_probability tracebackx[(i+1)*m + l] = i+1 tracebacky[(i+1)*m + l] = ki for l in range(self.silent_start, m): # Now the second pass through silent states, where we check the # silent states that could potentially reach here and see if # they're better than the non-silent states we found. for k in range(in_edges[l], in_edges[l+1]): ki = self.in_transitions[k] if ki < self.silent_start or ki >= l: continue # For each current-step preceding silent state k # This holds the log-probability coming that way state_log_probability = v[(i+1)*m + ki] + \ self.in_transition_log_probabilities[k] if state_log_probability > v[(i+1)*m + l]: v[(i+1)*m + l] = state_log_probability tracebackx[(i+1)*m + l] = i+1 tracebacky[(i+1)*m + l] = ki # Now the DP table is filled in. If this is a finite model, get the # log likelihood of ending up in the end state after following the # ML path through the model. If an infinite sequence, find the state # which the ML path ends in, and begin there. if self.finite == 1: log_probability = v[n*m + self.end_index] end_index = self.end_index else: end_index = -1 log_probability = NEGINF for i in range(m): if v[n*m + i] > log_probability: log_probability = v[n*m + i] end_index = i if log_probability == NEGINF: free(tracebackx) free(tracebacky) free(v) free(e) return log_probability # Otherwise, do the traceback # This holds the path, which we construct in reverse order cdef int px = n, py = end_index, npx cdef int length = 0 while px != 0 or py != self.start_index: # Until we've traced back to the start... # Put the position in the path, making sure to look up the state # object to use instead of the state index. path[length] = py length += 1 # Go backwards npx = tracebackx[px*m + py] py = tracebacky[px*m + py] px = npx # We've now reached the start (if we didn't raise an exception because # we messed up the traceback) # Record that we start at the start path[length] = py for i in range((length + 1) / 2): path[i], path[length-i] = path[length-i], path[i] free(tracebackx) free(tracebacky) free(v) free(e) return log_probability def predict_proba(self, sequence): """Calculate the state probabilities for each observation in the sequence. Run the forward-backward algorithm on the sequence and return the emission matrix. This is the normalized probability that each each state generated that emission given both the symbol and the entire sequence. This is a sklearn wrapper for the forward backward algorithm. See also: - Forward and backward algorithm implementations. A comprehensive description of the forward, backward, and forward-background algorithm is here: http://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm Parameters ---------- sequence : array-like An array (or list) of observations. Returns ------- emissions : array-like, shape (len(sequence), n_nonsilent_states) The normalized probabilities of each state generating each emission. """ if self.d == 0: raise ValueError("must bake model before prediction") return numpy.exp(self.predict_log_proba(sequence)) def predict_log_proba(self, sequence): """Calculate the state log probabilities for each observation in the sequence. Run the forward-backward algorithm on the sequence and return the emission matrix. This is the log normalized probability that each each state generated that emission given both the symbol and the entire sequence. This is a sklearn wrapper for the forward backward algorithm. See also: - Forward and backward algorithm implementations. A comprehensive description of the forward, backward, and forward-background algorithm is here: http://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm Parameters ---------- sequence : array-like An array (or list) of observations. Returns ------- emissions : array-like, shape (len(sequence), n_nonsilent_states) The log normalized probabilities of each state generating each emission. """ if self.d == 0: raise ValueError("must bake model before prediction") cdef int n = len(sequence), m = len(self.states) cdef int mv = self.multivariate cdef numpy.ndarray sequence_ndarray cdef numpy.ndarray r_ndarray = numpy.zeros((n, self.silent_start), dtype='float64') cdef double* sequence_data cdef double* r = r_ndarray.data sequence_ndarray = _check_input(sequence, self) sequence_data = sequence_ndarray.data with nogil: self._predict_log_proba(sequence_data, r, n, NULL) return r_ndarray cdef void _predict_log_proba(self, double* sequence, double* r, int n, double* emissions) nogil: cdef int i, k, l, li cdef int m = self.n_states, dim = self.d cdef double log_sequence_probability cdef double* f cdef double* b cdef double* e cdef void** distributions = self.distributions_ptr if emissions is NULL: e = malloc(n*self.silent_start*sizeof(double)) for l in range(self.silent_start): for i in range(n): if self.cython == 1: ( distributions[l])._log_probability(sequence+i*dim, e+l*n+i, 1) else: with gil: python_log_probability(self.distributions[l], sequence+i*dim, e+l*n+i, 1) e[l*n + i] += self.state_weights[l] else: e = emissions # Fill in both the F and B DP matrices. f = self._forward(sequence, n, emissions) b = self._backward(sequence, n, emissions) # Find out the probability of the sequence if self.finite == 1: log_sequence_probability = f[n*m + self.end_index] else: log_sequence_probability = NEGINF for i in range(self.silent_start): log_sequence_probability = pair_lse( log_sequence_probability, f[n*m + i]) # Is the sequence impossible? If so, don't bother calculating any more. if log_sequence_probability == NEGINF: with gil: print("Warning: Sequence is impossible.") for k in range(m): if k < self.silent_start: for i in range(n): # For each symbol that came out # What's the weight of this symbol for that state? # Probability that we emit index characters and then # transition to state l, and that from state l we # continue on to emit len(sequence) - (index + 1) # characters, divided by the probability of the # sequence under the model. # According to http://www1.icsi.berkeley.edu/Speech/ # docs/HTKBook/node7_mn.html, we really should divide by # sequence probability. r[i*self.silent_start + k] = f[(i+1)*m + k] + b[(i+1)*m + k] - \ log_sequence_probability free(f) free(b) free(e) def predict(self, sequence, algorithm='map'): """Calculate the most likely state for each observation. This can be either the Viterbi algorithm or maximum a posteriori. It returns the probability of the sequence under that state sequence and the actual state sequence. This is a sklearn wrapper for the Viterbi and maximum_a_posteriori methods. Parameters ---------- sequence : array-like An array (or list) of observations. algorithm : "map", "viterbi" The algorithm with which to decode the sequence Returns ------- path : list of integers A list of the ids of states along the MAP or the Viterbi path. """ if self.d == 0: raise ValueError("must bake model before prediction") if algorithm == 'map': return [state_id for state_id, state in self.maximum_a_posteriori(sequence)[1]] elif algorithm == 'viterbi': return [state_id for state_id, state in self.viterbi(sequence)[1]] else: raise ValueError("algorithm must be map or viterbi") def maximum_a_posteriori(self, sequence): """Run posterior decoding on the sequence. MAP decoding is an alternative to viterbi decoding, which returns the most likely state for each observation, based on the forward-backward algorithm. This is also called posterior decoding. This method is described on p. 14 of http://ai.stanford.edu/~serafim/CS262_2007/ notes/lecture5.pdf WARNING: This may produce impossible sequences. Parameters ---------- sequence : array-like An array (or list) of observations. Returns ------- logp : double The log probability of the sequence under the Viterbi path path : list of tuples Tuples of (state index, state object) of the states along the posterior path. """ if self.d == 0: raise ValueError("must bake model before using MAP decoding") return self._maximum_a_posteriori(numpy.array(sequence)) cdef tuple _maximum_a_posteriori(self, numpy.ndarray sequence): cdef int i, k, l, li cdef int m=len(self.states), n=len(sequence) cdef double [:,:] emission_weights = self.predict_log_proba(sequence) cdef list path = [] cdef double maximum_emission_weight cdef double log_probability_sum = 0 cdef int maximum_index # Go through each symbol and determine what the most likely state # that it came from is. for k in range(n): maximum_index = -1 maximum_emission_weight = NEGINF # Go through each hidden state and see which one has the maximal # weight for emissions. Tied states are not taken into account # here, because we are not performing training. for l in range(self.silent_start): if emission_weights[k, l] > maximum_emission_weight: maximum_emission_weight = emission_weights[k, l] maximum_index = l path.append((maximum_index, self.states[maximum_index])) log_probability_sum += maximum_emission_weight return log_probability_sum, path def fit(self, sequences, weights=None, labels=None, stop_threshold=1E-9, min_iterations=0, max_iterations=1e8, algorithm='baum-welch', pseudocount=None, transition_pseudocount=0, emission_pseudocount=0.0, use_pseudocount=False, inertia=None, edge_inertia=0.0, distribution_inertia=0.0, batches_per_epoch=None, lr_decay=0.0, callbacks=[], return_history=False, verbose=False, n_jobs=1, multiple_check_input=True): """Fit the model to data using either Baum-Welch, Viterbi, or supervised training. Given a list of sequences, performs re-estimation on the model parameters. The two supported algorithms are "baum-welch", "viterbi", and "labeled", indicating their respective algorithm. "labeled" corresponds to supervised learning that requires passing in a matching list of labels for each symbol seen in the sequences. Training supports a wide variety of other options including using edge pseudocounts and either edge or distribution inertia. Parameters ---------- sequences : array-like An array of some sort (list, numpy.ndarray, tuple..) of sequences, where each sequence is a numpy array, which is 1 dimensional if the HMM is a one dimensional array, or multidimensional if the HMM supports multiple dimensions. weights : array-like or None, optional An array of weights, one for each sequence to train on. If None, all sequences are equally weighted. Default is None. labels : array-like or None, optional An array of state labels for each sequence. This is only used in 'labeled' training. If used this must be comprised of n lists where n is the number of sequences to train on, and each of those lists must have one label per observation. A None in this list corresponds to no labels for the entire sequence and triggers semi-supervised learning, where the labeled sequences are summarized using labeled fitting and the unlabeled are summarized using the specified algorithm. Default is None. stop_threshold : double, optional The threshold the improvement ratio of the models log probability in fitting the scores. Default is 1e-9. min_iterations : int, optional The minimum number of iterations to run Baum-Welch training for. Default is 0. max_iterations : int, optional The maximum number of iterations to run Baum-Welch training for. Default is 1e8. algorithm : 'baum-welch', 'viterbi', 'labeled' The training algorithm to use. Baum-Welch uses the forward-backward algorithm to train using a version of structured EM. Viterbi iteratively runs the sequences through the Viterbi algorithm and then uses hard assignments of observations to states using that. Default is 'baum-welch'. Labeled training requires that labels are provided for each observation in each sequence. pseudocount : double, optional A pseudocount to add to both transitions and emissions. If supplied, it will override both transition_pseudocount and emission_pseudocount in the same way that specifying `inertia` will override both `edge_inertia` and `distribution_inertia`. Default is None. transition_pseudocount : double, optional A pseudocount to add to all transitions to add a prior to the MLE estimate of the transition probability. Default is 0. emission_pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Only effects hidden Markov models defined over discrete distributions. Default is 0. use_pseudocount : bool, optional Whether to use the pseudocounts defined in the `add_edge` method for edge-specific pseudocounts when updating the transition probability parameters. Does not effect the `transition_pseudocount` and `emission_pseudocount` parameters, but can be used in addition to them. Default is False. inertia : double or None, optional, range [0, 1] If double, will set both edge_inertia and distribution_inertia to be that value. If None, will not override those values. Default is None. edge_inertia : bool, optional, range [0, 1] Whether to use inertia when updating the transition probability parameters. Default is 0.0. distribution_inertia : double, optional, range [0, 1] Whether to use inertia when updating the distribution parameters. Default is 0.0. batches_per_epoch : int or None, optional The number of batches in an epoch. This is the number of batches to summarize before calling `from_summaries` and updating the model parameters. This allows one to do minibatch updates by updating the model parameters before setting the full dataset. If set to None, uses the full dataset. Default is None. lr_decay : double, optional, positive The step size decay as a function of the number of iterations. Functionally, this sets the inertia to be (2+k)^{-lr_decay} where k is the number of iterations. This causes initial iterations to have more of an impact than later iterations, and is frequently used in minibatch learning. This value is suggested to be between 0.5 and 1. Default is 0, meaning no decay. callbacks : list, optional A list of callback objects that describe functionality that should be undertaken over the course of training. return_history : bool, optional Whether to return the history during training as well as the model. verbose : bool, optional Whether to print the improvement in the model fitting at each iteration. Default is True. n_jobs : int, optional The number of threads to use when performing training. This leads to exact updates. Default is 1. multiple_check_input : bool, optional Whether to check and transcode input at each iteration. This leads to copying whole input data in each iteration. Which can introduce significant overhead (up to 2 times slower) so should be turned off when you know that data won't be changed between fitting iteration. Default is True. Returns ------- improvement : HiddenMarkovModel The trained model itself. """ if self.d == 0: raise ValueError("must bake model before fitting") cdef int iteration = 0 cdef int mv = self.multivariate cdef double improvement = INF cdef double total_improvement = 0 cdef double initial_log_probability_sum cdef double log_probability_sum cdef double last_log_probability_sum cdef str alg = algorithm.lower() cdef bint check_input = alg == 'viterbi' training_start_time = time.time() if multiple_check_input: # if we should check input multiple times # use old code, where we only change class of input # checking input will be in `summarize` function if not isinstance(sequences, BaseGenerator): data_generator = SequenceGenerator(sequences, weights, labels) else: data_generator = sequences else: if not isinstance(sequences, BaseGenerator): #check_input: #sequences have elements which are ndarrays. For each dimension in #our HMM model we have one row in this array, so we have to #iterate over all sequences for all dimensions checked_sequences = [] for sequence in sequences: sequence_ndarray = _check_input(sequence, self) checked_sequences.append(sequence_ndarray) if labels is not None: labels = numpy.array(labels) data_generator = SequenceGenerator(checked_sequences, weights, labels) else: checked_sequences, checked_weights, checked_labels = [], [], [] for batch in sequences.batches(): sequence_ndarray= _check_input(batch[0][0],self) checked_sequences.append(sequence_ndarray) checked_weights.append(batch[1]) if len(batch) == 3: checked_labels.append(batch[2][0]) else: checked_labels = None data_generator = SequenceGenerator(checked_sequences, checked_weights, checked_labels) n = data_generator.shape[0] semisupervised = False if labels is not None: for l in labels: if l is None: semisupervised = True break batches_per_epoch = batches_per_epoch n_seen_batches = 0 callbacks = [History()] + callbacks for callback in callbacks: callback.model = self callback.on_training_begin() with Parallel(n_jobs=n_jobs, backend='threading') as parallel: f = delayed(self.summarize) while improvement > stop_threshold or iteration < min_iterations + 1: epoch_start_time = time.time() step_size = None if inertia is None else 1 - ((1 - inertia) * (2 + iteration) ** -lr_decay) self.from_summaries(step_size, pseudocount, transition_pseudocount, emission_pseudocount, use_pseudocount, edge_inertia, distribution_inertia) if iteration >= max_iterations + 1: break if semisupervised: log_probability_sum = sum(parallel(f(*batch, algorithm='labeled', check_input=multiple_check_input) for batch in data_generator.labeled_batches())) log_probability_sum += sum(parallel(f(*batch, algorithm=algorithm, check_input=multiple_check_input) for batch in data_generator.unlabeled_batches())) elif labels is not None: log_probability_sum = sum(parallel(f(*batch, algorithm=algorithm, check_input=multiple_check_input) for batch in data_generator.batches())) else: log_probability_sum = sum(parallel(f(*batch, algorithm=algorithm, check_input=multiple_check_input) for batch in data_generator.batches())) if iteration == 0: initial_log_probability_sum = log_probability_sum else: epoch_end_time = time.time() time_spent = epoch_end_time - epoch_start_time improvement = log_probability_sum - last_log_probability_sum if verbose: print("[{}] Improvement: {}\tTime (s): {:.4}".format( iteration, improvement, time_spent)) total_improvement += improvement logs = {'learning_rate': step_size, 'n_seen_batches' : n_seen_batches, 'epoch' : iteration, 'improvement' : improvement, 'total_improvement' : total_improvement, 'log_probability' : log_probability_sum, 'last_log_probability' : last_log_probability_sum, 'initial_log_probability' : initial_log_probability_sum, 'epoch_start_time' : epoch_start_time, 'epoch_end_time' : epoch_end_time, 'duration' : time_spent } for callback in callbacks: callback.on_epoch_end(logs) iteration += 1 last_log_probability_sum = log_probability_sum for callback in callbacks: callback.on_training_end(logs) self.clear_summaries() if verbose: print("Total Training Improvement: {}".format(total_improvement)) total_training_time = time.time() - training_start_time print("Total Training Time (s): {:.4f}".format(total_training_time)) history = callbacks[0] if return_history: return self, history return self def summarize(self, sequences, weights=None, labels=None, algorithm='baum-welch', check_input=True): """Summarize data into stored sufficient statistics for out-of-core training. Only implemented for Baum-Welch training since Viterbi is less memory intensive. Parameters ---------- sequences : array-like An array of some sort (list, numpy.ndarray, tuple..) of sequences, where each sequence is a numpy array, which is 1 dimensional if the HMM is a one dimensional array, or multidimensional if the HMM supports multiple dimensions. weights : array-like or None, optional An array of weights, one for each sequence to train on. If None, all sequences are equally weighted. Default is None. labels : array-like or None, optional An array of state labels for each sequence. This is only used in 'labeled' training. If used this must be comprised of n lists where n is the number of sequences to train on, and each of those lists must have one label per observation. Default is None. algorithm : 'baum-welch', 'viterbi', 'labeled' The training algorithm to use. Baum-Welch uses the forward-backward algorithm to train using a version of structured EM. Viterbi iteratively runs the sequences through the Viterbi algorithm and then uses hard assignments of observations to states using that. Default is 'baum-welch'. Labeled training requires that labels are provided for each observation in each sequence. check_input : bool, optional Check the input. This casts the input sequences as numpy arrays, and converts non-numeric inputs into numeric inputs for faster processing later. Default is True. Returns ------- logp : double The log probability of the sequences. """ cdef int mv = self.multivariate cdef list X = [] if self.d == 0: raise ValueError("must bake model before summarizing data") if check_input: if weights is None: weights = numpy.ones(len(sequences), dtype='float64') else: weights = numpy.array(weights, dtype='float64') if labels is not None: labels = numpy.array(labels) for sequence in sequences: sequence_ndarray = _check_input(sequence, self) X.append(sequence_ndarray) else: X = sequences if algorithm == 'baum-welch': return sum([self._baum_welch_summarize(sequence, weight) for sequence, weight in zip(X, weights)]) elif algorithm == 'viterbi': return sum([self._viterbi_summarize(sequence, weight) for sequence, weight in zip(X, weights)]) elif algorithm == 'labeled': return sum([self._labeled_summarize(sequence, label, weight) for sequence, label, weight in zip(X, labels, weights)]) cpdef double _baum_welch_summarize(self, numpy.ndarray sequence_ndarray, double weight): """Python wrapper for the summarization step. This is done to ensure compatibility with joblib's multithreading API. It just calls the cython update, but provides a Python wrapper which joblib can easily wrap. """ cdef double* sequence = sequence_ndarray.data cdef int n = sequence_ndarray.shape[0] cdef double log_sequence_probability with nogil: log_sequence_probability = self._summarize(sequence, &weight, n, 0, self.d) return log_sequence_probability cdef double _summarize(self, double* sequence, double* weight, int n, int column_idx, int d) nogil: """Collect sufficient statistics on a single sequence.""" cdef int i, k, l, li cdef int m = self.n_states cdef void** distributions = self.distributions_ptr cdef double log_sequence_probability cdef double log_transition_emission_probability_sum cdef double* expected_transitions = calloc(self.n_edges, sizeof(double)) cdef double* f cdef double* b cdef double* e cdef int* tied_edges = self.tied_edge_group_size cdef int* tied_states = self.tied_state_count cdef int* out_edges = self.out_edge_count cdef double* weights = calloc(n, sizeof(double)) e = malloc(n*self.silent_start*sizeof(double)) for l in range(self.silent_start): for i in range(n): if self.cython == 1: ( distributions[l])._log_probability(sequence+i*d, e+l*n+i, 1) else: with gil: python_log_probability(self.distributions[l], sequence+i*d, e+l*n+i, 1) e[l*n + i] += self.state_weights[l] f = self._forward(sequence, n, e) b = self._backward(sequence, n, e) if self.finite == 1: log_sequence_probability = f[n*m + self.end_index] else: log_sequence_probability = NEGINF for i in range(self.silent_start): log_sequence_probability = pair_lse(f[n*m + i], log_sequence_probability) # Is the sequence impossible? If so, we can't train on it, so skip # it if log_sequence_probability != NEGINF: for k in range(m): # For each state we could have come from for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] if li >= self.silent_start: continue # For each state we could go to (and emit a character) # Sum up probabilities that we later normalize by # probability of sequence. log_transition_emission_probability_sum = NEGINF for i in range(n): # For each character in the sequence # Add probability that we start and get up to state k, # and go k->l, and emit the symbol from l, and go from l # to the end. log_transition_emission_probability_sum = pair_lse( log_transition_emission_probability_sum, f[i*m + k] + self.out_transition_log_probabilities[l] + e[i + li*n] + b[(i+1)*m + li]) # Now divide by probability of the sequence to make it given # this sequence, and add as this sequence's contribution to # the expected transitions matrix's k, l entry. expected_transitions[l] += cexp( log_transition_emission_probability_sum - log_sequence_probability) for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] if li < self.silent_start: continue # For each silent state we can go to on the same character # Sum up probabilities that we later normalize by # probability of sequence. log_transition_emission_probability_sum = NEGINF for i in range(n+1): # For each row in the forward DP table (where we can # have transitions to silent states) of which we have 1 # more than we have symbols... # Add probability that we start and get up to state k, # and go k->l, and go from l to the end. In this case, # we use forward and backward entries from the same DP # table row, since no character is being emitted. log_transition_emission_probability_sum = pair_lse( log_transition_emission_probability_sum, f[i*m + k] + self.out_transition_log_probabilities[l] + b[i*m + li]) # Now divide by probability of the sequence to make it given # this sequence, and add as this sequence's contribution to # the expected transitions matrix's k, l entry. expected_transitions[l] += cexp( log_transition_emission_probability_sum - log_sequence_probability) if k < self.silent_start: for i in range(n): # For each symbol that came out # What's the weight of this symbol for that state? # Probability that we emit index characters and then # transition to state l, and that from state l we # continue on to emit len(sequence) - (index + 1) # characters, divided by the probability of the # sequence under the model. # According to http://www1.icsi.berkeley.edu/Speech/ # docs/HTKBook/node7_mn.html, we really should divide by # sequence probability. weights[i] = cexp(f[(i+1)*m + k] + b[(i+1)*m + k] - log_sequence_probability) * weight[0] if self.cython == 0: with gil: python_summarize(self.distributions[k], sequence, weights, n) else: (distributions[k])._summarize(sequence, weights, n, 0, self.d) # Update the master expected transitions vector representing the sparse matrix. with gil: for i in range(self.n_edges): self.expected_transitions[i] += expected_transitions[i] * weight[0] self.summaries += 1 free(expected_transitions) free(e) free(weights) free(f) free(b) return log_sequence_probability * weight[0] cpdef double _viterbi_summarize(self, numpy.ndarray sequence_ndarray, double weight): """Python wrapper for the summarization step. This is done to ensure compatibility with joblib's multithreading API. It just calls the cython update, but provides a Python wrapper which joblib can easily wrap. """ cdef double* sequence = sequence_ndarray.data cdef int n = sequence_ndarray.shape[0], m = len(self.states) cdef double log_sequence_probability with nogil: log_sequence_probability = self.__viterbi_summarize(sequence, weight, n, m) return self.log_probability(sequence_ndarray, check_input=False) cdef double __viterbi_summarize(self, double* sequence, double weight, int n, int m) nogil: """Perform Viterbi re-estimation on the model parameters. The sequence is tagged using the viterbi algorithm, and both emissions and transitions are updated based on the probabilities in the observations. """ cdef int* path = malloc((n+m+1)*sizeof(int)) memset(path, -1, (n+m+1)*sizeof(int)) cdef double log_probability = self._viterbi(sequence, path, n, m) self.__labeled_summarize(sequence, path, weight, n, m) free(path) return log_probability * weight cpdef double _labeled_summarize(self, numpy.ndarray sequence_ndarray, numpy.ndarray label_ndarray, double weight): """Python wrapper for the summarization step. This is done to ensure compatibility with joblib's multithreading API. It just calls the cython update, but provides a Python wrapper which joblib can easily wrap. """ cdef double log_sequence_probability cdef double* sequence = sequence_ndarray.data cdef int i, n = sequence_ndarray.shape[0], m = len(self.states) cdef int* labels = malloc((n+m+1)*sizeof(int)) memset(labels, -1, (n+m+1)*sizeof(int)) for i in range(label_ndarray.shape[0]): if isinstance(label_ndarray[i], State): labels[i] = self.states.index(label_ndarray[i]) else: labels[i] = self.state_name_mapping[label_ndarray[i]] with nogil: log_sequence_probability = self.__labeled_summarize(sequence, labels, weight, n, m) free(labels) return self.log_probability(sequence_ndarray, check_input=False) cdef double __labeled_summarize(self, double* sequence, int* states, double weight, int n, int m) nogil: """Perform a re-estimation of the model parameters using labeled data. This assumes that labels are passed in alongside the sequence and will only update the appropriate states and transitions. """ cdef int i, j, k, l, li cdef int past, present cdef int* out_edges = self.out_edge_count cdef void** distributions = self.distributions_ptr cdef double* transitions = calloc(m*m, sizeof(double)) j = 0 for i in range(1, n+m+1): past = states[i-1] present = states[i] if present == -1: break else: transitions[past*m + present] += weight if present < self.silent_start: if self.cython == 0: with gil: python_summarize(self.distributions[present], sequence+j*self.d, &weight, 1) else: ( distributions[present])._summarize(sequence+j*self.d, &weight, 1, 0, self.d) j += 1 with gil: for k in range(m): for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] self.expected_transitions[l] += transitions[k*m + li] self.summaries += 1 free(transitions) return 0 def from_summaries(self, inertia=None, pseudocount=None, transition_pseudocount=0.0, emission_pseudocount=0.0, use_pseudocount=False, edge_inertia=0.0, distribution_inertia=0.0): """Fit the model to the stored summary statistics. Parameters ---------- inertia : double or None, optional The inertia to use for both edges and distributions without needing to set both of them. If None, use the values passed in to those variables. Default is None. pseudocount : double, optional A pseudocount to add to both transitions and emissions. If supplied, it will override both transition_pseudocount and emission_pseudocount in the same way that specifying `inertia` will override both `edge_inertia` and `distribution_inertia`. Default is None. transition_pseudocount : double, optional A pseudocount to add to all transitions to add a prior to the MLE estimate of the transition probability. Default is 0. emission_pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Only effects hidden Markov models defined over discrete distributions. Default is 0. use_pseudocount : bool, optional Whether to use the pseudocounts defined in the `add_edge` method for edge-specific pseudocounts when updating the transition probability parameters. Does not effect the `transition_pseudocount` and `emission_pseudocount` parameters, but can be used in addition to them. Default is False. edge_inertia : bool, optional, range [0, 1] Whether to use inertia when updating the transition probability parameters. Default is 0.0. distribution_inertia : double, optional, range [0, 1] Whether to use inertia when updating the distribution parameters. Default is 0.0. Returns ------- None """ if self.d == 0: raise ValueError("must bake model before using from summaries") if self.summaries == 0: return if inertia is not None: edge_inertia = inertia distribution_inertia = inertia if pseudocount is not None: transition_pseudocount = pseudocount emission_pseudocount = pseudocount self._from_summaries(transition_pseudocount, emission_pseudocount, use_pseudocount, edge_inertia, distribution_inertia) memset(self.expected_transitions, 0, self.n_edges*sizeof(double)) self.summaries = 0 cdef void _from_summaries(self, double transition_pseudocount, double emission_pseudocount, bint use_pseudocount, double edge_inertia, double distribution_inertia): """Update the transition matrix and emission distributions.""" cdef int k, i, l, li, m = len(self.states), n, idx cdef int* in_edges = self.in_edge_count cdef int* out_edges = self.out_edge_count cdef int* tied_states = self.tied_state_count cdef double* norm cdef double probability, tied_edge_probability cdef int start, end cdef int* tied_edges = self.tied_edge_group_size cdef double* expected_transitions = calloc(m*m, sizeof(double)) with nogil: for k in range(m): for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] expected_transitions[k*m + li] = self.expected_transitions[l] # We now have expected_transitions taking into account all sequences. # And a list of all emissions, and a weighting of each emission for each # state # Normalize transition expectations per row (so it becomes transition # probabilities) # See http://stackoverflow.com/a/8904762/402891 # Only modifies transitions for states a transition was observed from. norm = calloc(m, sizeof(double)) # Go through the tied state groups and add transitions from each member # in the group to the other members of the group. # For each group defined. for k in range(self.n_tied_edge_groups-1): tied_edge_probability = 0. # For edge in this group, get the sum of the edges for l in range(tied_edges[k], tied_edges[k+1]): start = self.tied_edges_starts[l] end = self.tied_edges_ends[l] tied_edge_probability += expected_transitions[start*m + end] # Update each entry for l in range(tied_edges[k], tied_edges[k+1]): start = self.tied_edges_starts[l] end = self.tied_edges_ends[l] expected_transitions[start*m + end] = tied_edge_probability # Calculate the regularizing norm for each node for k in range(m): for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] norm[k] += expected_transitions[k*m + li] + \ transition_pseudocount + \ self.out_transition_pseudocounts[l] * use_pseudocount # For every node, update the transitions appropriately for k in range(m): # Recalculate each transition out from that node and update # the vector of out transitions appropriately if norm[k] > 0: for l in range(out_edges[k], out_edges[k+1]): li = self.out_transitions[l] probability = (expected_transitions[k*m + li] + transition_pseudocount + self.out_transition_pseudocounts[l] * use_pseudocount)\ / norm[k] self.out_transition_log_probabilities[l] = _log( cexp(self.out_transition_log_probabilities[l]) * edge_inertia + probability * (1 - edge_inertia)) # Recalculate each transition in to that node and update the # vector of in transitions appropriately for l in range(in_edges[k], in_edges[k+1]): li = self.in_transitions[l] if norm[li] > 0: probability = (expected_transitions[li*m + k] + transition_pseudocount + self.in_transition_pseudocounts[l] * use_pseudocount)\ / norm[li] self.in_transition_log_probabilities[l] = _log( cexp(self.in_transition_log_probabilities[l]) * edge_inertia + probability * (1 - edge_inertia)) for k in range(self.silent_start): # Re-estimate the emission distribution for every non-silent state. # Take each emission weighted by the probability that we were in # this state when it came out, given that the model generated the # sequence that the symbol was part of. Take into account tied # states by only training that distribution one time, since many # states are pointing to the same distribution object. with gil: if self.discrete: self.states[k].distribution.from_summaries( distribution_inertia, emission_pseudocount) else: self.states[k].distribution.from_summaries( distribution_inertia) for k in range(self.n_states): for l in range(self.out_edge_count[k], self.out_edge_count[k+1]): li = self.out_transitions[l] prob = self.out_transition_log_probabilities[l] self.graph[self.states[k]][self.states[li]]['probability'] = prob free(norm) free(expected_transitions) def clear_summaries(self): """Clear the summary statistics stored in the object. Parameters ---------- None Returns ------- None """ memset(self.expected_transitions, 0, self.n_edges*sizeof(double)) self.summaries = 0 for state in self.states[:self.silent_start]: state.distribution.clear_summaries() def to_dict(self): model = { 'class' : 'HiddenMarkovModel', 'name' : self.name, 'start' : self.start.to_dict(), 'end' : self.end.to_dict(), 'states' : [state.to_dict() for state in self.states], 'end_index' : self.end_index, 'start_index' : self.start_index, 'silent_index' : self.silent_start } indices = { state: i for i, state in enumerate(self.states)} # Get the number of groups of edges which are tied groups = [] n = self.n_tied_edge_groups-1 # Go through each group one at a time for i in range(n): # Create an empty list for that group groups.append([]) # Go through each edge in that group start, end = self.tied_edge_group_size[i], self.tied_edge_group_size[i+1] # Add each edge as a tuple of indices for j in range(start, end): groups[i].append((self.tied_edges_starts[j], self.tied_edges_ends[j])) # Now reverse this into a dictionary, such that each pair of edges points # to a label (a number in this case) d = { tup : i for i in range(n) for tup in groups[i] } # Get all the edges from the graph edges = [] for start, end, data in list(self.graph.edges(data=True)): # If this edge is part of a group of tied edges, annotate this group # it is a part of s, e = indices[start], indices[end] prob, pseudocount = math.e**data['probability'], data['pseudocount'] edge = (s, e) edges.append((s, e, prob, pseudocount, d.get(edge, None))) model['edges'] = edges # Get distribution tie information ties = [] for i in range(self.silent_start): start, end = self.tied_state_count[i], self.tied_state_count[i+1] for j in range(start, end): ties.append((i, self.tied[j])) model['distribution ties'] = ties return model @classmethod def from_dict(cls, d, verbose=False): # Make a new generic HMM model = cls(str(d['name'])) states = [State.from_dict(j) for j in d['states']] for i, j in d['distribution ties']: # Tie appropriate states together states[i].tie(states[j]) # Add all the states to the model model.add_states(states) # Indicate appropriate start and end states model.start = states[d['start_index']] model.end = states[d['end_index']] # Add all the edges to the model for start, end, probability, pseudocount, group in d['edges']: model.add_transition(states[start], states[end], probability, pseudocount, group) # Bake the model model.bake(verbose=verbose) return model @classmethod def from_matrix(cls, transition_probabilities, distributions, starts, ends=None, state_names=None, name=None, verbose=False, merge='None'): """Create a model from a more standard matrix format. Take in a 2D matrix of floats of size n by n, which are the transition probabilities to go from any state to any other state. May also take in a list of length n representing the names of these nodes, and a model name. Must provide the matrix, and a list of size n representing the distribution you wish to use for that state, a list of size n indicating the probability of starting in a state, and a list of size n indicating the probability of ending in a state. Parameters ---------- transition_probabilities : array-like, shape (n_states, n_states) The probabilities of each state transitioning to each other state. distributions : array-like, shape (n_states) The distributions for each state. Silent states are indicated by using None instead of a distribution object. starts : array-like, shape (n_states) The probabilities of starting in each of the states. ends : array-like, shape (n_states), optional If passed in, the probabilities of ending in each of the states. If ends is None, then assumes the model has no explicit end state. Default is None. state_names : array-like, shape (n_states), optional The name of the states. If None is passed in, default names are generated. Default is None name : str, optional The name of the model. Default is None verbose : bool, optional The verbose parameter for the underlying bake method. Default is False. merge : 'None', 'Partial', 'All', optional The merge parameter for the underlying bake method. Default is All Returns ------- model : HiddenMarkovModel The baked model ready to go. Examples -------- matrix = [[0.4, 0.5], [0.4, 0.5]] distributions = [NormalDistribution(1, .5), NormalDistribution(5, 2)] starts = [1., 0.] ends = [.1., .1] state_names= ["A", "B"] model = Model.from_matrix(matrix, distributions, starts, ends, state_names, name="test_model") """ # Build the initial model model = cls(name=name) state_names = state_names or ["s{}".format(i) for i in range(len(distributions))] # Build state objects for every state with the appropriate distribution states = [State(distribution, name=name) for name, distribution in zip(state_names, distributions)] n = len(states) # Add all the states to the model for state in states: model.add_state(state) # Connect the start of the model to the appropriate state for i, prob in enumerate(starts): if prob != 0: model.add_transition(model.start, states[i], prob) # Connect all states to each other if they have a non-zero probability for i in range(n): for j, prob in enumerate(transition_probabilities[i]): if prob != 0.: model.add_transition(states[i], states[j], prob) if ends is not None: # Connect states to the end of the model if a non-zero probability for i, prob in enumerate(ends): if prob != 0: model.add_transition(states[i], model.end, prob) model.bake(verbose=verbose, merge=merge) return model @classmethod def from_samples(cls, distribution, n_components, X, weights=None, labels=None, algorithm='baum-welch', inertia=None, edge_inertia=0.0, distribution_inertia=0.0, pseudocount=None, transition_pseudocount=0, emission_pseudocount=0.0, use_pseudocount=False, stop_threshold=1e-9, min_iterations=0, max_iterations=1e8, n_init=1, init='kmeans++', max_kmeans_iterations=1, initialization_batch_size=None, batches_per_epoch=None, lr_decay=0.0, end_state=False, state_names=None, name=None, keys=None, random_state=None, callbacks=[], return_history=False, verbose=False, n_jobs=1, multiple_check_input=True): """Learn the transitions and emissions of a model directly from data. This method will learn both the transition matrix, emission distributions, and start probabilities for each state. This will only return a dense graph without any silent states or explicit transitions to an end state. Currently all components must be defined as the same distribution, but soon this restriction will be removed. If learning a multinomial HMM over discrete characters, the initial emisison probabilities are initialized randomly. If learning a continuous valued HMM, such as a Gaussian HMM, then kmeans clustering is used first to identify initial clusters. Regardless of the type of model, the transition matrix and start probabilities are initialized uniformly. Then the specified learning algorithm (Baum-Welch recommended) is used to refine the parameters of the model. Parameters ---------- distribution : callable The emission distribution of the components of the model. n_components : int The number of states (or components) to initialize. X : array-like or generator An array of some sort (list, numpy.ndarray, tuple..) of sequences, where each sequence is a numpy array, which is 1 dimensional if the HMM is a one dimensional array, or multidimensional if the HMM supports multiple dimensions. Alternatively, a data generator object that yields sequences. weights : array-like or None, optional An array of weights, one for each sequence to train on. If None, all sequences are equally weighted. Default is None. labels : array-like or None, optional An array of state labels for each sequence. This is only used in 'labeled' training. If used this must be comprised of n lists where n is the number of sequences to train on, and each of those lists must have one label per observation. A None in this list corresponds to no labels for the entire sequence and triggers semi-supervised learning, where the labeled sequences are summarized using labeled fitting and the unlabeled are summarized using the specified algorithm. Default is None. algorithm : 'baum-welch', 'viterbi', 'labeled' The training algorithm to use. Baum-Welch uses the forward-backward algorithm to train using a version of structured EM. Viterbi iteratively runs the sequences through the Viterbi algorithm and then uses hard assignments of observations to states using that. Default is 'baum-welch'. Labeled training requires that labels are provided for each observation in each sequence. inertia : double or None, optional, range [0, 1] If double, will set both edge_inertia and distribution_inertia to be that value. If None, will not override those values. Default is None. edge_inertia : bool, optional, range [0, 1] Whether to use inertia when updating the transition probability parameters. Default is 0.0. distribution_inertia : double, optional, range [0, 1] Whether to use inertia when updating the distribution parameters. Default is 0.0. pseudocount : double, optional A pseudocount to add to both transitions and emissions. If supplied, it will override both transition_pseudocount and emission_pseudocount in the same way that specifying `inertia` will override both `edge_inertia` and `distribution_inertia`. Default is None. transition_pseudocount : double, optional A pseudocount to add to all transitions to add a prior to the MLE estimate of the transition probability. Default is 0. emission_pseudocount : double, optional A pseudocount to add to the emission of each distribution. This effectively smoothes the states to prevent 0. probability symbols if they don't happen to occur in the data. Only effects hidden Markov models defined over discrete distributions. Default is 0. use_pseudocount : bool, optional Whether to use the pseudocounts defined in the `add_edge` method for edge-specific pseudocounts when updating the transition probability parameters. Does not effect the `transition_pseudocount` and `emission_pseudocount` parameters, but can be used in addition to them. Default is False. stop_threshold : double, optional The threshold the improvement ratio of the models log probability in fitting the scores. Default is 1e-9. min_iterations : int, optional The minimum number of iterations to run Baum-Welch training for. Default is 0. max_iterations : int, optional The maximum number of iterations to run Baum-Welch training for. Default is 1e8. n_init : int, optional The number of times to initialize the k-means clustering before taking the best value. Default is 1. init : str, optional The initialization method for kmeans. Must be one of 'first-k', 'random', 'kmeans++', or 'kmeans||'. Default is kmeans++. max_kmeans_iterations : int, optional The number of iterations to run k-means for before starting EM. initialization_batch_size : int or None, optional The number of batches to use to initialize the model. None means use the entire data set. Default is None. batches_per_epoch : int or None, optional The number of batches in an epoch. This is the number of batches to summarize before calling `from_summaries` and updating the model parameters. This allows one to do minibatch updates by updating the model parameters before setting the full dataset. If set to None, uses the full dataset. Default is None. lr_decay : double, optional, positive The step size decay as a function of the number of iterations. Functionally, this sets the inertia to be (2+k)^{-lr_decay} where k is the number of iterations. This causes initial iterations to have more of an impact than later iterations, and is frequently used in minibatch learning. This value is suggested to be between 0.5 and 1. Default is 0, meaning no decay. end_state : bool, optional Whether to calculate the probability of ending in each state or not. Default is False. state_names : array-like, shape (n_states), optional The name of the states. If None is passed in, default names are generated. Default is None name : str, optional The name of the model. Default is None keys : list A list of sets where each set is the keys present in that column. If there are d columns in the data set then this list should have d sets and each set should have at least two keys in it. random_state : int, numpy.random.RandomState, or None The random state used for generating samples. If set to none, a random seed will be used. If set to either an integer or a random seed, will produce deterministic outputs. callbacks : list, optional A list of callback objects that describe functionality that should be undertaken over the course of training. return_history : bool, optional Whether to return the history during training as well as the model. verbose : bool, optional Whether to print the improvement in the model fitting at each iteration. Default is True. n_jobs : int, optional The number of threads to use when performing training. This leads to exact updates. Default is 1. multiple_check_input : bool, optional Whether to check and transcode input at each iteration. This leads to copying whole input data in each iteration. Which can introduce significant overhead (up to 2 times slower) so should be turned off when you know that data won't be changed between fitting iteration. Default is True. Returns ------- model : HiddenMarkovModel The model fit to the data. """ random_state = check_random_state(random_state) if not isinstance(X, BaseGenerator): data_generator = SequenceGenerator(X, weights, labels) else: data_generator = X if initialization_batch_size is None: initialization_batch_size = len(data_generator) X_, labels_ = [], [] data = data_generator.batches() for i in range(initialization_batch_size): batch = next(data) X_.extend(batch[0]) if labels is not None: labels_.extend(batch[2]) # Discrete distributions are handled differently because I make bad decisions. if distribution is DiscreteDistribution: X_concat = numpy.concatenate(X_) if keys is None: if X_concat.ndim == 1: keys = numpy.unique(X_concat) else: keys = [numpy.unique(X_concat[:,i] for i in range(X_concat.shape[1]))] distributions = [] for i in range(n_components): weights = random_state.uniform(0, 1, size=len(X_concat)) weights /= weights.sum() if X_concat.ndim == 1: distribution = DiscreteDistribution({key: weight for key, weight in zip(keys, weights)}) else: distribution = IndependentComponentsDistribution.from_samples(X_concat, distributions=DiscreteDistribution) distributions.append(distribution) # If labels are provided, use them to initialize the distributions elif labels is not None: X_concat = [x for x, label in zip(X_, labels_) if label is not None] X_concat = numpy.concatenate(X_concat) if X_concat.ndim == 1: X_concat = X_concat.reshape(-1, 1) labels_concat = numpy.concatenate([l for l in labels if l is not None]) labels_concat = numpy.array([l for l in labels_concat if l != str(name)+"-start" and l != str(name)+"-end"]) label_set = numpy.unique(labels_concat) X_ = [X_concat[labels_concat == label] for label in label_set] distributions = _initialize_distributions(X_, distribution) # If labels are not provided, run K-means to initialize the distributions else: X_concat = numpy.concatenate(X_) if X_concat.ndim == 1: X_concat = X_concat.reshape(-1, 1) clf = Kmeans(n_components, init=init, n_init=n_init) clf.fit(X_concat, max_iterations=max_kmeans_iterations, batches_per_epoch=batches_per_epoch, random_state=random_state) y = clf.predict(X_concat) X_ = [X_concat[y == i] for i in range(n_components)] distributions = _initialize_distributions(X_, distribution) k = n_components transition_matrix = numpy.ones((k, k)) / k start_probabilities = numpy.ones(k) / k end_probabilities = None if end_state: end_probabilities = numpy.ones(k) / k model = cls.from_matrix(transition_matrix, distributions, start_probabilities, state_names=state_names, name=name, ends=end_probabilities) _, history = model.fit(data_generator, weights=weights, labels=labels, stop_threshold=stop_threshold, min_iterations=min_iterations, max_iterations=max_iterations, algorithm=algorithm, verbose=verbose, pseudocount=pseudocount, transition_pseudocount=transition_pseudocount, emission_pseudocount=emission_pseudocount, use_pseudocount=use_pseudocount, inertia=inertia, edge_inertia=edge_inertia, distribution_inertia=distribution_inertia, batches_per_epoch=batches_per_epoch, lr_decay=lr_decay, callbacks=callbacks, return_history=True, n_jobs=n_jobs, multiple_check_input=multiple_check_input) if return_history: return model, history return model pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/io.py000066400000000000000000000301771420461712600241640ustar00rootroot00000000000000import numpy try: import pandas except: pandas = None class BaseGenerator(object): """The base data generator class. This object is inherited by data generator objects in order to specify that they are data generators. Do not use this object directly. """ def __init__(self): pass def __len__(self): return NotImplementedError @property def shape(self): return NotImplementedError @property def classes(self): return NotImplementedError @property def ndim(self): return NotImplementedError class DataGenerator(BaseGenerator): """A generator that returns batches of a data set. This object will wrap a data set and optionally a set of labels and will return batches of data as requested. When it reaches the end of a data set it will not roll over but rather return a batch of data smaller than the other batches. Parameters ---------- X : numpy.ndarray or list The data set to iterate over. weights : numpy.ndarray or list or None, optional The weights for each example. Default is None. y: numpy.ndarray or list or None, optional The set of labels for each example in the data set. Default is None. batch_size : int or None, optional The size of the batches to return. If None will return the full data set each time. Default is None batches_per_epoch : int or None, optional The number of batches to return before resetting the index. If the value is too low you may not see all examples from the data set. If None, will return enough batches to cover the entire data set. Default is None. """ def __init__(self, X, weights=None, y=None, batch_size=None, batches_per_epoch=None): self.X = numpy.array(X) self.y = y self.idx = 0 if y is not None and len(y) != len(X): raise ValueError("Size of label vector y does not match size of data.") if weights is None: self.weights = numpy.ones(len(X), dtype='float64') else: if len(weights) != len(X): raise ValueError("Size of weight vector does not match size of data.") self.weights = numpy.array(weights) if batch_size is None: self.batch_size = len(self) else: self.batch_size = int(batch_size) if batches_per_epoch is None: self.batches_per_epoch = float("inf") else: self.batches_per_epoch = batches_per_epoch def __len__(self): return len(self.X) @property def shape(self): return self.X.shape @property def classes(self): if self.y is None: raise ValueError("Classes cannot be found on an unlabeled data set.") return numpy.unique(self.y) @property def ndim(self): return self.X.ndim def batches(self): if self.batch_size == len(self): while True: if self.y is not None: yield self.X, self.y, self.weights else: yield self.X, self.weights break else: start, end = 0, self.batch_size iteration = 0 while start < len(self) and iteration < self.batches_per_epoch: if self.y is not None: yield (self.X[start:end], self.y[start:end], self.weights[start:end]) else: yield self.X[start:end], self.weights[start:end] start += self.batch_size end += self.batch_size iteration += 1 def labeled_batches(self): X = self.X[self.y != -1] weights = self.weights[self.y != -1] y = self.y[self.y != -1] start, end = 0, self.batch_size while start < len(X): yield X[start:end], y[start:end], weights[start:end] start += self.batch_size end += self.batch_size def unlabeled_batches(self): X = self.X[self.y == -1] weights = self.weights[self.y == -1] start, end = 0, self.batch_size while start < len(X): yield X[start:end], weights[start:end] start += self.batch_size end += self.batch_size class SequenceGenerator(BaseGenerator): """A generator that returns batches of sequences from a data set. This object will wrap a data set and optionally a set of labels and will return sequences as requested. Due to the processing in pomegranate, only batches of size 1 are supported. Parameters ---------- X : numpy.ndarray or list The data set to iterate over. weights : numpy.ndarray or list or None, optional The weights for each example. Default is None. y: numpy.ndarray or list or None, optional The set of labels for each example in the data set. Default is None. """ def __init__(self, X, weights=None, y=None, batches_per_epoch=None): self.X = X self.y = y self.idx = 0 if weights is None: self.weights = numpy.ones(len(X), dtype='float64') else: self.weights = weights if batches_per_epoch is None: self.batches_per_epoch = float("inf") else: self.batches_per_epoch = batches_per_epoch def __len__(self): return len(self.X) @property def shape(self): x_ = numpy.asarray(self.X[0]) if x_.ndim == 1: return len(self.X), 1 elif x_.ndim == 2: return len(self.X), x_.shape[1] else: raise ValueError("Data must be passed in as a list of numpy arrays.") @property def ndim(self): return len(self.X[0]) @property def classes(self): if self.y is None: raise ValueError("No labels found for this data set.") return numpy.unique(self.y) def batches(self): for idx in range(len(self)): if self.y is not None: yield self.X[idx:idx+1], self.weights[idx:idx+1], self.y[idx:idx+1] else: yield self.X[idx:idx+1], self.weights[idx:idx+1] def labeled_batches(self): X = [x for x, y in zip(self.X, self.y) if y is not None] weights = [w for w, y in zip(self.weights, self.y) if y is not None] y = [y for y in self.y if y is not None] for idx in range(len(X)): yield X[idx:idx+1], weights[idx:idx+1], y[idx:idx+1] def unlabeled_batches(self): X = [x for x, y in zip(self.X, self.y) if y is None] weights = [w for w, y in zip(self.weights, self.y) if y is None] for idx in range(len(X)): yield X[idx:idx+1], weights[idx:idx+1] class DataFrameGenerator(BaseGenerator): """A generator that returns batches of sequences from a DataFrame. This object will wrap a DataFrame and generate batches of data from it. This gives a natural support for pandas DataFrames to all pomegranate models. Weights and labels for examples can either be passed in separately (potentially either as Series or array objects) or specified as a column in the main DataFrame object. Parameters ---------- X : pandas.DataFrame The DataFrame containing data. The weights and labels (if used) can be columns in this DataFrame. weights : pandas.Series, numpy.ndarray, str, or None, optional The weights for each example. This can either be a series, a numpy array, a string key for the column of the DataFrame, or None. None means that all weights are uniform. Default is None. y : pandas.Series, numpy.ndarray, str, or None, optional The labels for each example. This can either be a series, a numpy array, a string key for the column of the DataFrame, or None. None means that all examples are unlabeled. Default is None. batch_size : int or None, optional The size of the batches to return. If None will return the full data set each time. Default is None batches_per_epoch : int or None, optional The number of batches to return before resetting the index. If the value is too low you may not see all examples from the data set. If None, will return enough batches to cover the entire data set. Default is None. """ def __init__(self, X, weights=None, y=None, batch_size=None, batches_per_epoch=None): if pandas is None: raise ValueError("Must have pandas installed to use DataFrameGenerator.") self.X = X if isinstance(weights, pandas.Series): self.weights = weights.values elif isinstance(weights, numpy.ndarray): self.weights = weights elif isinstance(weights, str): self.weights = self.X[weights].values self.X = self.X.drop(weights, axis=1) else: self.weights = numpy.ones(self.X.shape[0]) if isinstance(y, pandas.Series): self.y = y.values elif isinstance(y, numpy.ndarray): self.y = y elif isinstance(y, str): self.y = self.X[y].values self.X = self.X.drop(y, axis=1) else: self.y = None if len(self.weights) != len(self.X): raise ValueError("Size of weights does not match size of data set.") if self.y is not None and len(self.y) != len(self.X): raise ValueError("Size of labels does not match size of data set.") self.batch_size = batch_size or len(self.X) if batches_per_epoch is None: self.batches_per_epoch = float("inf") else: self.batches_per_epoch = batches_per_epoch def __len__(self): return len(self.X) @property def shape(self): return self.X.shape @property def classes(self): if self.y is None: raise ValueError("Must specify y to return classes.") return numpy.unique(self.y) @property def ndim(self): return self.X.ndim def batches(self): if self.batch_size == len(self): while True: if self.y is not None: yield self.X.values, self.y, self.weights else: yield self.X.values, self.weights break else: start, end = 0, self.batch_size iteration = 0 while start < len(self) and iteration < self.batches_per_epoch: x = self.X.iloc[start:end].values if self.y is not None: yield x, self.y[start:end], self.weights[start:end] else: yield x, self.weights[start:end] start += self.batch_size end += self.batch_size iteration += 1 def labeled_batches(self): X = self.X.iloc[self.y != -1] weights = self.weights[self.y != -1] y = self.y[self.y != -1] start, end = 0, self.batch_size while start < len(X): x_ = X.iloc[start:end].values yield x_, y[start:end], weights[start:end] start += self.batch_size end += self.batch_size def unlabeled_batches(self): X = self.X.iloc[self.y == -1] weights = self.weights[self.y == -1] start, end = 0, self.batch_size while start < len(X): x_ = X.iloc[start:end].values yield x_, weights[start:end] start += self.batch_size end += self.batch_size class CSVGenerator(BaseGenerator): """A generator that returns batches of sequences from a data file. This object will wrap a file, such as a CSV file, and generate batches of data from it. It will not load the entire file into memory except for particular model methods that force it to do so. It is mostly a wrapper around a call to `pandas.read_csv`. Parameters ---------- filename : str The name of the file to open. weight_column : str or int or None, optional The column to use for the weights. If None, assume uniform weights. y_column: str or int or None, optional The column to use for the labels. If None, assume no labels. kwargs : keyword arguments, optional Any other argument to pass into `pandas.read_csv`. """ def __init__(self, filename, weight_column=None, y_column=None, batch_size=32, **kwargs): self.filename = filename self.weight_column = weight_column self.y_column = y_column self.kwargs = kwargs self.file = pandas.read_csv(filename, iterator=True, chunksize=batch_size, **kwargs) def __len__(self): return len(self.X) @property def shape(self): raise ValueError("Cannot get shape of a file.") @property def classes(self): if self.y_column is None: raise ValueError("Must specify y_column to return classes.") return numpy.unique(numpy.concatenate([numpy.unique( batch[self.y_column]) for batch in self.batches])) def batches(self): for batch in self.file: if self.weight_column is not None: weights = batch[self.weight_column].values.astype('float64') X = batch.drop(self.weight_column) else: weights = numpy.ones(batch.shape[0], dtype='float64') if self.y_column is not None: y = X[self.y_column].values.astype('float64') X = X.drop(self.y_column).values.astype('float64') yield X, weights, y else: X = X.values.astype('float64') yield X, weights def labeled_batches(self): X = [x for x, y in zip(self.X, self.y) if y is not None] weights = [w for w, y in zip(self.weights, self.y) if y is not None] y = [y for y in self.y if y is not None] for idx in range(len(X)): yield X[idx:idx+1], weights[idx:idx+1], y[idx:idx+1] def unlabeled_batches(self): X = [x for x, y in zip(self.X, self.y) if y is None] weights = [w for w, y in zip(self.weights, self.y) if y is None] for idx in range(len(X)): yield X[idx:idx+1], weights[idx:idx+1] pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/kmeans.pyx000066400000000000000000000606261420461712600252250ustar00rootroot00000000000000#cython: boundscheck=False #cython: cdivision=True # kmeans.pyx # Contact: Jacob Schreiber ( jmschreiber91@gmail.com ) from libc.stdlib cimport calloc from libc.stdlib cimport free from libc.stdlib cimport malloc from libc.string cimport memset from libc.math cimport log10 as clog10 from scipy.linalg.cython_blas cimport ddot from .base cimport Model from .utils cimport mdot from .utils cimport isnan from .utils import check_random_state import time import numpy cimport numpy from joblib import Parallel from joblib import delayed DEF NEGINF = float("-inf") DEF INF = float("inf") cdef double distance(double* X, double* centroid, int d) nogil: cdef int i cdef double distance = 0.0 for i in range(d): if not isnan(X[i]): distance += (X[i] - centroid[i]) ** 2 return distance cpdef numpy.ndarray _initialize_centroids(numpy.ndarray X, weights, int k, init='first-k', double oversampling_factor=0.95, random_state=None): """Initialize the centroids for kmeans given a dataset. This function will take in a dataset and return the centroids found using some method. This is the initialization step of kmeans. Parameters ---------- X : numpy.ndarray, shape=(n_samples, n_dim) The dataset to identify centroids in. weights : numpy.ndarray, shape=(n_samples,) The weights associated with each of the samples. k : int The number of centroids to extract. init : str, one of 'first-k', 'random', 'kmeans++', 'kmeans||' 'first-k' : use the first k samples as the centroids 'random' : randomly select k samples as the centroids 'kmeans++' : use the kmeans++ initialization algorithm, which iteratively selects the next centroid randomly, but weighted based on distance to nearest centroid, to be likely to choose good initializations 'kmeans||' : use the scalable kmeans++ initialization algorithm, as described in http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf random_state : int, numpy.random.RandomState, or None The random state used for generating samples. If set to none, a random seed will be used. If set to either an integer or a random seed, will produce deterministic outputs. Returns ------- centroids : numpy.ndarray, shape=(k, d) The centroids to use initially for clustering. There are k centroids, one for each cluster, and they span d dimensions. """ cdef int n = X.shape[0], d = X.shape[1] cdef int count, i, j, l, m cdef double dist cdef numpy.ndarray centroids cdef numpy.ndarray min_distance cdef numpy.ndarray min_idxs cdef numpy.ndarray mask cdef double* X_ptr = X.data cdef double* weights_ptr cdef double* min_distance_ptr cdef double* centroids_ptr cdef double phi random_state = check_random_state(random_state) if weights is None: weights = numpy.ones(len(X), dtype='float64') / len(X) else: weights = weights.astype('float64') / sum(weights) weights_ptr = ( weights).data if init not in ('first-k', 'random', 'kmeans++', 'kmeans||'): raise ValueError("initialization must be one of 'first-k', 'random', 'kmeans++', or 'kmeans||'") if init == 'first-k': centroids = X[:k].copy() elif init == 'random': idxs = random_state.choice(n, size=k, replace=False, p=weights) centroids = X[idxs].copy() elif init == 'kmeans++': centroids = numpy.zeros((k, d), dtype='float64') centroids_ptr = centroids.data idx = random_state.choice(n, p=weights) centroids[0] = X[idx] centroids[0][numpy.isnan(centroids[0])] = 0.0 min_distance = numpy.full(n, INF, dtype='float64') min_distance_ptr = min_distance.data for m in range(k-1): for i in range(n): dist = distance(X_ptr + i*d, centroids_ptr + m*d, d) dist *= weights_ptr[i] if dist < min_distance_ptr[i]: min_distance_ptr[i] = dist idx = random_state.choice(n, p=min_distance / min_distance.sum()) centroids[m+1] = X[idx] centroids[m+1][numpy.isnan(centroids[m+1])] = 0.0 elif init == 'kmeans||': centroids = numpy.zeros((1, d)) idx = random_state.choice(n, p=weights) centroids[0] = X[idx] centroids[0][numpy.isnan(centroids[0])] = 0 centroids_ptr = centroids.data min_distance = numpy.zeros(n, dtype='float64') min_distance_ptr = min_distance.data for i in range(n): min_distance_ptr[i] = distance(X_ptr + i*d, centroids_ptr, d) min_idxs = numpy.zeros(n, dtype='int32') min_idxs_ptr = min_idxs.data phi = min_distance.sum() count = 1 for iteration in range(int(clog10(phi))): prob = random_state.uniform(0, 1, size=(n,)) thresh = oversampling_factor * min_distance / phi centroids = numpy.concatenate((centroids, X[prob < thresh])) centroids[numpy.isnan(centroids)] = 0.0 centroids_ptr = centroids.data m = centroids.shape[0] - count if m > 0: for i in range(n): for l in range(m): dist = distance(X_ptr + i*d, centroids_ptr + (l+count)*d, d) if dist < min_distance_ptr[i]: phi += dist - min_distance_ptr[i] min_distance_ptr[i] = dist min_idxs_ptr[i] = l + count count = centroids.shape[0] w = numpy.bincount(min_idxs) clf = Kmeans(k, 'kmeans++', 1) clf.fit(centroids, w) centroids = clf.centroids centroids[numpy.isnan(centroids)] = 0.0 return numpy.array(centroids) cdef class Kmeans(Model): """A kmeans model. Kmeans is not a probabilistic model, but it is used in the kmeans++ initialization for GMMs. In essence, a point is selected as the center for one component and then remaining points are selected. Parameters ---------- k : int The number of centroids. init : str or list, optional The initialization scheme for k-means. If str, must be one of 'first-k', 'random', 'kmeans++', or 'kmeans||'. First-k uses the first k samples as the centroids. Random will randomly assign points to be the centroids. Kmeans++ uses the kmeans++ initialization algorithm, and kmeans|| uses the scalable kmeans algorithm. If a list or array is passed in, these values are used as the centroids. Default is kmeans++. n_init : int, optional The number of initializations to do before taking the best. This can help reduce the effect of converging to a local optima by sampling multiple local optima and taking the best. Attributes ---------- k : int The number of centroids centroids : array-like, shape (k, n_dim) The means of the centroid points. """ cdef public int k cdef int n_init cdef str init cdef public numpy.ndarray centroids cdef numpy.ndarray centroids_T cdef double* centroids_ptr cdef double* centroids_T_ptr cdef double* summary_sizes cdef double* summary_weights cdef double* centroid_norms def __init__(self, k, init='kmeans++', n_init=10): self.k = k if init != 'first-k': self.n_init = n_init else: self.n_init = 1 self.centroid_norms = calloc(self.k, sizeof(double)) if isinstance(init, (list, numpy.ndarray)): self.centroids = numpy.array(init, dtype='float64', ndmin=2, order='C') self.centroids_ptr = self.centroids.data self.centroids_T = self.centroids.T.copy() self.centroids_T_ptr = self.centroids_T.data self.d = self.centroids.shape[1] for i in range(self.k): self.centroid_norms[i] = self.centroids[i].dot(self.centroids[i]) self.init = 'fixed' elif isinstance(init, str): self.init = init def __dealloc__(self): free(self.centroid_norms) def predict(self, X, n_jobs=1): """Predict nearest centroid for each point. Parameters ---------- X : array-like, shape (n_samples, n_dim) The data to fit to. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. -1 means use all available resources. Default is 1. Returns ------- y : array-like, shape (n_samples,) The index of the nearest centroid. """ starts = [int(i*len(X)/n_jobs) for i in range(n_jobs)] ends = [int(i*len(X)/n_jobs) for i in range(1, n_jobs+1)] if n_jobs > 1: with Parallel(n_jobs=n_jobs, backend='threading') as parallel: y_pred = parallel(delayed(self.predict)( X[start:end]) for start, end in zip(starts, ends)) return numpy.concatenate(y_pred) X = numpy.asarray(X, dtype='float64', order='C') cdef double* X_ptr = ( X).data cdef int n = len(X) cdef numpy.ndarray y = numpy.zeros(n, dtype='int32') cdef int* y_ptr = y.data self.d = X.shape[1] with nogil: self._predict(X_ptr, y_ptr, n) return y cdef void _predict(self, double* X, int* y, int n) nogil: cdef int i, j, l, k = self.k, d = self.d cdef double dist, min_dist cdef double* centroid for i in range(n): min_dist = INF for j in range(k): centroid = self.centroids_ptr + j*d dist = distance(X + i*d, centroid, d) if dist < min_dist: min_dist = dist y[i] = j def distance(self, X): """Calculate the distance to each centroid. Parameters ---------- X : array-like, shape (n_samples, n_dim) The data to fit to. Returns ------- y : array-like, shape (n_samples, n_centroids) The index of the nearest centroid. """ cdef int i, j, n = X.shape[0], d = self.d cdef double* X_ptr cdef double* centroid X = numpy.asarray(X, dtype='float64', order='C') X_ptr = ( X).data dist = numpy.empty((X.shape[0], self.k)) for i in range(X.shape[0]): for j in range(self.k): centroid = self.centroids_ptr + j*d dist[i, j] = distance(X_ptr + i*d, centroid, d) return dist def fit(self, X, weights=None, inertia=0.0, stop_threshold=1e-3, max_iterations=1e3, batch_size=None, batches_per_epoch=None, clear_summaries=False, verbose=False, n_jobs=1, random_state=None): """Fit the model to the data using k centroids. Parameters ---------- X : array-like, shape (n_samples, n_dim) The data to fit to. weights : array-like, shape (n_samples,), optional The initial weights of each sample in the matrix. If nothing is passed in then each sample is assumed to be the same weight. Default is None. inertia : double, optional The weight of the previous parameters of the model. The new parameters will roughly be old_param*inertia + new_param*(1-inertia), so an inertia of 0 means ignore the old parameters, whereas an inertia of 1 means ignore the new parameters. Default is 0.0. stop_threshold : double, optional, positive The threshold at which EM will terminate for the improvement of the model. If the model does not improve its fit of the data by a log probability of the threshold then terminate. Default is 1e-3. max_iterations : int, optional The maximum number of iterations to run for. Default is 1e3. batch_size : int or None, optional The number of samples in a batch to summarize on. This controls the size of the set sent to `summarize` and so does not make the update any less exact. This is useful when training on a memory map and cannot load all the data into memory. If set to None, batch_size is 1 / n_jobs. Default is None. batches_per_epoch : int or None, optional The number of batches in an epoch. This is the number of batches to summarize before calling `from_summaries` and updating the model parameters. This allows one to do minibatch updates by updating the model parameters before setting the full dataset. If set to None, uses the full dataset. Default is None. clear_summaries : bool or 'auto', optional Whether to clear the stored sufficient statistics after an update. Typically this would be set to True as you want to recollect new statistics after each epoch. However, in either the streaming or the minibatching setting one may want to set this to False to collect statistics on the entire dataset. 'auto' means set to True if batches_per_epoch is None, otherwise False. verbose : bool, optional Whether or not to print out improvement information over iterations. Default is False. n_jobs : int, optional The number of threads to use when processing data. Default to 1, meaning no parallelism. random_state : int, numpy.random.RandomState, or None The random state used for generating samples. If set to none, a random seed will be used. If set to either an integer or a random seed, will produce deterministic outputs. Returns ------- self : Kmeans This is the fit kmeans object. """ X = numpy.asarray(X, dtype='float64', order='C') n, d = X.shape best_centroids, best_distance = None, INF training_start_time = time.time() self.d = d self.summary_sizes = calloc(self.k*d, sizeof(double)) self.summary_weights = calloc(self.k*d, sizeof(double)) if batch_size is None: starts = [int(i*len(X)/n_jobs) for i in range(n_jobs)] ends = [int(i*len(X)/n_jobs) for i in range(1, n_jobs+1)] else: starts = list(range(0, n, batch_size)) if starts[-1] == n: del starts[-1] ends = list(range(batch_size, n, batch_size)) + [n] if clear_summaries == 'auto': clear_summaries = True if batches_per_epoch == None else False batches_per_epoch = batches_per_epoch or len(starts) n_seen_batches = 0 if self.centroids is not None: initial_centroids = self.centroids.copy() else: initial_centroids = None with Parallel(n_jobs=n_jobs, backend='threading') as parallel: for i in range(self.n_init): self.clear_summaries() initial_distance_sum = INF iteration, improvement = 0, INF if weights is not None and initial_centroids is None: self.centroids = _initialize_centroids(X=X[:ends[0]], weights=weights[:ends[0]], k=self.k, init=self.init, random_state=random_state) elif weights is None and initial_centroids is None: self.centroids = _initialize_centroids(X=X[:ends[0]], weights=None, k=self.k, init=self.init, random_state=random_state) else: self.centroids = initial_centroids.copy() self.centroids_ptr = self.centroids.data self.centroids_T = self.centroids.T.copy() self.centroids_T_ptr = self.centroids_T.data for i in range(self.k): self.centroid_norms[i] = self.centroids[i].dot(self.centroids[i]) while improvement > stop_threshold and iteration < max_iterations + 1: epoch_start_time = time.time() epoch_starts = starts[n_seen_batches:n_seen_batches+batches_per_epoch] epoch_ends = ends[n_seen_batches:n_seen_batches+batches_per_epoch] n_seen_batches += batches_per_epoch if n_seen_batches >= len(starts): n_seen_batches = 0 self.from_summaries(inertia, clear_summaries) if weights is not None: distance_sum = sum(parallel(delayed(self.summarize)(X[start:end], weights[start:end]) for start, end in zip(epoch_starts, epoch_ends))) else: distance_sum = sum(parallel(delayed(self.summarize)(X[start:end]) for start, end in zip(epoch_starts, epoch_ends))) if iteration == 0: initial_distance_sum = distance_sum else: improvement = last_distance_sum - distance_sum time_spent = time.time() - epoch_start_time if verbose: print("[{}] Improvement: {}\tTime (s): {:4.4}".format(iteration, improvement, time_spent)) iteration += 1 last_distance_sum = distance_sum total_improvement = initial_distance_sum - last_distance_sum if verbose: total_time_spent = time.time() - training_start_time print("Total Improvement: {}".format(total_improvement)) print("Total Time (s): {:.4f}".format(total_time_spent)) if last_distance_sum < best_distance: best_centroids = self.centroids.copy() best_distance = last_distance_sum free(self.summary_sizes) free(self.summary_weights) self.centroids = best_centroids self.centroids_ptr = self.centroids.data self.centroids_T = self.centroids.T.copy() self.centroids_T_ptr = self.centroids_T.data for i in range(self.k): self.centroid_norms[i] = self.centroids[i].dot(self.centroids[i]) return self def summarize(self, X, weights=None): """Summarize the points into sufficient statistics for a future update. Parameters ---------- X : array-like, shape (n_samples, n_dim) The data to fit to. weights : array-like, shape (n_samples,), optional The initial weights of each sample in the matrix. If nothing is passed in then each sample is assumed to be the same weight. Default is None. Returns ------- dist : double The negative total euclidean distance between each point and its nearest centroid. This is not a probability, and the negative is returned to fit in with the idea of large negative numbers being worse than smaller negative numbers, such as with log probabilities. """ cdef numpy.ndarray X_ndarray = numpy.asarray(X, dtype='float64', order='C') cdef double* X_ptr = X_ndarray.data cdef numpy.ndarray weights_ndarray cdef int i, j, n = X_ndarray.shape[0], d = X_ndarray.shape[1] cdef double dist if weights is None: weights_ndarray = numpy.ones(n, dtype='float64') else: weights_ndarray = numpy.asarray(weights, dtype='float64') cdef double* weights_ptr = weights_ndarray.data with nogil: dist = self._summarize(X_ptr, weights_ptr, n, 0, self.d) return dist cdef double _summarize(self, double* X, double* weights, int n, int column_idx, int d) nogil: cdef int i, j, l, y, k = self.k, inc = 1 cdef double min_dist, dist, total_dist = 0.0, pdist cdef double* summary_sizes = calloc(k*d, sizeof(double)) cdef double* summary_weights = calloc(k*d, sizeof(double)) cdef double* dists = calloc(n*k, sizeof(double)) cdef double* X_ = malloc(n*d*sizeof(double)) cdef double* bias = calloc(n*k, sizeof(double)) for i in range(n): for j in range(d): idx = i*d + j if isnan(X[idx]): X_[idx] = 0.0 for l in range(k): bias[i*k + l] += self.centroids_T_ptr[j*k + l] ** 2 else: X_[idx] = X[idx] mdot(X_, self.centroids_T_ptr, dists, n, k, d) for i in range(n): min_dist = INF pdist = ddot(&d, X_ + i*d, &inc, X_ + i*d, &inc) for j in range(k): dist = self.centroid_norms[j] + pdist - 2*dists[i*k + j] - bias[i*k + j] if dist < min_dist: min_dist = dist y = j total_dist += min_dist for l in range(d): if not isnan(X[i*d + l]): summary_sizes[y*d + l] += weights[i] summary_weights[y*d + l] += X[i*d + l] * weights[i] with gil: for j in range(k): for l in range(d): self.summary_sizes[j*d + l] += summary_sizes[j*d + l] self.summary_weights[j*d + l] += summary_weights[j*d + l] free(summary_sizes) free(summary_weights) free(dists) free(X_) free(bias) return total_dist def from_summaries(self, double inertia=0.0, clear_summaries=True): """Fit the model to the collected sufficient statistics. Fit the parameters of the model to the sufficient statistics gathered during the summarize calls. This should return an exact update. Parameters ---------- inertia : double, optional The weight of the previous parameters of the model. The new parameters will roughly be old_param*inertia + new_param*(1-inertia), so an inertia of 0 means ignore the old parameters, whereas an inertia of 1 means ignore the new parameters. Default is 0.0. clear_summaries : boolean, optional Whether to clear the stored summaries after updating the models based on them. If set to False, this can be used for streaming updates where one updates the parameters of the model while seeing more data. Default is True. Returns ------- None """ cdef int i, l, j, k = self.k, d = self.d cdef double w_sum = 0 with nogil: for i in range(k*d): w_sum += self.summary_sizes[i] if w_sum > 1e-8: for j in range(k): for l in range(d): i = j*d + l if self.summary_weights[i] == 0: continue self.centroids_ptr[i] = \ inertia * self.centroids_ptr[i] \ + (1-inertia) * self.summary_weights[i] \ / self.summary_sizes[i] self.centroids_T = self.centroids.T.copy() self.centroids_T_ptr = self.centroids_T.data for i in range(self.k): self.centroid_norms[i] = self.centroids[i].dot(self.centroids[i]) if clear_summaries: self.clear_summaries() def clear_summaries(self): memset(self.summary_sizes, 0, self.k*self.d*sizeof(double)) memset(self.summary_weights, 0, self.k*self.d*sizeof(double)) def to_dict(self): return { 'class' : 'Kmeans', 'k' : self.k, 'centroids' : self.centroids.tolist() } @classmethod def from_dict(cls, d): model = cls(d['k'], d['centroids']) return model @classmethod def from_samples(cls, k, X, weights=None, init='kmeans++', n_init=10, inertia=0.0, stop_threshold=0.1, max_iterations=1e3, batch_size=None, batches_per_epoch=None, clear_summaries=False, verbose=False, n_jobs=1, random_state=None): """Fit a k-means object to the data directly. Parameters ---------- k : int The number of centroids. X : array-like, shape (n_samples, n_dim) The data to fit to. weights : array-like, shape (n_samples,), optional The initial weights of each sample in the matrix. If nothing is passed in then each sample is assumed to be the same weight. Default is None. init : str or list, optional The initialization scheme for k-means. If str, must be one of 'first-k', 'random', 'kmeans++', or 'kmeans||'. First-k uses the first k samples as the centroids. Random will randomly assign points to be the centroids. Kmeans++ uses the kmeans++ initialization algorithm, and kmeans|| uses the scalable kmeans algorithm. If a list or array is passed in, these values are used as the centroids. Default is kmeans++. n_init : int, optional The number of initializations to do before taking the best. This can help reduce the effect of converging to a local optima by sampling multiple local optima and taking the best. inertia : double, optional The weight of the previous parameters of the model. The new parameters will roughly be old_param*inertia + new_param*(1-inertia), so an inertia of 0 means ignore the old parameters, whereas an inertia of 1 means ignore the new parameters. Default is 0.0. stop_threshold : double, optional, positive The threshold at which EM will terminate for the improvement of the model. If the model does not improve its fit of the data by a log probability of 0.1 then terminate. Default is 0.1. max_iterations : int, optional The maximum number of iterations to run for. Default is 1e3. batch_size : int or None, optional The number of samples in a batch to summarize on. This controls the size of the set sent to `summarize` and so does not make the update any less exact. This is useful when training on a memory map and cannot load all the data into memory. If set to None, batch_size is 1 / n_jobs. Default is None. batches_per_epoch : int or None, optional The number of batches in an epoch. This is the number of batches to summarize before calling `from_summaries` and updating the model parameters. This allows one to do minibatch updates by updating the model parameters before setting the full dataset. If set to None, uses the full dataset. Default is None. clear_summaries : bool or 'auto', optional Whether to clear the stored sufficient statistics after an update. Typically this would be set to True as you want to recollect new statistics after each epoch. However, in either the streaming or the minibatching setting one may want to set this to False to collect statistics on the entire dataset. 'auto' means set to True if batches_per_epoch is None, otherwise False. verbose : bool, optional Whether or not to print out improvement information over iterations. Default is False. n_jobs : int, optional The number of threads to use. Default is 1, indicating no parallelism is used. random_state : int, numpy.random.RandomState, or None The random state used for generating samples. If set to none, a random seed will be used. If set to either an integer or a random seed, will produce deterministic outputs. """ X = numpy.asarray(X, dtype='float64', order='C') n, d = X.shape model = cls(k=k, init=init, n_init=n_init) model.fit(X, weights, inertia=inertia, stop_threshold=stop_threshold, max_iterations=max_iterations, batch_size=batch_size, batches_per_epoch=batches_per_epoch, clear_summaries=clear_summaries, verbose=verbose, n_jobs=n_jobs, random_state=random_state) return model pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/parallel.pyx000066400000000000000000000371501420461712600255370ustar00rootroot00000000000000# parallel.py # Contact: Jacob Schreiber import numpy cimport numpy from .hmm import HiddenMarkovModel from .NaiveBayes import NaiveBayes from .distributions import Distribution from joblib import Parallel from joblib import delayed NEGINF = float("-inf") INF = float("inf") def parallelize(model, X, func, n_jobs, backend): """The main parallelization function. This function takes in a model, a dataset, a function to parallelize, the number of jobs to do, and the backend, and will chunk up the dataset and parallelize the function. Parameters ---------- model : base.Model This is any pomegranate model. All pomegranate models have a cython backend which releases the GIL and allows for multithreaded parallelization. X : numpy.ndarray or list The dataset to operate on. For most models this is a numpy array with columns corresponding to features and rows corresponding to samples. For markov chains and HMMs this will be a list of variable length sequences. func : The function to parallelize. Typically this is one of the model methods for prediction or fitting. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. backend : str, 'multiprocessing' or 'threading' The parallelization backend of joblib to use. If 'multiprocessing' then use the processing backend, if 'threading' then use the threading backend. Returns ------- results : object The results of the method concatenated together across processes. """ delay = delayed(getattr(model, func)) with Parallel(n_jobs=n_jobs, backend=backend) as parallel: if isinstance(model, HiddenMarkovModel): y = parallel(delay(x) for x in X) else: n = len(X) starts = [n/n_jobs*i for i in range(n_jobs)] ends = starts[1:] + [n] y = parallel(delay(X[start:end]) for start, end in zip(starts, ends)) return numpy.concatenate(y) if n_jobs > 1 and n_jobs != len(X) else y def predict(model, X, n_jobs=1, backend='threading'): """Provides for a parallelized predict function. This function takes in a model, a dataset, the number of jobs to do, and the backend, and will chunk up the dataset and parallelize the predict function. Parameters ---------- model : base.Model This is any pomegranate model. All pomegranate models have a cython backend which releases the GIL and allows for multithreaded parallelization. X : numpy.ndarray or list The dataset to operate on. For most models this is a numpy array with columns corresponding to features and rows corresponding to samples. For markov chains and HMMs this will be a list of variable length sequences. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. backend : str, 'multiprocessing' or 'threading' The parallelization backend of joblib to use. If 'multiprocessing' then use the processing backend, if 'threading' then use the threading backend. Returns ------- results : object The predictions concatenated together across processes. """ return parallelize(model, X, 'predict', n_jobs, backend) def predict_proba(model, X, n_jobs=1, backend='threading'): """Provides for a parallelized predict_proba function. This function takes in a model, a dataset, the number of jobs to do, and the backend, and will chunk up the dataset and parallelize the predict_proba function. Parameters ---------- model : base.Model This is any pomegranate model. All pomegranate models have a cython backend which releases the GIL and allows for multithreaded parallelization. X : numpy.ndarray or list The dataset to operate on. For most models this is a numpy array with columns corresponding to features and rows corresponding to samples. For markov chains and HMMs this will be a list of variable length sequences. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. backend : str, 'multiprocessing' or 'threading' The parallelization backend of joblib to use. If 'multiprocessing' then use the processing backend, if 'threading' then use the threading backend. Returns ------- results : object The predictions concatenated together across processes. """ return parallelize(model, X, 'predict_proba', n_jobs, backend) def predict_log_proba(model, X, n_jobs=1, backend='threading'): """Provides for a parallelized predict_log_proba function. This function takes in a model, a dataset, the number of jobs to do, and the backend, and will chunk up the dataset and parallelize the predict_log_proba function. Parameters ---------- model : base.Model This is any pomegranate model. All pomegranate models have a cython backend which releases the GIL and allows for multithreaded parallelization. X : numpy.ndarray or list The dataset to operate on. For most models this is a numpy array with columns corresponding to features and rows corresponding to samples. For markov chains and HMMs this will be a list of variable length sequences. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. backend : str, 'multiprocessing' or 'threading' The parallelization backend of joblib to use. If 'multiprocessing' then use the processing backend, if 'threading' then use the threading backend. Returns ------- results : object The predictions concatenated together across processes. """ return parallelize(model, X, 'predict_log_proba', n_jobs, backend) def log_probability(model, X, n_jobs=1, backend='threading'): """Provides for a parallelized log_probability function. This function takes in a model, a dataset, the number of jobs to do, and the backend, and will chunk up the dataset and parallelize the log_probability function. Parameters ---------- model : base.Model This is any pomegranate model. All pomegranate models have a cython backend which releases the GIL and allows for multithreaded parallelization. X : numpy.ndarray or list The dataset to operate on. For most models this is a numpy array with columns corresponding to features and rows corresponding to samples. For markov chains and HMMs this will be a list of variable length sequences. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. backend : str, 'multiprocessing' or 'threading' The parallelization backend of joblib to use. If 'multiprocessing' then use the processing backend, if 'threading' then use the threading backend. Returns ------- results : object The log probabilities concatenated together across processes. """ return parallelize(model, X, 'log_probability', n_jobs, backend) def probability(model, X, n_jobs=1, backend='threading'): """Provides for a parallelized probability function. This function takes in a model, a dataset, the number of jobs to do, and the backend, and will chunk up the dataset and parallelize the log_probability function followed by exponentiation. Parameters ---------- model : base.Model This is any pomegranate model. All pomegranate models have a cython backend which releases the GIL and allows for multithreaded parallelization. X : numpy.ndarray or list The dataset to operate on. For most models this is a numpy array with columns corresponding to features and rows corresponding to samples. For markov chains and HMMs this will be a list of variable length sequences. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. backend : str, 'multiprocessing' or 'threading' The parallelization backend of joblib to use. If 'multiprocessing' then use the processing backend, if 'threading' then use the threading backend. Returns ------- results : object The probabilities concatenated together across processes. """ return numpy.exp(parallelize(model, X, 'log_probability', n_jobs, backend)) def summarize(model, X, weights=None, y=None, n_jobs=1, backend='threading', parallel=None): """Provides for a parallelized summarization function. This function takes in a model, a dataset, the number of jobs to do, and the backend, and will chunk up the dataset and parallelize the summarization function. Parameters ---------- model : base.Model This is any pomegranate model. All pomegranate models have a cython backend which releases the GIL and allows for multithreaded parallelization. X : numpy.ndarray or list The dataset to operate on. For most models this is a numpy array with columns corresponding to features and rows corresponding to samples. For markov chains and HMMs this will be a list of variable length sequences. y : numpy.ndarray or list or None, optional Data labels for supervised training algorithms. Default is None n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. backend : str, 'multiprocessing' or 'threading' The parallelization backend of joblib to use. If 'multiprocessing' then use the processing backend, if 'threading' then use the threading backend. parallel : joblib.Parallel or None The worker pool. If you're calling summarize multiple times, it may be more efficient to reuse the worker pool rather than create a new one each time it is called. Returns ------- logp : double The log probability of the dataset being summarized. """ if isinstance(X, list) and isinstance(model, HiddenMarkovModel): n, n_jobs = len(X), len(X) elif isinstance(X, list): n, d = len(X), model.d elif X.ndim == 1 and model.d > 1: n, d = 1, X.shape[0] elif X.ndim == 1 and model.d == 1: n, d = X.shape[0], 1 else: n, d = X.shape if weights is None: weights = numpy.ones(len(X), dtype='float64') else: weights = numpy.asarray(weights, dtype='float64') starts = [n/n_jobs*i for i in range(n_jobs)] ends = starts[1:] + [n] parallel = parallel or Parallel(n_jobs=n_jobs, backend=backend) delay = delayed(model.summarize) if isinstance(model, NaiveBayes): y = parallel(delay(X[start:end], y[start:end], weights[start:end]) for start, end in zip(starts, ends)) else: y = parallel(delay(X[start:end], weights[start:end]) for start, end in zip(starts, ends)) return sum(y) def fit(model, X, weights=None, y=None, n_jobs=1, backend='threading', stop_threshold=1e-3, max_iterations=1e8, inertia=0.0, verbose=False, batch_size=1240, algorithm='exact', **kwargs): """Provides for a parallelized fit function. This function takes in a model, a dataset, the number of jobs to do, and the backend, and appropriate arguments for fitting, and will chunk up the dataset and parallelize the fit function. Parameters ---------- model : base.Model This is any pomegranate model. All pomegranate models have a cython backend which releases the GIL and allows for multithreaded parallelization. X : numpy.ndarray or list The dataset to operate on. For most models this is a numpy array with columns corresponding to features and rows corresponding to samples. For markov chains and HMMs this will be a list of variable length sequences. y : numpy.ndarray or list or None, optional Data labels for supervised training algorithms. Default is None weights : array-like or None, shape (n_samples,), optional The initial weights of each sample in the matrix. If nothing is passed in then each sample is assumed to be the same weight. Default is None. n_jobs : int The number of jobs to use to parallelize, either the number of threads or the number of processes to use. Default is 1. backend : str, 'multiprocessing' or 'threading' The parallelization backend of joblib to use. If 'multiprocessing' then use the processing backend, if 'threading' then use the threading backend. Default is 'threading' stop_threshold : double, optional, positive The threshold at which EM will terminate for the improvement of the model. If the model does not improve its fit of the data by a log probability of 0.1 then terminate. Default is 1e-3. max_iterations : int, optional, positive The maximum number of iterations to run EM for. If this limit is hit then it will terminate training, regardless of how well the model is improving per iteration. Default is 1e8. inertia : double, optional The weight of the previous parameters of the model. The new parameters will roughly be old_param*inertia + new_param*(1-inertia), so an inertia of 0 means ignore the old parameters, whereas an inertia of 1 means ignore the new parameters. Default is 0.0. verbose : bool, optional Whether or not to print out improvement information over iterations. Default is False. batch_size : int, optional The number of points to summarize at a time. The more the faster. Default is 1240. algorithm : str, 'exact' or 'batch', optional The algorithm to use. Exact requires the dataset can be fully loaded in memory for the first update. Batch updates will begin with the first batch but don't require the full dataset to be stored in memory. Both techniques allow parallelization, but 'batch' allows for out of core updates. Default is 'exact'. Returns ------- improvement : double The improvement in log probability after fitting a model, or None if fitting a basic distribution. """ if weights is None: weights = numpy.ones(len(X), dtype='float64') else: weights = numpy.asarray(weights, dtype='float64') if isinstance(model, HiddenMarkovModel): return model.fit(X, weights=weights, n_jobs=n_jobs, stop_threshold=stop_threshold, max_iterations=max_iterations, inertia=inertia, verbose=verbose, **kwargs) elif isinstance(model, Distribution): summarize(model, X, weights, n_jobs, backend) model.from_summaries(inertia) elif isinstance(model, NaiveBayes): model.fit(X, y, weights, n_jobs=n_jobs, inertia=inertia) else: if isinstance(X, list): n, d = len(X), model.d elif X.ndim == 1 and model.d == 1: n, d = X.shape[0], 1 else: n, d = X.shape if algorithm == 'exact': starts = [n/n_jobs*i for i in range(n_jobs)] elif algorithm == 'batch': starts = [batch_size*i for i in range(n/batch_size+1)] ends = starts[1:] + [n] delay = delayed(model.summarize) initial_log_probability_sum = NEGINF iteration, improvement = 0, INF with Parallel(n_jobs=n_jobs, backend=backend) as parallel: while improvement > stop_threshold and iteration < max_iterations + 1: if model.d == 0 and algorithm == 'exact': log_probability_sum = model.summarize(X, weights) initial_log_probability_sum = log_probability_sum elif model.d == 0 and algorithm == 'batch': log_probability_sum = model.summarize(X[starts[0]:ends[0]], weights[starts[0]:ends[0]]) log_probability_sum += sum(parallel(delay(X[start:end], weights[start:end]) for start, end in zip(starts[1:], ends[1:]))) initial_log_probability_sum = log_probability_sum elif iteration == 0: log_probability_sum = sum(parallel(delay(X[start:end], weights[start:end]) for start, end in zip(starts, ends))) initial_log_probability_sum = log_probability_sum else: model.from_summaries(inertia, **kwargs) log_probability_sum = sum(parallel(delay(X[start:end], weights[start:end]) for start, end in zip(starts, ends))) improvement = log_probability_sum - last_log_probability_sum if verbose: print( "Improvement: {}".format(improvement) ) iteration += 1 last_log_probability_sum = log_probability_sum model.clear_summaries() if verbose: print( "Total Improvement: {}".format(last_log_probability_sum - initial_log_probability_sum) ) return last_log_probability_sum - initial_log_probability_sum pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/utils.pxd000066400000000000000000000013511420461712600250500ustar00rootroot00000000000000# utils.pxd # Contact: Jacob Schreiber ( jmschreiber91@gmail.com ) cimport numpy cdef extern from "numpy/npy_math.h": bint npy_isnan(double x) nogil cdef inline bint isnan(double x) nogil: return npy_isnan(x) cdef bint _is_gpu_enabled() nogil cdef python_log_probability(model, double* X, double* log_probability, int n) cdef python_summarize(model, double* X, double* weights, int n) cdef ndarray_wrap_cpointer(void* data, int n) cdef void mdot(double* X, double* Y, double* A, int m, int n, int k) nogil cdef double _log (double x) nogil cdef double _log2 (double x) nogil cdef double pair_lse(double x, double y) nogil cdef double gamma(double x) nogil cdef double lgamma(double x) nogil cdef choose_one(double [:] weights, int length) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pomegranate/utils.pyx000066400000000000000000000317001420461712600250760ustar00rootroot00000000000000# utils.pyx # Contact: Jacob Schreiber ( jmschreiber91@gmail.com ) from libc.math cimport log as clog from libc.math cimport log2 as clog2 from libc.math cimport exp as cexp from libc.math cimport floor from libc.math cimport fabs from libc.stdlib cimport rand, RAND_MAX from libc.math cimport isnan from scipy.linalg.cython_blas cimport dgemm cimport cython import numpy cimport numpy import numbers import heapq cdef bint GPU = False cdef int has_cupy = -1 numpy.import_array() cdef extern from "numpy/ndarraytypes.h": void PyArray_ENABLEFLAGS(numpy.ndarray X, int flags) # Define some useful constants DEF NEGINF = float("-inf") DEF INF = float("inf") DEF SQRT_2_PI = 2.50662827463 DEF GAMMA = 0.577215664901532860606512090 DEF HALF_LOG2_PI = 0.91893853320467274178032973640562 cdef class PriorityQueue(object): cdef int n cdef public list pq cdef dict entries def __init__(self): self.pq = [] self.entries = {} def push(self, item, weight): entry = [weight, item] self.entries[item[0]] = entry heapq.heappush(self.pq, entry) self.n += 1 def get(self, variables): return self.entries.get(variables, None) def delete(self, variables): entry = self.entries.pop(variables) entry[-1] = ((-1,),) self.n -= 1 def empty(self): return self.n == 0 def pop(self): while not self.empty(): weight, item = heapq.heappop(self.pq) if item[0] != (-1,): del self.entries[item[0]] self.n -= 1 return weight, item else: raise KeyError("Attempting to pop from an empty priority queue") def init_cupy(): global has_cupy try: from cupy import cuda cuda.Device().cublas_handle has_cupy = 1 except: has_cupy = 0 return has_cupy def is_gpu_enabled(): global GPU if has_cupy == -1 and init_cupy(): GPU = True return GPU cdef bint _is_gpu_enabled() nogil: global GPU if has_cupy == -1: with gil: if init_cupy(): GPU = True return GPU cpdef enable_gpu(): global GPU if has_cupy == -1: init_cupy() if not has_cupy: raise Warning("Please install cupy before attempting to utilize a GPU.") else: GPU = True cpdef disable_gpu(): global GPU GPU = False cdef ndarray_wrap_cpointer(void* data, numpy.npy_intp n): cdef numpy.ndarray[numpy.float64_t, ndim=1] X = numpy.PyArray_SimpleNewFromData(1, &n, numpy.NPY_FLOAT64, data) return X cdef python_log_probability(model, double* X, double* log_probability, int n): cdef int i cdef numpy.npy_intp dim = n * model.d cdef numpy.ndarray X_ndarray X_ndarray = numpy.PyArray_SimpleNewFromData(1, &dim, numpy.NPY_FLOAT64, X) X_ndarray = X_ndarray.reshape(n, model.d) logp = model.log_probability(X_ndarray) if n == 1: log_probability[0] = logp else: for i in range(n): log_probability[i] = logp[i] cdef python_summarize(model, double* X, double* weights, int n): cdef int i cdef numpy.npy_intp dim = n * model.d cdef numpy.npy_intp n_elements = n cdef numpy.ndarray X_ndarray cdef numpy.ndarray w_ndarray X_ndarray = numpy.PyArray_SimpleNewFromData(1, &dim, numpy.NPY_FLOAT64, X) if model.d > 1: X_ndarray = X_ndarray.reshape(n, model.d) w_ndarray = numpy.PyArray_SimpleNewFromData(1, &n_elements, numpy.NPY_FLOAT64, weights) model.summarize(X_ndarray, w_ndarray) cdef void mdot(double* X, double* Y, double* A, int m, int n, int k) nogil: cdef double alpha = 1 cdef double beta = 0 dgemm('N', 'N', &n, &m, &k, &alpha, Y, &n, X, &k, &beta, A, &n) cpdef bdot(numpy.ndarray X_ndarray): cdef int n = X_ndarray.shape[0] cdef int d = X_ndarray.shape[1] cdef double* x = X_ndarray.data cdef double alpha = 1 cdef double beta = 1 cdef numpy.ndarray c_ndarray = numpy.zeros((d, d), dtype='float64') cdef double* c = c_ndarray.data dgemm('N', 'T', &d, &d, &n, &alpha, x, &d, x, &d, &beta, c, &d) #dgemm('T', 'N', &n, &n, &d, &alpha, x, &d, x, &d, &beta, c, &n) return c_ndarray cpdef numpy.ndarray _convert( data ): if type(data) is numpy.ndarray: return data if type(data) is int: return numpy.array( [data] ) if type(data) is float: return numpy.array( [data] ) if type(data) is list: return numpy.array( data ) # Useful speed optimized functions cdef double _log(double x) nogil: ''' A wrapper for the c log function, by returning negative infinity if the input is 0. ''' return clog(x) if x > 0 else NEGINF cdef double _log2(double x) nogil: ''' A wrapper for the c log function, by returning negative infinity if the input is 0. ''' return clog2(x) if x > 0 else NEGINF cdef double pair_lse(double x, double y) nogil: ''' Perform log-sum-exp on a pair of numbers in log space.. This is calculated as z = log( e**x + e**y ). However, this causes underflow sometimes when x or y are too negative. A simplification of this is thus z = x + log( e**(y-x) + 1 ), where x is the greater number. If either of the inputs are infinity, return infinity, and if either of the inputs are negative infinity, then simply return the other input. ''' if x == INF or y == INF: return INF if x == NEGINF: return y if y == NEGINF: return x if x > y: return x + clog(cexp(y-x) + 1) return y + clog(cexp(x-y) + 1) def logsumexp(X): """Calculate the log-sum-exp of an array to add in log space.""" X = numpy.asarray(X, dtype='float64') cdef double* X_ptr = ( X).data cdef double x cdef int i, n = X.shape[0] cdef double y = 0. cdef double x_max = NEGINF with nogil: for i in range(n): x = X_ptr[i] if x > x_max: x_max = x for i in range(n): x = X_ptr[i] if x == NEGINF: continue y += cexp(x - x_max) return x_max + clog(y) def logaddexp(X, Y): """Calculate the log-add-exp of a pair of arrays.""" X = numpy.asarray(X, dtype='float64') Y = numpy.asarray(Y, dtype='float64') if len(X.shape) != len(Y.shape): raise ValueError("Both arrays must be of the same shape.") if X.shape[0] != Y.shape[0]: raise ValueError("Both arrays must be of the same shape.") if len(X.shape) > 1: raise ValueError("Both arrays must of one dimensional.") Z = numpy.zeros_like(Y) cdef int i, n = X.shape[0] cdef double* X_ptr = ( X).data cdef double* Y_ptr = ( Y).data cdef double* Z_ptr = ( Z).data with nogil: for i in range(n): Z_ptr[i] = pair_lse(X_ptr[i], Y_ptr[i]) return Z cdef double gamma(double x) nogil: """Calculate the gamma function on a number.""" # Split the function domain into three intervals: # (0, 0.001), [0.001, 12), and (12, infinity). # First interval: (0, 0.001). # For small x, 1/Gamma(x) has power series x + gamma x^2 - ... # So in this range, 1/Gamma(x) = x + gamma x^2 with error # on the order of x^3. # The relative error over this interval is less than 6e-7. cdef double p[8] p[0] = -1.71618513886549492533811E+0 p[1] = 2.47656508055759199108314E+1 p[2] = -3.79804256470945635097577E+2 p[3] = 6.29331155312818442661052E+2 p[4] = 8.66966202790413211295064E+2 p[5] = -3.14512729688483675254357E+4 p[6] = -3.61444134186911729807069E+4 p[7] = 6.64561438202405440627855E+4 cdef double q[8] q[0] = -3.08402300119738975254353E+1 q[1] = 3.15350626979604161529144E+2 q[2] = -1.01515636749021914166146E+3 q[3] = -3.10777167157231109440444E+3 q[4] = 2.25381184209801510330112E+4 q[5] = 4.75584627752788110767815E+3 q[6] = -1.34659959864969306392456E+5 q[7] = -1.15132259675553483497211E+5 cdef double den, num, result, z, y cdef int i, n, arg_was_less_than_one if x == 0.0: return INF if x < 0.001: return 1.0 / (x * (1.0 + GAMMA * x)) # Second interval: [0.001, 12). if x < 12.0: # The algorithm directly approximates gamma over (1,2) and uses # reduction identities to reduce other arguments to this interval. y = x n = 0 arg_was_less_than_one = (y < 1.0) # Add or subtract integers as necessary to bring y into (1,2) # Will correct for this below */ if arg_was_less_than_one: y += 1.0 else: n = floor(y) - 1 y -= n num = 0.0 den = 1.0 z = y - 1 for i in range(8): num = (num + p[i]) * z den = den * z + q[i] result = num/den + 1.0 # Apply correction if argument was not initially in (1,2) if arg_was_less_than_one: # Use identity gamma(z) = gamma(z+1)/z # The variable "result" now holds gamma of the original y + 1 # Thus we use y-1 to get back the original y. result /= (y-1.0) else: # Use the identity gamma(z+n) = z*(z+1)* ... *(z+n-1)*gamma(z) for i in range(n): result *= y+i return result # Third interval: [12, infinity). if x > 171.624: # Correct answer too large to display, force +infinity. return INF return cexp(lgamma(x)) cdef double lgamma(double x) nogil: # Abramowitz and Stegun 6.1.41 # Asymptotic series should be good to at least 11 or 12 figures # For error analysis, see Whittiker and Watson # A Course in Modern Analysis (1927), page 252 cdef double c[8] c[0] = 1.0 / 12.0 c[1] = -1.0 / 360.0 c[2] = 1.0 / 1260.0 c[3] = -1.0 / 1680.0 c[4] = 1.0 / 1188.0 c[5] = -691.0 / 360360.0 c[6] = 1.0 / 156.0 c[7] = -3617.0 / 122400.0 cdef double z, sum cdef int i if x < 12.0: return clog(fabs(gamma(x))) z = 1.0 / (x * x) sum = c[7] for i in range(7): sum *= z sum += c[6-i] return (x - 0.5) * clog(x) - x + HALF_LOG2_PI + sum / x def plot_networkx(Q, edge_label=None, filename=None): import tempfile import pygraphviz import matplotlib.pyplot as plt import matplotlib.image G = pygraphviz.AGraph(directed=True) for state in Q.nodes(): G.add_node(state, color='red') for parent, child, data in Q.edges(data=True): if edge_label: G.add_edge(parent, child, label=data[edge_label]) else: G.add_edge(parent, child) if filename is None: with tempfile.NamedTemporaryFile() as tf: G.draw(tf.name, format='png', prog='dot') img = matplotlib.image.imread(tf.name) plt.imshow(img) plt.axis('off') else: G.draw(filename, format='pdf', prog='dot') def _check_input(X, keymap=None): """Check the input to make sure that it is a properly formatted array.""" cdef numpy.ndarray X_ndarray try: X_ndarray = numpy.array(X, dtype='float64', ndmin=2, order='C') except: if not isinstance(X, (numpy.ndarray, list, tuple)): X_ndarray = numpy.array(keymap[0][X], dtype='float64', ndmin=2, order='C') else: X = numpy.array(X) X_ndarray = numpy.empty(X.shape, dtype='float64', order='C') if X.ndim == 1: for i in range(X.shape[0]): X_ndarray[i] = keymap[0][X[i]] X_ndarray = X_ndarray.reshape(-1, 1) else: for j in range(X.shape[1]): if len(keymap[j]) == 0: # No keymap for non-discrete distributions # convert the whole column to floats; X_ndarray[:, j] = X[:, j].astype(numpy.float64) else: # else convert entries via the keymap for i in range(X.shape[0]): X_ndarray[i, j] = keymap[j][X[i, j]] return X_ndarray def parallelize_function(X, cls, func, filename, **kwargs): """Parallelize a function using joblib multiprocessing.""" model = cls.from_json(filename) return getattr(model, func)(X, **kwargs) def weight_set(items, weights): """Converts both items and weights to appropriate numpy arrays. Convert the items into a numpy array with 64-bit floats, and the weight array to the same. If no weights are passed in, then return a numpy array with uniform weights. """ items = numpy.array(items, dtype=numpy.float64) if weights is None: # Weight everything 1 if no weights specified weights = numpy.ones(items.shape[0], dtype=numpy.float64) else: # Force whatever we have to be a Numpy array weights = numpy.asarray(weights, dtype=numpy.float64) return items, weights def _check_nan(X): """Checks to see if a value is nan, either as a float or a string.""" if isinstance(X, (str, unicode, numpy.string_)): return X == 'nan' if isinstance(X, (float, numpy.float32, numpy.float64)): return isnan(X) return X is None def check_random_state(seed): """Turn seed into a np.random.RandomState instance. This function will check to see whether the input seed is a valid seed for generating random numbers. This is a slightly modified version of the code from sklearn.utils.validation. Parameters ---------- seed : None | int | instance of RandomState If seed is None, return the RandomState singleton used by np.random. If seed is an int, return a new RandomState instance seeded with seed. If seed is already a RandomState instance, return it. Otherwise raise ValueError. """ if seed is None or seed is numpy.random: return numpy.random.mtrand._rand if isinstance(seed, (numbers.Integral, numpy.integer)): return numpy.random.RandomState(seed) if isinstance(seed, numpy.random.RandomState): return seed raise ValueError('%r cannot be used to seed a numpy.random.RandomState' ' instance' % seed) @cython.boundscheck(False) @cython.wraparound(False) @cython.nonecheck(False) @cython.cdivision(True) cdef choose_one(double [:] weights, int length): cdef int i cdef double cs cdef double random random = rand()*1./(RAND_MAX) cs = 0.0 i = 0 while cs <= random and i < length: cs += weights[i] i += 1 return i-1 pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/pyproject.toml000066400000000000000000000001161420461712600236030ustar00rootroot00000000000000[build-system] requires = ["cython", "numpy", "scipy", "setuptools", "wheel"] pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/rebuildconda000077500000000000000000000034601420461712600232550ustar00rootroot00000000000000#!/bin/bash # Setting up conda: # Pass in the version of python as an argument with default of 2.7 ########## Current setup BEGIN: _PYVER=${1:-2.7} #_PYVER=2.7 #_PYVER=3.4 #_PYVER=3.6 penv_version=py${_PYVER} echo rebuilding conda environment $penv_version # List envs: conda info --envs # List all info: conda info -a source deactivate conda env remove --yes --name $penv_version conda create --yes --name $penv_version python=${_PYVER} ; source activate $penv_version conda config --get channels conda config --add channels conda-forge conda config --get channels # wip add packages that should be in base: base_packages=" cython " pom_packages=" scipy joblib nose networkx=1.11 " pom_nbtest_packages=" scikit-learn pandas seaborn pygraphviz pillow xlrd memory_profiler jupyter jupyter_contrib_nbextensions jupyter_nbextensions_configurator " echo "Don't forget to also install gnuplot-py since it isn't yet in the tools Makefile" conda install --yes $base_packages $pom_packages $pom_nbtest_packages source activate $penv_version # It would be nice if cmckeague would submit libpgm to conda-forge... Also doesn't have a python 3 version if [ ${penv_version/py2.7.*/py2.7} == "py2.7" ]; then conda install --yes -c cmckeague libpgm # Note that omnia hmmlearn causes numpy downgrade to 1.12.1 conda install --yes -c omnia hmmlearn fi conda env export > $penv_version.yml ########## Current setup END # exit 0 ########## Current setup Remove: source deactivate conda env remove --yes --name $penv_version ########## ########## Purge packages: conda clean --dry-run --all ########## Bash aliases for quick change of environment: alias py27="source activate py2.7; p=~/miniconda2/envs/py2.7/lib/python2.7/site-packages " alias py36="source activate py3.6; p=~/miniconda2/envs/py3.6/lib/python3.6/site-packages " pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/requirements.txt000066400000000000000000000001331420461712600241520ustar00rootroot00000000000000numpy >= 1.20.0 joblib >= 0.9.0b4 networkx >= 2.4 scipy >= 0.17.0 pyyaml cython >= 0.29.14 pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/setup.py000066400000000000000000000057231420461712600224120ustar00rootroot00000000000000from setuptools import setup from setuptools import Extension from setuptools.command.build_ext import build_ext as _build_ext # https://stackoverflow.com/a/11181607/541202 # import __builtin__ as __builtins__ try: from Cython.Build import cythonize except ImportError: use_cython = False ext = 'c' else: use_cython = True ext = 'pyx' filenames = [ "base", "bayes", "BayesianNetwork", "MarkovNetwork", "FactorGraph", "hmm", "gmm", "kmeans", "NaiveBayes", "BayesClassifier", "MarkovChain", "utils", "parallel" ] distributions = [ 'distributions', 'UniformDistribution', 'BernoulliDistribution', 'NormalDistribution', 'LogNormalDistribution', 'ExponentialDistribution', 'BetaDistribution', 'GammaDistribution', 'DiscreteDistribution', 'PoissonDistribution', 'KernelDensities', 'IndependentComponentsDistribution', 'MultivariateGaussianDistribution', 'DirichletDistribution', 'ConditionalProbabilityTable', 'JointProbabilityTable' ] if not use_cython: extensions = [ Extension("pomegranate.{}".format( name ), [ "pomegranate/{}.{}".format(name, ext) ]) for name in filenames ] + [Extension("pomegranate.distributions.{}".format(dist), ["pomegranate/distributions/{}.{}".format(dist, ext)]) for dist in distributions] else: extensions = [ Extension("pomegranate.*", ["pomegranate/*.pyx"]), Extension("pomegranate.distributions.*", ["pomegranate/distributions/*.pyx"]) ] extensions = cythonize(extensions, compiler_directives={'language_level' : "2"}) class build_ext(_build_ext): def finalize_options(self): _build_ext.finalize_options(self) # Prevent numpy from thinking it is still in its setup process: if hasattr(__builtins__, '__NUMPY_SETUP__'): __builtins__.__NUMPY_SETUP__ = False import numpy self.include_dirs.append(numpy.get_include()) setup( name='pomegranate', version='0.14.8', author='Jacob Schreiber', author_email='jmschreiber91@gmail.com', packages=[ 'pomegranate', 'pomegranate/distributions', ], url='http://pypi.python.org/pypi/pomegranate/', license='MIT', description='Pomegranate is a graphical models library for Python, implemented in Cython for speed.', ext_modules=extensions, cmdclass={'build_ext':build_ext}, setup_requires=[ "cython >= 0.22.1", "numpy >= 1.20.0", "scipy >= 0.17.0" ], install_requires=[ "numpy >= 1.20.0", "joblib >= 0.9.0b4", "networkx >= 2.4", "scipy >= 0.17.0", "pyyaml" ], extras_require={ "Plotting": ["pygraphviz", "matplotlib"], "GPU": ["cupy"], }, test_suite = 'nose.collector', package_data={ 'pomegranate': ['*.pyd', '*.pxd'], 'pomegranate/distributions': ['*.pyd', '*.pxd'], }, include_package_data=True, zip_safe=False, ) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/slides/000077500000000000000000000000001420461712600221545ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/slides/pomegranate ODSC East 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/ImageI ] >> endobj 40 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 46 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 52 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 61 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 66 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 71 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 76 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 82 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 89 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 94 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 100 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 105 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 114 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 120 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 125 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 131 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 137 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 143 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 149 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 155 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 160 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 167 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 173 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 181 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 187 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 193 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 198 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 203 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 208 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 215 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 221 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 227 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 233 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 241 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 247 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 253 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 259 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 265 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 271 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 277 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 284 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 290 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 295 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 300 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 305 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 311 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 317 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 323 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 328 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 334 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 339 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 344 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 351 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 357 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 363 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 369 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 376 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image3 14 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 32 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 43 0 R /Image20 56 0 R /Image21 57 0 R /Image22 58 0 R /Image23 79 0 R /Image24 85 0 R /Image25 86 0 R /Image26 97 0 R /Image27 108 0 R /Image28 109 0 R /Image29 110 0 R /Image30 111 0 R /Image31 117 0 R /Image32 128 0 R /Image33 134 0 R /Image34 140 0 R /Image35 146 0 R /Image36 152 0 R /Image37 163 0 R /Image38 164 0 R /Image39 170 0 R /Image40 176 0 R /Image41 177 0 R /Image42 178 0 R /Image43 184 0 R /Image44 190 0 R /Image45 211 0 R /Image46 212 0 R /Image47 218 0 R /Image48 224 0 R /Image49 230 0 R /Image50 236 0 R /Image51 237 0 R /Image52 238 0 R /Image53 244 0 R /Image54 250 0 R /Image55 256 0 R /Image56 262 0 R /Image57 268 0 R /Image58 274 0 R /Image59 280 0 R /Image60 281 0 R /Image61 287 0 R /Image62 308 0 R /Image63 314 0 R /Image64 320 0 R /Image65 331 0 R /Image66 347 0 R /Image67 348 0 R /Image68 354 0 R /Image69 360 0 R /Image70 366 0 R /Image71 372 0 R /Image72 373 0 R /Image73 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 382 0 obj << /Font << /Font4 15 0 R /Font5 16 0 R /Font6 17 0 R /Font18 49 0 R /Font19 55 0 R >> /Pattern << >> /XObject << /Image2 13 0 R 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47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 86 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 91 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 96 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 105 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 111 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 116 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 122 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 128 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 134 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 140 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 145 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 151 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 159 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 165 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 171 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 176 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 181 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 186 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 193 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 199 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 205 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 211 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 217 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 222 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 227 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 233 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 238 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 243 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 248 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 253 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 259 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 265 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 271 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 278 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 283 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 288 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 293 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 300 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 306 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 312 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 317 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 324 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 329 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 336 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R 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0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R /Image60 351 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 354 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R /Font51 297 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 76 0 R /Image21 82 0 R /Image22 83 0 R /Image23 99 0 R /Image24 100 0 R /Image25 101 0 R /Image26 102 0 R /Image27 108 0 R /Image28 119 0 R /Image29 125 0 R /Image30 131 0 R /Image31 137 0 R /Image32 148 0 R /Image33 154 0 R /Image34 155 0 R /Image35 156 0 R /Image36 162 0 R /Image37 168 0 R /Image38 189 0 R /Image39 190 0 R /Image40 196 0 R /Image41 202 0 R /Image42 208 0 R /Image43 214 0 R /Image44 230 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 274 0 R /Image49 275 0 R /Image50 296 0 R /Image52 303 0 R /Image53 309 0 R /Image54 320 0 R /Image55 321 0 R /Image56 332 0 R /Image57 333 0 R /Image58 339 0 R /Image59 345 0 R 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/Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 20 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 31 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 37 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 42 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 48 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 53 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 58 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 64 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R 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/Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 78 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 83 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R 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/Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 93 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 98 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R 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/Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 108 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 114 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 119 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 125 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 131 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 137 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 143 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 149 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 154 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 159 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 164 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 171 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 177 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 183 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 189 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 196 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 203 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 209 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 215 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 222 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 228 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 234 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 240 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 246 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 251 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 256 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 261 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 267 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 273 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 278 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 284 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 290 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 296 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 301 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 307 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 313 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 319 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 325 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image14 45 0 R /Image15 61 0 R /Image16 67 0 R /Image17 68 0 R /Image18 74 0 R /Image19 75 0 R /Image20 111 0 R /Image21 122 0 R /Image22 128 0 R /Image23 134 0 R /Image24 140 0 R /Image25 146 0 R /Image26 167 0 R /Image27 168 0 R /Image28 174 0 R /Image29 180 0 R /Image30 186 0 R /Image31 192 0 R /Image33 199 0 R /Image34 200 0 R /Image35 206 0 R /Image36 212 0 R /Image39 225 0 R /Image40 231 0 R /Image41 237 0 R /Image42 243 0 R /Image43 264 0 R /Image44 270 0 R /Image45 281 0 R /Image46 287 0 R /Image47 293 0 R /Image48 304 0 R /Image49 310 0 R /Image50 316 0 R /Image51 322 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 330 0 obj << /Font << /Font2 13 0 R /Font32 193 0 R /Font37 218 0 R /Font38 219 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R 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/Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 81 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 86 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 91 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 96 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 103 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 109 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 114 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 120 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 126 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 132 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 138 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 143 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 150 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 156 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 162 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 168 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 174 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 179 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 184 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 189 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 196 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 202 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 208 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 214 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 220 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 225 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 230 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 235 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 241 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 246 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 251 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 256 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 261 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 266 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 272 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 278 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 284 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 291 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 298 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 304 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 310 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 316 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 322 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 327 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 333 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 339 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 345 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 351 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R /Image24 100 0 R /Image25 106 0 R /Image26 117 0 R /Image27 123 0 R /Image28 129 0 R /Image29 135 0 R /Image30 146 0 R /Image31 147 0 R /Image32 153 0 R /Image33 159 0 R /Image34 165 0 R /Image35 171 0 R /Image36 192 0 R /Image37 193 0 R /Image38 199 0 R /Image39 205 0 R /Image40 211 0 R /Image41 217 0 R /Image42 238 0 R /Image43 269 0 R /Image44 275 0 R /Image45 281 0 R /Image46 287 0 R /Image47 288 0 R /Image48 294 0 R /Image49 295 0 R /Image50 301 0 R /Image51 307 0 R /Image52 313 0 R /Image53 319 0 R /Image54 330 0 R /Image55 336 0 R /Image56 342 0 R /Image57 348 0 R /Image58 354 0 R /Image59 360 0 R /Image60 366 0 R /Image61 372 0 R /Image62 373 0 R /Image63 379 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 357 0 obj << /Font << /Font2 13 0 R /Font3 14 0 R /Font15 36 0 R /Font16 47 0 R >> /Pattern << >> /XObject << /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 18 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 29 0 R /Image14 35 0 R /Image17 48 0 R /Image18 49 0 R /Image19 50 0 R /Image20 71 0 R /Image21 77 0 R /Image22 78 0 R /Image23 99 0 R 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81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 68 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 73 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 78 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 84 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 92 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 97 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 102 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 111 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 118 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 123 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 129 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 136 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 142 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 148 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 153 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 159 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 167 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 173 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 179 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 184 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 189 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 194 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 202 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 208 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 214 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 220 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 228 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 234 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 240 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 246 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 252 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 258 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 264 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 271 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 277 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 282 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 287 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 292 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 298 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 305 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 311 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 316 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 323 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 328 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 333 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 340 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 346 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 352 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 358 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 364 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R /Image18 44 0 R /Image20 51 0 R /Image22 58 0 R /Image23 59 0 R /Image24 60 0 R /Image25 81 0 R /Image26 87 0 R /Image27 88 0 R /Image28 89 0 R /Image29 105 0 R /Image30 106 0 R /Image31 107 0 R /Image32 108 0 R /Image33 114 0 R /Image34 115 0 R /Image35 126 0 R /Image36 132 0 R /Image37 133 0 R /Image38 139 0 R /Image39 145 0 R /Image40 156 0 R /Image41 162 0 R /Image42 163 0 R /Image43 164 0 R /Image44 170 0 R /Image45 176 0 R /Image46 197 0 R /Image47 198 0 R /Image48 199 0 R /Image49 205 0 R /Image50 211 0 R /Image51 217 0 R /Image52 223 0 R /Image53 224 0 R /Image54 225 0 R /Image55 231 0 R /Image56 237 0 R /Image57 243 0 R /Image58 249 0 R /Image59 255 0 R /Image60 261 0 R /Image61 267 0 R /Image62 268 0 R /Image63 274 0 R /Image64 295 0 R /Image65 301 0 R /Image66 302 0 R /Image67 308 0 R /Image68 319 0 R /Image69 320 0 R /Image70 336 0 R /Image71 337 0 R /Image72 343 0 R /Image73 349 0 R /Image74 355 0 R /Image75 361 0 R /Image76 367 0 R /Image77 368 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 371 0 obj << /Font << /Font3 14 0 R /Font4 15 0 R /Font6 22 0 R /Font19 50 0 R /Font21 57 0 R >> /Pattern << >> /XObject << /Image2 13 0 R /Image5 16 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 33 0 R /Image13 34 0 R /Image14 35 0 R /Image15 36 0 R /Image16 37 0 R /Image17 38 0 R 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/Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 84 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 89 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 94 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 99 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 106 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 112 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 117 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 123 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 129 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 136 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 142 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 148 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 154 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 160 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 165 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 170 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 175 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 182 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 188 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 194 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 200 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 206 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 213 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 219 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 226 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 232 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 238 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 243 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 249 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 256 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 262 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 268 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 274 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 280 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 286 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 292 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 298 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 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/Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 314 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 319 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 324 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 329 0 obj << /Font << /Font2 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/Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R /Image59 370 0 R /Image60 376 0 R /Image61 392 0 R /Image62 398 0 R /Image63 404 0 R /Image64 410 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 341 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R 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endobj 384 0 obj << /Font << /Font2 13 0 R /Font14 50 0 R /Font52 283 0 R /Font57 338 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 34 0 R /Image15 51 0 R /Image16 52 0 R /Image17 53 0 R /Image18 74 0 R /Image19 80 0 R /Image20 81 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 133 0 R /Image28 139 0 R /Image29 145 0 R /Image30 151 0 R /Image31 157 0 R /Image32 178 0 R /Image33 179 0 R /Image34 185 0 R /Image35 191 0 R /Image36 197 0 R /Image37 203 0 R /Image38 209 0 R /Image39 210 0 R /Image40 216 0 R /Image41 222 0 R /Image42 223 0 R /Image43 229 0 R /Image44 235 0 R /Image45 246 0 R /Image46 252 0 R /Image47 253 0 R /Image48 259 0 R /Image49 265 0 R /Image50 271 0 R /Image51 277 0 R /Image53 289 0 R /Image54 295 0 R /Image55 301 0 R /Image56 332 0 R /Image58 364 0 R 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/Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 31 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 36 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 41 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 46 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 52 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 58 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 63 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 68 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 76 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 82 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 88 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 94 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 101 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R 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0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 114 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 121 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 126 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 132 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 138 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 144 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 150 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 156 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 161 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 166 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 171 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 176 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 183 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 189 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 195 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 201 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 206 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 212 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 218 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 224 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 230 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 236 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 242 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 248 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 253 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 259 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 265 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 272 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 278 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 284 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 289 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 294 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 299 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 304 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 309 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 315 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 321 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 326 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 332 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 338 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 344 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 349 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 356 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 362 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 368 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 374 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 380 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 387 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 393 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 399 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 405 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 410 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 415 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 420 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 426 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 432 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 437 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 443 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 449 0 obj << /Font << /Font2 13 0 R /Font17 73 0 R /Font62 383 0 R /Font63 384 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 28 0 R /Image13 49 0 R /Image14 55 0 R /Image15 71 0 R /Image16 72 0 R /Image18 79 0 R /Image19 85 0 R /Image20 91 0 R /Image21 97 0 R /Image22 98 0 R /Image23 104 0 R /Image24 110 0 R /Image25 111 0 R /Image26 117 0 R /Image27 118 0 R /Image28 129 0 R /Image29 135 0 R /Image30 141 0 R /Image31 147 0 R /Image32 153 0 R /Image33 179 0 R /Image34 180 0 R /Image35 186 0 R /Image36 192 0 R /Image37 198 0 R /Image38 209 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 233 0 R /Image43 239 0 R /Image44 245 0 R /Image45 256 0 R /Image46 262 0 R /Image47 268 0 R /Image48 269 0 R /Image49 275 0 R /Image50 281 0 R /Image51 312 0 R /Image52 318 0 R /Image53 329 0 R /Image54 335 0 R /Image55 341 0 R /Image56 352 0 R /Image57 353 0 R /Image58 359 0 R /Image59 365 0 R /Image60 371 0 R /Image61 377 0 R /Image64 390 0 R /Image65 396 0 R /Image66 402 0 R /Image67 423 0 R /Image68 429 0 R /Image69 440 0 R /Image70 446 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> 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/Text /ImageB /ImageC /ImageI ] >> endobj 63 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 68 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 73 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 81 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 87 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 93 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 99 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 106 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 113 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 119 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 125 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 131 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 136 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 141 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 146 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 151 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 158 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 164 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 170 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 176 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 181 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 187 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 192 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 198 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 204 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 210 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 216 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 222 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 228 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 233 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 239 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 245 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 251 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 258 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 265 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 270 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 276 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 282 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 287 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 292 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 297 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 303 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 309 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 314 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 319 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 324 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 330 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 336 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 341 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 346 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 352 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 358 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 363 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R 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/Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 380 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 386 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 392 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 398 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 403 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 409 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 415 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 421 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 428 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 434 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> 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418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 439 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 445 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 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/ImageC /ImageI ] >> endobj 451 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R /Image49 300 0 R /Image50 306 0 R /Image51 327 0 R /Image52 333 0 R /Image53 349 0 R /Image54 355 0 R /Image55 371 0 R /Image56 377 0 R /Image57 383 0 R /Image58 389 0 R /Image59 395 0 R /Image60 406 0 R /Image61 412 0 R /Image62 418 0 R /Image65 431 0 R /Image66 442 0 R /Image67 448 0 R /Image68 469 0 R /Image69 470 0 R /Image70 476 0 R /Image71 487 0 R /Image72 493 0 R /Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 456 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R 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/Image73 499 0 R /Image74 510 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 466 0 obj << /Font << /Font2 13 0 R /Font17 78 0 R /Font63 424 0 R /Font64 425 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 24 0 R /Image9 25 0 R /Image10 26 0 R /Image11 27 0 R /Image12 38 0 R /Image13 44 0 R /Image14 50 0 R /Image15 76 0 R /Image16 77 0 R /Image18 84 0 R /Image19 90 0 R /Image20 96 0 R /Image21 102 0 R /Image22 103 0 R /Image23 109 0 R /Image24 110 0 R /Image25 116 0 R /Image26 122 0 R /Image27 128 0 R /Image28 154 0 R /Image29 155 0 R /Image30 161 0 R /Image31 167 0 R /Image32 173 0 R /Image33 184 0 R /Image34 195 0 R /Image35 201 0 R /Image36 207 0 R /Image37 213 0 R /Image38 219 0 R /Image39 225 0 R /Image40 236 0 R /Image41 242 0 R /Image42 248 0 R /Image43 254 0 R /Image44 255 0 R /Image45 261 0 R /Image46 262 0 R /Image47 273 0 R /Image48 279 0 R 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108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 61 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 67 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 73 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 79 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 86 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 93 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 100 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 105 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 111 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 117 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 123 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 129 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 135 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 140 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 145 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 150 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 155 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 162 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R /Image44 250 0 R /Image45 251 0 R /Image46 252 0 R /Image47 253 0 R /Image48 254 0 R >> /ExtGState << /Alpha0 11 0 R /Alpha1 12 0 R >> /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 168 0 obj << /Font << /Font2 13 0 R /Font12 58 0 R /Font37 208 0 R /Font38 209 0 R >> /Pattern << >> /XObject << /Image3 14 0 R /Image4 15 0 R /Image5 16 0 R /Image6 17 0 R /Image7 23 0 R /Image8 34 0 R /Image9 40 0 R /Image10 56 0 R /Image11 57 0 R /Image13 64 0 R /Image14 70 0 R /Image15 76 0 R /Image16 82 0 R /Image17 83 0 R /Image18 89 0 R /Image19 90 0 R /Image20 96 0 R /Image21 97 0 R /Image22 108 0 R /Image23 114 0 R /Image24 120 0 R /Image25 126 0 R /Image26 132 0 R /Image27 158 0 R /Image28 159 0 R /Image29 165 0 R /Image30 171 0 R /Image31 177 0 R /Image32 183 0 R /Image33 184 0 R /Image34 190 0 R /Image35 196 0 R /Image36 202 0 R /Image39 215 0 R /Image40 221 0 R /Image41 227 0 R /Image42 238 0 R /Image43 244 0 R 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pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/000077500000000000000000000000001420461712600220335ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/__init__.py000066400000000000000000000000001420461712600241320ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/test_bayes_classifier.py000066400000000000000000000562631420461712600267670ustar00rootroot00000000000000from __future__ import (division) from pomegranate import * from pomegranate.bayes import BayesModel from pomegranate.io import DataGenerator from pomegranate.io import DataFrameGenerator from nose.tools import with_setup from nose.tools import assert_almost_equal from nose.tools import assert_equal from nose.tools import assert_not_equal from nose.tools import assert_less_equal from nose.tools import assert_raises from nose.tools import assert_true from numpy.testing import assert_array_almost_equal import pandas import random import pickle import numpy as np nan = numpy.nan def setup_multivariate_gaussian(): mu, cov = [0, 0, 0], numpy.eye(3) d1 = MultivariateGaussianDistribution(mu, cov) mu, cov = [2, 2, 2], numpy.eye(3) d2 = MultivariateGaussianDistribution(mu, cov) global model model = BayesClassifier([d1, d2]) global X X = numpy.array([[ 0.3, 0.5, 0.1], [ 0.8, 1.4, 0.5], [ 1.4, 2.6, 1.8], [ 4.2, 3.3, 3.7], [ 2.6, 3.6, 3.3], [ 3.1, 2.2, 1.7], [ 1.8, 2.2, 1.8], [-1.2, -1.8, -1.5], [-1.8, 0.3, 0.5], [ 0.7, -1.3, -0.1]]) global y y = [0, 0, 0, 1, 1, 1, 1, 0, 0, 0] global X_nan X_nan = numpy.array([[ 0.3, nan, 0.1], [ nan, 1.4, nan], [ 1.4, 2.6, nan], [ nan, nan, nan], [ nan, 3.6, 3.3], [ 3.1, nan, 1.7], [ nan, nan, 1.8], [-1.2, -1.8, -1.5], [ nan, 0.3, 0.5], [ nan, -1.3, nan]]) def setup_multivariate_mixed(): mu, cov = [0, 0, 0], numpy.eye(3) d1 = MultivariateGaussianDistribution(mu, cov) d21 = ExponentialDistribution(5) d22 = LogNormalDistribution(0.2, 0.8) d23 = PoissonDistribution(3) d2 = IndependentComponentsDistribution([d21, d22, d23]) global model model = BayesClassifier([d1, d2]) global X X = numpy.array([[ 0.3, 0.5, 0.1], [ 0.8, 1.4, 0.5], [ 1.4, 2.6, 1.8], [ 4.2, 3.3, 3.7], [ 2.6, 3.6, 3.3], [ 3.1, 2.2, 1.7], [ 1.8, 2.2, 1.8], [ 1.2, 1.8, 1.5], [ 1.8, 0.3, 0.5], [ 0.7, 1.3, 0.1]]) global y y = [0, 0, 0, 1, 1, 1, 1, 0, 0, 0] global X_nan X_nan = numpy.array([[ 0.3, nan, 0.1], [ nan, 1.4, nan], [ 1.4, 2.6, nan], [ nan, nan, nan], [ nan, 3.6, 3.3], [ 3.1, nan, 1.7], [ nan, nan, 1.8], [ 1.2, 1.8, 1.5], [ nan, 0.3, 0.5], [ nan, 1.3, nan]]) def setup_hmm(): global model global hmm1 global hmm2 global hmm3 rigged = State( DiscreteDistribution({ 'H': 0.8, 'T': 0.2 }) ) unrigged = State( DiscreteDistribution({ 'H': 0.5, 'T':0.5 }) ) hmm1 = HiddenMarkovModel() hmm1.start = rigged hmm1.add_transition(rigged, rigged, 1) hmm1.bake() hmm2 = HiddenMarkovModel() hmm2.start = unrigged hmm2.add_transition(unrigged, unrigged, 1) hmm2.bake() hmm3 = HiddenMarkovModel() hmm3.add_transition(hmm3.start, unrigged, 0.5) hmm3.add_transition(hmm3.start, rigged, 0.5) hmm3.add_transition(rigged, rigged, 0.5) hmm3.add_transition(rigged, unrigged, 0.5) hmm3.add_transition(unrigged, rigged, 0.5) hmm3.add_transition(unrigged, unrigged, 0.5) hmm3.bake() model = BayesClassifier([hmm1, hmm2, hmm3]) def setup_multivariate(): pass def teardown(): pass def test_unpickle_bayes_model(): """Test that `BayesModel` can be pickled and unpickled.""" dists = [BernoulliDistribution(0.2), BernoulliDistribution(0.3)] model = BayesModel(distributions=dists) unpickled_model = pickle.loads(pickle.dumps(model)) np.testing.assert_almost_equal(model.weights, unpickled_model.weights) # Check weights of individual distributions. np.testing.assert_almost_equal( model.distributions[0].parameters, unpickled_model.distributions[0].parameters, ) np.testing.assert_almost_equal( model.distributions[1].parameters, unpickled_model.distributions[1].parameters, ) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_initialization(): assert_equal(model.d, 3) assert_equal(model.n, 2) assert_equal(model.is_vl_, False) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_initialization(): assert_equal(model.d, 3) assert_equal(model.n, 2) assert_equal(model.is_vl_, False) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_predict_log_proba(): y_hat = model.predict_log_proba(X) y = [[ -1.48842547e-02, -4.21488425e+00], [ -4.37487950e-01, -1.03748795e+00], [ -5.60369104e+00, -3.69104343e-03], [ -1.64000001e+01, -7.54345812e-08], [ -1.30000023e+01, -2.26032685e-06], [ -8.00033541e+00, -3.35406373e-04], [ -5.60369104e+00, -3.69104343e-03], [ -3.05902274e-07, -1.50000003e+01], [ -3.35406373e-04, -8.00033541e+00], [ -6.11066022e-04, -7.40061107e+00]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_predict_log_proba(): y_hat = model.predict_log_proba(X) y = [[ -5.03107596e-01, -9.27980626e-01], [ -1.86355320e-01, -1.77183117e+00], [ -5.58542088e-01, -8.48731256e-01], [ -7.67315597e-01, -6.24101927e-01], [ -2.32860808e+00, -1.02510436e-01], [ -3.06641866e-03, -5.78877778e+00], [ -9.85292840e-02, -2.36626165e+00], [ -2.61764180e-01, -1.46833995e+00], [ -2.01640009e-03, -6.20744952e+00], [ -1.47371167e-01, -1.98758175e+00]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_nan_predict_log_proba(): y_hat = model.predict_log_proba(X_nan) y = [[ -3.99533332e-02, -3.23995333e+00], [ -1.17110067e+00, -3.71100666e-01], [ -4.01814993e+00, -1.81499279e-02], [ -6.93147181e-01, -6.93147181e-01], [ -9.80005545e+00, -5.54500620e-05], [ -5.60369104e+00, -3.69104343e-03], [ -1.78390074e+00, -1.83900741e-01], [ -3.05902274e-07, -1.50000003e+01], [ -8.68361522e-02, -2.48683615e+00], [ -1.00016521e-02, -4.61000165e+00]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_nan_predict_log_proba(): y_hat = model.predict_log_proba(X_nan) y = [[ -3.57980882e-01, -1.20093223e+00], [ -1.20735130e+00, -3.55230506e-01], [ -2.43174286e-01, -1.53310132e+00], [ -6.93147181e-01, -6.93147181e-01], [ -9.31781101e+00, -8.98143220e-05], [ -6.29755079e-04, -7.37049444e+00], [ -1.31307006e+00, -3.13332194e-01], [ -2.61764180e-01, -1.46833995e+00], [ -2.29725479e-01, -1.58353505e+00], [ -1.17299253e+00, -3.70251760e-01]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_predict_log_proba_parallel(): y_hat = model.predict_log_proba(X, n_jobs=2) y = [[ -1.48842547e-02, -4.21488425e+00], [ -4.37487950e-01, -1.03748795e+00], [ -5.60369104e+00, -3.69104343e-03], [ -1.64000001e+01, -7.54345812e-08], [ -1.30000023e+01, -2.26032685e-06], [ -8.00033541e+00, -3.35406373e-04], [ -5.60369104e+00, -3.69104343e-03], [ -3.05902274e-07, -1.50000003e+01], [ -3.35406373e-04, -8.00033541e+00], [ -6.11066022e-04, -7.40061107e+00]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_predict_log_proba_parallel(): y_hat = model.predict_log_proba(X, n_jobs=2) y = [[ -5.03107596e-01, -9.27980626e-01], [ -1.86355320e-01, -1.77183117e+00], [ -5.58542088e-01, -8.48731256e-01], [ -7.67315597e-01, -6.24101927e-01], [ -2.32860808e+00, -1.02510436e-01], [ -3.06641866e-03, -5.78877778e+00], [ -9.85292840e-02, -2.36626165e+00], [ -2.61764180e-01, -1.46833995e+00], [ -2.01640009e-03, -6.20744952e+00], [ -1.47371167e-01, -1.98758175e+00]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_predict_proba(): y_hat = model.predict_proba(X) y = [[ 9.85225968e-01, 1.47740317e-02], [ 6.45656306e-01, 3.54343694e-01], [ 3.68423990e-03, 9.96315760e-01], [ 7.54345778e-08, 9.99999925e-01], [ 2.26032430e-06, 9.99997740e-01], [ 3.35350130e-04, 9.99664650e-01], [ 3.68423990e-03, 9.96315760e-01], [ 9.99999694e-01, 3.05902227e-07], [ 9.99664650e-01, 3.35350130e-04], [ 9.99389121e-01, 6.10879359e-04]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_predict_proba(): y_hat = model.predict_proba(X) y = [[ 0.60464873, 0.39535127], [ 0.82997863, 0.17002137], [ 0.57204244, 0.42795756], [ 0.46425765, 0.53574235], [ 0.09743127, 0.90256873], [ 0.99693828, 0.00306172], [ 0.90616916, 0.09383084], [ 0.76969251, 0.23030749], [ 0.99798563, 0.00201437], [ 0.86297361, 0.13702639]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_nan_predict_proba(): y_hat = model.predict_proba(X_nan) y = [[ 9.60834277e-01, 3.91657228e-02], [ 3.10025519e-01, 6.89974481e-01], [ 1.79862100e-02, 9.82013790e-01], [ 5.00000000e-01, 5.00000000e-01], [ 5.54485247e-05, 9.99944551e-01], [ 3.68423990e-03, 9.96315760e-01], [ 1.67981615e-01, 8.32018385e-01], [ 9.99999694e-01, 3.05902227e-07], [ 9.16827304e-01, 8.31726965e-02], [ 9.90048198e-01, 9.95180187e-03]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_nan_predict_proba(): y_hat = model.predict_proba(X_nan) y = [[ 6.99086440e-01, 3.00913560e-01], [ 2.98988163e-01, 7.01011837e-01], [ 7.84134838e-01, 2.15865162e-01], [ 5.00000000e-01, 5.00000000e-01], [ 8.98102888e-05, 9.99910190e-01], [ 9.99370443e-01, 6.29556825e-04], [ 2.68992964e-01, 7.31007036e-01], [ 7.69692511e-01, 2.30307489e-01], [ 7.94751748e-01, 2.05248252e-01], [ 3.09439547e-01, 6.90560453e-01]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_predict_proba_parallel(): y_hat = model.predict_proba(X, n_jobs=2) y = [[ 9.85225968e-01, 1.47740317e-02], [ 6.45656306e-01, 3.54343694e-01], [ 3.68423990e-03, 9.96315760e-01], [ 7.54345778e-08, 9.99999925e-01], [ 2.26032430e-06, 9.99997740e-01], [ 3.35350130e-04, 9.99664650e-01], [ 3.68423990e-03, 9.96315760e-01], [ 9.99999694e-01, 3.05902227e-07], [ 9.99664650e-01, 3.35350130e-04], [ 9.99389121e-01, 6.10879359e-04]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_predict_proba_parallel(): y_hat = model.predict_proba(X, n_jobs=2) y = [[ 0.60464873, 0.39535127], [ 0.82997863, 0.17002137], [ 0.57204244, 0.42795756], [ 0.46425765, 0.53574235], [ 0.09743127, 0.90256873], [ 0.99693828, 0.00306172], [ 0.90616916, 0.09383084], [ 0.76969251, 0.23030749], [ 0.99798563, 0.00201437], [ 0.86297361, 0.13702639]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_predict(): y_hat = model.predict(X) y = [0, 0, 1, 1, 1, 1, 1, 0, 0, 0] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_predict(): y_hat = model.predict(X) y = [0, 0, 0, 1, 1, 0, 0, 0, 0, 0] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_nan_predict(): y_hat = model.predict(X_nan) y = [0, 1, 1, 0, 1, 1, 1, 0, 0, 0] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_nan_predict(): y_hat = model.predict(X_nan) y = [0, 1, 0, 0, 1, 0, 1, 0, 0, 1] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_predict_parallel(): y_hat = model.predict(X, n_jobs=2) y = [0, 0, 1, 1, 1, 1, 1, 0, 0, 0] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_predict_parallel(): y_hat = model.predict(X, n_jobs=2) y = [0, 0, 0, 1, 1, 0, 0, 0, 0, 0] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_fit_parallel(): model.fit(X, y, n_jobs=2) mu1 = model.distributions[0].parameters[0] cov1 = model.distributions[0].parameters[1] mu1_t = [0.03333333, 0.28333333, 0.21666666] cov1_t = [[1.3088888, 0.9272222, 0.6227777], [0.9272222, 2.2513888, 1.3402777], [0.6227777, 1.3402777, 0.9547222]] mu2 = model.distributions[1].parameters[0] cov2 = model.distributions[1].parameters[1] mu2_t = [2.925, 2.825, 2.625] cov2_t = [[0.75687499, 0.23687499, 0.4793750], [0.23687499, 0.40187499, 0.5318749], [0.47937500, 0.53187499, 0.7868750]] assert_array_almost_equal(mu1, mu1_t) assert_array_almost_equal(cov1, cov1_t) assert_array_almost_equal(mu2, mu2_t) assert_array_almost_equal(cov2, cov2_t) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_fit_parallel(): model.fit(X, y, n_jobs=2) mu1 = model.distributions[0].parameters[0] cov1 = model.distributions[0].parameters[1] mu1_t = [1.033333, 1.3166667, 0.75] cov1_t = [[0.242222, 0.0594444, 0.178333], [0.059444, 0.5980555, 0.414166], [0.178333, 0.4141666, 0.439166]] d21 = model.distributions[1].distributions[0] d22 = model.distributions[1].distributions[1] d23 = model.distributions[1].distributions[2] assert_array_almost_equal(mu1, mu1_t) assert_array_almost_equal(cov1, cov1_t) assert_array_almost_equal(d21.parameters, [0.34188034]) assert_array_almost_equal(d22.parameters, [1.01294275, 0.22658346]) assert_array_almost_equal(d23.parameters, [2.625]) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_from_samples(): model = BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y) mu1 = model.distributions[0].parameters[0] cov1 = model.distributions[0].parameters[1] mu1_t = [0.03333333, 0.2833333, 0.21666666] cov1_t = [[1.308888888, 0.9272222222, 0.6227777777], [0.927222222, 2.251388888, 1.340277777], [0.622777777, 1.340277777, 0.9547222222]] mu2 = model.distributions[1].parameters[0] cov2 = model.distributions[1].parameters[1] mu2_t = [2.925, 2.825, 2.625] cov2_t = [[0.75687500, 0.23687499, 0.47937500], [0.23687499, 0.40187499, 0.53187499], [0.47937500, 0.53187499, 0.78687500]] assert_array_almost_equal(mu1, mu1_t) assert_array_almost_equal(cov1, cov1_t) assert_array_almost_equal(mu2, mu2_t) assert_array_almost_equal(cov2, cov2_t) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_pickle(): model2 = pickle.loads(pickle.dumps(model)) assert_true(isinstance(model2, BayesClassifier)) assert_true(isinstance(model2.distributions[0], MultivariateGaussianDistribution)) assert_true(isinstance(model2.distributions[1], MultivariateGaussianDistribution)) assert_array_almost_equal(model.weights, model2.weights) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_pickle(): model2 = pickle.loads(pickle.dumps(model)) assert_true(isinstance(model2, BayesClassifier)) assert_true(isinstance(model2.distributions[0], MultivariateGaussianDistribution)) assert_true(isinstance(model2.distributions[1], IndependentComponentsDistribution)) assert_array_almost_equal(model.weights, model2.weights) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_to_json(): model2 = BayesClassifier.from_json(model.to_json()) assert_true(isinstance(model2, BayesClassifier)) assert_true(isinstance(model2.distributions[0], MultivariateGaussianDistribution)) assert_true(isinstance(model2.distributions[1], MultivariateGaussianDistribution)) assert_array_almost_equal(model.weights, model2.weights) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_to_json(): model2 = BayesClassifier.from_json(model.to_json()) assert_true(isinstance(model2, BayesClassifier)) assert_true(isinstance(model2.distributions[0], MultivariateGaussianDistribution)) assert_true(isinstance(model2.distributions[1], IndependentComponentsDistribution)) assert_array_almost_equal(model.weights, model2.weights) @with_setup(setup_multivariate_gaussian, teardown) def test_bc_multivariate_gaussian_robust_from_json(): model2 = from_json(model.to_json()) assert_true(isinstance(model2, BayesClassifier)) assert_true(isinstance(model2.distributions[0], MultivariateGaussianDistribution)) assert_true(isinstance(model2.distributions[1], MultivariateGaussianDistribution)) assert_array_almost_equal(model.weights, model2.weights) @with_setup(setup_multivariate_mixed, teardown) def test_bc_multivariate_mixed_robust_from_json(): model2 = from_json(model.to_json()) assert_true(isinstance(model2, BayesClassifier)) assert_true(isinstance(model2.distributions[0], MultivariateGaussianDistribution)) assert_true(isinstance(model2.distributions[1], IndependentComponentsDistribution)) assert_array_almost_equal(model.weights, model2.weights) @with_setup(setup_hmm, teardown) def test_model(): assert_almost_equal(hmm1.log_probability(list('H')), -0.2231435513142097 ) assert_almost_equal(hmm1.log_probability(list('T')), -1.6094379124341003 ) assert_almost_equal(hmm1.log_probability(list('HHHH')), -0.8925742052568388 ) assert_almost_equal(hmm1.log_probability(list('THHH')), -2.2788685663767296 ) assert_almost_equal(hmm1.log_probability(list('TTTT')), -6.437751649736401 ) assert_almost_equal(hmm2.log_probability(list('H')), -0.6931471805599453 ) assert_almost_equal(hmm2.log_probability(list('T')), -0.6931471805599453 ) assert_almost_equal(hmm2.log_probability(list('HHHH')), -2.772588722239781 ) assert_almost_equal(hmm2.log_probability(list('THHH')), -2.772588722239781 ) assert_almost_equal(hmm2.log_probability(list('TTTT')), -2.772588722239781 ) assert_almost_equal(hmm3.log_probability(list('H')), -0.43078291609245417) assert_almost_equal(hmm3.log_probability(list('T')), -1.0498221244986776) assert_almost_equal(hmm3.log_probability(list('HHHH')), -1.7231316643698167) assert_almost_equal(hmm3.log_probability(list('THHH')), -2.3421708727760397) assert_almost_equal(hmm3.log_probability(list('TTTT')), -4.1992884979947105) assert_almost_equal(hmm3.log_probability(list('THTHTHTHTHTH')), -8.883630243546788) assert_almost_equal(hmm3.log_probability(list('THTHHHHHTHTH')), -7.645551826734343) assert_equal(model.d, 1) @with_setup(setup_hmm, teardown) def test_hmm_log_proba(): logs = model.predict_log_proba(np.array([list('H'), list('THHH'), list('TTTT'), list('THTHTHTHTHTH'), list('THTHHHHHTHTH')])) assert_almost_equal(logs[0][0], -0.89097292388986515) assert_almost_equal(logs[0][1], -1.3609765531356006) assert_almost_equal(logs[0][2], -1.0986122886681096) assert_almost_equal(logs[1][0], -0.93570553121744293) assert_almost_equal(logs[1][1], -1.429425687080494) assert_almost_equal(logs[1][2], -0.9990078376167526) assert_almost_equal(logs[2][0], -3.9007882563128864) assert_almost_equal(logs[2][1], -0.23562532881626597) assert_almost_equal(logs[2][2], -1.6623251045711958) assert_almost_equal(logs[3][0], -3.1703366478831185) assert_almost_equal(logs[3][1], -0.49261403211260379) assert_almost_equal(logs[3][2], -1.058478108940049) assert_almost_equal(logs[4][0], -1.3058441172130273) assert_almost_equal(logs[4][1], -1.4007102236822906) assert_almost_equal(logs[4][2], -0.7284958836972919) @with_setup(setup_hmm, teardown) def test_hmm_proba(): probs = model.predict_proba(np.array([list('H'), list('THHH'), list('TTTT'), list('THTHTHTHTHTH'), list('THTHHHHHTHTH')])) assert_almost_equal(probs[0][0], 0.41025641025641024) assert_almost_equal(probs[0][1], 0.25641025641025639) assert_almost_equal(probs[0][2], 0.33333333333333331) assert_almost_equal(probs[1][0], 0.39230898163446098) assert_almost_equal(probs[1][1], 0.23944639992337707) assert_almost_equal(probs[1][2], 0.36824461844216183) assert_almost_equal(probs[2][0], 0.020225961918306088) assert_almost_equal(probs[2][1], 0.79007663743383105) assert_almost_equal(probs[2][2], 0.18969740064786292) assert_almost_equal(probs[3][0], 0.041989459861032523) assert_almost_equal(probs[3][1], 0.61102706038265642) assert_almost_equal(probs[3][2], 0.346983479756311) assert_almost_equal(probs[4][0], 0.27094373022369794) assert_almost_equal(probs[4][1], 0.24642188711704707) assert_almost_equal(probs[4][2], 0.48263438265925512) @with_setup(setup_hmm, teardown) def test_hmm_prediction(): predicts = model.predict(np.array([list('H'), list('THHH'), list('TTTT'), list('THTHTHTHTHTH'), list('THTHHHHHTHTH')])) assert_equal(predicts[0], 0) assert_equal(predicts[1], 0) assert_equal(predicts[2], 1) assert_equal(predicts[3], 1) assert_equal(predicts[4], 2) @with_setup(setup_multivariate_gaussian, teardown) def test_io_log_probability(): X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) logp1 = model.log_probability(X) logp2 = model.log_probability(X2) logp3 = model.log_probability(X3) assert_array_almost_equal(logp1, logp2) assert_array_almost_equal(logp1, logp3) @with_setup(setup_multivariate_gaussian, teardown) def test_io_predict(): X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) y_hat1 = model.predict(X) y_hat2 = model.predict(X2) y_hat3 = model.predict(X3) assert_array_almost_equal(y_hat1, y_hat2) assert_array_almost_equal(y_hat1, y_hat3) @with_setup(setup_multivariate_gaussian, teardown) def test_io_predict_proba(): X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) y_hat1 = model.predict_proba(X) y_hat2 = model.predict_proba(X2) y_hat3 = model.predict_proba(X3) assert_array_almost_equal(y_hat1, y_hat2) assert_array_almost_equal(y_hat1, y_hat3) @with_setup(setup_multivariate_gaussian, teardown) def test_io_predict_log_proba(): X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) y_hat1 = model.predict_log_proba(X) y_hat2 = model.predict_log_proba(X2) y_hat3 = model.predict_log_proba(X3) assert_array_almost_equal(y_hat1, y_hat2) assert_array_almost_equal(y_hat1, y_hat3) def test_io_fit(): X = numpy.random.randn(100, 5) + 0.5 weights = numpy.abs(numpy.random.randn(100)) y = numpy.random.randint(2, size=100) data_generator = DataGenerator(X, weights, y) mu1 = numpy.array([0, 0, 0, 0, 0]) mu2 = numpy.array([1, 1, 1, 1, 1]) cov = numpy.eye(5) d1 = MultivariateGaussianDistribution(mu1, cov) d2 = MultivariateGaussianDistribution(mu2, cov) bc1 = BayesClassifier([d1, d2]) bc1.fit(X, y, weights) d1 = MultivariateGaussianDistribution(mu1, cov) d2 = MultivariateGaussianDistribution(mu2, cov) bc2 = BayesClassifier([d1, d2]) bc2.fit(data_generator) logp1 = bc1.log_probability(X) logp2 = bc2.log_probability(X) assert_array_almost_equal(logp1, logp2) def test_io_from_samples(): X = numpy.random.randn(100, 5) + 0.5 weights = numpy.abs(numpy.random.randn(100)) y = numpy.random.randint(2, size=100) data_generator = DataGenerator(X, weights, y) d = MultivariateGaussianDistribution bc1 = BayesClassifier.from_samples(d, X=X, y=y, weights=weights) bc2 = BayesClassifier.from_samples(d, X=data_generator) logp1 = bc1.log_probability(X) logp2 = bc2.log_probability(X) assert_array_almost_equal(logp1, logp2)pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/test_bayesian_network.py000066400000000000000000001643561420461712600270270ustar00rootroot00000000000000# test_bayes_net.py # Authors: Jacob Schreiber # Nelson Liu # ''' These are unit tests for the Bayesian network part of pomegranate. ''' from __future__ import division from pomegranate import from_json from pomegranate import DiscreteDistribution from pomegranate import ConditionalProbabilityTable from pomegranate import State, Node from pomegranate import BayesianNetwork from pomegranate.BayesianNetwork import _check_input from pomegranate.io import DataGenerator from pomegranate.io import DataFrameGenerator from nose.tools import with_setup from nose.tools import assert_equal from nose.tools import assert_not_equal from nose.tools import assert_raises from nose.tools import assert_almost_equal from networkx import DiGraph from numpy.testing import assert_array_equal from numpy.testing import assert_array_almost_equal import pandas import random, numpy import sys nan = numpy.nan numpy.random.seed(1) datasets = [numpy.random.randint(2, size=(10, 4)), numpy.random.randint(2, size=(100, 5)), numpy.random.randint(2, size=(1000, 7)), numpy.random.randint(2, size=(100, 9))] datasets_nan = [] for dataset in datasets: X = dataset.copy().astype('float64') n, d = X.shape idx = numpy.random.choice(n*d, replace=False, size=n*d//5) i, j = idx // d, idx % d X[i, j] = numpy.nan datasets_nan.append(X) def setup_monty(): # Build a model of the Monty Hall Problem global monty_network, monty_index, prize_index, guest_index random.seed(0) # Friends emissions are completely random guest = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3}) # The actual prize is independent of the other distributions prize = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3}) # Monty is dependent on both the guest and the prize. monty = ConditionalProbabilityTable( [['A', 'A', 'A', 0.0], ['A', 'A', 'B', 0.5], ['A', 'A', 'C', 0.5], ['A', 'B', 'A', 0.0], ['A', 'B', 'B', 0.0], ['A', 'B', 'C', 1.0], ['A', 'C', 'A', 0.0], ['A', 'C', 'B', 1.0], ['A', 'C', 'C', 0.0], ['B', 'A', 'A', 0.0], ['B', 'A', 'B', 0.0], ['B', 'A', 'C', 1.0], ['B', 'B', 'A', 0.5], ['B', 'B', 'B', 0.0], ['B', 'B', 'C', 0.5], ['B', 'C', 'A', 1.0], ['B', 'C', 'B', 0.0], ['B', 'C', 'C', 0.0], ['C', 'A', 'A', 0.0], ['C', 'A', 'B', 1.0], ['C', 'A', 'C', 0.0], ['C', 'B', 'A', 1.0], ['C', 'B', 'B', 0.0], ['C', 'B', 'C', 0.0], ['C', 'C', 'A', 0.5], ['C', 'C', 'B', 0.5], ['C', 'C', 'C', 0.0]], [guest, prize]) # Make the states s1 = State(guest, name="guest") s2 = State(prize, name="prize") s3 = State(monty, name="monty") # Make the bayes net, add the states, and the conditional dependencies. monty_network = BayesianNetwork("test") monty_network.add_nodes(s1, s2, s3) monty_network.add_edge(s1, s3) monty_network.add_edge(s2, s3) monty_network.bake() monty_index = monty_network.states.index(s3) prize_index = monty_network.states.index(s2) guest_index = monty_network.states.index(s1) def setup_titanic(): # Build a model of the titanic disaster global titanic_network, passenger, gender, tclass # Passengers on the Titanic either survive or perish passenger = DiscreteDistribution({'survive': 0.6, 'perish': 0.4}) # Gender, given survival data gender = ConditionalProbabilityTable( [['survive', 'male', 0.0], ['survive', 'female', 1.0], ['perish', 'male', 1.0], ['perish', 'female', 0.0]], [passenger]) # Class of travel, given survival data tclass = ConditionalProbabilityTable( [['survive', 'first', 0.0], ['survive', 'second', 1.0], ['survive', 'third', 0.0], ['perish', 'first', 1.0], ['perish', 'second', 0.0], ['perish', 'third', 0.0]], [passenger]) # State objects hold both the distribution, and a high level name. s1 = State(passenger, name="passenger") s2 = State(gender, name="gender") s3 = State(tclass, name="class") # Create the Bayesian network object with a useful name titanic_network = BayesianNetwork("Titanic Disaster") # Add the three nodes to the network titanic_network.add_nodes(s1, s2, s3) # Add transitions which represent conditional dependencies, where the # second node is conditionally dependent on the first node (Monty is # dependent on both guest and prize) titanic_network.add_edge(s1, s2) titanic_network.add_edge(s1, s3) titanic_network.bake() def setup_large_monty(): # Build the huge monty hall large_monty_network. This is an example I made # up with which may not exactly flow logically, but tests a varied type of # tables ensures heterogeneous types of data work together. global large_monty_network, large_monty_friend, large_monty_guest, large_monty global large_monty_remaining, large_monty_randomize, large_monty_prize # large_monty_Friend large_monty_friend = DiscreteDistribution({True: 0.5, False: 0.5}) # large_monty_Guest emissions are completely random large_monty_guest = ConditionalProbabilityTable( [[True, 'A', 0.50], [True, 'B', 0.25], [True, 'C', 0.25], [False, 'A', 0.0], [False, 'B', 0.7], [False, 'C', 0.3]], [large_monty_friend]) # Number of large_monty_remaining cars large_monty_remaining = DiscreteDistribution({0: 0.1, 1: 0.7, 2: 0.2}) # Whether they large_monty_randomize is dependent on the number of # large_monty_remaining cars large_monty_randomize = ConditionalProbabilityTable( [[0, True, 0.05], [0, False, 0.95], [1, True, 0.8], [1, False, 0.2], [2, True, 0.5], [2, False, 0.5]], [large_monty_remaining]) # Where the large_monty_prize is depends on if they large_monty_randomize or # not and also the large_monty_guests large_monty_friend large_monty_prize = ConditionalProbabilityTable( [[True, True, 'A', 0.3], [True, True, 'B', 0.4], [True, True, 'C', 0.3], [True, False, 'A', 0.2], [True, False, 'B', 0.4], [True, False, 'C', 0.4], [False, True, 'A', 0.1], [False, True, 'B', 0.9], [False, True, 'C', 0.0], [False, False, 'A', 0.0], [False, False, 'B', 0.4], [False, False, 'C', 0.6]], [large_monty_randomize, large_monty_friend]) # Monty is dependent on both the large_monty_guest and the large_monty_prize. large_monty = ConditionalProbabilityTable( [['A', 'A', 'A', 0.0], ['A', 'A', 'B', 0.5], ['A', 'A', 'C', 0.5], ['A', 'B', 'A', 0.0], ['A', 'B', 'B', 0.0], ['A', 'B', 'C', 1.0], ['A', 'C', 'A', 0.0], ['A', 'C', 'B', 1.0], ['A', 'C', 'C', 0.0], ['B', 'A', 'A', 0.0], ['B', 'A', 'B', 0.0], ['B', 'A', 'C', 1.0], ['B', 'B', 'A', 0.5], ['B', 'B', 'B', 0.0], ['B', 'B', 'C', 0.5], ['B', 'C', 'A', 1.0], ['B', 'C', 'B', 0.0], ['B', 'C', 'C', 0.0], ['C', 'A', 'A', 0.0], ['C', 'A', 'B', 1.0], ['C', 'A', 'C', 0.0], ['C', 'B', 'A', 1.0], ['C', 'B', 'B', 0.0], ['C', 'B', 'C', 0.0], ['C', 'C', 'A', 0.5], ['C', 'C', 'B', 0.5], ['C', 'C', 'C', 0.0]], [large_monty_guest, large_monty_prize]) # Make the states s0 = State(large_monty_friend, name="large_monty_friend") s1 = State(large_monty_guest, name="large_monty_guest") s2 = State(large_monty_prize, name="large_monty_prize") s3 = State(large_monty, name="large_monty") s4 = State(large_monty_remaining, name="large_monty_remaining") s5 = State(large_monty_randomize, name="large_monty_randomize") # Make the bayes net, add the states, and the conditional dependencies. large_monty_network = BayesianNetwork("test") large_monty_network.add_nodes(s0, s1, s2, s3, s4, s5) large_monty_network.add_transition(s0, s1) large_monty_network.add_transition(s1, s3) large_monty_network.add_transition(s2, s3) large_monty_network.add_transition(s4, s5) large_monty_network.add_transition(s5, s2) large_monty_network.add_transition(s0, s2) large_monty_network.bake() def setup_random_mixed(): numpy.random.seed(0) global X X = numpy.array([ numpy.random.choice([True, False], size=50), numpy.random.choice(['A', 'B'], size=50), numpy.random.choice(2, size=50) ], dtype=object).T.copy() global weights weights = numpy.abs(numpy.random.randn(50)) global data_generator data_generator = DataGenerator(X, weights) global model model = BayesianNetwork.from_samples(X) def teardown(): pass @with_setup(setup_monty, teardown) def test_check_input_dict(): obs = {'guest' : 'A'} _check_input(obs, monty_network) obs = {'guest' : 'NaN'} assert_raises(ValueError, _check_input, obs, monty_network) obs = {'guest' : None} assert_raises(ValueError, _check_input, obs, monty_network) obs = {'guest' : numpy.nan} assert_raises(ValueError, _check_input, obs, monty_network) obs = {'guest' : 'NaN', 'prize' : 'B'} assert_raises(ValueError, _check_input, obs, monty_network) obs = {'guest' : 'A', 'prize' : 'C'} _check_input(obs, monty_network) obs = {'guest' : 'A', 'prize' : 'C', 'monty' : 'C'} _check_input(obs, monty_network) obs = {'guest' : DiscreteDistribution({'A' : 0.25, 'B' : 0.25, 'C' : 0.50})} _check_input(obs, monty_network) obs = {'hello' : 'A', 'prize' : 'B'} assert_raises(ValueError, _check_input, obs, monty_network) @with_setup(setup_monty, teardown) def test_check_input_list_of_dicts(): obs = {'guest' : 'A'} _check_input([obs], monty_network) obs = {'guest' : 'NaN'} assert_raises(ValueError, _check_input, [obs], monty_network) obs = {'guest' : None} assert_raises(ValueError, _check_input, [obs], monty_network) obs = {'guest' : numpy.nan} assert_raises(ValueError, _check_input, [obs], monty_network) obs = {'guest' : 'NaN', 'prize' : 'B'} assert_raises(ValueError, _check_input, [obs], monty_network) obs = {'guest' : 'A', 'prize' : 'C'} _check_input([obs], monty_network) obs = {'guest' : 'A', 'prize' : 'C', 'monty' : 'C'} _check_input([obs], monty_network) obs = {'guest' : DiscreteDistribution({'A' : 0.25, 'B' : 0.25, 'C' : 0.50})} _check_input([obs], monty_network) obs = {'hello' : 'A', 'prize' : 'B'} assert_raises(ValueError, _check_input, [obs], monty_network) obs = [{'guest' : 'A'}, {'guest' : 'A', 'prize' : 'C'}, {'guest' : 'A', 'prize' : 'C', 'monty' : 'C'}, {'guest' : DiscreteDistribution({'A' : 0.25, 'B' : 0.25, 'C' : 0.50})}] _check_input(obs, monty_network) obs.append({'guest' : 'NaN', 'prize' : 'B'}) assert_raises(ValueError, _check_input, obs, monty_network) @with_setup(setup_monty, teardown) def test_check_input_list_of_lists(): obs = ['A', None, None] _check_input([obs], monty_network) obs = ['A', numpy.nan, numpy.nan] _check_input([obs], monty_network) obs = numpy.array(['A', None, None]) _check_input([obs], monty_network) obs = numpy.array(['A', numpy.nan, numpy.nan]) _check_input([obs], monty_network) obs = numpy.array(['A', 'B', 'C']) _check_input([obs], monty_network) obs = numpy.array(['NaN', numpy.nan, numpy.nan]) assert_raises(ValueError, _check_input, [obs], monty_network) obs = numpy.array(['A', 'B', 'D']) assert_raises(ValueError, _check_input, [obs], monty_network) obs = ['A'] assert_raises(ValueError, _check_input, [obs], monty_network) obs = ['A', 'C', 'E', 'F'] assert_raises(ValueError, _check_input, [obs], monty_network) d = DiscreteDistribution({'A': 0.25, 'B': 0.25, 'C': 0.25}) obs = [d, None, None] _check_input([obs], monty_network) e = DiscreteDistribution({'A': 0.25, 'B': 0.25, 'D': 0.25}) obs = [e, None, None] assert_raises(ValueError, _check_input, [obs], monty_network) obs = [['A', None, None], ['A', numpy.nan, numpy.nan], ['A', 'B', 'C'], ['A', None, 'C'], [None, 'B', 'C'], [d, None, None]] _check_input(obs, monty_network) obs.append([e, None, None]) assert_raises(ValueError, _check_input, obs, monty_network) @with_setup(setup_titanic, teardown) def test_titanic_network(): assert_almost_equal(passenger.log_probability('survive'), numpy.log(0.6)) assert_almost_equal(passenger.log_probability('survive'), numpy.log(0.6)) assert_almost_equal(gender.log_probability(('survive', 'male')), float("-inf")) assert_almost_equal(gender.log_probability(('survive', 'female')), 0.0) assert_almost_equal(gender.log_probability(('perish', 'male')), 0.0) assert_almost_equal(gender.log_probability(('perish', 'female')), float("-inf")) assert_almost_equal(tclass.log_probability(('survive', 'first')), float("-inf")) assert_almost_equal(tclass.log_probability(('survive', 'second')), 0.0) assert_almost_equal(tclass.log_probability(('survive', 'third')), float("-inf")) assert_almost_equal(tclass.log_probability(('perish', 'first')), 0.0) assert_almost_equal(tclass.log_probability(('perish', 'second')), float("-inf")) assert_almost_equal(tclass.log_probability(('perish', 'third')), float("-inf")) @with_setup(setup_titanic, teardown) def test_guest_titanic(): male = titanic_network.predict_proba({'gender': 'male'}) female = titanic_network.predict_proba({'gender': 'female'}) assert_equal(female[0].log_probability("survive"), 0.0) assert_equal(female[0].log_probability("perish"), float("-inf")) assert_equal(female[1], 'female') assert_equal(female[2].log_probability("first"), float("-inf")) assert_equal(female[2].log_probability("second"), 0.0) assert_equal(female[2].log_probability("third"), float("-inf")) assert_equal(male[0].log_probability("survive"), float("-inf")) assert_equal(male[0].log_probability("perish"), 0.0) assert_equal(male[1], 'male') assert_equal(male[2].log_probability("first"), 0.0) assert_equal(male[2].log_probability("second"), float("-inf")) assert_equal(male[2].log_probability("third"), float("-inf")) titanic_network2 = BayesianNetwork.from_json(titanic_network.to_json()) @with_setup(setup_large_monty, teardown) def test_large_monty(): assert_almost_equal(large_monty.log_probability(('A', 'A', 'C')), numpy.log(0.5)) assert_almost_equal(large_monty.log_probability(('B', 'B', 'C')), numpy.log(0.5)) assert_equal(large_monty.log_probability(('C', 'C', 'C')), float("-inf")) data = [[True, 'A', 'A', 'C', 1, True], [True, 'A', 'A', 'C', 0, True], [False, 'A', 'A', 'B', 1, False], [False, 'A', 'A', 'A', 2, False], [False, 'A', 'A', 'C', 1, False], [False, 'B', 'B', 'B', 2, False], [False, 'B', 'B', 'C', 0, False], [True, 'C', 'C', 'A', 2, True], [True, 'C', 'C', 'C', 1, False], [True, 'C', 'C', 'C', 0, False], [True, 'C', 'C', 'C', 2, True], [True, 'C', 'B', 'A', 1, False]] large_monty_network.fit(data) assert_almost_equal(large_monty.log_probability(('A', 'A', 'C')), numpy.log(0.6)) assert_almost_equal(large_monty.log_probability(('B', 'B', 'C')), numpy.log(0.5)) assert_almost_equal(large_monty.log_probability(('C', 'C', 'C')), numpy.log(0.75)) @with_setup(setup_large_monty, teardown) def test_large_monty_friend(): assert_almost_equal(large_monty_friend.log_probability(True), numpy.log(0.5)) assert_almost_equal(large_monty_friend.log_probability(False), numpy.log(0.5)) data = [[True, 'A', 'A', 'C', 1, True], [True, 'A', 'A', 'C', 0, True], [False, 'A', 'A', 'B', 1, False], [False, 'A', 'A', 'A', 2, False], [False, 'A', 'A', 'C', 1, False], [False, 'B', 'B', 'B', 2, False], [False, 'B', 'B', 'C', 0, False], [True, 'C', 'C', 'A', 2, True], [True, 'C', 'C', 'C', 1, False], [True, 'C', 'C', 'C', 0, False], [True, 'C', 'C', 'C', 2, True], [True, 'C', 'B', 'A', 1, False]] large_monty_network.fit(data) assert_almost_equal(large_monty_friend.log_probability(True), numpy.log(7. / 12)) assert_almost_equal(large_monty_friend.log_probability(False), numpy.log(5. / 12)) @with_setup(setup_large_monty, teardown) def test_large_monty_remaining(): model = large_monty_remaining assert_almost_equal(model.log_probability(0), numpy.log(0.1)) assert_almost_equal(model.log_probability(1), numpy.log(0.7)) assert_almost_equal(model.log_probability(2), numpy.log(0.2)) data = [[True, 'A', 'A', 'C', 1, True], [True, 'A', 'A', 'C', 0, True], [False, 'A', 'A', 'B', 1, False], [False, 'A', 'A', 'A', 2, False], [False, 'A', 'A', 'C', 1, False], [False, 'B', 'B', 'B', 2, False], [False, 'B', 'B', 'C', 0, False], [True, 'C', 'C', 'A', 2, True], [True, 'C', 'C', 'C', 1, False], [True, 'C', 'C', 'C', 0, False], [True, 'C', 'C', 'C', 2, True], [True, 'C', 'B', 'A', 1, False]] large_monty_network.fit(data) assert_almost_equal(model.log_probability(0), numpy.log(3. / 12)) assert_almost_equal(model.log_probability(1), numpy.log(5. / 12)) assert_almost_equal(model.log_probability(2), numpy.log(4. / 12)) @with_setup(setup_large_monty, teardown) def test_large_monty_network_log_probability(): model = large_monty_network data = numpy.array([[True, 'A', 'A', 'C', 1, True], [True, 'A', 'A', 'C', 0, True], [False, 'A', 'A', 'B', 1, False], [False, 'A', 'A', 'A', 2, False], [False, 'A', 'A', 'C', 1, False], [False, 'B', 'B', 'B', 2, False], [False, 'B', 'B', 'C', 0, False], [True, 'C', 'C', 'A', 2, True], [True, 'C', 'C', 'C', 1, False], [True, 'C', 'C', 'C', 0, False], [True, 'C', 'C', 'C', 2, True], [True, 'C', 'B', 'A', 1, False]], dtype=object) logp = [-3.863233, -8.581732, float("-inf"), float("-inf"), float("-inf"), float("-inf"), -5.013138, -6.279147, float("-inf"), float("-inf"), float("-inf"), -4.150915] logp1 = model.log_probability(data) assert_array_almost_equal(logp, logp1) model.fit(data) logp = [-5.480639, -5.480639, -4.60517 , -5.298317, -3.506558, -5.192957, -5.192957, -4.74667 , -2.667228, -3.360375, -3.648057, -3.072693] logp2 = model.log_probability(data) assert_array_almost_equal(logp2, logp) @with_setup(setup_large_monty, teardown) def test_large_monty_network_log_probability_parallel(): model = large_monty_network data = numpy.array([[True, 'A', 'A', 'C', 1, True], [True, 'A', 'A', 'C', 0, True], [False, 'A', 'A', 'B', 1, False], [False, 'A', 'A', 'A', 2, False], [False, 'A', 'A', 'C', 1, False], [False, 'B', 'B', 'B', 2, False], [False, 'B', 'B', 'C', 0, False], [True, 'C', 'C', 'A', 2, True], [True, 'C', 'C', 'C', 1, False], [True, 'C', 'C', 'C', 0, False], [True, 'C', 'C', 'C', 2, True], [True, 'C', 'B', 'A', 1, False]], dtype=object) logp = [-3.863233, -8.581732, float("-inf"), float("-inf"), float("-inf"), float("-inf"), -5.013138, -6.279147, float("-inf"), float("-inf"), float("-inf"), -4.150915] logp1 = model.log_probability(data, n_jobs=2) assert_array_almost_equal(logp, logp1) model.fit(data) logp = [-5.480639, -5.480639, -4.60517 , -5.298317, -3.506558, -5.192957, -5.192957, -4.74667 , -2.667228, -3.360375, -3.648057, -3.072693] logp2 = model.log_probability(data, n_jobs=2) assert_array_almost_equal(logp2, logp) @with_setup(setup_large_monty, teardown) def test_large_monty_prize(): assert_almost_equal(large_monty_prize.log_probability( (True, True, 'A')), numpy.log(0.3)) assert_almost_equal(large_monty_prize.log_probability( (True, False, 'C')), numpy.log(0.4)) assert_almost_equal(large_monty_prize.log_probability( (False, True, 'B')), numpy.log(0.9)) assert_almost_equal(large_monty_prize.log_probability( (False, False, 'A')), float("-inf")) data = [[True, 'A', 'A', 'C', 1, True], [True, 'A', 'A', 'C', 0, True], [False, 'A', 'A', 'B', 1, False], [False, 'A', 'A', 'A', 2, False], [False, 'A', 'A', 'C', 1, False], [False, 'B', 'B', 'B', 2, False], [False, 'B', 'B', 'C', 0, False], [True, 'C', 'C', 'A', 2, True], [True, 'C', 'C', 'C', 1, False], [True, 'C', 'C', 'C', 0, False], [True, 'C', 'C', 'C', 2, True], [True, 'C', 'B', 'A', 1, False]] large_monty_network.fit(data) assert_almost_equal(large_monty_prize.log_probability( (True, True, 'C')), numpy.log(0.5)) assert_equal(large_monty_prize.log_probability( (True, True, 'B')), float("-inf")) a = large_monty_prize.log_probability((True, False, 'A')) b = large_monty_prize.log_probability((True, False, 'B')) c = large_monty_prize.log_probability((True, False, 'C')) assert_almost_equal(a, b) assert_almost_equal(b, c) assert_equal(large_monty_prize.log_probability( (False, False, 'C')), float("-inf")) assert_almost_equal(large_monty_prize.log_probability( (False, True, 'C')), numpy.log(2. / 3)) def assert_discrete_equal(x, y, z=8): xd, yd = x.parameters[0], y.parameters[0] for key, value in xd.items(): if round(yd[key], z) != round(value, z): raise ValueError("{} != {}".format(yd[key], value)) @with_setup(setup_monty, teardown) def test_guest_monty(): a = monty_network.predict_proba({'guest': 'A'}) b = monty_network.predict_proba({'guest': 'B'}) c = monty_network.predict_proba({'guest': 'C'}) prize_correct = DiscreteDistribution( {'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3}) assert_discrete_equal(a[prize_index], b[prize_index]) assert_discrete_equal(a[prize_index], c[prize_index]) assert_discrete_equal(a[prize_index], prize_correct) assert_discrete_equal(a[monty_index], DiscreteDistribution( {'A': 0.0, 'B': 1. / 2, 'C': 1. / 2})) assert_discrete_equal(b[monty_index], DiscreteDistribution( {'A': 1. / 2, 'B': 0.0, 'C': 1. / 2})) assert_discrete_equal(c[monty_index], DiscreteDistribution( {'A': 1. / 2, 'B': 1. / 2, 'C': 0.0})) @with_setup(setup_monty, teardown) def test_guest_with_monty(): b = monty_network.predict_proba({'guest': 'A', 'monty': 'B'}) c = monty_network.predict_proba({'guest': 'A', 'monty': 'C'}) assert_equal(b[guest_index], 'A') assert_equal(b[monty_index], 'B') assert_discrete_equal(b[prize_index], DiscreteDistribution( {'A': 1. / 3, 'B': 0.0, 'C': 2. / 3})) assert_equal(c[guest_index], 'A') assert_equal(c[monty_index], 'C') assert_discrete_equal(c[prize_index], DiscreteDistribution( {'A': 1. / 3, 'B': 2. / 3, 'C': 0.0})) @with_setup(setup_monty, teardown) def test_monty(): a = monty_network.predict_proba({'monty': 'A'}) assert_equal(a[monty_index], 'A') assert_discrete_equal(a[guest_index], a[prize_index]) assert_discrete_equal(a[guest_index], DiscreteDistribution( {'A': 0.0, 'B': 1. / 2, 'C': 1. / 2})) @with_setup(setup_monty, teardown) def test_predict(): obs = [['A', None, 'B'], ['A', None, 'C'], ['A', 'B', 'C']] predictions = monty_network.predict(obs) assert_array_equal(predictions, [ ['A', 'C', 'B'], ['A', 'B', 'C'], ['A', 'B', 'C'] ]) assert_array_equal(obs, [ ['A', None, 'B'], ['A', None, 'C'], ['A', 'B', 'C'] ]) @with_setup(setup_monty, teardown) def test_rejection_sampling(): numpy.random.seed(0) predictions = monty_network._rejection(n=10,evidences=[{'guest':'A', 'monty':'B'}]) (unique, counts) = numpy.unique(predictions[:,1], return_counts=True) assert_array_equal(unique, ['A', 'C']) assert counts[0] > 0 and counts[0] < 4 # Need to find where random seed is changed so next test can work # assert_array_equal(predictions, # [['A', 'C', 'B'], # ['A', 'C', 'B'], # ['A', 'A', 'B'], # ['A', 'C', 'B'], # ['A', 'C', 'B'], # ['A', 'C', 'B'], # ['A', 'C', 'B'], # ['A', 'C', 'B'], # ['A', 'C', 'B'], # ['A', 'C', 'B']]) @with_setup(setup_monty, teardown) def test_gibbs_sampling(): # evidences = [['A', None, 'B'], # ['A', None, 'C'], # ['A', 'B', 'C' ]] predictions = monty_network._gibbs(n=1000,evidences=[{'guest': 'A', 'monty': 'B'}]) values, counts = numpy.unique(predictions[:, 1], return_counts=True) # will fail from time to time # need to fix the seed assert(abs(counts[0]-340) < 34) @with_setup(setup_monty, teardown) def test_predict_parallel(): obs = [['A', None, 'B'], ['A', None, 'C'], ['A', 'B', 'C']] predictions = monty_network.predict(obs, n_jobs=2) assert_array_equal(predictions, [ ['A', 'C', 'B'], ['A', 'B', 'C'], ['A', 'B', 'C'] ]) assert_array_equal(obs, [ ['A', None, 'B'], ['A', None, 'C'], ['A', 'B', 'C'] ]) @with_setup(setup_monty, teardown) def test_predict_datagenerator(): obs = [['A', None, 'B'], ['A', None, 'C'], ['A', 'B', 'C']] X = DataGenerator(obs) predictions = monty_network.predict(X) assert_array_equal(predictions, [ ['A', 'C', 'B'], ['A', 'B', 'C'], ['A', 'B', 'C'] ]) assert_array_equal(obs, [ ['A', None, 'B'], ['A', None, 'C'], ['A', 'B', 'C'] ]) @with_setup(setup_monty, teardown) def test_numpy_predict(): obs = numpy.array([['A', None, 'B'], ['A', None, 'C'], ['A', 'B', 'C']]) predictions = monty_network.predict(obs) assert_array_equal(predictions, [ ['A', 'C', 'B'], ['A', 'B', 'C'], ['A', 'B', 'C'] ]) assert_array_equal(obs, [ ['A', None, 'B'], ['A', None, 'C'], ['A', 'B', 'C'] ]) @with_setup(setup_monty, teardown) def test_numpy_predict_parallel(): obs = numpy.array([['A', None, 'B'], ['A', None, 'C'], ['A', 'B', 'C']]) predictions = monty_network.predict(obs, n_jobs=2) assert_array_equal(predictions, [ ['A', 'C', 'B'], ['A', 'B', 'C'], ['A', 'B', 'C'] ]) assert_array_equal(obs, [ ['A', None, 'B'], ['A', None, 'C'], ['A', 'B', 'C'] ]) @with_setup(setup_monty, teardown) def test_numpy_predict_datagenerator(): obs = numpy.array([['A', None, 'B'], ['A', None, 'C'], ['A', 'B', 'C']]) X = DataGenerator(obs) predictions = monty_network.predict(X) assert_array_equal(predictions, [ ['A', 'C', 'B'], ['A', 'B', 'C'], ['A', 'B', 'C'] ]) assert_array_equal(obs, [ ['A', None, 'B'], ['A', None, 'C'], ['A', 'B', 'C'] ]) @with_setup(setup_monty, teardown) def test_single_dict_predict_proba(): obs = {'guest': 'A', 'monty': 'B'} y = DiscreteDistribution({'A': 1./3, 'B': 0., 'C': 2./3}) y_hat = monty_network.predict_proba(obs) assert_equal(y_hat[0], 'A') assert_equal(y_hat[2], 'B') assert_discrete_equal(y_hat[1], y) @with_setup(setup_large_monty, teardown) def test_single_dict_large_predict_proba(): obs = {'large_monty_friend' : True, 'large_monty_guest': 'A', 'large_monty_prize': 'A', 'large_monty': 'C'} y1 = DiscreteDistribution({0: 0.0472, 1: 0.781, 2: 0.17167}) y2 = DiscreteDistribution({True: 0.8562, False: 0.143776}) y_hat = large_monty_network.predict_proba(obs) assert_equal(y_hat[0], True) assert_equal(y_hat[1], 'A') assert_equal(y_hat[2], 'A') assert_equal(y_hat[3], 'C') assert_discrete_equal(y_hat[4], y1, 3) assert_discrete_equal(y_hat[5], y2, 3) obs = {'large_monty_friend' : True, 'large_monty_prize': 'A', 'large_monty': 'C', 'large_monty_remaining' : 2} y1 = DiscreteDistribution({'A': 0.5, 'B': 0.5, 'C': 0.0}) y2 = DiscreteDistribution({True: 0.75, False: 0.25}) y_hat = large_monty_network.predict_proba(obs) assert_equal(y_hat[0], True) assert_equal(y_hat[2], 'A') assert_equal(y_hat[3], 'C') assert_equal(y_hat[4], 2) assert_discrete_equal(y_hat[1], y1) assert_discrete_equal(y_hat[5], y2) @with_setup(setup_monty, teardown) def test_list_of_lists_predict_proba(): obs = [['A', None, 'B']] y = DiscreteDistribution({'A': 1./3, 'B': 0., 'C': 2./3}) y_hat = monty_network.predict_proba(obs) assert_equal(y_hat[0][0], 'A') assert_equal(y_hat[0][2], 'B') assert_discrete_equal(y_hat[0][1], y) @with_setup(setup_large_monty, teardown) def test_list_of_lists_large_predict_proba(): obs = [[True, 'A', 'A', 'C', None, None]] y1 = DiscreteDistribution({0: 0.0472, 1: 0.781, 2: 0.17167}) y2 = DiscreteDistribution({True: 0.8562, False: 0.143776}) y_hat = large_monty_network.predict_proba(obs) assert_equal(y_hat[0][0], True) assert_equal(y_hat[0][1], 'A') assert_equal(y_hat[0][2], 'A') assert_equal(y_hat[0][3], 'C') assert_discrete_equal(y_hat[0][4], y1, 3) assert_discrete_equal(y_hat[0][5], y2, 3) obs = [[True, None, 'A', 'C', 2, None]] y1 = DiscreteDistribution({'A': 0.5, 'B': 0.5, 'C': 0.0}) y2 = DiscreteDistribution({True: 0.75, False: 0.25}) y_hat = large_monty_network.predict_proba(obs) assert_equal(y_hat[0][0], True) assert_equal(y_hat[0][2], 'A') assert_equal(y_hat[0][3], 'C') assert_equal(y_hat[0][4], 2) assert_discrete_equal(y_hat[0][1], y1) assert_discrete_equal(y_hat[0][5], y2) @with_setup(setup_monty, teardown) def test_list_of_dicts_predict_proba(): obs = [{'guest': 'A', 'monty': 'B'}] y = DiscreteDistribution({'A': 1./3, 'B': 0., 'C': 2./3}) y_hat = monty_network.predict_proba(obs) assert_equal(y_hat[0][0], 'A') assert_equal(y_hat[0][2], 'B') assert_discrete_equal(y_hat[0][1], y) @with_setup(setup_large_monty, teardown) def test_list_of_dicts_large_predict_proba(): obs = [{'large_monty_friend' : True, 'large_monty_guest': 'A', 'large_monty_prize': 'A', 'large_monty': 'C'}] y1 = DiscreteDistribution({0: 0.0472, 1: 0.781, 2: 0.17167}) y2 = DiscreteDistribution({True: 0.8562, False: 0.143776}) y_hat = large_monty_network.predict_proba(obs) assert_equal(y_hat[0][0], True) assert_equal(y_hat[0][1], 'A') assert_equal(y_hat[0][2], 'A') assert_equal(y_hat[0][3], 'C') assert_discrete_equal(y_hat[0][4], y1, 3) assert_discrete_equal(y_hat[0][5], y2, 3) obs = [{'large_monty_friend' : True, 'large_monty_prize': 'A', 'large_monty': 'C', 'large_monty_remaining' : 2}] y1 = DiscreteDistribution({'A': 0.5, 'B': 0.5, 'C': 0.0}) y2 = DiscreteDistribution({True: 0.75, False: 0.25}) y_hat = large_monty_network.predict_proba(obs) assert_equal(y_hat[0][0], True) assert_equal(y_hat[0][2], 'A') assert_equal(y_hat[0][3], 'C') assert_equal(y_hat[0][4], 2) assert_discrete_equal(y_hat[0][1], y1) assert_discrete_equal(y_hat[0][5], y2) @with_setup(setup_monty, teardown) def test_list_of_dicts_predict_proba_parallel(): obs = [{'guest': 'A', 'monty': 'B'}, {'guest': 'B', 'prize': 'A'}, {'monty': 'C', 'prize': 'B'}, {'monty': 'B'}, {'prize': 'A'}] y = DiscreteDistribution({'A': 1./3, 'B': 0., 'C': 2./3}) y_hat = monty_network.predict_proba(obs, n_jobs=2) assert_equal(y_hat[0][0], 'A') assert_equal(y_hat[0][2], 'B') assert_discrete_equal(y_hat[0][1], y) assert_equal(y_hat[1][0], 'B') assert_equal(y_hat[1][1], 'A') assert_equal(y_hat[3][2], 'B') assert_equal(y_hat[4][1], 'A') @with_setup(setup_monty, teardown) def test_raise_error(): obs = [['green', 'cat', None]] assert_raises(ValueError, monty_network.predict, obs) obs = [['A', 'b', None]] assert_raises(ValueError, monty_network.predict, obs) obs = [['none', 'B', None]] assert_raises(ValueError, monty_network.predict, obs) obs = [['NaN', 'B', None]] assert_raises(ValueError, monty_network.predict, obs) obs = [['A', 'C', 'D']] assert_raises(ValueError, monty_network.predict, obs) def test_exact_structure_learning(): logps = -19.8282, -345.9527, -4847.59688, -604.0190 for X, logp in zip(datasets, logps): model = BayesianNetwork.from_samples(X, algorithm='exact') model2 = BayesianNetwork.from_samples(X, algorithm='exact-dp') assert_almost_equal(model.log_probability(X).sum(), model2.log_probability(X).sum()) assert_almost_equal(model.log_probability(X).sum(), logp, 4) def test_exact_low_memory_structure_learning(): logps = -19.8282, -345.9527, -4847.59688, -604.0190 for X, logp in zip(datasets, logps): model = BayesianNetwork.from_samples(X, low_memory=True, algorithm='exact') model2 = BayesianNetwork.from_samples(X, low_memory=True, algorithm='exact-dp') assert_almost_equal(model.log_probability(X).sum(), model2.log_probability(X).sum()) assert_almost_equal(model.log_probability(X).sum(), logp, 4) def test_exact_penalized_structure_learning(): n_parents = [(5, 3, 4), (10, 0, 1), (21, 0, 8), (26, 3, 21)] for X, n_parents in zip(datasets, n_parents): model = BayesianNetwork.from_samples(X, algorithm='exact', penalty=0) model2 = BayesianNetwork.from_samples(X, algorithm='exact-dp', penalty=0) assert_equal(sum(map(len, model.structure)), n_parents[0]) assert_equal(sum(map(len, model2.structure)), n_parents[0]) model = BayesianNetwork.from_samples(X, algorithm='exact') model2 = BayesianNetwork.from_samples(X, algorithm='exact-dp') assert_equal(sum(map(len, model.structure)), n_parents[1]) assert_equal(sum(map(len, model2.structure)), n_parents[1]) model = BayesianNetwork.from_samples(X, algorithm='exact', penalty=1) model2 = BayesianNetwork.from_samples(X, algorithm='exact-dp', penalty=1) assert_equal(sum(map(len, model.structure)), n_parents[2]) assert_equal(sum(map(len, model2.structure)), n_parents[2]) model = BayesianNetwork.from_samples(X, algorithm='exact', penalty=100) model2 = BayesianNetwork.from_samples(X, algorithm='exact-dp', penalty=100) assert_equal(sum(map(len, model.structure)), 0) assert_equal(sum(map(len, model2.structure)), 0) def test_exact_penalized_low_memory_structure_learning(): n_parents = [(5, 3, 4), (10, 0, 1), (21, 0, 8), (26, 3, 21)] for X, n_parents in zip(datasets, n_parents): model = BayesianNetwork.from_samples(X, low_memory=True, algorithm='exact', penalty=0) model2 = BayesianNetwork.from_samples(X, low_memory=True, algorithm='exact-dp', penalty=0) assert_equal(sum(map(len, model.structure)), n_parents[0]) assert_equal(sum(map(len, model2.structure)), n_parents[0]) model = BayesianNetwork.from_samples(X, low_memory=True, algorithm='exact') model2 = BayesianNetwork.from_samples(X, low_memory=True, algorithm='exact-dp') assert_equal(sum(map(len, model.structure)), n_parents[1]) assert_equal(sum(map(len, model2.structure)), n_parents[1]) model = BayesianNetwork.from_samples(X, low_memory=True, algorithm='exact', penalty=1) model2 = BayesianNetwork.from_samples(X, low_memory=True, algorithm='exact-dp', penalty=1) assert_equal(sum(map(len, model.structure)), n_parents[2]) assert_equal(sum(map(len, model2.structure)), n_parents[2]) model = BayesianNetwork.from_samples(X, low_memory=True, algorithm='exact', penalty=100) model2 = BayesianNetwork.from_samples(X, low_memory=True, algorithm='exact-dp', penalty=100) assert_equal(sum(map(len, model.structure)), 0) assert_equal(sum(map(len, model2.structure)), 0) def test_exact_structure_learning_include_edges(): for X in datasets: model = BayesianNetwork.from_samples(X, algorithm='exact', include_edges=[(1, 3)]) assert_equal(model.structure[3], (1,)) model = BayesianNetwork.from_samples(X, algorithm='exact') assert_not_equal(model.structure[3], (1,)) def test_exact_low_memory_structure_learning_include_edges(): for X in datasets: model = BayesianNetwork.from_samples(X, algorithm='exact', low_memory=True, include_edges=[(1, 3)]) assert_equal(model.structure[3], (1,)) model = BayesianNetwork.from_samples(X, low_memory=True, algorithm='exact') assert_not_equal(model.structure[3], (1,)) def test_exact_dp_structure_learning_include_edges(): for X in datasets: model = BayesianNetwork.from_samples(X, algorithm='exact-dp', include_edges=[(1, 3)]) assert_equal(model.structure[3], (1,)) model = BayesianNetwork.from_samples(X, algorithm='exact-dp') assert_not_equal(model.structure[3], (1,)) def test_exact_structure_learning_exclude_edges(): for X in datasets: X = X.copy() X[:,1] = X[:,-1] X[:,-2] = X[:,-1] d = X.shape[1] # Learn constrained network model = BayesianNetwork.from_samples(X, algorithm='exact', exclude_edges=[(1, d-1), (d-1, d-2)]) assert_not_equal(model.structure[-1], (1,)) assert_not_equal(model.structure[-2], (d-1,)) assert_equal(model.structure[-2], (1,)) def test_exact_low_memory_structure_learning_exclude_edges(): for X in datasets: X = X.copy() X[:,1] = X[:,-1] X[:,-2] = X[:,-1] d = X.shape[1] # Learn constrained network model = BayesianNetwork.from_samples(X, algorithm='exact', low_memory=True, exclude_edges=[(1, d-1), (d-1, d-2)]) assert_not_equal(model.structure[-1], (1,)) assert_not_equal(model.structure[-2], (d-1,)) assert_equal(model.structure[-2], (1,)) def test_exact_dp_structure_learning_exclude_edges(): for X in datasets: X = X.copy() X[:,1] = X[:,-1] X[:,-2] = X[:,-1] d = X.shape[1] # Learn constrained network model = BayesianNetwork.from_samples(X, algorithm='exact-dp', exclude_edges=[(1, d-1), (d-1, d-2)]) assert_not_equal(model.structure[-1], (1,)) assert_not_equal(model.structure[-2], (d-1,)) def test_constrained_sl_structure_learning_exclude_edges(): for X in datasets: X = X.copy() X[:,1] = X[:,-1] X[:,-2] = X[:,-1] d = X.shape[1] cg = DiGraph() n = tuple(range(d)) cg.add_edge(n, n) # Learn constrained network model = BayesianNetwork.from_samples(X, algorithm='greedy', constraint_graph=cg, exclude_edges=[(1, d-1), (d-1, d-2)]) assert_not_equal(model.structure[-1], (1,)) assert_not_equal(model.structure[-2], (d-1,)) assert_equal(model.structure[-2], (1,)) def test_low_memory_constrained_sl_structure_learning_exclude_edges(): for X in datasets: X = X.copy() X[:,1] = X[:,-1] X[:,-2] = X[:,-1] d = X.shape[1] cg = DiGraph() n = tuple(range(d)) cg.add_edge(n, n) # Learn constrained network model = BayesianNetwork.from_samples(X, algorithm='greedy', low_memory=True, constraint_graph=cg, exclude_edges=[(1, d-1), (d-1, d-2)]) assert_not_equal(model.structure[-1], (1,)) assert_not_equal(model.structure[-2], (d-1,)) assert_equal(model.structure[-2], (1,)) def test_constrained_parents_structure_learning_exclude_edges(): for X in datasets: X = X.copy() X[:,1] = X[:,-1] X[:,-2] = X[:,-1] d = X.shape[1] d1 = int(numpy.ceil(d / 2)) # Node groups g1 = tuple(range(0, d1)) g2 = tuple(range(d1, d)) # Constraint graph: cg = DiGraph() cg.add_edge(g1, g2) # Learn constrained network model1 = BayesianNetwork.from_samples(X, algorithm='exact', constraint_graph=cg, exclude_edges=[(1, d-1)]) assert_not_equal(model1.structure[-1], (1,)) assert_equal(model1.structure[-2], (1,)) model2 = BayesianNetwork.from_samples(X, algorithm='exact', constraint_graph=cg) assert_equal(model2.structure[-1], (1,)) assert_equal(model2.structure[-2], (1,)) X = numpy.random.randint(2, size=(50, 8)) X[:,0] = X[:,4] X[:,1] = X[:,7] X[:,2] = X[:,7] cg = DiGraph() n1 = (0, 2, 3, 5, 6) n2 = (1, 4, 7) cg.add_edge(n1, n2) model = BayesianNetwork.from_samples(X, algorithm='exact', constraint_graph=cg, exclude_edges=[(0, 4), (2, 7)]) assert_not_equal(model.structure[7], (2,)) assert_not_equal(model.structure[4], (0,)) model = BayesianNetwork.from_samples(X, algorithm='exact', constraint_graph=cg) assert_equal(model.structure[7], (2,)) assert_equal(model.structure[4], (0,)) def test_low_memory_constrained_parents_structure_learning_exclude_edges(): for X in datasets: X = X.copy() X[:,1] = X[:,-1] X[:,-2] = X[:,-1] d = X.shape[1] d1 = int(numpy.ceil(d / 2)) # Node groups g1 = tuple(range(0, d1)) g2 = tuple(range(d1, d)) # Constraint graph: cg = DiGraph() cg.add_edge(g1, g2) # Learn constrained network model1 = BayesianNetwork.from_samples(X, algorithm='exact', low_memory=True, constraint_graph=cg, exclude_edges=[(1, d-1)]) assert_not_equal(model1.structure[-1], (1,)) assert_equal(model1.structure[-2], (1,)) model2 = BayesianNetwork.from_samples(X, algorithm='exact', low_memory=True, constraint_graph=cg) assert_equal(model2.structure[-1], (1,)) assert_equal(model2.structure[-2], (1,)) X = numpy.random.randint(2, size=(50, 8)) X[:,0] = X[:,4] X[:,1] = X[:,7] X[:,2] = X[:,7] cg = DiGraph() n1 = (0, 2, 3, 5, 6) n2 = (1, 4, 7) cg.add_edge(n1, n2) model = BayesianNetwork.from_samples(X, algorithm='exact', low_memory=True, constraint_graph=cg, exclude_edges=[(0, 4), (2, 7)]) assert_not_equal(model.structure[7], (2,)) assert_not_equal(model.structure[4], (0,)) model = BayesianNetwork.from_samples(X, algorithm='exact', low_memory=True, constraint_graph=cg) assert_equal(model.structure[7], (2,)) assert_equal(model.structure[4], (0,)) def test_constrained_slap_structure_learning_exclude_edges(): for X in datasets: X = X.copy() X[:,1] = X[:,-1] X[:,-2] = X[:,-1] d = X.shape[1] d1 = int(numpy.ceil(d / 2)) # Node groups g1 = tuple(range(0, d1)) g2 = tuple(range(d1, d)) # Constraint graph: cg = DiGraph() cg.add_edge(g1, g2) cg.add_edge(g2, g2) # Learn constrained network model1 = BayesianNetwork.from_samples(X, algorithm='exact', constraint_graph=cg, exclude_edges=[(1, d-1)]) assert_not_equal(model1.structure[-1], (1,)) assert_equal(model1.structure[-1], (d-2,)) #model2 = BayesianNetwork.from_samples(X, algorithm='exact', # constraint_graph=cg) #assert_equal(model2.structure[-1], (d-2,)) X = numpy.random.randint(2, size=(50, 8)) X[:,0] = X[:,4] X[:,1] = X[:,7] X[:,2] = X[:,7] cg = DiGraph() n1 = (0, 2, 3, 5, 6) n2 = (1, 4, 7) cg.add_edge(n1, n2) cg.add_edge(n2, n2) model = BayesianNetwork.from_samples(X, algorithm='exact', constraint_graph=cg, exclude_edges=[(0, 4), (2, 7)]) assert_not_equal(model.structure[7], (2,)) assert_not_equal(model.structure[4], (0,)) model = BayesianNetwork.from_samples(X, algorithm='exact', constraint_graph=cg) assert_equal(model.structure[7], (2,)) assert_equal(model.structure[4], (0,)) def test_constrained_parents_structure_learning(): logps1 = [-12.2173, -207.3633, -3462.7469, -480.0970] logps2 = [-10.8890, -207.3633, -3462.7469, -480.0970] for X, logp1, logp2 in zip(datasets, logps1, logps2): X = X.copy() X[:,1] = X[:,-1] X[:,-2] = X[:,-1] d = X.shape[1] d1 = int(numpy.ceil(d / 2)) # Node groups g1 = tuple(range(0, d1)) g2 = tuple(range(d1, d)) # Constraint graph: cg = DiGraph() cg.add_edge(g1, g2) # Learn constrained network model1 = BayesianNetwork.from_samples(X, algorithm='exact', constraint_graph=cg) assert_almost_equal(model1.log_probability(X).sum(), logp1, 4) # Check structure constraints satisfied for node in g1: assert_equal(0, len(model1.structure[node])) assert_equal(model1.structure[-1], (1,)) assert_equal(model1.structure[-2], (1,)) model2 = BayesianNetwork.from_samples(X, algorithm='exact') assert_almost_equal(model2.log_probability(X).sum(), logp2, 4) assert_equal(model2.structure[-1], (d-2,)) assert_equal(model2.structure[-2], (1,)) def test_constrained_slap_structure_learning(): logps = [-21.7780, -345.9527, -4847.5969, -611.0356] for X, logp in zip(datasets, logps): d = X.shape[1] d1 = int(numpy.ceil(d / 2)) # Node groups g1 = tuple(range(0, d1)) g2 = tuple(range(d1, d)) # Constraint graph: cg = DiGraph() cg.add_edge(g1, g2) cg.add_edge(g2, g2) # Learn constrained network model = BayesianNetwork.from_samples(X, algorithm='exact', constraint_graph=cg) assert_almost_equal(model.log_probability(X).sum(), logp, 4) # Check structure constraints satisfied for node in g1: assert_equal(0, len(model.structure[node])) def test_from_structure(): X = datasets[1] structure = ((1, 2), (4,), (), (), (3,)) model = BayesianNetwork.from_structure(X, structure=structure) assert_equal(model.structure, structure) assert_almost_equal(model.log_probability(X).sum(), -344.38287, 4) model2 = BayesianNetwork.from_json(model.to_json()) assert_equal(model2.structure, structure) assert_almost_equal(model.log_probability(X).sum(), -344.38287, 4) model_dtype = type(model.states[0].distribution.parameters[0][0][0]) model2_dtype = type(model2.states[0].distribution.parameters[0][0][0]) assert_equal(model_dtype, model2_dtype) def test_robust_from_structure(): X = datasets[1] structure = ((1, 2), (4,), (), (), (3,)) model = BayesianNetwork.from_structure(X, structure=structure) assert_equal(model.structure, structure) assert_almost_equal(model.log_probability(X).sum(), -344.38287, 4) model2 = from_json(model.to_json()) assert_equal(model2.structure, structure) assert_almost_equal(model.log_probability(X).sum(), -344.38287, 4) model_dtype = type(model.states[0].distribution.parameters[0][0][0]) model2_dtype = type(model2.states[0].distribution.parameters[0][0][0]) assert_equal(model_dtype, model2_dtype) @with_setup(setup_random_mixed) def test_from_json(): model2 = BayesianNetwork.from_json(model.to_json()) logp1 = model.log_probability(X) logp2 = model2.log_probability(X) logp = [-2.304186, -1.898721, -1.898721, -2.224144, -1.898721, -1.978764, -1.898721, -1.898721, -1.898721, -1.898721, -1.818679, -2.384229, -2.304186, -1.978764, -2.304186, -2.384229, -2.304186, -2.384229, -2.304186, -1.978764, -2.224144, -1.818679, -1.898721, -2.304186, -2.304186, -1.898721, -1.818679, -1.898721, -1.818679, -2.304186, -1.978764, -2.224144, -1.898721, -2.304186, -1.898721, -1.818679, -2.304186, -1.898721, -1.898721, -2.384229, -2.224144, -1.818679, -2.384229, -1.978764, -1.818679, -1.978764, -1.898721, -1.818679, -2.224144, -1.898721] assert_array_almost_equal(logp1, logp2) assert_array_almost_equal(logp1, logp) assert_array_almost_equal(logp2, logp) model_dtype = type(list(model.states[0].distribution.parameters[0].keys())[0]) model2_dtype = type(list(model2.states[0].distribution.parameters[0].keys())[0]) assert_equal(model_dtype, model2_dtype) @with_setup(setup_random_mixed) def test_robust_from_json(): model2 = from_json(model.to_json()) logp1 = model.log_probability(X) logp2 = model2.log_probability(X) logp = [-2.304186, -1.898721, -1.898721, -2.224144, -1.898721, -1.978764, -1.898721, -1.898721, -1.898721, -1.898721, -1.818679, -2.384229, -2.304186, -1.978764, -2.304186, -2.384229, -2.304186, -2.384229, -2.304186, -1.978764, -2.224144, -1.818679, -1.898721, -2.304186, -2.304186, -1.898721, -1.818679, -1.898721, -1.818679, -2.304186, -1.978764, -2.224144, -1.898721, -2.304186, -1.898721, -1.818679, -2.304186, -1.898721, -1.898721, -2.384229, -2.224144, -1.818679, -2.384229, -1.978764, -1.818679, -1.978764, -1.898721, -1.818679, -2.224144, -1.898721] assert_array_almost_equal(logp1, logp2) assert_array_almost_equal(logp1, logp) assert_array_almost_equal(logp2, logp) model_dtype = type(list(model.states[0].distribution.parameters[0].keys())[0]) model2_dtype = type(list(model2.states[0].distribution.parameters[0].keys())[0]) assert_equal(model_dtype, model2_dtype) def test_float64_from_json(): X = datasets[1].astype('float64') structure = ((1, 2), (4,), (), (), (3,)) model = BayesianNetwork.from_structure(X, structure=structure) assert_equal(model.structure, structure) assert_almost_equal(model.log_probability(X).sum(), -344.38287, 4) model2 = BayesianNetwork.from_json(model.to_json()) assert_equal(model2.structure, structure) assert_almost_equal(model.log_probability(X).sum(), -344.38287, 4) model_dtype = type(model.states[0].distribution.parameters[0][0][0]) model2_dtype = type(model2.states[0].distribution.parameters[0][0][0]) assert_equal(model_dtype, model2_dtype) def test_robust_float64_from_json(): X = datasets[1].astype('float64') structure = ((1, 2), (4,), (), (), (3,)) model = BayesianNetwork.from_structure(X, structure=structure) assert_equal(model.structure, structure) assert_almost_equal(model.log_probability(X).sum(), -344.38287, 4) model2 = from_json(model.to_json()) assert_equal(model2.structure, structure) assert_almost_equal(model.log_probability(X).sum(), -344.38287, 4) model_dtype = type(model.states[0].distribution.parameters[0][0][0]) model2_dtype = type(model2.states[0].distribution.parameters[0][0][0]) assert_equal(model_dtype, model2_dtype) def test_parallel_structure_learning(): logps = -19.8282, -345.9527, -4847.59688, -604.0190 for X, logp in zip(datasets, logps): model = BayesianNetwork.from_samples(X, algorithm='exact') model2 = BayesianNetwork.from_samples(X, algorithm='exact', n_jobs=2) assert_equal(model.log_probability(X).sum(), model2.log_probability(X).sum()) assert_almost_equal(model.log_probability(X).sum(), logp, 4) def test_greedy_structure_learning(): logps = -19.8282, -345.9527, -4847.59688, -611.0356 for X, logp in zip(datasets, logps): model = BayesianNetwork.from_samples(X, algorithm='greedy') assert_almost_equal(model.log_probability(X).sum(), logp, 4) def test_greedy_structure_learning_include_edges(): for X in datasets: model = BayesianNetwork.from_samples(X, algorithm='greedy', include_edges=[(1, 3)]) assert_equal(model.structure[3], (1,)) model = BayesianNetwork.from_samples(X, algorithm='greedy') assert_not_equal(model.structure[3], (1,)) def test_greedy_structure_learning_exclude_edges(): for X in datasets: X = X.copy() X[:,1] = X[:,-1] X[:,-2] = X[:,-1] d = X.shape[1] # Learn constrained network model = BayesianNetwork.from_samples(X, algorithm='greedy', exclude_edges=[(1, d-1), (d-1, d-2)]) assert_not_equal(model.structure[-1], (1,)) assert_not_equal(model.structure[-2], (d-1,)) assert_equal(model.structure[-2], (1,)) def test_greedy_penalized_structure_learning(): n_parents = [(5, 3, 4), (10, 0, 1), (21, 0, 5), (26, 1, 21)] for X, n_parents in zip(datasets, n_parents): model = BayesianNetwork.from_samples(X, algorithm='greedy', penalty=0) assert_equal(sum(map(len, model.structure)), n_parents[0]) model = BayesianNetwork.from_samples(X, algorithm='greedy') assert_equal(sum(map(len, model.structure)), n_parents[1]) model = BayesianNetwork.from_samples(X, algorithm='greedy', penalty=1) assert_equal(sum(map(len, model.structure)), n_parents[2]) model = BayesianNetwork.from_samples(X, algorithm='greedy', penalty=100) assert_equal(sum(map(len, model.structure)), 0) def test_chow_liu_structure_learning(): logps = -19.8282, -344.248785, -4842.40158, -603.2370 for X, logp in zip(datasets, logps): model = BayesianNetwork.from_samples(X, algorithm='chow-liu') assert_almost_equal(model.log_probability(X).sum(), logp, 4) def test_exact_nan_structure_learning(): logps = -6.13764, -159.6505, -2055.76364, -201.73615 for X, logp in zip(datasets_nan, logps): model = BayesianNetwork.from_samples(X, algorithm='exact') model2 = BayesianNetwork.from_samples(X, algorithm='exact-dp') assert_equal(model.log_probability(X).sum(), model2.log_probability(X).sum()) assert_almost_equal(model.log_probability(X).sum(), logp, 4) def test_greedy_nan_structure_learning(): logps = -7.5239, -159.6505, -2058.5706, -203.7662 for X, logp in zip(datasets_nan, logps): model = BayesianNetwork.from_samples(X, algorithm='greedy') assert_almost_equal(model.log_probability(X).sum(), logp, 4) @with_setup(setup_random_mixed, teardown) def test_io_log_probability(): X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) logp1 = model.log_probability(X) logp2 = model.log_probability(X2) logp3 = model.log_probability(X3) assert_array_almost_equal(logp1, logp2) assert_array_almost_equal(logp1, logp3) @with_setup(setup_random_mixed, teardown) def test_io_predict(): X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) y_hat1 = model.predict(X) y_hat2 = model.predict(X2) y_hat3 = model.predict(X3) assert_array_equal(y_hat1, y_hat2) assert_array_equal(y_hat1, y_hat3) @with_setup(setup_random_mixed, teardown) def test_io_fit(): d1 = DiscreteDistribution({True: 0.6, False: 0.4}) d2 = ConditionalProbabilityTable([ [True, 'A', 0.2], [True, 'B', 0.8], [False, 'A', 0.3], [False, 'B', 0.7]], [d1]) d3 = ConditionalProbabilityTable([ ['A', 0, 0.3], ['A', 1, 0.7], ['B', 0, 0.8], ['B', 1, 0.2]], [d2]) n1 = Node(d1) n2 = Node(d2) n3 = Node(d3) model1 = BayesianNetwork() model1.add_nodes(n1, n2, n3) model1.add_edge(n1, n2) model1.add_edge(n2, n3) model1.bake() model1.fit(X, weights=weights) d1 = DiscreteDistribution({True: 0.2, False: 0.8}) d2 = ConditionalProbabilityTable([ [True, 'A', 0.7], [True, 'B', 0.2], [False, 'A', 0.4], [False, 'B', 0.6]], [d1]) d3 = ConditionalProbabilityTable([ ['A', 0, 0.9], ['A', 1, 0.1], ['B', 0, 0.0], ['B', 1, 1.0]], [d2]) n1 = Node(d1) n2 = Node(d2) n3 = Node(d3) model2 = BayesianNetwork() model2.add_nodes(n1, n2, n3) model2.add_edge(n1, n2) model2.add_edge(n2, n3) model2.bake() model2.fit(data_generator) logp1 = model1.log_probability(X) logp2 = model2.log_probability(X) assert_array_almost_equal(logp1, logp2) @with_setup(setup_random_mixed, teardown) def test_io_from_samples(): model1 = BayesianNetwork.from_samples(X, weights=weights) model2 = BayesianNetwork.from_samples(data_generator) logp1 = model1.log_probability(X) logp2 = model2.log_probability(X) assert_array_almost_equal(logp1, logp2) @with_setup(setup_random_mixed, teardown) def test_io_from_structure(): structure = ((2,), (0, 2), ()) model1 = BayesianNetwork.from_structure(X=X, weights=weights, structure=structure) model2 = BayesianNetwork.from_structure(X=data_generator, structure=structure) logp1 = model1.log_probability(X) logp2 = model2.log_probability(X) assert_array_almost_equal(logp1, logp2) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/test_custom_distributions.py000066400000000000000000000660251420461712600277510ustar00rootroot00000000000000from __future__ import (division) from pomegranate import * from nose.tools import with_setup from nose.tools import assert_almost_equal from nose.tools import assert_equal from nose.tools import assert_not_equal from nose.tools import assert_less_equal from nose.tools import assert_raises from nose.tools import assert_true from numpy.testing import assert_array_equal from numpy.testing import assert_array_almost_equal import numpy import scipy.stats nan = numpy.nan class NormalDistribution2(): def __init__(self, mu, std): self.mu = mu self.std = std self.d = 1 self.summaries = numpy.zeros(3) def log_probability(self, X): return scipy.stats.norm.logpdf(X, self.mu, self.std) def summarize(self, X, w=None): if w is None: w = numpy.ones(X.shape[0]) X = X.reshape(X.shape[0]) self.summaries[0] += w.sum() self.summaries[1] += X.dot(w) self.summaries[2] += (X ** 2.).dot(w) def from_summaries(self, inertia=0.0): self.mu = self.summaries[1] / self.summaries[0] self.std = self.summaries[2] / self.summaries[0] - self.summaries[1] ** 2 / (self.summaries[0] ** 2) self.std = numpy.sqrt(self.std) self.clear_summaries() def clear_summaries(self, inertia=0.0): self.summaries = numpy.zeros(3) @classmethod def from_samples(cls, X, weights=None): d = NormalDistribution2(0, 0) d.summarize(X, weights) d.from_summaries() return d @classmethod def blank(cls): return NormalDistribution2(0, 0) class MultivariateGaussianDistribution2(): def __init__(self, mu, cov): self.mu = mu self.cov = cov self.d = len(mu) self.summaries = [0, numpy.zeros(self.d), numpy.zeros((self.d, self.d))] def log_probability(self, X): return scipy.stats.multivariate_normal.logpdf(X, self.mu, self.cov) def summarize(self, X, w=None): if w is None: w = numpy.ones(X.shape[0]) self.summaries[0] += w.sum() self.summaries[1] += X.T.dot(w) self.summaries[2] += (X.T * numpy.sqrt(w)).dot((X.T * numpy.sqrt(w)).T) def from_summaries(self, inertia=0.0): self.mu = self.summaries[1] / self.summaries[0] w = self.summaries[0] x_ij = self.summaries[2] x_i = self.summaries[1].reshape(self.d, 1) self.cov = (x_ij - x_i.T * x_i / w) / w self.clear_summaries() def clear_summaries(self, inertia=0.0): self.summaries = [0, numpy.zeros(self.d), numpy.zeros((self.d, self.d))] @classmethod def from_samples(cls, X, weights=None): mu = numpy.zeros(X.shape[1]) cov = numpy.eye(X.shape[1]) d = MultivariateGaussianDistribution2(mu, cov) d.summarize(X, weights) d.from_summaries() return d @classmethod def blank(cls, d=2): mu = numpy.zeros(d) cov = numpy.eye(d) return MultivariateGaussianDistribution2(mu, cov) def build_model(d1, d2): s1 = State(d1, "s1") s2 = State(d2, "d2") model = HiddenMarkovModel() model.add_states(s1, s2) model.add_transition(model.start, s1, 0.8) model.add_transition(model.start, s2, 0.2) model.add_transition(s1, s1, 0.6) model.add_transition(s1, s2, 0.4) model.add_transition(s2, s2, 0.3) model.add_transition(s2, s1, 0.7) model.bake() return model def setup_normal_hmm(): global model1 global model2 d1 = NormalDistribution(0, 1) d2 = NormalDistribution(1, 1) model1 = build_model(d1, d2) d3 = NormalDistribution2(0, 1) d4 = NormalDistribution2(1, 1) model2 = build_model(d3, d4) def setup_mgd_hmm(): global model1 global model2 d1 = MultivariateGaussianDistribution(numpy.zeros(3), numpy.eye(3)) d2 = MultivariateGaussianDistribution(numpy.ones(3), numpy.eye(3)) model1 = build_model(d1, d2) d3 = MultivariateGaussianDistribution2(numpy.zeros(3), numpy.eye(3)) d4 = MultivariateGaussianDistribution2(numpy.ones(3), numpy.eye(3)) model2 = build_model(d3, d4) def setup_icd_hmm(): global model1 global model2 d1 = IndependentComponentsDistribution([NormalDistribution(0, 1) for _ in range(5)]) d2 = IndependentComponentsDistribution([NormalDistribution(1, 1) for _ in range(5)]) model1 = build_model(d1, d2) d3 = IndependentComponentsDistribution([NormalDistribution2(0, 1) for _ in range(5)]) d4 = IndependentComponentsDistribution([NormalDistribution2(1, 1) for _ in range(5)]) model2 = build_model(d3, d4) def test_custom_normal_gmm_init(): d1 = NormalDistribution2(0, 1) d2 = NormalDistribution2(1, 1) model = GeneralMixtureModel([d1, d2]) assert_equal(d1.d, 1) assert_equal(d2.d, 1) assert_equal(model.d, 1) def test_custom_normal_gmm_logp(): X = numpy.random.normal(0.5, 1, size=(20, 1)) d1 = NormalDistribution(0, 1) d2 = NormalDistribution(1, 1) model1 = GeneralMixtureModel([d1, d2]) d3 = NormalDistribution(0, 1) d4 = NormalDistribution(1, 1) model2 = GeneralMixtureModel([d3, d4]) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_normal_gmm_predict(): X = numpy.random.normal(0.5, 1, size=(20, 1)) d1 = NormalDistribution(0, 1) d2 = NormalDistribution(1, 1) model1 = GeneralMixtureModel([d1, d2]) d3 = NormalDistribution(0, 1) d4 = NormalDistribution(1, 1) model2 = GeneralMixtureModel([d3, d4]) assert_array_equal(model1.predict(X), model2.predict(X)) def test_custom_normal_gmm_predict_proba(): X = numpy.random.normal(0.5, 1, size=(20, 1)) d1 = NormalDistribution(0, 1) d2 = NormalDistribution(1, 1) model1 = GeneralMixtureModel([d1, d2]) d3 = NormalDistribution(0, 1) d4 = NormalDistribution(1, 1) model2 = GeneralMixtureModel([d3, d4]) assert_array_almost_equal(model1.predict_proba(X), model2.predict_proba(X)) def test_custom_normal_gmm_predict_log_proba(): X = numpy.random.normal(0.5, 1, size=(20, 1)) d1 = NormalDistribution(0, 1) d2 = NormalDistribution(1, 1) model1 = GeneralMixtureModel([d1, d2]) d3 = NormalDistribution(0, 1) d4 = NormalDistribution(1, 1) model2 = GeneralMixtureModel([d3, d4]) assert_array_almost_equal(model1.predict_log_proba(X), model2.predict_log_proba(X)) def test_custom_normal_gmm_fit(): X = numpy.random.normal(0.5, 1, size=(20, 1)) d1 = NormalDistribution(0, 1) d2 = NormalDistribution(1, 1) model1 = GeneralMixtureModel([d1, d2]) model1.fit(X) d3 = NormalDistribution2(0, 1) d4 = NormalDistribution2(1, 1) model2 = GeneralMixtureModel([d3, d4]) model2.fit(X) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_normal_gmm_from_samples(): X = numpy.random.normal(0.5, 1, size=(200, 1)) X[::2] += 1 model1 = GeneralMixtureModel.from_samples(NormalDistribution, 2, X, init='first-k', max_iterations=5) model2 = GeneralMixtureModel.from_samples(NormalDistribution2, 2, X, init='first-k', max_iterations=5) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_mgd_gmm_init(): d1 = MultivariateGaussianDistribution2(numpy.zeros(3), numpy.eye(3)) d2 = MultivariateGaussianDistribution2(numpy.ones(3), numpy.eye(3)) model = GeneralMixtureModel([d1, d2]) assert_equal(d1.d, 3) assert_equal(d2.d, 3) assert_equal(model.d, 3) def test_custom_mgd_gmm_logp(): X = numpy.random.normal(0.5, 1, size=(20,3)) d1 = MultivariateGaussianDistribution(numpy.zeros(3), numpy.eye(3)) d2 = MultivariateGaussianDistribution(numpy.ones(3), numpy.eye(3)) model1 = GeneralMixtureModel([d1, d2]) d3 = MultivariateGaussianDistribution2(numpy.zeros(3), numpy.eye(3)) d4 = MultivariateGaussianDistribution2(numpy.ones(3), numpy.eye(3)) model2 = GeneralMixtureModel([d3, d4]) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_mgd_gmm_predict(): X = numpy.random.normal(0.5, 1, size=(20,3)) d1 = MultivariateGaussianDistribution(numpy.zeros(3), numpy.eye(3)) d2 = MultivariateGaussianDistribution(numpy.ones(3), numpy.eye(3)) model1 = GeneralMixtureModel([d1, d2]) d3 = MultivariateGaussianDistribution2(numpy.zeros(3), numpy.eye(3)) d4 = MultivariateGaussianDistribution2(numpy.ones(3), numpy.eye(3)) model2 = GeneralMixtureModel([d3, d4]) assert_array_equal(model1.predict(X), model2.predict(X)) def test_custom_mgd_gmm_predict_proba(): X = numpy.random.normal(0.5, 1, size=(20,3)) d1 = MultivariateGaussianDistribution(numpy.zeros(3), numpy.eye(3)) d2 = MultivariateGaussianDistribution(numpy.ones(3), numpy.eye(3)) model1 = GeneralMixtureModel([d1, d2]) d3 = MultivariateGaussianDistribution2(numpy.zeros(3), numpy.eye(3)) d4 = MultivariateGaussianDistribution2(numpy.ones(3), numpy.eye(3)) model2 = GeneralMixtureModel([d3, d4]) assert_array_almost_equal(model1.predict_proba(X), model2.predict_proba(X)) def test_custom_mgd_gmm_predict_log_proba(): X = numpy.random.normal(0.5, 1, size=(20,3)) d1 = MultivariateGaussianDistribution(numpy.zeros(3), numpy.eye(3)) d2 = MultivariateGaussianDistribution(numpy.ones(3), numpy.eye(3)) model1 = GeneralMixtureModel([d1, d2]) d3 = MultivariateGaussianDistribution2(numpy.zeros(3), numpy.eye(3)) d4 = MultivariateGaussianDistribution2(numpy.ones(3), numpy.eye(3)) model2 = GeneralMixtureModel([d3, d4]) assert_array_almost_equal(model1.predict_log_proba(X), model2.predict_log_proba(X)) def test_custom_mgd_gmm_fit(): X = numpy.random.normal(0, 1, size=(500,3)) X[::2] += 1 d1 = MultivariateGaussianDistribution(numpy.zeros(3), numpy.eye(3)) d2 = MultivariateGaussianDistribution(numpy.ones(3), numpy.eye(3)) model1 = GeneralMixtureModel([d1, d2]) model1.fit(X) d3 = MultivariateGaussianDistribution2(numpy.zeros(3), numpy.eye(3)) d4 = MultivariateGaussianDistribution2(numpy.ones(3), numpy.eye(3)) model2 = GeneralMixtureModel([d3, d4]) model2.fit(X) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_mgd_gmm_from_samples(): X = numpy.random.normal(0, 1, size=(500,3)) X[::2] += 1 model1 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X, init='first-k', max_iterations=5) model2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution2, 2, X, init='first-k', max_iterations=5) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_icd_gmm_init(): d1 = IndependentComponentsDistribution([NormalDistribution2(0, 1) for _ in range(5)]) d2 = IndependentComponentsDistribution([NormalDistribution2(1, 1) for _ in range(5)]) model = GeneralMixtureModel([d1, d2]) assert_equal(d1.d, 5) assert_equal(d2.d, 5) assert_equal(model.d, 5) def test_custom_icd_gmm_logp(): X = numpy.random.normal(0.5, 1, size=(20,5)) d1 = IndependentComponentsDistribution([NormalDistribution(0, 1) for _ in range(5)]) d2 = IndependentComponentsDistribution([NormalDistribution(1, 1) for _ in range(5)]) model1 = GeneralMixtureModel([d1, d2]) d3 = IndependentComponentsDistribution([NormalDistribution2(0, 1) for _ in range(5)]) d4 = IndependentComponentsDistribution([NormalDistribution2(1, 1) for _ in range(5)]) model2 = GeneralMixtureModel([d3, d4]) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_icd_gmm_predict(): X = numpy.random.normal(0.5, 1, size=(20,5)) d1 = IndependentComponentsDistribution([NormalDistribution(0, 1) for _ in range(5)]) d2 = IndependentComponentsDistribution([NormalDistribution(1, 1) for _ in range(5)]) model1 = GeneralMixtureModel([d1, d2]) d3 = IndependentComponentsDistribution([NormalDistribution2(0, 1) for _ in range(5)]) d4 = IndependentComponentsDistribution([NormalDistribution2(1, 1) for _ in range(5)]) model2 = GeneralMixtureModel([d3, d4]) assert_array_almost_equal(model1.predict(X), model2.predict(X)) def test_custom_icd_gmm_predict_proba(): X = numpy.random.normal(0.5, 1, size=(20,5)) d1 = IndependentComponentsDistribution([NormalDistribution(0, 1) for _ in range(5)]) d2 = IndependentComponentsDistribution([NormalDistribution(1, 1) for _ in range(5)]) model1 = GeneralMixtureModel([d1, d2]) d3 = IndependentComponentsDistribution([NormalDistribution2(0, 1) for _ in range(5)]) d4 = IndependentComponentsDistribution([NormalDistribution2(1, 1) for _ in range(5)]) model2 = GeneralMixtureModel([d3, d4]) assert_array_almost_equal(model1.predict_proba(X), model2.predict_proba(X)) def test_custom_icd_gmm_predict_log_proba(): X = numpy.random.normal(0.5, 1, size=(20,5)) d1 = IndependentComponentsDistribution([NormalDistribution(0, 1) for _ in range(5)]) d2 = IndependentComponentsDistribution([NormalDistribution(1, 1) for _ in range(5)]) model1 = GeneralMixtureModel([d1, d2]) d3 = IndependentComponentsDistribution([NormalDistribution2(0, 1) for _ in range(5)]) d4 = IndependentComponentsDistribution([NormalDistribution2(1, 1) for _ in range(5)]) model2 = GeneralMixtureModel([d3, d4]) assert_array_almost_equal(model1.predict_log_proba(X), model2.predict_log_proba(X)) def test_custom_icd_gmm_fit(): X = numpy.random.normal(0.5, 1, size=(20,5)) d1 = IndependentComponentsDistribution([NormalDistribution(0, 1) for _ in range(5)]) d2 = IndependentComponentsDistribution([NormalDistribution(1, 1) for _ in range(5)]) model1 = GeneralMixtureModel([d1, d2]) model1.fit(X) d3 = IndependentComponentsDistribution([NormalDistribution2(0, 1) for _ in range(5)]) d4 = IndependentComponentsDistribution([NormalDistribution2(1, 1) for _ in range(5)]) model2 = GeneralMixtureModel([d3, d4]) model2.fit(X) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_icd_gmm_from_samples(): X = numpy.random.normal(0.0, 1, size=(200,5)) X[::2] += 1 model1 = GeneralMixtureModel.from_samples(NormalDistribution, 2, X, init='first-k', max_iterations=5) model2 = GeneralMixtureModel.from_samples(NormalDistribution2, 2, X, init='first-k', max_iterations=5) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_normal_nb_init(): d1 = NormalDistribution2(0, 1) d2 = NormalDistribution2(1, 1) model = NaiveBayes([d1, d2]) assert_equal(d1.d, 1) assert_equal(d2.d, 1) assert_equal(model.d, 1) def test_custom_normal_nb_logp(): X = numpy.random.normal(0.5, 1, size=(20, 1)) d1 = NormalDistribution(0, 1) d2 = NormalDistribution(1, 1) model1 = NaiveBayes([d1, d2]) d3 = NormalDistribution(0, 1) d4 = NormalDistribution(1, 1) model2 = NaiveBayes([d3, d4]) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_normal_nb_predict(): X = numpy.random.normal(0.5, 1, size=(20, 1)) d1 = NormalDistribution(0, 1) d2 = NormalDistribution(1, 1) model1 = NaiveBayes([d1, d2]) d3 = NormalDistribution(0, 1) d4 = NormalDistribution(1, 1) model2 = NaiveBayes([d3, d4]) assert_array_equal(model1.predict(X), model2.predict(X)) def test_custom_normal_nb_predict_proba(): X = numpy.random.normal(0.5, 1, size=(20, 1)) d1 = NormalDistribution(0, 1) d2 = NormalDistribution(1, 1) model1 = NaiveBayes([d1, d2]) d3 = NormalDistribution(0, 1) d4 = NormalDistribution(1, 1) model2 = NaiveBayes([d3, d4]) assert_array_almost_equal(model1.predict_proba(X), model2.predict_proba(X)) def test_custom_normal_nb_predict_log_proba(): X = numpy.random.normal(0.5, 1, size=(20, 1)) d1 = NormalDistribution(0, 1) d2 = NormalDistribution(1, 1) model1 = NaiveBayes([d1, d2]) d3 = NormalDistribution(0, 1) d4 = NormalDistribution(1, 1) model2 = NaiveBayes([d3, d4]) assert_array_almost_equal(model1.predict_log_proba(X), model2.predict_log_proba(X)) def test_custom_normal_nb_fit(): X = numpy.random.normal(0.5, 1, size=(20, 1)) y = numpy.zeros(20) y[::2] = 1 d1 = NormalDistribution(0, 1) d2 = NormalDistribution(1, 1) model1 = NaiveBayes([d1, d2]) model1.fit(X, y) d3 = NormalDistribution2(0, 1) d4 = NormalDistribution2(1, 1) model2 = NaiveBayes([d3, d4]) model2.fit(X, y) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_normal_nb_from_samples(): X = numpy.random.normal(0.5, 1, size=(200, 1)) X[::2] += 1 y = numpy.zeros(200) y[::2] = 1 model1 = NaiveBayes.from_samples(NormalDistribution, X, y) model2 = NaiveBayes.from_samples(NormalDistribution2, X, y) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_mgd_bc_init(): d1 = MultivariateGaussianDistribution2(numpy.zeros(3), numpy.eye(3)) d2 = MultivariateGaussianDistribution2(numpy.ones(3), numpy.eye(3)) model = BayesClassifier([d1, d2]) assert_equal(d1.d, 3) assert_equal(d2.d, 3) assert_equal(model.d, 3) def test_custom_mgd_bc_logp(): X = numpy.random.normal(0.5, 1, size=(20,3)) d1 = MultivariateGaussianDistribution(numpy.zeros(3), numpy.eye(3)) d2 = MultivariateGaussianDistribution(numpy.ones(3), numpy.eye(3)) model1 = BayesClassifier([d1, d2]) d3 = MultivariateGaussianDistribution2(numpy.zeros(3), numpy.eye(3)) d4 = MultivariateGaussianDistribution2(numpy.ones(3), numpy.eye(3)) model2 = BayesClassifier([d3, d4]) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_mgd_bc_predict(): X = numpy.random.normal(0.5, 1, size=(20,3)) d1 = MultivariateGaussianDistribution(numpy.zeros(3), numpy.eye(3)) d2 = MultivariateGaussianDistribution(numpy.ones(3), numpy.eye(3)) model1 = BayesClassifier([d1, d2]) d3 = MultivariateGaussianDistribution2(numpy.zeros(3), numpy.eye(3)) d4 = MultivariateGaussianDistribution2(numpy.ones(3), numpy.eye(3)) model2 = BayesClassifier([d3, d4]) assert_array_equal(model1.predict(X), model2.predict(X)) def test_custom_mgd_bc_predict_proba(): X = numpy.random.normal(0.5, 1, size=(20,3)) d1 = MultivariateGaussianDistribution(numpy.zeros(3), numpy.eye(3)) d2 = MultivariateGaussianDistribution(numpy.ones(3), numpy.eye(3)) model1 = BayesClassifier([d1, d2]) d3 = MultivariateGaussianDistribution2(numpy.zeros(3), numpy.eye(3)) d4 = MultivariateGaussianDistribution2(numpy.ones(3), numpy.eye(3)) model2 = BayesClassifier([d3, d4]) assert_array_almost_equal(model1.predict_proba(X), model2.predict_proba(X)) def test_custom_mgd_bc_predict_log_proba(): X = numpy.random.normal(0.5, 1, size=(20,3)) d1 = MultivariateGaussianDistribution(numpy.zeros(3), numpy.eye(3)) d2 = MultivariateGaussianDistribution(numpy.ones(3), numpy.eye(3)) model1 = BayesClassifier([d1, d2]) d3 = MultivariateGaussianDistribution2(numpy.zeros(3), numpy.eye(3)) d4 = MultivariateGaussianDistribution2(numpy.ones(3), numpy.eye(3)) model2 = BayesClassifier([d3, d4]) assert_array_almost_equal(model1.predict_log_proba(X), model2.predict_log_proba(X)) def test_custom_mgd_bc_fit(): X = numpy.random.normal(0, 1, size=(500,3)) X[::2] += 1 y = numpy.zeros(500) y[::2] = 1 d1 = MultivariateGaussianDistribution(numpy.zeros(3), numpy.eye(3)) d2 = MultivariateGaussianDistribution(numpy.ones(3), numpy.eye(3)) model1 = BayesClassifier([d1, d2]) model1.fit(X, y) d3 = MultivariateGaussianDistribution2(numpy.zeros(3), numpy.eye(3)) d4 = MultivariateGaussianDistribution2(numpy.ones(3), numpy.eye(3)) model2 = BayesClassifier([d3, d4]) model2.fit(X, y) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_mgd_bc_from_samples(): X = numpy.random.normal(0, 1, size=(500,3)) X[::2] += 1 y = numpy.zeros(500) y[::2] = 1 model1 = BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y) model2 = BayesClassifier.from_samples(MultivariateGaussianDistribution2, X, y) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_icd_nb_init(): d1 = IndependentComponentsDistribution([NormalDistribution2(0, 1) for _ in range(5)]) d2 = IndependentComponentsDistribution([NormalDistribution2(1, 1) for _ in range(5)]) model = NaiveBayes([d1, d2]) assert_equal(d1.d, 5) assert_equal(d2.d, 5) assert_equal(model.d, 5) def test_custom_icd_nb_logp(): X = numpy.random.normal(0.5, 1, size=(20,5)) d1 = IndependentComponentsDistribution([NormalDistribution(0, 1) for _ in range(5)]) d2 = IndependentComponentsDistribution([NormalDistribution(1, 1) for _ in range(5)]) model1 = NaiveBayes([d1, d2]) d3 = IndependentComponentsDistribution([NormalDistribution2(0, 1) for _ in range(5)]) d4 = IndependentComponentsDistribution([NormalDistribution2(1, 1) for _ in range(5)]) model2 = NaiveBayes([d3, d4]) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_icd_nb_predict(): X = numpy.random.normal(0.5, 1, size=(20,5)) d1 = IndependentComponentsDistribution([NormalDistribution(0, 1) for _ in range(5)]) d2 = IndependentComponentsDistribution([NormalDistribution(1, 1) for _ in range(5)]) model1 = NaiveBayes([d1, d2]) d3 = IndependentComponentsDistribution([NormalDistribution2(0, 1) for _ in range(5)]) d4 = IndependentComponentsDistribution([NormalDistribution2(1, 1) for _ in range(5)]) model2 = NaiveBayes([d3, d4]) assert_array_almost_equal(model1.predict(X), model2.predict(X)) def test_custom_icd_nb_predict_proba(): X = numpy.random.normal(0.5, 1, size=(20,5)) d1 = IndependentComponentsDistribution([NormalDistribution(0, 1) for _ in range(5)]) d2 = IndependentComponentsDistribution([NormalDistribution(1, 1) for _ in range(5)]) model1 = NaiveBayes([d1, d2]) d3 = IndependentComponentsDistribution([NormalDistribution2(0, 1) for _ in range(5)]) d4 = IndependentComponentsDistribution([NormalDistribution2(1, 1) for _ in range(5)]) model2 = NaiveBayes([d3, d4]) assert_array_almost_equal(model1.predict_proba(X), model2.predict_proba(X)) def test_custom_icd_nb_predict_log_proba(): X = numpy.random.normal(0.5, 1, size=(20,5)) d1 = IndependentComponentsDistribution([NormalDistribution(0, 1) for _ in range(5)]) d2 = IndependentComponentsDistribution([NormalDistribution(1, 1) for _ in range(5)]) model1 = NaiveBayes([d1, d2]) d3 = IndependentComponentsDistribution([NormalDistribution2(0, 1) for _ in range(5)]) d4 = IndependentComponentsDistribution([NormalDistribution2(1, 1) for _ in range(5)]) model2 = NaiveBayes([d3, d4]) assert_array_almost_equal(model1.predict_log_proba(X), model2.predict_log_proba(X)) def test_custom_icd_nb_fit(): X = numpy.random.normal(0.5, 1, size=(20,5)) y = numpy.zeros(20) y[::2] = 1 d1 = IndependentComponentsDistribution([NormalDistribution(0, 1) for _ in range(5)]) d2 = IndependentComponentsDistribution([NormalDistribution(1, 1) for _ in range(5)]) model1 = NaiveBayes([d1, d2]) model1.fit(X, y) d3 = IndependentComponentsDistribution([NormalDistribution2(0, 1) for _ in range(5)]) d4 = IndependentComponentsDistribution([NormalDistribution2(1, 1) for _ in range(5)]) model2 = NaiveBayes([d3, d4]) model2.fit(X, y) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_icd_nb_from_samples(): X = numpy.random.normal(0.0, 1, size=(200,5)) X[::2] += 1 y = numpy.zeros(200) y[::2] = 1 model1 = NaiveBayes.from_samples(NormalDistribution, X, y) model2 = NaiveBayes.from_samples(NormalDistribution2, X, y) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) @with_setup(setup_normal_hmm) def test_custom_normal_hmm_init(): assert_equal(model1.d, 1) assert_equal(model2.d, 1) @with_setup(setup_normal_hmm) def test_custom_normal_hmm_logp(): X = numpy.random.normal(0.5, 1, size=(5, 20, 1)) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) @with_setup(setup_normal_hmm) def test_custom_normal_hmm_predict(): X = numpy.random.normal(0.5, 1, size=(5, 20, 1)) assert_array_equal(model1.predict(X), model2.predict(X)) @with_setup(setup_normal_hmm) def test_custom_normal_hmm_predict_proba(): X = numpy.random.normal(0.5, 1, size=(5, 20, 1)) assert_array_almost_equal(model1.predict_proba(X), model2.predict_proba(X)) @with_setup(setup_normal_hmm) def test_custom_normal_hmm_predict_log_proba(): X = numpy.random.normal(0.5, 1, size=(5, 20, 1)) assert_array_almost_equal(model1.predict_log_proba(X), model2.predict_log_proba(X)) @with_setup(setup_normal_hmm) def test_custom_normal_hmm_fit(): X = numpy.random.normal(0.5, 1, size=(5, 20, 1)) model1.fit(X, max_iterations=5) model2.fit(X, max_iterations=5) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_normal_hmm_from_samples(): X = numpy.random.normal(0.5, 1, size=(2, 200, 1)) X[::2] += 1 model1 = HiddenMarkovModel.from_samples(NormalDistribution, 2, X, init='first-k', max_iterations=5) model2 = HiddenMarkovModel.from_samples(NormalDistribution2, 2, X, init='first-k', max_iterations=5) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) @with_setup(setup_mgd_hmm) def test_custom_mgd_hmm_init(): assert_equal(model1.d, 3) assert_equal(model2.d, 3) @with_setup(setup_mgd_hmm) def test_custom_mgd_hmm_logp(): X = numpy.random.normal(0.5, 1, size=(5, 20, 3)) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) @with_setup(setup_mgd_hmm) def test_custom_mgd_hmm_predict(): X = numpy.random.normal(0.5, 1, size=(5, 20, 3)) assert_array_equal(model1.predict(X), model2.predict(X)) @with_setup(setup_mgd_hmm) def test_custom_mgd_hmm_predict_proba(): X = numpy.random.normal(0.5, 1, size=(5, 20, 3)) assert_array_almost_equal(model1.predict_proba(X), model2.predict_proba(X)) @with_setup(setup_mgd_hmm) def test_custom_mgd_hmm_predict_log_proba(): X = numpy.random.normal(0.5, 1, size=(5, 20, 3)) assert_array_almost_equal(model1.predict_log_proba(X), model2.predict_log_proba(X)) @with_setup(setup_mgd_hmm) def test_custom_mgd_hmm_fit(): X = numpy.random.normal(0, 0.1, size=(2, 50, 3)) X[:, ::2] += 1 model1.fit(X, max_iterations=5) model2.fit(X, max_iterations=5) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_mgd_hmm_from_samples(): X = numpy.random.normal(0, 1, size=(2, 50, 3)) X[:, ::2] += 1 model1 = HiddenMarkovModel.from_samples(MultivariateGaussianDistribution, 2, X, init='first-k', max_iterations=5) model2 = HiddenMarkovModel.from_samples(MultivariateGaussianDistribution2, 2, X, init='first-k', max_iterations=5) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) @with_setup(setup_icd_hmm) def test_custom_icd_hmm_init(): assert_equal(model1.d, 5) assert_equal(model2.d, 5) @with_setup(setup_icd_hmm) def test_custom_icd_hmm_logp(): X = numpy.random.normal(0.5, 1, size=(5, 20, 5)) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) @with_setup(setup_icd_hmm) def test_custom_icd_hmm_predict(): X = numpy.random.normal(0.5, 1, size=(5, 20, 5)) assert_array_almost_equal(model1.predict(X), model2.predict(X)) @with_setup(setup_icd_hmm) def test_custom_icd_hmm_predict_proba(): X = numpy.random.normal(0.5, 1, size=(5, 20, 5)) assert_array_almost_equal(model1.predict_proba(X), model2.predict_proba(X)) @with_setup(setup_icd_hmm) def test_custom_icd_hmm_predict_log_proba(): X = numpy.random.normal(0.5, 1, size=(5, 20, 5)) assert_array_almost_equal(model1.predict_log_proba(X), model2.predict_log_proba(X)) @with_setup(setup_icd_hmm) def test_custom_icd_hmm_fit(): X = numpy.random.normal(0.5, 1, size=(3, 20, 5)) model1.fit(X, max_iterations=5) model2.fit(X, max_iterations=5) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) def test_custom_icd_hmm_from_samples(): X = numpy.random.normal(0.0, 1, size=(2, 20, 5)) X[::2] += 1 model1 = HiddenMarkovModel.from_samples(NormalDistribution, 2, X, init='first-k', max_iterations=5) model2 = HiddenMarkovModel.from_samples(NormalDistribution2, 2, X, init='first-k', max_iterations=5) assert_array_almost_equal(model1.log_probability(X), model2.log_probability(X)) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/test_distributions.py000066400000000000000000001307421420461712600263550ustar00rootroot00000000000000from __future__ import (division) from pomegranate import (Distribution, UniformDistribution, NormalDistribution, DiscreteDistribution, LogNormalDistribution, ExponentialDistribution, GammaDistribution, PoissonDistribution, BetaDistribution, GaussianKernelDensity, TriangleKernelDensity, UniformKernelDensity, IndependentComponentsDistribution, MultivariateGaussianDistribution, ConditionalProbabilityTable, BernoulliDistribution, from_json) from nose.tools import with_setup from nose.tools import assert_almost_equal from nose.tools import assert_equal from nose.tools import assert_not_equal from nose.tools import assert_less_equal from nose.tools import assert_true from nose.tools import assert_raises from numpy.testing import assert_array_equal from numpy.testing import assert_array_almost_equal import pickle import numpy nan = numpy.nan inf = float("inf") def setup(): pass def teardown(): pass def discrete_equality(x, y, z=8): ''' Test to see if two discrete distributions are equal to each other to z decimal points. ''' xd, yd = x.parameters[0], y.parameters[0] for key, value in xd.items(): if round(yd[key], z) != round(value, z): return False return True def test_distributions_uniform_initialization(): d = UniformDistribution(0, 10) assert_equal(d.name, "UniformDistribution") assert_array_equal(d.parameters, [0, 10]) assert_array_equal(d.summaries, [inf, -inf, 0]) def test_distributions_uniform_blank(): d = UniformDistribution.blank() assert_equal(d.name, "UniformDistribution") assert_array_equal(d.parameters, [0, 0]) assert_array_equal(d.summaries, [inf, -inf, 0]) def test_distributions_uniform_initialization_error(): assert_raises(TypeError, UniformDistribution, 0) assert_raises(TypeError, UniformDistribution, [0, 10]) assert_raises(TypeError, UniformDistribution, 0, 10, 4, 7, 3) def test_distributions_uniform_log_probability(): d = UniformDistribution(0, 10) e = UniformDistribution(0., 10.) assert_almost_equal(d.log_probability(5), -2.302585092) assert_equal(d.log_probability(5), e.log_probability(5)) assert_equal(d.log_probability(5), d.log_probability(5.)) assert_almost_equal(d.log_probability(0), -2.302585092) assert_equal(d.log_probability(0), e.log_probability(0.)) assert_equal(d.log_probability(-1), -inf) assert_equal(d.log_probability(11), -inf) def test_distributions_uniform_nan_log_probability(): d = UniformDistribution(0, 10) assert_equal(d.log_probability(nan), 0) assert_array_almost_equal(d.log_probability([nan, 5]), [0, -2.302585092]) def test_distributions_uniform_underflow_log_probability(): d = UniformDistribution(0, 10) assert_equal(d.log_probability(1e100), float("-inf")) def test_distributions_uniform_probability(): d = UniformDistribution(0, 10) e = UniformDistribution(0., 10.) assert_almost_equal(d.probability(5), 0.0999999999) assert_equal(d.probability(5), e.probability(5)) assert_equal(d.probability(5), d.probability(5.)) assert_almost_equal(d.probability(0), 0.0999999999) assert_equal(d.probability(0), e.probability(0.)) assert_equal(d.probability(-1), 0) assert_equal(d.probability(11), 0) def test_distributions_uniform_nan_probability(): d = UniformDistribution(0, 10) assert_equal(d.probability(nan), 1) assert_array_almost_equal(d.probability([nan, 5]), [1, 0.0999999999]) def test_distributions_uniform_underflow_probability(): d = UniformDistribution(0, 10) assert_almost_equal(d.probability(1e100), 0.0) def test_distributions_uniform_fit(): d = UniformDistribution(5, 2) e = UniformDistribution(5, 2) d.fit([5, 4, 5, 4, 6, 5, 6, 5, 4, 6, 5, 4]) assert_array_equal(d.parameters, [4, 6]) assert_not_equal(d.log_probability(4), e.log_probability(4)) assert_almost_equal(d.log_probability(4), -0.69314718055994529) assert_equal(d.log_probability(18), -inf) assert_equal(d.log_probability(1e8), -inf) assert_array_equal(d.summaries, [inf, -inf, 0]) def test_distributions_uniform_nan_fit(): d = UniformDistribution(5, 2) e = UniformDistribution(5, 2) d.fit([5, 4, nan, 5, 4, nan, 6, 5, 6, nan, nan, 5, 4, 6, nan, 5, 4, nan]) assert_array_equal(d.parameters, [4, 6]) assert_not_equal(d.log_probability(4), e.log_probability(4)) assert_almost_equal(d.log_probability(4), -0.69314718055994529) assert_equal(d.log_probability(18), -inf) assert_equal(d.log_probability(1e8), -inf) assert_array_equal(d.summaries, [inf, -inf, 0]) def test_distributions_uniform_exclusive_nan_fit(): d = UniformDistribution(0, 10) e = UniformDistribution(0, 10) d.fit([nan, nan, nan, nan, nan]) assert_array_equal(d.parameters, [0, 10]) assert_almost_equal(d.log_probability(4), e.log_probability(4.)) assert_array_equal(d.summaries, [inf, -inf, 0]) def test_distributions_uniform_weighted_fit(): d = UniformDistribution(0, 10) d.fit([0, 2, 3, 2, 100], weights=[0, 5, 2, 3, 200]) assert_array_equal(d.parameters, [2, 100]) assert_almost_equal(d.log_probability(50), -4.58496747867) assert_array_equal(d.summaries, [inf, -inf, 0]) def test_distributions_uniform_inertia_fit(): d = UniformDistribution(0, 10) d.fit([0, 5, 3, 5, 7, 3, 4, 5, 2], inertia=0.5) assert_array_equal(d.parameters, [0, 8.5]) assert_array_equal(d.summaries, [inf, -inf, 0]) def test_distributions_uniform_fit_ooc(): d = UniformDistribution(0, 10) d.summarize([0, 2], weights=[0, 5]) d.summarize([3, 2], weights=[2, 3]) d.summarize([100], weights=[200]) assert_array_equal(d.summaries, [2, 100, 210]) d.from_summaries() assert_array_equal(d.summaries, [inf, -inf, 0]) assert_array_equal(d.parameters, [2, 100]) def test_distributions_uniform_freeze_fit(): d = UniformDistribution(0, 10) d.freeze() d.fit([0, 1, 1, 2, 3, 2, 1, 2, 2]) assert_array_almost_equal(d.parameters, [0, 10]) assert_array_equal(d.summaries, [inf, -inf, 0]) def test_distributions_uniform_freeze_thaw_fit(): d = UniformDistribution(0, 10) d.freeze() d.thaw() d.fit([5, 4, 5, 4, 6, 5, 6, 5, 4, 6, 5, 4]) assert_array_equal(d.parameters, [4, 6]) def test_distributions_uniform_from_samples(): d = UniformDistribution.from_samples([5, 2, 4, 6, 8, 3, 6, 8, 3]) assert_array_equal(d.parameters, [2, 8]) assert_array_equal(d.summaries, [inf, -inf, 0]) def test_distributions_uniform_nan_from_samples(): d = UniformDistribution.from_samples([5, nan, 2, nan, 4, 6, nan, 8, 3, nan, 6, nan, 8, 3]) assert_array_equal(d.parameters, [2, 8]) assert_array_equal(d.summaries, [inf, -inf, 0]) def test_distributions_uniform_pickle_serialization(): d = UniformDistribution(0, 10) e = pickle.loads(pickle.dumps(d)) assert_equal(e.name, "UniformDistribution") assert_array_equal(e.parameters, [0, 10]) assert_array_equal(d.summaries, [inf, -inf, 0]) def test_distributions_uniform_json_serialization(): d = UniformDistribution(0, 10) e = Distribution.from_json(d.to_json()) assert_equal(e.name, "UniformDistribution") assert_array_equal(e.parameters, [0, 10]) assert_array_equal(d.summaries, [inf, -inf, 0]) def test_distributions_uniform_robust_json_serialization(): d = UniformDistribution(0, 10) e = from_json(d.to_json()) assert_equal(e.name, "UniformDistribution") assert_array_equal(e.parameters, [0, 10]) assert_array_equal(d.summaries, [inf, -inf, 0]) def test_distributions_uniform_random_sample(): d = UniformDistribution(0, 10) x = numpy.array([2.21993171, 8.70732306, 2.06719155, 9.18610908, 4.88411189]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x) def test_distributions_normal_initialization(): d = NormalDistribution(5, 2) assert_equal(d.name, "NormalDistribution") assert_array_equal(d.parameters, [5, 2]) assert_array_equal(d.summaries, [0, 0, 0]) def test_distributions_normal_blank(): d = NormalDistribution.blank() assert_equal(d.name, "NormalDistribution") assert_array_equal(d.parameters, [0, 1]) assert_array_equal(d.summaries, [0, 0, 0]) def test_distributions_normal_initialization_error(): assert_raises(TypeError, NormalDistribution, 5) assert_raises(TypeError, NormalDistribution, [5, 1]) assert_raises(TypeError, NormalDistribution, 5, 1, 4, 7, 3) def test_distributions_normal_log_probability(): d = NormalDistribution(5, 2) e = NormalDistribution(5., 2.) assert_almost_equal(d.log_probability(5), -1.61208571) assert_equal(d.log_probability(5), e.log_probability(5)) assert_equal(d.log_probability(5), d.log_probability(5.)) assert_almost_equal(d.log_probability(0), -4.737085713764219) assert_equal(d.log_probability(0), e.log_probability(0.)) def test_distributions_normal_nan_log_probability(): d = NormalDistribution(5, 2) assert_equal(d.log_probability(nan), 0) assert_array_almost_equal(d.log_probability([nan, 5]), [0, -1.61208571]) def test_distributions_normal_underflow_log_probability(): d = NormalDistribution(5, 1e-10) assert_almost_equal(d.log_probability(1e100), -4.9999999999999987e+219, delta=6.270570637641398e+203) def test_distributions_normal_probability(): d = NormalDistribution(5, 2) e = NormalDistribution(5., 2.) assert_almost_equal(d.probability(5), 0.19947114) assert_equal(d.probability(5), e.probability(5)) assert_equal(d.probability(5), d.probability(5.)) assert_almost_equal(d.probability(0), 0.0087641502) assert_equal(d.probability(0), e.probability(0.)) def test_distributions_normal_nan_probability(): d = NormalDistribution(5, 2) assert_equal(d.probability(nan), 1) assert_array_almost_equal(d.probability([nan, 5]), [1, 0.199471]) def test_distributions_normal_underflow_probability(): d = NormalDistribution(5, 1e-10) assert_almost_equal(d.probability(1e100), 0.0) def test_distributions_normal_fit(): d = NormalDistribution(5, 2) e = NormalDistribution(5, 2) d.fit([5, 4, 5, 4, 6, 5, 6, 5, 4, 6, 5, 4]) assert_array_almost_equal(d.parameters, [4.9167, 0.7592], 4) assert_not_equal(d.log_probability(4), e.log_probability(4)) assert_almost_equal(d.log_probability(4), -1.3723678499651766) assert_almost_equal(d.log_probability(18), -149.13140399454429) assert_almost_equal(d.log_probability(1e8), -8674697942168743.0, -4) assert_array_equal(d.summaries, [0, 0, 0]) def test_distributions_normal_nan_fit(): d = NormalDistribution(5, 2) e = NormalDistribution(5, 2) d.fit([5, 4, nan, 5, 4, nan, 6, 5, 6, nan, nan, 5, 4, 6, nan, 5, 4, nan]) assert_array_almost_equal(d.parameters, [4.9167, 0.7592], 4) assert_not_equal(d.log_probability(4), e.log_probability(4)) assert_almost_equal(d.log_probability(4), -1.3723678499651766) assert_almost_equal(d.log_probability(18), -149.13140399454429) assert_almost_equal(d.log_probability(1e8), -8674697942168743.0, -4) assert_array_equal(d.summaries, [0, 0, 0]) def test_distributions_normal_exclusive_nan_fit(): d = NormalDistribution(5, 2) e = NormalDistribution(5, 2) d.fit([nan, nan, nan, nan, nan]) assert_array_equal(d.parameters, [5, 2]) assert_almost_equal(d.log_probability(4), e.log_probability(4.)) assert_array_equal(d.summaries, [0, 0, 0]) def test_distributions_normal_weighted_fit(): d = NormalDistribution(5, 2) d.fit([0, 2, 3, 2, 100], weights=[0, 5, 2, 3, 200]) assert_array_almost_equal(d.parameters, [95.3429, 20.8276], 4) assert_almost_equal(d.log_probability(50), -6.32501194) assert_array_equal(d.summaries, [0, 0, 0]) def test_distributions_normal_inertia_fit(): d = NormalDistribution(5, 2) d.fit([0, 5, 3, 5, 7, 3, 4, 5, 2], inertia=0.5) assert_array_almost_equal(d.parameters, [4.3889, 1.9655], 4) assert_array_equal(d.summaries, [0, 0, 0]) def test_distributions_normal_fit_ooc(): d = NormalDistribution(5, 2) d.summarize([0, 2], weights=[0, 5]) d.summarize([3, 2], weights=[2, 3]) d.summarize([100], weights=[200]) assert_array_equal(d.summaries, [2.100000e+02, 2.002200e+04, 2.000050e+06]) d.from_summaries() assert_array_equal(d.summaries, [0, 0, 0]) assert_array_almost_equal(d.parameters, [95.3429, 20.8276], 4) def test_distributions_normal_freeze_fit(): d = NormalDistribution(5, 2) d.freeze() d.fit([0, 1, 1, 2, 3, 2, 1, 2, 2]) assert_array_almost_equal(d.parameters, [5, 2]) assert_array_equal(d.summaries, [0, 0, 0]) def test_distributions_normal_freeze_thaw_fit(): d = NormalDistribution(5, 2) d.freeze() d.thaw() d.fit([5, 4, 5, 4, 6, 5, 6, 5, 4, 6, 5, 4]) assert_array_almost_equal(d.parameters, [4.9166, 0.7592], 4) def test_distributions_normal_from_samples(): d = NormalDistribution.from_samples([5, 2, 4, 6, 8, 3, 6, 8, 3]) assert_array_almost_equal(d.parameters, [5.0, 2.05480466]) assert_array_equal(d.summaries, [0, 0, 0]) def test_distributions_normal_nan_from_samples(): d = NormalDistribution.from_samples([5, nan, 2, nan, 4, 6, nan, 8, 3, nan, 6, nan, 8, 3]) assert_array_almost_equal(d.parameters, [5.0, 2.05480466]) assert_array_equal(d.summaries, [0, 0, 0]) def test_distributions_normal_pickle_serialization(): d = NormalDistribution(5, 2) e = pickle.loads(pickle.dumps(d)) assert_equal(e.name, "NormalDistribution") assert_array_equal(e.parameters, [5, 2]) assert_array_equal(e.summaries, [0, 0, 0]) def test_distributions_normal_json_serialization(): d = NormalDistribution(5, 2) e = Distribution.from_json(d.to_json()) assert_equal(e.name, "NormalDistribution") assert_array_equal(e.parameters, [5, 2]) assert_array_equal(e.summaries, [0, 0, 0]) def test_distributions_normal_robust_json_serialization(): d = NormalDistribution(5, 2) e = from_json(d.to_json()) assert_equal(e.name, "NormalDistribution") assert_array_equal(e.parameters, [5, 2]) assert_array_equal(e.summaries, [0, 0, 0]) def test_distributions_normal_random_sample(): d = NormalDistribution(0, 1) x = numpy.array([ 0.44122749, -0.33087015, 2.43077119, -0.25209213, 0.10960984]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x) @with_setup(setup, teardown) def test_distributions_discrete(): d = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25}) assert_equal(d.log_probability('C'), -1.3862943611198906) assert_equal(d.log_probability('A'), d.log_probability('C')) assert_equal(d.log_probability('G'), d.log_probability('T')) assert_equal(d.log_probability('a'), float('-inf')) seq = "ACGTACGTTGCATGCACGCGCTCTCGCGC" d.fit(list(seq)) assert_equal(d.log_probability('C'), -0.9694005571881036) assert_equal(d.log_probability('A'), -1.9810014688665833) assert_equal(d.log_probability('T'), -1.575536360758419) seq = "ACGTGTG" d.fit(list(seq), weights=[0., 1., 2., 3., 4., 5., 6.]) assert_equal(d.log_probability('A'), float('-inf')) assert_equal(d.log_probability('C'), -3.044522437723423) assert_equal(d.log_probability('G'), -0.5596157879354228) d.summarize(list("ACG"), weights=[0., 1., 2.]) d.summarize(list("TGT"), weights=[3., 4., 5.]) d.summarize(list("G"), weights=[6.]) d.from_summaries() assert_equal(d.log_probability('A'), float('-inf')) assert_equal(round(d.log_probability('C'), 4), -3.0445) assert_equal(round(d.log_probability('G'), 4), -0.5596) d = DiscreteDistribution({'A': 0.0, 'B': 1.0}) d.summarize(list("ABABABAB")) d.summarize(list("ABAB")) d.summarize(list("BABABABABABABABABA")) d.from_summaries(inertia=0.75) assert_equal(d.parameters[0], {'A': 0.125, 'B': 0.875}) d = DiscreteDistribution({'A': 0.0, 'B': 1.0}) d.summarize(list("ABABABAB")) d.summarize(list("ABAB")) d.summarize(list("BABABABABABABABABA")) d.from_summaries(inertia=0.5) assert_equal(d.parameters[0], {'A': 0.25, 'B': 0.75}) d.freeze() d.fit(list('ABAABBAAAAAAAAAAAAAAAAAA')) assert_equal(d.parameters[0], {'A': 0.25, 'B': 0.75}) d = DiscreteDistribution.from_samples(['A', 'B', 'A', 'A']) assert_equal(d.parameters[0], {'A': 0.75, 'B': 0.25}) # Test vector input instead of flat array. d = DiscreteDistribution.from_samples(numpy.array(['A', 'B', 'A', 'A']).reshape(-1,1)) assert_equal(d.parameters[0], {'A': 0.75, 'B': 0.25}) d = DiscreteDistribution.from_samples(['A', 'B', 'A', 'A'], pseudocount=0.5) assert_equal(d.parameters[0], {'A': 0.70, 'B': 0.30}) d = DiscreteDistribution.from_samples(['A', 'B', 'A', 'A'], pseudocount=6) assert_equal(d.parameters[0], {'A': 0.5625, 'B': 0.4375}) e = Distribution.from_json(d.to_json()) assert_equal(e.name, "DiscreteDistribution") assert_equal(e.parameters[0], {'A': 0.5625, 'B': 0.4375}) f = pickle.loads(pickle.dumps(e)) assert_equal(f.name, "DiscreteDistribution") assert_equal(f.parameters[0], {'A': 0.5625, 'B': 0.4375}) def test_discrete_robust_json_serialization(): d = DiscreteDistribution.from_samples(['A', 'B', 'A', 'A'], pseudocount=6) e = from_json(d.to_json()) assert_equal(e.name, "DiscreteDistribution") assert_equal(e.parameters[0], {'A': 0.5625, 'B': 0.4375}) @with_setup(setup, teardown) def test_lognormal(): d = LogNormalDistribution(5, 2) assert_equal(round(d.log_probability(5), 4), -4.6585) d.fit([5.1, 5.03, 4.98, 5.05, 4.91, 5.2, 5.1, 5., 4.8, 5.21]) assert_equal(round(d.parameters[0], 4), 1.6167) assert_equal(round(d.parameters[1], 4), 0.0237) d.summarize([5.1, 5.03, 4.98, 5.05]) d.summarize([4.91, 5.2, 5.1]) d.summarize([5., 4.8, 5.21]) d.from_summaries() assert_equal(round(d.parameters[0], 4), 1.6167) assert_equal(round(d.parameters[1], 4), 0.0237) e = Distribution.from_json(d.to_json()) assert_equal(e.name, "LogNormalDistribution") assert_equal(round(e.parameters[0], 4), 1.6167) assert_equal(round(e.parameters[1], 4), 0.0237) f = pickle.loads(pickle.dumps(e)) assert_equal(f.name, "LogNormalDistribution") assert_equal(round(f.parameters[0], 4), 1.6167) assert_equal(round(f.parameters[1], 4), 0.0237) def test_distributions_lognormal_random_sample(): d = LogNormalDistribution(0, 1) x = numpy.array([1.55461432, 0.71829843, 11.36764528, 0.77717313, 1.11584263]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x) @with_setup(setup, teardown) def test_gamma(): d = GammaDistribution(5, 2) assert_equal(round(d.log_probability(4), 4), -2.1671) d.fit([2.3, 4.3, 2.7, 2.3, 3.1, 3.2, 3.4, 3.1, 2.9, 2.8]) assert_equal(round(d.parameters[0], 4), 31.8806) assert_equal(round(d.parameters[1], 4), 10.5916) d = GammaDistribution(2, 7) assert_not_equal(round(d.log_probability(4), 4), -2.1671) d.summarize([2.3, 4.3, 2.7]) d.summarize([2.3, 3.1, 3.2]) d.summarize([3.4, 3.1, 2.9, 2.8]) d.from_summaries() assert_equal(round(d.parameters[0], 4), 31.8806) assert_equal(round(d.parameters[1], 4), 10.5916) e = Distribution.from_json(d.to_json()) assert_equal(e.name, "GammaDistribution") assert_equal(round(e.parameters[0], 4), 31.8806) assert_equal(round(e.parameters[1], 4), 10.5916) f = pickle.loads(pickle.dumps(e)) assert_equal(f.name, "GammaDistribution") assert_equal(round(f.parameters[0], 4), 31.8806) assert_equal(round(f.parameters[1], 4), 10.5916) def test_distributions_gamma_random_sample(): d = GammaDistribution(0.5, 1) x = numpy.array([0.049281, 0.042733, 0.238545, 0.773426, 0.088091]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x) @with_setup(setup, teardown) def test_exponential(): d = ExponentialDistribution(3) assert_equal(round(d.log_probability(8), 4), -22.9014) d.fit([2.7, 2.9, 3.8, 1.9, 2.7, 1.6, 1.3, 1.0, 1.9]) assert_equal(round(d.parameters[0], 4), 0.4545) d = ExponentialDistribution(4) assert_not_equal(round(d.log_probability(8), 4), -22.9014) d.summarize([2.7, 2.9, 3.8]) d.summarize([1.9, 2.7, 1.6]) d.summarize([1.3, 1.0, 1.9]) d.from_summaries() assert_equal(round(d.parameters[0], 4), 0.4545) e = Distribution.from_json(d.to_json()) assert_equal(e.name, "ExponentialDistribution") assert_equal(round(e.parameters[0], 4), 0.4545) f = pickle.loads(pickle.dumps(e)) assert_equal(f.name, "ExponentialDistribution") assert_equal(round(f.parameters[0], 4), 0.4545) def test_distributions_exponential_random_sample(): d = ExponentialDistribution(7) x = numpy.array([0.03586, 0.292267, 0.033083, 0.358359, 0.095748]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x) @with_setup(setup, teardown) def test_poisson(): d = PoissonDistribution(5) assert_almost_equal(d.log_probability(5), -1.7403021806115442) assert_almost_equal(d.log_probability(10), -4.0100334487345126) assert_almost_equal(d.log_probability(1), -3.3905620875658995) assert_equal(d.log_probability(-1), float("-inf")) d = PoissonDistribution(0) assert_equal(d.log_probability(1), float("-inf")) assert_equal(d.log_probability(7), float("-inf")) d.fit([1, 6, 4, 9, 1]) assert_equal(d.parameters[0], 4.2) d.fit([1, 6, 4, 9, 1], weights=[0, 0, 0, 1, 0]) assert_equal(d.parameters[0], 9) d.fit([1, 6, 4, 9, 1], weights=[1, 0, 0, 1, 0]) assert_equal(d.parameters[0], 5) assert_almost_equal(d.log_probability(5), -1.7403021806115442) assert_almost_equal(d.log_probability(10), -4.0100334487345126) assert_almost_equal(d.log_probability(1), -3.3905620875658995) assert_equal(d.log_probability(-1), float("-inf")) e = pickle.loads(pickle.dumps(d)) assert_equal(e.name, "PoissonDistribution") assert_equal(e.parameters[0], 5) def test_distributions_poisson_random_sample(): d = PoissonDistribution(1) x = numpy.array([0, 1, 2, 2, 0]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x) def test_beta(): """Test pickling of beta distribution.""" d = BetaDistribution(2, 3) e = pickle.loads(pickle.dumps(d)) assert_equal(e.name, "BetaDistribution") assert_equal(e.parameters, [2, 3]) def test_distributions_beta_random_sample(): d = BetaDistribution(1, 1) x = numpy.array([0.612564, 0.098563, 0.735983, 0.583171, 0.69296 ]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x) @with_setup(setup, teardown) def test_gaussian_kernel(): d = GaussianKernelDensity([0, 4, 3, 5, 7, 4, 2]) assert_equal(round(d.log_probability(3.3), 4), -1.7042) d.fit([1, 6, 8, 3, 2, 4, 7, 2]) assert_equal(round(d.log_probability(1.2), 4), -2.0237) d.fit([1, 0, 108], weights=[2., 3., 278.]) assert_equal(round(d.log_probability(110), 4), -2.9368) assert_equal(round(d.log_probability(0), 4), -5.1262) d.summarize([1, 6, 8, 3]) d.summarize([2, 4, 7]) d.summarize([2]) d.from_summaries() assert_equal(round(d.log_probability(1.2), 4), -2.0237) d.summarize([1, 0, 108], weights=[2., 3., 278.]) d.from_summaries() assert_equal(round(d.log_probability(110), 4), -2.9368) assert_equal(round(d.log_probability(0), 4), -5.1262) d.freeze() d.fit([1, 3, 5, 4, 6, 7, 3, 4, 2]) assert_equal(round(d.log_probability(110), 4), -2.9368) assert_equal(round(d.log_probability(0), 4), -5.1262) e = Distribution.from_json(d.to_json()) assert_equal(e.name, "GaussianKernelDensity") assert_equal(round(e.log_probability(110), 4), -2.9368) assert_equal(round(e.log_probability(0), 4), -5.1262) f = pickle.loads(pickle.dumps(e)) assert_equal(f.name, "GaussianKernelDensity") assert_equal(round(f.log_probability(110), 4), -2.9368) assert_equal(round(f.log_probability(0), 4), -5.1262) def test_distributions_gaussian_kernel_random_sample(): d = GaussianKernelDensity([0, 4, 3, 5, 7, 4, 2]) x = numpy.array([5.367586, 2.574708, 2.114238, 2.170925, 4.596907]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x) @with_setup(setup, teardown) def test_triangular_kernel(): d = TriangleKernelDensity([1, 6, 3, 4, 5, 2]) assert_equal(round(d.log_probability(6.5), 4), -2.4849) d = TriangleKernelDensity([1, 8, 100]) assert_not_equal(round(d.log_probability(6.5), 4), -2.4849) d.summarize([1, 6]) d.summarize([3, 4, 5]) d.summarize([2]) d.from_summaries() assert_equal(round(d.log_probability(6.5), 4), -2.4849) d.freeze() d.fit([1, 4, 6, 7, 3, 5, 7, 8, 3, 3, 4]) assert_equal(round(d.log_probability(6.5), 4), -2.4849) e = Distribution.from_json(d.to_json()) assert_equal(e.name, "TriangleKernelDensity") assert_equal(round(e.log_probability(6.5), 4), -2.4849) f = pickle.loads(pickle.dumps(e)) assert_equal(f.name, "TriangleKernelDensity") assert_equal(round(f.log_probability(6.5), 4), -2.4849) def test_distributions_triangle_kernel_random_sample(): d = TriangleKernelDensity([0, 4, 3, 5, 7, 4, 2]) x = numpy.array([4.118801, 2.31576 , 4.018591, 1.770455, 4.612734]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x) @with_setup(setup, teardown) def test_uniform_kernel(): d = UniformKernelDensity([1, 3, 5, 6, 2, 2, 3, 2, 2]) assert_equal(round(d.log_probability(2.2), 4), -0.4055) assert_equal(round(d.log_probability(6.2), 4), -2.1972) assert_equal(d.log_probability(10), float('-inf')) d = UniformKernelDensity([1, 100, 200]) assert_not_equal(round(d.log_probability(2.2), 4), -0.4055) assert_not_equal(round(d.log_probability(6.2), 4), -2.1972) d.summarize([1, 3, 5, 6, 2]) d.summarize([2, 3, 2, 2]) d.from_summaries() assert_equal(round(d.log_probability(2.2), 4), -0.4055) assert_equal(round(d.log_probability(6.2), 4), -2.1972) e = Distribution.from_json(d.to_json()) assert_equal(e.name, "UniformKernelDensity") assert_equal(round(e.log_probability(2.2), 4), -0.4055) assert_equal(round(e.log_probability(6.2), 4), -2.1972) f = pickle.loads(pickle.dumps(e)) assert_equal(e.name, "UniformKernelDensity") assert_equal(round(f.log_probability(2.2), 4), -0.4055) assert_equal(round(f.log_probability(6.2), 4), -2.1972) def test_distributions_uniform_kernel_random_sample(): d = UniformKernelDensity([0, 4, 3, 5, 7, 4, 2]) x = numpy.array([4.223488, 2.531816, 4.036836, 1.593601, 4.375442]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x) @with_setup(setup, teardown) def test_bernoulli(): d = BernoulliDistribution(0.6) assert_equal(d.probability(0), 0.4) assert_equal(d.probability(1), 0.6) assert_equal(d.parameters[0], 1-d.probability(0)) assert_equal(d.parameters[0], d.probability(1)) d.fit([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) assert_not_equal(d.probability(1), 1.0) assert_equal(d.probability(0), 1.0) a = [0.0, 0.0, 0.0] b = [1.0, 1.0, 1.0] c = [1.0, 1.0, 1.0] d.summarize(a) d.from_summaries() assert_equal(d.probability(0), 1) assert_equal(d.probability(1), 0) d.summarize(a) d.summarize(b) d.from_summaries() assert_equal(d.probability(0), 0.5) assert_equal(d.probability(1), 0.5) assert_equal(d.parameters[0], d.probability(0)) assert_equal(d.parameters[0], d.probability(1)) d.summarize(a) d.summarize(b) d.summarize(c) d.from_summaries() assert_equal(round(d.probability(0), 4), 0.3333) assert_equal(round(d.probability(1), 4), 0.6667) assert_equal(d.parameters[0], d.probability(1)) d = BernoulliDistribution.from_samples([0.0, 0.0, 0.0, 0.0, 0.0, 1.0]) assert_equal(round(d.probability(0), 4), 0.8333) assert_equal(round(d.probability(1), 4), 0.1667) assert_almost_equal(d.parameters[0], d.probability(1)) e = Distribution.from_json(d.to_json()) assert_equal(e.name, "BernoulliDistribution") assert_equal(round(e.parameters[0], 4), 0.1667) f = pickle.loads(pickle.dumps(e)) assert_equal(f.name, "BernoulliDistribution") assert_equal(round(f.parameters[0], 4), 0.1667) def test_distributions_uniform_kernel_random_sample(): d = BernoulliDistribution(0.2) x = numpy.array([0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]) assert_array_equal(d.sample(20, random_state=5), x) assert_raises(AssertionError, assert_array_equal, d.sample(20), x) @with_setup(setup, teardown) def test_independent(): d = IndependentComponentsDistribution( [NormalDistribution(5, 2), ExponentialDistribution(2)]) assert_equal(round(d.log_probability((4, 1)), 4), -3.0439) assert_equal(round(d.log_probability((100, 0.001)), 4), -1129.0459) d = IndependentComponentsDistribution([NormalDistribution(5, 2), ExponentialDistribution(2)], weights=[18., 1.]) assert_equal(round(d.log_probability((4, 1)), 4), -32.5744) assert_equal(round(d.log_probability((100, 0.001)), 4), -20334.5764) d.fit([(5, 1), (5.2, 1.7), (4.7, 1.9), (4.9, 2.4), (4.5, 1.2)]) assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.86) assert_equal(round(d.parameters[0][0].parameters[1], 4), 0.2417) assert_equal(round(d.parameters[0][1].parameters[0], 4), 0.6098) d = IndependentComponentsDistribution([NormalDistribution(5, 2), UniformDistribution(0, 10)]) d.fit([(0, 0), (5, 0), (3, 0), (5, -5), (7, 0), (3, 0), (4, 0), (5, 0), (2, 20)], inertia=0.5) assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889) assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655) assert_equal(d.parameters[0][1].parameters[0], -2.5) assert_equal(d.parameters[0][1].parameters[1], 15) d.fit([(0, 0), (5, 0), (3, 0), (5, -5), (7, 0), (3, 0), (4, 0), (5, 0), (2, 20)], inertia=0.75) assert_not_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889) assert_not_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655) assert_not_equal(d.parameters[0][1].parameters[0], -2.5) assert_not_equal(d.parameters[0][1].parameters[1], 15) d = IndependentComponentsDistribution([NormalDistribution(5, 2), UniformDistribution(0, 10)]) d.summarize([(0, 0), (5, 0), (3, 0)]) d.summarize([(5, -5), (7, 0)]) d.summarize([(3, 0), (4, 0), (5, 0), (2, 20)]) d.from_summaries(inertia=0.5) assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889) assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655) assert_equal(d.parameters[0][1].parameters[0], -2.5) assert_equal(d.parameters[0][1].parameters[1], 15) d.freeze() d.fit([(1, 7), (7, 2), (2, 4), (2, 4), (1, 4)]) assert_equal(round(d.parameters[0][0].parameters[0], 4), 4.3889) assert_equal(round(d.parameters[0][0].parameters[1], 4), 1.9655) assert_equal(d.parameters[0][1].parameters[0], -2.5) assert_equal(d.parameters[0][1].parameters[1], 15) e = Distribution.from_json(d.to_json()) assert_equal(e.name, "IndependentComponentsDistribution") assert_equal(round(e.parameters[0][0].parameters[0], 4), 4.3889) assert_equal(round(e.parameters[0][0].parameters[1], 4), 1.9655) assert_equal(e.parameters[0][1].parameters[0], -2.5) assert_equal(e.parameters[0][1].parameters[1], 15) f = pickle.loads(pickle.dumps(e)) assert_equal(e.name, "IndependentComponentsDistribution") assert_equal(round(f.parameters[0][0].parameters[0], 4), 4.3889) assert_equal(round(f.parameters[0][0].parameters[1], 4), 1.9655) assert_equal(f.parameters[0][1].parameters[0], -2.5) assert_equal(f.parameters[0][1].parameters[1], 15) X = numpy.array([[0.5, 0.2, 0.7], [0.3, 0.1, 0.9], [0.4, 0.3, 0.8], [0.3, 0.3, 0.9], [0.3, 0.2, 0.6], [0.5, 0.2, 0.8]]) d = IndependentComponentsDistribution.from_samples(X, distributions=NormalDistribution) assert_almost_equal(d.parameters[0][0].parameters[0], 0.38333, 4) assert_almost_equal(d.parameters[0][0].parameters[1], 0.08975, 4) assert_almost_equal(d.parameters[0][1].parameters[0], 0.21666, 4) assert_almost_equal(d.parameters[0][1].parameters[1], 0.06872, 4) assert_almost_equal(d.parameters[0][2].parameters[0], 0.78333, 4) assert_almost_equal(d.parameters[0][2].parameters[1], 0.10672, 4) d = IndependentComponentsDistribution.from_samples(X, distributions=ExponentialDistribution) assert_almost_equal(d.parameters[0][0].parameters[0], 2.6087, 4) assert_almost_equal(d.parameters[0][1].parameters[0], 4.6154, 4) assert_almost_equal(d.parameters[0][2].parameters[0], 1.2766, 4) d = IndependentComponentsDistribution.from_samples(X, distributions=[NormalDistribution, NormalDistribution, NormalDistribution]) assert_almost_equal(d.parameters[0][0].parameters[0], 0.38333, 4) assert_almost_equal(d.parameters[0][0].parameters[1], 0.08975, 4) assert_almost_equal(d.parameters[0][1].parameters[0], 0.21666, 4) assert_almost_equal(d.parameters[0][1].parameters[1], 0.06872, 4) assert_almost_equal(d.parameters[0][2].parameters[0], 0.78333, 4) assert_almost_equal(d.parameters[0][2].parameters[1], 0.10672, 4) d = IndependentComponentsDistribution.from_samples(X, distributions=[NormalDistribution, LogNormalDistribution, ExponentialDistribution]) assert_almost_equal(d.parameters[0][0].parameters[0], 0.38333, 4) assert_almost_equal(d.parameters[0][0].parameters[1], 0.08975, 4) assert_almost_equal(d.parameters[0][1].parameters[0], -1.5898, 4) assert_almost_equal(d.parameters[0][1].parameters[1], 0.36673, 4) assert_almost_equal(d.parameters[0][2].parameters[0], 1.27660, 4) def test_distributions_independent_random_sample(): d = IndependentComponentsDistribution([NormalDistribution(5, 2), UniformDistribution(0, 10), ExponentialDistribution(7), LogNormalDistribution(0, 0.4)]) x = numpy.array([[5.882455, 2.219932, 0.03586 , 1.193024], [4.33826 , 8.707323, 0.292267, 0.876036], [9.861542, 2.067192, 0.033083, 2.644041]]) assert_array_almost_equal(d.sample(3, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x) def test_conditional(): phditis = DiscreteDistribution({True: 0.01, False: 0.99}) test_result = ConditionalProbabilityTable( [[True, True, 0.95], [True, False, 0.05], [False, True, 0.05], [False, False, 0.95]], [phditis]) assert discrete_equality(test_result.marginal(), DiscreteDistribution({False: 0.941, True: 0.059})) def setup_cpt(): guest = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3}) prize = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3}) global monty monty = ConditionalProbabilityTable( [['A', 'A', 'A', 0.0], ['A', 'A', 'B', 0.5], ['A', 'A', 'C', 0.5], ['A', 'B', 'A', 0.0], ['A', 'B', 'B', 0.0], ['A', 'B', 'C', 1.0], ['A', 'C', 'A', 0.0], ['A', 'C', 'B', 1.0], ['A', 'C', 'C', 0.0], ['B', 'A', 'A', 0.0], ['B', 'A', 'B', 0.0], ['B', 'A', 'C', 1.0], ['B', 'B', 'A', 0.5], ['B', 'B', 'B', 0.0], ['B', 'B', 'C', 0.5], ['B', 'C', 'A', 1.0], ['B', 'C', 'B', 0.0], ['B', 'C', 'C', 0.0], ['C', 'A', 'A', 0.0], ['C', 'A', 'B', 1.0], ['C', 'A', 'C', 0.0], ['C', 'B', 'A', 1.0], ['C', 'B', 'B', 0.0], ['C', 'B', 'C', 0.0], ['C', 'C', 'A', 0.5], ['C', 'C', 'B', 0.5], ['C', 'C', 'C', 0.0]], [guest, prize]) global X X = [['A', 'A', 'C'], ['A', 'A', 'B'], ['A', 'A', 'C'], ['A', 'A', 'B'], ['A', 'A', 'A'], ['A', 'B', 'A'], ['A', 'B', 'A'], ['A', 'B', 'B'], ['A', 'B', 'C'], ['A', 'C', 'A'], ['A', 'C', 'C'], ['A', 'C', 'C'], ['A', 'C', 'C'], ['A', 'C', 'B'], ['B', 'A', 'A'], ['B', 'A', 'B'], ['B', 'A', 'B'], ['B', 'A', 'B'], ['B', 'B', 'B'], ['B', 'B', 'C'], ['B', 'C', 'A'], ['B', 'C', 'B'], ['B', 'C', 'A'], ['B', 'C', 'B'], ['C', 'A', 'B'], ['C', 'B', 'B'], ['C', 'B', 'C'], ['C', 'C', 'A'], ['C', 'C', 'C'], ['C', 'C', 'C'], ['C', 'C', 'C']] global X_nan X_nan = [['nan', 'A', 'C'], ['A', 'A', 'nan'], ['A', 'nan', 'C'], ['A', 'A', 'B'], ['A', 'A', 'A'], ['A', 'B', 'nan'], ['A', 'B', 'A'], ['A', 'B', 'nan'], ['A', 'B', 'C'], ['A', 'C', 'A'], ['A', 'nan', 'C'], ['A', 'C', 'C'], ['A', 'C', 'C'], ['A', 'C', 'B'], ['B', 'nan', 'A'], ['B', 'A', 'B'], ['nan', 'A', 'B'], ['B', 'A', 'B'], ['B', 'B', 'B'], ['B', 'B', 'C'], ['B', 'C', 'A'], ['nan', 'C', 'B'], ['B', 'C', 'A'], ['nan', 'C', 'B'], ['C', 'A', 'B'], ['C', 'B', 'B'], ['C', 'nan', 'C'], ['C', 'nan', 'A'], ['C', 'nan', 'C'], ['C', 'C', 'C'], ['C', 'C', 'C']] @with_setup(setup_cpt) def test_distributions_cpt_initialization(): assert_equal(monty.name, "ConditionalProbabilityTable") assert_array_equal(monty.parameters[0], [['A', 'A', 'A', 0.0], ['A', 'A', 'B', 0.5], ['A', 'A', 'C', 0.5], ['A', 'B', 'A', 0.0], ['A', 'B', 'B', 0.0], ['A', 'B', 'C', 1.0], ['A', 'C', 'A', 0.0], ['A', 'C', 'B', 1.0], ['A', 'C', 'C', 0.0], ['B', 'A', 'A', 0.0], ['B', 'A', 'B', 0.0], ['B', 'A', 'C', 1.0], ['B', 'B', 'A', 0.5], ['B', 'B', 'B', 0.0], ['B', 'B', 'C', 0.5], ['B', 'C', 'A', 1.0], ['B', 'C', 'B', 0.0], ['B', 'C', 'C', 0.0], ['C', 'A', 'A', 0.0], ['C', 'A', 'B', 1.0], ['C', 'A', 'C', 0.0], ['C', 'B', 'A', 1.0], ['C', 'B', 'B', 0.0], ['C', 'B', 'C', 0.0], ['C', 'C', 'A', 0.5], ['C', 'C', 'B', 0.5], ['C', 'C', 'C', 0.0]]) assert_equal(monty.n_columns, 3) assert_equal(monty.m, 2) @with_setup(setup_cpt) def test_distributions_cpt_log_probability(): assert_equal(monty.log_probability(('A', 'B', 'C')), 0.) assert_equal(monty.log_probability(('C', 'B', 'A')), 0.) assert_equal(monty.log_probability(('C', 'C', 'C')), -inf) assert_equal(monty.log_probability(('A', 'A', 'A')), -inf) assert_equal(monty.log_probability(('B', 'A', 'C')), 0.) assert_equal(monty.log_probability(('C', 'A', 'B')), 0.) @with_setup(setup_cpt) def test_distributions_cpt_nan_log_probability(): assert_equal(monty.log_probability(('A', 'nan', 'C')), 0.) assert_equal(monty.log_probability(('nan', 'nan', 'C')), 0.) assert_equal(monty.log_probability(('A', 'nan', 'nan')), 0.) assert_equal(monty.log_probability(('nan', 'nan', 'nan')), 0.) assert_equal(monty.log_probability(('nan', 'B', 'C')), 0.) assert_equal(monty.log_probability(('A', 'B', 'nan')), 0.) @with_setup(setup_cpt) def test_distributions_cpt_probability(): assert_equal(monty.probability(('A', 'B', 'C')), 1.) assert_equal(monty.probability(('C', 'B', 'A')), 1.) assert_equal(monty.probability(('C', 'C', 'C')), 0.) assert_equal(monty.probability(('A', 'A', 'A')), 0.) assert_equal(monty.probability(('B', 'A', 'C')), 1.) assert_equal(monty.probability(('C', 'A', 'B')), 1.) @with_setup(setup_cpt) def test_distributions_cpt_nan_probability(): assert_equal(monty.probability(('A', 'nan', 'C')), 1.) assert_equal(monty.probability(('nan', 'nan', 'C')), 1.) assert_equal(monty.probability(('A', 'nan', 'nan')), 1.) assert_equal(monty.probability(('nan', 'nan', 'nan')), 1.) assert_equal(monty.probability(('nan', 'B', 'C')), 1.) assert_equal(monty.probability(('A', 'B', 'nan')), 1.) @with_setup(setup_cpt) def test_distributions_cpt_fit(): monty.fit(X) assert_array_equal(monty.parameters[0], [['A', 'A', 'A', 0.2], ['A', 'A', 'B', 0.4], ['A', 'A', 'C', 0.4], ['A', 'B', 'A', 0.5], ['A', 'B', 'B', 0.25], ['A', 'B', 'C', 0.25], ['A', 'C', 'A', 0.2], ['A', 'C', 'B', 0.2], ['A', 'C', 'C', 0.6], ['B', 'A', 'A', 0.25], ['B', 'A', 'B', 0.75], ['B', 'A', 'C', 0.0], ['B', 'B', 'A', 0.0], ['B', 'B', 'B', 0.5], ['B', 'B', 'C', 0.5], ['B', 'C', 'A', 0.5], ['B', 'C', 'B', 0.5], ['B', 'C', 'C', 0.0], ['C', 'A', 'A', 0.0], ['C', 'A', 'B', 1.0], ['C', 'A', 'C', 0.0], ['C', 'B', 'A', 0.0], ['C', 'B', 'B', 0.5], ['C', 'B', 'C', 0.5], ['C', 'C', 'A', 0.25], ['C', 'C', 'B', 0.0], ['C', 'C', 'C', 0.75]]) @with_setup(setup_cpt) def test_distributions_cpt_nan_fit(): monty.fit(X_nan) assert_array_equal(monty.parameters[0], [['A', 'A', 'A', 0.5], ['A', 'A', 'B', 0.5], ['A', 'A', 'C', 0.0], ['A', 'B', 'A', 0.5], ['A', 'B', 'B', 0.0], ['A', 'B', 'C', 0.5], ['A', 'C', 'A', 0.25], ['A', 'C', 'B', 0.25], ['A', 'C', 'C', 0.5], ['B', 'A', 'A', 0.0], ['B', 'A', 'B', 1.0], ['B', 'A', 'C', 0.0], ['B', 'B', 'A', 0.0], ['B', 'B', 'B', 0.5], ['B', 'B', 'C', 0.5], ['B', 'C', 'A', 1.0], ['B', 'C', 'B', 0.0], ['B', 'C', 'C', 0.0], ['C', 'A', 'A', 0.0], ['C', 'A', 'B', 1.0], ['C', 'A', 'C', 0.0], ['C', 'B', 'A', 0.0], ['C', 'B', 'B', 1.0], ['C', 'B', 'C', 0.0], ['C', 'C', 'A', 0.0], ['C', 'C', 'B', 0.0], ['C', 'C', 'C', 1.0]]) @with_setup(setup_cpt) def test_distributions_cpt_exclusive_nan_fit(): X = [['nan', 'nan', 'nan'], ['nan', 'nan', 'nan'], ['nan', 'nan', 'nan'], ['nan', 'nan', 'nan'], ['nan', 'nan', 'nan']] monty.fit(X) assert_array_equal(monty.parameters[0], [['A', 'A', 'A', 0.0], ['A', 'A', 'B', 0.5], ['A', 'A', 'C', 0.5], ['A', 'B', 'A', 0.0], ['A', 'B', 'B', 0.0], ['A', 'B', 'C', 1.0], ['A', 'C', 'A', 0.0], ['A', 'C', 'B', 1.0], ['A', 'C', 'C', 0.0], ['B', 'A', 'A', 0.0], ['B', 'A', 'B', 0.0], ['B', 'A', 'C', 1.0], ['B', 'B', 'A', 0.5], ['B', 'B', 'B', 0.0], ['B', 'B', 'C', 0.5], ['B', 'C', 'A', 1.0], ['B', 'C', 'B', 0.0], ['B', 'C', 'C', 0.0], ['C', 'A', 'A', 0.0], ['C', 'A', 'B', 1.0], ['C', 'A', 'C', 0.0], ['C', 'B', 'A', 1.0], ['C', 'B', 'B', 0.0], ['C', 'B', 'C', 0.0], ['C', 'C', 'A', 0.5], ['C', 'C', 'B', 0.5], ['C', 'C', 'C', 0.0]]) @with_setup(setup_cpt) def test_distributions_cpt_weighted_fit(): weights = [1, 3, 2, 3, 7, 4, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 1, 2, 1, 3, 1, 1, 1, 2, 1, 1, 1, 3, 1, 1, 1] monty.fit(X, weights=weights) assert_array_equal(monty.parameters[0], [['A', 'A', 'A', 0.4375], ['A', 'A', 'B', 0.375], ['A', 'A', 'C', 0.1875], ['A', 'B', 'A', 0.6], ['A', 'B', 'B', 0.2], ['A', 'B', 'C', 0.2], ['A', 'C', 'A', 0.25], ['A', 'C', 'B', 0.0], ['A', 'C', 'C', 0.75], ['B', 'A', 'A', 0.0], ['B', 'A', 'B', 1.0], ['B', 'A', 'C', 0.0], ['B', 'B', 'A', 0.0], ['B', 'B', 'B', 0.25], ['B', 'B', 'C', 0.75], ['B', 'C', 'A', 0.4], ['B', 'C', 'B', 0.6], ['B', 'C', 'C', 0.0], ['C', 'A', 'A', 0.0], ['C', 'A', 'B', 1.0], ['C', 'A', 'C', 0.0], ['C', 'B', 'A', 0.0], ['C', 'B', 'B', 0.5], ['C', 'B', 'C', 0.5], ['C', 'C', 'A', 0.5], ['C', 'C', 'B', 0.0], ['C', 'C', 'C', 0.5]]) def test_univariate_log_probability(): distributions = [UniformDistribution, NormalDistribution, ExponentialDistribution, GammaDistribution, LogNormalDistribution] X = numpy.abs(numpy.random.randn(100)) for distribution in distributions: d = distribution.from_samples(X) logp = d.log_probability(X) for i in range(100): assert_almost_equal(d.log_probability(X[i]), logp[i]) def test_multivariate_log_probability(): X = numpy.random.randn(100, 5) d = MultivariateGaussianDistribution.from_samples(X) logp = d.log_probability(X) for i in range(100): assert_almost_equal(d.log_probability(X[i]), logp[i]) d = IndependentComponentsDistribution.from_samples(X, distributions=NormalDistribution) logp = d.log_probability(X) for i in range(100): assert_almost_equal(d.log_probability(X[i]), logp[i]) def test_distributions_mgd_random_sample(): mu = numpy.array([5., 1.]) cov = numpy.eye(2) d = MultivariateGaussianDistribution(mu, cov) x = numpy.array([[5.441227, 0.66913 ], [7.430771, 0.747908], [5.10961 , 2.582481]]) assert_array_almost_equal(d.sample(3, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x) def test_distributions_independent_random_sample(): d = IndependentComponentsDistribution([NormalDistribution(5, 2), UniformDistribution(0, 10), ExponentialDistribution(7), LogNormalDistribution(0, 0.4)]) x = numpy.array([[5.882455, 2.219932, 0.03586 , 1.193024], [4.33826 , 8.707323, 0.292267, 0.876036], [9.861542, 2.067192, 0.033083, 2.644041]]) assert_array_almost_equal(d.sample(3, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x) def test_cpd_sampling(): d1 = DiscreteDistribution({"A": 0.1, "B": 0.9}) d2 = ConditionalProbabilityTable( [["A", "A", 0.1], ["A", "B", 0.9], ["B", "A", 0.7], ["B", "B", 0.3]], [d1]) # P(A) = 0.1*0.1 + 0.9*0.7 = 0.64 # P(B) = 0.1*0.9 + 0.9*0.3 = 0.36 true = [0.64, 0.36] est = numpy.bincount([0 if d2.sample() == "A" else 1 for i in range(2000)]) / 2000.0 assert_almost_equal(est[0], true[0], 1) assert_almost_equal(est[1], true[1], 1) # when A is observed, it reduces to [0.1, 0.9] true1 = [0.1, 0.9] par_val = {} par_val[d1] = "A" est = numpy.bincount( [0 if d2.sample(parent_values=par_val) == "A" else 1 for i in range(1000)] ) / 1000.0 assert_almost_equal(est[0], true1[0], 1) assert_almost_equal(est[1], true1[1], 1) true2= [0.7, 0.3] par_val = {} par_val[d1] = "B" est = numpy.bincount( [0 if d2.sample(parent_values=par_val) == "A" else 1 for i in range(1000)] ) / 1000.0 assert_almost_equal(est[0], true2[0], 1) assert_almost_equal(est[1], true2[1], 1) def test_distributions_cpt_random_sample(): d1 = DiscreteDistribution({"A": 0.1, "B": 0.9}) d = ConditionalProbabilityTable( [["A", "A", 0.1], ["A", "B", 0.9], ["B", "A", 0.7], ["B", "B", 0.3]], [d1]) # Not true with actual seed # x = numpy.array(['B', 'A', 'B', 'B', 'A', 'B', 'A', 'A', 'B', 'A', 'A', # 'B', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A']) x = numpy.array(['B', 'A', 'B', 'A', 'A', 'B', 'A', 'A', 'A', 'A', 'B', 'A', 'A', 'B', 'A', 'A', 'A', 'A', 'A', 'A']) assert_array_equal(d.sample(n=20, random_state=5), x) assert_raises(AssertionError, assert_array_equal, d.sample(n=10), x) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/test_factor_graphs.py000066400000000000000000000031101420461712600262610ustar00rootroot00000000000000from pomegranate import * from nose.tools import assert_equal def setup(): ''' No setup or teardown needs to be done in this case. ''' pass def teardown(): ''' No setup or teardown needs to be done in this case. ''' pass def test_json(): d1 = DiscreteDistribution({"A": 0.1, "B": 0.9}) d2 = ConditionalProbabilityTable( [["A", "A", 0.1], ["A", "B", 0.9], ["B", "A", 0.7], ["B", "B", 0.3]], [d1]) bayes_net = BayesianNetwork("test") s1 = Node(d1, name="d1") s2 = Node(d2, name="d2") bayes_net.add_states(s1, s2) bayes_net.add_edge(s1, s2) bayes_net.bake() fg = bayes_net.graph also_fg = FactorGraph.from_json(fg.to_json()) assert_equal(fg.to_json(), also_fg.to_json()) assert_equal(len(fg.edges), len(also_fg.edges)) for e1, e2 in zip(fg.edges, also_fg.edges): assert_equal(e1[0], e2[0]) assert_equal(e1[1], e2[1]) def test_robust_json(): d1 = DiscreteDistribution({"A": 0.1, "B": 0.9}) d2 = ConditionalProbabilityTable( [["A", "A", 0.1], ["A", "B", 0.9], ["B", "A", 0.7], ["B", "B", 0.3]], [d1]) bayes_net = BayesianNetwork("test") s1 = Node(d1, name="d1") s2 = Node(d2, name="d2") bayes_net.add_states(s1, s2) bayes_net.add_edge(s1, s2) bayes_net.bake() fg = bayes_net.graph also_fg = from_json(fg.to_json()) assert_equal(fg.to_json(), also_fg.to_json()) assert_equal(len(fg.edges), len(also_fg.edges)) for e1, e2 in zip(fg.edges, also_fg.edges): assert_equal(e1[0], e2[0]) assert_equal(e1[1], e2[1]) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/test_gmm.py000066400000000000000000001443131420461712600242320ustar00rootroot00000000000000from pomegranate import * from pomegranate.io import DataGenerator from pomegranate.io import DataFrameGenerator from nose.tools import with_setup from nose.tools import assert_true from nose.tools import assert_equal from nose.tools import assert_greater from nose.tools import assert_raises from nose.tools import assert_not_equal from numpy.testing import assert_almost_equal from numpy.testing import assert_array_equal from numpy.testing import assert_array_almost_equal import pandas import random import pickle import numpy as np np.random.seed(0) random.seed(0) nan = numpy.nan def setup_nothing(): pass def setup_multivariate_gaussian(): """ Set up a five component Gaussian mixture model, where each component is a multivariate Gaussian distribution. """ global gmm mu = np.arange(5) cov = np.eye(5) mgs = [MultivariateGaussianDistribution(mu*i, cov) for i in range(5)] gmm = GeneralMixtureModel(mgs) def setup_multivariate_mixed(): d11 = NormalDistribution(1, 1) d12 = ExponentialDistribution(5) d13 = LogNormalDistribution(0.5, 0.78) d14 = NormalDistribution(0.4, 0.8) d15 = PoissonDistribution(4) d1 = IndependentComponentsDistribution([d11, d12, d13, d14, d15]) d21 = NormalDistribution(3, 1) d22 = ExponentialDistribution(35) d23 = LogNormalDistribution(1.8, 1.33) d24 = NormalDistribution(0.5, 0.7) d25 = PoissonDistribution(6) d2 = IndependentComponentsDistribution([d21, d22, d23, d24, d25]) global gmm gmm = GeneralMixtureModel([d1, d2]) def setup_univariate_gaussian(): """ Set up a three component univariate Gaussian model. """ global gmm gmm = GeneralMixtureModel([NormalDistribution(i*3, 1) for i in range(3)]) def setup_univariate_mixed(): """Set up a four component univariate mixed model.""" global gmm d1 = ExponentialDistribution(5) d2 = NormalDistribution(0, 1.2) d3 = LogNormalDistribution(0.3, 1.4) d4 = PoissonDistribution(5) gmm = GeneralMixtureModel([d1, d2, d3, d4]) def setup_multivariate_discrete(): d1 = IndependentComponentsDistribution([DiscreteDistribution({'A':0.5, 'B':0.5}), DiscreteDistribution({'0':0.2, '1':0.2, '2':0.2, '3':0.4})]) d2 = IndependentComponentsDistribution([DiscreteDistribution({'A':0.1, 'B':0.9}), DiscreteDistribution({'0':0.1, '1':0.6, '2':0.1, '3':0.2})]) global gmm gmm = GeneralMixtureModel([d1, d2], weights=np.array([0.4,0.6])) def setup_multivariate_mixed_discrete_other(): d1 = IndependentComponentsDistribution([PoissonDistribution(4.0), DiscreteDistribution({'A':0.5, 'B':0.5}), DiscreteDistribution({'0':0.2, '1':0.2, '2':0.2, '3':0.4})]) d2 = IndependentComponentsDistribution([PoissonDistribution(1.0), DiscreteDistribution({'A':0.1, 'B':0.9}), DiscreteDistribution({'0':0.1, '1':0.6, '2':0.1, '3':0.2})]) global gmm gmm = GeneralMixtureModel([d1, d2], weights=np.array([0.4,0.6])) def teardown(): """ Teardown the model, so delete it. """ pass @with_setup(setup_multivariate_gaussian, teardown) def test_gmm_multivariate_gaussian_log_probability(): X = numpy.array([[1.1, 2.7, 3.0, 4.8, 6.2], [1.8, 2.1, 3.1, 5.2, 6.5], [0.9, 2.2, 3.2, 5.0, 5.8], [1.0, 2.1, 3.5, 4.3, 5.2], [1.2, 2.9, 3.1, 4.2, 5.5], [1.8, 1.9, 3.0, 4.9, 5.7], [1.2, 3.1, 2.9, 4.2, 5.9], [1.0, 2.9, 3.9, 4.1, 6.0]]) logp_t = [ -9.8405678, -9.67171158, -9.71615297, -9.89404726, -10.93812212, -11.06611533, -11.31473392, -10.79220257] logp = gmm.log_probability(X) assert_array_almost_equal(logp, logp_t) @with_setup(setup_multivariate_mixed, teardown) def test_gmm_multivariate_mixed_log_probability(): X = numpy.array([[1.1, 2.7, 3.0, 4.8, 6.2], [1.8, 2.1, 3.1, 5.2, 6.5], [0.9, 2.2, 3.2, 5.0, 5.8], [1.0, 2.1, 3.5, 4.3, 5.2], [1.2, 2.9, 3.1, 4.2, 5.5], [1.8, 1.9, 3.0, 4.9, 5.7], [1.2, 3.1, 2.9, 4.2, 5.9], [1.0, 2.9, 3.9, 4.1, 6.0]]) logp_t = [-33.75384631, -34.1714099, -32.59702495, -27.39375394, -30.66715208, -30.52489174, -31.71056782, -30.79589904] logp = gmm.log_probability(X) assert_array_almost_equal(logp, logp_t) @with_setup(setup_univariate_gaussian, teardown) def test_gmm_univariate_gaussian_log_probability(): X = np.array([[1.1], [2.7], [3.0], [4.8], [6.2]]) logp = [-2.35925975, -2.03120691, -1.99557605, -2.39638244, -2.03147258] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[1.8], [2.1], [3.1], [5.2], [6.5]]) logp = [-2.39618117, -2.26893273, -1.9995911, -2.22202965, -2.14007514] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[0.9], [2.2], [3.2], [5.0], [5.8]]) logp = [-2.26957032, -2.22113386, -2.01155305, -2.31613252, -2.01751101] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[1.0], [2.1], [3.5], [4.3], [5.2]]) logp = [-2.31613252, -2.26893273, -2.09160506, -2.42491769, -2.22202965] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[1.2], [2.9], [3.1], [4.2], [5.5]]) logp = [-2.39638244, -1.9995911, -1.9995911, -2.39618117, -2.09396318] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[1.8], [1.9], [3.0], [4.9], [5.7]]) logp = [-2.39618117, -2.35895351, -1.99557605, -2.35925975, -2.03559364] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[1.2], [3.1], [2.9], [4.2], [5.9]]) logp = [-2.39638244, -1.9995911, -1.9995911, -2.39618117, -2.00766654] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[1.0], [2.9], [3.9], [4.1], [6.0]]) logp = [-2.31613252, -1.9995911, -2.26893273, -2.35895351, -2.00650306] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) @with_setup(setup_univariate_mixed, teardown) def test_gmm_mixed_log_probability(): X = np.array([[1.1], [2.7], [3.0], [4.8], [6.2]]) logp = [-2.01561437061559, -2.7951359521294536, -2.8314639809821918, -2.9108132001193265, -3.1959940375620945] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[1.8], [2.1], [3.1], [5.2], [6.5]]) logp = [-2.4758296236378774, -2.6201420691379314, -2.8383034405278975, -2.966292939154318, -3.2891059316267657] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[0.9], [2.2], [3.2], [5.0], [5.8]]) logp = [-1.8326789033955484, -2.660084457680723, -2.8432332382831653, -2.9359363402629808, -3.0888100501157312] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[1.0], [2.1], [3.5], [4.3], [5.2]]) logp = [-1.9296484854978633, -2.6201420691379314, -2.850660742142904, -2.8698462265150058, -2.966292939154318] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[1.2], [2.9], [3.1], [4.2], [5.5]]) logp = [-2.0938404063492411, -2.8222781320451804, -2.8383034405278975, -2.8650550479352965, -3.0216937107055277] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[1.8], [1.9], [3.0], [4.9], [5.7]]) logp = [-2.4758296236378774, -2.527780058213545, -2.8314639809821918, -2.9227254358998933, -3.0651517040535605] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[1.2], [3.1], [2.9], [4.2], [5.9]]) logp = [-2.0938404063492411, -2.8383034405278975, -2.8222781320451804, -2.8650550479352965, -3.1137381280223324] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) X = np.array([[1.0], [2.9], [3.9], [4.1], [6.0]]) logp = [-1.9296484854978633, -2.8222781320451804, -2.8560160466782816, -2.8612291168066575, -3.1399218398841575] assert_array_almost_equal(gmm.log_probability(X), logp) assert_array_almost_equal(gmm.log_probability(X, n_jobs=2), logp) assert_array_almost_equal(gmm.log_probability(X, batch_size=2), logp) @with_setup(setup_multivariate_gaussian, teardown) def test_gmm_multivariate_gaussian_json(): gmm_2 = GeneralMixtureModel.from_json(gmm.to_json()) X = np.array([[1.1, 2.7, 3.0, 4.8, 6.2]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -9.8406, 4) X = np.array([[1.8, 2.1, 3.1, 5.2, 6.5]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -9.6717, 4) X = np.array([[0.9, 2.2, 3.2, 5.0, 5.8]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -9.7162, 4) X = np.array([[1.0, 2.1, 3.5, 4.3, 5.2]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -9.894, 4) X = np.array([[1.2, 2.9, 3.1, 4.2, 5.5]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -10.9381, 4) X = np.array([[1.8, 1.9, 3.0, 4.9, 5.7]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -11.0661, 4) X = np.array([[1.2, 3.1, 2.9, 4.2, 5.9]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -11.3147, 4) X = np.array([[1.0, 2.9, 3.9, 4.1, 6.0]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -10.7922, 4) @with_setup(setup_multivariate_mixed, teardown) def test_gmm_multivariate_mixed_json(): gmm2 = GeneralMixtureModel.from_json(gmm.to_json()) X = numpy.array([[1.1, 2.7, 3.0, 4.8, 6.2], [1.8, 2.1, 3.1, 5.2, 6.5], [0.9, 2.2, 3.2, 5.0, 5.8], [1.0, 2.1, 3.5, 4.3, 5.2], [1.2, 2.9, 3.1, 4.2, 5.5], [1.8, 1.9, 3.0, 4.9, 5.7], [1.2, 3.1, 2.9, 4.2, 5.9], [1.0, 2.9, 3.9, 4.1, 6.0]]) logp_t = [-33.75384631, -34.1714099, -32.59702495, -27.39375394, -30.66715208, -30.52489174, -31.71056782, -30.79589904] logp1 = gmm.log_probability(X) logp2 = gmm2.log_probability(X) assert_array_almost_equal(logp2, logp_t) assert_array_almost_equal(logp1, logp2) @with_setup(setup_multivariate_gaussian, teardown) def test_gmm_multivariate_gaussian_robust_from_json(): gmm_2 = from_json(gmm.to_json()) X = np.array([[1.1, 2.7, 3.0, 4.8, 6.2]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -9.8406, 4) X = np.array([[1.8, 2.1, 3.1, 5.2, 6.5]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -9.6717, 4) X = np.array([[0.9, 2.2, 3.2, 5.0, 5.8]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -9.7162, 4) X = np.array([[1.0, 2.1, 3.5, 4.3, 5.2]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -9.894, 4) X = np.array([[1.2, 2.9, 3.1, 4.2, 5.5]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -10.9381, 4) X = np.array([[1.8, 1.9, 3.0, 4.9, 5.7]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -11.0661, 4) X = np.array([[1.2, 3.1, 2.9, 4.2, 5.9]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -11.3147, 4) X = np.array([[1.0, 2.9, 3.9, 4.1, 6.0]]) assert_almost_equal(gmm_2.log_probability(X).sum(), -10.7922, 4) @with_setup(setup_multivariate_mixed, teardown) def test_gmm_multivariate_mixed_robust_from_json(): gmm2 = from_json(gmm.to_json()) X = numpy.array([[1.1, 2.7, 3.0, 4.8, 6.2], [1.8, 2.1, 3.1, 5.2, 6.5], [0.9, 2.2, 3.2, 5.0, 5.8], [1.0, 2.1, 3.5, 4.3, 5.2], [1.2, 2.9, 3.1, 4.2, 5.5], [1.8, 1.9, 3.0, 4.9, 5.7], [1.2, 3.1, 2.9, 4.2, 5.9], [1.0, 2.9, 3.9, 4.1, 6.0]]) logp_t = [-33.75384631, -34.1714099, -32.59702495, -27.39375394, -30.66715208, -30.52489174, -31.71056782, -30.79589904] logp1 = gmm.log_probability(X) logp2 = gmm2.log_probability(X) assert_array_almost_equal(logp2, logp_t) assert_array_almost_equal(logp1, logp2) @with_setup(setup_multivariate_gaussian, teardown) def test_gmm_multivariate_gaussian_predict_log_proba(): posterior = np.array([[-2.10001234e+01, -1.23402948e-04, -9.00012340e+00, -4.80001234e+01, -1.17000123e+02], [-2.30009115e+01, -9.11466556e-04, -7.00091147e+00, -4.40009115e+01, -1.11000911e+02]]) X = np.array([[2., 5., 7., 3., 2.], [1., 2., 5., 2., 5.]]) assert_almost_equal(gmm.predict_log_proba(X), posterior, 4) assert_almost_equal(numpy.exp(gmm.predict_log_proba(X)), gmm.predict_proba(X), 4) @with_setup(setup_multivariate_gaussian, teardown) def test_gmm_multivariate_gaussian_predict(): X = np.array([[2., 5., 7., 3., 2.], [1., 2., 5., 2., 5.], [2., 1., 8., 2., 1.], [4., 3., 8., 1., 2.]]) assert_almost_equal(gmm.predict(X), gmm.predict_proba(X).argmax(axis=1)) assert_almost_equal(gmm.predict(X, n_jobs=2), gmm.predict_proba(X).argmax(axis=1)) assert_almost_equal(gmm.predict(X), gmm.predict_proba(X, n_jobs=2).argmax(axis=1)) assert_almost_equal(gmm.predict(X, batch_size=2), gmm.predict_proba(X).argmax(axis=1)) assert_almost_equal(gmm.predict(X), gmm.predict_proba(X, batch_size=3).argmax(axis=1)) def test_gmm_multivariate_gaussian_fit(): d1 = MultivariateGaussianDistribution([0, 0], [[1, 0], [0, 1]]) d2 = MultivariateGaussianDistribution([2, 2], [[1, 0], [0, 1]]) gmm = GeneralMixtureModel([d1, d2]) X = np.array([[0.1, 0.7], [1.8, 2.1], [-0.9, -1.2], [-0.0, 0.2], [1.4, 2.9], [1.8, 2.5], [1.4, 3.1], [1.0, 1.0]]) _, history = gmm.fit(X, return_history=True) total_improvement = history.total_improvement[-1] assert_almost_equal(total_improvement, 15.242416, 4) @with_setup(setup_multivariate_gaussian, teardown) def test_gmm_multivariate_gaussian_fit_iterations(): numpy.random.seed(0) X = numpy.concatenate([numpy.random.normal(i, 1, size=(100, 5)) for i in range(2)]) mu, cov = numpy.ones(5), numpy.eye(5) d = [MultivariateGaussianDistribution(mu*i, cov) for i in range(2)] gmm = GeneralMixtureModel(d) gmm2 = gmm.copy() gmm3 = gmm.copy() gmm.fit(X) gmm2.fit(X, max_iterations=1) gmm3.fit(X, max_iterations=1) logp1 = gmm.log_probability(X).sum() logp2 = gmm2.log_probability(X).sum() logp3 = gmm3.log_probability(X).sum() assert_greater(logp1, logp2) assert_equal(logp2, logp3) @with_setup(setup_multivariate_mixed, teardown) def test_gmm_multivariate_mixed_fit_iterations(): numpy.random.seed(0) X = numpy.concatenate([numpy.random.normal(i, 1, size=(100, 5)) for i in range(2)]) X = numpy.abs(X) gmm2 = gmm.copy() gmm3 = gmm.copy() gmm.fit(X) gmm2.fit(X, max_iterations=1) gmm3.fit(X, max_iterations=1) logp1 = gmm.log_probability(X).sum() logp2 = gmm2.log_probability(X).sum() logp3 = gmm3.log_probability(X).sum() assert_raises(AssertionError, assert_equal, logp1, logp2) assert_equal(logp2, logp2) assert_greater(logp1, logp2) def test_gmm_initialization(): assert_raises(ValueError, GeneralMixtureModel, []) assert_raises(TypeError, GeneralMixtureModel, [NormalDistribution(5, 2), MultivariateGaussianDistribution([5, 2], [[1, 0], [0, 1]])]) assert_raises(TypeError, GeneralMixtureModel, [NormalDistribution(5, 2), NormalDistribution]) X = numpy.concatenate((numpy.random.randn(300, 5) + 0.5, numpy.random.randn(200, 5))) MGD = MultivariateGaussianDistribution gmm1 = GeneralMixtureModel.from_samples(MGD, 2, X, init='first-k') gmm2 = GeneralMixtureModel.from_samples(MGD, 2, X, init='first-k', max_iterations=1) assert_greater(gmm1.log_probability(X).sum(), gmm2.log_probability(X).sum()) assert_equal(gmm1.d, 5) assert_equal(gmm2.d, 5) def test_gmm_multivariate_discrete_initialization(): d1 = IndependentComponentsDistribution([DiscreteDistribution({'A' : 0.5, 'B' : 0.5}), DiscreteDistribution({'0' : 0.2, '1' : 0.2, '2' : 0.2, '3' : 0.4})]) d2 = IndependentComponentsDistribution([DiscreteDistribution({'A' : 0.1, 'B' : 0.9}), DiscreteDistribution({'0' : 0.1, '1' : 0.6, '2' : 0.1, '3' : 0.2})]) GeneralMixtureModel([d1, d2], weights=np.array([0.4,0.6])) @with_setup(setup_multivariate_gaussian, teardown) def test_gmm_dimension(): gmm1 = GeneralMixtureModel([NormalDistribution(0, 1), UniformDistribution(0, 10)]) assert_equal(gmm.d, 5) assert_equal(gmm1.d, 1) @with_setup(setup_multivariate_gaussian, teardown) def test_gmm_json(): univariate = GeneralMixtureModel([NormalDistribution(5, 2), UniformDistribution(0, 10)]) j_univ = univariate.to_json() j_multi = gmm.to_json() new_univ = univariate.from_json(j_univ) assert_true(isinstance(new_univ.distributions[0], NormalDistribution)) assert_true(isinstance(new_univ.distributions[1], UniformDistribution)) assert_true(isinstance(new_univ, GeneralMixtureModel)) assert_array_equal(univariate.weights, new_univ.weights) new_multi = gmm.from_json(j_multi) for i in range(5): assert_true(isinstance(new_multi.distributions[i], MultivariateGaussianDistribution)) assert_true(isinstance(new_multi, GeneralMixtureModel)) assert_array_almost_equal(gmm.weights, new_multi.weights) def test_gmm_univariate_pickling(): univariate = GeneralMixtureModel( [NormalDistribution(5, 2), UniformDistribution(0, 10)], weights=np.array([1.0, 2.0])) j_univ = pickle.dumps(univariate) new_univ = pickle.loads(j_univ) assert_true(isinstance(new_univ.distributions[0], NormalDistribution)) assert_true(isinstance(new_univ.distributions[1], UniformDistribution)) assert_true(isinstance(new_univ, GeneralMixtureModel)) assert_array_equal(univariate.weights, new_univ.weights) @with_setup(setup_multivariate_gaussian, teardown) def test_gmm_multivariate_gaussian_pickling(): gmm2 = pickle.loads(pickle.dumps(gmm)) for d in gmm2.distributions: assert_true(isinstance(d, MultivariateGaussianDistribution)) assert_true(isinstance(gmm2, GeneralMixtureModel)) assert_array_almost_equal(gmm.weights, gmm2.weights) @with_setup(setup_multivariate_mixed, teardown) def test_gmm_multivariate_gaussian_pickling(): gmm2 = pickle.loads(pickle.dumps(gmm)) d1 = gmm2.distributions[0] d2 = gmm2.distributions[1] assert_true(isinstance(d1, IndependentComponentsDistribution)) assert_true(isinstance(d2, IndependentComponentsDistribution)) assert_true(isinstance(d1.distributions[0], NormalDistribution)) assert_true(isinstance(d1.distributions[1], ExponentialDistribution)) assert_true(isinstance(d1.distributions[2], LogNormalDistribution)) assert_true(isinstance(d1.distributions[3], NormalDistribution)) assert_true(isinstance(d1.distributions[4], PoissonDistribution)) assert_true(isinstance(d2.distributions[0], NormalDistribution)) assert_true(isinstance(d2.distributions[1], ExponentialDistribution)) assert_true(isinstance(d2.distributions[2], LogNormalDistribution)) assert_true(isinstance(d2.distributions[3], NormalDistribution)) assert_true(isinstance(d2.distributions[4], PoissonDistribution)) assert_true(isinstance(gmm2, GeneralMixtureModel)) assert_array_equal(gmm.weights, gmm2.weights) def test_gmm_multivariate_gaussian_ooc(): X = numpy.concatenate([numpy.random.randn(1000, 3) + i for i in range(3)]) gmm = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 3, X, init='first-k', max_iterations=5) gmm2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 3, X, init='first-k', max_iterations=5, batch_size=3000) gmm3 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 3, X, init='first-k', max_iterations=5, batch_size=500, batches_per_epoch=6) gmm4 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 3, X, init='first-k', max_iterations=5, batch_size=500, batches_per_epoch=2) assert_almost_equal(gmm.log_probability(X).sum(), gmm2.log_probability(X).sum()) assert_almost_equal(gmm.log_probability(X).sum(), gmm3.log_probability(X).sum(), -2) assert_not_equal(gmm.log_probability(X).sum(), gmm4.log_probability(X).sum()) def test_gmm_multivariate_mixed_ooc(): X = numpy.concatenate([numpy.random.randn(1000, 3) + i for i in range(3)]) X = numpy.abs(X) d = [NormalDistribution, ExponentialDistribution, LogNormalDistribution] gmm = GeneralMixtureModel.from_samples(d, 3, X, init='first-k', max_iterations=5) gmm2 = GeneralMixtureModel.from_samples(d, 3, X, init='first-k', max_iterations=5, batch_size=3000) gmm3 = GeneralMixtureModel.from_samples(d, 3, X, init='first-k', max_iterations=5, batch_size=500, batches_per_epoch=6) gmm4 = GeneralMixtureModel.from_samples(d, 3, X, init='first-k', max_iterations=5, batch_size=500, batches_per_epoch=2) assert_almost_equal(gmm.log_probability(X).sum(), gmm2.log_probability(X).sum()) assert_not_equal(gmm.log_probability(X).sum(), gmm4.log_probability(X).sum()) def test_gmm_multivariate_gaussian_minibatch(): X = numpy.concatenate([numpy.random.randn(1000, 3) + i for i in range(3)]) gmm = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 3, X, init='first-k', max_iterations=5) gmm2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 3, X, init='first-k', max_iterations=5, batch_size=500, batches_per_epoch=1) gmm3 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 3, X, init='first-k', max_iterations=5, batch_size=500, batches_per_epoch=6) gmm4 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 3, X, init='first-k', max_iterations=5, batch_size=3000, batches_per_epoch=1) assert_not_equal(gmm.log_probability(X).sum(), gmm2.log_probability(X).sum()) assert_not_equal(gmm2.log_probability(X).sum(), gmm3.log_probability(X).sum()) assert_raises(AssertionError, assert_array_almost_equal, gmm3.log_probability(X), gmm.log_probability(X)) assert_array_equal(gmm.log_probability(X), gmm4.log_probability(X)) def test_gmm_multivariate_mixed_minibatch(): X = numpy.concatenate([numpy.random.randn(1000, 3) + i for i in range(3)]) X = numpy.abs(X) d = [NormalDistribution, ExponentialDistribution, LogNormalDistribution] gmm = GeneralMixtureModel.from_samples(d, 3, X, init='first-k', max_iterations=5) gmm2 = GeneralMixtureModel.from_samples(d, 3, X, init='first-k', max_iterations=5, batch_size=500, batches_per_epoch=1) gmm3 = GeneralMixtureModel.from_samples(d, 3, X, init='first-k', max_iterations=5, batch_size=500, batches_per_epoch=6) gmm4 = GeneralMixtureModel.from_samples(d, 3, X, init='first-k', max_iterations=5, batch_size=3000, batches_per_epoch=1) assert_not_equal(gmm.log_probability(X).sum(), gmm2.log_probability(X).sum()) assert_not_equal(gmm2.log_probability(X).sum(), gmm3.log_probability(X).sum()) assert_raises(AssertionError, assert_array_almost_equal, gmm3.log_probability(X), gmm.log_probability(X)) assert_array_equal(gmm.log_probability(X), gmm4.log_probability(X)) def test_gmm_multivariate_gaussian_nan_from_samples(): numpy.random.seed(1) X = numpy.concatenate([numpy.random.normal(0, 1, size=(300, 3)), numpy.random.normal(8, 1, size=(300, 3))]) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(1800), replace=False, size=500) i, j = idxs // 3, idxs % 3 X_nan = X.copy() X_nan[i, j] = numpy.nan mu1t = [-0.036813615311095164, 0.05802948506749107, 0.09725454186262805] cov1t = [[ 1.02529437, -0.11391075, 0.03146951], [-0.11391075, 1.03553592, -0.07852064], [ 0.03146951, -0.07852064, 0.83874547]] mu2t = [8.088079704231793, 7.927924504375215, 8.000474719123183] cov2t = [[ 0.95559825, -0.02582016, 0.07491681], [-0.02582016, 0.99427793, 0.03304442], [ 0.07491681, 0.03304442, 1.15403456]] for init in 'first-k', 'random', 'kmeans++': model = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_nan, init=init, n_init=1) mu1 = model.distributions[0].parameters[0] cov1 = model.distributions[0].parameters[1] mu2 = model.distributions[1].parameters[0] cov2 = model.distributions[1].parameters[1] assert_array_almost_equal(mu1, mu1t) assert_array_almost_equal(mu2, mu2t) assert_array_almost_equal(cov1, cov1t) assert_array_almost_equal(cov2, cov2t) def test_gmm_multivariate_mixed_nan_from_samples(): numpy.random.seed(1) X = numpy.concatenate([numpy.random.normal(0, 1, size=(100, 5)), numpy.random.normal(4, 1, size=(100, 5))]) X = numpy.abs(X) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(1000), replace=False, size=500) i, j = idxs // 5, idxs % 5 X_nan = X.copy() X_nan[i, j] = numpy.nan p1 = [4.11799, 0.257289, 1.38576, 4.11644, 3.775064] p2 = [0.77275, 1.323, -0.4297, 0.707097, 0.81196] d = [NormalDistribution, ExponentialDistribution, LogNormalDistribution, NormalDistribution, PoissonDistribution] model = GeneralMixtureModel.from_samples(d, 2, X_nan, init='first-k', n_init=1) for i in range(5): d1 = model.distributions[0].distributions[i] assert_almost_equal(d1.parameters[0], p1[i], 3) d2 = model.distributions[1].distributions[i] assert_almost_equal(d2.parameters[0], p2[i], 3) def test_gmm_multivariate_gaussian_nan_fit(): mu1, mu2, mu3 = numpy.zeros(3), numpy.zeros(3)+3, numpy.zeros(3)+5 cov1, cov2, cov3 = numpy.eye(3), numpy.eye(3)*2, numpy.eye(3)*0.5 d1 = MultivariateGaussianDistribution(mu1, cov1) d2 = MultivariateGaussianDistribution(mu2, cov2) d3 = MultivariateGaussianDistribution(mu3, cov3) model = GeneralMixtureModel([d1, d2, d3]) numpy.random.seed(1) X = numpy.concatenate([numpy.random.normal(0, 1, size=(300, 3)), numpy.random.normal(2.5, 1, size=(300, 3)), numpy.random.normal(6, 1, size=(300, 3))]) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(2700), replace=False, size=1000) i, j = idxs // 3, idxs % 3 model.fit(X) mu1t = [-0.003165176330948316, 0.07462401273020161, 0.04001352280548061] cov1t = [[ 0.98556769, -0.10062447, 0.08213565], [-0.10062447, 1.06955989, -0.03085883], [ 0.08213565, -0.03085883, 0.89728992]] mu2t = [2.601485766170187, 2.48231424824341, 2.52771758325412] cov2t = [[ 0.94263451, -0.00361101, -0.02668448], [-0.00361101, 1.06339061, -0.00408865], [-0.02668448, -0.00408865, 1.14789789]] mu3t = [5.950490843670593, 5.9572969419328725, 6.025950220056731] cov3t = [[ 1.03991941, -0.0232587, -0.02457755], [-0.0232587, 1.01047466, -0.04948464], [-0.02457755, -0.04948464, 0.85671553]] mu1 = model.distributions[0].parameters[0] cov1 = model.distributions[0].parameters[1] mu2 = model.distributions[1].parameters[0] cov2 = model.distributions[1].parameters[1] mu3 = model.distributions[2].parameters[0] cov3 = model.distributions[2].parameters[1] assert_array_almost_equal(mu1, mu1t) assert_array_almost_equal(mu2, mu2t) assert_array_almost_equal(mu3, mu3t) assert_array_almost_equal(cov1, cov1t) assert_array_almost_equal(cov2, cov2t) assert_array_almost_equal(cov3, cov3t) @with_setup(setup_multivariate_mixed, teardown) def test_gmm_multivariate_mixed_nan_fit(): numpy.random.seed(1) X = numpy.concatenate([numpy.random.normal(0, 1, size=(300, 5)), numpy.random.normal(2.5, 1, size=(300, 5))]) X = numpy.abs(X) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(3000), replace=False, size=1500) i, j = idxs // 5, idxs % 5 gmm.fit(X) p1 = [2.400978, 0.4117966, 0.72697, 2.47768, 2.30139] p2 = [0.73931, 1.24874, -0.68713, 0.67972, 0.82206] for i in range(5): d1 = gmm.distributions[0].distributions[i] assert_almost_equal(d1.parameters[0], p1[i], 4) d2 = gmm.distributions[1].distributions[i] assert_almost_equal(d2.parameters[0], p2[i], 4) @with_setup(setup_multivariate_gaussian, teardown) def test_gmm_multivariate_gaussian_nan_log_probability(): numpy.random.seed(1) X = numpy.concatenate([numpy.random.normal(0, 1, size=(5, 5)), numpy.random.normal(2.5, 1, size=(5, 5)), numpy.random.normal(6, 1, size=(5, 5))]) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(75), replace=False, size=35) i, j = idxs // 5, idxs % 5 X[i, j] = numpy.nan logp_t = [ -7.73923053e+00, -7.81725880e+00, -4.55182482e+00, -2.45359578e+01, -8.01289941e+00, -1.08630517e+01, -3.10356303e+00, -3.06193233e+01, -5.46424483e+00, -1.84952128e+01, -3.15420910e+01, -1.01635415e+01, 1.66533454e-16, -2.70671185e+00, -5.69860159e+00] logp = gmm.log_probability(X) logp2 = gmm.log_probability(X, n_jobs=2) logp4 = gmm.log_probability(X, n_jobs=4) assert_array_almost_equal(logp, logp_t) assert_array_almost_equal(logp2, logp_t) assert_array_almost_equal(logp4, logp_t) @with_setup(setup_multivariate_mixed, teardown) def test_gmm_multivariate_mixed_nan_log_probability(): X = numpy.array([[ 1.014, nan, 1.076, 1.012, nan], [ 0.745, nan, nan, nan, 1.226], [ 0.012, nan, nan, 0.010, 0.006], [ nan, nan, 0.979, 1.031, nan], [ 0.006, 0.003, nan, 0.006, nan], [ nan, nan, nan, nan, 1.041], [ 1.176, 1.040, 1.098, 1.224, 1.186], [ nan, 0.004, nan, nan, 0.005], [ nan, nan, nan, 0.025, nan], [ 0.0116, nan, 0.022, 0.006, nan]]) logp = gmm.log_probability(X) logp2 = gmm.log_probability(X, n_jobs=2) logp4 = gmm.log_probability(X, n_jobs=4) logp_t = [ -3.447266, -4.038374, -6.900469, -2.282948, -1.216055, -3.080241, -9.789394, -2.4831 , -0.799054, -13.052867] assert_array_almost_equal(logp, logp_t) assert_array_almost_equal(logp2, logp_t) assert_array_almost_equal(logp4, logp_t) def test_gmm_multivariate_gaussian_nan_fit_predict(): X = numpy.concatenate([numpy.random.normal(0, 1, size=(300, 5)), numpy.random.normal(8, 1, size=(300, 5))]) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(3000), replace=False, size=900) i, j = idxs // 5, idxs % 5 X_nan = X.copy() X_nan[i, j] = numpy.nan for init in 'first-k', 'random', 'kmeans++': model = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_nan, init=init, n_init=1) for d in model.distributions: assert_equal(numpy.isnan(d.parameters[0]).sum(), 0) assert_equal(numpy.isnan(d.parameters[1]).sum(), 0) y_hat = model.predict(X) assert_equal(y_hat.sum(), 300) @with_setup(setup_multivariate_gaussian, teardown) def test_gmm_multivariate_gaussian_nan_predict(): numpy.random.seed(0) X = numpy.concatenate([numpy.random.normal(0, 1, size=(5, 5)), numpy.random.normal(2, 1, size=(5, 5))]) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(50), replace=False, size=25) i, j = idxs // 5, idxs % 5 X[i, j] = numpy.nan y_hat = gmm.predict(X) y = [0, 1, 0, 0, 0, 0, 1, 0, 1, 1] assert_array_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_gmm_multivariate_mixed_nan_predict(): X = [[ 1.014, nan, 1.076, 1.012, nan], [ 0.745, nan, nan, nan, 1.226], [ 0.012, nan, nan, 0.010, 0.006], [ nan, nan, 0.979, 1.031, nan], [ 0.006, 0.003, nan, 0.006, nan], [ nan, nan, nan, nan, 1.041], [ 1.176, 1.040, 1.098, 1.224, 1.186], [ nan, 0.004, nan, nan, 0.005], [ nan, nan, nan, 0.025, nan], [ 0.0116, nan, 0.022, 0.006, nan]] y_hat = gmm.predict(X) y = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1] assert_array_equal(y, y_hat) @with_setup(setup_multivariate_discrete) def test_gmm_multivariate_discrete_predict_proba(): probs = gmm.predict_proba(numpy.array([['A', '0'], ['B', '1']])) assert_almost_equal(probs, [[0.86956522, 0.13043478], [0.10989011, 0.89010989]]) @with_setup(setup_multivariate_gaussian, teardown) def test_gmm_multivariate_gaussian_nan_predict_proba(): numpy.random.seed(0) X = numpy.concatenate([numpy.random.normal(0, 1, size=(5, 5)), numpy.random.normal(2, 1, size=(5, 5))]) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(50), replace=False, size=25) i, j = idxs // 5, idxs % 5 X[i, j] = numpy.nan y_hat = gmm.predict_proba(X) y = [[ 9.90174494e-01, 9.82550625e-03, 2.20378830e-10, 1.11726590e-23, 1.28030945e-42], [ 2.53691739e-01, 7.46308013e-01, 2.47068952e-07, 9.20463413e-21, 3.85907464e-41], [ 9.97082301e-01, 2.91769906e-03, 1.18573581e-16, 6.69226809e-41, 5.24561818e-76], [ 9.94236788e-01, 5.76321240e-03, 7.55111628e-11, 2.23629671e-24, 1.49699183e-43], [ 9.56664759e-01, 4.33351517e-02, 8.91201785e-08, 8.32083585e-18, 3.52706158e-32], [ 9.98269488e-01, 1.73051193e-03, 3.37590087e-13, 7.41127732e-30, 1.83098477e-53], [ 1.75007605e-01, 8.24992395e-01, 3.63922167e-13, 1.50221681e-38, 5.80259513e-77], [ 8.76384248e-01, 1.23615751e-01, 7.21849843e-10, 1.74507352e-25, 1.74652336e-48], [ 2.12508922e-03, 9.45913785e-01, 5.19607732e-02, 3.52248634e-07, 2.94694950e-16], [ 8.63688760e-03, 9.91106040e-01, 2.57071963e-04, 1.50716583e-13, 1.99728068e-28]] assert_array_almost_equal(y, y_hat, 2) @with_setup(setup_multivariate_mixed, teardown) def test_gmm_multivariate_mixed_nan_predict_proba(): X = [[ 1.014, nan, 1.076, 1.012, nan], [ 0.745, nan, nan, nan, 1.226], [ 0.012, nan, nan, 0.010, 0.006], [ nan, nan, 0.979, 1.031, nan], [ 0.006, 0.003, nan, 0.006, nan], [ nan, nan, nan, nan, 1.041], [ 1.176, 1.040, 1.098, 1.224, 1.186], [ nan, 0.004, nan, nan, 0.005], [ nan, nan, nan, 0.025, nan], [ 0.0116, nan, 0.022, 0.006, nan]] y_hat = gmm.predict_proba(X) y = [[ 9.54354263e-01, 4.56457374e-02], [ 9.82240224e-01, 1.77597761e-02], [ 9.97442611e-01, 2.55738927e-03], [ 7.48577347e-01, 2.51422653e-01], [ 8.93432925e-01, 1.06567075e-01], [ 8.28908436e-01, 1.71091564e-01], [ 1.00000000e+00, 2.78899961e-15], [ 5.42909820e-01, 4.57090180e-01], [ 4.96708777e-01, 5.03291223e-01], [ 1.31011533e-01, 8.68988467e-01]] assert_array_almost_equal(y, y_hat) def test_gmm_multivariate_gaussian_ooc_nan_from_samples(): numpy.random.seed(2) X = numpy.concatenate([numpy.random.normal(i*3, 0.5, size=(200, 3)) for i in range(2)]) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(1200), replace=False, size=100) i, j = idxs // 3, idxs % 3 X[i, j] = numpy.nan model1 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X, init='first-k', batch_size=None, max_iterations=3) model2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X, init='first-k', batch_size=400, max_iterations=3) model3 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X, init='first-k', batch_size=100, max_iterations=3) cov1 = model1.distributions[0].parameters[1] cov2 = model2.distributions[0].parameters[1] cov3 = model3.distributions[0].parameters[1] assert_array_almost_equal(cov1, cov2) assert_array_almost_equal(cov1, cov3) def test_gmm_multivariate_mixed_ooc_nan_from_samples(): numpy.random.seed(2) X = numpy.concatenate([numpy.random.normal(i*3, 0.5, size=(200, 3)) for i in range(2)]) numpy.abs(X) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(1200), replace=False, size=100) i, j = idxs // 3, idxs % 3 X[i, j] = numpy.nan d = [NormalDistribution, LogNormalDistribution, PoissonDistribution] model1 = GeneralMixtureModel.from_samples(d, 2, X, init='first-k', batch_size=None, max_iterations=3) model2 = GeneralMixtureModel.from_samples(d, 2, X, init='first-k', batch_size=400, max_iterations=3) model3 = GeneralMixtureModel.from_samples(d, 2, X, init='first-k', batch_size=100, max_iterations=3) p01 = model1.distributions[0].distributions[0].parameters[0] p02 = model2.distributions[0].distributions[0].parameters[0] p03 = model3.distributions[0].distributions[0].parameters[0] assert_almost_equal(p01, p02) assert_almost_equal(p01, p03) p11 = model1.distributions[0].distributions[1].parameters[0] p12 = model2.distributions[0].distributions[1].parameters[0] p13 = model3.distributions[0].distributions[1].parameters[0] assert_almost_equal(p11, p12) assert_almost_equal(p11, p13) p21 = model1.distributions[0].distributions[2].parameters[0] p22 = model2.distributions[0].distributions[2].parameters[0] p23 = model3.distributions[0].distributions[2].parameters[0] assert_almost_equal(p21, p22) assert_almost_equal(p21, p23) def test_gmm_multivariate_gaussian_ooc_nan_fit(): X = numpy.concatenate([numpy.random.normal(i*3, 0.5, size=(100, 3)) for i in range(2)]) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(600), replace=False, size=100) i, j = idxs // 3, idxs % 3 X[i, j] = numpy.nan mus = [numpy.ones(3)*i*3 for i in range(2)] covs = [numpy.eye(3) for i in range(2)] distributions = [MultivariateGaussianDistribution(mu, cov) for mu, cov in zip(mus, covs)] model1 = GeneralMixtureModel(distributions) model1.fit(X, max_iterations=3) distributions = [MultivariateGaussianDistribution(mu, cov) for mu, cov in zip(mus, covs)] model2 = GeneralMixtureModel(distributions) model2.fit(X, batch_size=10, max_iterations=3) distributions = [MultivariateGaussianDistribution(mu, cov) for mu, cov in zip(mus, covs)] model3 = GeneralMixtureModel(distributions) model3.fit(X, batch_size=1, max_iterations=3) cov1 = model1.distributions[0].parameters[1] cov2 = model2.distributions[0].parameters[1] cov3 = model3.distributions[0].parameters[1] assert_array_almost_equal(cov1, cov2) assert_array_almost_equal(cov1, cov3) def test_gmm_multivariate_mixed_ooc_nan_fit(): X = numpy.concatenate([numpy.random.normal(i*3, 0.5, size=(100, 3)) for i in range(2)]) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(600), replace=False, size=100) i, j = idxs // 3, idxs % 3 X[i, j] = numpy.nan d1 = [NormalDistribution(0, 1), LogNormalDistribution(0.4, 0.2), PoissonDistribution(3)] d2 = [NormalDistribution(2, 1), LogNormalDistribution(0.8, 1.2), PoissonDistribution(6)] d = [IndependentComponentsDistribution([a.copy() for a in d1]), IndependentComponentsDistribution([a.copy() for a in d2])] model1 = GeneralMixtureModel(d) model1.fit(X, max_iterations=3) d = [IndependentComponentsDistribution([a.copy() for a in d1]), IndependentComponentsDistribution([a.copy() for a in d2])] model2 = GeneralMixtureModel(d) model2.fit(X, batch_size=10, max_iterations=3) d = [IndependentComponentsDistribution([a.copy() for a in d1]), IndependentComponentsDistribution([a.copy() for a in d2])] model3 = GeneralMixtureModel(d) model3.fit(X, batch_size=1, max_iterations=3) p01 = model1.distributions[0].distributions[0].parameters[0] p02 = model2.distributions[0].distributions[0].parameters[0] p03 = model3.distributions[0].distributions[0].parameters[0] assert_almost_equal(p01, p02) assert_almost_equal(p01, p03) p11 = model1.distributions[0].distributions[1].parameters[0] p12 = model2.distributions[0].distributions[1].parameters[0] p13 = model3.distributions[0].distributions[1].parameters[0] assert_almost_equal(p11, p12) assert_almost_equal(p11, p13) p21 = model1.distributions[0].distributions[2].parameters[0] p22 = model2.distributions[0].distributions[2].parameters[0] p23 = model3.distributions[0].distributions[2].parameters[0] assert_almost_equal(p21, p22) assert_almost_equal(p21, p23) def test_gmm_multivariate_gaussian_minibatch_nan_from_samples(): X = numpy.concatenate([numpy.random.normal(i*2, 1, size=(100, 3)) for i in range(2)]) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(600), replace=False, size=100) i, j = idxs // 3, idxs % 3 X[i, j] = numpy.nan model1 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X, init='first-k', batch_size=None, max_iterations=5) model2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X, init='first-k', batch_size=200, max_iterations=5) model3 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X, init='first-k', batch_size=50, batches_per_epoch=4, max_iterations=5) model4 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X, init='first-k', batch_size=50, batches_per_epoch=1, max_iterations=5) cov1 = model1.distributions[0].parameters[1] cov2 = model2.distributions[0].parameters[1] cov3 = model3.distributions[0].parameters[1] cov4 = model4.distributions[0].parameters[1] assert_array_almost_equal(cov1, cov2) assert_raises(AssertionError, assert_array_almost_equal, cov1, cov3) assert_raises(AssertionError, assert_array_almost_equal, cov1, cov4) def test_gmm_multivariate_mixed_minibatch_nan_from_samples(): X = numpy.concatenate([numpy.random.normal(i*2, 1, size=(100, 3)) for i in range(2)]) X = numpy.abs(X) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(600), replace=False, size=100) i, j = idxs // 3, idxs % 3 X[i, j] = numpy.nan d = [NormalDistribution, LogNormalDistribution, PoissonDistribution] model1 = GeneralMixtureModel.from_samples(d, 2, X, init='first-k', batch_size=None, max_iterations=5) model2 = GeneralMixtureModel.from_samples(d, 2, X, init='first-k', batch_size=200, max_iterations=5) model3 = GeneralMixtureModel.from_samples(d, 2, X, init='first-k', batch_size=50, batches_per_epoch=4, max_iterations=5) model4 = GeneralMixtureModel.from_samples(d, 2, X, init='first-k', batch_size=50, batches_per_epoch=1, max_iterations=5) p1 = model1.distributions[0].distributions[0].parameters[0] p2 = model2.distributions[0].distributions[0].parameters[0] p3 = model3.distributions[0].distributions[0].parameters[0] p4 = model4.distributions[0].distributions[0].parameters[0] assert_almost_equal(p1, p2) assert_raises(AssertionError, assert_almost_equal, p1, p3) assert_raises(AssertionError, assert_almost_equal, p1, p4) p1 = model1.distributions[0].distributions[1].parameters[0] p2 = model2.distributions[0].distributions[1].parameters[0] p3 = model3.distributions[0].distributions[1].parameters[0] p4 = model4.distributions[0].distributions[1].parameters[0] assert_almost_equal(p1, p2) assert_raises(AssertionError, assert_almost_equal, p1, p3) assert_raises(AssertionError, assert_almost_equal, p1, p4) p1 = model1.distributions[0].distributions[2].parameters[0] p2 = model2.distributions[0].distributions[2].parameters[0] p3 = model3.distributions[0].distributions[2].parameters[0] p4 = model4.distributions[0].distributions[2].parameters[0] assert_almost_equal(p1, p2) assert_raises(AssertionError, assert_almost_equal, p1, p3) assert_raises(AssertionError, assert_almost_equal, p1, p4) def test_gmm_multivariate_gaussian_minibatch_nan_fit(): X = numpy.concatenate([numpy.random.normal(i*3, 0.5, size=(100, 3)) for i in range(2)]) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(600), replace=False, size=100) i, j = idxs // 3, idxs % 3 X[i, j] = numpy.nan mus = [numpy.ones(3)*i*3 for i in range(2)] covs = [numpy.eye(3) for i in range(2)] distributions = [MultivariateGaussianDistribution(mu, cov) for mu, cov in zip(mus, covs)] model1 = GeneralMixtureModel(distributions) model1.fit(X, batch_size=None, max_iterations=3) distributions = [MultivariateGaussianDistribution(mu, cov) for mu, cov in zip(mus, covs)] model2 = GeneralMixtureModel(distributions) model2.fit(X, batch_size=10, max_iterations=3) distributions = [MultivariateGaussianDistribution(mu, cov) for mu, cov in zip(mus, covs)] model3 = GeneralMixtureModel(distributions) model3.fit(X, batch_size=10, batches_per_epoch=5, max_iterations=3) cov1 = model1.distributions[0].parameters[1] cov2 = model2.distributions[0].parameters[1] cov3 = model3.distributions[0].parameters[1] assert_array_almost_equal(cov1, cov2) assert_raises(AssertionError, assert_array_equal, cov1, cov3) def test_gmm_multivariate_mixed_minibatch_nan_fit(): X = numpy.concatenate([numpy.random.normal(i*3, 0.5, size=(100, 3)) for i in range(2)]) X = numpy.abs(X) numpy.random.shuffle(X) idxs = numpy.random.choice(numpy.arange(600), replace=False, size=100) i, j = idxs // 3, idxs % 3 X[i, j] = numpy.nan d1 = [NormalDistribution(0, 1), LogNormalDistribution(0.4, 0.2), PoissonDistribution(3)] d2 = [NormalDistribution(2, 1), LogNormalDistribution(0.8, 1.2), PoissonDistribution(6)] d = [IndependentComponentsDistribution([a.copy() for a in d1]), IndependentComponentsDistribution([a.copy() for a in d2])] model1 = GeneralMixtureModel(d) model1.fit(X, batch_size=None, max_iterations=3) d = [IndependentComponentsDistribution([a.copy() for a in d1]), IndependentComponentsDistribution([a.copy() for a in d2])] model2 = GeneralMixtureModel(d) model2.fit(X, batch_size=10, max_iterations=3) d = [IndependentComponentsDistribution([a.copy() for a in d1]), IndependentComponentsDistribution([a.copy() for a in d2])] model3 = GeneralMixtureModel(d) model3.fit(X, batch_size=10, batches_per_epoch=5, max_iterations=3) p1 = model1.distributions[0].distributions[0].parameters[0] p2 = model2.distributions[0].distributions[0].parameters[0] p3 = model3.distributions[0].distributions[0].parameters[0] assert_almost_equal(p1, p2) assert_raises(AssertionError, assert_almost_equal, p1, p3) p1 = model1.distributions[0].distributions[1].parameters[0] p2 = model2.distributions[0].distributions[1].parameters[0] p3 = model3.distributions[0].distributions[1].parameters[0] assert_almost_equal(p1, p2) assert_raises(AssertionError, assert_almost_equal, p1, p3) p1 = model1.distributions[0].distributions[2].parameters[0] p2 = model2.distributions[0].distributions[2].parameters[0] p3 = model3.distributions[0].distributions[2].parameters[0] assert_almost_equal(p1, p2) assert_raises(AssertionError, assert_almost_equal, p1, p3) @with_setup(setup_multivariate_mixed, teardown) def test_gmm_multivariate_mixed_random_sample(): x = numpy.array([[ 4.61023781e+00, 2.68284944e-02, 5.15017758e+01, 1.62716647e+00, 5.00000000e+00], [ 1.97078941e+00, 1.02011059e-02, 1.53902626e+00, -2.20447410e-01, 4.00000000e+00], [-2.37630710e-01, 2.01296137e-02, 8.15899779e-02, -1.76634150e+00, 1.00000000e+01]]) assert_array_almost_equal(gmm.sample(3, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, gmm.sample(3), x) @with_setup(setup_multivariate_gaussian, teardown) def test_gmm_multivariate_mixed_random_sample(): x = numpy.array([[-0.937128, 3.919795, 7.066424, 11.901844, 16.691532], [ 1.875753, 0.915462, 2.591164, 3.173006, 4.656439], [ 0.307347, 3.411364, 7.450107, 11.520762, 16.511734]]) assert_array_almost_equal(gmm.sample(3, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, gmm.sample(3), x) @with_setup(setup_multivariate_mixed_discrete_other) def test_gmm_multivariate_mixed_discrete_other_fit(): x = numpy.array( [ [0, 'A', '0'], [5, 'A', '1'], [4, 'A', '3'], [5, 'B', '0'] ], dtype=object ) gmm.fit(x) assert_array_almost_equal(gmm.weights, numpy.array([-0.28544206, -1.39304465])) def test_io_log_probability(): X = numpy.random.randn(100, 5) + 0.5 X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) d = MultivariateGaussianDistribution model = GeneralMixtureModel.from_samples(d, n_components=2, X=X) logp1 = model.log_probability(X) logp2 = model.log_probability(X2) logp3 = model.log_probability(X3) assert_array_almost_equal(logp1, logp2) assert_array_almost_equal(logp1, logp3) def test_io_predict(): X = numpy.random.randn(100, 5) + 0.5 X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) d = MultivariateGaussianDistribution model = GeneralMixtureModel.from_samples(d, n_components=2, X=X) y_hat1 = model.predict(X) y_hat2 = model.predict(X2) y_hat3 = model.predict(X3) assert_array_almost_equal(y_hat1, y_hat2) assert_array_almost_equal(y_hat1, y_hat3) def test_io_predict_proba(): X = numpy.random.randn(100, 5) + 0.5 X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) d = MultivariateGaussianDistribution model = GeneralMixtureModel.from_samples(d, n_components=2, X=X) y_hat1 = model.predict_proba(X) y_hat2 = model.predict_proba(X2) y_hat3 = model.predict_proba(X3) assert_array_almost_equal(y_hat1, y_hat2) assert_array_almost_equal(y_hat1, y_hat3) def test_io_predict_log_proba(): X = numpy.random.randn(100, 5) + 0.5 X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) d = MultivariateGaussianDistribution model = GeneralMixtureModel.from_samples(d, n_components=2, X=X) y_hat1 = model.predict_log_proba(X) y_hat2 = model.predict_log_proba(X2) y_hat3 = model.predict_log_proba(X3) assert_array_almost_equal(y_hat1, y_hat2) assert_array_almost_equal(y_hat1, y_hat3) def test_io_fit(): X = numpy.random.randn(100, 5) + 0.5 weights = numpy.abs(numpy.random.randn(100)) data_generator = DataGenerator(X, weights) mu1 = numpy.array([0, 0, 0, 0, 0]) mu2 = numpy.array([1, 1, 1, 1, 1]) cov = numpy.eye(5) d1 = MultivariateGaussianDistribution(mu1, cov) d2 = MultivariateGaussianDistribution(mu2, cov) gmm1 = GeneralMixtureModel([d1, d2]) gmm1.fit(X, weights, max_iterations=5) d1 = MultivariateGaussianDistribution(mu1, cov) d2 = MultivariateGaussianDistribution(mu2, cov) gmm2 = GeneralMixtureModel([d1, d2]) gmm2.fit(data_generator, max_iterations=5) logp1 = gmm1.log_probability(X) logp2 = gmm2.log_probability(X) assert_array_almost_equal(logp1, logp2) def test_io_from_samples_gmm(): X = numpy.random.randn(100, 5) + 0.5 weights = numpy.abs(numpy.random.randn(100)) data_generator = DataGenerator(X, weights) d = MultivariateGaussianDistribution gmm1 = GeneralMixtureModel.from_samples(d, n_components=2, X=X, weights=weights, max_iterations=5, init='first-k') gmm2 = GeneralMixtureModel.from_samples(d, n_components=2, X=data_generator, max_iterations=5, init='first-k') logp1 = gmm1.log_probability(X) logp2 = gmm2.log_probability(X) assert_array_almost_equal(logp1, logp2) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/test_hmm.py000066400000000000000000002364641420461712600242440ustar00rootroot00000000000000from __future__ import (division) from pomegranate import * from pomegranate.parallel import log_probability from pomegranate.io import SequenceGenerator from nose.tools import with_setup from nose.tools import assert_almost_equal from nose.tools import assert_equal from nose.tools import assert_not_equal from nose.tools import assert_less_equal from nose.tools import assert_raises from nose.tools import assert_greater from numpy.testing import assert_array_almost_equal from numpy.testing import assert_array_equal import pickle import random import numpy import time numpy.random.seed(0) random.seed(0) inf = float("inf") nan = numpy.nan def sparse_model(d1, d2, d3, i_d): model = HiddenMarkovModel("Global Alignment") # Create the insert states i0 = State(i_d, name="I0") i1 = State(i_d, name="I1") i2 = State(i_d, name="I2") i3 = State(i_d, name="I3") # Create the match states m1 = State(d1, name="M1") m2 = State(d2, name="M2") m3 = State(d3, name="M3") # Create the delete states d1 = State(None, name="D1") d2 = State(None, name="D2") d3 = State(None, name="D3") # Add all the states to the model model.add_states(i0, i1, i2, i3, m1, m2, m3, d1, d2, d3) # Create transitions from match states model.add_transition(model.start, m1, 0.9) model.add_transition(model.start, i0, 0.1) model.add_transition(m1, m2, 0.9) model.add_transition(m1, i1, 0.05) model.add_transition(m1, d2, 0.05) model.add_transition(m2, m3, 0.9) model.add_transition(m2, i2, 0.05) model.add_transition(m2, d3, 0.05) model.add_transition(m3, model.end, 0.9) model.add_transition(m3, i3, 0.1) # Create transitions from insert states model.add_transition(i0, i0, 0.70) model.add_transition(i0, d1, 0.15) model.add_transition(i0, m1, 0.15) model.add_transition(i1, i1, 0.70) model.add_transition(i1, d2, 0.15) model.add_transition(i1, m2, 0.15) model.add_transition(i2, i2, 0.70) model.add_transition(i2, d3, 0.15) model.add_transition(i2, m3, 0.15) model.add_transition(i3, i3, 0.85) model.add_transition(i3, model.end, 0.15) # Create transitions from delete states model.add_transition(d1, d2, 0.15) model.add_transition(d1, i1, 0.15) model.add_transition(d1, m2, 0.70) model.add_transition(d2, d3, 0.15) model.add_transition(d2, i2, 0.15) model.add_transition(d2, m3, 0.70) model.add_transition(d3, i3, 0.30) model.add_transition(d3, model.end, 0.70) # Call bake to finalize the structure of the model. model.bake() return model def setup(): global model i_d = DiscreteDistribution({ 'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25 }) d1 = DiscreteDistribution({ "A": 0.95, 'C': 0.01, 'G': 0.01, 'T': 0.02 }) d2 = DiscreteDistribution({ "A": 0.003, 'C': 0.99, 'G': 0.003, 'T': 0.004 }) d3 = DiscreteDistribution({ "A": 0.01, 'C': 0.01, 'G': 0.01, 'T': 0.97 }) model = sparse_model(d1, d2, d3, i_d) def setup_multivariate_discrete_sparse(): global model i1 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25}) i2 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25}) i_d = IndependentComponentsDistribution([i1, i2]) d11 = DiscreteDistribution({ "A": 0.95, 'C': 0.01, 'G': 0.01, 'T': 0.02 }) d12 = DiscreteDistribution({ "A": 0.92, 'C': 0.02, 'G': 0.02, 'T': 0.03 }) d21 = DiscreteDistribution({ "A": 0.005, 'C': 0.96, 'G': 0.005, 'T': 0.003 }) d22 = DiscreteDistribution({ "A": 0.003, 'C': 0.99, 'G': 0.003, 'T': 0.004 }) d31 = DiscreteDistribution({ "A": 0.01, 'C': 0.01, 'G': 0.01, 'T': 0.97 }) d32 = DiscreteDistribution({ "A": 0.05, 'C': 0.03, 'G': 0.02, 'T': 0.90 }) d1 = IndependentComponentsDistribution([d11, d12]) d2 = IndependentComponentsDistribution([d21, d22]) d3 = IndependentComponentsDistribution([d31, d32]) model = sparse_model(d1, d2, d3, i_d) def setup_multivariate_gaussian_sparse(): global model i1 = UniformDistribution(-20, 20) i2 = UniformDistribution(-20, 20) i_d = IndependentComponentsDistribution([i1, i2]) d11 = NormalDistribution(5, 1) d12 = NormalDistribution(7, 1) d21 = NormalDistribution(13, 1) d22 = NormalDistribution(17, 1) d31 = NormalDistribution(-2, 1) d32 = NormalDistribution(-5, 1) d1 = IndependentComponentsDistribution([d11, d12]) d2 = IndependentComponentsDistribution([d21, d22]) d3 = IndependentComponentsDistribution([d31, d32]) model = sparse_model(d1, d2, d3, i_d) def dense_model(d1, d2, d3, d4): s1 = State(d1, "s1") s2 = State(d2, "s2") s3 = State(d3, "s3") s4 = State(d4, "s4") model = HiddenMarkovModel() model.add_states(s1, s2, s3, s4) model.add_transition(model.start, s1, 0.1) model.add_transition(model.start, s2, 0.3) model.add_transition(model.start, s3, 0.2) model.add_transition(model.start, s4, 0.4) model.add_transition(s1, s1, 0.5) model.add_transition(s1, s2, 0.1) model.add_transition(s1, s3, 0.1) model.add_transition(s1, s4, 0.2) model.add_transition(s2, s1, 0.2) model.add_transition(s2, s2, 0.1) model.add_transition(s2, s3, 0.4) model.add_transition(s2, s4, 0.2) model.add_transition(s3, s1, 0.1) model.add_transition(s3, s2, 0.1) model.add_transition(s3, s3, 0.3) model.add_transition(s3, s4, 0.4) model.add_transition(s4, s1, 0.2) model.add_transition(s4, s2, 0.2) model.add_transition(s4, s3, 0.1) model.add_transition(s4, s4, 0.4) model.add_transition(s1, model.end, 0.1) model.add_transition(s2, model.end, 0.1) model.add_transition(s3, model.end, 0.1) model.add_transition(s4, model.end, 0.1) model.bake() return model def setup_univariate_discrete_dense(): global model d1 = DiscreteDistribution({'A': 0.90, 'B': 0.02, 'C': 0.03, 'D': 0.05}) d2 = DiscreteDistribution({'A': 0.02, 'B': 0.90, 'C': 0.03, 'D': 0.05}) d3 = DiscreteDistribution({'A': 0.03, 'B': 0.02, 'C': 0.90, 'D': 0.05}) d4 = DiscreteDistribution({'A': 0.05, 'B': 0.02, 'C': 0.03, 'D': 0.90}) model = dense_model(d1, d2, d3, d4) def setup_univariate_gaussian_dense(): global model d1 = NormalDistribution(5, 1) d2 = NormalDistribution(1, 1) d3 = NormalDistribution(13, 2) d4 = NormalDistribution(16, 0.5) model = dense_model(d1, d2, d3, d4) def setup_univariate_poisson_dense(): global model d1 = PoissonDistribution(12.1) d2 = PoissonDistribution(8.7) d3 = PoissonDistribution(1) d4 = PoissonDistribution(5) model = dense_model(d1, d2, d3, d4) def setup_multivariate_mixed_dense(): global model d11 = NormalDistribution(1, 1) d12 = ExponentialDistribution(5) d13 = LogNormalDistribution(0.5, 0.78) d14 = NormalDistribution(0.4, 0.8) d15 = PoissonDistribution(4) d1 = IndependentComponentsDistribution([d11, d12, d13, d14, d15]) d21 = NormalDistribution(3, 1) d22 = ExponentialDistribution(35) d23 = LogNormalDistribution(1.8, 1.33) d24 = NormalDistribution(0.5, 0.7) d25 = PoissonDistribution(6) d2 = IndependentComponentsDistribution([d21, d22, d23, d24, d25]) d31 = NormalDistribution(5, 1) d32 = ExponentialDistribution(7) d33 = LogNormalDistribution(1.2, 0.38) d34 = NormalDistribution(0.2, 0.8) d35 = PoissonDistribution(8) d3 = IndependentComponentsDistribution([d31, d32, d33, d34, d35]) d41 = NormalDistribution(9, 1) d42 = ExponentialDistribution(15) d43 = LogNormalDistribution(2.2, 1.0) d44 = NormalDistribution(2.3, 0.2) d45 = PoissonDistribution(10) d4 = IndependentComponentsDistribution([d41, d42, d43, d44, d45]) model = dense_model(d1, d2, d3, d4) def setup_multivariate_gaussian_dense(): global model random_state = numpy.random.RandomState(0) mu = random_state.normal(0, 1, size=(4, 5)) d1 = MultivariateGaussianDistribution(mu[0], numpy.eye(5)) d2 = MultivariateGaussianDistribution(mu[1], numpy.eye(5)) d3 = MultivariateGaussianDistribution(mu[2], numpy.eye(5)) d4 = MultivariateGaussianDistribution(mu[3], numpy.eye(5)) model = dense_model(d1, d2, d3, d4) def setup_general_mixture_gaussian(): global model # should be able to pass list of weights gmm1 = GeneralMixtureModel([NormalDistribution(5, 2), NormalDistribution(1, 2)], weights=[0.33, 0.67]) gmm2 = GeneralMixtureModel([NormalDistribution(3, 2), NormalDistribution(-1, 2)], weights=numpy.array([0.67, 0.33])) s1 = State(gmm1, "s1") s2 = State(gmm2, "s2") model = HiddenMarkovModel() model.add_states([s1, s2]) model.add_transition(model.start, s1, 0.5) model.add_transition(model.start, s2, 0.5) model.add_transition(s1, s1, 0.7) model.add_transition(s1, s2, 0.3) model.add_transition(s2, s2, 0.8) model.add_transition(s2, s1, 0.2) model.bake() def teardown(): ''' Remove the model at the end of the unit testing. Since it is stored in a global variance, simply delete it. ''' pass @with_setup(setup_univariate_discrete_dense) def test_hmm_univariate_discrete_dense_forward(): f = model.forward(['A', 'B', 'D', 'D', 'C']) logp = numpy.array([[-inf, -inf, -inf, -inf, 0., -inf], [-2.40794561, -5.11599581, -5.11599581, -3.91202301, -inf, -4.40631933], [-6.89188193, -4.35987383, -8.09848286, -7.43200392, -inf, -6.52303724], [-8.73634472, -9.47926612, -8.22377759, -5.87605232, -inf, -8.01305991], [-10.28388158, -10.39501067, -10.80077009, -6.76858029, -inf, -8.99969597], [-11.780373, -11.84820305, -9.00308599, -11.14654124, -inf, -11.09251037]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_gaussian_dense) def test_hmm_univariate_gaussian_dense_forward(): f = model.forward([3, 5, 8, 19, 13]) logp = numpy.array([[-inf, -inf, -inf, -inf, 0.0, -inf], [-5.221523626198319, -4.122911337530209, -15.72152362619832, -339.14208208451845, -inf, -6.137807473983832], [-6.045149476287824, -15.056746007188107, -14.571238720950003, -247.67044476202696, -inf, -8.347414404915495], [-12.157146753448874, -33.766352938119766, -13.083738234853534, -135.8798604314077, -inf, -14.126191869688759], [-111.69303081629936, -177.04513040289305, -19.78896563212587, -31.409154369913637, -inf, -22.091541742268795], [-55.01047129270264, -95.01047129270263, -22.605021155923353, -38.93103873379323, -inf, -24.90760616769035]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_poisson_dense) def test_hmm_univariate_poisson_dense_forward(): f = model.forward([5, 8, 2, 4, 7, 8, 2]) logp = numpy.array([[-inf, -inf, -inf, -inf, 0.0, -inf], [-6.724049572762615, -3.874849418805291, -7.396929655216146, -2.6565929124856993, -inf, -4.680333421931903], [-6.730147189571273, -6.114939268804046, -15.76343567997591, -6.1491172641470415, -inf, -7.498440536740427], [-14.331825716875938, -12.23889386200279, -8.404629462451046, -8.953498331843619, -inf, -10.236032891838605], [-15.218653579546471, -13.152885951823485, -13.58596603773139, -10.59764914181018, -inf, -12.771059542322048], [-15.25983657200538, -14.222315575321783, -22.039008189195016, -13.683075848076015, -inf, -15.403406295745581], [-17.30957501126636, -16.957613004675117, -26.32610484066788, -16.995462506806398, -inf, -18.279525087145316], [-25.05974969431663, -23.03771236899512, -19.218787612604835, -19.75231189091333, -inf, -21.044274521213687]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_forward(): f = model.forward([[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [4, 6, 2, 0, 1]]) logp = numpy.array([[ -inf, -inf, -inf, -inf, 0., -inf], [ -14.73222089, -46.16604623, -29.00420739, -62.64844211, -inf, -17.03480535], [ -55.5993327, -180.07255998, -74.2437335, -195.58972567, -inf, -57.90191778], [ -94.93076444, -273.03467427, -107.55659346, -233.79896288, -inf, -97.23334625]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_forward(): f = model.forward([[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [-4, 6, -2, 0, 1]]) logp = numpy.array([[-inf, -inf, -inf, -inf, 0.0, -inf], [-17.388625105797296, -25.109952452723487, -20.33305760532214, -28.085443290422877, -inf, -19.639474084287432], [-41.518178585896166, -62.62215498003203, -56.463304390840634, -63.21955480161304, -inf, -43.820763354673296], [-86.42085732368164, -67.62244823776697, -71.30752972353059, -70.64290643117542, -inf, -69.85376066028503]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_discrete_dense) def test_hmm_univariate_discrete_dense_nan_forward(): f = model.forward(['A', nan, 'D', nan, 'C']) logp = numpy.array([[ -inf, -inf, -inf, -inf, 0., -inf], [-2.40794561, -5.11599581, -5.11599581, -3.91202301, -inf, -4.40631933], [-2.97985892, -4.25451331, -4.18645985, -3.51998092, -inf, -4.51167984], [-6.32889724, -7.26872515, -6.99767999, -3.58171257, -inf, -5.76918537], [-5.0073806, -5.13193889, -5.65094338, -4.42343213, -inf, -5.87454588], [-8.42983637, -9.02567905, -5.34834914, -8.24851276, -inf, -7.53208902]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_gaussian_dense) def test_hmm_univariate_gaussian_dense_nan_forward(): f = model.forward([3, 5, 8, nan, 13]) logp = numpy.array([[ -inf, -inf, -inf, -inf, 0., -inf], [ -5.22152363, -4.12291134, -15.72152363, -339.14208208, -inf, -6.13780747], [ -6.04514948, -15.05674601, -14.57123872, -247.67044476, -inf, -8.3474144 ], [ -12.15714675, -33.76635294, -13.08373823, -135.87986043, -inf, -14.12619187], [ -12.77409228, -14.12619187, -13.67687992, -13.18336302, -inf, -14.23155239], [ -46.01446276, -86.89953909, -15.32388795, -31.38618129, -inf, -17.62647294]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_poisson_dense) def test_hmm_univariate_poisson_dense_nan_forward(): f = model.forward([5, 8, 2, nan, 7, nan, 2]) logp = numpy.array([[ -inf, -inf, -inf, -inf, 0., -inf], [ -6.72404957, -3.87484942, -7.39692966, -2.65659291, -inf, -4.68033342], [ -6.73014719, -6.11493927, -15.76343568, -6.14911726, -inf, -7.49844054], [-14.33182572, -12.23889386, -8.40462946, -8.95349833, -inf, -10.23603289], [ -9.91342156, -9.92812422, -9.40791221, -8.85734696, -inf, -10.34139341], [-12.72297734, -12.0579037, -19.19249097, -11.37941729, -inf, -13.11175535], [-12.21880511, -12.66362307, -12.49060859, -11.97032983, -inf, -13.21711586], [-20.06552788, -17.98505855, -14.24996743, -14.55695817, -inf, -15.98581404]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_nan_forward(): f = model.forward([[0, nan, 5, nan, 3], [nan, 4, 1, 5, 6], [4, nan, 2, nan, 1]]) logp = numpy.array([[-inf, -inf, -inf, -inf, 0.0, -inf], [-8.64586382, -11.86321233, -20.72307256, -49.92199169, -inf, -10.90916394], [-48.07813868, -172.52798004, -62.58979428, -164.0451409, -inf, -50.38072328], [-58.19821326, -58.2514622, -59.25426314, -73.55131746, -inf, -59.66964241]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_nan_forward(): f = model.forward([[0, nan, 5, nan, 3], [nan, 4, 1, 5, 6], [-4, nan, -2, nan, 1]]) logp = numpy.array([[ -inf, -inf, -inf, -inf, 0., -inf], [-15.34182759, -21.05906503, -16.62794596, -24.70263887, -inf, -17.39777437], [-38.48018922, -55.02937919, -51.31243927, -58.5238134, -inf, -40.78277157], [-63.35506959, -49.99044883, -56.0924389, -55.56636784, -inf, -52.28702405]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_discrete_dense) def test_hmm_univariate_discrete_dense_backward(): f = model.backward(['A', 'B', 'D', 'D', 'C']) logp = numpy.array([[-9.86805902419294, -10.666561769922483, -11.09973677168472, -10.617074536069564, -11.092510372852566, -inf], [-9.120551817416588, -9.07513780778706, -9.061129343592423, -8.517934491110973, -8.143527673240188, -inf], [-6.950743680969357, -6.918207210615943, -6.3171849919534315, -6.328223424806821, -6.314201940019015, -inf], [-5.926238762190273, -5.832923518979429, -5.343210135438772, -5.352351255040521, -5.296020007499352, -inf], [-4.474141923581687, -3.2834143460057716, -3.547379891840237, -4.474141923581686, -3.8922203781319653, -inf], [-2.3025850929940455, -2.3025850929940455, -2.3025850929940455, -2.3025850929940455, -inf, 0.0]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_gaussian_dense) def test_hmm_univariate_gaussian_dense_backward(): f = model.backward([3, 5, 8, 19, 13]) logp = numpy.array([[-24.010022764471987, -24.820878919065986, -25.359784144328874, -24.666641886986977, -24.907606167690343, -inf], [-20.47495458748052, -21.390489786220005, -22.08295469697486, -21.390527588974052, -22.081667004696868, -inf], [-18.86308443640411, -18.00715991329825, -18.32109321121691, -19.183852463904262, -18.700307092256082, -inf], [-13.533310680731095, -12.147019061523556, -12.434699610689767, -13.533307024859651, -12.840163500171148, -inf], [-6.217255777912353, -4.830961508172448, -5.1186435298576365, -6.217255656072613, -5.524108597352521, -inf], [-2.3025850929940455, -2.3025850929940455, -2.3025850929940455, -2.3025850929940455, -inf, 0.0]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_poisson_dense) def test_hmm_univariate_poisson_dense_backward(): f = model.backward([5, 8, 2, 4, 7, 8, 2]) logp = numpy.array([[-21.691907586187032, -21.73799328948721, -21.138366991878907, -21.083291604178275, -21.044274521213687, -inf], [-18.8959690490499, -19.147279755417475, -18.96895836891973, -18.554384880883024, -18.344028493972242, -inf], [-16.436767297608355, -15.50289984292892, -15.520463678389667, -16.044844087024796, -15.741349651348344, -inf], [-13.746179478239421, -13.67833968609671, -13.103892290217432, -13.104515632505755, -13.064497897488891, -inf], [-10.900016194765097, -11.134592598694933, -10.70914236553333, -10.534402907712945, -10.472655441966987, -inf], [-8.08666946806842, -8.338743855593863, -8.159657556177146, -7.746210358522981, -7.536781914211483, -inf], [-5.624801928499422, -4.698024820874441, -4.71562372030326, -5.232043004902404, -4.927999971986549, -inf], [-2.3025850929940455, -2.3025850929940455, -2.3025850929940455, -2.3025850929940455, -inf, 0.0]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_backward(): f = model.backward([[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [4, 6, 2, 0, 1]]) logp = numpy.array([[-95.62390845, -96.54019907, -97.23334619, -96.54019916, -97.23334625, -inf], [-82.50112549, -83.41741621, -84.11056338, -83.41741622, -84.11056339, -inf], [-41.63401355, -42.55027471, -43.24340546, -42.55029936, -43.24342189, -inf], [-2.30258509, -2.30258509, -2.30258509, -2.30258509, -inf, 0.0]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_backward(): f = model.backward([[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [-4, 6, -2, 0, 1]]) logp = numpy.array([[-68.2539137567533, -69.16076639610863, -69.84868309756291, -69.16857768159394, -69.85376066028503, -inf], [-52.47579136451719, -53.392079325925124, -54.08522496164164, -53.392081629466915, -54.08522649261687, -inf], [-28.335582428803374, -28.267824434424867, -28.247424322493245, -27.65418843714621, -27.260167817399534, -inf], [-2.3025850929940455, -2.3025850929940455, -2.3025850929940455, -2.3025850929940455, -inf, 0.0]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_discrete_dense) def test_hmm_univariate_discrete_dense_nan_backward(): f = model.backward(['A', nan, 'D', nan, 'C']) logp = numpy.array([[-6.2351892, -7.03937052, -7.53995041, -7.0284468, -7.53208902, -inf], [-5.47529921, -5.28136881, -5.22459211, -5.34120086, -5.21037792, -inf], [-5.61180968, -5.59762678, -5.01422924, -5.0155398, -4.99812389, -inf], [-4.22604905, -3.92055934, -4.01239598, -4.06168378, -3.78494992, -inf], [-4.47414192, -3.28341435, -3.54737989, -4.47414192, -3.89222038, -inf], [-2.30258509, -2.30258509, -2.30258509, -2.30258509, -inf, 0.0]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_gaussian_dense) def test_hmm_univariate_gaussian_dense_nan_backward(): f = model.backward([3, 5, 8, nan, 13]) logp = numpy.array([[-16.72877261, -17.53965174, -18.0785865, -17.38544424, -17.62647294, -inf], [-13.19368605, -14.10946925, -14.80215433, -14.10948639, -14.80126502, -inf], [-11.58174695, -11.10331706, -11.44733525, -12.0888188, -11.79646424, -inf], [-5.88078356, -5.52410863, -5.62946913, -5.68662755, -5.38434669, -inf], [-6.21725578, -4.83096151, -5.11864353, -6.21725566, -5.5241086, -inf], [-2.30258509, -2.30258509, -2.30258509, -2.30258509, -inf, 0.0]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_poisson_dense) def test_hmm_univariate_poisson_dense_nan_backward(): f = model.backward([5, 8, 2, nan, 7, nan, 2]) logp = numpy.array([[-16.63348003, -16.67956771, -16.0798998, -16.02482554, -15.98581404, -inf], [-13.83793763, -14.08893409, -13.91233676, -13.49588981, -13.28439885, -inf], [-11.37983721, -10.43981663, -10.46546269, -10.99407821, -10.68304212, -inf], [-8.14995891, -8.11105833, -8.03603323, -8.07865567, -7.99445884, -inf], [-8.13575931, -8.359288, -8.01322363, -7.75426045, -7.62580822, -inf], [-5.36468956, -5.06990563, -5.10022463, -5.18918048, -4.95862159, -inf], [-5.62480193, -4.69802482, -4.71562372, -5.232043, -4.92799997, -inf], [-2.30258509, -2.30258509, -2.30258509, -2.30258509, -inf, 0.0]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_nan_backward(): f = model.backward([[0, nan, 5, nan, 3], [nan, 4, 1, 5, 6], [4, nan, 2, nan, 1]]) logp = numpy.array([[-58.07503525, -58.9897255, -59.68021202, -58.98706625, -59.66964241, -inf], [-51.03967719, -51.95596417, -52.64910927, -51.95596729, -52.64911135, -inf], [-11.5915042, -11.41490257, -11.63801707, -11.45032367, -11.10355162, -inf], [-2.30258509, -2.30258509, -2.30258509, -2.30258509, -inf, 0.0]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_nan_backward(): f = model.backward([[0, nan, 5, nan, 3], [nan, 4, 1, 5, 6], [-4, nan, -2, nan, 1]]) logp = numpy.array([[-50.72705835, -51.59493939, -52.26201549, -51.63477927, -52.28702405, -inf], [-37.00027105, -37.91654779, -38.60968708, -37.91655924, -38.60969427, -inf], [-13.80683759, -13.80018574, -13.79865713, -13.11480477, -12.71022551, -inf], [-2.30258509, -2.30258509, -2.30258509, -2.30258509, -inf, 0.0]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_discrete_dense) def test_hmm_univariate_discrete_dense_predict_log_proba(): f = model.predict_log_proba(['A', 'B', 'D', 'D', 'C']) logp = numpy.array([[-0.43598705, -3.09862324, -3.08461478, -1.33744712], [-2.75011524, -0.18557067, -3.32315748, -2.66771698], [-3.57007311, -4.21967926, -2.47447735, -0.1358932], [-3.66551313, -2.58591465, -3.25563961, -0.15021184], [-2.99044772, -3.05827777, -0.21316071, -2.35661596]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_gaussian_dense) def test_hmm_univariate_gaussian_dense_predict_log_proba(): f = model.predict_log_proba([3, 5, 8, 19, 13]) logp = numpy.array([[-0.78887205, -0.60579496, -12.89687216, -335.62500351], [-0.00062775, -8.15629975, -7.98472576, -241.94669106], [-0.78285127, -21.00576583, -0.61083168, -124.50556129], [-93.00268043, -156.96848574, -2.99e-06, -12.71880386], [-32.40545022, -72.40545022, -8e-08, -16.32601766]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_poisson_dense) def test_hmm_univariate_poisson_dense_predict_log_proba(): f = model.predict_log_proba([5, 8, 2, 4, 7, 8, 2]) logp = numpy.array([[-4.5757441, -1.97785465, -5.3216135, -0.16670327], [-2.12263997, -0.57356459, -10.23962484, -1.14968683], [-7.03373067, -4.87295903, -0.46424723, -1.01373944], [-5.07439525, -3.24320403, -3.25083388, -0.08777753], [-2.30223152, -1.51678491, -9.15439122, -0.38501169], [-1.89010242, -0.6113633, -9.99745404, -1.18323099], [-6.31806027, -4.29602294, -0.47709818, -1.01062246]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_predict_log_proba(): f = model.predict_log_proba([[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [4, 6, 2, 0, 1]]) logp = numpy.array([[-1.3e-07, -32.35011618, -15.88142452, -48.83251208], [-0.0, -125.38948844, -20.25379271, -140.90667878], [-3.29e-06, -178.10391312, -12.6258323, -138.86820172]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_predict_log_proba_from_json(): logp = numpy.array([[-1.3e-07, -32.35011618, -15.88142452, -48.83251208], [-0.0, -125.38948844, -20.25379271, -140.90667878], [-3.29e-06, -178.10391312, -12.6258323, -138.86820172]]) s = [[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [4, 6, 2, 0, 1]] hmm_json = HiddenMarkovModel.from_json(model.to_json()) f = hmm_json.predict_log_proba(s) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_predict_log_proba_from_yaml(): logp = numpy.array([[-1.3e-07, -32.35011618, -15.88142452, -48.83251208], [-0.0, -125.38948844, -20.25379271, -140.90667878], [-3.29e-06, -178.10391312, -12.6258323, -138.86820172]]) s = [[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [4, 6, 2, 0, 1]] hmm_yaml = HiddenMarkovModel.from_yaml(model.to_yaml()) f = hmm_yaml.predict_log_proba(s) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_robust_from_json(): logp = numpy.array([[-1.3e-07, -32.35011618, -15.88142452, -48.83251208], [-0.0, -125.38948844, -20.25379271, -140.90667878], [-3.29e-06, -178.10391312, -12.6258323, -138.86820172]]) s = [[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [4, 6, 2, 0, 1]] hmm_json = from_json(model.to_json()) f = hmm_json.predict_log_proba(s) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_predict_log_proba(): f = model.predict_log_proba([[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [-4, 6, -2, 0, 1]]) logp = numpy.array([[-0.01065581, -8.64827112, -4.56452191, -11.62376426], [-3.5e-07, -21.03621875, -14.85696805, -21.01998258], [-18.86968176, -0.07127267, -3.75635416, -3.09173086]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_predict_log_proba_from_json(): logp = numpy.array([[-0.01065581, -8.64827112, -4.56452191, -11.62376426], [-3.5e-07, -21.03621875, -14.85696805, -21.01998258], [-18.86968176, -0.07127267, -3.75635416, -3.09173086]]) s = [[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [-4, 6, -2, 0, 1]] hmm_json = HiddenMarkovModel.from_json(model.to_json()) f = hmm_json.predict_log_proba(s) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_predict_log_proba_from_yaml(): logp = numpy.array([[-0.01065581, -8.64827112, -4.56452191, -11.62376426], [-3.5e-07, -21.03621875, -14.85696805, -21.01998258], [-18.86968176, -0.07127267, -3.75635416, -3.09173086]]) s = [[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [-4, 6, -2, 0, 1]] hmm_yaml = HiddenMarkovModel.from_yaml(model.to_yaml()) f = hmm_yaml.predict_log_proba(s) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_robust_from_json(): logp = numpy.array([[-0.01065581, -8.64827112, -4.56452191, -11.62376426], [-3.5e-07, -21.03621875, -14.85696805, -21.01998258], [-18.86968176, -0.07127267, -3.75635416, -3.09173086]]) s = [[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [-4, 6, -2, 0, 1]] hmm_json = from_json(model.to_json()) f = hmm_json.predict_log_proba(s) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_discrete_dense) def test_hmm_univariate_discrete_dense_nan_predict_log_proba(): f = model.predict_log_proba(['A', nan, 'D', nan, 'C']) logp = numpy.array([[-0.35115579, -2.8652756, -2.8084989, -1.72113484], [-1.05957958, -2.32005107, -1.66860008, -1.0034317], [-3.02285728, -3.65719546, -3.47798695, -0.11130733], [-1.9494335, -0.88326422, -1.66623425, -1.36548504], [-3.20033245, -3.79617513, -0.11884521, -3.01900883]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_gaussian_dense) def test_hmm_univariate_gaussian_dense_nan_predict_log_proba(): f = model.predict_log_proba([3, 5, 8, nan, 13]) logp = numpy.array([[-0.78873673, -0.60590764, -12.89720502, -335.62509553], [-0.00042349, -8.53359013, -8.39210104, -242.13279062], [-0.41145737, -21.66398863, -1.08673442, -123.94001504], [-1.36487512, -1.33068044, -1.16905051, -1.77414573], [-30.69057492, -71.57565125, -1.1e-07, -16.06229345]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_poisson_dense) def test_hmm_univariate_poisson_dense_nan_predict_log_proba(): f = model.predict_log_proba([5, 8, 2, nan, 7, nan, 2]) logp = numpy.array([[-4.57617316, -1.97796947, -5.32345238, -0.16666868], [-2.12417036, -0.56894186, -10.24308433, -1.15738144], [-6.49597059, -4.36413816, -0.45484866, -1.04633997], [-2.06336683, -2.30159819, -1.4353218, -0.62579337], [-2.10185287, -1.14199529, -8.30690156, -0.58278374], [-1.857793, -1.37583385, -1.22041828, -1.2165588], [-6.38229894, -4.3018296, -0.56673849, -0.87372922]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_nan_predict_log_proba(): f = model.predict_log_proba([[0, nan, 5, nan, 3], [nan, 4, 1, 5, 6], [4, nan, 2, nan, 1]]) logp = numpy.array([[-0.0158986, -4.14953409, -13.70253942, -42.20831658], [-4.8e-07, -124.2732402, -14.55816895, -115.82582217], [-0.83115595, -0.88440489, -1.88720582, -16.18426015]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_nan_predict_log_proba(): f = model.predict_log_proba([[0, nan, 5, nan, 3], [nan, 4, 1, 5, 6], [-4, nan, -2, nan, 1]]) logp = numpy.array([[-0.05507459, -6.68858877, -2.95060899, -10.33217406], [-2.76e-06, -16.54254089, -12.82407236, -19.35159412], [-13.37063063, -0.00600988, -6.10799995, -5.58192889]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_discrete_dense) def test_hmm_univariate_discrete_dense_predict_proba(): f = model.predict_proba(['A', 'B', 'D', 'D', 'C']) logp = numpy.array([[0.6466261, 0.04511127, 0.04574765, 0.26251498], [0.06392049, 0.83063013, 0.03603886, 0.06941051], [0.0281538, 0.01470336, 0.08420699, 0.87293586], [0.02559104, 0.07532715, 0.03855615, 0.86052566], [0.05026493, 0.04696852, 0.80802627, 0.09474029]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_gaussian_dense) def test_hmm_univariate_gaussian_dense_predict_proba(): f = model.predict_proba([3, 5, 8, 19, 13]) logp = numpy.array([[0.454357, 0.54564049, 2.51e-06, 0.0], [0.99937245, 0.00028692, 0.00034063, 0.0], [0.45710084, 0.0, 0.54289916, 0.0], [0.0, 0.0, 0.99999701, 2.99e-06], [0.0, 0.0, 0.99999992, 8e-08]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_poisson_dense) def test_hmm_univariate_poisson_dense_predict_proba(): f = model.predict_proba([5, 8, 2, 4, 7, 8, 2]) logp = numpy.array([[0.01029863, 0.13836576, 0.00488487, 0.84645074], [0.11971517, 0.56351316, 3.573e-05, 0.31673595], [0.00088164, 0.00765069, 0.62860812, 0.36285955], [0.00625487, 0.03903861, 0.03874189, 0.91596463], [0.10003536, 0.2194162, 0.00010575, 0.68044268], [0.15105634, 0.54261062, 4.552e-05, 0.30628753], [0.00180344, 0.01362263, 0.62058159, 0.36399234]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_predict_proba(): f = model.predict_proba([[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [4, 6, 2, 0, 1]]) logp = numpy.array([[0.99999987, 0.0, 1.3e-07, 0.0], [1.0, 0.0, 0.0, 0.0], [0.99999671, 0.0, 3.29e-06, 0.0]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_predict_proba(): f = model.predict_proba([[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [-4, 6, -2, 0, 1]]) logp = numpy.array([[0.98940076, 0.00017543, 0.01041486, 8.95e-06], [0.99999965, 0.0, 3.5e-07, 0.0], [1e-08, 0.93120794, 0.02336878, 0.04542326]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_discrete_dense) def test_hmm_univariate_discrete_dense_nan_predict_proba(): f = model.predict_proba(['A', nan, 'D', nan, 'C']) logp = numpy.array([[0.70387409, 0.05696743, 0.06029543, 0.17886305], [0.3466015, 0.09826857, 0.18851078, 0.36661915], [0.04866198, 0.02580478, 0.03086949, 0.89466375], [0.14235469, 0.41343118, 0.18895729, 0.25525684], [0.04074865, 0.0224565, 0.88794523, 0.04884961]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_gaussian_dense) def test_hmm_univariate_gaussian_dense_nan_predict_proba(): f = model.predict_proba([3, 5, 8, nan, 13]) logp = numpy.array([[0.45441848, 0.54557901, 2.51e-06, 0.0], [0.9995766, 0.00019675, 0.00022665, 0.0], [0.66268377, 0.0, 0.33731623, 0.0], [0.25541257, 0.26429736, 0.31066177, 0.1696283], [0.0, 0.0, 0.99999989, 1.1e-07]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_poisson_dense) def test_hmm_univariate_poisson_dense_nan_predict_proba(): f = model.predict_proba([5, 8, 2, nan, 7, nan, 2]) logp = numpy.array([[0.01029422, 0.13834988, 0.00487589, 0.84648002], [0.1195321, 0.56612416, 3.56e-05, 0.31430814], [0.00150951, 0.01272562, 0.63454399, 0.35122088], [0.12702558, 0.10009874, 0.23803875, 0.53483693], [0.12222974, 0.31918153, 0.00024681, 0.55834192], [0.15601658, 0.25262885, 0.29510671, 0.29624786], [0.00169123, 0.01354376, 0.56737292, 0.4173921]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_nan_predict_proba(): f = model.predict_proba([[0, nan, 5, nan, 3], [nan, 4, 1, 5, 6], [4, nan, 2, nan, 1]]) logp = numpy.array([[0.98422712, 0.01577176, 1.12e-06, 0.0], [0.99999952, 0.0, 4.8e-07, 0.0], [0.43554553, 0.41295986, 0.15149452, 9e-08]]) assert_array_almost_equal(f, logp) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_nan_predict_proba(): f = model.predict_proba([[0, nan, 5, nan, 3], [nan, 4, 1, 5, 6], [-4, nan, -2, nan, 1]]) logp = numpy.array([[0.94641455, 0.00124504, 0.05230784, 3.257e-05], [0.99999724, 7e-08, 2.7e-06, 0.0], [1.56e-06, 0.99400815, 0.002225, 0.0037653]]) assert_array_almost_equal(f, logp) @with_setup(setup_univariate_discrete_dense) def test_hmm_univariate_discrete_dense_predict(): f = model.predict(['A', 'B', 'D', 'D', 'C']) path = [0, 1, 3, 3, 2] assert_array_almost_equal(f, path) @with_setup(setup_univariate_gaussian_dense) def test_hmm_univariate_gaussian_dense_predict(): f = model.predict([3, 5, 8, 19, 13]) path = [1, 0, 2, 2, 2] assert_array_almost_equal(f, path) @with_setup(setup_univariate_poisson_dense) def test_hmm_univariate_poisson_dense_predict(): f = model.predict([5, 8, 2, 4, 7, 8, 2]) path = [3, 1, 2, 3, 3, 1, 2] assert_array_almost_equal(f, path) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_predict(): f = model.predict([[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [4, 6, 2, 0, 1]]) path = [0, 0, 0] assert_array_almost_equal(f, path) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_predict(): f = model.predict([[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [-4, 6, -2, 0, 1]]) path = [0, 0, 1] assert_array_almost_equal(f, path) @with_setup(setup_univariate_discrete_dense) def test_hmm_univariate_discrete_dense_nan_predict(): f = model.predict(['A', nan, 'D', nan, 'C']) path = [0, 3, 3, 1, 2] assert_array_almost_equal(f, path) @with_setup(setup_univariate_gaussian_dense) def test_hmm_univariate_gaussian_dense_nan_predict(): f = model.predict([3, 5, 8, nan, 13]) path = [1, 0, 0, 2, 2] assert_array_almost_equal(f, path) @with_setup(setup_univariate_poisson_dense) def test_hmm_univariate_poisson_dense_nan_predict(): f = model.predict([5, 8, 2, nan, 7, nan, 2]) path = [3, 1, 2, 3, 3, 3, 2] assert_array_almost_equal(f, path) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_nan_predict(): f = model.predict([[0, nan, 5, nan, 3], [nan, 4, 1, 5, 6], [4, nan, 2, nan, 1]]) path = [0, 0, 0] assert_array_almost_equal(f, path) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_nan_predict(): f = model.predict([[0, nan, 5, nan, 3], [nan, 4, 1, 5, 6], [-4, nan, -2, nan, 1]]) path = [0, 0, 1] assert_array_almost_equal(f, path) @with_setup(setup_univariate_discrete_dense) def test_hmm_univariate_discrete_dense_predict_viterbi(): f = model.predict(['A', 'B', 'D', 'D', 'C'], algorithm='viterbi') path = [4, 0, 1, 3, 3, 2, 5] assert_array_almost_equal(f, path) @with_setup(setup_univariate_gaussian_dense) def test_hmm_univariate_gaussian_dense_predict_viterbi(): f = model.predict([3, 5, 8, 19, 13], algorithm='viterbi') path = [4, 1, 0, 2, 2, 2, 5] assert_array_almost_equal(f, path) @with_setup(setup_univariate_poisson_dense) def test_hmm_univariate_poisson_dense_predict_viterbi(): f = model.predict([5, 8, 2, 4, 7, 8, 2], algorithm='viterbi') path = [4, 3, 1, 2, 3, 3, 1, 2, 5] assert_array_almost_equal(f, path) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_predict_viterbi(): f = model.predict([[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [4, 6, 2, 0, 1]], algorithm='viterbi') path = [4, 0, 0, 0, 5] assert_array_almost_equal(f, path) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_predict_viterbi(): f = model.predict([[0, 1, 5, 2, 3], [2, 4, 1, 5, 6], [-4, 6, -2, 0, 1]], algorithm='viterbi') path = [4, 0, 0, 1, 5] assert_array_almost_equal(f, path) @with_setup(setup_univariate_discrete_dense) def test_hmm_univariate_discrete_dense_nan_predict_viterbi(): f = model.predict(['A', nan, 'D', nan, 'C'], algorithm='viterbi') path = [4, 0, 0, 3, 1, 2, 5] assert_array_almost_equal(f, path) @with_setup(setup_univariate_gaussian_dense) def test_hmm_univariate_gaussian_dense_nan_predict_viterbi(): f = model.predict([3, 5, 8, nan, 13], algorithm='viterbi') path = [4, 1, 0, 0, 0, 2, 5] assert_array_almost_equal(f, path) @with_setup(setup_univariate_poisson_dense) def test_hmm_univariate_poisson_dense_nan_predict_viterbi(): f = model.predict([5, 8, 2, nan, 7, nan, 2], algorithm='viterbi') path = [4, 3, 1, 2, 3, 3, 1, 2, 5] assert_array_almost_equal(f, path) @with_setup(setup_multivariate_mixed_dense) def test_hmm_multivariate_mixed_dense_nan_predict_viterbi(): f = model.predict([[0, nan, 5, nan, 3], [nan, 4, 1, 5, 6], [4, nan, 2, nan, 1]], algorithm='viterbi') path = [4, 0, 0, 0, 5] assert_array_almost_equal(f, path) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_multivariate_gaussian_dense_nan_predict_viterbi(): f = model.predict([[0, nan, 5, nan, 3], [nan, 4, 1, 5, 6], [-4, nan, -2, nan, 1]], algorithm='viterbi') path = [4, 0, 0, 1, 5] assert_array_almost_equal(f, path) @with_setup(setup, teardown) def test_hmm_viterbi_fit(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='viterbi', verbose=False, use_pseudocount=True) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 83.2834) @with_setup(setup, teardown) def test_hmm_viterbi_fit_no_pseudocount(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='viterbi', verbose=False, use_pseudocount=False) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 84.9318) @with_setup(setup, teardown) def test_hmm_viterbi_fit_w_pseudocount(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='viterbi', verbose=False, transition_pseudocount=1.) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 79.4713) @with_setup(setup, teardown) def test_hmm_viterbi_fit_w_pseudocount_priors(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='viterbi', verbose=False, transition_pseudocount=0.278, use_pseudocount=True) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 81.7439) @with_setup(setup, teardown) def test_hmm_viterbi_fit_w_inertia(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='viterbi', verbose=False, edge_inertia=0.193) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 84.9318) @with_setup(setup, teardown) def test_hmm_viterbi_fit_w_inertia2(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='viterbi', verbose=False, edge_inertia=0.82) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 84.9318) @with_setup(setup, teardown) def test_hmm_viterbi_fit_w_pseudocount_inertia(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='viterbi', verbose=False, edge_inertia=0.23, use_pseudocount=True) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 83.2834) @with_setup(setup, teardown) def test_hmm_viterbi_fit_one_check_input(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='viterbi', verbose=False, use_pseudocount=True, multiple_check_input=False) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 83.2834) @with_setup(setup, teardown) def test_hmm_bw_fit(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=True, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 83.1132) @with_setup(setup_multivariate_discrete_sparse, teardown) def test_hmm_bw_multivariate_discrete_fit(): seqs = [[['A', 'A'], ['A', 'C'], ['C', 'T']], [['A', 'A'], ['C', 'C'], ['T', 'T']], [['A', 'A'], ['A', 'C'], ['C', 'C'], ['T', 'T']], [['A', 'A'], ['C', 'C']]] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=True, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 13.3622) @with_setup(setup_multivariate_discrete_sparse, teardown) def test_hmm_bw_multivariate_discrete_fit_json_yaml(): seqs = [[['A', 'A'], ['A', 'C'], ['C', 'T']], [['A', 'A'], ['C', 'C'], ['T', 'T']], [['A', 'A'], ['A', 'C'], ['C', 'C'], ['T', 'T']], [['A', 'A'], ['C', 'C']]] hmm_json = HiddenMarkovModel.from_json(model.to_json()) _, history = hmm_json.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=True, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 13.3622) @with_setup(setup_multivariate_discrete_sparse, teardown) def test_hmm_bw_multivariate_discrete_fit_robust_from_json(): seqs = [[['A', 'A'], ['A', 'C'], ['C', 'T']], [['A', 'A'], ['C', 'C'], ['T', 'T']], [['A', 'A'], ['A', 'C'], ['C', 'C'], ['T', 'T']], [['A', 'A'], ['C', 'C']]] hmm_json = from_json(model.to_json()) _, history = hmm_json.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=True, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 13.3622) @with_setup(setup_multivariate_discrete_sparse, teardown) def test_hmm_bw_multivariate_discrete_fit_from_yaml(): seqs = [[['A', 'A'], ['A', 'C'], ['C', 'T']], [['A', 'A'], ['C', 'C'], ['T', 'T']], [['A', 'A'], ['A', 'C'], ['C', 'C'], ['T', 'T']], [['A', 'A'], ['C', 'C']]] hmm_yaml = HiddenMarkovModel.from_yaml(model.to_yaml()) _, history = hmm_yaml.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=True, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 13.3622) @with_setup(setup_multivariate_gaussian_sparse, teardown) def test_hmm_bw_multivariate_gaussian_fit(): seqs = [[[5, 8], [8, 10], [13, 17], [-3, -4]], [[6, 7], [13, 16], [12, 11], [-6, -7]], [[4, 6], [13, 15], [-4, -7]], [[6, 5], [14, 18], [-7, -5]]] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=True, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 24.7013) @with_setup(setup_multivariate_gaussian_sparse, teardown) def test_hmm_bw_multivariate_gaussian_from_json(): seqs = [[[5, 8], [8, 10], [13, 17], [-3, -4]], [[6, 7], [13, 16], [12, 11], [-6, -7]], [[4, 6], [13, 15], [-4, -7]], [[6, 5], [14, 18], [-7, -5]]] hmm_json = HiddenMarkovModel.from_json(model.to_json()) _, history = hmm_json.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=True, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 24.7013) @with_setup(setup_multivariate_gaussian_sparse, teardown) def test_hmm_bw_multivariate_gaussian_robust_from_json(): seqs = [[[5, 8], [8, 10], [13, 17], [-3, -4]], [[6, 7], [13, 16], [12, 11], [-6, -7]], [[4, 6], [13, 15], [-4, -7]], [[6, 5], [14, 18], [-7, -5]]] hmm_json = from_json(model.to_json()) _, history = hmm_json.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=True, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 24.7013) @with_setup(setup_multivariate_gaussian_sparse, teardown) def test_hmm_bw_multivariate_gaussian_from_yaml(): seqs = [[[5, 8], [8, 10], [13, 17], [-3, -4]], [[6, 7], [13, 16], [12, 11], [-6, -7]], [[4, 6], [13, 15], [-4, -7]], [[6, 5], [14, 18], [-7, -5]]] hmm_yaml = HiddenMarkovModel.from_yaml(model.to_yaml()) _, history = hmm_yaml.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=True, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 24.7013) @with_setup(setup, teardown) def test_hmm_bw_fit_json(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=True, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 83.1132) assert_almost_equal(sum(model.log_probability(seq) for seq in seqs), -42.2341, 4) hmm = HiddenMarkovModel.from_json(model.to_json()) assert_almost_equal(sum(model.log_probability(seq) for seq in seqs), -42.2341, 4) @with_setup(setup, teardown) def test_hmm_bw_fit_robust_from_json(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=True, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 83.1132) assert_almost_equal(sum(model.log_probability(seq) for seq in seqs), -42.2341, 4) hmm = from_json(model.to_json()) assert_almost_equal(sum(model.log_probability(seq) for seq in seqs), -42.2341, 4) @with_setup(setup, teardown) def test_hmm_bw_fit_no_pseudocount(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=False, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 85.681) @with_setup(setup, teardown) def test_hmm_bw_fit_w_pseudocount(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, transition_pseudocount=0.123, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 84.9408) @with_setup(setup, teardown) def test_hmm_bw_fit_w_pseudocount_priors(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, transition_pseudocount=0.278, use_pseudocount=True, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 81.2265) @with_setup(setup, teardown) def test_hmm_bw_fit_w_inertia(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, edge_inertia=0.193, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 85.0528) @with_setup(setup, teardown) def test_hmm_bw_fit_w_inertia2(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, edge_inertia=0.82, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 72.5134) @with_setup(setup, teardown) def test_hmm_bw_fit_w_pseudocount_inertia(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, edge_inertia=0.02, use_pseudocount=True, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 83.0764) @with_setup(setup, teardown) def test_hmm_bw_fit_w_frozen_distributions(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, distribution_inertia=1.00, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 64.474) @with_setup(setup, teardown) def test_hmm_bw_fit_w_frozen_edges(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, edge_inertia=1.00, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 44.0208) @with_setup(setup, teardown) def test_hmm_bw_fit_w_edge_a_distribution_inertia(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, edge_inertia=0.5, distribution_inertia=0.5, max_iterations=5) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 81.5447) @with_setup(setup, teardown) def test_hmm_bw_fit_parallel(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=True, max_iterations=5, n_jobs=2) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 83.1132) @with_setup(setup, teardown) def test_hmm_bw_fit_no_pseudocount_parallel(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=False, max_iterations=5, n_jobs=2) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 85.681) @with_setup(setup, teardown) def test_hmm_bw_fit_w_pseudocount_parallel(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, transition_pseudocount=0.123, max_iterations=5, n_jobs=2) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 84.9408) @with_setup(setup, teardown) def test_hmm_bw_fit_w_pseudocount_priors_parallel(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, transition_pseudocount=0.278, use_pseudocount=True, max_iterations=5, n_jobs=2) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 81.2265) @with_setup(setup, teardown) def test_hmm_bw_fit_w_inertia_parallel(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, edge_inertia=0.193, max_iterations=5, n_jobs=2) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 85.0528) @with_setup(setup, teardown) def test_hmm_bw_fit_w_inertia2_parallel(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, edge_inertia=0.82, max_iterations=5, n_jobs=2) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 72.5134) @with_setup(setup, teardown) def test_hmm_bw_fit_w_pseudocount_inertia_parallel(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, edge_inertia=0.02, use_pseudocount=True, max_iterations=5, n_jobs=2) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 83.0764) @with_setup(setup, teardown) def test_hmm_bw_fit_w_frozen_distributions_parallel(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, distribution_inertia=1.00, max_iterations=5, n_jobs=2) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 64.474) @with_setup(setup, teardown) def test_hmm_bw_fit_w_frozen_edges_parallel(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, edge_inertia=1.00, max_iterations=5, n_jobs=2) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 44.0208) @with_setup(setup, teardown) def test_hmm_bw_fit_w_edge_a_distribution_inertia(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, edge_inertia=0.5, distribution_inertia=0.5, max_iterations=5, n_jobs=2) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 81.5447) @with_setup(setup, teardown) def test_hmm_bw_fit_one_check_input(): seqs = [list(x) for x in ['ACT', 'ACT', 'ACC', 'ACTC', 'ACT', 'ACT', 'CCT', 'CCC', 'AAT', 'CT', 'AT', 'CT', 'CT', 'CT', 'CT', 'CT', 'CT', 'ACT', 'ACT', 'CT', 'ACT', 'CT', 'CT', 'CT', 'CT']] _, history = model.fit(seqs, return_history=True, algorithm='baum-welch', verbose=False, use_pseudocount=True, max_iterations=5, multiple_check_input=False) total_improvement = history.total_improvement[-1] assert_equal(round(total_improvement, 4), 83.1132) def test_hmm_initialization(): hmmd1 = HiddenMarkovModel() assert_equal(hmmd1.d, 0) def test_hmm_univariate_initialization(): s1d1 = State(NormalDistribution(5, 2)) s2d1 = State(NormalDistribution(0, 1)) s3d1 = State(UniformDistribution(0, 10)) hmmd1 = HiddenMarkovModel() hmmd1.add_transition(hmmd1.start, s1d1, 0.5) hmmd1.add_transition(hmmd1.start, s2d1, 0.5) hmmd1.add_transition(s1d1, s3d1, 1) hmmd1.add_transition(s2d1, s3d1, 1) hmmd1.add_transition(s3d1, s1d1, 0.5) hmmd1.add_transition(s3d1, s2d1, 0.5) assert_equal(hmmd1.d, 0) hmmd1.bake() assert_equal(hmmd1.d, 1) def test_hmm_multivariate_initialization(): s1d3 = State(MultivariateGaussianDistribution([1, 4, 3], [[3, 0, 1],[0, 3, 0],[1, 0, 3]])) s2d3 = State(MultivariateGaussianDistribution([7, 7, 7], [[1, 0, 0],[0, 5, 0],[0, 0, 3]])) s3d3 = State(IndependentComponentsDistribution([UniformDistribution(0, 10), UniformDistribution(0, 10), UniformDistribution(0, 10)])) hmmd3 = HiddenMarkovModel() assert_equal(hmmd3.d, 0) hmmd3.add_transition(hmmd3.start, s1d3, 0.5) hmmd3.add_transition(hmmd3.start, s2d3, 0.5) hmmd3.add_transition(s1d3, s3d3, 1) hmmd3.add_transition(s2d3, s3d3, 1) hmmd3.add_transition(s3d3, s1d3, 0.5) hmmd3.add_transition(s3d3, s2d3, 0.5) assert_equal(hmmd3.d, 0) hmmd3.bake() assert_equal(hmmd3.d, 3) def test_hmm_initialization_error(): sbd1 = State(UniformDistribution(0, 10)) sbd3 = State(MultivariateGaussianDistribution([1, 4, 3], [[3, 0, 1],[0, 3, 0],[1, 0, 3]])) hmmb = HiddenMarkovModel() hmmb.add_transition(hmmb.start, sbd1, 0.5) hmmb.add_transition(hmmb.start, sbd3, 0.5) hmmb.add_transition(sbd1, sbd1, 0.5) hmmb.add_transition(sbd1, sbd3, 0.5) hmmb.add_transition(sbd3, sbd1, 0.5) hmmb.add_transition(sbd3, sbd3, 0.5) assert_raises(ValueError, hmmb.bake) @with_setup(setup_univariate_gaussian_dense) def test_hmm_pickle_univariate(): model2 = pickle.loads(pickle.dumps(model)) random_state = numpy.random.RandomState(0) for i in range(10): sequence = random_state.normal(0, 1, size=10) logp1 = model.log_probability(sequence) logp2 = model2.log_probability(sequence) assert_almost_equal(logp1, logp2) @with_setup(setup_univariate_gaussian_dense) def test_hmm_json_univariate(): model2 = HiddenMarkovModel.from_json(model.to_json()) random_state = numpy.random.RandomState(0) for i in range(10): sequence = random_state.normal(0, 1, size=10) logp1 = model.log_probability(sequence) logp2 = model2.log_probability(sequence) assert_almost_equal(logp1, logp2) @with_setup(setup_univariate_gaussian_dense) def test_hmm_robust_from_json_univariate(): model2 = from_json(model.to_json()) random_state = numpy.random.RandomState(0) for i in range(10): sequence = random_state.normal(0, 1, size=10) logp1 = model.log_probability(sequence) logp2 = model2.log_probability(sequence) assert_almost_equal(logp1, logp2) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_pickle_multivariate(): model2 = pickle.loads(pickle.dumps(model)) random_state = numpy.random.RandomState(0) for i in range(10): sequence = random_state.normal(0, 1, size=(10, 5)) logp1 = model.log_probability(sequence) logp2 = model2.log_probability(sequence) assert_almost_equal(logp1, logp2) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_json_multivariate(): model2 = HiddenMarkovModel.from_json(model.to_json()) random_state = numpy.random.RandomState(0) for i in range(10): sequence = random_state.normal(0, 1, size=(10, 5)) logp1 = model.log_probability(sequence) logp2 = model2.log_probability(sequence) assert_almost_equal(logp1, logp2) @with_setup(setup_multivariate_gaussian_dense) def test_hmm_robust_from_json_multivariate(): model2 = from_json(model.to_json()) random_state = numpy.random.RandomState(0) for i in range(10): sequence = random_state.normal(0, 1, size=(10, 5)) logp1 = model.log_probability(sequence) logp2 = model2.log_probability(sequence) assert_almost_equal(logp1, logp2) @with_setup(setup_univariate_discrete_dense, teardown) def test_hmm_univariate_discrete_from_samples(): X = [model.sample(random_state=0) for i in range(25)] model2 = HiddenMarkovModel.from_samples(DiscreteDistribution, 4, X, max_iterations=25) logp1 = sum(map(model.log_probability, X)) logp2 = sum(map(model2.log_probability, X)) assert_greater(logp2, logp1) @with_setup(setup_univariate_discrete_dense, teardown) def test_hmm_univariate_discrete_from_samples_with_labels(): X, y = zip(*model.sample(25, path=True, random_state=0)) y = [[state.name for state in seq if not state.is_silent()] for seq in y] model2 = HiddenMarkovModel.from_samples(DiscreteDistribution, 4, X, max_iterations=25, labels=y) logp1 = sum(map(model.log_probability, X)) logp2 = sum(map(model2.log_probability, X)) assert_greater(logp2, logp1) @with_setup(setup_univariate_discrete_dense, teardown) def test_hmm_univariate_discrete_from_samples_one_check_input(): X = [model.sample(random_state=0) for i in range(25)] model2 = HiddenMarkovModel.from_samples(DiscreteDistribution, 4, X, max_iterations=25, multiple_check_input=False) logp1 = sum(map(model.log_probability, X)) logp2 = sum(map(model2.log_probability, X)) assert_greater(logp2, logp1) @with_setup(setup_univariate_gaussian_dense, teardown) def test_hmm_univariate_gaussian_from_samples(): X = model.sample(n=25, random_state=0) model2 = HiddenMarkovModel.from_samples(NormalDistribution, 4, X, max_iterations=25) logp1 = sum(map(model.log_probability, X)) logp2 = sum(map(model2.log_probability, X)) assert_greater(logp2, logp1) @with_setup(setup_multivariate_gaussian_dense, teardown) def test_hmm_multivariate_gaussian_from_samples(): X = model.sample(n=25, random_state=0) model2 = HiddenMarkovModel.from_samples(MultivariateGaussianDistribution, 4, X, max_iterations=25) logp1 = sum(map(model.log_probability, X)) logp2 = sum(map(model2.log_probability, X)) assert_greater(logp2, logp1) @with_setup(setup_univariate_discrete_dense, teardown) def test_hmm_univariate_discrete_from_samples_end_state(): X = model.sample(n=25, random_state=0) model2 = HiddenMarkovModel.from_samples(DiscreteDistribution, 4, X, max_iterations=25, end_state=True) #We get non-zero end probabilities for each state assert_greater(model2.dense_transition_matrix()[0][model2.end_index],0) assert_greater(model2.dense_transition_matrix()[1][model2.end_index],0) assert_greater(model2.dense_transition_matrix()[2][model2.end_index],0) assert_greater(model2.dense_transition_matrix()[3][model2.end_index],0) @with_setup(setup_univariate_discrete_dense, teardown) def test_hmm_univariate_discrete_from_samples_no_end_state(): X = [model.sample(random_state=0) for i in range(25)] model2 = HiddenMarkovModel.from_samples(DiscreteDistribution, 4, X, max_iterations=25, end_state=False) #We don't have end probabilities for each state assert_equal(model2.dense_transition_matrix()[0][model2.end_index],0) assert_equal(model2.dense_transition_matrix()[1][model2.end_index],0) assert_equal(model2.dense_transition_matrix()[2][model2.end_index],0) assert_equal(model2.dense_transition_matrix()[3][model2.end_index],0) @with_setup(setup_general_mixture_gaussian, teardown) def test_hmm_json_general_mixture_gaussian(): model2 = HiddenMarkovModel.from_json(model.to_json()) random_state = numpy.random.RandomState(0) for i in range(10): sequence = random_state.normal(0, 1, size=10) logp1 = model.log_probability(sequence) logp2 = model2.log_probability(sequence) assert_almost_equal(logp1, logp2) @with_setup(setup_general_mixture_gaussian, teardown) def test_hmm_robust_from_json_general_mixture_gaussian(): model2 = from_json(model.to_json()) random_state = numpy.random.RandomState(0) for i in range(10): sequence = random_state.normal(0, 1, size=10) logp1 = model.log_probability(sequence) logp2 = model2.log_probability(sequence) assert_almost_equal(logp1, logp2) def test_io_fit(): X = [numpy.random.choice(['A', 'B'], size=25) for i in range(20)] weights = numpy.abs(numpy.random.randn(20)) data_generator = SequenceGenerator(X, weights) s1 = State(DiscreteDistribution({'A': 0.3, 'B': 0.7})) s2 = State(DiscreteDistribution({'A': 0.9, 'B': 0.1})) model1 = HiddenMarkovModel() model1.add_states(s1, s2) model1.add_transition(model1.start, s1, 0.8) model1.add_transition(model1.start, s2, 0.2) model1.add_transition(s1, s1, 0.3) model1.add_transition(s1, s2, 0.7) model1.add_transition(s2, s1, 0.4) model1.add_transition(s2, s2, 0.6) model1.bake() model1.fit(X, weights=weights, max_iterations=5) s1 = State(DiscreteDistribution({'A': 0.3, 'B': 0.7})) s2 = State(DiscreteDistribution({'A': 0.9, 'B': 0.1})) model2 = HiddenMarkovModel() model2.add_states(s1, s2) model2.add_transition(model2.start, s1, 0.8) model2.add_transition(model2.start, s2, 0.2) model2.add_transition(s1, s1, 0.3) model2.add_transition(s1, s2, 0.7) model2.add_transition(s2, s1, 0.4) model2.add_transition(s2, s2, 0.6) model2.bake() model2.fit(X, weights=weights, max_iterations=5) logp1 = [model1.log_probability(x) for x in X] logp2 = [model2.log_probability(x) for x in X] assert_array_almost_equal(logp1, logp2) def test_io_from_samples_hmm(): X = [numpy.random.choice(['A', 'B'], size=25) for i in range(20)] weights = numpy.abs(numpy.random.randn(20)) data_generator = SequenceGenerator(X, weights) model1 = HiddenMarkovModel.from_samples(DiscreteDistribution, n_components=2, X=X, weights=weights, max_iterations=5, init='first-k', random_state=1) model2 = HiddenMarkovModel.from_samples(DiscreteDistribution, n_components=2, X=data_generator, max_iterations=5, init='first-k', random_state=1) logp1 = [model1.log_probability(x) for x in X] logp2 = [model2.log_probability(x) for x in X] assert_array_almost_equal(logp1, logp2) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/test_io.py000066400000000000000000000563541420461712600240700ustar00rootroot00000000000000from pomegranate import * from pomegranate.io import DataGenerator from pomegranate.io import SequenceGenerator from pomegranate.io import DataFrameGenerator from nose.tools import with_setup from nose.tools import assert_true from nose.tools import assert_equal from nose.tools import assert_raises from numpy.testing import assert_almost_equal from numpy.testing import assert_array_equal from numpy.testing import assert_array_almost_equal import random import numpy import pandas numpy.random.seed(0) random.seed(0) nan = numpy.nan def test_io_datagenerator_shape(): X = numpy.random.randn(500, 13) data = DataGenerator(X) assert_array_equal(data.shape, X.shape) def test_io_datagenerator_classes_fail(): X = numpy.random.randn(500, 13) data = DataGenerator(X) assert_raises(ValueError, lambda data: data.classes, data) def test_io_datagenerator_classes(): X = numpy.random.randn(500, 13) y = numpy.random.randint(5, size=500) data = DataGenerator(X, y=y) assert_array_equal(data.classes, [0, 1, 2, 3, 4]) def test_io_datagenerator_x_batches(): X = numpy.random.randn(500, 13) w = numpy.ones(500) data = DataGenerator(X) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X, X_) assert_almost_equal(w, w_) data = DataGenerator(X, batch_size=123) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X, X_) assert_almost_equal(w, w_) data = DataGenerator(X, batch_size=1) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X, X_) assert_almost_equal(w, w_) data = DataGenerator(X, batch_size=506) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X, X_) assert_almost_equal(w, w_) def test_io_datagenerator_w_batches(): X = numpy.random.randn(500, 13) w = numpy.abs(numpy.random.randn(500)) data = DataGenerator(X, w) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(w, w_) data = DataGenerator(X, w, batch_size=123) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(w, w_) data = DataGenerator(X, w, batch_size=1) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(w, w_) data = DataGenerator(X, w, batch_size=506) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(w, w_) def test_io_datagenerator_y_batches(): X = numpy.random.randn(500, 13) w = numpy.abs(numpy.random.randn(500)) y = numpy.random.randint(5, size=500) data = DataGenerator(X, y=y) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(y, y_) data = DataGenerator(X, y=y, batch_size=123) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(y, y_) data = DataGenerator(X, y=y, batch_size=1) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(y, y_) data = DataGenerator(X, y=y, batch_size=506) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(y, y_) def test_io_datagenerator_wy_batches(): X = numpy.random.randn(500, 13) w = numpy.abs(numpy.random.randn(500)) y = numpy.random.randint(5, size=500) data = DataGenerator(X, w, y) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) w_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_almost_equal(y, y_) assert_almost_equal(w, w_) data = DataGenerator(X, w, y, batch_size=123) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) w_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_almost_equal(y, y_) assert_almost_equal(w, w_) data = DataGenerator(X, w, y, batch_size=1) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) w_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_almost_equal(y, y_) assert_almost_equal(w, w_) data = DataGenerator(X, w, y, batch_size=506) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) w_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_almost_equal(y, y_) assert_almost_equal(w, w_) def test_io_datagenerator_wy_unlabeled(): X = numpy.random.randn(500, 13) w = numpy.abs(numpy.random.randn(500)) y = numpy.random.randint(5, size=500) - 1 data = DataGenerator(X, w, y) X_ = numpy.concatenate([batch[0] for batch in data.unlabeled_batches()]) w_ = numpy.concatenate([batch[1] for batch in data.unlabeled_batches()]) assert_true(X.shape[0] > X_.shape[0]) assert_almost_equal(X[y == -1], X_) assert_almost_equal(w[y == -1], w_) def test_io_datagenerator_wy_labeled(): X = numpy.random.randn(500, 13) w = numpy.abs(numpy.random.randn(500)) y = numpy.random.randint(5, size=500) - 1 data = DataGenerator(X, w, y) X_ = numpy.concatenate([batch[0] for batch in data.labeled_batches()]) y_ = numpy.concatenate([batch[1] for batch in data.labeled_batches()]) w_ = numpy.concatenate([batch[2] for batch in data.labeled_batches()]) assert_true(X.shape[0] > X_.shape[0]) assert_almost_equal(X[y != -1], X_) assert_almost_equal(y[y != -1], y_) assert_almost_equal(w[y != -1], w_) def test_io_seqgenerator_uni_shape(): X = [numpy.random.randn(15) for i in range(500)] data = SequenceGenerator(X) assert_array_equal(data.shape, (len(X), 1)) def test_io_seqgenerator_symbol_shape(): X = [[numpy.random.choice(['A', 'C', 'G', 'T']) for j in range(15)] for i in range(500)] data = SequenceGenerator(X) assert_array_equal(data.shape, (len(X), 1)) def test_io_seqgenerator_shape(): X = [numpy.random.randn(15, 8) for i in range(500)] data = SequenceGenerator(X) assert_array_equal(data.shape, (len(X), 8)) def test_io_seqgenerator_x_batches(): X = [numpy.random.randn(15, 8) for i in range(500)] w = numpy.ones(500) data = SequenceGenerator(X) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X, X_) assert_almost_equal(w, w_) def test_io_seqgenerator_x_symbol_batches(): X = [[numpy.random.choice(["A", "C", "G", "T"]) for i in range(18)] for i in range(500)] w = numpy.ones(500) data = SequenceGenerator(X) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_array_equal(X, X_) assert_almost_equal(w, w_) def test_io_seqgenerator_w_batches(): X = [numpy.random.randn(15, 8) for i in range(500)] w = numpy.abs(numpy.random.randn(500)) data = SequenceGenerator(X, w) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(w, w_) def test_io_seqgenerator_w_symbol_batches(): X = [[numpy.random.choice(["A", "C", "G", "T"]) for i in range(18)] for i in range(500)] w = numpy.abs(numpy.random.randn(500)) data = SequenceGenerator(X, w) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(w, w_) def test_io_seqgenerator_y_batches(): X = [numpy.random.randn(15, 8) for i in range(500)] y = [numpy.random.randint(6, size=15) for i in range(500)] data = SequenceGenerator(X, y=y) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_array_equal(X, X_) assert_almost_equal(y, y_) def test_io_seqgenerator_y_symbol_batches(): X = [[numpy.random.choice(["A", "C", "G", "T"]) for i in range(18)] for i in range(500)] y = [numpy.random.randint(6, size=15) for i in range(500)] data = SequenceGenerator(X, y=y) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_array_equal(X, X_) assert_almost_equal(y, y_) def test_io_seqgenerator_wy_batches(): X = [numpy.random.randn(15, 8) for i in range(500)] y = [numpy.random.randint(6, size=15) for i in range(500)] w = numpy.abs(numpy.random.randn(500)) data = SequenceGenerator(X, w, y) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) y_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_almost_equal(X, X_) assert_almost_equal(w, w_) assert_almost_equal(y, y_) def test_io_seqgenerator_wy_symbol_batches(): X = [[numpy.random.choice(["A", "C", "G", "T"]) for i in range(18)] for i in range(500)] y = [numpy.random.randint(6, size=15) for i in range(500)] w = numpy.abs(numpy.random.randn(500)) data = SequenceGenerator(X, w, y) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) y_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_array_equal(X, X_) assert_almost_equal(w, w_) assert_almost_equal(y, y_) def test_io_seqgenerator_wy_unlabeled(): X = [numpy.random.randn(15, 8) for i in range(500)] y = [numpy.random.randint(6, size=15) for i in range(500)] w = numpy.abs(numpy.random.randn(500)) idx = numpy.random.choice(500, size=100, replace=False) for i in idx: y[i] = None data = SequenceGenerator(X, w, y) X_ = numpy.concatenate([batch[0] for batch in data.unlabeled_batches()]) w_ = numpy.concatenate([batch[1] for batch in data.unlabeled_batches()]) assert_true(len(X) > len(X_)) assert_true(len(w) > len(w_)) i = 0 for j in range(500): if y[j] is None: assert_almost_equal(X[j], X_[i]) assert_almost_equal(w[j], w_[i]) i += 1 def test_io_seqgenerator_wy_symbol_unlabeled(): X = [[numpy.random.choice(["A", "C", "G", "T"]) for i in range(18)] for i in range(500)] y = [numpy.random.randint(6, size=15) for i in range(500)] w = numpy.abs(numpy.random.randn(500)) idx = numpy.random.choice(500, size=100, replace=False) for i in idx: y[i] = None data = SequenceGenerator(X, w, y) X_ = numpy.concatenate([batch[0] for batch in data.unlabeled_batches()]) w_ = numpy.concatenate([batch[1] for batch in data.unlabeled_batches()]) assert_true(len(X) > len(X_)) assert_true(len(w) > len(w_)) i = 0 for j in range(500): if y[j] is None: assert_array_equal(X[j], X_[i]) assert_almost_equal(w[j], w_[i]) i += 1 def test_io_seqgenerator_wy_labeled(): X = [numpy.random.randn(15, 8) for i in range(500)] y = [numpy.random.randint(6, size=15) for i in range(500)] w = numpy.abs(numpy.random.randn(500)) idx = numpy.random.choice(500, size=100, replace=False) for i in idx: y[i] = None data = SequenceGenerator(X, w, y) X_ = numpy.concatenate([batch[0] for batch in data.labeled_batches()]) w_ = numpy.concatenate([batch[1] for batch in data.labeled_batches()]) y_ = numpy.concatenate([batch[2] for batch in data.labeled_batches()]) assert_true(len(X) > len(X_)) assert_true(len(w) > len(w_)) assert_true(len(y) > len(y_)) i = 0 for j in range(500): if y[j] is not None: assert_almost_equal(X[j], X_[i]) assert_almost_equal(w[j], w_[i]) assert_almost_equal(y[j], y_[i]) i += 1 def test_io_seqgenerator_wy_symbol_labeled(): X = [[numpy.random.choice(["A", "C", "G", "T"]) for i in range(18)] for i in range(500)] y = [numpy.random.randint(6, size=15) for i in range(500)] w = numpy.abs(numpy.random.randn(500)) idx = numpy.random.choice(500, size=100, replace=False) for i in idx: y[i] = None data = SequenceGenerator(X, w, y) X_ = numpy.concatenate([batch[0] for batch in data.labeled_batches()]) w_ = numpy.concatenate([batch[1] for batch in data.labeled_batches()]) y_ = numpy.concatenate([batch[2] for batch in data.labeled_batches()]) assert_true(len(X) > len(X_)) assert_true(len(w) > len(w_)) assert_true(len(y) > len(y_)) i = 0 for j in range(500): if y[j] is not None: assert_array_equal(X[j], X_[i]) assert_almost_equal(w[j], w_[i]) assert_almost_equal(y[j], y_[i]) i += 1 def test_io_dfgenerator_shape(): X = pandas.DataFrame(numpy.random.randn(500, 13)) data = DataFrameGenerator(X) assert_array_equal(data.shape, X.shape) def test_io_dfgenerator_classes_fail(): X = pandas.DataFrame(numpy.random.randn(500, 13)) data = DataFrameGenerator(X) assert_raises(ValueError, lambda data: data.classes, data) def test_io_dfgenerator_numpy_classes(): X = pandas.DataFrame(numpy.random.randn(500, 13)) y = numpy.random.randint(5, size=500) data = DataFrameGenerator(X, y=y) assert_array_equal(data.classes, [0, 1, 2, 3, 4]) def test_io_dfgenerator_pandas_classes(): X = pandas.DataFrame(numpy.random.randn(500, 13)) y = pandas.Series(numpy.random.randint(5, size=500)) data = DataFrameGenerator(X, y=y) assert_array_equal(data.classes, [0, 1, 2, 3, 4]) def test_io_dfgenerator_str_classes(): X = pandas.DataFrame(numpy.random.randn(500, 13)) X['y'] = numpy.random.randint(5, size=500) data = DataFrameGenerator(X, y='y') assert_array_equal(data.classes, [0, 1, 2, 3, 4]) def test_io_dfgenerator_x_batches(): X = pandas.DataFrame(numpy.random.randn(500, 13)) w = numpy.ones(500) data = DataFrameGenerator(X) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, batch_size=123) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, batch_size=1) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, batch_size=506) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(w, w_) def test_io_dfgenerator_w_batches(): X = pandas.DataFrame(numpy.random.randn(500, 13)) w = numpy.abs(numpy.random.randn(500)) data = DataFrameGenerator(X, w) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, w, batch_size=123) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, w, batch_size=1) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, w, batch_size=506) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(w, w_) def test_io_dfgenerator_w_str_batches(): X = pandas.DataFrame(numpy.random.randn(500, 13)) X2 = X.copy() w = numpy.abs(numpy.random.randn(500)) X['w'] = w data = DataFrameGenerator(X, 'w') X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X2.values, X_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, 'w', batch_size=123) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X2.values, X_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, 'w', batch_size=1) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X2.values, X_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, 'w', batch_size=506) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) w_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X2.values, X_) assert_almost_equal(w, w_) def test_io_dfgenerator_y_batches(): X = pandas.DataFrame(numpy.random.randn(500, 13)) y = numpy.random.randint(5, size=500) data = DataFrameGenerator(X, y=y) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(y, y_) data = DataFrameGenerator(X, y=y, batch_size=123) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(y, y_) data = DataFrameGenerator(X, y=y, batch_size=1) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(y, y_) data = DataFrameGenerator(X, y=y, batch_size=506) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(y, y_) def test_io_dfgenerator_y_str_batches(): X = pandas.DataFrame(numpy.random.randn(500, 13)) X2 = X.copy() y = numpy.random.randint(5, size=500) X['y'] = y data = DataFrameGenerator(X, y='y') X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X2.values, X_) assert_almost_equal(y, y_) data = DataFrameGenerator(X, y='y', batch_size=123) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X2.values, X_) assert_almost_equal(y, y_) data = DataFrameGenerator(X, y='y', batch_size=1) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X2.values, X_) assert_almost_equal(y, y_) data = DataFrameGenerator(X, y='y', batch_size=506) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) assert_almost_equal(X2.values, X_) assert_almost_equal(y, y_) def test_io_dfgenerator_wy_batches(): X = pandas.DataFrame(numpy.random.randn(500, 13)) w = numpy.abs(numpy.random.randn(500)) y = numpy.random.randint(5, size=500) data = DataFrameGenerator(X, w, y) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) w_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(y, y_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, w, y, batch_size=123) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) w_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(y, y_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, w, y, batch_size=1) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) w_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(y, y_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, w, y, batch_size=506) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) w_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_almost_equal(X.values, X_) assert_almost_equal(y, y_) assert_almost_equal(w, w_) def test_io_dfgenerator_wy_str_batches(): X = pandas.DataFrame(numpy.random.randn(500, 13)) X2 = X.copy() w = numpy.abs(numpy.random.randn(500)) y = numpy.random.randint(5, size=500) X['w'] = w X['y'] = y data = DataFrameGenerator(X, 'w', 'y') X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) w_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_almost_equal(X2.values, X_) assert_almost_equal(y, y_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, 'w', 'y', batch_size=123) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) w_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_almost_equal(X2.values, X_) assert_almost_equal(y, y_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, 'w', 'y', batch_size=1) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) w_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_almost_equal(X2.values, X_) assert_almost_equal(y, y_) assert_almost_equal(w, w_) data = DataFrameGenerator(X, 'w', 'y', batch_size=506) X_ = numpy.concatenate([batch[0] for batch in data.batches()]) y_ = numpy.concatenate([batch[1] for batch in data.batches()]) w_ = numpy.concatenate([batch[2] for batch in data.batches()]) assert_almost_equal(X2.values, X_) assert_almost_equal(y, y_) assert_almost_equal(w, w_) def test_io_dfgenerator_wy_unlabeled(): X = pandas.DataFrame(numpy.random.randn(500, 13)) w = numpy.abs(numpy.random.randn(500)) y = numpy.random.randint(5, size=500) - 1 data = DataFrameGenerator(X, w, y) X_ = numpy.concatenate([batch[0] for batch in data.unlabeled_batches()]) w_ = numpy.concatenate([batch[1] for batch in data.unlabeled_batches()]) assert_true(X.shape[0] > X_.shape[0]) assert_almost_equal(X.loc[y == -1], X_) assert_almost_equal(w[y == -1], w_) def test_io_dfgenerator_wy_str_unlabeled(): X = pandas.DataFrame(numpy.random.randn(500, 13)) X2 = X.copy() w = numpy.abs(numpy.random.randn(500)) y = numpy.random.randint(5, size=500) - 1 X['w'] = w X['y'] = y data = DataFrameGenerator(X, 'w', 'y') X_ = numpy.concatenate([batch[0] for batch in data.unlabeled_batches()]) w_ = numpy.concatenate([batch[1] for batch in data.unlabeled_batches()]) assert_true(X.shape[0] > X_.shape[0]) assert_almost_equal(X2.loc[y == -1], X_) assert_almost_equal(w[y == -1], w_) def test_io_dfgenerator_wy_labeled(): X = pandas.DataFrame(numpy.random.randn(500, 13)) w = numpy.abs(numpy.random.randn(500)) y = numpy.random.randint(5, size=500) - 1 data = DataFrameGenerator(X, w, y) X_ = numpy.concatenate([batch[0] for batch in data.labeled_batches()]) y_ = numpy.concatenate([batch[1] for batch in data.labeled_batches()]) w_ = numpy.concatenate([batch[2] for batch in data.labeled_batches()]) assert_true(X.shape[0] > X_.shape[0]) assert_almost_equal(X.loc[y != -1], X_) assert_almost_equal(y[y != -1], y_) assert_almost_equal(w[y != -1], w_) def test_io_dfgenerator_wy_str_labeled(): X = pandas.DataFrame(numpy.random.randn(500, 13)) X2 = X.copy() w = numpy.abs(numpy.random.randn(500)) y = numpy.random.randint(5, size=500) - 1 X['w'] = w X['y'] = y data = DataFrameGenerator(X, 'w', 'y') X_ = numpy.concatenate([batch[0] for batch in data.labeled_batches()]) y_ = numpy.concatenate([batch[1] for batch in data.labeled_batches()]) w_ = numpy.concatenate([batch[2] for batch in data.labeled_batches()]) assert_true(X.shape[0] > X_.shape[0]) assert_almost_equal(X2.loc[y != -1], X_) assert_almost_equal(y[y != -1], y_) assert_almost_equal(w[y != -1], w_) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/test_kmeans.py000066400000000000000000000326621420461712600247330ustar00rootroot00000000000000from pomegranate import * from nose.tools import with_setup from nose.tools import assert_true from nose.tools import assert_equal from nose.tools import assert_greater_equal from nose.tools import assert_greater from nose.tools import assert_raises from nose.tools import assert_not_equal from numpy.testing import assert_almost_equal from numpy.testing import assert_array_almost_equal from numpy.testing import assert_array_equal import random import pickle import numpy as np numpy.random.seed(0) def setup_three_dimensions(): global X X = numpy.array([[-0.13174492, 0.51895916, -1.13141796], [ 7.92260379, 7.86325294, 7.9884075 ], [-0.63378039, -0.96394236, -1.34125012], [ 8.16216236, 8.04655182, 6.68825619], [-0.69595565, -0.19004012, 0.40768949], [ 7.76271281, 8.94969945, 7.03687617], [-1.92481462, -1.03905815, -0.44048926], [ 7.90926091, 7.21944418, 7.15989354], [-0.97493454, -0.04714556, -0.38607725], [ 9.65781658, 7.04832845, 6.47613347]]) idxs = numpy.array([29, 19, 26, 11, 8, 27, 21, 7, 14, 13]) i, j = idxs // 3, idxs % 3 global X_nan X_nan = X.copy() X_nan[i, j] = numpy.nan global centroids centroids = numpy.array([[0, 0, 0], [8, 8, 8]]) global model model = Kmeans(2, centroids) def setup_five_dimensions(): global X X = numpy.array([[-0.04320239, 2.25402395, -0.3075753 , 0.01710706, 2.88816037], [ 3.6483074 , 5.03958367, 3.14457941, 4.94180558, 4.32880698], [ 7.48485345, 8.54100011, 7.90936486, 8.12260819, 6.6466098 ], [ 12.15394848, 10.52091121, 13.55495735, 10.48190106, 10.94417476], [ 1.21068778, 0.77311369, -0.31479566, -0.51865649, 0.4408653 ], [-0.62796182, -0.34947675, -1.09050772, -0.34591408, 0.78866514], [ 0.5661847 , 0.30785453, 0.38823634, 1.99717206, -0.99415221], [ 0.10871016, 2.06244903, -0.19580087, -0.22100353, -0.43777027], [ 3.06987578, 4.8633418 , 4.23645519, 4.20563589, 3.40046883], [ 3.0471144 , 3.43070459, 3.88690894, 3.61962816, 3.52399965], [ 3.3020318 , 5.16491752, 3.85249134, 2.7075964 , 4.03831846], [ 3.55266908, 2.69803949, 4.13340743, 5.72527752, 4.9840009 ], [ 7.27689336, 8.99614296, 7.10109146, 7.81354687, 7.27320546], [ 9.55443921, 7.70358635, 8.9762396 , 7.8054752 , 7.95933534], [ 7.55150108, 9.09523173, 8.38379803, 8.18932292, 7.70853 ], [ 9.59329137, 8.26811547, 9.82226673, 8.35257773, 8.21768809], [ 11.77294852, 12.33135372, 13.02160394, 12.05536766, 11.96375761], [ 11.08768408, 13.15689157, 12.59002102, 11.16137415, 9.84335332], [ 11.41978669, 11.45646564, 11.77622614, 11.96590564, 12.33083825], [ 12.13323296, 11.89683824, 12.18373541, 13.21432431, 11.79987739]]) idxs = numpy.array([77, 26, 61, 46, 18, 30, 94, 96, 45, 67, 4, 20, 23, 73, 37, 21, 58, 99, 51, 7, 69, 53, 81, 85, 95, 9, 98, 24, 28, 38]) i, j = idxs // 5, idxs % 5 global X_nan X_nan = X.copy() X_nan[i, j] = numpy.nan global centroids centroids = numpy.array([[0, 0, 0, 0, 0], [4, 4, 4, 4, 4], [8, 8, 8, 8, 8], [12, 12, 12, 12, 12]]) global model model = Kmeans(4, centroids) def test_kmeans_init(): centroids = [[2, 3], [5, 7]] model = Kmeans(2, centroids) assert_equal(model.d, 2) assert_equal(model.k, 2) assert_array_equal(model.centroids, centroids) @with_setup(setup_three_dimensions) def test_kmeans_from_samples(): model = Kmeans.from_samples(2, X, init='first-k') centroids = [[-0.872246, -0.344245, -0.578309], [ 8.282911, 7.825455, 7.069913]] assert_array_almost_equal(model.centroids, centroids) @with_setup(setup_three_dimensions) def test_kmeans_from_samples_parallel(): model = Kmeans.from_samples(2, X, init='first-k', n_jobs=2) centroids = [[-0.872246, -0.344245, -0.578309], [ 8.282911, 7.825455, 7.069913]] assert_array_almost_equal(model.centroids, centroids) @with_setup(setup_three_dimensions) def test_kmeans_predict(): y = numpy.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1]) y_hat = model.predict(X) assert_array_equal(y, y_hat) @with_setup(setup_three_dimensions) def test_kmeans_predict_parallel(): y = numpy.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1]) y_hat = model.predict(X, n_jobs=2) assert_array_equal(y, y_hat) y_hat = model.predict(X, n_jobs=4) assert_array_equal(y, y_hat) @with_setup(setup_five_dimensions) def test_kmeans_predict_large(): y = [0, 1, 2, 3, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3] y_hat = model.predict(X) assert_array_equal(y, y_hat) @with_setup(setup_five_dimensions) def test_kmeans_predict_large_parallel(): y = [0, 1, 2, 3, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3] y_hat = model.predict(X, n_jobs=2) assert_array_equal(y, y_hat) y_hat = model.predict(X, n_jobs=4) assert_array_equal(y, y_hat) @with_setup(setup_three_dimensions) def test_kmeans_fit(): model.fit(X) centroids = [[-0.872246, -0.344245, -0.578309], [ 8.282911, 7.825455, 7.069913]] assert_array_almost_equal(model.centroids, centroids) @with_setup(setup_three_dimensions) def test_kmeans_fit_parallel(): model.fit(X, n_jobs=2) centroids = [[-0.872246, -0.344245, -0.578309], [ 8.282911, 7.825455, 7.069913]] assert_array_almost_equal(model.centroids, centroids) model.fit(X, n_jobs=4) centroids = [[-0.872246, -0.344245, -0.578309], [ 8.282911, 7.825455, 7.069913]] assert_array_almost_equal(model.centroids, centroids) @with_setup(setup_five_dimensions) def test_kmeans_multiple_init(): model1 = Kmeans.from_samples(4, X, init='kmeans++', n_init=1) model2 = Kmeans.from_samples(4, X, init='kmeans++', n_init=25) dist1 = model1.distance(X).min(axis=1).sum() dist2 = model2.distance(X).min(axis=1).sum() assert_greater_equal(dist1, dist2) model1 = Kmeans.from_samples(4, X, init='first-k', n_init=1) model2 = Kmeans.from_samples(4, X, init='first-k', n_init=5) dist1 = model1.distance(X).min(axis=1).sum() dist2 = model2.distance(X).min(axis=1).sum() assert_equal(dist1, dist2) @with_setup(setup_five_dimensions) def test_kmeans_ooc_from_samples(): numpy.random.seed(0) model1 = Kmeans.from_samples(5, X, init='first-k', batch_size=20) model2 = Kmeans.from_samples(5, X, init='first-k', batch_size=None) assert_array_equal(model1.centroids, model2.centroids) @with_setup(setup_three_dimensions) def test_kmeans_ooc_fit(): centroids_copy = numpy.copy(centroids) model1 = Kmeans(2, centroids_copy, n_init=1) model1.fit(X) centroids_copy = numpy.copy(centroids) model2 = Kmeans(2, centroids_copy, n_init=1) model2.fit(X, batch_size=10) centroids_copy = numpy.copy(centroids) model3 = Kmeans(2, centroids_copy, n_init=1) model3.fit(X, batch_size=1) assert_array_almost_equal(model1.centroids, model2.centroids) assert_array_almost_equal(model1.centroids, model3.centroids) @with_setup(setup_five_dimensions) def test_kmeans_minibatch_from_samples(): model1 = Kmeans.from_samples(4, X, init='first-k', batch_size=10) model2 = Kmeans.from_samples(4, X, init='first-k', batch_size=None) model3 = Kmeans.from_samples(4, X, init='first-k', batch_size=10, batches_per_epoch=1) assert_array_almost_equal(model1.centroids, model2.centroids) assert_raises(AssertionError, assert_array_equal, model1.centroids, model3.centroids) @with_setup(setup_five_dimensions) def test_kmeans_minibatch_fit(): centroids_copy = numpy.copy(centroids) model1 = Kmeans(4, centroids_copy) model1.fit(X, batch_size=10) centroids_copy = numpy.copy(centroids) model2 = Kmeans(4, centroids_copy) model2.fit(X, batch_size=None) centroids_copy = numpy.copy(centroids) model3 = Kmeans(4, centroids_copy) model3.fit(X, batch_size=5, batches_per_epoch=1) assert_array_almost_equal(model1.centroids, model2.centroids) assert_raises(AssertionError, assert_array_equal, model1.centroids, model3.centroids) @with_setup(setup_three_dimensions) def test_kmeans_nan_from_samples(): model = Kmeans.from_samples(2, X_nan, init='first-k') centroids = [[-0.872246, 0.235907, -0.785954], [ 7.94916 , 7.825455, 7.395059]] assert_array_almost_equal(model.centroids, centroids) @with_setup(setup_three_dimensions) def test_kmeans_nan_from_samples_parallel(): model = Kmeans.from_samples(2, X_nan, init='first-k', n_jobs=2) centroids = [[-0.872246, 0.235907, -0.785954], [ 7.94916 , 7.825455, 7.395059]] assert_array_almost_equal(model.centroids, centroids) @with_setup(setup_three_dimensions) def test_kmeans_nan_fit(): model.fit(X_nan) centroids = [[-0.872246, 0.235907, -0.785954], [ 7.94916 , 7.825455, 7.395059]] assert_array_almost_equal(model.centroids, centroids) @with_setup(setup_three_dimensions) def test_kmeans_nan_fit_parallel(): model.fit(X_nan, n_jobs=2) centroids = [[-0.872246, 0.235907, -0.785954], [ 7.94916 , 7.825455, 7.395059]] assert_array_almost_equal(model.centroids, centroids) @with_setup(setup_five_dimensions) def test_kmeans_nan_fit_large(): model.fit(X_nan) centroids = [[ -0.187485, 1.541443, -0.331161, 1.00714 , -0.214419], [ 3.393221, 4.200322, 4.027316, 4.25569 , 3.986697], [ 8.292196, 8.401983, 7.798085, 8.023552, 7.461508], [ 11.782228, 11.711423, 12.625309, 11.727549, 10.917095]] assert_array_almost_equal(model.centroids, centroids) @with_setup(setup_five_dimensions) def test_kmeans_nan_fit_large_parallel(): model.fit(X_nan, n_jobs=2) centroids = [[ -0.187485, 1.541443, -0.331161, 1.00714 , -0.214419], [ 3.393221, 4.200322, 4.027316, 4.25569 , 3.986697], [ 8.292196, 8.401983, 7.798085, 8.023552, 7.461508], [ 11.782228, 11.711423, 12.625309, 11.727549, 10.917095]] assert_array_almost_equal(model.centroids, centroids) @with_setup(setup_three_dimensions) def test_kmeans_nan_predict(): y = numpy.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1]) y_hat = model.predict(X_nan) assert_array_almost_equal(y, y_hat) @with_setup(setup_three_dimensions) def test_kmeans_nan_predict_parallel(): y = numpy.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1]) y_hat = model.predict(X_nan, n_jobs=2) assert_array_almost_equal(y, y_hat) @with_setup(setup_five_dimensions) def test_kmeans_nan_large_predict(): y = numpy.array([0, 1, 2, 3, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]) y_hat = model.predict(X_nan) assert_array_almost_equal(y, y_hat) @with_setup(setup_five_dimensions) def test_kmeans_nan_large_predict_parallel(): y = numpy.array([0, 1, 2, 3, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]) y_hat = model.predict(X_nan, n_jobs=2) assert_array_almost_equal(y, y_hat) @with_setup(setup_five_dimensions) def test_kmeans_nan_multiple_init(): numpy.random.seed(0) model1 = Kmeans.from_samples(4, X_nan, init='kmeans++', n_init=1) numpy.random.seed(0) model2 = Kmeans.from_samples(4, X_nan, init='kmeans++', n_init=25) dist1 = model1.distance(X).min(axis=1).sum() dist2 = model2.distance(X).min(axis=1).sum() assert_greater_equal(dist1, dist2) model1 = Kmeans.from_samples(4, X_nan, init='first-k', n_init=1) model2 = Kmeans.from_samples(4, X_nan, init='first-k', n_init=5) dist1 = model1.distance(X).min(axis=1).sum() dist2 = model2.distance(X).min(axis=1).sum() assert_equal(dist1, dist2) @with_setup(setup_five_dimensions) def test_kmeans_ooc_nan_from_samples(): model1 = Kmeans.from_samples(4, X_nan, init='first-k', batch_size=20) model2 = Kmeans.from_samples(4, X_nan, init='first-k', batch_size=None) assert_array_almost_equal(model1.centroids, model2.centroids) @with_setup(setup_five_dimensions) def test_kmeans_ooc_nan_fit(): centroids_copy = numpy.copy(centroids) model1 = Kmeans(4, centroids_copy, n_init=1) model1.fit(X_nan) centroids_copy = numpy.copy(centroids) model2 = Kmeans(4, centroids_copy, n_init=1) model2.fit(X_nan, batch_size=10) centroids_copy = numpy.copy(centroids) model3 = Kmeans(4, centroids_copy, n_init=1) model3.fit(X_nan, batch_size=1) assert_array_almost_equal(model1.centroids, model2.centroids, 4) assert_array_almost_equal(model1.centroids, model3.centroids, 4) @with_setup(setup_five_dimensions) def test_kmeans_minibatch_nan_from_samples(): model1 = Kmeans.from_samples(4, X_nan, init='first-k', batch_size=10) model2 = Kmeans.from_samples(4, X_nan, init='first-k', batch_size=None) model3 = Kmeans.from_samples(4, X_nan, init='first-k', batch_size=10, batches_per_epoch=1) model4 = Kmeans.from_samples(4, X_nan, init='first-k', batch_size=10, batches_per_epoch=2) assert_array_almost_equal(model1.centroids, model2.centroids) assert_array_almost_equal(model1.centroids, model4.centroids) assert_raises(AssertionError, assert_array_equal, model1.centroids, model3.centroids) @with_setup(setup_five_dimensions) def test_kmeans_minibatch_nan_fit(): centroids_copy = numpy.copy(centroids) model1 = Kmeans(4, centroids_copy, n_init=1) model1.fit(X, batch_size=10) centroids_copy = numpy.copy(centroids) model2 = Kmeans(4, centroids_copy, n_init=1) model2.fit(X, batch_size=None) centroids_copy = numpy.copy(centroids) model3 = Kmeans(4, centroids_copy, n_init=1) model3.fit(X, batch_size=10, batches_per_epoch=1) centroids_copy = numpy.copy(centroids) model4 = Kmeans(4, centroids_copy, n_init=1) model4.fit(X, batch_size=10, batches_per_epoch=2) assert_array_almost_equal(model1.centroids, model2.centroids) assert_array_almost_equal(model1.centroids, model4.centroids) assert_raises(AssertionError, assert_array_equal, model1.centroids, model3.centroids) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/test_markov_chain.py000066400000000000000000000475121420461712600261160ustar00rootroot00000000000000from __future__ import (division) from pomegranate import * from nose.tools import with_setup from nose.tools import assert_almost_equal from nose.tools import assert_equal from nose.tools import assert_not_equal from nose.tools import assert_less_equal from nose.tools import assert_raises import random import pickle import numpy as np def setup(): global data global weights global zeroth_dist global first_dist global second_dist data = [ list('AAACBDAA'), list('DBBCBABD'), list('CBDCCDCBC'), list('DDDDC'), list('DADBBCBBBACBC'), list('CBDBAC'), list('CACDDDBCAABA'), list('BBABDAADCABCCAD'), list('DCBBBADBBCBC'), list('DCADDAAA'), list('AABBBCC'), list('CABBADACDBBDCC'), list('AADDCDDDB'), list('CCDBDCDDDABDAC'), list('DDADB'), list('BCDACDBBBBAC'), list('ADCBCCCB'), list('BDDBCA'), list('ACCDCBADCBBBB'), list('ACCBABCBA'), list('DAABCCBBD'), list('CCAADACBDCDBABC'), list('BCDCACBBAD'), list('CBBBCDCA'), list('CBDADACAADBCC'), list('DCDABCC'), list('AAAABCBC'), list('BDDDDACBBCABA'), list('CCBDCBDCBADBBDB'), list('CBDAA'), list('CCDAAAB'), list('ABDBAC'), list('DBCCDCC'), list('CCDCBB'), list('ACDBCDD'), list('CACCACCDBBDABB'), list('DCDDAB'), list('ADDDCDACACA'), list('AAACACADB'), list('BDBBDDBACDCCAAA') ] weights = [ 8, 7, 1, 2, 8, 1, 9, 2, 6, 2, 5, 6, 10, 10, 8, 4, 10, 10, 2, 9, 6, 2, 9, 9, 3, 1, 10, 6, 9, 1, 2, 9, 2, 1, 4, 1, 2, 7, 9, 4] zeroth_dist = DiscreteDistribution( { 'A': 0.1, 'B': 0.2, 'C': 0.3, 'D': 0.4 } ) first_dist = ConditionalProbabilityTable( [[ 'A', 'A', 0.8 ], [ 'A', 'B', 0.05 ], [ 'A', 'C', 0.05 ], [ 'A', 'D', 0.1 ], [ 'B', 'A', 0.1 ], [ 'B', 'B', 0.2 ], [ 'B', 'C', 0.6 ], [ 'B', 'D', 0.1 ], [ 'C', 'A', 0.15 ], [ 'C', 'B', 0.1 ], [ 'C', 'C', 0.25 ], [ 'C', 'D', 0.5 ], [ 'D', 'A', 0.25 ], [ 'D', 'B', 0.25 ], [ 'D', 'C', 0.4 ], [ 'D', 'D', 0.1 ]], [ zeroth_dist ] ) second_dist = ConditionalProbabilityTable( [[ 'A', 'A', 'A', 0.05 ], [ 'A', 'A', 'B', 0.25 ], [ 'A', 'A', 'C', 0.15 ], [ 'A', 'A', 'D', 0.55 ], [ 'A', 'B', 'A', 0.05 ], [ 'A', 'B', 'B', 0.05 ], [ 'A', 'B', 'C', 0.85 ], [ 'A', 'B', 'D', 0.05 ], [ 'A', 'C', 'A', 0.7 ], [ 'A', 'C', 'B', 0.1 ], [ 'A', 'C', 'C', 0.1 ], [ 'A', 'C', 'D', 0.1 ], [ 'A', 'D', 'A', 0.2 ], [ 'A', 'D', 'B', 0.4 ], [ 'A', 'D', 'C', 0.35 ], [ 'A', 'D', 'D', 0.05 ], [ 'B', 'A', 'A', 0.3 ], [ 'B', 'A', 'B', 0.05 ], [ 'B', 'A', 'C', 0.15 ], [ 'B', 'A', 'D', 0.5 ], [ 'B', 'B', 'A', 0.8 ], [ 'B', 'B', 'B', 0.1 ], [ 'B', 'B', 'C', 0.1 ], [ 'B', 'B', 'D', 0.0 ], [ 'B', 'C', 'A', 0.1 ], [ 'B', 'C', 'B', 0.35 ], [ 'B', 'C', 'C', 0.3 ], [ 'B', 'C', 'D', 0.25 ], [ 'B', 'D', 'A', 0.3 ], [ 'B', 'D', 'B', 0.1 ], [ 'B', 'D', 'C', 0.2 ], [ 'B', 'D', 'D', 0.4 ], [ 'C', 'A', 'A', 0.2 ], [ 'C', 'A', 'B', 0.3 ], [ 'C', 'A', 'C', 0.3 ], [ 'C', 'A', 'D', 0.2 ], [ 'C', 'B', 'A', 0.35 ], [ 'C', 'B', 'B', 0.45 ], [ 'C', 'B', 'C', 0.0 ], [ 'C', 'B', 'D', 0.2 ], [ 'C', 'C', 'A', 0.25 ], [ 'C', 'C', 'B', 0.0 ], [ 'C', 'C', 'C', 0.6 ], [ 'C', 'C', 'D', 0.15 ], [ 'C', 'D', 'A', 0.8 ], [ 'C', 'D', 'B', 0.1 ], [ 'C', 'D', 'C', 0.05 ], [ 'C', 'D', 'D', 0.05 ], [ 'D', 'A', 'A', 0.5 ], [ 'D', 'A', 'B', 0.0 ], [ 'D', 'A', 'C', 0.5 ], [ 'D', 'A', 'D', 0.0 ], [ 'D', 'B', 'A', 0.35 ], [ 'D', 'B', 'B', 0.5 ], [ 'D', 'B', 'C', 0.1 ], [ 'D', 'B', 'D', 0.05 ], [ 'D', 'C', 'A', 0.1 ], [ 'D', 'C', 'B', 0.45 ], [ 'D', 'C', 'C', 0.0 ], [ 'D', 'C', 'D', 0.45 ], [ 'D', 'D', 'A', 0.2 ], [ 'D', 'D', 'B', 0.1 ], [ 'D', 'D', 'C', 0.1 ], [ 'D', 'D', 'D', 0.6 ]], [ zeroth_dist, first_dist ] ) def teardown(): pass @with_setup( setup, teardown ) def test_zeroth_dist(): assert_almost_equal( zeroth_dist.log_probability( 'A' ), -2.3025850929940455 ) assert_almost_equal( zeroth_dist.log_probability( 'B' ), -1.6094379124341003 ) assert_almost_equal( zeroth_dist.log_probability( 'C' ), -1.2039728043259361 ) assert_almost_equal( zeroth_dist.log_probability( 'D' ), -0.916290731874155 ) assert_almost_equal( zeroth_dist.log_probability( 'E' ), -float('inf') ) @with_setup( setup, teardown ) def test_first_dist(): assert_almost_equal( first_dist.log_probability( ('A', 'A') ), -0.22314355131420971 ) assert_almost_equal( first_dist.log_probability( ('A', 'B') ), -2.9957322735539909 ) assert_almost_equal( first_dist.log_probability( ('B', 'B') ), -1.6094379124341003 ) assert_almost_equal( first_dist.log_probability( ('B', 'C') ), -0.51082562376599072 ) assert_almost_equal( first_dist.log_probability( ('C', 'C') ), -1.3862943611198906 ) assert_almost_equal( first_dist.log_probability( ('C', 'D') ), -0.69314718055994529 ) assert_almost_equal( first_dist.log_probability( ('D', 'D') ), -2.3025850929940455 ) assert_almost_equal( first_dist.log_probability( ('D', 'A') ), -1.3862943611198906 ) @with_setup( setup, teardown ) def test_second_dist(): assert_almost_equal( second_dist.log_probability( ('A', 'A', 'C') ), -1.89711998489 ) assert_almost_equal( second_dist.log_probability( ('A', 'A', 'A') ), -2.99573227355 ) assert_almost_equal( second_dist.log_probability( ('A', 'B', 'A') ), -2.99573227355 ) assert_almost_equal( second_dist.log_probability( ('A', 'B', 'C') ), -0.162518929498 ) assert_almost_equal( second_dist.log_probability( ('A', 'C', 'C') ), -2.30258509299 ) assert_almost_equal( second_dist.log_probability( ('A', 'C', 'D') ), -2.30258509299 ) assert_almost_equal( second_dist.log_probability( ('A', 'D', 'B') ), -0.916290731874 ) assert_almost_equal( second_dist.log_probability( ('A', 'D', 'D') ), -2.99573227355 ) assert_almost_equal( second_dist.log_probability( ('B', 'A', 'B') ), -2.99573227355 ) assert_almost_equal( second_dist.log_probability( ('B', 'A', 'D') ), -0.69314718056 ) assert_almost_equal( second_dist.log_probability( ('B', 'B', 'B') ), -2.30258509299 ) assert_almost_equal( second_dist.log_probability( ('B', 'B', 'D') ), -float('inf') ) assert_almost_equal( second_dist.log_probability( ('B', 'C', 'A') ), -2.30258509299 ) assert_almost_equal( second_dist.log_probability( ('B', 'C', 'B') ), -1.0498221245 ) assert_almost_equal( second_dist.log_probability( ('B', 'D', 'D') ), -0.916290731874 ) assert_almost_equal( second_dist.log_probability( ('B', 'D', 'B') ), -2.30258509299 ) assert_almost_equal( second_dist.log_probability( ('C', 'A', 'A') ), -1.60943791243 ) assert_almost_equal( second_dist.log_probability( ('C', 'A', 'B') ), -1.20397280433 ) assert_almost_equal( second_dist.log_probability( ('C', 'B', 'C') ), -float('inf') ) assert_almost_equal( second_dist.log_probability( ('C', 'B', 'A') ), -1.0498221245 ) assert_almost_equal( second_dist.log_probability( ('C', 'C', 'D') ), -1.89711998489 ) assert_almost_equal( second_dist.log_probability( ('C', 'C', 'B') ), -float('inf') ) assert_almost_equal( second_dist.log_probability( ('C', 'D', 'A') ), -0.223143551314 ) assert_almost_equal( second_dist.log_probability( ('C', 'D', 'C') ), -2.99573227355 ) assert_almost_equal( second_dist.log_probability( ('D', 'A', 'D') ), -float('inf') ) assert_almost_equal( second_dist.log_probability( ('D', 'A', 'A') ), -0.69314718056 ) assert_almost_equal( second_dist.log_probability( ('D', 'B', 'D') ), -2.99573227355 ) assert_almost_equal( second_dist.log_probability( ('D', 'B', 'C') ), -2.30258509299 ) assert_almost_equal( second_dist.log_probability( ('D', 'C', 'A') ), -2.30258509299 ) assert_almost_equal( second_dist.log_probability( ('D', 'C', 'D') ), -0.798507696218 ) assert_almost_equal( second_dist.log_probability( ('D', 'D', 'D') ), -0.510825623766 ) assert_almost_equal( second_dist.log_probability( ('D', 'D', 'A') ), -1.60943791243 ) @with_setup( setup, teardown ) def test_constructors(): # raises no errors first_chain = MarkovChain([ zeroth_dist, first_dist ]) second_chain = MarkovChain([ zeroth_dist, first_dist, second_dist ]) @with_setup( setup, teardown ) def test_first_log_probability(): # test going one state back first_chain = MarkovChain([ zeroth_dist, first_dist ]) assert_almost_equal( first_chain.log_probability( list('A') ), -2.3025850929940455 ) assert_almost_equal( first_chain.log_probability( list('B') ), -1.6094379124341003 ) assert_almost_equal( first_chain.log_probability( list('AC') ), -5.2983173665480363 ) assert_almost_equal( first_chain.log_probability( list('AD') ), -4.6051701859880909 ) assert_almost_equal( first_chain.log_probability( list('BD') ), -3.9120230054281455 ) assert_almost_equal( first_chain.log_probability( list('BA') ), -3.9120230054281455 ) assert_almost_equal( first_chain.log_probability( list('CA') ), -3.1010927892118172 ) assert_almost_equal( first_chain.log_probability( list('CB') ), -3.5065578973199818 ) assert_almost_equal( first_chain.log_probability( list('DB') ), -2.3025850929940455 ) assert_almost_equal( first_chain.log_probability( list('DC') ), -1.83258146374831 ) assert_almost_equal( first_chain.log_probability( list('ABDD') ), -9.9034875525361272 ) assert_almost_equal( first_chain.log_probability( list('CCCB') ), -6.2791466195597625 ) assert_almost_equal( first_chain.log_probability( list('CCBD') ), -7.1954373514339167 ) assert_almost_equal( first_chain.log_probability( list('ACAC') ), -10.191169624987909 ) assert_almost_equal( first_chain.log_probability( list('ABDBCCDC') ), -12.493754717981954 ) assert_almost_equal( first_chain.log_probability( list('DACCBDCB') ), -14.508657738524217 ) assert_almost_equal( first_chain.log_probability( list('BCCCACBDBDBABACD') ), -30.75583043175768 ) assert_almost_equal( first_chain.log_probability( list('DABBCBDACAAADCBDCDBCBDCACBDABBAA') ), -53.616465272807112 ) # test going two states back second_chain = MarkovChain([ zeroth_dist, first_dist, second_dist ]) assert_almost_equal( second_chain.log_probability( list('A') ), -2.30258509299 ) assert_almost_equal( second_chain.log_probability( list('B') ), -1.60943791243 ) assert_almost_equal( second_chain.log_probability( list('AC') ), -5.29831736655 ) assert_almost_equal( second_chain.log_probability( list('AD') ), -4.60517018599 ) assert_almost_equal( second_chain.log_probability( list('BD') ), -3.91202300543 ) assert_almost_equal( second_chain.log_probability( list('BA') ), -3.91202300543 ) assert_almost_equal( second_chain.log_probability( list('CA') ), -3.10109278921 ) assert_almost_equal( second_chain.log_probability( list('CB') ), -3.50655789732 ) assert_almost_equal( second_chain.log_probability( list('DB') ), -2.30258509299 ) assert_almost_equal( second_chain.log_probability( list('DC') ), -1.83258146375 ) assert_almost_equal( second_chain.log_probability( list('ABDD') ), -9.21034037198 ) assert_almost_equal( second_chain.log_probability( list('CCCD') ), -4.9982127741 ) assert_almost_equal( second_chain.log_probability( list('CCBD') ), -float('inf') ) assert_almost_equal( second_chain.log_probability( list('ACAC') ), -6.85896511481 ) assert_almost_equal( second_chain.log_probability( list('ABDBCCDC') ), -18.9960448889 ) assert_almost_equal( second_chain.log_probability( list('DACCBDCB') ), -float('inf') ) assert_almost_equal( second_chain.log_probability( list('BCCCACBDBDBABACD') ), -29.1792463442 ) assert_almost_equal( second_chain.log_probability( list('DABBCBDACAAADCBDCDBCBDCACBDABBAA') ), -float('inf') ) # if summarize and from summaries work, so does fit @with_setup( setup, teardown ) def test_summarize_no_weights_no_inertia(): first_chain = MarkovChain([ zeroth_dist, first_dist ]) # split in four first_chain.summarize( data[:10] ) first_chain.summarize( data[10:20] ) first_chain.summarize( data[20:30] ) first_chain.summarize( data[30:] ) first_chain.from_summaries() # check if probabilities are correct assert_almost_equal( first_chain.log_probability( list('A') ), -1.29098418132 ) assert_almost_equal( first_chain.log_probability( list('B') ), -1.89711998489 ) assert_almost_equal( first_chain.log_probability( list('AC') ), -2.52493781785 ) assert_almost_equal( first_chain.log_probability( list('AD') ), -2.82721868973 ) assert_almost_equal( first_chain.log_probability( list('BD') ), -3.35240721749 ) assert_almost_equal( first_chain.log_probability( list('BA') ), -3.56371631116 ) assert_almost_equal( first_chain.log_probability( list('CA') ), -2.66812748722 ) assert_almost_equal( first_chain.log_probability( list('CB') ), -2.31672960038 ) assert_almost_equal( first_chain.log_probability( list('DB') ), -2.74959920402 ) assert_almost_equal( first_chain.log_probability( list('DC') ), -2.70514744144 ) assert_almost_equal( first_chain.log_probability( list('ABDD') ), -5.69233078086 ) assert_almost_equal( first_chain.log_probability( list('CCCB') ), -5.2049574644 ) assert_almost_equal( first_chain.log_probability( list('CCBD') ), -5.216130765 ) assert_almost_equal( first_chain.log_probability( list('ACAC') ), -5.30308884496 ) assert_almost_equal( first_chain.log_probability( list('ABDBCCDC') ), -11.1281275339 ) assert_almost_equal( first_chain.log_probability( list('DACCBDCB') ), -10.6827162728 ) assert_almost_equal( first_chain.log_probability( list('BCCCACBDBDBABACD') ), -23.2162130846 ) assert_almost_equal( first_chain.log_probability( list('DABBCBDACAAADCBDCDBCBDCACBDABBAA') ), -43.7174844781 ) second_chain = MarkovChain([ zeroth_dist, first_dist, second_dist ]) # split into four second_chain.summarize( data[:10] ) second_chain.summarize( data[10:20] ) second_chain.summarize( data[20:30] ) second_chain.summarize( data[30:] ) second_chain.from_summaries() @with_setup( setup, teardown ) def test_summarize_no_weights_with_inertia(): first_chain = MarkovChain([ zeroth_dist, first_dist ]) first_chain.summarize( data[:10] ) first_chain.summarize( data[10:20] ) first_chain.summarize( data[20:30] ) first_chain.summarize( data[30:] ) first_chain.from_summaries( inertia=0.4 ) assert_almost_equal( first_chain.log_probability( list('A') ), -1.58474529984 ) assert_almost_equal( first_chain.log_probability( list('B') ), -1.77195684193 ) assert_almost_equal( first_chain.log_probability( list('AC') ), -3.22112518823 ) assert_almost_equal( first_chain.log_probability( list('AD') ), -3.3619279842 ) assert_almost_equal( first_chain.log_probability( list('BD') ), -3.48675527002 ) assert_almost_equal( first_chain.log_probability( list('BA') ), -3.6470979201 ) assert_almost_equal( first_chain.log_probability( list('CA') ), -2.82601794483 ) assert_almost_equal( first_chain.log_probability( list('CB') ), -2.66015931757 ) assert_almost_equal( first_chain.log_probability( list('DB') ), -2.54362030796 ) assert_almost_equal( first_chain.log_probability( list('DC') ), -2.30916483161 ) assert_almost_equal( first_chain.log_probability( list('ABDD') ), -6.88185009599 ) assert_almost_equal( first_chain.log_probability( list('CCCB') ), -5.50132618677 ) assert_almost_equal( first_chain.log_probability( list('CCBD') ), -5.79554118026 ) assert_almost_equal( first_chain.log_probability( list('ACAC') ), -6.52834038129 ) assert_almost_equal( first_chain.log_probability( list('ABDBCCDC') ), -11.1045309105 ) assert_almost_equal( first_chain.log_probability( list('DACCBDCB') ), -11.519936158 ) assert_almost_equal( first_chain.log_probability( list('BCCCACBDBDBABACD') ), -24.8604337068 ) assert_almost_equal( first_chain.log_probability( list('DABBCBDACAAADCBDCDBCBDCACBDABBAA') ), -44.8853484278 ) @with_setup( setup, teardown ) def test_summarize_with_weights_no_inertia(): first_chain = MarkovChain([ zeroth_dist, first_dist ]) # split in four first_chain.summarize( data[:10], weights=weights[:10] ) first_chain.summarize( data[10:20], weights=weights[10:20] ) first_chain.summarize( data[20:30], weights=weights[20:30] ) first_chain.summarize( data[30:], weights=weights[30:] ) first_chain.from_summaries() assert_almost_equal( first_chain.log_probability( list('A') ), -0.961056745744 ) assert_almost_equal( first_chain.log_probability( list('B') ), -1.82454929205 ) assert_almost_equal( first_chain.log_probability( list('AC') ), -2.13478966488 ) assert_almost_equal( first_chain.log_probability( list('AD') ), -2.47837936927 ) assert_almost_equal( first_chain.log_probability( list('BD') ), -3.41892100613 ) assert_almost_equal( first_chain.log_probability( list('BA') ), -3.45702085236 ) assert_almost_equal( first_chain.log_probability( list('CA') ), -2.84952832968 ) assert_almost_equal( first_chain.log_probability( list('CB') ), -2.43790935599 ) assert_almost_equal( first_chain.log_probability( list('DB') ), -2.88910833385 ) assert_almost_equal( first_chain.log_probability( list('DC') ), -2.98200208074 ) assert_almost_equal( first_chain.log_probability( list('ABDD') ), -5.56874179664 ) assert_almost_equal( first_chain.log_probability( list('CCCB') ), -5.71252297888 ) assert_almost_equal( first_chain.log_probability( list('CCBD') ), -5.66958788152 ) assert_almost_equal( first_chain.log_probability( list('ACAC') ), -4.78548674538 ) assert_almost_equal( first_chain.log_probability( list('ABDBCCDC') ), -11.3892431338 ) assert_almost_equal( first_chain.log_probability( list('DACCBDCB') ), -11.0991865874 ) assert_almost_equal( first_chain.log_probability( list('BCCCACBDBDBABACD') ), -23.5462387667 ) assert_almost_equal( first_chain.log_probability( list('DABBCBDACAAADCBDCDBCBDCACBDABBAA') ), -44.3127858762 ) @with_setup( setup, teardown ) def test_summarize_with_weights_with_inertia(): first_chain = MarkovChain([ zeroth_dist, first_dist ]) # split in four first_chain.summarize( data[:10], weights=weights[:10] ) first_chain.summarize( data[10:20], weights=weights[10:20] ) first_chain.summarize( data[20:30], weights=weights[20:30] ) first_chain.summarize( data[30:], weights=weights[30:] ) first_chain.from_summaries( inertia=0.4 ) assert_almost_equal( first_chain.log_probability( list('A') ), -1.3112125381 ) assert_almost_equal( first_chain.log_probability( list('B') ), -1.73288210353 ) assert_almost_equal( first_chain.log_probability( list('AC') ), -2.89339373397 ) assert_almost_equal( first_chain.log_probability( list('AD') ), -3.07392432189 ) assert_almost_equal( first_chain.log_probability( list('BD') ), -3.55414269165 ) assert_almost_equal( first_chain.log_probability( list('BA') ), -3.58268887356 ) assert_almost_equal( first_chain.log_probability( list('CA') ), -2.92624445535 ) assert_almost_equal( first_chain.log_probability( list('CB') ), -2.70099965753 ) assert_almost_equal( first_chain.log_probability( list('DB') ), -2.596587547 ) assert_almost_equal( first_chain.log_probability( list('DC') ), -2.43824119019 ) assert_almost_equal( first_chain.log_probability( list('ABDD') ), -6.77853581842 ) assert_almost_equal( first_chain.log_probability( list('CCCB') ), -5.75946483735 ) assert_almost_equal( first_chain.log_probability( list('CCBD') ), -6.05149283556 ) assert_almost_equal( first_chain.log_probability( list('ACAC') ), -6.10013721195 ) assert_almost_equal( first_chain.log_probability( list('ABDBCCDC') ), -11.2181867683 ) assert_almost_equal( first_chain.log_probability( list('DACCBDCB') ), -11.6681121956 ) assert_almost_equal( first_chain.log_probability( list('BCCCACBDBDBABACD') ), -25.0365515667 ) assert_almost_equal( first_chain.log_probability( list('DABBCBDACAAADCBDCDBCBDCACBDABBAA') ), -45.1660985662 ) @with_setup( setup, teardown ) def test_raise_errors(): pass @with_setup( setup, teardown ) def test_pickling(): chain1 = MarkovChain([ zeroth_dist, first_dist ]) chain2 = pickle.loads( pickle.dumps( chain1 ) ) assert_almost_equal( chain1.log_probability( list('BCCCACBDBDBABACD') ), chain2.log_probability( list('BCCCACBDBDBABACD') ) ) @with_setup( setup, teardown ) def test_json(): chain1 = MarkovChain([ zeroth_dist, first_dist ]) chain2 = MarkovChain.from_json(chain1.to_json()) assert_almost_equal( chain1.log_probability( list('BCCCACBDBDBABACD') ), chain2.log_probability( list('BCCCACBDBDBABACD') ) ) @with_setup( setup, teardown ) def test_robust_from_json(): chain1 = MarkovChain([ zeroth_dist, first_dist ]) chain2 = from_json(chain1.to_json()) assert_almost_equal( chain1.log_probability( list('BCCCACBDBDBABACD') ), chain2.log_probability( list('BCCCACBDBDBABACD') ) )pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/test_markov_network.py000066400000000000000000000431761420461712600265270ustar00rootroot00000000000000# test_bayes_net.py # Authors: Jacob Schreiber ''' These are unit tests for the Markov network model of pomegranate. ''' from __future__ import division from pomegranate import JointProbabilityTable from pomegranate import MarkovNetwork from pomegranate.io import DataGenerator from pomegranate.io import DataFrameGenerator from nose.tools import with_setup from nose.tools import assert_equal from nose.tools import assert_raises from nose.tools import assert_true from nose.tools import assert_almost_equal from numpy.testing import assert_array_equal from numpy.testing import assert_array_almost_equal import pandas import random, numpy import sys def setup_markov_network_int(): global d1, d2, d3 global model1, model2, model3, model4 d1 = JointProbabilityTable([ [0, 0, 0.1], [0, 1, 0.2], [1, 0, 0.4], [1, 1, 0.3]], [0, 1]) d2 = JointProbabilityTable([ [0, 0, 0, 0.05], [0, 0, 1, 0.15], [0, 1, 0, 0.07], [0, 1, 1, 0.03], [1, 0, 0, 0.12], [1, 0, 1, 0.18], [1, 1, 0, 0.10], [1, 1, 1, 0.30]], [1, 2, 3]) d3 = JointProbabilityTable([ [0, 0, 0, 0.08], [0, 0, 1, 0.12], [0, 1, 0, 0.11], [0, 1, 1, 0.19], [1, 0, 0, 0.04], [1, 0, 1, 0.06], [1, 1, 0, 0.23], [1, 1, 1, 0.17]], [2, 3, 4]) model1 = MarkovNetwork([d1]) model1.bake() model2 = MarkovNetwork([d1, d2]) model2.bake() model3 = MarkovNetwork([d1, d2, d3]) model3.bake() model4 = MarkovNetwork([d1, d3]) model4.bake() def setup_markov_network_str(): global d1, d2, d3 global model1, model2, model3, model4 d1 = JointProbabilityTable([ ['0', '0', 0.1], ['0', '1', 0.2], ['1', '0', 0.4], ['1', '1', 0.3]], [0, 1]) d2 = JointProbabilityTable([ ['0', '0', '0', 0.05], ['0', '0', '1', 0.15], ['0', '1', '0', 0.07], ['0', '1', '1', 0.03], ['1', '0', '0', 0.12], ['1', '0', '1', 0.18], ['1', '1', '0', 0.10], ['1', '1', '1', 0.30]], [1, 2, 3]) d3 = JointProbabilityTable([ ['0', '0', '0', 0.08], ['0', '0', '1', 0.12], ['0', '1', '0', 0.11], ['0', '1', '1', 0.19], ['1', '0', '0', 0.04], ['1', '0', '1', 0.06], ['1', '1', '0', 0.23], ['1', '1', '1', 0.17]], [2, 3, 4]) model1 = MarkovNetwork([d1]) model1.bake() model2 = MarkovNetwork([d1, d2]) model2.bake() model3 = MarkovNetwork([d1, d2, d3]) model3.bake() model4 = MarkovNetwork([d1, d3]) model4.bake() def setup_markov_network_bool(): global d1, d2, d3 global model1, model2, model3, model4 d1 = JointProbabilityTable([ [False, False, 0.1], [False, True, 0.2], [True, False, 0.4], [True, True, 0.3]], [0, 1]) d2 = JointProbabilityTable([ [False, False, False, 0.05], [False, False, True, 0.15], [False, True, False, 0.07], [False, True, True, 0.03], [True, False, False, 0.12], [True, False, True, 0.18], [True, True, False, 0.10], [True, True, True, 0.30]], [1, 2, 3]) d3 = JointProbabilityTable([ [False, False, False, 0.08], [False, False, True, 0.12], [False, True, False, 0.11], [False, True, True, 0.19], [True, False, False, 0.04], [True, False, True, 0.06], [True, True, False, 0.23], [True, True, True, 0.17]], [2, 3, 4]) model1 = MarkovNetwork([d1]) model1.bake() model2 = MarkovNetwork([d1, d2]) model2.bake() model3 = MarkovNetwork([d1, d2, d3]) model3.bake() model4 = MarkovNetwork([d1, d3]) model4.bake() def setup_markov_network_mixed(): global d1, d2, d3 global model1, model2, model3, model4 d1 = JointProbabilityTable([ [False, 'blue', 0.1], [False, 'red', 0.2], [True, 'blue', 0.4], [True, 'red', 0.3]], [0, 1]) d2 = JointProbabilityTable([ ['blue', False, 0, 0.05], ['blue', False, 1, 0.15], ['blue', True, 0, 0.07], ['blue', True, 1, 0.03], ['red', False, 0, 0.12], ['red', False, 1, 0.18], ['red', True, 0, 0.10], ['red', True, 1, 0.30]], [1, 2, 3]) d3 = JointProbabilityTable([ [False, 0, 'a', 0.08], [False, 0, 'b', 0.12], [False, 1, 'a', 0.11], [False, 1, 'b', 0.19], [True, 0, 'a', 0.04], [True, 0, 'b', 0.06], [True, 1, 'a', 0.23], [True, 1, 'b', 0.17]], [2, 3, 4]) model1 = MarkovNetwork([d1]) model1.bake() model2 = MarkovNetwork([d1, d2]) model2.bake() model3 = MarkovNetwork([d1, d2, d3]) model3.bake() model4 = MarkovNetwork([d1, d3]) model4.bake() def teardown(): pass def test_initialize(): assert_raises(ValueError, MarkovNetwork, []) d1 = JointProbabilityTable([ [0, 0, 0.2], [0, 1, 0.2], [1, 0, 0.4], [1, 1, 0.2]], [0, 1]) model = MarkovNetwork([d1]) @with_setup(setup_markov_network_int, teardown) def test_structure(): assert_equal(model1.structure, ((0, 1),)) assert_equal(model2.structure, ((0, 1), (1, 2, 3))) assert_equal(model3.structure, ((0, 1), (1, 2, 3), (2, 3, 4))) assert_equal(model4.structure, ((0, 1), (2, 3, 4))) @with_setup(setup_markov_network_int, teardown) def test_partition(): model3.bake() assert_true(model3.partition != float("inf")) model3.bake(calculate_partition=False) assert_true(model3.partition == float("inf")) @with_setup(setup_markov_network_int, teardown) def test_d(): assert_equal(model1.d, 2) assert_equal(model2.d, 4) assert_equal(model3.d, 5) assert_equal(model4.d, 5) @with_setup(setup_markov_network_mixed, teardown) def test_d_mixed(): assert_equal(model1.d, 2) assert_equal(model2.d, 4) assert_equal(model3.d, 5) assert_equal(model4.d, 5) @with_setup(setup_markov_network_int, teardown) def test_log_probability_int(): x = [1, 0] logp1 = model1.log_probability(x) logp2 = d1.log_probability(x) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4)) x = [1, 0, 1, 1] logp1 = model2.log_probability(x) logp2 = d1.log_probability(x[:2]) + d2.log_probability(x[1:]) assert_raises(AssertionError, assert_almost_equal, logp1, logp2) assert_almost_equal(logp1, -3.7297014467295373) x = [1, 0, 1, 0, 1] logp1 = model3.log_probability(x) logp2 = (d1.log_probability(x[:2]) + d2.log_probability(x[1:4]) + d3.log_probability(x[2:])) assert_raises(AssertionError, assert_almost_equal, logp1, logp2) assert_almost_equal(logp1, -4.429966143312331) logp3 = model4.log_probability(x) logp4 = d1.log_probability(x[:2]) + d3.log_probability(x[2:]) assert_almost_equal(logp3, logp4) assert_almost_equal(logp3, -3.7297014486341915) assert_raises(AssertionError, assert_almost_equal, logp1, logp3) @with_setup(setup_markov_network_str, teardown) def test_log_probability_str(): x = ['1', '0'] logp1 = model1.log_probability(x) logp2 = d1.log_probability(x) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4)) x = ['1', '0', '1', '1'] logp1 = model2.log_probability(x) logp2 = d1.log_probability(x[:2]) + d2.log_probability(x[1:]) assert_raises(AssertionError, assert_almost_equal, logp1, logp2) assert_almost_equal(logp1, -3.7297014467295373) x = ['1', '0', '1', '0', '1'] logp1 = model3.log_probability(x) logp2 = (d1.log_probability(x[:2]) + d2.log_probability(x[1:4]) + d3.log_probability(x[2:])) assert_raises(AssertionError, assert_almost_equal, logp1, logp2) assert_almost_equal(logp1, -4.429966143312331) logp3 = model4.log_probability(x) logp4 = d1.log_probability(x[:2]) + d3.log_probability(x[2:]) assert_almost_equal(logp3, logp4) assert_almost_equal(logp3, -3.7297014486341915) assert_raises(AssertionError, assert_almost_equal, logp1, logp3) @with_setup(setup_markov_network_bool, teardown) def test_log_probability_bool(): x = [True, False] logp1 = model1.log_probability(x) logp2 = d1.log_probability(x) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4)) x = [True, False, True, True] logp1 = model2.log_probability(x) logp2 = d1.log_probability(x[:2]) + d2.log_probability(x[1:]) assert_raises(AssertionError, assert_almost_equal, logp1, logp2) assert_almost_equal(logp1, -3.7297014467295373) x = [True, False, True, False, True] logp1 = model3.log_probability(x) logp2 = (d1.log_probability(x[:2]) + d2.log_probability(x[1:4]) + d3.log_probability(x[2:])) assert_raises(AssertionError, assert_almost_equal, logp1, logp2) assert_almost_equal(logp1, -4.429966143312331) logp3 = model4.log_probability(x) logp4 = d1.log_probability(x[:2]) + d3.log_probability(x[2:]) assert_almost_equal(logp3, logp4) assert_almost_equal(logp3, -3.7297014486341915) assert_raises(AssertionError, assert_almost_equal, logp1, logp3) @with_setup(setup_markov_network_mixed, teardown) def test_log_probability_mixed(): x = [True, 'blue'] logp1 = model1.log_probability(x) logp2 = d1.log_probability(x) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4)) x = [True, 'blue', True, 1] logp1 = model2.log_probability(x) logp2 = d1.log_probability(x[:2]) + d2.log_probability(x[1:]) assert_raises(AssertionError, assert_almost_equal, logp1, logp2) assert_almost_equal(logp1, -3.7297014467295373) x = [1, 'blue', True, 0, 'b'] logp1 = model3.log_probability(x) logp2 = (d1.log_probability(x[:2]) + d2.log_probability(x[1:4]) + d3.log_probability(x[2:])) assert_raises(AssertionError, assert_almost_equal, logp1, logp2) assert_almost_equal(logp1, -4.429966143312331) logp3 = model4.log_probability(x) logp4 = d1.log_probability(x[:2]) + d3.log_probability(x[2:]) assert_almost_equal(logp3, logp4) assert_almost_equal(logp3, -3.7297014486341915) assert_raises(AssertionError, assert_almost_equal, logp1, logp3) @with_setup(setup_markov_network_int, teardown) def test_log_probability_unnormalized_int(): x = [1, 0] logp1 = model1.log_probability(x, unnormalized=True) logp2 = d1.log_probability(x) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4)) x = [1, 0, 1, 1] logp1 = model2.log_probability(x, unnormalized=True) logp2 = d1.log_probability(x[:2]) + d2.log_probability(x[1:]) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4 * 0.03)) x = [1, 0, 1, 0, 1] logp1 = model3.log_probability(x, unnormalized=True) logp2 = (d1.log_probability(x[:2]) + d2.log_probability(x[1:4]) + d3.log_probability(x[2:])) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4 * 0.07 * 0.06)) logp3 = model4.log_probability(x, unnormalized=True) logp4 = d1.log_probability(x[:2]) + d3.log_probability(x[2:]) assert_almost_equal(logp3, logp4) assert_almost_equal(logp3, numpy.log(0.4 * 0.06)) assert_raises(AssertionError, assert_almost_equal, logp1, logp3) @with_setup(setup_markov_network_str, teardown) def test_log_probability_unnormalized_str(): x = ['1', '0'] logp1 = model1.log_probability(x, unnormalized=True) logp2 = d1.log_probability(x) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4)) x = ['1', '0', '1', '1'] logp1 = model2.log_probability(x, unnormalized=True) logp2 = d1.log_probability(x[:2]) + d2.log_probability(x[1:]) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4 * 0.03)) x = ['1', '0', '1', '0', '1'] logp1 = model3.log_probability(x, unnormalized=True) logp2 = (d1.log_probability(x[:2]) + d2.log_probability(x[1:4]) + d3.log_probability(x[2:])) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4 * 0.07 * 0.06)) logp3 = model4.log_probability(x, unnormalized=True) logp4 = d1.log_probability(x[:2]) + d3.log_probability(x[2:]) assert_almost_equal(logp3, logp4) assert_almost_equal(logp3, numpy.log(0.4 * 0.06)) assert_raises(AssertionError, assert_almost_equal, logp1, logp3) @with_setup(setup_markov_network_bool, teardown) def test_log_probability_unnormalized_bool(): x = [True, False] logp1 = model1.log_probability(x, unnormalized=True) logp2 = d1.log_probability(x) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4)) x = [True, False, True, True] logp1 = model2.log_probability(x, unnormalized=True) logp2 = d1.log_probability(x[:2]) + d2.log_probability(x[1:]) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4 * 0.03)) x = [True, False, True, False, True] logp1 = model3.log_probability(x, unnormalized=True) logp2 = (d1.log_probability(x[:2]) + d2.log_probability(x[1:4]) + d3.log_probability(x[2:])) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4 * 0.07 * 0.06)) logp3 = model4.log_probability(x, unnormalized=True) logp4 = d1.log_probability(x[:2]) + d3.log_probability(x[2:]) assert_almost_equal(logp3, logp4) assert_almost_equal(logp3, numpy.log(0.4 * 0.06)) assert_raises(AssertionError, assert_almost_equal, logp1, logp3) @with_setup(setup_markov_network_mixed, teardown) def test_log_probability_unnormalized_mixed(): x = [True, 'blue'] logp1 = model1.log_probability(x, unnormalized=True) logp2 = d1.log_probability(x) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4)) x = [True, 'blue', True, 1] logp1 = model2.log_probability(x, unnormalized=True) logp2 = d1.log_probability(x[:2]) + d2.log_probability(x[1:]) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4 * 0.03)) x = [1, 'blue', True, 0, 'b'] logp1 = model3.log_probability(x, unnormalized=True) logp2 = (d1.log_probability(x[:2]) + d2.log_probability(x[1:4]) + d3.log_probability(x[2:])) assert_almost_equal(logp1, logp2) assert_almost_equal(logp1, numpy.log(0.4 * 0.07 * 0.06)) logp3 = model4.log_probability(x, unnormalized=True) logp4 = d1.log_probability(x[:2]) + d3.log_probability(x[2:]) assert_almost_equal(logp3, logp4) assert_almost_equal(logp3, numpy.log(0.4 * 0.06)) assert_raises(AssertionError, assert_almost_equal, logp1, logp3) @with_setup(setup_markov_network_int, teardown) def test_predict_int(): assert_array_equal(model1.predict([[1, None]]), [[1, 0]]) assert_array_equal(model1.predict([[None, 1]]), [[1, 1]]) assert_array_equal(model2.predict([[1, 0, None, None]]), [[1, 0, 0, 1]]) assert_array_equal(model2.predict([[0, 0, None, None]]), [[0, 0, 0, 1]]) assert_array_equal(model2.predict([[None, 1, None, None]]), [[1, 1, 1, 1]]) assert_array_equal(model2.predict([[None, 1, 1, None]]), [[1, 1, 1, 1]]) assert_array_equal(model2.predict([[None, 1, None, 0]]), [[1, 1, 0, 0]]) assert_array_equal(model3.predict([[1, 0, None, 1, None]]), [[1, 0, 0, 1, 1]]) assert_array_equal(model3.predict([[None, 0, None, 1, None]]), [[1, 0, 0, 1, 1]]) assert_array_equal(model3.predict([[1, 0, None, 0, 1]]), [[1, 0, 0, 0, 1]]) assert_array_equal(model3.predict([[None, None, None, None, None]]), [[1, 1, 1, 1, 1]]) assert_array_equal(model3.predict([[None, None, None, None, 1]]), [[1, 1, 0, 1, 1]]) @with_setup(setup_markov_network_str, teardown) def test_predict_str(): assert_array_equal(model1.predict([['1', None]]), [['1', '0']]) assert_array_equal(model1.predict([[None, '1']]), [['1', '1']]) assert_array_equal(model2.predict([['1', '0', None, None]]), [['1', '0', '0', '1']]) assert_array_equal(model2.predict([['0', '0', None, None]]), [['0', '0', '0', '1']]) assert_array_equal(model2.predict([[None, '1', None, None]]), [['1', '1', '1', '1']]) assert_array_equal(model2.predict([[None, '1', '1', None]]), [['1', '1', '1', '1']]) assert_array_equal(model2.predict([[None, '1', None, '0']]), [['1', '1', '0', '0']]) assert_array_equal(model3.predict([['1', '0', None, '1', None]]), [['1', '0', '0', '1', '1']]) assert_array_equal(model3.predict([[None, '0', None, '1', None]]), [['1', '0', '0', '1', '1']]) assert_array_equal(model3.predict([['1', '0', None, '0', '1']]), [['1', '0', '0', '0', '1']]) assert_array_equal(model3.predict([[None, None, None, None, None]]), [['1', '1', '1', '1', '1']]) assert_array_equal(model3.predict([[None, None, None, None, '1']]), [['1', '1', '0', '1', '1']]) @with_setup(setup_markov_network_bool, teardown) def test_predict_bool(): assert_array_equal(model1.predict([[True, None]]), [[True, False]]) assert_array_equal(model1.predict([[None, True]]), [[True, True]]) assert_array_equal(model2.predict([[True, False, None, None]]), [[True, False, False, True]]) assert_array_equal(model2.predict([[False, False, None, None]]), [[False, False, False, True]]) assert_array_equal(model2.predict([[None, True, None, None]]), [[True, True, True, True]]) assert_array_equal(model2.predict([[None, True, True, None]]), [[True, True, True, True]]) assert_array_equal(model2.predict([[None, True, None, False]]), [[True, True, False, False]]) assert_array_equal(model3.predict([[True, False, None, True, None]]), [[True, False, False, True, True]]) assert_array_equal(model3.predict([[None, False, None, True, None]]), [[True, False, False, True, True]]) assert_array_equal(model3.predict([[True, False, None, False, True]]), [[True, False, False, False, True]]) assert_array_equal(model3.predict([[None, None, None, None, None]]), [[True, True, True, True, True]]) assert_array_equal(model3.predict([[None, None, None, None, True]]), [[True, True, False, True, True]]) @with_setup(setup_markov_network_mixed, teardown) def test_predict_mixed(): assert_array_equal(model1.predict([[True, None]]), numpy.array([[True, 'blue']], dtype=object)) assert_array_equal(model1.predict([[None, 'red']]), numpy.array([[True, 'red']], dtype=object)) assert_array_equal(model2.predict([[True, 'blue', None, None]]), numpy.array([[True, 'blue', False, 1]], dtype=object)) assert_array_equal(model2.predict([[False, 'blue', None, None]]), numpy.array([[False, 'blue', False, 1]], dtype=object)) assert_array_equal(model2.predict([[None, 'red', None, None]]), numpy.array([[True, 'red', True, 1]], dtype=object)) assert_array_equal(model2.predict([[None, 'red', True, None]]), numpy.array([[True, 'red', True, 1]], dtype=object)) assert_array_equal(model2.predict([[None, 'red', None, 0]]), numpy.array([[True, 'red', False, 0]], dtype=object)) assert_array_equal(model3.predict([[True, 'blue', None, 1, None]]), numpy.array([[True, 'blue', False, 1, 'b']], dtype=object)) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/test_naive_bayes.py000066400000000000000000000725651420461712600257500ustar00rootroot00000000000000from __future__ import (division) from pomegranate import * from pomegranate.io import DataGenerator from pomegranate.io import DataFrameGenerator from nose.tools import with_setup from nose.tools import assert_almost_equal from nose.tools import assert_equal from nose.tools import assert_not_equal from nose.tools import assert_less_equal from nose.tools import assert_raises from nose.tools import assert_true from numpy.testing import assert_array_equal from numpy.testing import assert_array_almost_equal import pandas import random import pickle import numpy as np nan = numpy.nan def setup_univariate_mixed(): normal = NormalDistribution(5, 2) uniform = UniformDistribution(0, 10) global model model = NaiveBayes([normal, uniform]) global X X = numpy.array([[5], [3], [1], [-1]]) def setup_multivariate_gaussian(): d11 = NormalDistribution(0.0, 1) d12 = NormalDistribution(0.5, 1) d13 = NormalDistribution(0.3, 1) d1 = IndependentComponentsDistribution([d11, d12, d13]) d21 = NormalDistribution(1.0, 1) d22 = NormalDistribution(1.2, 1) d23 = NormalDistribution(1.5, 1) d2 = IndependentComponentsDistribution([d21, d22, d23]) global model model = NaiveBayes([d1, d2]) global X X = numpy.array([[0.3, 0.5, 0.1], [0.8, 1.4, 0.5], [1.4, 2.6, 1.8], [4.2, 3.3, 3.7], [2.6, 3.6, 3.3]]) global y y = [0, 0, 0, 1, 1] global X_nan X_nan = numpy.array([[0.3, nan, 0.1], [nan, 1.4, nan], [1.4, 2.6, nan], [nan, nan, nan], [nan, 3.6, 3.3]]) def setup_multivariate_mixed(): d11 = ExponentialDistribution(5) d12 = LogNormalDistribution(0.5, 0.78) d13 = PoissonDistribution(4) d1 = IndependentComponentsDistribution([d11, d12, d13]) d21 = ExponentialDistribution(35) d22 = LogNormalDistribution(1.8, 1.33) d23 = PoissonDistribution(6) d2 = IndependentComponentsDistribution([d21, d22, d23]) global model model = NaiveBayes([d1, d2]) global X X = numpy.array([[0.3, 0.5, 0.1], [0.8, 1.4, 0.5], [1.4, 2.6, 1.8], [4.2, 3.3, 3.7], [2.6, 3.6, 3.3]]) global y y = [0, 0, 0, 1, 1] global X_nan X_nan = numpy.array([[0.3, nan, 0.1], [nan, 1.4, nan], [1.4, 2.6, nan], [nan, nan, nan], [nan, 3.6, 3.3]]) def teardown(): pass @with_setup(setup_univariate_mixed, teardown) def test_nb_univariate_initialization(): assert_equal(model.d, 1) assert_equal(model.n, 2) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_initialization(): assert_equal(model.d, 3) assert_equal(model.n, 2) def test_nb_univariate_constructors(): d1 = NormalDistribution(0.5, 1) d2 = MultivariateGaussianDistribution([0, 0], [[1, 0], [0, 1]]) d3 = IndependentComponentsDistribution([NormalDistribution(0, 1), NormalDistribution(2, 1), NormalDistribution(3, 1)]) assert_raises(TypeError, NaiveBayes, [d1, d2]) assert_raises(TypeError, NaiveBayes, [d1, d3]) assert_raises(ValueError, NaiveBayes, [NormalDistribution]) def test_nb_multivariate_constructors(): d1 = MultivariateGaussianDistribution([0, 0], [[1, 0], [0, 1]]) d2 = IndependentComponentsDistribution([NormalDistribution(0, 1), NormalDistribution(2, 1), NormalDistribution(3, 1)]) d3 = IndependentComponentsDistribution([NormalDistribution(0, 1), NormalDistribution(2, 1)]) NaiveBayes([d1, d3]) assert_raises(TypeError, NaiveBayes, [d2, d3]) assert_raises(TypeError, NaiveBayes, [d2, d1]) assert_raises(ValueError, NaiveBayes, [MultivariateGaussianDistribution]) assert_raises(ValueError, NaiveBayes, [IndependentComponentsDistribution]) @with_setup(setup_univariate_mixed, teardown) def test_nb_univariate_mixed_predict_log_proba(): y_hat = model.predict_log_proba(X) y = [[-0.4063484, -1.096847], [-0.6024268, -0.792926], [-1.5484819, -0.238981], [ 0.0, float('-inf')]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_predict_log_proba(): y_hat = model.predict_log_proba(X) y = [[ -2.194303e-01, -1.624430e+00], [ -8.00891133e-01, -5.95891133e-01], [ -3.24475797e+00, -3.97579742e-02], [ -8.77515454e+00, -1.54536960e-04], [ -6.90600226e+00, -1.00225665e-03]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_predict_log_proba(): y_hat = model.predict_log_proba(X) y = [[ -3.96979060e-05, -1.01342320e+01], [ -1.43325352e-11, -2.49684574e+01], [ 0.00000000e+00, -4.18889545e+01], [ 0.00000000e+00, -1.24795606e+02], [ 0.00000000e+00, -7.68246547e+01]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_nan_predict_log_proba(): y_hat = model.predict_log_proba(X_nan) y = [[-0.27268481, -1.43268481], [-0.90406199, -0.51906199], [-2.23782228, -0.11282228], [-0.69314718, -0.69314718], [-4.81315536, -0.00815536]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_nan_predict_log_proba(): y_hat = model.predict_log_proba(X_nan) y = [[ -1.21742279e-04, -9.01366508e+00], [ -2.83092062e-01, -1.40019217e+00], [ 0.00000000e+00, -4.06187917e+01], [ -6.93147181e-01, -6.93147181e-01], [ -3.80319311e-01, -1.15088421e+00]] assert_array_almost_equal(y, y_hat) @with_setup(setup_univariate_mixed, teardown) def test_nb_univariate_mixed_predict_log_proba_parallel(): y_hat = model.predict_log_proba(X, n_jobs=2) y = [[-0.4063484, -1.096847], [-0.6024268, -0.792926], [-1.5484819, -0.238981], [ 0.0, float('-inf')]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_predict_log_proba_parallel(): y_hat = model.predict_log_proba(X, n_jobs=2) y = [[ -2.194303e-01, -1.624430e+00], [ -8.00891133e-01, -5.95891133e-01], [ -3.24475797e+00, -3.97579742e-02], [ -8.77515454e+00, -1.54536960e-04], [ -6.90600226e+00, -1.00225665e-03]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_predict_log_proba_parallel(): y_hat = model.predict_log_proba(X, n_jobs=2) y = [[ -3.96979060e-05, -1.01342320e+01], [ -1.43325352e-11, -2.49684574e+01], [ 0.00000000e+00, -4.18889545e+01], [ 0.00000000e+00, -1.24795606e+02], [ 0.00000000e+00, -7.68246547e+01]] assert_array_almost_equal(y, y_hat) @with_setup(setup_univariate_mixed, teardown) def test_nb_univariate_mixed_predict_proba(): y_hat = model.predict_proba(X) y = [[ 0.66607801, 0.33392199], [ 0.54748134, 0.45251866], [ 0.21257042, 0.78742958], [ 1., 0. ]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_predict_proba(): y_hat = model.predict_proba(X) y = [[ 8.02976114e-01, 1.97023886e-01], [ 4.48928731e-01, 5.51071269e-01], [ 3.89779969e-02, 9.61022003e-01], [ 1.54525019e-04, 9.99845475e-01], [ 1.00175456e-03, 9.98998245e-01]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_predict_proba(): y_hat = model.predict_proba(X) y = [[ 9.99960303e-01, 3.96971181e-05], [ 1.00000000e+00, 1.43329876e-11], [ 1.00000000e+00, 6.42477904e-19], [ 1.00000000e+00, 6.33806932e-55], [ 1.00000000e+00, 4.31992661e-34]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_nan_predict_proba(): y_hat = model.predict_proba(X_nan) y = [[ 0.76133271, 0.23866729], [ 0.40492153, 0.59507847], [ 0.10669059, 0.89330941], [ 0.5, 0.5 ], [ 0.00812219, 0.99187781]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_nan_predict_proba(): y_hat = model.predict_proba(X_nan) y = [[ 9.99878265e-01, 1.21734869e-04], [ 7.53450421e-01, 2.46549579e-01], [ 1.00000000e+00, 2.28814158e-18], [ 5.00000000e-01, 5.00000000e-01], [ 6.83643080e-01, 3.16356920e-01]] assert_array_almost_equal(y, y_hat) @with_setup(setup_univariate_mixed, teardown) def test_nb_univariate_mixed_predict_proba_parallel(): y_hat = model.predict_proba(X, n_jobs=2) y = [[ 0.66607801, 0.33392199], [ 0.54748134, 0.45251866], [ 0.21257042, 0.78742958], [ 1., 0. ]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_predict_proba_parallel(): y_hat = model.predict_proba(X, n_jobs=2) y = [[ 8.02976114e-01, 1.97023886e-01], [ 4.48928731e-01, 5.51071269e-01], [ 3.89779969e-02, 9.61022003e-01], [ 1.54525019e-04, 9.99845475e-01], [ 1.00175456e-03, 9.98998245e-01]] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_predict_proba_parallel(): y_hat = model.predict_proba(X, n_jobs=2) y = [[ 9.99960303e-01, 3.96971181e-05,], [ 1.00000000e+00, 1.43329876e-11,], [ 1.00000000e+00, 6.42477904e-19,], [ 1.00000000e+00, 6.33806932e-55,], [ 1.00000000e+00, 4.31992661e-34,]] assert_array_almost_equal(y, y_hat) @with_setup(setup_univariate_mixed, teardown) def test_nb_univariate_mixed_predict(): y_hat = model.predict(X) y = [0, 0, 1, 0] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_predict(): y_hat = model.predict(X) y = [0, 1, 1, 1, 1] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_predict(): y_hat = model.predict(X) y = [0, 0, 0, 0, 0] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_nan_predict(): y_hat = model.predict(X_nan) y = [0, 1, 1, 0, 1] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_nan_predict(): y_hat = model.predict(X_nan) y = [0, 0, 0, 0, 0] assert_array_almost_equal(y, y_hat) @with_setup(setup_univariate_mixed, teardown) def test_nb_univariate_mixed_predict_parallel(): y_hat = model.predict(X, n_jobs=2) y = [0, 0, 1, 0] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_predict_parallel(): y_hat = model.predict(X, n_jobs=2) y = [0, 1, 1, 1, 1] assert_array_almost_equal(y, y_hat) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_predict_parallel(): y_hat = model.predict(X, n_jobs=2) y = [0, 0, 0, 0, 0] assert_array_almost_equal(y, y_hat) @with_setup(setup_univariate_mixed, teardown) def test_nb_univariate_mixed_fit(): X = np.array([5, 4, 5, 4, 6, 5, 6, 5, 4, 6, 5, 4, 0, 0, 1, 9, 8, 2, 0, 1, 1, 8, 10, 0]).reshape(-1, 1) y = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) model.fit(X, y) d1 = model.distributions[0] d2 = model.distributions[1] assert_array_almost_equal(d1.parameters, [4.916666666666667, 0.7592027982620252]) assert_array_almost_equal(d2.parameters, [0.0, 10.0]) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_fit(): model.fit(X, y) d11 = model.distributions[0].distributions[0] d12 = model.distributions[0].distributions[1] d13 = model.distributions[0].distributions[2] d21 = model.distributions[1].distributions[0] d22 = model.distributions[1].distributions[1] d23 = model.distributions[1].distributions[2] assert_array_almost_equal(d11.parameters, [0.8333333333333334, 0.4496912521077347]) assert_array_almost_equal(d12.parameters, [1.5, 0.8602325267042628]) assert_array_almost_equal(d13.parameters, [0.7999999999999999, 0.725718035235908]) assert_array_almost_equal(d21.parameters, [3.4000000000000004, 0.7999999999999993]) assert_array_almost_equal(d22.parameters, [3.45, 0.1499999999999969]) assert_array_almost_equal(d23.parameters, [3.5, 0.19999999999999787]) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_fit(): model.fit(X, y) d11 = model.distributions[0].distributions[0] d12 = model.distributions[0].distributions[1] d13 = model.distributions[0].distributions[2] d21 = model.distributions[1].distributions[0] d22 = model.distributions[1].distributions[1] d23 = model.distributions[1].distributions[2] assert_array_almost_equal(d11.parameters, [1.1999999920000004]) assert_array_almost_equal(d12.parameters, [0.199612167029568, 0.6799837375101412]) assert_array_almost_equal(d13.parameters, [0.7999999999999999]) assert_array_almost_equal(d21.parameters, [0.2941176574394461]) assert_array_almost_equal(d22.parameters, [1.2374281569672494, 0.04350568849481522]) assert_array_almost_equal(d23.parameters, [3.5]) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_nan_fit(): model.fit(X_nan, y) d11 = model.distributions[0].distributions[0] d12 = model.distributions[0].distributions[1] d13 = model.distributions[0].distributions[2] d21 = model.distributions[1].distributions[0] d22 = model.distributions[1].distributions[1] d23 = model.distributions[1].distributions[2] assert_array_almost_equal(d11.parameters, [0.85, 0.55]) assert_array_almost_equal(d12.parameters, [2.0, 0.6000000000000003]) assert_array_almost_equal(d13.parameters, [0.1, 0.0]) assert_array_almost_equal(d21.parameters, [1.0, 1.0]) assert_array_almost_equal(d22.parameters, [3.6, 0.0]) assert_array_almost_equal(d23.parameters, [3.3, 0.0]) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_nan_fit(): model.fit(X_nan, y) d11 = model.distributions[0].distributions[0] d12 = model.distributions[0].distributions[1] d13 = model.distributions[0].distributions[2] d21 = model.distributions[1].distributions[0] d22 = model.distributions[1].distributions[1] d23 = model.distributions[1].distributions[2] assert_array_almost_equal(d11.parameters, [1.1764705778546718]) assert_array_almost_equal(d12.parameters, [0.645991, 0.3095196]) assert_array_almost_equal(d13.parameters, [0.1]) assert_array_almost_equal(d21.parameters, [35.0]) assert_array_almost_equal(d22.parameters, [1.2809338454620642, 0.0]) assert_array_almost_equal(d23.parameters, [3.3]) @with_setup(setup_univariate_mixed, teardown) def test_nb_univariate_mixed_fit_parallel(): X = np.array([5, 4, 5, 4, 6, 5, 6, 5, 4, 6, 5, 4, 0, 0, 1, 9, 8, 2, 0, 1, 1, 8, 10, 0]).reshape(-1, 1) y = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) model.fit(X, y, n_jobs=2) d1 = model.distributions[0] d2 = model.distributions[1] assert_array_almost_equal(d1.parameters, [4.916666666666667, 0.7592027982620252]) assert_array_almost_equal(d2.parameters, [0.0, 10.0]) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_fit_parallel(): model.fit(X, y, n_jobs=2) d11 = model.distributions[0].distributions[0] d12 = model.distributions[0].distributions[1] d13 = model.distributions[0].distributions[2] d21 = model.distributions[1].distributions[0] d22 = model.distributions[1].distributions[1] d23 = model.distributions[1].distributions[2] assert_array_almost_equal(d11.parameters, [0.8333333333333334, 0.4496912521077347]) assert_array_almost_equal(d12.parameters, [1.5, 0.8602325267042628]) assert_array_almost_equal(d13.parameters, [0.7999999999999999, 0.725718035235908]) assert_array_almost_equal(d21.parameters, [3.4000000000000004, 0.7999999999999993]) assert_array_almost_equal(d22.parameters, [3.45, 0.1499999999999969]) assert_array_almost_equal(d23.parameters, [3.5, 0.19999999999999787]) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_fit_parallel(): model.fit(X, y, n_jobs=2) d11 = model.distributions[0].distributions[0] d12 = model.distributions[0].distributions[1] d13 = model.distributions[0].distributions[2] d21 = model.distributions[1].distributions[0] d22 = model.distributions[1].distributions[1] d23 = model.distributions[1].distributions[2] assert_array_almost_equal(d11.parameters, [1.1999999920000004]) assert_array_almost_equal(d12.parameters, [0.199612167029568, 0.6799837375101412]) assert_array_almost_equal(d13.parameters, [0.7999999999999999]) assert_array_almost_equal(d21.parameters, [0.2941176574394461]) assert_array_almost_equal(d22.parameters, [1.2374281569672494, 0.04350568849481522]) assert_array_almost_equal(d23.parameters, [3.5]) @with_setup(setup_univariate_mixed, teardown) def test_nb_univariate_mixed_from_samples(): X = np.array([5, 4, 5, 4, 6, 5, 6, 5, 4, 6, 5, 4, 0, 0, 1, 9, 8, 2, 0, 1, 1, 8, 10, 0]).reshape(-1, 1) y = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) model = NaiveBayes.from_samples([NormalDistribution, UniformDistribution], X, y) d1 = model.distributions[0] d2 = model.distributions[1] assert_array_almost_equal(d1.parameters, [4.916666666666667, 0.7592027982620252]) assert_array_almost_equal(d2.parameters, [0.0, 10.0]) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_from_samples(): model = NaiveBayes.from_samples(NormalDistribution, X, y) d11 = model.distributions[0].distributions[0] d12 = model.distributions[0].distributions[1] d13 = model.distributions[0].distributions[2] d21 = model.distributions[1].distributions[0] d22 = model.distributions[1].distributions[1] d23 = model.distributions[1].distributions[2] assert_array_almost_equal(d11.parameters, [0.8333333333333334, 0.4496912521077347]) assert_array_almost_equal(d12.parameters, [1.5, 0.8602325267042628]) assert_array_almost_equal(d13.parameters, [0.7999999999999999, 0.725718035235908]) assert_array_almost_equal(d21.parameters, [3.4000000000000004, 0.7999999999999993]) assert_array_almost_equal(d22.parameters, [3.45, 0.1499999999999969]) assert_array_almost_equal(d23.parameters, [3.5, 0.19999999999999787]) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_from_samples(): d = [ExponentialDistribution, LogNormalDistribution, PoissonDistribution] model = NaiveBayes.from_samples(d, X, y) d11 = model.distributions[0].distributions[0] d12 = model.distributions[0].distributions[1] d13 = model.distributions[0].distributions[2] d21 = model.distributions[1].distributions[0] d22 = model.distributions[1].distributions[1] d23 = model.distributions[1].distributions[2] assert_array_almost_equal(d11.parameters, [1.1999999920000004]) assert_array_almost_equal(d12.parameters, [0.199612167029568, 0.6799837375101412]) assert_array_almost_equal(d13.parameters, [0.7999999999999999]) assert_array_almost_equal(d21.parameters, [0.2941176574394461]) assert_array_almost_equal(d22.parameters, [1.2374281569672494, 0.04350568849481522]) assert_array_almost_equal(d23.parameters, [3.5]) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_nan_from_samples(): model = NaiveBayes.from_samples(NormalDistribution, X_nan, y) d11 = model.distributions[0].distributions[0] d12 = model.distributions[0].distributions[1] d13 = model.distributions[0].distributions[2] d21 = model.distributions[1].distributions[0] d22 = model.distributions[1].distributions[1] d23 = model.distributions[1].distributions[2] assert_array_almost_equal(d11.parameters, [0.85, 0.55]) assert_array_almost_equal(d12.parameters, [2.0, 0.6000000000000003]) assert_array_almost_equal(d13.parameters, [0.1, 0.0]) assert_array_almost_equal(d21.parameters, [0.0, 1.0]) assert_array_almost_equal(d22.parameters, [3.6, 0.0]) assert_array_almost_equal(d23.parameters, [3.3, 0.0]) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_nan_from_samples(): d = [ExponentialDistribution, LogNormalDistribution, PoissonDistribution] model = NaiveBayes.from_samples(d, X_nan, y) d11 = model.distributions[0].distributions[0] d12 = model.distributions[0].distributions[1] d13 = model.distributions[0].distributions[2] d21 = model.distributions[1].distributions[0] d22 = model.distributions[1].distributions[1] d23 = model.distributions[1].distributions[2] assert_array_almost_equal(d11.parameters, [1.1764705778546718]) assert_array_almost_equal(d12.parameters, [0.645991, 0.3095196]) assert_array_almost_equal(d13.parameters, [0.1]) assert_array_almost_equal(d21.parameters, [1]) assert_array_almost_equal(d22.parameters, [1.2809338454620642, 0.0]) assert_array_almost_equal(d23.parameters, [3.3]) @with_setup(setup_univariate_mixed, teardown) def test_nb_univariate_mixed_from_samples_parallel(): X = np.array([5, 4, 5, 4, 6, 5, 6, 5, 4, 6, 5, 4, 0, 0, 1, 9, 8, 2, 0, 1, 1, 8, 10, 0]).reshape(-1, 1) y = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) model = NaiveBayes.from_samples([NormalDistribution, UniformDistribution], X, y, n_jobs=2) d1 = model.distributions[0] d2 = model.distributions[1] assert_array_almost_equal(d1.parameters, [4.916666666666667, 0.7592027982620252]) assert_array_almost_equal(d2.parameters, [0.0, 10.0]) @with_setup(setup_multivariate_gaussian, teardown) def test_nb_multivariate_gaussian_from_samples_parallel(): model = NaiveBayes.from_samples(NormalDistribution, X, y, n_jobs=2) d11 = model.distributions[0].distributions[0] d12 = model.distributions[0].distributions[1] d13 = model.distributions[0].distributions[2] d21 = model.distributions[1].distributions[0] d22 = model.distributions[1].distributions[1] d23 = model.distributions[1].distributions[2] assert_array_almost_equal(d11.parameters, [0.8333333333333334, 0.4496912521077347]) assert_array_almost_equal(d12.parameters, [1.5, 0.8602325267042628]) assert_array_almost_equal(d13.parameters, [0.7999999999999999, 0.725718035235908]) assert_array_almost_equal(d21.parameters, [3.4000000000000004, 0.7999999999999993]) assert_array_almost_equal(d22.parameters, [3.45, 0.1499999999999969]) assert_array_almost_equal(d23.parameters, [3.5, 0.19999999999999787]) @with_setup(setup_multivariate_mixed, teardown) def test_nb_multivariate_mixed_from_samples_parallel(): d = [ExponentialDistribution, LogNormalDistribution, PoissonDistribution] model = NaiveBayes.from_samples(d, X, y, n_jobs=2) d11 = model.distributions[0].distributions[0] d12 = model.distributions[0].distributions[1] d13 = model.distributions[0].distributions[2] d21 = model.distributions[1].distributions[0] d22 = model.distributions[1].distributions[1] d23 = model.distributions[1].distributions[2] assert_array_almost_equal(d11.parameters, [1.1999999920000004]) assert_array_almost_equal(d12.parameters, [0.199612167029568, 0.6799837375101412]) assert_array_almost_equal(d13.parameters, [0.7999999999999999]) assert_array_almost_equal(d21.parameters, [0.2941176574394461]) assert_array_almost_equal(d22.parameters, [1.2374281569672494, 0.04350568849481522]) assert_array_almost_equal(d23.parameters, [3.5]) @with_setup(setup_univariate_mixed, teardown) def test_raise_errors(): # check raises no errors when converting values model.predict_log_proba([[5]]) model.predict_log_proba([[4.5]]) model.predict_log_proba([[5], [6]]) model.predict_log_proba(np.array([[5], [6]]) ) model.predict_proba([[5]]) model.predict_proba([[4.5]]) model.predict_proba([[5], [6]]) model.predict_proba(np.array([[5], [6]])) model.predict([[5]]) model.predict([[4.5]]) model.predict([[5], [6]]) model.predict(np.array([[5], [6]])) @with_setup(setup_univariate_mixed, teardown) def test_pickling(): j_univ = pickle.dumps(model) new_univ = pickle.loads(j_univ) assert_true(isinstance(new_univ.distributions[0], NormalDistribution)) assert_true(isinstance(new_univ.distributions[1], UniformDistribution)) assert_true(isinstance(new_univ, NaiveBayes)) numpy.testing.assert_array_equal(model.weights, new_univ.weights) @with_setup(setup_univariate_mixed, teardown) def test_json(): j_univ = model.to_json() new_univ = model.from_json(j_univ) assert_true(isinstance(new_univ.distributions[0], NormalDistribution)) assert_true(isinstance(new_univ.distributions[1], UniformDistribution)) assert_true(isinstance(new_univ, NaiveBayes)) numpy.testing.assert_array_equal( model.weights, new_univ.weights) @with_setup(setup_univariate_mixed, teardown) def test_robust_from_json(): j_univ = model.to_json() new_univ = from_json(j_univ) assert_true(isinstance(new_univ.distributions[0], NormalDistribution)) assert_true(isinstance(new_univ.distributions[1], UniformDistribution)) assert_true(isinstance(new_univ, NaiveBayes)) numpy.testing.assert_array_equal( model.weights, new_univ.weights) @with_setup(setup_multivariate_gaussian, teardown) def test_io_log_probability(): X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) logp1 = model.log_probability(X) logp2 = model.log_probability(X2) logp3 = model.log_probability(X3) assert_array_almost_equal(logp1, logp2) assert_array_almost_equal(logp1, logp3) @with_setup(setup_multivariate_gaussian, teardown) def test_io_predict(): X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) y_hat1 = model.predict(X) y_hat2 = model.predict(X2) y_hat3 = model.predict(X3) assert_array_almost_equal(y_hat1, y_hat2) assert_array_almost_equal(y_hat1, y_hat3) @with_setup(setup_multivariate_gaussian, teardown) def test_io_predict_proba(): X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) y_hat1 = model.predict_proba(X) y_hat2 = model.predict_proba(X2) y_hat3 = model.predict_proba(X3) assert_array_almost_equal(y_hat1, y_hat2) assert_array_almost_equal(y_hat1, y_hat3) @with_setup(setup_multivariate_gaussian, teardown) def test_io_predict_log_proba(): X2 = DataGenerator(X) X3 = DataFrameGenerator(pandas.DataFrame(X)) y_hat1 = model.predict_log_proba(X) y_hat2 = model.predict_log_proba(X2) y_hat3 = model.predict_log_proba(X3) assert_array_almost_equal(y_hat1, y_hat2) assert_array_almost_equal(y_hat1, y_hat3) def test_io_fit(): X = numpy.random.randn(100, 5) + 0.5 weights = numpy.abs(numpy.random.randn(100)) y = numpy.random.randint(2, size=100) data_generator = DataGenerator(X, weights, y) d1 = IndependentComponentsDistribution([ NormalDistribution(0, 1) for i in range(5)]) d2 = IndependentComponentsDistribution([ NormalDistribution(1, 1) for i in range(5)]) nb1 = NaiveBayes([d1, d2]) nb1.fit(X, y, weights) d1 = IndependentComponentsDistribution([ NormalDistribution(0, 1) for i in range(5)]) d2 = IndependentComponentsDistribution([ NormalDistribution(1, 1) for i in range(5)]) nb2 = NaiveBayes([d1, d2]) nb2.fit(data_generator) logp1 = nb1.log_probability(X) logp2 = nb2.log_probability(X) assert_array_almost_equal(logp1, logp2) def test_io_from_samples(): X = numpy.random.randn(100, 5) + 0.5 weights = numpy.abs(numpy.random.randn(100)) y = numpy.random.randint(2, size=100) data_generator = DataGenerator(X, weights, y) d = NormalDistribution nb1 = NaiveBayes.from_samples(d, X=X, weights=weights, y=y) nb2 = NaiveBayes.from_samples(d, X=data_generator) logp1 = nb1.log_probability(X) logp2 = nb2.log_probability(X) assert_array_almost_equal(logp1, logp2) def test_discrete_distribution(): """ Test fit NaiveBayes to discrete variables. """ X = np.array([ [0, 0], [1, 1], [2, 0], [0, 0], [1, 1], [2, 0], ]) y = np.array([0, 0, 0, 1, 1, 1]) m = NaiveBayes.from_samples( DiscreteDistribution, X, y, ) p_y0 = m.distributions[0].parameters[0] assert_almost_equal(list(p_y0[0].parameters[0].values()), [1/3, 1/3, 1/3]) assert_almost_equal(list(p_y0[1].parameters[0].values()), [2/3, 1/3]) p_y1 = m.distributions[1].parameters[0] assert_almost_equal(list(p_y1[0].parameters[0].values()), [1/3, 1/3, 1/3]) assert_almost_equal(list(p_y1[1].parameters[0].values()), [2/3, 1/3]) # Check the probability calculation for a test variable. X_test = np.array([[1, 0]]) p_groundtruth = np.array([[1/2, 1/2]]) assert_array_almost_equal(m.predict_log_proba(X_test), np.log(p_groundtruth)) assert_array_almost_equal(m.log_probability(X_test), [np.log(2/9)]) assert_array_almost_equal(m.predict_proba(X_test), p_groundtruth) assert_array_almost_equal(m.predict(X_test), [0]) def test_bernoulli_discrete_distribution(): """ Test model composed of a Bernoulli and discrete distribution. """ X = np.array([ # y = 0. [0, 1], [0, 1], [1, 0], [1, 0], [1, 0], # y = 1. [0, 0], [1, 0], [1, 1], [1, 1], ]) y = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1]) # Since the data is composed of binary values, `DiscreteDistribution` and # `BernoulliDistribution` should coincide. m1 = NaiveBayes.from_samples(DiscreteDistribution, X, y) m2 = NaiveBayes.from_samples(BernoulliDistribution, X, y) m3 = NaiveBayes.from_samples([DiscreteDistribution, BernoulliDistribution], X, y) m4 = NaiveBayes.from_samples([BernoulliDistribution, DiscreteDistribution], X, y) # Work out expression analytically for a single test point. X_test = np.array([[0, 1]]) py0_x01 = (2/5) * (2/5) * (5/9) py1_x01 = (1/4) * (1/2) * (4/9) py0_cond_x01 = py0_x01 / (py0_x01 + py1_x01) p_groundtruth = np.array([[py0_cond_x01, 1- py0_cond_x01]]) assert_array_almost_equal(m1.predict_proba(X_test), p_groundtruth) assert_array_almost_equal(m1.predict_log_proba(X_test), np.log(p_groundtruth)) assert_array_almost_equal(m1.log_probability(X_test), [np.log(py0_x01 + py1_x01)]) assert_array_almost_equal(m1.predict(X_test), [0]) # Check that all predictions are identical. assert_array_almost_equal(m1.predict_proba(X), m2.predict_proba(X)) assert_array_almost_equal(m2.predict_proba(X), m3.predict_proba(X)) assert_array_almost_equal(m3.predict_proba(X), m4.predict_proba(X)) pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tests/test_profile_hmm.py000066400000000000000000000427071420461712600257570ustar00rootroot00000000000000from __future__ import (division, print_function) from pomegranate import * from nose.tools import with_setup from nose.tools import assert_equal from nose.tools import assert_not_equal from nose.tools import assert_raises from nose.tools import assert_almost_equal import random import numpy as np import json def setup(): ''' Build a model that we want to use to test sequences. This model will be somewhat complicated, in order to extensively test YAHMM. This will be a three state global sequence alignment HMM. The HMM models a reference of 'ACT', with pseudocounts to allow for slight deviations from this reference. ''' random.seed(0) global model model = HiddenMarkovModel( "Global Alignment") # Define the distribution for insertions i_d = DiscreteDistribution( { 'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25 } ) # Create the insert states i0 = State( i_d, name="I0" ) i1 = State( i_d, name="I1" ) i2 = State( i_d, name="I2" ) i3 = State( i_d, name="I3" ) # Create the match states m1 = State( DiscreteDistribution({ "A": 0.95, 'C': 0.01, 'G': 0.01, 'T': 0.02 }) , name="M1" ) m2 = State( DiscreteDistribution({ "A": 0.003, 'C': 0.99, 'G': 0.003, 'T': 0.004 }) , name="M2" ) m3 = State( DiscreteDistribution({ "A": 0.01, 'C': 0.01, 'G': 0.01, 'T': 0.97 }) , name="M3" ) # Create the delete states d1 = State( None, name="D1" ) d2 = State( None, name="D2" ) d3 = State( None, name="D3" ) # Add all the states to the model model.add_states( [i0, i1, i2, i3, m1, m2, m3, d1, d2, d3 ] ) # Create transitions from match states model.add_transition( model.start, m1, 0.9 ) model.add_transition( model.start, i0, 0.1 ) model.add_transition( m1, m2, 0.9 ) model.add_transition( m1, i1, 0.05 ) model.add_transition( m1, d2, 0.05 ) model.add_transition( m2, m3, 0.9 ) model.add_transition( m2, i2, 0.05 ) model.add_transition( m2, d3, 0.05 ) model.add_transition( m3, model.end, 0.9 ) model.add_transition( m3, i3, 0.1 ) # Create transitions from insert states model.add_transition( i0, i0, 0.70 ) model.add_transition( i0, d1, 0.15 ) model.add_transition( i0, m1, 0.15 ) model.add_transition( i1, i1, 0.70 ) model.add_transition( i1, d2, 0.15 ) model.add_transition( i1, m2, 0.15 ) model.add_transition( i2, i2, 0.70 ) model.add_transition( i2, d3, 0.15 ) model.add_transition( i2, m3, 0.15 ) model.add_transition( i3, i3, 0.85 ) model.add_transition( i3, model.end, 0.15 ) # Create transitions from delete states model.add_transition( d1, d2, 0.15 ) model.add_transition( d1, i1, 0.15 ) model.add_transition( d1, m2, 0.70 ) model.add_transition( d2, d3, 0.15 ) model.add_transition( d2, i2, 0.15 ) model.add_transition( d2, m3, 0.70 ) model.add_transition( d3, i3, 0.30 ) model.add_transition( d3, model.end, 0.70 ) # Call bake to finalize the structure of the model. model.bake() def multitransition_setup(): ''' Build a model that we want to use to test sequences. This is the same as the above model, except that it uses the multiple transition methods for building. ''' random.seed(0) global model model = HiddenMarkovModel( "Global Alignment") # Define the distribution for insertions i_d = DiscreteDistribution( { 'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25 } ) # Create the insert states i0 = State( i_d, name="I0" ) i1 = State( i_d, name="I1" ) i2 = State( i_d, name="I2" ) i3 = State( i_d, name="I3" ) # Create the match states m1 = State( DiscreteDistribution({ "A": 0.95, 'C': 0.01, 'G': 0.01, 'T': 0.02 }) , name="M1" ) m2 = State( DiscreteDistribution({ "A": 0.003, 'C': 0.99, 'G': 0.003, 'T': 0.004 }) , name="M2" ) m3 = State( DiscreteDistribution({ "A": 0.01, 'C': 0.01, 'G': 0.01, 'T': 0.97 }) , name="M3" ) # Create the delete states d1 = State( None, name="D1" ) d2 = State( None, name="D2" ) d3 = State( None, name="D3" ) # Add all the states to the model model.add_states( [i0, i1, i2, i3, m1, m2, m3, d1, d2, d3 ] ) # Create transitions from match states model.add_transitions( model.start, [m1, i0], [0.9, 0.1] ) model.add_transitions( m1, [m2, i1, d2], [0.9, 0.05, 0.05] ) model.add_transitions( m2, [m3, i2, d3], [0.9, 0.05, 0.05] ) model.add_transitions( m3, [model.end, i3], [0.9, 0.1] ) # Create transitions from insert states model.add_transitions( i0, [i0, d1, m1], [0.7, 0.15, 0.15] ) model.add_transitions( i1, [i1, d2, m2], [0.7, 0.15, 0.15] ) model.add_transitions( i2, [i2, d3, m3], [0.7, 0.15, 0.15] ) model.add_transitions( [i3, i3], [i3, model.end], [0.85, 0.15] ) # Create transitions from delete states model.add_transitions( d1, [d2, i1, m2], [0.15, 0.15, 0.70] ) model.add_transitions( [d2, d2, d2, d3, d3], [d3, i2, m3, i3, model.end], [0.15, 0.15, 0.70, 0.30, 0.70 ] ) # Call bake to finalize the structure of the model. model.bake() def tied_edge_setup(): ''' Build a model that we want to use to test sequences. This model has tied edges. ''' random.seed(0) global model model = HiddenMarkovModel( "Global Alignment") # Define the distribution for insertions i_d = DiscreteDistribution( { 'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25 } ) # Create the insert states i0 = State( i_d, name="I0" ) i1 = State( i_d, name="I1" ) i2 = State( i_d, name="I2" ) i3 = State( i_d, name="I3" ) # Create the match states m1 = State( DiscreteDistribution({ "A": 0.95, 'C': 0.01, 'G': 0.01, 'T': 0.02 }) , name="M1" ) m2 = State( DiscreteDistribution({ "A": 0.003, 'C': 0.99, 'G': 0.003, 'T': 0.004 }) , name="M2" ) m3 = State( DiscreteDistribution({ "A": 0.01, 'C': 0.01, 'G': 0.01, 'T': 0.97 }) , name="M3" ) # Create the delete states d1 = State( None, name="D1" ) d2 = State( None, name="D2" ) d3 = State( None, name="D3" ) # Add all the states to the model model.add_states( [i0, i1, i2, i3, m1, m2, m3, d1, d2, d3 ] ) # Create transitions from match states model.add_transition( model.start, m1, 0.9 ) model.add_transition( model.start, i0, 0.1 ) model.add_transition( m1, m2, 0.9 ) model.add_transition( m1, i1, 0.05 ) model.add_transition( m1, d2, 0.05 ) model.add_transition( m2, m3, 0.9 ) model.add_transition( m2, i2, 0.05 ) model.add_transition( m2, d3, 0.05 ) model.add_transition( m3, model.end, 0.9 ) model.add_transition( m3, i3, 0.1 ) # Create transitions from insert states model.add_transition( i0, i0, 0.70, group="i_a" ) model.add_transition( i0, d1, 0.15, group="i_b" ) model.add_transition( i0, m1, 0.15, group="i_c" ) model.add_transition( i1, i1, 0.70, group="i_a" ) model.add_transition( i1, d2, 0.15, group="i_b" ) model.add_transition( i1, m2, 0.15, group="i_c" ) model.add_transition( i2, i2, 0.70, group="i_a" ) model.add_transition( i2, d3, 0.15, group="i_b" ) model.add_transition( i2, m3, 0.15, group="i_c" ) model.add_transition( i3, i3, 0.85, group="i_a" ) model.add_transition( i3, model.end, 0.15 ) # Create transitions from delete states model.add_transition( d1, d2, 0.15, group="d_a" ) model.add_transition( d1, i1, 0.15, group="d_b" ) model.add_transition( d1, m2, 0.70, group="d_c" ) model.add_transition( d2, d3, 0.15, group="d_a" ) model.add_transition( d2, i2, 0.15, group="d_b" ) model.add_transition( d2, m3, 0.70, group="d_c" ) model.add_transition( d3, i3, 0.30 ) model.add_transition( d3, model.end, 0.70 ) # Call bake to finalize the structure of the model. model.bake() def teardown(): ''' Remove the model at the end of the unit testing. Since it is stored in a global variance, simply delete it. ''' pass @with_setup( setup, teardown ) def test_same_length_viterbi(): scores = [ -0.5132449003570658, -11.048101241343396, -9.125519674022627, -5.0879558788604475 ] sequences = [ list(x) for x in [ 'ACT', 'GGC', 'GAT', 'ACC' ] ] for seq, score in zip( sequences, scores ): assert_almost_equal( model.viterbi( seq )[0], score ) assert_raises( ValueError, model.viterbi, list('XXX') ) @with_setup( setup, teardown ) def test_variable_length_viterbi(): scores = [ -5.406181012423981, -10.88681993576597, -3.6244718790494277, -3.644880750680635, -10.674332964640293, -10.393824835172445, -8.67126440174503, -16.903451796110275, -16.451699654050792 ] sequences = [ list(x) for x in ('A', 'GA', 'AC', 'AT', 'ATCC', 'ACGTG', 'ATTT', 'TACCCTC', 'TGTCAACACT') ] for seq, score in zip( sequences, scores ): assert_almost_equal( model.viterbi( seq )[0], score ) @with_setup( setup, teardown ) def test_log_probability(): scores = [ -5.3931, -0.5052, -11.8478, -14.3482 ] sequences = [ list(x) for x in ( 'A', 'ACT', 'GGCA', 'TACCTGT' ) ] for seq, score in zip( sequences, scores ): assert_equal( round( model.log_probability( seq ), 4 ), score ) @with_setup( setup, teardown ) def test_posterior_transitions(): a_scores = [ 0.0, 0.0021, 0.2017, 1.5105 ] b_scores = [ 0.013, 0.0036, 1.9836, 2.145 ] c_scores = [ 0.013, 0.0035, 0.817, 0.477 ] d_scores = [ 1.0, 0.0023, 0.2636, 0.3682 ] t_scores = [ 4.013, 4.0083, 6.457, 8.9812 ] sequences = [ list(x) for x in ( 'A', 'ACT', 'GGCA', 'TACCTGT' ) ] indices = { state.name: i for i, state in enumerate( model.states ) } i, j, k, l = indices['I2'], indices['I0'], indices['D1'], indices['D2'] scores = zip( sequences, a_scores, b_scores, c_scores, d_scores, t_scores ) for seq, a, b, c, d, t in scores: trans, ems = model.forward_backward( seq ) assert_equal( round( trans[i].sum(), 4 ), a ) assert_equal( round( trans[j].sum(), 4 ), b ) assert_equal( round( trans[k].sum(), 4 ), c ) assert_equal( round( trans[l].sum(), 4 ), d ) assert_equal( round( trans.sum(), 4 ), t ) @with_setup( setup, teardown ) def test_posterior_transitions_w_training(): sequences = [ list(x) for x in ( 'A', 'ACT', 'GGCA', 'TACCTGT' ) ] indices = { state.name: i for i, state in enumerate( model.states ) } transitions = model.dense_transition_matrix() i0, i1, i2 = indices['I0'], indices['I1'], indices['I2'] d1, d2, d3 = indices['D1'], indices['D2'], indices['D3'] m1, m2, m3 = indices['M1'], indices['M2'], indices['M3'] assert_equal( transitions[d1, i1], transitions[d2, i2] ) assert_equal( transitions[i0, i0], transitions[i1, i1] ) assert_equal( transitions[i0, i0], transitions[i2, i2] ) assert_equal( transitions[i0, m1], transitions[i1, m2] ) assert_equal( transitions[d1, d2], transitions[d2, d3] ) assert_equal( transitions[i0, d1], transitions[i1, d2] ) assert_equal( transitions[i0, d1], transitions[i2, d3] ) model.fit( sequences, verbose=False ) transitions = model.dense_transition_matrix() assert_not_equal( transitions[d1, i1], transitions[d2, i2] ) assert_not_equal( transitions[i0, m1], transitions[i1, m2] ) assert_not_equal( transitions[d1, d2], transitions[d2, d3] ) assert_not_equal( transitions[i0, d1], transitions[i1, d2] ) assert_not_equal( transitions[i0, d1], transitions[i2, d3] ) @with_setup( setup, teardown ) def test_posterior_transitions_w_vtraining(): sequences = [ list(x) for x in ( 'A', 'ACT', 'GGCA', 'TACCTGT' ) ] indices = { state.name: i for i, state in enumerate( model.states ) } transitions = model.dense_transition_matrix() i0, i1, i2, i3 = indices['I0'], indices['I1'], indices['I2'], indices['I3'] d1, d2, d3 = indices['D1'], indices['D2'], indices['D3'] m1, m2, m3 = indices['M1'], indices['M2'], indices['M3'] assert_equal( transitions[d1, i1], transitions[d2, i2] ) assert_equal( transitions[i0, i0], transitions[i1, i1] ) assert_equal( transitions[i0, i0], transitions[i2, i2] ) assert_equal( transitions[i0, m1], transitions[i1, m2] ) assert_equal( transitions[d1, d2], transitions[d2, d3] ) assert_equal( transitions[i0, d1], transitions[i1, d2] ) assert_equal( transitions[i0, d1], transitions[i2, d3] ) model.fit( sequences, verbose=False, algorithm='viterbi' ) transitions = model.dense_transition_matrix() assert_not_equal( transitions[i0, i0], transitions[i1, i1] ) assert_not_equal( transitions[d1, d2], transitions[d2, d3] ) assert_not_equal( transitions[i0, d1], transitions[i1, d2] ) assert_not_equal( transitions[i0, d1], transitions[i2, d3] ) @with_setup( tied_edge_setup, teardown ) def test_posterior_transitions_w_tied_training(): sequences = [ list(x) for x in ( 'A', 'ACT', 'GGCA', 'TACCTGT' ) ] indices = { state.name: i for i, state in enumerate( model.states ) } transitions = model.dense_transition_matrix() i0, i1, i2, i3 = indices['I0'], indices['I1'], indices['I2'], indices['I3'] d1, d2, d3 = indices['D1'], indices['D2'], indices['D3'] m1, m2, m3 = indices['M1'], indices['M2'], indices['M3'] assert_equal( transitions[d1, i1], transitions[d2, i2] ) assert_equal( transitions[i0, i0], transitions[i1, i1] ) assert_equal( transitions[i0, i0], transitions[i2, i2] ) assert_equal( transitions[i0, m1], transitions[i1, m2] ) assert_equal( transitions[d1, d2], transitions[d2, d3] ) assert_equal( transitions[i0, d1], transitions[i1, d2] ) assert_equal( transitions[i0, d1], transitions[i2, d3] ) model.fit( sequences, verbose=False ) transitions = model.dense_transition_matrix() assert_equal( transitions[i0, i0], transitions[i1, i1] ) assert_equal( transitions[d1, d2], transitions[d2, d3] ) assert_equal( transitions[i0, d1], transitions[i1, d2] ) assert_equal( transitions[i0, d1], transitions[i2, d3] ) @with_setup( tied_edge_setup, teardown ) def test_posterior_transitions_w_tied_vtraining(): sequences = [ list(x) for x in ( 'A', 'ACT', 'GGCA', 'TACCTGT' ) ] indices = { state.name: i for i, state in enumerate( model.states ) } transitions = model.dense_transition_matrix() i0, i1, i2 = indices['I0'], indices['I1'], indices['I2'] d1, d2, d3 = indices['D1'], indices['D2'], indices['D3'] m1, m2, m3 = indices['M1'], indices['M2'], indices['M3'] assert_equal( transitions[d1, i1], transitions[d2, i2] ) assert_equal( transitions[i0, i0], transitions[i1, i1] ) assert_equal( transitions[i0, i0], transitions[i2, i2] ) assert_equal( transitions[i0, m1], transitions[i1, m2] ) assert_equal( transitions[d1, d2], transitions[d2, d3] ) assert_equal( transitions[i0, d1], transitions[i1, d2] ) assert_equal( transitions[i0, d1], transitions[i2, d3] ) model.fit( sequences, verbose=False, algorithm='viterbi' ) transitions = model.dense_transition_matrix() assert_equal( transitions[d1, i1], transitions[d2, i2] ) assert_equal( transitions[i0, i0], transitions[i1, i1] ) assert_equal( transitions[i0, i0], transitions[i2, i2] ) assert_equal( transitions[i0, m1], transitions[i1, m2] ) assert_equal( transitions[d1, d2], transitions[d2, d3] ) assert_equal( transitions[i0, d1], transitions[i1, d2] ) assert_equal( transitions[i0, d1], transitions[i2, d3] ) @with_setup( setup, teardown ) def test_posterior_emissions(): a_scores = [ 0.987, 0.9965, 0.183, 0.523 ] b_scores = [ 0.0, 0.9977, 0.7364, 0.6318 ] c_scores = [ 0.0, 0.9975, 0.6237, 0.8641 ] d_scores = [ 0.0, 0.0021, 0.2017, 1.5105 ] sequences = [ list(x) for x in ( 'A', 'ACT', 'GGCA', 'TACCTGT' ) ] indices = { state.name: i for i, state in enumerate( model.states ) } i, j, k, l = indices['M1'], indices['M2'], indices['M3'], indices['I2'] for seq, a, b, c, d in zip( sequences, a_scores, b_scores, c_scores, d_scores ): trans, ems = model.forward_backward( seq ) ems = np.exp( ems ) assert_equal( round( ems[:,i].sum(), 4 ), a ) assert_equal( round( ems[:,j].sum(), 4 ), b ) assert_equal( round( ems[:,k].sum(), 4 ), c ) assert_equal( round( ems[:,l].sum(), 4 ), d ) assert_equal( round( ems.sum() ), len( seq ) ) @with_setup( multitransition_setup, teardown ) def test_posterior_emissions_w_multitransition_setup(): a_scores = [ 0.987, 0.9965, 0.183, 0.523 ] b_scores = [ 0.0, 0.9977, 0.7364, 0.6318 ] c_scores = [ 0.0, 0.9975, 0.6237, 0.8641 ] d_scores = [ 0.0, 0.0021, 0.2017, 1.5105 ] sequences = [ list(x) for x in ( 'A', 'ACT', 'GGCA', 'TACCTGT' ) ] indices = { state.name: i for i, state in enumerate( model.states ) } i, j, k, l = indices['M1'], indices['M2'], indices['M3'], indices['I2'] for seq, a, b, c, d in zip( sequences, a_scores, b_scores, c_scores, d_scores ): trans, ems = model.forward_backward( seq ) ems = np.exp( ems ) assert_equal( round( ems[:,i].sum(), 4 ), a ) assert_equal( round( ems[:,j].sum(), 4 ), b ) assert_equal( round( ems[:,k].sum(), 4 ), c ) assert_equal( round( ems[:,l].sum(), 4 ), d ) assert_equal( round( ems.sum() ), len( seq ) ) @with_setup( tied_edge_setup, teardown ) def test_posterior_emissions_w_tied_edge_setup(): a_scores = [ 0.987, 0.9965, 0.183, 0.523 ] b_scores = [ 0.0, 0.9977, 0.7364, 0.6318 ] c_scores = [ 0.0, 0.9975, 0.6237, 0.8641 ] d_scores = [ 0.0, 0.0021, 0.2017, 1.5105 ] sequences = [ list(x) for x in ( 'A', 'ACT', 'GGCA', 'TACCTGT' ) ] indices = { state.name: i for i, state in enumerate( model.states ) } i, j, k, l = indices['M1'], indices['M2'], indices['M3'], indices['I2'] for seq, a, b, c, d in zip( sequences, a_scores, b_scores, c_scores, d_scores ): trans, ems = model.forward_backward( seq ) ems = np.exp( ems ) assert_equal( round( ems[:,i].sum(), 4 ), a ) assert_equal( round( ems[:,j].sum(), 4 ), b ) assert_equal( round( ems[:,k].sum(), 4 ), c ) assert_equal( round( ems[:,l].sum(), 4 ), d ) assert_equal( round( ems.sum() ), len( seq ) ) @with_setup( setup, teardown ) def test_properties(): assert_equal( model.edge_count(), 29 ) assert_equal( model.state_count(), 12 ) assert_equal( model.name, "Global Alignment" ) @with_setup( setup, teardown ) def test_to_json(): b = json.loads(model.to_json()) assert_equal(b['name'], 'Global Alignment') assert_equal(len(b['edges']), 29) assert_equal(len(b['states']), 12) assert_equal(b['silent_index'], 7) @with_setup( setup, teardown ) def test_from_json(): hmm = HiddenMarkovModel.from_json( model.to_json() ) assert_equal(hmm.edge_count(), 29) assert_equal(hmm.state_count(), 12) assert_equal(hmm.name, "Global Alignment") pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/000077500000000000000000000000001420461712600227175ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/A_Overview.ipynb000066400000000000000000003133771420461712600260460ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## pomegranate: fast and flexible probabilistic modelling\n", "\n", "Author: Jacob Schreiber
\n", "Contact: " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fri Jan 10 2020 \n", "\n", "numpy 1.18.1\n", "scipy 1.4.1\n", "pomegranate 0.12.0\n", "\n", "compiler : Clang 10.0.0 (clang-1000.11.45.5)\n", "system : Darwin\n", "release : 17.7.0\n", "machine : x86_64\n", "processor : i386\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import time\n", "import pandas\n", "import random\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "import itertools\n", "\n", "from pomegranate import *\n", "\n", "random.seed(0)\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pomegranate is a probabilistic modeling library for Python that aims to be easy to use, flexible, and fast. It is easy to use because it maintains a consistent and minimal API that mirrors the scikit-learn API when possible. It is flexible because it allows complicated probabilistic models to be built from simpler ones by easily stacking components on top of each other. It is fast due to its Cython backend which allows for fast numerics and multi-threading without cluttering the interface. pomegranate was developed at the University of Washington with the generous support from the following organizations:\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The API\n", "\n", "One of the core tenant of pomegranate is that everything is fundamentally a probability distribution and should be treated the same way. Most people are familiar with simple probability distributions such as the normal or the uniform distribution. However, a mixture of many simple distributions is still a probability distribution because the density under it still adds up to 1. A hidden Markov model is still a probability distribution over sequences because the probability of all potential sequences must add up to 1. A Bayesian network is literally a probability distribution that has been factorized along a graphical structure, with a discrete Bayesian network factorizing a very large joint probability table and a linear Gaussian Bayesian network factorizing a multivariate Gaussian distribution that has many 0's in the inverse covariance matrix.\n", "\n", "A benefit of considering all methods as probability distributions is that it simplifies and unifies the API across all of the models. The common API is the following methods. Let's specify some distribution and take a look." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "model = NormalDistribution(5, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***model.probability(X)*** and ***model.log_probability(X)***\n", "\n", "These methods return the probability of a single sample or a vector of probabilities if provided a vector of samples. This is equal to P(D|M) where D is the data and M is the model." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.24197072 0.24197072 0.05399097]\n", "[-1.41893853 -1.41893853 -2.91893853]\n" ] } ], "source": [ "print(model.probability([4., 6., 7.]))\n", "print(model.log_probability([4., 6., 7.]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***model.sample(n=1)***\n", "\n", "This method will return a random sample from the probability distribution or a vector of random samples if n is set to be greater than 1." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([6.76405235, 5.40015721, 5.97873798, 7.2408932 , 6.86755799,\n", " 4.02272212, 5.95008842, 4.84864279, 4.89678115, 5.4105985 ])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.sample(n=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***model.fit(X, weights=None, inertia=0.0, other arguments)***\n", "\n", "This method will fit the distribution to the data. For simple distributions and Bayesian networks this corresponds to weighted maximum likelihood estimates. For other compositional distributions such as hidden Markov models this corresponds to an iterative expectation-maximization allgorithm. Optionally, weights can be provided for each of the samples. Different models will have different arguments that can be specified as well." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" :\"Distribution\",\n", " \"name\" :\"NormalDistribution\",\n", " \"parameters\" :[\n", " 7.036955079959913,\n", " 2.0668514243367655\n", " ],\n", " \"frozen\" :false\n", "}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = numpy.random.normal(7, 2, size=(100,))\n", "\n", "model.fit(X)\n", "model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***model.summary(X, weights=None)***\n", "\n", "This method implements the first part of the fitting process, which is summarizing a batch of data down to its sufficient statistics and storing them. These sufficient statistics are additive and will be updated for each successive batch that's seen. The sufficient statistics can be reset either through a call to `from_summaries` or a call to `clear_summaries`. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" :\"Distribution\",\n", " \"name\" :\"NormalDistribution\",\n", " \"parameters\" :[\n", " 7.036955079959913,\n", " 2.0668514243367655\n", " ],\n", " \"frozen\" :false\n", "}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = numpy.random.normal(8, 1.5, size=(100,))\n", "\n", "model.summarize(X)\n", "model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***model.from_summaries(inertia=0.0)***\n", "\n", "This method implements the second part of the fitting process, which is using the stored sufficient statistics in order to update the model parameters." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" :\"Distribution\",\n", " \"name\" :\"NormalDistribution\",\n", " \"parameters\" :[\n", " 8.068146018374335,\n", " 1.4490322240381275\n", " ],\n", " \"frozen\" :false\n", "}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.from_summaries()\n", "model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***model.clear_summaries()***\n", "\n", "This method resets the sufficient statistics stored to the model." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" :\"Distribution\",\n", " \"name\" :\"NormalDistribution\",\n", " \"parameters\" :[\n", " 8.068146018374335,\n", " 1.4490322240381275\n", " ],\n", " \"frozen\" :false\n", "}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = numpy.random.normal(3, 0.2, size=(100,))\n", "\n", "model.summarize(X)\n", "model.clear_summaries()\n", "model.from_summaries()\n", "model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Model.from_samples(distributions, n_components, X, weights=None)*** or ***Model.from_samples(X, weights=None)***\n", "\n", "This class method will initialize and then fit the parameters of a model to some data. This differs from the `fit` function in that the `fit` function will update the parameters of a pre-defined model whereas the `from_samples` method will initialize a model to data and return the best parameters given the data." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" :\"Distribution\",\n", " \"name\" :\"NormalDistribution\",\n", " \"parameters\" :[\n", " 5.878631757664192,\n", " 1.0120288481111088\n", " ],\n", " \"frozen\" :false\n", "}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = numpy.random.normal(6, 1, size=(250, 1))\n", "\n", "model = NormalDistribution.from_samples(X)\n", "model" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"GeneralMixtureModel\",\n", " \"distributions\" : [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 5.901815709887363,\n", " 0.47865628749453537\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 5.175959799664007,\n", " 0.8774489898709738\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 6.9902780582522634,\n", " 0.597808757836137\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " \"weights\" : [\n", " 0.3148521575093671,\n", " 0.42381846635660686,\n", " 0.261329376134026\n", " ]\n", "}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = GeneralMixtureModel.from_samples(NormalDistribution, 3, X)\n", "model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***model.to_json(separators=(',', ' :'), indent=4)***\n", "\n", "This method returns the JSON serialization of the distribution as a string." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"class\" : \"GeneralMixtureModel\",\n", " \"distributions\" : [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 5.901815709887363,\n", " 0.47865628749453537\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 5.175959799664007,\n", " 0.8774489898709738\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 6.9902780582522634,\n", " 0.597808757836137\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " \"weights\" : [\n", " 0.3148521575093671,\n", " 0.42381846635660686,\n", " 0.261329376134026\n", " ]\n", "}\n" ] } ], "source": [ "print(model.to_json())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Model.from_json(s)***\n", "\n", "This class method returns the deserialization of the JSON string back to the model object. s can either be the JSON string or a filename ending in `.json` to read from." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" :\"Distribution\",\n", " \"name\" :\"NormalDistribution\",\n", " \"parameters\" :[\n", " 5.0,\n", " 2.0\n", " ],\n", " \"frozen\" :false\n", "}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = NormalDistribution(5, 2)\n", "\n", "model2 = Distribution.from_json(model.to_json())\n", "model2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compositional models, i.e., those that are not simple distributions, have three additional methods whose named are inspired by scikit-learn. These methods relate to the posterior probabilities P(M|D) of each of the components of the model given some data." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"GeneralMixtureModel\",\n", " \"distributions\" : [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"ExponentialDistribution\",\n", " \"parameters\" : [\n", " 5.0\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"ExponentialDistribution\",\n", " \"parameters\" : [\n", " 0.3\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " \"weights\" : [\n", " 0.5,\n", " 0.5\n", " ]\n", "}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1 = ExponentialDistribution(5.0)\n", "d2 = ExponentialDistribution(0.3)\n", "\n", "model = GeneralMixtureModel([d1, d2])\n", "model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***model.predict(X)***\n", "\n", "This method returns the most likely component for each sample. In the case of a mixture model it returns the component that is most likely, in the case of a hidden Markov model it returns the most likely component for each observation in the sequence." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 0, 0, 0, 1, 0, 0, 1, 0, 1], dtype=int32)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = numpy.random.exponential(3, size=(10,1))\n", "\n", "model.predict(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***model.predict_proba(X)***\n", "\n", "This method returns the probability of each component for each sample. It is similar to the `predict` method except that it returns the probabilities instead of simply the most likely component." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.00046883, 0.99953117],\n", " [0.74132615, 0.25867385],\n", " [0.58846532, 0.41153468],\n", " [0.89449455, 0.10550545],\n", " [0. , 1. ],\n", " [0.94041259, 0.05958741],\n", " [0.50991095, 0.49008905],\n", " [0.00002574, 0.99997426],\n", " [0.83434281, 0.16565719],\n", " [0. , 1. ]])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict_proba(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***model.predict_log_proba(X)***\n", "\n", "Like predict_proba except that it returns the log probabilities instead of the probabilities." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ -7.66527111, -0.00046894],\n", " [ -0.29931461, -1.35218726],\n", " [ -0.53023729, -0.88786198],\n", " [ -0.11149647, -2.24899266],\n", " [-46.97709403, 0. ],\n", " [ -0.06143657, -2.820311 ],\n", " [ -0.67351918, -0.71316817],\n", " [-10.56732239, -0.00002574],\n", " [ -0.18111092, -1.79783472],\n", " [-27.30692393, -0. ]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict_log_proba(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Flexibility\n", "\n", "#### Modeling different features as different distributions\n", "\n", "A second benefit of treating all models as probability distributions is that it greatly increases the flexibility that pomegranate provides. When people build naive Bayes classifiers, they typically will use a Gaussian distribution. However, there's no reason that one has to use a Gaussian distribution, you can drop in any type of distribution that you'd like. For example:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "X = numpy.random.normal(5, 1, size=(100, 2))\n", "X[50:] += 1\n", "\n", "y = numpy.zeros(100)\n", "y[50:] = 1\n", "\n", "model1 = NaiveBayes.from_samples(NormalDistribution, X, y)\n", "model2 = NaiveBayes.from_samples(LogNormalDistribution, X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is easy to drop in whatever probability distribution you'd like because it should be easy. Mathematically, the naive Bayes model relies on Bayes' rule, which says:\n", "\n", "\\begin{equation}\n", "P(M|D) = \\frac{P(D|M)P(M)}{P(D)}\n", "\\end{align}\n", "\n", "Because the \"naive\" part of a \"naive\" Bayes model means that the model treats each feature independently, we can rewrite the $P(D|M)$ aspect as the product of these probabilities over all $d$ features:\n", "\n", "\\begin{equation}\n", "P(M|D) = \\frac{P(M) \\prod\\limits_{i=1}^{d} P(D_{i}|M)}{P(D)}\n", "\\end{equation}\n", "\n", "Now, because each feature is independent, they can be modeled by different probability distributions." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mu = numpy.random.normal(7, 2, size=1000)\n", "std = numpy.random.lognormal(-0.8, 0.8, size=1000)\n", "dur = numpy.random.exponential(50, size=1000)\n", "\n", "data = numpy.concatenate([numpy.random.normal(mu_, std_, int(t)) for mu_, std_, t in zip(mu, std, dur)])\n", "\n", "plt.figure(figsize=(14, 4))\n", "plt.title(\"Randomly Generated Signal\", fontsize=16)\n", "plt.plot(data)\n", "plt.xlabel(\"Time\", fontsize=14)\n", "plt.ylabel(\"Signal\", fontsize=14)\n", "plt.xlim(0, 3000)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If someone was trying to model this signal, they could potentially try to segment it and then extract properties of those segments, such as the mean, the variance, and the duration. We can plot what thos would look like:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 3))\n", "plt.subplot(131)\n", "plt.title(\"mu\", fontsize=14)\n", "plt.hist(mu, bins=numpy.arange(0, 15))\n", "\n", "plt.subplot(132)\n", "plt.title(\"sigma\", fontsize=14)\n", "plt.hist(std, bins=numpy.arange(0.00, 1.75, 0.05))\n", "\n", "plt.subplot(133)\n", "plt.title(\"Duration\", fontsize=14)\n", "plt.hist(dur, bins=numpy.arange(0, 150, 10))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the mean of the segments does have a Gaussian distribution, but that neither the standard deviation or the duration do. It would be suboptimal to assume all features were Gaussian, as a simple Gaussian naive Bayes model would. pomegranate allows you to define a different distribution for each feature, like the following:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"NaiveBayes\",\n", " \"models\" : [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"IndependentComponentsDistribution\",\n", " \"parameters\" : [\n", " [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 6.905835442537852,\n", " 2.09604452629164\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"LogNormalDistribution\",\n", " \"parameters\" : [\n", " -0.880499005246798,\n", " 0.7642112111076668\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"ExponentialDistribution\",\n", " \"parameters\" : [\n", " 0.01979944881580867\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " [\n", " 1.0,\n", " 1.0,\n", " 1.0\n", " ]\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"IndependentComponentsDistribution\",\n", " \"parameters\" : [\n", " [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 8.03534029261696,\n", " 2.063613757125447\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"LogNormalDistribution\",\n", " \"parameters\" : [\n", " -1.2016720901139963,\n", " 0.5891919855702662\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"ExponentialDistribution\",\n", " \"parameters\" : [\n", " 0.010167268183697146\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " [\n", " 1.0,\n", " 1.0,\n", " 1.0\n", " ]\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " \"weights\" : [\n", " 0.4,\n", " 0.6\n", " ]\n", "}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X1 = numpy.array([numpy.random.normal(7, 2, size=400),\n", " numpy.random.lognormal(-0.8, 0.8, size=400),\n", " numpy.random.exponential(50, size=400)]).T\n", "\n", "X2 = numpy.array([numpy.random.normal(8, 2, size=600),\n", " numpy.random.lognormal(-1.2, 0.6, size=600),\n", " numpy.random.exponential(100, size=600)]).T\n", "\n", "X = numpy.concatenate([X1, X2])\n", "y = numpy.zeros(1000)\n", "y[400:] = 1\n", "\n", "NaiveBayes.from_samples([NormalDistribution, LogNormalDistribution, ExponentialDistribution], X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Modeling each feature independently is allowed for naive Bayes, mixtures, and hidden Markov models. This is very useful when trying to capture different dynamics in different features.\n", "\n", "#### Stacking models on top of each other\n", "\n", "Next, another feature that emerges when you treat all models as probability distributions is that they can be easily stacked within each other. For example, if a mixture is just a probability distribution, then naturally you should be able to make a mixture Bayes classifier by dropping a `GeneralMixtureModel` into a `BayesClassifier` just as easily as a Gaussian Bayes classifier by dropping a `MultivariateGaussianDistribution` into the `BayesClassifier`." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-4012.4121095570604\n", "-3904.723017794966\n" ] } ], "source": [ "X = numpy.concatenate([numpy.random.normal((5, 1), 1, size=(200, 2)),\n", " numpy.random.normal((6, 4), 1, size=(200, 2)),\n", " numpy.random.normal((3, 5), 1, size=(350, 2)),\n", " numpy.random.normal((7, 6), 1, size=(250, 2))])\n", "\n", "y = numpy.zeros(1000)\n", "y[400:] = 1\n", "\n", "model = BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y)\n", "print(model.log_probability(X).sum())\n", "\n", "\n", "d1 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X[y == 0])\n", "d2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X[y == 1])\n", "model2 = BayesClassifier([d1, d2], [0.4, 0.6])\n", "print(model2.log_probability(X).sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case we build data that intentionally has 4 clusters, of which each class is composed of two of the clusters. A simple normal distribution is unlikely to capture this well, but a mixture model is built specifically to model distributions that are composed of multiple parts. We can see that the correspond log probability is much higher when we incorporate the mixture model in. There is an implementation detail that doesn't allow you yet to pass in a compositional model into the `from_samples` method quite yet, but in this case it is fairly simple to break up the data such that we build the model ourselves." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Speed\n", "\n", "pomegranate uses a Cython backend for the computationally intensive aspects of the calculation, both dramatically speeding up calculations and allowing for multithreading to be utilized.\n", "\n", "#### numpy\n", "\n", "pomegranate used to be consistently faster than numpy at fitting probability distributions to data, when compared to performing the same operations in numpy. However, due to consistent improvements in numpy, it is now a more complicated picture. For example, let's look at fitting a normal distribution to 1,000 samples." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "152 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n", "336 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" ] } ], "source": [ "X = numpy.random.normal(4, 1, size=1000)\n", "\n", "%timeit -n 1 -r 1 numpy.mean(X), numpy.std(X)\n", "%timeit -n 1 -r 1 NormalDistribution.from_samples(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pomegranate appears to be a bit faster, but the total amount of time is negligble one way or another. If we increase the size of the data we're using to 10 million samples:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "52.9 ms ± 903 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "67.8 ms ± 577 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "X = numpy.random.normal(4, 1, size=10000000)\n", "\n", "%timeit numpy.mean(X), numpy.std(X)\n", "%timeit NormalDistribution.from_samples(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like pomegranate is more comparable to numpy.\n", "\n", "Let's now look at fitting a multivariate Gaussian distribution. The calculation of a covariance matrix requires a dot product, which numpy accelerates using BLAS. pomegranate uses a Cython wrapper of BLAS and so can take advantage of those speed improvements. Let's look at fitting to one million data points with 3 dimensions." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "51.2 ms ± 838 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "33.2 ms ± 247 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "X = numpy.random.normal(4, 1, size=(1000000, 3))\n", "\n", "%timeit numpy.mean(X, axis=0), numpy.cov(X, rowvar=False, ddof=0)\n", "%timeit MultivariateGaussianDistribution.from_samples(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like pomegranate is around the same speed here. However, if we fit to a 1000 dimensional sample rather than a 3 dimensional one:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.45 s ± 76.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "4.99 s ± 612 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "X = numpy.random.normal(4, 1, size=(100000, 1000))\n", "\n", "%timeit numpy.mean(X, axis=0), numpy.cov(X, rowvar=False, ddof=0)\n", "%timeit MultivariateGaussianDistribution.from_samples(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it looks like pomegranate is slower than numpy.\n", "\n", "The main take-away when compared to numpy is that pomegranate and numpy appear to perform similarly. As the samples become larger numpy becomes faster than pomegranate, but they're both within an order of magnitude of each other one way or another. If you're trying to do basic operations on a large amount of data, you may want to stick with using numpy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### scipy\n", "\n", "scipy can be used to calculate probabilities of samples given a distribution. This corresponds to the `probability` and `log_probability` functions in pomegranate. Let's see how long it takes to calculate these probabilities in the two packages given a normal distrubution." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "581 ms ± 12.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "47.2 ms ± 883 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "\n", "logp difference: -3.990151116406437e-06\n" ] } ], "source": [ "from scipy.stats import norm\n", "\n", "d = NormalDistribution(0, 1)\n", "x = numpy.random.normal(0, 1, size=(10000000,))\n", "\n", "%timeit norm.logpdf(x, 0, 1)\n", "%timeit NormalDistribution(0, 1).log_probability(x)\n", "\n", "print(\"\\nlogp difference: {}\".format((norm.logpdf(x, 0, 1) - NormalDistribution(0, 1).log_probability(x)).sum()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like it can be significantly faster. Let's also look at a large multivariate normal distribution with 2500 dimensions." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.39 s ± 25.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "796 ms ± 6.07 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "171 ms ± 1.5 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "\n", "logp difference: 8.183596946764737e-07\n" ] } ], "source": [ "from scipy.stats import multivariate_normal\n", "\n", "dim = 2500\n", "n = 1000\n", "\n", "mu = numpy.random.normal(6, 1, size=dim)\n", "cov = numpy.eye(dim)\n", "\n", "X = numpy.random.normal(8, 1, size=(n, dim))\n", "\n", "d = MultivariateGaussianDistribution(mu, cov)\n", "\n", "%timeit multivariate_normal.logpdf(X, mu, cov)\n", "%timeit MultivariateGaussianDistribution(mu, cov).log_probability(X)\n", "%timeit d.log_probability(X)\n", "\n", "print(\"\\nlogp difference: {}\".format((multivariate_normal.logpdf(X, mu, cov) - d.log_probability(X)).sum()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the reasons which pomegranate can be so fast at calculating log probabilities is that it is able to cache parts of the logpdf equation so that it doesn't need to do all of the calculations each time. For example, let's look at the Normal distribution pdf equation:\n", "\n", "\\begin{equation}\n", "P(X|\\mu, \\sigma) = \\frac{1}{\\sqrt{2\\pi}\\sigma} exp \\left( -\\frac{(x - \\mu)^{2}}{2\\sigma^{2}} \\right) \\\\\n", "\\end{equation}\n", "\n", "We can take the log of this to simplify it.\n", "\n", "\\begin{equation}\n", "logP(X|\\mu, \\sigma) = -\\log \\left(\\sqrt{2\\pi}\\sigma \\right) - \\frac{(x-\\mu)^{2}}{2\\sigma^{2}}\n", "\\end{equation}\n", "\n", "pomegranate speeds up this calculation by caching $-\\log(\\sqrt{2\\pi}\\sigma)$ and $2\\sigma^{2}$ when the object is created. This means that the equation is simplified to the following:\n", "\n", "\\begin{equation}\n", "logP(X|\\mu, \\sigma) = \\alpha - \\frac{(x - \\mu)^{2}}{\\beta}\n", "\\end{equation}\n", "\n", "We don't need to calculate any logs or exponentials here, just a difference, a multiplication, a division, and a subtraction." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### scikit-learn\n", "\n", "scikit-learn and pomegranate overlap when it comes to naive Bayes classifiers and mixture models. In pomegranate, both of these model types can be used with any distribution, allowing mixtures of exponentials or log-normals to be made just as easily as mixtures of Gaussians, whereas in scikit-learn only Gaussians or multinomials are allowed. Let's compare speed of the overlap." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1.79 s ± 160 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "1.43 s ± 107 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "from sklearn.mixture import GaussianMixture\n", "\n", "X = numpy.random.normal(8, 1, size=(10000, 100))\n", "\n", "%timeit model1 = GaussianMixture(5, max_iter=10).fit(X)\n", "%timeit model2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 5, X, max_iterations=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like the two are approximately the same speed on a single thread. Let's look at naive Bayes now." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.14 s ± 104 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "1.37 s ± 10.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "\n", "X = numpy.random.normal(8, 1, size=(100000, 500))\n", "X[:50000] += 1\n", "\n", "y = numpy.zeros(100000)\n", "y[:50000] = 1\n", "\n", "%timeit GaussianNB().fit(X, y)\n", "%timeit NaiveBayes.from_samples(NormalDistribution, X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like scikit-learn is a bit faster when it comes to training a Gaussian naive Bayes model. \n", "\n", "Much like with the comparisons to numpy, if scikit-learn has something easily implemented, it's likely better to try to use their implementation. The goal of pomegranate is not to step on the feet of these other packages, but rather to provide similarly-fast implementations that extend to other probability distributions. For example, you can train a log normal naive Bayes in the following manner:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "model = NaiveBayes.from_samples(LogNormalDistribution, X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No such functionality yet exists in scikit-learn to train naive Bayes models for arbitrary distributions." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } B_Model_Tutorial_1_Distributions.ipynb000066400000000000000000004153511420461712600322420ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Probability Distributions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial will cover the various distributions that are implemented in pomegranate. These distributions can be used by themselves, or more typically as a part of a larger compositional model like a hidden Markov model. \n", "\n", "The distributions that are currently available:\n", "\n", "Univariate Distributions\n", " 1. BernoulliDistribution\n", " 2. BetaDistribution\n", " 3. DiscreteDistribution\n", " 4. ExponentialDistribution\n", " 5. GammaDistribution\n", " 6. LogNormalDistribution\n", " 7. NormalDistribution\n", " 8. PoissonDistribution\n", " 9. UniformDistribution\n", " \n", "Univariate Kernel Densities\n", " 1. GaussianKernelDensity\n", " 2. UniformKernelDensity\n", " 3. TriangleKernelDensity\n", " \n", "Multivariate Distributions\n", " 1. IndependentComponentsDistribution\n", " 2. Dirichlet Distribution\n", " 3. MultivariateGaussianDistribution\n", " 4. ConditionalProbabilityTable\n", " 5. JointProbabilityTable\n", "\n", "The following methods are supported by all distributions:\n", "\n", "```\n", "1. d.probability(X): Return the probability of a sample under the distribution or a vector of samples\n", "2. d.log_probability(X): Return the log probability of a sample under the distribution or a vector of samples\n", "3. d.fit(X, weights=None, inertia): Use MLE estimates to update the parameters of the distribution. Optional weight vector is accepted, or inertia that pushes parameters from where they currently are to the update that portion of the way.\n", "4. d.summarize(X, weights=None): Extract and update the stored sufficient statistics on a data set with optional weights, but don't update the parameters yet\n", "5. d.from_summaries(X, inertia=0.0): Use the stored sufficient statistics to do a parameter update\n", "6. d.sample(n=1): Generate a sample, or a vector of samples, from the distribution\n", "\n", "7. d.to_json(): Serialize the distribution to a string json\n", "8. Distribution.from_json(s): Load up a stored distriibution, either from a file if passed a filename that ends in '.json', or from the string passed in itself.\n", "\n", "9. d.copy(): Make a deep copy of the distribution\n", "10. d.freeze(): Freeze the distribution, making 'fit' and 'from_summaries' not change model parameters\n", "11. d.thaw(): Unfreeze the distribution so that it can be updated agin.\n", "```\n", "\n", "Univariate distributions also have a `plot(n=1000, **kwargs)` command, where you can plot a histogram of `n` randomly generated samples from the distribution, which will pass matplotlib keyword arguments to the `plt.hist` function. \n", "\n", "Lets look at a few examples." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fri Jan 10 2020 \n", "\n", "numpy 1.18.1\n", "scipy 1.4.1\n", "pomegranate 0.12.0\n", "\n", "compiler : Clang 10.0.0 (clang-1000.11.45.5)\n", "system : Darwin\n", "release : 17.7.0\n", "machine : x86_64\n", "processor : i386\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "import numpy\n", "\n", "from pomegranate import *\n", "\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The API\n", "\n", "### Initialization\n", "\n", "Let's first look at an example of using the methods in the API. First, we can create probability distributions by passing in the parameters if we know them." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "d = NormalDistribution(5, 2)\n", "d2 = ExponentialDistribution(5)\n", "d3 = LogNormalDistribution(2, 0.4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If what you have is data and you'd like to fit your distribution to the data, rather than having parameters beforehand, you can use the `from_samples` class method to pass in the points and optional weights." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "x = numpy.random.normal(10, 1, size=100)\n", "\n", "d4 = NormalDistribution.from_samples(x)\n", "d5 = ExponentialDistribution.from_samples(x)\n", "d6 = LogNormalDistribution.from_samples(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the data is univariate we can pass in a simple vector. If our data is multivariate and we'd like to fit a multivariate distribution we can pass in a matrix." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([9.94235563, 9.96112858, 9.99785915]),\n", " array([[ 0.98534191, -0.01989778, -0.03028855],\n", " [-0.01989778, 0.95247322, 0.0396411 ],\n", " [-0.03028855, 0.0396411 , 0.88819551]]))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = numpy.random.normal(10, 1, size=(1000, 3))\n", "\n", "d7 = MultivariateGaussianDistribution.from_samples(x)\n", "d7.mu, d7.cov" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to model our multivariate data with a different distribution on each feature, assuming that each feature is independent of each other, we can use an IndependentComponentsDistribution. This can be done either by passing in the distributions to the initialization, or as `from_samples`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"IndependentComponentsDistribution\",\n", " \"parameters\" : [\n", " [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 10.05980801553448,\n", " 1.0078822447166023\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"ExponentialDistribution\",\n", " \"parameters\" : [\n", " 0.09940547566677038\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"LogNormalDistribution\",\n", " \"parameters\" : [\n", " 2.303462554064328,\n", " 0.1012880395101563\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " [\n", " 1.0,\n", " 1.0,\n", " 1.0\n", " ]\n", " ],\n", " \"frozen\" : false\n", "}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d8 = IndependentComponentsDistribution([d4, d5, d6])\n", "d8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we wanted to learn a multivariate Gaussian distribution but with a forced diagonal covariance matrix we can model each feature are independent normal distributions, like such." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"IndependentComponentsDistribution\",\n", " \"parameters\" : [\n", " [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 9.942355632560583,\n", " 0.9926439000824223\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 9.96112857784113,\n", " 0.9759473472188426\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 9.997859147101435,\n", " 0.9424412506637966\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " [\n", " 1.0,\n", " 1.0,\n", " 1.0\n", " ]\n", " ],\n", " \"frozen\" : false\n", "}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d8.from_samples(x, distributions=NormalDistribution)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the distributions here are displaying the mean and standard deviation, whereas the `MultivariateGaussianDistribution` is showing the mean and ~covariance~, which is the standard deviation squared. When adjusted accordingly, the means here line up with the vector of means from before, and the standard deviations are equal to the square root of the diagonal of the covariance matrix.\n", "\n", "Lastly, if we wanted to learn an independent components distribution with different distributions on each feature, we can pass in a list of distributions." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"IndependentComponentsDistribution\",\n", " \"parameters\" : [\n", " [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 9.942355632560583,\n", " 0.9926439000824223\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"ExponentialDistribution\",\n", " \"parameters\" : [\n", " 0.10039023111442362\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"LogNormalDistribution\",\n", " \"parameters\" : [\n", " 2.297868979078352,\n", " 0.09529758318186476\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " [\n", " 1.0,\n", " 1.0,\n", " 1.0\n", " ]\n", " ],\n", " \"frozen\" : false\n", "}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d8.from_samples(x, distributions=[NormalDistribution, ExponentialDistribution, LogNormalDistribution])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting\n", "\n", "If we want to visualize them, we can use the plot command, which randomly draws samples from the model and plots the histogram of those samples." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 4))\n", "plt.subplot(231)\n", "plt.title(\"Normal Distribution\", fontsize=14)\n", "d.plot(1000, edgecolor='c', color='c', bins=20)\n", "\n", "plt.subplot(232)\n", "plt.title(\"Exponential Distribution\", fontsize=14)\n", "d2.plot(1000, edgecolor='m', color='m', bins=20)\n", "\n", "plt.subplot(233)\n", "plt.title(\"Log Normal Distribution\", fontsize=14)\n", "d3.plot(1000, edgecolor='g', color='g', bins=20)\n", "\n", "plt.subplot(234)\n", "plt.title(\"Fit Normal Distribution\", fontsize=14)\n", "d4.plot(1000, edgecolor='c', color='c', bins=20)\n", "\n", "plt.subplot(235)\n", "plt.title(\"Fit Exponential Distribution\", fontsize=14)\n", "d5.plot(1000, edgecolor='m', color='m', bins=20)\n", "\n", "plt.subplot(236)\n", "plt.title(\"Fit Log Normal Distribution\", fontsize=14)\n", "d6.plot(1000, edgecolor='g', color='g', bins=20)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Naturally, if you wanted smoother probability distributions, you could calculate the probability at each point along a given grid and plot those probabilities." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x1 = numpy.arange(-1, 11.1, 0.1)\n", "x2 = numpy.arange(0, 2, 0.1)\n", "x3 = numpy.arange(0, 20, 0.1)\n", "\n", "plt.figure(figsize=(14, 3))\n", "plt.subplot(231)\n", "plt.title(\"Normal Distribution\", fontsize=14)\n", "plt.fill_between(x1, 0, d.probability(x1), color='c')\n", "\n", "plt.subplot(232)\n", "plt.title(\"Exponential Distribution\", fontsize=14)\n", "plt.fill_between(x2, 0, d2.probability(x2), color='m')\n", "\n", "plt.subplot(233)\n", "plt.title(\"Log Normal Distribution\", fontsize=14)\n", "plt.fill_between(x3, 0, d3.probability(x3), color='g')\n", "\n", "plt.subplot(234)\n", "plt.title(\"Fit Normal Distribution\", fontsize=14)\n", "plt.fill_between(x3, 0, d4.probability(x3), color='c')\n", "\n", "plt.subplot(235)\n", "plt.title(\"Fit Exponential Distribution\", fontsize=14)\n", "plt.fill_between(x3, 0, d5.probability(x3), color='m')\n", "\n", "plt.subplot(236)\n", "plt.title(\"Fit Log Normal Distribution\", fontsize=14)\n", "plt.fill_between(x3, 0, d6.probability(x3), color='g')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Probability / Log Probability\n", "\n", "All distributions can calculate probabilities and log probabilities using those respective methods. Different distributions will give very different probabilities." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.026995483256604804, 0.03368973499542734, 3.7167987868372287e-06)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.probability(1), d2.probability(1), d3.probability(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A vector of values can also be passed in." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.02699548, 0.19947114, 0.12098536, 0.0647588 , 0.12098536])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.probability([1, 5, 7, 8, 3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The same works for multivariate data." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.026023208688747114, 0.0019422749531612764)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d7.probability([[9, 10, 11]]), d8.probability([[9, 10, 11]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the independent components distribution the probability is the multiplication of the probabilities for all three dimensions." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0019422749531612768" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d8.distributions[0].probability(9) * d8.distributions[1].probability(10) * d8.distributions[2].probability(11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fitting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Probability distributions can be fit directly to data, as shown above, using the `from_samples` class method. However, a fit distribution can also be fit to new data using the `fit` method. This method uses maximum likelihood estimates to derive new parameters and is not affected by the current values of the distribution unless an inertia is set, which defines the proportion of the original distribution to use, the default being 0." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" :\"Distribution\",\n", " \"name\" :\"NormalDistribution\",\n", " \"parameters\" :[\n", " 5.906921950798634,\n", " 2.000502667681352\n", " ],\n", " \"frozen\" :false\n", "}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = numpy.random.normal(6, 2, size=1000,)\n", "\n", "d = NormalDistribution(100, 1)\n", "d.fit(X)\n", "d" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" :\"Distribution\",\n", " \"name\" :\"NormalDistribution\",\n", " \"parameters\" :[\n", " 52.95346097539932,\n", " 1.500251333840676\n", " ],\n", " \"frozen\" :false\n", "}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = NormalDistribution(100, 1)\n", "d.fit(X, inertia=0.5)\n", "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fit method can also be given weights, in which case weighted maximum likelihood estimates are used. This is how expectation-maximization is done for compositional models. In this next example we'll give 50 samples with mean 5 and 50 samples with mean 9 and weight those samples with mean 9 as 4 times more than those with weight 5. If the samples were unweighted one would expect the result to be 7. However, with the higher magnitude samples being weighted more heavily, the result will actually be closer to 9." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" :\"Distribution\",\n", " \"name\" :\"NormalDistribution\",\n", " \"parameters\" :[\n", " 8.228881313766124,\n", " 2.5257422669054432\n", " ],\n", " \"frozen\" :false\n", "}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = numpy.random.normal(5, 2, size=100,)\n", "X[50:] += 4\n", "\n", "w = numpy.ones(100)\n", "w[:50] = 0.25\n", "\n", "d = NormalDistribution(10, 1)\n", "d.fit(X, w)\n", "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discrete Distributions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A notable deviant from the numerics logic is the Discrete Distribution. Instead of passing parameters as floats, you instead pass in a dictionary where keys can be any objects and values are the probability of them occurring. Internally the objects that are the keys get converted to integer indices to an array whose values are the probability of that integer occuring. All models store a keymap that converts the inputs to those models to the indices of the discrete distribution. If you try to calculate the log probability of an item not present in the distribution, the default behavior is to return negative infinity, or 0.0 probability." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.10000000000000002\n", "0.5\n", "0.0\n" ] } ], "source": [ "d = DiscreteDistribution({'A': 0.1, 'C': 0.25, 'G': 0.50, 'T': 0.15})\n", "print(d.probability( 'A'))\n", "print(d.probability( 'G'))\n", "print(d.probability('????'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Updating a discrete distributon is the same as updating other distributions." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "Missing parentheses in call to 'print'. Did you mean print(d.parameters[0])? (, line 5)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m print d.parameters[0]\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(d.parameters[0])?\n" ] } ], "source": [ "X = list('ACGATACACTGAATGACAGCAGTCACTGACAGTAGTACGAGTAGTAGCAGAGAGTAATAAAGAATTAATATATGACACTACGAAAAAAATGCATCG')\n", "\n", "d.fit(X)\n", "\n", "print d.parameters[0]\n", "print 1. * numpy.unique(X, return_counts=True)[1] / len(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Kernel Density" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Gaussian Kernel Density is a non-parametric distribution that, instead of parameters, stores the data points that it was trained on directly. When initializing a kernel density you simply pass in the points. These points are converted to a probability distribution by essentially dropping some probability distribution on top of each point and then dividing the density by the number of points, ensuring that the area still integrates to 1. We can see this in action with the Gaussian kernel density which drops a normal distribution on each point with standard deviation equal to the bandwidth." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d = GaussianKernelDensity( [0.5, 1.2, 4.2, 1.9, 5.4, 3.4], bandwidth=1 )\n", "d.plot(n=100000, edgecolor='c', color='c', bins=50)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Changing the bandwidth parameter is equivalent to reducing the variance on a Gaussian. It makes the densities more central around the points, but can cause overfitting." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d = GaussianKernelDensity( [0.5, 1.2, 4.2, 1.9, 5.4, 3.4], bandwidth=0.2 )\n", "d.plot(n=100000, edgecolor='c', color='c', bins=50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Passing in weights can change the influence of each sample for the final probability distribution." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d = GaussianKernelDensity( [0.5, 1.2, 4.2, 1.9, 5.4, 3.4], weights=[1, 1, 1, 3, 1, 1], bandwidth=0.2 )\n", "d.plot( n=100000, edgecolor='c', color='c', bins=100 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Out-of-core learning\n", "\n", "Distributions also have the `summarize` and `from_summaries` method that implement out-of-core learning. More will be available in the out-of-core learning tutorial, but the gist is that you can train on an amount of data that doesn't fit in memory and still yield exact updates. This works for any model in pomegranate, but lets illustrate it for an independent components distribution made up of three very different distributions." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"IndependentComponentsDistribution\",\n", " \"parameters\" : [\n", " [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 15.975331623897382,\n", " 0.9839454537443147\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"ExponentialDistribution\",\n", " \"parameters\" : [\n", " 0.06273786783336628\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"LogNormalDistribution\",\n", " \"parameters\" : [\n", " 2.7687181529109424,\n", " 0.06401250783319416\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " [\n", " 1.0,\n", " 1.0,\n", " 1.0\n", " ]\n", " ],\n", " \"frozen\" : false\n", "}" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pomegranate import *\n", "import numpy\n", "\n", "X = numpy.random.normal(16, 1, size=(1000, 3))\n", "\n", "d = IndependentComponentsDistribution([NormalDistribution(5, 1), \n", " ExponentialDistribution(7), \n", " LogNormalDistribution(0.6, 0.3)])\n", "\n", "d2 = d.copy()\n", "\n", "d.summarize(X[:500])\n", "d.summarize(X[500:])\n", "d.from_summaries()\n", "d" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"IndependentComponentsDistribution\",\n", " \"parameters\" : [\n", " [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 15.975331623897368,\n", " 0.9839454537444303\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"ExponentialDistribution\",\n", " \"parameters\" : [\n", " 0.06273786783336632\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"LogNormalDistribution\",\n", " \"parameters\" : [\n", " 2.7687181529109472,\n", " 0.06401250783306929\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " [\n", " 1.0,\n", " 1.0,\n", " 1.0\n", " ]\n", " ],\n", " \"frozen\" : false\n", "}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d2.fit(X)\n", "d2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also take a look at the JSON serialization. JSONs are the perfect way to store these models given their human interpretability, and the ability to recursively store JSONs within other JSONs, such as more complicated models (such as HMMs) will need to store the JSON of each distribution. It is useful to store these models after the computationally intensive task of training them, so that it need not be repeated." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"IndependentComponentsDistribution\",\n", " \"parameters\" : [\n", " [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 15.975331623897368,\n", " 0.9839454537444303\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"ExponentialDistribution\",\n", " \"parameters\" : [\n", " 0.06273786783336632\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"LogNormalDistribution\",\n", " \"parameters\" : [\n", " 2.7687181529109472,\n", " 0.06401250783306929\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " [\n", " 1.0,\n", " 1.0,\n", " 1.0\n", " ]\n", " ],\n", " \"frozen\" : false\n", "}\n" ] } ], "source": [ "print(d.to_json())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Serialization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Distributions (except tables right now) support serialization to JSONs using `to_json()` and `Distribution.from_json(json)`. We can see a few examples easily." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"class\" :\"Distribution\",\n", " \"name\" :\"NormalDistribution\",\n", " \"parameters\" :[\n", " 5.0,\n", " 2.0\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "print(NormalDistribution(5, 2).to_json())" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.5,\n", " \"B\" :0.25,\n", " \"C\" :0.25\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "print(DiscreteDistribution({'A': 0.5, 'B': 0.25, 'C': 0.25}).to_json())" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"class\" :\"Distribution\",\n", " \"name\" :\"GammaDistribution\",\n", " \"parameters\" :[\n", " 5.0,\n", " 2.0\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "print(GammaDistribution(5, 2).to_json())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can load them up using the base Distribution class. This is done because, when storing compositional models, they typically don't know what type of distribution is being stored." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.05399096651320961" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "NormalDistribution(1, 1).probability(3)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.05399096651320961" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Distribution.from_json(NormalDistribution(1, 1).to_json()).probability(3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } B_Model_Tutorial_2_General_Mixture_Models.ipynb000066400000000000000000010450771420461712600340030ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## General Mixture Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is frequently the case that the data you have is not explained by a single underlying distribution. Typically this is because there are multiple phenomena occuring in the data set, each with their own underlying distribution. If we want to try to recover the underlying distributions, we need to have a model which has multiple components. An example could be sensor readings where the majority of the time a sensor shows no signal, but sometimes it detects some phenomena. Modeling both phenomena as a single distribution would be silly because the readings would come from two distinct phenomena.\n", "\n", "A solution to the problem of having more than one single underlying distribution is to use a mixture of distributions instead of a single distribution, commonly called a mixture model. This type of compositional model builds a more complex probability distribution from a set of simpler ones. A common type, called a Gaussian Mixture Model, is composed of Gaussian distributions, but mathematically there is no need for these distributions to all be Gaussian. In fact, there is no need for these distributions to be simple probability distributions.\n", "\n", "In this tutorial we'll explore how to do mixture modeling in pomegranate, compare against scikit-learn's implementation of Gaussian mixture models, and explore more complex types of mixture modeling that one can do with probabilistic modeling." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sat Oct 06 2018 \n", "\n", "numpy 1.14.2\n", "scipy 1.0.0\n", "pomegranate 0.10.0\n", "\n", "compiler : GCC 7.2.0\n", "system : Linux\n", "release : 4.15.0-34-generic\n", "machine : x86_64\n", "processor : x86_64\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "import numpy\n", "\n", "from pomegranate import *\n", "\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A simple example: Gaussian mixture models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start off with a simple example. Perhaps we have a data set like the one below, which is made up of not a single blob, but many blobs. It doesn't seem like any of the simple distributions that pomegranate has implemented can fully capture what's going on in the data." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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6TnR5yKP4iH1gFV08dzfPisWJ39mbmRJUTU5uMCqeQFUnfqm6Aam+6foJ1BxG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\nFGJgJSIiUsgoFAoFv2/S09Ojoi1ERES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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.concatenate([numpy.random.normal((7, 2), 1, size=(100, 2)),\n", " numpy.random.normal((2, 3), 1, size=(150, 2)),\n", " numpy.random.normal((7, 7), 1, size=(100, 2))])\n", "\n", "plt.figure(figsize=(8, 6))\n", "plt.scatter(X[:,0], X[:,1])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems clear to us that this data is composed of three blobs. Accordingly, rather than trying to find some complex single distribution that can describe this data, we can describe it as a mixture of three Gaussian distributions. In the same way that we could initialize a basic distribution using the `from_samples` method, we can initialize a mixture model using it, additionally passing in the type(s) of distribution(s) to use and the number of components." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 3, X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can look at the probability densities if we had used a single Gaussian distribution versus using this mixture of three Gaussian models." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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SMCMkWAiCKFdE12jW42iMVXaRJSmZULEoAZNkGTVtq7GgLGRYFLMsCyv7MptG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\nlqhuuKUuTAKlBCCxY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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = numpy.arange(-1, 10.1, .1)\n", "y = numpy.arange(-1, 10.1, .1)\n", "\n", "xx, yy = numpy.meshgrid(x, y)\n", "x_ = numpy.array(list(zip(xx.flatten(), yy.flatten())))\n", "\n", "p1 = MultivariateGaussianDistribution.from_samples(X).probability(x_).reshape(len(x), len(y))\n", "p2 = model.probability(x_).reshape(len(x), len(y))\n", "\n", "\n", "plt.figure(figsize=(14, 6))\n", "plt.subplot(121)\n", "plt.contourf(xx, yy, p1, cmap='Blues', alpha=0.8)\n", "plt.scatter(X[:,0], X[:,1])\n", "\n", "plt.subplot(122)\n", "plt.contourf(xx, yy, p2, cmap='Blues', alpha=0.8)\n", "plt.scatter(X[:,0], X[:,1])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like, unsurprisingly, the mixture model is able to better capture the structure of the data. The single model is so bad that the region of highest density (the darkest blue ellipse) has very few real points in it. In contrast, the darkest regions in the mixture densities correspond to where there are the most points in the clusters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A more complex example: independent component mixture models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most data is more complex than the Gaussian case above. A common case in the domain of signal processing is analyzing segments of steady signal based on the mean, noise, and duration of each segment. Let's say that we have a signal like the following which is made up of long low-noise signals and shorter high-noise signals." ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "image/png": 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N7EyJIQDgRlyOsYtAiEnYcuUF3vojQuH79FRVtzglPHgvOod7SRy57z96VYD4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\nERERERGRGQyaiIiIiIiIzPh/DA3O2fXHLucAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mu = numpy.random.normal(7, 1, size=250)\n", "\n", "std = numpy.random.lognormal(-2.0, 0.4, size=250)\n", "std[::2] += 0.3\n", "\n", "dur = numpy.random.exponential(250, size=250)\n", "dur[::2] -= 140\n", "dur = numpy.abs(dur)\n", "\n", "data = numpy.concatenate([numpy.random.normal(mu_, std_, int(t)) for mu_, std_, t in zip(mu, std, dur)])\n", "\n", "plt.figure(figsize=(14, 4))\n", "plt.title(\"Randomly Generated Signal\", fontsize=16)\n", "plt.plot(data)\n", "plt.xlabel(\"Time\", fontsize=14)\n", "plt.ylabel(\"Signal\", fontsize=14)\n", "plt.xlim(0, 10000)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can show the distribution of segment properties to highlight the differences." ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "image/png": 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yxGHBANQcH374oXr16mVz/ZAhQ+Tl5aX+/furf3/bH9SvygRbzXJzylxfVFik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\nyP9fW3Jyc+Xr41Pl/VRW8Tk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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 4))\n", "plt.subplot(131)\n", "plt.title(\"Segment Means\", fontsize=14)\n", "plt.hist(mu[::2], bins=20, alpha=0.7)\n", "plt.hist(mu[1::2], bins=20, alpha=0.7)\n", "\n", "plt.subplot(132)\n", "plt.title(\"Segment STDs\", fontsize=14)\n", "plt.hist(std[::2], bins=20, alpha=0.7)\n", "plt.hist(std[1::2], bins=20, alpha=0.7)\n", "\n", "plt.subplot(133)\n", "plt.title(\"Segment Durations\", fontsize=14)\n", "plt.hist(dur[::2], bins=numpy.arange(0, 1000, 25), alpha=0.7)\n", "plt.hist(dur[1::2], bins=numpy.arange(0, 1000, 25), alpha=0.7)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this situation, it looks like the segment noise is going to be the feature with the most difference between the learned distributions. Let's use pomegranate to learn this mixture, modeling each feature with an appropriate distribution." ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"weights\" : [\n", " 0.5126040711398554,\n", " 0.4873959288601447\n", " ],\n", " \"distributions\" : [\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " [\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " 6.987501980099923,\n", " 0.9214383552866655\n", " ],\n", " \"name\" : \"NormalDistribution\"\n", " },\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " -0.8201943718176836,\n", " 0.13668606405233233\n", " ],\n", " \"name\" : \"LogNormalDistribution\"\n", " },\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " 0.0055513741389845135\n", " ],\n", " \"name\" : \"ExponentialDistribution\"\n", " }\n", " ],\n", " [\n", " 1.0,\n", " 1.0,\n", " 1.0\n", " ]\n", " ],\n", " \"name\" : \"IndependentComponentsDistribution\"\n", " },\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " [\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " 7.01255052332367,\n", " 0.9030007437921286\n", " ],\n", " \"name\" : \"NormalDistribution\"\n", " },\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " -1.9984343269872586,\n", " 0.37492907121195773\n", " ],\n", " \"name\" : \"LogNormalDistribution\"\n", " },\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " 0.003718687576817513\n", " ],\n", " \"name\" : \"ExponentialDistribution\"\n", " }\n", " ],\n", " [\n", " 1.0,\n", " 1.0,\n", " 1.0\n", " ]\n", " ],\n", " \"name\" : \"IndependentComponentsDistribution\"\n", " }\n", " ],\n", " \"class\" : \"GeneralMixtureModel\"\n", "}" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = numpy.array([mu, std, dur]).T.copy()\n", "\n", "model = GeneralMixtureModel.from_samples([NormalDistribution, LogNormalDistribution, ExponentialDistribution], 2, X)\n", "model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like the model is easily able to fit the differences that we observed in the data. The durations of the two components are similar, the means are almost identical to each other, but it's the noise that's the most different between the two." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The API\n", "\n", "### Initialization\n", "\n", "The API for general mixture models is similar to that of the rest of pomegranate. The model can be initialized either through passing in some pre-initialized distributions or by calling `from_samples` on a data set, specifying the types of distributions you'd like. \n", "\n", "You can simply pass in some distributions to initialize the mixture." ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = GeneralMixtureModel([NormalDistribution(4, 1), NormalDistribution(7, 1)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is no reason these distributions have to be the same type. You can create a non-homogenous mixture by passing in different types of distributions." ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model2 = GeneralMixtureModel([NormalDistribution(5, 1), ExponentialDistribution(0.3)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "They will produce very different probability distributions, but both work as a mixture." ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [ { "data": { "image/png": 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OiNSt2nmOnisqKkKzZs0APBuRx8XFBRYWFujfvz+WLVum8sGGDBmCIUOGKC2b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\n7A1AfinfTBDpgh9Oh8PS3BRje7cTOorOY50iIn0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = numpy.arange(0, 10.01, 0.05)\n", "\n", "plt.figure(figsize=(14, 3))\n", "plt.subplot(121)\n", "plt.title(\"~Norm(4, 1) + ~Norm(7, 1)\", fontsize=14)\n", "plt.ylabel(\"Probability Density\", fontsize=14)\n", "plt.fill_between(x, 0, model.probability(x))\n", "plt.ylim(0, 0.25)\n", "\n", "plt.subplot(122)\n", "plt.title(\"~Norm(5, 1) + ~Exp(0.3)\", fontsize=14)\n", "plt.ylabel(\"Probability Density\", fontsize=14)\n", "plt.fill_between(x, 0, model2.probability(x))\n", "plt.ylim(0, 0.25)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pomegranate offers a lot of flexibility when it comes to making mixtures directly from data. The normal option is just to specify the type of distribution, the number of components, and then pass in the data. Make sure that if you're making 1 dimensional data that you're still passing in a matrix whose second dimension is just set to 1." ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = numpy.random.normal(3, 1, size=(200, 1))\n", "X[::2] += 3\n", "\n", "model = GeneralMixtureModel.from_samples(NormalDistribution, 2, X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can take a quick look at what it looks like:" ] }, { "cell_type": "code", "execution_count": 160, "metadata": {}, "outputs": [ { "data": { "image/png": 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7d08TGYkGpRdWIexe68YEEOMUQw+qDR7rAvF3MXj//fcxceJE3LhxAxs2bEBs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\nVPr/fuN4MK2oIsMAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6, 3))\n", "plt.title(\"Learned Model\", fontsize=14)\n", "plt.ylabel(\"Probability Density\", fontsize=14)\n", "plt.fill_between(x, 0, model.probability(x))\n", "plt.ylim(0, 0.25)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, we can also fit a non-homogenous mixture by passing in a list of univariate distributions to be fit to univariate data. Let's try to fit to data of which some of it is normally distributed and some is exponentially distributed. " ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = numpy.concatenate([numpy.random.normal(5, 1, size=(200, 1)), numpy.random.exponential(1, size=(50, 1))])\n", "\n", "model = GeneralMixtureModel.from_samples([NormalDistribution, ExponentialDistribution], 2, X)" ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [ { "data": { "image/png": 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TriOoPaFIrJLzEkuruk6MyLR8rmMQ0ixWhaC4uFgyk1iHDh3w8uVLAMDo0aNV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\nAHjx4gWWLFmC3NxcdOvWDUF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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = numpy.arange(0, 12.01, 0.01)\n", "\n", "plt.figure(figsize=(6, 3))\n", "plt.title(\"Learned Model\", fontsize=14)\n", "plt.ylabel(\"Probability Density\", fontsize=14)\n", "plt.fill_between(x, 0, model.probability(x))\n", "plt.ylim(0, 0.5)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like the mixture is capturing the distribution fairly well.\n", "\n", "Next, if we want to make a mixture model that describes each feature using a different distribution, we can pass in a list of distributions---one or each feature---and it'll use that distribution to model the corresponding feature. For example, in the example below, the first feature will be modeled using a normal distribution, the second feature will be modeled using a log normal distribution, and the last feature will be modeled using an exponential distribution." ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = numpy.array([mu, std, dur]).T.copy()\n", "\n", "model = GeneralMixtureModel.from_samples([NormalDistribution, LogNormalDistribution, ExponentialDistribution], 2, X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the same command we used in the second example. It creates two components, each of which model the three features respectively with those three distributions. The difference between this and the previous example is that the data here is multivariate and so univariate distributions are assumed to model different distributions.\n", "\n", "### Prediction\n", "\n", "Now that we have a mixture model, we can make predictions as to which component it is most likely to fall under. This is similar to the situation in which you predict which cluster a point falls under in k-means clustering. However, one of the benefits of using probabilistic models is that we can get a softer assignment of points based on probability, rather than a hard assignment to clusters for each point. " ] }, { "cell_type": "code", "execution_count": 179, "metadata": {}, "outputs": [ { "data": { "image/png": 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n9TV04njVzIPHyywvA/ekQ9zo+AI3zp+XMTLDVWDWjLzmvpicWMPV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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.random.normal(5, 1, size=(500, 1))\n", "X[::2] += 3\n", "\n", "x = numpy.arange(0, 10, .01)\n", "\n", "model = GeneralMixtureModel.from_samples(NormalDistribution, 2, X)\n", "\n", "plt.figure(figsize=(8, 4))\n", "plt.hist(X, bins=50, normed=True)\n", "plt.plot(x, model.distributions[0].probability(x), label=\"Distribution 1\")\n", "plt.plot(x, model.distributions[1].probability(x), label=\"Distribution 2\")\n", "plt.plot(x, model.probability(x), label=\"Mixture\")\n", "plt.legend(fontsize=14, loc=2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The prediction task is identifying, for each point, whether it falls under the orange distribution or the green distribution. This is done using Bayes' rule, where the probability of each sample under each component is divided by the probability of the sample under all components of the mixture. The posterior probability of a sample belonging to component $i$ out of $k$ components is\n", "\n", "\\begin{equation}\n", "P(M_{i}|D) = \\frac{P(D|M_{i})P(M_{i})}{\\sum\\limits_{i=1}^{k} P(D|M_{i})P(M_{i})}\n", "\\end{equation}\n", "\n", "where $D$ is the data, $M_{i}$ is the component of the model (such as the green or orange distributions), $P(D|M_{i}$ is the likelihood calculated by the `probability` function, $P(M_{i})$ is the prior probability of each component, stored under the `weights` attribute of the model, and $P(M_{i}|D)$ is the posterior probability that sums to 1 over all components.\n", "\n", "We can calculate these posterior probabilites using the `predict_proba` method." ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.99969547, 0.00030453],\n", " [0.99849679, 0.00150321],\n", " [0.99284427, 0.00715573],\n", " [0.96765651, 0.03234349],\n", " [0.86944497, 0.13055503],\n", " [0.60478603, 0.39521397],\n", " [0.26632011, 0.73367989],\n", " [0.08163 , 0.91837 ],\n", " [0.02197475, 0.97802525],\n", " [0.00582886, 0.99417114]])" ] }, "execution_count": 183, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = numpy.arange(4, 9, 0.5).reshape(10, 1)\n", "\n", "model.predict_proba(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we scan from left to right, we can see that the probability of the first component (in orange) decreases, while the probability of the second component increases. This makes sense, because the green component is centered at a higher value. The posterior probability is not the probability of the point given the distribution, but rather the probability of the distribution given the point, i.e., which component is more likely to have generated the distribution. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 1 } B_Model_Tutorial_3_Hidden_Markov_Models.ipynb000066400000000000000000001652701420461712600334210ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Hidden Markov Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hidden Markov models (HMMs) are the flagship of the pomegranate package in that they have the most features of all of the models and that they were the first algorithm implemented.\n", "\n", "Hidden Markov models are a form of structured prediction method that are popular for tagging all elements in a sequence with some \"hidden\" state. They can be thought of as extensions of Markov chains where, instead of the probability of the next observation being dependant on the current observation, the probability of the next **hidden state** is dependant on the current hidden state, and the next observation is derived from that hidden state. An example of this can be part of speech tagging, where the observations are words and the hidden states are parts of speech. Each word gets tagged with a part of speech, but dynamic programming is utilized to search through all potential word-tag combinations to identify the best set of tags across the entire sentence.\n", "\n", "Another perspective of HMMs is that they are an extension on mixture models that includes a transition matrix. Conceptually, a mixture model has a set of \"hidden\" states---the mixture components---and one can calculate the probability that each sample belongs to each component. This approach treats each observations independently. However, like in the part-of-speech example we know that an adjective typically is followed by a noun, and so position in the sequence matters. A HMM adds a transition matrix between the hidden states to incorporate this information across the sequence, allowing for higher probabilities of transitioning from the \"adjective\" hidden state to a noun or verb.\n", "\n", "pomegranate implements HMMs in a flexible manner that goes beyond what other packages allow. Let's see some examples." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fri Jan 10 2020 \n", "\n", "numpy 1.18.1\n", "scipy 1.4.1\n", "pomegranate 0.12.0\n", "\n", "compiler : Clang 10.0.0 (clang-1000.11.45.5)\n", "system : Darwin\n", "release : 17.7.0\n", "machine : x86_64\n", "processor : i386\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "import numpy\n", "\n", "from pomegranate import *\n", "\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CG rich region identification example\n", "\n", "Lets take the simplified example of CG island detection on a sequence of DNA. DNA is made up of the four canonical nucleotides, abbreviated 'A', 'C', 'G', and 'T'. We can say that regions of the genome that are enriched for nucleotides 'C' and 'G' are 'CG islands', which is a simplification of the real biological concept but sufficient for our example. The issue with identifying these regions is that they are not exclusively made up of the nucleotides 'C' and 'G', but have some 'A's and 'T's scatted amongst them. A simple model that looked for long stretches of C's and G's would not perform well, because it would miss most of the real regions.\n", "\n", "We can start off by building the model. Because HMMs involve the transition matrix, which is often represented using a graph over the hidden states, building them requires a few more steps that a simple distribution or the mixture model. Our simple model will be composed of two distributions. One distribution wil be a uniform distribution across all four characters and one will have a preference for the nucleotides C and G, while still allowing the nucleotides A and T to be present." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "d1 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})\n", "d2 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the HMM we have to first define states, which are a pair of a distribution and a name." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "s1 = State(d1, name='background')\n", "s2 = State(d2, name='CG island')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we define the HMM and pass in the states." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "model = HiddenMarkovModel()\n", "model.add_states(s1, s2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we have to define the transition matrix, which is the probability of going from one hidden state to the next hidden state. In some cases, like this one, there are high self-loop probabilities, indicating that it's likely that one will stay in the same hidden state from one observation to the next in the sequence. Other cases have a lower probability of staying in the same state, like the part of speech tagger. A part of the transition matrix is the start probabilities, which is the probability of starting in each of the hidden states. Because we create these transitions one at a time, they are very amenable to sparse transition matrices, where it is impossible to transition from one hidden state to the next." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "model.add_transition(model.start, s1, 0.5)\n", "model.add_transition(model.start, s2, 0.5)\n", "model.add_transition(s1, s1, 0.9)\n", "model.add_transition(s1, s2, 0.1)\n", "model.add_transition(s2, s1, 0.1)\n", "model.add_transition(s2, s2, 0.9)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, finally, we need to bake the model in order to finalize the internal structure. Bake must be called when the model has been fully specified." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can make predictions on some sequence. Let's create some sequence that has a CG enriched region in the middle and see whether we can identify it." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sequence: CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC\n", "hmm pred: 111111111111111000000000000000011111111111111110000\n" ] } ], "source": [ "seq = numpy.array(list('CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC'))\n", "\n", "hmm_predictions = model.predict(seq)\n", "\n", "print(\"sequence: {}\".format(''.join(seq)))\n", "print(\"hmm pred: {}\".format(''.join(map( str, hmm_predictions))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like it successfully identified a CG island in the middle (the long stretch of 0's) and another shorter one at the end. The predicted integers don't correspond to the order in which states were added to the model, but rather, the order that they exist in the model after a topological sort. More importantly, the model wasn't tricked into thinking that every CG or even pair of CGs was an island. It required many C's and G's to be part of a longer stretch to identify that region as an island. Naturally, the balance of the transition and emission probabilities will heavily influence what regions are detected.\n", "\n", "Let's say, though, that we want to get rid of that CG island prediction at the end because we don't believe that real islands can occur at the end of the sequence. We can take care of this by adding in an explicit end state that only the non-island hidden state can get to. We enforce that the model has to end in the end state, and if only the non-island state gets there, the sequence of hidden states must end in the non-island state. Here's how:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "model = HiddenMarkovModel()\n", "model.add_states(s1, s2)\n", "model.add_transition(model.start, s1, 0.5)\n", "model.add_transition(model.start, s2, 0.5)\n", "model.add_transition(s1, s1, 0.89 )\n", "model.add_transition(s1, s2, 0.10 )\n", "model.add_transition(s1, model.end, 0.01)\n", "model.add_transition(s2, s1, 0.1 )\n", "model.add_transition(s2, s2, 0.9)\n", "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that all we did was add a transition from `s1` to `model.end` with some low probability. This probability doesn't have to be high if there's only a single transition there, because there's no other possible way of getting to the end state." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sequence: CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC\n", "hmm pred: 111111111111111000000000000000011111111111111111111\n" ] } ], "source": [ "seq = numpy.array(list('CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC'))\n", "\n", "hmm_predictions = model.predict(seq)\n", "\n", "print(\"sequence: {}\".format(''.join(seq)))\n", "print(\"hmm pred: {}\".format(''.join(map( str, hmm_predictions))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This seems far more reasonable. There is a single CG island surrounded by background sequence, and something at the end. If we knew that CG islands cannot occur at the end of sequences, we need only modify the underlying structure of the HMM in order to say that the sequence must end from the background state.\n", "\n", "In the same way that mixtures could provide probabilistic estimates of class assignments rather than only hard labels, hidden Markov models can do the same. These estimates are the posterior probabilities of belonging to each of the hidden states given the observation, but also given the rest of the sequence." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.19841088 0.80158912]\n", " [0.32919701 0.67080299]\n", " [0.38366073 0.61633927]\n", " [0.58044619 0.41955381]\n", " [0.69075524 0.30924476]\n", " [0.74653183 0.25346817]\n", " [0.76392808 0.23607192]]\n" ] } ], "source": [ "print(model.predict_proba(seq)[12:19])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see here the transition from the first non-island region to the middle island region, with high probabilities in one column turning into high probabilities in the other column. The `predict` method is just taking the most likely element, the maximum-a-posteriori estimate." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to using the forward-backward algorithm to just calculate posterior probabilities for each observation, we can count the number of transitions that are predicted to occur between the hidden states." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[15.78100555 2.89559314 0. 0. ]\n", " [ 2.41288774 28.91051356 0. 1. ]\n", " [ 0.4827054 0.5172946 0. 0. ]\n", " [ 0. 0. 0. 0. ]]\n" ] } ], "source": [ "trans, ems = model.forward_backward(seq)\n", "print(trans)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the transition table, which has the soft count of the number of transitions across an edge in the model given a single sequence. It is a square matrix of size equal to the number of states (including start and end state), with number of transitions from (row_id) to (column_id). This is exemplified by the 1.0 in the first row, indicating that there is one transition from background state to the end state, as that's the only way to reach the end state. However, the third (or fourth, depending on ordering) row is the transitions from the start state, and it only slightly favors the background state. These counts are not normalized to the length of the input sequence, but can easily be done so by dividing by row sums, column sums, or entire table sums, depending on your application.\n", "\n", "A possible reason not to normalize is to run several sequences through and add up their tables, because normalizing in the end and extracting some domain knowledge. It is extremely useful in practice. For example, we can see that there is an expectation of ~2.9 transitions from CG island to background, and ~2.4 from background to CG island. This could be used to infer that there are ~2-3 edges, which makes sense if you consider that the start and end of the sequence seem like they might be part of the CG island states except for the strict transition probabilities used (look at the first few rows of the emission table above.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sequence Alignment Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets move on to a more complicated structure, that of a profile HMM. A profile HMM is used to align a sequence to a reference 'profile', where the reference profile can either be a single sequence, or an alignment of many sequences (such as a reference genome). In essence, this profile has a 'match' state for every position in the reference profile, and 'insert' state, and a 'delete' state. The insert state allows the external sequence to have an insertion into the sequence without throwing off the entire alignment, such as the following:\n", "\n", "`ACCG : Sequence`
\n", "`|| |`
\n", "`AC-G : Reference`\n", "\n", "or a deletion, which is the opposite:\n", "\n", "`A-G : Sequence`
\n", "`| |`
\n", "`ACG : Reference`\n", "\n", "The bars in the middle refer to a perfect match, whereas the lack of a bar means either a deletion/insertion, or a mismatch. A mismatch is where two positions are aligned together, but do not match. This models the biological phenomena of mutation, where one nucleotide can convert to another over time. It is usually more likely in biological sequences that this type of mutation occurs than that the nucleotide was deleted from the sequence (shifting all nucleotides over by one) and then another was inserted at the exact location (moving all nucleotides over again). Since we are using a probabilistic model, we get to define these probabilities through the use of distributions! If we want to model mismatches, we can just set our 'match' state to have an appropriate distribution with non-zero probabilities over mismatches. \n", "\n", "Lets now create a three nucleotide profile HMM, which models the sequence 'ACT'. We will fuzz this a little bit in the match states, pretending to have some prior information about what mutations occur at each position. If you don't have any information, setting a uniform, small, value over the other values is usually okay." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "model = HiddenMarkovModel( \"Global Alignment\")\n", "\n", "# Define the distribution for insertions\n", "i_d = DiscreteDistribution( { 'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25 } )\n", "\n", "# Create the insert states\n", "i0 = State( i_d, name=\"I0\" )\n", "i1 = State( i_d, name=\"I1\" )\n", "i2 = State( i_d, name=\"I2\" )\n", "i3 = State( i_d, name=\"I3\" )\n", "\n", "# Create the match states\n", "m1 = State( DiscreteDistribution({ \"A\": 0.95, 'C': 0.01, 'G': 0.01, 'T': 0.02 }) , name=\"M1\" )\n", "m2 = State( DiscreteDistribution({ \"A\": 0.003, 'C': 0.99, 'G': 0.003, 'T': 0.004 }) , name=\"M2\" )\n", "m3 = State( DiscreteDistribution({ \"A\": 0.01, 'C': 0.01, 'G': 0.01, 'T': 0.97 }) , name=\"M3\" )\n", "\n", "# Create the delete states\n", "d1 = State( None, name=\"D1\" )\n", "d2 = State( None, name=\"D2\" )\n", "d3 = State( None, name=\"D3\" )\n", "\n", "# Add all the states to the model\n", "model.add_states( [i0, i1, i2, i3, m1, m2, m3, d1, d2, d3 ] )\n", "\n", "# Create transitions from match states\n", "model.add_transition( model.start, m1, 0.9 )\n", "model.add_transition( model.start, i0, 0.1 )\n", "model.add_transition( m1, m2, 0.9 )\n", "model.add_transition( m1, i1, 0.05 )\n", "model.add_transition( m1, d2, 0.05 )\n", "model.add_transition( m2, m3, 0.9 )\n", "model.add_transition( m2, i2, 0.05 )\n", "model.add_transition( m2, d3, 0.05 )\n", "model.add_transition( m3, model.end, 0.9 )\n", "model.add_transition( m3, i3, 0.1 )\n", "\n", "# Create transitions from insert states\n", "model.add_transition( i0, i0, 0.70 )\n", "model.add_transition( i0, d1, 0.15 )\n", "model.add_transition( i0, m1, 0.15 )\n", "\n", "model.add_transition( i1, i1, 0.70 )\n", "model.add_transition( i1, d2, 0.15 )\n", "model.add_transition( i1, m2, 0.15 )\n", "\n", "model.add_transition( i2, i2, 0.70 )\n", "model.add_transition( i2, d3, 0.15 )\n", "model.add_transition( i2, m3, 0.15 )\n", "\n", "model.add_transition( i3, i3, 0.85 )\n", "model.add_transition( i3, model.end, 0.15 )\n", "\n", "# Create transitions from delete states\n", "model.add_transition( d1, d2, 0.15 )\n", "model.add_transition( d1, i1, 0.15 )\n", "model.add_transition( d1, m2, 0.70 ) \n", "\n", "model.add_transition( d2, d3, 0.15 )\n", "model.add_transition( d2, i2, 0.15 )\n", "model.add_transition( d2, m3, 0.70 )\n", "\n", "model.add_transition( d3, i3, 0.30 )\n", "model.add_transition( d3, model.end, 0.70 )\n", "\n", "# Call bake to finalize the structure of the model.\n", "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets try to align some sequences to it and see what happens!" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sequence: 'ACT' -- Log Probability: -0.5132449003570658 -- Path: M1 M2 M3\n", "Sequence: 'GGC' -- Log Probability: -11.048101241343396 -- Path: I0 I0 D1 M2 D3\n", "Sequence: 'GAT' -- Log Probability: -9.125519674022627 -- Path: I0 M1 D2 M3\n", "Sequence: 'ACC' -- Log Probability: -5.0879558788604475 -- Path: M1 M2 M3\n" ] } ], "source": [ "for sequence in map( list, ('ACT', 'GGC', 'GAT', 'ACC') ):\n", " logp, path = model.viterbi( sequence )\n", " print(\"Sequence: '{}' -- Log Probability: {} -- Path: {}\".format(\n", " ''.join( sequence ), logp, \" \".join( state.name for idx, state in path[1:-1] ) ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first and last sequence are entirely matches, meaning that it thinks the most likely alignment between the profile ACT and ACT is A-A, C-C, and T-T, which makes sense, and the most likely alignment between ACT and ACC is A-A, C-C, and T-C, which includes a mismatch. Essentially, it's more likely that there's a T-C mismatch at the end then that there was a deletion of a T at the end of the sequence, and a separate insertion of a C.\n", "\n", "The two middle sequences don't match very well, as expected! G's are not very likely in this profile at all. It predicts that the two G's are inserts, and that the C matches the C in the profile, before hitting the delete state because it can't emit a T. The third sequence thinks that the G is an insert, as expected, and then aligns the A and T in the sequence to the A and T in the master sequence, missing the middle C in the profile.\n", "\n", "By using deletes, we can handle other sequences which are shorter than three characters. Lets look at some more sequences of different lengths." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sequence: 'A' -- Log Probability: -5.406181012423981 -- Path: M1 D2 D3\n", "Sequence: 'GA' -- Log Probability: -10.88681993576597 -- Path: I0 M1 D2 D3\n", "Sequence: 'AC' -- Log Probability: -3.6244718790494277 -- Path: M1 M2 D3\n", "Sequence: 'AT' -- Log Probability: -3.644880750680635 -- Path: M1 D2 M3\n", "Sequence: 'ATCC' -- Log Probability: -10.674332964640293 -- Path: M1 D2 M3 I3 I3\n", "Sequence: 'ACGTG' -- Log Probability: -10.393824835172445 -- Path: M1 M2 I2 I2 I2 D3\n", "Sequence: 'ATTT' -- Log Probability: -8.67126440174503 -- Path: M1 I1 I1 D2 M3\n", "Sequence: 'TACCCTC' -- Log Probability: -16.903451796110275 -- Path: I0 I0 I0 I0 D1 M2 M3 I3\n", "Sequence: 'TGTCAACACT' -- Log Probability: -16.451699654050792 -- Path: I0 I0 I0 I0 I0 I0 I0 M1 M2 M3\n" ] } ], "source": [ "for sequence in map( list, ('A', 'GA', 'AC', 'AT', 'ATCC', 'ACGTG', 'ATTT', 'TACCCTC', 'TGTCAACACT') ):\n", " logp, path = model.viterbi( sequence )\n", " print(\"Sequence: '{}' -- Log Probability: {} -- Path: {}\".format(\n", " ''.join( sequence ), logp, \" \".join( state.name for idx, state in path[1:-1] ) ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, more of the same expected. You'll notice most of the use of insertion states are at I0, because most of the insertions are at the beginning of the sequence. It's more probable to simply stay in I0 at the beginning instead of go from I0 to D1 to I1, or going to another insert state along there. You'll see other insert states used when insertions occur in other places in the sequence, like 'ATTT' and 'ACGTG'.\n", "Now that we have the path, we need to convert it into an alignment, which is significantly more informative to look at." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sequence: A, Log Probability: -5.406181012423981\n", "ACT\n", "A--\n", "\n", "Sequence: GA, Log Probability: -10.88681993576597\n", "-ACT\n", "GA--\n", "\n", "Sequence: AC, Log Probability: -3.6244718790494277\n", "ACT\n", "AC-\n", "\n", "Sequence: AT, Log Probability: -3.644880750680635\n", "ACT\n", "A-T\n", "\n", "Sequence: ATCC, Log Probability: -10.674332964640293\n", "ACT--\n", "A-TCC\n", "\n", "Sequence: ACGTG, Log Probability: -10.393824835172445\n", "AC---T\n", "ACGTG-\n", "\n", "Sequence: ATTT, Log Probability: -8.67126440174503\n", "A--CT\n", "ATT-T\n", "\n", "Sequence: TACCCTC, Log Probability: -16.903451796110275\n", "----ACT-\n", "TACC-CTC\n", "\n", "Sequence: TGTCAACACT, Log Probability: -16.451699654050792\n", "-------ACT\n", "TGTCAACACT\n", "\n" ] } ], "source": [ "def path_to_alignment( x, y, path ):\n", " \"\"\"\n", " This function will take in two sequences, and the ML path which is their alignment,\n", " and insert dashes appropriately to make them appear aligned. This consists only of\n", " adding a dash to the model sequence for every insert in the path appropriately, and\n", " a dash in the observed sequence for every delete in the path appropriately.\n", " \"\"\"\n", " \n", " for i, (index, state) in enumerate( path[1:-1] ):\n", " name = state.name\n", " \n", " if name.startswith( 'D' ):\n", " y = y[:i] + '-' + y[i:]\n", " elif name.startswith( 'I' ):\n", " x = x[:i] + '-' + x[i:]\n", "\n", " return x, y\n", "\n", "for sequence in map( list, ('A', 'GA', 'AC', 'AT', 'ATCC', 'ACGTG', 'ATTT', 'TACCCTC', 'TGTCAACACT') ):\n", " logp, path = model.viterbi( sequence )\n", " x, y = path_to_alignment( 'ACT', ''.join(sequence), path )\n", " \n", " print(\"Sequence: {}, Log Probability: {}\".format( ''.join(sequence), logp ))\n", " print(\"{}\\n{}\".format( x, y ))\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training Hidden Markov Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two main algorithms for training hidden Markov models-- Baum Welch (structured version of Expectation Maximization), and Viterbi training. Since we don't start off with labels on the data, these are both unsupervised training algorithms. In order to assign labels, Baum Welch uses EM to assign soft labels (weights in this case) to each point belonging to each state, and then using weighted MLE estimates to update the distributions. Viterbi assigns hard labels to each observation using the Viterbi algorithm, and then updates the distributions based on these hard labels.\n", "\n", "pomegranate is extremely well featured when it comes to regularization methods for training, supporting tied emissions and edges, edge and emission inertia, freezing nodes or edges, edge pseudocounts, and multithreaded training. Lets look at some examples of the following:\n", "\n", "### Tied Emissions\n", "\n", "Sometimes we want to say that multiple states model the same phenomena, but are simply at different points in the graph because we are utilizing complicated edge structure. An example is in the example of the global alignment HMM we saw. All insert states represent the same phenomena, which is nature randomly inserting a nucleotide, and this probability should be the same regardless of position. However, we can't simply have a single insert state, or we'd be allowed to transition from any match state to any other match state.\n", "\n", "You can tie emissions together simply by passing the same distribution object to multiple states. That's it." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "d = NormalDistribution( 5, 2 )\n", "\n", "s1 = State( d, name=\"Tied1\" )\n", "s2 = State( d, name=\"Tied2\" )\n", "\n", "s3 = State( NormalDistribution( 5, 2 ), name=\"NotTied1\" )\n", "s4 = State( NormalDistribution( 5, 2 ), name=\"NotTied2\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You have now indicated that these two states are tied, and when training, the weights of all points going to s2 will be added to the weights of all points going to s1 when updating d. As a side note, this is implemented in a computationally efficient manner such that d will only be updated once, not twice (but giving the same result). s3 and s4 are not tied together, because while they have the same distribution, it is not the same python object.\n", "\n", "### Tied Edges\n", "\n", "Edges can be tied together for the same reason. If you have a modular structure to your HMM, perhaps you believe this repeating structure doesn't (or shouldn't) have a position specific edge structure. You can do this simply by adding a group when you add transitions." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "model = HiddenMarkovModel()\n", "model.add_states( [s1, s2] )\n", "model.add_transition( model.start, s1, 0.5, group='a' )\n", "model.add_transition( model.start, s2, 0.5, group='b' )\n", "model.add_transition( s1, s2, 0.5, group='a' )\n", "model.add_transition( s2, s1, 0.5, group='b' )\n", "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above model doesn't necessarily make sense, but it shows how simple it is to tie edges as well. You can go ahead and train normally from this point, without needing to change any code.\n", "\n", "### Inertia\n", "\n", "The next options are inertia on edges or on distributions. This simply means that you update your parameters as (previous_parameter * inertia) + (new_parameter * (1-inertia) ). It is a way to prevent your updates from overfitting immediately. You can specify this in the train function using either `edge_inertia` or `distribution_inertia`. These default to 0, with 1 being the maximum, meaning that you don't update based on new evidence, the same as freezing a distribution or the edges." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"name\" : \"None\",\n", " \"start\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-start\",\n", " \"weight\" : 1.0\n", " },\n", " \"end\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-end\",\n", " \"weight\" : 1.0\n", " },\n", " \"states\" : [\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 4.0000059049,\n", " 1.563474497595802\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"Tied1\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 4.0000059049,\n", " 1.563474497595802\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"Tied2\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-start\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-end\",\n", " \"weight\" : 1.0\n", " }\n", " ],\n", " \"end_index\" : 3,\n", " \"start_index\" : 2,\n", " \"silent_index\" : 2,\n", " \"edges\" : [\n", " [\n", " 2,\n", " 0,\n", " 0.5,\n", " 0.5,\n", " 0\n", " ],\n", " [\n", " 2,\n", " 1,\n", " 0.5,\n", " 0.5,\n", " 1\n", " ],\n", " [\n", " 0,\n", " 1,\n", " 1.0,\n", " 0.5,\n", " 0\n", " ],\n", " [\n", " 1,\n", " 0,\n", " 1.0,\n", " 0.5,\n", " 1\n", " ]\n", " ],\n", " \"distribution ties\" : [\n", " [\n", " 0,\n", " 1\n", " ],\n", " [\n", " 1,\n", " 0\n", " ]\n", " ]\n", "}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit( [[5, 2, 3, 4], [5, 7, 2, 3, 5]], distribution_inertia=0.3, edge_inertia=0.25 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pseudocounts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another way of regularizing your model is to add pseudocounts to your edges (which have non-zero probabilities). When updating your edges in the future, you add this pseudocount to the count of transitions across that edge in the future. This gives a more Bayesian estimate of the edge probability, and is useful if you have a large model and don't expect to cross most of the edges with your training data. An example might be a complicated profile HMM, where you don't expect to see deletes or inserts at all in your training data, but don't want to change from the default values.\n", "\n", "In pomegranate, pseudocounts default to the initial probabilities, so that if you don't see data, the edge values simply aren't updated. You can define both edge specific pseudocounts when you define the transition. When you train, you must define `use_pseudocount=True`." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"name\" : \"None\",\n", " \"start\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-start\",\n", " \"weight\" : 1.0\n", " },\n", " \"end\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-end\",\n", " \"weight\" : 1.0\n", " },\n", " \"states\" : [\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 3.967631778032427,\n", " 1.3388904282425669\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"s1\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 4.038616148637643,\n", " 1.7942514140960613\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"s2\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-start\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-end\",\n", " \"weight\" : 1.0\n", " }\n", " ],\n", " \"end_index\" : 3,\n", " \"start_index\" : 2,\n", " \"silent_index\" : 2,\n", " \"edges\" : [\n", " [\n", " 2,\n", " 0,\n", " 0.7883901790047138,\n", " 4.2,\n", " null\n", " ],\n", " [\n", " 2,\n", " 1,\n", " 0.21160982099528625,\n", " 1.3,\n", " null\n", " ],\n", " [\n", " 0,\n", " 1,\n", " 1.0,\n", " 5.2,\n", " null\n", " ],\n", " [\n", " 1,\n", " 0,\n", " 1.0,\n", " 0.9,\n", " null\n", " ]\n", " ],\n", " \"distribution ties\" : []\n", "}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = State( NormalDistribution( 3, 1 ), name=\"s1\" )\n", "s2 = State( NormalDistribution( 6, 2 ), name=\"s2\" )\n", "\n", "model = HiddenMarkovModel()\n", "model.add_states( [s1, s2] )\n", "model.add_transition( model.start, s1, 0.5, pseudocount=4.2 )\n", "model.add_transition( model.start, s2, 0.5, pseudocount=1.3 )\n", "model.add_transition( s1, s2, 0.5, pseudocount=5.2 )\n", "model.add_transition( s2, s1, 0.5, pseudocount=0.9 )\n", "model.bake()\n", "model.fit( [[5, 2, 3, 4], [5, 7, 2, 3, 5]], max_iterations=5, use_pseudocount=True )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The other way is to put a blanket pseudocount on all edges." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"name\" : \"None\",\n", " \"start\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-start\",\n", " \"weight\" : 1.0\n", " },\n", " \"end\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-end\",\n", " \"weight\" : 1.0\n", " },\n", " \"states\" : [\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 3.982144723850736,\n", " 1.547455896814475\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"s1\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 4.018144756842671,\n", " 1.5793744688756404\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"s2\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-start\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-end\",\n", " \"weight\" : 1.0\n", " }\n", " ],\n", " \"end_index\" : 3,\n", " \"start_index\" : 2,\n", " \"silent_index\" : 2,\n", " \"edges\" : [\n", " [\n", " 2,\n", " 0,\n", " 0.5007412253170119,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 2,\n", " 1,\n", " 0.49925877468298807,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 0,\n", " 1,\n", " 1.0,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 1,\n", " 0,\n", " 1.0,\n", " 0.5,\n", " null\n", " ]\n", " ],\n", " \"distribution ties\" : []\n", "}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = State( NormalDistribution( 3, 1 ), name=\"s1\" )\n", "s2 = State( NormalDistribution( 6, 2 ), name=\"s2\" )\n", "\n", "model = HiddenMarkovModel()\n", "model.add_states( [s1, s2] )\n", "model.add_transition( model.start, s1, 0.5 )\n", "model.add_transition( model.start, s2, 0.5 )\n", "model.add_transition( s1, s2, 0.5 )\n", "model.add_transition( s2, s1, 0.5 )\n", "model.bake()\n", "model.fit( [[5, 2, 3, 4], [5, 7, 2, 3, 5]], max_iterations=5, transition_pseudocount=20, use_pseudocount=True )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that there isn't as much of an improvement. This is part of regularization, though. We sacrifice fitting the data exactly in order for our model to generalize better to future data. The majority of the training improvement is likely coming from the emissions better fitting the data, though." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multithreaded Training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since pomegranate is implemented in cython, the majority of functions are written with the GIL released. A benefit of doing this is that we can use multithreading in order to make some computationally intensive tasks take less time. However, a downside is that python doesn't play nicely with multithreading, and so there are some cases where training using multithreading can make your model training take significantly longer. I investigate this in an early multithreading pull request here. Things have improved since then, but the gist is that if you have a small model (less than 15 states), it may be detrimental, but the larger your model is, the more it scales towards getting a speed improvement exactly the number of threads you use. You can specify multithreading using the `n_jobs` keyword. All structures in pomegranate are thread safe, so you don't need to worry about race conditions." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"name\" : \"None\",\n", " \"start\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-start\",\n", " \"weight\" : 1.0\n", " },\n", " \"end\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-end\",\n", " \"weight\" : 1.0\n", " },\n", " \"states\" : [\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 3.200000252045981,\n", " 1.6613245827659173\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"s1\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 4.6363634085403405,\n", " 1.431638224527499\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"s2\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-start\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-end\",\n", " \"weight\" : 1.0\n", " }\n", " ],\n", " \"end_index\" : 3,\n", " \"start_index\" : 2,\n", " \"silent_index\" : 2,\n", " \"edges\" : [\n", " [\n", " 2,\n", " 0,\n", " 4.036978946423165e-07,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 2,\n", " 1,\n", " 0.9999995963021053,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 0,\n", " 1,\n", " 1.0,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 1,\n", " 0,\n", " 1.0,\n", " 0.5,\n", " null\n", " ]\n", " ],\n", " \"distribution ties\" : []\n", "}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = State( NormalDistribution( 3, 1 ), name=\"s1\" )\n", "s2 = State( NormalDistribution( 6, 2 ), name=\"s2\" )\n", "\n", "model = HiddenMarkovModel()\n", "model.add_states( [s1, s2] )\n", "model.add_transition( model.start, s1, 0.5 )\n", "model.add_transition( model.start, s2, 0.5 )\n", "model.add_transition( s1, s2, 0.5 )\n", "model.add_transition( s2, s1, 0.5 )\n", "model.bake()\n", "model.fit( [[5, 2, 3, 4, 7, 3, 6, 3, 5, 2, 4], [5, 7, 2, 3, 5, 1, 3, 5, 6, 2]], max_iterations=5 )" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"name\" : \"None\",\n", " \"start\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-start\",\n", " \"weight\" : 1.0\n", " },\n", " \"end\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-end\",\n", " \"weight\" : 1.0\n", " },\n", " \"states\" : [\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 3.200000252045981,\n", " 1.6613245827659173\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"s1\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 4.6363634085403405,\n", " 1.431638224527499\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"s2\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-start\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-end\",\n", " \"weight\" : 1.0\n", " }\n", " ],\n", " \"end_index\" : 3,\n", " \"start_index\" : 2,\n", " \"silent_index\" : 2,\n", " \"edges\" : [\n", " [\n", " 2,\n", " 0,\n", " 4.036978946423165e-07,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 2,\n", " 1,\n", " 0.9999995963021053,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 0,\n", " 1,\n", " 1.0,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 1,\n", " 0,\n", " 1.0,\n", " 0.5,\n", " null\n", " ]\n", " ],\n", " \"distribution ties\" : []\n", "}" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = State( NormalDistribution( 3, 1 ), name=\"s1\" )\n", "s2 = State( NormalDistribution( 6, 2 ), name=\"s2\" )\n", "\n", "model = HiddenMarkovModel()\n", "model.add_states( [s1, s2] )\n", "model.add_transition( model.start, s1, 0.5 )\n", "model.add_transition( model.start, s2, 0.5 )\n", "model.add_transition( s1, s2, 0.5 )\n", "model.add_transition( s2, s1, 0.5 )\n", "model.bake()\n", "model.fit( [[5, 2, 3, 4, 7, 3, 6, 3, 5, 2, 4], [5, 7, 2, 3, 5, 1, 3, 5, 6, 2]], max_iterations=5, n_jobs=4 )" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Serialization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "General Mixture Models support serialization to JSONs using `to_json()` and `from_json( json )`. This is useful is you want to train a GMM on large amounts of data, taking a significant amount of time, and then use this model in the future without having to repeat this computationally intensive step (sounds familiar by now). Lets look at the original CG island model, since it's significantly smaller." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "seq = list('CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC')\n", "\n", "d1 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})\n", "d2 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})\n", "\n", "s1 = State( d1, name='background' )\n", "s2 = State( d2, name='CG island' )\n", "\n", "hmm = HiddenMarkovModel()\n", "hmm.add_states(s1, s2)\n", "hmm.add_transition( hmm.start, s1, 0.5 )\n", "hmm.add_transition( hmm.start, s2, 0.5 )\n", "hmm.add_transition( s1, s1, 0.5 )\n", "hmm.add_transition( s1, s2, 0.5 )\n", "hmm.add_transition( s2, s1, 0.5 )\n", "hmm.add_transition( s2, s2, 0.5 )\n", "hmm.bake()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"name\" : \"None\",\n", " \"start\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-start\",\n", " \"weight\" : 1.0\n", " },\n", " \"end\" : {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-end\",\n", " \"weight\" : 1.0\n", " },\n", " \"states\" : [\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.1,\n", " \"C\" : 0.4,\n", " \"G\" : 0.4,\n", " \"T\" : 0.1\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"CG island\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : {\n", " \"class\" : \"Distribution\",\n", " \"dtype\" : \"str\",\n", " \"name\" : \"DiscreteDistribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.25,\n", " \"C\" : 0.25,\n", " \"G\" : 0.25,\n", " \"T\" : 0.25\n", " }\n", " ],\n", " \"frozen\" : false\n", " },\n", " \"name\" : \"background\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-start\",\n", " \"weight\" : 1.0\n", " },\n", " {\n", " \"class\" : \"State\",\n", " \"distribution\" : null,\n", " \"name\" : \"None-end\",\n", " \"weight\" : 1.0\n", " }\n", " ],\n", " \"end_index\" : 3,\n", " \"start_index\" : 2,\n", " \"silent_index\" : 2,\n", " \"edges\" : [\n", " [\n", " 2,\n", " 1,\n", " 0.5,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 2,\n", " 0,\n", " 0.5,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 1,\n", " 1,\n", " 0.5,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 1,\n", " 0,\n", " 0.5,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 0,\n", " 1,\n", " 0.5,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 0,\n", " 0,\n", " 0.5,\n", " 0.5,\n", " null\n", " ]\n", " ],\n", " \"distribution ties\" : []\n", "}\n" ] } ], "source": [ "print(hmm.to_json())" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-69.70121909739684\n" ] } ], "source": [ "seq = list('CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC')\n", "print(hmm.log_probability( seq ))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-69.70121909739684\n" ] } ], "source": [ "hmm_2 = HiddenMarkovModel.from_json( hmm.to_json() )\n", "print(hmm_2.log_probability( seq ))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } B_Model_Tutorial_4_Bayesian_Networks.ipynb000066400000000000000000001064111420461712600330240ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Bayesian Networks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bayesian networks are a powerful inference tool, in which a set of variables are represented as nodes, and the lack of an edge represents a conditional independence statement between the two variables, and an edge represents a dependence between the two variables. One of the powerful components of a Bayesian network is the ability to infer the values of certain variables, given observed values for another set of variables. These are referred to as the 'hidden' and 'observed' variables respectively, and need not be set at the time the network is created. The same network can have a different set of variables be hidden or observed between two data points. The more values which are observed, the closer the inferred values will be to the truth.\n", "\n", "While Bayesian networks can have extremely complex emission probabilities, usually Gaussian or conditional Gaussian distributions, pomegranate currently supports only discrete Bayesian networks. Bayesian networks are explicitly turned into Factor Graphs when inference is done, wherein the Bayesian network is turned into a bipartite graph with all variables having marginal nodes on one side, and joint tables on the other." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sat Mar 14 2020 \n", "\n", "numpy 1.18.1\n", "scipy 1.3.2\n", "pomegranate 0.12.0\n", "\n", "compiler : GCC 7.3.0\n", "system : Linux\n", "release : 4.15.0-88-generic\n", "machine : x86_64\n", "processor : x86_64\n", "CPU cores : 8\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "import numpy\n", "\n", "from pomegranate import *\n", "\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Monty Hall Gameshow\n", "\n", "The Monty Hall problem arose from the gameshow Let's Make a Deal, where a guest had to choose which one of three doors had a prize behind it. The twist was that after the guest chose, the host, originally Monty Hall, would then open one of the doors the guest did not pick and ask if the guest wanted to switch which door they had picked. Initial inspection may lead you to believe that if there are only two doors left, there is a 50-50 chance of you picking the right one, and so there is no advantage one way or the other. However, it has been proven both through simulations and analytically that there is in fact a 66% chance of getting the prize if the guest switches their door, regardless of the door they initially went with.\n", "\n", "We can reproduce this result using Bayesian networks with three nodes, one for the guest, one for the prize, and one for the door Monty chooses to open. The door the guest initially chooses and the door the prize is behind are completely random processes across the three doors, but the door which Monty opens is dependent on both the door the guest chooses (it cannot be the door the guest chooses), and the door the prize is behind (it cannot be the door with the prize behind it).\n", "\n", "To create the Bayesian network in pomegranate, we first create the distributions which live in each node in the graph. For a discrete (aka categorical) bayesian network we use DiscreteDistribution objects for the root nodes and ConditionalProbabilityTable objects for the inner and leaf nodes. The columns in a ConditionalProbabilityTable correspond to the order in which the parents (the second argument) are specified, and the last column is the value the ConditionalProbabilityTable itself takes. In the case below, the first column corresponds to the value 'guest' takes, then the value 'prize' takes, and then the value that 'monty' takes. 'B', 'C', 'A' refers then to the probability that Monty reveals door 'A' given that the guest has chosen door 'B' and that the prize is actually behind door 'C', or P(Monty='A'|Guest='B', Prize='C')." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# The guests initial door selection is completely random\n", "guest = DiscreteDistribution({'A': 1./3, 'B': 1./3, 'C': 1./3})\n", "\n", "# The door the prize is behind is also completely random\n", "prize = DiscreteDistribution({'A': 1./3, 'B': 1./3, 'C': 1./3})\n", "\n", " # Monty is dependent on both the guest and the prize. \n", "monty = ConditionalProbabilityTable(\n", " [[ 'A', 'A', 'A', 0.0 ],\n", " [ 'A', 'A', 'B', 0.5 ],\n", " [ 'A', 'A', 'C', 0.5 ],\n", " [ 'A', 'B', 'A', 0.0 ],\n", " [ 'A', 'B', 'B', 0.0 ],\n", " [ 'A', 'B', 'C', 1.0 ],\n", " [ 'A', 'C', 'A', 0.0 ],\n", " [ 'A', 'C', 'B', 1.0 ],\n", " [ 'A', 'C', 'C', 0.0 ],\n", " [ 'B', 'A', 'A', 0.0 ],\n", " [ 'B', 'A', 'B', 0.0 ],\n", " [ 'B', 'A', 'C', 1.0 ],\n", " [ 'B', 'B', 'A', 0.5 ],\n", " [ 'B', 'B', 'B', 0.0 ],\n", " [ 'B', 'B', 'C', 0.5 ],\n", " [ 'B', 'C', 'A', 1.0 ],\n", " [ 'B', 'C', 'B', 0.0 ],\n", " [ 'B', 'C', 'C', 0.0 ],\n", " [ 'C', 'A', 'A', 0.0 ],\n", " [ 'C', 'A', 'B', 1.0 ],\n", " [ 'C', 'A', 'C', 0.0 ],\n", " [ 'C', 'B', 'A', 1.0 ],\n", " [ 'C', 'B', 'B', 0.0 ],\n", " [ 'C', 'B', 'C', 0.0 ],\n", " [ 'C', 'C', 'A', 0.5 ],\n", " [ 'C', 'C', 'B', 0.5 ],\n", " [ 'C', 'C', 'C', 0.0 ]], [guest, prize]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we pass these distributions into state objects along with the name for the node." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# State objects hold both the distribution, and a high level name.\n", "s1 = State(guest, name=\"guest\")\n", "s2 = State(prize, name=\"prize\")\n", "s3 = State(monty, name=\"monty\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we add the states to the network, exactly like we did when making a HMM. In the future, all matrices of data should have their columns organized in the same order that the states are added to the network. The way the states are added to the network makes no difference to it, and so you should add the states according to how the columns are organized in your data." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Create the Bayesian network object with a useful name\n", "model = BayesianNetwork(\"Monty Hall Problem\")\n", "\n", "# Add the three states to the network \n", "model.add_states(s1, s2, s3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we need to add edges to the model. The edges represent which states are parents of which other states. This is currently a bit redundant with passing in the distribution objects that are parents for each ConditionalProbabilityTable. For now edges are added from parent -> child by calling `model.add_edge(parent, child)`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Add edges which represent conditional dependencies, where the second node is \n", "# conditionally dependent on the first node (Monty is dependent on both guest and prize)\n", "model.add_edge(s1, s3)\n", "model.add_edge(s2, s3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lastly, the model must be baked to finalize the internals. Since Bayesian networks use factor graphs for inference, an explicit factor graph is produced from the Bayesian network during the bake step." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Predicting Probabilities\n", "\n", "We can calculate probabilities of a sample under the Bayesian network in the same way that we can calculate probabilities under other models. In this case, let's calculate the probability that you initially said door A, that Monty then opened door B, but that the actual car was behind door C." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.11111111111111109" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.probability([['A', 'B', 'C']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That seems in line with what we know, that there is a 1/9th probability of that happening. \n", "\n", "Next, let's look at an impossible situation. What is the probability of initially saying door A, that Monty opened door B, and that the car was actually behind door B." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.probability([['A', 'B', 'B']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The reason that situation is impossible is because Monty can't open a door that has the car behind it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Performing Inference\n", "\n", "Perhaps the most powerful aspect of Bayesian networks is their ability to perform inference. Given any set of observed variables, including no observations, Bayesian networks can make predictions for all other variables. Obviously, the more variables that are observed, the more accurate the predictions will get of the remaining variables. \n", "\n", "pomegranate uses the loopy belief propagation algorithm to do inference. This is an approximate algorithm which can yield exact results in tree-like graphs, and in most other cases still yields good results. Inference on a Bayesian network consists of taking in observations for a subset of the variables and using that to infer the values that the other variables take. The most variables which are observed, the closer the inferred values will be to truth. One of the powers of Bayesian networks is that the set of observed and 'hidden' (or unobserved) variables does not need to be specified beforehand, and can change from sample to sample.\n", "\n", "We can run inference using the `predict_proba` method and passing in a dictionary of values, where the key is the name of the state and the value is the observed value for that state. If we don't supply any values, we get the marginal of the graph, which is just the frequency of each value for each variable over an infinite number of randomly drawn samples from the graph.\n", "\n", "Lets see what happens when we look at the marginal of the Monty hall network." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.33333333333333337,\n", " \"B\" :0.33333333333333337,\n", " \"C\" :0.33333333333333337\n", " }\n", " ],\n", " \"frozen\" :false\n", "},\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.33333333333333337,\n", " \"B\" :0.33333333333333337,\n", " \"C\" :0.33333333333333337\n", " }\n", " ],\n", " \"frozen\" :false\n", "},\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"C\" :0.3333333333333333,\n", " \"B\" :0.3333333333333333,\n", " \"A\" :0.3333333333333333\n", " }\n", " ],\n", " \"frozen\" :false\n", "}], dtype=object)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict_proba({})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are returned three `DiscreteDistribution` objects, each representing the marginal distribution for each variable, in the same order they were put into the model. In this case, they represent the guest, prize, and monty variables respectively. We see that everything is equally likely.\n", "\n", "We can also pass in an array where `None` (or `np.nan`) correspond to the unobserved values." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.33333333333333337,\n", " \"B\" :0.33333333333333337,\n", " \"C\" :0.33333333333333337\n", " }\n", " ],\n", " \"frozen\" :false\n", " },\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.33333333333333337,\n", " \"B\" :0.33333333333333337,\n", " \"C\" :0.33333333333333337\n", " }\n", " ],\n", " \"frozen\" :false\n", " },\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"C\" :0.3333333333333333,\n", " \"B\" :0.3333333333333333,\n", " \"A\" :0.3333333333333333\n", " }\n", " ],\n", " \"frozen\" :false\n", " }], dtype=object)]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict_proba([[None, None, None]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All of the variables that were observed will be the observed value, and all of the variables that were unobserved will be a `DiscreteDistribution` object. This means that `parameters[0]` will return the underlying dictionary used by the distribution.\n", "\n", "Now lets do something different, and say that the guest has chosen door 'A'. We do this by passing a dictionary to `predict_proba` with key pairs consisting of the name of the state (in the state object), and the value which that variable has taken, or by passing in a list where the first index is our observation." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array(['A',\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.3333333333333333,\n", " \"B\" :0.3333333333333333,\n", " \"C\" :0.3333333333333333\n", " }\n", " ],\n", " \"frozen\" :false\n", " },\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"C\" :0.49999999999999994,\n", " \"B\" :0.49999999999999994,\n", " \"A\" :0.0\n", " }\n", " ],\n", " \"frozen\" :false\n", " }], dtype=object)]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict_proba([['A', None, None]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that now Monty will not open door 'A', because the guest has chosen it. At the same time, the distribution over the prize has not changed, it is still equally likely that the prize is behind each door.\n", "\n", "Now, lets say that Monty opens door 'C' and see what happens. Here we use a dictionary rather than a list simply to show how one can use both input forms depending on what is more convenient." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array(['A',\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.3333333333333334,\n", " \"B\" :0.6666666666666664,\n", " \"C\" :0.0\n", " }\n", " ],\n", " \"frozen\" :false\n", " },\n", " 'C'], dtype=object)]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict_proba([{'guest': 'A', 'monty': 'C'}])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suddenly, we see that the distribution over prizes has changed. It is now twice as likely that the car is behind the door labeled 'B'. This demonstrates that when on the game show, it is always better to change your initial guess after being shown an open door. Now you could go and win tons of cars, except that the game show got cancelled." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imputation Given Structured Constraints\n", "\n", "The task of filling in an incomplete matrix can be called imputation, and there are many approaches for doing so. One of the most well known is that of matrix factorization, where a latent representation is learned for each of the columns and each of the rows such that the dot product between the two can reconstruct values in the matrix. Due to the manner that these latent representations are learned, the matrix does not need to be complete, and the dot product can then be used to fill in all of the missing values.\n", "\n", "One weakness of the matrix factorization approach is that constraints and known relationships can't be enforced between the features. A simple example is that the rule \"when column 1 is 'A' and column 2 is 'B', column 3 must be 'C'\" can potentially be learned in the representation, but can't be simply hard-coded like a conditional probability table would. A more complex example would say that a pixel in an image can be predicted from its neighbors, whereas the notion of neighbors is more difficult to specify for a factorization approach.\n", "\n", "The process for imputing data given a Bayesian network is to either first learn the structure of the network from the given data, or have a known structure, and then use loopy-belief propogation to predict the best values for the missing features.\n", "\n", "Let's see an example of this on the digits data set, binarizing the data based on the median value. We'll only use the first two rows because learning large, unconstrained Bayesian networks is difficult. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import load_digits\n", "\n", "data = load_digits()\n", "X, _ = data.data, data.target\n", "\n", "plt.imshow(X[0].reshape(8, 8))\n", "plt.grid(False)\n", "plt.show()\n", "\n", "X = X[:,:16]\n", "X = (X > numpy.median(X)).astype('float64')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's remove a large portion of the pixels randomly from each of the images. We can do that with numpy arrays by setting missing values to `np.nan`." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 1., ..., 1., 1., 0.],\n", " [ 0., nan, nan, ..., nan, 0., 0.],\n", " [ 0., 0., nan, ..., 1., 0., 0.],\n", " ...,\n", " [ 0., 0., nan, ..., 1., 0., nan],\n", " [ 0., nan, nan, ..., 1., nan, 0.],\n", " [nan, 0., 1., ..., nan, 0., nan]])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numpy.random.seed(111)\n", "\n", "i = numpy.random.randint(X.shape[0], size=10000)\n", "j = numpy.random.randint(X.shape[1], size=10000)\n", "\n", "X_missing = X.copy()\n", "X_missing[i, j] = numpy.nan\n", "X_missing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can set up a baseline for how good an imputation is by using the average pixel value as a replacement. Because this is binary data, we can use the mean absolute error to measure how good these approaches are on imputing the pixels that are not observed." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n" ] }, { "data": { "text/plain": [ "0.1954958904004812" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from fancyimpute import SimpleFill\n", "\n", "y_pred = SimpleFill().fit_transform(X_missing)[i, j]\n", "numpy.abs(y_pred - X[i, j]).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we can see how good an IterativeSVD approach is, which is similar to a matrix factorization." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.26645818594371573" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from fancyimpute import IterativeSVD\n", "\n", "y_pred = IterativeSVD(verbose=False).fit_transform(X_missing)[i, j]\n", "numpy.abs(y_pred - X[i, j]).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can try building a Bayesian network using the Chow-Liu algorithm and then use the resulting network to fill in the matrix." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.1092" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_hat = BayesianNetwork.from_samples(X_missing, max_parents=1).predict(X_missing)\n", "numpy.abs(numpy.array(y_hat)[i, j] - X[i, j]).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can compare this to a better imputation approach, that of K-nearest neighbors, and see how good using a Bayesian network is." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.16290500020624946" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from fancyimpute import KNN\n", "\n", "y_pred = KNN(verbose=False).fit_transform(X_missing)[i, j]\n", "numpy.abs(y_pred - X[i, j]).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like in this case the Bayesian network is better than KNN for imputing the pixels. It is, however, slower than the fancyimpute methods." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The API\n", "\n", "### Initialization\n", "\n", "While the methods are similar across all models in pomegranate, Bayesian networks are more closely related to hidden Markov models than the other models. This makes sense, because both of them rely on graphical structures.\n", "\n", "The first way to initialize Bayesian networks is to pass in one distribution and state at a time, and then pass in edges. This is similar to hidden Markov models." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "d1 = DiscreteDistribution({True: 0.2, False: 0.8})\n", "d2 = DiscreteDistribution({True: 0.6, False: 0.4})\n", "d3 = ConditionalProbabilityTable(\n", " [[True, True, True, 0.2],\n", " [True, True, False, 0.8],\n", " [True, False, True, 0.3],\n", " [True, False, False, 0.7],\n", " [False, True, True, 0.9],\n", " [False, True, False, 0.1],\n", " [False, False, True, 0.4],\n", " [False, False, False, 0.6]], [d1, d2])\n", "\n", "s1 = State(d1, name=\"s1\")\n", "s2 = State(d2, name=\"s2\")\n", "s3 = State(d3, name=\"s3\")\n", "\n", "model = BayesianNetwork()\n", "model.add_states(s1, s2, s3)\n", "model.add_edge(s1, s3)\n", "model.add_edge(s2, s3)\n", "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The other way is to use the `from_samples` method if given a data set." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "must have pygraphviz installed for visualization", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBayesianNetwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pomegranate-0.12.0-py3.7-linux-x86_64.egg/pomegranate/BayesianNetwork.pyx\u001b[0m in \u001b[0;36mpomegranate.BayesianNetwork.BayesianNetwork.plot\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: must have pygraphviz installed for visualization" ] } ], "source": [ "numpy.random.seed(111)\n", "\n", "X = numpy.random.randint(2, size=(15, 15))\n", "X[:,5] = X[:,4] = X[:,3]\n", "X[:,11] = X[:,12] = X[:,13]\n", "\n", "model = BayesianNetwork.from_samples(X)\n", "model.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The structure learning algorithms are covered more in depth in the accompanying notebook.\n", "\n", "We can look at the structure of the network by using the `structure` attribute. Each tuple is a node, and the integers in the tuple correspond to the parents of the node." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prediction\n", "\n", "The prediction method is similar to the other models. Inference is done using loopy belief propogation, which is an approximate version of the forward-backward algorithm that can be significantly faster while still precise. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.predict([[False, False, False, False, None, None, False, None, False, None, True, None, None, True, False]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The predict method will simply return the argmax of all the distributions after running the `predict_proba` method. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.predict_proba([[False, False, False, False, None, None, False, None, False, None, \n", " True, None, None, True, False]])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 1 } B_Model_Tutorial_4b_Bayesian_Network_Structure_Learning.ipynb000066400000000000000000010323701420461712600367050ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Bayesian Network Structure Learning\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com\n", " \n", "Learning the structure of Bayesian networks can be complicated for two main reasons: (1) difficulties in inferring causality and (2) the super-exponential number of directed edges that could exist in a dataset. The first issue presents itself when the structure lerning algorithm considers only correlation or another measure of co-occurrence to determine if an edge should exist. The first point presents challenges which deserve a far more in depth treatment unrelated to implementations in pomegranate, so instead this tutorial will focus on how pomegranate implements fast Bayesian network structure learning. It will also cover a new concept called the \"constraint graph\" which can be used to massively speed up structure search while also making causality assignment a bit more reasonable.\n", "\n", "### Introduction to Bayesian Network Structure Learning\n", "\n", "Most methods for Bayesian network structure learning (BNSL) can be put into one of the following three categories: \n", "\n", "(1) Search and Score: The most intuitive method is that of 'search and score,' where one searches over the space of all possible directed acyclic graphs (DAGs) and identifies the one that minimizes some objective function. Typical objective functions attempt to balance the log probability of the data given the model (the likelihood) with the complexity of the model to encourage sparser models. A naive implementation of this search is super-exponential in time with the number of variables, and becomes infeasible when considering even less than a dozen variables. However, dynamic programming can efficiently remove the many repeated calculations and reduce this to be simply exponential in time. This allows exact BNSL to scale to ~25-30 variables. In addition, the A\\* algorithm can be used to smartly search the space and reduce computational time even further by not even considering all possibile networks.\n", "\n", "(2) Constraint learning: These methods typically involve calculating some measure of correlation or co-occurrence to identify an undirected backbone of edges that could exist, and then prune these edges systematically until a DAG is reached. A common method is to iterate over all triplets of variables to identify conditional independencies that specify both presence and direction of the edges. This algorithm is asymptotically faster (quadratic in time) than search-and-score, but it does not have a simple probabilistic interpretation.\n", "\n", "(3) Approximate algorithms: In many real world examples, one wishes to merge the interpretability of the search and score method with the attractiveness of the task finishing before the universe ends. To this end, several heuristics have been developed with different properties to yield good structures in a reasonable amount of time. These methods include the Chow-Liu tree building algorithm, the hill-climbing algorithm, and optimal reinsertion, though there are others.\n", "\n", "pomegranate currently implements a search-and-score method based on the minimum description length score which utilizes the dynamic programming and A\\* algorithm (DP/A\\*), a greedy algorithm based off of DP/A\\*, and the Chow-Liu tree building algorithm, though there are plans to soon add other algorithms.\n", "\n", "### Structure Learning in pomegranate\n", "#### Exact Learning\n", "\n", "Structure learning in pomegranate is done using the `from_samples` method. All you pass in is the samples, their associated weights (if not uniform), and the algorithm which you'd like to use, and it will learn the network for you using the dynamic programming implementation. Lets see a quick synthetic example to make sure that appropriate connections are found. Lets add connections between variables 1, 3, 6, and variables 0 and 2, and variables 4 and 5." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "((), (), (0,), (1,), (), (4,), (3,))\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%pylab inline\n", "%load_ext memory_profiler\n", "from pomegranate import BayesianNetwork\n", "import seaborn, time\n", "seaborn.set_style('whitegrid')\n", "\n", "X = numpy.random.randint(2, size=(2000, 7))\n", "X[:,3] = X[:,1]\n", "X[:,6] = X[:,1]\n", "\n", "X[:,0] = X[:,2]\n", "\n", "X[:,4] = X[:,5]\n", "\n", "model = BayesianNetwork.from_samples(X, algorithm='exact')\n", "print(model.structure)\n", "model.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The structure attribute returns a tuple of tuples, where each inner tuple corresponds to that node in the graph (and the column of data learned on). The numbers in that inner tuple correspond to the parents of that node. The results from this structure are that node 3 has node 1 as a parent, that node 2 has node 0 as a parent, and so forth. It seems to faithfully recapture the underlying dependencies in the data.\n", "\n", "Now, two algorithms for performing search-and-score were mentioned, the traditional shortest path algorithm and the A\\* algorithm. These both work by essentially turning the Bayesian network structure learning problem into a shortest path problem over an 'order graph.' This order graph is a lattice made up of layers of variable sets from the BNSL problem, with the root node having no variables, the leaf node having all variables, and layer `i` in the lattice having all subsets of variables of size `i`. Each path from the root to the leaf represents a certain topological sort of the variables, with the shortest path corresponding to the optimal topological sort and Bayesian network. Details can be found here. The traditional shortest path algorithm calculates the values of all edges in the order lattice before finding the shortest path, while the A\\* algorithm searches only a subset of the order lattice and begins searching immediately. Both methods yield optimal Bayesian networks.\n", "\n", "A major problem that arises in the traditional shortest path algorithm is that the size of the order graph grows exponentially with the number of variables, and can make tasks infeasible that have otherwise-reasonable computational times. While the A\\* algorithm is faster computationally, another advantage is that it uses a much smaller amount of memory since it doesn't explore the full order graph, and so can be applied to larger problems.\n", "\n", "In order to see the differences between these algorithms in practice, let's turn to the task of learning a Bayesian network over the digits dataset. The digits dataset is comprised of over a thousand 8x8 pictures of handwritten digits. We binarize the values into 'on' or 'off' for simplicity, and try to learn dependencies between the pixels." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import load_digits\n", "\n", "X, y = load_digits(10, True)\n", "X = X > numpy.mean(X)\n", "\n", "plt.figure(figsize=(14, 4))\n", "plt.subplot(131)\n", "plt.imshow(X[0].reshape(8, 8), interpolation='nearest')\n", "plt.grid(False)\n", "plt.subplot(132)\n", "plt.imshow(X[1].reshape(8, 8), interpolation='nearest')\n", "plt.grid(False)\n", "plt.subplot(133)\n", "plt.imshow(X[2].reshape(8, 8), interpolation='nearest')\n", "plt.grid(False)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shortest Path\n", "Time (s): 42.78771615028381\n", "P(D|M): -8267.950447476833\n", "peak memory: 2234.61 MiB, increment: 1932.45 MiB\n", "\n", "A* Search\n", "Time (s): 25.080483198165894\n", "P(D|M): -8267.950447476833\n", "peak memory: 860.71 MiB, increment: 552.22 MiB\n" ] } ], "source": [ "X = X[:,:18]\n", " \n", "tic = time.time()\n", "model = BayesianNetwork.from_samples(X, algorithm='exact-dp') # << BNSL done here!\n", "t1 = time.time() - tic\n", "p1 = model.log_probability(X).sum()\n", "\n", "tic = time.time()\n", "model = BayesianNetwork.from_samples(X, algorithm='exact')\n", "t2 = time.time() - tic\n", "p2 = model.log_probability(X).sum()\n", "\n", "\n", "print(\"Shortest Path\")\n", "print(\"Time (s): \", t1)\n", "print(\"P(D|M): \", p1)\n", "%memit BayesianNetwork.from_samples(X, algorithm='exact-dp')\n", "print()\n", "print(\"A* Search\")\n", "print(\"Time (s): \", t2)\n", "print(\"P(D|M): \", p2)\n", "%memit BayesianNetwork.from_samples(X, algorithm='exact')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These results show that the A\\* algorithm is both computationally faster and requires far less memory than the traditional algorithm, making it a better default for the 'exact' algorithm. The amount of memory used by the BNSL process is under 'increment', not 'peak memory', as 'peak memory' returns the total memory used by everything, while increment shows the difference in peak memory before and after the function has run. \n", "\n", "#### Approximate Learning: Greedy Search (pomegranate default)\n", "\n", "A natural heuristic when a non-greedy algorithm is too slow is to consider the greedy version. This simple implementation iteratively finds the best variable to add to the growing topological sort, allowing the new variable to draw only from variables already in the topological sort. This is the default in pomegranate because it has a nice balance between producing good (often optimal) graphs and having a small computational cost and memory footprint. However, there is no guarantee that this produces the globally optimal graph.\n", "\n", "Let's see how it performs on the same dataset as above." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Greedy\n", "Time (s): 1.4977431297302246\n", "P(D|M): -8434.745069065655\n", "peak memory: 216.68 MiB, increment: 0.07 MiB\n" ] } ], "source": [ "tic = time.time()\n", "model = BayesianNetwork.from_samples(X) # << Default BNSL setting\n", "t = time.time() - tic\n", "p = model.log_probability(X).sum()\n", "\n", "print(\"Greedy\")\n", "print(\"Time (s): \", t)\n", "print(\"P(D|M): \", p)\n", "%memit BayesianNetwork.from_samples(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Approximate Learning: Chow-Liu Trees\n", "\n", "However, there are even cases where the greedy heuristic is too slow, for example hundreds of variables. One of the first heuristics for BNSL is that of Chow-Liu trees, which learns the optimal tree from data. Essentially it calculates the mutual information between all pairs of variables and then finds the maximum spanning tree. A root node has to be input to turn the undirected edges based on mutual information into directed edges for the Bayesian network. The algorithm is is $O(d^{2})$ and practically is extremely fast and memory efficient, though it produces structures with a worse $P(D|M)$." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chow-Liu\n", "Time (s): 0.045445919036865234\n", "P(D|M): -8752.32666094451\n", "peak memory: 190.02 MiB, increment: 0.00 MiB\n" ] } ], "source": [ "tic = time.time()\n", "model = BayesianNetwork.from_samples(X, algorithm='chow-liu') # << Default BNSL setting\n", "t = time.time() - tic\n", "p = model.log_probability(X).sum()\n", "\n", "print(\"Chow-Liu\")\n", "print(\"Time (s): \", t)\n", "print(\"P(D|M): \", p)\n", "%memit BayesianNetwork.from_samples(X, algorithm='chow-liu')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Comparison\n", "\n", "We can then compare the algorithms directly to each other on the digits dataset as we expand the number of pixels to consider." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "X, _ = load_digits(10, True)\n", "X = X > numpy.mean(X)\n", "\n", "t1, t2, t3, t4 = [], [], [], []\n", "p1, p2, p3, p4 = [], [], [], []\n", "n_vars = range(8, 19)\n", "\n", "for i in n_vars:\n", " X_ = X[:,:i]\n", "\n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X_, algorithm='exact-dp') # << BNSL done here!\n", " t1.append(time.time() - tic)\n", " p1.append(model.log_probability(X_).sum())\n", "\n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X_, algorithm='exact')\n", " t2.append(time.time() - tic)\n", " p2.append(model.log_probability(X_).sum())\n", "\n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X_, algorithm='greedy')\n", " t3.append(time.time() - tic)\n", " p3.append(model.log_probability(X_).sum())\n", "\n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X_, algorithm='chow-liu')\n", " t4.append(time.time() - tic)\n", " p4.append(model.log_probability(X_).sum())" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 4))\n", "plt.subplot(121)\n", "plt.title(\"Time to Learn Structure\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.ylabel(\"Time (s)\", fontsize=14)\n", "plt.xlabel(\"Variables\", fontsize=14)\n", "plt.plot(n_vars, t1, c='c', label=\"Exact Shortest\")\n", "plt.plot(n_vars, t2, c='m', label=\"Exact A*\")\n", "plt.plot(n_vars, t3, c='g', label=\"Greedy\")\n", "plt.plot(n_vars, t4, c='r', label=\"Chow-Liu\")\n", "plt.legend(fontsize=14, loc=2)\n", "\n", "plt.subplot(122)\n", "plt.title(\"$P(D|M)$ with Resulting Model\", fontsize=14)\n", "plt.xlabel(\"Variables\", fontsize=14)\n", "plt.ylabel(\"logp\", fontsize=14)\n", "plt.plot(n_vars, p1, c='c', label=\"Exact Shortest\")\n", "plt.plot(n_vars, p2, c='m', label=\"Exact A*\")\n", "plt.plot(n_vars, p3, c='g', label=\"Greedy\")\n", "plt.plot(n_vars, p4, c='r', label=\"Chow-Liu\")\n", "plt.legend(fontsize=14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see the expected results-- that the A\\* algorithm works faster than the shortest path, the greedy one faster than that, and Chow-Liu the fastest. The purple and cyan lines superimpose on the right plot as they produce graphs with the same score, followed closely by the greedy algorithm and then Chow-Liu performing the worst." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Constraint Graphs\n", "\n", "Now, sometimes you have prior information about how groups of nodes are connected to each other and want to exploit that. This can take the form of a global ordering, where variables can be ordered in such a manner that edges only go from left to right, for example. However, sometimes you have layers in your network where variables are a part of these layers and can only have parents in another layer.\n", "\n", "Lets consider a diagnostics Bayesian network like the following (no need to read code, the picture is all that is important for now):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pomegranate import DiscreteDistribution, ConditionalProbabilityTable, Node\n", "\n", "BRCA1 = DiscreteDistribution({0: 0.999, 1: 0.001})\n", "BRCA2 = DiscreteDistribution({0: 0.985, 1: 0.015})\n", "LCT = DiscreteDistribution({0: 0.950, 1: 0.050})\n", "\n", "OC = ConditionalProbabilityTable([[0, 0, 0, 0.999],\n", " [0, 0, 1, 0.001],\n", " [0, 1, 0, 0.750],\n", " [0, 1, 1, 0.250],\n", " [1, 0, 0, 0.700],\n", " [1, 0, 1, 0.300],\n", " [1, 1, 0, 0.050],\n", " [1, 1, 1, 0.950]], [BRCA1, BRCA2])\n", "\n", "LI = ConditionalProbabilityTable([[0, 0, 0.99],\n", " [0, 1, 0.01],\n", " [1, 0, 0.20],\n", " [1, 1, 0.80]], [LCT])\n", "\n", "PREG = DiscreteDistribution({0: 0.90, 1: 0.10})\n", "\n", "LE = ConditionalProbabilityTable([[0, 0, 0.99],\n", " [0, 1, 0.01],\n", " [1, 0, 0.25],\n", " [1, 1, 0.75]], [OC])\n", "\n", "BLOAT = ConditionalProbabilityTable([[0, 0, 0, 0.85],\n", " [0, 0, 1, 0.15],\n", " [0, 1, 0, 0.70],\n", " [0, 1, 1, 0.30],\n", " [1, 0, 0, 0.40],\n", " [1, 0, 1, 0.60],\n", " [1, 1, 0, 0.10],\n", " [1, 1, 1, 0.90]], [OC, LI])\n", "\n", "LOA = ConditionalProbabilityTable([[0, 0, 0, 0.99],\n", " [0, 0, 1, 0.01],\n", " [0, 1, 0, 0.30],\n", " [0, 1, 1, 0.70],\n", " [1, 0, 0, 0.95],\n", " [1, 0, 1, 0.05],\n", " [1, 1, 0, 0.95],\n", " [1, 1, 1, 0.05]], [PREG, OC])\n", "\n", "VOM = ConditionalProbabilityTable([[0, 0, 0, 0, 0.99],\n", " [0, 0, 0, 1, 0.01],\n", " [0, 0, 1, 0, 0.80],\n", " [0, 0, 1, 1, 0.20],\n", " [0, 1, 0, 0, 0.40],\n", " [0, 1, 0, 1, 0.60],\n", " [0, 1, 1, 0, 0.30],\n", " [0, 1, 1, 1, 0.70],\n", " [1, 0, 0, 0, 0.30],\n", " [1, 0, 0, 1, 0.70],\n", " [1, 0, 1, 0, 0.20],\n", " [1, 0, 1, 1, 0.80],\n", " [1, 1, 0, 0, 0.05],\n", " [1, 1, 0, 1, 0.95],\n", " [1, 1, 1, 0, 0.01],\n", " [1, 1, 1, 1, 0.99]], [PREG, OC, LI])\n", "\n", "AC = ConditionalProbabilityTable([[0, 0, 0, 0.95],\n", " [0, 0, 1, 0.05],\n", " [0, 1, 0, 0.01],\n", " [0, 1, 1, 0.99],\n", " [1, 0, 0, 0.40],\n", " [1, 0, 1, 0.60],\n", " [1, 1, 0, 0.20],\n", " [1, 1, 1, 0.80]], [PREG, LI])\n", "\n", "s1 = Node(BRCA1, name=\"BRCA1\")\n", "s2 = Node(BRCA2, name=\"BRCA2\")\n", "s3 = Node(LCT, name=\"LCT\")\n", "s4 = Node(OC, name=\"OC\")\n", "s5 = Node(LI, name=\"LI\")\n", "s6 = Node(PREG, name=\"PREG\")\n", "s7 = Node(LE, name=\"LE\")\n", "s8 = Node(BLOAT, name=\"BLOAT\")\n", "s9 = Node(LOA, name=\"LOA\")\n", "s10 = Node(VOM, name=\"VOM\")\n", "s11 = Node(AC, name=\"AC\")\n", "\n", "model = BayesianNetwork(\"Hut\")\n", "model.add_nodes(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11)\n", "model.add_edge(s1, s4)\n", "model.add_edge(s2, s4)\n", "model.add_edge(s3, s5)\n", "model.add_edge(s4, s7)\n", "model.add_edge(s4, s8)\n", "model.add_edge(s4, s9)\n", "model.add_edge(s4, s10)\n", "model.add_edge(s5, s8)\n", "model.add_edge(s5, s10)\n", "model.add_edge(s5, s11)\n", "model.add_edge(s6, s9)\n", "model.add_edge(s6, s10)\n", "model.add_edge(s6, s11)\n", "model.bake()\n", "\n", "plt.figure(figsize=(14, 10))\n", "model.plot()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This network contains three layer, with symptoms on the bottom (low energy, bloating, loss of appetite, vomitting, and abdominal cramps), diseases in the middle (overian cancer, lactose intolerance, and pregnancy), and genetic tests on the top for three different genetic mutations. The edges in this graph are constrainted such that symptoms are explained by diseases, and diseases can be partially explained by genetic mutations. There are no edges from diseases to genetic conditions, and no edges from genetic conditions to symptoms. If we were going to design a more efficient search algorithm, we would want to exploit this fact to drastically reduce the search space of graphs.\n", "\n", "Before presenting a solution, lets also consider another situation. In some cases you can define a global ordering of the variables, meaning you can order them from left to right and ensure that edges only go from the left to the right. This can represent some temporal separation (things on the left happen before things on the right), physical separation, or anything else. This would also dramatically reduce the search space. \n", "\n", "In addition to reducing the search space, an efficient algorithm can exploit this layered structure. A key property of most scoring functions is the idea of \"global parameter independence\", meaning that that the parents of node A are independent of the parents of node B assuming that they do not form a cycle in the graph. If you have a layered structure, either like in the diagnostics network or through a global ordering, it is impossible to form a cycle in the graph through any valid assignment of parent values. This means that the parents for each node can be identified independently, drastically reducing the runtime of the algorithm.\n", "\n", "Now, sometimes we know ~some things~ about the structure of the variables, but nothing about the others. For example, we might have a partial ordering on some variables but not know anything about the others. We could enforce an arbitrary ordering on the others, but this may not be well justified. In essence, we'd like to exploit whatever information we have.\n", "\n", "Abstractly, we can think about this in terms of constraint graphs. Lets say you have some symptoms, diseases, and genetic tests, and don't a priori know the connection between all of these pieces, but you do know the previous layer structure. You can define a \"constraint graph\" which is made up of three nodes, \"symptoms\", \"diseases\", and \"genetic mutations\". There is a directed edge from genetic mutations to diseases, and a directed edge from diseases to symptoms. This specifies that genetic mutations can be parents to diseases, and diseases to symptoms. It would look like the following:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import networkx\n", "from pomegranate.utils import plot_networkx\n", "\n", "constraints = networkx.DiGraph()\n", "constraints.add_edge('genetic conditions', 'diseases')\n", "constraints.add_edge('diseases', 'symptoms')\n", "plot_networkx(constraints)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All variables corresponding to these categories would be put in their appropriate name. This would define a scaffold for structure learning.\n", "\n", "Now, we can do the same thing for a global ordering. Lets say we have 3 variables in an order from 0-2. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "constraints = networkx.DiGraph()\n", "constraints.add_edge(0, 1)\n", "constraints.add_edge(1, 2)\n", "constraints.add_edge(0, 2)\n", "plot_networkx(constraints)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this graph, we're saying that variable 0 can be a parent for 1 or 2, and that variable 1 can be a parent for variable 2. In the same way that putting multiple variables in a node of the constraint graph allowed us to define layers, putting a single variable in the nodes of a constraint graph can allow us to define an ordering.\n", "\n", "To be specific, lets say we want to find the parents of the variables in node 1 given that those variables parents can only come from the variables in node 0. We can independently find the best parents for each variable in node 1 from the set of those in node 0. This is significantly faster than trying to find the best Bayesian network of all variables in nodes 0 and 1. We can also do the same thing for the variables in node 2 by going through the variables in both nodes 0 and 1 to find the best parent set for the variables in node 2.\n", "\n", "However, there are some cases where we know nothing about the parent structure of some variables. This can be solved by including self-loops in the graph, where a node is its own parent. This means that we know nothing about the parent structure of the variables in that node and that the full exponential time algorithm will have to be run. The naive structure learning algorithm can be thought of as putting all variables in a single node in the constraint graph and putting a self-loop on that node. \n", "\n", "We are thus left with two procedures; one for solving edges which are self edges, and one for solving edges which are not. Even though we have to use the exponential time procedure on variables in nodes with self loops, it will still be significantly faster because we will be using less variables (except in the naive case).\n", "\n", "Frequently though we will have some information about some of the nodes of the graph even if we don't have information about all of the nodes. Lets take the case where we know some variables have no children but can have parents, and know nothing about the other variables." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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U9KqrEn6qak/jxrEpyIYNRXVE4vXXA2lSsqFDElMkziFfeUV0yxZRTYwI+zx00OidResQaQKwXu+pU/1vUxKhQxJTxN/L3r1bVNPCvvhCNCsrrpcllfD996KayqbPmB06BNKcRBN/QGrK1LBhohMnxtkkckXo9OKKFaIffxxcWxIIb9nEFN4d8pNPRCumktUMX4xrnUitDxkKLlwQrZiy1r9/MO1JEOGLHlKt8T7s8+9/i2rFsBA54969ewEAzzzzDABEi+E/++yzQTWp6uj11mf2f/1LlA5JSOLw5pCnTwMrV8qx1sMJAfqsqLrPrfHOCvMQ1fjxok8+KaoL5SpW5QgJdEhiCm8OmZ8PuM2FopP+IUB70R3cIHKLFi2CbE5i0Ouvn0d+vqgOnIcMOiQxhTeHLCiIVf0iNtDPQ6u/0SEJiR9vDnnkCJciWEM/D1cnPazQIYkpGJDEFAxIYgpvz5Dp6bFEUWKDY8dE3bx8WPEWkB07Am++meCmkLjQov8PPBBoM+KFt2xiCm8OeeutwJ49cqzrrkM0haicP38eAGCpqnWV0euvn8ettwbXlgRAhySm8OaQaWnAvffK8TvviGrFCsOoE+bl5QEANm/eDAA4dOgQAGDw4MEAgOzs7ABa5xG9/vp5hDTtTKFDElNwkVdY4SIvQpKP90Ve+k3UDS0XLBBloQB/0DqRkYhoyJ1RoUMSUySuto9ugqlVz264Ia6XJZWgU7Y9eoiuWSOq2yWHHDokMUXiaoxrvcL//EdUN1gP+biYGXS/RN0ofswYUdaHJCR5JK6Crn5TXZkSNGggyhrj8XHypKg+M2oF3WrmjEryNk56+GHRnTtFtX5h48ZJOV215Ngx4Oqr5XjOHNG//U3U6n7hccJbNjEFt5azCLeWI8QGwW5PrBW7wlS5Nhm4amx4/nlRbk9MiA38c0iluPj3KWu5uaIjRvjalMBZvlx0xgzRiqlkrVr536aAoUMSU/jvkJeyerVoTo6oVn994gnR++4TDfv2dDrtt3Sp6Msvi+q06uzZorrnTwpDhySmCNYhK7JqlahuvatbHI8bJzpkSGwLO6szFXo5164VXbECWLxYjnX6b8oU0YED/W1bCKBDElPYcsiKFBWJLlkimpcHuJ0TcOedouqYWkG2UyfRZM2ZnzghqnP0WsJEHVE3H23TRnToUGDsWDnOzExOm6oRdEhiCtsO+UeUlIhqArCqzv9qje1mzUR1EVQkEluQpjND2nvX51Ht5evMycGDsXPqeQ8fFlUn1nl5TQvTBNqMjKq/NxLCgPwz9O3s3y964IBoSUksmDTwdDhG/0aHYVSbNxeNRGIB1rq1qNVOVcjhLZuYovo5JAk1dEhiCgYkMQUDkpiCAUlMwYAkpmBAElMwIIkpGJDEFAxIYgoGJDEFA5KYggFJTPH/SZjA7uHS+icAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "constraints = networkx.DiGraph()\n", "constraints.add_edge(0, 1)\n", "constraints.add_edge(0, 0)\n", "plot_networkx(constraints)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this situation we would have to run the exponential time algorithm on the variables in node 0 to find the optimal parents, and then run the independent parents algorithm on the variables in node 1 drawing only from the variables in node 0. To be specific:\n", "\n", "(1) Use exponential time procedure to find optimal structure amongst variables in node 0 \n", "(2) Use independent-parents procedure to find the best parents of variables in node 1, restricting the parents to be in node 0\n", "(3) Concatenate these parent sets together to get the optimal structure of the network given the constraints.\n", "\n", "We can generalize this to any arbitrary constraint graph:\n", "\n", "(1) Use exponential time procedure to find optimal structure amongst variables in nodes with self loops (including parents from other nodes if needed)\n", "(2) Use independent-parents procedure to find best parents of variables in a node given the constraint that the parents must come from variables in the node which is this nodes parent\n", "(3) Concatenate these parent sets together to get the optimal structure of the network given the constraints.\n", "\n", "According to the global parameter independence property of Bayesian networks, this procedure will give the globally optimal Bayesian network while exploring a significantly smaller part of the network.\n", "\n", "pomegranate supports constraint graphs in an extremely easy to use manner. Lets say that we have a graph with three layers like the diagnostic model, and five variables in each layer. We can define the constraint graph as a networkx DiGraph, with the nodes being tuples containing the column ids of each variable belonging to that variable.\n", "\n", "In this case, we're saying that (0, 1, 2, 3, 4) is the first node, (5, 6, 7, 8, 9) is the second node, and (10, 11, 12, 13, 14) is the final node. Lets make nodes 1, 7, and 12 related, 11, 13, 14 related, and 3 and 5 related. In this case, where should be an edge from 1 to 7, and 7 to 12. 11, 13, and 14 are all a part of the same layer and so that connection should be ignored, and then there should be a connection from 3 to 5." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Constraint Graph\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Learned Bayesian Network\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "pomegranate time: 0.42864394187927246 ((), (), (), (), (), (3,), (), (1,), (), (), (), (), (7,), (), ())\n" ] } ], "source": [ "numpy.random.seed(6)\n", "\n", "X = numpy.random.randint(2, size=(200, 15))\n", "\n", "X[:,1] = X[:,7]\n", "X[:,12] = 1 - X[:,7]\n", "\n", "X[:,5] = X[:,3]\n", "\n", "X[:,13] = X[:,11]\n", "X[:,14] = X[:,11]\n", "\n", "a = networkx.DiGraph()\n", "b = tuple((0, 1, 2, 3, 4))\n", "c = tuple((5, 6, 7, 8, 9))\n", "d = tuple((10, 11, 12, 13, 14))\n", "\n", "a.add_edge(b, c)\n", "a.add_edge(c, d)\n", "print(\"Constraint Graph\")\n", "plot_networkx(a)\n", "plt.show()\n", "\n", "print(\"Learned Bayesian Network\")\n", "\n", "tic = time.time()\n", "model = BayesianNetwork.from_samples(X, algorithm='exact', constraint_graph=a)\n", "\n", "plt.figure(figsize=(16, 8))\n", "model.plot()\n", "plt.show()\n", "print(\"pomegranate time: \", time.time() - tic, model.structure)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that reconstructed perfectly here. Lets see what would happen if we didn't use the exact algorithm." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "pomegranate time: 2.7319540977478027 ((), (), (), (), (), (3,), (), (1,), (), (), (), (), (7,), (11,), (13,))\n" ] } ], "source": [ "tic = time.time()\n", "model = BayesianNetwork.from_samples(X, algorithm='exact')\n", "plt.figure(figsize=(16, 8))\n", "model.plot()\n", "plt.show()\n", "print(\"pomegranate time: \", time.time() - tic, model.structure)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like we got three desirable attributes by using a constraint graph. The first is that there was over an order of magnitude speed improvement in finding the optimal graph. The second is that we were able to remove some edges we didn't want in the final Bayesian network, such as those between 11, 13, and 14. We also removed the edge between 1 and 12 and 1 and 3, which are spurious given the model that we originally defined. The third desired attribute is that we can specify the direction of some of the edges and get a better causal model.\n", "\n", "Lets take a look at how big of a model we can learn given a three layer constraint graph like before. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "constraint_times, times = [], []\n", "\n", "x = numpy.arange(1, 7)\n", "for i in x:\n", " symptoms = tuple(range(i))\n", " diseases = tuple(range(i, i*2))\n", " genetic = tuple(range(i*2, i*3))\n", "\n", " constraints = networkx.DiGraph()\n", " constraints.add_edge(genetic, diseases)\n", " constraints.add_edge(diseases, symptoms)\n", " \n", " X = numpy.random.randint(2, size=(2000, i*3))\n", " \n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X, algorithm='exact', constraint_graph=constraints)\n", " constraint_times.append( time.time() - tic )\n", " \n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X, algorithm='exact')\n", " times.append( time.time() - tic ) \n", "\n", "\n", "plt.figure(figsize=(14, 6))\n", "plt.title('Time To Learn Bayesian Network', fontsize=18)\n", "plt.xlabel(\"Number of Variables\", fontsize=14)\n", "plt.ylabel(\"Time (s)\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.plot( x*3, times, linewidth=3, color='c', label='Exact')\n", "plt.plot( x*3, constraint_times, linewidth=3, color='m', label='Constrained')\n", "plt.legend(loc=2, fontsize=16)\n", "plt.yscale('log')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like by including a constraint graph, we can far more quickly find the optimal Bayesian network. This can allow us to create Bayesian networks on dozens of variables as long as we can order them into layers.\n", "\n", "## Other Parameters\n", "\n", "There are three other parameters that are worth mentioning. The first is that a vector of weights can be passed in alongside a matrix of samples, with each entry in the vector corresponding to the weight of that sample. By default all weights are set to 1, but they can be any non-negative number.\n", "\n", "```\n", "model = BayesianNetwork.from_samples(X, weights=weights)\n", "```\n", "\n", "Next is the pseudocounts parameter that indicates the number added to the observations of each observed variable combination. This acts as a regularizer by smoothing over the counts and is typically used to prevent the model from saying it is impossible for a combination of variables to occur together just because they weren't observed in the data. Currently all learning algorithms support pseudocounts. By default this is set to 0.\n", "\n", "```\n", "model = BayesianNetwork.from_samples(X, pseudocount=1.7\n", "```\n", "\n", "Lastly, the maximum number of parents that each variable can have can be limited. This is sometimes referred to as the k-learn problem in literature. In practice this can dramatically speed up the BNSL task as the majority of time is spent determining the best parent sets. Currently this only affects the exact and greedy algorithm as in the Chow-Liu algorithm each variable only has a single parent.\n", "\n", "```\n", "model = BayesianNetwork.from_samples(X, algorithm='exact', max_parents=2)\n", "```\n", "\n", "Since exact inference uses minimum description length as a score function, the maximum number of parents is by default set to $\\log \\left(\\frac{n}{\\log(n)} \\right)$ with $n$ being the number of samples in the dataset.\n", "\n", "# Conclusions\n", "\n", "pomegranate currently supports exact BNSL through an a shortest path dynamic programming algorithm, an A\\* algorithm, a greedy algorithm, the Chow-Liu tree building algorithm, and a constraint-graph based algorithm which can significantly speed up structure learning if you have any prior knowledge of the interactions between variables.\n", "\n", "If you have any suggestions for how to improve pomegranate or ideas for algorithms to implement, feel free to open an issue on the issue tracker! I'd love to hear feedback." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } B_Model_Tutorial_5_Bayes_Classifiers.ipynb000066400000000000000000026042431420461712600330000ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Naive Bayes and Bayes Classifiers\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com\n", "\n", "Bayes classifiers are some of the simplest machine learning models that exist, due to their intuitive probabilistic interpretation and simple fitting step. Each class is modeled as a probability distribution, and the data is interpreted as samples drawn from these underlying distributions. Fitting the model to data is as simple as calculating maximum likelihood parameters for the data that falls under each class, and making predictions is as simple as using Bayes' rule to determine which class is most likely given the distributions. Bayes' Rule is the following: \n", "\n", "\\begin{equation}\n", "P(M|D) = \\frac{P(D|M)P(M)}{P(D)}\n", "\\end{equation}\n", "\n", "where M stands for the model and D stands for the data. $P(M)$ is known as the prior, because it is the probability that a sample is of a certain class before you even know what the sample is. This is generally just the frequency of each class. Intuitively, it makes sense that you would want to model this, because if one class occurs 10x more than another class, it is more likely that a given sample will belong to that distribution. $P(D|M)$ is the likelihood, or the probability, of the data under a given model. Lastly, $P(M|D)$ is the posterior, which is the probability of each component of the model, or class, being the component which generated the data. It is called the posterior because the prior corresponds to probabilities before seeing data, and the posterior corresponds to probabilities after observing the data. In cases where the prior is uniform, the posterior is just equal to the normalized likelihoods. This equation forms the basis of most probabilistic modeling, with interesting priors allowing the user to inject sophisticated expert knowledge into the problem directly.\n", "\n", "pomegranate implements two distinct models of this format with the simpler being the naive Bayes classifier. The naive Bayes classifier assumes that each feature is independent from each other feature, and so breaks down $P(D|M)$ to be $\\prod\\limits_{i=1}^{d} P(D_{i}|M_{i})$ where $i$ is a specific feature in a data set that has $d$ features in it. This typically means faster calculations because covariance across features doesn't need to be considered, but it also is a natural way to model each feature as a different distribution because it ignores the complexities of modeling the covariance between, say, an exponential distribution and a normal distribution.\n", "\n", "The more general model is the Bayes classifier which does not assume that each feature is independent of the others. This allows you to plug in anything for the likelihood function, whether it be a multivariate Gaussian distribution or a whole other compositional model. For instance, one could create a classifier whose components are each large mixture models. This enables much more complex models to be learned within the simple framework of Bayes' rule." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fri Jan 10 2020 \n", "\n", "numpy 1.18.1\n", "scipy 1.4.1\n", "pomegranate 0.12.0\n", "\n", "compiler : Clang 10.0.0 (clang-1000.11.45.5)\n", "system : Darwin\n", "release : 17.7.0\n", "machine : x86_64\n", "processor : i386\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "import numpy\n", "\n", "from pomegranate import *\n", "\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simple Gaussian Example" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.concatenate((numpy.random.normal(3, 1, 200), numpy.random.normal(10, 2, 1000)))\n", "y = numpy.concatenate((numpy.zeros(200), numpy.ones(1000)))\n", "\n", "x1 = X[:200]\n", "x2 = X[200:]\n", "\n", "plt.figure(figsize=(16, 5))\n", "plt.hist(x1, bins=25, color='m', edgecolor='m', label=\"Class A\")\n", "plt.hist(x2, bins=25, color='c', edgecolor='c', label=\"Class B\")\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.ylabel(\"Count\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data seems like it comes from two normal distributions, with the cyan class being more prevalent than the magenta class. A natural way to model this data would be to create a normal distribution for the cyan data, and another for the magenta distribution.\n", "\n", "Let's take a look at doing that. All we need to do is use the `from_samples` class method of the `NormalDistribution` class." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d1 = NormalDistribution.from_samples(x1)\n", "d2 = NormalDistribution.from_samples(x2)\n", "idxs = numpy.arange(0, 15, 0.1)\n", "\n", "p1 = list(map(d1.probability, idxs))\n", "p2 = list(map(d2.probability, idxs))\n", "\n", "plt.figure(figsize=(16, 5))\n", "plt.plot(idxs, p1, color='m'); plt.fill_between(idxs, 0, p1, facecolor='m', alpha=0.2)\n", "plt.plot(idxs, p2, color='c'); plt.fill_between(idxs, 0, p2, facecolor='c', alpha=0.2)\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.ylabel(\"Probability\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like some aspects of the data are captured well by doing things this way-- specifically the mean and variance of the normal distributions. This allows us to easily calculate $P(D|M)$ as the probability of a sample under either the cyan or magenta distributions using the normal (or Gaussian) probability density equation:\n", "\n", "\\begin{align}\n", "P(D|M) &= P(x|\\mu, \\sigma) \\\\\n", " &= \\frac{1}{\\sqrt{2\\pi\\sigma^{2}}} exp \\left(-\\frac{(x-u)^{2}}{2\\sigma^{2}} \\right) \n", "\\end{align}\n", "\n", "However, if we look at the original data, we see that the cyan distributions is both much wider than the purple distribution and much taller, as there were more samples from that class in general. If we reduce that data down to these two distributions, we lose the class imbalance. We want our prior to model this class imbalance, with the reasoning being that if we randomly draw a sample from the samples observed thus far, it is far more likely to be a cyan than a magenta sample. Let's take a look at this class imbalance exactly." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "magenta_prior = 1. * len(x1) / len(X)\n", "cyan_prior = 1. * len(x2) / len(X)\n", "\n", "plt.figure(figsize=(4, 6))\n", "plt.title(\"Prior Probabilities P(M)\", fontsize=14)\n", "plt.bar(0, magenta_prior, facecolor='m', edgecolor='m')\n", "plt.bar(1, cyan_prior, facecolor='c', edgecolor='c')\n", "plt.xticks([0, 1], ['P(Magenta)', 'P(Cyan)'], fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The prior $P(M)$ is a vector of probabilities over the classes that the model can predict, also known as components. In this case, if we draw a sample randomly from the data that we have, there is a ~83% chance that it will come from the cyan class and a ~17% chance that it will come from the magenta class.\n", "\n", "Let's multiply the probability densities we got before by this imbalance." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d1 = NormalDistribution.from_samples(x1)\n", "d2 = NormalDistribution.from_samples(x2)\n", "idxs = numpy.arange(0, 15, 0.1)\n", "\n", "p_magenta = numpy.array(list(map(d1.probability, idxs))) * magenta_prior\n", "p_cyan = numpy.array(list(map(d2.probability, idxs))) * cyan_prior\n", "\n", "plt.figure(figsize=(16, 5))\n", "plt.plot(idxs, p_magenta, color='m'); plt.fill_between(idxs, 0, p_magenta, facecolor='m', alpha=0.2)\n", "plt.plot(idxs, p_cyan, color='c'); plt.fill_between(idxs, 0, p_cyan, facecolor='c', alpha=0.2)\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.ylabel(\"P(M)P(D|M)\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This looks a lot more faithful to the original data, and actually corresponds to $P(M)P(D|M)$, the prior multiplied by the likelihood. However, these aren't actually probability distributions anymore, as they no longer integrate to 1. This is why the $P(M)P(D|M)$ term has to be normalized by the $P(D)$ term in Bayes' rule in order to get a probability distribution over the components. However, $P(D)$ is difficult to determine exactly-- what is the probability of the data? Well, we can sum over the classes to get that value, since $P(D) = \\sum_{i=1}^{c} P(D|M)P(M)$ for a problem with c classes. This translates into $P(D) = P(M=Cyan)P(D|M=Cyan) + P(M=Magenta)P(D|M=Magenta)$ for this specific problem, and those values can just be pulled from the unnormalized plots above.\n", "\n", "This gives us the full Bayes' rule, with the posterior $P(M|D)$ being the proportion of density of the above plot coming from each of the two distributions at any point on the line. Let's take a look at the posterior probabilities of the two classes on the same line." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "magenta_posterior = p_magenta / (p_magenta + p_cyan)\n", "cyan_posterior = p_cyan / (p_magenta + p_cyan)\n", "\n", "plt.figure(figsize=(16, 5))\n", "plt.subplot(211)\n", "plt.plot(idxs, p_magenta, color='m'); plt.fill_between(idxs, 0, p_magenta, facecolor='m', alpha=0.2)\n", "plt.plot(idxs, p_cyan, color='c'); plt.fill_between(idxs, 0, p_cyan, facecolor='c', alpha=0.2)\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.ylabel(\"P(M)P(D|M)\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "\n", "plt.subplot(212)\n", "plt.plot(idxs, magenta_posterior, color='m')\n", "plt.plot(idxs, cyan_posterior, color='c')\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.ylabel(\"P(M|D)\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The top plot shows the same densities as before, while the bottom plot shows the proportion of the density belonging to either class at that point. This proportion is known as the posterior $P(M|D)$, and can be interpreted as the probability of that point belonging to each class. This is one of the native benefits of probabilistic models, that instead of providing a hard class label for each sample, they can provide a soft label in the form of the probability of belonging to each class.\n", "\n", "We can implement all of this simply in pomegranate using the `NaiveBayes` class." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "idxs = idxs.reshape(idxs.shape[0], 1)\n", "X = X.reshape(X.shape[0], 1)\n", "\n", "model = NaiveBayes.from_samples(NormalDistribution, X, y)\n", "posteriors = model.predict_proba(idxs)\n", "\n", "plt.figure(figsize=(14, 4))\n", "plt.plot(idxs, posteriors[:,0], color='m')\n", "plt.plot(idxs, posteriors[:,1], color='c')\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.ylabel(\"P(M|D)\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like we're getting the same plots for the posteriors just through fitting the naive Bayes model directly to data. The predictions made will come directly from the posteriors in this plot, with cyan predictions happening whenever the cyan posterior is greater than the magenta posterior, and vice-versa." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Naive Bayes\n", "\n", "In the univariate setting, naive Bayes is identical to a general Bayes classifier. The divergence occurs in the multivariate setting, the naive Bayes model assumes independence of all features, while a Bayes classifier is more general and can support more complicated interactions or covariances between features. Let's take a look at what this means in terms of Bayes' rule. \n", "\n", "\\begin{align}\n", "P(M|D) &= \\frac{P(M)P(D|M)}{P(D)} \\\\\n", " &= \\frac{P(M)\\prod_{i=1}^{d}P(D_{i}|M_{i})}{P(D)}\n", "\\end{align}\n", "\n", "This looks fairly simple to compute, as we just need to pass each dimension into the appropriate distribution and then multiply the returned probabilities together. This simplicity is one of the reasons why naive Bayes is so widely used. Let's look closer at using this in pomegranate, starting off by generating two blobs of data that overlap a bit and inspecting them." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.concatenate([numpy.random.normal(3, 2, size=(150, 2)), numpy.random.normal(7, 1, size=(250, 2))])\n", "y = numpy.concatenate([numpy.zeros(150), numpy.ones(250)])\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c')\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m')\n", "plt.xlim(-2, 10)\n", "plt.ylim(-4, 12)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's fit our naive Bayes model to this data using pomegranate. We can use the `from_samples` class method, pass in the distribution that we want to model each dimension, and then the data. We choose to use `NormalDistribution` in this particular case, but any supported distribution would work equally well, such as `BernoulliDistribution` or `ExponentialDistribution`. To ensure we get the correct decision boundary, let's also plot the boundary recovered by sklearn." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "\n", "model = NaiveBayes.from_samples(NormalDistribution, X, y)\n", "clf = GaussianNB().fit(X, y)\n", "\n", "xx, yy = numpy.meshgrid(numpy.arange(-2, 10, 0.02), numpy.arange(-4, 12, 0.02))\n", "Z1 = model.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "Z2 = clf.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "\n", "plt.figure(figsize=(16, 8))\n", "plt.subplot(121)\n", "plt.title(\"pomegranate naive Bayes\", fontsize=16)\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c')\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m')\n", "plt.contour(xx, yy, Z1, cmap='Blues')\n", "plt.xlim(-2, 10)\n", "plt.ylim(-4, 12)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "\n", "plt.subplot(122)\n", "plt.title(\"sklearn naive Bayes\", fontsize=16)\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c')\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m')\n", "plt.contour(xx, yy, Z2, cmap='Blues')\n", "plt.xlim(-2, 10)\n", "plt.ylim(-4, 12)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Drawing the decision boundary helps to verify that we've produced a good result by cleanly splitting the two blobs from each other.\n", "\n", "Bayes' rule provides a great deal of flexibility in terms of what the actually likelihood functions are. For example, when considering a multivariate distribution, there is no need for each dimension to be modeled by the same distribution. In fact, each dimension can be modeled by a different distribution, as long as we can multiply the $P(D|M)$ terms together. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Classifying Signal with Different Distributions on Different Features\n", "\n", "Let's consider the example of some noisy signals that have been segmented. We know that they come from two underlying phenomena, the cyan phenomena and the magenta phenomena, and want to classify future segments. To do this, we have three features-- the mean signal of the segment, the standard deviation, and the duration. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_signal(X, n):\n", " plt.figure(figsize=(16, 6))\n", " t_current = 0\n", " for i in range(n):\n", " mu, std, t = X[i]\n", " chunk = numpy.random.normal(mu, std, int(t))\n", " plt.plot(numpy.arange(t_current, t_current+t), chunk, c='cm'[i % 2])\n", " t_current += t\n", " \n", " plt.xticks(fontsize=14)\n", " plt.yticks(fontsize=14)\n", " plt.xlabel(\"Time (s)\", fontsize=14)\n", " plt.ylabel(\"Signal\", fontsize=14)\n", " plt.ylim(20, 40)\n", " plt.show()\n", "\n", "def create_signal(n):\n", " X, y = [], []\n", " for i in range(n):\n", " mu = numpy.random.normal(30.0, 0.4)\n", " std = numpy.random.lognormal(-0.1, 0.4)\n", " t = int(numpy.random.exponential(50)) + 1\n", " X.append([mu, std, int(t)])\n", " y.append(0)\n", "\n", " mu = numpy.random.normal(30.5, 0.8)\n", " std = numpy.random.lognormal(-0.3, 0.6)\n", " t = int(numpy.random.exponential(200)) + 1\n", " X.append([mu, std, int(t)])\n", " y.append(1)\n", " \n", " return numpy.array(X), numpy.array(y)\n", "\n", "X_train, y_train = create_signal(1000)\n", "X_test, y_test = create_signal(250)\n", "plot_signal(X_train, 20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can start by modeling each variable as Gaussians, like before, and see what accuracy we get." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gaussian Naive Bayes: 0.794\n", "sklearn Gaussian Naive Bayes: 0.794\n" ] } ], "source": [ "model = NaiveBayes.from_samples(NormalDistribution, X_train, y_train)\n", "print(\"Gaussian Naive Bayes: \", (model.predict(X_test) == y_test).mean())\n", "\n", "clf = GaussianNB().fit(X_train, y_train)\n", "print(\"sklearn Gaussian Naive Bayes: \", (clf.predict(X_test) == y_test).mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We get identical values for sklearn and for pomegranate, which is good. However, let's take a look at the data itself to see whether a Gaussian distribution is the appropriate distribution for the data." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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8Hrxeb2h/r9dLcnJypMIVkS6gZEZERERs5Y033uDFF18EICEhAYfDwY9+9CMOHToEwL59+xg2bBhXXnklu3fvxjRNTp06FVrdXUTOHxpmJiIiIrby3e9+lwULFnDvvfdSW1tLXl4eF198MUuWLCEuLo5evXqxZMkSPB4PWVlZTJ48GdM0yc/Pj3ToItLJlMyIiIiIrSQmJvL88883ad+0aVOTttzcXHJzc7sjLBGJAA0zExERERERW1IyIyIiIiIitqRkRkREREREbEnJjIiIiIiI2FKbCgAcPHiQZ599lg0bNlBQUMCSJUtwOp24XC6WLVtGr1692Lx5M5s2bSI2NpY5c+Zw4403dnXscp4LlAUwKowW+50pTlzprm6MSOQM/WyKCEBaTBq+Ql+L/boWiHS9VpOZdevWsX37dhISEgBYunQpixYtIjMzk02bNrFu3Tpmz57Nhg0b2LZtG36/n6lTp3LNNdfgcukXWDrOqDAoXl/cYn/GjAzQcgESAfrZFBEAh89B8W90LRCJpFaHmfXv35+VK1eGtlesWEFmZiYAhmHgdrs5dOgQ3/zmN3G5XCQnJ9O/f3+OHTvWdVGLiIiIiMgFr9UnMzk5OZw4cSK0fdFFFwHw3nvvsXHjRl599VX+9Kc/kZycHNonKSmJqqqqVl/c7/dTUFDQkbg7RU1NTURfPxzFBj38PSgtKW2xP6E8gcKCwgZtet86JppjExEREWlJhxbNfPPNN1mzZg0vvfQS6enpeDwevF5vqN/r9TZIblridrtDT3kioaCgIKKvH45iA1+hD1/vlscip6alknFJRoM2vW8dUz82JTUiIiJiF+2uZvbb3/6WjRs3smHDBvr16wfAiBEjePfdd/H7/VRWVvLxxx8zZMiQTg9WIqcsEKDQ56PQ58PfowdlgUCkQxIRERGRC1y7nswYhsHSpUu5+OKLyc3NBeDb3/42jzzyCNOmTWPq1KlYlsWPf/xj3G53lwQskVFhGKwvPjPJsaS0lMdTUzWnUUREREQiqk3JTN++fdm8eTMABw4caHafu+++m7vvvrvzIhMREREREQmjQ3NmRETaKxgMkpeXx8mTJwkEAsyZM4dLL72U+fPn43A4GDx4MIsXLyYmJoZVq1axc+dOYmNjycvLY8SIEZEOX0RERKKQkhkR6Rbbt28nLS2N5cuXU15ezoQJE7j88suZO3cuo0ePJj8/nx07dtCnTx8OHDjAli1bKCoqIjc3l23btkU6fBEREYlCSmZEpFuMHTuWnJwcACzLwul0cuTIEUaNGgXAddddx549exg4cCDZ2dk4HA769OmDYRiUlZWRnq5ZWiIiItKQkhkR6RZJSUkAVFVV8cgjjzB37lyWLVuGw+EI9VdWVlJVVUVaWlqD76usrGw1mQm3blVnr6PTkTWQWmOXtX7sEKcdYgR7xGmHGEXkwqZkRkS6TVFREQ8//DBTp05l3LhxLF++PNTn9XpJSUnpknWrOnuNn46sgdSaaF6HqD47xGmHGMEecZ5LjEqCRKQ7tHudGRGRjigtLWXmzJk8/vjjTJw4EYChQ4eyf/9+AHbt2kVWVhZXXnklu3fvxjRNTp06hWmaGmImIiIizdKTGRHpFmvXrqWiooLVq1ezevVqAJ588kmeeeYZVqxYwaBBg8jJycHpdJKVlcXkyZMxTZP8/PxujTNQFsCoMMLuY/rNbopGREREwlEyI12itQ+EzhQnrnRXN0YkkbZw4UIWLlzYpH3jxo1N2nJzc0ML83Y3o8KgeH1x2H16T+zdTdGIiIhIOEpmpEu09oEwY0YGaOSQiIiIiJwDzZkRERERERFbUjIjIiIiIiK2pGFmIiIiYiuGYbBw4UKOHz+Ow+Hg6aefxu12M3/+fBwOB4MHD2bx4sXExMSwatUqdu7cSWxsLHl5eYwYMSLS4YtIJ1IyIyIiIrby9ttvA7Bp0yb279/Pc889h2VZzJ07l9GjR5Ofn8+OHTvo06cPBw4cYMuWLRQVFZGbm8u2bdsiHL2IdCYlMyIiImIrt9xyCzfccAMAp06dIiUlhb179zJq1CgArrvuOvbs2cPAgQPJzs7G4XDQp08fDMOgrKws7NpVfr+/zQt+phgplJaUttifUJ5AYUFhm88rkmpqas6bhU51LtGpq85FyYyIiIjYTmxsLPPmzeN///d/+eUvf8mePXtwOBwAJCUlUVlZSVVVFWlpaaHvqWsPl8y43W4yMzPbFEPx+8X06t2rxf7UtFQyLslo4xlFVkFBQZvPO9rpXKLTuZxLuCRIyYxEhgm+Ql/4XbQwoYiIhLFs2TIee+wx7r77bvx+f6jd6/WSkpKCx+PB6/U2aE9OTo5EqCLSRZTMSEQY1QYlW0vC7qOFCUVEpDlvvPEGn3/+OQ899BAJCQk4HA6GDx/O/v37GT16NLt27eKqq66if//+LF++nFmzZlFcXIxpmmGfyoiI/SiZEREREVv57ne/y4IFC7j33nupra0lLy+Pb3zjGyxatIgVK1YwaNAgcnJycDqdZGVlMXnyZEzTJD8/P9Khi0gnUzIjIiIitpKYmMjzzz/fpH3jxo1N2nJzc8nNze2OsEQkArRopoiIiIiI2JKSGRERERERsSUlMyIiIiIiYktKZkRERERExJaUzIiIiIiIiC2pmpl0iAkU+s4sepnidJLuckU2ILGNgwcP8uyzz7JhwwZ+/OMfU1paCsDJkycZOXIkzz33HHPmzOHvf/87cXFxuN1ufvWrX0U4ahEREYlGSmakQ6oNg60lZxa9nJGRgZYgk7ZYt24d27dvJyEhAYDnnnsOgNOnTzN9+nQWLFgAwCeffMLvfvc7HA5HxGIVERGR6NemYWYHDx5k2rRpwJkPGVOmTGHq1KksXrwY0zQBWLVqFRMnTuSee+7h0KFDXRexiNhW//79WblyZZP2lStXct9993HRRRdRWlpKRUUFP/jBD5gyZQpvv/12BCIVERERO2j1yUzjO6k/+9nPmDt3LqNHjyY/P58dO3bQp08fDhw4wJYtWygqKiI3N5dt27Z1efAiYi85OTmcOHGiQduXX37Jvn37Qk9lgsEgM2fOZPr06Zw+fZopU6YwYsQIevbsGfbYfr+fgoKCZvtqampa7Gush78HpSWlYfdJDaSG3SehPIHCgsI2vV6d9sQYSXaI0w4xgj3itEOMInJhazWZqbuT+sQTTwBw5MgRRo0aBcB1113Hnj17GDhwINnZ2TgcDvr06YNhGJSVlZGersFHIhLe73//e26//XacTicAvXr14p577iE2NpaePXuSmZnJ8ePHW01m3G43mZmZzfYVFBS02NeYr9CHr7cv7D4ul4tevXu12J+alkrGJRlter2OxBhJdojTDjGCPeI8lxiVBIlId2g1mWl8J9WyrNA49qSkJCorK6mqqiItLS20T117a8lMuDup3SGa7zhFW2z+Hj0o+Wqidm1tLYFAILRdnZbG+6dPh/ZNsCxSyx1h71y3dme7Lfs0d/c72t63+hRb8/bt28ecOXNC23v37mXjxo2sW7cOr9fLX//6VwYNGhSR2ERERCS6tbsAQEzM2Wk2Xq+XlJQUPB4PXq+3QXtycnKrxwp3J7U7RPNdsWiLrdDno/dX1ctKSktxuVz07nXmzrQZF8cb5eWhfWdkZJDqJ+zd7dbubLdln+bufkfb+1afXWLr7qTm+PHj9OvXL7R9/fXXs3v3bu6++25iYmJ49NFH9ZRXREREmtXuZGbo0KHs37+f0aNHs2vXLq666ir69+/P8uXLmTVrFsXFxZimqQ8fItKsvn37snnz5tD27373uyb7PPnkk90ZkoiIiNhUu5OZefPmsWjRIlasWMGgQYPIycnB6XSSlZXF5MmTMU2T/Pz8rohVREREREQkpE3JTP07qQMHDmTjxo1N9snNzSU3N7dzoxMREREREWlBm9aZERERERERiTZKZkRERERExJaUzIiIiIiIiC21uwCAiIi0wjyz+GY4zhQnrnRXNwUkIiJyflIyIyLSyYxqg5KtJWH3yZiRAapgLyIick6UzIiIdDGfYeA3rQZtnloDXyBAuktPZ0RERDpKyYyISBfzmxYHq6oatvl8ZBhOPZwRERE5B0pmpEN6VDoYW+UBoGetxdgaT6gv6aSBaTgiFZqIiIiIXCCUzEiHxFSZHPvVCQAcSYkc81aH+tweD4OnXByp0ERE5DwXDAbJy8vj5MmTBAIB5syZw8UXX8xDDz3EJZdcAsCUKVO47bbbWLVqFTt37iQ2Npa8vDxGjBgR2eBFpFMpmRERERFb2b59O2lpaSxfvpzy8nImTJjAww8/zAMPPMDMmTND+x05coQDBw6wZcsWioqKyM3NZdu2bRGMXEQ6m5IZERERsZWxY8eSk5MDgGVZOJ1ODh8+zPHjx9mxYwcDBgwgLy+Pd999l+zsbBwOB3369MEwDMrKykhPb3m2mt/vp6CgoE1xpBgplJaUttifUJ5AYUFhu84tUmpqatp83tFO5xKduupclMyIiIiIrSQlJQFQVVXFI488wty5cwkEAkyaNInhw4ezZs0aXnjhBZKTk0lLS2vwfZWVlWGTGbfbTWZmZpviKH6/mF69e7XYn5qWSsYlGW08q8gqKCho83lHO51LdDqXcwmXBMV0NCARERGRSCkqKmL69OmMHz+ecePGMWbMGIYPHw7AmDFjOHr0KB6PB6/XG/oer9dLcnJypEIWkS6gZEZERERspbS0lJkzZ/L4448zceJEAGbNmsWhQ4cA2LdvH8OGDePKK69k9+7dmKbJqVOnME0z7FMZEbEfDTMTEelEPsOg1rQoD9aG2gzLCvMdItJea9eupaKigtWrV7N69WoA5s+fz09/+lPi4uLo1asXS5YswePxkJWVxeTJkzFNk/z8/AhHLiKdTcmMiHSrgwcP8uyzz7JhwwaOHj163pVS9ZsWFYbRYJHMzKTECEYkcv5ZuHAhCxcubNK+adOmJm25ubnk5uZ2R1giEgFKZkSk26xbt47t27eTkJAAnCmbqlKqIiIi0lGaMyP2ZYKv0Nfgq4e/R+jfgbJApCOURvr378/KlStD24cPH2bnzp3ce++95OXlUVVV1WIpVREREZHG9GRGbMuoNijZWtKgrbSkFF9vHwAZMzJA8zyjSk5ODidOnAhtjxgxolNKqUL4tSHaU9u+h79H2HUjAFIDqS3uU5sQj2kYeKurQ21GvLvBNkCN30/5aYOCwsJ2xxhJdojTDjGCPeK0Q4wicmFTMiMiETNmzBhSUlJC/16yZAk333xzh0qphlsboj217X2FvlBC3BKXy9Xi2hLlwVpinE6SEs/Ok3E22gaId7tJS/VwSUZGu2OMJDvEaYcYwR5xdtW6ECIinUXDzEQkYuxeStVnGJQHaxt8qXKZiIhI99GTGRGJmKeeeoolS5bYtpSq37QaVC0DVS4TkXq+mtsZjjPFiSvd1U0BiZx/lMyISLfq27cvmzdvBmDYsGEqpSoi563m5nY2pvmdIudGw8xERERERMSWlMyIiIiIiIgtKZkRERERERFb6tCcmWAwyPz58zl58iQxMTEsWbKE2NhY5s+fj8PhYPDgwSxevJiYGOVKIiIiIiLSNTqUzPzxj3+ktraWTZs2sWfPHn7xi18QDAaZO3cuo0ePJj8/nx07djBmzJjOjle6SFkgQIVhhLbjHA6C9UrM+k0zEmGJiIiIiLSoQ8nMwIEDMQwD0zSpqqoiNjaW999/n1GjRgFw3XXXsWfPHiUzNlJhGKwvLg5tT+zdm60lJQ22RURERESiSYeSmcTERE6ePMmtt97K3//+d9auXcs777yDw+EAICkpicrKylaP4/f7I7pCcE1NTdSuUNzdsfl79KCktDS0HUhNbXG7trYW0zTxVlcDYMS7Q/8GqImLJRAIUFpy9vsbSw2khu1vyz7N9dfW1oba0qrTOP3+6bCvYSVYlJvlYffpLPp5ExEREelcHUpm1q9fT3Z2Nv/v//0/ioqKuP/++wkGg6F+r9dLSkpKq8dxu91kZmZ2JIROUVBQENHXD6e7Yyv0+ejtO7uwl8vlonevXs1ul5SWEhMTQ1LimcUBnU5n6N8A8e54XC4XvXqf/f7GWutvyz7N9ZeWlIba4sw4yt03ItMAACAASURBVN8In6hkzMjg4ksuDrtPZ7HLz5uSGhGRtnG4XPiMKhKczkiHInLB6tAM/ZSUFJKTkwFITU2ltraWoUOHsn//fgB27dpFVlZW50UpIiIiEmVqHeA3rdZ3FJEu06EnMzNmzCAvL4+pU6cSDAb58Y9/zPDhw1m0aBErVqxg0KBB5OTkdHasIiIiIiIiIR1KZpKSknj++eebtG/cuPGcAxIREREREWkLLQQjIiIiIiK2pGRGRERERERsqUPDzETCsYBa06I8WAuAO8ahSi8iIiIi0umUzEgT2aaHnkUWY2s8obaetWe3axxxuIItfTcELZMKw+BgVRUAIz0eEpTLiIiIiEgnUzIjTXiq4dPfnOKY9+xCmI6kxNC2t7qaEbkjIhWeiIiIiAigZEZEutnBgwd59tln2bBhAwUFBSxZsgSn04nL5WLZsmX06tWLZ555hvfee4+kpCQAVq9eHVrbSkQkGAySl5fHyZMnCQQCzJkzh0svvZT58+fjcDgYPHgwixcvJiYmhlWrVrFz505iY2PJy8tjxAjdjBM5nyiZEZFus27dOrZv305CQgIAS5cuZdGiRWRmZrJp0ybWrVvHggULOHLkCL/61a9IT0+PcMQiEo22b99OWloay5cvp7y8nAkTJnD55Zczd+5cRo8eTX5+Pjt27KBPnz4cOHCALVu2UFRURG5uLtu2bYt0+CLSiZTMXKDKAgEqDCO07TfNCEYjF4r+/fuzcuVKnnjiCQBWrFjBRRddBIBhGLjdbkzT5JNPPiE/P5/S0lImTpzIxIkTIxm2iESZsWPHhhbntiwLp9PJkSNHGDVqFADXXXcde/bsYeDAgWRnZ+NwOOjTpw+GYVBWVhb2Ronf76egoKBNcSSbF1Hjr6G0vKbZ/tRAKqUlpWGPkVCeQGFBYZteryvV1NS0+byjnc4lOnXVuSiZuUBVGAbri4tD2xN7945gNHKhyMnJ4cSJE6HtukTmvffeY+PGjbz66qtUV1dz33338cADD2AYBtOnT2f48OFcfvnlYY8d7gNIey6gPfw9Wv3wUfcBpTYhHm91dYM+I96NaRgN2o14d5P9avx+qmtief/06TP7pKTwfnExCZaFWV7eplgjwQ5/WO0QI9gjzmiNsW4IalVVFY888ghz585l2bJlOByOUH9lZSVVVVWkpaU1+L7KysqwyYzb7SYzM7NNcZwsKCPeHU+ax9Nsv8vlolfvXmGPkZqWSsYlGW16va5UUFDQ5vOOdjqX6HQu5xLuOqRkRkQi6s0332TNmjW89NJLpKenhxKYuqFoV111FceOHWs1mQn3AaQ9F1BfoQ9fb1/Yfeo+oJQHa0lKrG3Q53Q6iXE6SUpMbNBWfxsg3u3GjIvjja8Sl5LSUnr36sWMjAwuufjiNsUaCXb4w2qHGMEecXbVh4/OUFRUxMMPP8zUqVMZN24cy5cvD/V5vV5SUlLweDx4vd4G7Zp/J3J+0aKZIhIxv/3tb9m4cSMbNmygX79+ABQWFjJlyhQMwyAYDPLee+8xbNiwCEcqItGktLSUmTNn8vjjj4eGoQ4dOpT9+/cDsGvXLrKysrjyyivZvXs3pmly6tQpTNPUXDyR84yezIhIRBiGwdKlS7n44ovJzc0F4Nvf/jaPPPII48eP5+677yYuLo7x48czePDgCEcrItFk7dq1VFRUsHr1alavXg3Ak08+yTPPPMOKFSsYNGgQOTk5OJ1OsrKymDx5MqZpkp+fH+HIRaSzKZkRkW7Vt29fNm/eDMCBAwea3Wf27NnMnj27O8MSERtZuHAhCxcubNK+cePGJm25ubmhGyYicv7RMDMREREREbElJTMiIiIiImJLSmZERERERMSWlMyIiIiIiIgtKZkRERERERFbUjUzEZEI+FpsHPFFFmNrzqwcXuOII77KTdJJA1+sD2eKE1e6K8JRioiIRDclMyIiEeCsgU+3nOKYtxoAb3U1SYmJuD0e0uJiyZiRAVrbT0REJCwNMxMREREREVtSMiMiIiIiIrakZEZERERERGxJyYyIiIiIiNiSCgBIl7OA8mBtaNsd44hcMCIiIiJy3lAyI10uaJkUfFWxCWCkxxPBaERERETkfNHhZObFF1/krbfeIhgMMmXKFEaNGsX8+fNxOBwMHjyYxYsXExOjUWwiIiIiItI1OpRt7N+/n7/85S/85je/YcOGDRQXF/Ozn/2MuXPn8utf/xrLstixY0dnxyqdJFAWIOmkwdgqT+irZ5EV+vdFVlykQxQRERERaVWHnszs3r2bIUOG8PDDD1NVVcUTTzzB5s2bGTVqFADXXXcde/bsYcyYMZ0arHQOo8LgxL8UcayqKtTmSEoMLd7XZ8bASIUmIiIiItJmHUpm/v73v3Pq1CnWrl3LiRMnmDNnDpZl4XCcmdidlJREZWVlq8fx+/0UFBR0JIROUVNTE9HXD6crY+vh70GNvwZv9dl5LEa8O7RtGgaGYbTcb5pYlhXart9Xt23W+/7G/TVxsQQCAUpLSsPGmRpIDbtPc/21tbWhtta+HyChPIHCgsKw+3SWC/XnTURERKSrdCiZSUtLY9CgQbhcLgYNGoTb7aa4uDjU7/V6SUlJafU4brebzMzMjoTQKQoKCiL6+uF0ZWy+Qh/x7kqSEs9WGHM6nSQlJgIQ43Q22G7c762uxuFwhLab2zemXlvj/nh3PC6Xi169e4WNs7V9musvLSkNtbXlNVLTUsm4JCPsPp3FLj9vXZ3UHDx4kGeffZYNGzbwySefNDvXbtWqVezcuZPY2Fjy8vIYMWJEl8YkIiIi9tShOTPf+ta3+NOf/oRlWXz++ef4fD6uvvpq9u/fD8CuXbvIysrq1EBFxP7WrVvHwoUL8fv9AM3OtTty5AgHDhxgy5YtrFixgqeffjrCUYuIiEi06lAyc+ONN5KZmcnEiROZM2cO+fn5zJs3j5UrVzJ58mSCwSA5OTmdHauI2Fz//v1ZuXJlaPvIkSMN5trt3buXd999l+zsbBwOB3369MEwDMrKyiIVsoiIiESxDpdmfuKJJ5q0bdy48ZyCEZHzW05ODidOnAhtNzfXrqqqirS0tNA+de3p6elhjx1uDl575gT18Pdo83yu2oT4BvPBoOmcsbq2xvs1nptmmibe6mpq4mIpLa/p1vlc7WGH+VV2iBHsEacdYow0C/AZBglOZ6RDEbkgadFMEYmY+mtR1c2183g8eL3eBu3JycmtHivcHLz2zFfyFfrw9faF3aduPlZ5sLbB3DNoOmesrq3+NjSdm+atriYpMZF4dzxpHk+3zudqj2ie+1XHDjGCPeI8lxgvlCQoaJk4zBgSlMuIRIRWtRSRiBk6dGiTuXZXXnklu3fvxjRNTp06hWmarT6VEZEL08GDB5k2bRoAR48e5dprr2XatGlMmzaNN998E4BVq1YxceJE7rnnHg4dOhTJcEWkC+jJjIhEzLx581i0aBErVqxg0KBB5OTk4HQ6ycrKYvLkyZimSX5+fqTDFJEotG7dOrZv305CQgJwZg7eAw88wMyZM0P71C8oUlRURG5uLtu2bYtUyCLSBZTMiEi36tu3L5s3bwZg4MCBzc61y83NJTc3t7tDExEbqSsoUjeH9/Dhwxw/fpwdO3YwYMAA8vLyWiwooqe9IucPJTMiIiJiO40LiowYMYJJkyYxfPhw1qxZwwsvvEBycnK7C4q0Z0HvZPMiDMOgxghQWl7TpD/aFm8O53wq9qBziU5ddS5KZuTCZp6Z8N0SZ4oTV7qrGwMSEZGOGDNmTGjB7jFjxrBkyRJuvvnmdhcUac+C3icLynA6nbhi40jzeJr0R9vizeHYoSBFW+lcolNXFRRRAQC5oBnVBsXri1v8MiqMSIcoIiJtMGvWrNAE/3379jFs2DAVFBG5AOjJjIhIFLGA8mAtnlqDz31nnxqmOJ2ku/SUUKQlTz31FEuWLCEuLo5evXqxZMkSPB5PtxQU0VozIpGjZEZEJIoELZMCbzV+n4/fF1eF2mdkZKD7ySIN1S8oMmzYMDZt2tRkn+4oKKK1ZkQiR8PMRERERETElpTMiIiIiIiILWmYmYiIiEikqKqmyDlRMiMiIiISIUa1QcnWkhb7M2ZkoAlzIi3TMDMREREREbElJTMiIiIiImJLGmZ2gSgLBKgwziwAmVRrYFhWhCM6y2cY+M2z8bhjHKrVLyIiIiKtUjJzgagwDNYXFwMw1ufBQfQkM37T4mDV2fU0Rno8qtUvIiIiIq3SMDMREREREbElJTMiIiIiImJLSmZERERERMSWNGfmPBQoC2BUGA3akmoNxvo8AFxkxdFyRXsREREREXtQMnMeMioMitcXN2grD9Zy7KtJ9n1mDIxEWCLNev311/n3f/93APx+PwUFBaxYsYJly5Zx8cUXA5Cbm8uoUaMiGaaIiIhEISUzIhJRd911F3fddRcATz/9NN/73vc4fPgwjz/+ODk5ORGOTkRERKKZkhkRiQoffPABf/vb31i8eDGzZ8+moKCAf/3Xf2XEiBE89thjxMaGv1zVPdVpTk1NTYt9jfXw96C0pDTsPqmBVEpLSqlNiMdbXd2gz4h3YxpGg3Yj3t1kP9MwMOrtZ5om3urq0L41fj8lNWfjKE9IoKCwsE3n0JXa815Gih1iBHvEaYcYReTCpmRGup0F1JoW5cFagKhawFMi58UXX+Thhx8G4JprruGWW26hb9++LF68mE2bNnHfffeF/X63201mZmazfQUFBS32NeYr9OHr7Qu7j8vlolfvXpQHa0lKrG3Q53Q6iXE6SUpMbNBWfxsgxuls0O6triYpMTHUFu9209vTK7R/Wmoql2RktOkculJ73stIsUOMYI84zyVGJUEi0h1UzUy6XdAyqTAMDlZVcbCqCiOKFvCUyKioqOD48eNcddVVAHzve9+jX79+OBwObr75Zo4ePRrhCEVERCQanVMy8+WXX3L99dfz8ccf88knnzBlyhSmTp3K4sWLMU2zs2IUkfPcO++8w9VXXw2AZVnccccdFBefKWKxb98+hg0bFsnwREREJEp1OJkJBoPk5+cTHx8PwM9+9jPmzp3Lr3/9ayzLYseOHZ0WpLSfzzAoD9aGvjSUS6LZ8ePH6du3LwAOh4NnnnmGH/3oR9x33334fD7uvvvuCEcoIiIi0ajDc2aWLVvGPffcw0svvQTAkSNHQqVTr7vuOvbs2cOYMWM6J0ppN79pcfCrUswAmUmJYfYWiazZs2c32M7OziY7OztC0YiIiIhddCiZef3110lPT+faa68NJTOWZeFwOABISkqisrKy1eOEqz7UHaK5Ssu5xNbD34Maf02L1ZQaV1Fqd79pYllWaLtxpabG1Zza218TF0tpeU2oYlRLmuuvra0NtbX2/W3ZJ6E8gcKCwrDHaKvz9edNREREJFI6lMxs27YNh8PBvn37KCgoYN68eZSVlYX6vV4vKSkprR4nXPWh7hDNlWTOJTZfoY94d2WDKkv1qyY1rqLU3n5vdTUOhyO03dy+9as5tbc/3h1PmscTqhjVkub6S0tKQ22tfX9b9klNSyXjks6pIGWXnzclNU35DAO/eXaoZo+vqvF15fDNr8XGMbbKE9pOOmngiz1bZc2Z4sSV7uqy1xcREbGDDiUzr776aujf06ZN46mnnmL58uXs37+f0aNHs2vXrlBVIhERu2s8bLPnV9X4unL4prMGjq0/Edp2eTw46vX3feBinB5IdymhERGRC1enlWaeN28eK1euZPLkyQSDQa3cLSLSiYKWGSpnfrCqio99PioMI9JhiUTUwYMHmTZtGkCLVVVXrVrFxIkTueeeezh06FAkwxWRLnDOi2Zu2LAh9O+NGzee6+FEREREWrVu3Tq2b99OQkICcLaq6ujRo8nPz2fHjh306dOHAwcOsGXLFoqKisjNzWXbtm0RjlxEOpMWzRQRERHb6d+/PytXrgxtN66qunfvXt59912ys7NxOBz06dMHwzAazPEVEfs75yczIiIiIt0tJyeHEyfOzitrrqpqVVUVaWlpoX3q2tPT01s8bnsqrSabF2F8NdyzxghQWl7ToD/aqmqGcz5VtdS5RKeuOhclMyIiImJ7MTFnB5vUVVX1eDx4vd4G7cnJyWGP055KqycLynA6nQC4YuNI83ga9HdKVc2UVFJjUlvs76zKhtFccbO9dC7R6VzOJVwSpGRGREREbG/o0KFNqqr279+f5cuXM2vWLIqLizFNM+xTmXNhAeXBWtwxDhK+SnA6g1FtULK1pMX+jBkZ0DWnJGILSmZERETE9ubNm8eiRYtYsWIFgwYNIicnB6fTSVZWFpMnT8Y0TfLz87vs9YOWSYG3mpEeDwmdl8uISCuUzIiIiIgt9e3bl82bNwMwcODAZquq5ubmkpub292hiUg3UTUzERERERGxJT2ZEQnHBF+hr8Xuzpp4Kd0nUBbAqGh5sUnTb3ZjNCIiInIulMyIhNHqxMvpGfgqWk52QAlPtDEqDIrXF7fY33ti726MRkTONxbgM4xOLQIgIi1TMiNyDlpLdkCVZkRELiRBywQzBmj5CbCIdB4lMyIiNvS12DiSThr4YjUMUiTaBC0Th6lpySLdQcmMiETcnXfeieerxeb69u3L5MmTWbp0KU6nk+zsbH70ox9FOMLo46yBExuLSItr+TKup4IiInK+UzIjIhHl9/uxLIsNGzaE2saPH8/KlSvp168f3//+9zl69ChDhw6NYJQiIiISjfQMVEQi6tixY/h8PmbOnMn06dN55513CAQC9O/fH4fDQXZ2Nnv37o10mCIiIhKF9GTmPFEWCFBhnJlsmFRrYFhWhCMSaZv4+HhmzZrFpEmTKCws5MEHHyQlJSXUn5SUxGeffdbqcfx+PwUFBc321dTUhPp6+HtQWlLa4nFSA6lN+msT4vFWV4e2TcPAW12NEe9u0A5gxLtD/fXbGu9nGgZGvf1M02xwzNaOYRoGNf4aSstrWjyXhPIECgsKW+zviPrvZbSyQ4xgjzjtEKOIXNiUzJwnKgyD9cVnys2O9XlwoGRG7GHgwIEMGDAAh8PBwIEDSU5Opry8PNTv9XobJDctcbvdZGZmNttXUFAQ6vMV+vD1bnnSvMvlolfvXg3ayoO1JCXWhrZjnE6SEhNxfvXf+pxOZ6i/flvj/WKczgbt3urqBsds7RgxTifx7njSvppr1JzUtFQyLslosb8j6r+X0coOMYI94jyXGJUEiUh30DAzEYmorVu38vOf/xyAzz//HJ/PR2JiIp9++imWZbF7926ysrIiHKWIiIhEIz2ZEZGImjhxIgsWLGDKlCk4HA5++tOfEhMTw2OPPYZhGGRnZzNy5MhIhykiIiJRSMmMiESUy+Xin//5n5u0b968OQLRiIiIiJ1omJmIiIiIiNiSnsyIiJxnfIaB37Tw1Bp87jtT7CDF6STd5YpwZCIiIp1LyYyIiE1ZnKm01phhWRz2evH7fPy+uAqAGRkZpHdzfCIiIl1NyYzNBMoCGBVGk/akWoOxvjMlWi+y4ijp7sBEpNsFLZMCb3WT9sykMyWcvxYbx9iqM9eFpJMGvtiGJamdKU5c6XpaIyIi9qVkxmaMCoPi9cVN2suDtRyrOnMHts+Mgd0dlohEIWcNHFt/AgC3x0NaXMNLfsaMDPS4RkRE7EzJjIiIiIhdmWcWAw5HT2HlfKZkRqJO3TyAHqZFebAWd4yDBKcz0mGJiIhEHaPaoGRr+MHlegor57MOJTPBYJC8vDxOnjxJIBBgzpw5XHrppcyfPx+Hw8HgwYNZvHgxMTGq/CztVzcPoKdhcLCqipEeDwnKZURERESkkQ4lM9u3byctLY3ly5dTXl7OhAkTuPzyy5k7dy6jR48mPz+fHTt2MGbMmM6O97xXN8G/h79Hs4+NTb8ZgahERERERKJPh5KZsWPHkpOTA4BlWTidTo4cOcKoUaMAuO6669izZ4+SmQ6om+BfWlKKr3fTZKb3xN4RiEpEREREJPp0KJlJSkoCoKqqikceeYS5c+eybNkyHA5HqL+ysrLV4/j9fgoKCjoSQqeoqamJ6Os3p4e/B6UlpdTW1lJaUtqkPzWQ2mx7bUI83uozJVpNw8AwjNA2gBHv7rx+08SyrNB2/b66bbPe93ek31tdHdqnJi6W0vKaNr0X9d+3lt6r1o7Rmf0ACeUJFBYURuXPW51ojk1EpD3uvPNOPJ4zJcn79u3L5MmTWbp0KU6nk+zsbH70ox9FOEIR6UwdLgBQVFTEww8/zNSpUxk3bhzLly8P9Xm9XlJSUlo9htvtJjMzs6MhnLOCgoKIvn5zfIU+fL19lJaU0qt3ryb9Lper2fbyYC1JiWcWz4txOnE6nSQlJob662+fa7+3uhqHwxHabm7fmHptHelPSkwM7RPvjiftqz9Mrb0X9d+3lt6r1o7Rmf0AqWmpZFySEZU/b3Xqx6akRkTsyu/3Y1kWGzZsCLWNHz+elStX0q9fP77//e9z9OhRhg4dGsEoRaQzdWiGfmlpKTNnzuTxxx9n4sSJAAwdOpT9+/cDsGvXLrKysjovSmnCZxiUB2tDX4ZlRTokERGRiDp27Bg+n4+ZM2cyffp03nnnHQKBAP3798fhcJCdnc3evXsjHaaIdKIOPZlZu3YtFRUVrF69mtWrVwPw5JNP8swzz7BixQoGDRoUmlMjXcNvWhz8apFMOLvi9/morlRzHZVqFhGR5sTHxzNr1iwmTZpEYWEhDz74YIORIklJSXz22Wdhj9GeIfDJ5kUYhhHa9lZXY8S7AagxAgQCgagY7pxWncbp90+32G8lWOfVcGOdS3TqqnPpUDKzcOFCFi5c2KR948aN5xyQSGN1pZrrqFSziIg0Z+DAgQwYMACHw8HAgQNJTk6mvLw81N+WYfDtGQJ/sqAMZ72ba0mJiaFtV2xc1Ax3jjPjKH+jvMX+jBkZlJvlUTsUur2ieVh3e+lczn5vS7QQjIjIBaDuCWf9r6pag7JAINKhiXSarVu38vOf/xyAzz//HJ/PR2JiIp9++imWZbF79+5uGwZvAaapIeAiXa3DBQBERMQ+Gj/hBPD7fGQYTi0MLueNiRMnsmDBAqZMmYLD4eCnP/0pMTExPPbYYxiGQXZ2NiNHjuyWWIKWiVaGE+l6SmZEJKKCwSB5eXmcPHmSQCDAnDlzuPjii3nooYe45JJLAJgyZQq33XZbZAO9ANUt4ltf4wV9nSlOXOmu7g5NpFkul4t//ud/btK+efPmCEQjIt1ByYyIRNT27dtJS0tj+fLllJeXM2HCBB5++GEeeOABZs6cGenwLmh1i/jW13hB34wZGejRjoiIRIqSGRGJqLFjx4aqH1qWhdPp5PDhwxw/fpwdO3YwYMAA8vLyQovgtSRcBaL6FVTqFqZtSbOLsdZblBYILejaeMFXaLoobF1b4/0aL05rmmaDY7Z2jOYWt228b7jFaQFq/H7KTxsUFBY2+1409141XtC3blHYaGKX6j92iNMOMYrIhU3JjIhEVFJSEgBVVVU88sgjzJ07l0AgwKRJkxg+fDhr1qzhhRdeYN68eWGPE64CUf0KKnUL07akucpA9RelBUILujZe8BWaLgpb19Z4v8aL03qrqxscs7VjNLe4beN9wy1OCxDvdpOW6uGSjIxm34vm3qvGC/rWLQobTexS/ccOcXZV9SERkc6iamYiEnFFRUVMnz6d8ePHM27cOMaMGcPw4cMBGDNmDEePHo1whCIiIhKNlMyIdDXzzB3uuonTjb8CZRd2adzS0lJmzpzJ448/zsSJEwGYNWsWhw4dAmDfvn0MGzYskiGKiIhIlNIwM5EuZlQblGwtaTJxus6FPoF67dq1VFRUsHr1alavXg3A/Pnz+elPf0pcXBy9evViyZIlEY5SRKRjfIZBgjPKV3o2m1YqrE9VCyWaKZkRkYhauHAhCxcubNK+adOmCEQjItJ5TMBvWiREeS5jVBt8tu6zFucTXug33SS6KZnpZs2t21Cf6dcSWyKRZJoW5cHaBm2GpVW8RUREopGSmW7W3LoN9fWe2LsboxGRxkzgYFVVg7bMpKYVw84HX4uNI+mkgS+2+buxurkicu4szlREdMc4on+4mYgNKZkREblAOWvgxMYi0uKa/1Ogmysi5y5omRR4qxnp8UT9cDMRO1I1MxERERERsSU9melErc2HAQ3bEJHoUjcEpj4NhxEREbtQMtOJWpsPAxq2Ic34ah2acFQWU7pK3RCY+jQcRqTzWdikTLOIzSiZEYmwunVowlFZTBERewtaJg4zxp43CnTTTaKYkhmb8BkGftOix1dlY1UqVkTsoLXht/oAJBL9dNNNopmSGZvwmxYHq6roaRgcrKo6b0vFikjk1c2j6dFozR13jKPdx2pt+K0+AMmFxAIqa2uJdWhemkhnUTITpRzx8Q0+ROhJzFmNP2hpsrJI56qbR1N386TOSI+n6c6tDD9R0RORs4KWSdACl8Omw81EopCSmSgVdMDheh8i9CTmrMYftDRZWSRyWht+oqInIiLSlbTOjIiIiIiI2JKezLRDaxNZNZwiOjQulnAhDEPTJGsRERG5ECmZaYfWJrJqOEV0aFws4UIYhqZJ1hIpZqMiAXU68yaCknURG2hl/px+T6WrKJmJkLqnB3WcDjAsQk8THE4nNPMBQZqqv4J5bUI8DhVLEOk2JjQoElCnzTcR2rB+hek3+eI3X7TYr2Rd7Kbu75Y7xoFpWudFhbPW5s/p91S6ipKZCKl7elAnMymxwaT2IfHuCEZnL/VXMPdWV5PVu1eD/vrJDpxNHOFM8miLFZlVMUrOU0a1waevFTe4uQMNn+yc61Pv1p7sgO4aS/eq+7s10uPBBHymqQpnIh3UqcmMaZo89dRTfPjhh7hcLp555hkGDBjQmS8h0m71kx04mzgC9PzqCVm0/wG5ECtG6XoSfSzOPP2sf3MgLcy+jdeqqX8joU4PoRYs1AAACuFJREFU06LaMDns9TZo78zhoa0NwwTdNT6fRfO1xPrq64LQyk05R5wDKxj+3TjXmw66sXF+6tRk5g9/+AOBQIDXXnuN999/n5///OesWbPmnI+rHz6RC09XXU+k44KWyXunK0hKPJvMtPSRsLm1aurfSKjT0zAwzvHjXG2tScnfzj7pTuFrlPytCpfDgcsZY5snl9Hyt65+HD38PZp8ALXb39tovpYELTP002+BPUYKdFBbbsqF64fWbzrU/ew293MLrQ9ZbctrSPTp1GTm3Xff5dprrwXgiiuu4PDhw51yXN1VE7nwdNX1RM4/RrXJ717+W2jbW11NUmIiIz0e0uJi2/bkspm7xgHDJFA3By/ZiTflzD9TnE7SXe3/MN84WWn8gatNH7SmZ+Cr6Pjd7bYkIvX/5paWlOLr3fD17Pb31i7XkqBlghmD36zF6TjbXn84tM8wqLUs28+v6bA2DLn+4jdfNPtzC20cxRAFT5DsorUbMI3fq666OeKwrM6bLf3kk0/y3e9+l+uvvx6AG264gT/84Q/ExjafM73//vu43ZobIhJN/H4/V1xxRaTD0PVExOZ0LRGRzhLuetKpT2Y8Hg/eeuOeTdNs8WIBRMVFTkSik64nItIZdC0ROb/FdObBrrzySnbt2gWcubMxZMiQzjy8iFxAdD0Rkc6ga4nI+a1Th5nVVQz56KOPsKz/397dhUS1tXEA/29HrSk9lZmUZUf7gCwpEK2LprroO1JTipLQSgvNPkyxzLFhihmnDBHCuqgoCLVALLzqw4hMwqyYslD7ICgjDVFTakZrauZ5L8LBM27jwCvutY/P7yrHjfyf1vistbZ77yFYLBbMnj17uH48Y2wU4X7CGBsO3EsY+28b1s0MY4wxxhhjjI2UYb3MjDHGGGOMMcZGCm9mGGOMMcYYY6rEmxnGGGOMMcaYKg3ro5lF9vPnT+j1erS2tsLhcGDv3r0IDg6G0WiERqNBaGgoCgoK4OU18vs7uWxTp06F0WiEr68vwsPDkZ+fr0g2p9OJY8eO4f3795AkCSdOnMCYMWNw9OhRSJKEuXPnwmg0CpOt/yk1FosFYWFhSExMHPFcQ2VzOp0wmUzQaDTw9fVFYWEhAgMDhcjm5eUFg8EAIkJoaCjMZvMfH12qBv03/b558wa+vr4wm834+++hPq9eWS9evEBRURFKS0uVjiJLrketXLlS6ViD/KkniKarqwsJCQm4fPmysDejx8fHw8/PDwAwY8YMnDx5UuFEYlBTb/HkOaZbt25FQUEBNBoNdDod9u/fL3x9A/tlS0uL7Hrk7NmzqKmpgbe3N/R6PRYuXDjksaLU0tzcjLS0NISGhgIAEhMTsWHDBuFrkZsf5syZM7LjQqNEZWUlmc1mIiLq7u6mFStWUEZGBtXU1BARUXZ2Nt27d0+YbPHx8WS1WomIqLi4mKqqqhTJdvfuXTp69CgREdXX11N6ejqlpaVRfX09EREZDAaqrq4WJltXVxelpqbSypUr6erVq4rkGirb9u3bqbm5mYiIrl27RhaLRZhse/fupSdPnhARUW5urmJjOpzu3LlDubm5RET0/PlzSk9PVziRvAsXLtDGjRtpy5YtSkcZklyPEpHce1tEDoeDMjIyaM2aNfTu3Tul48j6/v07xcXFKR1DSGrpLZ7kxjQ2NpZaWlrI5XLR7t27qampSej6PPul3HqksbGRkpKSyOVyUWtrKyUkJAx5rJI8a6moqKBLly794xg11CI3P4z0uIyay8zWrVuHzMxMAAARQaPRIDw8HD09PSAi2O12xc5Ey2Vrb29HZGQkgN/PyLdarYpkW7VqFUwmEwCgra0Nf/31F5qamrB48WIAwPLly1FXVydMNrvdjgMHDiAuLk6RTH/KVlxcjPDwcAC/zyAr9QnTctlKSkoQHR0Nh8OBjo4O95k7NbNarVi2bBmA3x+C19jYqHAieTNnzkRJSYnSMf5IrkeJSO69LaLCwkJs27YNQUFBSkcZ0uvXr9HX14eUlBQkJyejoaFB6UjCUEtv8eQ5pk+fPoXD4cDMmTMhSRJ0Oh3q6uqErs+zX8qtR6xWK3Q6HSRJQnBwMJxOJ758+SLM2qWfZy2NjY2oqanB9u3bodfrYbPZVFGL3Pww0uMyajYz48ePh5+fH2w2Gw4ePIhDhw65Ly1bv349urq6sGTJEmGyhYSE4MmTJwCA+/fvo6+vT5FsAODt7Y3c3FyYTCbExMSAiCBJkjv7t2/fhMkWEhKCRYsWKZZnIM9s/QuXZ8+eoaysDDt37hQmm0ajQWtrKzZu3Iju7m7MmzdPsWzDxWaz/WNTptFo8OvXLwUTyVu7dq3wl/TJ9ShReb63RXPjxg0EBAS4F4uiGjt2LFJTU3Hp0iWcOHECOTk5Qv7+KEEtvcWT55jm5eVBq9W6v98/n4tcn2e/lFuPeObvf12ktQswuJaFCxfiyJEjKC8vR0hICM6dO6eKWuTmh5Eel1GzmQGAz58/Izk5GXFxcYiJiUFBQQHKy8tx+/ZtbNq0CadOnRImm8Viwfnz57Fjxw5MnjwZkyZNUiwb8PtM4p07d2AwGPDjxw/363a7XfGznwOz9fb2KprFk2e2mzdvwmg04sKFCwgICBAq2/Tp01FdXY3ExERFfxeGi5+fH+x2u/trl8sl/KZBZJ49SmQi94Tr16+jrq4OSUlJePXqFXJzc9HR0aF0rEHCwsIQGxsLSZIQFhaGiRMnCplTCWrtLZ5j6u/vj56eHvf3++dzNdU38N6KofLb7Xb4+/vLHiuS1atXIyIiwv3v5uZm1dTiOT+M9LiMms1MZ2cnUlJScPjwYWzevBkAMGHCBPcuMSgoCF+/fhUm24MHD1BUVIQrV66gp6cHS5cuVSRbVVUVzp8/DwDQarWQJAkRERF4/PgxAKC2thZRUVHCZFP6Zr5+ctmqq6tRVlaG0tJShISECJVt3759+PDhA4DfZ0ZE+X/8f0RGRqK2thYA0NDQIOyN4Gog16NEJHJP6FdeXu7uA+Hh4SgsLMSUKVOUjjVIZWWl+6RGe3s7bDabkDmVoNbe4jmmfX19GDduHD5+/AgiwsOHDxEVFaWq+ubPnz9oPRIZGYmHDx/C5XKhra0NLpcLAQEBsseKJDU1FS9fvgQAPHr0CAsWLFBFLXLzw0iPi0RENHwlictsNuPWrVuYNWuW+7XMzEwUFRXB29sbPj4+MJlMmDFjhhDZdu3ahTNnzkCr1WLJkiXIysoa8VwA0Nvbi7y8PHR2duLXr1/Ys2cPZs+eDYPBgJ8/f2LWrFkwm82KXEMvl23VqlUAgJKSEgQGBir2NDO5bHq9HtOmTXOfdYiOjsbBgweFyBYQEIDTp0/Dx8cHWq0WZrNZ6Ov5/43+J/K8ffsWRASLxSLsU6M+ffqE7OxsVFRUKB1FllyPunjxIsaOHatgqsH+1BNElJSUhOPHjwv5vnQ4HMjLy0NbWxskSUJOTo77Ps7RTk29ZSC5MfXy8oLFYoHT6YROp0NWVpbw9Q3sl+/fv5ddj5SUlKC2thYulwt5eXmIiooa8lhRamlqaoLJZIKPjw8CAwNhMpng5+cnfC1y80N+fj7MZvOIjcuo2cwwxhhjjDHG/lvE+vs7Y4wxxhhjjP1LvJlhjDHGGGOMqRJvZhhjjDHGGGOqxJsZxhhjjDHGmCrxZoYxxhhjjDGmSryZYYwxxhhjjKkSb2YYY4wxxhhjqvQ/w5re4osGwysAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 4))\n", "plt.subplot(131)\n", "plt.title(\"Mean\")\n", "plt.hist(X_train[y_train == 0, 0], color='c', alpha=0.5, bins=25)\n", "plt.hist(X_train[y_train == 1, 0], color='m', alpha=0.5, bins=25)\n", "\n", "plt.subplot(132)\n", "plt.title(\"Standard Deviation\")\n", "plt.hist(X_train[y_train == 0, 1], color='c', alpha=0.5, bins=25)\n", "plt.hist(X_train[y_train == 1, 1], color='m', alpha=0.5, bins=25)\n", "\n", "plt.subplot(133)\n", "plt.title(\"Duration\")\n", "plt.hist(X_train[y_train == 0, 2], color='c', alpha=0.5, bins=25)\n", "plt.hist(X_train[y_train == 1, 2], color='m', alpha=0.5, bins=25)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, unsurprisingly (since you can see that I used non-Gaussian distributions to generate the data originally), it looks like only the mean follows a normal distribution, whereas the standard deviation seems to follow either a gamma or a log-normal distribution. We can take advantage of that by explicitly using these distributions instead of approximating them as normal distributions. pomegranate is flexible enough to allow for this, whereas sklearn currently is not." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gaussian Naive Bayes: 0.794\n", "sklearn Gaussian Naive Bayes: 0.794\n", "Heterogeneous Naive Bayes: 0.822\n" ] } ], "source": [ "model = NaiveBayes.from_samples(NormalDistribution, X_train, y_train)\n", "print(\"Gaussian Naive Bayes: \", (model.predict(X_test) == y_test).mean())\n", "\n", "clf = GaussianNB().fit(X_train, y_train)\n", "print(\"sklearn Gaussian Naive Bayes: \", (clf.predict(X_test) == y_test).mean())\n", "\n", "model = NaiveBayes.from_samples([NormalDistribution, LogNormalDistribution, ExponentialDistribution], X_train, y_train)\n", "print(\"Heterogeneous Naive Bayes: \", (model.predict(X_test) == y_test).mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like we're able to get a small improvement in accuracy just by using appropriate distributions for the features, without any type of data transformation or filtering. This certainly seems worthwhile if you can determine what the appropriate underlying distribution is.\n", "\n", "Next, there's obviously the issue of speed. Let's compare the speed of the pomegranate implementation and the sklearn implementation." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "710 µs ± 15.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n", "547 µs ± 3.91 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n", "568 µs ± 5.77 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%timeit GaussianNB().fit(X_train, y_train)\n", "%timeit NaiveBayes.from_samples(NormalDistribution, X_train, y_train)\n", "%timeit NaiveBayes.from_samples([NormalDistribution, LogNormalDistribution, ExponentialDistribution], X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks as if on this small dataset they're all taking approximately the same time. This is pretty much expected, as the fitting step is fairly simple and both implementations use C-level numerics for the calculations. We can give a more thorough treatment of the speed comparison on larger datasets. Let's look at the average time it takes to fit a model to data of increasing dimensionality across 25 runs." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "pom_time, skl_time = [], []\n", "\n", "n1, n2 = 15000, 60000,\n", "for d in range(1, 101, 5): \n", " X = numpy.concatenate([numpy.random.normal(3, 2, size=(n1, d)), numpy.random.normal(7, 1, size=(n2, d))])\n", " y = numpy.concatenate([numpy.zeros(n1), numpy.ones(n2)])\n", "\n", " tic = time.time()\n", " for i in range(25):\n", " GaussianNB().fit(X, y)\n", " skl_time.append((time.time() - tic) / 25)\n", " \n", " tic = time.time()\n", " for i in range(25):\n", " NaiveBayes.from_samples(NormalDistribution, X, y)\n", " pom_time.append((time.time() - tic) / 25)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 6))\n", "plt.plot(range(1, 101, 5), pom_time, color='c', label=\"pomegranate\")\n", "plt.plot(range(1, 101, 5), skl_time, color='m', label=\"sklearn\")\n", "plt.xticks(fontsize=14)\n", "plt.xlabel(\"Number of Dimensions\", fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.ylabel(\"Time (s)\")\n", "plt.legend(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It appears as if the two implementations are basically the same speed. This is unsurprising given the simplicity of the calculations, and as mentioned before, the low level implementation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multivariate Gaussian Bayes Classifiers\n", "\n", "The natural generalization of the naive Bayes classifier is to allow any multivariate function take the place of $P(D|M)$ instead of it being the product of several univariate probability distributions. One immediate difference is that now instead of creating a Gaussian model with effectively a diagonal covariance matrix, you can now create one with a full covariance matrix. Let's see an example of that at work." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tilt_a = [[-2, 0.5], [5, 2]]\n", "tilt_b = [[-1, 1.5], [3, 3]]\n", "\n", "X = numpy.concatenate((numpy.random.normal(4, 1, size=(250, 2)).dot(tilt_a), numpy.random.normal(3, 1, size=(800, 2)).dot(tilt_b)))\n", "y = numpy.concatenate((numpy.zeros(250), numpy.ones(800)))\n", "\n", "model_a = NaiveBayes.from_samples(NormalDistribution, X, y)\n", "model_b = BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y)\n", "\n", "xx, yy = numpy.meshgrid(numpy.arange(-5, 30, 0.02), numpy.arange(0, 25, 0.02))\n", "Z1 = model_a.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "Z2 = model_b.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "\n", "plt.figure(figsize=(18, 8))\n", "plt.subplot(121)\n", "plt.contour(xx, yy, Z1, cmap='Blues')\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c', alpha=0.3)\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m', alpha=0.3)\n", "plt.xlim(-5, 30)\n", "plt.ylim(0, 25)\n", "\n", "plt.subplot(122)\n", "plt.contour(xx, yy, Z2, cmap='Blues')\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c', alpha=0.3)\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m', alpha=0.3)\n", "plt.xlim(-5, 30)\n", "plt.ylim(0, 25)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like we are able to get a better boundary between the two blobs of data. The primary for this is because the data don't form spherical clusters, like you assume when you force a diagonal covariance matrix, but are tilted ellipsoids, that can be better modeled by a full covariance matrix. We can quantify this quickly by looking at performance on the training data." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "naive training accuracy: 0.9219\n", "bayes classifier training accuracy: 0.9695\n" ] } ], "source": [ "print(\"naive training accuracy: {:4.4}\".format((model_a.predict(X) == y).mean()))\n", "print(\"bayes classifier training accuracy: {:4.4}\".format((model_b.predict(X) == y).mean()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like there is a significant boost. Naturally you'd want to evaluate the performance of the model on separate validation data, but for the purposes of demonstrating the effect of a full covariance matrix this should be sufficient." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gaussian Mixture Model Bayes Classifier\n", "\n", "While using a full covariance matrix is certainly more complicated than using only the diagonal, there is no reason that the $P(D|M)$ has to even be a single simple distribution versus a full probabilistic model. After all, all probabilistic models, including general mixtures, hidden Markov models, and Bayesian networks, can calculate $P(D|M)$. Let's take a look at an example of using a mixture model instead of a single gaussian distribution." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.empty(shape=(0, 2))\n", "X = numpy.concatenate((X, numpy.random.normal(4, 1, size=(200, 2)).dot([[-2, 0.5], [2, 0.5]])))\n", "X = numpy.concatenate((X, numpy.random.normal(3, 1, size=(350, 2)).dot([[-1, 2], [1, 0.8]])))\n", "X = numpy.concatenate((X, numpy.random.normal(7, 1, size=(700, 2)).dot([[-0.75, 0.8], [0.9, 1.5]])))\n", "X = numpy.concatenate((X, numpy.random.normal(6, 1, size=(120, 2)).dot([[-1.5, 1.2], [0.6, 1.2]])))\n", "y = numpy.concatenate((numpy.zeros(550), numpy.ones(820)))\n", "\n", "model_a = BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y)\n", "\n", "gmm_a = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X[y == 0])\n", "gmm_b = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X[y == 1])\n", "model_b = BayesClassifier([gmm_a, gmm_b], weights=numpy.array([1-y.mean(), y.mean()]))\n", "\n", "xx, yy = numpy.meshgrid(numpy.arange(-10, 10, 0.02), numpy.arange(0, 25, 0.02))\n", "Z1 = model_a.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "Z2 = model_b.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "centroids1 = numpy.array([distribution.mu for distribution in model_a.distributions])\n", "centroids2 = numpy.concatenate([[distribution.mu for distribution in component.distributions] for component in model_b.distributions])\n", "\n", "plt.figure(figsize=(18, 8))\n", "plt.subplot(121)\n", "plt.contour(xx, yy, Z1, cmap='Blues')\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c', alpha=0.3)\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m', alpha=0.3)\n", "plt.scatter(centroids1[:,0], centroids1[:,1], color='k', s=100)\n", "\n", "plt.subplot(122)\n", "plt.contour(xx, yy, Z2, cmap='Blues')\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c', alpha=0.3)\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m', alpha=0.3)\n", "plt.scatter(centroids2[:,0], centroids2[:,1], color='k', s=100)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Frequently in the real world you will end up with data that looks like the ones in the above plots, not neatly falling into any single simple distribution. Using a mixture here allowed us to model the various components separately and get a more sophisticated decision boundary that doesn't seem to be extremely overfit.\n", "\n", "If one wanted to use hidden Markov models to model sequences, all that needs to change is passing in `HiddenMarkovModel` objects instead of `GeneralMixtureModel` ones. Likewise, Bayesian networks are similarly supported and can be passed in just as easily. In fact, the most stacking one could reasonably do is to create a Bayes classifier that distinguishes mixtures of hidden Markov models with mixture emissions (GMM-HMM-GMMs) from each other!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/B_Model_Tutorial_6_Markov_Chain.ipynb000066400000000000000000000205151420461712600320170ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Markov Chains\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com\n", "\n", "Markov Chains are a simple model based on conditional probability, where a sequence is modelled as the product of conditional probabilities. A n-th order Markov chain looks back n emissions to base its conditional probability on. For example, a 3rd order Markov chain models $P(X_{t} | X_{t-1}, X_{t-2}, X_{t-3})$.\n", "\n", "However, a full Markov model needs to model the first observations, and the first n-1 observations. The first observation can't really be modelled well using $P(X_{t} | X_{t-1}, X_{t-2}, X_{t-3})$, but can be modelled by $P(X_{t})$. The second observation has to be modelled by $P(X_{t} | X_{t-1} )$. This means that these distributions have to be passed into the Markov chain as well. \n", "\n", "We can initialize a Markov chain easily enough by passing in a list of the distributions." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fri Jan 10 2020 \n", "\n", "numpy 1.18.1\n", "scipy 1.4.1\n", "pomegranate 0.12.0\n", "\n", "compiler : Clang 10.0.0 (clang-1000.11.45.5)\n", "system : Darwin\n", "release : 17.7.0\n", "machine : x86_64\n", "processor : i386\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import time\n", "import pandas\n", "import random\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "import itertools\n", "\n", "from pomegranate import *\n", "\n", "random.seed(0)\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['random']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] } ], "source": [ "from pomegranate import *\n", "%pylab inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "d1 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})\n", "d2 = ConditionalProbabilityTable([['A', 'A', 0.10],\n", " ['A', 'C', 0.50],\n", " ['A', 'G', 0.30],\n", " ['A', 'T', 0.10],\n", " ['C', 'A', 0.10],\n", " ['C', 'C', 0.40],\n", " ['C', 'T', 0.40],\n", " ['C', 'G', 0.10],\n", " ['G', 'A', 0.05],\n", " ['G', 'C', 0.45],\n", " ['G', 'G', 0.45],\n", " ['G', 'T', 0.05],\n", " ['T', 'A', 0.20],\n", " ['T', 'C', 0.30],\n", " ['T', 'G', 0.30],\n", " ['T', 'T', 0.20]], [d1])\n", "\n", "clf = MarkovChain([d1, d2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Markov chains have log probability, fit, summarize, and from summaries methods implemented. They do not have classification capabilities by themselves, but when combined with a Naive Bayes classifier can be used to do discrimination between multiple models (see the Naive Bayes tutorial notebook).\n", "\n", "Lets see the log probability of some data." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-17.532789486599906" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.log_probability( list('CAGCATCAGT') ) " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.916290731874155" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.log_probability( list('C') )" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-38.55615991599665" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.log_probability( list('CACATCACGACTAATGATAAT') )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can fit the model to sequences which we pass in, and as expected, get better performance on sequences which we train on. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-9.496270911389157\n", "-0.6931471805599453\n", "-25.257514389300717\n" ] } ], "source": [ "clf.fit(list(map(list,('CAGCATCAGT', 'C', 'ATATAGAGATAAGCT', 'GCGCAAGT', 'GCATTGC', 'CACATCACGACTAATGATAAT'))))\n", "print(clf.log_probability( list('CAGCATCAGT') ) )\n", "print(clf.log_probability( list('C') ))\n", "print(clf.log_probability( list('CACATCACGACTAATGATAAT') ))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"str\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.16666666666666666,\n", " \"C\" :0.5,\n", " \"G\" :0.3333333333333333,\n", " \"T\" :0.0\n", " }\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "print(clf.distributions[0])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\tA\t0.18181818181818182\n", "A\tC\t0.13636363636363635\n", "A\tG\t0.2727272727272727\n", "A\tT\t0.4090909090909091\n", "C\tA\t0.6666666666666666\n", "C\tC\t0.0\n", "C\tT\t0.16666666666666669\n", "C\tG\t0.16666666666666669\n", "G\tA\t0.3333333333333333\n", "G\tC\t0.5\n", "G\tG\t0.0\n", "G\tT\t0.16666666666666669\n", "T\tA\t0.5\n", "T\tC\t0.2\n", "T\tG\t0.2\n", "T\tT\t0.10000000000000002\n" ] } ], "source": [ "print(clf.distributions[1])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } B_Model_Tutorial_7_Markov_Networks.ipynb000066400000000000000000000453371420461712600325440ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Markov Networks\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com\n", "\n", "Markov networks are probabilistic models that are usually represented as an undirected graph, where the nodes represent variables and the edges represent associations. Markov networks are similar to Bayesian networks with the primary difference being that Bayesian networks can be represented as directed graphs with known parental relations. Generally, Bayesian networks are easier to interpret and can be used to calculate probabilities faster but, naturally, require that causality is known. However, in many settings, one can only know the associations between variables and not necessarily the direction of causality.\n", "\n", "The underlying implementation of inference in pomegranate for both Markov networks and Bayesian networks is the same, because both get converted to their factor graph representations. However, there are many important differences between the two models that should be considered before choosing one. \n", "\n", "In this tutorial we will go over how to use Markov networks in pomegranate and what some of the current limitations of the implementation are." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mon Dec 02 2019 \n", "\n", "numpy 1.17.2\n", "scipy 1.3.1\n", "pomegranate 0.11.3\n", "\n", "compiler : GCC 7.3.0\n", "system : Linux\n", "release : 4.15.0-66-generic\n", "machine : x86_64\n", "processor : x86_64\n", "CPU cores : 8\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy\n", "import itertools\n", "\n", "from pomegranate import *\n", "\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Defining a Markov Network\n", "\n", "A Markov network is defined by passing in a list of the joint probability tables associated with a series of cliques rather than an explicit graph structure. This is because the probability distributions for a particular variable are not defined by themselves, but rather through associations with other variables. While a Bayesian network has root variables that do not hav e parents, the undirected nature of the edges in a Markov networks means that variables are generally grouped together.\n", "\n", "Let's define a simple Markov network where the cliques are A-B, B-C-D, and C-D-E. B-C-D-E is almost a clique but are missing the connection between B and E." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "d1 = JointProbabilityTable([\n", " [0, 0, 0.1],\n", " [0, 1, 0.2],\n", " [1, 0, 0.4],\n", " [1, 1, 0.3]], [0, 1])\n", "\n", "d2 = JointProbabilityTable([\n", " [0, 0, 0, 0.05],\n", " [0, 0, 1, 0.15],\n", " [0, 1, 0, 0.07],\n", " [0, 1, 1, 0.03],\n", " [1, 0, 0, 0.12],\n", " [1, 0, 1, 0.18],\n", " [1, 1, 0, 0.10],\n", " [1, 1, 1, 0.30]], [1, 2, 3])\n", "\n", "d3 = JointProbabilityTable([\n", " [0, 0, 0, 0.08],\n", " [0, 0, 1, 0.12],\n", " [0, 1, 0, 0.11],\n", " [0, 1, 1, 0.19],\n", " [1, 0, 0, 0.04],\n", " [1, 0, 1, 0.06],\n", " [1, 1, 0, 0.23],\n", " [1, 1, 1, 0.17]], [2, 3, 4])\n", "\n", "model = MarkovNetwork([d1, d2, d3])\n", "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the initialization is fairly straightforward. An important note is that the JointProbabilityTable object requires as the second argument a list of variables that are included in that clique in the order that they appear in the table, from left to right." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculating the probability of examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similar to the other probabilistic models in pomegranate, Markov networks can be used to calculate the probability or log probability of examples. However, unlike the other models, calculating the log probability for Markov networks is generally computationally intractable for data with even a modest number of variables (~30). \n", "\n", "The process for calculating the log probability begins by calculating the \"unnormalized\" log probability $\\hat{P}$, which is just the product of the probabilities for the variables $c$ in each clique $c \\in C$ under their joint probability table $JPT(c)$. This step is easy because it just involves, for each clique, taking the columns corresponding to that clique and performing a table lookup.\n", "\n", "\\begin{equation}\n", "\\hat{P}(X=x) = \\prod\\limits_{c \\in C} JPT(c)\n", "\\end{equation}\n", "\n", "The reason this is called the unnormalized probability is because the sum of all combinations of variables that $X$ can take $\\sum\\limits_{x \\in X} \\hat{P}(X=x)$ does not sum to 1; thus, it is not a true probability distribution. \n", "\n", "We can calculate the normalized probability $P(X)$ by dividing by the sum of probabilities under all combinations of variables, frequently referred to as the \"partition function\". Calculating the partition function $Z$ is as simple as summing the unnormalized probabilities over all possible combinations of variables $x \\in X$. \n", "\n", "\\begin{equation}\n", "Z = \\sum\\limits_{x \\in X} \\hat{P}(X=x)\n", "\\end{equation}\n", "\n", "Finally, we can divide any unnormalized probability calculation by the partition function to get the correct probability.\n", "\n", "\\begin{equation}\n", "P(X = x) = \\frac{1}{Z} \\hat{P}(X = x)\n", "\\end{equation}\n", "\n", "The `probability` method returns the normalized probability value. We can check this by seeing that it is different than simply passing the columns of data in to the distributions for their respective cliques." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.020425530968183916" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.probability([0, 1, 0, 0, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the probability if we simply passed the columns into the corresponding cliques:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0028800000000000006" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1.probability([0, 1]) * d2.probability([1, 0, 0]) * d3.probability([0, 0, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, by passing the `unnormalized=True` parameter in to the `probability` method we can return the unnormalized probability values." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0028799999999999997" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.probability([0, 1, 0, 0, 1], unnormalized=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the two are identical, subject to machine precision." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculating the partition function\n", "\n", "Calculating the partition function involves summing the unnormalized probabilities of all combinations of variables that an example can take. Unfortunately, the time it takes to calculate the partition function grows exponentially with the number of dimensions. This means that it may not be able to calculate the partition function exactly for more than ~25 variables, depending on your machine. While pomegranate does not currently support any methods for calculating the partition function other than the exact method, it is flexible enough to allow users to get around this limitation.\n", "\n", "The partition function itself is calculated in the `bake` method because, at that point, all combinations of variables are known to the model. This value is then cached so that calls to `probability` or `log_probability` are just as fast regardless of if the normalized or unnormalized probabilities are calculated. However, if the user passes in `calculate_partition=False` the model will not spend time calculating the partition function. We can see the difference in time here:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.67 s ± 50.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "1.06 ms ± 31.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "X = numpy.random.randint(2, size=(100, 14))\n", "model2 = MarkovNetwork.from_samples(X)\n", "\n", "%timeit model2.bake()\n", "%timeit model2.bake(calculate_partition=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two main reasons that one might not want to calculate the partition function when creating the model. The first is when the user will only be inspecting the model, such as after structure learning, or only doing inference, which uses the approximate loopy belief propogation. The second is if the user wants to estimate the partition function themselves using an approximate algorithm.\n", "\n", "Let's look at how one would manually calculate the exact partition function to see how an approximate algorithm could be substituted in. First, what happens if we don't calculate the partition but try to calculate probabilities?" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Must calculate partition before computing probability", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalculate_partition\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprobability\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/Desktop/pomegranate/pomegranate/MarkovNetwork.pyx\u001b[0m in \u001b[0;36mpomegranate.MarkovNetwork.MarkovNetwork.probability\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m~/Desktop/pomegranate/pomegranate/MarkovNetwork.pyx\u001b[0m in \u001b[0;36mpomegranate.MarkovNetwork.MarkovNetwork.log_probability\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Must calculate partition before computing probability" ] } ], "source": [ "model.bake(calculate_partition=False)\n", "model.probability([0, 1, 0, 0, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like we get an error. We can still calculate unnormalized probabilities though." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0028799999999999997" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.probability([0, 1, 0, 0, 1], unnormalized=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can calculate the partition function by calculating the unnormalized probability of all combinations." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.141" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Z = model.probability(list(itertools.product(*model.keys_)), unnormalized=True).sum()\n", "Z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can set the stored partition function in the model to be this value (or specifically the log partition function) and then calculate normalized probabilities as before." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.020425530968183916" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.partition = numpy.log(Z)\n", "model.probability([0, 1, 0, 0, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can calculate $Z$ however you'd like and simply plug it in." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inference\n", "\n", "Similar to Bayesian networks, Markov networks can be used to infer missing values in data sets. In pomegranate, inference is done for Markov networks by converting them to their factor graph representations and then using loopy belief propagation. This results in fast, but approximate, inference." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([1, 1, 0, 1, 1], dtype=object), array([1, 1, 1, 1, 0], dtype=object)]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict([[None, 1, 0, None, None], [None, None, 1, None, 0]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we go back and inspect the joint probability tables we can easily see that the inferred values are the correct ones.\n", "\n", "If we want the full probability distribution rather than just the most likely value we can use the `predict_proba` method." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"int\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"0\" :0.37654171704957684,\n", " \"1\" :0.623458282950423\n", " }\n", " ],\n", " \"frozen\" :false\n", " },\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"int\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"0\" :0.11729141475211632,\n", " \"1\" :0.8827085852478838\n", " }\n", " ],\n", " \"frozen\" :false\n", " },\n", " 1,\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"int\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"0\" :0.08222490931076197,\n", " \"1\" :0.917775090689238\n", " }\n", " ],\n", " \"frozen\" :false\n", " },\n", " 0], dtype=object)]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict_proba([[None, None, 1, None, 0]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Structure Learning\n", "\n", "Markov networks can be learned from data just as Bayesian networks can. While there are many algorithms that have been proposed for Bayesian network structure learning, there are fewer for Markov networks. Currently, pomegranate only supports the Chow-Liu tree-building algorithm for learning a tree-like network. Let's see a simple example where we learn a Markov network over a chunk of features from the digits data set." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-12699.71644426486, -13141.625038482453)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.datasets import load_digits\n", "\n", "X = load_digits().data[:, 22:40]\n", "X = (X > numpy.median(X)).astype(int)\n", "\n", "model = MarkovNetwork.from_samples(X)\n", "icd = IndependentComponentsDistribution.from_samples(X, distributions=DiscreteDistribution)\n", "\n", "model.log_probability(X).sum(), icd.log_probability(X).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like the Markov network is somewhat better than simply modeling each pixel individually for this set of features." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } C_Feature_Tutorial_1_Parallelization.ipynb000066400000000000000000002162641420461712600330700ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Multi-threaded Parallelism\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com\n", "\n", "pomegranate supports parallelization through a set of built in functions based off of joblib. All computationally intensive functions in pomegranate are implemented in cython with the global interpreter lock (GIL) released, allowing for multithreading to be used for efficient parallel processing. \n", "\n", "These functions can all be simply parallelized by passing in `n_jobs=X` to the method calls. This tutorial will demonstrate how to use those calls. First we'll look at a simple multivariate Gaussian mixture model, and compare its performance to sklearn. Then we'll look at a hidden Markov model with Gaussian emissions, and lastly we'll look at a mixture of Gaussian HMMs. These can all utilize the build-in parallelization that pomegranate has.\n", "\n", "Let's dive right in!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fri Jan 10 2020 \n", "\n", "numpy 1.18.1\n", "scipy 1.4.1\n", "pomegranate 0.12.0\n", "\n", "compiler : Clang 10.0.0 (clang-1000.11.45.5)\n", "system : Darwin\n", "release : 17.7.0\n", "machine : x86_64\n", "processor : i386\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "\n", "from pomegranate import *\n", "\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def create_dataset(n_samples, n_dim, n_classes, alpha=1):\n", " \"\"\"Create a random dataset with n_samples in each class.\"\"\"\n", " \n", " X = numpy.concatenate([numpy.random.normal(i*alpha, 1, size=(n_samples, n_dim)) for i in range(n_classes)])\n", " y = numpy.concatenate([numpy.zeros(n_samples) + i for i in range(n_classes)])\n", " idx = numpy.arange(X.shape[0])\n", " numpy.random.shuffle(idx)\n", " return X[idx], y[idx]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Naive Bayes\n", "\n", "Let's start off with a simple model like naive Bayes." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sklearn GNB\n", "2.24 s ± 204 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "\n", "pomegranate GNB\n", "3.06 s ± 294 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "\n", "pomegranate GNB (2 jobs)\n", "1.95 s ± 125 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "\n", "pomegranate GNB (4 jobs)\n", "1.65 s ± 191 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "\n", "n, d, k = 50000, 1000, 2\n", "X, y = create_dataset(n, d, k)\n", "\n", "print(\"sklearn GNB\")\n", "%timeit GaussianNB().fit(X, y)\n", "print() \n", "print(\"pomegranate GNB\")\n", "%timeit NaiveBayes.from_samples(NormalDistribution, X, y)\n", "print()\n", "print(\"pomegranate GNB (2 jobs)\")\n", "%timeit NaiveBayes.from_samples(NormalDistribution, X, y, n_jobs=2)\n", "print()\n", "print(\"pomegranate GNB (4 jobs)\")\n", "%timeit NaiveBayes.from_samples(NormalDistribution, X, y, n_jobs=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like scikit-learn starts off as faster on a single thread, but that adding multiple threads will improve pomegranate's performance, typically past that of scikit-learn on a single thread. The reason this is the case is likely because pomegranate has to create one object per feature per class, whereas scikit-learn will just define the means and variances in a matrix. It's efficient when you know what distribution you're implementing in the naive Bayes, but not as modular." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Bayes' Classifier\n", "\n", "The next complex model is the Bayes' classifier. This model is similar to the naive Bayes' model, but does not require that the features are independent from each other." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pomegranate GBC\n", "6.59 s ± 1.21 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "\n", "pomegranate GBC (2 jobs)\n", "5.3 s ± 317 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "\n", "pomegranate GBC (4 jobs)\n", "5.58 s ± 502 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "print(\"pomegranate GBC\")\n", "%timeit BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y)\n", "print()\n", "print(\"pomegranate GBC (2 jobs)\")\n", "%timeit BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y, n_jobs=2)\n", "print()\n", "print(\"pomegranate GBC (4 jobs)\")\n", "%timeit BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y, n_jobs=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see a nice spead improvement here as well. There is no model to compare against in scikit-learn because their naive Bayes' model assumes that all features are independent." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. General Mixture Models\n", "\n", "Next, lets take a look at Gaussian mixture models." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sklearn GMM\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/mixture/_base.py:267: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "30.5 s ± 1.46 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "\n", "pomegranate GMM\n", "17 s ± 406 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "\n", "pomegranate GMM (2 jobs)\n", "15.6 s ± 273 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "pomegranate GMM (4 jobs)\n", "15.6 s ± 259 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "from sklearn.mixture import GaussianMixture\n", "\n", "n, d, k = 1000000, 5, 3\n", "X, y = create_dataset(n, d, k)\n", "\n", "print(\"sklearn GMM\")\n", "%timeit GaussianMixture(n_components=k, covariance_type='full', max_iter=15, tol=1e-10).fit(X)\n", "print()\n", "print(\"pomegranate GMM\")\n", "%timeit GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, k, X, max_iterations=15, stop_threshold=1e-10)\n", "print()\n", "print(\"pomegranate GMM (2 jobs)\")\n", "%timeit GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, k, X, n_jobs=2, max_iterations=15, stop_threshold=1e-10)\n", "print\n", "print(\"pomegranate GMM (4 jobs)\")\n", "%timeit GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, k, X, n_jobs=4, max_iterations=15, stop_threshold=1e-10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like pomegranate can be faster than sklearn at performing 15 iterations of EM on this data set, and that parallelization can speed things up even further. We can ignore the convergence warnings. Here we're just timing a fixed amount of computational work. Different models might converge after a different number of iterations, and we don't want the pseedup to come just from that.\n", "\n", "Lets now take a look at the time it takes to make predictions using GMMs. Lets fit the model to a small amount of data, and then predict a larger amount of data drawn from the same underlying distributions." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sklearn GMM\n", "3.72 s ± 270 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "\n", "pomegranate GMM\n", "2.07 s ± 65.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "\n", "pomegranate GMM (4 jobs)\n", "1.52 s ± 76 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "X, _ = create_dataset(1000, 25, 2)\n", "\n", "a = GaussianMixture(k, n_init=1, max_iter=25).fit(X)\n", "b = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, k, X, max_iterations=25)\n", "\n", "X, _ = create_dataset(1000000, 25, 2)\n", "\n", "print(\"sklearn GMM\")\n", "%timeit -n 1 a.predict_proba(X)\n", "print()\n", "print(\"pomegranate GMM\")\n", "%timeit -n 1 b.predict_proba(X)\n", "print()\n", "print(\"pomegranate GMM (4 jobs)\")\n", "%timeit -n 1 b.predict_proba(X, n_jobs=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like pomegranate can be slightly slower than sklearn when using a single processor, but that it can be parallelized to get faster performance.\n", "\n", "To ensure that we're getting the exact same results, only faster, lets subtract the predictions from each other and make sure that the sum is equal to 0." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0\n" ] } ], "source": [ "print((b.predict_proba(X) - b.predict_proba(X, n_jobs=4)).sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, no difference between the two.\n", "\n", "Lets now make sure that pomegranate and sklearn are learning basically the same thing. Lets fit both models to some 2 dimensional 2 component data and make sure that they both extract the underlying clusters by plotting them." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X, y = create_dataset(1000, 2, 2, alpha=2)\n", "\n", "a = GaussianMixture(2, n_init=1, max_iter=25).fit(X)\n", "b = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X, max_iterations=25)\n", "\n", "y1, y2 = a.predict(X), b.predict(X)\n", "\n", "plt.figure(figsize=(16,6))\n", "plt.subplot(121)\n", "plt.title(\"sklearn clusters\", fontsize=14)\n", "plt.scatter(X[y1==0, 0], X[y1==0, 1], color='m', edgecolor='m')\n", "plt.scatter(X[y1==1, 0], X[y1==1, 1], color='c', edgecolor='c')\n", "\n", "plt.subplot(122)\n", "plt.title(\"pomegranate clusters\", fontsize=14)\n", "plt.scatter(X[y2==0, 0], X[y2==0, 1], color='m', edgecolor='m')\n", "plt.scatter(X[y2==1, 0], X[y2==1, 1], color='c', edgecolor='c')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like we're getting the same basic results for the two. The two algorithms are initialized a bit differently, and so it can be difficult to directly compare the results between them, but it looks like they're getting roughly the same results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Multivariate Gaussian HMM\n", "\n", "Now let's move on to training a hidden Markov model with multivariate Gaussian emissions with a diagonal covariance matrix. We'll randomly generate some Gaussian distributed numbers and use pomegranate with either one or four threads to fit our model to the data." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pomegranate Gaussian HMM (1 job)\n", "17.2 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n", "\n", "pomegranate Gaussian HMM (2 jobs)\n", "26.1 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" ] } ], "source": [ "X = numpy.random.randn(1000, 500, 50)\n", "\n", "print(\"pomegranate Gaussian HMM (1 job)\")\n", "%timeit -n 1 -r 1 HiddenMarkovModel.from_samples(NormalDistribution, 5, X, max_iterations=5)\n", "print()\n", "print(\"pomegranate Gaussian HMM (2 jobs)\")\n", "%timeit -n 1 -r 1 HiddenMarkovModel.from_samples(NormalDistribution, 5, X, max_iterations=5, n_jobs=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All we had to do was pass in the n_jobs parameter to the fit function in order to get a speed improvement. It looks like we're getting a really good speed improvement, as well! This is mostly because the HMM algorithms perform a lot more operations than the other models, and so spend the vast majority of time with the GIL released. You may not notice as strong speedups when using a MultivariateGaussianDistribution because BLAS uses multithreaded operations already internally, even when only one job is specified.\n", "\n", "Now lets look at the prediction function to make sure the we're getting speedups there as well. You'll have to use a wrapper function to parallelize the predictions for a HMM because it returns an annotated sequence rather than a single value like a classic machine learning model might." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pomegranate Gaussian HMM (1 job)\n", "3.87 s ± 103 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "\n", "pomegranate Gaussian HMM (2 jobs)\n", "8.96 s ± 191 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "model = HiddenMarkovModel.from_samples(NormalDistribution, 5, X, max_iterations=2, verbose=False)\n", "\n", "print(\"pomegranate Gaussian HMM (1 job)\")\n", "%timeit predict_proba(model, X)\n", "print()\n", "print(\"pomegranate Gaussian HMM (2 jobs)\")\n", "%timeit predict_proba(model, X, n_jobs=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, we're getting a speedup on that as well, albiet a minor one. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conclusions\n", "\n", "Hopefully you'll find pomegranate useful in your work! Parallelization should allow you to train complex models faster than before. Keep in mind though that there is an overhead to using parallel processing, and so it's possible that on some smaller examples it does not work as well. In general, the bigger the dataset, the closer to a linear speedup you'll get with pomegranate.\n", "\n", "If you have any interesting examples of how you've used pomegranate in your work, I'd love to hear about them. In addition I'd like to hear any feedback you may have on features you'd like to see. Please shoot me an email. Good luck!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } C_Feature_Tutorial_2_Out_Of_Core_Learning.ipynb000066400000000000000000000316161420461712600337150ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Out-of-Core Learning\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com
\n", "\n", "Out-of-core learning refers to the process of training a model on an amount of data that cannot fit in memory. There are several approaches that can be described as out-of-core, but here we refer to the ability to derive exact updates to a model from a massive data set, despite not being able to fit the entire thing in memory.\n", "\n", "This out-of-core learning approach is implemented for all of pomegranate's models using two methods. The first is a `summarize` method that will take in a batch of data and reduce it down to additive sufficient statistics. Because these summaries are additive, after the first call, these summaries are added to the previously stored summaries. Once the entire data set has been seen, the stored sufficient statistics will be identical to those that would have been derived if the entire data set had been seen at once. The second method is the `from_summaries` method, which uses the stored sufficient statistics to derive parameter updates for the model.\n", "\n", "A common solution to having too much data is to randomly select an amount of data that does fit in memory to use in the place of the full data set. While simple to implement, this approach is likely to yield lower performance models because it is exposed to less data. However, by using out-of-core learning, on can train their models on a massive amount of data without being limited by the amount of memory their computer has." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tue Nov 27 2018 \n", "\n", "numpy 1.15.1\n", "scipy 1.1.0\n", "pomegranate 0.10.0\n", "\n", "compiler : GCC 7.2.0\n", "system : Linux\n", "release : 4.15.0-39-generic\n", "machine : x86_64\n", "processor : x86_64\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import time\n", "import pandas\n", "import random\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "import itertools\n", "\n", "from pomegranate import *\n", "\n", "random.seed(0)\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Training a Probability Distribution\n", "\n", "Let's start off simple with training a multivariate Gaussian distribution in an out-of-core manner. First, we'll generate some random data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = numpy.random.normal([5, 7], [1.5, 0.4], size=(1000, 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can make a blank distribution with 2 dimensions. This is equivalent to filling in the mean and standard deviation with dummy values that will be overwritten, and don't effect the calculation." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " [\n", " 0.0,\n", " 0.0\n", " ],\n", " [\n", " [\n", " 1.0,\n", " 0.0\n", " ],\n", " [\n", " 0.0,\n", " 1.0\n", " ]\n", " ]\n", " ],\n", " \"name\" :\"MultivariateGaussianDistribution\"\n", "}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1 = MultivariateGaussianDistribution.blank(2)\n", "d1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's summarize through a few batches of data." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "d1.summarize(X[:250])\n", "d1.summarize(X[250:500])\n", "d1.summarize(X[500:750])\n", "d1.summarize(X[750:])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1.summaries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've seen the entire data set let's use the `from_summaries` method to update the parameters." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " [\n", " 4.967395273422254,\n", " 6.996038686884455\n", " ],\n", " [\n", " [\n", " 2.1362097536595757,\n", " -0.007992485878115985\n", " ],\n", " [\n", " -0.007992485878115985,\n", " 0.15420875108571636\n", " ]\n", " ]\n", " ],\n", " \"name\" :\"MultivariateGaussianDistribution\"\n", "}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1.from_summaries()\n", "d1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And what do we get if we learn directly from the data?" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " [\n", " 4.967395273422257,\n", " 6.996038686884458\n", " ],\n", " [\n", " [\n", " 2.136209753659561,\n", " -0.007992485878137813\n", " ],\n", " [\n", " -0.007992485878137813,\n", " 0.1542087510856727\n", " ]\n", " ]\n", " ],\n", " \"name\" :\"MultivariateGaussianDistribution\"\n", "}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "MultivariateGaussianDistribution.from_samples(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The exact same model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Training a Mixture Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This summarization option enables a variety of different training strategies that can be written by hand. This notebook focuses on out-of-core learning, so let's make a data set and \"read it in\" one batch at a time to train a mixture model with a custom training function. We'll make another data set here, but one could easily have a function that read through some number of lines in a CSV, or loaded up a chunk from a numpy memory map, or whatever other massive data store you had." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = numpy.concatenate([numpy.random.normal(0, 1, size=(5000, 10)), numpy.random.normal(1, 1, size=(7500, 10))])\n", "n = X.shape[0]\n", "\n", "idx = numpy.arange(n)\n", "numpy.random.shuffle(idx)\n", "\n", "X = X[idx]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we have to initialize our model. We can do that either by hand to some value we think is good, or by fitting to the first chunk of data, anticipating that it will be a decent representation of the remainder. We can also calculate the log probability of the data set now to see how much we improved. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# First we initialize our model on some small chunk of data.\n", "model = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X[:200], max_iterations=1, init='first-k')\n", "\n", "# The base performance on the data set.\n", "base_logp = model.log_probability(X).sum()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2b14bbabfb784fb2958f59567f60c26c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=5), HTML(value=u'')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from tqdm import tqdm_notebook as tqdm\n", "\n", "# Now we write our own iterator. This outer loop will be the number of times we iterate---hard coded to 5 in this case.\n", "for iteration in tqdm(range(5)):\n", "\n", " # This internal loop goes over chunks from the data set. We're just loading chunks of a fixed size iteratively\n", " # until we've seen the entire data set.\n", " for i in range(10):\n", " model.summarize(X[i * (n // 10):(i+1) * (n //10)])\n", " \n", " # Now we've seen the entire data set and summarized it. We can update the parameters now.\n", " model.from_summaries() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How we does did our model do on the data originally, and how well does it do now?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-188806.57618011418, -184074.12225611554)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "base_logp, model.log_probability(X).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like a decent improvement.\n", "\n", "Now, let's compare to having fit our model to the entire loaded data set for five epochs." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-188806.57618011418, -184074.12225611557)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X[:200], max_iterations=1, init='first-k')\n", "base_logp = model.log_probability(X).sum()\n", "\n", "model.fit(X, max_iterations=5)\n", "base_logp, model.log_probability(X).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like the exact same values.\n", "\n", "You may ask why we bothered to write a summarization function for data that did fit in memory. The purpose here was entirely illustrative. Our function that use the summarize method would scale to any amount of data that could be loaded in batches, whereas the fit function can only scale to the amount of data that can fit in memory. However, they yield identical answers at the end, suggesting that if one wanted to scale to massive data sets but still get the same performance, this summarize function is the way to go." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 } C_Feature_Tutorial_3_Semisupervised_Learning.ipynb000066400000000000000000035144151420461712600345700ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Semi-supervised Learning\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com\n", "\n", "Most classical machine learning algorithms either assume that an entire dataset is either labeled (supervised learning) or that there are no labels (unsupervised learning). However, frequently it is the case that some labeled data is present but there is a great deal of unlabeled data as well. A great example of this is that of computer vision where the internet is filled of pictures (mostly of cats) that could be useful, but you don't have the time or money to label them all in accordance with your specific task. Typically what ends up happening is that either the unlabeled data is discarded in favor of training a model solely on the labeled data, or that an unsupervised model is initialized with the labeled data and then set free on the unlabeled data. Neither method uses both sets of data in the optimization process.\n", "\n", "Semi-supervised learning is a method to incorporate both labeled and unlabeled data into the training task, typically yield better performing estimators than using the labeled data alone. There are many methods one could use for semisupervised learning, and scikit-learn has a good write-up on some of these techniques.\n", "\n", "pomegranate natively implements semi-supervised learning through the a merger of maximum-likelihood and expectation-maximization. As an overview, the models are initialized by first fitting to the labeled data directly using maximum-likelihood estimates. The models are then refined by running expectation-maximization (EM) on the unlabeled datasets and adding the sufficient statistics to those acquired from maximum-likelihood estimates on the labeled data. Under the hood both a supervised model and an unsupervised mixture model are created using the same underlying distribution objects. The summarize method is first called using the supervised method on the labeled data, and then the summarize method is called again using the unsupervised method on the unlabeled data. This causes the sufficient statistics to be updated appropriately given the results of first maximum-likelihood and then EM. This process continues until convergence in the EM step.\n", "\n", "Let's take a look!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fri Jan 10 2020 \n", "\n", "numpy 1.18.1\n", "scipy 1.4.1\n", "pomegranate 0.12.0\n", "\n", "compiler : Clang 10.0.0 (clang-1000.11.45.5)\n", "system : Darwin\n", "release : 17.7.0\n", "machine : x86_64\n", "processor : i386\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import time\n", "import pandas\n", "import random\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "import itertools\n", "\n", "from pomegranate import *\n", "\n", "random.seed(0)\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['random']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] } ], "source": [ "%pylab inline\n", "from pomegranate import *\n", "from sklearn.semi_supervised import LabelPropagation\n", "from sklearn.datasets import make_blobs\n", "import seaborn, time\n", "seaborn.set_style('whitegrid')\n", "numpy.random.seed(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first generate some data in the form of blobs that are close together. Generally one tends to have far more unlabeled data than labeled data, so let's say that a person only has 50 samples of labeled training data and 4950 unlabeled samples. In pomegranate you a sample can be specified as lacking a label by providing the integer -1 as the label, just like in scikit-learn. Let's also say there there is a bit of bias in the labeled samples to inject some noise into the problem, as otherwise Gaussian blobs are trivially modeled with even a few samples." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "X, y = make_blobs(10000, 2, 3, cluster_std=2)\n", "x_min, x_max = X[:,0].min()-2, X[:,0].max()+2\n", "y_min, y_max = X[:,1].min()-2, X[:,1].max()+2\n", "\n", "X_train = X[:5000]\n", "y_train = y[:5000]\n", "\n", "# Set the majority of samples to unlabeled.\n", "y_train[numpy.random.choice(5000, size=4950, replace=False)] = -1\n", "\n", "# Inject noise into the problem\n", "X_train[y_train != -1] += 2.5\n", "\n", "X_test = X[5000:]\n", "y_test = y[5000:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's take a look at the data when we plot it." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 8))\n", "plt.scatter(X_train[y_train == -1, 0], X_train[y_train == -1, 1], color='0.6')\n", "plt.scatter(X_train[y_train == 0, 0], X_train[y_train == 0, 1], color='c')\n", "plt.scatter(X_train[y_train == 1, 0], X_train[y_train == 1, 1], color='m')\n", "plt.scatter(X_train[y_train == 2, 0], X_train[y_train == 2, 1], color='r')\n", "plt.xlim(x_min, x_max)\n", "plt.ylim(y_min, y_max)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The clusters of unlabeled data seem clear to us, and it doesn't seem like the labeled data is perfectly faithful to these clusters. This can typically happen in a semisupervised setting as well, as the data that is labeled is sometimes biased either because the labeled data was chosen as it was easy to label, or the data was chosen to be labeled in a biased maner.\n", "\n", "Now let's try fitting a simple naive Bayes classifier to this data and compare the results when using only the labeled data to when using both the labeled and unlabeled data together." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Supervised Learning Accuracy: 0.8706\n", "Semisupervised Learning Accuracy: 0.9274\n" ] } ], "source": [ "model_a = NaiveBayes.from_samples(NormalDistribution, X_train[y_train != -1], y_train[y_train != -1])\n", "print(\"Supervised Learning Accuracy: {}\".format((model_a.predict(X_test) == y_test).mean()))\n", "\n", "model_b = NaiveBayes.from_samples(NormalDistribution, X_train, y_train)\n", "print(\"Semisupervised Learning Accuracy: {}\".format((model_b.predict(X_test) == y_test).mean()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems like we get a big bump in test set accuracy when we use semi-supervised learning. Let's visualize the data to get a better sense of what is happening here." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_contour(X, y, Z):\n", " plt.scatter(X[y == -1, 0], X[y == -1, 1], color='0.2', alpha=0.5, s=20)\n", " plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c', s=20)\n", " plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m', s=20)\n", " plt.scatter(X[y == 2, 0], X[y == 2, 1], color='r', s=20)\n", " plt.contour(xx, yy, Z)\n", " plt.xlim(x_min, x_max)\n", " plt.ylim(y_min, y_max)\n", " plt.xticks(fontsize=14)\n", " plt.yticks(fontsize=14)\n", "\n", "xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, 0.1), numpy.arange(y_min, y_max, 0.1))\n", "Z1 = model_a.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "Z2 = model_b.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "\n", "plt.figure(figsize=(16, 16))\n", "plt.subplot(221)\n", "plt.title(\"Training Data, Supervised Boundaries\", fontsize=16)\n", "plot_contour(X_train, y_train, Z1)\n", "\n", "plt.subplot(223)\n", "plt.title(\"Training Data, Semi-supervised Boundaries\", fontsize=16)\n", "plot_contour(X_train, y_train, Z2)\n", "\n", "plt.subplot(222)\n", "plt.title(\"Test Data, Supervised Boundaries\", fontsize=16)\n", "plot_contour(X_test, y_test, Z1)\n", "\n", "plt.subplot(224)\n", "plt.title(\"Test Data, Semi-supervised Boundaries\", fontsize=16)\n", "plot_contour(X_test, y_test, Z2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The contours plot the decision boundaries between the different classes with the left figures corresponding to the partially labeled training set and the right figures corresponding to the test set. We can see that the boundaries learning using only the labeled data look a bit weird when considering the unlabeled data, particularly in that it doesn't cleanly separate the cyan cluster from the other two. In addition, it seems like the boundary between the magenta and red clusters is a bit curved in an unrealistic way. We would not expect points that fell around (-18, -7) to actually come from the red class. Training the model in a semi-supervised manner cleaned up both of these concerns by learning better boundaries that are also flatter and more generalizable.\n", "\n", "Let's next compare the training times to see how much slower it is to do semi-supervised learning than it is to do simple supervised learning." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Supervised Learning: \n", "464 µs ± 9.22 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n", "\n", "Semi-supervised Learning: \n", "22.2 ms ± 52.4 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "\n", "Label Propagation (sklearn): \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:277: RuntimeWarning: invalid value encountered in true_divide\n", " self.label_distributions_ /= normalizer\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:288: ConvergenceWarning: max_iter=1000 was reached without convergence.\n", " category=ConvergenceWarning\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:277: RuntimeWarning: invalid value encountered in true_divide\n", " self.label_distributions_ /= normalizer\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:288: ConvergenceWarning: max_iter=1000 was reached without convergence.\n", " category=ConvergenceWarning\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:277: RuntimeWarning: invalid value encountered in true_divide\n", " self.label_distributions_ /= normalizer\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:288: ConvergenceWarning: max_iter=1000 was reached without convergence.\n", " category=ConvergenceWarning\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:277: RuntimeWarning: invalid value encountered in true_divide\n", " self.label_distributions_ /= normalizer\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:288: ConvergenceWarning: max_iter=1000 was reached without convergence.\n", " category=ConvergenceWarning\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:277: RuntimeWarning: invalid value encountered in true_divide\n", " self.label_distributions_ /= normalizer\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:288: ConvergenceWarning: max_iter=1000 was reached without convergence.\n", " category=ConvergenceWarning\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:277: RuntimeWarning: invalid value encountered in true_divide\n", " self.label_distributions_ /= normalizer\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:288: ConvergenceWarning: max_iter=1000 was reached without convergence.\n", " category=ConvergenceWarning\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:277: RuntimeWarning: invalid value encountered in true_divide\n", " self.label_distributions_ /= normalizer\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:288: ConvergenceWarning: max_iter=1000 was reached without convergence.\n", " category=ConvergenceWarning\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:277: RuntimeWarning: invalid value encountered in true_divide\n", " self.label_distributions_ /= normalizer\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "54.9 s ± 6.36 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:288: ConvergenceWarning: max_iter=1000 was reached without convergence.\n", " category=ConvergenceWarning\n" ] } ], "source": [ "print(\"Supervised Learning: \")\n", "%timeit NaiveBayes.from_samples(NormalDistribution, X_train[y_train != -1], y_train[y_train != -1])\n", "print()\n", "print(\"Semi-supervised Learning: \")\n", "%timeit NaiveBayes.from_samples(NormalDistribution, X_train, y_train)\n", "print()\n", "print(\"Label Propagation (sklearn): \")\n", "%timeit LabelPropagation().fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is quite a bit slower to do semi-supervised learning than simple supervised learning in this example. This is expected as the simple supervised update for naive Bayes is a trivial MLE across each dimension whereas the semi-supervised case requires EM to converge to complete. However, it is still faster to do semi-supervised learning this setting to learn a naive Bayes classifier than it is to fit the label propagation estimator from sklearn. \n", "\n", "However, though it is widely used, the naive Bayes classifier is still a fairly simple model. One can construct a more complicated model that does not assume feature independence called a Bayes classifier that can also be trained using semi-supervised learning in pretty much the same manner. You can read more about the Bayes classifier in its tutorial in the tutorial folder. Let's move on to more complicated data and try to fit a mixture model Bayes classifier, comparing the performance between using only labeled data and using all data.\n", "\n", "First let's generate some more complicated, noisier data." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.empty(shape=(0, 2))\n", "X = numpy.concatenate((X, numpy.random.normal(4, 1, size=(3000, 2)).dot([[-2, 0.5], [2, 0.5]])))\n", "X = numpy.concatenate((X, numpy.random.normal(3, 1, size=(6500, 2)).dot([[-1, 2], [1, 0.8]])))\n", "X = numpy.concatenate((X, numpy.random.normal(7, 1, size=(8000, 2)).dot([[-0.75, 0.8], [0.9, 1.5]])))\n", "X = numpy.concatenate((X, numpy.random.normal(6, 1, size=(2200, 2)).dot([[-1.5, 1.2], [0.6, 1.2]])))\n", "X = numpy.concatenate((X, numpy.random.normal(8, 1, size=(3500, 2)).dot([[-0.2, 0.8], [0.7, 0.8]])))\n", "X = numpy.concatenate((X, numpy.random.normal(9, 1, size=(6500, 2)).dot([[-0.0, 0.8], [0.5, 1.2]])))\n", "x_min, x_max = X[:,0].min()-2, X[:,0].max()+2\n", "y_min, y_max = X[:,1].min()-2, X[:,1].max()+2\n", "\n", "y = numpy.concatenate((numpy.zeros(9500), numpy.ones(10200), numpy.ones(10000)*2))\n", "idxs = numpy.arange(29700)\n", "numpy.random.shuffle(idxs)\n", "\n", "X = X[idxs]\n", "y = y[idxs]\n", "\n", "X_train, X_test = X[:25000], X[25000:]\n", "y_train, y_test = y[:25000], y[25000:]\n", "y_train[numpy.random.choice(25000, size=24920, replace=False)] = -1\n", "\n", "plt.scatter(X_train[y_train == -1, 0], X_train[y_train == -1, 1], color='0.6', s=1)\n", "plt.scatter(X_train[y_train == 0, 0], X_train[y_train == 0, 1], color='c', s=10)\n", "plt.scatter(X_train[y_train == 1, 0], X_train[y_train == 1, 1], color='m', s=10)\n", "plt.scatter(X_train[y_train == 2, 0], X_train[y_train == 2, 1], color='r', s=10)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's take a look at the accuracies that we get when training a model using just the labeled examples versus all of the examples in a semi-supervised manner." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Supervised Learning Accuracy: 0.9361702127659575\n", "Semisupervised Learning Accuracy: 0.9846808510638297\n" ] } ], "source": [ "d1 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 0], max_iterations=1)\n", "d2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 1], max_iterations=1)\n", "d3 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 2], max_iterations=1)\n", "model_a = BayesClassifier([d1, d2, d3]).fit(X_train[y_train != -1], y_train[y_train != -1])\n", "print(\"Supervised Learning Accuracy: {}\".format((model_a.predict(X_test) == y_test).mean()))\n", "\n", "d1 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 0], max_iterations=1)\n", "d2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 1], max_iterations=1)\n", "d3 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 2], max_iterations=1)\n", "model_b = BayesClassifier([d1, d2, d3])\n", "model_b.fit(X_train, y_train)\n", "print(\"Semisupervised Learning Accuracy: {}\".format((model_b.predict(X_test) == y_test).mean()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the semi-supervised method performs better. Let's visualize the landscape in the same manner as before in order to see why this is the case." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, 0.1), numpy.arange(y_min, y_max, 0.1))\n", "Z1 = model_a.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "Z2 = model_b.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "\n", "plt.figure(figsize=(16, 16))\n", "plt.subplot(221)\n", "plt.title(\"Training Data, Supervised Boundaries\", fontsize=16)\n", "plot_contour(X_train, y_train, Z1)\n", "\n", "plt.subplot(223)\n", "plt.title(\"Training Data, Semi-supervised Boundaries\", fontsize=16)\n", "plot_contour(X_train, y_train, Z2)\n", "\n", "plt.subplot(222)\n", "plt.title(\"Test Data, Supervised Boundaries\", fontsize=16)\n", "plot_contour(X_test, y_test, Z1)\n", "\n", "plt.subplot(224)\n", "plt.title(\"Test Data, Semi-supervised Boundaries\", fontsize=16)\n", "plot_contour(X_test, y_test, Z2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Immediately, one would notice that the decision boundaries when using semi-supervised learning are smoother than those when using only a few samples. This can be explained mostly because having more data can generally lead to smoother decision boundaries as the model does not overfit to spurious examples in the dataset. It appears that the majority of the correctly classified samples come from having a more accurate decision boundary for the magenta samples in the left cluster. When using only the labeled samples many of the magenta samples in this region get classified incorrectly as cyan samples. In contrast, when using all of the data these points are all classified correctly.\n", "\n", "Lastly, let's take a look at a time comparison in this more complicated example." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Supervised Learning: \n", "1.01 ms ± 119 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n", "\n", "Semi-supervised Learning: \n", "177 ms ± 5.04 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "\n", "Label Propagation (sklearn): \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:277: RuntimeWarning: invalid value encountered in true_divide\n", " self.label_distributions_ /= normalizer\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:288: ConvergenceWarning: max_iter=1000 was reached without convergence.\n", " category=ConvergenceWarning\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:277: RuntimeWarning: invalid value encountered in true_divide\n", " self.label_distributions_ /= normalizer\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:288: ConvergenceWarning: max_iter=1000 was reached without convergence.\n", " category=ConvergenceWarning\n", "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py:277: RuntimeWarning: invalid value encountered in true_divide\n", " self.label_distributions_ /= normalizer\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Label Propagation (sklearn): \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'LabelPropagation().fit(X_train, y_train)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2305\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2306\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2307\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m 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BayesClassifier([d1, d2, d3])\n", "\n", "print(\"Supervised Learning: \")\n", "%timeit model.fit(X_train[y_train != -1], y_train[y_train != -1])\n", "print()\n", "\n", "d1 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 0], max_iterations=1)\n", "d2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 1], max_iterations=1)\n", "d3 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 2], max_iterations=1)\n", "model = BayesClassifier([d1, d2, d3])\n", "\n", "print(\"Semi-supervised Learning: \")\n", "%timeit model.fit(X_train, y_train)\n", "\n", "print()\n", "print(\"Label Propagation (sklearn): \")\n", "%timeit LabelPropagation().fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like the difference, while still large, is not as large as in the previous example, being only a ~40x difference instead of a ~1000x difference. This is likely because even without the unlabeled data the supervised model is performing EM to train each of the mixtures that are the components of the Bayes classifier. Again, it is faster to do semi-supervised learning in this manner for generative models than it is to perform LabelPropagation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "In the real world (ack) there are frequently situations where only a small fraction of the available data has useful labels. Semi-supervised learning provides a framework for leveraging both the labeled and unlabeled aspects of a dataset to learn a sophisticated estimator. In this case, semi-supervised learning plays well with probabilistic models as normal maximum likelihood estimates can be done on the labeled data and expectation-maximization can be run on the unlabeled data using the same distributions.\n", "\n", "This notebook has covered how to implement semi-supervised learning in pomegranate using both naive Bayes and a Bayes classifier. All one has to do is set the labels of unlabeled samples to -1 and pomegranate will take care of the rest. This can be particularly useful when encountering complex, noisy, data in the real world that aren't neat Gaussian blobs." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 } C_Feature_Tutorial_4_Missing_Values.ipynb000066400000000000000000006143231420461712600326670ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Missing Value Support\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The majority of machine learning algorithms assume that they are operating on a fully observed data set. In contast, a great deal of data sets in the real world are missing some values. Sometimes, this missingness is missing at random (MAR), which means that there is no important pattern to the missingness, and sometimes the missingness itself can be interpreted as a feature. For example, in the Titanic data set, males were more likely to have missing records than females were, and those without children were more likely to have missing records. \n", "\n", "A common approach to bridging this gap is to impute the missing values and then treat the entire data set as observed. For continuous features this is commonly done by replacing the missing values with the mean or median of the column. For categorical variables it is commonly done by replacing the missing values with the most common category observed in that column. While these techniques are simple and allow for almost any ML algorithm to be run, they are frequently suboptimal. Consider the follow simple example of continuous data that is bimodally distributed:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fri Jan 10 2020 \n", "\n", "numpy 1.18.1\n", "scipy 1.4.1\n", "pomegranate 0.12.0\n", "\n", "compiler : Clang 10.0.0 (clang-1000.11.45.5)\n", "system : Darwin\n", "release : 17.7.0\n", "machine : x86_64\n", "processor : i386\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import time\n", "import pandas\n", "import random\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "import itertools\n", "\n", "from pomegranate import *\n", "\n", "random.seed(0)\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['random']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] } ], "source": [ "%pylab inline\n", "from pomegranate import *\n", "import seaborn\n", "seaborn.set_style('whitegrid')\n", "numpy.random.seed(0)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.concatenate([numpy.random.normal(0, 1, size=(1000)), numpy.random.normal(6, 1, size=(1250))])\n", "\n", "plt.title(\"Bimodal Distribution\", fontsize=14)\n", "plt.hist(X, bins=numpy.arange(-3, 9, 0.1), alpha=0.6)\n", "plt.ylabel(\"Count\", fontsize=14)\n", "plt.yticks(fontsize=12)\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.yticks(fontsize=12)\n", "plt.vlines(numpy.mean(X), 0, 80, color='r', label=\"Mean\")\n", "plt.vlines(numpy.median(X), 0, 80, color='b', label=\"Median\")\n", "plt.legend(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data peaks around 0 and around 6. Replacing the missing values with ~3 will be inserting values into the data set that mostly don't exist in the observed values. The median is slightly better, but will still cause the imputed values to be in one of the two clusters. This has the effect of essentially increasing the variance of the appropriate distribution. Let's take a look at what the distribution looks like if we add 500 missing values and then impute them using the mean of the observed values." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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eWLp0qeYm4rfffhsPHz6ETCbTzGPatGkYPHgwevfujSlTpuDatWv6a6QWPKIgIsPSYQ+/wkjuo/Dx8cHMmTOhUCjwww8/oFevXppL+StlZWXh1q1bSExM1AyTy+Xo378/ACAgIAAXL17EgQMHkJ2djX//+98AUO3eMWdnZ83Pbdu2hUKhaM5m1YlBQUSkI09PT4jFYly+fBmnT5/GyJEja0yjVCoxY8YMBAUFVRteeVopIiICV65cQWBgIKZMmYJOnTohODi42rRmZmbVPqvV6iZuSf0wKIiIdCQSiTB8+HCcOXMGZ8+exZ49e2pM06NHD9y5cwfdu3fXDPvss89gZ2eHoKAgHD9+HPHx8Rg4cCCAJx3igOHDQAj7KIiI6sHHxwcHDhyAnZ0dunXrVmP89OnTkZycjF27diEnJwfx8fGIjY1F9+7dYW5uDktLS5w8eRK5ubm4cOECoqKiADx5cKqxYlAQEdXDq6++CqVSCV9fX63jPTw8sHbtWuzfvx9SqRS7du3CihUrMGzYMJibm2P58uVISUmBn58fVqxYgdDQUDg4OOD69et6bonueOqJiKgON2/e1PxsaWmJK1euVBtfeUVTJT8/P/j5+Wmdl7e3d41xEydO1LosAAgKCqrR36FvPKIgIiJBDAoiIhLEoCAiIkEMCiIiEsSgICIiQQwKIiISxKAgIiJBDAoiIhLEoCAiIkEMCiKiOkgkEkgkEty5c6fGuPj4eEgkEqxfv75B887JyYFEIkFubq5mWZcuXWpUvU2NQUFEpAMzMzPNS4uqSklJgYmJSZMt58KFC/Dy8mqy+TUFBgURkQ68vLxqBEVRUREyMjLQp0+fJltOp06dNO+uMBYMCiIiHfj4+ODy5ct4/PixZlhqaiq8vLxgbW1dbdqUlBRIpVIMGDAA48ePR1pammacXC5HVFQUvLy8MGzYMJw/f77ad6ueerp37x7mzJmDl156Ce7u7hg3bhx+/PFHAEBubi4kEglOnDiBkSNHol+/fvjv//5v3L9/v8nbzqfHEpFBDR9e9zQqlTlETbRbq8ObV7VydXWFk5MT0tLSIJVKAQCnT5+Gr68vjh49qpnuxo0bCA8Px+LFi+Hp6YmLFy9i9uzZSEhIgJubG7Zs2YJz585hy5YtMDU1xfz582td5rx582BlZYV9+/ZBrVZj7dq1WLx4MZKSkjTTbN26FWvXrgUAhIWFYfv27QgPD29YI2vBIwoiIh2NGDFCc/pJLpfjwoUL8PHxqTbN9u3b8cYbb2DcuHFwdnbGlClTIJVKERcXB7VajW+//RazZ8/GSy+9hIEDBwoGhbe3NyIjI+Hq6opevXrhH//4B7Kysqq9DW/27NkYMGAABgwYgICAAFy7dq3J280jCiIyKF328EtLK2BpadnstdTFx8cHM2fOhEKhwA8//IBevXrB3t6+2jRZWVm4desWEhMTNcPkcjn69++PwsJCFBYW4oUXXtCMc3d3r3V5U6ZMQVJSEn7++Wf8/vvv+PXXXwE8eS93JWdnZ83Pbdu2hUKhaHQ7n8agICLSkaenJ8RiMS5fvozTp09j5MiRNaZRKpWYMWNGjZcNVe2grnpEYGqqfTOsUqnw9ttv4+HDh/Dz88OIESMgl8sxe/bsatOZmZlV+9wc795mUBAR6UgkEmH48OE4c+YMzp49iz179tSYpkePHrhz5w66d++uGfbZZ5/Bzs4OU6dOhb29Pa5du4a+ffsCADIzM7Uu67fffsOPP/6I8+fPo3PnzgCAvXv3AmieMBDCPgoionrw8fHBgQMHYGdnh27dutUYP336dCQnJ2PXrl3IyclBfHw8YmNj0b17d5iYmCA4OBibNm3CxYsXcfXqVcTExGhdTrt27SASiZCUlIQ///wTycnJ2LhxIwCgoqKiWdv4NAYFEVE9vPrqq1AqlfD19dU63sPDA2vXrsX+/fshlUqxa9curFixAsOGDQMAvPPOOxg/fjw+/PBDhIaGIjg4WOt8HB0dsWTJEuzcuRNSqRRbt25FREQEzMzMaj0KaS4man0fwzSzzMxMuLm5GboMQS2hRl2xLU0vt7AEn5/NwixvV3Rtb9WgeRhLW57W0LpKS0uNojO7KRiyLUL//kLjeERBRESCGBRERCRI70GRkpICT09PzefY2FiMGTMGI0eOxMaNGzW9+ffv38c777wDPz8/+Pv74+eff9Z3qUREBD0Hxe3bt7Fq1SpNGKSmpiI5ORkHDx7EsWPHkJ6ejuPHjwMAli5dCi8vLyQlJWHNmjV4//33UVpaqs9yiYgIegyK0tJShIeHV7td/dSpU/D394eVlRUsLCwQFBSEI0eOQKFQ4Ny5c5g0aRIAwM3NDS4uLjUenkVELU8ru36mxWjMv7vebrhbtGgRgoODIZFINMPy8vIwePBgzWdHR0fIZDIUFhZCpVKhQ4cOmnEODg7Iz8+vcznl5eV6v3SsvsrKyoy+Rl2xLU1P1LYjHjwoRHFRMTLzcxo0D2Npy9MUCgUePHiANm3a1Ot7arW61ZxRMFRbysrKoFAoGrRe6CUo9u7dC1NTU0yYMEHzFidAe8KJRCKoVCqt8xGLxXUuy8LCwigvC6zKWC9dbAi2penlFpbAzq49rNtao2u3Tg2ah7G05WmPHj2CTCaDk5MTLC0tdX7hDy+PbbjKYPrrr7/QvXt3tGvXTut0QgGil6D49ttvUVZWhsDAQMjlcs3Pffr0QUFBgWY6mUwGR0dHzUO2Hj58CFtbW804BwcHfZRLRM2kciN19+5dyOVynb8nl8trPNOopTJEW8zMzODg4FBrSNRFL0HxzTffaH7Ozc1FQEAADh8+jDNnzmDTpk2YNGkSTE1NcfDgQQQFBcHU1BTDhw9HQkIC3n33Xdy4cQNZWVl45ZVX9FEuETWjdu3a1XuDZaxHSA3REtti0IcCjhgxArdu3cLEiRMhl8vh4+ODcePGAQAWL16MiIgI+Pv7w8TEBKtXr4aNjY0hyyUieibpPSi6du2KjIwMzefQ0FCEhobWmK5jx46IjY3VZ2lERKQF78wmIiJBDAoiIhLEoCAiIkEMCiIiEsSgICIiQQwKIiISxKAgIiJBDAoiIhLEoCAiIkEMCiIiEsSgICIiQQwKIiISxKAgIiJBDAoiIhLEoCAiIkEMCiIiEsSgICIiQQwKIiISxKAgIiJBDAoiIhLEoCAiIkEMCiIiEsSgICIiQQwKIiISxKAgIiJBDAoiIhLEoCAiIkEMCiIiEsSgICIiQQwKIiISxKAgIiJBDAoiIhKk16DYs2cPpFIp/P39ERYWhr///htKpRLR0dEYM2YMRo4cifj4eM30t2/fRkhICPz8/DBhwgRkZWXps1wiIgJgqq8F/frrr9ixYwcOHz4MGxsbrFq1Cp9++ikkEglycnJw7NgxFBcXIzg4GH379kX//v0xd+5cTJs2DQEBAUhNTcWcOXNw7NgxmJiY6KtsIqJnnt6OKNzd3XHixAnY2NigvLwcMpkMdnZ2SElJQVBQEExNTWFrawupVIojR45AJpMhOzsbUqkUADBs2DCUlpbi+vXr+iqZiIjQREFx//59naYzMzNDSkoKhg4dih9//BFBQUHIy8tDly5dNNM4OjoiPz8feXl56Ny5M0Si/5To4OCA/Pz8piiZiIh0pPOpJzc3N1y8eBEdOnSoNjw3NxcBAQHIyMjQaT6+vr7w9fXF/v37MWPGDJia1ixBJBJBpVJp/b5YLBacf3l5OTIzM3WqxVDKysqMvkZdsS1NT9S2Ix48KERxUTEy83MaNA9jaUtTaU3taYltEQyKb7/9Ft988w0AQK1WIywsrMaGvaCgAJ07d65zQTk5OSgoKICXlxcA4I033sDixYvh5eWFgoICzXQymQyOjo547rnn8Ndff0GtVmv6JCrHCbGwsICbm1ud9RhSZmam0deoK7al6eUWlsDOrj2s21qja7dODZqHsbSlqbSm9hhrW4TCSzAoRo8ejT///BMAcPnyZXh6esLa2rraNNbW1hg1alSdRRQUFODDDz/EoUOH0KFDBxw9ehTPP/88Ro0ahcTERHh7e6OkpATfffcdli5dCkdHRzg7OyMpKQlSqRTnz5+HSCRC7969dWkzERE1EcGgsLKywuzZswEATk5O8PPzg4WFRYMW5OXlhdDQULz11lsQi8Xo3LkzPv/8c3Tp0gV//PEHAgMDIZfLERwcjJdffhkAsG7dOkRGRmLLli0wNzfHp59+Wq3PgoiImp/OfRTjx49HVlYWfv31VygUCqjV6mrjJ0yYUOc8QkJCEBISUmP4woULtU7v4uKCuLg4XUskIqJmoHNQbNu2DevWrYOtrW2N008mJiY6BQUREbU8OgfFzp07ER4ejhkzZjRnPUREZGR0PuEvl8t16rQmIqLWReegCAwMxN69e2v0TRARUeum86mnwsJCnDx5EkePHoWTkxPMzMyqjd+7d2+TF0dERIanc1D07NkToaGhzVkLEREZIZ2DovJ+CiIierboHBTz5s0THL969epGF0NERMZH585ssVhc7T+1Wo0//vgDJ06cqPP5S0RE1HLpfESxcuVKrcN37tzJd0QQEbVijX5w0siRI5GSktIUtRARkRHS+YhC2/shiouLsW/fPrRv375JiyIiIuOhc1D06dNH67uqLSwsEB0d3aRFERGR8dA5KHbv3l3ts4mJCczMzNCrVy+0bdu2yQsjIiLjoHNQVL4jIisrC1lZWVAqlejRowdDgoioldM5KB4+fIh//etfOHfuHGxtbaFUKlFcXAwvLy9s3rwZNjY2zVknEREZiM5XPS1btgwFBQVISkpCeno6fvrpJxw9ehSlpaW1XjpLREQtn85BcfbsWSxduhQ9e/bUDOvVqxcWLVqE06dPN0txRERkeDoHRZs2bbQONzExgVKpbLKCiIjIuOgcFCNGjEBUVBR+//13zbDs7GwsW7YM3t7ezVIcEREZns6d2eHh4Zg1axZef/11zZVOxcXFGDZsGCIjI5utQCIiMiydguLq1auQSCSIi4vDzZs3kZWVhYqKCnTt2hVeXl7NXSMRERmQ4KknhUKB8PBwBAcH48qVKwAAiUQCPz8/pKamYurUqYiIiGAfBRFRKyYYFDt27EB6ejp2796tueGu0vr167Fz506cPn0acXFxzVokEREZjmBQfPvtt4iMjMRLL72kdfygQYMwb948fPPNN81SHBERGZ5gUOTl5aFPnz6CM/Dy8kJubm6TFkVERMZDMCg6duxYZwjcvXuXjxknImrFBINi5MiR2LhxI+RyudbxcrkcmzZtwtChQ5ulOCIiMjzBy2NnzpyJCRMmICgoCFOnToW7uztsbGzw8OFDXL16FXv37kV5eTnWrVunr3qJiEjPBIPCxsYG+/fvx5o1axATE4PS0lIAgFqthq2tLfz9/TFr1ix06NBBL8USEZH+1XnDna2tLaKjo7Fo0SLcuXMHjx49Qvv27eHs7AyRqNGv3CYiIiOn8yM8zM3N4erq2py1EBGREeIhARERCdJrUBw+fBhjx45FYGAgJk+ejGvXrgEAYmNjMWbMGM1VVmq1GgBw//59vPPOO/Dz84O/vz9+/vlnfZZLRESox6mnxsrOzsaaNWtw8OBBdO7cGampqXjvvfewdOlSJCcn4+DBgxCLxZgxYwZcXV3h5+eHpUuXwsvLC6GhocjMzMS7776LkydPwtLSUl9lExE98/R2RGFubo7o6Gh07twZAODu7o6//voLycnJ8Pf3h5WVFSwsLBAUFIQjR45AoVDg3LlzmDRpEgDAzc0NLi4uOH/+vL5KJiIi6PGIomvXrujatSuAJ5fXrly5EiNGjMC9e/cwZMgQzXSOjo6QyWQoLCyESqWqdumtg4MD8vPzBZdTXl6OzMzM5mlEEykrKzP6GnXFtjQ9UduOePCgEMVFxcjMz2nQPIylLU2lNbWnJbZFb0FRqaSkBPPnz0d+fj6+/PJLfPDBBzWmEYlEUKlUWr8vFosF529hYQE3N7cmqbW5ZGZmGn2NumJbml5uYQns7NrDuq01unbr1KB5GEtbmkprao+xtkUovPTamX337l1MnjwZYrEYu3fvRrt27dClSxcUFBRoppHJZHB0dIS9vT0A4OHDh9XGOT3jdAUAAA+OSURBVDg46LNkIqJnnt6C4sGDB3jzzTcxatQorF+/Hm3atAEA+Pj44MiRIygpKUFFRQUOHjwIX19fmJqaYvjw4UhISAAA3LhxA1lZWXjllVf0VTIREUGPp57i4+ORl5eHU6dO4dSpU5rhu3btwqhRozBx4kTI5XL4+Phg3LhxAIDFixcjIiIC/v7+MDExwerVq2FjY6OvkomICHoMirCwMISFhWkdFxoaitDQ0BrDO3bsiNjY2OYujYiIBPDObCIiEsSgICIiQQwKIiISxKAgIiJBDAoiIhLEoCAiIkEMCiIiEsSgICIiQQwKIiISxKAgIiJBDAoiIhLEoCAiIkEMCiIiEsSgICIiQQwKIiISxKAgIiJBDAoiIhLEoCAiIkEMCiIiEsSgICIiQQwKIiISxKAgIiJBDAoiIhLEoCAiIkEMCiIiEsSgICIiQQwKIiISxKAgIiJBDAoiIhLEoCAiIkEMCiIiEsSgICIiQXoPCrVajfnz52P79u0AAKVSiejoaIwZMwYjR45EfHy8Ztrbt28jJCQEfn5+mDBhArKysvRdLhHRM0+vQZGVlYVp06bh+PHjmmH79u1DTk4Ojh07hm+++QZfffUVrl69CgCYO3cupkyZgqSkJLz33nuYM2cO1Gq1PksmInrm6TUo9u7di6CgILz++uuaYSkpKQgKCoKpqSlsbW0hlUpx5MgRyGQyZGdnQyqVAgCGDRuG0tJSXL9+XZ8lExE980z1ubBFixYBAH744QfNsLy8PHTp0kXz2dHRETdv3kReXh46d+4Mkeg/Webg4ID8/Hz07du31mWUl5cjMzOzGapvOmVlZUZfo67YlqYnatsRDx4UorioGJn5OQ2ah7G0pam0pva0xLboNSi00XYqSSQSQaVSaZ1eLBYLzs/CwgJubm5NUltzyczMNPoadcW2NL3cwhLY2bWHdVtrdO3WqUHzMJa2NJXW1B5jbYtQeBn8qqcuXbqgoKBA81kmk8HR0RHPPfcc/vrrr2pBUjmOiIj0x+BB4ePjg8TERCgUCjx69AjfffcdfH194ejoCGdnZyQlJQEAzp8/D5FIhN69exu4YiKiZ4vBTz1NmTIFf/zxBwIDAyGXyxEcHIyXX34ZALBu3TpERkZiy5YtMDc3x6efflqtz4KIiJqfQYIiJibmPwWYmmLhwoVap3NxcUFcXJy+yiIiIi24e05ERIIYFEREJIhBQUREghgUREQkiEFBRESCGBRERCSIQUFERIIYFEREJIhBQUREghgUREQkiEFBREavTbsOyC0sQWFJhaFLeSYxKIjI6FWoRPj8bBaKyxWGLuWZxKAgIiJBBn/MOBFRUyssqdAcfVhbmKK9lbmBK2rZGBREZDR02cDnFpYIjgeA4nIFPj+bBQCY5e3KoGgkBgVpVfkHy70x0qenN/CVoSEyNQMAlMmV2H7hdrXxVdfRyvVWrlTXnDk1GPsoSKvKP1h2HlJTKyyp0OkKpjK5Ep+fzcLnZ7OgEhhfdR2tXG+VKm3foIZiUFCjVF62WNsfvq4bBWo9Kn/ntf3euRPS8jAoqFEqL1us7Q+/6kZBaONBLV9lQDwokQuuE1VxnWgZGBSks7r2FIVUPY3APcnWqb6nfZ5eJ3ILS9i3YKTYmU0aVa840fYHq62jsbKTsT7zoWdPXetEZSf1jCEueq6MdMGgeAZou+RQ27CqQVD1D7bycsSqf+CVf9jTB3ersbza5kOtj647Bc29TmhbR7WprNdMbKKZllf21Y1B8QzQdk25tqMDoT09gBt9euLpcNiWlg3AcOuHLuto1SDZlpaNGUNcql1my6AQxqCgJj3sr8955rpuruLdtcapvkcHhu570DVIuI7Vjp3Z1GQqOyd17cys3OA83cHdkKtnyDjVd50whKr3Y/AqLO0YFK1Y5Qb36b05Q+/h1YU3TZEh6HplXmOu/mupGBStQG0rrrYNbkvYw6Pm9yxu7JpKbUfCrRn7KFqBlvgAtNqOdIDar1zheeSmo22dqdonVHlVEP+9CeARRatUuadorKeXtB3VVD3s13a0o+25PtS0qu4pPy6r+ZiN2k5ltla5hSWQPSrV/D1V/vystL8qBoUREno+UtVTBtpW3Lo2uK0RT6Pox7PUd1T5d1QZmFV/frr9z8K6x1NPRqjyD7LqaaSqj0+uet36s3g369PvI6jt0dQ8baI7oT1lXW9mexZpe+x5a7yZj0cURq5yb6XyUtFnYW9OSF3PB+IzpepP6AKHZ/EItaG0HYW0lnWQRxRGQtujEHhXtDBdbhR8ugP8WXwhk7ZOah4d6E9ruAjD6I8ozp07h4CAAIwePRpz5sxBUVGRoUuqk7Zz5lWHtWnXocZ3qnYkcs+taWi7karyyKzqsMq+Hm2/l5ZA1/c/CJ1np+ahbR1sieuZUR9R3L9/Hx9//DHi4+Ph4uKCNWvWYO3atViyZImhSxNU17OV/meIs+a8L/fwmp+2I7Onh22/cFvr76Xq+WZjPfesbX2rfKEUwL4FY1B1fZv+kiMA7Ud6xrReVWXUQXHhwgX069cPLi4uAIApU6YgMDAQixcvhomJSbMuW9svUdtGoyHv6y1XqrH1wn+elfMsdkgbo9p+L08HCqC947K+60ltIVR1/REKrto2KrmFJVCILLCNT/A1ShYW5pq+tacvTKl6MUZd65Y+Q8VErVYb7e7Gtm3bkJubi6ioKACAQqFA3759cfnyZbRt21brd3755RdYWFjos0wiohavvLwcHh4eWscZ9RGFqpbzqCJR7V0rtTWUiIgaxqg7s7t06YKCggLNZ5lMBltbW1hZWRmwKiKiZ4tRB8WQIUNw5coV3L59GwCwb98++Pj4GLYoIqJnjFH3UQBAamoqPvnkE8jlcjg7O2PVqlWws7MzdFlERM8Mow8KIiIyLKM+9URERIbHoCAiIkEMCgN4/PgxQkND8eabbyI4OBgZGRmGLqneVCoVFi1ahODgYEydOhU5OTmGLqnB5HI5wsPDERISggkTJuD06dOGLqnR/v77bwwbNgxZWVmGLqVRtm7diuDgYAQFBeHAgQOGLqfB5HI5PvroI0yePBkhISEt7vfCoDCAnTt3YtCgQdizZw9WrlypuaGwJUlJSUFFRQUSEhLw0UcfISYmxtAlNdiRI0dgZ2eHr7/+Gl9++SWWLVtm6JIaRS6XY9GiRWjTpo2hS2mU9PR0ZGRkID4+HnFxccjPzzd0SQ2WmpoKhUKBffv2YdasWdiwYYOhS6oXo77hrrWaPn06zM2f3HqvVCpb5J3kly9fxmuvvQbgyU2Ov/76q4ErargxY8Zg9OjRAAC1Wg2xWGzgihpn1apVmDx5MrZt22boUhrlwoUL6N27N2bNmoWioiLMmzfP0CU1WI8ePaBUKqFSqVBUVART05a16W1Z1bZABw4cwFdffVVt2IoVK9C/f38UFBQgPDwcCxYsMFB1DVdUVFTtMSpisRgKhaLF/QEAgLW1NYAnbZozZw4++OADA1fUcAcPHkSHDh3w2muvtfigKCwsxN27dxEbG4vc3FyEhYUhOTm52Z/z1hysrKzw559/4vXXX0dhYSFiY2MNXVK9tLy/6hZm4sSJmDhxYo3hN2/exIcffoh58+bh5ZdfNkBljdO2bVsUFxdrPqtUqhYZEpXy8vIwa9YshISEICAgwNDlNFhiYiJMTEzw/fffIzMzE//617+wZcsWdOrUydCl1ZudnR169uwJc3Nz9OzZExYWFrh//z7s7e0NXVq97dq1C0OGDMFHH32EvLw8TJs2DUePHm0xZxPYR2EAv/32G95//3188sknGDZsmKHLaRBPT0+kpaUBePIgxt69exu4oob766+/8PbbbyM8PBwTJkwwdDmNsnfvXuzZswdxcXFwc3PDqlWrWmRIAMCLL76I8+fPQ61WQyaTobS0tMXebNuuXTvY2NgAAGxtbaFQKKBUKg1cle54w50BhIWF4ebNm3BycgLwZO98y5YtBq6qflQqFZYsWYJbt25BrVZjxYoVcHV1NXRZDRIdHY3jx4+jZ8+emmFffPFFi+8Mnjp1KpYsWdJify8AsHr1aqSnp0OtVuOf//ynpl+spSkuLsaCBQtQUFAAuVyOt956q0UduTIoiIhIEE89ERGRIAYFEREJYlAQEZEgBgUREQliUBARkSAGBZEOQkJCar1j+9y5c3B3d0dhYWGt31coFJBIJEhPT2+uEomaDYOCSAcBAQFITU1FeXl5jXFJSUkYMmQI2rdvb4DKiJofg4JIB2PGjIFcLsf58+erDa+oqMCZM2cwduxYA1VG1PwYFEQ6aN++PYYMGYLk5ORqw9PS0qBSqTBixAgUFRXh448/xuDBg+Hu7o4xY8YgJSVF6/yGDh2KgwcPaj5funQJEolE8/nu3bsIDQ2Fh4cHvL29sX79esjl8uZpHFEdGBREOvL398e5c+dQUVGhGXb8+HGMGjUKbdq0QXR0NHJycrBz504cO3YMAwcOxMKFC6tNrwuVSoWZM2fC3t4eiYmJWL16NU6dOtXi3mFArQeDgkhHPj4+UCqVuHTpEgCgvLwcZ86c0Tyzx8vLC0uXLsULL7wAFxcXvP3223jw4AH+/vvvei3n4sWLkMlkWLZsGVxdXfHSSy8hMjISe/bsgUqlavJ2EdWl5T4XmkjPLC0t4ePjgxMnTmD48OFITU2FtbU1Bg0aBAAICgrCyZMnsW/fPmRnZ+Pf//43ANT7KaFZWVl48OABXnzxRc0wtVqNsrIy5OXlaR4mSaQvDAqieggICMC8efOgUCiQlJQEPz8/zRvxPvroI1y9ehWBgYEICQlBhw4dEBISonU+T798R6FQaH5WKpXo2bMnNm/eXON7LfWR4dSyMSiI6uHVV1+FSCTC999/j9TUVMTFxQEAHj58iKSkJBw4cAD9+/cHAJw+fRrAk6OBp5mZmVV78dOdO3c0P/fo0QN3795Fhw4dNO8w+N///V98/fXXWL16dbO1jag27KMgqgdTU1O8/vrr+OSTT+Dg4AB3d3cAQJs2bWBpaYmTJ08iNzcXaWlpWL58OQBo7czu168fEhMT8X//939IT0/H7t27NeOGDh0KR0dHzJ07Fzdu3MDPP/+MiIgIiMVizbvWifSJQUFUTwEBAcjMzKz24hkLCwusXr0aycnJkEqlWLVqFWbNmoWOHTsiMzOzxjw+/PBDWFtbY/z48Vi+fDnef/99zThTU1Ns3boVarUakydPxsyZMzFo0CAsW7ZML+0jehpfXERERIJ4REFERIIYFEREJIhBQUREghgUREQkiEFBRESCGBRERCSIQUFERIIYFEREJOj/ATu7KX1Yb6yaAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.concatenate([X, [numpy.nan]*500])\n", "X_imp = X.copy()\n", "X_imp[numpy.isnan(X_imp)] = numpy.mean(X_imp[~numpy.isnan(X_imp)])\n", "\n", "plt.title(\"Bimodal Distribution\", fontsize=14)\n", "plt.hist(X_imp, bins=numpy.arange(-3, 9, 0.1), alpha=0.6)\n", "plt.ylabel(\"Count\", fontsize=14)\n", "plt.yticks(fontsize=12)\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.yticks(fontsize=12)\n", "plt.vlines(numpy.mean(X), 0, 80, color='r', label=\"Mean\")\n", "plt.vlines(numpy.median(X), 0, 80, color='b', label=\"Median\")\n", "plt.legend(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It doesn't appear to be that great. We can see the issue with increased variance by trying to fit a Gaussian mixture model to the data with the imputed values, versus fitting it to the data and ignoring missing values." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = numpy.arange(-3, 9, 0.1)\n", "model1 = GeneralMixtureModel.from_samples(NormalDistribution, 2, X_imp.reshape(X_imp.shape[0], 1))\n", "model2 = GeneralMixtureModel.from_samples(NormalDistribution, 2, X.reshape(X.shape[0], 1))\n", "p1 = model1.probability(x.reshape(x.shape[0], 1))\n", "p2 = model2.probability(x.reshape(x.shape[0], 1))\n", "\n", "plt.figure(figsize=(12, 3))\n", "plt.subplot(121)\n", "plt.title(\"Mean Impute Missing Values\", fontsize=14)\n", "plt.hist(X_imp, bins=x, alpha=0.6, density=True)\n", "plt.plot(x, p1, color='b')\n", "plt.ylabel(\"Count\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.xlabel(\"Value\", fontsize=14); plt.yticks(fontsize=12)\n", "\n", "plt.subplot(122)\n", "plt.title(\"Ignore Missing Values\", fontsize=14)\n", "plt.hist(X[~numpy.isnan(X)], bins=x, alpha=0.6, density=True)\n", "plt.plot(x, p2, color='b')\n", "plt.ylabel(\"Count\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.xlabel(\"Value\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we impute the missing values, it seems that one component is fit properly and one has drastically increased variance. In contrast, when we ignore the missing values, we fit a model that represents the underlying data much more faithfully.\n", "\n", "At this point, you may think that as long as the data comes from a single distribution it shouldn't matter if you do a mean imputation of the data. However, this has the effect of shrinking the variance inappropriately. Let's take a look quickly at data drawn from a single Gaussian." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.concatenate([numpy.random.normal(0, 1, size=(750)), [numpy.nan]*250])\n", "X_imp = X.copy()\n", "X_imp[numpy.isnan(X_imp)] = numpy.mean(X_imp[~numpy.isnan(X_imp)])\n", "\n", "x = numpy.arange(-3, 3, 0.1)\n", "d1 = NormalDistribution.from_samples(X_imp)\n", "d2 = NormalDistribution.from_samples(X)\n", "p1 = d1.probability(x.reshape(x.shape[0], 1))\n", "p2 = d2.probability(x.reshape(x.shape[0], 1))\n", "\n", "plt.figure(figsize=(12, 3))\n", "plt.subplot(121)\n", "plt.title(\"Mean Impute Missing Values\", fontsize=14)\n", "plt.hist(X_imp, bins=x, alpha=0.6, density=True, label=\"$\\sigma$ = {:4.4}\".format(d1.parameters[1]))\n", "plt.plot(x, p1, color='b')\n", "plt.ylabel(\"Count\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.xlabel(\"Value\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.legend(fontsize=14)\n", "\n", "plt.subplot(122)\n", "plt.title(\"Ignore Missing Values\", fontsize=14)\n", "plt.hist(X[~numpy.isnan(X)], bins=x, alpha=0.6, density=True, label=\"$\\sigma$ = {:4.4}\".format(d2.parameters[1]))\n", "plt.plot(x, p2, color='b')\n", "plt.ylabel(\"Count\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.xlabel(\"Value\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.legend(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even when the data is all drawn from a single, Gaussian, distribution, it is not a great idea to do mean imputation. We can see that the standard deviation of the learned distribution is significantly smaller than the true standard deviation (of 1), whereas if the missing data is ignored the value is closer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This might all be intuitive for a single variable. However, the concept of only collecting sufficient statistics from values that are present in the data and ignoring the missing values can be used in much more complicated, and/or multivariate models. Let's take a look at how well one can estimate the covariance matrix of a multivariate Gaussian distribution using these two strategies. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "n, d, steps = 1000, 10, 50\n", "diffs1 = numpy.zeros(int(steps*0.86))\n", "diffs2 = numpy.zeros(int(steps*0.86))\n", "\n", "X = numpy.random.normal(6, 3, size=(n, d))\n", "\n", "for k, size in enumerate(range(0, int(n*d*0.86), n*d // steps)):\n", " idxs = numpy.random.choice(numpy.arange(n*d), replace=False, size=size)\n", " i, j = idxs // d, idxs % d\n", "\n", " cov_true = numpy.cov(X, rowvar=False, bias=True)\n", " X_nan = X.copy()\n", " X_nan[i, j] = numpy.nan\n", "\n", " X_mean = X_nan.copy()\n", " for col in range(d):\n", " mask = numpy.isnan(X_mean[:,col])\n", " X_mean[mask, col] = X_mean[~mask, col].mean()\n", "\n", " diff = numpy.abs(numpy.cov(X_mean, rowvar=False, bias=True) - cov_true).sum()\n", " diffs1[k] = diff\n", "\n", " dist = MultivariateGaussianDistribution.from_samples(X_nan)\n", " diff = numpy.abs(numpy.array(dist.parameters[1]) - cov_true).sum()\n", " diffs2[k] = diff" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.title(\"Error in Multivariate Gaussian Covariance Matrix\", fontsize=16)\n", "plt.plot(diffs1, label=\"Mean\")\n", "plt.plot(diffs2, label=\"Ignore\")\n", "\n", "plt.xlabel(\"Percentage Missing\", fontsize=14)\n", "plt.ylabel(\"L1 Errors\", fontsize=14)\n", "plt.xticks(range(0, 51, 10), numpy.arange(0, 5001, 1000) / 5000.)\n", "plt.xlim(0, 50)\n", "plt.legend(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In even the simplest case of Gaussian distributed data with a diagonal covariance matrix, it is more accurate to use the ignoring strategy rather than imputing the mean. When the data set is mostly unobserved the mean imputation strategy tends to do better in this case, but only because there is so little data for the ignoring strategy to actually train on. The deflation of the variance benefits the mean imputation strategy because all of the off-diagonal elements should be 0, but are likely to be artificially high when there are only few examples of the pairs of the variables co-existing in the dataset. This weakness in the ignoring strategy also makes it more likely to encounter linear algebra errors, such as a non-invertable covariance matrix." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This long introduction is a way of saying that pomegranate uses a strategy of ignoring missing values instead of attempting to impute them, followed by fitting to the newly complete data set. There are other imputation strategies, such as those based on EM, that would be a natural fit with the types of probabilistic models implemented in pomegranate. While those have not yet been added, they would be a good addition that I hope to get to this year.\n", "\n", "Let's now take a look at how to use missing values in some pomegranate models!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Distributions\n", "\n", "We've seen some examples of fitting distributions to missing data. For univariate distributions, the missing values are simply ignored when fitting to the data." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting only to observed values:\n", "{\n", " \"class\" :\"Distribution\",\n", " \"name\" :\"NormalDistribution\",\n", " \"parameters\" :[\n", " -0.0844412750220139,\n", " 0.9166407920894343\n", " ],\n", " \"frozen\" :false\n", "}\n", "\n", "Fitting to observed and missing values:\n", "{\n", " \"class\" :\"Distribution\",\n", " \"name\" :\"NormalDistribution\",\n", " \"parameters\" :[\n", " -0.0844412750220139,\n", " 0.9166407920894343\n", " ],\n", " \"frozen\" :false\n", "}\n" ] } ], "source": [ "X = numpy.random.randn(100)\n", "X_nan = numpy.concatenate([X, [numpy.nan]*100])\n", "\n", "print(\"Fitting only to observed values:\")\n", "print(NormalDistribution.from_samples(X))\n", "print()\n", "print(\"Fitting to observed and missing values:\")\n", "print(NormalDistribution.from_samples(X_nan))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This may seem to be an obvious thing to do. However, it suggests a way for dealing with multivariate data being modeled with an IndependentComponentsDistribution when some of the features are missing. Specifically, treat each column independently, and update based on the observed values, regardless of if there is an unobserved value in the same sample but another column. For example:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"IndependentComponentsDistribution\",\n", " \"parameters\" : [\n", " [\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " -0.08339536402140717,\n", " 0.9112109573110577\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 0.048834959770246114,\n", " 0.9877919452098168\n", " ],\n", " \"frozen\" : false\n", " },\n", " {\n", " \"class\" : \"Distribution\",\n", " \"name\" : \"NormalDistribution\",\n", " \"parameters\" : [\n", " 0.014464502073682939,\n", " 0.9392312251035693\n", " ],\n", " \"frozen\" : false\n", " }\n", " ],\n", " [\n", " 1.0,\n", " 1.0,\n", " 1.0\n", " ]\n", " ],\n", " \"frozen\" : false\n", "}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = numpy.random.normal(0, 1, size=(500, 3))\n", "idxs = numpy.random.choice(1500, replace=False, size=500)\n", "i, j = idxs // 3, idxs % 3\n", "X[i, j] = numpy.nan\n", "\n", "d = IndependentComponentsDistribution.from_samples(X, distributions=[NormalDistribution]*3)\n", "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Easy. As we saw above, we can do the same to learn a multivariate Gaussian distribution in the presence of missing data. Again, we don't need to change anything about how we interact with the data, and there are no flags to toggle.\n", "\n", "The last aspect is that the probability of missing data under any univariate distribution is 1, for the purposes of downstream algorithms." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "NormalDistribution(1, 2).probability(numpy.nan)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In an IndependentComponentsDistribution, this just means that when multiplying together the probabilities of each feature to get the total probability, that some dimensions don't factor into the calculation." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.00015597177674513935" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.probability((numpy.nan, 2, 3))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0001559717767451392" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.distributions[1].probability(2) * d.distributions[2].probability(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. K-Means Clustering\n", "\n", "K-means clustering mostly serves a helper role in initializing mixture models and hidden Markov models. However, it can still be used by itself if desired. In addition to having the same parallelization and out-of-core capabilities of the main models, it also supports missing values now." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "X = numpy.concatenate([numpy.random.normal(0, 1, size=(50, 2)), numpy.random.normal(3, 1, size=(75, 2))])\n", "X_nan = X.copy()\n", "\n", "idxs = numpy.random.choice(250, replace=False, size=50)\n", "i, j = idxs // 2, idxs % 2\n", "X_nan[i, j] = numpy.nan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like the other models, you don't need to change the method calls in order to handle missing data. You can fit a K-means model to data sets with missing values and make predictions on samples with missing values in the same way you would without missing values. The prediction step will assign samples to the nearest centroid in the dimensions that are observed, ignoring the missing values." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model1 = Kmeans.from_samples(2, X)\n", "model2 = Kmeans.from_samples(2, X_nan)\n", "\n", "y1 = model1.predict(X)\n", "y2 = model2.predict(X_nan)\n", "\n", "plt.figure(figsize=(14, 6))\n", "plt.subplot(121)\n", "plt.title(\"Fit w/o Missing Values\", fontsize=16)\n", "plt.scatter(X[y1 == 0,0], X[y1 == 0,1], color='b')\n", "plt.scatter(X[y1 == 1,0], X[y1 == 1,1], color='r')\n", "\n", "plt.subplot(122)\n", "plt.title(\"Fit w/ Missing Values\", fontsize=16)\n", "plt.scatter(X[y2 == 0,0], X[y2 == 0,1], color='b')\n", "plt.scatter(X[y2 == 1,0], X[y2 == 1,1], color='r')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that there are some blue points in the red cluster on the right plot because those samples are entirely NaN. Any sample that is entirely NaN is assigned to cluster 0. Otherwise, it's still able to identify the two clusters even there there are many missing values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. General Mixture Models\n", "\n", "Missing value support for mixture models follows that of k-means fairly closely. Essentially, one passes in a data set containing missing values denoted as `numpy.nan` and they get used appropriately for the fit and predict steps. All methods automatically handle missing values appropriately without any additional flags or methods. \n", "\n", "Since training is an iterative procedure that involves calculating the probabilities of samples given each component, multivariate Gaussian mixtures will be much slower when handling missing values than they would be when using only observed values. This is because an inverse covariance matrix has to be calculated by subsetting the covariance matrix and inverting it based only on the observed dimensions for each sample. Each sample, then, needs its own matrix inversion. Since there is no single inverse covariance matrix, one also cannot use BLAS or a GPU to accelerate this step. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.79 ms ± 194 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", "2.15 s ± 636 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "X = numpy.concatenate([numpy.random.normal(0, 1, size=(1000, 10)), numpy.random.normal(2, 1, size=(1250, 10))])\n", "\n", "idxs = numpy.random.choice(22500, replace=False, size=5000)\n", "i, j = idxs // 10, idxs % 10\n", "\n", "X_nan = X.copy()\n", "X_nan[i, j] = numpy.nan\n", "\n", "%timeit GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X, max_iterations=10)\n", "%timeit GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_nan, max_iterations=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, if one was modeling each dimension independently, there should be no hit at all!" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.93 ms ± 403 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", "3.7 ms ± 124 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%timeit -n 100 GeneralMixtureModel.from_samples([NormalDistribution]*2, 2, X, max_iterations=10)\n", "%timeit -n 100 GeneralMixtureModel.from_samples([NormalDistribution]*2, 2, X_nan, max_iterations=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Naive Bayes / Bayes Classifiers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Support for these models mirrors what's been seen before. However, since the fitting step does not involve calculating probabilities of samples, it should be no slower to train them on data sets involving missing values than to train them on dense data sets." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.22 ms ± 142 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", "1.41 ms ± 44.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "y = numpy.concatenate([numpy.zeros(1000), numpy.ones(1250)])\n", "\n", "%timeit -n 100 BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y)\n", "%timeit -n 100 BayesClassifier.from_samples(MultivariateGaussianDistribution, X_nan, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since pomegranate also has semi-supervised learning built-in, this means that one can now fit Bayes classifiers on data sets with missingness in both the labels and in the values! Since semi-supervised learning does rely on EM, it will be slower to train multivariate Gaussian models with missing values than not to." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] Improvement: 44.19320307574344\tTime (s): 0.1736\n", "[2] Improvement: 0.15779521180411393\tTime (s): 0.1557\n", "[3] Improvement: 0.009774176905921195\tTime (s): 0.165\n", "Total Improvement: 44.360772464453476\n", "Total Time (s): 0.6666\n" ] } ], "source": [ "idx = numpy.random.choice(2250, replace=False, size=750)\n", "y_nan = y.copy()\n", "y_nan[idx] = -1\n", "\n", "model = BayesClassifier.from_samples(MultivariateGaussianDistribution, X_nan, y_nan, verbose=True)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.95 ms ± 88.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", "631 ms ± 7.69 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%timeit BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y_nan)\n", "%timeit BayesClassifier.from_samples(MultivariateGaussianDistribution, X_nan, y_nan)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Hidden Markov Models\n", "\n", "Hidden Markov models are slightly different from the others, in that they operate over sequences. This adds another level of complication to the model because the forward and backward algorithms are needed in order to identify the best component for each observation. Typically this involves calculating the probability of each observation given each state, and taking the sum of all paths through the model, multiplying the transition probability of each edge crossed by the emission probability of the state you transition to emitting the next character. If this is a univariate model and the character is missing, you ignore the emission probability and just multiply by the transition probability. This is easily done by having the probability of missing values be 1 under all univariate models." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "d1 = DiscreteDistribution({'A': 0.25, 'B': 0.75})\n", "d2 = DiscreteDistribution({'A': 0.67, 'B': 0.33})\n", "\n", "s1 = State(d1, name=\"s1\")\n", "s2 = State(d2, name=\"s2\")\n", "\n", "model = HiddenMarkovModel()\n", "model.add_states(s1, s2)\n", "model.add_transition(model.start, s1, 1.0)\n", "model.add_transition(s1, s1, 0.5)\n", "model.add_transition(s1, s2, 0.5)\n", "model.add_transition(s2, s2, 0.5)\n", "model.add_transition(s2, s1, 0.5)\n", "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's run the forward algorithm on a simple sequence." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0. , 0. , 1. , 0. ],\n", " [0.25 , 0. , 0. , 0. ],\n", " [0.09375 , 0.04125 , 0. , 0. ],\n", " [0.016875 , 0.045225 , 0. , 0. ],\n", " [0.0077625, 0.0208035, 0. , 0. ]])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numpy.exp(model.forward(['A', 'B', 'A', 'A']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see what happens when we remove one of the characters." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0. , 0. , 1. , 0. ],\n", " [0.25 , 0. , 0. , 0. ],\n", " [0.125 , 0.125 , 0. , 0. ],\n", " [0.03125 , 0.08375 , 0. , 0. ],\n", " [0.014375, 0.038525, 0. , 0. ]])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numpy.exp(model.forward(['A', 'nan', 'A', 'A']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that initially the first character is aligned to s1 because there is a 100% chance of going from the start state to s1. The value is 0.25 because it is equal to the transition probability (1.0) multiplied by the emission probability (0.25). In the next step, you can see that the probability is equally diffused between two options, staying in the current state (transition probability of 0.5) and moving to s2 (also transition probability of 0.5). Since the character is missing, there is no emission probability to multiply by. \n", "\n", "If we want to decode the sequence we can call the same methods as before." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 0, 0, 1, 1]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict(['A', 'A', 'B', 'B', 'A', 'A'])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 0, 0, 0, 0, 1]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict(['A', 'nan', 'B', 'B', 'nan', 'A'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fitting is pretty much the same story as the previous models. Like the Bayes classifiers, one can now train a hidden Markov model in a supervised manner, having some observations in the sequence missing, but also labels on each observation. Labeled missing data can still be used to train the transition parameters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. Bayesian Networks\n", "\n", "Bayesian networks could previously handle missing data in the `predict` and `predict_proba` methods. In fact, handling missing data was the area in which they excelled. However, one couldn't calculate the probability of a sample containing missing values, fit a Bayesian network to incomplete data sets, or learn the structure of a network on incomplete data sets. The new missing value support allows all of these things (except Chow-Liu tree building on missing data sets)! To be clear, there is a very common iterative technique for fitting/learning models on incomplete data sets using an iterative EM based approach. This does not do that. This only ignores sufficient statistics from missing samples, and so is not an iterative approach.\n", "\n", "Learning the structure of a network on an incomplete data set should take a similar amount of time as learning it on a complete data set. If you are indicating missing values in numeric data sets, you will have to convert your data set to floats, whereas previously integers would be fine. If your data set is complete, there is no need." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "101 ms ± 4.45 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", "129 ms ± 5.92 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "X = numpy.random.randint(3, size=(500, 10)).astype('float64')\n", "\n", "idxs = numpy.random.choice(5000, replace=False, size=2000)\n", "i, j = idxs // 10, idxs % 10\n", "X_nan = X.copy()\n", "X_nan[i, j] = numpy.nan\n", "\n", "%timeit -n 100 BayesianNetwork.from_samples(X, algorithm='exact')\n", "%timeit -n 100 BayesianNetwork.from_samples(X_nan, algorithm='exact')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusions\n", "\n", "Missing value support has been added to pomegranate as of v0.9.0! A lot of care was taken to make the interface as simple to the end user while not compromising on speed. While multivariate Gaussian distributions, and the compositional models that are built on top of them, may be slower on incomplete data sets than on complete ones, everything else should take a similar amount of time!\n", "\n", "The implemented missing value support focuses on ignoring data that is missing. Another approach that works well is to use an EM based algorithm to impute the missing values based on the observed values and the model and fit to that complete data set. This works well in the framework of probabilistic modeling and is a natural addition to pomegranate that I hope to add in the coming year.\n", "\n", "As always, feedback and questions are always welcome!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 } C_Feature_Tutorial_5_Custom_Distributions.ipynb000066400000000000000000004071311420461712600341310ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Custom Distributions\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com\n", "\n", "One of the main implementation details of pomegranate is that it decouples the mathematics behind the various models from the likelihood functions that are used in them. For example, the class probabilities calculated for mixture models and Bayes' classifiers involve multiplying a likelihood function and a prior probability. Typically, this likelihood function is assumed to be Gaussian, but there is no algorithmic requirement that it be any specific probability distribution. This is why pomegranate is much more flexible than other packages in terms of the types of distributions that various models can be built using.\n", "\n", "However, a major limitation in pomegranate for a long time was that while users were free to use any of the built in distributions to build models, they could not define their own in Python. The reason behind this is the Cython backend, which essentially required that the distributions be implemented in Cython.\n", "\n", "Fortunatly, a recent patch has allowed users to define their own custom distributions. This means that users can define their own distributions, entirely in Python, and plug them in to existing models without modification. This tutorial will show you how to do that." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fri Jan 10 2020 \n", "\n", "numpy 1.18.1\n", "scipy 1.4.1\n", "pomegranate 0.12.0\n", "\n", "compiler : Clang 10.0.0 (clang-1000.11.45.5)\n", "system : Darwin\n", "release : 17.7.0\n", "machine : x86_64\n", "processor : i386\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import time\n", "import pandas\n", "import random\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "import itertools\n", "\n", "from pomegranate import *\n", "\n", "random.seed(0)\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Building a new normal distribution\n", "\n", "Let's start off by building a custom normal distribution. Our goal is to build a pure Python object that has the exact same functionality as the normal distribution that is currently implemented, to demonstrate that the internals are all working correctly." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import scipy.stats\n", "\n", "class NormalDistribution2():\n", " def __init__(self, mu, std):\n", " self.mu = mu\n", " self.std = std\n", " self.parameters = (self.mu, self.std)\n", " self.d = 1\n", " self.summaries = numpy.zeros(3)\n", "\n", " def log_probability(self, X):\n", " return scipy.stats.norm.logpdf(X, self.mu, self.std)\n", "\n", " def summarize(self, X, w=None):\n", " if w is None:\n", " w = numpy.ones(X.shape[0])\n", "\n", " X = X.reshape(X.shape[0])\n", " self.summaries[0] += w.sum()\n", " self.summaries[1] += X.dot(w)\n", " self.summaries[2] += (X ** 2.).dot(w)\n", "\n", " def from_summaries(self, inertia=0.0):\n", " self.mu = self.summaries[1] / self.summaries[0]\n", " self.std = self.summaries[2] / self.summaries[0] - self.summaries[1] ** 2 / (self.summaries[0] ** 2)\n", " self.std = numpy.sqrt(self.std)\n", " self.parameters = (self.mu, self.std)\n", " self.clear_summaries()\n", "\n", " def clear_summaries(self, inertia=0.0):\n", " self.summaries = numpy.zeros(3)\n", "\n", " @classmethod\n", " def from_samples(cls, X, weights=None):\n", " d = NormalDistribution2(0, 0)\n", " d.summarize(X, weights)\n", " d.from_summaries()\n", " return d\n", "\n", " @classmethod\n", " def blank(cls):\n", " return NormalDistribution2(0, 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The custom objects have a few requirements.\n", "\n", "(1) An attribute, `d`, that stores the number of dimensions represented by the distribution. For univariate distributions like this normal distribution, this should just be 1.\n", "\n", "\n", "(2) A method, `log_probability(X)`, that takes in a vector of shape (n_samples,) or a matrix of shape (n_samples, d) if multivariate, and returns the log probability of each sample. In this case we're just using the built-in scipy function for calculating log probabilities under a normal distribution for simplicity.\n", "\n", "(3) A method, `summarize(X, weights=None)`, that takes in a vector of shape (n_samples,) or a matrix of shape (n_samples, d) if multivariate, calculates the sufficient statistics of that batch, and adds them to the growing sufficient statistics. In the case of a normal distribution, our sufficient statistics are the sum of the weights, the sum of the weighted points, and the sum of the weighted points squared. \n", "\n", "(4) A method, `from_summaries(inertia=0.0)`, that takes uses the stored sufficient statistics in order to update the model parameters. This should also clear the stored sufficient statistics for the next iteration of training.\n", "\n", "(5) A method, `clear_summaries()`, that clears the stored sufficient statistics. Here, all we're doing is resetting the three stored summaries to 0.\n", "\n", "(6) A class method, `from_samples(X, weights=None)` that rreturns a distribution that has been fit to the data. Generally this will initialize a dummy distribution and then overwrite the initial parameters using those derived from the data.\n", "\n", "(7) A class method, `blank()` or `blank(d)` if multivariate, that creates a dummy distribution with the appropriate number of parameters. Here we return a distribution with a mean and a variance of 0. These dummies generally aren't meant to be used.\n", "\n", "Let's test this against the built in distributions!" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-77.23179709134088, -77.23179709134129)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "NormalDistribution(2.532, 8.211).log_probability(102.563), NormalDistribution2(2.532, 8.211).log_probability(102.563)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks good so far. Looks like there might be a small difference at very smalll precisions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.8729100873003006, 0.5756744412108401]\n", "(0.8729100873002954, 0.5756744412108428)\n" ] } ], "source": [ "X = numpy.random.normal(0.872, 0.57721, size=100000)\n", "\n", "print(NormalDistribution.from_samples(X).parameters)\n", "print(NormalDistribution2.from_samples(X).parameters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! Now let's try putting this into a more complex model." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] Improvement: 27.262773607714053\tTime (s): 0.000407\n", "[2] Improvement: 8.072984397975915\tTime (s): 0.0006638\n", "[3] Improvement: 3.6296182644623514\tTime (s): 0.0005722\n", "[4] Improvement: 1.9879748588534767\tTime (s): 0.0005398\n", "[5] Improvement: 1.2248102842011122\tTime (s): 0.0005431\n", "Total Improvement: 42.17816141320691\n", "Total Time (s): 0.0040\n" ] } ], "source": [ "X = numpy.random.normal(0, 1, size=(1000, 1))\n", "X[::2] += 1\n", "\n", "model1 = GeneralMixtureModel.from_samples(NormalDistribution, 2, X, max_iterations=5, init='first-k', verbose=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] Improvement: 27.26277360771337\tTime (s): 0.002283\n", "[2] Improvement: 8.072984397971823\tTime (s): 0.002548\n", "[3] Improvement: 3.6296182644625787\tTime (s): 0.002187\n", "[4] Improvement: 1.9879748588559778\tTime (s): 0.002138\n", "[5] Improvement: 1.2248102842013395\tTime (s): 0.002576\n", "Total Improvement: 42.17816141320509\n", "Total Time (s): 0.0160\n" ] } ], "source": [ "model2 = GeneralMixtureModel.from_samples(NormalDistribution2, 2, X, max_iterations=5, init='first-k', verbose=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-1528.71286897159, -1528.712868971989)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model1.log_probability(X).sum(), model2.log_probability(X).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! It looks like both for fitting a mixture model and performing inference that our new Python distribution is identical to the built-in one. Now, how much slower is it to use the Python object versus the Cython one?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.64 s ± 102 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "12.1 s ± 737 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "X = numpy.random.normal(0, 1, size=(300000, 1))\n", "X[::2] += 1\n", "\n", "%timeit GeneralMixtureModel.from_samples(NormalDistribution, 2, X, max_iterations=100, init='first-k')\n", "%timeit GeneralMixtureModel.from_samples(NormalDistribution2, 2, X, max_iterations=100, init='first-k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like it can be a fair bit slower. Another drawback of using a Python distibution is that it may be less efficient to do multi-threaded parallelism, because the Python object requires the GIL. However, many numpy operations will drop the GIL and so can actually be used with multi-threading, but that isn't guaranteed." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.6 s ± 7.76 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "12 s ± 720 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%timeit GeneralMixtureModel.from_samples(NormalDistribution, 2, X, max_iterations=100, init='first-k', n_jobs=2)\n", "%timeit GeneralMixtureModel.from_samples(NormalDistribution2, 2, X, max_iterations=100, init='first-k', n_jobs=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This distribution acts exactly like any other distribution. If we want to use a different distribution to model different features, we can use a mix of custom and built-in distrbutions with ease." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] Improvement: 23.311879238614438\tTime (s): 0.1243\n", "[2] Improvement: 6.699529281400373\tTime (s): 0.1171\n", "[3] Improvement: 3.0210554091190716\tTime (s): 0.1278\n", "[4] Improvement: 1.7027921401574986\tTime (s): 0.1284\n", "[5] Improvement: 1.0990971926314614\tTime (s): 0.128\n", "[6] Improvement: 0.7793100178469103\tTime (s): 0.1179\n", "[7] Improvement: 0.5912596644188852\tTime (s): 0.1501\n", "[8] Improvement: 0.468752338889999\tTime (s): 0.1177\n", "[9] Improvement: 0.3796901625521514\tTime (s): 0.1326\n", "[10] Improvement: 0.30861288860546665\tTime (s): 0.1318\n", "[11] Improvement: 0.24885399990603219\tTime (s): 0.1276\n", "[12] Improvement: 0.19804543157511034\tTime (s): 0.1194\n", "[13] Improvement: 0.15545944917175802\tTime (s): 0.1291\n", "[14] Improvement: 0.12065643278288007\tTime (s): 0.1178\n", "[15] Improvement: 0.09298774676676658\tTime (s): 0.1293\n", "Total Improvement: 39.1779813944388\n", "Total Time (s): 2.0407\n" ] } ], "source": [ "X = numpy.random.normal(0, 1, size=(500, 3))\n", "X[::2] += 1\n", "X[:,1] = numpy.abs(X[:,1])\n", "\n", "distributions = [NormalDistribution, ExponentialDistribution, NormalDistribution2]\n", "model = GeneralMixtureModel.from_samples(distributions, 2, X, verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Building a new distribution, the Student T Distribution\n", "\n", "There have been a few requests to add in the Student T distribution to pomegranate. This is, essentially, a version of the normal distribution that has a heavy tail to reduce the effect of outliers on the model. Now, instead of waiting for me to find time to add it, you can add it in yourself! Here is an example of what a custom student T distribution might look like." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "class StudentTDistribution():\n", " def __init__(self, mu, std, df=1.0):\n", " self.mu = mu\n", " self.std = std\n", " self.df = df\n", " self.parameters = (self.mu, self.std)\n", " self.d = 1\n", " self.summaries = numpy.zeros(3)\n", "\n", " def probability(self, X):\n", " return numpy.exp(self.log_probability(X))\n", " \n", " def log_probability(self, X):\n", " return scipy.stats.t.logpdf(X, self.df, self.mu, self.std)\n", "\n", " def summarize(self, X, w=None):\n", " if w is None:\n", " w = numpy.ones(X.shape[0])\n", "\n", " X = X.reshape(X.shape[0])\n", " self.summaries[0] += w.sum()\n", " self.summaries[1] += X.dot(w)\n", " self.summaries[2] += (X ** 2.).dot(w)\n", "\n", " def from_summaries(self, inertia=0.0):\n", " self.mu = self.summaries[1] / self.summaries[0]\n", " self.std = self.summaries[2] / self.summaries[0] - self.summaries[1] ** 2 / (self.summaries[0] ** 2)\n", " self.std = numpy.sqrt(self.std)\n", " self.parameters = (self.mu, self.std)\n", " self.clear_summaries()\n", "\n", " def clear_summaries(self, inertia=0.0):\n", " self.summaries = numpy.zeros(3)\n", "\n", " @classmethod\n", " def from_samples(cls, X, weights=None, df=1):\n", " d = StudentTDistribution(0, 0, df)\n", " d.summarize(X, weights)\n", " d.from_summaries()\n", " return d\n", "\n", " @classmethod\n", " def blank(cls):\n", " return StudentTDistribution(0, 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The primary difference between the Student T distribution and the normal distribution is the degree of freedom parameter that has to be set in advance---it is not meant to be learned from the data. The higher this parameter, the more like a normal distribution it is." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dn = NormalDistribution(0, 1)\n", "dt1 = StudentTDistribution(0, 1, 1)\n", "dt3 = StudentTDistribution(0, 1, 3)\n", "dt8 = StudentTDistribution(0, 1, 8)\n", "\n", "x = numpy.arange(-6, 6, 0.1)\n", "\n", "plt.figure(figsize=(7, 4))\n", "plt.plot(x, dn.probability(x), label=\"Normal\")\n", "plt.plot(x, dt1.probability(x), label=\"Student T, df=1\")\n", "plt.plot(x, dt3.probability(x), label=\"Student T, df=3\")\n", "plt.plot(x, dt8.probability(x), label=\"Student T, df=8\")\n", "plt.ylabel(\"Probability\", fontsize=12)\n", "plt.legend(fontsize=12)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's stick it in a mixture model now and see what the normal and the Student T versions look like." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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abA7LV199lY0bN2K1WunevTsPPfQQKSkppKenM2zYMPbu3Utubi4jR47kmWeeYdmyZRQUFPDggw/y7LPPcuGFF3b4n2Hq1Kl8/PHHGI1G9Hpl0oHPP/+cKVOmYLVaAUhPT2f+/PlERkZy7733sm7dOuLi4rj55psZPXo0dXV1TeJ5/vnnmT9/PpMnT25y/uTJk5strBscHMzixYuprKzEYrGQnp7Or3/96w4/z9kk6QnhBVn5VcSH6YkM1jk9LrV+rFtWfqV/Jb3iGib2j2/zuNhQHUEBGk6Wdu4SP74SERHBggULuPXWW4mNjWXEiBGMHj2aa665Bp1Ox5IlS1izZk2bpanMzExefvllPvnkE+Li4njssccc+9auXUtWVhYfffQRWq2WVatW8eSTTzoWos3JyeGdd96hpqaGKVOm8MMPP3Dfffexfv16nn/+eUeC7ajo6GiGDx/OV199xdSpU9m9ezd9+/YlIiKC0tLSJsdecsklzJkzh4ULF3LhhRei0+n44x//iFqtdjke+8K6y5Ytw2w2M2PGDJ566ilGjBhBZWUls2fPpl+/fgwbNsyt57KTpCeEF/xcUOUYvO2Mvfrz54IqrkhrO8l0hmqjmaIqo6O9zhmVSkXP6CBySrxc0hs2t1kpzOSjRWRvueUWbrjhBnbt2sWuXbv45z//yT//+U8+/vhjx9JobdmxYwdjx44lLi4OgNmzZ/Ptt98C8PXXX7N//35mzZoFKCvUNC5ZTpgwAbVaTWhoKCkpKZSXl3v4CZUlkz755BOmTp3K2rVruf7661ttn7v77ruZO3cu77//PuvXr0etbn9XEfvCuidOnCAnJ4fHH3/ccR2DwcChQ4ck6Qnhz7KLq5kypO218qJCdEQGB3CiuLrNYzuLPYG11XPTLjk6mJPeTnp+Ys+ePezbt49bb72VCRMmMGHCBO6//36mT5/O9u3bHdV3jdlsNsfPJpMJUD4sNN5uX1AblCR36623Mm/ePMc5+fkNy1DZ17dr6TqeMnHiRBYvXkxubi67du3iiSeeaDXpVVZWUlRUhEqlIjs7u9USbuM46+qaTsgQHKy81iwWC+Hh4Xz44YeODzRFRUUuf5hwhfTeFMLDymvqKK2po3dMiEvHp8SEcKLIf5LGqfqqyrZ6btr1iFKSnjf++Pqb6OhoXn31VXbv3u3YVlhYSG1traNdzr5gtv14e7LIyMigsLAQgEsvvZTt27eTl5cHwH//+1/H9caNG8fHH39MVZUy/nHZsmU8+uijbcbW+L7u0ul0TJo0iT//+c9ceeWVra7DB/DII49w7bXXsmTJEh588EHH4t+t/Tvk5ORw5MiRFq/Vu3dv9Ho9n332GaCs8zdt2jSP9gKVkp4QHpZdopTaXC0p9YoJZk92adsHdpIzZUrS6+5iG2NydDDVJgsl1SZizvPVFnr37s0rr7zCiy++SF5eHnq9nrCwMBYvXkyfPn0AZVm1efPmsXz5ch588EGeeOIJVq1axaBBgxw9PtPS0liwYAE333wzISEhDB061HGPG264gfz8fG688UZUKhVJSUnNVqxpyVVXXcV9993H008/zbhx49x+1hkzZjBv3jwWLVrU6jHvvvsueXl5vPTSSwQEBDBu3DgWLVrEP/7xjybx/OEPf+Dhhx9my5Yt9OnTx1GdeTadTsfy5ctZvHgxK1euxGw2c++99zJy5Ei3n8ehHev6+SVZRLb9utLz+uJZ1+47ZUt56FPbkbwKl45/YeMRW++HP7UZ6sxu39sTz/vXzw7ZLnj0fzar1erS8RsO5NpSHvrU9uPJUrfvbSeLyDbVlZ7VZvPuIrJSvSmEh2XXd993pcs/KCU9q62hWtHXTpfV0j0yyOVxg/Zep2fKDN4MSwiPkKQnhIedKK4mKaLtgd12vWKVtr9sP+nMcqaslm6RLa+h15KkCOXY3HL/SNpCOCNJTwgPyy6ucbk9D6BXfYeX437SmeV0aa3L7XkA0SE69Fq1oy3QU2xdoGOM6LiOvj4k6QnhYdnF1aREu9ZzEyAqOICwQC0ninxf0jOaLRRUGts1UF6lUtEtMogz5Z6r3tRoNM26tQvRWF1dndNepa2RpCeEB9WYzBRVmVwa2G2nUqnoGRXsF5M255cbAdd7btolRQSS68GSXmRkJPn5+Y5pr4RozGq1kp+fT0RERLvPlSELQniQvYqvtYVjW9M9Ksj781e64HQ7hyvYJUUEsf3nIo/FERsby6lTp1odzwXKJ/2AgACP3dOfdaVnBdeeNyQkhNjY2HZfW5KeEB50srSDSS8yiB3Hin2+2oI96bV3HtDukYEUVBowW6xoNe5XIKnVapKTk50ek5mZyYABA9y+17mgKz0rePd5pXpTCA9q72wmdt0jg6gymqkweGZGjY7Kr1Da5RIjXO+9CZAUGYTVBvmVRm+EJYTHSNITwoNOldag06iJa+fMJN3rS4anfTxWL7/CQERQgMvDLezswxY83YNTCE+TpCeEB50uraV7VBBqdfuqKO3Viad9nDTyKwwkhLd/KrGGAeqS9IR/k6QnhAedaucYN7vufpI0CiqNJIS3r2oTICFMOadQqjeFn5OkJ4QHnSqtbXcnFoCYEB06rdrnJb2CCiPxYe1PeuFBWvRataNNUAh/JUlPCA8x1FkoqjJ2KOmp1Sq6Rwb5NOlZrTYKKg3Ed6B6U6VSkRAeSIGU9ISfk6QnhIec6WB3f7vukUE+7chSWmOizmIjIaxjywMlhOulpCf8niQ9ITwkr4Pd/e26RQb6tE3PXkrrSJseQHxYIAUVUtIT/k2SnhAe4hjj1sGk0T0ymIJKI0azxZNhucwef0eqN+3nSfWm8HeS9ITwkLz6eSvdKekp1/FNFaG9lNaRjiyglBCrjGaqjL4dYC+EM5L0hPCQ/AoDYYFagnUdm93P1wPU3S3p2cf3FUi7nvBjkvSE8JC8ckOHqzYBekQqU5ed8lG7XkGlkajgAPTa9s3GYmcvIeZLu57wY5L0hPCQvApDh6s2QakWVal8N0A9v8LQ4apNaFTSq5SSnvBfkvSE8BBlCq+OJw2dVk18mN531ZuVxg5XbQLE1z+79OAU/kySnhAeYLHaKKg0ulW9CdSvQO6j6k03k3aYXktQgEbG6gm/JklPCA8orjJisdpIcKN6E3w3QN1qtVFYaezQZNN2KpWK+HC9LC8k/JokPSE8IM/NMXp23SODOFNmwGq1eSIsl5XUmDBbbW616YEy8bT03hT+zGtJz2q18thjjzF79mzS09PJzs5udkxJSQlXX301RqPyydBmszF+/HjS09NJT0/nhRde8FZ4QniUfWydJ6o3TRYrJTUmT4TlMnuVpDslPZAB6sL/dWxAkQs2bdqEyWRi1apVZGRksHTpUl599VXH/m3btvHCCy9QWFjo2JaTk8OgQYN47bXXvBWWEF5hL+klRLiXNOy9P/PKDcS2cyFad9gTVbybSTshPJDNhwuw2WyoVO1bU1CIzuC1kt6ePXsYP348AMOGDePAgQNNb6xWs2LFCiIjIx3bDh48SH5+Punp6dx222388ssv3gpPCI/KKzegVauIDXEvUdlXIM/t5FlZ7FWS8R2cbNouPkxPjckis7IIv+W1kl5VVRWhoaGO3zUaDWazGa1WueXYsWObnRMXF8ftt9/OlClT2L17NwsWLGD16tVO72M0GsnMzHQrVoPB4PY1ziVd6Xk761mPnCwgKkjNkSOH3bpOZa2SLDKOHKeHqqTd53f0eQ8cKwWg+PQJKvM6XkIzV1UCsDPjED0idB2+jivkdXz+8ubzei3phYaGUl1d7fjdarU6El5rBg8ejEajzAYxatQoCgraribR6/UMGDDArVgzMzPdvsa5pCs9b2c9q2F7OT1itG7fy2q1EbD6JLagSAYM6N/u8zv6vNbD+4kOqebCwQPbfW5jpboi2FZISFx3BvSNdetabZHX8fnL3ed1ljC9Vr05YsQItm7dCkBGRgapqaltnvPyyy+zcuVKAA4fPkxSUpK0C4hzgrtTkNmp1cpirJ1evVlpdLtqExqWJZIB6sJfea2kN2nSJLZv386cOXOw2Wz87W9/Y8WKFSQnJzNx4sQWz7n99ttZsGABW7ZsQaPRsGTJEm+FJ4RH5VcYGX9BnEeulRQRSG4nD1AvqDC43YkFGtoEZYC68FdeS3pqtZrFixc32da3b99mx23evNnxc0REBG+88Ya3QhLCK+zL6bgz72ZjiRFB7D9V5pFruSq/wkhqQpjb1wnVawnWaWTYgvBbMjhdCDd5aoyenVLSM2Czdc4AdYvVRmGV0a0pyOxUKqV6Nk9KesJPSdITwk0NA7s9VNILD8RotlJWU+eR67WlpNqExWpza7LpxuLC9BRKSU/4KUl6QrjJUdLzUPVmZ4/Vcywe6+YUZHbxYXqKJOkJPyVJTwg3eWreTTvHrCwVndOZxb7+nbtTkNnFhclUZMJ/SdITwk35FQbCA7UE6Tq24vjZkiKCgM4s6SkJylPVs3FheqqMZmpMMiuL8D+S9IRwU165eyumny0uTI9GrSK3rHOSnn1Mnafm+rRXk0q7nvBHkvSEcJO7K6afTaNWkRCm77ySXqWBmBAdOq1n/hzE1Y/Vk6Qn/JEkPSHclFfhmdlYGkuMCOy8Nj0PDUy3iwuVpCf8lyQ9IdxgtlgprDR6tHoTlHa9zirpFbi5YvrZ7EMfpDOL8EeS9IRwQ1GVCavNc51A7BIjAsnrpAHq+RUGj8y7aRcdrEOjVklJT/glSXpCuMHTwxXskiICqTFZqDB4twekxWqjsNIzs7HYqdUqYkN1kvSEX5KkJ4QbPD0w3a7xCureVFxlxGpzf8X0sylj9WQqMuF/JOkJ4QZPT0FmZ5+V5YyXV1uwt7t5snoTlM4shVVS0hP+R5KeEG7IqzAQoFERE+LZVcLtA9S9XdLzVtKODwuUNfWEX3Ip6W3evLnTZnwX4lySX24gPiwQtdqzix3HhelRq7w/K0vDbCweLumF6Smun8haCH/iUtL7z3/+w8SJE1m+fDmFhYXejkmIc0ZehaFjCaOuFna8Au9cD9++CMbKJrsDNGriwvTkebl6M7/CgErludlY7OLC9FisNkprTB69rhDucinpvfXWW7z99tvU1NRwww03cO+997Jjxw5vxyaE38ur6MAUZFYrfHQLbHgEio/BpifgvTlQ17RUl9gJY/UKKo3EhOgI0DT6U2CzQfZ3sH0ZnNnXoeva2wililP4G5fb9JKTk7nvvvv4y1/+woEDB7j//vuZPn06u3fv9mZ8Qvi1/PIOTEG2/UXI+hwmPwP/9xPM/BdkfwtfPNzksKTwQK+36RVUGJouKWSzwcaFsGIKfPkYvHEFfL2k3dd1TEUmnVmEn3Ep6WVnZ/Pcc89x+eWX8+GHH/LII4+wfft2nnzySf785z97O0Yh/FKloY5qk6V9Y/RqSuDbf0D/aTD6DmXb0Bvgkrtgz9uQ+6PjUPsAdW/KrzyrenbHK7DjZbjoNrjvIAydDVuWQtaGdl3XnkgLZAV14WdcSno33HADZrOZ//znP7z55ptMnDgRtVrNiBEjuPjii70doxB+yd7zsV3VmztfBWMFTHgEVI06v1z+ZwiOhg2POjYlRQRSaTRTafDeCur5FcaGkl5pNnz9V0idAlOfg4geMP0lSBwC6+4Bs+ulttgwpTerlPSEv3Ep6S1atIi//OUv9OrVy7Ft7dq1ACxdutQrgQnh7/LK27kOndkEu/6llPISBjXdFxQJ4+6DE9vg9F7A+wPUzRYrxVWN5t3c9ASgUhKePSEHBMKkp6AqDzLec/nawTotoXqtzMoi/I7W2c7NmzdjNptZtmwZgYGBjmELZrOZF198keuuu65TghTCH7V7CrKjG6G2BEbc3PL+ETfBN88o1Yu/fotukQ2LyV6QEOaJkJsorjY1zMZS9DMc/C+MvRciezY9sM8V0G0EbP+HEqPatcVy42UFdeGHnCa9zMxMdu7cSXFxMf/+978bTtJq+f3vf+/14ITwZ+2u3vzxfQiJg75Xtrw/MAJG3qxUgV69hMRwJdF5q6TXZGD6d8+DVg9j7mp+oEqlbF/9e8jeDr0vc+n6sWF6KekJv+M06d11113cddddvPvuuxCdCxQAACAASURBVMyfP7+zYhLinJBXbiAiKIDAABdKPoZypTPIxbeBxsnbbsRNSklv/0ckXPxHwHsD1O0D05N0tfDTh3DhHAiNb/ngtKmgC4OfVrmc9OLD9Bw6U+GpcIXwCKdJ75NPPmHGjBkYjUZWrFjRbP8tt9zitcCE8He55e1YPPbnr8BaBwOmOz8uLk2pSsx4D92Yu4gN1XttMVn7hNDJJz8BswFGOam90QXDwGvh0DqY+jwEBLV5/Tip3hR+yGnSy87OBuDo0aOdEowQ55L8CgMJrlZtZn0BQdHQw4XezsPmwf8ehLyfSIoI9GpJT6WyEXbwXeg+CpKGOj9hyK8h41049jX0n9rm9ePC9FQZzdSYzATrnP6pEaLTOH0l3nPPPQAsWdL+walCnO/yKgwMSHKhg4nFrHRiSZ3svGrTbvAsZbaWjPdJjPg1J0tq3A+2BQUVBi4PzkFVdEQZmtCWlHFKFWfWFy4lPftQiMJKIykxkvSEf3D6Spw+3XlVzPr16z0ajBDnijqLlaIqo2vVm6f3QG0ppF7t2sWDo5UEuf8juqfO4Yfj3inpFVQamaX9FlSBMMiFnthaHfS7UmmbtFpB7XzEk2NWlkojKTEhnghZCLc5TXqLFi3qrDiEOKcUVhqx2XCtevOXbwAV9L7c9RsMmweZ6xht3svbtQleqSIsLq/kirptMGCK0nPUFamT4dAnkPcjdBvu9NC40IakJ4S/cPpRLSYmhosvvpiQkJAWv4Toqto1Ru+XbyDpQqUE56p+V0FwDENLvgC804Ozb/n3hFkrYOgc10+64FfK9583tXlofP2gd+nMIvyJ04+Ozz77LK+//jp33313s30qlYqvvvrKa4EJ4c/yy10co2esglM/wJg/te8GmgAYPIukPSsJ4wbyyg30jQvtYLTNmS1WrqrbTE1gFMH9Jrp+YkgsxA+C49vgsgVOD40O1qFRq6SkJ/yK06T3+uuvA8rMLEKIBi6X9LK/A6tZmdWkvYbORv3DG0zW/EBu+Zj2n+9EcVEhE9X7ON7tBvprAtp3cu/xsGelMhentvV1+NRqFbGhOsfQCCH8gUtzb9bU1PDCCy8wc+ZMZs+ezSuvvILJJItDiq4rr8KATqMmOkTn/MCc70CthZ6j23+T7iOxRvXmOvV2jy8ma/ppDXpVHeUXzGr/yb3GgblW6aDThjiZlUX4GZeS3pNPPkleXh4LFizg//7v/zh69ChPP/20t2MTwm/llxuID9ejarxSQktydkLSMGVwd3upVKiHzmaM5hDVRSc7FmgrQo98zM/WbgSnjGr/ySljAZVSxdmGuFC9rLQg/IpLSe/QoUM899xzjBkzhjFjxvDCCy+wd+9eb8cmhN/Kq3BhNpY6g1IaSr6k4zcaeiNqbPTO/bzj1zhbaTZRRbtZYxlHfHtXfQelQ07iYGVFiDbEhwXK6unCr7iU9CIiIigrK3P8XlNTQ1iY52d9F+JckV9hbHu4Qm4GWEyQ7EZ7XExfjun6M7Liy45f42w/rQLgU9s4YkNbb5NzqtdlcPIHJbE7ERemp7jahMVq69h9hPAwpx1Z7FWYWq2WmTNn8qtf/Qq1Ws3mzZvp169fpwQohL+x2WzklRu4sn8rkzPb5exQvrtT0gMy4yYz7fQ/IP8QJAx061pYrbDvHbKCR2AJ7IlG3Ub1bGt6j4edr8CpXcrPrYgP12Ox2iitMXU8wQrhQU5LepGRkURGRjJq1ChmzpxJaGgowcHBTJs2jf79+3dWjEL4lQqDmdo6S9vVmznfQ8wFSjd/NxQkX4PZpsb84yq3rgPAL19DWQ4b9Fe3b8X3s6VcCip1m1Wc9gHqUsUp/IXTkt6f/tT62KKaGu/MByiEv3OsQ+csaVitcHKnskq6myJiu7HVOpTLfloFVz3m8iKuLdq7EoKi+dQ0kn5xbiS9wAhlwP2Jb50e5piKTDqzCD/hUpvepk2buPbaa7nqqquYOHEiEyZMYOzYsd6OTQi/ZF/U1WlJryhLmW/TzapNgKSIQD62XIa2KldZoqijqgrh8P+wXTiHnAoXSqptSRmrdNQxt57Q7JNOF1TIWD3hH1xKes8++yx33nknSUlJPP7444wfP545c5xPXWS1WnnssceYPXs26enpjmWKGispKeHqq6/GaFTeNAaDgbvvvpt58+Zx2223UVJS0oFHEsK7XBqY7mjPc39QeWJEIF9aR2HQx8Dutzp+oR/fA2sdVYN+Q22dhSR3qjdBSehmA5zJaPWQ2DBlHKOU9IS/cCnpBQUFMXXqVIYNG4Zer+eJJ55g586dTs/ZtGkTJpOJVatW8cADD7B06dIm+7dt28bvfvc7CgsLHdvef/99UlNTee+997juuutYvnx5Bx5JCO+yT0Fmn1uyRTk7ISQOovu4fb/EiEDq0HIwYQYc3QClzT9AtslqVWZR6XkJZwKSHdd1S8/6Uqw9wbcgWKclVK+VAerCb7iU9HQ6HSaTieTkZDIzM1Gr1W3OyLJnzx7Gj1d6dQ0bNowDBw40vbFazYoVK4iMjGzxnMsuu4wdO1p/MwnhK3kVBqKCAwgMcNK2lrNDKQm1NXjdBcE6LRFBAXwdNk3pPPL9a+2/SNbnUHIMLr6N3PrZXdwu6YXGQUw/JcE7ES8rqAs/4tJaJRMnTuT2229n6dKlzJkzhz179jRJVi2pqqoiNLRhglyNRoPZbEarVW7ZUptgVVWVY/xfSEgIlZWVbcZmNBrJzMx05TFaZTAY3L7GuaQrPa83nvXn00VE6lWtXldbU8AFZdnk97qOEg/dOypQxe4CNeU9ryJs9wqOJl2HVRfe7LgWn9dmI+WrJWhDkjimTmPf4eMAVBWcJrMm3624ksIHEHpiC0cPHVQScguCNRay80s9/v8gr+Pzlzef16Wkd+edd3LttdeSmJjI8uXL2bVrF9OmOe+VFhoaSnV1teN3q9XqSHiunFNdXU14ePM39dn0ej0DBgxw4Slal5mZ6fY1ziVd6Xm98azVm4pIiQ9p/boHlDdrwkUzSOjumXv33llJYZWRiCmL4LUvSCveBFc+2uy4Fp/32NdQvB+mPMeAQUP44kwWKlURl44YRIDGpcqe1hmmwPH1DIjVQHzLw5h67avl4JkKj/8/yOv4/OXu8zpLmC6/4o8dO8YzzzzD559/zsCBA4mJiXF6/IgRI9i6dSsAGRkZpKamtnmPESNGsGXLFgC2bt3KyJEjXQ1PiE6TV97Giuk5OyEgGBKHeuyeiRFBSq/RxMEw6HrY8QpUFbR9otUKmx6HiGQYeTOg9D6NDdW7n/CgoXdqznetHiKTTgt/4tKr/rXXXmPJkiUEBgai0WhYuHAh7777rtNzJk2ahE6nY86cOSxZsoS//OUvrFixwukafHPnzuXo0aPMnTuXVatWOR0nKIQv1FmsFFcbSXCW9E7uhB6jlDXxPCQpIpCiKhNGswUmLFR6TX71ZNsn/vge5P4IEx5xLAOUV2Fwvz3PLroPhMQ7bdeLC9NTZTRTYzJ75p5CuMGl6s1PP/2UDz/80NFGd8sttzBv3jzmz5/f6jlqtZrFixc32da3b99mxzVeqy8oKIiXXnrJpcCF8IWCSiM2m5Oej8ZKyNsP4x/06H3t9yuoMNIzth9cejds/wcMngV9r2z5pIpc2PAIJF8KQ2c7NueVG0iJ6cCqDy1RqZTSnpMenPaxeoWVRlJiXPqTI4TXuFTS0+v1hISEOH6PiIhAr5d59ETX0+bA9FO7wGb1yKD0xuwls9z6+3PFw8oUZ2tub3kIQ10tfHgTmE0w42VQN7zVc8trPVfSA2UsYlkOlJ9ucbdjVhap4hR+wGnS27hxIxs3bqR379788Y9/ZPPmzWzZsoX77ruPwYMHd1aMQvgNe3f/Vkt6OTuVXow9LvLofRuSXv1isgFBMOddZRWHd66DgsMNB9eUwAfzlAQ883WIaahhqTaaqTCYSYwI8lxwKfUD8E+2XMVpn39Tkp7wB07rGt55550mv69YscLxc3FxsXciEsKP5ZYpJa1urSWNnB2QMBgC2+553B72JGUvaQIQlwbzPoJV8+H1yyBtMkm1Flj3nVLNeu1LMHBGk+vYZ5PxaEkvYQgEhED2DqW69Sz2QfwyVk/4g3YlPbPZjM1mIyDAcw30QpxLzpTXEqzTEB7UwlvHUgen9sDw33j8vqF6LWF6bUP1pl3yaLhjG2x7AQ5/SqipFvpeDuPug27Dm13HUT3ryaSn0ULPi1rtzBIdrEOrVjkm6hbCl1xq0ysuLubWW29l2LBhDB06lJtuuon8fPcGtQpxLsotM9AtMghVSzOt5O2HumolEXlBUmRgQ/VmY+FJcM3z8MBhjl73Bdz47xYTHrg4WXZHJI+B/ANgKG+2S61WkRAe2LSUKoSPuJT0Fi9ezLBhw/juu+/47rvvGDVqFE888YSXQxPC/zjtBGIv6fT0bCcWO8dYPTc4Jsv2ZEkP6jvu2ODkrhZ3J0YEOu4thC+5lPROnDjBn/70J8LDw4mKiuKee+4hJyfH27EJ4XfOlBtab887uRMikyGiu1funRQe2Lx6s51yy2vbnje0I3pcBCpNq4PUE6WkJ/yES0nPbDY7lv8BqK2tbbl6R4jzmMlspajKSFJkC6Ukm00p6XlgKaHWJEYEUlhlpM5i7fA18soNzgfWd5QuRFlUtpV2vYRwpaRns9k8f28h2sGlkaJTp07lt7/9LTNnzgRgzZo1XH311V4NTAh/k19hwGZrpedm6XGoyoee3mnPA6XHpc2mxNEjqmODy0+V1tIjyoPDFRpLHgO731QWldU2HcebFBFIjclChcFMRJB0hBO+41LSu+uuu0hMTGTbtm1YrVZmzpzJr3/9a2/HJoRfOVNWvyRPSyU9ewnHiyU9e6I7WVLboaRns9k4XVrLJX2cz5vbYcmXwM5XlGnPel7cZFdCfRtifoVBkp7wKZeS3s0338zKlSuZNav5GBwhugp7e1pSSyW9nJ0QGAFxLa804AnJ0fakV8OYvu1PXBW1ZiqNZi+W9BotKntW0ms8o0xqQph37i+EC1xq06usrKSmpsbbsQjh187UDxfo1lpJr+clTab78rRukYFo1CpySjr2XjxZqpzX0arRNoXGK4vKZjefh9M+RCJfOrMIH3OppBcUFMSECRNIS0sjOLjhDfPaax1YwVmIc1RumVI1F6w7621TXQxFR+DC2S2f6CFajZrukUEdTnqnHEnPSyU9UEp7hz9TljRq9AHAPiuLDFsQvtZm0svKymLixImMGzeOxMTEzohJCL90pqyVMXonv1e+e7E9zy45OtiNpKeUVHt6q6QHyr/Bvv9AUVaTRWX1Wg0xITq3h1wI4S6nSW/16tU888wzpKSkkJOTw/PPP8/48eM7KzYh/MqZcmU2lmZyvgONrtVZUDypZ3QwGw/mdejcU6W1hOm1LU+h5in2xJ+zo9lK6gnhgTIVmfA5pw0Q77zzDuvXr+ejjz7itdde45///GdnxSWE32l1NpZftihDFQK8WG1YLzk6mOJqE1XG9i/Ieqq0hu5RrUyh5inRfSAkrsX19ZIi3B9cL4S72mx1T0hIAGD48OGUlpZ6PSAh/FGtyUJZTV3zkl5NiTLnZu/LOiWOxj042+tUaS09o71YtQnKorK9xsHxrcqA/UYSIqSkJ3zPadI7+xOhRuPhqYuEOEfYe242K+md+BawQe/LOyUOe9LLLm5f0rPZbOSU1Hi3E4tdnyugMheKjjbZnBQeSEm1CUOdxfsxCNGKdvWvlqnHRFdlX0ev2Ri941uUteS6j+iUOHrFKknveFF1u84rrDRSY7LQOzbEG2E1Zf8A8Ms3TTbbB6gXVMi6esJ3nLZoHzlyhBEjGt7MBoOBESNGYLPZUKlU7N271+sBCuEPWh2jd3wrpFwKms6ZZSQsMIC4MD3Hi6radZ49SfaK6YSkF90bIlOUpDf6dsfmxqu/J8d4uZpViFY4TXpffvllZ8UhhF+zl/SaLMlTkat0zR9xU6fG0js2hF8K21fSO1HciUkPlCrOg/8Fi1lZZJaGAeoyVk/4ktOk1727d5ZIEeJck1teS2yoDr22Ubv28a3K907qxGLXNy6EDQfbt4jz8aIaAjSqlmeT8YY+V8DelXBmn7KqOg3Vm7LEkPAl782ZJMR55GRpTfOej8e3QlAUJAzp1Fh6x4ZQUm2irMbk8jkniqrpGR2MVtNJb3n7B4Hj3zg2hem1hOg0UtITPiVJTwgX5JTUNJ3JxGZT2qx6jffqfJst6RMbCsAv7ejMcqK4mt6dVbUJEBILiUOUMYz1VCqVDFsQPidJT4g2mC1WzpQZHMMFACg4BBWnoN9VnR5P7zgleR13sV3PZrNxoriaXp3Rc7OxPlcoE3GbGuKUAerC1yTpCdGG3HIDFqutadLL2qB8v+BXnR5PcnQwWrWKnwtd68GZV2HAUGft/KTXdyJY65oMXUgMD3J0ChLCFyTpCdEG+wTPTdr0jm6ExKEQntTp8QRo1PSNCyUrr9Kl44/UH5caH+rNsJpLGQv6cMj6wrGpe1QQ+ZUGTGZr58YiRD1JekK0oSHp1Q9MrylRVlZIvdpnMfVPCuOwi0kvK78+6XX24q1aHfS9UikVW5Uk1yMyCJtNenAK35GkJ0QbTpbUoFWrGmZjObYZbFa4wHdJLy0xjNNltZTX1rV57JG8KuLD9ESF6DohsrOkTYGqfMjdByglPYBTZbIotfANSXpCtME+Z6VGXT8NX9YGCI7ptKnHWjIgMVwJJb/t0t6R/ArSEju5lGfXbxKo1I420O71E3afrl/bT4jOJklPiDacLGk0Rs9qgZ83KX/M1b6bgL1/kpLEDudWOD3OYrVxNL+KtM6u2rQLiYEeF8ORzwFIqh8cf7pMkp7wDUl6QrQhp3HSO/kD1JZAauf32mwsMTyQiKAAMtto18spqcFotpLqq5IeQNpkyPsJyk+j12qID9NLSU/4jCQ9IZyoNNRRWlPXMFzh0FrQ6H0yVKExlUrFwKRwDpwud3rcoTNKSbC/L5Ne6hTle5ZS2useFSQlPeEzkvSEcOJkifLHOTk6WKnaPLgWLpgEeh8mkXrDkyM5dKbC6fp0GSdL0WnV9K9vA/SJuDSITVX+7VDa9STpCV+RpCeEE47hClHByuwiVXkw6HofR6UYkRyF2Wrjp1Otl/b25pQxpHsEOq0P3+oqFQyepSy4W3GG7lFBnCmrxWq1tX2uEB4mSU8IJ06VKkkvOToYDq4BbRCkTvZxVIrhyZEA7M0pbXG/yWxl/+lyhveM7MywWjZ4FmCDg2vpGRVMncVGfqWM1ROdT5KeEE7klNQQHqglIlANhz5ROrDoO3lmk1bEhOrpFRPM3uyWk15mbgUms5URKVGdHFkLYi9QZrA58DEp9QvIZhfLWD3R+STpCeFETkmNssp39naoLvSbqk27ESlR7M4uxdJCVeGe+mRoLxH63OBZcHoPfTSFQEPVsRCdSZKeEE44lhTa/zEEBPt0FpaWXJ4aR0m1iYyTZc32fX2kgN6xIQ0zyfja4JkAJJ78DI1aRY6U9IQPSNITohVWq41TpbX0iQAOrIaB14EuuM3zOtMVqfFo1So2ZTZdSb3SUMfOX4qZNDDBR5G1IDIZeo5Gc+BjukXopaQnfMJrSc9qtfLYY48xe/Zs0tPTyc7ObrL/ww8/ZObMmdx44418/fXXAJSVlTF69GjS09NJT09n5cqV3gpPiDadLqvFZLZymWkbmKpg5M2+DqmZiOAALu4dzaZDTZPelqxC6iw2/0p6AMPmQ+FhrgrNlqQnfELrrQtv2rQJk8nEqlWryMjIYOnSpbz66qsAFBYW8s4777B69WqMRiPz5s1j7NixHDp0iGnTprFo0SJvhSWEy+wrkw/MXQuxadBztI8jatmvBibwxPpD/HiyDPuU0p/+mEt0iI4RyX7QiaWxwbNgw6NMr/uCTyp7+joa0QV5Lent2bOH8ePHAzBs2DAOHDjg2PfTTz8xfPhwdDodOp2O5ORkDh8+zIEDBzh48CC/+c1viI6OZuHChcTHxzu9j9FoJDMz061YDQaD29c4l3Sl53XnWXdklpOqOklY0T7yh91DyeHDHo7OMwaHWgnVqVmybh8PXRrJZ9sz+OJgHrOHRJJ1xP9iTuw5iaG/rMdSO4vdPx4kRNexCid5HZ+/vPm8Xkt6VVVVhIY2dO3WaDSYzWa0Wi1VVVWEhTXMaBESEkJVVRV9+vRh8ODBXHrppaxbt46nn36al156yel99Ho9AwYMcCvWzMxMt69xLulKz+vOs7535AA36b/Bpg4gYdL/kRAS4+HoPOf2Ih1//zKLjceD2F1gJSxQyyPXX0xEcICvQ2su6n54bQ2zNNsIjJ3MgO4RHbqMvI7PX+4+r7OE6bU2vdDQUKqrqx2/W61WtFpti/uqq6sJCwvjkksuYfRopQpp0qRJHDp0yFvhCdGm0wVFzFBtQzVgmrJagB/73bjejO4dzas/FPPjyXIemzbQPxMeQOIQauKHM0/zFSeKqnwdjehivJb0RowYwdatWwHIyMggNTXVsW/o0KHs2bMHo9FIZWUlx44dIzU1lYULF7Jhg7Lu1o4dOxg0aJC3whOiTYMK1hNmq4KL7/B1KG0K1Wt577ZLWDA+jo33XcYNo/y7vUx78e/ppz6D6eg3vg5FdDFeq96cNGkS27dvZ86cOdhsNv72t7+xYsUKkpOTmThxIunp6cybNw+bzcZ9992HXq/ngQce4JFHHuH9998nKCiIp59+2lvhCeFUjcHAjaZPyI0cSlLKGF+H4xKNWsWVfcLoFRvi61DapLvwBoo+XcSg428C830djuhCvJb01Go1ixcvbrKtb9++jp9vvPFGbrzxxib7e/bsyTvvvOOtkIRwWfEPH9FTXciegY+T5OtgzkcBgXwZMYu55f+C03t9ugq96FpkcLoQZ7PZCN2znGPWJCKGz/B1NOetX1JupMIWgu3bF30diuhCJOkJcbZfviaq/BBvWafTK9Y/Jpc+H3VLiGelZRJkrofCLF+HI7oISXpCNGa1wqYnKdLEsz92MlqNvEW8pU9cKG+br8aq0cPW53wdjugi5B0tRGOH1kJuBstVs+mTGO3raM5rfWJDKCaCwynzYf+HcCbD1yGJLkCSnhB2ljr4ajGWuAG8XTWa1MSwts8RHdY9MohgnYZ1YbMhOAa+XAQ2WU1deJckPSHs9rwNpcf55cIHsaImLUGSnjep1SouSAjjp0IbXP4QHN8KR7/0dVjiPCdJTwiA6iL4+q+QMo4ftKMASJWk53X9E8LIyq+EkbdAdB/YuBDMJl+HJc5jkvSEANjwCBir4JoXOHCmkvBALT2i/GTx1fNYamIYxdUmigw2uPpvUHQEti/zdVjiPCZJT4hjm+GnVTDu/yC+PwfPlDO4ewQqlcrXkZ337FXIR/IqIW0KDLoetj4LhUd8HJk4X0nSE12bqRo+vQ+i+8L4B6mzWDmcW8ngDs78L9onLbFR0gOY8iwEBMO6e5ThI0J4mCQ90bV9/mcozYZrX4KAQI7mV2GyWBnULdzXkXUJsaE6okN0ZOZWKBtC42HyEji5E3b8P98GJ85LkvRE1/XTR7DvPzD+Aeg1DoCDZ8oBpKTXSVQqFYO6hXPgTEXDxgvnwoBrYdOTkPO974IT5yVJeqJrKj6mVGv2vASu+Itj8/7T5YToNPSO8f+VCs4XQ3tEcDS/EkOdRdmgUsGMlyGyJ3z8O6gp8W2A4rwiSU90PbVl8P5cUGtg1j9B07DYyN6cUi7sGYlaLZ1YOsuQ7hGYrbaGKk6AwAi44W2oLoDVv1cmDhDCAyTpia7FUgcf3Qwlx2D2fyAy2bGrxmQmM7eSEclRPgyw67FXJR84Xd50R7fhcM0LSu/azx6Q2VqER3htPT0h/I7NBp/dD798AzNegd7jm+z+8WQ5FquNkSmS9DpT98ggooID2H920gMYcROUnoBtL0BUitL+KoQbJOmJrsFmg88fgr3/hvEPwvDfNDtkb04pAMOTIzs7ui5NpVIxtEckGSfLWj7gykVQlgNfLQZ9OFx8W+cGKM4rUr0pzn82G2x4FH54Hcb8Ca5c2OJhu0+U0CcuhMhgXScHKEalRJGVX0V5TQttdyoVzFgOadfA/x6E79/o/ADFeUOSnji/Wc2w/h7Y+QqMvhN+9bTyR/QsZouVXSdKGd07xgdBilG9lGWc9uS00lNTq1M6tvSfBp8vgG9flDY+0SGS9MT5y1hFz28XNFRpTl7aYsID+PFUOVVGM2P7SdLzhWE9IwnQqPjheGnrB9kT3+BZsOkJEvc8CxZzp8Uozg/SpifOT8XHYFU6IYWZMO0fMOoWp4d/93MRAGP6SNLzhSCdhsHdI9h9oo0xeZoAmPkviEwh6tu/w7tlMOtNCJH/N+EaKemJ88+hdfDGFVCZy8nLXmwz4QFsP1bEgKRwYkL13o9PtOjiXtH8eKqMGlMbpTe1Gq56nDMXPQrZ38Hr42XmFuEySXri/GEoh7V/hA/TIfYCuGMr1Ymj2zyt0lDHnuxSxknVpk+NvyCOOouNnb8Uu3R8eZ/pcOuXoNHBiimw+a+yFp9okyQ9ce6z2eDwZ7D8UvjxA6X97pYvlGmsXLAlq5A6i41JAxO9HKhwZlSvKIICNGw5Uuj6SUkXwh1bYOiNypJEb1wBp3Z7LUZx7pOkJ85tBZnwznXwwTzQh8Hvv4SJi5RODy768lA+0SE6GZTuY4EBGsb0jWFLVjuSHihTll3/Gsz7EGpL4F8TYc0dUH7aO4GKc5okPXFuKj+tTE316lg4kwFTnoM7v4UeI9t1mTqLla8PF3Bl/3g0Mt+mz12eGseJ4hp+Kaxq/8mpV8Ofdimzthz8L/y/kfD1EqXaW4h6kvTEuaXkOKy/F5ZdCHvehpG/hbv3wujbm0wc7aqtWYVUGMxMHiRVm/5g0sAEAP63P7djF9CH6XigbAAAD7tJREFUwcTHlOSXNhm2LIUXByuzuVS1swQpzkuS9IT/s9ng+Fb46LfKp/eM95Q5Ge/ZB9P+7lZ39dV7TxETouPytDjPxSs6rFtkEKNSovj0pw4mPbuoFGVM3x1boe+VsO3v8I8hynJSefs9Eqs4N8k4PeG/KvPhwGrYswKKsiAwEi75gzKVWHiS25cvqzGx6VAB8y9JJkAjn//8xTVDk3hy/SF+LqikX3yYexdLuhBuXAlFR+Hbf8C+d2H3W9B9lDKUZeAMpXQougx5pwv/UlMCe9+Bf8+Av/eHDX9R/ijNWA4PHIar/+qRhAfw8Z5TmCxWZo3o4ZHrCc+4ZkgSGrWKD3ef8txFYy+A616pfw0tAWMFfHIXPNcPVqXDwbVQV+u5+wm/JSU94Vs2G+QfhKMbIGsjnPoBbFaI6q10SBj8a4jv7/Hbmi1WVmw/wcW9oh3ruQn/EB8eyNWDEvhw90nun5RKYIDGcxcPjoYxf1RqDE7+oNQkHPwvZK4DbZCy3NQFv4J+V0F0b8/dV/gNSXqic9lsUPILZG9XZtM4vhUq6ruWJ12ojLFLmwzdRrQ6T6YnfH4gj9NltTw2faDX7iE67jeXpPC//Xms+/EMN45ybbxlu6hUkDxa+Zq8BE5sgyOfw9GNyhdATD/ocwUkj4GUSyG8m+fjEJ1Okp7wrpoSyP0RcjPgzD7I2QlV+cq+4Fjlj8kVD0O/SR6rtmxLncXKi19m0S8+lKsGJHTKPUX7jOkTw4CkcJZ//TMzh3dH6802V7VGSW59roApzyjzth79Ukl+Ge/Drn8px0WmKK/XbiOUD2iJg0EX4r24hFdI0hOeYalThhMUHYHCw5D7k5LoynIajolMht6XK384UsYq7SxeLM215oNdJ/mlqJp/3TRKxub5KZVKxf2TUrnt37tZvfcUsy9K7rybx/RVvi65U1nFIe8nyNmh1Ez8vAl+fL8+SDXEXKAkwKShEDdAeU1H9FTmBxV+SZKecJ2lDspPQVk2lGYr34uyoDALSo4pa9fZRfWG7iNh1O8gaZjyhyE42nex18str+XZLw4zpk8MEwfE+zoc4cRVA+IZ1jOS5zZkcfWgRN8s7qvRQvcRyteYu5Tq+crc+tqL+q/s7bD/w4ZztEEQ2w9iU5WvqF5KKTEyGcKSJCH6mCQ9obDUQVUBVOYpb+rK3Pqf8xqSXMVpsFkazlFplMb+2DToP1X5HpemfNr1w27gZouVBR/9hNliY+msIah8UMoUrlOpVPz1+sFc+/J2Fn96iL/fOMzXISk1E+HdlK+0KQ3bq4uVD4BFR5ThEYVHlDlAD6wBGi12q9EpJcGo+iQY3h3CEpVkaP8eFC2J0Ysk6Z2vLGaoLVXmIqwpVtrWaoqhtoT4k0fhCFBd2JDcqoto8uYEpfomJF55cyaPVj6tRqU0fHIN796hWVB8wWaz8dSnh/j25yKenTWUlBhpizkXDOoWwV0T+vHSV0cZ3jOS9DG9fB1Sy0JiIGQMpIxpur3OAOUnm9aO2L/n/qi8J8+mDoDQBCUJhsZDcDRxtUBJKgTHNP8KjPBJM8G56tz4i9WV2GxgMYGpGupqwFABxkplXJGxounvLe6rUBKcoazVW0Rp9BASq7xhwropDfOOT5qJDZ84Q+KURv5znMWqJLyVO7K5bXxvbrzIC70BhdfcO/ECDpwu54n1hwgN1HL98HNoXGVAoFLzEXtBy/vNRqVjl6OGpdH3ijNKm/iZfURXF8HhupavodIoTQeBEaAPh8DwRt8jzvq90XddCAQEN3zX6rtE8pSk54w9AZmN9d8NjX5u9N1sBIux+b66GmXAq6la+V5Xo3yZahp+bmm/zepCcKpGL+Aw5efQeKUBPjhGqSIJjlHeDMHRTbYdOZbNgAEDvP7P5w/yq+p46s3v+e5YMb8f15u/TOkaz30+0ahV/L+5w7l15W7uW/UjPxdU8X9Xpfo6LM/Q6pWalEjnHXWOHDrEgL4962ttGtXcNP4yNPrwW5nX8LvJxcm7VWoICAFdcNNkqAtuYXsQaAOV6lptoPIcju/6Rr/Xb9Pozzqm/lwfVON6LelZrVaeeOIJjhw5gk6n4+mnnyYlJcWx/8MPP+SDDz5Aq9Xyhz/8gQkTJlBSUsKDDz6IwWAgPj6eJUuWEBQU5K0QFUU/02vjLbDR0DyJWTy0IKU2SHmROF5AQcqLKDBSKVHZX0QB9u9BDdsafzLT1ye4wHDQhXaJT2Ud9XNBFe9+n827O0+hUatZOnMIsy/qKe1456gQvZYVt1zEY58c4JWvj/H5/jxm9g+mdz+LZwev+yuVqv7DbZjSvNAeVkvTmiBHMqxu9CG8uuHDeLPt1cpk3Y2PMRuadlzrKI1OSYiaAOXnsES46RP3r+uE15Lepk2bMJlMrFq1ioyMDJYuXcqrr74KQGFhIe+88w6rV6/GaDQyb948xo4dy/Lly5k2bRozZ87kjTfeYNWqVfz2t7/9/+3db0wTeRoH8O90+g9aWFHcPe60HpIld+AhIqebCNzloGH1hYnRIqA1EQLGxBcaJQQTAYWUZnPyBgwmnjHGN/7jjS9cjZpIAxqNva2khd7eyQq6uyLH/UmnwkDbuRdli8W5tSudndB5PklDZ/rrzPdJO31mphMqVcQwbTL4jzKRlGKY2xvRvrUHo5v/KzrvrT0YsXmapHDDoy+lJTETCMHPBzDp5/Hdf6YxOunH05f/xV/H/o2RCT80LIM//NqAU5ZN+OUyiXeeiOT0GhZf7FqPz9f9AvYvvfhz/wTOPLqD32cux2drlyNrpRGm5clIN+qQoldDp1bRTg4Q/ooiKS18i6dgYP4M1w9nwaLuT0efIfu/Y2bnDzJ0qeHPTglJ1vScTieKi4sBAPn5+XC73ZHHBgcHsWHDBmi1Wmi1WphMJni9XjidThw4cAAAUFJSgs7OTsmb3gifigMTtRD+pYmaLwjCO2PfnYO5qxnfzN3Ex8W6LJFhEERGio4TDSc2TsBsYBYa9XeLziI+JtZsMdYl8sxgSMCbmQBmg+8+usKgRd6qj7DvszXY9rsMTH77DTW8BPOn33yCP2Z/jMv3v8LXnA4D//gnvrj1t3fGaVgGyVo11CoGKhUDlmHAqhioVADLhOfJ1RJ/ajPmeR66L1/HZ91xWUqstHO32KQZtPhLUNpv3SRbOsdxMBqNkWmWZREIBKBWq8FxHFJS5i9pNxgM4Dguar7BYIDP53vveniex/Dw8AfnnHwTwK9SVAiKfOLG+r4UGxb9XEZ0lNjyRZcV40pjfW4wxIBl2QXjPjxfvJ4Xfq5IDpFpvYZBkkaFZI0KqToWKw0sPjFqkJ7Mzi1jGpPffoPp6elFvT+WGiXV+9s0BhsyVNj96cfw8SvwvS+A732z8PFB+GdDeDMjYGo2hKAgICRg7iZE/ZVD+KPmp608qGPBqhYfWKaSY5aqmcWzv38NBGYkex9L1vSMRiP8fn9kOhQKQa1Wiz7m9/uRkpISma/X6+H3+5Gamvre9eh0ukVflLEieVgxF3YAwPCwcupVUq2AsuqlWhPXYuv9sYYp2ZdNBQUFcDgcAACXy4Xs7PmrrfLy8uB0OsHzPHw+H549e4bs7GwUFBSgr68PAOBwOLBx40ap4hFCCFEgyY70zGYzBgYGUFlZCUEQYLPZcOHCBZhMJpSWlsJqtaK6uhqCIODIkSPQ6XQ4ePAgGhsbcfXqVaSlpeH06dNSxSOEEKJAkjU9lUqFU6dORc3LysqK3K+oqEBFRUXU4+np6Th//rxUkQghhCgcXUtPCCFEMajpEUIIUQxqeoQQQhSDmh4hhBDFoKZHCCFEMRhB7H9kLSEulws6nbT/q40QQsjSwfM88vPFf3R4yTc9QgghJFZ0epMQQohiUNMjhBCiGNT0CCGEKAY1PUIIIYpBTY8QQohiUNMjhBCiGIpvenfu3MHRo0ejpsvKymC1WmG1WvH48WMZ08XfwnpdLhcsFgsqKyvR3d0tYzJpCIKA4uLiyOuZiD9XFQqF0NzcjN27d8NqtWJ0dFTuSJLbsWNH5DVtamqSO44knj59CqvVCgAYHR1FVVUVqqur0dLSglAoJHO6+Hq71qGhoaht9ubNm3Fdl2Q/LbQUtLe3o7+/P+oXet1uNxoaGlBeXi5jMmmI1dvS0oKuri6sXr0a9fX1GBoaQk5Ojowp42tsbAy5ubk4e/as3FEkc/fuXczMzODKlStwuVyw2+3o6emRO5ZkeJ6HIAi4dOmS3FEkc+7cOdy4cQNJSUkAgI6ODhw+fBibN29Gc3Mz7t27B7PZLHPK+FhYq8fjwf79+1FTUyPJ+hR9pFdQUIDW1taoeR6PB729vaiurobdbkcgEJAnnAQW1stxHGZmZmAymcAwDIqKivDgwQP5AkrA4/FgfHwcVqsVdXV1GBkZkTtS3DmdThQXFwMA8vPz4Xa7ZU4kLa/Xi6mpKdTU1GDfvn1wuVxyR4o7k8mErq6uyLTH48GmTZsAACUlJQm1nS6s1e124/79+9izZw+OHz8OjuPiuj5FHOldu3YNFy9ejJpns9mwbds2PHr0KGr+li1bUFZWhlWrVqGlpQWXL1/G3r17f864ixZrvRzHwWg0RqYNBgNevHjxs+WMN7G6m5ubUV9fj61bt+LJkydoaGhAb2+vTAmlsfB1ZFkWgUAAanVibt56vR61tbWwWCx4/vw56urqcOvWrYSqt7y8HC9fvoxMC4IAhmEAhLdTn88nV7S4W1hrXl4eLBYL1q1bh56eHpw5cwaNjY1xW1/ivEt+hMVigcViiWnszp07kZqaCgAoLS3F7du3pYwmiVjrNRqN8Pv9kWm/3x+pfSkSq3tqagosywIACgsL8fr166gPkESw8HUMhUIJ1QAWyszMxJo1a8AwDDIzM7Fs2TJMTEwgIyND7miSUanmT8ot9e30fcxmc6Q+s9mMtra2uC5f0ac3FxIEAdu3b8erV68AAA8fPkRubq7MqaRjNBqh0WgwNjYGQRDQ39+PwsJCuWPFVXd3d+Toz+v1IiMjI6EaHhA+be1wOACEL0zKzs6WOZG0rl+/DrvdDgAYHx8Hx3FYuXKlzKmklZOTEzlL43A4Em47fVttbS0GBwcBSPMZnLi7gx+AYRi0t7fj0KFD0Ov1yMrKQkVFhdyxJHXy5EkcO3YMwWAQRUVFWL9+vdyR4qq+vh4NDQ3o6+sDy7Lo6OiQO1Lcmc1mDAwMoLKyEoIgwGazyR1JUrt27UJTUxOqqqrAMAxsNltCH9kCQGNjI06cOIHOzk6sXbs2IS+0+0Frayva2tqg0WiQnp4e9yM9+pUFQgghikGnNwkhhCgGNT1CCCGKQU2PEEKIYlDTI4QQohjU9AghhCgGNT1CCCGKQU2PEEKIYvwPC/2aeFOaP0gAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.random.normal(-2, 1, size=(1000, 1))\n", "X[::2] += 4\n", "\n", "modeln = GeneralMixtureModel.from_samples(NormalDistribution, 2, X)\n", "modelt = GeneralMixtureModel.from_samples(StudentTDistribution, 2, X)\n", "\n", "x = numpy.arange(-15, 15, 0.1)\n", "\n", "plt.figure(figsize=(7, 4))\n", "plt.plot(x, modeln.probability(x), label=\"Normal Mixture\")\n", "plt.plot(x, modelt.probability(x), label=\"Student T Mixture\")\n", "plt.ylabel(\"Probability\", fontsize=12)\n", "plt.legend(fontsize=12)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The results look reasonable. The Student T distributions have a smaller valley separating the peaks because they have heavier tails. Additionally, the peaks are closer together for the Student T distribution because the heavier tails assign more credit from points in one cluster to the distribution modeling the other cluster. This pulls the centers together slightly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Block Distributions\n", "\n", "Currently, pomegranate supports distributions where the features are either independent, through `IndependentComponentsDistribution`, or have a Gaussian covariance structure, as in `MultivariateGaussianDistribution`. However, sometimes one would want to model their features in blocks, where the covariance amongst features in the block are accounted for. When all your variables are Gaussian, this is simply having an enforced block structure on your covariance matrix. In the more general case, this can take the form of a Bayesian network. \n", "\n", "This new custom support allows us to do that easily. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "class BlockGaussianDistribution():\n", " def __init__(self, distributions):\n", " self.distributions = distributions\n", " self.d = sum([d.d for d in distributions])\n", " \n", " def log_probability(self, X):\n", " i, log_probability = 0, numpy.zeros(X.shape[0])\n", " for distribution in self.distributions:\n", " log_probability += distribution.log_probability(X[:, i:i+distribution.d].copy())\n", " i += distribution.d\n", " return log_probability\n", " \n", " def summarize(self, X, w=None):\n", " i = 0\n", " for distribution in self.distributions:\n", " distribution.summarize(X[:, i:i+distribution.d].copy(), w)\n", " i += distribution.d\n", " \n", " def from_summaries(self, inertia=0.0):\n", " for distribution in self.distributions:\n", " distribution.from_summaries(inertia)\n", "\n", " @classmethod\n", " def from_samples(cls, X, weights=None, ds=[]):\n", " distributions = [MultivariateGaussianDistribution.blank(d) for d in ds]\n", " d = BlockGaussianDistribution(distributions)\n", " d.summarize(X, weights)\n", " d.from_summaries()\n", " return d\n", "\n", " @classmethod\n", " def blank(cls, ds):\n", " distributions = [MultivariateGaussianDistribution.blank(d) for d in ds]\n", " return BlockGaussianDistribution(distributions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the code above, `BlockGaussianDistribution` creates multiple `MultivariateGaussianDistribution` objects, each modeling a different set of features. The probability of an example is then the product of the probability of each set of features under their respective distribution.\n", "\n", "To evaluate this, let's look at the diabetes data set. This is normally a regression task, so we'll have to binarize the response using the median value. Additionally, the data has been scaled for us already, so we'll have to go ahead an unscale it for the purposes of our demonstration." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import load_diabetes\n", "from sklearn.model_selection import train_test_split\n", "\n", "from pomegranate import LogNormalDistribution, BernoulliDistribution, NormalDistribution\n", "\n", "data = load_diabetes()\n", "X, y = data.data, data.target\n", "X[:,1] = (X[:,1] - X[:,1].min()) / (X[:,1].max() - X[:,1].min())\n", "X[:,0] -= X[:,0].min() - 0.001\n", "X[:,2] -= X[:,2].min() - 0.001\n", "X[:,3] -= X[:,3].min() - 0.001\n", "y = y > numpy.median(y)\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first four variables correspond to age, sex, BMI, and average blood pressure. The remaining six features are different blood serum measurements. In this example, let's use univariate distributions to model the first four variables, and a multivariate Gaussian with a full covariance matrix to model the last six.\n", "\n", "To get a sense for what distributions might be a good fit for the first four features, we can visualize the training data." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ksachdeva/Desktop/Dev/myoss/pomegranate/env/lib/python3.7/site-packages/ipykernel_launcher.py:11: MatplotlibDeprecationWarning: \n", "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n", " # This is added back by InteractiveShellApp.init_path()\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "names = 'Age', 'Sex', 'BMI', 'Average Blood Pressure'\n", "plt.figure(figsize=(14, 2))\n", "\n", "for i in range(4):\n", " plt.subplot(1, 4, i+1)\n", " \n", " x = numpy.arange(0.01, 0.3 if i != 1 else 1, 0.01)\n", " \n", " plt.hist(X_train[:,i], bins=10, normed=True)\n", " plt.plot(x, LogNormalDistribution.from_samples(X_train[:,i]).probability(x), label=\"LogNormal\")\n", " plt.plot(x, NormalDistribution.from_samples(X_train[:,i]).probability(x), label=\"Normal\")\n", " plt.title(names[i], fontsize=12)\n", " \n", "plt.legend(loc=(1.05, 0.4))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like age and BMI may be better modeled by a normal distribution than a log normal distribution, but that average blood pressure is better modeled with a log normal distribution. Due to the binary nature of sex in this data set, a Bernoulli distribution seems like the right choice.\n", "\n", "Let's now train a Bayes' classifier using our custom block distribution! To evaluate its performance, let's compare against a model that simply uses a single multivariate Gaussian distribution per class." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.7303370786516854, 0.6966292134831461)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = [BlockGaussianDistribution([NormalDistribution.blank(),\n", " BernoulliDistribution.blank(),\n", " NormalDistribution.blank(),\n", " LogNormalDistribution.blank(),\n", " MultivariateGaussianDistribution.blank(6)]) for j in range(2)]\n", "model = BayesClassifier(ds)\n", "model.fit(X_train, y_train)\n", "\n", "model2 = BayesClassifier.from_samples(MultivariateGaussianDistribution, X_train, y_train)\n", "\n", "model.score(X_test, y_test), model2.score(X_test, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like the custom distribution can outperform the simpler, full covariance Gaussian, in this setting." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 } C_Feature_Tutorial_6_Deep_Models.ipynb000066400000000000000000001516071420461712600321220ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Incorporating Neural Networks\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com\n", "\n", "Neural networks have become exceedingly popular recently due, in part, to their ability to achieve state-of-the-art performance on a variety of tasks without requiring complicated feature extraction pipelines. These models are frequently applied to domains where there is a great deal of raw structured data, such as computer vision, where neighboring pixels are strongly correlated, and natural language processing, where words are organized and modified in specific ways to convey meaning.\n", "\n", "There have been mergers between neural networks and probabilistic models. For example, deep hidden Markov models (DHMMs) are models where the input to the neural network is some observation, such as an image, and the output is the state in the hidden Markov model that the observation belongs to. These resulting probabilities are then treated as the likelihood function P(D|M) by the model, regularized using the transition matrix, and then re-normalized to get the posterior probabilities. Another example is a deep mixture model, where expectation-maximization is used to train the model on unlabeled samples.\n", "\n", "Thus far, pomegranate has stuck to probabilistic models that are not coupled with a neural network. However, with the recent inclusion of custom distributions, one can use a quick hack in order to turn pomegranate's models into deep models." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fri Feb 15 2019 \n", "\n", "numpy 1.15.1\n", "scipy 1.1.0\n", "pomegranate 0.10.0\n", "\n", "compiler : GCC 7.2.0\n", "system : Linux\n", "release : 4.15.0-45-generic\n", "machine : x86_64\n", "processor : x86_64\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy\n", "import seaborn; seaborn.set_style('whitegrid')\n", "\n", "from pomegranate import *\n", "\n", "numpy.random.seed(0)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What?\n", "\n", "Two natural questions emerge when considering the notion of combining a neural network and a probabilistic model. The first is: what is the benefit? The second: how does one do that?\n", "\n", "There are many benefits of merging a neural network and a probabilistic model. The simplest is that one can expand the expressiveness of probabilistic models by incorporating a neural network. Instead of forcing your data to come neatly in the form of a single distribution or a mixture of distributions, one can process data that comes in more structured forms such as images. Additionally, a neural network may be able to extract more complicated relationships amongst the features of existing data sets than a simple covariance matrix could.\n", "\n", "Practically, this works by having a single neural network replace the entire set of distributions in your model where the model has the same number of outputs as probability distributions that used to be in your model. For example, a HMM that has eight states would be coupled with a neural network that makes predictions for eight classes. First, each observation in your sequence would be run through the neural network to get the predictions for each observation. This calculation is essentially the same as calculating the probability of an observation given each state and then normalizing, i.e., taking a softmax. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Neural Network Wrapper\n", "\n", "We can hack pomegranate models by using a custom distribution that wraps our neural network (using the package of your choice). The following class will store a pointer to your model, an identifying of which class it's representing, and train or make predictions using the given class. When asked for a log probability, it returns the posterior probability of the observation given the class it's storing. When training, it stores data and labels (it's own class ID) and then performs a single round of training to update. Note that this is the exact code that is already implemented in pomegranate, except this version is called \"NeuralNetworkWrapperExample\" instead of \"NeuralNetworkWrapper\", and is only shown here for reference as to what's going on in the backend." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "class NeuralNetworkWrapperExample():\n", " '''A wrapper for a neural network model for use in pomegranate.\n", " \n", " This wrapper will store a pointer to the model, as well as an indicator\n", " of what class it represents. It needs information about the number of\n", " dimensions of the input and the total number of classes in the input.\n", " It is currently built to work with keras models, but can be easily\n", " modified to work with the package of your choice.\n", " \n", " Training works in a somewhat hacky manner. Internally, pomegranate\n", " will scan over all components of the model, calling `summarize` for each\n", " one, and then `from_summaries` at the end. During the EM procedure, the\n", " samples and their associated weights are passed in to the `summarize`\n", " function. The associated weights are the responsibilities calculated\n", " during the EM algorithm. In theory, one could simply update the model\n", " using the samples and their corresponding weights. In practice, it's much\n", " better to reconstruct the whole responsibility matrix for the batch of\n", " data and then train on soft labels.\n", " \n", " The process for training is as follows. When pomegranate starts a round of\n", " optimization, this wrapper will store a pointer to the data set to the\n", " neural network model object. This data set is the same one passed to each\n", " NeuralNetworkWrapper, the only difference being the ~weights~. Thus, each\n", " successive call will store the weights that are passed in (the responsibilities\n", " from the EM algorithm) to an associated label matrix. The result is a single\n", " copy of the data batch and a corresponding matrix of soft labels. Keras\n", " allows us to train a classifier on soft labels, and this is the preferred\n", " strategy.\n", " \n", " Parameters\n", " ----------\n", " model : object\n", " The neural network model being utilized.\n", " \n", " i : int\n", " The class that this distribution represents.\n", " \n", " n_dimensions : int\n", " The number of dimensions in the input.\n", " \n", " n_classes : int\n", " The total number of classes that the model can output.\n", " '''\n", " \n", " def __init__(self, model, i, n_dimensions, n_classes):\n", " self.d = n_dimensions\n", " self.n_classes = n_classes\n", " self.model = model\n", " self.i = i\n", " self.model.X = []\n", " self.model.y = []\n", " self.model.w = []\n", " \n", " def log_probability(self, X):\n", " ''' Return pseudo-log probabilities from the neural network.\n", " \n", " This method returns the log probability of the class that this\n", " wrapper represents given the model. Thus, it's not strictly a\n", " likelihood function, but rather, a posterior. However, because\n", " the HMM takes log probabilities, multiplies them with a prior,\n", " and then normalizes them, mathematically they work out to be\n", " equivalent.\n", "\n", " This method uses the `predict` function from the neural network,\n", " which should take in a single batch of data, and returns the\n", " posterior probability of each class given the network. Typically,\n", " this is calculated using a softmax function on the outputs. The\n", " output of this function should be a matrix of size (n, k), where\n", " n is the number of samples and k is the number of classes, where\n", " the sum over each sample is equal to 1. \n", "\n", " Parameters\n", " ----------\n", " X : numpy.ndarray, shape=(n, d)\n", " The batch of data to calculcate probabilities over.\n", " '''\n", " \n", " return numpy.log(self.model.predict(X)[:,self.i])\n", " \n", " def summarize(self, X, w):\n", " '''When shown a batch of data, store the data.\n", "\n", " This will store the batch of data, and associated weights, to the\n", " object. The actual update occurs when `from_summaries` is called.\n", "\n", " Parameters\n", " ----------\n", " X : numpy.ndarray, shape=(n, d)\n", " The batch of data to be passed in.\n", "\n", " w : numpy.ndarray, shape=(n,)\n", " The associated weights. These can be uniform if unweighted.\n", "\n", " Returns\n", " -------\n", " None\n", " '''\n", "\n", " if self.i == 0:\n", " self.model.X = X.copy()\n", " self.model.y = numpy.zeros((X.shape[0], self.n_classes))\n", " \n", " self.model.y[:, self.i] = w\n", " \n", " def from_summaries(self, inertia=0.0):\n", " '''Perform a single gradient update to the network.\n", "\n", " This will perform a single gradient update step, using the\n", " `train_on_batch` method from the network. This is already implemented\n", " for keras networks, but for other networks this method will have to\n", " be implemented. It should take in a single batch of data, along with\n", " associated sample weights, and update the model weights.\n", "\n", " Parameters\n", " ----------\n", " inertia : double, optional\n", " This parameter is ignored for neural networks, but required for\n", " compatibility reasons.\n", "\n", " Returns\n", " -------\n", " None\n", " '''\n", "\n", "\n", " if self.i == 0:\n", " self.model.train_on_batch(self.model.X, self.model.y)\n", " self.clear_summaries()\n", " \n", " def clear_summaries(self):\n", " self.model.X = None\n", " self.model.y = None\n", "\n", "\n", " @classmethod\n", " def from_samples(self, X, weights):\n", " '''The training of this wrapper on data should be performed in the main model.\n", "\n", " This method should not be used directly to train the network.\n", " '''\n", "\n", " return self\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Deep mixture models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first model we'll merge with a deep network is the general mixture model. Here, our neural network model can have any architecture as long as it outputs a number of classes equal to the number of components that you'd like in the mixture model. Our goal is to then train the neural network in an unsupervised manner using EM. This can be very difficult to do correctly and fragile, so use with care.\n", "\n", "Let's demonstrate this on the MNIST handwritten digits data set, where we want to learn a model that can distinguish 2's from 9's without any training labels. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n" ] } ], "source": [ "from keras.datasets import mnist\n", "\n", "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n", "\n", "X_train = X_train[(y_train == 2) | (y_train == 9)]\n", "X_test = X_test[(y_test == 2) | (y_test == 9)]\n", "y_test = y_test[(y_test == 2) | (y_test == 9)]\n", "\n", "n, d = X_train.shape[0], 784\n", "\n", "X_train = X_train.reshape(n, d)\n", "X_test = X_test.reshape(X_test.shape[0], d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we have to implement the initialization step by hand because it's currently not built-in. First we use K-means to cluster the data and assign preliminary labels. Then, we train our neural network model for some amount of time on those labels. Lastly, we use EM to refine our labels just in case the initial K-means cluster was not good enough. This procedure is essentially identical to the built-in initialization for mixtures, with the only difference being that all the complexities of training a neural network replace the simplicity of fitting a probability distribution." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from sklearn.cluster import KMeans\n", "from pomegranate import GeneralMixtureModel\n", "from pomegranate import NeuralNetworkWrapper\n", "\n", "from keras.layers import Dense\n", "from keras.models import Sequential\n", "\n", "numpy.random.seed(111)\n", "\n", "# Use k-means to get an initial clustering of our data\n", "y_init = KMeans(2).fit_predict(X_train)\n", "y_kmeans = numpy.zeros((n, 2))\n", "y_kmeans[numpy.arange(n), y_init] = 1\n", "\n", "# Define the neural network model. Here we use a simple model for illustration. \n", "nn = Sequential([\n", " Dense(128, input_dim=d, activation='relu'), \n", " Dense(2, activation='softmax')\n", "])\n", "\n", "nn.compile(loss='categorical_crossentropy', optimizer='adam')\n", "\n", "# Now we fit to the k-means defined labels. \n", "nn.fit(X_train, y_kmeans, epochs=10, verbose=False)\n", "\n", "# Here we define our wrappers---one for each component in our mixture / each class in our neural model\n", "d1 = NeuralNetworkWrapper(nn, 0, d, 2)\n", "d2 = NeuralNetworkWrapper(nn, 1, d, 2)\n", "\n", "# We feed the wrapper into the mixture model as you would a normal distribution\n", "model1 = GeneralMixtureModel([d1, d2])\n", "\n", "# Now we fit the model using EM, just as one would with built-in distributions\n", "model1.fit(X_train, max_iterations=10, stop_threshold=0.1)\n", "\n", "# And make predictions just as one would with normal distributions\n", "y_hat = model1.predict(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the clusters that are found using this approach." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 3))\n", "\n", "X_test_r = X_test.reshape(X_test.shape[0], 28, 28)\n", "\n", "for i in range(1, 11):\n", " plt.subplot(2, 10, i)\n", " plt.imshow(X_test_r[y_hat == 0][i], cmap='Greens')\n", " plt.axis('off')\n", " \n", " plt.subplot(2, 10, i+10)\n", " plt.imshow(X_test_r[y_hat == 1][i], cmap='Reds')\n", " plt.axis('off')\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks good! One cluster clearly seems to be 2's, and one clearly appears to be 9's. \n", "\n", "But, how much of this was just because K-means did a good job of clustering MNIST, which is a notoriously easy data set? Well, we can evaluate that by seeing how good the predictions using K-means would be versus our deep mixture model. Let's print out the accuracy using the predicted labels, and those labels flipped, just because we don't know which cluster ends up corresponding to which original label when you use unsupervised methods for training." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('kmeans: ', 0.9608035276825085, 0.039196472317491425)\n", "('deep mixture model: ', 0.9696227339539442, 0.030377266046055854)\n" ] } ], "source": [ "y_kmeans_hat = KMeans(2).fit(X_train).predict(X_test)\n", "\n", "y_test[y_test == 2] = 0\n", "y_test[y_test == 9] = 1\n", "\n", "print(\"kmeans: \", (y_kmeans_hat == y_test).mean(), ((1 - y_kmeans_hat) == y_test).mean())\n", "print(\"deep mixture model: \", (y_hat == y_test).mean(), ((1 - y_hat) == y_test).mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like we get a small improvement in performance. Now, MNIST is notoriously easy and one could likely get much better performance by tuning the K-means model. However, this does demonstrate that deep mixture models have potential for much larger and more complicated phenomena." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Deep hidden Markov models\n", "\n", "The next type of model we could merge with a neural network is a hidden Markov model. In this situation, the classes in the neural network correspond to hidden states in the HMM, and the edges in the HMM correspond to probabilistic constraints in the network predictions over a sequence.\n", "\n", "To demonstrate how this would work we consider the situation in which one has a pre-trained object recognition network and wants to adapt it to work over videos (sequences of images) but doesn't have labels for each frame in their data. The goal is not to refine the network, which has been pre-trained, but to train the HMM such that it learns a useful transition matrix to constrain the predictions from the neural network alone applied to each frame sequentially.\n", "\n", "Let's use the MNIST data set again, but this time use data from each of the 10 classes." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from keras.utils import to_categorical\n", "\n", "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n", "\n", "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1] * X_train.shape[2])\n", "y_train_ohe = to_categorical(y_train, num_classes=10)\n", "\n", "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1] * X_test.shape[2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because keras doesn't have any pre-trained MNIST models, let's pre-train some simple model on the training set and then freeze it. We use L2 regularization because unregularized dense networks with lots of parameters have a tendancy to predict probabilities of 1 rather than smooth probability distributions. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from keras.regularizers import l2\n", "\n", "# Define our neural network model. In this case it's a small dense network for fast training, but in\n", "# practice it can be anything that has 10 classes as the output.\n", "nn = Sequential([\n", " Dense(32, activation='relu', input_dim=784, kernel_regularizer=l2(1.)),\n", " Dense(10, activation='softmax')\n", "])\n", "\n", "nn.compile(loss='categorical_crossentropy', optimizer='adam')\n", "\n", "# Now we fit our model to the data to get a \"pre-trained\" model\n", "nn.fit(X_train, y_train_ohe, epochs=25, verbose=False, batch_size=256)\n", "\n", "# After training we go through and freeze the model so that training our HMM later on doesn't affect the parameters\n", "for layer in nn.layers:\n", " layer.trainable = False\n", "\n", "nn.compile(loss='categorical_crossentropy', optimizer='adam')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have to generate sequences from our training data of images. Our \"videos\" will be sequences of images, and we add structure by enforcing that the images are of digits that either stay the same value or increase. For example, a \"video\" might be images of the digits 1, 1, 2, and 5 sequentially, but not 1, 4, and 2 sequentially. Our goal is to train the HMM to learn this constraint. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def generate_videos(X_train, y_train, n=1000):\n", " seqs, labels = [], []\n", " \n", " for _ in range(n):\n", " seq, label, number = [], [], 0\n", " \n", " # Our videos can be a maximum of 25 images\n", " for i in range(25):\n", " chance = numpy.random.choice(4)\n", " \n", " # There's a 25% chance that the next image skips a number, but only as long as it\n", " # stays between 1 and 10, e.g. 1 followed by a 3. \n", " if chance == 2:\n", " if number < 8:\n", " number += 2\n", " \n", " # There's a 25% chance that the next image proceeds to the next number, e.g. 1 followed by a 2\n", " elif chance == 3:\n", " if number < 9:\n", " number += 1\n", " \n", " # There's a 50% chance that the next image is of the same number, e.g. 1 followed by a 1\n", " \n", " # If we've seen at least one image already, there's an independent 10% chance that\n", " # the sequence terminates to give us a distribution of sequence lengths\n", " if i > 1 and numpy.random.choice(10) == 9:\n", " break\n", " \n", " idx = numpy.random.randint((y_train == number).sum())\n", " seq.append(X_train[y_train == number][idx])\n", " label.append(number)\n", " \n", " labels.append(label)\n", " seqs.append(numpy.array(seq))\n", " \n", " return seqs, labels\n", "\n", "X_train_seqs, y_train_seqs = generate_videos(X_train, y_train, 1000)\n", "X_test_seqs, y_test_seqs = generate_videos(X_test, y_test, 100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's hand define a model where you can either stay in the same hidden state of proceed to any of the next three states. In this case, a state corresponds to a number / class in the MNIST data set. If one doesn't have prior knowledge of what the transition structure should look like they can train the model similarly to the deep mixture model example above. However, this will allow us to see whether training the model using EM actually works to improve on rough hand-specified constraints." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "d0 = NeuralNetworkWrapper(nn, 0, 28*28, 10)\n", "d1 = NeuralNetworkWrapper(nn, 1, 28*28, 10)\n", "d2 = NeuralNetworkWrapper(nn, 2, 28*28, 10)\n", "d3 = NeuralNetworkWrapper(nn, 3, 28*28, 10)\n", "d4 = NeuralNetworkWrapper(nn, 4, 28*28, 10)\n", "d5 = NeuralNetworkWrapper(nn, 5, 28*28, 10)\n", "d6 = NeuralNetworkWrapper(nn, 6, 28*28, 10)\n", "d7 = NeuralNetworkWrapper(nn, 7, 28*28, 10)\n", "d8 = NeuralNetworkWrapper(nn, 8, 28*28, 10)\n", "d9 = NeuralNetworkWrapper(nn, 9, 28*28, 10)\n", "\n", "s0 = State(d0, \"s0\")\n", "s1 = State(d1, \"s1\")\n", "s2 = State(d2, \"s2\")\n", "s3 = State(d3, \"s3\")\n", "s4 = State(d4, \"s4\")\n", "s5 = State(d5, \"s5\")\n", "s6 = State(d6, \"s6\")\n", "s7 = State(d7, \"s7\")\n", "s8 = State(d8, \"s8\")\n", "s9 = State(d9, \"s9\")\n", "\n", "model = HiddenMarkovModel()\n", "model.add_states(s0, s1, s2, s3, s4, s5, s6, s7, s8, s9)\n", "model.add_transition(model.start, s0, 0.33)\n", "model.add_transition(model.start, s1, 0.33)\n", "model.add_transition(model.start, s2, 0.34)\n", "\n", "model.add_transition(s0, s0, 0.24)\n", "model.add_transition(s0, s1, 0.24)\n", "model.add_transition(s0, s2, 0.24)\n", "model.add_transition(s0, s3, 0.24)\n", "model.add_transition(s0, model.end, 0.04)\n", "\n", "model.add_transition(s1, s1, 0.24)\n", "model.add_transition(s1, s2, 0.24)\n", "model.add_transition(s1, s3, 0.24)\n", "model.add_transition(s1, s4, 0.24)\n", "model.add_transition(s1, model.end, 0.04)\n", "\n", "model.add_transition(s2, s2, 0.24)\n", "model.add_transition(s2, s3, 0.24)\n", "model.add_transition(s2, s4, 0.24)\n", "model.add_transition(s2, s5, 0.24)\n", "model.add_transition(s2, model.end, 0.04)\n", " \n", "model.add_transition(s3, s3, 0.24)\n", "model.add_transition(s3, s4, 0.24)\n", "model.add_transition(s3, s5, 0.24)\n", "model.add_transition(s3, s6, 0.24)\n", "model.add_transition(s3, model.end, 0.04)\n", "\n", "model.add_transition(s4, s4, 0.24)\n", "model.add_transition(s4, s5, 0.24)\n", "model.add_transition(s4, s6, 0.24)\n", "model.add_transition(s4, s7, 0.24)\n", "model.add_transition(s4, model.end, 0.04)\n", "\n", "model.add_transition(s5, s5, 0.24)\n", "model.add_transition(s5, s6, 0.24)\n", "model.add_transition(s5, s7, 0.24)\n", "model.add_transition(s5, s8, 0.24)\n", "model.add_transition(s5, model.end, 0.04)\n", "\n", "model.add_transition(s6, s6, 0.24)\n", "model.add_transition(s6, s7, 0.24)\n", "model.add_transition(s6, s8, 0.24)\n", "model.add_transition(s6, s9, 0.24)\n", "model.add_transition(s6, model.end, 0.04)\n", "\n", "model.add_transition(s7, s7, 0.25)\n", "model.add_transition(s7, s8, 0.25)\n", "model.add_transition(s7, s9, 0.25)\n", "model.add_transition(s7, model.end, 0.25)\n", "\n", "model.add_transition(s8, s8, 0.33)\n", "model.add_transition(s8, s9, 0.33)\n", "model.add_transition(s8, model.end, 0.34)\n", "\n", "model.add_transition(s9, s9, 0.5)\n", "model.add_transition(s9, model.end, 0.5)\n", "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can make predictions using the maximum a posteriori (MAP) algorithm." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "y_pred = numpy.concatenate([model.predict(seq) for seq in X_test_seqs])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternately, we could apply our pre-trained model to each image individually to make predictions. If our deep HMM model does not outperform this, there is no benefit to combining the neural network with a probabilistic model." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "y_nn_pred = numpy.argmax(nn.predict(numpy.concatenate(X_test_seqs)), axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's evaluate our models using simple accuracy at predicting the value of each image." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('pre-trained model: ', 0.9231464737793852)\n", "('deep HMM before training: ', 0.9792043399638336)\n" ] } ], "source": [ "y_true = numpy.concatenate(y_test_seqs)\n", "\n", "print(\"pre-trained model: \", (y_nn_pred == y_true).mean())\n", "print(\"deep HMM before training: \", (y_pred == y_true).mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like there is a significant improvement using the HMM, even before refinement of the transition matrix using EM. This is because there are predictions that the pre-trained neural network alone may have gotten incorrect, but when combined with the transition matrix the correct label became more likely. For example, there may be a situation where the model alone thought that a 0 came after a 3. However, our transition matrix says that that is impossible. Essentially, our HMM allows our model to consider context better. \n", "\n", "Now, what happens when we train the model? Remember that we froze our model, so this refinement is only done on the transition matrix of the HMM, not the parameters of the pre-trained neural network." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] Improvement: 3167.82880474\tTime (s): 7.191\n", "[2] Improvement: 46.46103046\tTime (s): 7.029\n", "[3] Improvement: 5.49539624626\tTime (s): 7.207\n", "[4] Improvement: 1.37985857847\tTime (s): 7.154\n", "[5] Improvement: 0.488223125971\tTime (s): 7.188\n", "[6] Improvement: 0.211493207351\tTime (s): 7.188\n", "[7] Improvement: 0.103808710392\tTime (s): 7.27\n", "[8] Improvement: 0.0552500054655\tTime (s): 7.145\n", "[9] Improvement: 0.0311245813919\tTime (s): 7.147\n", "[10] Improvement: 0.018309211895\tTime (s): 7.189\n", "Total Training Improvement: 3222.07329887\n", "Total Training Time (s): 78.8414\n" ] } ], "source": [ "_ = model.fit(X_train_seqs, max_iterations=10, verbose=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('pre-trained model: ', 0.9231464737793852)\n", "('deep HMM before training: ', 0.9792043399638336)\n", "('deep HMM after training: ', 0.9855334538878843)\n" ] } ], "source": [ "y_pred2 = numpy.concatenate([model.predict(seq) for seq in X_test_seqs])\n", "\n", "print(\"pre-trained model: \", (y_nn_pred == y_true).mean())\n", "print(\"deep HMM before training: \", (y_pred == y_true).mean())\n", "print(\"deep HMM after training: \", (y_pred2 == y_true).mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like the addition of the constraints by themselves leads to a large improvement in performance, and that refining the transition matrix leads to further improvements. \n", "\n", "Even if we don't know the structure of the HMM beforehand we can still get good performance." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] Improvement: 11385.6094235\tTime (s): 6.5\n", "[2] Improvement: 1495.15851959\tTime (s): 6.434\n", "[3] Improvement: 221.541352097\tTime (s): 6.438\n", "[4] Improvement: 36.4806422466\tTime (s): 6.501\n", "[5] Improvement: 8.603361119\tTime (s): 6.425\n", "[6] Improvement: 2.28461893309\tTime (s): 6.604\n", "[7] Improvement: 0.693117308972\tTime (s): 6.444\n", "[8] Improvement: 0.281554052133\tTime (s): 6.427\n", "[9] Improvement: 0.140599682802\tTime (s): 6.488\n", "[10] Improvement: 0.0812708375324\tTime (s): 6.431\n", "Total Training Improvement: 13150.8744594\n", "Total Training Time (s): 71.1533\n", "('pre-trained model: ', 0.918625678119349)\n", "('deep HMM before training: ', 0.9819168173598554)\n" ] } ], "source": [ "nn = Sequential([\n", " Dense(32, activation='relu', input_dim=784, kernel_regularizer=l2(1.)),\n", " Dense(10, activation='softmax')\n", "])\n", "\n", "nn.compile(loss='categorical_crossentropy', optimizer='adam')\n", "\n", "nn.fit(X_train, y_train_ohe, epochs=25, verbose=False, batch_size=256)\n", "\n", "for layer in nn.layers:\n", " layer.trainable = False\n", "\n", "nn.compile(loss='categorical_crossentropy', optimizer='adam')\n", "y_nn_pred = numpy.argmax(nn.predict(numpy.concatenate(X_test_seqs)), axis=1)\n", "\n", "distributions = [NeuralNetworkWrapper(nn, i, 28*28, 10) for i in range(10)]\n", "\n", "model = HiddenMarkovModel.from_samples(distributions, 10, X_train_seqs, max_iterations=10, verbose=True)\n", "y_pred = numpy.concatenate([model.predict(seq) for seq in X_test_seqs])\n", "\n", "print(\"pre-trained model: \", (y_nn_pred == y_true).mean())\n", "print(\"deep HMM before training: \", (y_pred == y_true).mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a situation where one has labels for all images in a sequence, a recurrent neural network is likely to have superior performance to a HMM at this type of classification. However, when one doesn't have class labels, deep HMMs can be a very powerful tool." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 2 } C_Feature_Tutorial_7_Data_Generators.ipynb000066400000000000000000000511111420461712600327720ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Generators\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com\n", "\n", "The most common way to pass data to machine learning models, like those defined using scikit-learn or keras, is in the format of a numpy array. However, there are times when this is either not possible, like in the case where the data set is so big that it cannot fit in memory, or simply inconvenient, like in the case where the data exists in some other format. Further, once the numpy array is passed in, control over the training process becomes limited to whatever built-in commands the package happens to support.\n", "\n", "The concept of a data generator is a solution to both of these problems. Essentially, a data generator wraps a data set and yields batches of data. The pomegranate models then simply request batches from the generators, rather than handling the work of partitioning the data set themselves. Thus, generators become a flexible solution to a variety of problems because the user can define how exactly these batches are generated, including how data is read in, what pre-processing steps are performed on it, and when the epoch ends.\n", "\n", "The internal fitting loop of most pomegranate models looks something like this:\n", "\n", "```python\n", "with Parallel(n_jobs=n_jobs, backend='threading') as parallel:\n", " f = delayed(self.summarize, check_pickle=False)\n", "\n", " while improvement > stop_threshold and iteration < max_iterations + 1:\n", " # Update parameters from the stored sufficient statistics\n", " self.from_summaries(step_size, pseudocount)\n", "\n", " # Calculate new sufficient statistics from the data\n", " log_probability_sum = sum(parallel(f(*batch) for batch in \n", " data_generator.batches()))\n", "```\n", "\n", "The code is fairly simple. First, the model creates a thread pool for parallel processing (defaulting back to sequential execution if `n_jobs=1`). Then, there is a loop that continues until either convergence or the maximum number of iterations is reached. Inside that loop, there is a parameter update step and a batch summarization step. This may seem out of order but is written this way for efficiency reasons we won't get into here. However, the important aspect is that the data generator has a method, `batches`, that generates batches for each epoch. These batches are then processed, either sequentially or in parallel, by the `summarize` method. Importantly, it is the data generator that returns these batches rather than some hard-coded process." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sun Dec 01 2019 \n", "\n", "numpy 1.17.2\n", "scipy 1.3.1\n", "pomegranate| not installed\n", "\n", "compiler : GCC 7.3.0\n", "system : Linux\n", "release : 4.15.0-66-generic\n", "machine : x86_64\n", "processor : x86_64\n", "CPU cores : 8\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy\n", "import pandas\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "\n", "from pomegranate import *\n", "\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using a pre-defined generator\n", "\n", "Currently, there are three generators that are built-in to pomegranate: `DataGenerator`, `SequenceGenerator`, and `DataFrameGenerator`. `DataGenerator` is a somewhat bland generator that takes in a data set and yields sequential chunks of data until the entire data set has been seen. SequenceGenerator takes a list of sequences and yields them one at a time, for use mostly for `HiddenMarkovModel` objects. Finally, `DataFrameGenerator` takes a pandas DataFrame and yields batches from it.\n", "\n", "Let's take a look at learning a Bayesian network using a pandas DataFrame." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " bool str int1 int2 color int3 int4 int5\n", "0 True B 0 1 red 0 1 1\n", "1 False A 1 0 green 1 0 1\n", "2 False A 1 3 orange 1 1 2\n", "3 True B 0 0 blue 1 0 0\n", "4 False A 0 3 green 1 1 0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numpy.random.seed(0)\n", "\n", "X = numpy.array([\n", " numpy.random.choice([True, False], size=10),\n", " numpy.random.choice(['A', 'B'], size=10),\n", " numpy.random.choice(2, size=10),\n", " numpy.random.choice(4, size=10),\n", " numpy.random.choice(['orange', 'blue', 'red', 'green'], size=10),\n", " numpy.random.choice(2, size=10),\n", " numpy.random.choice(2, size=10),\n", " numpy.random.choice(3, size=10)\n", "], dtype=object).T.copy()\n", "\n", "X_df = pandas.DataFrame(X, columns=['bool', 'str', 'int1', 'int2', 'color', 'int3', 'int4', 'int5'])\n", "X_df.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((), (0,), (1,), (5,), (), (), (), (5,))" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model1 = BayesianNetwork.from_samples(X_df, state_names=X_df.columns)\n", "model1.structure" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((), (0,), (1,), (5,), (), (), (), (5,))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model2 = BayesianNetwork.from_samples(X, state_names=X_df.columns)\n", "model2.structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that we're getting the same structure regardless of if we use a numpy array or a DataFrame. This is because, internally, each object that is not a data generator gets wrapped by `DataGenerator` before being used internally." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([{\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"bool\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"True\" :0.6666666666666666,\n", " \"False\" :0.3333333333333333\n", " }\n", " ],\n", " \"frozen\" :false\n", "},\n", " 'B',\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"int\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"0\" :1.0,\n", " \"1\" :0.0\n", " }\n", " ],\n", " \"frozen\" :false\n", "},\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"int\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"0\" :0.29999999999999993,\n", " \"1\" :0.2000000000000001,\n", " \"2\" :0.1000000000000001,\n", " \"3\" :0.3999999999999999\n", " }\n", " ],\n", " \"frozen\" :false\n", "},\n", " 'blue',\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"int\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"0\" :0.4000000000000002,\n", " \"1\" :0.5999999999999999\n", " }\n", " ],\n", " \"frozen\" :false\n", "},\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"int\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"1\" :0.30000000000000004,\n", " \"0\" :0.6999999999999998\n", " }\n", " ],\n", " \"frozen\" :false\n", "},\n", " {\n", " \"class\" :\"Distribution\",\n", " \"dtype\" :\"int\",\n", " \"name\" :\"DiscreteDistribution\",\n", " \"parameters\" :[\n", " {\n", " \"0\" :0.29999999999999993,\n", " \"1\" :0.4,\n", " \"2\" :0.3\n", " }\n", " ],\n", " \"frozen\" :false\n", "}], dtype=object)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model1.predict_proba({'color': 'blue', 'str': 'B'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is important to note that Bayesian network structure learning does not operate in a batch-wise fashion. Rather, the data set is entirely generated by exhausting the generator in the `batches` method. In this case, the generator serves primarily as a way to convert a pandas DataFrame to a numpy array for use by the model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How do you define a data generator?\n", "\n", "The `BaseGenerator` looks basically like the following:\n", "\n", "```python\n", "class BaseGenerator(object):\n", "\tdef __init__(self):\n", "\t\tpass\n", "\n", "\tdef __len__(self):\n", "\t\treturn NotImplementedError\n", "\n", "\t@property\n", "\tdef shape(self):\n", "\t\treturn NotImplementedError\n", " \n", " @property\n", " def ndim(self):\n", " return NotImplementedError\n", " \n", " def batches(self):\n", " return NotImplementedError\n", " \n", " def unlabeled_batches(self):\n", " return NotImplementedError\n", " \n", " def labeled_batches(self):\n", " return NotImplementedError\n", "```\n", "\n", "Your custom data generator has to implement the subset of methods that will be relevant for your task. \n", "\n", "The first few methods are related to managing the generator. The `__init__` method should take in the parameters involved with loading the data set and how batches are to be generated, e.g. batch size, shuffling, etc.. The `__len__` method should define the total number of examples in the data set, even when they aren't all read into disk. The `shape` and `ndim` methods are the counterparts to the numpy methods. While `shape` and `__len__` are somewhat redundant with each other they are both necessary because some operations within pomegranate need to work regardless if a list, a numpy array, or a data generator have been passed in.\n", "\n", "The remaining methods involve generating batches of data to operate on. The `batches` method should be a generator that yields batches until the epoch is over. This can be as simple as contiguous chunks of data until the entire data set has been seen (as in `DataGenerator`) or as complicated as randomly sampling examples until some pre-specified number of examples have been seen. Because each iteration of the training loop involves another call to `batches` the generator becomes reset each epoch. The `unlabeled_batches` and `labeled_batches` are used in the case of semi-supervised learning and, respectively, generate only examples that do are unlabeled from the data set and those that are labeled. These do not have to be implemented if the use case will never rely on semi-supervised learning.\n", "\n", "### Building a custom out-of-core data generator\n", "\n", "As an example, let's build an out-of-core data generator that wraps a numpy array that lives on disk. The purpose of this generator would be to be able to generate batches from a data set without having to read the entire thing into memory. Because we are writing a data generator to do this, we don't have to rely on pomegranate internally being able to handle out-of-core operations; all we have to do is define how one would generate batches from it." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from pomegranate.io import BaseGenerator\n", "\n", "class MemoryMapGenerator(BaseGenerator):\n", " def __init__(self, filename, weights=None, batch_size=None):\n", " self.X = numpy.load(filename, mmap_mode='r')\n", " \n", " if weights is None:\n", " self.weights = numpy.ones(self.X.shape[0])\n", " else:\n", " self.weights = numpy.load(weights, mmap_mode='r')\n", " \n", " if batch_size is None:\n", " self.batch_size = self.X.shape[0]\n", " else:\n", " self.batch_size = batch_size\n", " \n", " def __len__(self):\n", " return len(self.X)\n", "\n", " @property\n", " def shape(self):\n", " return self.X.shape\n", "\n", " @property\n", " def ndim(self):\n", " return self.X.ndim\n", "\n", " def batches(self):\n", " start, end = 0, self.batch_size\n", " iteration = 0\n", "\n", " while start < len(self):\n", " yield self.X[start:end], self.weights[start:end]\n", "\n", " start += self.batch_size\n", " end += self.batch_size\n", " iteration += 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The implementation here was fairly straightforward, mostly because numpy is already super convenient when it comes to handling out-of-core data sets that are stored as numpy formatted arrays. All the generator does is load up slices of data (and corresponding slices of weights) and yields them until all examples have been seen." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "X = numpy.random.randn(10000, 70)\n", "numpy.save(\"X_test.npy\", X)\n", "\n", "X_generator = MemoryMapGenerator(\"X_test.npy\", batch_size=1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's train a model using the original data set and by using a generator defined on the data set that now lives on disk" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] Improvement: 22728.663993260125\tTime (s): 0.01303\n", "[2] Improvement: 351.98156800703146\tTime (s): 0.01358\n", "[3] Improvement: 196.71634020493366\tTime (s): 0.01322\n", "[4] Improvement: 136.9181309074629\tTime (s): 0.0132\n", "[5] Improvement: 98.06255334301386\tTime (s): 0.01312\n", "[6] Improvement: 73.51235836837441\tTime (s): 0.01331\n", "[7] Improvement: 57.17729074554518\tTime (s): 0.01358\n", "[8] Improvement: 46.950419555883855\tTime (s): 0.01334\n", "[9] Improvement: 40.84007772023324\tTime (s): 0.01323\n", "[10] Improvement: 36.80416104476899\tTime (s): 0.01291\n", "Total Improvement: 23767.626893157372\n", "Total Time (s): 0.1473\n" ] } ], "source": [ "from pomegranate import GeneralMixtureModel, MultivariateGaussianDistribution\n", "\n", "d = MultivariateGaussianDistribution\n", "model1 = GeneralMixtureModel.from_samples(d, 2, X, verbose=True, batch_size=1000, max_iterations=10, init='first-k')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] Improvement: 22728.663993260125\tTime (s): 0.01284\n", "[2] Improvement: 351.98156800703146\tTime (s): 0.01327\n", "[3] Improvement: 196.71634020493366\tTime (s): 0.013\n", "[4] Improvement: 136.9181309074629\tTime (s): 0.01306\n", "[5] Improvement: 98.06255334301386\tTime (s): 0.01332\n", "[6] Improvement: 73.51235836837441\tTime (s): 0.01341\n", "[7] Improvement: 57.17729074554518\tTime (s): 0.01342\n", "[8] Improvement: 46.950419555883855\tTime (s): 0.01333\n", "[9] Improvement: 40.84007772023324\tTime (s): 0.01305\n", "[10] Improvement: 36.80416104476899\tTime (s): 0.01355\n", "Total Improvement: 23767.626893157372\n", "Total Time (s): 0.1456\n" ] } ], "source": [ "model2 = GeneralMixtureModel.from_samples(d, 2, X_generator, verbose=True, max_iterations=10, init='first-k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like we're getting the same improvements and that it's not actually significantly slower to load up batches in an out-of-core manner than to generate them from a data set that lives in memory." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/C_Feature_Tutorial_8_Callbacks.ipynb000066400000000000000000001045361420461712600317010ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Callbacks\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com\n", "\n", "It is sometimes convenient to be able to implement custom code at certain points in the training process. A \"callback\" is one way of doing this. Essentially, a callback is an object that has certain methods implemented. When the object is passed in to the `fit` method of a pomegranate object, these methods will be automatically called at predetermined points during training. These methods an implement a wide variety of functionality, but a few common callbacks include model checkpointing, where the model is written out to disk after each epoch, early stopping, where the training of a model stops early based on performance on a validation set, and even TensorBoard, which displays the results of training of multiple models.\n", "\n", "Callbacks are implemented in pomegranate using a similar approach to that of keras. The base callback looks like the following: \n", "\n", "```python\n", "class Callback(object):\n", "\t\"\"\"An object that adds functionality during training.\n", "\n", "\tA callback is a function or group of functions that can be executed during\n", "\tthe training process for any of pomegranate's models that have iterative\n", "\ttraining procedures. A callback can be called at three stages-- the\n", "\tbeginning of training, at the end of each epoch (or iteration), and at\n", "\tthe end of training. Users can define any functions that they wish in\n", "\tthe corresponding functions.\n", "\t\"\"\"\n", "\n", "\tdef __init__(self):\n", "\t\tself.model = None\n", "\t\tself.params = None\n", "\n", "\tdef on_training_begin(self):\n", "\t\t\"\"\"Functionality to add to the beginning of training.\n", "\n", "\t\tThis method will be called at the beginning of each model's training\n", "\t\tprocedure.\n", "\t\t\"\"\"\n", "\n", "\t\tpass\n", "\n", "\tdef on_training_end(self, logs):\n", "\t\t\"\"\"Functionality to add to the end of training.\n", "\n", "\t\tThis method will be called at the end of each model's training\n", "\t\tprocedure.\n", "\t\t\"\"\"\n", "\n", "\t\tpass\n", "\n", "\tdef on_epoch_end(self, logs):\n", "\t\t\"\"\"Functionality to add to the end of each epoch.\n", "\n", "\t\tThis method will be called at the end of each epoch during the model's\n", "\t\titerative training procedure.\n", "\t\t\"\"\"\n", "\n", "\t\tpass\n", "```\n", "\n", "During the training process the `self.model` attribute gets set to the model that is being trained, allowing users to interact with it and use the methods. \n", "\n", "A user can define a custom callback by simply by inheriting from this object (in pomegranate.callbacks) and implementing the methods that they care about. This doesn't have to be all of the methods.\n", "\n", "There are a few callbacks that are built-in to pomegranate:\n", "1. ModelCheckpoint: This callback will save a copy of the model every few epochs in case the user is performing a long-running job.\n", "2. History: This callback will save the logs generated during training and return them in a convenient object.\n", "3. CSVLogger: This callback is similar to History except that it will save the logs to a given CSV file\n", "4. LambdaCallback: This callback allows one to pass in function objects for each of the three methods to execute rather than define ones own custom callback." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sun Dec 01 2019 \n", "\n", "numpy 1.17.2\n", "scipy 1.3.1\n", "pomegranate 0.11.3\n", "\n", "compiler : GCC 7.3.0\n", "system : Linux\n", "release : 4.15.0-66-generic\n", "machine : x86_64\n", "processor : x86_64\n", "CPU cores : 8\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "\n", "from pomegranate import *\n", "\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using a callback\n", "\n", "Let's first take a look at how to use the built-in callbacks. We'll start off with the History callback, which is already automatically created and updated during training. You can return the history object with the data stored in it using the `return_history` parameter during training." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "X = numpy.random.randn(10000, 13)\n", "\n", "d1 = MultivariateGaussianDistribution(numpy.zeros(13), numpy.eye(13))\n", "d2 = MultivariateGaussianDistribution(numpy.ones(13), numpy.eye(13))\n", "\n", "model = GeneralMixtureModel([d1, d2])\n", "_, history = model.fit(X, return_history=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After training we can use the history object to make several useful plots. An intuitive plot is the log probability of the data set given the model $P(D|M)$ over the number of epochs of training." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Log Probability')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(history.epochs, history.log_probabilities)\n", "plt.xlabel(\"Epoch\", fontsize=12)\n", "plt.ylabel(\"Log Probability\", fontsize=12)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we expected, the log probability of the data set goes up during training, because the model is being explicitly fit to the data set.\n", "\n", "Now let's look at how to pass in additional callbacks. Let's take a look at the CSV logger. All we have to do is create the CSVLogger object by passing in the name of the file to save to and then pass that object in to the fit function." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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epochdurationtotal_improvementimprovementlog_probabilitylast_log_probabilityepoch_start_timeepoch_end_timen_seen_batcheslearning_rate
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" ], "text/plain": [ " epoch duration total_improvement improvement log_probability \\\n", "0 1 0.002165 6079.062778 6079.062778 -184116.511097 \n", "1 2 0.002047 6102.391290 23.328513 -184093.182584 \n", "2 3 0.002156 6112.050803 9.659512 -184083.523072 \n", "3 4 0.002037 6117.923677 5.872874 -184077.650198 \n", "4 5 0.002295 6122.236641 4.312964 -184073.337234 \n", "\n", " last_log_probability epoch_start_time epoch_end_time n_seen_batches \\\n", "0 -190195.573874 1.575228e+09 1.575228e+09 None \n", "1 -184116.511097 1.575228e+09 1.575228e+09 None \n", "2 -184093.182584 1.575228e+09 1.575228e+09 None \n", "3 -184083.523072 1.575228e+09 1.575228e+09 None \n", "4 -184077.650198 1.575228e+09 1.575228e+09 None \n", "\n", " learning_rate \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas\n", "from pomegranate.callbacks import CSVLogger\n", "\n", "d1 = MultivariateGaussianDistribution(numpy.zeros(13), numpy.eye(13))\n", "d2 = MultivariateGaussianDistribution(numpy.ones(13), numpy.eye(13))\n", "\n", "model = GeneralMixtureModel([d1, d2])\n", "model.fit(X, callbacks=[CSVLogger(\"logs.csv\")])\n", "\n", "logs = pandas.read_csv(\"logs.csv\")\n", "logs.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The CSV will now contain the information that the History object stores, but in a convenient written format. Note that some of the columns will correspond to information that isn't particularly useful for normal training, such as \"learning rate.\" While conceptually similar to the learning rate used in training neural networks, EM does not necessarily benefit in the same way that gradient descent does from tuning it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementing a custom callback\n", "\n", "Now let's look at an example of creating a custom callback. This callback will take in a training and a validation set and output both the training and validation set log probabilities. Currently, pomegranate does not allow for a user to pass a validation set in to the fit function and monitor performance that way, so this custom callback is an easy way around that limitation." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from pomegranate.callbacks import Callback\n", "\n", "class ValidationSetCallback(Callback):\n", " \"\"\"This callback evaluates a validation set after each epoch.\"\"\"\n", "\n", " def __init__(self, X_train, X_valid):\n", " self.X_train = X_train\n", " self.X_valid = X_valid\n", " self.model = None\n", " self.params = None\n", "\n", " def on_epoch_end(self, logs):\n", " \"\"\"Functionality to add to the end of each epoch.\n", "\n", " This method will be called at the end of each epoch during the model's\n", " iterative training procedure.\n", " \"\"\"\n", " \n", " epoch = logs['epoch']\n", " train_logp = self.model.log_probability(self.X_train).sum()\n", " valid_logp = self.model.log_probability(self.X_valid).sum()\n", " \n", " print(\"Epoch {} -- Training LogP: {:4.4} -- Validation LogP: {:4.4}\".format(epoch, train_logp, valid_logp))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above code seems fairly simple. All we do is store the data sets that are passed in and then calculate their respective log probabilities at the end of each epoch and print that out to the screen. Let's see how it works on a data set." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1 -- Training LogP: -5.874e+03 -- Validation LogP: -2.949e+03\n", "Epoch 2 -- Training LogP: -5.825e+03 -- Validation LogP: -2.93e+03\n", "Epoch 3 -- Training LogP: -5.747e+03 -- Validation LogP: -2.895e+03\n", "Epoch 4 -- Training LogP: -5.675e+03 -- Validation LogP: -2.859e+03\n", "Epoch 5 -- Training LogP: -5.647e+03 -- Validation LogP: -2.845e+03\n", "Epoch 6 -- Training LogP: -5.64e+03 -- Validation LogP: -2.842e+03\n", "Epoch 7 -- Training LogP: -5.637e+03 -- Validation LogP: -2.842e+03\n", "Epoch 8 -- Training LogP: -5.635e+03 -- Validation LogP: -2.842e+03\n", "Epoch 9 -- Training LogP: -5.634e+03 -- Validation LogP: -2.843e+03\n", "Epoch 10 -- Training LogP: -5.633e+03 -- Validation LogP: -2.843e+03\n", "Epoch 11 -- Training LogP: -5.632e+03 -- Validation LogP: -2.844e+03\n", "Epoch 12 -- Training LogP: -5.632e+03 -- Validation LogP: -2.844e+03\n", "Epoch 13 -- Training LogP: -5.631e+03 -- Validation LogP: -2.844e+03\n", "Epoch 14 -- Training LogP: -5.631e+03 -- Validation LogP: -2.844e+03\n", "Epoch 15 -- Training LogP: -5.631e+03 -- Validation LogP: -2.845e+03\n", "Epoch 16 -- Training LogP: -5.63e+03 -- Validation LogP: -2.845e+03\n", "Epoch 17 -- Training LogP: -5.63e+03 -- Validation LogP: -2.845e+03\n", "Epoch 18 -- Training LogP: -5.63e+03 -- Validation LogP: -2.845e+03\n" ] } ], "source": [ "numpy.random.seed(0)\n", "\n", "X_train = numpy.concatenate([\n", " numpy.random.normal(0, 1.0, size=(500, 5)),\n", " numpy.random.normal(0.3, 0.8, size=(500, 5)),\n", " numpy.random.normal(-0.3, 0.4, size=(500, 5))\n", "])\n", "\n", "idx = numpy.arange(X_train.shape[0])\n", "numpy.random.shuffle(idx)\n", "X_train = X_train[idx]\n", "\n", "X_valid = X_train[:500]\n", "X_train = X_train[500:]\n", "\n", "\n", "callback = ValidationSetCallback(X_train, X_valid)\n", "\n", "d1 = MultivariateGaussianDistribution(numpy.zeros(5), numpy.eye(5))\n", "d2 = MultivariateGaussianDistribution(numpy.ones(5), numpy.eye(5))\n", "d3 = MultivariateGaussianDistribution(-numpy.ones(5), numpy.eye(5))\n", "\n", "model = GeneralMixtureModel([d1, d2, d3])\n", "_ = model.fit(X_train, callbacks=[callback])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simple! All we did was create the object and then pass it in to the callbacks parameter during fitting. We can see that the validation log probability initially goes down with the training set log probability, but then goes up a little bit, showing that the model is beginning to overfit a tiny bit to the data set." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/GGBlasts.xlsx000066400000000000000000001331321420461712600253100ustar00rootroot00000000000000PK!ª÷X¤z[Content_Types].xml ¢( ÌTÉjÃ0½úF×+I¡”'‡.Ç6ôkl‹Ø’ÐLÒäï;vJÈ‚i ½ØØÒ¼e†yƒÑª*£%4Î&¢wE6uÚØ<ŸÓ·Î£ˆ”Õªt±£áíÍ`ºö€W[LDA䟤Ĵ€JaìÉ\¨ñgÈ¥Wé\å ûÝîƒL%°Ô¡C /©EIÑëŠo”ÌŒÑóæ^M•å}iRE,T.­> é¸,3)h—.*†ŽÑP ªÊØÃŒaDl …<Ê Äv¤[W1W6°0ïØú †úä´«mÝ# 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The tutorials show probabilistic models of increasing complexity, with a single distribution being the simplest, and a Bayesian network being the most sophisticated. To read a tutorial, just click on the notebook above. 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xl/theme/theme1.xmlPK-!;m2KÁB#nxl/worksheets/_rels/sheet1.xml.relsPK-!~ÁŠå`tpxl/worksheets/sheet2.xmlPK-!~ÁŠå`txl/worksheets/sheet3.xmlPK-!ÕÊza¶œxl/worksheets/sheet1.xmlPK-! —ÅÖuì'Ó2xl/sharedStrings.xmlPK-!‡8+ßóI Û¨xl/styles.xmlPK-!{£–Pù«docProps/app.xmlPK-!±ë è'Å®xl/printerSettings/printerSettings1.binPK-!·Ñs¹F]°docProps/core.xmlPK²’²pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/old/README.md000066400000000000000000000007411420461712600247560ustar00rootroot00000000000000# Tutorial This tutorial covers both the concept of probabilistic modelling, and how to do such using pomegranate. The tutorials show probabilistic models of increasing complexity, with a single distribution being the simplest, and a Bayesian network being the most sophisticated. To read a tutorial, just click on the notebook above. Github can render IPython notebooks natively, so you don't need to go through the complicated procedure of rendering them somewhere else. Tutorial_0_pomegranate_overview.ipynb000066400000000000000000064224001420461712600330230ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/old{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pomegranate: fast and flexible probabilistic modelling\n", "\n", "Author: Jacob Schreiber
\n", "Contact: " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import seaborn, pandas, itertools, time, random\n", "seaborn.set_style('whitegrid')\n", "from pomegranate import *\n", "numpy.set_printoptions(suppress=True)\n", "\n", "random.seed(0)\n", "numpy.random.seed(0)\n", "\n", "import matplotlib\n", "matplotlib.rcParams['xtick.labelsize'] = 14\n", "matplotlib.rcParams['ytick.labelsize'] = 14\n", "matplotlib.rcParams['axes.labelsize'] = 16\n", "matplotlib.rcParams['axes.titlesize'] = 16\n", "matplotlib.rcParams['figure.figsize'] = (10, 6)\n", "matplotlib.rcParams['legend.fontsize'] = 16" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pomegranate is a probabilistic modelling library which focuses on combining an ease of use with a speedy cython backend. Its precursor, yahmm, began while I was an undergraduate working in the nanopore group at UC Santa Cruz which was developing a new DNA sequencing tool. Since it was in its beginning stages, sequence analysis using this tool was primarily done by hand, which was extremely slow and could suffer significantly from human bias. Through the use of hidden Markov models implemented in yahmm this process was automated, speeding up the analysis by several orders of magnitude while removing human bias and cutting out around 80% of the error (increasing accuracy from ~90% to ~98%).\n", "\n", "pomegranate began when I was in graduate school at the University of Washington and taking machine learning courses. Homework assignments frequently required the implementation of a model, and I noticed that many implementations simply involved rearranging the components of yahmm into other useful models, such as Naive Bayes, mixture models, and Bayesian networks. Thus, pomegranate was born.\n", "\n", "While efficient, pomegranate didn't fully utilize the benefits that cython can provide until last summer, while I was doing a summer internship working on sklearn. During the internship I was working on speeding up decision tree regressors, and a mathematical trick I proposed was able to speed them up up to 4x faster in some cases. This taught my how to write bare-bones and parallelizable cython code and utilize computational tricks to speed up seemingly-complex code. I've taken this knowledge to significantly improve the internals of pomegranate and make it extremely fast.\n", "\n", "The main models which pomegranate implements and I will be discussing in this talk are\n", "\n", "1. Probability Distributions\n", "2. Naive Bayes\n", "3. Markov Chains \n", "4. General Mixture Models\n", "5. Hidden Markov Models\n", "6. Bayesian Networks\n", "\n", "One of the core tenets of pomegranate is that every model is really a probability distribution. We're all familiar with simple distributions such as normal or uniform distributions, but even a Bayesian network can be thought of as a distribution which is factored along a graph. This idea makes pomegranate extremely flexible, allowing for models to be stacked within each other to neatly produce hidden Markov models with general mixture model emissions, Naive Bayes classifiers with Bayesian network emissions, and mixtures of hidden Markov models.\n", "\n", "Here is a table of the ways in which models can be stacked. On the left is a chart of what is possible, with dark blue indicating that models can be stacked in a certain way, and light blue indicating a way models can be stacked which will be added in the near future. On the right is a chart of the comparison of pomegranates models to other known packages, with light red indicating model stacking which is known to exist in other packages but pomegranate provides more support for. For example, sklearn implements mixture models, but these are limited to Gaussian emissions, while pomegranate supports mixture models of arbitrary distributions. Dark red indicates new model stacking that no other known package supports, such as mixtures of hidden Markov models. If you know of a package which supports these meshes I'd love to know!" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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CFAAAAAAoGgpR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\nA23atFE/zsrKwpEjRzBgwAD89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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = 'Distributions', 'Naive Bayes', 'Markov Chains', 'GMM', 'HMM', 'Bayesian Networks' \n", "X = [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n", " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", " [0.0, 1.0, 0.5, 0.0, 0.0, 0.0],\n", " [1.0, 1.0, 0.0, 1.0, 1.0, 0.0],\n", " [0.0, 1.0, 0.0, 1.0, 0.0, 0.0],\n", " [0.0, 1.0, 0.0, 0.5, 0.5, 0.0]]\n", "\n", "Y = [[0.0, 0.5, 0.0, 0.5, 0.5, 0.0],\n", " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", " [0.0, 0.5, 0.0, 0.0, 0.0, 0.0],\n", " [0.5, 1.0, 0.0, 1.0, 0.5, 0.0],\n", " [0.0, 1.0, 0.0, 1.0, 0.0, 0.0],\n", " [0.0, 1.0, 0.0, 0.0, 0.0, 0.0]]\n", "\n", "plt.figure(figsize=(20, 10))\n", "plt.subplot(121)\n", "plt.title(\"Model Stacking Allowed\", fontsize=24)\n", "plt.imshow(X, interpolation='nearest', cmap='Blues')\n", "plt.xticks(range(6), labels, rotation=90, fontsize=20)\n", "plt.xlabel(\"..is allowed in this model\", fontsize=20)\n", "plt.ylabel(\"This model...\", fontsize=20)\n", "plt.yticks(range(6), labels, fontsize=20)\n", "\n", "plt.subplot(122)\n", "plt.title(\"Model Stacking Compared To Others\", fontsize=24)\n", "plt.imshow(Y, interpolation='nearest', cmap='Reds')\n", "plt.xticks(range(6), labels, rotation=90, fontsize=20)\n", "plt.xlabel(\"..is allowed in this model\", fontsize=20)\n", "plt.yticks(range(6), '', fontsize=18)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All of the models (and stacks of models) in pomegranate support the same core functionality and methods. You can read about the API more at the readthedocs site. These methods are almost all implemented with a cython backend which releases the GIL. This makes the operations especially fast, but it also makes them all parallelizable using ~multithreading~. In many cases, this can be much more efficient than multiprocessing.\n", "\n", "To utilize this parallelization, pomegranate has implemented some parallelization wrappers, which can run model prediction, log probability calculations, or model fitting in parallel for any model or stack of models. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Models\n", "\n", "Since all models share the same core set of functionality, let's start with a quick API reference. All models have the following methods:\n", "\n", "1. probability\n", "2. log_probability\n", "3. fit\n", "4. sample\n", "5. summarize\n", "6. from_summaries\n", "7. clear_summaries\n", "8. to_json\n", "9. from_json\n", "\n", "The first three methods are likely the most used functions---updating parameters of the distribution, and evaluating samples using the model parameters. \n", "\n", "Methods 5-7 implement pomegranate's out-of-core learning scheme. All models can summarize data down to its sufficient statistics using 'summarize', update model parameters exactly using these sufficent statistics using 'from_summaries', and then clear the stored sufficent statistics using 'clear_sumaries'. This will ultimately allow you to decide if you want to clear the summaries after each parameter updates, or continue with online learning to get better estimates of a single process.\n", "\n", "In addition, for models composed of simple distributions, a sklearn-like API is exposed for the common operations:\n", "\n", "1. predict\n", "2. predict_proba\n", "3. predict_log_proba\n", "\n", "The 'predict' method returns the index of the model which most likely generated the sample, 'predict_proba' returns the posterior probabilities of each model having generated the sample, and 'predict_log_proba' returns the log probabilities.\n", "\n", "\n", "## 1. Probability Distributions\n", "\n", "The basic unit of probabilistic modelling is the probability distribution. Most people are familiar with simple ones like the Normal distribution, but pomegranate supports a wide variety of them. Here is a full list of the probability distributions which pomegranate currently (8/12/2016) supports.\n", "\n", "Univariate Distributions\n", "1. UniformDistribution\n", "2. NormalDistribution\n", "3. LogNormalDistribution\n", "4. ExponentialDistribution\n", "5. BetaDistribution\n", "6. GammaDistribution\n", "7. DiscreteDistribution\n", "8. PoissonDistribution\n", "\n", "Kernel Densities\n", "1. GaussianKernelDensity\n", "2. UniformKernelDensity\n", "3. TriangleKernelDensity\n", "\n", "Multivariate Distributions\n", "1. IndependentComponentsDistribution\n", "2. MultivariateGaussianDistribution\n", "3. DirichletDistribution\n", "4. ConditionalProbabilityTable\n", "5. JointProbabilityTable\n", "\n", "The first two methods are likely the most used functions---updating parameters of the distribution, and evaluating samples using the model parameters. The next three are involved with the out-of-core learning API, where `summarize` will store sufficient statistics from a dataset, `from_summaries` will update the model parameters using the stored sufficient statistics, and `clear_summaries` will reset the sufficient statistics if you don't want past points to influence the update anymore. Lastly, `to/from_json` allow you to serialize your models.\n", "\n", "Let's take a look at one of the simpler distributions, the Normal Distribution. We can specify it easily in terms of the mean $\\mu$ and the standard deviation $\\sigma$." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "a = NormalDistribution(0, 2)\n", "b = NormalDistribution(1, 0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can look at the log probability of some points under these distributions using the `log_probability` method." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-4.737085713764219\n", "-32.22579135264433\n" ] } ], "source": [ "print a.log_probability(5)\n", "print b.log_probability(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can fit any type of distribution to the data using the class method `Model.from_sample(X)`. This uses maximum likelihood estimates on the data to derive parameters for the distribution. In the case of the normal distribution, it just calculates the mean and standard deviation of the data.\n", "\n", "Let's fit a normal distribution to that data and take a look at it" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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zVqcR8U6FOsPlq6++Ytu2bRw8eJDIyEiaNm1K27ZtXZ1NRIpg6VI4dAiGDTMj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\nccxf2uXBB+G++2DWrGjWri3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0dFPUnTxpiriTJ//qFDl71vzXHzspLHlINgez8znaltff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\nnz59Lm5PSkqiV69eFiYTb/Xkk0+yfv16li1bRq1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = numpy.random.randn(100)\n", "\n", "a = NormalDistribution.from_samples(data)\n", "\n", "x = numpy.arange(-3.5, 3.5, 0.1)\n", "y = a.probability(x)\n", "\n", "plt.plot(x, y, c='b')\n", "plt.fill_between(x, 0, y, color='b', alpha=0.3)\n", "plt.scatter(data, numpy.zeros(100)-0.05)\n", "plt.ylabel('Probability')\n", "plt.xlim(-4, 4)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the basic fit method, pomegranate also supports out-of-core learning to train on datasets which don't fit in memory through the `summarize` and `from_summaries` methods. Essentially, the `summarize` method will reduce a batch of data down to its sufficient statistics, and the `from_summaries` method will calculate an exact update to the model parameters based on the stored sufficient statistics. A simple example of this is the Normal Distribution, where the three sufficient statistics are $\\sum\\limits_{i=1}^{n}w_{i}$, $\\sum\\limits_{i=1}^{n}w_{i}x_{i}$, and $\\sum\\limits_{i=1}^{n} w_{i}x_{i}^{2}$. Since this sum can be broken into parts, you can run `summarize` as many times as necessary to analyze the entire dataset. Once you're done summarizing data, you can then run `from_summaries`, which calculates the following:\n", "\n", "\\begin{align}\n", "\\mu &= \\frac{\\sum\\limits_{i=1}^{n}w_{i}x_{i}}{\\sum\\limits_{i=1}^{n}w_{i}} \\\\\n", "\\sigma^{2} &= \\frac{\\sum\\limits_{i=1}^{n} w_{i}x_{i}^{2}}{\\sum\\limits_{i=1}^{n} w_{i}} - \\frac{\\left(\\sum\\limits_{i=1}^{n} w_{i}x_{i} \\right)^{2}}{\\left( \\sum\\limits_{i=1}^{n} w_{i} \\right)^{2}}\n", "\\end{align}\n", "\n", "Let's confirm that this is true by taking a look at two distributions, one fit to the entire thing, and one summarized over five chunks of data." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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SQvdC+8Med3Bw3CrRUYj0Dos4InpqNz5IRTlMYNm1M19ooAY5h7XFb1IPtPqZ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\nE+fURiqcDFTpDdS7eNPg6oXZzROrhyd4eaH29kRr8ELr7Y6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Fit Mean: -0.0174820965846, Fit STD: 0.986767322871\n", "Summarize Mean: -0.0174820965846, Summarize STD: 0.986767322871\n" ] } ], "source": [ "data = numpy.random.randn(5000)\n", "a = NormalDistribution(5, 2)\n", "b = NormalDistribution(5, 2)\n", "\n", "a.fit(data)\n", "b.summarize(data[:1000])\n", "b.summarize(data[1000:2000])\n", "b.summarize(data[2000:3000])\n", "b.summarize(data[3000:4000])\n", "b.summarize(data[4000:])\n", "b.from_summaries()\n", "\n", "\n", "x = numpy.arange(-3.5, 3.5, 0.1)\n", "ya = map( a.probability, x )\n", "yb = map( b.probability, x )\n", "\n", "plt.plot(x, ya, c='b')\n", "plt.plot(x, yb, c='r')\n", "plt.fill_between(x, 0, ya, color='b', alpha=0.3)\n", "plt.fill_between(x, 0, yb, color='r', alpha=0.3)\n", "plt.ylabel('Probability')\n", "plt.show()\n", "\n", "print \"Fit Mean: {}, Fit STD: {}\".format( *a.parameters )\n", "print \"Summarize Mean: {}, Summarize STD: {}\".format( *b.parameters )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Speed\n", "\n", "Let's compare the speed against numpy for extracting model parameters and scipy for calculating the log probability of a point under these parameters." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "numpy time:\n", "100 loops, best of 3: 25 µs per loop\n", "\n", "pomegranate time:\n", "100 loops, best of 3: 13 µs per loop\n" ] } ], "source": [ "data = numpy.random.randn(1000)\n", "\n", "print \"numpy time:\"\n", "%timeit -n 100 data.mean(), data.std()\n", "print\n", "print \"pomegranate time:\"\n", "%timeit -n 100 NormalDistribution.from_samples(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like we're around twice as fast at extracting the mean and standard deviation. This is mostly because the mean and variances are calculated together in pomegranate through the use of the sufficient statistics, instead of going through the entire array twice to calculate the mean and then the standard deviation.\n", "\n", "\n", "Lets take a look at doing this with multivariate gaussian distributions on large amounts of data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "numpy time:\n", "10 loops, best of 3: 904 ms per loop\n", "\n", "pomegranate time:\n", "10 loops, best of 3: 1.39 s per loop\n" ] } ], "source": [ "data = numpy.random.randn(10000000, 10)\n", "\n", "print \"numpy time:\"\n", "%timeit -n 10 data.mean(), numpy.cov(data.T)\n", "print\n", "print \"pomegranate time:\"\n", "%timeit -n 10 MultivariateGaussianDistribution.from_samples(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like pomegranate is able to be faster than numpy on large amounts of data. This effect diminishes with the number of dimensions, converging to be just as fast as numpy the more dimensions are added, but outperforming numpy as more data points are added." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "scipy time:\n", "100 loops, best of 3: 92.7 µs per loop\n", "\n", "pomegranate time:\n", "100 loops, best of 3: 3.09 µs per loop\n", "\n", "pomegranate with (w/ created object)\n", "100 loops, best of 3: 2.58 µs per loop\n", "\n", "logp difference: -3.99236199655e-13\n" ] } ], "source": [ "from scipy.stats import norm\n", "\n", "d = NormalDistribution(0, 1)\n", "\n", "print \"scipy time:\"\n", "%timeit -n 100 norm.logpdf(2, 0, 1)\n", "print\n", "print \"pomegranate time:\"\n", "%timeit -n 100 NormalDistribution(0, 1).log_probability(2)\n", "print\n", "print \"pomegranate with (w/ created object)\"\n", "%timeit -n 100 d.log_probability(2)\n", "print\n", "print \"logp difference: {}\".format( norm.logpdf(2, 0, 1) - NormalDistribution(0, 1).log_probability( 2 ) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the reasons which pomegranate can be so fast at calculating log probabilities is that it is able to cache parts of the logpdf equation so that it doesn't need to do all of the calculations each time. For example, let's look at the Normal distribution pdf equation:\n", "\n", "\\begin{equation}\n", "P(X|\\mu, \\sigma) = \\frac{1}{\\sqrt{2\\pi}\\sigma} exp \\left( -\\frac{(x - \\mu)^{2}}{2\\sigma^{2}} \\right) \\\\\n", "\\end{equation}\n", "\n", "We can take the log of this to simplify it.\n", "\n", "\\begin{equation}\n", "logP(X|\\mu, \\sigma) = -\\log \\left(\\sqrt{2\\pi}\\sigma \\right) - \\frac{(x-\\mu)^{2}}{2\\sigma^{2}}\n", "\\end{equation}\n", "\n", "pomegranate speeds up this calculation by caching $-\\log(\\sqrt{2\\pi}\\sigma)$ and $2\\sigma^{2}$ when the object is created. This means that the equation is simplified to the following:\n", "\n", "\\begin{equation}\n", "logP(X|\\mu, \\sigma) = \\alpha - \\frac{(x - \\mu)^{2}}{\\beta}\n", "\\end{equation}\n", "\n", "We don't need to calculate any logs or exponentials here, just a difference, a multiplication, a division, and a subtraction.\n", "\n", "It is worth noting that numpy and scipy supports vectorized operations across an array of samples at the same time. As the size of this array grows, it may become faster to use numpy or scipy than to use pomegranate. However, most of the models inside pomegranate only require the calculation of one value at a time. Because of this, it's more useful for pomegranate to have fast one-sample calculations than vectorized ones." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example: Gossip Girl\n", "\n", "Let's turn now to a practical and useful example of how probabilistic modelling can help us.\n", "\n", "A few years ago, my girlfriend wanted me to watch Gossip Girl with her. The show follows a group of angsty teenagers in Manhattan who take turns being in relationships with each other while constantly disappointing their parents. In the backdrop, an enigmatic figure known as 'gossip girl' sends out text message 'blasts' to stir up trouble--usually just in time to cause needless drama which could be solved if the characters ever decided to talk to each other. A mystery of the show is 'Who is Gossip Girl', with the implication that one of the main characters is secretly gossip girl.\n", "\n", "Let's take a look at some of the blasts." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Spotted: Lonely Boy. Can't believe the love of his life has returned. If only she knew who he was. But everyone knows Serena. And everyone is talking. Wonder what Blair Waldorf thinks. Sure, they're BFF's, but we always thought Blair's boyfriend Nate had a thing for Serena. \n", "\n", "Why'd she leave? Why'd she return? Send me all the deets. And who am I? That's the secret I'll never tell. The only one. —XOXO. Gossip Girl. \n" ] } ], "source": [ "data = pandas.read_excel('GGBlasts.xlsx')\n", "blasts = data['Blast']\n", "\n", "print blasts[1]\n", "print\n", "print blasts[5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I took that last blast as a challenge. I wanted to figure out which of the characters was gossip girl, and I wanted to use data science in order to do it.\n", "\n", "To do this, the blasts were first all encoded as being either in favor of someone's agenda, or against someone's agenda. Presumably, the character who the blasts help the most would be gossip girl. So let's now take a look at a bar chart of the number of positive blasts minus the number of negative blasts. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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SpKNHj+qnn37SyJEjVbVqVQUFBenTTz/VDz/8oGPHjtk9EwAAgCswVOB8fHx0\n/fp1SVKRIkV08OBBy75Lly4pOTnZMenuc+3aNXl7eytbtmySpKioKOXIkcPyVAhJCg4OVo4cORQV\nFZUpmQAAADKboUkMzz//vGJiYlSvXj298sormjp1qq5fvy5PT0/Nnj1bwcHBjs6phIQETZ06VW3b\ntpWX153Y58+fl6+vr0wmk+U4k8kkX19fnT9/Pt33edomWzxNnzc5Ofmp+rz2xNg9Hsbv8TB+j4fx\ne3TuPHaGClzPnj115swZSVLfvn0VGxuradOmKSUlRVWqVNEnn3xi+C+cPHnyAyc9zJ07VzVr1rS8\nTkxMVJ8+fVSoUCENHTrU6th7y9tdZrM53e2SFBgYaDjrg8Qr3m7v5Sj2/LyuLiYm5qn6vPbE2D0e\nxu/xMH6Ph/F7dK4+drt377a5z1CBe+655/Tcc89JknLlyqXp06fr5s2bunnzpnLlyvVQYTp37qzm\nzZtneIy/v7/l6+vXr6tXr16SpIiICGXNmtWyr0CBArpw4YJVYTObzbp06RLPZwUAAE+sh1rI917e\n3t7y9vZ+6D/n6+srX19fQ8deu3ZNPXv2lNlsVmRkpHLmzGm1PygoSImJiYqKirLcBxcVFaXExEQF\nBQU9dDYAAAB3YLPALV++/KHeqGXLlo8d5l7Xrl1T9+7dde3aNYWHhyspKcky+zVv3rzy9vZW6dKl\nVbduXY0YMUKjRo2S2WzWiBEj1KBBA5UqVcqueQAAAFyFzQL3wQcfGH4Tk8lk9wJ34MAB7d27V5LU\nqFEjq3333iM3YcIEjR49Wt26dZMkhYSE6OOPP7ZrFgAAAFdis8Bt2rQpM3OkUbNmTR06dOiBx/n4\n+GjChAmZkAgAAMA12CxwRYsWzcwcAAAAMMjQQr73u3r1qn777TfFxcXZOw8AAAAeIMNZqP/3f/+n\n9evX6/bt22rSpIkaN26sWbNmaerUqbp586YkqV69epo6darV8h4AAABwHJsFbvXq1RoyZIiKFCmi\nHDlyaNCgQTp+/LjCw8PVtm1blSlTRgcPHtTSpUv1zTffWNZqAwAAgGPZLHDffPONmjZtqgkTJshk\nMikyMlKTJ09Wnz59NGDAAMtx+fLl06pVqyhwAAAAmcTmPXAnTpxQy5YtLU84aN26tVJSUqwecSXd\nmS3KvXAAAACZx2aBu3r1qvLmzWt5fffr+5+GkDNnTiUnJzsoHgAAAO73SLNQAQAA4DwZzkJdvHix\nfvjhB0myPDB+4cKFKliwoOWY+Ph4xyYEAACAlQwL3NKlS9Ns++6779Jsu3ufHAAAABzPZoE7ePBg\nZuYAAACAQdwDBwAA4GYocAD4RlcsAAAgAElEQVQAAG6GAgcAAOBmKHAAAABuhgIHAADgZihwAAAA\nbibDdeDud/HiRe3bt0+XL19WgwYN5OPjoxs3bihLlizy8KALAgAAZAZDBc5sNuvzzz/XvHnzdOvW\nLZlMJn333Xfy8fHR22+/rapVq6pfv36OzgoAAAAZvIT65Zdfav78+erXr5+WLFkis9ls2degQQP9\n5z//cVQ+AAAA3MfQGbhvv/1W/fr1U+/evZWSkmK1r3jx4oqNjXVIOAAAAKRl6AxcfHy8nn/++XT3\nZcmSRUlJSXYNBQAAANsMFbhChQrpyJEj6e47dOiQihUrZtdQAAAAsM1QgWvcuLHCw8O1e/duyzaT\nyaTjx49r1qxZatKkicMCAgAAwJqhe+AGDBigqKgodezYUf7+/pKkgQMH6syZMwoKClKvXr0cGhIA\nAAB/MVTgsmXLpn/9619atWqVtm7dqhIlSliWEGnWrJm8vB5qOTkAAAA8BsPNy9PTUy1btlTLli0d\nmQcAAAAPYOgeuP79+2vjxo26deuWo/MAAADgAQydgTt27Jj69++vvHnzqkmTJmrRooWqVKni6GwA\nAABIh6ECt3btWv3+++9asWKF1q1bp0WLFikgIEAtWrRQ8+bNFRAQ4OicAAAA+B/DT6CvVKmS/vnP\nf2rLli2KiIjQc889p8jISL3yyitq3769IzMCAADgHoYL3F2enp6qV6+eJk6cqOnTp8vPz09RUVGO\nyAYAAIB0PPT6H7GxsVq5cqVWrVql2NhYFSxYUF27dnVENgAAAKTDUIG7cuWK1q5dqxUrVmjfvn3K\nli2bGjZsqI8//li1atWSyWRydE4AAAD8j6ECV7t2baWmpuqFF15QaGioGjVqpOzZszs6GwAAANJh\nqMC9++67at68ufz8/BydBwAAAA9gqMD16NHD0TkAAABgkM0Ct3z5ctWrV0/58uXT8uXLH/hGPGIL\nAAAgc9gscB988IGWLFmifPny6YMPPsjwTUwmEwUOAAAgk9gscJs2bVLBggUtXwMAAMA12CxwRYsW\nTfdrAAAAOJehJzEEBgbqt99+S3ff77//rsDAQLuGkqTLly9r1KhRaty4sSpXrqx69eppxIgRunTp\nktVxISEhKleunNV/EyZMsHseAAAAV2FoFqrZbLa5LzU11SEL+Z49e1bx8fEaOnSoypQpo/j4eH36\n6acaPHiwZs2aZXVsv3799Oabb1pe58iRw+55AAAAXEWGBS41NdVS3lJTU5Wammq1Pzk5WT/++KPy\n5ctn92Bly5ZVWFiY5XWJEiU0bNgw9e7dW9euXVOuXLks+3LmzGm5Xw8AAOBJZ7PAhYWFKTw8XNKd\nWab3nuG6X/v27e2fLB3Xrl2Tt7e3smXLZrV91qxZmjlzpgoXLqzGjRure/fu8vb2zpRMAAAAmc1m\ngatRo4akO5dPw8PD9frrr6tw4cJWx3h7e6t06dJq0KCBY1NKSkhI0NSpU9W2bVt5ef0V+6233lJg\nYKB8fHy0f/9+TZgwQXFxcfrss8/SfZ+YmBiHZ3UlT9PnTU5Ofqo+rz0xdo+H8Xs8jN/jYfwenTuP\nncmc0Q1u/xMWFqY2bdqoUKFCj/0XTp48WRERERkeM3fuXNWsWdPyOjExUT169JCnp6ciIyOVNWtW\nm3927dq1GjRokH755Zc0l3Z3796t4ODgx/sA9/iP6T92ey9HqW+u7+wImSYmJsYhE2qeBozd42H8\nHg/j93gYv0fn6mOXUW8xNImhf//+dgvTuXNnNW/ePMNj/P39LV9fv35dvXr1kiRFRERkWN4k6fnn\nn5ckxcbGOuTePAAAAGezWeCGDx+ut99+WwEBARo+fHiGb2IymTRmzBhDf6Gvr698fX0NHXvt2jX1\n7NlTZrNZkZGRypkz5wP/zN1ToUxqAAAATyqbBe7XX39V586dLV9nxBHLiFy7dk3du3fXtWvXFB4e\nrqSkJCUlJUmS8ubNK29vb0VFRWnfvn2qWbOmcuXKpf379ys0NFQhISFWZ/EAAACeJDYL3ObNm9P9\nOrMcOHBAe/fulSQ1atTIat/de+S8vb21du1ahYWF6ebNm/L391fbtm3Vo0ePTM8LAACQWQzdA+cM\nNWvW1KFDhzI8pmLFilqyZEkmJQIAAHANhh6ltWfPHv3www+W15cuXdJ7772nZs2aady4cUpJSXFY\nQAAAAFgzVOAmTpyoAwcOWF5//vnn2rJli0qWLKmFCxc+cFkQAAAA2I+hAnf06FFVqlRJknTr1i1t\n2LBBw4cP1/Tp0/Xuu+9q9erVDg0JAACAvxgqcImJiZZnj/72229KSkqyPH2hYsWKOnPmjOMSAgAA\nwIqhAleoUCEdPHhQkvTjjz/q2WefVf78+SVJV65cSfNsUgAAADiOoVmoTZs21aRJk7Rjxw5t2bJF\nAwYMsOyLjo5WyZIlHZUPAAAA9zFU4AYMGKCsWbNq37596tmzp7p06WLZd/DgQTVu3NhR+QAAAHAf\nQwXO09NTffv2TXffjBkz7BoIAAAAGXuohXwPHz6snTt36vLly/Lx8VH16tVVtmxZR2UDAABAOgwV\nuNu3b+uDDz7QmjVrZDabLdtNJpNeffVVjR07Vp6eng4LCQAAgL8YKnBhYWFav3693nnnHTVv3lwF\nCxbUuXPntHLlSoWHhysgIEDvvPOOo7MCAABABgvcypUr1bdvX6v74IoWLaq+ffsqJSVFy5Yto8AB\nAABkEkPrwJ09e1ZBQUHp7qtatarOnj1r11AAAACwzVCB8/Pz0549e9Ldt2fPHvn5+dk1FAAAAGwz\ndAm1WbNmioiIkMlksroHbu3atYqIiFDPnj0dnRMAAAD/Y3gh37i4OE2fPl1hYWGW7WazWU2bNlX/\n/v0dFhAAAADWDBU4Ly8vTZw4Ub1799auXbt05coV5c2bVzVq1FCZMmUcnREAAAD3MFTgLl68qJw5\nc6ps2bIs3AsAAOBkNicxpKSkaPr06apWrZpq166t4OBgDRgwQAkJCZmZDwAAAPexeQZu0aJFCg8P\nV40aNfTcc8/p1KlT2rhxo3LlyqXQ0NDMzAgAAIB72CxwS5YsUdu2bTVy5EjLtkWLFmnUqFH69NNP\n5e3tnSkBAQAAYM3mJdRTp06pcePGVtuaNGmilJQUnT592uHBAAAAkD6bBS4xMVG5cuWy2pYzZ05J\n0vXr1x2bCgAAADZlOAs1Pj5ep06dsrxOSUmxbM+TJ4/VsQEBAQ6IBwAAgPtlWOBsPaC+X79+abbF\nxMTYJxEAAAAyZLPAMdMUAADANdkscK1atcrMHAAAADDI5iQGAAAAuCYKHAAAgJuhwAEAALgZChwA\nAICbocABAAC4GQocAACAm6HAAQAAuBkKHAAAgJuhwAEAALgZChwAAICbocABAAC4GQocAACAm6HA\nAQAAuBkKHAAAgJtx6QL34Ycf6uWXX1blypX1wgsvqG/fvjp69KjVMVeuXNHQoUMVHBys4OBgDR06\nVAkJCU5KDAAA4HguXeAqVaqksWPHau3atfr6669lNpvVpUsX3bp1y3LM4MGDFR0dra+++kqRkZGK\njo7WsGHDnJgaAADAsbycHSAj7dq1s3xdrFgxvfvuu2rRooVOnTqlUqVK6ejRo/rpp5+0YMECVa1a\nVZL06aefqkOHDjp27JhKlSrlrOgAAAAO49Jn4O6VmJioZcuWyd/fX8WKFZMkRUVFKUeOHJbyJknB\nwcHKkSOHoqKinBUVAADAoVz6DJwkzZ8/XxMmTFBiYqKeeeYZzZkzR97e3pKk8+fPy9fXVyaTyXK8\nyWSSr6+vzp8/n+77xcTE2C2bR34PpV5Itdv72ZtHfg+7fl5Xl5yc/FR9Xnti7B4P4/d4GL/Hw/g9\nOnceu0wvcJMnT1ZERESGx8ydO1c1a9aUJDVv3ly1a9fWuXPn9PXXX2vgwIFauHChsmfPLklW5e0u\ns9mc7nZJCgwMfMxPcM97nbffe0l3yqU98z1tGL9Hx9g9Hsbv8TB+j4fxe3SuPna7d++2uS/TC1zn\nzp3VvHnzDI/x9/e3fJ07d27lzp1bJUuW1PPPP68aNWpow4YNatmypQoUKKALFy5YFTaz2axLly4p\nf/78Dv0cAAAAzpLpBc7X11e+vr6P/OfNZrNlFmpQUJASExMVFRVluQ8uKipKiYmJCgoKskteAAAA\nV+Oy98CdPHlSGzZsUK1ateTr66s///xTM2fOlLe3t+rXry9JKl26tOrWrasRI0Zo1KhRMpvNGjFi\nhBo0aMAMVAAA8MRy2QLn7e2tHTt2aPbs2bp69ary58+vatWqadGiRSpYsKDluAkTJmj06NHq1q2b\nJCkkJEQff/yxs2IDAAA4nMsWuCJFiigyMvKBx/n4+GjChAmZkAgAAMA1uM06cAAAALiDAgcAAOBm\nKHAAAABuhgIHAADgZihwAAAAboYCBwAA4GYocAAAAG6GAgcAAOBmTGaz2ezsEJll9+7dzo4AAABg\nWHBwcLrbn6oCBwAA8CTgEioAAICbocABAAC4GQocAACAm6HAAYBBMTExzo4AAJIocE6VlJSk1NRU\nZ8dwWxmN3bVr1zIxCZ4WrVq1UuvWrbVgwQJdvXrV2XEAPMWYheokKSkpqly5slasWKEyZco4O45b\nGj58uEJDQ9Nsv3r1qrp166Zvv/3WCanwJDtx4oSWLl2qFStW6MqVK2rYsKFef/11vfDCC86OhqfU\nuXPndOvWLatt/v7+Tkrj+pYvX641a9bozJkzacZtw4YNTkr1aLycHeBp5enpKX9//zTfQDBu7969\nGjdunN5//33LtqtXr6pLly7KkyePE5O5l/j4eF28eDHNGc2KFSs6KZHrKlmypAYPHqxBgwbpxx9/\n1LJly9SjRw8VLlxYr732mlq1aqXChQs7O6ZbuHHjhn744QfFxsaqXbt2ypMnj2JjY5UnTx75+Pg4\nO55Lu3r1qkaPHq1169al+zOES/3pmzVrlmbMmKE2bdrol19+0RtvvKGTJ08qKipKXbp0cXa8h8YZ\nOCf6/vvvtXr1ao0fP16+vr7OjuN2Tp8+rfbt26t9+/bq1auXEhIS1LVrV+XOnVszZ86Ut7e3syO6\ntOjoaA0dOlTHjh3T/f8MmEwmfggYcOPGDS1cuFATJ07UrVu35OXlpYYNG+qDDz5QoUKFnB3PZZ08\neVJdu3bV9evXdfXqVW3YsEEBAQEaN26cEhIS9Nlnnzk7okv78MMPtX//fg0ZMkQDBgzQmDFjFB8f\nr7lz5+r9999X48aNnR3RJTVq1EgDBw5UkyZNFBQUpJUrVyogIEBhYWE6e/asRo4c6eyID4UzcE40\na9YsxcXF6cUXX1ThwoWVPXt2q/2rVq1yUjL34O/vr6+//lodOnSQl5eX1qxZozx58igiIoLyZsBH\nH32kwoULa9SoUfLz85PJZHJ2JLfx22+/6bvvvtO6deuUK1cu9erVS6+99prOnTunqVOn6u2339bS\npUudHdNljRkzRrVr19Ynn3yiatWqWbaHhIRo+PDhTkzmHn788UdNmjRJ1apVk4eHhypWrKgmTZqo\nYMGCWrx4MQXOhj///FNVqlSRJGXLls1yr3SzZs3Utm1bChyMa9SokbMjuL3SpUtr5syZ6tKli4KC\ngvTFF19Q3gw6evSovv/+ez3zzDPOjuI2Zs+eraVLl+rEiROqX7++JkyYoLp168rD4858MH9/f40Y\nMUJ///vfnZzUtUVFRWnx4sXy9PS02l6kSBGdPXvWSancx9WrVy33ueXOnVuXL19WiRIlVKVKFX34\n4YdOTue6ChQooEuXLsnf31/+/v7at2+fAgMDderUqTRXIdwBBc6J+vfv7+wIbqdZs2bpbvfy8lJc\nXJxee+01yzbOYGasbNmyOn/+PAXuISxcuFCvv/66WrdurQIFCqR7TJEiRbgEaMDt27fTbDtz5oxy\n587thDTuJSAgQKdOnZK/v79Kly6tNWvWqHLlyvr3v/+tvHnzOjuey6pZs6Z++OEHVaxYUa1bt1Zo\naKg2bNig33//XQ0bNnR2vIfGPXBwK2FhYYaPpSBnbPv27Zo8ebLeffddlS1bVl5e1r/PcSM5HGXQ\noEHKnj27xowZY7kXKV++fOrbt68CAgI0ZswYZ0d0aXPmzJGHh4c6deqk7du3q0+fPrp9+7ZSU1P1\nz3/+Ux07dnR2RJd0d4zuXqVZuXKl9uzZo5IlS6p9+/Zud/WGApfJqlatqo0bN8rX11dBQUEZ3ne0\nZ8+eTEyGp0358uUtX9/7fWg2m5nEkIGkpCTFxMSkO3P3lVdecVIq9xIfH69OnTpJkuLi4hQYGKjY\n2Fjlz59f8+fPZ1LXQzp9+rR+//13lShRQuXKlXN2HGQSClwm+/7779W0aVN5e3vr+++/z/DYVq1a\nZVIq97Rjxw5JUo0aNdJsN5lMql69ujNiuY2742fL/eMKadu2bXrvvfd0+fLlNPsovQ8nOTlZq1ev\nVnR0tFJTU1WxYkU1a9ZM2bJlc3Y0PKF27dolb29vVa5cWdKdNeGWLl2qMmXKaNiwYWkmEro6Chzc\nVqtWrdSvXz+9/PLLVts3b96ssLAwLVu2zEnJ8KRq2rSpnnvuOQ0aNIhlQh7Dzp07FRQUlOay/e3b\ntxUVFcUvXwbs27dP27dv14ULF9LcgM9EhvTd+zPjxIkTevXVV9WqVSvt2bNH1atX1yeffOLsiA+F\nSQxwW8ePH0/3ckHZsmV1/PhxJyRyT/Hx8emuSs4P0bT++OMPffHFF5S3x9SpUydt3bpV+fPnt9p+\n9epVderUiTOZD/D1119r/PjxKlGihPz8/Kz2sRyQbSdPnrT8zNiwYYNq1aqlUaNGae/evXrnnXco\ncDDu5s2bioiI0Jo1a3T69Ok0s7L4RyxjWbNm1dmzZxUQEGC1/c8//1SWLFmclMp9xMfHa8iQIdq5\nc6dMJpPl3re7+P5Lq2rVqjp+/LiKFy/u7Chu7f7vtbsuX77sdpexnGHu3Ln68MMPmazwkEwmk1JS\nUiRJv/zyixo0aCBJ8vPzS/e2CFdHgXOiqVOnat26derVq5dCQ0M1bNgw/fHHH1qzZo0GDhzo7Hgu\nr06dOpo4caK++OILy9T5y5cva/LkyapTp46T07m+MWPGyMPDQ2vWrNHrr7+uyMhIXbhwQdOmTWMx\nVRvatWuncePG6ezZs+nO3OXxYxnr06ePpDs/SIcOHWr1i1ZqaqqOHDmioKAgZ8VzG9euXVO9evWc\nHcPtVKpUSV9++aVq166tnTt3Ws64/fHHHzaXBXJlFDgnWrdunT755BO9+OKLGjdunF566SUVL15c\npUqV0rZt29SuXTtnR3Rp77//vjp27KiQkBDLafFDhw4pf/78mjRpkpPTub6dO3fqyy+/VOnSpWUy\nmeTr66vg4GB5e3tr6tSpql27trMjupx33nlH0p2nWNyPSQwPli9fPkl3zsDlyZPHasJClixZFBwc\nrDZt2jgrntto2rSpfvzxR3Xo0MHZUdzK8OHD9d5772n9+vXq2bOnSpQoIenO5VR3/MWBAudEFy5c\nUJkyZSRJOXPmVEJCgiSpbt26mjBhgjOjuQU/Pz+tWLFCq1atUkxMjMxms1q1aqVXX32VyzAGJCcn\nW36g+vj46MKFC3rmmWdUunRpHTp0yMnpXNOmTZucHcGthYaGSpKKFi2qbt26KUeOHE5O5J6KFCmi\n6dOna8+ePSpXrlyaW0a6du3qpGSurXz58lq7dm2a7e+9916as+nuwP0SP0HuPjbG399fxYsX19at\nW1WpUiXt3buXqfQGZc+eXW3btnV2DLdUqlQpHTt2TMWKFVP58uW1aNEiFSlSRAsWLOAmfRuKFi3q\n7AhPBBbZfjzffvutcuTIoaioKEVFRVntM5lMFDiDLly4oC1btqhMmTKWpUXcCQXOiRo2bKjt27er\nSpUq6tSpkwYPHqwlS5bo7Nmz6t69u7Pjubz/+7//y3A/i6pmrFOnTjp37pwkqV+/furRo4dWr16t\nrFmzauzYsU5O57rOnDmjXbt2pbt8Az84jVu6dKllAtf9M6A505mxzZs3OzuCW+rZs6dq166tLl26\nKDExUa+99poSEhJ048YNhYaGqnnz5s6O+FBYB86F7N27V1FRUSpZsqRldgxsu/dJAve6O7uN+5Ee\nTlJSko4dO6YiRYqwEr4NK1eu1D/+8Q95eXmlGSOTyUTxMCgyMlIzZ87UG2+8oTlz5qh9+/Y6efKk\ndu3apW7duuntt992dkS3cf78efn6+srDw8PZUVze3/72N82ePVvly5fX8uXL9eWXX2rFihVauXKl\n5s6dq5UrVzo74kPhDJwLqVKliqpUqeLsGG7j4MGDVq9v376t6OhojR8/Xu+++66TUrmH5ORkRUZG\nasOGDYqLi5N05wHZjRs3Vrdu3ZycznV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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = data.fillna(0)\n", "data.sum(axis=0).plot(kind='bar', facecolor='m', edgecolor='m')\n", "plt.ylabel('Positive Blasts - Negative Blasts')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This chart seems to have some important information in it. Specifically, it seems like Blair has by far the most blasts working against her agenda. It seems unlikely that she is gossip girl. Likewise, it seems unlikely that Chuck is gossip girl due to the negativity. This leaves Dan, Nate, and Vanessa as the most likely contendors to be Gossip Girl based entirely on the negativity index. In order to break this tie, let's attempt to use probabilistic modelling to see if we can figure out who it is.\n", "\n", "This brings us to the concept of the beta distribution. Basically, the beta distribution is a probability distribution between 0 and 1, which is parameterized by two shape parameters $\\alpha$ and $\\beta$. Typically, this represents a distribution over the probability that an event occurs, with $\\alpha$ representing the number of times which an event happened, and $\\beta$ representing the number of times which it did not happen.\n", "\n", "A classic example of using a beta distribution is modelling the probability of a coin coming up heads. A coin coming up heads is considered a 'success', and an event coming up tails is considered a 'failure'. Typically a count of 1 is added to the beta distribution on top of the number of observations, for math reasons. Let's take a look." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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FF19QUFBQ4bGxsbFER0ej1WqrEzaxsbH4+fkRGxur2/bCCy/wwgsvVGu82kqS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\nJlI7Oljs6clQvZ6X2tu5qTd6afUnnp62wmv6dFtLit27bS0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PzKPR15WEZ/7NpEk3EfH2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\n/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a = BetaDistribution(0+1, 0+1)\n", "b = BetaDistribution(0+1, 2+2)\n", "c = BetaDistribution(1+1, 2+2)\n", "d = BetaDistribution(25+1, 25+1)\n", "\n", "plt.figure(figsize=(10, 6))\n", "\n", "x = numpy.arange(0, 1, 0.01)\n", "ya = map( a.probability, x )\n", "yb = map( b.probability, x )\n", "yc = map( c.probability, x )\n", "yd = map( d.probability, x )\n", "\n", "plt.plot(x, ya, c='b', label=\"0 heads, 0 tails\")\n", "plt.plot(x, yb, c='c', label=\"0 heads, 2 tails\")\n", "plt.plot(x, yc, c='r', label=\"1 head, 2 tails\")\n", "plt.plot(x, yd, c='g', label=\"25 heads, 25 tails\")\n", "\n", "plt.fill_between(x, 0, ya, color='b', alpha=0.3)\n", "plt.fill_between(x, 0, yb, color='c', alpha=0.3)\n", "plt.fill_between(x, 0, yc, color='r', alpha=0.3)\n", "plt.fill_between(x, 0, yd, color='g', alpha=0.3)\n", "\n", "plt.legend()\n", "plt.ylabel('Probability')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This seems intuitive. If we haven't seen any heads or any tails (blue), we really have a uniform distribution over the possible values of the probability of the coin flipping heads. If we've seen two tails (cyan) then it becomes impossible that there is a 100% chance of seeing heads, and there is a growing probability that there is a 100% probability of tails. However, as soon as we see a single head (red), the probability of only flipping tails goes to 0. This makes sense, because we've observed a head. Continuing, if we flip 10 heads and 10 tails, we get a tighter distribution around 0.5, representing our increased confidence that the parameter is around there. This makes sense, because if we flip a coin 2 times and get 1 head and 1 tail, we're less confident than if we've flipped it 2000 times and get 1000 heads and 1000 tails.\n", "\n", "Let's apply this concept to the Gossip Girl data. We can start off by saying that a blast in favor of a characters agenda is a 'success', and a blast against their agenda is a 'failure'. Let's then learn beta distributions for each character for each of the four seasons, and see how these distributions evolve over time." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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Dg2nQoAH9+vUDoEqVKlStWpUlS5Zw6tQpfH19iYyM5K233sq7uGOLFi3YtGkT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\nWLv69Q02LMuMdnYh6s92v3H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ESQT6lGNC+wl80P8DGlZqyIQvJ9B1eVeS05Pt9ElERK6k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yFhp6nfGPTqkxxJXYhVXNql56y22MuKtpV8ezgcDtQ1JJplqwSf+Go4rZdpYVG4fq8HXjYVcXG5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pZvTZjXGlE8CsHEEI6ebrzNfYTSVIQhHI+ICrpm5Ik8Soq/xJ40kvr5DcptrnFh182s2NyZ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\nfJPpEdAJL039Vzddhq+M0tmBdGMJUpAb4aQZcWV3WNQR9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0oQBKYmTOXe2Hv5OftnBq4f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\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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "characters = { \n", " 'Blair': [[1, 1, 1, 1], [1, 1, 1, 1]],\n", " 'Serena' : [[1, 1, 1, 1], [1, 1, 1, 1]],\n", " 'Dan' : [[1, 1, 1, 1], [1, 1, 1, 1]],\n", " 'Vanessa' : [[1, 1, 1, 1], [1, 1, 1, 1]],\n", " 'Jenny' : [[1, 1, 1, 1], [1, 1, 1, 1]],\n", " 'Chuck' : [[1, 1, 1, 1], [1, 1, 1, 1]]\n", " }\n", "\n", "\n", "for i, row in data.iterrows():\n", " season_s = row['Season/episode']\n", " if isinstance( season_s, unicode ):\n", " season = int( row['Season/episode'][1] ) - 1 \n", "\n", " for character in characters.keys():\n", " if row[character] == 1:\n", " characters[character][0][season] += 1.\n", " elif row[character] == -1:\n", " characters[character][1][season] += 1.\n", "\n", "for character, (good, bad) in characters.items():\n", " characters[character] = [ np.cumsum(good), np.cumsum(bad) ]\n", "\n", "x = np.linspace(0, 1, 100)\n", "plt.figure( figsize=(16, 8))\n", "\n", "colors = { \n", " 'Blair' : 'r',\n", " 'Serena' : 'y',\n", " 'Dan' : 'g',\n", " 'Jenny' : 'c',\n", " 'Vanessa' : 'm',\n", " 'Chuck' : 'b'\n", " }\n", "\n", "for k in xrange(4):\n", " plt.subplot(len(good) / 2, 2, k + 1)\n", " plt.xlabel(\"After Season {}\".format( k+1 ))\n", " \n", " for character, (good, bad) in characters.items():\n", " B = BetaDistribution(good[k], bad[k])\n", " y = numpy.exp([B.log_probability(i) for i in x])\n", " \n", " plt.plot( x, y, label=character, c=colors[character] )\n", " plt.fill_between(x, 0, y, color=colors[character], alpha=0.2)\n", " plt.vlines( good[k] / ( good[k]+bad[k] ), 0, 9, colors=colors[character], linestyles='dashed' )\n", " \n", " plt.legend()\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above we've plotted both the distributions and the maximum likelihood estimates of these distributions plotted as vertical dotted bars. We can see that over the course of the show, the distributions tighten. Two interesting observations are that Dan appears to be the favorite to be Gossip Girl across all seasons, though not by much. Also, except for by the end of Season 3, Blair is not the least likely to be Gossip Girl, being surpassed by Chuck. In fact, by the end of season 1, she is above the median for being Gossip Girl.\n", "\n", "Hopefully this practical example has shown how even basic probabilistic modelling can be applied to derive insight in important, real world, problems." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Naive Bayes Classifiers\n", "\n", "Now that we've covered basic probability distributions, a typical thing you may want to do is create a machine learning classifier using them. After all, you can fit these distributions to some data, and use them to evaluate the likelihood of some future points. Wouldn't it be simple to just see which distribution produced the highest likelihood of a given point, and then assign that label to the point?\n", "\n", "This is the basis of Naive Bayes classifiers, which are a form of probabilistic classifier which rely on Bayes Rule, shown below. M stands for model, and D stands for data.\n", "\n", "

\n", "\\begin{equation}\n", "P(M|D) P(D) = P(D|M) P(M) \n", "\\end{equation}\n", "

\n", "\n", "Let's look at the different parts of this equation. The first part of P(M|D), which is the probability of the model given the data. This is ultimately what we would like to calculate for each model (distribution) in the Naive Bayes classifier. We then return the model label corresponding to the model with the highest P(M|D) score for each point. \n", "\n", "However, P(D) is a bit more ambiguous. What exactly is the probability of that dataset? Instead of worrying about that, we realize that we can simply get rid of that term, since it does not depend on the model, and so will be the same for each component. We can get rid of that term, and are left with the following:\n", "\n", "

\n", "\\begin{equation}\n", "P(M|D) = P(D|M) P(M) \n", "\\end{equation}\n", "

\n", "\n", "Next, we get to P(D|M), which is our basic likelihood function. All distributions implement `model.log_probability(X)`, which calculates logP(D|M). \n", "\n", "Lastly, we come to P(M), which is the prior distribution over the distributions in the Naive Bayes classifier. This term represents the probability that, without even having seeing the data point yet, we would assign a specific label to it. This typically will weight the likelihood by the frequency of that label, so that labels which occur far more frequently are recognized as being more likely. \n", "\n", "pomegranate implements Naive Bayes in a fast, flexible, and simple to use format. Let's see a basic example using two normal distributions.\n", "\n", "We can specify the model in two ways, depending on if we want to fit the model to a dataset, or have specific distributions in mind which we'd like to use to predict." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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UTWioeTxwQImeiAtpjZ6IiDuzNUv29K3DbFPPBw5YG4dINaOGySIi7urcOe+Z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\nvHvvBT9Lfs0Rb1L/68/xP3mEczffTlFNf7te6+M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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a = NormalDistribution(0, 2)\n", "b = NormalDistribution(5, 1)\n", "\n", "x = numpy.arange(-8, 14, 0.1)\n", "ya = a.probability(x)\n", "yb = b.probability(x)\n", "\n", "plt.plot(x, ya, c='b')\n", "plt.plot(x, yb, c='r')\n", "\n", "plt.fill_between(x, 0, ya, color='b', alpha=0.2)\n", "plt.fill_between(x, 0, yb, color='r', alpha=0.2)\n", "\n", "plt.ylabel(\"Probability\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use the predict function on a grid to see where it is predicting 0 and where it is predicting 1s, corresponding respectively to the blue and red distributions." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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Nw/333z9isQMRkelpuzro3XpE43TKer32duDkUhci0p8uCx1Wr159xma+W7ZsGfT91772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\nzowgOzOIXh9wotGG8nftSMtwIi9XRdG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FY3q+MpGjMcnJyRn3QulPPvkE4XC4/8WHjMnh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\nEZkUEzkiIiIik2IiR0RERGRS/x9pA8PLVMykagAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = numpy.arange(-5, 10, 0.5).reshape(-1, 1)\n", "\n", "clf = NaiveBayes([a, b])\n", "y = clf.predict(data)\n", "\n", "n0 = data.shape[0] - y.sum()\n", "n1 = y.sum()\n", "\n", "x = numpy.arange(-8, 14, 0.1)\n", "ya = a.probability(x)\n", "yb = b.probability(x)\n", "\n", "plt.plot(x, ya, c='b', alpha=0.2)\n", "plt.plot(x, yb, c='r', alpha=0.2)\n", "\n", "plt.fill_between(x, 0, ya, color='b', alpha=0.2)\n", "plt.fill_between(x, 0, yb, color='r', alpha=0.2)\n", "\n", "plt.scatter( data[y==0], numpy.zeros(n0)-0.02, color='b', edgecolor='b' )\n", "plt.scatter( data[y==1], numpy.zeros(n1)+0.42, color='r', edgecolor='r' )\n", "plt.ylabel(\"Probability\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This makes sense. The model switches from predicting 0 (blue) to 1 (red) at the position in the plot were the red distribution has a higher probability than the blue distribution. This is pretty much what we would expect from a Naive Bayes classifier with no prior distribution on the components.\n", "\n", "Now, we can get a probabilistic view of the classification by looking at the predicted probabilities instead of just a hard classification. This ends up being P(M|D), which is a vector of probabilities that each model component generated that sample, which will sum to 1 for each sample. We can get these values by using `model.predict_proba`." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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fERGRGzE1pVYqpY7ENoGBQFOTaJMTGip1NEQ+yeVdQHU6Hc6ePYvU1FSz46mp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\nWBvrAIsKb/osCLu5jlIUBUiMF0iM9yLS1oq4uFg0NltQ3aT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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = numpy.arange(-3, 7, 0.25).reshape(-1, 1)\n", "y = clf.predict_proba(data)\n", "\n", "x = numpy.arange(-8, 14, 0.1)\n", "ya = a.probability(x)\n", "yb = b.probability(x)\n", "\n", "plt.plot(x, ya, c='b', alpha=0.2)\n", "plt.plot(x, yb, c='r', alpha=0.2)\n", "\n", "plt.fill_between(x, 0, ya, color='b', alpha=0.2)\n", "plt.fill_between(x, 0, yb, color='r', alpha=0.2)\n", "\n", "plt.scatter( data[y[:,1] <= 0.5], y[y[:,1] <= 0.5, 1]*0.4, color='b', edgecolor='b' )\n", "plt.scatter( data[y[:,1] > 0.5], y[y[:,1] > 0.5, 1]*0.4, color='r', edgecolor='r' )\n", "\n", "plt.yticks(numpy.arange(0, 0.41, 0.05), numpy.arange(0, 1.1, 0.125))\n", "plt.ylim(-0.05, 0.45)\n", "plt.ylabel(\"P(Red Model | Data)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These results mesh pretty well with what we saw before. As the input values increases, there is a higher probability that the same was generated by the red distribution. The point at which the dots switch colors lines up with the point at which the red distribution has a higher probability than the blue distribution.\n", "\n", "We can fit Naive Bayes classifiers in two ways. The first is by passing in an explicit list of instantiated models, possibly with a prior distribution over them, and then calling fit. The second is similar to sklearn, where we only pass a callable for the type of distribution which we would like to fit. We know the number of distributions which we'd like to fit from the label set." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"models\" : [\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " -0.01013271317851311,\n", " 2.0996425031230492\n", " ],\n", " \"name\" : \"NormalDistribution\"\n", " },\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " 3.9719553014786957,\n", " 1.4745674916478821\n", " ],\n", " \"name\" : \"NormalDistribution\"\n", " }\n", " ],\n", " \"weights\" : [\n", " 0.5,\n", " 0.5\n", " ],\n", " \"class\" : \"NaiveBayes\"\n", "}\n" ] } ], "source": [ "X = numpy.concatenate([numpy.random.normal(0, 2, (100, 1)), numpy.random.normal(4, 1.5, (100, 1))])\n", "y = numpy.concatenate([numpy.zeros(100), numpy.ones(100)])\n", "\n", "clf = NaiveBayes.from_samples(NormalDistribution, X, y)\n", "print(clf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Speed\n", "\n", "sklearn implements a few versions of Naive Bayes classifiers, of which Gaussian naive bayes is probably used the most. Let's run a speed test comparing the speed of pomegranate with the speed of sklearn as well as the accuracy of the models." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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lPc3VOlcJRX7BJ7Y0RusJrVhdFns275nspdVFwpE4aHL2khBCCDHzKKVQJUV5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s3evXvV3lhjx47V6Y1V5MGDBxQWFqoN7/XN+Msvv/xS30G8jlavXs3Ro0dZsmQJXbp0oVOnTlSr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\nciSEEEL8Q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VhqSkpOAqOyIlpetGzoeuGzkfv71u/vjHP1KvXj2ef/75MuxVyX311VcMHz6c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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "\n", "def create_dataset(n_samples, n_dim, n_classes):\n", " \"\"\"Create a random dataset with n_samples in each class.\"\"\"\n", " \n", " X = numpy.concatenate([numpy.random.randn(n_samples, n_dim) + i for i in range(n_classes)])\n", " y = numpy.concatenate([numpy.zeros(n_samples) + i for i in range(n_classes)])\n", " return X, y\n", "\n", "def plot( fit, predict, skl_error, pom_error, sizes, xlabel ):\n", " \"\"\"Plot the results.\"\"\"\n", " \n", " idx = numpy.arange(fit.shape[1])\n", " \n", " plt.figure( figsize=(14, 4))\n", " plt.plot( fit.mean(axis=0), c='c', label=\"Fitting\")\n", " plt.plot( predict.mean(axis=0), c='m', label=\"Prediction\")\n", " plt.plot( [0, fit.shape[1]-1], [1, 1], c='k', label=\"Baseline\" )\n", " \n", " plt.fill_between( idx, fit.min(axis=0), fit.max(axis=0), color='c', alpha=0.3 )\n", " plt.fill_between( idx, predict.min(axis=0), predict.max(axis=0), color='m', alpha=0.3 )\n", " \n", " plt.xticks(idx, sizes, rotation=65, fontsize=14)\n", " plt.xlabel('{}'.format(xlabel), fontsize=14)\n", " plt.ylabel('pomegranate is x times faster', fontsize=14)\n", " plt.legend(fontsize=12, loc=4)\n", " plt.show()\n", " \n", " \n", " plt.figure( figsize=(14, 4))\n", " plt.plot( 1 - skl_error.mean(axis=0), alpha=0.5, c='c', label=\"sklearn accuracy\" )\n", " plt.plot( 1 - pom_error.mean(axis=0), alpha=0.5, c='m', label=\"pomegranate accuracy\" )\n", " \n", " plt.fill_between( idx, 1-skl_error.min(axis=0), 1-skl_error.max(axis=0), color='c', alpha=0.3 )\n", " plt.fill_between( idx, 1-pom_error.min(axis=0), 1-pom_error.max(axis=0), color='m', alpha=0.3 )\n", " \n", " plt.xticks(idx, sizes, rotation=65, fontsize=14)\n", " plt.xlabel('{}'.format(xlabel), fontsize=14)\n", " plt.ylabel('Accuracy', fontsize=14)\n", " plt.legend(fontsize=14) \n", " plt.show()\n", " \n", "def evaluate_models():\n", " sizes = numpy.around(numpy.exp( numpy.arange(7, 13))).astype('int')\n", " n, m = sizes.shape[0], 5\n", " \n", " skl_predict, pom_predict = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_fit, pom_fit = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_error, pom_error = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " \n", " for i in range(m):\n", " for j, size in enumerate(sizes):\n", " X, y = create_dataset(size, 20, 2)\n", " \n", " # bench fit times\n", " tic = time.time()\n", " skl = GaussianNB()\n", " skl.fit( X, y )\n", " skl_fit[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom = NaiveBayes.from_samples(NormalDistribution, X, y)\n", " pom_fit[i, j] = time.time() - tic\n", "\n", " # bench predict times\n", " tic = time.time()\n", " skl_predictions = skl.predict( X )\n", " skl_predict[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom_predictions = pom.predict( X )\n", " pom_predict[i, j] = time.time() - tic\n", " \n", " # check number wrong\n", " skl_e = (y != skl_predictions).mean()\n", " pom_e = (y != pom_predictions).mean()\n", "\n", " skl_error[i, j] = min(skl_e, 1-skl_e)\n", " pom_error[i, j] = min(pom_e, 1-pom_e)\n", " \n", " fit = skl_fit / pom_fit\n", " predict = skl_predict / pom_predict\n", " \n", " plot(fit, predict, skl_error, pom_error, sizes, \"samples per component\")\n", "\n", "evaluate_models()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example: Classifying Noisy Signals\n", "\n", "A common case in the analysis of signals is to have a signal which is a mixture of two different underlying phenomena, which each have different characteristics. For example, this could be the electricity usage in a house when the lights are on versus off, or a pedometer which measures when a person is running versus walking. \n", "\n", "Let's assume that we have a perfect segmenter right now, which will divide the signal in regions which are different from the adjacent regions. The task becomes to take this segment of the signal, and classify which phenomena are happening during it. Let's take a look at some data, colored red and blue to describe the underlying phenomena." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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/rDwqr0VHV0h5sOI4k3uaFZvsXbIXOyftNHyMlrfHi95C/UaGxNXggr8t8mPZ\ncobXYmR1a2kpXmQGypBHJ2gdjIl/BJ3XMdxWOgYC33H7x+2qie30HxT4n06njFbHl8EskDSHgN8R\n+/OuURlppFeMx2trTe8/BlYIikpvQW9oZ5oJn8OH7JnZKLkltFRbmSlUqvpTFfK/no/SNaXIPTsX\n9f8Qq+yKbyjG/rP2Y/9Z+9G5TQzk5DHWmqdSHjv1z4qP78npQeMLjTh47UH5Pm+XVxVYCYKA3PNz\nsTVxq+r5BJ8QUee14BdQ/3w9fE7jk2Ljy+I5UJVpVGzek9ODvAvzkLcqT/3cijk2pNUE5HXkfeJ7\nKLmpBD0PibNmS59RuN+h3EmkOWi1nQv7z9qPfSv2mT5X+3/FKpEDVwSHDWivgdLr+Rw+NP+7GTvG\n7UBPbg+q1lfJnRHHEwatxzFpTKv2lObyD48yAhoYbXmw/kY6t/mhm1VKnpo80F2yxF3nRuX9lej4\nYnDWzXy/tRWP10Q/RowGQHNctX/cDlejmI2RGiHKBrXziE42wOT0V/jtQuxbti+kUe53+7Ft5DY0\nvtKI7OnZaP9h+MlGInldABFnWsmcVxNMdno88m2xJJcHW1nuRQpAIygBl7a1ErTmf10xe7IrUP4Y\nYdBq5X0Y7YnZY/Wmg3zCIGjNDSyvo5ctPx7sW77PNGDp2tWFhheCpbrS0JrGFxvh9/hx4JoDKPyO\nuMyNUdAqlbhK2bymf4kls13bg+OE3Y2BTJ+UoVSce5o2N6G/RB30CIKAhJHiNyllJz0dHuyYsAN7\n5u+Rtyu+vlh34kap1N2q5jeaUXprKaofUmc3963chwPfPyD+NqTflFeQqxHk34wgIOc0/UoG3X2R\nzsE+Aa6a4HApX58PnlZx+3Cd+T5HoN3t9KuqKLwdoRlQd4NxplUZ5DqOiJ91+2ft2Jq4FT05octP\n+fv9aP9EvC61f9KOI785gvxL8kO2O9YxaD2ObOvshNfvx/bALIZOnfJggEErGds+frv872jLgwWf\ngOY3m0NuT5l1dBdprX9ucMZvf7eoCL8qj88xYsc7bWO/+Lpi5F0g9qDrZVrlxwmCfL9ZUNi1LdCg\n05wiPS0e+J1+HPqJWI7rq7WWDlUGv4M5prXD48Hh/uOvV30g5EyrzYYapxMTd+7Ew9XmpX2SOpcL\nt0QwAZFV0jEgl/rqnD87t3XC3SrOeuysdspZI6Py4N7CXjhrQjtnpE4cqREd+c4aZ1SjyVhHsoZr\nndst7YL4WPVuHRdcVS7D+3LPzlUtOafseKi4pwItb7ag7QNxHLRR0Cqdw6T7u3d1o+WdFlWr35as\nTQeK/8u/NB/F1xWjaHWR6u49VTzXAAAgAElEQVS6J+vkzhBPsxjEHb7zcMhLN78aev0HIAd+VkkB\nt7bEvTe3Fy1vtaDygcrgb8oTLEeWbpNKdPXknpMbcptcMuwTVOXP28dsR8W94uROHZ92YO+yveL+\ntbhVJb4+hw++rkDQ6vCjZ28wuPS2B9+DturM7/GHZN6lzxcQx9p27epC67utAICurNAJynx9PrnN\nJmWzXdX6JeXHMgatx4kPW1txYV4ekrZtwwV5efisvT0YtGouMAxajx5BEI5aWWwsSBMZtH3cZjgW\nQ0XnDOJp9aDh+dAJHU769UkD3b2IHG8na9IP/BxlYi/0kd8Gys50vvaGFxqwbcQ2MQiw8HPUvk7U\ns2oqHmb5eIzidHHKvn2Yv2dP+A2PIneTGzWP1QzZEiXuwHXOBnFCHwD4T2ur4fbK/byjrAwbLC71\nYnUipu8WFqLaEagKMBmfmndhHvK/lo+6p+qwa9Yu1D1dZ/qYfcv2YddJu0KOL5/DB8EnRF4erHgj\nRo+M5ooWTWNTLg+OIOA14un0oGvn0ZuNuNPjQbc3NLumPM7q/l6n2+Gg3VYZmHZlq9+DUdAqj2Ht\nDnZaHLjyQLAEFoA9WfxWpEBUOu91fKpfpdT0apOqDNdV5wo7M64gCPD1i/vgaYksaJUmf7SP0D96\nOrd2yj9Av9sf/I0EdtGo9N7onCQFrX0FfaZjdvsKxfGtWWlZyJqWJd/etSP43Wi/F0+bIgjV/CaL\nryvGzkk70b23W570Ss6CB9Q+USs/zp5sh8/hw5H7gqXWLW+16P5OtCXaxzoGrceJSqf6B1brchln\nWjmm9agpXVOKbcnbhno3Ilb4zUJrG+q0JZQXScm0n01DwqjBXy9YGWxEOnEJHQMMkkbFNwQnQ9Ir\no2zeLPb895f2W8p4hgSY0casgddyHHGEzMyZf1m+XALoqHTIPfTRdLZUu4yzNkPl4I8OovyucvQV\n9A3J6yvHtEqfqNnswcr7ImkYWQ1a83p74fRa+477Cvtw5H/FBmnNo+JQhHDnM22mZvfs3dizaE9I\nAzmSTgSjjKrR7WafgT2KwDOS8cJdO7tQ+L1Cw8+28NuFyD0vVw7QBtvEnTuRtnNnyO3K77HsF2XY\nddIu+Pp9qPtHXcgM/gWXFaAnpwc9+4IZO2XQ6ap3GXdKBF5GG1QqHy99sHKA5Yfp+FF3o1u1ioDf\n6Q/boZB9Yja2j94OV53LMNPq9/rR9FoTBL+ATFsmKv9UKd4e+K6MgtaurV3BYSGeYHmwr8cnBsuB\nsuoRJ41AytxgpVdvrn6HvDSZEQA5qxkJ5ZAq7feiLAVu3Kiei6DlLXECrP1n7EfB5eKSQdry5ZY3\ngtl1W5INre+2our/gpNaNb3SJI871hJ8AgRBQO3TtXC3mE/+FO8YtMYBT4cHJbeUDGhBY73TtBSc\nckyryN3kRvk95Uc1A9fwnNgoPZYWRI+knMzxpiOkp1jvOFZdKAeRMmBhpvX4YxRwKpc40K1sCDys\n+dVm1QQchgKHcOPLjXC3uCMOGII7I/4v/5L8kAlIOj7pkEsAd8/ejd68XtVjrFq6d29kDzhKpDFc\nA6008bv98pIWEb1+4Jyb19uLC/ICJeQWtgeA5AGubb6jsxOn7tunutZ6BSG4lLBXgM/p0137WiJ1\nYpjp3hccN6iXxXIcdoQGNQaHcufWTnlSG2k7oyD/aGVaIzmDF11ZhLb32uBp88Dn8KHjqw5U/blK\nPv56c8Tf19G8LuglCPQ61cp+WYay28tQ+4R6jG/Hpx1ofLkRxT8KdsrJkyYByJ6RjYM/OAhd0pxf\n2k5kxcu3vd+Gzh2dctAq+AWUrzMe+mJLsiH/4uA4ScErhP1i3XVikOSocMDXE3pMe3u9aHiuAcU/\nKkbt38T3X/m/Ytlv9x7x+LaNCN9+2D1nN6r+FAzi2j5ok9siKbNSVJ/7kd/rTwZVeltwfVZHSZgJ\n0nRIS+YAoUGrs8aJqj9XYee0nbol1ZLurG40v9Gsu/yQclytfaT1X1Ttk7XoK+rD4TsO49ANx/aM\n84lDvQMEVD9UjYYNDRg5fyRGLRoF+wg7Jn1jUsh2nk4PkiYkWXpO1RgQlgcDEHs1W95qwYQLJ2Dy\ntyYf1dcWvILcC+f3+iF4ghMaxBuzE6qW74gPu07apb5NZz3Wo9VQUDWQB3l9i+fq63FNaiomJFn7\nTdLAWTqOdL53Kdht3Nho6TkEvwBPmweHfnoIiRMTsXLXStPti75XpHu79LrK8UxhXzvC30pR39Bk\nMsOSI7Twm/pdfsNsSsE3C9D5ZSdWCasienkpCK1wmi/NIVEeNkkRZAX1soH/U1aGwr4+lPT3Y9mY\nMQDEzK9dMWlM1R+r0PlFJyLV/kk7nJVOTL91Ovafvj+4/zoVLgBC1280+D6k2VUnXS62PQRBMC4P\nPkqZ1khmD1ZmUEvXlKLpZbEjK2lyEqbfOl2+z9LvXxBQ92Qd0q5Ni/kEgnpBqxSMuOpdcoZdElL+\nq/mZ9B8KZgelrOzEiyfKM89qM63KJVHqn61H/bP1mPe3eeK+uQTTYUH2FPWLC15BFUSbkeYeUO17\naT/2LNyDkQtGAoBqPeGqB6vQvkV8D1bLw5tfC46ldVY55c7yxEmJcG13oem1JiRPTZY/GzPuZvOM\nZM0TwckYfU4fElISVB32hd9WV6uVr9XvDNDL/CtnZtYjeAX4+62348t/VY6xp48FoC5TPhYx0xpD\nnkIPOjL1xwI4jjjQ8JL+GBkpmKm4pwJF3ylCwaUFIdv07O/Bzok70fS6tYXslT9yTsQkki5WhhMX\nGPB7/Wj72Hjxd0uvrbhQFX67ENtHbTfZemhpZwyMmE6bQDkhwWA6mpnW20pLsaasbFBfgzQsdETo\nfu+Kn7yykWf2HNKYIm+HV7eh2bgpmJWSxjgZvW7CGOMOKm2jUi+bfKivDx0esbHh9vvxs+Nofdb+\nkn5sS9lmeG3r/DLywA4Iv7SbtjNXmWmNKGgN/F95zZX+JQV32V1daPF4YFMErZFOSiMpuKwApbeV\nhhwnRtc1baNYetz+c/bj0I3icdTybkvI4+CLbaY1mlobvQolo29VqqDo3t0tB6yAWPmjnLjGyrCR\nvgN9OPz/DquGHcSCq8GlW54pZdU7Pu9QZQsBqLPfgGmQmHNaDkpvK1WtJarN2Ot1bkgd1T37etCd\nHTrrryRkyTGvMKCpnaUZiPXGnlb+b6X8b7/Tj6bNTSi4vMDydV3wBMuDE8eJ+bniHxWrMsVK2ooQ\n5URIepRBqLStNHY3EspxqVY1vNCgml3aCqkN1rO3B3kXh3YgHCsYtMZQ+zXtyL9I/weRe24uSn5W\norsQtLb3So+0MLLeNOKAeWd2SHlwuIu5LzaTB3naPKj/Z2xmcO0v6zedsMAKaaa8SN9bzV9qUPjN\nQrR9FH3gqrxQdnwSu6VY3E1u9B2IbaYl0qDe0nMOwlqBerRB6+G7DiPTlinf1rm1U/X3QLV6ju1e\ny8EkCAL6Dsb22LQ0HlVnG+VteksPhPCLa7zKf+qcM1RlVoYtafF/ZkGrNCulTKfdk7F3L87cL2bV\nPuvowEtxtj6rt9eL2qdqVUGGFMS56lz6QVGANDmOlexHJDp1JsFR0gZkvhiWB0tZRQFiJ/E5ueJM\npXZpgmvfwBr7QGjwZTRWs+5vdaq/W/8jjtXrzu6Wx9Yp14Fs/6hd3kejs7bR4W6aaTW5z4j0+srX\nW7lXQPFPQoNJKaNc9B111cOR+44g99zcYAmshaBVul55O7zoK+6LeHiP0fbZ07NVS8NIpOEFeoGS\ndjiD2Yy4Emm5G/2dC/twXbYkW8j+dW7tHFAUYXXuDL/Tj+LritH+33bLgaHgFeTMZ8gsyTqUszVH\nSvpOlOXBSuPOHmf4WNPvykB3VnfUnXkAoqrwiBcMWo8SeRC24vfmOOKAt8erW5uuPelJ41WS0iIv\nRYy0PLjgmwW6kwe5W93YMWWHahyNmeIfF6P05tKYNFz3LNgTUoYaKXuS+DlbGtOm4KoWJ0dQXixa\nP2xFpi3T8rpjUjDlqo/thCm7F+xWNa5jQW/cyYCf02Cc7OtNTfhv28Cy2ErKoLX/YD9qHxPHyEi/\ngZrH1eur1v2jTh43E43hegIV/ELYHu+mV5uwd/HeAXX2hLxulOXByiUTDMefmrxOuDUyDWejDATL\n2qBVOdmJthFmFJiXOcTGTTTLjQiCgPdaW6N6rBUVd1fg8C8Po21L6Hd94IoDOHDFAcPPSDqHJk2O\nbZn9V53mDTPtVTBcplUQBN1rp95ETNJ5wQ+gXjFJli2wVdmasqhOHqollDTHaOMr1joyDl570FIH\nsOATDDOt0jqqkYhmBmDpeFUetw+uE9D0SlPoucDgsqUtozQ7h5TfU47ye4PzXvTs7cHek/eiYUMw\nq1X9l2r0l5pXa/RHWM0WyfABK0FruIBm1v/Osvx6SWlJOC3vNJxwywkhHX56Q4kSxsV+2JOy/dCb\nb2FVA4idgc4KJ+yj7ZYqdJpesVbFqKd7VzfatrSFzudhA6b/z3RMvWGq4WOlTiSyZri2uYaMssd+\n95zdyP9avm6mVdtrKq0FlZwWOr6iwuEwnRVRO7Pwd4uKcMSh7t1x1bnQWySeDDo+088EdnzeAW+b\nFzV/qdG9X8tVG356/1gTBAH1z9XrBklSGba70a0a1xGO9P0oM5A1D4ufgdUsp9/jh6vehewZ2ZZf\n1wplqU+mLROH77I+HtXwOQchaDUaf/HD4mJcXmhxpmIrr6P4fammjDdoQ5TdXob9Z+633Jnwabs6\nG2QD0O/zYfauXfiiI3YZ9HiXdUIWdqWbdyJJMzTGNNtqZbkaaa0+ReO0/0CwkamXaRV86qxwSNAa\nYUeXzKA8eNes4GcXUu5n8h47PR7Tj0AblHr9fvgFAS82NuJ7RUX4p8VlXCIljZNSZRq0MYrBjksN\n9sRJR3eKjZDPyiRorXU68UhNDVK2bUO7prpCL2hVlgfXKoJWu+Iz0Bv7H3afFe2Cru3qpU+M1sbU\nIy0TZUbwCYadHFceOKB7u1lgqm3l/Oig+bg9QD/TKol24kqz9kjNX2pQ80hNSJDTvVvs2PT2elFx\nT4U8/teI3lI3ehInise8q+bozv6dlGqtg2jq9VNxbtO5GLN8DBJG6wejzgp1+3LmL2cOeP+0lG2v\nvPOtl7Y2/asJCWMSBr0NWnFvBQq/XRjaoSCI7U4r1ZRkDT/Jo0z68Ug9pj37enQn5NFe0KTxL/ZR\n6q+syunE3N278dsj6rp4ZY/s2vLQAeDKNegEQUD2zGzsW7rP0r5LCxiHE+n2sdD6XitKbyvFkd+F\njhOQSkQq7q3A7rm7LT+nlAlXlrhaLhcKfF2CRwg7sD8WpMyiVZ3bOtH6gbqnL5rGlMSoDOZolQcb\nTXYgX7QMvrbsGdmWAtd/t6jLHO02Gzq8XlQ6nSEdQcczT7NH7pQyFMFEPFZJweSoRaPCbmNY5q5z\nc+ltpdi7eK/hNtEOl5CyptpGi6rMTnN6DAmYFeeaiTt3mlbKaDsvk7Ztw/eKilAeODZvLS3Fk7W1\naHKrz0WtH7aqzk+dOzp1x7Y7yh3iWrdattB91dI2HPtL+yEIghzwJk4wD1pjPQO79iznEwR4/X5s\nqK8PmTTo90eO4KXANbPFZEhATk8PkrZuRUPg8xUAVdBqU7yFSIJMiXK8od7cF1YpO24Ml4DxGSeo\nOiwGZUraVsBrzeHfv9SRoPzm/YEnijZo7c3tRaYtE23/Na4ACZm8SloRJnBOCbc2aY8vdN/0hoYl\nnxDbSZ6sSpoSDFqn/WxayP1SJ1vq1anB2wyC1p69PRh39jgkjBfv11YOTrhoQkT7Jn0myv3yO/xR\nl9MnjI48aFUuXRMJR4UD9pF2nPzvk4PPlcigNZb4SR5l8jpSihOu3gEtBQ7uZjea32gOWTRZ0hy4\nODo1DZlI1mK1um7ZsRC0yp9bIDMtCAI87R40vNSA7l3mywMYkYNWvUaw4q3pjn2RGnNeQS5PHgzR\nTjqUd2FeyBiggQStRifnwRgnq8dobbVwQStgXLrtEwTcUlKCvJ4enDxKHSwl2GxywyoxivK3o8Xb\n60X2idny2PijQc66xPCrl47zSd8MnV1dFni9SJZu0i4vEGl5sOFxJY1hNHu85iejLQ/WBqLXmGSo\n9CpuPmhrUwW6dx4+jJX7gh2U7hY3ilYX4eB1wefNOz8PexbtQfO/1YHF7nm7sWvWLrT9t019rtPr\noNAG44qGY+eOTuxZuAfVf66WKzvClo9qjiPHEQcaNkafOfZorpleQcCz9fW4pbQ0JCN9UkqK4Uy2\n0ufwZWcnnqythVcQUK8IWtsVAZ59gEUs+V/TnzMjUsoSeaNyebNMqxGzK1w0swd7NEHrzBrIMzBH\ne52SMtRSWWbHFx3YNXuXqkw/ZG1bqSPMYudrv07Qqre/ieP1O2pmrp2JyasnY+Q8cVbdaT+dhsTJ\n6m0nr45+BYQxy8bI/5baNxO/MRGAWA6csTkDADD21LHyds4q47JkW4JNnqlXO350xi9nWN6v0UtH\nY+XulUiZnYIT150o3+7vN55dPJxoMq0JY6Mrce7e1Y2EcQkhnaoRBa02YOVu89nqtWb+v5lY/sVy\ny9uflndaRM8fTxi0HmVS40VVgqnz+/D1+NBb2IusqVk4eO1BOcgS/ALK7y6XJ5MxuhB4w1xsVF+8\nUUJCm10I7LKVILTjiw44DgcyTwOdcCKCgEw6cUqPqXm0Bjsn70TJz0rQlx9dmaJ0wtFtBAd2rf2z\nduw9eS+aXmnCoRsPyd+PVPoneISQ79nKpDISv8ePptebDLMNkcxa1+bxyJmWxAmJql5XYGDl3EYn\n54x/ZYTcZrS+ZLPbjQN9fXJ2KBasBK16JaCbGhvxXmsrNjQ04Lri4pAZSe0INn7NglZvjxcNLzYM\n2Xq9fQV9cNW6UPGbivAbx4pB9q3to7boJ1ULnJLMesIFn4Dmt5rRsy/6Gau155yCb0SX1ZLGa5oF\n0CHBmmbTcLPgGj3UqWg4azs1691ubG4Sx3BJ5X16HXnamUwlhZcXouWtYNWB/B7MglbFZyqVpx75\n/RE0bxYD43DnHe39uefmouRG/ckNrXDrTMTUHMiiajOJf6qqkscUa6+t0l/rq6tDOg38gqAqFbXF\nyXrdyjHeRsNBBK/xmFYjZoFpNI1Nd+C4lYLnKYo+yWiDVml4k3Q8Hl57GM5KJ3aM3yFvo52UR7pW\ny0FrmI9F+3sD9GftTRiXgDErx4TcPu+xeVj6/lJMukzsnBu9bDTm/HmOapul7y81zdRmvJaBMaeo\nn3vkfDEIHrUwGFRNOF/MhI4/dzwA8XOZ8u0pWCWswogZI+TtTvj5CRiVoV/hIgiCfP1UZmSXfrgU\nY5aHvj8jpxecjpQTU3BWxVkYnTFavt3b5dUdPqDMBBsxClonfcu449No4rxw68X6unzwNHlUw/gE\nr/nyhtrnPHXfqRh3xjgxEP3SOBBN/1M6Fm1ahNOLTsecR+Zg/LnjkTI3BYvfWmy6jwAi+k7iDYPW\nQeZqcKHygUr57+Y3xAu03LucaNMNGn29PuxbFuwNl390PjEQA4CtrR3YaDCLZLiLTYJySRyD4Knq\n/9QNFjlzmhA+ClVNKx7Bdc/X58O+FfvQvTeYFVVenDJtmaaTIEj71vJGC3x9PrS+N/BB7vKYVpNe\nVmk68b7iPnlGRvHB4v8EjxDSGBV8AhpebEDeReHHaFSvr0bxD4vR8rbYUHTWOuHtUjQ8TC7g2gb4\nlJ07MS0rC4CYsTKb2TRSRgte611cjNaXnJqVhSV792Lebusl3OFoy/J1t9HJht1w6BCuCozf8gpC\nSIPVBljKtJb9ogwlPy8xnP170Nk0/9fRd6gv7AQjWo5Kh2rqfVeDK3i86byWIAgo/FYh9i0Pntt8\nfT50fGFtPLD03OGC1oPfP2h5ZkpdEcZBRmtk7lu+D36P3/Tcoc10SefjJrcb+b29YTsglZTbKkvZ\n9RrR1xWLM7BKnYvKBqq8L4rfhHYeAG+7F4IgqJcDM+sUUjYc9Y6NwP19h/rQVxx6btCex+RqmijH\nG7t1Mq1WMoum5dmav/0AuhWdB7ZBXjvaKuUyKIZBq8nswUbMzq/RTMTk1mRaXYpDdCAVQYDieLRp\n/oZi4kz5xcRrbtsH1iaVcyiOEWlcuV5JceLYRCz/1DgwkX5zCWMT5EyjLdGGc1rOAWCewbOPsIcE\nRCuyVuCMkjNUbbi0a9JwVuVZmPwdMXNr1Hk0/pzxOOPgGRh/4fjQO4Vgx/moBcHA1p5iD5shHblw\npOF9K3auwNgzxsLb6dU95+vuC4CJl0yU/21UHjz1x1MN136W2kRJU9Ud+toOfiPKSeUEr2D6PS18\nfqHqb6kNNe/xeZh40cSQ7U8rPA2rhFVI/306pv14GkYvHg17kvg5n3X4LKReFT6QP5YxaB1kh244\nhMo/VMp/l68tR/febvlCYR9p180khjRypESRP5ixW1WUj6fr1NPZSyIJWo2uTFUPVMknotb3WlH3\njPhaZpnWur/XyYG5vOuK92eUNe34qgNbU7aien01evN6UX5XcGyi9uLU+r5JIKo4ol31LksBdjh6\npUHaC7B0QdKW+0iZ1q6dXSGZa8EnoOTnJejM7AybgZMmFZL+v+vEXdi7PJipNGo0d+/rxtbErejc\noV8WakuwWS4Pt8Lw5Bz7CQUjYiVbr/x+2j9pR0++OlNnR2jWy64oD06y2+GsdqrGBnq7vSj9Ram8\nDt1AG1pW5F6Qqyr1BGCp42hvxl7sWSgux+D3+NG5LXwpcd6FeSi5qQQ+pw+eNg+yp2ej7I4y8bPU\nKRmVx4QpxtSV3FqC/Ivz5TX7TAW+IrNS+0gqMzJtmbqTqcVqjV+/w49tydvQu994xsvWt9Xns8O/\nPIyu7C4s3bsXp+zbF1mmVRDwRUcHDvT14QbFWq4Ok0BLClr1sjbKsX2uOnX5fMfnHThw5QEUfrMQ\nza8HsqUm1SPKhqNeACPdvzdjL/aevBeCIKDu2eD1TfudSOfWHZN2wNPpQZ9OSaYZ7efqFQRL/ava\nDgDlY0KysJpM60DLg2PFUqbVZPZgI2ZvL5rGpkeTaXUrDtGBzroqeAV0ZHaEH5sP8bgu+k4RytcF\n2iURZFqTtm3DJfn5uo9J+1GaafZuxi9mwD7KjsmXT5bLblPmpCB5ivhBGHUSA2LQmjBKvPBO/u5k\nrBJWIXlKsiqolKTMSkHiWLHtEq7iYdmWZaoxmxIpU6vMxtpG2MIGrSu2rghsHHrf+HPGY9TCUejN\n7YWrKvR7Uj63sqR3zsNzMPUn4oy9CWMS5Gu7fbS1o1B6rhEnqDvyxp1hvHSNkrLdKXgF+bvTSxCM\nmKl+jXClxGZZ2+GAQesg052l1h+cWMmWbFOv9xegu4hz4LFWDtpwDR0paK34TQXaPzZZHy/wNEXf\nK5LLa82C1rJflOHgtZoGc+D8XfG7CmxN3Bo6yQHEWXgFlxDM7ip2X9vQV15k2z9tR6YtU+51V+6b\nzW6LyXhavdJSbZAp9VxLi1gHd0L8X+mtpSEXA+Xf4bIFHZ+LmShl5kN5EtceZ+X3iiXkXdvE8Tv1\nzxqsl5ug06s8AEYn3Gg7D1x+P07btw9f6czMu6OzU57J0ycIeLqsOmQbiaXyYMVnW3BZAfaszFHd\nX+VyhYyDs0Odad01axeypmbJ99c+WYv6v9fL46kLLitQzVJb9WAVDv0s9PcfqZq/1sjjD7u2d8kl\nl1rdWd3ImpGle5/SkfuOIO/CvLDLAUnHjuAW5N9g/TP1KPpOkW7JqF7nijSzr7JywIiVTGuk6aGC\ny0NLf5WdZkOh/b/t8oQ/H7dbX7/UJwi4OD8fSzSl95tNJr3pLwtk1/Uqfnp88meuDW5a3moJHUOu\nTKZqAlPVNU3nNCF4BVWpb39xv7g0jOJ+FamKxSWgZ0+PPL+DVSHlwQjf2QuEZlqVj9Bman0IZlrT\nmoARRwZ/XefTCsKPV2v6V3B5D8OZ9H2RD0c3+/yiuQJI31FVYDIrZdBa+0Qt/G4/Dnz/QNi1t2fe\nFTqjreARkH9RvqXlZtwNbnk2dCueqFVPiPhlZydKZ/kx99PFmHixmD1b+uFSpH4v1TSoG3faOFzQ\ndwFGTB8Be7K4nTI4MwtabSNs8rbJU0M7pCZeMhELnl8g/y1tGy5oTRidoCovFh8ELH57MU5+42SM\nmB4MwuwpodnekOcLBHJGJavSDMsAkH5/OqbdqJg4SvHUys/FlmiTEwgJYxIw5btTAAAn3XuS6b7I\nrxkI4JXL96RenSqP+1UGmnP+oi7blpz0W/G1BI8g76cyUzt6mVj+rP1ukiaZZ3PNvnM9cx+bG9H2\n8Y5B62DT+f1X/V8VCi4TG0pGmRdt0NpX1CffbuWg1ZY+adkBHLn/CKrXV+PgNcYTe+hlHCINBKXe\n97onxV5zaTF51Taa0kzpMa0ftoZMkqL8zGqfFC8OUuNa1cPlE0z3VZUBFgRUP1KNjswOtH6obohJ\nJ/HGjY1yw1zid/nh9/rlTKuyJ0/wC6qTqlnQ6uvzoXNbJ0puK4Gnw6Oa9MDd5JYzdUYXFG2DsuYR\nsYRc7lEXgOyuLvxQM4lLLDLRSoZBq6IBO3b7dlyWb21CkQqHAzm9vVhTWqq63ScIOD8vD5cWiL+j\nFxsa8PsDxuM1rY5pVY499GiuHU6/HyWacbZ2m03uINKWB/vdfvTmhTZ06p4OZo+O/P4IGl/SL/H3\ne/yWx8CWryvHwR+E/o77y8QZWpUZMHd9IGvf4kb2rGw0vxka0PQViuebhhcb0JUd+nuVyGO9XOoS\n2PaP23UzrV1ZOs8VaBfkrMxB8Q3Fhq+lfL1w5cER0Xkqq2WAg0bxFn5UbP6ZKEVSSixxHhHPNXrj\nbj0tHrlTxWjZKhXly5tMxBSyzE/gfmXWS5pVWL7fINMKiJ2/zSaz+urRLQ+28DizSQ71xslKmdaL\nvopo96KWclJK2G1UmX8eTskAACAASURBVFaDKh3BJ6D7UGTzQJgFrdFMxKT9jlyaCvb+Q/2qsdVG\nkqeFBm1W1juVaJcXCpdp/VSnk/XU/ftx1aRqOesmnduVbZT0+9Ox+D/6YxKlc9+U70yRbzNLYNhH\n2OXxpXpLJS7/dDmm3zw9+FyBoE/vt6kV0q4SgOTUZKT9IC1kH8JlWhNGJ2DxfxZj2SfLdO9Xzio+\nYuYIeQWNeU/Ow7TrpyH1mlTV/kv7Jz3OPtKO6bdNx3ld5+mOyU2/P13+97hzxEyqFEjbk+2Y/P5k\nnNd5Hhb/e7EcCI87d5wcPU29Tr0G6ylbTwEQLJMWPIJ8vCiD1hXbVuCM0jOQlKZuaIRb51a7fTgz\nbrc+EdaxgEHrYNM5ubV9qGgQGVwljYITwW9eHy8J1zufYLOh6o/6k2xo90O7VItRoGPUwJYaG9IP\nVm8m2ZASVSnDu7pIHsMrUQZoUpmgFPQqT7h+l9+0cSs10hyVDnTt7ELFvRXIvygfRavF2XSdVU50\nbu8MycDlnJEDV7XYuCq4pAB5F+YFL/zKbKxPUO2PapkLqL/jyvsrceDqA2h4rgHZJ2ar1sBUzjQt\neATdiUeUDQ/lVP6Ocoe8X+fk5uJ1TcblaAWtSr0+Hz4xWNNUm8l06kxy5BME3FEmZmD2BRa5r3A6\nkWTSZrUStDqrndg+ajtqnxY7Qrw6Ezu+pVnyxmxMa9kvy0JKPwF1g1He9s4y1d+CIGBb8jYUXxcM\nWASfgLaP2iwHsv1l/dizYA+qHqjSHa/rKHXAVe1C8Y91gqLA19DwXANyz8k1fhFpKQiXP/R9BT6O\nlndaUP98PQRBwIEr1es7NrzQoArsmzY1wdtrkvkIHOZS1kFPpGMcj+bs5lZFO2FXJOWcM5LFhqzU\n2WA07rbplSb0FvaibUv4QF6vikYSbkxr5f2VcgUSoH++9PZ4UfDtAvG8pmjb2ZPtaIkw06pXHmwl\n6A8pD1Y8RnufTxDkTKs24BoMKXNTIl6uw6g82FntROF3i3TvM2IW9A8k0ypRfYZ2qMbGm9GbpXcg\nE7VZpV3vd29Pj3zuks5Tyg7d9D+kI/UK/TGJU743BYvfWYxZv58l3yZl6yTKiXvsI+zyb85KoGNP\nseOkX5+EU7adEnZb7TnT6HxlH2GHLdGG0ctHY9GmRfLtJ/1OzELO//t8AEDqFam6gTUApKQHO2Fs\nI2zBNp9bgH2EHRn/ykDq1ak4efPJ8jnBlhAMWgWfAJvNhsRxiboBdPof0uV/L/vvMpy6/1Q5MWRL\ntiFxXqJ8/EhZ0eS0ZCzdshSTV08OWaprwgUTVJ+R4BXk7OnY04KzMSeOT8So+aNCMqvaCpWJl05E\nytwULPtsGU7ZfgrsieHbWNKYZ0Ds4E2enozZD80O+7hjwdFdzfs4ZjiWJ8w10HApBJ/YQxTSkPBb\nKw/Y2W1e1pdgsddT8AnIv0STFTPoCAq3LqI8GY6ix7xzeyfyLshD2g/VPXRmn5vyIiv1WkqNJeXJ\nU3ALpkGZ4BXgc/qwe/bukJN/R2aHOJmUD5j1v8GLhF7mrDurGxO+Lp6olB0S1Q9Xq67UIRltRVuh\n7qk6eUyZv098Lz6HDwkjE1THluARdBuXykke2j8JdlhI5Zsej37DJFzQevIbJ5tm4iX20Xb4+/wR\nl65o9WsafZ2BLIUyIPy8owPP1KvLnddXV2OaSaxjZSImqZEsdZIoM612ALNTUlDuVPfOv9nSgjcD\ngWxSs3oHurbqZyi9nV74nD7VWsJ1T9bhpHtOkifD6T8klmw2v94sXowhlgBX3FuBJe8vwZTVYm97\n36E+w4FkUkVC48ZGjDtLPRbH7/XL5bi2BFvocASrM1sHNuvM7Azt9Q98Zb05vSi9tVQurZL3we0P\nmaUTAHaM3YEL/Rfqj3sMVx5sj2ypGyD2HTcxEeWQWrOxq0oj7Xac/5YHlV9Uyp9p1/YuHPnDEcz+\nY2jj5sD3D8BREn7MsVmHgfQbzDk9xzBgUE5UFrIElU8sSW7f0o7KSZXqTGuSzfJ7l2izeO0ej+5S\nJVpm5cEOzeO9giBv7wy0vW2JtgHN0G5kzMoxOC3ntIhnU+74Ur8D0d3ohi/wkx5tt6PPwudrFvTH\nPNNqsDu2ETYILvV+hFsDWGvuo3OD41cN/KW6Gl5BwG9mzTLcJllRiSOZ9M1JaH23VV7Oxip7kh2p\n31MHtPMem4dJl01C+bpyOEodqlJT+4hg+9EoIFSy2WyY85B+qWvItjqZVt19TrHDZrPh9LzTxcfZ\nxTk0TrjxBMz5P2uvNe2n0zBm2Rg0v9GMtO+niR3jT4jL4wCAPdGOxf8Ws9P2JDv8Pr8YKJ8s3i+1\npwD1GNjJl4cuGZQ4LhFjV4yVO9+1naMTLpqAjFczMOV7U5AwKgGTL5usulYueC5Ybq0MWkctHIVT\ntp6CcWeOCxmqpbz+6I25Xf6x9aVsJNKYZ0D8Xs+pE4PYI785YvSQYwaD1hiJ5WQ2QKAMOCU0aLVa\nHhyO1aHcgkdAX4GmRMjgreplkABx2YST3zhZNQOypOlVcWyNdlyt6YQeivukxqsU/CsbA64GF9re\nN84OCB4BPq+4M9r3mH9RMFC30sCQAnbl5BCV91XqbmP0vEmpSarxpc5KJ/oK+lSD9/0ev26ZnjKQ\nV46rlbLkHqP3EOZAGH+e/ux8WtKJd6CLaGsbfR06Qat2m97ANuEyrX2H+tDxifEstdL3IY0VVmZa\nE222kIBVy35OMACrfKDScKyYt8OLxhcaUfuYetxTV3YX0q5OQ29BrzxLtHKaf6nDRPk725uhv2wQ\nAPl36qx0hhx725K2YeEL4qyFtmSbquS+48sOwxJbb7cX1etDxw4fuv4Qpv/PdJ1HBGnLEM0mpWp4\noQHTbwp9vnDlwbYEW8RrAh9PQeuCPXssbTcxMRG3Pu5GJSoxarFYxubt8KLqgSpV5kFiJWAFxI6I\nruyukKU2AHFehDnr55hmuJRVJdoleASfAFeN+Nts2tQE9Z2Rj7/UZvFqXS70DjRo1dx3eWGh3G8p\nBa3R1rfN/etc07HW0qQ7kR7PLW/ql9cKbgFC4KlezsjA1QcO6G6nZBa0RlM9oP2O3OFjLySOTYTH\npT52Ig1aR8wyT4sLfgH3VIjDUcyC1iS7HdAcEyfcfAKmfHeK7jjTSNlH2DFl9RQcvvOw+Lfi+msb\nYZM78CJ9/+Foz79JqepsYdoP09C8uVku5ZVoS2ktvZbNhrGnjpXXjE27Og3jKschZVZoGbwUKNoS\nbJj4jYmY88gceTwrEAxax545VtVOmnHHDNVSPdI4Wu2aszabLeQ9KDvPpt8SvGYpg1YgmIFd9Moi\n1bhfADir8izAFtvv6fSDp4cMsThl6ykRXx/jDYPWGDEKWqMt8xJ8gv7FzeJETGaWFAJpmcbj1JSy\nZ2SH7ptBdtgoaG19txX1z9TLM7gJXgGCIMDv8svBYkhZmcnHZrPbxCVf2r0hpTbKgNhsxk55P4wy\n3crtLGxj5USQfEKyKijVBq3asSSuOlfIpFaCV1A17CTKoFXZcy5lD6vH6H83ygbOhIsmoPMrzYyx\nFhtYVoPWcEtKaDOtyqDV6/fj8drakMZpg7TmbJhMa/7XzcfRaj9XZQMpyWYLaUCF6Ag+XjljuFZ/\nWT+c1aEBcP9BMbuqLHlTjoGRSicr/1CJ0UtG6/7ein8aLPVVLqVScnNoRlMKgm2JNnlmcAC6n5Oj\n3IGiK4tCO7AU6v+h6UHWZFa0QWrDBvVYdSVpluwQ0lrRJpnWiIPWOCwPPjg2dpOj6ZmYmAgg8Bqa\n04mVWaONeDu8yD0nVxzzpflY+w/2o+g75uWmymNEu0as4BXQk6Mf8EYz0+0RpxMXABifkIAun89y\n0HpbaSmunxacDMYsaFXeL2da7TaEm6c49fupaPm3Opic/J3JaHixQZ64TEsuaYwgo2mW9W35dwv8\ngTlcrMbZXkEwbPNIkylFQptp9Vto+oxeMhppP0pD4vhEeZx/pMGAXhnptBunyfMPiPerjxVbZibu\nOfFEPDQnmEFM1vkubDZbTAJWJan9pAyy7CPs8rkwFokOJeU5c/7T8+VxpZJFGxdh5q9mqjJ+saQX\nsALB64It0QabzYaT7lZPvCRPCqU5ROc/OV/1tzQGOmF0QtjfqhEpkNcuJTbt+mkh2xq9n4FQrnMr\nkQLnYxnHtMaI1MsZIsoec8Er6JfH+QWMOGlgg2Mu3Aqc8KdgRlA5SUFPXvhxHtqlWyR6wZTE2+2V\nL44Hrj6A2r/VYvvI7ejOFsvBQsrKzD43u7jky77l++STlDIglp8iTLBpVGqrt104ViYp0QbmIRMz\naZdL0Cm1EzyCvAyRkrI8uHtnsMROylb86RL9YEMZtCpn6ZPvtzApg7ih+L9wQWu4Er7XmtQZlOpA\ndjPJbscrTU24p6ICf9PMzFgfCFrDZVr11slT0n6u2kxrrHjbvPJkSEqVf6jE4V8dVt0mnVd8fT50\nfCZ2RjiPOJG3Kg/5XwsNLpteDn5+BZcEZ8XVZq0AsSQdENfbDFc2tHvebtOAVZfmIyv+kRhQS2OU\nKn5tPHGW0eQdUqbVaMkbW4JN9ZvWW8ZF7zHx5om51joVo/W98cHSOO15SFllEilpKaPund1RDWI0\nGl8JiOc+o8oZwRt50PrTQ4eQ29MjD5VpdLvRYyFo7ff7DddqNXu83AlmodWVdm1ayG3hjtNoApNw\nj/EH7rY6nMjqZFZWhe0o1Eh/IB2L316M6TdNR9r3g59hNEGrtspI+Z0YVYI9UlOjKgeOpiQ6GlIb\nxZ5kD05AlGzH/KfnY8JFEzD29LFmD4+YMmidcfuMkODUnmzHuNOsLQ8TS3JnpkFzXN7vMAepckxr\ntCZcMAEnv3Ey5jxirQyarGHQGiP2ZDtG/Uwss9LLokTKKNMq+MTB58rB6ZHqHwXYFD/aEScGg+Cc\nFTk6j1Cr/3u9PG6mv7QfmbZM9OT1mAd3fqg6JsvXqsuc9GYP1p1pFFAFtNpMqzK7pC2N0HI3ulH3\nD/11bpWslH73F+v3fitJDbKTfi32/mmDfG0mqkhnEozax2p1ZzY1mgFS4kzUCYAF9Zhf3XEvEZ4h\nwgWt4caM3VdZqfr7jcDEUYIgoDEQnGovI00WM63hLlTKsS8AsOVbwX/HMmgFAE+7/rFZ+7g6IPc5\nfGj/vB1tH6m/83DfdzySZuu0slae0XEUbkyrzW5TdfZoe7l1xeGyd/YYD3n82gR1D/vKSePkSUGk\ndVpjwXFkYM9V/7zB0lwQrzVGBF90wVKl0yk/zun3W8q0AsCBvmAHjrKzsdYkmyiNDzXK7Ct/FyNm\njMD4C9RBky3BptuRLQ0hiDRonXjxRMtBq9VnrnQ68XeDteOjEW4VBKUl7y9B+n3pukuGKGeWtWLs\naWOxYvsK1W3KwFd7rVBSTiYYrrIoVmY/KI5DT5yYKM8PYh9px9hTxuKUL0+J+dqe8VidAoQPSqVO\n+LAVkIHTgGEyyqK0H6QN+3VVY83SuejVV1/F6tWrsXLlSqxcuRLXXHMNMjMz5fsFQcBTTz2F8847\nD8uWLcP111+PsrIy4ycM+OSTT3D55ZdjyZIluPzyy/HZZ59F/UbiQuDT3DUrOPNrtJlW+KDfU+0P\nNNwG8DvoU1QNJE1NMp3x0UhvjlhaKM0o2fhSo+nYT8EvRDb5hAAcuEp/DI1q6QTFmNam15pU6/qF\nC1r3n7lfXobHjFkGORJSY1pqkMuZv8BxI2Up5O0NPq8jvw3NioXNIvaGfsffKCjAJ13BscR6PYJG\nmVZtg0oSbo2xPouNQom0xEyvz2eYpZVm7AyXaQ13odJ+z19dFPx3kt2OotNPt7DH1vSXqBvgetPc\nj5w/Eo4SBwouKdBdzibeGR2/VhoChsskBA4Bs/JgJSszqcZjAyyWQevf58/HB0uX4vPlyzElSfx9\nJtlsmPzd0IlIBqp9S/B8YtawNyJNoKO3tqNUPq/HVeWKONMKAKMSEuTAwhUIWq0cDdKwBSD8pIcS\nqXLD6Nie+f+Ca4mOPWMslr6/VHW/YbArrd9p8rvSe+zyz5aH/S1GmmkFgDsPHw6/kUV6mdZfPAOc\nVXNWyO1mHaaRBA4LX1woX8emXBkcD6k3AzEQGgApM63RHJPRmH7TdKwSVsGebMe8J+fhzPIzw16L\nByIez5kAMOFCsXPO8Poh3Rzm1DTtp9OQ9sM0OcFA8cNS0Dp16lSsW7cO77zzDt5++22cddZZuP32\n23HokLh+24YNG/Diiy/ivvvuw1tvvYVJkybhxhtvRG+v8ZjC3NxcrF27FqtXr8Z7772H1atX4847\n70S+xfUb45GygS+fyCI8Z028RJxhU7tcivy8fkG8bwAlbf2KpapGzBgR8RIRAOSZR6WTg9/pNw9K\nBWsTGim3Txivf6HRC1prn6hF6Rr1Wp5WFg23IhZBa/J0xax+gYurlC2b+DXxO490f5UNcrOyOgCw\n6Xz0n3d0IMchZgymXDFFXlBbxeAMYXQBDzdWx2wGSqdBQDs+IQFuQZAnYNIuKyH1xg8006rNdI9Q\nJE0SbTYsHh06RsQqadIjibNcXY2hXUdw9oOzMXpx9K83WEbO15/xUjlhlESvimHxW4sx96/hFzs3\nLA8Ol2nVnBdjGbQqK1IGW0IMayyvSUvDqIQEfH3iRJwyRpwgKclmU12bplw5Jeywk2WfLcPMu2aa\nbqOkN9O6VUkTxQa38vsz64Q89NNDIct5WdHv88mnBZcgoMfnQ2qScWP/qXnzAIidaB+1tWFTo/4a\ny3p6A3NT6a0VCQRL3lOvSYXNZgs5Lo2u+VIJo1nW1Ki6wXKm9SiVuWrpZVpLMmxImZmCxf9ZjPQ/\npmPixYFZyU1+M9oJgcz8f/buPD6q8t4f+OfMlslk3xNISEJCNhISCDtEKFWx2tarePuz1g2rFm5d\nqlbtvXZRiqC2Viu1LSrWlFupWgVbsJbWulS5igRcqJFNUMIiSUCWQNY5vz9mzsk5Z842yYRMyOf9\nevEiM3Nm5szMc855vs/yfZSJD8c+N1Ye1eaIdWDihxMx8mb1upcn/er1tKWgNXTWq7H0i9ORPT90\nrmNfOFwOxI4OLzNxuKI1aC1dUYraLbVwp+kfw3JPq0V2fFeSCxVPV9jKugwA498cj0kfRa5Rm4zZ\nOpLPPvtszJo1C/n5+SgsLMQtt9yCuLg4vPfeexBFEb///e9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amgSH0Kc5rXbm/wouod9L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\n4XQ6MX/+f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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_signal(X, n):\n", " plt.figure(figsize=(16, 6))\n", "\n", " t_current = 0\n", " for i in range(n):\n", " mu, std, t = X[i]\n", " t = int(t)\n", " chunk = numpy.random.normal(mu, std, t)\n", " plt.plot(numpy.arange(t_current, t_current+t), chunk, c='cm'[i % 2])\n", " t_current += t\n", " \n", " plt.ylim(20, 40)\n", " plt.show()\n", "\n", "def create_signal(n):\n", " X, y = [], []\n", "\n", " for i in range(n):\n", " mu = numpy.random.normal(30.0, 0.4)\n", " std = numpy.random.lognormal(-0.1, 0.4)\n", " t = int(numpy.random.exponential(45)) + 1\n", " X.append([mu, std, t])\n", " y.append(0)\n", "\n", " mu = numpy.random.normal(30.5, 0.4)\n", " std = numpy.random.lognormal(-0.4, 0.4)\n", " t = int(numpy.random.exponential(250)) + 1\n", " X.append([mu, std, t])\n", " y.append(1)\n", " \n", " return numpy.array(X), numpy.array(y)\n", "\n", "X_train, y_train = create_signal(1000)\n", "X_test, y_test = create_signal(250)\n", "plot_signal(X_train, 20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each segment is drawn from a normal distribution with the mean drawn from a normal distribution, the standard deviation drawn from a log normal distribution, and the duration drawn from an exponential distribution. These distributions are all fairly similar to each other, but together they may have enough of a difference to produce a good classifier. Let's say that when the classifier gets a segment of signal, it will be classifying based on the mean, standard deviation, and duration of the signal, and now the raw data points. This turns into a basic classification task with three features.\n", "\n", "We can create two classifers in pomegranate and also see how the more well used sklearn classifier does. One uses the appropriate underlying distribution for the features, a Normal distribution for the mean, a log normal distribution for the standard deviation, and an exponential distribution for the duration. We can pass these directly into the `from_samples` method. The second classifier is a, more typical, multivariate Gaussian Naive Bayes model.\n", "\n", "Let's fit our models to 1000 example segments, and then test the validation performance on 250 new segments." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Naive Bayes Using Normal, LogNormal, Exponential: 0.844\n", "Multivariate Gaussian Naive Bayes: 0.828\n", "sklearn Multivariate Gaussian Naive Bayes: 0.828\n" ] } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "\n", "distributions = [NormalDistribution, LogNormalDistribution, ExponentialDistribution]\n", "model = NaiveBayes.from_samples(distributions, X_train, y_train)\n", "print \"Naive Bayes Using Normal, LogNormal, Exponential: \", (model.predict(X_test) == y_test).mean()\n", "\n", "model = NaiveBayes.from_samples(NormalDistribution, X_train, y_train)\n", "print \"Multivariate Gaussian Naive Bayes: \", (model.predict(X_test) == y_test).mean()\n", "\n", "clf = GaussianNB()\n", "clf.fit(X_train, y_train)\n", "print \"sklearn Multivariate Gaussian Naive Bayes: \", ( clf.predict(X_test) == y_test).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like using a mutlviariate Gaussian model is not a bad choice for this data, but it looks like using the proper distributions to model each of the features does allow you to get significantly better performance and generalizability. This improved performance is not just in the hard classifications, but the soft calls (using `model.predict_proba`) will be more reliable and accurate.\n", "\n", "A difference between the pomegranate Naive Bayes clasifier and the sklearn classifier is that pomegranate will learn the full covariance matrix for the classifier, while sklearn will only learn the diagonal. In many applications, learning the full covariance matrix can improve performance as the underlying features are correlated. In this example, all of the data is generated from independent distributions, and so there isn't much correlation.\n", "\n", "## Example: Bayesian Network Naive Bayes\n", "\n", "Bayesian networks are just distributions which are factorized along a graphical structure. As such, we can plug them into a Naive Bayes classifier to compare different structures. For example, let's compare a Bayesian network to a simple independent components distribution. This will let us compare whether or not adding structure to the variables is better than treating them indepdently." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "intelligence = DiscreteDistribution({0: 0.7, 1: 0.3})\n", "difficulty = DiscreteDistribution({0: 0.4, 1: 0.6})\n", "SAT = ConditionalProbabilityTable([[0, 0, 0.95],\n", " [0, 1, 0.05],\n", " [1, 0, 0.20],\n", " [1, 1, 0.80]], [intelligence])\n", "grade = ConditionalProbabilityTable([[0, 0, 0, 0.70],\n", " [0, 0, 1, 0.30],\n", " [0, 1, 0, 0.95],\n", " [0, 1, 1, 0.05],\n", " [1, 0, 0, 0.90],\n", " [1, 0, 1, 0.10],\n", " [1, 1, 0, 0.30],\n", " [1, 1, 1, 0.70]], [intelligence, difficulty])\n", "letter = ConditionalProbabilityTable([[0, 0, 0.90],\n", " [0, 1, 0.10],\n", " [1, 0, 0.40],\n", " [1, 1, 0.60]], [grade])\n", "\n", "s1 = State(intelligence, name=\"intelligence\")\n", "s2 = State(difficulty, name=\"difficulty\")\n", "s3 = State(SAT, name=\"SAT\")\n", "s4 = State(grade, name=\"grade\")\n", "s5 = State(letter, name=\"letter\")\n", "\n", "dN = BayesianNetwork()\n", "dN.add_nodes(s1, s2, s3, s4, s5)\n", "dN.add_edge(s1, s3)\n", "dN.add_edge(s1, s4)\n", "dN.add_edge(s2, s4)\n", "dN.add_edge(s4, s5)\n", "dN.bake()\n", "\n", "intelligence = DiscreteDistribution({0: 0.95, 1: 0.05})\n", "difficulty = DiscreteDistribution({0: 0.4, 1: 0.6})\n", "SAT = ConditionalProbabilityTable([[0, 0, 0.95],\n", " [0, 1, 0.05],\n", " [1, 0, 0.20],\n", " [1, 1, 0.80]], [intelligence])\n", "grade = DiscreteDistribution({0: 0.2, 1: 0.8})\n", "letter = ConditionalProbabilityTable([[0, 0, 0.90],\n", " [0, 1, 0.10],\n", " [1, 0, 0.40],\n", " [1, 1, 0.60]], [grade])\n", "\n", "s1 = State(intelligence, name=\"intelligence\")\n", "s2 = State(difficulty, name=\"difficulty\")\n", "s3 = State(SAT, name=\"SAT\")\n", "s4 = State(grade, name=\"grade\")\n", "s5 = State(letter, name=\"letter\")\n", "\n", "dC = BayesianNetwork()\n", "dC.add_nodes(s1, s2, s3, s4, s5)\n", "dC.add_edge(s1, s3)\n", "dC.add_edge(s4, s5)\n", "dC.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have two fully created Bayesian networks, one without any dependency on grades because the student is cheating. The two changes that we make are that the distribution over intelligence favors those who are not exceptional, because they are more likely to cheat in this example. Secondly, we remove the dependency of grade on difficulty and intelligence because cheating gives a better score. \n", "\n", "Let's build a Bayes classifier using these two networks to try to identify students who are cheating just based on this network structure." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.72058824, 0.27941176],\n", " [0.42857143, 0.57142857],\n", " [0.9 , 0.1 ],\n", " [0.72058824, 0.27941176],\n", " [0.72058824, 0.27941176],\n", " [0.77777778, 0.22222222],\n", " [0.96428571, 0.03571429],\n", " [0.04402516, 0.95597484]])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = numpy.array([[0, 0, 1, 0, 0],\n", " [1, 0, 1, 1, 0],\n", " [1, 1, 0, 0, 0],\n", " [0, 0, 1, 0, 1],\n", " [0, 0, 1, 0, 1],\n", " [0, 1, 1, 0, 1],\n", " [1, 0, 1, 0, 1],\n", " [0, 1, 0, 1, 0]])\n", "\n", "clf = BayesClassifier([dN, dC])\n", "clf.predict_proba(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The student who is most likely to not be cheating makes a lot of sense. It's a smart student taking an easy class who does not do well, but does well on their SAT, and ultimately ends up getting a letter of recommendation. Conversely, the student most likely to be cheating was a poor student who was taking a difficult class, did not end up doing well on the SAT but somehow gets a good grade in the class and does not get a letter. \n", "\n", "It is easy to see how these networks could be expanded to allow for more granularity at each level. For example, each grade could be the actual letter grade and SAT performance might be the number of standard deviations above or below the mean. More variables and connections could allow for more sophisticated differences between the networks. No matter how complicated these networks get, they can be fed into pomegranate." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## 3. Markov Chains\n", "\n", "Markov Chains are a simple model based on conditional probability, where a sequence is modelled as the product of conditional probabilities. A n-th order Markov chain looks back n emissions to base its conditional probability on. For example, a 3rd order Markov chain models $P(X_{t}|X_{t−1},X_{t−2},X_{t−3})$.\n", "\n", "However, a full Markov model needs to model the first observations, and the first n-1 observations. The first observation can't really be modelled well using $P(X_{t}|X+{t−1},X_{t−2},X_{t−3})$, but can be modelled by $P(X_{t})$. The second observation has to be modelled by $P(X_{t}|X_{t−1})$. This means that these distributions have to be passed into the Markov chain as well.\n", "\n", "We can initialize a Markov chain easily enough by passing in a list of the distributions." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "a = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})\n", "b = ConditionalProbabilityTable([['A', 'A', 0.10],\n", " ['A', 'C', 0.50],\n", " ['A', 'G', 0.30],\n", " ['A', 'T', 0.10],\n", " ['C', 'A', 0.10],\n", " ['C', 'C', 0.40],\n", " ['C', 'T', 0.40],\n", " ['C', 'G', 0.10],\n", " ['G', 'A', 0.05],\n", " ['G', 'C', 0.45],\n", " ['G', 'G', 0.45],\n", " ['G', 'T', 0.05],\n", " ['T', 'A', 0.20],\n", " ['T', 'C', 0.30],\n", " ['T', 'G', 0.30],\n", " ['T', 'T', 0.20]], [a])\n", "\n", "model = MarkovChain([a, b])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Markov chains have log probability, fit, summarize, and from summaries methods implemented. They do not have classification capabilities by themselves, but when combined with a Naive Bayes classifier can be used to do discrimination between multiple models.\n", "\n", "Let's see the log probability of some data." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-17.532789486599906" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.log_probability( list('CAGCATCAGT') ) " ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.916290731874155" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.log_probability( list('C') )" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-38.55615991599665" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.log_probability( list('CACATCACGACTAATGATAAT') )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can fit the model to sequences which we pass in, and as expected, see that these sequences subsequently have a higher likelihood." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-9.49627091139\n", "-0.69314718056\n", "-25.2575143893\n" ] } ], "source": [ "model.fit( map( list, ('CAGCATCAGT', 'C', 'ATATAGAGATAAGCT', 'GCGCAAGT', 'GCATTGC', 'CACATCACGACTAATGATAAT') ) )\n", "print model.log_probability( list('CAGCATCAGT') ) \n", "print model.log_probability( list('C') )\n", "print model.log_probability( list('CACATCACGACTAATGATAAT') )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After fitting, we can take a look at the initial distribution and see that it has different parameters now." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"frozen\" :false,\n", " \"dtype\" :\"str\",\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.16666666666666666,\n", " \"C\" :0.5,\n", " \"T\" :0.0,\n", " \"G\" :0.3333333333333333\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}\n" ] } ], "source": [ "print model.distributions[0] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. General Mixture Models\n", "\n", "\n", "It is frequently the case that the data you have is not explained by a single underlying distribution. If we want to try to recover the underlying distributions, we need to have a model which has multiple components. An example is the following data." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = np.concatenate((np.random.normal(1, 2, (250, 1)), np.random.normal(7.85, 1.2, (500, 1))))\n", "numpy.random.shuffle(data)\n", "\n", "plt.figure(figsize=(10, 4))\n", "plt.hist( data, edgecolor='c', color='c', bins=50 )\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This data is clearly made up of two Gaussian distributions, one centered around 8, and one centered around 1. To be exact, the distributions from which the samples were generated are centered around 1 and 7.85 respectively. If we had labels for each sample, we could easily train a Naive Bayes classifier using this data, or just learn the two underlying distributions simply by grouping points with the same label and fitting a Gaussian to that data.\n", "\n", "Unfortunately, we don't have labels for these data points. Without labels for this data, we can't directly fit a Gaussian distribution to some points. If we had explicit parameters for a distribution, we could try to infer labels based on the likelihood of each sample under each distribution (like a Naive Bayes from earlier), but we don't have explicit parameters for the distributions. We're stuck in this chicken-and-egg problem of being able to figure out one given the other, but ultimately having neither. \n", "\n", "We can solve this problem using the Expectation-Maximization (EM) algorithm. The gist is that you start off with a rough estimate of the parameters (such as randomly choosing a sample to be the $\\mu$ and use unit variance for each component in a 1d Gaussian mixture), and then you iterate between inferring labels and performing parameter updates.\n", "\n", "Expectation: Calculate 'responsibility' of each component for each sample, which is the probability that component generated that sample. This sums to 1 for each sample.\n", "\n", "Maximization: Use weighted maximum likelihood estimates to update the parameters of each component, with the weight being the probability of that component generating that sample.\n", "\n", "The difficulty can be in getting an appropriate initial estimate. pomegranate current defaults to using a single iteration of kmeans clustering in order to get initial estimates. Those clusters are then used to fit the initial distributions, and EM is run to refine those estimates until convergence." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] Improvement: 0.935615744235\tTime (s): 0.00016\n", "[2] Improvement: 0.213706399069\tTime (s): 0.000381\n", "[3] Improvement: 0.0611823687568\tTime (s): 0.0002742\n", "Total Improvement: 1.21050451206\n", "Total Time (s): 0.0016\n" ] } ], "source": [ "model = GeneralMixtureModel.from_samples(NormalDistribution, 2, data, verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look both at the underlying data which was used, and at the resulting distributions and weights (prior probabilities) which were learned by the model. " ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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LF5qdRBJZSmt2HjVqFFu2bMHBwYHGjRszePBg8ufP/+T51KlT069fP2rVqhXr8YsWLaJp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\nuHHj2Qutv/KKMa1L4cJGYViyJNSoAVm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kEgnui8A7sVj8YeOJSCSCRCKBUqlklIo9g8GA\n3t5eOBwOtLa24vT0FG63GxaLhXU0purr6+F0OiGXy5OPazc2NtDR0cE62o+KxWK4vr4GALy+viIc\nDiMYDEIikUAmk0Gv12N1dRXl5eVQKBRwOBwQiURoa2tjnDx9vromxcXFGB8fx8nJCVZXV5GVlZW8\n/xYUFPx1U2RWQuhbnn5TqVRYWlpCc3Mz6yjMHBwcQK/X//Fv/16zx3cejwfr6+uIRCJQKpWw2Wyo\nq6tjHYuZz85/M5lMGBsb++E0mau/vx9VVVWYmppiHYWp/f19LC4uIhQKQSaToa+vD/39/YJeaxWN\nRrG0tIS9vT3c3NyA4zhotVqMjo4KaknMZ58znZ2dmJubQyKRwPLyMra2tnB3d4fa2lpMTU3x+ovT\nV9fEZDJ9+oRxdnYWXV1dX/5vKnmEEEIIITwk2HPyCCGEEEL4jEoeIYQQQggPUckjhBBCCOEhKnmE\nEEIIITxEJY8QQgghhIeo5BFCCCGE8BCVPEIIIYQQHqKSRwghhBDCQ1TyCCGEEEJ46Bd968c4xplE\niQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[0.6693856 0.3306144]\n" ] } ], "source": [ "a, b = model.distributions\n", "\n", "plt.figure(figsize=(10, 4))\n", "\n", "x = numpy.arange(-4, 12, 0.1)\n", "p = numpy.exp(model.log_probability(x))\n", "plt.plot(x, p, color='r')\n", "plt.hist( data, edgecolor='c', color='c', normed=True, bins=50)\n", "\n", "plt.ylabel(\"Probability\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.xlim(-4, 12)\n", "plt.show()\n", "\n", "print numpy.exp(model.weights)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems like it did a fairly good job on this simple example. The two learned distributions (in the background) do seem to fit what we expected that distribution to by eye. In addition, the weights are pretty spot on with the process which randomly generated the data (one third in the left hand group, two thirds in the right hand group).\n", "\n", "We can now use these underlying distributions, and Bayes rule, to calculate the probabilities of samples having been generated from the distributions (the posterior probability, P(M|D)). This is the exact same calculation as the Naive Bayes classifier, since we have some underlying models and we want to know which one was most likely to have generated the sample." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y = model.predict(data)\n", "\n", "plt.figure(figsize=(10, 4))\n", "\n", "plt.hist( data[y==0], edgecolor='b', color='b', normed=True, bins=25 )\n", "plt.hist( data[y==1], edgecolor='r', color='r', normed=True, bins=25 )\n", "\n", "plt.ylabel(\"Probability\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.xlim(-4, 12)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like with the Naive Bayes classifier, we can plot the posterior probability of the two models (the distributions) over a grid of points. Since the two distributions are further away and have closer variances than the Naive Bayes example, we see a simpler logistic function representing the change in posterior probability for the two models." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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PHti1axeKi4vVy2WePHlS4zwBAQHYu3cvOnTooPGhQ0xyuRypqakYMWKExhz+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\nUkVFBd5991389ddf2Lx5M1q3bm3QcUzoyeq4ubn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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = numpy.array([numpy.arange(-3, 11, .2)]).T\n", "y = model.predict_proba(x)\n", "\n", "plt.figure(figsize=(10, 5))\n", "\n", "plt.hist( data, edgecolor='c', color='c', normed=True, bins=50, alpha=0.3 )\n", "plt.scatter(x, y[:,0]*0.30, c='b', edgecolor='b', label=\"P(Model 1|D)\")\n", "plt.scatter(x, y[:,1]*0.30, c='r', edgecolor='r', label=\"P(Model 2|D)\")\n", "\n", "plt.ylabel(\"Probability\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(numpy.arange(0, 0.31, 0.06), numpy.arange(0, 1.1, 0.2), fontsize=14)\n", "plt.xlim(-4, 12)\n", "plt.legend(loc=6, fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results of this make sense. But this is still pretty cool, we can take entirely unlabelled data and derive similar results to a Naive Bayes classifier, which requires labeled data. \n", "\n", "The reason which the model is called a General Mixture Model is because it supports mixtures of all types of distributions, not just Gaussians. This is one reason I chose to not abbreviate it to be GMM, just to make this point explicit. We can create a mixtue model of exponential distributions just as easily." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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M1ZBUfM28G9kjV1aoZC8fAPgXAhp8rip8z+i1NboKXq64muPuFa5V9Vw8DhMD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\n3w/T0tKMHj16GG3atDE6dux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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = numpy.concatenate([ numpy.random.exponential(5, (1000, 1)), numpy.random.exponential(0.25, (1000, 1))])\n", "\n", "plt.figure(figsize=(10, 5))\n", "plt.hist( data[data < 20], edgecolor='c', color='c', normed=True, bins=100 )\n", "\n", "plt.ylabel(\"Probability\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.legend(loc=6, fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above data was created from two exponential distributions: one with a high peak which decays early ($\\lambda = 5$), and one with a lower peak which slowly decays over time ($\\lambda = 0.25$). Let's first fit a single exponential distribution to the data and see what it looks like." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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WLdK+ffuUnJys6dOn67777it2mcTERL344ovas2eP/P399be//U2PPvpovp+k+/LLL/X6\n66/r0KFDaty4sUaPHq2uXbs6rW4AAIDKxK3nAJ49e1YhISGaOHFivl8TKcqZM2c0fPhw1alTRytW\nrNCkSZO0aNEivfvuu/Y+u3bt0ujRo9WzZ0+tWbNGPXv21BNPPKGff/7ZlZsCAABQYbn1PoAxMTGK\niYmRJD3zzDMlFrd27VqdO3dOM2fOlK+vr0JCQnTw4EG9++67GjZsmCwWixYvXqwOHTro4YcfliQ9\n/PDD2rZtmxYvXqxXXnmlxNcAAAAwm2v6PoC7d+9Wu3bt8u0tjI6O1uuvv64jR47o+uuv1+7duzVo\n0KB8y0VHR2vJkiVurRUAAKCiuKbvA5iamqp69erlawsMDLTPu/7665Wammpvy9snJSXFbXUCAABU\nJGX6LeBchw4d0sGDByVJzZo1U+PGjZ1aVF55L/aQJMMwHLYX7FPc/IKsVuuVFVeJnD9/nnEogDFx\njHFxjHFxjHEpjDFxjHFxrzIFwPT0dE2cOFGbNm2Sh4ft+hHDMPSXv/xF06ZNU+3atZ1aXGBgoFJT\nU/O1paWlSZLq1KlTbJ+CewWLExoaepWVVnxWq5VxKIAxcYxxcYxxcYxxKYwxcYxxcSwhIcEl6y3T\nVcCTJk3SoUOHtGTJEu3Zs0d79uzRhx9+qCNHjmjy5MlOLy4iIkI7d+7UhQsX7G3x8fGqW7euGjVq\nZO8THx+fb7n4+HhFRkY6vR4AAIDKoEwB8IcfftCLL76otm3bysvLS15eXmrbtq1eeOEF/fDDDyUu\nn5GRIavVKqvVqpycHCUlJclqtSopKUmSNHv2bA0ZMsTev2fPnvLz89P48eP1v//9T1999ZUWLFhg\nvwJYkgYPHqytW7dq/vz5OnjwoObPn69t27blWw8AAAAuK1MADAgIkJ+fX6F2Pz8/1apVq8Tl9+7d\nq969e6t37946f/684uLi1Lt3b82ZM0eSlJKSosOHD9v716hRQ++8846Sk5N1//3364UXXtDw4cM1\nbNgwe582bdrolVde0apVq9SrVy+tXr1ar776qsLDw8uyaQAAAKZRpnMAH3nkEU2bNk0vv/yy/erc\nEydOaMaMGXr00UdLXL5Dhw5KTEwscv6MGTMKtTVv3rzEW7p0795d3bt3L/H1AQAAUMobQed15MgR\nde7cOV8A9PHxUVpamvr27euaKgEAAOA0pboRNAAAACqPMt0IGgAAABVfmS4CAQAAQMVXpotAMjMz\nNW/ePK1bt05JSUnKzs7ON587eAMAAFz7yrQH8PXXX9fq1as1bNgweXh4aOzYsRo4cKBq1aql5557\nzlU1AgAAwInKFAC/+OILTZkyRf3795eHh4e6dOmiSZMm6bHHHiv0axwAAAC4NpUpAKalpSk4OFiS\nVK1aNf3555+SpI4dO5bql0AAAABQ/soUAK+77jolJydLkho3bmwPfbt375avr6/zqwMAAIDTleki\nkK5du+rHH39URESEBg8erDFjxmj58uVKTk7WP/7xD1fVCAAAACcqUwAcM2aM/XH37t1Vv3597dq1\nS02aNFGnTp2cXhwAAACcr0wBsKCIiAhFREQ4q5ZyY9m8WZJUz9tbx6OiyrcYAAAAFytzANy3b58W\nL16sAwcOSJKaNWumoUOHKiwszOnFuduJrKzyLgEAAMDlynQRyNq1a9WnTx+lpKQoJiZGMTExSktL\nU9++fbVmzRpX1QgAAAAnKtMewNdee01PPPGERo4cma99/vz5ev3119WrVy+nFgcAAADnK9MewJMn\nT+ruu+8u1N69e3elpaU5rSgAAAC4TpkCYIcOHbR9+/ZC7du3b1f79u2dVlR5smzerPpbtpR3GQAA\nAC5T4iHgr776yv74jjvu0OzZs/Wf//zHfvXv7t279fXXX2vUqFGuq9LNuBgEAABUZiUGwMcff7xQ\n2/Lly7V8+fJ8bS+99JIGDhzovMoAAADgEiUGwP3797ujDgAAALhJmc4BBAAAQMVX5htBb968WQsX\nLtSBAwdksVgUHBysESNGKCYmxhX1AQAAwMnKtAfwk08+0ahRo9S4cWM99dRTGjNmjBo1aqRHH31U\nK1ascFWNAAAAcKIy7QFcuHChxo8fr0GDBtnb+vbtq7CwMC1cuFB9+vRxeoEAAABwrjLtAUxKSlLH\njh0Ltd9xxx06evSo04oCAACA65QpADZo0EBbHNwk+YcfflDDhg2dVhQAAABcp0yHgIcPH66XXnpJ\n//3vfxUZGSmLxaKEhAStWbNGkydPdlWNAAAAcKIyBcD+/furTp06euedd/T1119Lkpo2barXXntN\nd955p0sKBAAAgHOVOgBmZ2dry5Ytateunbp27erKmgAAAOBCpT4H0MvLS6NGjVJGRoYr6wEAAICL\nlekikJtvvlmHDh1yVS0AAABwgzIFwFGjRmnGjBnauHGjjh07plOnTuWbAAAAcO0r00UgDz30kCRb\nELRYLPZ2wzBksVhktVpLXMeSJUu0aNEipaSk6KabbtKECRPUrl07h33Hjx+vVatWFWr38/PT7t27\nJUnbtm3T4MGDC/VZv369mjVrVqrtAgAAMJMyBcD333//ql5s/fr1mjZtmp577jm1bdtWS5cu1YgR\nI7Ru3To1aNCgUP+JEydqzJgx+doGDBig9u3bF+q7bt06+fv7258HBARcVa0AAACVVakC4Llz5zRr\n1ixt3LhR2dnZuv322zVx4sQyh6x3331XsbGx6tevnyRp8uTJ+v7777Vs2bJCQU+SatSooRo1atif\nJyQk6PDhw5o1a1ahvgEBAYQ+AACAUijVOYBz5szRqlWr9Je//EX33HOPtmzZoilTppTphTIzM7Vv\n3z5FRUXla4+KitKuXbtKtY5PPvlEN910k9q0aVNoXp8+fRQdHa0hQ4Zo69atZaoNAADATEq1B/Dr\nr7/W1KlTdc8990iS7r33Xg0YMEAXL16Up6dnqV4oPT1dFy9eVGBgYL72OnXqKD4+vsTlT58+rQ0b\nNmj06NH52oOCgjRlyhS1atVKWVlZWrNmjYYOHaoPPvjA4aFiAAAAsytVADx+/Hi+CzVat24tT09P\nJScn67rrrivTC+a9eKS4toLWrl2rixcvqlevXvnamzZtqqZNm9qfR0ZG6ujRo1q0aNFVBcDSXNBS\nmZw/f95021wSxsQxxsUxxsUxxqUwxsQxxsW9ShUAL168KG9v73xtnp6eys7OLvUL1a5dW56enkpJ\nScnXnpaWVmivoCPLly/XXXfdpVq1apXYNzw8XOvWrSt1bY6EhoZe1fIVjdVqNd02l4QxcYxxcYxx\ncYxxKYwxcYxxcSwhIcEl6y1VADQMQ08//XS+EJiZmanJkyfL19fX3jZv3rwi1+Hj46OwsDDFx8fr\n7rvvtrfHx8frrrvuKvb19+zZo/3792vChAmlKVdWq1VBQUGl6gsAAGA2pQqAsbGxhdruvffeMr/Y\nsGHDNHbsWLVu3Vpt2rTRsmXLlJycrP79+0uSxo4dK0mFrvL9+OOP1aRJE91yyy2F1vnee++pUaNG\nCg4OVlZWltauXauNGzcqLi6uzPUBAACYQakC4PTp053yYj169FB6errmzp2r5ORkhYSEaMGCBWrY\nsKEk6dixY4WWOXPmjNavX69HHnnE4bmCWVlZmjlzpk6cOCFfX18FBwdrwYIFiomJcUrNAAAAlU2Z\nbgTtDAMHDtTAgQMdzvvggw8KtVWvXr3Y28SMGDFCI0aMcFp9AAAAlV2ZfgsYAAAAFR8BEAAAwGQI\ngAAAACZDAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmAwBEAAAwGQIgAAAACZD\nAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmAwBEAAAwGQIgAAAACZDAAQAADAZ\nAiAAAIDJEAABAABMhgBYBMvmzaq/ZUt5lwEAAOB0BMBinMjKKu8SAAAAnI4ACAAAYDIEQAAAAJMh\nAAIAAJgMARAAAMBkCIAAAAAmQwAEAAAwGQIgAACAyRAAAQAATMbtAXDJkiXq3LmzWrVqpfvuu087\nd+4ssu+2bdvUvHnzQtPBgwfz9fvyyy/Vo0cPtWzZUj169NDXX3/t6s0AAACosNwaANevX69p06Zp\n5MiRWr16tSIjIzVixAglJSUVu9y6dev0ww8/2KcmTZrY5+3atUujR49Wz549tWbNGvXs2VNPPPGE\nfv75ZxdvDQAAQMXk1gD47rvvKjY2Vv369VOzZs00efJkBQUFadmyZcUuFxAQoKCgIPvk6elpn7d4\n8WJ16NBBDz/8sJo1a6aHH35Yt9xyixYvXuzqzQEAAKiQ3BYAMzMztW/fPkVFReVrj4qK0q5du4pd\ntk+fPoqOjtaQIUO0devWfPN2795daJ3R0dElrhMAAMCs3BYA09PTdfHiRQUGBuZrr1OnjlJSUhwu\nExQUpClTpmjOnDmKi4vTjTfeqKFDh2rHjh32PqmpqYXWGRgYWOQ6AQAAzM7L3S9osVhK1SZJTZs2\nVdOmTe3PIyMjdfToUS1atEjt27cv8jUMwyhynWVltVqdsp5r3fnz502zraXFmDjGuDjGuDjGuBTG\nmDjGuLiX2wJg7dq15enpWWjPXFpaWqE9eMUJDw/XunXr7M8DAwOVmpp6VessTmhoqFPWc62zWq2m\n2dbSYkwcY1wcY1wcY1wKY0wcY1wcS0hIcMl63XYI2MfHR2FhYYqPj8/XHh8fr8jIyFKvx2q1Kigo\nyP48IiLiqtcJAABgJm49BDxs2DCNHTtWrVu3Vps2bbRs2TIlJyerf//+kqSxY8dKkmbNmiVJeu+9\n99SoUSMFBwcrKytLa9eu1caNGxUXF2df5+DBgzVo0CDNnz9fd955pzZu3Kht27Zp6dKl7tw0AACA\nCsOtAbBHjx5KT0/X3LlzlZycrJCQEC1YsEANGzaUJB07dixf/6ysLM2cOVMnTpyQr6+vgoODtWDB\nAsXExNj7tGnTRq+88opee+01xcXF6frrr9err76q8PBwd24aAABAheH2i0AGDhyogQMHOpz3wQcf\n5Hs+YsQIjRgxosR1du/eXd27d3dKfQAAAJUdvwUMAABgMgTAElg2b1b9LVvKuwwAAACnIQCWwoms\nrPIuAQAAwGkIgAAAACZDAAQAADAZAiAAAIDJEAABAABMhgAIAABgMgRAAAAAkyEAAgAAmAwBEAAA\nwGQIgKXEL4IAAIDKwpQBsOq5c1e0HL8IAgAAKgNTBsBvxoxR4KlT5V0GAABAuTBlAAw/eFBbHntM\nNyYllXcpAAAAbmfKANhl9mzV+fNP/ThqlNokJpZ3OQAAAG5lygD4Y8uWioqL0zkfH337f/+nbtu3\nl3dJAAAAbmPKAChJiY0b67Y339QvjRrp82ee0UNr15Z3SQAAAG5h2gAoScfr1FHMa6/py/btNe/V\nV/XGa6/JKzu7vMsCAABwKVMHQEk6Xa2a7p06VbP+9jc9umaNvhw7VgF//FHeZQEAALiM6QOgJOV4\nemrcyJEaPH68ovbu1fZHHlGLX38t77IAAABcggCYxwfduinmtddU9fx5/ThqlP4aH1/eJQEAADgd\nAbCAbS1aqP28efpfo0b6bOJETV24UJ4XL5Z3WQAAAE5DAHTgaFCQOs6ZowX33KMJS5dq45gxqp+W\nVt5lAQAAOAUBsAjnq1TRQ089pcHjx+uW/fu1a8QIxezeXd5lAQAAXDUCYAk+6NZNt8ydq1PVq+ub\nMWOkadOknJzyLgsAAOCKEQBLYd+NN6r9vHla/pe/SBMnSt26SUePlndZAAAAV4QAWEpnqlbVA5Mm\nSfPnS/HxUuvW0qeflndZAAAAZUYALAuLRXrwQWnXLqlpU6lPH2nYMOn06fKuDAAAoNQIgFciJMS2\nF3DiROn996WICNtzAACACoAAeKW8vaWXXpK+/dZ2UUjHjtKYMdLZs+VdGQAAQLEIgFcrOlr6+Wdp\nxAjplVds5wZu3lzeVQEAABTJ7QFwyZIl6ty5s1q1aqX77rtPO3fuLLLvV199peHDh+vWW29VZGSk\n+vbtq2+++SZfn5UrV6p58+aFpgsXLrh6Uy6rWVOaN0/atMn2vFMnaeRI6c8/3VcDAABAKbk1AK5f\nv17Tpk3TyJEjtXr1akVGRmrEiBFKSkpy2H/79u269dZbtWDBAq1evVoxMTEaNWpUodDo5+enH374\nId9UpUoVd2xSfp06SXv2SE/Bt4MPAAAfJ0lEQVQ+KS1cKIWFSZ995v46AAAAiuHWAPjuu+8qNjZW\n/fr1U7NmzTR58mQFBQVp2bJlDvtPmjRJDz74oFq3bq0bbrhBo0aNUlhYmDZu3Jivn8ViUVBQUL7J\nVSybN8uyebPqb9niuEPVqtLs2baLQvz9pXvvtU2//uqymgAAAMrCbQEwMzNT+/btU1RUVL72qKgo\n7dq1q9TrycjIUM2aNfO1nT9/Xp06ddIdd9yhhx56SP/973+dUnNxTmRlFd+hQwfb7WJeftl2aLhF\nC+nFF6Xz511eGwAAQHG83PVC6enpunjxogIDA/O116lTR/GlvIXKkiVLdPz4cfXq1cveduONN2ra\ntGm6+eablZGRoffff18DBgzQmjVr1KRJE2duQiFWq7XkTvfcI6+2bVVv1izVfPZZZb79to5PnKiM\njh1dWltZnT9/vnTbYyKMiWOMi2OMi2OMS2GMiWOMi3u5LQDmslgspWor6Msvv9SsWbP0yiuvqGHD\nhvb2yMhIRUZG5nveu3dvffjhh5o0aZJzii5CixMnVM/bW8cL7NUsJDTUdn7g11/LZ9QoNX7oIalH\nD9vewRYtXFpjaVmtVoWGhpZ3GdcUxsQxxsUxxsUxxqUwxsQxxsWxhIQEl6zXbYeAa9euLU9PT6Wk\npORrT0tLK7RXsKAvv/xSY8eO1cyZM9WlS5di+3p6eqply5b67bffrrbkUinxUHBeXbvaLhJ5+WVp\nyxbbLWMeflhKTnZdgQAAAAW4LQD6+PgoLCys0OHe+Pj4fHvwClq/fr2efvppTZ8+Xd27dy/xdQzD\nUGJioksvBLkqVapITz0lHTggPfKI9PbbUnCwNH26dO5ceVcHAABMwK1XAQ8bNkyrVq3SJ598ooMH\nD+qll15ScnKy+vfvL0kaO3asxo4da++/bt06Pf300xozZozat2+vlJQUpaSk6NSpU/Y+b7zxhr7/\n/nsdPnxYVqtVEyZMUGJiogYMGODOTSu7wEBpzhxp3z6pc2dpwgTpppuk+fOlzMzyrg4AAFRibj0H\nsEePHkpPT9fcuXOVnJyskJAQLViwwH5O37Fjx/L1/+ijj5Sdna1p06Zp2rRp9vZbbrlFH3zwgSTp\nzz//1LPPPquUlBTVqFFDLVq00IcffqjWrVu7b8OuRkiItHq17SflJkyw3UB65kzpueekgQMlL7ef\npgkAACo5t6eLgQMHauDAgQ7n5Ya6op47MmHCBE2YMMEptZWrmBjphx+kDRukSZOkoUNth4Wff17q\n21fy4Ff7AACAc5AqriUWi3T33dLOndLKlba9f/37Sy1bSu+/L5XlghMAAIAiEACvRRaLFBsr/fyz\ntGyZ5O0tDRliO1w8dy43kwYAAFeFAHgt8/S07QHcvdv2m8L169uuHL7xRtutZPJcDAMAAFBaBMCK\nwGKR/vpX2+8Lb9okhYVJY8dK118vPfGEdPBgeVcIAAAqEAJgRWKx2H5RZONG6aefpPvusx0Svukm\n2+Pvv5cMo7yrBAAA1zgCYEUVGSktXiz99pvt9jHffivdcYetff586cyZ8q4QAABcowiATmDZvFn1\nt2wpnxdv0EB66SXp8GFb8JNs9xJs0MB2vuB//lM+dQEAgGsWAdBJyvSbwK5Qtar04IPSrl3Sjz/a\nriJ+5x3b7w1HRUmLFkmnT5dvjQAA4JpAAKxsLBbp1ltth4ePHpX+9S8pLU365z9tVxEPGSJt3izl\n5JR3pQAAoJwQACuzOnWkMWMkq9V2BfHAgdKqVbYLSW66SZoyRdq/v7yrBAAAbkYANAOLRbrtNmnB\nAun4cemDD6QmTaQXXpBCQ6WICNVZuFD69dfyrhQAALgBAdCN6m/ZUr4XjEi2cwUHDZK++cZ2iPj1\n16WqVVX31Velpk1tQfG116SkpPKrEQAAuBQB0I1yLxQp9wtGcl13nfT441J8vA58/bU0c6Z04YI0\nerTUqJEUEyO9+io3mgYAoJIhAEKSlNWwoe3XRX76yXZe4JQp0smT0pNPSsHBUsuWtvsNbt3KBSQA\nAFRwBEAU1ry59OyztnsIHjxoOyRct640a5btEHGDBtKIEbbfJ87IKO9qAQBAGREAUbymTW2/N7xp\nk5SSIi1ZYjs0/PHH0r33SgEBUufO0owZtr2H7B0EAOCaRwBE6dWuLT3wgC38paZKX31lO4fw5Enp\nmWektm2levWkAQOkd9+Vjhwp74oBAIADXuVdAEqn/pYt9otHPCTlSKrn7a3jUVHlU5CPj9S1q22S\nbLeX2bjRFgq/+kr66CNbe/Pmtj2Gd9xhm66/vnzqBQAAdgRAJ8sNannDWd7wdqXyLp/joK3c1a9v\nu73MoEGSYdjOH/zqK9uvjnz8se0ehJLt/oO5YfCOO2wXmFgs5Vk5AACmQwB0IsvmzfbHecPZNRXU\n3MFisf0GcevW0lNPSRcv2gLhd99J334rrV8vvf++rW+9erafrrv1VqlDB6ldO6lGjfKtHwCASo4A\n6EJ5A6ErOdrreE3x9JQiImzT44/b9hDu328LhD/8IG3bJq1ZY+vr4SGFhdnCYO7UooVtHQAAwCkI\ngNeI3BBX8Py+0hw+LssNpos7RF3v5En3BEiLxfYTdKGh0kMP2drS0qTt221hcNs26dNPpbffts3z\n87PtTWzTRoqMtE0tW0q+vq6vFQCASogAeI3IDW8Fz+9z1uHjgkHS0eNyPVRdp4509922SbLtJfzl\nF1sY/Oknadcu2y1o5s61zffysgXINm2k8HDbXsOwMNs9CjmnEACAYhEAy5EzLg4pab25e/pK+zqW\nzZvL9VByoT2UISHS3/9um5mTI/36qy0M5k4bNkiLF19eQa1atkPGuYEwd6pfn2AIAMAlBMBy4qzz\nAx2t52ovQLmaUOoofF7JazuswcNDatbMNvXpc7k9OVnaty//9Omn0sKFl/vUrm0Lgi1aSDfddHlq\n2tRUh5Kv9r8PAKByIACawJWEzSvdE1hc+CzqPMerVreuberU6XKbYUgnThQOhitW2G5cnctikRo3\ntt2OJm8wDA6WJTPz6mu7xpj66nQAgB0B0MRKCoYnsrKceki4qPMcXcJisR32rV9f6tIl/7yTJ6UD\nB2znGOadPvpIOnXK3q25xWI7p7BJE+mGG2x/8z5u3Piq9x5e81dwAwAqJQIgSlTp9hQFBEi33GKb\nCkpLswfC1B9/VNDZs9Lvv0vx8bYbWl+8mL9//fqXQ2GjRlLDhrapQYPLf6tUKbKUa+ICHACA6RAA\ncUVKezj3avcgOjpnreBes2JvbVPW165TxzbdeqtS27VTUGjo5XnZ2VJSkvTbb7ZQmPdvQoK0dq10\n7lzhdQYGXg6GuVP9+lK9err16FEl16qlEwEBVzQ+AABcCQLgNexKz91zhdz15ga+XKU5nJt7KLks\n6827bMHHxf0t+Dq5bVdz3mGhEHrHHYXm1fP21vHbb5dOnVLL1avVMCVFDVNT9U716tLRo5enHTuk\nlBT78j/mfaGqVW2/jFK3rjZ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UVNUrcEPh3PdZamqqrs97h/hKYsuWLTpy5Ij69u1bbL/H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\nAAEAAEyGAAgAAGAyBEAAAACT+f9RqujVRjVZ0AAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = ExponentialDistribution.from_samples(data)\n", "\n", "x = numpy.arange(0, 20, 0.1)\n", "y = map(model.probability, x)\n", "plt.plot(x, y, color='r')\n", "plt.hist(data[data < 20], edgecolor='c', facecolor='c', normed=True, bins=250)\n", "\n", "plt.ylabel(\"Probability\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.xlim(0, 20)\n", "plt.legend(loc=6, fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Doesn't quite look like it is fitting the underlying data. Let's now fit a two component mixture model and see what we get." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] Improvement: 87.5176450482\tTime (s): 0.0003252\n", "[2] Improvement: 75.6222821623\tTime (s): 0.0004339\n", "[3] Improvement: 67.8899702346\tTime (s): 0.0004389\n", "[4] Improvement: 54.2432197739\tTime (s): 0.0004501\n", "[5] Improvement: 37.7426503622\tTime (s): 0.000536\n", "[6] Improvement: 22.6741117564\tTime (s): 0.0005429\n", "[7] Improvement: 11.9478628925\tTime (s): 0.0004289\n", "[8] Improvement: 5.74704751234\tTime (s): 0.000478\n", "[9] Improvement: 2.62637502629\tTime (s): 0.000392\n", "[10] Improvement: 1.17003282288\tTime (s): 0.0003731\n", "[11] Improvement: 0.514874545412\tTime (s): 0.000479\n", "[12] Improvement: 0.225203323178\tTime (s): 0.0003729\n", "[13] Improvement: 0.0981964839511\tTime (s): 0.0003819\n", "[14] Improvement: 0.042745322789\tTime (s): 0.0004869\n", "[15] Improvement: 0.0185896474368\tTime (s): 0.00051\n", "[16] Improvement: 0.00808004420423\tTime (s): 0.0004258\n", "Total Improvement: 368.088886959\n", "Total Time (s): 0.0086\n" ] } ], "source": [ "model = GeneralMixtureModel.from_samples(ExponentialDistribution, 2, data, verbose=True, stop_threshold=1e-2)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " 0.2073803445118798\n", " ],\n", " \"name\" :\"ExponentialDistribution\"\n", "}\n", "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " 4.015765550925078\n", " ],\n", " \"name\" :\"ExponentialDistribution\"\n", "}\n" ] } ], "source": [ "a, b = model.distributions\n", "print a\n", "print b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like we can get close, but not perfect. $\\lambda = 3.8$ and $\\lambda = 0.2$ is close to 5 and 0.25 respectively. This is likely made more difficult by exponential distributions both overlapping each other significantly, and sharing a mode of 0. Let's take a look at what the distributions look like superimposed with the underlying data." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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FDg6otLGBB3cBExERkQgYFAArKytha2urtUwqlUKlUpmkKHMTbGxwx8mJxwAS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\nERERNSdmDYAeHh6QSqXIzc3VWp6fn68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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = numpy.arange(0, 20, 0.1)\n", "y = model.probability(x)\n", "plt.plot(x, y, color='r')\n", "plt.hist(data[data < 20], edgecolor='c', facecolor='c', normed=True, bins=250)\n", "\n", "plt.ylabel(\"Probability\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.xlim(0, 20)\n", "plt.legend(loc=6, fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fit doesn't seem too bad. Much better than we would get using Gaussians. We can also calculate the log probability under either of the distributions, and the log probability of the data under the model. This returns the log likelihood of the data, which is log P(D|M), not the posterior P(M|D)." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mixture Fit: -3134.8553098220236\n", "Exp 1 Fit : -4215.57091756185\n", "Exp 2 Fit : -17923.211242070414\n" ] } ], "source": [ "print \"Mixture Fit: \", model.log_probability(data).sum()\n", "print \"Exp 1 Fit : \", sum(a.log_probability(d) for d in data)\n", "print \"Exp 2 Fit : \", sum(b.log_probability(d) for d in data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Continuing with the theme of being a ~General~ Mixture Model, you can also manually specify any combination of pre-instantiated distributions of any types, with any priors. In this case, let's try out using an exponential distribution and a uniform distribution together, with the exponential distribution modelling the spike near 0, and the uniform distribution modelling the tail." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] Improvement: 1445.28046819\tTime (s): 0.000896\n", "[2] Improvement: 168.600898799\tTime (s): 0.000638\n", "[3] Improvement: 55.0567980604\tTime (s): 0.0005751\n", "[4] Improvement: 23.3706548977\tTime (s): 0.000397\n", "[5] Improvement: 11.2217235527\tTime (s): 0.000632\n", "[6] Improvement: 5.76738335229\tTime (s): 0.0003259\n", "[7] Improvement: 3.09542728295\tTime (s): 0.0002921\n", "[8] Improvement: 1.71181971053\tTime (s): 0.000283\n", "[9] Improvement: 0.96731109368\tTime (s): 0.0003381\n", "[10] Improvement: 0.555393796187\tTime (s): 0.000385\n", "[11] Improvement: 0.322726013273\tTime (s): 0.0003898\n", "[12] Improvement: 0.189237034512\tTime (s): 0.000524\n", "[13] Improvement: 0.111733512519\tTime (s): 0.0004809\n", "[14] Improvement: 0.0663230028631\tTime (s): 0.000315\n", "Total Improvement: 1716.3178983\n", "Total Time (s): 0.0112\n" ] }, { "data": { "image/png": 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r1q0L3YZhGIWuszDzu3XTy/Pn65nly9WreXNru9lsdmo9JVVmZqbP7KujGBP7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\nhiqvUyepUydJUrlLl3S+alU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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = GeneralMixtureModel([ExponentialDistribution(5), UniformDistribution(0, 20)])\n", "model.fit(data, stop_threshold=1e-1, verbose=True)\n", "\n", "x = numpy.arange(0, 20, 0.1)\n", "y = model.probability(x)\n", "plt.plot(x, y, color='r')\n", "plt.hist(data[data < 20], edgecolor='c', facecolor='c', normed=True, bins=250)\n", "\n", "\n", "plt.ylabel(\"Probability\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.xlim(0, 20)\n", "plt.legend(loc=6, fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That doesn't look better than the previous fit, but this is just an example of what you ~can~ do in pomegranate, not necessarily what you ~should~ do.\n", "\n", "## Speed\n", "\n", "We can compare the speed of pomegranate's GMM implementation to sklearn's GMM implementation for Gaussians." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jmschr/anaconda2/lib/python2.7/site-packages/sklearn/mixture/base.py:237: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n" ] }, { "data": { "image/png": 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gG+PBBcnNSZh7TQC5O/RBseSCC0RDRMVpFag4rQJWv4XoC3zD0c57OtH5s074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\ny0xNTWFub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AjRf24ofjSC5NInpadLJf9iljyCKi4mR11XNLkudOget/6FUwOHmlyHUvOOkM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pl/FlwjepuHDx/CwMCASRYRfbB/1uQzMjJCYGBgmTX5EhIS4OLiosdoqbJIS0vDiBEjkJiYKD0O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\nT5+iZcuWm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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.mixture import GaussianMixture\n", "\n", "def evaluate_models():\n", " sizes = numpy.around( numpy.exp( numpy.arange(7, 13) ) ).astype('int')\n", " n, m = sizes.shape[0], 5\n", " \n", " skl_predict, pom_predict = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_fit, pom_fit = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_error, pom_error = numpy.zeros((m, n)), numpy.zeros((m, n))\n", "\n", " for i in range(m):\n", " for j, size in enumerate(sizes):\n", " X, y = create_dataset(size, 1, 2)\n", " \n", " # bench fit times\n", " tic = time.time()\n", " skl = GaussianMixture(n_components=2, max_iter=10, tol=1e-10)\n", " skl.fit(X)\n", " skl_fit[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom = GeneralMixtureModel.from_samples(NormalDistribution, 2, X, max_iterations=10)\n", " pom_fit[i, j] = time.time() - tic\n", "\n", " # bench predict times\n", " tic = time.time()\n", " skl_predictions = skl.predict( X )\n", " skl_predict[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom_predictions = pom.predict( X )\n", " pom_predict[i, j] = time.time() - tic\n", "\n", " # check number wrong\n", " skl_e = (y != skl_predictions).mean()\n", " pom_e = (y != pom_predictions).mean()\n", "\n", " skl_error[i, j] = min(skl_e, 1-skl_e)\n", " pom_error[i, j] = min(pom_e, 1-pom_e)\n", " \n", " fit = skl_fit / pom_fit\n", " predict = skl_predict / pom_predict\n", " plot(fit, predict, skl_error, pom_error, sizes, \"samples per component\")\n", " del X, y\n", "\n", "evaluate_models()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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zKPgkE6ui4Oihhx7Ct7/9bfzFX/xFtdtDToLoFuF0O+Gc5wRfw6HH8r1K+zPQ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\nCXzxi1/EhRdeiF/84hdIJpP46le/Wu1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xyhJqY9EF0lo11FhFVbhRHDngeB49tk2XZbHD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LK7iMhvzbtKFbp5e9PWYmFvZiZ7s7JwMpmoeXV43KWCAtLz83ExmWhtsRDo4YG3nYfOye1zS9NM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\nokuXLgwZM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KFf64T1AKiHXHyL47i7O5Oa5aCiHEQlhrLWKrY/h5v6V6NjRdI74hTnxDHH/c\np3SsROX1CuVXyljrLBIDCaz+Sy+bcblaYSruhVJKEbohyd3t12sEkhyJFTSzdO6NcpmKG7J2FD5w\n1GBrKUZ/1iE7GOfNN8vYmda5sj69gOsLxWghtDUWmeszl1zAdaHcIZfUvlRLfTjVa+Y4pdTVKYJi\nQPWtajRO6eS5cUpGl9HUPYehF+KP+KhQYW+xSe1JYa1d/g9fIYRYLpqmkT6QZuyRsZb9/DG7owkO\nUntT0cQHr5SZ+NYEZsaMpgLfsrQLa7fSVNyL4Y/5OFsdYt2t/7PMpzXPctEypkrnHquVzr3pusRL\nim2nYferGptNh7VrHJyNrfcHNu8CrjfXt4DrQoVuiKZrpPamlvR5m5WRNEhsT5DYniB0zxun5NbG\nKWVNdLs5EiV/0sfP+eiWTuraVFS2kZa3VyFEe7A32hgZg6AUNO2U0vXQbZ3k7mhSpOqpKqVjJSa/\nP0nxB0XsbVHJ3eWUwLfiVNyLEZZC0vvbo4plPvLpLZbFdOncxAQvlUoMVV36cho3vKKxYyTGxpTN\n6m1OU/cCXMycBVwPdxHfVP8CrgvlDXlk3pVp6fUbFku3dOyNNvZGm+z1WbwRj8rJ6MPHG/FAa8w4\nJRUovDEPVVHEVsfoua0He9PSXn0UQohmoBka6YNpJr450RaNfM3QsDfb2JttvJGoh6d8rEz5aBlr\nfW3GuNX1zxjXqlNxL4af97HWRKWW7UqSI7Gkpkrn/j2X40S5TKUasP5tjQ8dNdjqW/T3OWSubN0y\no6VYwHWhgnKA7ugkd7Znbe9CaIaGtcbCWmORPpDGn6iNUzpWwn3TBaJeJ6Nr+cYphdUwSsoUJAYT\nJHcnia1qjWlXhRBisZwtDjkrR+iGLTmBwIXE+mJ09XVFCc4rUYIzfnocM2uSGExcNMFp9am4F8PP\n+XRd39W2Px9IciSWwJzSuWoVu6DY/obG7hMGmx2Htesc4onWPd3OX8A1tS8qnVqJdXq8YY+e9/a0\n1YfRUtA0jVh3jFh3jNTuGeOUXq19UAWg2bXyuyXozfFzPkE+SlQz12VI7Ei05Sw9QggxH93SSV+T\nJv/dPPH1zbnO3uUwEgapvSmSu5JUTkTVCfnv5ik8V8DZHk21bSSMaJzxqUrLT8W9GEEpwOwysTfa\njQ5lWbX3b1Esq6nSuW9NTPBSschwxaNvTHHzUZ3tkxYbux36Bm10o3Ub9Uu9gOuCX78QYHabbbf6\n9HKYM05pqDZO6ZVonBIGxLpjCxqnpAKFN+KhXIW1zqLr+i7iG+LoZuue00IIsViJwQST35tEBart\nysWmaKaGs83B3mpPT6pQ+mFgrldcAAAgAElEQVSJ0pES8fVxqq9XycVyLT8V92L4Yz7dt3a37Cx7\n9ZLkSCzYdOncxARvVCpUiz4b39K4/pjOVj3O+jUJ0huWdqa2lTZnAdftDoldl7eA62L4Yz49H+hp\n2w+h5aJbOvYGG3vDeeOUXq5vnFJQDvDHfDRNI3FVoqXXlhJCiKViJAySVycpvFQgvrb9eo9m0jQN\na62FtdbCn/QpHytTeaOCZmt0vaurpafiXoywGqLFNewt7d1rBJIciTrNLJ17Mp/nzUqVRF5x5Wtw\n1RmTzWmHtZsTxOKtXWY0vYDryyWUt7QLuC6UP+5j9VvYm9r/jWg5abqGtdrCWm2RvjaNn/Nx33Qp\nHi3OGqekgtraRIUAI22QvSGLvdXuyEkwhBDiQpK7khSeK6BC1THJgZk2SV+bJn1tmuqpatuXlc3H\nG/XIHMp0xKRDkhyJizq/dG6k5NI3DO8+qjFQtdjQm6BnZxxdb+0/lnkXcN2TwuxuzJ+IUgp/0qf7\ntu62HvS40jRNI5aNEcvGSO5KRuOU3o7WUwqPh5hbTLLvzhLv76wrgkIIUS+zK1obqHqqSqxPetQ7\ngfIVGhqJwUSjQ1kRkhyJec0pncv7XHEabnxVZ5vt0L82QTLZ+m+KKlRUT1QpvFCIFnBdvTwLuC6U\nN+LhbHPavmyh0YykQWJbgsS2BGc3nqVvV1+jQxJCiKaX2puidKxEjNZvB4hLc4ddUntSHVNJIcmR\nmDazdO6JfJ43y1WS4yFXHdO4asxkcybBmoEEZht0qa7kAq4Lji1UqIoi845MQ+PoNNJTJIQQ9bFW\nWcTXx/EnfMysNCXbmQoVBFE5ZaeQM1pMl879e610brTgsvosvPeoxkDosHF1guzOxicNS8Udcik8\nt3ILuC6UO+RGa+f0yBU5IYQQzSlzIMPI10YkOWpz3qiHM9D+05TP1Dk/qZhjqnTu3yYmeKNcxpsI\nuOINxc2nTLYnHPo3JnDs9jlFvPHaAq5nVm4B14VSfnSFJrU/1ehQhBBCiAuy1lmYvWY0iU0DJi0S\ny0+pqJIltaez2iQr3vL98pe/zBe/+EWGh4fZsWMHn/zkJzlw4MBF9/8//+f/8Oabb7Ju3To++tGP\n8qEPfWgFI24vs0rncjlOF6ukR0N2H9XYVYyxuTvJqittzBZem+h8jVzAdaHcsy7pa9OY6fZJSoUQ\nQrQfTddIH0wz9i9jkhy1qWAiwFpvNXwc9kpb0RbYI488wu///u/zO7/zO1x77bU88MADfOQjH+Hh\nhx+mv79/zv4PPPAAf/AHf8B//+//nb179/L888/zqU99ikwmw7vf/e6VDL3lzSyde7FYZCxXZe0Z\nxfte0RmIJdmwOkF2U3ud/EEpoPhSkcprjVnAdaFCN0QzNZJXd05drxBCiNZlX2FjJA3Caogeb87P\nVrF4QSGg+9buRoex4lY0Obr//vu5/fbb+emf/mkAfuu3fovvfOc7PPjgg3ziE5+Ys//Xv/51fuqn\nfooPfOADAGzcuJEXXniB//k//6ckR3XK+z7fq806d7xYJhj32PoqvHvIZEc6ybptCWyrva74NMsC\nrgvlDXl0Xd+FYTd3nEIIIQSAbuqkr02T+06O+AaZXbWdBIUAs8fE6m+vC+f1WLHkyHVdXnrpJe66\n665Z2w8fPsyzzz57wcfE47P/2OLxOC+88AKe5xGLyYD1+UyVzj2ay/FkLsfpyQqZIcX+o7CzGmfz\nmjSrroqjN8kEBEsl9ELKx8qUjjR+AdeFCkoBekIncWVnrCEghBCiPSR2JMg/kSf0wo5YILRT+OM+\n3T/SmWstrlhyND4+ThAE9PXNXkekt7eXxx9/fN7HXH/99XzlK1/hve99L1dffTUvvvgiX/nKV/A8\nj/HxcVavXj3nMUeOHFmW+BeqUqmseCy+Urzi+3zPdXnV88gXQtafhMPHNbZoJqt6DdJphVeqcqa0\noqEtiOu6nD51uu79VagITgb4r/qoqsJYY2DuNHEzLvnxPIwvY7BLJHg7IPbOGBOvTlzW8zTivGsX\ncuwWT47d4smxWzw5dou31MfO7XLxX/AxVjX/xcjLtdA2SitS1Wgihlwph3Zk6ZKjVvmbXfFR3/Nl\noBfKSj/2sY8xPDzMz/zMz6CUore3lw996EP85V/+JYYx/x/gzp07lzTexTpy5MiKxTJVOvdvExMc\nnywR5k0GXzHZPR5joCfFumtsrFjrvGGdPnWaDRs3XHK/OQu4brRI7k223MBBf9JH69FY/SOrL3ut\nnZU879qNHLvFk2O3eHLsFk+O3eIt9bHzN/qcHT0brRXYRDPALod62yitrHq6Sva27JKvbdRMf7NP\nP/30Be9bseSou7sbwzAYHh6etX10dHROb9IU27a59957+b3f+z1GR0dZtWoVf/M3f0MymaS7u/MG\niM00c9a5x3M5Tk9UyL4dcO2rOrsCmyv60/Q1wYKmy0EphXvGpfCD5lvAdTH8cZ++/6dPFiEVQgjR\nksyUSXJXktKxEtaa1rpAKWYLvRAtpuFsdxodSsOsWHJkWRa7du3i8ccf50d/9Eentz/++OPcdttt\nF31sLBZj7dq1QDTj3S233IKud2Zd69Ssc/82McGLkwUmRqusP654/xmTnckMGzYlScXbdxroZl/A\ndaG8cY/4hrgMZBVCCNHSkruTFF4ooJRq2c9kAf6wT+qaVEfPPriireg777yTu+++mz179nDNNdfw\n4IMPMjQ0xIc//GEA7r77bgA+97nPAXD8+HF+8IMfsG/fPvL5PPfffz+vvPIKn/nMZ1Yy7KYwq3Qu\nVyIc9tnxquK9eYvBVWnW7XQw27jnoRUWcF0opRTBZEDv+3rlg0QIIURLi/XEcLY6uGddYr0yYVYr\nUoFChYrEzs6eHGpFk6P3v//9jI+Pc9999zE0NMTAwABf+MIXWL9+PQBvvfXWrP3DMOR//+//zfHj\nxzFNk0OHDvHggw+yYUN713pOmZ51bmKCx/N53hwr0/NmyLXHdXZrDlvWp+je0JqlZPXyJ32Kz7fG\nAq4L5Y14JAYSWKulBEEIIUTrS+1PMfLVEehtdCRiMbwRj+SuJGaqfSuQ6rHiP/0dd9zBHXfcMe99\nX/rSl2Z9v23bNr72ta+tRFhNZWbp3Au5ArmRChtfhx8bNtmVSdO/NUEy1t4nbqst4LpQKohmgkkf\nTDc6FCGEEGJJWGssYqtj+HkfM9Pe7ZR2o5QidEOSu2Uhejlzm8jM0rnXx4uosx4Dr2r8aMViYG2a\ntVc6GG1cOgfRQEDvZY/R8VEIW2cB14Xyhj2Se5LEslJ6IIQQoj1omkb6YJrRh0clOWox/piPs8Uh\n1iPtEjlzG2xW6Vwuz5sjJXpPhhx802CPmeKK/jRZ22zr0rkpQSFg4tsTBCcD4nvipPakWmIB14UK\nvRAUpPdLr5EQQoj2Ym+0MTMmQSnASLTfZ3i7Ckshqf2pRofRFCQ5apCp0rlvTkzw4vgkuaEqm15X\nfGDcYndviv5tNo7ZOb8e96xL7tEcKIgditG1v6vRIS0bb8gjfTCNkZQPDSGEEO1F06Peo4lvTkhy\n1CL8vE9sdUymYa/pnNZ3k5gqnfvm+DivjxTR3vIYOKHzY77N4No0q9bE27507nzlV8tMfn8SI2XQ\ndWMX1Vy10SEtm7AarR+Q2i1XZ4QQQrQnZ6tD7rEcoRu2zVjhdubnfHp/TGbOnSLJ0QqYWTr32HiO\nM0Nl+k6GHBoy2BfPsGl9iq5459V4qlBReLZA6WiJ+Lo4mcOZ6E001+jIlo837JG9KdvR6wcIIYRo\nb3pMJ31Nmvx388T7ZR2/ZhaUAsyMib3RbnQoTUOSo2U0s3TuhZE8k2drpXPFOHtWpejfliBudGYj\nOayG5B7L4b7tkrgyQWpfCq3Ne8yCUoCRMnAGOnfVaSGEEJ0hMZhg8ruTKF+1xfIb7cof88m+O9v2\nbbCFkORoGYRK8WS1ypdeP87rw0WM0x4DZ3SuDhJc2Z+mb4OF3sFdl37eJ/ftHEEhIHMog7OtM5IF\nb8Sj50d70M3OTIiFEEJ0DsMxSF6dpPBSgfha6T1qRqEbolkaztbOaIfVS5KjZTDu+3xzrIzzTMB1\nYzH2pbJcsTFJus3XJqpH9UyV/ON50CF7axZrVWcM/vPzPtYqC2ezvAEJIYToDMndSQo/KKBCJT0T\nTcgb9shcl0GPyUXbmaS1vgx6YzHumoiTymfZsD1FrENL52ZSSlE+WqbwbAEza9J1Q1dbTtN9If6E\nz6oPrZIPByGEEB3DzJg4Aw6VE5WOuRjaKlSg0DSNxGCi0aE0HUmOlkmvYdLrxCUxIvoDnHxqkvLr\nZeyNNunr0h11lcIb87A32Vj98sEghBCis6T2pCgdLaGUktnQmog77JK6OoXhdM6F6npJciSWVVgJ\nyX0nhzvsktydJHl1sqPeHFWoCIshmfdnOurnFkIIIQCsVRbx9XGCiQCzW5qdzUCFCjxI7ko2OpSm\n1DmX78WK88Y9xv5lDG/Mo+twF6k9qY5LELxhD2fQkXICIYQQHStzIENQCBodhqjxx3ycAQezS5LV\n+chREcuicqpC/ok8uqXT/Z5uYr0duI5ToFCeInMg0+hQhBBCiIax1lmYPSZBIeio8cbNKigHpPbK\nYvQXIj1HYkkppSi+WCT3nRxml0n3bZ2ZGAF4Qx6pvSm5MiOEEKKjabpG+mAaf9xvdCgdz5/wia+P\nS0XLRUirTSwZ5Svy381TOVHB3myTOZRBMzqrjG5K6IWgILVPrswIIYQQ9hU2elInrITotlybb5Rg\nMiB7S7bRYTQ1OTvFkghKAeP/3zjVk1VS+1Jk3tm5iRFEvUbpd6QxElI+IIQQQuimTuZABm/Ea3Qo\nHSsoBJg9JvF+WZT3YiQ5EpfNG/EY/5dx/LxP1w1dJK/qrBnpzhdWQrS4JrPACCGEEDM42x20mBZV\nV4gV5417pA+kZc3FS6grOfr0pz/NsWPHljsW0YIqxyuMf2McDOi5rYf4Brka4Q67dF3XhW7JtQch\nhBBiih7XSe1L4Q/L2KOVFlZDjISBvdludChNr67W2wsvvMB/+A//gZ/8yZ/kb/7mbygUCssdl2hy\nSikKzxXIPZEj1hej50d6MLMyhC0oBphdJs4Op9GhCCGEEE0nuTOJChUqUI0OpaN4Ix7pa9Poply4\nvZS6jtBf//Vf8/DDD3Po0CE+//nPc8MNN3D33Xfzve99b7njE00o9EJy385R/GERZ4dD9uYselz+\n2IBoTad3dcmbjxBCCDEPI2mQ3JXEG5axRysl9EI0Q5MLt3WquwW3detWfv3Xf51vfetb/NEf/RGl\nUom77rqL2267jS984QtMTEwsZ5yiSQSFgPH/dxz3LZf0gXRUu9rBEy/M5Od84mvi2FdIl7UQQghx\nIcmrk4ReiFLSe7QS/BGf1L4Uhi2TRNVjwZe3fd+nUCgwOTlJGIasW7eOf/iHf+CWW27hoYceWo4Y\nRZNwz7qM/csYYTkke3OWxECioydemEkpRZALyLwrIwMdhRBCiIuIdcdwtjj4YzL2aLmpICphTOxM\nNDqUllH3IJEXXniBv/u7v+ORRx7Btm1uv/127rnnHjZu3AjAX/3VX3HvvffywQ9+cNmCFY1TfrXM\n5PcnMVIGXTd2YWZkfNFM/phPfEuc+DqZkEIIIYS4lNT+FCN/NwK9jY6kvXmjHomdCcyUtNvqVdeR\n+uAHP8jx48e5/vrruffee7n55psxDGPOPvfee++yBCkaR4WKwjMFSsdKxNfFyRzOyCxs51GhIigG\n9B3qa3QoQgghREuw1lhYay38vC8XXJeJUgpVVaT2yIL0C1HX2fi+972Pn/zJn2TNmjUX3Kenp4eX\nX355yQITjRdWQ3KP5XDfdknsTJDam5KSsXl4wx7Jq5LEemONDkUIIYRoCZqmkTqQYvThUUmOlok/\n7mNvton1SPtkIeo6Gz/ykY/MO2iuWq2iaRqWZS15YKKx/LxP7ls5gmJA5roMzlaZ4WQ+KlAoT5G+\nNt3oUIQQQoiWYm+0MbtMglKAkZDJApZaWAxJ3Sa9RgtVV33Ur/zKr/DAAw/M2f7ggw/y8Y9/fMmD\nEo1VPVNl/F/GCb2Q7K1ZSYwuwhvySO1PyVUvIYQQYoE0XSNzMCMTMywDf9IntjqGtVY6MBaqruTo\nmWee4fDhw3O2Hz58mGeffXbJgxKNoZSi9HKJ3LdyGCmDnh/pwVolf1QXEnohaJDaK1dlhBBCiMWw\nt9hocY2wGjY6lLYSTATRcisyq/CC1ZUcVSqVORMwAOi6TrFYXPKgxMpTgWLyu5NMPjNJfGOc7Huy\nGEnp4r4Y96xL+mAaw5HjJIQQQiyGHtNJX5PGG5VFYZdKUA4w0gb2Rll3cTHqSo4GBwd5+OGH52x/\n6KGH2LFjx5IHJVZWUA4Y/8Y45dfLJHcnoxnpYjIj3cWElRDDMUhelWx0KEIIIURLSwwm0NBQviwK\nuxT8UZ/0wTSaIb1Gi1HXQImPfexj/NIv/RInTpzguuuuA+DJJ5/kn//5n/n85z+/rAGK5eWNe+S+\nnSOshnRd34W9Sa4y1MMdcul5b49May6EEEJcJsMxSO5NUni+IOsFXqbQDdEsTcaLX4a6kqObb76Z\n++67j/vuu49Pf/rTAOzcuZM/+7M/46abblrWAMXyqZyqkH8ij27pdL+nW6Z6rFNQCDC7TZxt8sYj\nhBBCLIXkVUkKzxZQoZJlQy6DN+yROSRrUl6OuqfYuvHGG7nxxhuXMxaxQpRSlF4qUXi+QKwvRtf1\nXTKF5gL4Yz49H+iR7mohhBBiiZgZE2fAoXKiIpNBLZIKFGhRmaJYPJl/uMMoX5F/Mk/lZAVni0P6\nHVKTuhD+hI+1zpLyQyGEEGKJpfamKB8to5SSWdYWwRvxSO5OygXvy1RXcuS6Ln/+53/Oww8/zJkz\nZ/D92fPRHzlypO4X/PKXv8wXv/hFhoeH2bFjB5/85Cc5cODABfd/6KGH+Mu//EveeOMNUqkU73zn\nO/mv//W/smrVqrpfU0SCUkDu2zn8cZ/UvhSJnQl581kApRT+pE/3e7vluAkhhBBLzOqzsDZYBBNR\n+bqonwqjRemTu2SiqMtVV0HiH//xH/O1r32NO++8E13Xufvuu7njjjvIZrP8zu/8Tt0v9sgjj/D7\nv//7/OIv/iJf+9rX2L9/Px/5yEc4c+bMvPs//fTT3H333dx+++384z/+I3/6p3/Ka6+9xq/92q/V\n/Zoi4o14jP/LOH7ep+vGLpJXJaWBv0D+qI+z1SG+VgaLCiGEEMshc20GvyCLwi6UP+bjbHeIZWX8\n+OWqKzn6p3/6J373d3+XD3/4w+i6zq233sqnPvUpfvmXf5nHH3+87he7//77uf322/npn/5ptm3b\nxm/91m+xatUqHnzwwXn3f+6551i7di2/8Au/wMaNG9m3bx8/+7M/y/PPP1/3awooHy8z/o1xMKDn\nth7i66Vxv1AqVITlkMw7Mo0ORQghhGhbVr+F1WsRFIJGh9JSgnIgi9IvkbqSo9HRUbZv3w5AMpkk\nn88DcMMNN/Doo4/W9UKu6/LSSy9x+PDhWdsPHz7Ms88+O+9jrrnmGoaHh/nmN7+JUoqxsTEeeeQR\nmRiiTipUFJ4tkH8iT6wvRs+P9GBmpZt6Mdwhl+TupMzoJ4QQQiwjTdNIHUzhj0vvUb38nE+8P461\nWiayWAp1tZTXrVvH0NAQ/f39bNq0iUcffZTdu3fz3HPPYdv1DUwfHx8nCAL6+vpmbe/t7b1g79P+\n/fv5wz/8Q37t136NarWK7/scPnyYz372sxd8nYWMf1pO1WqVt996GyNozKA45Sm85zyCoQBzk4m7\n3aU4VGxILAvlui6nT51udBjTVKAIRgOc/Q5vHXmr0eFcVKVSaZq/gVYjx27x5Ngtnhy7xZNjt3jN\nfuxUoKjkKmhVDS3eXEMAmq2NAhC8HRC/Oc7IkZFGh3JRzX7eTakrOXrve9/LE088wb59+/j5n/95\nPvGJT/C3f/u3DA0N8R//439c0AvON87lQmNfXn31Ve655x4+9rGPcf311zM8PMznPvc5fvu3f5vP\nfe5z8z5m586dC4pnuTx/7Hl61/US71/5EragEDDx7QkCNyB1a4rEjtaa0vH0qdNs2Lih0WFMq75Z\nJf1jaTIHmr+k7siRI03zN9Bq5Ngtnhy7xZNjt3hy7BavFY5dUS8y8a0J4huaayhAs7VRgmKAyirW\n3Lym6deHaqbz7umnn77gfXUlR5/4xCemb7/vfe9j3bp1PPPMM2zevJlbbrmlriC6u7sxDIPh4eFZ\n20dHR+f0Jk35i7/4C/bs2cN/+k//CYArr7wSx3G44447+NVf/VXWrVtX12t3EvesS+7RHADZm7NY\na6WL9XKEbohmaiSvltlfhBBCiJXibHPIPZ4j9EL0mCxoeiHeuEfPe3qaPjFqJZc82zzP4+Mf/zgn\nT56c3rZ3717uvPPOuhMjAMuy2LVr15wSuscff5z9+/fP+5hKpYJhzC5Lm/peKVX3a3eK8itlJv5t\nAj2u031btyRGS8AbilaaNmxZM0AIIYRYKXpcJ7U/hTfkNTqUphVWQwzbwN4iay8upUsmR7FYjMce\ne2xJpn2+8847+fu//3v+7//9v7z22mvcc889DA0N8eEPfxiAu+++m7vvvnt6/1tuuYVvfOMbPPDA\nA5w6dYqnn36ae+65h127dtHf33/Z8bQLFSgmvz9J/qk81lqL7tu6MdMy8cLlCkoBekIncWVrlSUK\nIYQQ7SB5ZVS1oQK5ID4fb8QjdW0K3ZSetaVU95ijf/3Xf13w+KLzvf/972d8fJz77ruPoaEhBgYG\n+MIXvsD69esBeOut2YPdf/zHf5xisciXv/xlPvvZz5JOpzl06BC//uu/fllxtJOwGpJ7LIf7tkti\nZ4LU3pR0rS4RfzRa8FW684UQQoiVZyQNkruSlI6WsNZINcxMyldoukZiQC7gLrW6kqP+/n7uu+8+\nvv/977N7924Sidm/iDvvvLPuF7zjjju444475r3vS1/60pxtP/dzP8fP/dzP1f38ncTP+eS+nSMo\nBmSuy+BsdRodUtvwJ33MHhNnmxxTIYQQolGSu5MUXiiglJLF62fwhj2Se5NS9r8M6kqOvvrVr5LJ\nZDh69ChHjx6ddZ+maQtKjsTSqJ6pkn8sDwZkb81irZIrKkvJH/fp/WCv9MIJIYQQDRTrjuFsdXDf\ndon1ylqDEK1jqQJF8iqZLGo51JUcffOb31zuOESdlFKUXy5TeK6AmTXpurELIylXDZaSN+4R3xDH\n3igDHIUQQohGS+9PM/x3w9Db6EiagzfqkbgyIePLl4kc1RaiAsXkU5OUXy9jb7JJH0rLeJglppQi\nmAzofV+vdN8LIYQQTSC2Ooa11sLP+5iZzm66KqVQFUVqT6rRobStus6we+6556L3f+pTn1qSYMSF\nBeWA3HdyeCMeyauTJHcnpfG+DLwRj8RAAmu1lCkKIYQQzUDTNNIH04w8NNLxyZE/4RO/Ii4lhsuo\nrjPs/HFGvu/z+uuvEwQBV1111bIEJs7xxjxy38kRVkO6ru/C3iTlXstBBdHVmPSBdKNDEUIIIcQM\n8fVxzC6ToBRgJDp3OEFQCOh5T0+jw2hrdSVH880iV61W+eQnP8mBAweWPChxTuVkhfyTeXRLp/s9\n3cR65ErBcvGGPZJ7ksS65RgLIYQQzUTTNTIHM4x/Y7xjkyN/0sfqs7DWSXXLclr0gJV4PM5HP/pR\n/vzP/3wp4xE1SimKLxbJPZrDzJp0/4gkRssp9EJQkNonNbxCCCFEM7K32OhxnbAaNjqUhggmAtIH\n0zKsYpld1mj+sbExSqXSUsUiapSvyD+Wp/B8AWeLQ/et3RhOZ14lWSnecLTKtJnq7FpmIYQQolnp\nMZ3UtSm8Ea/Roay4oBxgpAwZWrEC6moJ3n///bO+V0oxPDzMQw89xI033rgsgXWqoBhNvOCP+6T3\np3GudOQKwTILqyGaqZHcJesFCCGEEM0sMZhg8slJlK/QzM5pH/mjPtmbsmhG5/zMjbKoMUe6rtPT\n08OP//iP85//839elsA6kTcSTbygfEXXjV3E18cbHVJH8IY9sjdmZZVpIYQQoskZtkFyb5Li88WO\nGXsTeiFaTMPZ7jQ6lI4gi8A2ifLxMpPfncRIGmTfncXskvKulRCUom5qZ1DecIQQQohWkLwqSeG5\nAipUaHr796R4wx6Zgxl0S9a2XAl1HWXXdalWq3O2V6tVXNdd8qA6iQoVhWcL5J/IE1sVo/u2bkmM\nVpA34pF5VwbdlDccIYQQohWYGZPEQAJvtP3HHqlAAVE5oVgZdbUIf+VXfoUHHnhgzvYHH3yQj3/8\n40seVKcIvZDcd3IUjxRJ7EiQvTmLHpdG+krx8z7WKgtni/QaCSGEEK0kuSeJqiiUUo0OZVl5Ix7J\nXUmMpJT+r5S6WuLPPPMMhw8fnrP98OHDPPvss0seVCfwJ33G/3Uc9y2X9MF0NDWjDLJbUf6ET9fh\nro7okhdCCCHaidVnEd8Ux5/wGx3KslFKoVxFcrdMGLWS6kqOKpUKhjE3Y9V1nWKxuORBtTv3bZfx\nfx0nrIRkb86S2CFdpSvNG/OwN9lY/Z0xmFMIIYRoN+lr0gSFoNFhLBt/zMfeZhPLyjqXK6mu5Ghw\ncJCHH354zvaHHnqIHTt2LHlQ7az0SomJf59At3W6b+vGWiuN85WmQkVYDMlcl5Fp0oUQQogWZa2z\nsPos/Mn27D0KigHpfelGh9Fx6hr5/7GPfYxf+qVf4sSJE1x33XUAPPnkk/zzP/8zn//855c1wHah\nAkXhmQKlV0rE++PRJAAy60hDeCMezqCDtUoSUyGEEKJVaZpG+mCasX8aw0y312RWfs4n3h8ntlp6\njVZaXWfSzTffzH333cd9993Hpz/9aQB27tzJn/3Zn3HTTTcta4DtIKyG5B7N4Z51SexMkNqbknEu\nDaIChfIUmQOZRocihAy5D1kAACAASURBVBBCiMtkb7LRkzphJUS32+eis5+vLfoqFS4rru40+8Yb\nb+TGG29czljakp/zyX07R1AMyFyXwdkqM6M1kjfkkdqTkunShRBCiDagGRqZgxkmvjVBfEO80eEs\niaAUYHaZxNe3x8/TaupKsb/3ve/xve99b97tTz311JIH1S7cs9HEC8pXZG/NSmLUYKEXApDam2pw\nJEIIIYRYKs52By2mTX/OtzpvNFr0VaqMGqOu5Ojee+8ln8/P2V4oFLj33nuXPKhWp5TCe9lj8qlJ\njLQRTbwg41sazhvySB9My1oBQgghRBvRLZ30NWm84dZfFDashhi2gb3FbnQoHauu5Oj48eMMDg7O\n2T4wMMDx48eXPKhW5416eM96WGstsrdmpTHeBMJqiGZpJK+StQKEEEKIdpMYTICKxha3Mm/EI3Ug\nhR5rn/FT/397dx4WVdk+cPw7LAMDw45iuIsK7iLuuIXmzpuaW2mr4q7llltqr5nmUlaamqVgbvWq\nqWmmmZWJqK/ilorkVuIKAsomzHZ+f/BjXhFURGEGvT/XxVWcec4599zA47nnOed5SpoCZd7BwYH4\n+Pg8269fv469vcyicS97L3scOzqiDZJfbmuhi9fh1swNGwf5eQghhBBPG1tnW5xrO6O/WXJHjxSD\ngkqlkvUvLaxAV4otWrTg448/5vbt2+Ztt27dYsGCBbRo0aLIgiupVCoVNh42MsOIlTCmZz/YqKkm\nz3wJIYQQTyvn2s4oOgXFVDJHj/QJepzrOWOrkTuOLKlAU3ZNmDCB/v37ExISYr69LjY2Fk9PTz75\n5JMiDVCIx6VP0uPVyQsbOxk1EkIIIZ5W9u72OPo5orumw96rZN3ZpJiyizq5/d/yClQclS5dmi1b\ntrB161ZiYmJQFIXu3bsTGhrKkSNH8PHxKeo4hSgUw20DDj4OOFaUBxuFEEKIp51LfRfiz8eXuOJI\nn6jHqboTdq6y1IilFfgnoNFo6N27NwA3btxg48aNdOnShatXrxITE1NkAQpRWIqiYLxtxCPEQ6bD\nFEIIIZ4B9qXtcXjOAcNtQ4lZ01BRFJRMBee6MmpkDQr8W2M0Gvn1119Zv349+/btw9/fn759+9Kx\nY8eijE+IQjMkGXCo5ICDryyiJoQQQjwLVCoVLg1duLn1Zokpjoy3jDiUd0DtLcu+WIOH/tZcuHCB\n9evXs2XLFjQaDV27diUyMpK5c+dStWrV4ohRiEemmBSM6Ua8unpZOhQhhBBCFCOHsg7YudthzDBi\n62T9kxsY0gx4tPWwdBji/z3wCfVXXnmFPn36kJqayqeffsru3bsZPXq0zMImrJ4+QY9zTWf5FEYI\nIYR4xqhsVLg2ckWfaP3TehvTjKi91Kh95XrFWjywODp27Bgvvvgir732Go0bNy6umIR4LIpRQdEr\nuAS5WDoUIYQQQliAYyVHbB1tMWWZLB3KAxmSDWgbaWXgwYo8sDjauHEjRqORfv360a1bNyIiIkhI\nSCiu2IQoFH28Hm2gVmZ8EUIIIZ5RNvY2aBtqrXpRWFOmCRtnGzQVZR1Ga/LA4qhGjRpMnz6dyMhI\n3njjDXbv3k2bNm0wmUz8/vvvuRaFFcIamPQmUIG2rtbSoQghhBDCgpyqO6GyUaEYrHNRWP1NPa4N\nXVHZyqiRNSnQqpgODg5069aNVatWsX37dgYMGEBERATBwcEMHDiwqGMUosD0N/S4NHIpEQ9gCiGE\nEKLo2Dra4lzPGX2C9Y0emfQmVPYqNFVl1MjaFKg4ulvFihUZN24ce/bs4dNPP8Xe/tEW2VqzZg0h\nISHUqVOHHj16cPjw4fu2nThxIv7+/nm+6tev/6hhi2eAKdOEjcZGVpcWQgghBADONZ2zn0U2Wdfo\nkSHBgDZQi43DI1+KiyJW6J+Ira0t7dq1Y8mSJQXeZ/v27cyaNYshQ4awefNmAgMDCQsL4+rVq/m2\nnzJlCpGRkbm+ypcvT6dOnQobtniK6RJ0uDZzxUYtHY0QQgghwM7FDqcaTlY1c51iVFAUBecA+TDX\nGhXrVWR4eDjdu3end+/e+Pn5MXXqVEqVKsW6devybe/i4kKpUqXMX5cuXSIuLo5evXoVZ9iiBDCm\nGbFzt8OpqpOlQxFCCCGEFdHW0aJkZhck1iBnuRFbZ3kEwBoVW3Gk0+k4deoUwcHBubYHBwdz9OjR\nAh1j/fr1VKtWjQYNGhRFiKIEMyQZcAt2k4cahRBCCJGLvZc9DhUdMNwyWDoUFEXBpDfhXEdGjaxV\nsc11nJycjNFoxNvbO9d2Ly8voqKiHrp/amoqO3bsYPTo0Q9sFxMT81hxPilZWVlcv3YdW6N8KvCo\ndDodl+MuF7i9KdWESqPidvptVDHPdnGUmZlpNX8DJY3krvAkd4UnuSs8yV3hPYu5M7oYyTqQhe1z\nj3dd9qjXKPcy3TJh42VD6vVUuP5YoZQ4JeX3rtgXgslvkauCLHz1ww8/YDQaefHFFx/YrkaNGoWO\n7Uk68dcJvJ7zwsHXwdKhlDiX4y5Trny5ArVVFIWsy1mU6lEKhzKS65iYGKv5GyhpJHeFJ7krPMld\n4UnuCu9ZzJ0SoJBwIwGT3oSdS+Evfx/lGiU/WWTh/aL3M3nNYk2/d9HR0fd9rdhuq/Pw8MDW1jbP\nIrKJiYl5RpPy85///If27dvj7u5eVCGKEsiQaEBTWfNMdjJCCCGEKBiVSoVLQxeMt4wWi8GQYkDt\no0bto7ZYDOLhiq04UqvV1KpVK88tdFFRUQQGBj5w3xMnTnDmzBl69+5dlCGKEkYxKZjumHBt4mrp\nUIQQQghh5RwrOGLrYovxjmUKJMNtA9qG2gLdMSUsp1hnq3vzzTfZtGkT69ev5/z588ycOZP4+Hj6\n9u0LwLvvvsu7776bZ7/vvvuOSpUq0bhx4+IMV1g5fYIe59rO2Hs+2lpbQgghhHj2qGyzR48MicU/\nMYMxw4idmx2O5R2L/dzi0RTrM0edO3cmOTmZJUuWEB8fT/Xq1Vm2bBlly5YF4Nq1a3n2SUtLY/v2\n7QwbNkwqbWGmGBQUg4I2UGvpUIQQQghRQmj8NNzedxuTzlSs6yIakgy4h7ijspFrWWtX7BMy9OvX\nj379+uX72qpVq/Js02q1BZ7qWzw7dPE6XIJcHuuhSiGEEEI8W2zUNrg0cCHlUEqxTZpl0plQOajQ\nVNEUy/nE4ynW2+qEeBJMOhMqW5WsESCEEEKIR+YUkL1gvGIsnkVh9Tf1uDRwwcZeLrtLAvkpiRJH\nH6/HtYkrto6yhpQQQgghHo2tky3OtZ3R39QX+bkUg4IKFU7+TkV+LvFkSHEkShTjHSM2TjbmT32E\nEEIIIR6Vcy1nFL2CYira0SNdgg7nOs7YauQD3ZJCiiNRohgSDLg1d5OhaSGEEEIUmr27PZqqGgxJ\nRTdznWJSwAjOteUxgJJErjBFiWFINWDnZYfGTx5oFEIIIcTj0dbTFumaR/pEPZrqGuxcZfKokkSK\nI1FiGJINuDZ3lWkwhRBCCPHY1KXVOPg6YLj95EePFEXBlGlCW1eWHClppDgSJYI+WY9DOQdZPE0I\nIYQQT4xLQxeMKU9+9Mh4y4hDWQfUpdRP/NiiaElxJKyeoigY04y4NXWThYCFEEII8cQ4+Dpg626L\nMf3JFkjGNCOuDV2f6DFF8ZDiSFg9/U09TlWdUPvIpy9CCCGEeHJUNipcG7miT35y03ob04zYedqh\nfk6uW0oiKY6EVVOMCkqmgksjF0uHIoQQQoinkGNlR2w1tpiyTE/keIZkAy6NXOQZ6RJKiiNh1fQJ\nepzrOmPvYW/pUIQQQgjxFLKxs8ElyOWJLApryjRh42yDY0V5RrqkkuJIWC3FoIAC2voy04sQQggh\nio6mmgaVjQqT/vFGj/Q39bg2dMXGTi6xSyr5yQmrpYvXoQ3SYqeV9QGEEEIIUXRsHW1xrueM4Wbh\np/U26U2o7FRoqsp6jCWZFEfCKpmysjsY51qyqrQQQgghip5zTefsZ52NSqH2NyQY0NbXYuMgl9cl\nmfz0hFXSJ+hxa+qGraOtpUMRQgghxDPAzsUOpxpO6BMf/dkjxaigmBScajgVQWSiOElxJKyOckfB\nVmuLxl+GpYUQQghRfLR1tShZCoryaKNH+pt6nGs5y6MATwEpjoTVUEwKugQdplsm3ILd5GFGIYQQ\nQhQre097HCs5Ykgu+LNHiqKg6BSca8ujAE8DufoUFpdTFGVdycKxgiOOnR3RVJFRIyGEEEIUP22g\nFmO6scDtDckGHCs7Yu8py448DaQ4KsF+PPQj7Sa3u+/r15KuETwumJi4mGKMquDuLYp8+vrg2c4T\nG3f5tRRCCCGEZajLqFGXVmNILdjokSndhDZQlh15WsiNkaLYKSYFfaIeU6YJp+pOuDRwkU9bhBBC\nCGEVVCoVLg1dSNqehJ3Lgy+VDSkG7Evbo/ZRF1N0oqhJcSQem96gx97u4cVNSSmKdDodarV0ckII\nIcSzyrG8I7YuthjvGLHV3H/mXONtI26d3VCpVMUYnShKcv+SlTt2/hhhn4fRbnI72r/XnrDPwrhw\n7UK+bVMyUhiyaAijl43mTtadfNtcvH6RcV+Po92UdnSZ3oXpq6eTmJJofj3mUgzvLHuHztM688KU\nFxi6aCgn/z6Z6xjB44LZuG8jkyIm0XZSW7786UuOnDtC8LhgDp89TNhnYYRMCuGtT98i9nLsfW+f\ns/e0Z8uWLbz00ksEBgbSrFkzRo0aRWJiYq7znT9/niFDhhAUFERgYCB9+vQhNjbW/PqmTZsIDQ2l\ndu3aNG/enIkTJ5pf8/f3Z8eOHbmOFxISwvLly3O1WbNmDSNGjKB+/fosWLAAo9HI5MmTCQkJoW7d\nurRv356vvvoKkyn3ytn3O/ekSZMYPHhwrrYmk4k2bdoQHh6e789GCCGEENZBZavCpZELhsT731pn\nzDBi62qLYwXHYoxMFDUZObJiBqOBiRET6dq4K9NfmY7RZCT2ciw2Nnlr2pspNxm9bDSVSldi2ivT\nsLez51b6rTxthi8eTtfGXRkROgKD0cCyn5YxIXwCy0Yuw8bGhoysDDoGdeSdF99BpVKxIXID45aP\n49uJ3+Lu7G4+1oqfVzC482BGhI5AhYrrydcBWLp9KUO7DMXb1ZtPN3/K+9+8z4r+K3AOcMYlMO9I\nkV6vZ9SoUVSpUoXk5GTmzZvHxx9/TIsWLQC4ceMGr7zyCg0aNCA8PBwXFxdOnDhhLlK+/fZbPvzw\nQ8aMGUPr1q3JyMjgwIEDj5zrRYsWMWbMGCZMmABkFzI+Pj58+umneHp6cuLECaZNm4a7uzu9evV6\n6Ll79+5Nv379iI+Pp3Tp0gDs27ePmzdv8uKLLz5yfEIIIYQoXpoqGm7vu41JZ8JGnffay5BkwD3E\nHZWNjBo9TZ7p4uh4WhpHU1OL5Nhx9lloXVOws8ud4jomR2qbCjYTW0ZWBql3UgmuGUw573IAVCxd\nMU+7yzcvM3rZaBr7N2Zs97H5Fk8Am6I2UdW3KsO6DjNve+/l9+g0rRNnLp+hZoWaBFULyrXPmO5j\n2PPnHg6eOUiHoA7m7W3rt+VfTf5l/j6nOArrGEaDKg3QJ+rpV68fb69/G9qCZ4BnvjH17NnT/P/l\ny5fn/fffp3Pnzly/fp0yZcqwdu1anJyc+Oyzz8y3ulWuXNm8z+LFi3n99dd58803zdtq166d77ke\npHPnzuaiJ8fbb79t/v9y5cpx+vRpfvzxR3O7B507MDCQKlWqsHnzZgYNGgTAxo0bCQkJwdMz/1wI\nIYQQwnrYqG1waeBCysEUHMo65HrNpDOhUqvQVJbZdZ82z3RxZO1cnVzp3LAzY74aQ1DVIBpWa8jz\n9Z7Hx93H3EZv1DP0i6G0qdOGsT3GPvB4sZdjOXbhWL4z3F1JvELNCjVJTk3mq51fceTcEZLSkjCZ\nTGTps7h+63qu9gHlAvI9RwV1BbKuZOHk74R/Y39YDynGlPvGdOrUKRYtWsSZM2e4det/I11Xr16l\nTJkynD59mgYNGuT7DFBiYiI3btygWbNmD3zfBZFfQbVu3TrWr1/P1atXycrKQq/XU7Zs2QKfu1ev\nXqxdu5ZBgwZx69Ytdu/ezaJFix47ViGEEEIUDyd/J1IPpaIYFVS2/xsh0ifocW3imu+IkijZnuni\nqJ5WSz1t0Uy9eOK/CXiluOKgdXh44weY0ncKvVv15uCZg0SeiuTLn77kozc/ool/EwDsbOxoXL0x\n+2P2cz3pOmU8y9z3WIqi0LxGc0aEjsjzmofWA4CZ384kKS2JUf8aRRnPMqjt1IxaOgqDIfc9txr1\n/z4pUUwKhtvZrztXdManpQ/2nvZkXM4AyPOcTo6MjAwGDBhA8+bNmTt3Lp6eniQnJ9OvXz/0er05\n5ge9n4dRqVR52uUcO9f70eT+5Gf79u3MmjWLCRMmEBgYiFarZc2aNfzyyy8FPveLL77I/PnzOXz4\nMDExMXh4eJhvFxRCCCGE9bN1ssW5tjPpp9PNM9IpRgWVSoWTv5OFoxNFQcrdEqCabzX6h/Rn0bBF\nNPBrwE+HfzK/plKpmNJnCnUr12Xk0pHm29vyU71cdS7euEgZjzKU8y6X68vZMXtV5+N/H6dncE+a\n12xOlTJVcHJwIjE1Md/j3T3RQk6H4d7SvcAz0F24cIHk5GRGjx5No0aN8PPzIykpKVebWrVqceTI\nEXQ6XZ79vb298fHxYf/+/fc9h6enJwkJCebvb968mev7+4mOjqZevXr079+fWrVqUbFiRS5duvRI\n53Z3d6d9+/Zs3LiRjRs30r17d2xt7z/jjRBCCCGsj3MtZxSdgmLK/mBUl6DDuY4ztk7yb/rTSIoj\nK3Y18SpLflzCn3//yfWk60Sfi+bctXNU8qmUq52NjQ3v9X2P2pVqM3LJ/QukHs17kHYnjamrpnLq\nn1NcSbzCob8OMWf9HNIz0wGo4F2BnUd2cvH6RWIuxTBt9TTsbfMWO4YUQ/bscxWzZ59zaeDyyO/P\n19cXtVrNmjVriIuL4/fff+ezzz7L1eaVV14hIyODd955hxMnTvDPP/+wbds2YmKyF7YdMmQIK1eu\nJCIigosXLxITE8OKFSvM+zdt2pQ1a9bw559/cvr0aSZOnIiDw8NH8ypVqsSpU6fYs2cPf//9N198\n8QWHDh3K1eZh54bsW+u2bt3KmTNneOmllx45R0IIIYSwLDs3OzTVNBiSDNkFkj67YBJPp2f6tjpr\n56h25FLCJd775j1up9/G08WT9g3a0//5/nna5hRIM7+dycilI1k4ZGGeNqXcSrF0xFKWbl/K2K/H\nkqXPwsfDh8bVG6O2yx75mdRnEnPXz+WtT9/C282bAe0HcCst+1mgnHWKAOxL2ePT1+ex1iny9PRk\nzpw5fPLJJ6xZswZ/f38mTpzIwIEDzW18fHxYvXo1c+fO5fXXXweyp96eMWMGkF082dvbEx4ezvz5\n83Fzc6NVq1bm/SdMmMCUKVN47bXX8PLyYvz48Vy4kP9U6Hfr06cPMTExjBs3DkVRaN++PW+++Sbf\nf/+9uc3Dzg3QpEkTypQpg6+vLxUqVCh0roQQQghhOdr6WjLOZqDcVtA00WDnJpfQTyuVUpCHJ0qI\n6OhogoKCHt6wGJzYcgKv6144+D7eM0fWIKcoUrIUNNU1+U7J/STFxMRQo0aNIjt+ccrMzKRVq1a8\n9957/Otf/3r4Do/pacpdcZPcFZ7krvAkd4UnuSs8yV3hJGxJ4J/9/1B3bF3UpWSx+EdlTb93D6oZ\npOwV92UuijIVNP5FXxQ9TUwmEzdv3iQiIgIHBwc6duxo6ZCEEEII8RhcG7liG28rhdFTToojkYcU\nRY/v6tWrtG3bljJlyjB79ux8pyIXQgghRMnh4OuAQ3DJvyNIPJgUR8JMiqInp1y5csTGxlo6DCGE\nEEI8QSqV6uGNRIkmxZGQokgIIYQQQggsMJX3mjVrCAkJoU6dOvTo0YPDhw8/sL1Op+Ozzz4jJCSE\n2rVr06ZNG7755ptiivbpppgUdPE6dFd0OFZ0pPTLpfFs6ymFkRBCCCGEeCYV68jR9u3bmTVrFtOn\nTycoKIi1a9cSFhbGjz/+iK+vb777jB07lmvXrvHBBx9QsWJFEhMTyczMLM6wnzqKSUF/8/9nn/PX\n4NLABXsPKYiEEEIIIcSzrViLo/DwcLp3707v3r0BmDp1Knv37mXdunWMHTs2T/vIyEiioqLYtWsX\nnp6eQPazHKJwpCgSQgghhBDi/oqtONLpdJw6dYq33nor1/bg4GCOHj2a7z6//PILderUISIigs2b\nN+Po6EirVq0YPXo0zs6yMnFBSVEkhBBCCCHEwxVbcZScnIzRaMTb2zvXdi8vL6KiovLdJy4ujujo\naNRqNQsXLiQlJYWZM2cSHx/P559/Xhxhl2h3F0VOAU5oA7VSFAkhhBBCCHEfxT5bXX5TIN5vWkRF\nUVCpVHz88ce4uLgA2bfiDRgwgJs3b+YptCB79V1rkJWVxfVr17E12hb7uRWTgpKsoOgVbKvYYt/A\nnhS3FLhO9peVy8zMtJqfY0kjuSs8yV3hSe4KT3JXeJK7wpPcFZ7krvBKSu6KrTjy8PDA1taWhISE\nXNsTExPzLXIASpUqhY+Pj7kwAvDz8wOyF9nMb78aNWo8wagL78RfJ/B6zgsH3+JbLCzXSFFwyR0p\niomJeWI/x++//54PPvjgvrduPm2eZO6eNZK7wpPcFZ7krvAkd4UnuSs8yV3hWVPuoqOj7/tasU3l\nrVarqVWrVp5b6KKioggMDMx3nwYNGhAfH096erp5299//w1A2bJliyzWkubuKbk1lTWUfrk0HiEe\nVlsY6fV6S4fw1NDpdJYOQQghhBDiqVGs6xy9+eabbNq0ifXr13P+/Hnz80N9+/YF4N133+Xdd981\nt+/atSvu7u5MmjSJs2fPEh0dzYcffkiHDh3w8vIqztAtZsTiEczdMJdPN39Kx6kd6Ti1I4u2LsJk\nMpmLosQLiczbM49uK7rR+p3WhI0O4+zZs+ZjfP/99wQGBrJnzx46duxIvXr1GDJkCKmpqezYsYP2\n7dsTFBTE+PHjc02TrigKX331Fe3ataNu3bqEhoayZcuWXPEdP36c7t27U6dOHbp168aePXvw9/fn\n4MGDABw8eBB/f3/27NlDz549qV27NpGRkVy6dImhQ4cSHBxM/fr16d69O7/99luuY4eEhLB48WKm\nTZtGgwYNaNWqFV9//XWuNuHh4YSGhlK/fn1atmzJlClTSElJMZ970qRJZGRk4O/vj7+/PwsXLgSy\ni4p58+bRqlUr6tevz0svvcTevXsf+LP4448/eOWVV2jUqBGNGzdmwIABnD9/PlebGzduMHbsWJo0\naUK9evV48cUXOXDggPn133//nV69elG3bl2aNGnCkCFDyMrKMr/f5cuX5zreq6++yowZM3LlZOHC\nhUyaNImGDRsybtw4AObPn0+HDh3o3bs3ISEhzJ0713zch5170aJFdO3aNc/77du3LzNnznxgToQQ\nQgghnibF+sxR586dSU5OZsmSJcTHx1O9enWWLVtmHgW6du1arvbOzs6Eh4czc+ZMevbsiaurK+3a\ntct32u+n2c9Hf6Zzw858OfJLzl09x5wNc3C3cadXvV44+TsxY+0M/o7/m8VLFuPq6sqCBQsYOHAg\nO3fuxNHREcguBsLDw5k/fz56vZ6RI0cyatQoHBwc+Pzzz7l16xYjR45k7dq15hkFP/30U3bs2MG0\nadOoXLkyx44dY+rUqbi5udGmTRvS09MZPHgwwcHBzJ07l/j4eGbNmpXve5g/fz4TJkygYsWKODs7\nEx8fT6tWrXjnnXdwdHRk+/btjBw5Mk/xtXLlSkaOHMmmTZv4448/mDlzJkFBQebRRpVKxeTJkylf\nvjxXr17lgw8+4IMPPmDevHkEBgYyefJkFixYwK5duwBwcnICYNKkScTFxfHxxx9TpkwZ9uzZw9Ch\nQ9mwYQMBAQH5voc7d+7w+uuv4+/vT2ZmJkuWLGHIkCH8+OOPqNVqMjIyePXVV/H09GTRokX4+Phw\n5swZ8/5//PEHw4YNIywsjNmzZ2MwGNi3bx8mk+mRfh/Cw8MZOnQoGzduRFEUADQaDbNmzSI1NRWV\nSsX06dNRq9W88847Dz13z549Wbx4MSdOnKBu3boAXLhwgaNHj/L+++8/UmxCCCGEECVZsU/I0K9f\nP/r165fva6tWrcqzrUqVKqxYsaJIYkk7nkbq0dQiOXbWkSxSbqdg55E7xY5VHNFU1jzSsbxdvBnd\nbTQo8BzPcTHwIhuObmD47OFcuX2F3/b+xurVq2nUqBEA8+bNo02bNmzdupVevXoBYDAYmDZtGlWq\nVAEgNDSUiIgI9u3bZ15Dqm3bthw8eJC33nqLjIwMwsPDWbFiBQ0bNgSgfPnynDhxgjVr1piPbzKZ\n+PDDD3F0dKRatWoMGTLEPJpxtxEjRtCiRQvz956enrmKkKFDh/Lbb7+xc+dOnn/+efP24OBg+vfv\nD2SPoqxatYr9+/ebi6M33njD3LZcuXKMHz+eYcOGMWfOHNRqNS4uLqhUKkqVKmVud+nSJX788Ud+\n/fVX8+LD/fv3Jyoqim+//fa+BUGHDh1yfT979myCgoI4ceIEDRs2ZNu2bSQkJPDtt9+ac1qhQgVz\n+8WLF9OhQwdGjx5t3na/QuxBGjduTFhYWK5tw4cPB/53P+/gwYNZsWKFuTh60Lk1Gg0tW7Zkw4YN\n5uJo48aN1KpVq1DxCSGEEEKUVMVeHIlHV7NCTfQJehSdgpO/E8HlgwkfGU6WfRbnz5/HxsaG+vXr\nm9u7uLhQvXp1zp07Z96mVqvNhRFkT6Hu7e1tvojP2Zazz7lz58jKymLgwIG5ZhPU6/Xmkb4LFy5Q\nrVo18+gUQL169fJ9D7Vr1871fUZGBosWLeL3338nISEBg8FAVlYW/v7+udrd+33p0qVJSkoyf79/\n/36WLVvG+fPnp+vExAAAHORJREFUSU1NxWQyodfrSUhIwMfHJ99YTp06haIodOnSJdd2nU5H06ZN\n890Hsouqzz77jOPHj5OUlISiKJhMJvOI5+nTp/H398+V07vFxMTQo0eP+x6/oO7NJcCOHTtYuXIl\nFy5cQKfTYTQac41IPezcvXr1YuLEiUyePBl7e3u2bNnCsGHDHjtWIYQQQoiS5JkujrT1tGjraYvk\n2AkeCbhed32s2eoUk4JJb8J0x4SmsgZtAy327vbY7f/fjy3ntqr83F3U2NnZ5XnN3t4+z7acC+qc\n4y5ZssQ8unLvsXKmWi8IjSb3aNmcOXPYu3ev+VY7jUbDhAkT8kzWkF/cOTFeuXKFwYMH07t3b0aN\nGoW7uzunT59mzJgxD5z0ISfuDRs25Dn+3YXevYYMGYKPjw8zZszAx8cHW1tbunTpYj7Xg34WBaFS\nqfIcI7/3cW8ujx07xpgxYxg+fDgvv/wydevW5ddff2XOnDkFPnebNm1wdHRk586duLi4kJqamu9z\nSEIIIYQQT7NinZBBFIxiUtDd0KG7qsPG0Yaz6Wdxf94de/fsYubYsWOULl0arVZL1apVMZlMHDt2\nzLx/Wloaf/31l3na88Lw8/NDrVZz9epVKlasmOsrZ+TIz8+Pv/76K9ckDidOnCjQ8Y8cOUK3bt3o\n0KEDAQEBlClThkuXLj1SjCdPnkSv1zNp0iQCAwOpXLky8fHxudrY29tjNBpzbatRowaKopCQkJDn\nvd1vtCk5OZnz588zePBgmjdvjp+fH+np6RgMBnObWrVqERsbm2tk697z7t+//77vx9PTM9dU91lZ\nWVy8ePGheThy5Ag+Pj4MHz6catWqUalSJa5evfpI57azs6NHjx5s3LiRjRs38sILL+Dq6vrQcwsh\nhBBCPE2kOLIidxdFGr/sKbntPeyJvxnPhx9+yIULF9ixYwfLly83P2tTqVIl2rZty7Rp0zh8+DCx\nsbGMGzcOrVZLaGhooWPRarW89dZbzJ07lw0bNvDPP/8QExPDunXr+O6774Ds55ZsbGx47733OHfu\nHFFRUXz55ZfA/Rf2zVGpUiV27drFqVOniI2NZfz48XlmV3uYihUrYjK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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def evaluate_models():\n", " sizes = numpy.arange(5, 51, 5).astype('int')\n", " n, m = sizes.shape[0], 5\n", " \n", " skl_predict, pom_predict = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_fit, pom_fit = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " skl_error, pom_error = numpy.zeros((m, n)), numpy.zeros((m, n))\n", " \n", " for i in range(m):\n", " for j, size in enumerate(sizes):\n", " X, y = create_dataset(50000, size, 2)\n", " \n", " # bench fit times\n", " tic = time.time()\n", " skl = GaussianMixture(n_components=2, max_iter=10, covariance_type='full')\n", " skl.fit( X )\n", " skl_fit[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X, max_iterations=10)\n", " pom_fit[i, j] = time.time() - tic\n", "\n", " # bench predict times\n", " tic = time.time()\n", " skl_predictions = skl.predict( X )\n", " skl_predict[i, j] = time.time() - tic\n", "\n", " tic = time.time()\n", " pom_predictions = pom.predict( X )\n", " pom_predict[i, j] = time.time() - tic\n", " \n", " # check number wrong\n", " skl_e = (y != skl_predictions).mean()\n", " pom_e = (y != pom_predictions).mean()\n", "\n", " skl_error[i, j] = min(skl_e, 1-skl_e)\n", " pom_error[i, j] = min(pom_e, 1-pom_e)\n", " \n", " fit = skl_fit / pom_fit\n", " predict = skl_predict / pom_predict\n", " plot(fit, predict, skl_error, pom_error, sizes, \"dimensions\")\n", " del X, y\n", "\n", "evaluate_models()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example: Classifying Noisy Signals\n", "\n", "Let's take a look at the same example we tried in the Naive Bayes classifying case, but this time using General Mixture Models. Since GMMs and Naive Bayes can be thought of as unsupervised and supervised versions of the same technique (basically, Bayes Rule), let's try dropping the labels we had for the noisy signal segments, and see how well we can do using clustering." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GMM Using Normal, LogNormal, Exponential: 0.831\n", "Multivariate Gaussian Naive Bayes: 0.768\n", "sklearn Multivariate Gaussian Naive Bayes: 0.768\n" ] } ], "source": [ "from sklearn.mixture import GMM\n", "\n", "X, y = create_signal(1000)\n", "\n", "d1 = NormalDistribution(0, 1)\n", "d2 = LogNormalDistribution(0, 1)\n", "d3 = ExponentialDistribution(0.1)\n", "da = IndependentComponentsDistribution([d1, d2, d3])\n", "\n", "d1 = NormalDistribution(1, 1)\n", "d2 = LogNormalDistribution(1, 1)\n", "d3 = ExponentialDistribution(1)\n", "db = IndependentComponentsDistribution([d1, d2, d3])\n", "\n", "model = GeneralMixtureModel([da, db])\n", "model.fit(X, max_iterations=10)\n", "print \"GMM Using Normal, LogNormal, Exponential: \", max((model.predict(X) == y).mean(), (1-model.predict(X) == y).mean())\n", "\n", "model = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train, max_iterations=10)\n", "print \"Multivariate Gaussian Naive Bayes: \", max((model.predict(X) == y).mean(), (1-model.predict(X) == y).mean())\n", "\n", "clf = GaussianMixture(n_components=2, max_iter=10)\n", "clf.fit(X_train)\n", "print \"sklearn Multivariate Gaussian Naive Bayes: \", max((model.predict(X) == y).mean(), (1-model.predict(X) == y).mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like we're doing significantly better using appropriate distributions than we do when we try to use Gaussian distributions. In fact, we're getting similar performance to the Naive Bayes classifier which used supervised learning. A good note is that our implementation of Gaussian mixtures and sklearns both give the exact same results when the same restraints are put on the EM algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example: Mixtures of Hidden Markov Models\n", "\n", "In the same vein as how it is easy to view a GMM as a \"distribution\" which could be used in larger models, hidden Markov models are just a structured distribution over sequences. Now, since HMMs are just a distribution, we can create a mixture of HMMs which can operate over arbitrarily lengthed sequences of data in the same way we created a mixture of anything else." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def build_model(a, b, name):\n", " d1 = State(NormalDistribution(a, 1), \"d1\")\n", " d2 = State(NormalDistribution(b, 1), \"d2\")\n", "\n", " model = HiddenMarkovModel(name)\n", " model.add_states(d1, d2)\n", "\n", " model.add_transition(model.start, d1, 0.9)\n", " model.add_transition(model.start, d2, 0.1)\n", " model.add_transition(d1, d2, 0.65)\n", " model.add_transition(d1, d1, 0.20)\n", " model.add_transition( d1, model.end, 0.15)\n", " model.add_transition(d2, d1, 0.20)\n", " model.add_transition(d2, d2, 0.70)\n", " model.add_transition(d2, model.end, 0.10)\n", "\n", " model.bake()\n", " return model\n", "\n", "a = build_model(1, 7, \"a\")\n", "b = build_model(3, 10, \"b\")\n", "gmm = GeneralMixtureModel([a, b])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It was just as simple to make a mixture of GMMs as it is to make a mixture of any other model. Doing this may be simple if everything were a python object, but one of the benefits of pomegranate is that even though they are stacked, these objects will still using a cython backend with the GIL released when communicating with each other, as opposed to simply having a python wrapper around cythonized functions. This makes the process faster, and makes multithreading far more efficient.\n", "\n", "Now let's take a look at the two HMMs prior to training. We can see that they have the same graph structure and edge probabilities, but the emission distributions on the two are different." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": 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6ZQ7liy++AIz52rKzs63PR0VFERUVxfDhw3nvvffwer14HR72HBERQXp6Ort2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 10))\n", "plt.subplot(121)\n", "a.plot()\n", "\n", "plt.subplot(122)\n", "b.plot()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now generate a bunch of data from the two underlying HMMs in order to train our model. Let's generate twice as much data from the second HMM as the first one to see if the mixture can learn the appropriate weighting (prior distribution) on the two models as well." ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.17827769, 0.82172231],\n", " [0.07720039, 0.92279961],\n", " [0.28729868, 0.71270132],\n", " [0.98155977, 0.01844023],\n", " [0.281161 , 0.718839 ],\n", " [0.19855272, 0.80144728],\n", " [0.5893465 , 0.4106535 ],\n", " [0.03095709, 0.96904291],\n", " [0.30105166, 0.69894834],\n", " [0.21623513, 0.78376487],\n", " [0.06231708, 0.93768292],\n", " [0.11703583, 0.88296417],\n", " [0.03414845, 0.96585155],\n", " [0.57320448, 0.42679552],\n", " [0.06706606, 0.93293394]])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = numpy.array([ a.sample() for i in range(5) ] + [ b.sample() for i in range(10) ])\n", "gmm.predict_proba(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the samples seem to mostly come from one model or the other. The first five are more likely to come from the first HMM, which makes sense given that they were generated by it, while the last ten sequences were more likely to come from the second HMM, which they were generated from.\n", "\n", "Now, we can fit our model just as simply as before. Let's get a larger amount of data here to get an appropriate training set." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] Improvement: 591.322527071\tTime (s): 0.002\n", "[2] Improvement: 0.161381672237\tTime (s): 0.002785\n", "[3] Improvement: 0.0669529333561\tTime (s): 0.002479\n", "Total Improvement: 591.550861677\n", "Total Time (s): 0.0103\n" ] }, { "data": { "text/plain": [ "{\n", " \"weights\" : [\n", " 0.41816624241565364,\n", " 0.5818337575843464\n", " ],\n", " \"distributions\" : [\n", " {\n", " \"end\" : {\n", " \"distribution\" : null,\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"a-end\"\n", " },\n", " \"name\" : \"a\",\n", " \"distribution ties\" : [],\n", " \"silent_index\" : 2,\n", " \"end_index\" : 3,\n", " \"states\" : [\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " 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"metadata": {}, "source": [ "We can now take a look at the underlying graphs again to see that the edge probabilities for the two have shifted. We cannot see the emissions on this graph, but we could extract the explicit distributions from the model itself if we so wanted." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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n06VLFwCKi4spLCzkkUceAaBHjx4nfM8PP/zA8uXL7fMfOHCAdevWnbR9wzBY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\na0V8LSMjw953+ODBg+Tk5Jzwh9RwbU5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uSoCvv/6aurq6E55fV1dHXV0d33//Pe+8846v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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 10))\n", "plt.subplot(121)\n", "a.plot()\n", "\n", "plt.subplot(122)\n", "b.plot()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now look at the weight of each component in the mixture. This should be 0.33 and 0.66 respectively, since that's the relative frequency with which we generated data. " ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.41816624, 0.58183376])" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numpy.exp(gmm.weights)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great. In addition to updating model parameters in an unsupervised manner, we're able to learn the prior distribution of the two components based on their frequency in the underlying data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Hidden Markov Models\n", "\n", "Hidden Markov models (HMMs) are the flagship of the pomegranate package, in that the majority of my time has been spent improving their performance, and these improvements trickle down into other models which use the same components. As such, they are very high performing and very feature rich. Let's compare the feature set between pomegranate and hmmlearn, which is another package which implements hidden Markov models in Python.\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Featurepomegranatehmmlearn
Graph Structure
Silent States✓
Optional Explicit End State✓
Sparse Implementation✓
Arbitrary Emissions Allowed on States✓
Discrete/Gaussian/GMM Emissions✓✓
Large Library of Other Emissions✓
Build Model from Matrices✓✓
Build Model Node-by-Node✓
Serialize to JSON✓
Serialize using Pickle/Joblib✓✓
Algorithms
Priors✓
Sampling✓✓
Log Probability Scoring✓✓
Forward-Backward Emissions✓✓
Forward-Backward Transitions✓
Viterbi Decoding✓✓
MAP Decoding✓✓
Baum-Welch Training✓✓
Viterbi Training✓
Labeled Training✓
Tied Emissions✓
Tied Transitions✓
Emission Inertia✓
Transition Inertia✓
Emission Freezing✓✓
Transition Freezing✓✓
Multi-threaded Training✓
\n", "

\n", "\n", "\n", "Hidden Markov models are a form of structured prediction method which extend general mixture models to sequences of data, where position in the sequence is relevant. If each point in this sequence is completely independent of the other points, then HMMs are not the right tools and GMMs (or more complicated Bayesian networks) may be a better tool.\n", "The most common examples of HMMs come from bioinformatics and natural language processing. Since I am a bioinformatician, I will predominately use examples from bioinformatics.\n", "\n", "## Speed\n", "\n", "hmmlearn is a package for hidden Markov models with a scikit-learn like API. Let's compare the speed of pomegranate versus the speed of hmmlearn when it comes to a sparse graph with discrete emissions, such as those which are commonly seen in bioinformatics settings." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jmschr/anaconda2/lib/python2.7/site-packages/hmmlearn/base.py:460: RuntimeWarning: divide by zero encountered in log\n", " np.log(self.transmat_),\n", "/home/jmschr/anaconda2/lib/python2.7/site-packages/hmmlearn/base.py:452: RuntimeWarning: divide by zero encountered in log\n", " np.log(self.transmat_), framelogprob)\n", "/home/jmschr/anaconda2/lib/python2.7/site-packages/hmmlearn/base.py:469: RuntimeWarning: divide by zero encountered in log\n", " np.log(self.transmat_),\n" ] }, { "data": { "image/png": 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EEEIIAwq9ncLM3Vc4cyeV+q4V2PDv5rzo5WzsWIWi5OWRvH4DCV9+CUCVCeNx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Y/7Wt/HqPBReLl42bvMKq8kP58za9aSsWgRefHxmGvVwm/0KPxGj8atWbNKrycjL8Oy8l/ycpIy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\njB5lmd7h5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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from hmmlearn.hmm import *\n", "\n", "def initialize_components(n_components, n_seqs):\n", " \"\"\"\n", " Initialize a transition matrix for a model with a fixed number of components,\n", " for Gaussian emissions with a certain number of dimensions, and a data set\n", " with a certain number of sequences.\n", " \"\"\"\n", " \n", " transmat = numpy.zeros((n_components, n_components))\n", " transmat[-1, -1] = 1\n", " for i in range(n_components-1):\n", " transmat[i, i] = 1\n", " transmat[i, i+1] = 1\n", " transmat[ transmat < 0 ] = 0\n", " transmat = (transmat.T / transmat.sum( axis=1 )).T\n", " \n", " start_probs = numpy.abs( numpy.random.randn(n_components) )\n", " start_probs /= start_probs.sum()\n", "\n", " dists = numpy.abs(numpy.random.randn(n_components, 4))\n", " dists = (dists.T / dists.T.sum(axis=0)).T\n", " \n", " seqs = numpy.random.randint(0, 4, (n_seqs, n_components*2, 1))\n", " return transmat, start_probs, dists, seqs\n", "\n", "def hmmlearn_model(transmat, start_probs, dists):\n", " \"\"\"Return a hmmlearn model.\"\"\"\n", "\n", " model = MultinomialHMM(n_components=transmat.shape[0], n_iter=1, tol=1e-8)\n", " model.startprob_ = start_probs\n", " model.transmat_ = transmat\n", " model.emissionprob_ = dists\n", " return model\n", "\n", "def pomegranate_model(transmat, start_probs, dists):\n", " \"\"\"Return a pomegranate model.\"\"\"\n", " \n", " states = [ DiscreteDistribution({ 'A': d[0],\n", " 'C': d[1],\n", " 'G': d[2], \n", " 'T': d[3] }) for d in dists ]\n", " model = HiddenMarkovModel.from_matrix(transmat, states, start_probs, merge='None')\n", " return model\n", "\n", "def evaluate_models(n_seqs):\n", " hllp, plp = [], []\n", " hlv, pv = [], []\n", " hlm, pm = [], []\n", " hls, ps = [], []\n", " hlt, pt = [], []\n", "\n", " dna = 'ACGT'\n", " \n", " for i in range(10, 112, 10):\n", " transmat, start_probs, dists, seqs = initialize_components(i, n_seqs)\n", " model = hmmlearn_model(transmat, start_probs, dists)\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.score(seq)\n", " hllp.append( time.time() - tic )\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.predict(seq)\n", " hlv.append( time.time() - tic )\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.predict_proba(seq)\n", " hlm.append( time.time() - tic ) \n", " \n", " tic = time.time()\n", " model.fit(seqs.reshape(n_seqs*i*2, 1), lengths=[i*2]*n_seqs)\n", " hlt.append( time.time() - tic )\n", "\n", " model = pomegranate_model(transmat, start_probs, dists)\n", " seqs = [[dna[i[0]] for i in seq] for seq in seqs]\n", " \n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.log_probability(seq)\n", " plp.append( time.time() - tic )\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.predict(seq)\n", " pv.append( time.time() - tic )\n", "\n", " tic = time.time()\n", " for seq in seqs:\n", " model.predict_proba(seq)\n", " pm.append( time.time() - tic ) \n", " \n", " tic = time.time()\n", " model.fit(seqs, max_iterations=1, verbose=False)\n", " pt.append( time.time() - tic )\n", "\n", " plt.figure( figsize=(12, 8))\n", " plt.xlabel(\"# Components\", fontsize=12 )\n", " plt.ylabel(\"pomegranate is x times faster\", fontsize=12 )\n", " plt.plot( numpy.array(hllp) / numpy.array(plp), label=\"Log Probability\")\n", " plt.plot( numpy.array(hlv) / numpy.array(pv), label=\"Viterbi\")\n", " plt.plot( numpy.array(hlm) / numpy.array(pm), label=\"Maximum A Posteriori\")\n", " plt.plot( numpy.array(hlt) / numpy.array(pt), label=\"Training\")\n", " plt.xticks( xrange(11), xrange(10, 112, 10), fontsize=12 )\n", " plt.yticks( fontsize=12 )\n", " plt.legend( fontsize=12 )\n", " \n", "evaluate_models(50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at an example case where we compare the performance of a sparse HMM with discrete emisions, such as those common in bionformatic settings (such as sequence alignment). This is a case that pomegranate should do very well at due to its sparse implementation of hidden Markov models.\n", "\n", "Let's take the simplified example of CG island detection on a sequence of DNA. DNA is made up of the four canonical nucleotides, abbreviated 'A', 'C', 'G', and 'T'. Specific organizations of these nucleotides encode enough information to build you, a human being. One simple region in the genome is called the 'CG' island, where the nucleotides 'C' and 'G' are enriched. Let's implement a CG island detector in pomegranate to see how simple it is to build structured modls." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "image/png": 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Ul5dTVlZmTFVVVZhMJuP3pBdI8rtqGuXl5RQVFVFUVER5eTlKKcLCwozhRPRc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\n6tWr7xsfKC4uDlu2bMGWLVtw8OBBDBo0CElJSQCAkydPQq1WY9asWXB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d1 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})\n", "d2 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})\n", "\n", "s1 = State(d1, name=\"background\")\n", "s2 = State(d2, name=\"CG island\")\n", "\n", "hmm = HiddenMarkovModel(\"CG-detector\")\n", "hmm.add_states(s1, s2)\n", "hmm.add_transition(hmm.start, s1, 0.5)\n", "hmm.add_transition(hmm.start, s2, 0.5)\n", "hmm.add_transition(s1, s1, 0.9)\n", "hmm.add_transition(s1, s2, 0.1)\n", "hmm.add_transition(s2, s1, 0.1)\n", "hmm.add_transition(s2, s2, 0.9)\n", "hmm.bake()\n", "hmm.plot()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sequence: CGACTACTGACTACTCGCGCGCACGCGGCACGCGTGCCGTCTATACTGCGCATACGGC\n", "hmm pred: 1111111111111110000000000000000000000001111111111111110000\n", "\n", "hmm state 0: CG island\n", "hmm state 1: background\n" ] } ], "source": [ "seq = list(\"CGACTACTGACTACTCGCGCGCACGCGGCACGCGTGCCGTCTATACTGCGCATACGGC\")\n", "hmm_predictions = hmm.predict( seq )\n", "\n", "print \"sequence: {}\".format( ''.join( seq ) )\n", "\n", "print \"hmm pred: {}\".format( ''.join( map( str, hmm_predictions ) ) )\n", "print\n", "print \"hmm state 0: {}\".format( hmm.states[0].name )\n", "print \"hmm state 1: {}\".format( hmm.states[1].name )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This seems fairly reasonable, and the HMM was able to find the single CG island in the middle of the sequence. However, it also found something at the end of the sequence which it called a CG island. Let's say that we want to enforce that the model cannot end on a CG island. We can do this by changing the structure of the model to add an exit distribution in addition to the start distribution and transition matrix." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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8efMoKSmJBg4cSGfPnqUdO3bQs88+S+Hh4VRZWUmBgYEUHx9Pv/76KxER1dbW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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hmm = HiddenMarkovModel(\"CG-detector\")\n", "hmm.add_states(s1, s2)\n", "hmm.add_transition(hmm.start, s1, 0.5)\n", "hmm.add_transition(hmm.start, s2, 0.5)\n", "hmm.add_transition(s1, s1, 0.89)\n", "hmm.add_transition(s1, s2, 0.10)\n", "hmm.add_transition(s1, hmm.end, 0.01)\n", "hmm.add_transition(s2, s1, 0.1)\n", "hmm.add_transition(s2, s2, 0.9)\n", "hmm.bake()\n", "hmm.plot()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sequence: CGACTACTGACTACTCGCGCGCACGCGGCACGCGTGCCGTCTATACTGCGCATACGGC\n", "hmm pred: 1111111111111110000000000000000000000001111111111111111111\n", "\n", "hmm state 0: CG island\n", "hmm state 1: background\n" ] } ], "source": [ "hmm_predictions = hmm.predict( seq )\n", "\n", "print \"sequence: {}\".format( ''.join( seq ) )\n", "print \"hmm pred: {}\".format( ''.join( map( str, hmm_predictions ) ) )\n", "print\n", "print \"hmm state 0: {}\".format( hmm.states[0].name )\n", "print \"hmm state 1: {}\".format( hmm.states[1].name )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like we managed to get rid of that pesky end (again, the numbers may have flipped, look at the indices). Modifying transition probabilities and using non-dense graphical structures are two major ways in which HMMs account for data in a sequence not being independent and identically distributed (i.i.d.). In fact, in most applications, the graphical structure of a HMM is very sparse.\n", "If we want a more probabilistic view of what's going on, we can get the probability of each symbol in the sequence being in each of the states in the model easily. This is useful to get a soft estimate of classification, which allows us to include confidence as well as prediction. Values close to 50-50 get masked when you make hard classifications, but this uncertainty can be passed to future applications if you use soft assignments. Each row in the matrix is one symbol in the sequence, and the columns correspond to the two states identified above (CG island or background)." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(40, 30))\n", "plt.imshow( hmm.predict_proba(seq)[::3].T, interpolation='nearest', cmap='Blues' )\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding More Structure to CpG Island Detector\n", "\n", "So far we've dealt with a dense HMM, where edges existed between all states in the graph. However, pomegranate efficiently implements sparse HMMs, where the only edge which are stored are those with nonzero values. This makes the model take significantly less memory, and also increases computational speed versus using the full matrix. \n", "\n", "Let's see an example with this. Let's consider the idea that we know there are three CpG islands in a sequence. We don't know where they occur, but we know that there are exactly 3, and that they all occur somewhere. Pursuing this example will allow us to investigate multiple aspects of HMM training which pomegranate supports." ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "image/png": 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GWq1Gz549UVRUhNjYWNjb24tdEmOV4pazGTtw4AB3aTwmmUyGjRs3Ij09HdOm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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d1 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})\n", "d2 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})\n", "d3 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})\n", "d4 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})\n", "d5 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})\n", "d6 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})\n", "d7 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})\n", "\n", "s1 = State(d1, name='background 1')\n", "s2 = State(d2, name='CG island 1')\n", "s3 = State(d3, name='background 2')\n", "s4 = State(d4, name='CG island 2')\n", "s5 = State(d5, name='background 3')\n", "s6 = State(d6, name='CG island 3')\n", "s7 = State(d7, name='background 4')\n", "\n", "model = HiddenMarkovModel(\"CG-detector\")\n", "model.add_states(s1, s2, s3, s4, s5, s6, s7)\n", "model.add_transition(model.start, s1, 0.9)\n", "model.add_transition(model.start, s2, 0.1)\n", "model.add_transition(s1, s1, 0.90)\n", "model.add_transition(s1, s2, 0.10)\n", "model.add_transition(s2, s3, 0.10)\n", "model.add_transition(s2, s2, 0.90)\n", "model.add_transition(s3, s3, 0.90)\n", "model.add_transition(s3, s4, 0.10)\n", "model.add_transition(s4, s5, 0.10)\n", "model.add_transition(s4, s4, 0.90)\n", "model.add_transition(s5, s5, 0.90)\n", "model.add_transition(s5, s6, 0.10)\n", "model.add_transition(s6, s7, 0.10)\n", "model.add_transition(s6, s6, 0.90)\n", "model.add_transition(s7, s7, 0.90)\n", "model.add_transition(s7, model.end, 0.10)\n", "model.bake()\n", "\n", "plt.figure(figsize=(6, 18))\n", "model.plot()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sequence: CGACTACGCTGCACTACTCGCGCGCATCTATACGCCGCGGCATCTCCGCGTGCCGTCTATACTGCGCATACGGC\n", "hmm pred: 11111111111111111111111111111111000000000000000000000001111111111111111111\n", "3GC pred: 33333333333333333300000004444444111111111555522222222226666666666666666666\n", "\n", "hmm state 0: CG island\n", "hmm state 1: background\n", "\n", "3GC state 0: CG island 1\n", "3GC state 1: CG island 2\n", "3GC state 2: CG island 3\n", "3GC state 3: background 1\n", "3GC state 4: background 2\n", "3GC state 5: background 3\n", "3GC state 6: background 4\n" ] } ], "source": [ "seq = list(\"CGACTACGCTGCACTACTCGCGCGCATCTATACGCCGCGGCATCTCCGCGTGCCGTCTATACTGCGCATACGGC\")\n", "\n", "hmm_predictions = hmm.predict( seq, algorithm='viterbi' )[1:-1]\n", "model_predictions = model.predict( seq, algorithm='viterbi' )[1:-1]\n", "\n", "print \"sequence: {}\".format( ''.join( seq ) )\n", "print \"hmm pred: {}\".format( ''.join( map( str, hmm_predictions ) ) )\n", "print \"3GC pred: {}\".format( ''.join( map( str, model_predictions ) ) )\n", "print\n", "print \"hmm state 0: {}\".format( hmm.states[0].name )\n", "print \"hmm state 1: {}\".format( hmm.states[1].name )\n", "print\n", "print \"3GC state 0: {}\".format( model.states[0].name )\n", "print \"3GC state 1: {}\".format( model.states[1].name )\n", "print \"3GC state 2: {}\".format( model.states[2].name )\n", "print \"3GC state 3: {}\".format( model.states[3].name )\n", "print \"3GC state 4: {}\".format( model.states[4].name )\n", "print \"3GC state 5: {}\".format( model.states[5].name )\n", "print \"3GC state 6: {}\".format( model.states[6].name )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the 3GC model seems to identify the 3 GC islands significantly better than the last HMM model we had before. Adding structural regularization like this can significantly improve the quality of your model, if you have prior information on the underlying structure of the data.\n", "\n", "However, if we were to train this model on data, we would notice some interesting results..." ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"end\" : {\n", " \"distribution\" : null,\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG-detector-end\"\n", " },\n", " \"name\" : \"CG-detector\",\n", " \"distribution ties\" : [],\n", " \"silent_index\" : 7,\n", " \"end_index\" : 8,\n", " \"states\" : [\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.2559919317969498,\n", " \"C\" : 0.4055565010789328,\n", " \"T\" : 0.12735010205182726,\n", " \"G\" : 0.21110146507229005\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG island 1\"\n", " },\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.08111462496280261,\n", " \"C\" : 0.5165008946612588,\n", " \"T\" : 0.08216457855785053,\n", " \"G\" : 0.32021990181808807\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG island 2\"\n", " },\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.2858227592966243,\n", " \"C\" : 0.3696834168275769,\n", " \"T\" : 0.13351784022248617,\n", " \"G\" : 0.21097598365331263\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG island 3\"\n", " },\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.37739936644252575,\n", " \"C\" : 0.2426622456340626,\n", " \"T\" : 0.17866900448893733,\n", " \"G\" : 0.2012693834344743\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"background 1\"\n", " },\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.4479860847204273,\n", " \"C\" : 0.11072777682293997,\n", " \"T\" : 0.317085260446504,\n", " \"G\" : 0.1242008780101286\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"background 2\"\n", " },\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.36290818122609164,\n", " \"C\" : 0.2893638110340463,\n", " \"T\" : 0.1899597021531147,\n", " \"G\" : 0.1577683055867474\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"background 3\"\n", " },\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.3985086666775567,\n", " \"C\" : 0.15885547024195112,\n", " \"T\" : 0.3339121151175033,\n", " \"G\" : 0.10872374796298888\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"background 4\"\n", " },\n", " {\n", " \"distribution\" : null,\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG-detector-start\"\n", " },\n", " {\n", " \"distribution\" : null,\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG-detector-end\"\n", " }\n", " ],\n", " \"start\" : {\n", " \"distribution\" : null,\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG-detector-start\"\n", " },\n", " \"edges\" : [\n", " [\n", " 5,\n", " 5,\n", " 0.9230320339598991,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 5,\n", " 2,\n", " 0.07696796604010098,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 4,\n", " 1,\n", " 0.10887786302302128,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 4,\n", " 4,\n", " 0.8911221369769787,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 0,\n", " 4,\n", " 0.0684476933557151,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 0,\n", " 0,\n", " 0.9315523066442849,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 3,\n", " 3,\n", " 0.9041626441035805,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 3,\n", " 0,\n", " 0.0958373558964195,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 6,\n", " 6,\n", " 0.8586794168719292,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 6,\n", " 8,\n", " 0.1413205831280709,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 1,\n", " 5,\n", " 0.1475819862151563,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 1,\n", " 1,\n", " 0.8524180137848437,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 7,\n", " 3,\n", " 0.9999995892327924,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 7,\n", " 0,\n", " 4.1076720755569046e-07,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 2,\n", " 6,\n", " 0.10896881905267802,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 2,\n", " 2,\n", " 0.891031180947322,\n", " 0.9,\n", " null\n", " ]\n", " ],\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"start_index\" : 7\n", "}" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "seqs = map(list, ['ATAGATCATCGACGCTACGCGCCATATATATAGACCTCGCCGCTCATATACGACTCGACACTCGATAT',\n", " 'GATCGACCATGCACGACTCGCATCAGCTATAAGATGAATGCCAGCGACGACTACGACGACGATCTATA',\n", " 'CGATAATAGCATCGACACGACGCGCGCATATAGCATCGACACGCATACATGCAACATACGACCGCTA',\n", " 'AGACTACGACACGACATCGCACCTACTATATAGACTCGCGCGCGCCTAAATGCACGTACGCCTAATAATATAGCTACG'])\n", "\n", "model.fit(seqs, stop_threshold=1e-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at two of the CG island states." ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'A': 0.08111462496280261, 'C': 0.5165008946612588, 'T': 0.08216457855785053, 'G': 0.32021990181808807}\n", "{'A': 0.2858227592966243, 'C': 0.3696834168275769, 'T': 0.13351784022248617, 'G': 0.21097598365331263}\n" ] } ], "source": [ "print model.states[1].distribution.parameters[0]\n", "print model.states[2].distribution.parameters[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also take a look at two of the background states..." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'A': 0.37739936644252575, 'C': 0.2426622456340626, 'T': 0.17866900448893733, 'G': 0.2012693834344743}\n", "{'A': 0.4479860847204273, 'C': 0.11072777682293997, 'T': 0.317085260446504, 'G': 0.1242008780101286}\n" ] } ], "source": [ "print model.states[3].distribution.parameters[0]\n", "print model.states[4].distribution.parameters[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like we're getting pretty drastically different emissions in these states, despite wanting them to model the same phenomena. It is certainly possible that each of these states should model slightly different distributions, but frequently we want them to model exactly the same emissions, but allowing for different structures. We can solve this problem through the use of tied emissions. This basically means that every observation which aligns to one CG island state is thought to be aligned to all CG island states.\n", "\n", "We can indicated tied emissions fairly early. Specifically, we just pass the same distribution object into the State object. That's it. " ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"end\" : {\n", " \"distribution\" : null,\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG-detector-end\"\n", " },\n", " \"name\" : \"CG-detector\",\n", " \"distribution ties\" : [\n", " [\n", " 0,\n", " 1\n", " ],\n", " [\n", " 0,\n", " 2\n", " ],\n", " [\n", " 1,\n", " 0\n", " ],\n", " [\n", " 1,\n", " 2\n", " ],\n", " [\n", " 2,\n", " 0\n", " ],\n", " [\n", " 2,\n", " 1\n", " ],\n", " [\n", " 3,\n", " 4\n", " ],\n", " [\n", " 3,\n", " 5\n", " ],\n", " [\n", " 3,\n", " 6\n", " ],\n", " [\n", " 4,\n", " 3\n", " ],\n", " [\n", " 4,\n", " 5\n", " ],\n", " [\n", " 4,\n", " 6\n", " ],\n", " [\n", " 5,\n", " 3\n", " ],\n", " [\n", " 5,\n", " 4\n", " ],\n", " [\n", " 5,\n", " 6\n", " ],\n", " [\n", " 6,\n", " 3\n", " ],\n", " [\n", " 6,\n", " 4\n", " ],\n", " [\n", " 6,\n", " 5\n", " ]\n", " ],\n", " \"silent_index\" : 7,\n", " \"end_index\" : 8,\n", " \"states\" : [\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.20104742253057367,\n", " \"C\" : 0.43767073623938646,\n", " \"T\" : 0.08961740057361418,\n", " \"G\" : 0.27166444065642575\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG island 1\"\n", " },\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.20104742253057367,\n", " \"C\" : 0.43767073623938646,\n", " \"T\" : 0.08961740057361418,\n", " \"G\" : 0.27166444065642575\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG island 2\"\n", " },\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.20104742253057367,\n", " \"C\" : 0.43767073623938646,\n", " \"T\" : 0.08961740057361418,\n", " \"G\" : 0.27166444065642575\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG island 3\"\n", " },\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.362938051161472,\n", " \"C\" : 0.25413487654821093,\n", " \"T\" : 0.22402431373533832,\n", " \"G\" : 0.1589027585549785\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"background 1\"\n", " },\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.362938051161472,\n", " \"C\" : 0.25413487654821093,\n", " \"T\" : 0.22402431373533832,\n", " \"G\" : 0.1589027585549785\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"background 2\"\n", " },\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.362938051161472,\n", " \"C\" : 0.25413487654821093,\n", " \"T\" : 0.22402431373533832,\n", " \"G\" : 0.1589027585549785\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"background 3\"\n", " },\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"dtype\" : \"str\",\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.362938051161472,\n", " \"C\" : 0.25413487654821093,\n", " \"T\" : 0.22402431373533832,\n", " \"G\" : 0.1589027585549785\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"background 4\"\n", " },\n", " {\n", " \"distribution\" : null,\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG-detector-start\"\n", " },\n", " {\n", " \"distribution\" : null,\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG-detector-end\"\n", " }\n", " ],\n", " \"start\" : {\n", " \"distribution\" : null,\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG-detector-start\"\n", " },\n", " \"edges\" : [\n", " [\n", " 5,\n", " 5,\n", " 0.9175235906949126,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 5,\n", " 2,\n", " 0.08247640930508743,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 4,\n", " 1,\n", " 0.08129984747264099,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 4,\n", " 4,\n", " 0.918700152527359,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 0,\n", " 4,\n", " 0.1404132658213802,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 0,\n", " 0,\n", " 0.8595867341786199,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 7,\n", " 3,\n", " 0.9929254979854489,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 7,\n", " 0,\n", " 0.007074502014550999,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 2,\n", " 6,\n", " 0.1957873385435523,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 2,\n", " 2,\n", " 0.8042126614564478,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 6,\n", " 6,\n", " 0.9314464131741013,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 6,\n", " 8,\n", " 0.0685535868258987,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 1,\n", " 1,\n", " 0.8407481932393318,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 1,\n", " 5,\n", " 0.15925180676066822,\n", " 0.1,\n", " null\n", " ],\n", " [\n", " 3,\n", " 3,\n", " 0.9219966390656136,\n", " 0.9,\n", " null\n", " ],\n", " [\n", " 3,\n", " 0,\n", " 0.07800336093438649,\n", " 0.1,\n", " null\n", " ]\n", " ],\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"start_index\" : 7\n", "}" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "background = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})\n", "cg_island = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})\n", "\n", "s1 = State(background, name='background 1')\n", "s2 = State(cg_island, name='CG island 1')\n", "s3 = State(background, name='background 2')\n", "s4 = State(cg_island, name='CG island 2')\n", "s5 = State(background, name='background 3')\n", "s6 = State(cg_island, name='CG island 3')\n", "s7 = State(background, name='background 4')\n", "\n", "model = HiddenMarkovModel(\"CG-detector\")\n", "model.add_states(s1, s2, s3, s4, s5, s6, s7)\n", "model.add_transition(model.start, s1, 0.9)\n", "model.add_transition(model.start, s2, 0.1)\n", "model.add_transition(s1, s1, 0.90)\n", "model.add_transition(s1, s2, 0.10)\n", "model.add_transition(s2, s3, 0.10)\n", "model.add_transition(s2, s2, 0.90)\n", "model.add_transition(s3, s3, 0.90)\n", "model.add_transition(s3, s4, 0.10)\n", "model.add_transition(s4, s5, 0.10)\n", "model.add_transition(s4, s4, 0.90)\n", "model.add_transition(s5, s5, 0.90)\n", "model.add_transition(s5, s6, 0.10)\n", "model.add_transition(s6, s7, 0.10)\n", "model.add_transition(s6, s6, 0.90)\n", "model.add_transition(s7, s7, 0.90)\n", "model.add_transition(s7, model.end, 0.10)\n", "model.bake()\n", "model.fit(seqs, stop_threshold=1e-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that there is less training improvement. This is a good thing, because we are restraining the number of parameters which the model has. Basically, we are regularizing the model to fight overfitting, which will cause our training accuracy/improvement to be less good in order to get better generalization.\n", "\n", "Let's take a look at the distributions." ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'A': 0.20104742253057367, 'C': 0.43767073623938646, 'T': 0.08961740057361418, 'G': 0.27166444065642575}\n", "{'A': 0.20104742253057367, 'C': 0.43767073623938646, 'T': 0.08961740057361418, 'G': 0.27166444065642575}\n" ] } ], "source": [ "print model.states[1].distribution.parameters[0]\n", "print model.states[2].distribution.parameters[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks good, they have the same emissions! Let's look at background as well." ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'A': 0.362938051161472, 'C': 0.25413487654821093, 'T': 0.22402431373533832, 'G': 0.1589027585549785}]\n", "[{'A': 0.362938051161472, 'C': 0.25413487654821093, 'T': 0.22402431373533832, 'G': 0.1589027585549785}]\n" ] } ], "source": [ "print model.states[3].distribution.parameters\n", "print model.states[4].distribution.parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Through the use of tied emissions, we were able to ensure that distributions with the same emissions before training continue to have the same (better) emissions after fitting. This backend for this does it extremely efficiently, and should not come at a cost of computational time at all.\n", "\n", "Let's now consider the case, where we think that we can have ~at most~ 3 CG islands, but we don't know how many will occur. We could theoretically accomplish this by adding an edge from every background state to every background state further in the chain. However, since all of the HMM algorithms take time proportional to the number of edges in the model, it would be better to try to limit this (not noticable in this case, but likely noticable in large examples). pomegranate supports 'silent states', which are states which the model can transition to, which do not correspond to a specific emission in the sequence. Basically, they can be thought of like 'secret passageways' between states. You can create a silent state by passing None in as the distribution to a state." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "image/png": 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MICuLuxBkZnKP1FRAreZMNzISaN6ce46OBtq2BcLD+VZvszAztRGCg4MxefJk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\n4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i4uAMSx1NvJZDKeYeohxcbGYv/+/YiPj8eCBQvumOGJ32umxS1TE6Idb6rtHS4sLKyx\nXKFQwNfXV4rSjI62RZqfn3/fqfL4vWZaHKYmZNq0aTh06BCWLl0KV1dXbN68ucbyzZs34+OPP9b9\nrFKpGrrERq/2VHkpKSn3nSqP32umxWFqQlq0aAEvLy+4u7vjzTffxJo1a1BSUgJAnOq4du1aBAQE\nAAAWLVoET09PpKen11iHUqkEAKjV6gatXWqPOlUev9dMi4+ZmpguXboAAKZPnw4PDw+8+uqraNas\nGc6dO4fp06frHufg4AAXFxdYWdX8E3j99dcBiMtqvPfee+jXrx86dOjQcBsgge3bt2Py5MlwcnLC\n7t27H3qGJ36vGQDIiIikLoLpz9KlSzFz5kypyzAahYWFkMvlGDVqFP7zn//wDE+PICEhAXFxcVAo\nFHBzc5O6HMnwbr6J0c5vyh6Mp8pj+sRhamL4MiYPxlPlMUPgMDUx9vb2HKb3kZiYiE6dOuH777/H\n9u3b8c0330hdEjMRHKYm6JNPPtH1FDPg4MGDaNWqFdzc3HD69GmcP39e13vPmL5wmJqg6upqHD16\nVOoyGoXq6mr06NEDzZs3R2pqKiZOnCh1ScxEcZiaIF9fX555H7emylu+fDl27tx5x2QkjOkTh6kJ\nun1uU3NUe6q8qVOn3nHuPGP6xmFqgqKjo802TO82VR5jDYHD1ARFR0cjNzcXly5dkrqUBvOgqfIY\nMzT+SzNBnTt3ho2Njdm0Ti9fvozY2FhMmTIFr732GhITE/m6SqzBcZiaIDs7O3To0MEswpSI0K5d\nuwdOlceYoXGYmqj4+Hh8+umnKC0tRWVlJZKSkrBixQqpy9Kb26fKKykpeeBUeYwZGoepCcrKykJh\nYSGaN2+O7t27w9nZGd26dcO0adOkLq3e7jZVHmONAU/BZ0JOnDiBJUuW4JtvvoFMJoOtrS0qKip0\ny419grD6TpXHmCFxy9SEnDp1SneuORHVCFIAsLS0lKIsvZg0aRKGDRuGAQMG4OTJkxykrNHhMDUh\nI0aM0E0OfTe1JyduLB7UYt61axd+/PFHniqPNWocpiZEJpNhw4YN91zeWC+PMWzYsLteyfP2qfJO\nnTrFU+WxRo3D1MRERETcswXaGC/q9umnn+K7777DvHnzatyfmJiIjh076qbKc3d3l6hCxuqGw9QE\n/fzzz1KX8EBlZWUICQnBa6+9BiLCu+++C39/f4SEhMDd3R0ZGRm4cOECT5XHjAaHqQmKi4vD0KFD\n76K0Q4wAACAASURBVBi87uTkJFFFd5o8eTLS09N1rWW1Wo3r168DELM9vfjii1KWx9hD4zA1UatW\nrYKtrW2N+/z8/CSqpqatW7fiiy++uOOwg1qtxsWLF3H+/HmJKmPs0XGYmihvb2+8++67NaaeCw4O\nlrAi4fz58xg7duw9l1tYWGDkyJG6a9AzZiw4TE3YK6+8gs6dO8PKygrW1tYICgqStJ6Kigo8/fTT\n9+0I0+7u//vf/27AyhirPw5TE2ZhYYF169ZBo9GguroaAQEBktbzr3/9C2fPnkV1dfVdl8tkMtjY\n2ECtVmPnzp0NXB1j9dM4R3GzR1dVBWRlAUolUFGB9lVV+Nczz2D59u1oVlwMfP89ULtl6OwMaIdT\nWVoCLi6ATAa4uQGOjoC9vbivnj799NO73m9rawuVSoU2bdqgf//+6NOnD3r16lXv12OsIXGYNnb5\n+cDVq0B2tvj+xg3gzBkgL098f+MGUFJy63aXXeh5ADIBdFuypH61eHkBTk6AhwfQpIm4ab/39ATC\nwwF/f8DXF7CxqfHUy5cvQyaTgYhgbW0NtVoNIkJYWBj+8Y9/YP78+XB0dKxffYxJiMO0IalUIgiT\nk4H//Q84fx64cAEoLRXLnZyAwECgWzfAzw9o3lyEk58f0LOnWP4IHAF8rbeNuI/sbODKFeDoUfEB\ncOWK+Hr1Kr5JTUUaEcIsLMT2hISIW2go0K6daAEzZsRkZOxTCTVWZWUiVE6cEOGZnAykpgKVlaLV\nNnDgrUBp2VKEio+P1FUb1smT4gNEe7twAUhLE63sZs2A9u1v3SIjxYcJa/QSEhIQFxcHhUIBNzc3\nqcuRDIepPmVniwDdvx9YuVIEp5sbEBEBdOokvoaHA507A3Z2UlfbeCgUwOefi/fu9GkgJUUc+/X2\nFu9Vjx7A1Kn8njVSHKYCh2l95OUBv/8O7NolvmZliY6cxx4Dpk0Tu+sS96AbJaVSBOuBA+KWlCSO\nB3ftCsTFAf36ifeYL9/cKHCYChymD0ujAQ4eBH79FViyRPR+R0cDffsCMTFi99TBQeoqTc+mTcAf\nfwAJCcD160DTpuI9j4sDRo2SujqzxmEqcAdUXWg0ooW0bRvw7beiBRocDGzdKv6hzfgPqMGMHi1u\nAHDpEvDTT8DPPwMTJwJr1gDDhgEvvCBCljEJ8KD9+zl9GvjXv0TnSEyM2JUfP150JF28KP6BOUgb\nXnCwOIb6229Abq4YljVzphj5MGAA8PXX4pgrYw2Iw/R2hYXA3LmidWNtLYb1LF8uvhIBp06J5RER\nUlfKtJydxYkI5eUiQHfsALp3BxYvFuFqaSmOuTJmYBymwK0QDQoCPvoImDwZSE8XHR0W/BYZHX9/\n8ftMTwe2bxcdgf37c6gyg+KkWL78VojGxwOXLwPz5omB5cy4WVkBTz8tOq1KS0WoPvmkOETDmJ6Z\ndwfU0aPA7NnAjBliKJMezj9njVDfvuL2++9AfDzy2rTBqyEhcOnUCa5yOQICAuDq6gp3d3d4e3sj\nICAAXl5ejfYChKxxMs+/FrVahOiyZeI4aFiY1BWxhtCnj/gAnTcPsqVLcTU9HaeDg/FNfj6Ki4tr\nzKFqaWkJLy8vREdHIywsDG3atEF4eDhatWp1x6TbjAHmOM60qAh4/nlg3z7gww+BCROkrohJ4cIF\n4KWXxIiNwkIAQFVVFa5du4arV68iKysL2dnZOHDgAE6fPo1z586huroaVlZWCA4ORlRUFB5//HGM\nGzdO4g2RHo8zFcwrTC9dEufEa6eii4w0yMsUFRXB1dVVr+usqqrC4cOH0aNHD72ut6EVFhY2nn+4\nykpg0iRxNtXUqfd9aHV1Nc6dO4fTp08jNTUVBw4cwIEDB9CkSRPExsZi+PDh6NevHywtLRuo+MaD\nw/QmMheZmUSBgUQdOxJlZel99dXV1bRkyRLq3r07WVpa6m29N27coJkzZ5KDgwMZ86+rvLycunbt\nShYWFvd9XGpqKg0ePJjc3d2pSZMmNHz4cMoywO+rBpmM6KOPHvpplZWVNH/+fOrevTvJZDLy9fWl\nGTNm0JkzZ+76eI1GQ+vXr6ehQ4fSrFmzaPPmzfddf0FBAb3yyiv01ltv0dSpU+mll14y/HvxCHbt\n2kUASKFQSF2KpIz3v/NhvPMOkZMT0T3+yPUpLCzMIKFnqPU2pAdtw8qVK6msrEz3c1VVFbm6upKj\no6Phixs+nMjXl6i4uF6rUavVtG3bNmrVqhWtW7dOd396ejpZWVnR4sWLdfctXLiQHBwcKD8//67r\nAkBnz57V/VxVVUVyuZzGjRtXrxr1jcNUMP2hUdevA++9J8YdckdTozZ16lQ41JrXQKVSNcxxydWr\ngYoKMVSuHiwsLDB06FAkJydj4sSJmDt3LgBg8+bNUKlUeOKJJ3SPjY2NhVKpxPr16++5vmbNmum+\nt7a2RqdOnbBt27Z61cgMw/TDdONGMfHw//1fg77soEGDIJfLERkZiT179ujuP3fuHIYOHYpRo0Yh\nJiYGJ0+e1C0rLS3FggUL8M9//hO9evXCypUrQXc5pL1s2TLY2tpi2rRpSExMBAAQEVatWoWRI0fC\n1tYWMplMd9u7dy9ef/11BAYGolevXmjWrBkUCgWKiorwxhtvYObMmYiPj0e/fv2gUCgAAGvWrNE9\nHwCKi4t1PxMR/ve//2HixInw9/eHQqHA6NGj0aZNG/z999+6OpVKJeLj4zFx4kTMmTMHZWVldX7/\nNBoN3n77baxcuRIrV658uDf/UXh4AFOmAJ9+eterFTwsGxsbrFu3DvPnz8f333+v+z35+/vrHqO9\nJldycvI911NQUFCrTA8UFxfXuz5mABK3jA2vRw+i8eMb7OW0u7IJCQn06aefkoODA1lYWFBycjIR\nEbVs2ZKCg4N1u7ARERFEJHbhevXqRSNHjiQioo0bNxIA+vHHH2usl4ho5MiRuvVpffTRR2RhYUH5\n+fm0ePFiAkDx8fFUUVFB+/fvJ3t7ewJAv/32G40bN46ys7MpJCSE3nnnHd06cnJyKCgoSLe7Fhwc\nXGO3XPuzRqOhzMxMcnR0JAC0cOFCSk9PJwAUFRVFROIYclRUFI0fP540Gg0REVlaWtbpUMV3331H\nMTExBIACAwNr7C4bVFoaEUB06JDeVjl69Ghq1aoVtW/fngCQUqnULSsrKyMA1LVr17s+FwBt2rSp\nxn2jRo1qdId7eDdfaFy/FUNo0oTo448b7OVqHxdcuXIlAaCXXnqJiIiWL19OW7ZsIbVaTcHBwWRl\nZaW7H4Cu86K6upo2btxIBQUFNdZ78eJFys3NveN1Bw0aRDKZjCorKyklJYUAUJcuXXTLQ0NDa9Q1\na9YsAkDZ2dk11gOA3njjjbtuS+2fa6+zadOmZGNjQ0REq1atIgB06tQp3fKQkJA6BUFBQQGdOnWK\nVq1apfsQaBAaDZGjI9Fnn+ltlQcPHiQAFBkZSQCovLxct0ypVBIA6tix412fK5PJyMfHhxITE6mw\nsJC2b99O3t7eeu3g1AcOU8H0d/PLyh752kn68NRTTwEATp8+DQCIj4/HoEGDsGjRIlRWVuquIb93\n714At3YDrays8PLLL0Mul9dY35NPPgkPD487Xqdv374gIvzyyy+wuzkjfWxsrG65rNZEyvv37wcA\nODs737GuAwcO1Gnbaq9TLpej6uZsTQkJCQCAoKAg3XKLOs5zIJfLER4ejilTpmDNmjV1eo5eyGTi\nb0V7TS498PX1BQD43LwkTeHNMa0AdIdUtI+p7ZdffoGPjw/i4uLQq1cvKJVKaDQa9O7dW2/1Mf0x\n/TD19BSdUBLx8vICcKsj4fDhw2jbti3eeustON0W8jk5OQCA8+fP33d9y5Ytw7vvvnvH/VOmTMG6\ndeswbtw4TJs2DfPmzcP8+fPvuR5tsKWnp9+xTB9jZLOysgAAN27cqNd6hgwZUu9a6qyyEigo0Ouc\nqMeOHYOFhQWioqIAANnZ2bpl165dA4B7jh0eMGAAjh49itLSUpw4cQKurq7Izc3FmDFj9FYf0yOp\nm8YGt3UrkZUVUXp6g7xcq1atdLulGo2GJk+eTIMHD9YdN3RxcSEA9OWXX5KnpycBoIMHD9LevXt1\nY0lXr15Nc+bMoYkTJ5JarSYiosDAQAJAarWaevfuTa6urnTs2DHd686bN4+Cg4Np/fr1tH//fjp7\n9ixVV1frljdv3rzG7nJZWRlFRESQn59fjV39bt26UVVVFRERPfXUUwSA5syZQ+fOnSO5XE4AaMeO\nHaRSqXTr1G6br68vAaCqqio6fvw4WVpakru7O+3YsYOUSiU5OzsTALp06dJd37vly5fT+vXrdbuL\n5eXlNGTIEHruuefq/Xupk+eeI4qM1Nvq+vfvT66urnT+/HkiIlq6dCmFhIRQ8c3hVy1btqR58+bp\nHr9w4UJyc3Ojy5cv11hPZWUlPffccxQaGkqZmZl6q09feDdfMP0wrawkatlS/KM0gISEBBo4cCCN\nHz+eXnvtNVq9ejWpVCrd8tWrV5OLiwslJSXRypUryc3NjQYPHkz5+fl08uRJ6tevH/n6+tLUqVOp\nsLCQbty4QfPnzycABIAWLVpEmzZtIgDk7OxMixcvJoVCQQkJCdS0aVPd4wCQh4cHbdq0iebNm6e7\n7/YALi4upjfeeIP69u1L8fHx9MYbb1BFRYVu+dmzZykqKoocHByob9++1KNHDxo5ciR99dVXumO8\nAGjBggVUWFio+3nGjBmkVCpp37591K1bN3JycqLg4GCKiYmhSZMm0e+//17jPdF65513qEWLFuTm\n5kavvPIK/fOf/6TffvtNF9YGlZQkBu9v317j7pEjR1JSUtJDr27v3r3k6+tLR48e1d2nHbQ/cuRI\nmjVrFq1du7bGtn3wwQfUrFmzGoGZmppKkZGRNGLECLp+/fojbJjhcZgKph+mREQ7doh/lM8/l7oS\ng9BoNLRhwwZaunQpERGpVCq6cuUKbdq0iTw9PSWuzggoFEQtWhD160dEovPv66+/po4dO5JMJqOF\nCxfWeVX79++nZ555hgDUuxU5d+5cWrBgAZ04caJe6zE0DlPBPMKUiGj6dCJbW6LffpO6Er1bsmQJ\nAbjjTJqLFy9Shw4dJKrKiPTsSeTnRyUXL9KaNWsoKCiIZDIZWVpako2NDU2dOvW+T7906RKtWLGC\n2rZtSwCoU6dOtGPHjgYqXnocpoL5TMG3ZIm4EN6gQWIg/wsvSF2R3mgHhH/66aeYNGkSPDw8cOzY\nMSxduhRffvmlxNU1cjk5uJ6Sgk9feAEfdOiA8vJy3QgLtVoNIkJubu4dT8vPz0dSUhJmzZqF1NRU\nuLm54dlnn8WGDRsQaaAJdFgjJ3WaNyiVimjGDLHLP2MG0c3OHWOXn59PU6ZMoaCgILK1taWhQ4fS\n2rVrqbKyUurSGrfkZDrm60uWlpZkbW1d43jz7beoqCj69ttvacGCBTR8+HDdeFuZTEavv/467d69\nW9dpZ464ZSqY1xR8Whs2AK++CvTsKa5wycyKpqoKf/zzn1i+fj12qtUPfLytrS2qq6sRFBSEiIgI\ntG/fHtHR0ejatesd44DNEU/BJ5jPbv7txo0D2rQRl21eulRcssTaWuqqWEM4ehQv9u2LrTcHzNeF\nvb09CgoK7piEhbHbmf6g/Xvp0gVISQGGDRMzrltaAm3bAt98A2g0UlfH9GncOMDGBggJAdLS8NWN\nGyDR+Yri4mLMmDEDbdq00U3kYl3rg7WwsBBKpVKi4pmxMN8w1WrRAvjqKxGsbdqIjqmICHFJE2a8\niIA9e4Dhw8UlatauBdLSgJEjxWmjNzk7O2Pp0qVISUlBVlYWvvjiC4waNUo3o5N25vy8vDxJNoMZ\nDw5TrfDwW6HasycwZw7w8svAoUNSV8YeRkEBsGIF0Lo1EBsLXLkCnDkDjBkjLv18Hz4+PnjxxRex\nYcMGXLlyBZcuXcLw4cPRunXrGqf+MnY35tkBVRclJSJUT5wAAgOBwYPFIQEjvwaTSVIqgd27gS++\nAH78UezSv/AC8Mor4vpOzKC4A0rglum9ODsDx4+LlunTT4sL8MXEiBbP228Dt02CzCSSmwt8/jng\n7g4MHQqUl4vd+exsYM0aDlLWoLhlWldEwMGDooPq22+BzExxyeh+/cTNz0/qCk1fVRWwfz+QkADs\n2iX2GqytRYAOGQKYcatIStwyFThMH1VKihhS9ddf4tpBERHiEEC3buLWsqXUFZqGkhKxd7BsGZCY\nKOanDQ299SHWu7ek89UyDlMt8xxnqg9t24oWUm2VlcDRo8D06SIEtPNX+voC7dvfuj31FHBzEmez\nlZkJnD0LJCeL452nTwPV1eJ96dABiI4GuncXX3fulLpaxu6LW6aGlpsrwuL225kz4qJtzZqJsY/a\nW1iY6OwKD5e6av1RqcTk3BkZwK+/AufOAefPi5t27KaPjxjr27490K6deB8e0PPOGg9umQocplKo\nqgJ++eVWqJw7J27aKwK4uAABAeLm5ye++viIGeCbNBFh4+EB1PEyIAZTWgrcuCE+MPLzxTjOK1eA\nq1fFpDJXrgA5OYD2lM127Wp+eISGim3x9JR2O1i9cJgK/PEvBRsbMUKgtuJiEUBJSSKQrlwRtwMH\nRDjdfolfmUwEq4eHuJS1XA7Y2wMODuKQgr29CGVA3GdrK763shIjFbSqq2te80h7mmVVlTg+WVgo\nesnLy8UhC22A5ueLY8W38/MTrW0/P6BrV+C558T3/v7i/tsuc8yYqeEwbUxcXMRZWG3a3H15VZUI\nsrNngbw8cbtxQ4SeQiF2m8vLRRgrlbdCsqTk1rXgKysBpRL/BdAVQCsLC+D2az65uIhTa62tRceO\nq+utkI6MFF89PG4FuYeHaFl6eNQMacbMDIepMbGxEbv7N690WR+vu7nh/fffR6sJE/RQGGOMB+0z\nxpgecJgyxpgecJgyxpgecJgyxpgecJgyxpgecJgyxpgecJgyxpgecJgyxpgecJgyxpgecJgyxpge\ncJgyxpgecJgyxpgecJgyxpgecJgyxpgecJgyxpgecJgyxpgecJgyxpgecJgyxpgecJgyxpgecJgy\nxpgecJgyxpgecJgyxpge8KWezRwRYePGjdi5cydCQ0MRERGBESNG3Pc5WVlZ2LVrF3bu3InMzEwk\nJSU1ULWMNV4cpmZuwYIF2LhxI44fPw65XI7AwEDk5eVh6tSp93yOn58f+vTpg3HjxiEsLKwBq2Ws\n8eIwNWO//vorfvjhBxw6dAhyuRwA8NVXX6Fbt26oqKjAjBkz7vncZs2aNVSZjBkFPmZqxi5cuAAA\n8Pf3190XEBAAAEhOTpakJsaMFYepGSssLAQAXasUANzd3QEAly9flqQmxowVh6kZs7e3BwDIZDLd\nfdrvq6qqJKmJMWPFYWrGvL29AdxqoQKAQqEAAPj6+kpSE2PGisPUjGkDMzs7W3fftWvXAAA9evSQ\npCbGjBWHqRnr2rUrXF1dsWfPHt19f/zxB6ytrWuMNVWpVFKUx5hR4TA1Y46OjlAoFHBzc8OoUaMw\ne/ZsuLm5obKyUterv2LFCrRo0QJXr17VPW/Pnj2YOHEiAODixYt47733cOLECUm2gbHGQkZEJHUR\nrOG5ubnh/fffx4QJE6QuhRm5hIQExMXF6T6YzRW3TBljTA84TBljTA84TBljTA84TBljTA84TBlj\nTA84TBljTA84TBljTA84TBljTA84TBljTA84TBljTA84TBljTA84TBljTA84TBljTA84TM3E+vXr\nIZPJdLeioiJMnDixxn0tWrSQukzGjBaHqZkYOnQorK2t77nc2toaY8aMabiCGDMxHKZmws3NDf37\n97/n8urqajz//PMNWBFjpoXD1Iy8+OKLd71fJpOhQ4cOCAkJaeCKGDMdHKZmZMiQIXe939LSEqNH\nj27gahgzLRymZsTOzu6ux03VajWGDRsmQUWMmQ4rqQtgDau6urrGzxYWFoiJiYGfn59EFTFmGrhl\namYmT54MGxsb3c8WFhb47rvvJKyIMdPAYWpmXnjhBVRVVQEArKysMGDAALi7u0tcFWPGj8PUzPTo\n0QO+vr4AxLHSkSNHSlwRY6aBw9TMyGQyXYDa2dlh4MCBElfEmGngDihTpFAAxcVAaSlQUQGUlQE3\nd+1RXo4XPDzwHoBnOnWCw6+/Am5ut57r5gbIZIBcDjg5Ac7OgL29JJvBmDHhMDUWKhVw9Spw5QqQ\nnAzk5QE5OeKWlydu2gAtK7vvqjoAmAhgQmIikJj44Ne2tARcXIBmzQBvb6BpU8DTE/DyuvV9s2ZA\n27Z62VTGjBGHaWNTXg788ANw7hyQnn7rdvWqCFRAhJinpwgyb28gMFD87OoqWpP+/qJF6eQkWpW2\ntoCDg3iujQ3g6Ig12tdTqYCSkluvr1AAGg1QVFQznIuLgYwM4Pp1IDcXOH8euHZNfF9eLp7r7i5q\nCQwEmjcHWrQAWrUCWrcGbh6nZcxUcZhKRaMRgXnkCJCSApw+DaSlieC0tASCg4GgICAsDIiLuxVQ\ngYGAj4/+6rCyErv0Wrd/X1elpaLFvHfvrfDfvx/YtAkoKBCPcXMDnn0WCA8HOnUCOnYUgc+YieAw\nNaTcXODQIREsv/8OHDsGWFiIgJw4UYTKsGHAqFFSV1o/Tk4iJMPD6/b47GwRvEePitvBg6J1Gx0N\n9OkDdO8OdOkC3GeWK8YaGxkRkdRFmBQi0docP160OK2sgM6dgd69gccfB7p1E+HDbiECPvkE2LMH\n2LdPHP+Vy4F+/YAhQ4ABA2p2krFGJSEhAXFxcVAoFHAz498Th6k+qNXAb7+JY50//iiOJb7+ugiD\nHj14d/ZhEAGnTgG7dwM//yzCFQA++EAcJtDnIQ6mFxymAodpfaSlieOCmzaJjpnwcGDQIGDgQBGi\nrP7KyoA//hCHQkpKgNhY8f2zzwKOjlJXx8BhqsWD9h9FRQWwdq0Iz2++Ecc/L1wQLaqlSzlI9cnR\nUXxAXb8ObNkiRiOMHStGLLz6KnD2rNQVMgaAO6AeTn4+8PHHwH/+I4YK7dsHxMSIQe7MsOzsgOHD\nxe3aNWDzZvGBtmaN6MyKiZG6QmbmuGVaFwUFQHy8GJr00UeiJZqeDvTsyUEqBR8f4N//Bs6cAbZv\nF7+H6GgRqoxJhMP0flq2FIPdP/gAmDdPHL/LzwcWLBAD55m0LCyAp58WnVZJSeKU2XbtxKGA6dOB\nykqpK2RmhHfz76aqCnj7bfGPuWcPEBAgdUWsLvr1A44fBzZsAN54A9i1C2ODg6F2cYGzszOcnJwg\nl8vh7OwMPz8/+Pr66r4yVl8cprVlZgJPPSXOTrr9NEtmHCwtxWGYvn2BUaPg+/PPOBEXh4yMDJSU\nlKCwsBDFxcXIz8/XPcXPzw9BQUFo3bo12rRpg4iICLRp0wZevPfBHgKH6e3OnBGnbrq6ihYOM15B\nQcC+fVg4b56Ym2D27BqLKysrkZWVhezsbBw+fBiXLl3C6dOn8f333+uCtmnTppg7dy6eeOIJhIaG\nSrEVzIhwmGodPy6CNCREDBZ/lHPUjUhOTg727duH8+fPY3atoNGXwsJCaccdWloC8+eLrwoFsGyZ\nbpGtrS2Cg4MRHByMHrWGsuXk5CA1NRUnTpzAjBkzUFJSAn9/fwwaNAhjx45F586dG3pLmBHgQfuA\n6K1/7DERpD/+eGuGJT0iImzbtg2ff/45srKyEBAQoLvl5eVh+fLlusdqNBps2rQJP/30EzIzM+Hi\n4gInJyd4eXmhVatWiI+Pv+9rvfHGG3j//ffvuTwtLQ2rV6/Gxx9/jLCwMJw5c0Zv21lRUYHly5fj\n559/xuHDh6FWq+/52FOnTmHWrFlITExE37598cEHHxjm+OXWrcCLLwKffgpMmPBQT1WpVDh8+DB2\n796NrVu34vTp02jfvj3Gjx+PMWPGwKnWqcFEhI0bN2Lnzp1wdXVFbGwsRowYcc/1KxQKzJo1C56e\nniguLoZCocCSJUuM6jiudtB+QUEBJk6ciNDQUOTk5Dz0tk+fPt2otvsOZO40GqIhQ4gCAojy8gzy\nErm5ufT4449TixYt6ODBg6TRaEitVtMXX3xB7u7uNHbs2BqPf/zxx6l169a0f/9+0mg0N8vU0I8/\n/kh+fn4PfL05c+Y88DHl5eUEgMLCwh5to+5DqVSSXC6n+/15nTp1ip566in67rvv6NixYwSAYmNj\n9V6Lzpw5RHZ2REeP1ms1+/fvp7Fjx5KjoyN5e3vfsXzevHnUvHlzKigooIKCAmrevDmtXLnyrutS\nKpUUGhpKixYt0t23bt068vLyoqtXr9arzoa0a9cuAkBvvvkmFRQUEBE90rYb23bXxmG6YQORtTXR\nwYMGWb1arabo6GiSy+WUn59/x/I9e/bQ8OHDazze29ubioqK7rq+06dP6602Q4UpEVFYWNh9w3Tl\nypVUVlam+9nV1ZUcHR0NUgsREalURLGxRO3aEanV9V5dXl4evfbaazR48GAqLi4mIqL09HSysrKi\nxYsX6x63cOFCcnBwuOvvfunSpQSAzp49q7uvqqqK5HI5jRs3rt41NhRtmFpZWdW4/2G33di2uzYO\nU5mM6Nw5g63+hRdeIAD0999/1+nxAwcOpOzs7Ed6LZVKRV9//TXFxMTo7jt8+DBFRUXRq6++SnPm\nzNH94xPdGaZnz56l6dOn08iRI6lHjx6UnJxMGo2GfvjhB5owYQIVFBTQSy+9RO7u7hQREUFHjhzR\nPbesrIwmTJhAs2fPppkzZ5K/v/99w/R2arWa1q1bp2uFG9TAgUT/+IfeVnfu3Dlq0qQJvfrqqzRg\nwAACQFlZWbrlmZmZBIBeeOGFO57bsWPHOx5PRPT888/X+b1rDLRhWrvmh9127XY/6t+/1IznN2Yo\n4eEGXX3nzp0JABUWFtbp8f7+/ncNlQMHDtD7779P77//Pq1YsYJKS0vv+vyMjIwaARkSEkJy31v/\nSwAAIABJREFUuVy3zpycHN2y2mHasmVLIhKtI1dXV4qIiCCNRkOZmZnk6OhICxcupPT0dPriiy8I\nAEVFRRERUXV1NUVFRele48KFC2RpaVmnQPjuu+8oJiaGAgMDGyZQ160jcnQkqq7W2yr/+9//krW1\nNYWHhxMAUiqVumVlZWUEgLp27XrH87QftJs2bapx/6hRo0wiTB9227XbfeXKFYPXbAjG8xszlGHD\nDLr6Ll26PNSnraur6z2XHTlyhADUCMTaNBpNjYD08PAgALRy5UpSq9U1Dh/UDtPly5cTkWgpBgcH\n19htCw0NrfEaTZs2JRsbGyIiWrVq1R3/SCEhIXUKhIKCAjp16hTZ29sTAPrss88e+Jx6OXiQCCBK\nT9fbKisqKsjGxoZatWpFAKi8vFy3TKlUEgDq2LHjHc87dOgQyWQy8vHxocTERCosLKTt27eTt7c3\nWVpa6q0+Q7tXmD7stmu3u1qPH3QNiU8nNfA0buE3Z59PS0ur0+Nbt26NoqKiuy577LHHAIjxj/ci\nqzVXwCeffAInJye8/vrriIqKgouLyz2fGx8fj//85z9YtGgRKisrodJec6rWemUyGeRyOapuXvE0\nISHhjnVZWNTtT0sulyM8PBxr1oirUn3++ed1et4j0/6+lUq9rdLW1hbu7u5wd3cHIIaEaSkUCgC4\nay91VFQUfvnlF/j4+CAuLg69evWCUqmERqNB79699VafVB5227XbbWVlnCM2OUzz8gy6+l69egEA\nDh48WKfH9+7d+67hBACWlpYP/fpDhw7FiRMn0K9fPxw9ehT//e9/7/nYw4cPIzg4GG+99dYdQ37u\nJysr66Hrqm3IkCEAABsbm3qv675ycsTXJk30tspr164hNzcXbdq0AQBkZ2fXWAbgjrGsWgMGDMDR\no0dRWlqKEydOwNXVFbm5uRgzZoze6pPKw2670W+31E1jyXl6ip5eA1GpVNSxY0dq2rTpHR0NRGKI\n0u27tiUlJRQYGHjPY6J1+ZXdvuv+1ltv6b7fsmVLjaFVqLWbX/tYKwDdMczavf6399aPGDHijroe\n1Jtf25kzZwgAffjhh3V+ziNZuJCoDsPLHsa4ceMoICCAsrKyyNXVlZYtW6Zb9t5775G1tXWN44D3\n2o0tKSmh0NBQiomJIbUeRhw0FO1uvouLS43777bt91JSUmJ0210bh6lMRrRjh0Ff4vTp09SsWTMK\nCgqib7/9lqqrq6msrIx2795NsbGxlJSUVOPxAQEB1Lp1azpw4ECNcaZ//vnnAwOquLiYfHx8dD/b\n29vrxv5VVVXpOo20nQPNmzfXPdbFxYUSEhLoyy+/JE9PTwJABw8epCtXrlDz5s1rdA75+voSAKqq\nqqLjx4+TpaUl7dixg5RKJe3evZucnZ0JAF26dOmudS5fvpzWr19PCoWCiIiGDBlCzz33nGH/mTQa\n0eE4ebLeVvnhhx+STCajb7/9lojEkJ+QkBAqLi6moqIiatmyJc2bN0/3+IULF5Kbmxtdvny5xnoq\nKyvpueeeo9DQUMrMzNRbfQ1BG6bvvPOObrTIvba99nYT3dp2Y9vu2vgMqGPHgK5dgUWLxExDzDRl\nZ4uz3J58Eti4UXf3yZMn0alTJ6hUKnTv3h2JiYkPXFVlZSW2bNmCt956C8uWLcPzzz//SCWlp6dj\n06ZNsLS0xKBBg9C+fftHWo/UHuWyJaay7TVIneaNwrJlRFZWRH/+KXUlzBCqq4liYohCQ4lujmb4\n66+/qH///iSTyXQ90S1atLjvalJSUmj27Nm6kQzjx49viOobPW3LVLuXYa6Ms9tM3+LjgcRE4Jln\ngF9/BSIjpa6I6dOIEcCxY6hKTMTWH37AkiVLcObMGVhZWYFu2zG7fVo+rV27dmHv3r34/vvvcfbs\nWfj6+mLSpEmYPHkyfPhKqew2HKaAuPTI1q3ACy+I69t/952YaJgZP6USRb/+iv+OHo3FcXE1AvP2\noV8AUFxcjH379uHUqVM4efIkjh49ir///hthYWF48sknsXHjRnTt2rXOw76YeeEw1bK1Bb7+Ghgz\nBhg8GHj3XWDqVKmrYvWRloZLQ4cioroaqrVr7wjP2ogIjz/+ONzc3NCuXTt069YNP/30E7y9vRuo\nYGbMOExvZ20NfPEF0Lq16IwaNgww5inBzBUR8J//YPO0aRhVVYWH6WHduXMn4uLiDFYaM128v1Kb\nhQUwZ444htq2LbB6NVBdLXVVrK5OnACeeAL417/Q49VXMWP6dN1uuZ2d3QOfbqxn3zDpcZjeS1QU\nMG6cuKRw27Zi0mjWeGVlAWPHAp06iVNF9+9H8xUrsOTdd1FcXIzffvsNU6dORbt27SCTySCTye44\n28rCwgI52jOkGHtIHKb38957QEWFuDaUlxcwZIi4BEZoqJi1nUnryBFg+HAgMBDYvh34+GNArQYO\nHhQfhjc5OjqiT58+WLp0KZKTk6HRaKDRaFBZWQkiwubNmzFu3DgEBQVJty3M6PGg/Yd18SLw0UfA\n+vXAs88CL70kditrTTDCDEShALZtAz7/HNi/H+jQATh8WBzvZpJ4lEH7pohbpg+rRQvgww+By5fF\n2VN9+4oOq8WLgQsXpK7OdH3/vRgH7O0NTJsmrtf155/iQogcpKwR4DB9VE2bAqmpYlezb1/ggw/E\nP3ibNsCsWYBGI3WFxi83F9iwQQxVGzoUKCwE1q0Drl8HPvsMiImRukLGdLjrsr46dxa3FSuAv/4S\nHVVbtwL//S/Qp484CaB3b3Fcj92fUgkcOADs2QP88YfYfbe1Fe9jdrY4bs1YI8XHTA1l8WJg924g\nKQkoLxdh2rs30LOnOF01IkLqChuH/HzRul+yBDh0CKiqEh18vXsD/fuLM9EMcOltpj98zFTgMDW0\nykoREnv2iNvhwyJcY2JEizYyUnSitGxp+sf+cnOBlBTg6FERoH//DaSni2XjxgGPPw7ExvKJEkaG\nw1TgMJVaRQWQliZup06JYVg//ghoT310cwOaNxe3wEBx69BBHLP19BS3hh5JUFYmZqzPyRFXKrh+\nHbh6Fdi7V4RjdrYYogQAHh6iFd66tTjuGREhOpGYyeAwFfiYqdTs7MQ8mzev7wRA7OpevAhkZIhw\n0n49ckQMC7p5OQgAYtyrNlRdXAAnJ8DfX3x1cgKcncXjXF3F2V0ymQhorfJyEehaCoU446u0VHxf\nWnrrdvmyCNDa109ycQECAoBevYBBg26Ff3CwqIsxM8Bh2hjZ2IiWXOvWd1+emytahNpWofbnkhIR\nepmZt74vKxPnqmsv8qZSASUlSANwCMAYG5uaFxWUywErKxHCbm63AtnXVxy/9PISAdm0qWhhenqK\nDwTGzByHqTFq2lTc6iFp40ZMnToVY0pK9FQUY+aNx5maKRsbG92lmhlj9cdhaqZsbGxQXV0N7n9k\nTD84TM2UjY0NiAjVPL0gY3rBYWqmnJycAAClpaUSV8KYaeAwNVPauTz5uClj+sFhaqY4TBnTLw5T\nM8Vhyph+cZiaKQ5TxvSLw9RMcZgypl8cpmaKw5Qx/eIwNVMcpozpF4epmeJxpozpF4epmeKWKWP6\nxWFqpjhMGdMvDlMzZWFhASsrKw5TxvSEw9SM8TR8jOkPh6kZs7GxQWVlpdRlMGYSOEzNGLdMGdMf\nDlMz5uTkxEOjGNMTDlMzxi1TxvSHw9SMaS9dwhirPw5TM2Zra8stU8b0hC/1bMZu380nImzcuBGX\nLl1CTk4OYmNjMWLEiPs+PysrC7t27cLOnTuRmZmJpKSkhiibsUaJw9SM3R6mCxYswMaNG5Geng6F\nQoHHHnsMeXl5mDp16j2f7+fnhz59+mDcuHEICwtrqLIZa5R4N9+MacM0IyMDCxYswKRJkwAAcrkc\nEyZMwKxZs3Djxo37rqNZs2YNUSpjjR6HqRnThunmzZuhUqnwxBNP6JbFxsZCqVRi/fr1ElbImPHg\nMDVj2nGm33zzDQCgbdu2umXt27cHAPzwww+S1MaYseEwNWPalqmLiwsAQCaT6ZZpv+fefsbqhsPU\njGnDtFWrVgCAwsJC3TKFQgEA8PX1laQ2xowNh6kZ04ZpREQEACA7O1u37Nq1awCAHj16SFIbY8aG\nw9SMacN01KhRcHV1xZ49e3TL/vjjD1hbWz9wrCljTOAwNWPaMHV3d8ebb76JNWvWAACKi4uxdu1a\nzJkzBwEBAQCARYsWIT09/Y51KJVKAIBarW6wuhlrjHjQvhm7fdD+9OnT4eHhgdmzZ+PcuXOYPn06\nxo8fr3usg4MDrKxq/rns2bMHX331FQAgPT0dJ06cQIcOHRpuAxhrRGRERFIXwaTx7rvvYs2aNbh0\n6ZLUpTAjlpCQgLi4OCgUCri5uUldjmR4N9+M8RR8jOkPh6kZ4zBlTH84TM0Yhylj+sNhasY4TBnT\nHw5TM8Zhypj+cJiaMRsbG6jVah4jypgecJiaMRsbGwA8mQlj+sBhasacnJwAgC/3zJgecJiaMW6Z\nMqY/HKZmjMOUMf3hMDVjHKaM6Q+HqRnjMGVMf3jWKDPTtm1bqFQqlJSUQKPRwNbWFtHR0ZDJZKis\nrERUVBQSExOlLpMxo8NhamZOnTqF2hOFVVZW6r4PCgpq6JIYMwm8m29mgoOD77t87dq1DVQJY6aF\nw9TMjB079o5JnrWsrKxgb2/fwBUxZho4TM3M6NGjodFo7rqMTytl7NFxmJoZPz8/xMbG3rV1am1t\nLUFFjJkGDlMzNG7cuDtaoZaWlujXr59EFTFm/DhMzdBTTz2lOy9fi4jw3HPPSVQRY8aPw9QM2dnZ\n4cUXX9QN2gcAmUyGgQMHSlgVY8aNw9RMjRkzRnfmk4WFBXr37g25XC5xVYwZLw5TM9WlSxe89957\nup83b94sYTWMGT8+A8pUlZYC5eVASQlQXAyo1eK+6mqxXKPBSFdXvGlhga6hoWi6bx9gYQG4ut5a\nh709YGcH2NoCDg6Ar6/4mTF2Bw5TY6FSAdevA1euAGlpQF4ekJ8vbjdu3PpaWgpcu1anVfoAmAWg\ny5kzQF07n7SB6+gIODsDTZoAHh7ia+vWt7738QH8/ABv70feZMaMCYdpY1JdDVy+DOzYAWRkAFev\nAllZ4vvr10XrEhAh5ukpbtowCwkRX52cRJA5OoqWpYuLeLyVlWhV3n6Gk6sr5ltY1Hz922fd17Zk\nKypEKzcrS3wtKgLKykSrVxvimZlAQoL4ubz81jpsbQF/fxGs3boBzZuLWkNCgIAAQCYz7HvKWAPh\nMJVKYiJw8iRw9ixw7hxw4QKQni5aoN7eInT8/YHISODZZ0UY+fsDzZqJEDIEa2vg9k6o2h1SnTrV\nbT1KJZCdLT4MMjPFLSsL2LlTbGNhoXicnd2tYB00CGjXDmjTBrhtlAFjxoLDtCFkZwNHjgDJyeJ2\n4gRw6ZIIq1atgNBQoEcPoGVLESwdO0pdcf04OIhtadny7svz8sSHx9mz4uv588Crr4oWrbW1OFzQ\nrh3Qvj3QoQMQFSVa2Iw1YhymhqBWAxs2iNbn0aPA6dPifh8f0bobNQoYNgwIDzfP3VztIYro6Fv3\nqdXicMapU+I9O3oU+OQT8aEDiPesRw+ge3fzfd9Yoyaj2pNbsrrbtg3YtUscK8zMBNzdgT59gCee\nAMaPF501TH+qqkTIHjgg3vO//hKt2WnTgP79gccfF8eGWYNKSEhAXFwcFAoF3NzcpC5HMhymD6u8\nHPj9dxGkW7aI3dA+fcStVy+xm8oaRnk5sH8/MHMmcOwY4OYGDBwoWv39+/PvooFwmAr8MV4XajXw\n66/AZ5+JTpTqaiA2FsjJEb3pTBr29uJD7O+/xeGAbdvEbfBgMbLhmWfEHkJkpNSVMjPA+6H3c+0a\nsHAhEBwMDBkiBr+vWiWGKe3axUHamAQHAzNmiGC9eBH497+BpCTRebVuXc0hX4wZAO/m30tKiugs\ncnUFxowBJk4UPe3MuOzfL1qvNjailfrGG3wigZ7xbr7ALdPaUlLEMbcOHYCNG0XH0vvvc5Aaq+7d\nxXjXOXOAr74CWrQA4uPF3gVjesRhqnX9OjBihAjR8+fFsbeRI/lcdFPQpIlokV68CCxeDGzdKkJ1\nzpyaZ2sxVg+8m9+zpzi29vrrwLx5YsA5Mw9ZWfhp2DB8mJGBQi8vFJWUoKioCMXFxbrLX9vZ2cHf\n3x++vr4IDg5G69atERERgfDwcAQGBkLG4115N/8m8+7NP3xYnEt++DDw2GNSV8Mamp8fBh04gEFN\nmohhVPv2iZmxAJSWliIzMxNHjhxBZmYmsrOzcfHiRezevRuZmZkAACcnJ0yYMAG9e/dGTEyMWQcJ\nM+eW6ddfi46lnByDnKpYVFQE19uns9OTqqqqGjPkG6vCwsLGEz7nz4vhVCUlwE8/PfCDtaioCGlp\naUhNTcWqVauQmpoKmUyGDh06oF+/fhg1ahRat27dQMVLj1umgnkeM92+HXjxReCVV/QepCqVCkuX\nLkUTPQ+bKigowJtvvmnUs+FXVFRg0aJFiI6Ofqj3Z9WqVQasCqJz8eBBMSdAv363Tv+9B1dXV3Tt\n2hXjx49HcnIycnNz8c0336B79+7YsmULwsPD0bVrVxQVFd31+USEDRs2YPbs2Rg/fjy2bNly39dT\nKBSYPHky3n77bbz++usYPXr0I28qMyAyN8nJRPb2RFOmGOwllEolGeKt1Wg05Onpqff1NiSlUkly\nubzO78/hw4fJ3t7ewFXdVFZG1L07UYsWRIWFj7QKtVpNv//+O40aNYrkcjktWrSIysvLazxm3rx5\n1Lx5cyIiKigooObNm9PKlSvvuj6lUkmhoaG0aNEi3X3r1q2jq1evPlJ9hrBr1y4CQAqFQupSJGVe\nYVpdTdS2LVHPnuJ7AzLU51RYWJhB1tuQwsLC6vT+FBQU0OzZsyk0NLQBqrrp2jUib2+icePqvap3\n3nmHnJycKDw8nJKTk4mIKD09naysrGjx4sW6xy1cuJAcHBwoPz//jnUsXbqUANDZs2d191VVVdE4\nPdSnLxymgnnt5n/2mZg7dP16nhCjkSMiLFiwAG+88UbD9ph7ewMrVoi/ldTUeq1q7ty5SE1NhYeH\nB3r27IkDBw5g8+bNUKlUeOKJJ3SPi42NhVKpxPr16+9Yx759+wAAzZo1091nbW2Nbdu2gcy0u6Ox\nMq8w/fhjcay0gQbgnz9/HoMGDYJcLkdk5P+3d+9xUdV5H8A/c0GG6yAoykUUFMV01Vy1J8tQNzRN\nTc20DHW9sdmu2UullMfaUGnrUctd6dnVyvLRrU0zX97vCXlBDfISGd4VAQG5zzD3Od/nj3FOjKJi\nDXNm4Pt+vc4LZubMOd85ymd+53d+55y+OHTokPjauHHj8Oabb2LSpEkYMGAAzp49K762ZMkSJCYm\n4rXXXkN8fDxWrlxZ7x/O8uXL4e3tjXnz5uHIkSMAbCGUmJiIWbNmwdvbGzKZTJwyMjLQoUMHFBYW\nIj4+HlFRUaisrER1dTWSk5OxYMECDBkyBHPnzkVlZSUAYPXq1eL7a2pqsGLFCvExEWHr1q1ISkpC\nZGQkpkyZgpCQEHTv3h3Z2dlinTqdDnPnzsWiRYuwcOFC1NbWPnDbrVq1ChMmTGiUg3gPNGEC0KWL\n7f/Lb9S+fXvs378f8fHxGDduHL799lsAQGRkpDhPu9sX+z5z5sxd7y8pKQFg6zOvq6amBsV84oF7\nkbRd7EoHDhABRPXsSjUGAFRdXS0+XrlyJQGgyZMnExHRF198QUS2PraYmBhSKpVERLRixQrKy8sT\n32c2m2nt2rVUUVFBRLZd5MuXL9O0adOotLT0rvWOHDmSjEYjERH9+OOPBIAee+wxh7rKy8vFxykp\nKQSAioqKxOfWrVtHACg5OVlcZ93/Knc+7ty5s/hYEAQKDQ2lFi1aEBHRqlWrCAD99NNP4vyxsbH3\n3M0vKyujadOmkdVqdVify+3YYfv/4kTz5s0jf39/AuDQj2rvY+/du/dd7zlx4gTJZDIKCwujI0eO\nUFVVFX399dekUCjI3MhdVQ3Fu/k2zadlam8N+fm5bJWBdUYKjB49GgBw7vaR4pEjR+Kjjz5CWloa\njEYjLBYLACAjI8Oh1aJUKjF16lSHo/jPPvssamtr0apVq7vWmZCQgJ07dwKwDTgHbLuRdQUHB4u/\nHz16FAAQEBAgPvfUU08BAI4dO9agz1l3N1wmk6Fly5YwmUwAbMNmACA6OlqcR36f67zOmjULiYmJ\nuHDhAvLy8pCXlwej0Yi8vDxcvny5QfU4hb+/0xcZEREh7mFU2W/dAoh7AeG3x7jW1a9fP+zcuRNh\nYWEYOnQo4uPjodPpMGjQICi5q8qtNJ8wbd3a9vP2bpOrtWnTBsAvfV+/+93vEBMTg7feegv+df5w\nS0pKcPHixfsua/ny5fjqq6/w/vvv3/XaX/7yF0yfPh3z58/HvHnzkJqaisWLF99zWfZgu3bt2l21\nOmMXu7CwEABQXl7eoPm3bduGwYMHo2vXruJ07do1dO3aFUOHDv3N9TTYzZtO71fPyckRA7OoqKjO\nqmx3k33yySfrfd+wYcOQk5MDrVaL06dPQ61W449//KNTa2O/XfMJ0549bVcOysiQZPX2s2ZGjBgB\nADCbzRg2bBgAQBAEALb+zp49eyItLU18DrAF3a5du8THzz77LFJSUpCSkuLwPABYrVYcP34cy5cv\nx9atW/H222/ftwVjb4XaW7N1a3366acBOLY8BUEQx09SAw6AxMXF3bX8+zEYDCDbKBNx6tKlC4gI\nly5datAynCIjw6lnxZ0+fRobN27EnDlzoFarHfrPv/32W3h5eWHixInic/Y9lTtptVokJyfjpZde\nclptv5X91Ftvb2+JK5GYdD0MEhg7lqh/f5esCnX6JgVBoFmzZtGoUaNIEATx9X379tGGDRuodevW\nBICOHz9OGRkZBIAGDRpE6enptGjRIkpKShL7EDt06EBWq5XMZjMNGjSI1Go1/fDDD+J6U1NT6ZNP\nPqHdu3fT0aNH6fz58w59awBIo9GIj2tra6lbt24UEREh9pu+9tpr1L9/fzKZTERENHr0aAJAFy5c\noA8++EAcJ7p7926yWCzUvn17hz7Q8PBwAkAmk4lOnTpFCoWCgoODSafT0cGDBykgIIAA0JUrVxq0\nLV3eZ1pdTRQYSLRsmVMW9/3331NYWBgNHjyYLBYLvffeexQbG3t7VdXUqVMnSk1NFedfunQpBQUF\n3bUco9FI48ePd+1QsQbYtGkTAXDo526OmleYZmURyWREX3/d6KsaMWIExcfH04wZM2j27NmUnp5O\nFotFfD0wMJD69u1LWVlZtHLlSgoKCqJRo0ZRWVkZDRkyhIKCgig8PJzmzJlDVVVVVF5eTosXLyYA\nlJaWRgUFBeKBooCAAHr33XepsrKS9u3bRwAcplatWtG6desoNTWVANDMmTMdArimpoaSk5MpISGB\nkpOTafHixWQwGMTXz58/T/369aOEhAQ6f/48Pfnkk5SYmEhffvklrVixQlxPVVUVffjhh+LjN998\nk3Q6HWVmZlL//v0pJiaG/va3v9GAAQPoT3/6Ex04cMBhm9yLy8N03jyikBCi2wdUDAYDffrpp/TS\nSy85bJcHMZlMtHLlSvLz86Phw4dTTU0NEdm+XD/55BNKSUmhcePG0Zo1a8QvWSKiDz74gKKiohyW\nlZubS3379qWJEydScXGxEz6k86xfv568vb2lLkNyzStMiYimTSNq1Yroxg2pK3E6QRDo008/FR9b\nLBbKz8+ndevWefyZUy6zfz+RXE708cdERLRkyRIKCQkhmUx21+D5+9Hr9RQXF0cqlYrefvvtBn1p\n1Ofq1av0zjvv0JIlS+j06dO/ahmN7eOPPya1Wi11GZJrfmFKZAvS9u2JunaVuhKnevbZZwkA3bp1\nS3wuJyeHXnjhBcrNzZWwMg+wfz9Ry5Z0uE8fen70aJLL5eTl5XVXK3/z5s0Ob8vLy6PPPvuMpkyZ\nInbXxMbGOpz+2dSlpaVRp06dpC5Dcs1zbEVkpO12waNHAwcP2m7N3ASsW7cO77zzDiIjI9G7d29E\nRERgyJAh2LBhQ5O40lRjEVavxs4//xmpgYHIyc6G8vRpCILgcBAQsA1T+/7771FTU4Nz584hNzcX\nu3fvho+PD/r164f58+dj1KhR4kG35qK0tFQcAdKcNd9L8AGATme7PfAbb9iuus5X1W92NJcuIS42\nFjcByBUKWK3We87bokULmEwmqFQqPPLII3jkkUcwe/ZsPProo/BqxreVfumll2A0GvHNN99IXYqk\nms/QqPr4+gIrVgD/+AfQowdw+1Q/1gwQQbNqFdp37owi2Pbh7xekdpMmTYJWq0VOTg7Wr1+Pfv36\nNesgBWwt09DQUKnLkFzzDlMAmD3bdv3Kbt1sd7F84YXffIEL5ub27AEefxz+c+fiH8OGofXtEzqU\nSuV9x+SazWYYjUYoFApXVeoROExtOEwBWx/qli3A1q22q0r17AmMHy91VczZbocohg0DQkIgy85G\n4s6dKC0txeXLl/HRRx/hxRdfFIPhztAkIoczl5hNcXExhyk4TB2NHAmcPm27M+n588CIEcCOHUAD\ndv+YG/vkE6BPH1uIBgcDJ04AO3favjRvi4mJQVJSEtavX4+bN2/i9OnTWL58OfxuX8vBfgUuvlKT\no5qaGpSVlSEmJkbqUiTXvA9A3Q8RkJBg60dt1w6YOROYPh0IC5O6MtZQubnA6tXAxx8D48YBc+YA\nffs+1CKsViuys7Nx6NAh7Nu3D0FBQc3+QEtdOTk56NOnD86fP4/OnTtLXY6kOEwbymgEtm2ztVp3\n7rQ9/t//tbV2bl+PkkkkN9d2I7xNm4BTp4DQUGDsWNt1SePjAb4dc6NJS0vDZ5995trrJrgpDtNf\no7bWFqhTp9qGVz3yiO1GbEOG2P54fX2lrrDpKysDDhwA9u4FPv/cdlWwsWNtBxAHDgQ0uS71AAAc\nxElEQVT4IJFLTJs2DYWFhdi7d6/UpUiueQ7a/638/GwHqEaNAg4fBvbts00rVwLe3raDHPPnA/37\nAx58N1G3cuMGcPQokJUFHDli69uWy23b+uRJ4Pe/tz1mLnX27FnEx8dLXYZb4JapM928aWspZWba\nWksyme32wY8/DvzXfwG9etmGYPn4SF2p+6uqAs6csYVmVhbw1Ve264v26mXbnoMH2yYn36qbNZzF\nYkFAQABWr16NyZMnS12O5DhMG0tZmS0Ejh2zTTk5tu4BpRLo3BmYPNl2NDkuDoiKat6tKpPJ1h99\n9qwtQM+cAa5ft73WqhXw2GO2ln7fvi69UwK7vx9//BE9evTAmTNn0KNHD6nLkRyHqasIAnDp0i9h\nsX49kJ9ve83bG+jUyRaysbHA8OFA+/ZAeLjtgtZNRV6eLSQvXrSN571wwfa7PThjY21fMHWniAhp\na2b3tH79esycORMajabZnwUGcJhKq7Lyl1CxB8uFC7Yj0oCtm6BtW9tJBRERtlEDkZG2roJWrWxT\naChQ5/5Nkikrs43NLSsDysuBggKgsNA25efbftrvsNmqlS04u3Sx/YyNtY3p5e4PjzJv3jxkZGQg\nJydH6lLcAoepJ6qttYXWrVu2n/YAKyuzvVZUZBtloNPZ+h51OttQLkEAbt9yBACg0QB1b4+hUv0S\naHI5YL8HlK+vbYqIsP3087O95udnC8aQECA0FAWBgRg4YQL8/P1x8ODBem/4x5oGo9GIoKAg/Otf\n/8KUKVOkLsctcJgyp7px4wYGDhyIgIAAHDx4ECEhIVKXxBrBkSNHMGDAAFy+fJnPfrqtGR/1YI2h\nXbt2OHToEGpqavD00083+K6kzLMcPnwYYWFhHKR1cJgyp4uKikJGRgaqqqqQkJCACntfKWsyjhw5\nIt7ZltlwmLJGYQ/UyspKJCQkoLKyUuqSmJNYLBYcO3YMTz75pNSluBUOU9Zo2rdvj/3796OkpATD\nhw+XuhzmJCdOnEBVVRWGDBkidSluhcOUNapOnTrh0KFDyM/Ph0ajkboc5gR79+5Fhw4dmv1Vou7E\nYcoaXWxsLA4dOoRnnnmGA7UJ2Lt3L5555hmpy3A7PDSKuUxeXh4GDRqETp06Yffu3fD395e6JPaQ\nvvzyS0yZMgVlZWUI5OsiOOCWKXOZuLg4HDp0CBcvXsTw4cNRW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "background = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})\n", "cg_island = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})\n", "\n", "s1 = State(background, name='background 1')\n", "s2 = State(cg_island, name='CG island 1')\n", "s3 = State(background, name='background 2')\n", "s4 = State(cg_island, name='CG island 2')\n", "s5 = State(background, name='background 3')\n", "s6 = State(cg_island, name='CG island 3')\n", "s7 = State(background, name='background 4')\n", "\n", "s8 = State(None, 'silent 2')\n", "s9 = State(None, 'silent 3')\n", "\n", "model = HiddenMarkovModel(\"CG-detector\")\n", "model.add_states(s1, s2, s3, s4, s5, s6, s7, s8, s9)\n", "model.add_transition(model.start, s1, 0.9)\n", "model.add_transition(model.start, s2, 0.1)\n", "model.add_transition(s1, s1, 0.80)\n", "model.add_transition(s1, s2, 0.10)\n", "model.add_transition(s2, s3, 0.10)\n", "model.add_transition(s2, s2, 0.90)\n", "model.add_transition(s3, s3, 0.80)\n", "model.add_transition(s3, s4, 0.10)\n", "model.add_transition(s4, s5, 0.10)\n", "model.add_transition(s4, s4, 0.90)\n", "model.add_transition(s5, s5, 0.90)\n", "model.add_transition(s5, s6, 0.10)\n", "model.add_transition(s6, s7, 0.10)\n", "model.add_transition(s6, s6, 0.90)\n", "model.add_transition(s7, s7, 0.90)\n", "model.add_transition(s7, model.end, 0.10)\n", "model.add_transition(s1, s8, 0.10)\n", "model.add_transition(s8, s9, 0.90)\n", "model.add_transition(s8, s3, 0.10)\n", "model.add_transition(s3, s9, 0.10)\n", "model.add_transition(s9, model.end, 0.90)\n", "model.add_transition(s9, s5, 0.10)\n", "model.bake()\n", "\n", "plt.figure(figsize=(8, 16))\n", "model.plot()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using silent states, we can construct a type of silent 'highway' which sequences can align to in order to avoid aligning to CG islands if the data does not support them. You can see that these silent states allow you to skip either the first CG island, the second one, or all of them, only aligning to background sequence. Let's see this in practice when aligning to a sequence with no CG islands." ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sequence: ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGT\n", "hmm pred: 11111111111111111111111111111111111111111111\n", "3GC pred: 33333333455555555555555555555566666666666666\n", "\n", "Prior HMM Model\n", "CG island : 0\n", "background : 1\n", "CG-detector-start : 2\n", "CG-detector-end : 3\n", "\n", "3GC Model\n", "CG island 1 : 0\n", "CG island 2 : 1\n", "CG island 3 : 2\n", "background 1 : 3\n", "background 2 : 4\n", "background 3 : 5\n", "background 4 : 6\n", "silent 2 : 7\n", "silent 3 : 8\n", "CG-detector-start : 9\n", "CG-detector-end : 10\n" ] } ], "source": [ "seq = list('ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGT')\n", "mod_predictions = model.predict(seq)\n", "hmm_predictions = hmm.predict(seq)\n", "\n", "print \"sequence: {}\".format( ''.join( seq ) )\n", "print \"hmm pred: {}\".format( ''.join( map( str, hmm_predictions ) ) )\n", "print \"3GC pred: {}\".format( ''.join( map( str, mod_predictions ) ) )\n", "print\n", "print \"Prior HMM Model\\n\", \"\\n\".join( \"{} : {}\".format(state.name, i) for i, state in enumerate(hmm.states))\n", "print\n", "print \"3GC Model\\n\", \"\\n\".join( \"{} : {}\".format(state.name, i) for i, state in enumerate(model.states))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we are using maximum a posteriori estimates for `model.predict_proba`, we get a mesh of all possible background states for the states predicted for each observation. This is because the maximum a posteriori estimates use a sum of all possible paths, which sometimes yield more accurate results, and sometimes yield more interesting results. If instead we were to look at the Viterbi path, which is the single most probable path through the model, we would get a far more stable and expected path." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. GMM-HMM\n", "\n", "As was mentioned previously, hidden Markov models add a structural element to distributions, allowing them to analyze arbitrarily lengthed sequences of data. The most common uses of HMMs involve the use of discrete distributions, such as in bioinformatics, or Gaussian distributions. However, HMMs really can be done over arbitrary probabilistic models, as long as they allow for the calculation of log probabilites and weighted updates. An example of this is to create a HMM with GeneralMixtureModel emissions. In essence, each node has a mixture of distributions representing it, allowing it to capture a fuller representation of the underlying data.\n", "\n", "Let's take a look at how easy this is in pomegranate." ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.65227912 0.34772088]\n", " [0.41463699 0.58536301]\n", " [0.13268346 0.86731654]\n", " [0.00185018 0.99814982]\n", " [0.01294739 0.98705261]\n", " [0.13511722 0.86488278]\n", " [0.25952093 0.74047907]\n", " [0.37707309 0.62292691]\n", " [0.45202487 0.54797513]\n", " [0.46337568 0.53662432]\n", " [0.2282899 0.7717101 ]\n", " [0.0064963 0.9935037 ]\n", " [0.01230074 0.98769926]]\n" ] } ], "source": [ "X = [9.6, 9.9, 11.4, 15.2, 13.1, 10.4, 10.2, 9.8, 9.5, 8.2, 10.3, 14.2, 13.1]\n", "\n", "d1 = GeneralMixtureModel([NormalDistribution(5, 2), NormalDistribution(5, 4)])\n", "d2 = GeneralMixtureModel([NormalDistribution(15, 1), NormalDistribution(15, 5)])\n", "\n", "s1 = State(d1, name=\"GMM1\")\n", "s2 = State(d2, name=\"GMM2\")\n", "\n", "model = HiddenMarkovModel()\n", "model.add_states(s1, s2)\n", "model.add_transition(model.start, s1, 0.75)\n", "model.add_transition(model.start, s2, 0.25)\n", "model.add_transition(s1, s1, 0.85)\n", "model.add_transition(s1, s2, 0.15)\n", "model.add_transition(s2, s2, 0.90)\n", "model.add_transition(s2, s1, 0.10)\n", "model.bake()\n", "\n", "print model.predict_proba(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All we had to do is make the distribution be a GeneralMixtureModel instead of an object which ends in Distribution. Since this isn't a separate object, all of the same benefits that you get from a more typical HMM can be used here including multithreaded training, pseudocounts for training, silent states, etc... In fact, it's worth noting that not all states even have to be mixtures--since GeneralMixtureModels are still just a probability distribution, you can use them interchangably." ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.99999978 0.00000022]\n", " [0.99999798 0.00000202]\n", " [0.95807881 0.04192119]\n", " [0.0228916 0.9771084 ]\n", " [0.10180952 0.89819048]\n", " [0.99957345 0.00042655]\n", " [0.99999662 0.00000338]\n", " [0.99999963 0.00000037]\n", " [0.99999994 0.00000006]\n", " [1. 0. ]\n", " [0.99972047 0.00027953]\n", " [0.01747232 0.98252768]\n", " [0.01685506 0.98314494]]\n" ] } ], "source": [ "d1 = GeneralMixtureModel([NormalDistribution(5, 2), NormalDistribution(5, 4)])\n", "d2 = NormalDistribution(15, 1)\n", "\n", "s1 = State(d1, name=\"GMM1\")\n", "s2 = State(d2, name=\"Norm2\")\n", "\n", "model = HiddenMarkovModel()\n", "model.add_states(s1, s2)\n", "model.add_transition(model.start, s1, 0.75)\n", "model.add_transition(model.start, s2, 0.25)\n", "model.add_transition(s1, s1, 0.85)\n", "model.add_transition(s1, s2, 0.15)\n", "model.add_transition(s2, s2, 0.90)\n", "model.add_transition(s2, s1, 0.10)\n", "model.bake()\n", "\n", "print model.predict_proba(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By adding in the second GMM, we were able to represent a spike in probability at 15, but also a wider base by mixing it with a distribution that had a large variance. Because of this, more of the symbols in X belonged to it than if we simply used a single distribution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Bayesian Networks\n", "\n", "Bayesian networks are a powerful inference tool, in which a set of variables are represented as nodes, and the lack of an edge represents a conditional independence statement between the two variables, and an edge represents a dependence between the two variables. One of the powerful components of a Bayesian network is the ability to infer the values of some variables given observed values for the remaining variables. These are referred to as the 'hidden' and 'observed' variables respectively, and need not be set at the time the network is created. The same network can have a different set of variables be hidden or observed between two data points. The more values which are observed, the closer the inferred values will be to the truth.\n", "\n", "Bayesian networks typically are either over discrete observations (like True/False, A/B/C) or are linear-gaussian (where all observations are continuous and parents effect their children in a linear manner). pomegranate currently only supports discrete observations, but has plans to both support linear-gaussian models, and meshes of both discrete and linear-gaussian models.\n", "\n", "Let's get down to a simple example from medical diagnosis. While not everyone can be as good at diagnosis as Dr. House, we can get pretty good with math. " ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "image/png": 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P1L9PmjQJP/zwA3744QesXr36qY8tLCzE559/jt9++w2+vr5V3na11K8P3Lql/e3oUJ6U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\nCGRCOFoCgUAgEAgEMvH/AWKtprsx4Fy0AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "BRCA1 = DiscreteDistribution({0: 0.999, 1: 0.001})\n", "BRCA2 = DiscreteDistribution({0: 0.985, 1: 0.015})\n", "LCT = DiscreteDistribution({0: 0.950, 1: 0.050})\n", "\n", "OC = ConditionalProbabilityTable([[0, 0, 0, 0.999],\n", " [0, 0, 1, 0.001],\n", " [0, 1, 0, 0.750],\n", " [0, 1, 1, 0.250],\n", " [1, 0, 0, 0.700],\n", " [1, 0, 1, 0.300],\n", " [1, 1, 0, 0.050],\n", " [1, 1, 1, 0.950]], [BRCA1, BRCA2])\n", "\n", "LI = ConditionalProbabilityTable([[0, 0, 0.99],\n", " [0, 1, 0.01],\n", " [1, 0, 0.20],\n", " [1, 1, 0.80]], [LCT])\n", "\n", "PREG = DiscreteDistribution({0: 0.90, 1: 0.10})\n", "\n", "LE = ConditionalProbabilityTable([[0, 0, 0.99],\n", " [0, 1, 0.01],\n", " [1, 0, 0.25],\n", " [1, 1, 0.75]], [OC])\n", "\n", "BLOAT = ConditionalProbabilityTable([[0, 0, 0, 0.85],\n", " [0, 0, 1, 0.15],\n", " [0, 1, 0, 0.70],\n", " [0, 1, 1, 0.30],\n", " [1, 0, 0, 0.40],\n", " [1, 0, 1, 0.60],\n", " [1, 1, 0, 0.10],\n", " [1, 1, 1, 0.90]], [OC, LI])\n", "\n", "LOA = ConditionalProbabilityTable([[0, 0, 0, 0.99],\n", " [0, 0, 1, 0.01],\n", " [0, 1, 0, 0.30],\n", " [0, 1, 1, 0.70],\n", " [1, 0, 0, 0.95],\n", " [1, 0, 1, 0.05],\n", " [1, 1, 0, 0.95],\n", " [1, 1, 1, 0.05]], [PREG, OC])\n", "\n", "VOM = ConditionalProbabilityTable([[0, 0, 0, 0, 0.99],\n", " [0, 0, 0, 1, 0.01],\n", " [0, 0, 1, 0, 0.80],\n", " [0, 0, 1, 1, 0.20],\n", " [0, 1, 0, 0, 0.40],\n", " [0, 1, 0, 1, 0.60],\n", " [0, 1, 1, 0, 0.30],\n", " [0, 1, 1, 1, 0.70],\n", " [1, 0, 0, 0, 0.30],\n", " [1, 0, 0, 1, 0.70],\n", " [1, 0, 1, 0, 0.20],\n", " [1, 0, 1, 1, 0.80],\n", " [1, 1, 0, 0, 0.05],\n", " [1, 1, 0, 1, 0.95],\n", " [1, 1, 1, 0, 0.01],\n", " [1, 1, 1, 1, 0.99]], [PREG, OC, LI])\n", "\n", "AC = ConditionalProbabilityTable([[0, 0, 0, 0.95],\n", " [0, 0, 1, 0.05],\n", " [0, 1, 0, 0.01],\n", " [0, 1, 1, 0.99],\n", " [1, 0, 0, 0.40],\n", " [1, 0, 1, 0.60],\n", " [1, 1, 0, 0.20],\n", " [1, 1, 1, 0.80]], [PREG, LI])\n", "\n", "s1 = Node(BRCA1, name=\"BRCA1\")\n", "s2 = Node(BRCA2, name=\"BRCA2\")\n", "s3 = Node(LCT, name=\"LCT\")\n", "s4 = Node(OC, name=\"OC\")\n", "s5 = Node(LI, name=\"LI\")\n", "s6 = Node(PREG, name=\"PREG\")\n", "s7 = Node(LE, name=\"LE\")\n", "s8 = Node(BLOAT, name=\"BLOAT\")\n", "s9 = Node(LOA, name=\"LOA\")\n", "s10 = Node(VOM, name=\"VOM\")\n", "s11 = Node(AC, name=\"AC\")\n", "\n", "model = BayesianNetwork(\"Hut\")\n", "model.add_nodes(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11)\n", "model.add_edge(s1, s4)\n", "model.add_edge(s2, s4)\n", "model.add_edge(s3, s5)\n", "model.add_edge(s4, s7)\n", "model.add_edge(s4, s8)\n", "model.add_edge(s4, s9)\n", "model.add_edge(s4, s10)\n", "model.add_edge(s5, s8)\n", "model.add_edge(s5, s10)\n", "model.add_edge(s5, s11)\n", "model.add_edge(s6, s9)\n", "model.add_edge(s6, s10)\n", "model.add_edge(s6, s11)\n", "model.bake()\n", "\n", "model.plot()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BRCA1 \tBRCA2 \tLCT \tOC \tLI \tPREG \tLE \tBLOAT \tLOA \tVOM \tAC \n", "0.001\t0.015\t0.05\t0.005\t0.05\t 0.1\t0.014\t0.16\t0.017\t0.091\t0.15\n" ] } ], "source": [ "d = model.predict_proba({})\n", "print \"\\t\".join( \"{:7}\".format(state.name) for state in model.states )\n", "print \"\\t\".join( \"{:4.2}\".format(m.parameters[0][1]) for m in d )" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BRCA1 \tBRCA2 \tLCT \tOC \tLI \tPREG \tLE \tBLOAT \tLOA \tVOM \tAC \n", "0.01\t0.13\t0.012\t0.16\t0.0015\t0.017\t 1.0\t 1.0\t 0.0\t 0.0\t 0.0\n" ] } ], "source": [ "d = model.predict_proba({'BLOAT' : 1, 'LE' : 1, 'LOA' : 0, 'VOM' : 0, 'AC' : 0})\n", "d = [m.parameters[0][1] if type(m) != int else float(m) for m in d]\n", "\n", "print \"\\t\".join( \"{:7}\".format(state.name) for state in model.states )\n", "print \"\\t\".join( \"{:4.2}\".format(m) for m in d )" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BRCA1 \tBRCA2 \tLCT \tOC \tLI \tPREG \tLE \tBLOAT \tLOA \tVOM \tAC \n", " 0.0\t 1.0\t0.011\t0.92\t0.0012\t0.02\t 1.0\t 1.0\t 0.0\t 0.0\t 0.0\n" ] } ], "source": [ "d = model.predict_proba({'BLOAT' : 1, 'LE' : 1, 'LOA' : 0, 'VOM' : 0, 'AC' : 0, 'BRCA1' : 0, 'BRCA2' : 1})\n", "d = [m.parameters[0][1] if type(m) != int else float(m) for m in d]\n", "\n", "print \"\\t\".join( \"{:7}\".format(state.name) for state in model.states )\n", "print \"\\t\".join( \"{:4.2}\".format(m) for m in d )" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BRCA1 \tBRCA2 \tLCT \tOC \tLI \tPREG \tLE \tBLOAT \tLOA \tVOM \tAC \n", "0.056\t0.68\t0.087\t0.91\t0.096\t 0.2\t 1.0\t 1.0\t0.52\t 1.0\t0.24\n" ] } ], "source": [ "d = model.predict_proba({'VOM' : 1, 'BLOAT' : 1, 'LE' : 1})\n", "d = [m.parameters[0][1] if type(m) != int else float(m) for m in d]\n", "\n", "print \"\\t\".join( \"{:7}\".format(state.name) for state in model.states )\n", "print \"\\t\".join( \"{:4.2}\".format(m) for m in d )" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BRCA1 \tBRCA2 \tLCT \tOC \tLI \tPREG \tLE \tBLOAT \tLOA \tVOM \tAC \n", "0.061\t0.75\t0.011\t 1.0\t0.0012\t0.0056\t 1.0\t 1.0\t 1.0\t 1.0\t 0.0\n" ] } ], "source": [ "d = model.predict_proba({'VOM' : 1, 'BLOAT' : 1, 'LE' : 1, 'LOA' : 1, 'VOM' : 1, 'AC' : 0})\n", "d = [m.parameters[0][1] if type(m) != int else float(m) for m in d]\n", "\n", "print \"\\t\".join( \"{:7}\".format(state.name) for state in model.states )\n", "print \"\\t\".join( \"{:4.2}\".format(m) for m in d )" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BRCA1 \tBRCA2 \tLCT \tOC \tLI \tPREG \tLE \tBLOAT \tLOA \tVOM \tAC \n", "0.05\t 0.6\t0.026\t0.81\t0.019\t0.31\t 1.0\t 1.0\t 0.0\t 1.0\t 0.0\n" ] } ], "source": [ "d = model.predict_proba({'VOM' : 1, 'BLOAT' : 1, 'LE' : 1, 'LOA' : 0, 'VOM' : 1, 'AC' : 0})\n", "d = [m.parameters[0][1] if type(m) != int else float(m) for m in d]\n", "\n", "print \"\\t\".join( \"{:7}\".format(state.name) for state in model.states )\n", "print \"\\t\".join( \"{:4.2}\".format(m) for m in d )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Parallelization\n", "\n", "pomegranate has built in functions for parallelization. Ultimately all you need to do is wrap the calling of summarization with some sort of parallelization library. In this case I am using joblib, as it is easy to use and performs extremely well. It is also used as the backend for scikit-learn, which lends it credibility.\n", "\n", "You can currently get parallelized versions of the following functions:\n", "\n", "1. predict\n", "2. predict_proba\n", "3. predict_log_proba\n", "4. summarize\n", "5. fit\n", "\n", "It's important to note that 'summarize' by itself does not completely fit unsupervised algorithms like HMM and GMMs, it has to be done iteratively. 'summarize' will simply calculate the suficient statistics for this particular dataset and store them in the model. However, 'fit' will perform a complete fit, and takes all the same arguments that 'model.fit' would take, specifically 'stop_threshold' and 'max_iterations'. You can specify which backend you would like for each of these functions, either 'multiprocessing' or 'threading', but 'threading' is the default for increased performance.\n", "\n", "Let's start off with parallelizing the predictions from a GeneralMixtureModel. The same code should work for all models in pomegranate, with differing leveels of efficiency." ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "mus = numpy.random.normal(0, 10, (25, 5))\n", "distributions = [MultivariateGaussianDistribution(mu, numpy.eye(5)) for mu in mus]\n", "model = GeneralMixtureModel(distributions)\n", "X = numpy.concatenate([numpy.random.normal(mu, 1.5, (100000, 5)) for mu in mus])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see how long it takes to do predictions with the two methods. " ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 2.66 s per loop\n", "1 loop, best of 3: 1.72 s per loop\n", "1 loop, best of 3: 1.62 s per loop\n" ] } ], "source": [ "%timeit model.predict(X)\n", "%timeit model.predict(X, n_jobs=2)\n", "%timeit model.predict(X, n_jobs=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's make sure that we are getting the same results for both of them." ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Complete Match: True\n", "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n" ] } ], "source": [ "print \"Complete Match: \", (model.predict(X) == model.predict(X, n_jobs=4)).all()\n", "print model.predict(X[:20])\n", "print model.predict(X[:20], n_jobs=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This can work just as easily with Naive Bayes as it would work with GMMs." ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = NaiveBayes(distributions)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 5.51 s per loop\n", "1 loop, best of 3: 3.56 s per loop\n" ] } ], "source": [ "%timeit model.predict_proba(X)\n", "%timeit model.predict_proba(X, n_jobs=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also confirm that this is producing the correct result." ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(model.predict_proba(X[:100]) - model.predict_proba(X[:100], n_jobs=4)).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Final Remarks\n", "\n", "Thank you for working through this tutorial of the basic functionality of the pomegranate package. I hope that you end up finding it relevant and useful for your work. If you have any exciting use cases I'd love to hear about how pomegranate was able to help you.\n", "\n", "pomegranate is a work in progress, and unfortunately is not how I am able to spend the majority of my time. If you notice a bug or inconsistency, please open an issue on github. I try to respond to fix the bugs as soon as possible!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 1 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/old/Tutorial_1_Distributions.ipynb000066400000000000000000002422121420461712600315100ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pomegranate Tutorial 1: Distributions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial will cover the various distributions that are implemented in pomegranate. These distributions can be used by themselves, or more typically as a part of a larger compositional model like a hidden Markov model. \n", "\n", "The distributions that are currently available:\n", "\n", "- Univariate Distributions\n", " 1. UniformDistribution\n", " 2. BernoulliDistribution\n", " 2. NormalDistribution\n", " 3. LogNormalDistribution\n", " 4. ExponentialDistribution\n", " 5. PoissonDistribution\n", " 6. BetaDistribution\n", " 7. GammaDistribution\n", " 8. DiscreteDistribution\n", " \n", "- Univariate Kernel Densities\n", " 1. GaussianKernelDensity\n", " 2. UniformKernelDensity\n", " 3. TriangleKernelDensity\n", " \n", "- Multivariate Distributions\n", " 1. IndependentComponentsDistribution\n", " 2. Dirichlet Distribution\n", " 3. MultivariateGaussianDistribution\n", " 4. ConditionalProbabilityTable\n", " 5. JointProbabilityTable\n", "\n", "The following methods are supported by all distributions:\n", "\n", "```\n", "1. d.probability(X): Return the probability of a sample under the distribution or a vector of samples\n", "2. d.log_probability(X): Return the log probability of a sample under the distribution or a vector of samples\n", "3. d.fit(X, weights=None, inertia): Use MLE estimates to update the parameters of the distribution. Optional weight vector is accepted, or inertia that pushes parameters from where they currently are to the update that portion of the way.\n", "4. d.summarize(X, weights=None): Extract and update the stored sufficient statistics on a data set with optional weights, but don't update the parameters yet\n", "5. d.from_summaries(X, inertia=0.0): Use the stored sufficient statistics to do a parameter update\n", "6. d.sample(n=1): Generate a sample, or a vector of samples, from the distribution\n", "\n", "7. d.to_json(): Serialize the distribution to a string json\n", "8. Distribution.from_json(s): Load up a stored distriibution, either from a file if passed a filename that ends in '.json', or from the string passed in itself.\n", "\n", "9. d.copy(): Make a deep copy of the distribution\n", "10. d.freeze(): Freeze the distribution, making 'fit' and 'from_summaries' not change model parameters\n", "11. d.thaw(): Unfreeze the distribution so that it can be updated agin.\n", "```\n", "\n", "Univariate distributions also have a `plot( n=1000, **kwargs )` command, where you can plot a histogram of `n` randomly generated samples from the distribution, which will pass matplotlib keyword arguments to the `plt.hist` function. \n", "\n", "Lets look at a few examples." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Normal Distribution" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "from pomegranate import *\n", "%pylab inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets start off by looking at a normal distribution. It is easy to specify, and take a look at." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d = NormalDistribution( 5, 2 )\n", "d.plot( n=100000, edgecolor='c', color='c', bins=50 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then calculate the log probability of some points under this distribution." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-3.612085713764219\n", "-1.612085713764219\n", "-3.612085713764219\n" ] } ], "source": [ "print d.log_probability( 1 )\n", "print d.log_probability( 5 )\n", "print d.log_probability( 9 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also fit this distribution to some data we have." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = np.random.randn(10000) * 0.4 + 9" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[9.004974667786882, 0.39447571955036087]\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d.fit( data )\n", "d.plot( n=100000, edgecolor='c', color='c', bins=50 )\n", "print d.parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We generated data from a normal distribution with mean 9 and standard deviation 0.4, and we can see that the distribution object was able to recover it. This is not surprising given the amount of data it received.\n", "\n", "The fitting process discards the previous parameters, and uses a pure MLE estimate on the new data. In this case, it just takes the mean and variance of the new data, discarding its previous parameters. It does not matter if the previous distribution was `NormalDistribution(1, 5)` or `NormalDistribution(10000000, 10000000)`, they would both recover the same parameters after.\n", "\n", "A way of taking into account current parameters is by using inertia, where the update is equal to `previous parameters * inertia + new parameters * ( 1 - inertia )`. This allows the update to take into account prior parameters a bit. Lets see an example." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "d -- mu: 9.00497466779 sigma 0.39447571955\n", "e -- mu: 104.502487334 sigma 0.697237859775\n" ] } ], "source": [ "d = NormalDistribution( 10, 1 )\n", "e = NormalDistribution( 200, 1 )\n", "\n", "d.fit( data, inertia=0.0 ) # The default setting\n", "e.fit( data, inertia=0.5 )\n", "\n", "print \"d -- mu: {} sigma {}\".format( *d.parameters )\n", "print \"e -- mu: {} sigma {}\".format( *e.parameters )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Out of core updates are also supported for distributions. This means that we can update a distribution using an amount of data which cannot fit into memory normally, but still produce an exact update. This is done by summarizing the dataset into sufficient statistics at each step, and then producing an update at the end. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "d -- mu: 9.00497466779 sigma 0.39447571955\n" ] } ], "source": [ "d = NormalDistribution( 105, 12 )\n", "\n", "for i in range(10):\n", " d.summarize( data[i::10] )\n", "\n", "d.from_summaries()\n", "print \"d -- mu: {} sigma {}\".format( *d.parameters )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this particular case, the data can all fit in memory, but it doesn't necessarily have to. You just have to call `summarize`, then load up the next set of data, and continue.\n", "\n", "If we want to weight points, we can easily pass in a weight vector. Weights must be non-negative, and the sum of the weight vector must be greater than 0. Numerical stability issues can arise if you have entirely small weight vectors, so try to avoid that if possible." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "d -- mu: 9.00249539503 sigma 0.395023883825\n" ] } ], "source": [ "d = NormalDistribution( 105, 12 )\n", "weights = np.random.exponential( 100, 10000 )\n", "d.fit( data, weights )\n", "\n", "print \"d -- mu: {} sigma {}\".format( *d.parameters )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It doesn't change much in this case since the data was drawn from a perfect normal distribution, but it illustrates the possibility. The analog for summarization is the following:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "d -- mu: 9.00249539503 sigma 0.395023883826\n" ] } ], "source": [ "d = NormalDistribution( 105, 12 )\n", "\n", "for i in range(10):\n", " d.summarize( data[i::10], weights=weights[i::10] )\n", "\n", "d.from_summaries()\n", "print \"d -- mu: {} sigma {}\".format( *d.parameters )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also easily sample from distributions by calling the `sample` function. `plot` does this internally when we call it, but to be explicit lets reproduce that." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 3., 4., 7., 20., 57., 114., 202., 362., 574.,\n", " 786., 1033., 1135., 1265., 1229., 1061., 819., 539., 356.,\n", " 246., 110., 39., 29., 5., 3., 2.]),\n", " array([ 58.28767007, 62.01813627, 65.74860246, 69.47906866,\n", " 73.20953485, 76.94000105, 80.67046725, 84.40093344,\n", " 88.13139964, 91.86186583, 95.59233203, 99.32279823,\n", " 103.05326442, 106.78373062, 110.51419681, 114.24466301,\n", " 117.97512921, 121.7055954 , 125.4360616 , 129.16652779,\n", " 132.89699399, 136.62746019, 140.35792638, 144.08839258,\n", " 147.81885877, 151.54932497]),\n", " )" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d = NormalDistribution( 105, 12 )\n", "samples = [ d.sample() for i in range(10000) ]\n", "plt.hist( samples, edgecolor='c', color='c', bins=25 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Discrete Distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A notable deviant from the numerics logic is the Discrete Distribution. Instead of passing parameters as floats, you instead pass in a dictionary where keys can be ~any object~ and values are the probability of them occurring. If you try to calculate the log probability of an item not present in the distribution, the default behavior is to return negative infinity." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-2.30258509299\n", "-0.69314718056\n", "-inf\n" ] } ], "source": [ "d = DiscreteDistribution({'A': 0.1, 'C': 0.25, 'G': 0.50, 'T': 0.15})\n", "print d.log_probability( 'A' )\n", "print d.log_probability( 'G' )\n", "print d.log_probability( '????' )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Updating a discrete distributon is the same as updating other distributions." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "logp(A) = -0.826678573184\n", "logp(C) = -1.85629799037\n", "logp(G) = -1.56861591791\n", "logp(T) = -1.6199092123\n", "[{'A': 0.4375, 'C': 0.15625, 'T': 0.19791666666666666, 'G': 0.20833333333333334}]\n" ] } ], "source": [ "items = list('ACGATACACTGAATGACAGCAGTCACTGACAGTAGTACGAGTAGTAGCAGAGAGTAATAAAGAATTAATATATGACACTACGAAAAAAATGCATCG')\n", "d.fit( items )\n", "\n", "for char in 'ACGT':\n", " print \"logp({}) = {}\".format( char, d.log_probability( char ) )\n", "\n", "print d.parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also take a look at the JSON serialization. JSONs are the perfect way to store these models given their human interpretability, and the ability to recursively store JSONs within other JSONs, such as more complicated models (such as HMMs) will need to store the JSON of each distribution. It is useful to store these models after the computationally intensive task of training them, so that it need not be repeated." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"frozen\" :false,\n", " \"dtype\" :\"str\",\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.4375,\n", " \"C\" :0.15625,\n", " \"T\" :0.19791666666666666,\n", " \"G\" :0.20833333333333334\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}\n" ] } ], "source": [ "print d.to_json()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Kernel Density" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Gaussian Kernel Density is a non-parametric distribution which stores a series of points, their associated weights, and the bandwidth representing the range of influence of each point. This will become apparent with an example." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d = GaussianKernelDensity( [0.5, 1.2, 4.2, 1.9, 5.4, 3.4], bandwidth=1 )\n", "d.plot( n=100000, edgecolor='c', color='c', bins=100 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Changing the bandwidth parameter is equivalent to reducing the variance on a Gaussian. It makes the densities more central around the points, but can cause overfitting." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d = GaussianKernelDensity( [0.5, 1.2, 4.2, 1.9, 5.4, 3.4], bandwidth=0.2 )\n", "d.plot( n=100000, edgecolor='c', color='c', bins=100 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily see the influence of weights by weighting a single point. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d = GaussianKernelDensity( [0.5, 1.2, 4.2, 1.9, 5.4, 3.4], weights=[1, 1, 1, 3, 1, 1], bandwidth=0.2 )\n", "d.plot( n=100000, edgecolor='c', color='c', bins=100 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the Gaussian kernel density, there is a Triangle kernel density, which has a slightly different shape. Mostly I liked how they looked like pointy hats, so I implemented it." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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L6Lr4nUgXytz2A9akTLLebZ6tZQCok7p4oOpimceprQfBrlxTMQkGgDQFkz7ItPXgulJb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\nASBJlTIAJKlS/w/k2sUPAGP+wwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d = TriangleKernelDensity( [0.5, 1.2, 4.2, 1.9, 5.4, 3.4], weights=[1, 1, 1, 3, 1, 1], bandwidth=0.5 )\n", "d.plot( n=500000, edgecolor='c', color='c', bins=250 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Independent Components Distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes, a univariate distribution isn't good enough. Many times data comes in the form of multiple, independent, dimensions, each with their own distribution. We can model this with a multivariate distribution which contains a tuple of independent components. \n", "\n", "A common example may be to look at data whose signal is normally distributed, and whose duration is modelled by an exponential. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "-3.612085713764219\n" ] } ], "source": [ "d = IndependentComponentsDistribution([ NormalDistribution(10, 1), ExponentialDistribution(0.5) ])\n", "print d.log_probability( (11, 3) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also weight the distributions. In this example, lets say that the signal is far more important than the duration." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "-6.449962780172767\n" ] } ], "source": [ "d = IndependentComponentsDistribution([ NormalDistribution(10, 1), ExponentialDistribution(0.5) ], weights=[3, 1])\n", "print d.log_probability( (11, 3) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the point becomes more probable, because we care more about the good fit to the signal dimension (the normal distribution) than it poorly fits the duration (exponential) distribution. You must be careful with the weightings though, because they don't have to sum to 1. In this sense, `weights = [3,1]` is not the same as `weights = [0.75, 0.25]`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can do updates in the same manner. Drawing sampels from a Gaussian with `mu = 12` and `sigma = 2`, and an exponential with `mu = 5` (0.2 in pomegranate notation, which is more prominent), we can recover the underlying distributions with 1000 points." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[12.160898634047232, 2.0366665283768683] [0.20419702218012306]\n" ] } ], "source": [ "data = numpy.zeros((1000, 2))\n", "data[:,0] = np.random.randn(1000) * 2 + 12 \n", "data[:,1] = np.random.exponential(5, 1000)\n", "d.fit(data)\n", "\n", "print d.parameters[0][0].parameters, d.parameters[0][1].parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multivariate Gaussian Distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's not always good enough to have independent distributions, as many signals are heavily correlated. A simple multivariate Gaussian has been written, as this is the most common use case, with full covariance updates. You provide a vector of mus, and a covariance matrix, and you're good to go! Unfortunately, plotting is not natively supported for this distribution." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.73749922 5.11476927 11.18206336 16.53134358 20.93125287]\n" ] } ], "source": [ "d = MultivariateGaussianDistribution( np.arange(5) * 5, np.eye(5) )\n", "print d.sample()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.02377337215938058, 7.969241783095424, 16.00711337421143, 23.988157982680665, 32.022657302705106], [[0.9431449057331389, -0.001642776696889797, 0.017314356941345806, 0.01096538884848701, -0.03258674233349018], [-0.001642776696889797, 0.9767716288924349, 0.018965950499477913, -0.007272981779940892, 0.04475422098013223], [0.017314356941345806, 0.018965950499477913, 0.9370010959472275, 0.018596776777878403, -0.028357435495068785], [0.01096538884848701, -0.007272981779940892, 0.018596776777878403, 1.0080029155391967, 0.020456741158268413], [-0.03258674233348995, 0.04475422098004492, -0.028357435495010578, 0.020456741158268413, 1.0468826718300115]]]\n" ] } ], "source": [ "data = np.random.randn(1000, 5) + np.arange(5) * 8\n", "\n", "d.fit(data)\n", "print d.parameters" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1.34675139 7.52499761 14.4882111 25.46624009 32.03538407]\n" ] } ], "source": [ "print d.sample()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conditional Probability Table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tables are a different type of multivariate distribution. In essence, a multivariate discrete distribution. To use them, we define the values, and give probabilities which sum to 1 for each instance of the parents. They must be linked to parent distributions (which can also be conditional probability tables). Large networks of tables are called Bayesian networks!\n", "\n", "Lets code up the Monty Hall problem very quickly, without inference (see Bayesian Network introduction for the inference component)." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\tA\tA\t0.0\n", "A\tA\tB\t0.5\n", "A\tA\tC\t0.5\n", "A\tB\tA\t0.5\n", "A\tB\tB\t0.0\n", "A\tB\tC\t0.5\n", "A\tC\tA\t0.5\n", "A\tC\tB\t0.5\n", "A\tC\tC\t0.0\n", "B\tA\tA\t0.0\n", "B\tA\tB\t0.5\n", "B\tA\tC\t0.5\n", "B\tB\tA\t0.5\n", "B\tB\tB\t0.0\n", "B\tB\tC\t0.5\n", "B\tC\tA\t0.5\n", "B\tC\tB\t0.5\n", "B\tC\tC\t0.0\n", "C\tA\tA\t0.0\n", "C\tA\tB\t0.5\n", "C\tA\tC\t0.5\n", "C\tB\tA\t0.5\n", "C\tB\tB\t0.0\n", "C\tB\tC\t0.5\n", "C\tC\tA\t0.5\n", "C\tC\tB\t0.5\n", "C\tC\tC\t0.0\n" ] } ], "source": [ "guest = DiscreteDistribution({'A': 0.33, 'B': 0.33, 'C': 0.33})\n", "prize = DiscreteDistribution({'A': 0.33, 'B': 0.33, 'C': 0.33})\n", "monty = ConditionalProbabilityTable(\n", " [['A', 'A', 'A', 0.0],\n", " ['A', 'A', 'B', 0.5],\n", " ['A', 'A', 'C', 0.5],\n", " ['A', 'B', 'A', 0.5],\n", " ['A', 'B', 'B', 0.0],\n", " ['A', 'B', 'C', 0.5],\n", " ['A', 'C', 'A', 0.5],\n", " ['A', 'C', 'B', 0.5],\n", " ['A', 'C', 'C', 0.0],\n", " ['B', 'A', 'A', 0.0],\n", " ['B', 'A', 'B', 0.5],\n", " ['B', 'A', 'C', 0.5],\n", " ['B', 'B', 'A', 0.5],\n", " ['B', 'B', 'B', 0.0],\n", " ['B', 'B', 'C', 0.5],\n", " ['B', 'C', 'A', 0.5],\n", " ['B', 'C', 'B', 0.5],\n", " ['B', 'C', 'C', 0.0],\n", " ['C', 'A', 'A', 0.0],\n", " ['C', 'A', 'B', 0.5],\n", " ['C', 'A', 'C', 0.5],\n", " ['C', 'B', 'A', 0.5],\n", " ['C', 'B', 'B', 0.0],\n", " ['C', 'B', 'C', 0.5],\n", " ['C', 'C', 'A', 0.5],\n", " ['C', 'C', 'B', 0.5],\n", " ['C', 'C', 'C', 0.0]], parents=[guest, prize]\n", " )\n", "\n", "print monty" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.69314718056\n", "-inf\n" ] } ], "source": [ "print monty.log_probability( ('A', 'A', 'C') )\n", "print monty.log_probability( ('A', 'B', 'B') )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The reason which we need the parent distributions to be explicit distributions is so that we can have seamless conversion from the conditional table to the joint table." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\tA\tA\t0.0\n", "A\tA\tB\t0.0555555555556\n", "A\tA\tC\t0.0555555555556\n", "A\tB\tA\t0.0555555555556\n", "A\tB\tB\t0.0\n", "A\tB\tC\t0.0555555555556\n", "A\tC\tA\t0.0555555555556\n", "A\tC\tB\t0.0555555555556\n", "A\tC\tC\t0.0\n", "B\tA\tA\t0.0\n", "B\tA\tB\t0.0555555555556\n", "B\tA\tC\t0.0555555555556\n", "B\tB\tA\t0.0555555555556\n", "B\tB\tB\t0.0\n", "B\tB\tC\t0.0555555555556\n", "B\tC\tA\t0.0555555555556\n", "B\tC\tB\t0.0555555555556\n", "B\tC\tC\t0.0\n", "C\tA\tA\t0.0\n", "C\tA\tB\t0.0555555555556\n", "C\tA\tC\t0.0555555555556\n", "C\tB\tA\t0.0555555555556\n", "C\tB\tB\t0.0\n", "C\tB\tC\t0.0555555555556\n", "C\tC\tA\t0.0555555555556\n", "C\tC\tB\t0.0555555555556\n", "C\tC\tC\t0.0\n" ] } ], "source": [ "print monty.joint()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are 18 non-zero entries in the table, each of equal weight. 1 / 18 is 0.556, so this makes sense. The joint distribution is it's own object, which can be stored and used later if desired." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "a = monty.joint()\n", "print type(a)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-inf" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.log_probability( ('C', 'A', 'A') )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both conditional and joint distributions can be trained in the same manner as the other distributions." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\tA\tA\t0.0\n", "A\tA\tB\t0.0\n", "A\tA\tC\t0.0\n", "A\tB\tA\tinf\n", "A\tB\tB\t0.0\n", "A\tB\tC\tinf\n", "A\tC\tA\t0.0\n", "A\tC\tB\t0.0\n", "A\tC\tC\t0.0\n", "B\tA\tA\t0.0\n", "B\tA\tB\t0.0\n", "B\tA\tC\t0.0\n", "B\tB\tA\tinf\n", "B\tB\tB\t0.0\n", "B\tB\tC\t0.0\n", "B\tC\tA\t0.0\n", "B\tC\tB\t0.0\n", "B\tC\tC\t0.0\n", "C\tA\tA\t0.0\n", "C\tA\tB\t0.0\n", "C\tA\tC\t0.0\n", "C\tB\tA\t0.0\n", "C\tB\tB\t0.0\n", "C\tB\tC\t0.0\n", "C\tC\tA\tinf\n", "C\tC\tB\tinf\n", "C\tC\tC\t0.0\n" ] } ], "source": [ "data = [['A', 'B', 'C'],\n", " ['B', 'B', 'A'],\n", " ['A', 'B', 'A'],\n", " ['C', 'C', 'B'],\n", " ['C', 'C', 'A'],\n", " ['C', 'C', 'A']]\n", "\n", "a.fit(data)\n", "print a" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\tA\tA\t0.333333333333\n", "A\tA\tB\t0.333333333333\n", "A\tA\tC\t0.333333333333\n", "A\tB\tA\t0.5\n", "A\tB\tB\t0.0\n", "A\tB\tC\t0.5\n", "A\tC\tA\t0.333333333333\n", "A\tC\tB\t0.333333333333\n", "A\tC\tC\t0.333333333333\n", "B\tA\tA\t0.333333333333\n", "B\tA\tB\t0.333333333333\n", "B\tA\tC\t0.333333333333\n", "B\tB\tA\t1.0\n", "B\tB\tB\t0.0\n", "B\tB\tC\t0.0\n", "B\tC\tA\t0.333333333333\n", "B\tC\tB\t0.333333333333\n", "B\tC\tC\t0.333333333333\n", "C\tA\tA\t0.333333333333\n", "C\tA\tB\t0.333333333333\n", "C\tA\tC\t0.333333333333\n", "C\tB\tA\t0.333333333333\n", "C\tB\tB\t0.333333333333\n", "C\tB\tC\t0.333333333333\n", "C\tC\tA\t0.666666666667\n", "C\tC\tB\t0.333333333333\n", "C\tC\tC\t0.0\n" ] } ], "source": [ "monty.fit(data)\n", "print monty" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the cases of conditional probability tables, if parent tuples haven't been seen before, it assumes a uniform distribution over them, explaining the prevalence of 0.3333... entries." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Serialization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Distributions (except tables right now) support serialization to JSONs using `to_json()` and `Distribution.from_json( json )`. We can see a few examples easily." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " 5.0,\n", " 2.0\n", " ],\n", " \"name\" :\"NormalDistribution\"\n", "}\n" ] } ], "source": [ "print NormalDistribution( 5, 2 ).to_json()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"frozen\" :false,\n", " \"dtype\" :\"str\",\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.5,\n", " \"C\" :0.25,\n", " \"B\" :0.25\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}\n" ] } ], "source": [ "print DiscreteDistribution( {'A': 0.5, 'B': 0.25, 'C': 0.25} ).to_json()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " 5.0,\n", " 2.0\n", " ],\n", " \"name\" :\"GammaDistribution\"\n", "}\n" ] } ], "source": [ "print Distribution.from_json( GammaDistribution( 5, 2 ).to_json() )" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " [\n", " 5.0,\n", " 6.0,\n", " 7.0,\n", " 3.0,\n", " 5.0,\n", " 7.0,\n", " 3.0,\n", " 3.0,\n", " 5.0,\n", " 2.0\n", " ],\n", " 1.0,\n", " [\n", " 0.1,\n", " 0.1,\n", " 0.1,\n", " 0.1,\n", " 0.1,\n", " 0.1,\n", " 0.1,\n", " 0.1,\n", " 0.1,\n", " 0.1\n", " ]\n", " ],\n", " \"name\" :\"GaussianKernelDensity\"\n", "}\n" ] } ], "source": [ "print Distribution.from_json( GaussianKernelDensity( [5,6,7,3,5,7,3,3,5,2] ).to_json() )" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 1 } Tutorial_2_General_Mixture_Models.ipynb000066400000000000000000000775231420461712600332000ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/old{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# General Mixture Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is frequently the case that the data you have is not explained by a single underlying distribution. If we want to try to recover the underlying distributions, we need to have a model which has multiple components. An example is the following data." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: pylab import has clobbered these variables: ['random', 'log']\n", "`%matplotlib` prevents importing * from pylab and numpy\n" ] } ], "source": [ "from pomegranate import *\n", "%pylab inline\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 1., 0., 0., 10., 13., 28., 26., 30., 38.,\n", " 28., 21., 21., 21., 20., 85., 149., 151., 77.,\n", " 27., 4.]),\n", " array([ -7.30907852, -6.36311627, -5.41715401, -4.47119176,\n", " -3.5252295 , -2.57926725, -1.633305 , -0.68734274,\n", " 0.25861951, 1.20458177, 2.15054402, 3.09650628,\n", " 4.04246853, 4.98843078, 5.93439304, 6.88035529,\n", " 7.82631755, 8.7722798 , 9.71824206, 10.66420431, 11.61016656]),\n", " )" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = np.concatenate( (np.random.randn(250, 1) * 2.75 + 1.25, np.random.randn(500, 1) * 1.2 + 7.85) )\n", "np.random.shuffle(data)\n", "plt.hist( data, edgecolor='c', color='c', bins=20 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can create our initial estimate of what this distribution is a General Mixture Model. This is a model which is comprised of multiple distributions, and weights on those distributions representing the prior probability of a point falling under that distribution given no knowledge of the point itself (defaults to equal). We can have univariate mixture models by using univariate distributions, or multivariate distributions by using multivariate distributions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gaussian Mixture Models" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d = GeneralMixtureModel( [NormalDistribution(2.5, 1), NormalDistribution(8, 1)] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now predict the class labels of each point under this mixture. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 1 1 1 0]\n", "507 1 labels, 243 0 labels\n" ] } ], "source": [ "labels = d.predict( data )\n", "print labels[:5]\n", "print \"{} 1 labels, {} 0 labels\".format( labels.sum(), labels.shape[0] - labels.sum() )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is fairly close to the number of underlying points from each distribution, off by 17 in each label. We still don't know if the labels are accurate, just the number of labels." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 9., 8., 20., 29., 36., 48., 46., 54., 61., 53., 40.,\n", " 35., 21., 16., 15., 11., 1., 2., 1., 1.]),\n", " array([ 5.33097483, 5.64493442, 5.95889401, 6.27285359,\n", " 6.58681318, 6.90077277, 7.21473235, 7.52869194,\n", " 7.84265153, 8.15661111, 8.4705707 , 8.78453029,\n", " 9.09848987, 9.41244946, 9.72640904, 10.04036863,\n", " 10.35432822, 10.6682878 , 10.98224739, 11.29620698, 11.61016656]),\n", " )" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JVqTtJFW1vgOTXwPmqurdvfXDQFXVTefst74TSNKUq6qhvvRgI4F+HnAKuBL4HvBl4H1V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\nDHRJaoSBLkmNMNAlqRH/D83lmncUE8cLAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist( data[ labels == 0 ], edgecolor='r', color='r', bins=20 )\n", "plt.hist( data[ labels == 1 ], edgecolor='c', color='c', bins=20 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is slightly more difficult to update the underlying components of the model because we don't have labels indicating which point came from which distribution. We could try to use the labels inferred from the model. It seems to cleanly split it, but what if our initial estimate was not very good? It could be difficult to get a good update if we had a bad prior. \n", "\n", "Another possibility is to predict the probability of each point under each component, to get a softer estimate of the labels. Lets take a look." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1.28630159e-08 9.99999987e-01]\n", " [ 3.14180327e-10 1.00000000e+00]\n", " [ 1.28508601e-04 9.99871491e-01]\n", " [ 5.37380480e-07 9.99999463e-01]\n", " [ 9.99999998e-01 2.39509083e-09]]\n", "[ 242.73703674 507.26296326]\n" ] } ], "source": [ "labels = d.predict_proba( data )\n", "print labels[:5]\n", "print labels.sum(axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is slightly closer to the truth, with 15.2 off instead of 17, around 10% closer.\n", "\n", "This is the beginning of a common unsupervised training algorithm called expectation maximization. It has two steps, expectation and maximization. The expectation step involves what we just did--assigning weights based on the probability of each point being generated by each component. The next step, maximization, is maximizing the probability that the distribution generated these points but performing weighted MLE.\n", "\n", "This process must be iterated until convergence. Sometimes this requires only a single update, but for overlapping distributions (such as this one) it can sometimes take many iterations." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Improvement: 892.969216325\n", "Improvement: 0.786696085199\n", "Improvement: 0.0368066512267\n", "Total Improvement: 893.792719061\n" ] }, { "data": { "text/plain": [ "893.7927190612211" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.fit( data, verbose=True )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multivariate Gaussian Mixture Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can do the same with multivariate distributions just as easily." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mu = np.arange(5)\n", "cov = np.eye(5)\n", "\n", "mgs = [ MultivariateGaussianDistribution( mu*i, cov ) for i in range(5) ]\n", "gmm = GeneralMixtureModel( mgs )" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = numpy.random.randn(1000, 5) * 5\n", "for i in range(5):\n", " data[i::5] += np.arange(5)*i" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets see how well some points fit under the mixture model." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point 0: logp -92.9968217129\n", "Point 1: logp -95.9044220626\n", "Point 2: logp -19.5299139507\n", "Point 3: logp -28.2330753263\n", "Point 4: logp -30.5658500441\n", "Point 5: logp -109.57535738\n", "Point 6: logp -54.4032830472\n", "Point 7: logp -204.647149088\n", "Point 8: logp -228.971188173\n", "Point 9: logp -52.1138274966\n" ] } ], "source": [ "for i in range(10):\n", " print \"Point {}: logp {}\".format( i, gmm.log_probability(data[i]) )" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Improvement: 44541.6683755\n", "Improvement: 30.6096878561\n", "Improvement: 11.5923550805\n", "Improvement: 6.24301245829\n", "Improvement: 3.90149691583\n", "Improvement: 2.65858549903\n", "Improvement: 1.93523543866\n", "Improvement: 1.50763034672\n", "Improvement: 1.25218891462\n", "Improvement: 1.0923188329\n", "Improvement: 0.986870608411\n", "Total Improvement: 44603.4477575\n" ] }, { "data": { "text/plain": [ "44603.44775749489" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gmm.fit(data, verbose=True, stop_threshold=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets see how well the previous points fit." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point 0: logp -15.7580845989\n", "Point 1: logp -16.114044174\n", "Point 2: logp -14.0475691373\n", "Point 3: logp -14.7997614439\n", "Point 4: logp -14.6995141746\n", "Point 5: logp -17.9272784665\n", "Point 6: logp -15.3165691235\n", "Point 7: logp -20.1189037538\n", "Point 8: logp -22.3676868448\n", "Point 9: logp -15.9103605907\n" ] } ], "source": [ "for i in range(10):\n", " print \"Point {}: logp {}\".format( i, gmm.log_probability(data[i]) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like they're being fit significantly better than before! Training works." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discrete Mixture Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to having general mixture models over continuous distributions, we can also have mixture models over discrete distributions. This is useful in many bioinformatics contexts, specifically sequence analysis. Lets use the toy analysis of trying to analyze CG island distribution.\n", "\n", "The problem is the following; DNA is made up of long sequences the four canonical nucleotides, abbreviated 'A', 'C', 'G', and 'T'. These nucleotides are not distributed randomly, and there is significant amounts of structure in the sequence. A major field in bioinformatics is trying to interpret this structure. One structured element is the CG content, where the Cs and the Gs appear more commonly than in the background. If we want to try to determine CG percentages in these islands, we can use a mixture model." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d1 = DiscreteDistribution( {'A' : 0.25, 'C': 0.25, 'G' : 0.25, 'T': 0.25 } ) # Background\n", "d2 = DiscreteDistribution( {'A' : 0.05, 'C': 0.45, 'G' : 0.45, 'T': 0.05 } ) # CG rich regions\n", "gmm = GeneralMixtureModel( [d1, d2] )" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3.4766541840931495" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "seq = numpy.array(list('CGACATCTGACTACGGCGCGCCTACTACTTGATCGATACGGCGTCAGCGACGACGATGATCGGCATCAGTCACTAC'))\n", "gmm.fit(seq)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.3500000000000001,\n", " \"C\" :0.19999999999999993,\n", " \"T\" :0.29166666666666674,\n", " \"G\" :0.15833333333333327\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}\n", " {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.0905172413793103,\n", " \"C\" :0.46551724137931033,\n", " \"T\" :0.07543103448275859,\n", " \"G\" :0.3685344827586208\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}]\n", "\n", "[ 0.56390977 0.43609023]\n" ] } ], "source": [ "print gmm.distributions\n", "print\n", "print numpy.exp(gmm.weights)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like in this case the concept was sound, that there many CG rich regions, but our initial estimates of the percentages were off. We can use a GMM like the one above to both identify and study the composition of these regions at the same time, updating the parameters of the distributions using expectation-maximization. We will go into a more complex way of dong this using HMMs in the next tutorial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Serialization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "General Mixture Models support serialization to JSONs using `to_json()` and `from_json( json )`. This is useful is you want to train a GMM on large amounts of data, taking a significant amount of time, and then use this model in the future without having to repeat this computationally intensive step." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"weights\" : [\n", " -0.5728610146854433,\n", " -0.8299061176753344\n", " ],\n", " \"distributions\" : [\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.3500000000000001,\n", " \"C\" : 0.19999999999999993,\n", " \"T\" : 0.29166666666666674,\n", " \"G\" : 0.15833333333333327\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.0905172413793103,\n", " \"C\" : 0.46551724137931033,\n", " \"T\" : 0.07543103448275859,\n", " \"G\" : 0.3685344827586208\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " }\n", " ],\n", " \"class\" : \"GeneralMixtureModel\"\n", "}\n" ] } ], "source": [ "print gmm.to_json()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not the prettiest thing to look at right now. However, we can easily load it and use it immediately." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.3500000000000001,\n", " \"C\" :0.19999999999999993,\n", " \"T\" :0.29166666666666674,\n", " \"G\" :0.15833333333333327\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "},\n", " {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.0905172413793103,\n", " \"C\" :0.46551724137931033,\n", " \"T\" :0.07543103448275859,\n", " \"G\" :0.3685344827586208\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}], dtype=object)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gmm_2 = GeneralMixtureModel.from_json( gmm.to_json() )\n", "gmm_2.distributions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`gmm_2` is now ready to go, as if it was the original model!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Concluding Remarks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "General Mixture Models are extremely powerful tools for unsupervised learning. pomegranate makes it easy to define, fit, and make predictions using these models. All the examples above have used the same distribution type for all the components of the mixture, but that is not a requirement, if your application requires varied distributions." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 } Tutorial_3_Hidden_Markov_Models.ipynb000066400000000000000000002465761420461712600326270ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/old{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hidden Markov Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hidden Markov models (HMMs) are the flagship of the pomegranate package, in that most time is spent improving their implementation, and these improvements sometimes trickle down into the other algorithms. Lets delve into the features which pomegranate offers.\n", "\n", "Hidden Markov models are a form of structured prediction method which extend general mixture models to sequences of data, where position in the sequence is relevant. If each point in this sequence is completely independent of the other points, then HMMs are not the right tools and GMMs (or more complicated Bayesian networks) may be a better tool.\n", "\n", "The most common examples of HMMs come from bioinformatics and natural language processing. Since I am a bioinformatician, I will predominately use examples from bioinformatics.\n", "\n", "## GMMs vs HMMs in the CG rich region example\n", "\n", "Lets take the simplified example of CG island detection on a sequence of DNA. DNA is made up of the four canonical nucleotides, abbreviated 'A', 'C', 'G', and 'T'. Specific organizations of these nucleotides encode enough information to build you, a human being. One simple region in the genome is called the 'CG' island, where the nucleotides 'C' and 'G' are enriched. Lets compare the predictions of a GMM with the predictions of a HMM, to both understand conceptually the differences between the two, and to see how easy it is to use pomegranate." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/nfliu/miniconda2/envs/pom/lib/python2.7/site-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['log', 'random']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] } ], "source": [ "from pomegranate import *\n", "import numpy as np\n", "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "seq = list('CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC')\n", "\n", "d1 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})\n", "d2 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})\n", "\n", "s1 = State( d1, name='background' )\n", "s2 = State( d2, name='CG island' )\n", "\n", "gmm = GeneralMixtureModel( [d1, d2] )\n", "hmm = HiddenMarkovModel()\n", "hmm.add_states(s1, s2)\n", "hmm.add_transition( hmm.start, s1, 0.5 )\n", "hmm.add_transition( hmm.start, s2, 0.5 )\n", "hmm.add_transition( s1, s1, 0.5 )\n", "hmm.add_transition( s1, s2, 0.5 )\n", "hmm.add_transition( s2, s1, 0.5 )\n", "hmm.add_transition( s2, s2, 0.5 )\n", "hmm.bake()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sequence: CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC\n", "gmm pred: 110100101010010111110111101011110100001011110001111\n", "hmm pred: 001011010101101000001000010100001011110100001110000\n" ] } ], "source": [ "gmm_predictions = gmm.predict( np.array(seq) )\n", "hmm_predictions = hmm.predict( seq )\n", "\n", "print \"sequence: {}\".format( ''.join( seq ) )\n", "print \"gmm pred: {}\".format( ''.join( map( str, gmm_predictions ) ) )\n", "print \"hmm pred: {}\".format( ''.join( map( str, hmm_predictions ) ) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: The HMM and GMM predictions may be the inverse of each other, because HMM states undergo a topological sort in order to properly handle silent states (more later), which can cause the order they were inserted into the model. \n", "\n", "Your first reaction may to say \"But Jacob, you just said that HMMs and GMMs are different. Why shold I make a HMM when making a GMM is so easy?\". \n", "\n", "My point in showing you this is that a dense HMM with equal probabilities between each state is ~equivalent~ to a GMM. However, this framework gives us great flexibility to add prior knowledge, whereas a GMM doesn't. If we look at the predictions, we see that it's bifurcating between \"background\" and \"CG island\" very quickly--in essence, calling every C or G a 'CG island'. This is not likely to be true. We know that CG islands have some As and Ts in them, and background sequence has Cs and Gs. We can change the transition probabilities to account for this, and prevent switching from occurring too rapidly." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hmm = HiddenMarkovModel()\n", "hmm.add_states(s1, s2)\n", "hmm.add_transition( hmm.start, s1, 0.5 )\n", "hmm.add_transition( hmm.start, s2, 0.5 )\n", "hmm.add_transition( s1, s1, 0.9 )\n", "hmm.add_transition( s1, s2, 0.1 )\n", "hmm.add_transition( s2, s1, 0.1 )\n", "hmm.add_transition( s2, s2, 0.9 )\n", "hmm.bake()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sequence: CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC\n", "gmm pred: 110100101010010111110111101011110100001011110001111\n", "hmm pred: 111111111111111000000000000000011111111111111110000\n", "\n", "hmm state 0: CG island\n", "hmm state 1: background\n" ] } ], "source": [ "hmm_predictions = hmm.predict( seq )\n", "\n", "print \"sequence: {}\".format( ''.join( seq ) )\n", "print \"gmm pred: {}\".format( ''.join( map( str, gmm_predictions ) ) )\n", "print \"hmm pred: {}\".format( ''.join( map( str, hmm_predictions ) ) )\n", "print\n", "print \"hmm state 0: {}\".format( hmm.states[0].name )\n", "print \"hmm state 1: {}\".format( hmm.states[1].name )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This seems far more reasonable. There is a single CG island surrounded by background sequence, and something at the end. If we knew that CG islands cannot occur at the end of sequences, we need only modify the underlying structure of the HMM in order to say that the sequence must end from the background state." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hmm = HiddenMarkovModel()\n", "hmm.add_states(s1, s2)\n", "hmm.add_transition( hmm.start, s1, 0.5 )\n", "hmm.add_transition( hmm.start, s2, 0.5 )\n", "hmm.add_transition( s1, s1, 0.89 )\n", "hmm.add_transition( s1, s2, 0.10 )\n", "hmm.add_transition( s1, hmm.end, 0.01 )\n", "hmm.add_transition( s2, s1, 0.1 )\n", "hmm.add_transition( s2, s2, 0.9 )\n", "hmm.bake()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sequence: CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC\n", "gmm pred: 110100101010010111110111101011110100001011110001111\n", "hmm pred: 111111111111111000000000000000011111111111111111111\n", "\n", "hmm state 0: CG island\n", "hmm state 1: background\n" ] } ], "source": [ "hmm_predictions = hmm.predict( seq )\n", "\n", "print \"sequence: {}\".format( ''.join( seq ) )\n", "print \"gmm pred: {}\".format( ''.join( map( str, gmm_predictions ) ) )\n", "print \"hmm pred: {}\".format( ''.join( map( str, hmm_predictions ) ) )\n", "print\n", "print \"hmm state 0: {}\".format( hmm.states[0].name )\n", "print \"hmm state 1: {}\".format( hmm.states[1].name )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like we managed to get rid of that pesky end (again, the numbers may have flipped, look at the indices). Modifying transition probabilities and using non-dense graphical structures are two major ways in which HMMs account for data in a sequence not being independent and identically distributed (i.i.d.). In fact, in most applications, the graphical structure of a HMM is very sparse. \n", "\n", "If we want a more probabilistic view of what's going on, we can get the probability of each symbol in the sequence being in each of the states in the model easily. This is useful to get a soft estimate of classification, which allows us to include confidence as well as prediction. Values close to 50-50 get masked when you make hard classifications, but this uncertainty can be passed to future applications if you use soft assignments. Each row in the matrix is one symbol in the sequence, and the columns correspond to the two states identified above (CG island or background)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.4827054 0.5172946 ]\n", " [ 0.42204064 0.57795936]\n", " [ 0.28598775 0.71401225]\n", " [ 0.25756456 0.74243544]\n", " [ 0.16040038 0.83959962]\n", " [ 0.13871983 0.86128017]\n", " [ 0.17217854 0.82782146]\n", " [ 0.14750165 0.85249835]\n", " [ 0.18021186 0.81978814]\n", " [ 0.15446181 0.84553819]\n", " [ 0.18788044 0.81211956]\n", " [ 0.16252887 0.83747113]\n", " [ 0.19841088 0.80158912]\n", " [ 0.32919701 0.67080299]\n", " [ 0.38366073 0.61633927]\n", " [ 0.58044619 0.41955381]\n", " [ 0.69075524 0.30924476]\n", " [ 0.74653183 0.25346817]\n", " [ 0.76392808 0.23607192]\n", " [ 0.7479817 0.2520183 ]\n", " [ 0.69407484 0.30592516]\n", " [ 0.74761829 0.25238171]\n", " [ 0.76321595 0.23678405]\n", " [ 0.74538467 0.25461533]\n", " [ 0.68896078 0.31103922]\n", " [ 0.57760471 0.42239529]\n", " [ 0.58391467 0.41608533]\n", " [ 0.53953778 0.46046222]\n", " [ 0.6030831 0.3969169 ]\n", " [ 0.61516689 0.38483311]\n", " [ 0.57928847 0.42071153]\n", " [ 0.48505793 0.51494207]\n", " [ 0.30518744 0.69481256]\n", " [ 0.25379428 0.74620572]\n", " [ 0.12610747 0.87389253]\n", " [ 0.08105965 0.91894035]\n", " [ 0.07637934 0.92362066]\n", " [ 0.10767468 0.89232532]\n", " [ 0.20431225 0.79568775]\n", " [ 0.23273876 0.76726124]\n", " [ 0.35851961 0.64148039]\n", " [ 0.40985726 0.59014274]\n", " [ 0.40161837 0.59838163]\n", " [ 0.33141706 0.66858294]\n", " [ 0.17892403 0.82107597]\n", " [ 0.12850549 0.87149451]\n", " [ 0.13285026 0.86714974]\n", " [ 0.19603531 0.80396469]\n", " [ 0.19760431 0.80239569]\n", " [ 0.13801161 0.86198839]\n", " [ 0. 1. ]]\n" ] } ], "source": [ "print hmm.predict_proba( seq )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a corresponding `hmm.predict_log_proba` method present if you want to get the log values. These are the emission probability values calculated by the forward backward algorithm, and can also be retrieved by calling `hmm.forward_backward( seq )`, which returns both the emission and the transition probability tables.\n", "\n", "Lets take a look at these tables!" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 15.78100555 2.89559314 0. 0. ]\n", " [ 2.41288774 28.91051356 0. 1. ]\n", " [ 0.4827054 0.5172946 0. 0. ]\n", " [ 0. 0. 0. 0. ]]\n" ] } ], "source": [ "trans, ems = hmm.forward_backward( seq )\n", "print trans" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the transition table, which has the soft count of the number of transitions across an edge in the model given a single sequence. It is a square matrix of size equal to the number of states (including start and end state), with number of transitions from (row_id) to (column_id). This is exemplified by the 1.0 in the first row, indicating that there is one transition from background state to the end state, as that's the only way to reach the end state. However, the third (or fourth, depending on ordering) row is the transitions from the start state, and it only slightly favors the background state. These counts are not normalized to the length of the input sequence, but can easily be done so by dividing by row sums, column sums, or entire table sums, depending on your application.\n", "\n", "A possible reason not to normalize is to run several sequences through and add up their tables, because normalizing in the end and extracting some domain knowledge. It is extremely useful in practice. For example, we can see that there is an expectation of 2.8956 transitions from CG island to background, and 2.4 from background to CG island. This could be used to infer that there are ~2-3 edges, which makes sense if you consider that the start and end of the sequence seem like they might be part of the CG island states except for the strict transition probabilities used (look at the first few rows of the emission table above.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've been using the forward backward algorithm and maximum a posteriori for decoding thus far, however maximum a posteriori decoding has the side effect that it is possible that it predicts impossible sequences in some edge cases. An alternative is Viterbi decoding, which at each step takes the most likely path, instead of sum of all paths, to produce hard assignments." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sequence: CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC\n", "gmm pred: 110100101010010111110111101011110100001011110001111\n", "hmm pred: 111111111111111111111111111111111111111111111111111\n", "\n", "hmm state 0: CG island\n", "hmm state 1: background\n" ] } ], "source": [ "hmm_predictions = hmm.predict( seq, algorithm='viterbi' )[1:-1]\n", "\n", "print \"sequence: {}\".format( ''.join( seq ) )\n", "print \"gmm pred: {}\".format( ''.join( map( str, gmm_predictions ) ) )\n", "print \"hmm pred: {}\".format( ''.join( map( str, hmm_predictions ) ) )\n", "print\n", "print \"hmm state 0: {}\".format( hmm.states[0].name )\n", "print \"hmm state 1: {}\".format( hmm.states[1].name )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see here a case in which it does not do too well. The Viterbi path can be more conservative in its transitions due to the hard assignments it makes. In essence, if multiple possibile paths are possible at a given point, it takes the most likely path, even if the sum of all other paths is greater than the sum of that path. In problems with a lower signal to noise ratio, this can mask the signal. As a side note, we can use the following to get the maximum a posteriori and Viterbi paths:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "v_logp, v_seq = hmm.viterbi( seq )\n", "m_logp, m_seq = hmm.maximum_a_posteriori( seq )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sequence predicted is a tuple of (state id, state object) for every state in the predicted path. The predict method simply takes the state ids from this path and returns those as an array. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sequence Alignment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets move on to a more complicated structure, that of a profile HMM. A profile HMM is used to align a sequence to a reference 'profile', where the reference profile can either be a single sequence, or an alignment of many sequences (such as a reference genome). In essence, this profile has a 'match' state for every position in the reference profile, and 'insert' state, and a 'delete' state. The insert state allows the external sequence to have an insertion into the sequence without throwing off the entire alignment, such as the following:\n", "\n", "`ACCG : Sequence`
\n", "`|| |`
\n", "`AC-G : Reference`\n", "\n", "or a deletion, which is the opposite:\n", "\n", "`A-G : Sequence`
\n", "`| |`
\n", "`ACG : Reference`\n", "\n", "The bars in the middle refer to a perfect match, whereas the lack of a bar means either a deletion/insertion, or a mismatch. A mismatch is where two positions are aligned together, but do not match. This models the biological phenomena of mutation, where one nucleotide can convert to another over time. It is usually more likely in biological sequences that this type of mutation occurs than that the nucleotide was deleted from the sequence (shifting all nucleotides over by one) and then another was inserted at the exact location (moving all nucleotides over again). Since we are using a probabilistic model, we get to define these probabilities through the use of distributions! If we want to model mismatches, we can just set our 'match' state to have an appropriate distribution with non-zero probabilities over mismatches. \n", "\n", "Lets now create a three nucleotide profile HMM, which models the sequence 'ACT'. We will fuzz this a little bit in the match states, pretending to have some prior information about what mutations occur at each position. If you don't have any information, setting a uniform, small, value over the other values is usually okay." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = HiddenMarkovModel( \"Global Alignment\")\n", "\n", "# Define the distribution for insertions\n", "i_d = DiscreteDistribution( { 'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25 } )\n", "\n", "# Create the insert states\n", "i0 = State( i_d, name=\"I0\" )\n", "i1 = State( i_d, name=\"I1\" )\n", "i2 = State( i_d, name=\"I2\" )\n", "i3 = State( i_d, name=\"I3\" )\n", "\n", "# Create the match states\n", "m1 = State( DiscreteDistribution({ \"A\": 0.95, 'C': 0.01, 'G': 0.01, 'T': 0.02 }) , name=\"M1\" )\n", "m2 = State( DiscreteDistribution({ \"A\": 0.003, 'C': 0.99, 'G': 0.003, 'T': 0.004 }) , name=\"M2\" )\n", "m3 = State( DiscreteDistribution({ \"A\": 0.01, 'C': 0.01, 'G': 0.01, 'T': 0.97 }) , name=\"M3\" )\n", "\n", "# Create the delete states\n", "d1 = State( None, name=\"D1\" )\n", "d2 = State( None, name=\"D2\" )\n", "d3 = State( None, name=\"D3\" )\n", "\n", "# Add all the states to the model\n", "model.add_states( [i0, i1, i2, i3, m1, m2, m3, d1, d2, d3 ] )\n", "\n", "# Create transitions from match states\n", "model.add_transition( model.start, m1, 0.9 )\n", "model.add_transition( model.start, i0, 0.1 )\n", "model.add_transition( m1, m2, 0.9 )\n", "model.add_transition( m1, i1, 0.05 )\n", "model.add_transition( m1, d2, 0.05 )\n", "model.add_transition( m2, m3, 0.9 )\n", "model.add_transition( m2, i2, 0.05 )\n", "model.add_transition( m2, d3, 0.05 )\n", "model.add_transition( m3, model.end, 0.9 )\n", "model.add_transition( m3, i3, 0.1 )\n", "\n", "# Create transitions from insert states\n", "model.add_transition( i0, i0, 0.70 )\n", "model.add_transition( i0, d1, 0.15 )\n", "model.add_transition( i0, m1, 0.15 )\n", "\n", "model.add_transition( i1, i1, 0.70 )\n", "model.add_transition( i1, d2, 0.15 )\n", "model.add_transition( i1, m2, 0.15 )\n", "\n", "model.add_transition( i2, i2, 0.70 )\n", "model.add_transition( i2, d3, 0.15 )\n", "model.add_transition( i2, m3, 0.15 )\n", "\n", "model.add_transition( i3, i3, 0.85 )\n", "model.add_transition( i3, model.end, 0.15 )\n", "\n", "# Create transitions from delete states\n", "model.add_transition( d1, d2, 0.15 )\n", "model.add_transition( d1, i1, 0.15 )\n", "model.add_transition( d1, m2, 0.70 ) \n", "\n", "model.add_transition( d2, d3, 0.15 )\n", "model.add_transition( d2, i2, 0.15 )\n", "model.add_transition( d2, m3, 0.70 )\n", "\n", "model.add_transition( d3, i3, 0.30 )\n", "model.add_transition( d3, model.end, 0.70 )\n", "\n", "# Call bake to finalize the structure of the model.\n", "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets try to align some sequences to it and see what happens!" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sequence: 'ACT' -- Log Probability: -0.513244900357 -- Path: M1 M2 M3\n", "Sequence: 'GGC' -- Log Probability: -11.0481012413 -- Path: I0 I0 D1 M2 D3\n", "Sequence: 'GAT' -- Log Probability: -9.12551967402 -- Path: I0 M1 D2 M3\n", "Sequence: 'ACC' -- Log Probability: -5.08795587886 -- Path: M1 M2 M3\n" ] } ], "source": [ "for sequence in map( list, ('ACT', 'GGC', 'GAT', 'ACC') ):\n", " logp, path = model.viterbi( sequence )\n", " print \"Sequence: '{}' -- Log Probability: {} -- Path: {}\".format(\n", " ''.join( sequence ), logp, \" \".join( state.name for idx, state in path[1:-1] ) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first and last sequence are entirely matches, meaning that it thinks the most likely alignment between the profile ACT and ACT is A-A, C-C, and T-T, which makes sense, and the most likely alignment between ACT and ACC is A-A, C-C, and T-C, which includes a mismatch. Essentially, it's more likely that there's a T-C mismatch at the end then that there was a deletion of a T at the end of the sequence, and a separate insertion of a C.\n", "\n", "The two middle sequences don't match very well, as expected! G's are not very likely in this profile at all. It predicts that the two G's are inserts, and that the C matches the C in the profile, before hitting the delete state because it can't emit a T. The third sequence thinks that the G is an insert, as expected, and then aligns the A and T in the sequence to the A and T in the master sequence, missing the middle C in the profile.\n", "\n", "By using deletes, we can handle other sequences which are shorter than three characters. Lets look at some more sequences of different lengths." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sequence: 'A' -- Log Probability: -5.40618101242 -- Path: M1 D2 D3\n", "Sequence: 'GA' -- Log Probability: -10.8868199358 -- Path: I0 M1 D2 D3\n", "Sequence: 'AC' -- Log Probability: -3.62447187905 -- Path: M1 M2 D3\n", "Sequence: 'AT' -- Log Probability: -3.64488075068 -- Path: M1 D2 M3\n", "Sequence: 'ATCC' -- Log Probability: -10.6743329646 -- Path: M1 D2 M3 I3 I3\n", "Sequence: 'ACGTG' -- Log Probability: -10.3938248352 -- Path: M1 M2 I2 I2 I2 D3\n", "Sequence: 'ATTT' -- Log Probability: -8.67126440175 -- Path: M1 I1 I1 D2 M3\n", "Sequence: 'TACCCTC' -- Log Probability: -16.9034517961 -- Path: I0 I0 I0 I0 D1 M2 M3 I3\n", "Sequence: 'TGTCAACACT' -- Log Probability: -16.4516996541 -- Path: I0 I0 I0 I0 I0 I0 I0 M1 M2 M3\n" ] } ], "source": [ "for sequence in map( list, ('A', 'GA', 'AC', 'AT', 'ATCC', 'ACGTG', 'ATTT', 'TACCCTC', 'TGTCAACACT') ):\n", " logp, path = model.viterbi( sequence )\n", " print \"Sequence: '{}' -- Log Probability: {} -- Path: {}\".format(\n", " ''.join( sequence ), logp, \" \".join( state.name for idx, state in path[1:-1] ) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, more of the same expected. You'll notice most of the use of insertion states are at I0, because most of the insertions are at the beginning of the sequence. It's more probable to simply stay in I0 at the beginning instead of go from I0 to D1 to I1, or going to another insert state along there. You'll see other insert states used when insertions occur in other places in the sequence, like 'ATTT' and 'ACGTG'.\n", "Now that we have the path, we need to convert it into an alignment, which is significantly more informative to look at." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sequence: A, Log Probability: -5.40618101242\n", "ACT\n", "A--\n", "\n", "Sequence: GA, Log Probability: -10.8868199358\n", "-ACT\n", "GA--\n", "\n", "Sequence: AC, Log Probability: -3.62447187905\n", "ACT\n", "AC-\n", "\n", "Sequence: AT, Log Probability: -3.64488075068\n", "ACT\n", "A-T\n", "\n", "Sequence: ATCC, Log Probability: -10.6743329646\n", "ACT--\n", "A-TCC\n", "\n", "Sequence: ACGTG, Log Probability: -10.3938248352\n", "AC---T\n", "ACGTG-\n", "\n", "Sequence: ATTT, Log Probability: -8.67126440175\n", "A--CT\n", "ATT-T\n", "\n", "Sequence: TACCCTC, Log Probability: -16.9034517961\n", "----ACT-\n", "TACC-CTC\n", "\n", "Sequence: TGTCAACACT, Log Probability: -16.4516996541\n", "-------ACT\n", "TGTCAACACT\n", "\n" ] } ], "source": [ "def path_to_alignment( x, y, path ):\n", " \"\"\"\n", " This function will take in two sequences, and the ML path which is their alignment,\n", " and insert dashes appropriately to make them appear aligned. This consists only of\n", " adding a dash to the model sequence for every insert in the path appropriately, and\n", " a dash in the observed sequence for every delete in the path appropriately.\n", " \"\"\"\n", " \n", " for i, (index, state) in enumerate( path[1:-1] ):\n", " name = state.name\n", " \n", " if name.startswith( 'D' ):\n", " y = y[:i] + '-' + y[i:]\n", " elif name.startswith( 'I' ):\n", " x = x[:i] + '-' + x[i:]\n", "\n", " return x, y\n", "\n", "for sequence in map( list, ('A', 'GA', 'AC', 'AT', 'ATCC', 'ACGTG', 'ATTT', 'TACCCTC', 'TGTCAACACT') ):\n", " logp, path = model.viterbi( sequence )\n", " x, y = path_to_alignment( 'ACT', ''.join(sequence), path )\n", " \n", " print \"Sequence: {}, Log Probability: {}\".format( ''.join(sequence), logp )\n", " print \"{}\\n{}\".format( x, y )\n", " print" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to getting this alignment, we can do some interesting things with this model! Lets score every sequence of length 5 of less and see what the distribution looks like." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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VFhYOO/PqdJYsWaInn3xSkyZNktvt1po1a1RVVaUlS5aMqqZQKKxgkC9YJF+q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\n/PLLiGmbN2+msLAQgEmTJrFp06bwvAMHDlBVVcXEiRN7qHo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ttyOm5eTk8P7773PnnXcS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\nUfL/AQmF7Tg6KczUAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import itertools as it\n", "import seaborn as sns\n", "\n", "sequences = reduce( lambda x, y: x+y, [[ seq for seq in it.product( 'ACGT', repeat=i ) ] for i in xrange( 1,6 )] )\n", "scores = map( model.log_probability, sequences )\n", "\n", "plt.figure( figsize=(10,5) )\n", "sns.kdeplot( numpy.array( scores ), shade=True )\n", "plt.ylabel('Density')\n", "plt.xlabel('Log Probability')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training Hidden Markov Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two main algorithms for training hidden Markov models-- Baum Welch (structured version of Expectation Maximization), and Viterbi training. Since we don't start off with labels on the data, these are both unsupervised training algorithms. In order to assign labels, Baum Welch uses EM to assign soft labels (weights in this case) to each point belonging to each state, and then using weighted MLE estimates to update the distributions. Viterbi assigns hard labels to each observation using the Viterbi algorithm, and then updates the distributions based on these hard labels.\n", "\n", "pomegranate is extremely well featured when it comes to regularization methods for training, supporting tied emissions and edges, edge and emission inertia, freezing nodes or edges, edge pseudocounts, and multithreaded training. Lets look at some examples of the following:\n", "\n", "### Tied Emissions\n", "\n", "Sometimes we want to say that multiple states model the same phenomena, but are simply at different points in the graph because we are utilizing complicated edge structure. An example is in the example of the global alignment HMM we saw. All insert states represent the same phenomena, which is nature randomly inserting a nucleotide, and this probability should be the same regardless of position. However, we can't simply have a single insert state, or we'd be allowed to transition from any match state to any other match state.\n", "\n", "You can tie emissions together simply by passing the same distribution object to multiple states. That's it." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "d = NormalDistribution( 5, 2 )\n", "\n", "s1 = State( d, name=\"Tied1\" )\n", "s2 = State( d, name=\"Tied2\" )\n", "\n", "s3 = State( NormalDistribution( 5, 2 ), name=\"NotTied1\" )\n", "s4 = State( NormalDistribution( 5, 2 ), name=\"NotTied2\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You have now indicated that these two states are tied, and when training, the weights of all points going to s2 will be added to the weights of all points going to s1 when updating d. As a side note, this is implemented in a computationally efficient manner such that d will only be updated once, not twice (but giving the same result). s3 and s4 are not tied together, because while they have the same distribution, it is not the same python object.\n", "\n", "### Tied Edges\n", "\n", "Edges can be tied together for the same reason. If you have a modular structure to your HMM, perhaps you believe this repeating structure doesn't (or shouldn't) have a position specific edge structure. You can do this simply by adding a group when you add transitions." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = HiddenMarkovModel()\n", "model.add_states( [s1, s2] )\n", "model.add_transition( model.start, s1, 0.5, group='a' )\n", "model.add_transition( model.start, s2, 0.5, group='b' )\n", "model.add_transition( s1, s2, 0.5, group='a' )\n", "model.add_transition( s2, s1, 0.5, group='b' )\n", "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above model doesn't necessarily make sense, but it shows how simple it is to tie edges as well. You can go ahead and train normally from this point, without needing to change any code.\n", "\n", "### Inertia\n", "\n", "The next options are inertia on edges or on distributions. This simply means that you update your parameters as (previous_parameter * inertia) + (new_parameter * (1-inertia) ). It is a way to prevent your updates from overfitting immediately. You can specify this in the train function using either `edge_inertia` or `distribution_inertia`. These default to 0, with 1 being the maximum, meaning that you don't update based on new evidence, the same as freezing a distribution or the edges." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training improvement: 1.42546236476\n", "Training improvement: 0.150770454545\n", "Training improvement: 0.0135693409091\n", "Training improvement: 0.00122124068182\n", "Training improvement: 0.000109911661365\n", "Training improvement: 9.89204952262e-06\n", "Training improvement: 8.90284457711e-07\n", "Training improvement: 8.01255986005e-08\n", "Training improvement: 7.21130533066e-09\n", "Training improvement: 6.49016840271e-10\n", "Total Training Improvement: 1.59114418288\n" ] }, { "data": { "text/plain": [ "1.5911441828758832" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit( [[5, 2, 3, 4], [5, 7, 2, 3, 5]], distribution_inertia=0.3, edge_inertia=0.25 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pseudocounts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another way of regularizing your model is to add pseudocounts to your edges (which have non-zero probabilities). When updating your edges in the future, you add this pseudocount to the count of transitions across that edge in the future. This gives a more Bayesian estimate of the edge probability, and is useful if you have a large model and don't expect to cross most of the edges with your training data. An example might be a complicated profile HMM, where you don't expect to see deletes or inserts at all in your training data, but don't want to change from the default values.\n", "\n", "In pomegranate, pseudocounts default to the initial probabilities, so that if you don't see data, the edge values simply aren't updated. You can define both edge specific pseudocounts when you define the transition. When you train, you must define `use_pseudocount=True`." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training improvement: 3.42346975202\n", "Training improvement: 0.217661114963\n", "Training improvement: 0.031578660226\n", "Training improvement: 0.00659919633505\n", "Training improvement: 0.00190080724972\n", "Total Training Improvement: 3.6812095308\n" ] }, { "data": { "text/plain": [ "3.6812095307959467" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = State( NormalDistribution( 3, 1 ), name=\"s1\" )\n", "s2 = State( NormalDistribution( 6, 2 ), name=\"s2\" )\n", "\n", "model = HiddenMarkovModel()\n", "model.add_states( [s1, s2] )\n", "model.add_transition( model.start, s1, 0.5, pseudocount=4.2 )\n", "model.add_transition( model.start, s2, 0.5, pseudocount=1.3 )\n", "model.add_transition( s1, s2, 0.5, pseudocount=5.2 )\n", "model.add_transition( s2, s1, 0.5, pseudocount=0.9 )\n", "model.bake()\n", "model.fit( [[5, 2, 3, 4], [5, 7, 2, 3, 5]], max_iterations=5, use_pseudocount=True )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The other way is to put a blanket pseudocount on all edges." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training improvement: 3.32270811471\n", "Training improvement: 0.160909798897\n", "Training improvement: 0.0329521413094\n", "Training improvement: 0.00953914051447\n", "Training improvement: 0.00315874050675\n", "Total Training Improvement: 3.52926793594\n" ] }, { "data": { "text/plain": [ "3.5292679359352164" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = State( NormalDistribution( 3, 1 ), name=\"s1\" )\n", "s2 = State( NormalDistribution( 6, 2 ), name=\"s2\" )\n", "\n", "model = HiddenMarkovModel()\n", "model.add_states( [s1, s2] )\n", "model.add_transition( model.start, s1, 0.5 )\n", "model.add_transition( model.start, s2, 0.5 )\n", "model.add_transition( s1, s2, 0.5 )\n", "model.add_transition( s2, s1, 0.5 )\n", "model.bake()\n", "model.fit( [[5, 2, 3, 4], [5, 7, 2, 3, 5]], max_iterations=5, transition_pseudocount=20, use_pseudocount=True )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that there isn't as much of an improvement. This is part of regularization, though. We sacrifice fitting the data exactly in order for our model to generalize better to future data. The majority of the training improvement is likely coming from the emissions better fitting the data, though." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multithreaded Training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since pomegranate is implemented in cython, the majority of functions are written with the GIL released. A benefit of doing this is that we can use multithreading in order to make some computationally intensive tasks take less time. However, a downside is that python doesn't play nicely with multithreading, and so there are some cases where training using multithreading can make your model training take significantly longer. I investigate this in an early multithreading pull request here. Things have improved since then, but the gist is that if you have a small model (less than 15 states), it may be detrimental, but the larger your model is, the more it scales towards getting a speed improvement exactly the number of threads you use. You can specify multithreading using the `n_jobs` keyword. All structures in pomegranate are thread safe, so you don't need to worry about race conditions." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training improvement: 6.91636682627\n", "Training improvement: 0.807997703056\n", "Training improvement: 0.225451019863\n", "Training improvement: 0.0142426402922\n", "Training improvement: 0.000547351061158\n", "Total Training Improvement: 7.96460554054\n" ] }, { "data": { "text/plain": [ "7.964605540540248" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = State( NormalDistribution( 3, 1 ), name=\"s1\" )\n", "s2 = State( NormalDistribution( 6, 2 ), name=\"s2\" )\n", "\n", "model = HiddenMarkovModel()\n", "model.add_states( [s1, s2] )\n", "model.add_transition( model.start, s1, 0.5 )\n", "model.add_transition( model.start, s2, 0.5 )\n", "model.add_transition( s1, s2, 0.5 )\n", "model.add_transition( s2, s1, 0.5 )\n", "model.bake()\n", "model.fit( [[5, 2, 3, 4, 7, 3, 6, 3, 5, 2, 4], [5, 7, 2, 3, 5, 1, 3, 5, 6, 2]], max_iterations=5 )" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training improvement: 6.91636682627\n", "Training improvement: 0.807997703056\n", "Training improvement: 0.225451019863\n", "Training improvement: 0.0142426402922\n", "Training improvement: 0.000547351061158\n", "Total Training Improvement: 7.96460554054\n" ] }, { "data": { "text/plain": [ "7.964605540540248" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = State( NormalDistribution( 3, 1 ), name=\"s1\" )\n", "s2 = State( NormalDistribution( 6, 2 ), name=\"s2\" )\n", "\n", "model = HiddenMarkovModel()\n", "model.add_states( [s1, s2] )\n", "model.add_transition( model.start, s1, 0.5 )\n", "model.add_transition( model.start, s2, 0.5 )\n", "model.add_transition( s1, s2, 0.5 )\n", "model.add_transition( s2, s1, 0.5 )\n", "model.bake()\n", "model.fit( [[5, 2, 3, 4, 7, 3, 6, 3, 5, 2, 4], [5, 7, 2, 3, 5, 1, 3, 5, 6, 2]], max_iterations=5, n_jobs=4 )" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Serialization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "General Mixture Models support serialization to JSONs using `to_json()` and `from_json( json )`. This is useful is you want to train a GMM on large amounts of data, taking a significant amount of time, and then use this model in the future without having to repeat this computationally intensive step (sounds familiar by now). Lets look at the original CG island model, since it's significantly smaller." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "seq = list('CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC')\n", "\n", "d1 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})\n", "d2 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})\n", "\n", "s1 = State( d1, name='background' )\n", "s2 = State( d2, name='CG island' )\n", "\n", "hmm = HiddenMarkovModel()\n", "hmm.add_states(s1, s2)\n", "hmm.add_transition( hmm.start, s1, 0.5 )\n", "hmm.add_transition( hmm.start, s2, 0.5 )\n", "hmm.add_transition( s1, s1, 0.5 )\n", "hmm.add_transition( s1, s2, 0.5 )\n", "hmm.add_transition( s2, s1, 0.5 )\n", "hmm.add_transition( s2, s2, 0.5 )\n", "hmm.bake()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"end\" : {\n", " \"distribution\" : null,\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"None-end\"\n", " },\n", " \"name\" : \"None\",\n", " \"distribution ties\" : [],\n", " \"silent_index\" : 2,\n", " \"end_index\" : 3,\n", " \"states\" : [\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.1,\n", " \"C\" : 0.4,\n", " \"T\" : 0.1,\n", " \"G\" : 0.4\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"CG island\"\n", " },\n", " {\n", " \"distribution\" : {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " {\n", " \"A\" : 0.25,\n", " \"C\" : 0.25,\n", " \"T\" : 0.25,\n", " \"G\" : 0.25\n", " }\n", " ],\n", " \"name\" : \"DiscreteDistribution\"\n", " },\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"background\"\n", " },\n", " {\n", " \"distribution\" : null,\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"None-start\"\n", " },\n", " {\n", " \"distribution\" : null,\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"None-end\"\n", " }\n", " ],\n", " \"start\" : {\n", " \"distribution\" : null,\n", " \"class\" : \"State\",\n", " \"weight\" : 1.0,\n", " \"name\" : \"None-start\"\n", " },\n", " \"edges\" : [\n", " [\n", " 0,\n", " 1,\n", " 0.5,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 0,\n", " 0,\n", " 0.5,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 2,\n", " 1,\n", " 0.5,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 2,\n", " 0,\n", " 0.5,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 1,\n", " 1,\n", " 0.5,\n", " 0.5,\n", " null\n", " ],\n", " [\n", " 1,\n", " 0,\n", " 0.5,\n", " 0.5,\n", " null\n", " ]\n", " ],\n", " \"class\" : \"HiddenMarkovModel\",\n", " \"start_index\" : 2\n", "}\n" ] } ], "source": [ "print hmm.to_json()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-69.7012190974\n" ] } ], "source": [ "seq = list('CGACTACTGACTACTCGCCGACGCGACTGCCGTCTATACTGCGCATACGGC')\n", "print hmm.log_probability( seq )" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-69.7012190974\n" ] } ], "source": [ "hmm_2 = HiddenMarkovModel.from_json( hmm.to_json() )\n", "print hmm_2.log_probability( seq )" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 } Tutorial_4_Bayesian_Networks.ipynb000066400000000000000000002610731420461712600322270ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/old{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bayesian Networks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bayesian networks are a powerful inference tool, in which a set of variables are represented as nodes, and the lack of an edge represents a conditional independence statement between the two variables, and an edge represents a dependence between the two variables. One of the powerful components of a Bayesian network is the ability to infer the values of certain variables, given observed values for another set of variables. These are referred to as the 'hidden' and 'observed' variables respectively, and need not be set at the time the network is created. The same network can have a different set of variables be hidden or observed between two data points. The more values which are observed, the closer the inferred values will be to the truth.\n", "\n", "While Bayesian networks can have extremely complex emission probabilities, usually Gaussian or conditional Gaussian distributions, pomegranate currently supports only discrete Bayesian networks. Bayesian networks are explicitly turned into Factor Graphs when inference is done, wherein the Bayesian network is turned into a bipartite graph with all variables having marginal nodes on one side, and joint tables on the other.\n", "\n", "If you didn't understand that, it's okay! Lets get down to a simple example, the Monty Hall example. The Monty Hall problem arose from the gameshow Let's Make a Deal, where a guest had to choose which one of three doors had a prize behind it. The twist was that after the guest chose, the host, originally Monty Hall, would then open one of the doors the guest did not pick and ask if the guest wanted to switch which door they had picked. Initial inspection may lead you to believe that if there are only two doors left, there is a 50-50 chance of you picking the right one, and so there is no advantage one way or the other. However, it has been proven both through simulations and analytically that there is in fact a 66% chance of getting the prize if the guest switches their door, regardless of the door they initially went with.\n", "\n", "We can reproduce this result using Bayesian networks with three nodes, one for the guest, one for the prize, and one for the door Monty chooses to open. The door the guest initially chooses and the door the prize is behind are completely random processes across the three doors, but the door which Monty opens is dependent on both the door the guest chooses (it cannot be the door the guest chooses), and the door the prize is behind (it cannot be the door with the prize behind it).\n", "\n", "## Defining a Bayesian Network in pomegranate\n", "\n", "To create the Bayesian network in pomegranate, we first create the distributions which live in each node in the graph. For a discrete (aka categorical) bayesian network we use DiscreteDistribution objects for the root nodes and ConditionalProbabilityTable objects for the inner and leaf nodes. The columns in a ConditionalProbabilityTable correspond to the order in which the parents (the second argument) are specified, and the last column is the value the ConditionalProbabilityTable itself takes. In the case below, the first column corresponds to the value 'guest' takes, then the value 'prize' takes, and then the value that 'monty' takes. 'B', 'C', 'A' refers then to the probability that Monty reveals door 'A' given that the guest has chosen door 'B' and that the prize is actually behind door 'C', or P(Monty='A'|Guest='B', Prize='C').\n", "\n", "Next, we pass these distributions into state objects along with the name for the node and add them to the network. In the future, all matrices of data should have their columns organized in the same order that the states are added to the network. The way the states are added to the network makes no difference to it, and so you should add the states in the same order your data has.\n", "\n", "Next, we need to add edges to the model. These represent which states are parents of which other states. This is currently a bit redundant with the parents step for ConditionalProbabilityTable objects and will be removed soon. For now edges are added from parent -> child by calling `model.add_transition(parent, child)`.\n", "\n", "Lastly, the model must be baked to finalize the internals. Since Bayesian networks use factor graphs for inference, an explicit factor graph is produced from the Bayesian network during the bake step." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pomegranate import *\n", "\n", "# The guests initial door selection is completely random\n", "guest = DiscreteDistribution( { 'A': 1./3, 'B': 1./3, 'C': 1./3 } )\n", "\n", "# The door the prize is behind is also completely random\n", "prize = DiscreteDistribution( { 'A': 1./3, 'B': 1./3, 'C': 1./3 } )\n", "\n", " # Monty is dependent on both the guest and the prize. \n", "monty = ConditionalProbabilityTable(\n", " [[ 'A', 'A', 'A', 0.0 ],\n", " [ 'A', 'A', 'B', 0.5 ],\n", " [ 'A', 'A', 'C', 0.5 ],\n", " [ 'A', 'B', 'A', 0.0 ],\n", " [ 'A', 'B', 'B', 0.0 ],\n", " [ 'A', 'B', 'C', 1.0 ],\n", " [ 'A', 'C', 'A', 0.0 ],\n", " [ 'A', 'C', 'B', 1.0 ],\n", " [ 'A', 'C', 'C', 0.0 ],\n", " [ 'B', 'A', 'A', 0.0 ],\n", " [ 'B', 'A', 'B', 0.0 ],\n", " [ 'B', 'A', 'C', 1.0 ],\n", " [ 'B', 'B', 'A', 0.5 ],\n", " [ 'B', 'B', 'B', 0.0 ],\n", " [ 'B', 'B', 'C', 0.5 ],\n", " [ 'B', 'C', 'A', 1.0 ],\n", " [ 'B', 'C', 'B', 0.0 ],\n", " [ 'B', 'C', 'C', 0.0 ],\n", " [ 'C', 'A', 'A', 0.0 ],\n", " [ 'C', 'A', 'B', 1.0 ],\n", " [ 'C', 'A', 'C', 0.0 ],\n", " [ 'C', 'B', 'A', 1.0 ],\n", " [ 'C', 'B', 'B', 0.0 ],\n", " [ 'C', 'B', 'C', 0.0 ],\n", " [ 'C', 'C', 'A', 0.5 ],\n", " [ 'C', 'C', 'B', 0.5 ],\n", " [ 'C', 'C', 'C', 0.0 ]], [guest, prize] ) \n", "\n", "# State objects hold both the distribution, and a high level name.\n", "s1 = State( guest, name=\"guest\" )\n", "s2 = State( prize, name=\"prize\" )\n", "s3 = State( monty, name=\"monty\" )\n", "\n", "# Create the Bayesian network object with a useful name\n", "model = BayesianNetwork( \"Monty Hall Problem\" )\n", "\n", "# Add the three states to the network \n", "model.add_states(s1, s2, s3)\n", "\n", "# Add transitions which represent conditional dependencies, where the second node is conditionally dependent on the first node (Monty is dependent on both guest and prize)\n", "model.add_transition(s1, s3)\n", "model.add_transition(s2, s3)\n", "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Probability\n", "\n", "We can calculate the probability or log probability of a point under the Bayesian network using the appropriately named `probability` and `log_probability` methods. The Bayesian network can give us a more intricate understanding of the connection between variables, and so in many cases is more sophisticated than a simple multivariate distribution." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.111111111111\n", "0.0\n", "\n", "-2.19722457734\n", "-inf\n" ] } ], "source": [ "print model.probability(['A', 'B', 'C'])\n", "print model.probability(['B', 'B', 'B'])\n", "print\n", "print model.log_probability(['C', 'A', 'B'])\n", "print model.log_probability(['B', 'A', 'A'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inference\n", "\n", "pomegranate uses the loopy belief propagation algorithm to do inference. This is an approximate algorithm which can yield exact results in tree-like graphs, and in most other cases still yields good results. Inference on a Bayesian network consists of taking in observations for a subset of the variables and using that to infer the values that the other variables take. The most variables which are observed, the closer the inferred values will be to truth. One of the powers of Bayesian networks is that the set of observed and 'hidden' (or unobserved) variables does not need to be specified beforehand, and can change from sample to sample.\n", "\n", "\n", "We can run inference using the `predict_proba` method and passing in a dictionary of values, where the key is the name of the state and the value is the observed value for that state. If we don't supply any values, we get the marginal of the graph, which is just the frequency of each value for each variable over an infinite number of randomly drawn samples from the graph.\n", "\n", "Lets see what happens when we look at the marginal of the Monty hall network." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.33333333333333337,\n", " \"C\" :0.33333333333333337,\n", " \"B\" :0.33333333333333337\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}\n", " {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.33333333333333337,\n", " \"C\" :0.33333333333333337,\n", " \"B\" :0.33333333333333337\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}\n", " {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.33333333333333337,\n", " \"C\" :0.33333333333333337,\n", " \"B\" :0.33333333333333337\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}]\n" ] } ], "source": [ "print model.predict_proba({})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are returned three `DiscreteDistribution` objects, each representing the marginal distribution for each variable, in the same order they were put into the model. In this case, they represent the guest, prize, and monty variables respectively. We see that everything is equally likely. If we want to access these distributions, we can do the following:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'A': 0.33333333333333337, 'C': 0.33333333333333337, 'B': 0.33333333333333337}\n" ] } ], "source": [ "marginals = model.predict_proba({})\n", "print marginals[0].parameters[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first element of `marginals` is a DiscreteDistribution, with all the same operations as a normal DiscreteDistribution objects. This means that parameters[0] will return the underlying dictionary used by the distribution, which we return here.\n", "\n", "Now lets do something different, and say that the guest has chosen door 'A'. We do this by passing a dictionary to `predict_proba` with key pairs consisting of the name of the state (in the state object), and the value which that variable has taken." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :1.0,\n", " \"C\" :0.0,\n", " \"B\" :0.0\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "},\n", " {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.33333333333333337,\n", " \"C\" :0.33333333333333337,\n", " \"B\" :0.33333333333333337\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "},\n", " {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.0,\n", " \"C\" :0.5,\n", " \"B\" :0.5\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}], dtype=object)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict_proba({'guest': 'A'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that now Monty will not open door 'A', because the guest has chosen it. At the same time, the distribution over the prize has not changed, it is still equally likely that the prize is behind each door.\n", "\n", "Now, lets say that Monty opens door 'C' and see what happens." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :1.0,\n", " \"C\" :0.0,\n", " \"B\" :0.0\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "},\n", " {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.3333333333333334,\n", " \"C\" :0.0,\n", " \"B\" :0.6666666666666666\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "},\n", " {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.0,\n", " \"C\" :1.0,\n", " \"B\" :0.0\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}], dtype=object)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict_proba({'guest': 'A', 'monty': 'C'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suddenly, we see that the distribution over prizes has changed. It is now twice as likely that the car is behind the door labeled 'B'. This illustrates the somewhat famous Monty Hall problem.\n", "\n", "## Imputation\n", "\n", "Bayesian networks also have an `predict` method which can be used to fill in missing values with their most likely value. If you have a bayesian network and the values for some variables in a sample, you can impute the most likely values for the remaining variables. You can indicate which values are missing by using `None`. Loopy belief propagation is then run to find the distribution of values for each variable like above, and the most likely variable is used. If all values are equally likely, it will randomly choose a value, which may be sub-optimal. For an example:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[['B', 'A', 'C'], ['C', 'A', 'B'], ['B', 'C', 'A'], ['A', 'B', 'C']]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict([['B', 'A', None],\n", " ['C', 'A', None],\n", " ['B', 'C', None],\n", " ['A', 'B', None]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parameter Fitting\n", "\n", "Networks can be trained by passing in a matrix with observation position corresponding to the state in the model. Currently it only works on datasets which do not contain any missing values, such as the following:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.fit([['A', 'B', 'C'],\n", " ['A', 'C', 'B'],\n", " ['A', 'A', 'C'],\n", " ['B', 'B', 'C'], \n", " ['B', 'C', 'A']])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.5999999999999999,\n", " \"C\" :0.0,\n", " \"B\" :0.4000000000000002\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}\n", " {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.20000000000000004,\n", " \"C\" :0.4000000000000001,\n", " \"B\" :0.4000000000000001\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}\n", " {\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.13333333333333333,\n", " \"C\" :0.3733333333333334,\n", " \"B\" :0.49333333333333323\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}]\n" ] } ], "source": [ "print model.predict_proba({})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Structure Learning\n", "\n", "The most difficult task involving Bayesian networks is the learning of structure. Naively this algorithm will take super-exponential time with the number of nodes, because there are a super-exponential number of DAGs to consider. Given the complicated nature of this task, researchers have proposed many different ways to tackle the structure learning problem, of which there are two main approaches: constraint based algorithms, and scoring based methods. The constraint based methods attempt to find conditional independence statements in the data and learn a Bayesian network using those, while scoring based methods attempt optimize some score to match the posterior well to the data.\n", "\n", "Currently pomegranate contains two scoring function based structure learning algorithms, the Chow-Liu tree and an exact network with an efficient dynamic programming backend. The Chow-Liu tree algorithm attempts to find the best single tree to approximate the underlying distribution, and can be extremely fast, but it limits each node to having a single parent. The exact algorithm can be very fast compared to more naive scoring techniques, but gets the problem down to \"only exponential\" in time with the number of nodes. Due to some programmatic efficiencies, in practice exactly optimal Bayesian networks can be found for 20-25 variables assuming that there is enough memory. \n", "\n", "Lets take a look at both of these learning algorithms on randomly generated data. Since the algorithms take the same amount of time given completely random data as structured data, we're going to use completely random data since it is easier to generate. We can use the `from_samples` method to do structure learning for Bayesian networks, just like we used it to initialize other models directly from data as well.\n", "\n", "Lets first take a look at the time it takes to calculate an exact Bayesian network using 10000 samples and between 2 and 17 variables." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jmschr/anaconda/lib/python2.7/site-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['log', 'random']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] } ], "source": [ "%pylab inline\n", "import time\n", "\n", "times = []\n", "for i in range(2, 18):\n", " tic = time.time()\n", " X = numpy.random.randint(2, size=(10000, i))\n", " model = BayesianNetwork.from_samples(X, algorithm='exact')\n", " times.append( time.time() - tic )" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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tYGCgZsbE6P/atSNUoQbhCgAAADjGf0tL9VhKiv5x8GCd2rCgIM2MjdWgoCAX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\nDerdu7dyc3N5VNlLioqKZDab3Y6ZzWYVFhZ6qSJI0oYNG7R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khcqb1xitAAAAAElFTkSu\nQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn\n", "seaborn.set_style('whitegrid')\n", "\n", "plt.figure(figsize=(14, 6))\n", "plt.title('Time To Learn Bayesian Network', fontsize=18)\n", "plt.xlabel(\"Number of Variables\", fontsize=14)\n", "plt.ylabel(\"Time (s)\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.plot(range(2, 18), times, linewidth=3, color='c')\n", "plt.yscale('log')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The algorithm looks like it's running in exponential time, which is a major feat for the prospect of Bayesian network structure learning. Combined with an efficient implementation of the algorithm, this seems like calculating a graph with mid-20 variables would be possible on a normal computer.\n", "\n", "Lets take a look at the speed of the time it takes to calculate the Chow-Liu tree. Since the algorithm is much faster than finding the exact graph, lets check out how long it takes to learn for a larger range of variables (up to 100)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "times = []\n", "for i in range(2, 253, 10):\n", " tic = time.time()\n", " X = numpy.random.randint(2, size=(10000, i))\n", " model = BayesianNetwork.from_samples(X, algorithm='chow-liu')\n", " times.append( time.time() - tic )" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Pdf1aBwdXCVQdQ0IUzQgf4ILRKMOWVLaqTn3uBwAAcDY2u10HCgr0g82mHwoK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\nboqrPqFstn4AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn\n", "seaborn.set_style('whitegrid')\n", "\n", "plt.figure(figsize=(14, 6))\n", "plt.title('Time To Learn Bayesian Network', fontsize=18)\n", "plt.xlabel(\"Number of Variables\", fontsize=14)\n", "plt.ylabel(\"Time (s)\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.plot( range(2, 253, 10), times, linewidth=3, color='c')\n", "plt.yscale('log')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like this is a quadratic time algorithm, and can be used to efficiently find tree-like graph approximations for datasets with a large number of variables. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 } Tutorial_4b_Bayesian_Network_Structure_Learning.ipynb000066400000000000000000020017001420461712600360740ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/old{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bayesian Network Structure Learning in Pomegranate\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com\n", " \n", "Learning the structure of Bayesian networks can be complicated for two main reasons: (1) difficulties in inferring causality and (2) the super-exponential number of directed edges that could exist in a dataset. The first issue presents itself when the structure lerning algorithm considers only correlation or another measure of co-occurrence to determine if an edge should exist. The first point presents challenges which deserve a far more in depth treatment unrelated to implementations in pomegranate, so instead this tutorial will focus on how pomegranate implements fast Bayesian network structure learning. It will also cover a new concept called the \"constraint graph\" which can be used to massively speed up structure search while also making causality assignment a bit more reasonable.\n", "\n", "## Introduction to Bayesian Network Structure Learning\n", "\n", "Most methods for Bayesian network structure learning (BNSL) can be put into one of the following three categories: \n", "\n", "(1) Search and Score: The most intuitive method is that of 'search and score,' where one searches over the space of all possible directed acyclic graphs (DAGs) and identifies the one that minimizes some objective function. Typical objective functions attempt to balance the log probability of the data given the model (the likelihood) with the complexity of the model to encourage sparser models. A naive implementation of this search is super-exponential in time with the number of variables, and becomes infeasible when considering even less than a dozen variables. However, dynamic programming can efficiently remove the many repeated calculations and reduce this to be simply exponential in time. This allows exact BNSL to scale to ~25-30 variables. In addition, the A\\* algorithm can be used to smartly search the space and reduce computational time even further by not even considering all possibile networks.\n", "\n", "(2) Constraint learning: These methods typically involve calculating some measure of correlation or co-occurrence to identify an undirected backbone of edges that could exist, and then prune these edges systematically until a DAG is reached. A common method is to iterate over all triplets of variables to identify conditional independencies that specify both presence and direction of the edges. This algorithm is asymptotically faster (quadratic in time) than search-and-score, but it does not have a simple probabilistic interpretation.\n", "\n", "(3) Approximate algorithms: In many real world examples, one wishes to merge the interpretability of the search and score method with the attractiveness of the task finishing before the universe ends. To this end, several heuristics have been developed with different properties to yield good structures in a reasonable amount of time. These methods include the Chow-Liu tree building algorithm, the hill-climbing algorithm, and optimal reinsertion, though there are others.\n", "\n", "pomegranate currently implements a search-and-score method based on the minimum description length score which utilizes the dynamic programming and A\\* algorithm (DP/A\\*), a greedy algorithm based off of DP/A\\*, and the Chow-Liu tree building algorithm, though there are plans to soon add other algorithms.\n", "\n", "## Structure Learning in pomegranate\n", "### Exact Learning\n", "\n", "Structure learning in pomegranate is done using the `from_samples` method. All you pass in is the samples, their associated weights (if not uniform), and the algorithm which you'd like to use, and it will learn the network for you using the dynamic programming implementation. Lets see a quick synthetic example to make sure that appropriate connections are found. Lets add connections between variables 1, 3, 6, and variables 0 and 2, and variables 4 and 5." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "((), (), (0,), (1,), (), (4,), (3,))\n" ] }, { "data": { "image/png": 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JvPYaE8o6Hfdx3HEHUFISPqKuolbKqCHT6Gja8tAhnhXkdvO1MSpAwhq3m6Wp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\nPwWKi4Fvf5uVRVp9uEV8PCftwUFWY7W3899xcWH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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%pylab inline\n", "%load_ext memory_profiler\n", "from pomegranate import BayesianNetwork\n", "import seaborn, time\n", "seaborn.set_style('whitegrid')\n", "\n", "X = numpy.random.randint(2, size=(2000, 7))\n", "X[:,3] = X[:,1]\n", "X[:,6] = X[:,1]\n", "\n", "X[:,0] = X[:,2]\n", "\n", "X[:,4] = X[:,5]\n", "\n", "model = BayesianNetwork.from_samples(X, algorithm='exact')\n", "print model.structure\n", "model.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The structure attribute returns a tuple of tuples, where each inner tuple corresponds to that node in the graph (and the column of data learned on). The numbers in that inner tuple correspond to the parents of that node. The results from this structure are that node 3 has node 1 as a parent, that node 2 has node 0 as a parent, and so forth. It seems to faithfully recapture the underlying dependencies in the data.\n", "\n", "Now, two algorithms for performing search-and-score were mentioned, the traditional shortest path algorithm and the A\\* algorithm. These both work by essentially turning the Bayesian network structure learning problem into a shortest path problem over an 'order graph.' This order graph is a lattice made up of layers of variable sets from the BNSL problem, with the root node having no variables, the leaf node having all variables, and layer `i` in the lattice having all subsets of variables of size `i`. Each path from the root to the leaf represents a certain topological sort of the variables, with the shortest path corresponding to the optimal topological sort and Bayesian network. Details can be found here. The traditional shortest path algorithm calculates the values of all edges in the order lattice before finding the shortest path, while the A\\* algorithm searches only a subset of the order lattice and begins searching immediately. Both methods yield optimal Bayesian networks.\n", "\n", "A major problem that arises in the traditional shortest path algorithm is that the size of the order graph grows exponentially with the number of variables, and can make tasks infeasible that have otherwise-reasonable computational times. While the A\\* algorithm is faster computationally, another advantage is that it uses a much smaller amount of memory since it doesn't explore the full order graph, and so can be applied to larger problems.\n", "\n", "In order to see the differences between these algorithms in practice, let's turn to the task of learning a Bayesian network over the digits dataset. The digits dataset is comprised of over a thousand 8x8 pictures of handwritten digits. We binarize the values into 'on' or 'off' for simplicity, and try to learn dependencies between the pixels." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import load_digits\n", "\n", "X, y = load_digits(10, True)\n", "X = X > numpy.mean(X)\n", "\n", "plt.figure(figsize=(14, 4))\n", "plt.subplot(131)\n", "plt.imshow(X[0].reshape(8, 8), interpolation='nearest')\n", "plt.grid(False)\n", "plt.subplot(132)\n", "plt.imshow(X[1].reshape(8, 8), interpolation='nearest')\n", "plt.grid(False)\n", "plt.subplot(133)\n", "plt.imshow(X[2].reshape(8, 8), interpolation='nearest')\n", "plt.grid(False)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shortest Path\n", "Time (s): 70.1325659752\n", "P(D|M): -8267.95044748\n", "peak memory: 2548.30 MiB, increment: 1805.16 MiB\n", "\n", "A* Search\n", "Time (s): 54.0429809093\n", "P(D|M): -8267.95044748\n", "peak memory: 1339.90 MiB, increment: 458.89 MiB\n" ] } ], "source": [ "X = X[:,:18]\n", " \n", "tic = time.time()\n", "model = BayesianNetwork.from_samples(X, algorithm='exact-dp') # << BNSL done here!\n", "t1 = time.time() - tic\n", "p1 = model.log_probability(X).sum()\n", "\n", "tic = time.time()\n", "model = BayesianNetwork.from_samples(X, algorithm='exact')\n", "t2 = time.time() - tic\n", "p2 = model.log_probability(X).sum()\n", "\n", "\n", "print \"Shortest Path\"\n", "print \"Time (s): \", t1\n", "print \"P(D|M): \", p1\n", "%memit BayesianNetwork.from_samples(X, algorithm='exact-dp')\n", "print\n", "print \"A* Search\"\n", "print \"Time (s): \", t2\n", "print \"P(D|M): \", p2\n", "%memit BayesianNetwork.from_samples(X, algorithm='exact')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These results show that the A\\* algorithm is both computationally faster and requires far less memory than the traditional algorithm, making it a better default for the 'exact' algorithm. The amount of memory used by the BNSL process is under 'increment', not 'peak memory', as 'peak memory' returns the total memory used by everything, while increment shows the difference in peak memory before and after the function has run. \n", "\n", "### Approximate Learning: Greedy Search (pomegranate default)\n", "\n", "A natural heuristic when a non-greedy algorithm is too slow is to consider the greedy version. This simple implementation iteratively finds the best variable to add to the growing topological sort, allowing the new variable to draw only from variables already in the topological sort. This is the default in pomegranate because it has a nice balance between producing good (often optimal) graphs and having a small computational cost and memory footprint. However, there is no guarantee that this produces the globally optimal graph.\n", "\n", "Let's see how it performs on the same dataset as above." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Greedy\n", "Time (s): 3.88651204109\n", "P(D|M): -8434.74506907\n", "peak memory: 850.02 MiB, increment: 3.01 MiB\n" ] } ], "source": [ "tic = time.time()\n", "model = BayesianNetwork.from_samples(X) # << Default BNSL setting\n", "t = time.time() - tic\n", "p = model.log_probability(X).sum()\n", "\n", "print \"Greedy\"\n", "print \"Time (s): \", t\n", "print \"P(D|M): \", p\n", "%memit BayesianNetwork.from_samples(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Approximate Learning: Chow-Liu Trees\n", "\n", "However, there are even cases where the greedy heuristic is too slow, for example hundreds of variables. One of the first heuristics for BNSL is that of Chow-Liu trees, which learns the optimal tree from data. Essentially it calculates the mutual information between all pairs of variables and then finds the maximum spanning tree. A root node has to be input to turn the undirected edges based on mutual information into directed edges for the Bayesian network. The algorithm is is $O(d^{2})$ and practically is extremely fast and memory efficient, though it produces structures with a worse $P(D|M)$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chow-Liu\n", "Time (s): 0.0929050445557\n", "P(D|M): -8752.32666094\n", "peak memory: 834.77 MiB, increment: 0.00 MiB\n" ] } ], "source": [ "tic = time.time()\n", "model = BayesianNetwork.from_samples(X, algorithm='chow-liu') # << Default BNSL setting\n", "t = time.time() - tic\n", "p = model.log_probability(X).sum()\n", "\n", "print \"Chow-Liu\"\n", "print \"Time (s): \", t\n", "print \"P(D|M): \", p\n", "%memit BayesianNetwork.from_samples(X, algorithm='chow-liu')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comparison\n", "\n", "We can then compare the algorithms directly to each other on the digits dataset as we expand the number of pixels to consider." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X, _ = load_digits(10, True)\n", "X = X > numpy.mean(X)\n", "\n", "t1, t2, t3, t4 = [], [], [], []\n", "p1, p2, p3, p4 = [], [], [], []\n", "n_vars = range(8, 19)\n", "\n", "for i in n_vars:\n", " X_ = X[:,:i]\n", "\n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X_, algorithm='exact-dp') # << BNSL done here!\n", " t1.append(time.time() - tic)\n", " p1.append(model.log_probability(X_).sum())\n", "\n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X_, algorithm='exact')\n", " t2.append(time.time() - tic)\n", " p2.append(model.log_probability(X_).sum())\n", "\n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X_, algorithm='greedy')\n", " t3.append(time.time() - tic)\n", " p3.append(model.log_probability(X_).sum())\n", "\n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X_, algorithm='chow-liu')\n", " t4.append(time.time() - tic)\n", " p4.append(model.log_probability(X_).sum())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+/mhra792DmdiYsKCBQvUHvt9yOHOnDmDvr5+vn4uuZEnSoXY7NmzsbS0VP1r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\n/fVKM6JdWt8cOHCAr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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 4))\n", "plt.subplot(121)\n", "plt.title(\"Time to Learn Structure\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.ylabel(\"Time (s)\", fontsize=14)\n", "plt.xlabel(\"Variables\", fontsize=14)\n", "plt.plot(n_vars, t1, c='c', label=\"Exact Shortest\")\n", "plt.plot(n_vars, t2, c='m', label=\"Exact A*\")\n", "plt.plot(n_vars, t3, c='g', label=\"Greedy\")\n", "plt.plot(n_vars, t4, c='r', label=\"Chow-Liu\")\n", "plt.legend(fontsize=14, loc=2)\n", "\n", "plt.subplot(122)\n", "plt.title(\"$P(D|M)$ with Resulting Model\", fontsize=14)\n", "plt.xlabel(\"Variables\", fontsize=14)\n", "plt.ylabel(\"logp\", fontsize=14)\n", "plt.plot(n_vars, p1, c='c', label=\"Exact Shortest\")\n", "plt.plot(n_vars, p2, c='m', label=\"Exact A*\")\n", "plt.plot(n_vars, p3, c='g', label=\"Greedy\")\n", "plt.plot(n_vars, p4, c='r', label=\"Chow-Liu\")\n", "plt.legend(fontsize=14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see the expected results-- that the A\\* algorithm works faster than the shortest path, the greedy one faster than that, and Chow-Liu the fastest. The purple and cyan lines superimpose on the right plot as they produce graphs with the same score, followed closely by the greedy algorithm and then Chow-Liu performing the worst." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constraint Graphs\n", "\n", "Now, sometimes you have prior information about how groups of nodes are connected to each other and want to exploit that. This can take the form of a global ordering, where variables can be ordered in such a manner that edges only go from left to right, for example. However, sometimes you have layers in your network where variables are a part of these layers and can only have parents in another layer.\n", "\n", "Lets consider a diagnostics Bayesian network like the following (no need to read code, the picture is all that is important for now):" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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/Nzc3h5s3b+LMmTNobm5esQ5MTk6iqakJPT090Gq12Lp1KxITEyGXy59qyKpU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\nhhHEAwMw4uEwHKZt2/Ac6+pgxCwWaXXhKVs3b6J6deECDH0shnvU0gI9sX0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L7YYO5SCX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\nHQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pomegranate import DiscreteDistribution, ConditionalProbabilityTable, Node\n", "\n", "BRCA1 = DiscreteDistribution({0: 0.999, 1: 0.001})\n", "BRCA2 = DiscreteDistribution({0: 0.985, 1: 0.015})\n", "LCT = DiscreteDistribution({0: 0.950, 1: 0.050})\n", "\n", "OC = ConditionalProbabilityTable([[0, 0, 0, 0.999],\n", " [0, 0, 1, 0.001],\n", " [0, 1, 0, 0.750],\n", " [0, 1, 1, 0.250],\n", " [1, 0, 0, 0.700],\n", " [1, 0, 1, 0.300],\n", " [1, 1, 0, 0.050],\n", " [1, 1, 1, 0.950]], [BRCA1, BRCA2])\n", "\n", "LI = ConditionalProbabilityTable([[0, 0, 0.99],\n", " [0, 1, 0.01],\n", " [1, 0, 0.20],\n", " [1, 1, 0.80]], [LCT])\n", "\n", "PREG = DiscreteDistribution({0: 0.90, 1: 0.10})\n", "\n", "LE = ConditionalProbabilityTable([[0, 0, 0.99],\n", " [0, 1, 0.01],\n", " [1, 0, 0.25],\n", " [1, 1, 0.75]], [OC])\n", "\n", "BLOAT = ConditionalProbabilityTable([[0, 0, 0, 0.85],\n", " [0, 0, 1, 0.15],\n", " [0, 1, 0, 0.70],\n", " [0, 1, 1, 0.30],\n", " [1, 0, 0, 0.40],\n", " [1, 0, 1, 0.60],\n", " [1, 1, 0, 0.10],\n", " [1, 1, 1, 0.90]], [OC, LI])\n", "\n", "LOA = ConditionalProbabilityTable([[0, 0, 0, 0.99],\n", " [0, 0, 1, 0.01],\n", " [0, 1, 0, 0.30],\n", " [0, 1, 1, 0.70],\n", " [1, 0, 0, 0.95],\n", " [1, 0, 1, 0.05],\n", " [1, 1, 0, 0.95],\n", " [1, 1, 1, 0.05]], [PREG, OC])\n", "\n", "VOM = ConditionalProbabilityTable([[0, 0, 0, 0, 0.99],\n", " [0, 0, 0, 1, 0.01],\n", " [0, 0, 1, 0, 0.80],\n", " [0, 0, 1, 1, 0.20],\n", " [0, 1, 0, 0, 0.40],\n", " [0, 1, 0, 1, 0.60],\n", " [0, 1, 1, 0, 0.30],\n", " [0, 1, 1, 1, 0.70],\n", " [1, 0, 0, 0, 0.30],\n", " [1, 0, 0, 1, 0.70],\n", " [1, 0, 1, 0, 0.20],\n", " [1, 0, 1, 1, 0.80],\n", " [1, 1, 0, 0, 0.05],\n", " [1, 1, 0, 1, 0.95],\n", " [1, 1, 1, 0, 0.01],\n", " [1, 1, 1, 1, 0.99]], [PREG, OC, LI])\n", "\n", "AC = ConditionalProbabilityTable([[0, 0, 0, 0.95],\n", " [0, 0, 1, 0.05],\n", " [0, 1, 0, 0.01],\n", " [0, 1, 1, 0.99],\n", " [1, 0, 0, 0.40],\n", " [1, 0, 1, 0.60],\n", " [1, 1, 0, 0.20],\n", " [1, 1, 1, 0.80]], [PREG, LI])\n", "\n", "s1 = Node(BRCA1, name=\"BRCA1\")\n", "s2 = Node(BRCA2, name=\"BRCA2\")\n", "s3 = Node(LCT, name=\"LCT\")\n", "s4 = Node(OC, name=\"OC\")\n", "s5 = Node(LI, name=\"LI\")\n", "s6 = Node(PREG, name=\"PREG\")\n", "s7 = Node(LE, name=\"LE\")\n", "s8 = Node(BLOAT, name=\"BLOAT\")\n", "s9 = Node(LOA, name=\"LOA\")\n", "s10 = Node(VOM, name=\"VOM\")\n", "s11 = Node(AC, name=\"AC\")\n", "\n", "model = BayesianNetwork(\"Hut\")\n", "model.add_nodes(s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11)\n", "model.add_edge(s1, s4)\n", "model.add_edge(s2, s4)\n", "model.add_edge(s3, s5)\n", "model.add_edge(s4, s7)\n", "model.add_edge(s4, s8)\n", "model.add_edge(s4, s9)\n", "model.add_edge(s4, s10)\n", "model.add_edge(s5, s8)\n", "model.add_edge(s5, s10)\n", "model.add_edge(s5, s11)\n", "model.add_edge(s6, s9)\n", "model.add_edge(s6, s10)\n", "model.add_edge(s6, s11)\n", "model.bake()\n", "\n", "plt.figure(figsize=(14, 10))\n", "model.plot()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This network contains three layer, with symptoms on the bottom (low energy, bloating, loss of appetite, vomitting, and abdominal cramps), diseases in the middle (overian cancer, lactose intolerance, and pregnancy), and genetic tests on the top for three different genetic mutations. The edges in this graph are constrainted such that symptoms are explained by diseases, and diseases can be partially explained by genetic mutations. There are no edges from diseases to genetic conditions, and no edges from genetic conditions to symptoms. If we were going to design a more efficient search algorithm, we would want to exploit this fact to drastically reduce the search space of graphs.\n", "\n", "Before presenting a solution, lets also consider another situation. In some cases you can define a global ordering of the variables, meaning you can order them from left to right and ensure that edges only go from the left to the right. This can represent some temporal separation (things on the left happen before things on the right), physical separation, or anything else. This would also dramatically reduce the search space. \n", "\n", "In addition to reducing the search space, an efficient algorithm can exploit this layered structure. A key property of most scoring functions is the idea of \"global parameter independence\", meaning that that the parents of node A are independent of the parents of node B assuming that they do not form a cycle in the graph. If you have a layered structure, either like in the diagnostics network or through a global ordering, it is impossible to form a cycle in the graph through any valid assignment of parent values. This means that the parents for each node can be identified independently, drastically reducing the runtime of the algorithm.\n", "\n", "Now, sometimes we know ~some things~ about the structure of the variables, but nothing about the others. For example, we might have a partial ordering on some variables but not know anything about the others. We could enforce an arbitrary ordering on the others, but this may not be well justified. In essence, we'd like to exploit whatever information we have.\n", "\n", "Abstractly, we can think about this in terms of constraint graphs. Lets say you have some symptoms, diseases, and genetic tests, and don't a priori know the connection between all of these pieces, but you do know the previous layer structure. You can define a \"constraint graph\" which is made up of three nodes, \"symptoms\", \"diseases\", and \"genetic mutations\". There is a directed edge from genetic mutations to diseases, and a directed edge from diseases to symptoms. This specifies that genetic mutations can be parents to diseases, and diseases to symptoms. It would look like the following:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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2bNmCnJyc4G9ZLBbIZDJkZmYiOzs7mF5QX18Pn8+HlJQU5OXlISMjA9nZ2Sgp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\nlyMnJwcAkJ2djby8PNTX12PXrl0YGhpCUlISEhISfCIzHQi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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import networkx\n", "from pomegranate.utils import plot_networkx\n", "\n", "constraints = networkx.DiGraph()\n", "constraints.add_edge('genetic conditions', 'diseases')\n", "constraints.add_edge('diseases', 'symptoms')\n", "plot_networkx(constraints)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All variables corresponding to these categories would be put in their appropriate name. This would define a scaffold for structure learning.\n", "\n", "Now, we can do the same thing for a global ordering. Lets say we have 3 variables in an order from 0-2. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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XtG0b8LWvUTImmeLW8SQzE9iwgVySvj7yxc+dozqbCEmmBaX81TEwQIulpiZK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\nCXGnp6fH7bWjBRO3jOF5Hjq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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "constraints = networkx.DiGraph()\n", "constraints.add_edge(0, 1)\n", "constraints.add_edge(1, 2)\n", "constraints.add_edge(0, 2)\n", "plot_networkx(constraints)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this graph, we're saying that variable 0 can be a parent for 1 or 2, and that variable 1 can be a parent for variable 2. In the same way that putting multiple variables in a node of the constraint graph allowed us to define layers, putting a single variable in the nodes of a constraint graph can allow us to define an ordering.\n", "\n", "To be specific, lets say we want to find the parents of the variables in node 1 given that those variables parents can only come from the variables in node 0. We can independently find the best parents for each variable in node 1 from the set of those in node 0. This is significantly faster than trying to find the best Bayesian network of all variables in nodes 0 and 1. We can also do the same thing for the variables in node 2 by going through the variables in both nodes 0 and 1 to find the best parent set for the variables in node 2.\n", "\n", "However, there are some cases where we know nothing about the parent structure of some variables. This can be solved by including self-loops in the graph, where a node is its own parent. This means that we know nothing about the parent structure of the variables in that node and that the full exponential time algorithm will have to be run. The naive structure learning algorithm can be thought of as putting all variables in a single node in the constraint graph and putting a self-loop on that node. \n", "\n", "We are thus left with two procedures; one for solving edges which are self edges, and one for solving edges which are not. Even though we have to use the exponential time procedure on variables in nodes with self loops, it will still be significantly faster because we will be using less variables (except in the naive case).\n", "\n", "Frequently though we will have some information about some of the nodes of the graph even if we don't have information about all of the nodes. Lets take the case where we know some variables have no children but can have parents, and know nothing about the other variables." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hwPnz5/Hll19iZGTkvbTmHjyhC+x2ij2ZpPueSADffqsstc3MMF5XWz/zYaZe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\nwy7cdWnJhw9xTUUCrtFgjiabZY3873/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "constraints = networkx.DiGraph()\n", "constraints.add_edge(0, 1)\n", "constraints.add_edge(0, 0)\n", "plot_networkx(constraints)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this situation we would have to run the exponential time algorithm on the variables in node 0 to find the optimal parents, and then run the independent parents algorithm on the variables in node 1 drawing only from the variables in node 0. To be specific:\n", "\n", "(1) Use exponential time procedure to find optimal structure amongst variables in node 0 \n", "(2) Use independent-parents procedure to find the best parents of variables in node 1, restricting the parents to be in node 0\n", "(3) Concatenate these parent sets together to get the optimal structure of the network given the constraints.\n", "\n", "We can generalize this to any arbitrary constraint graph:\n", "\n", "(1) Use exponential time procedure to find optimal structure amongst variables in nodes with self loops (including parents from other nodes if needed)\n", "(2) Use independent-parents procedure to find best parents of variables in a node given the constraint that the parents must come from variables in the node which is this nodes parent\n", "(3) Concatenate these parent sets together to get the optimal structure of the network given the constraints.\n", "\n", "According to the global parameter independence property of Bayesian networks, this procedure will give the globally optimal Bayesian network while exploring a significantly smaller part of the network.\n", "\n", "pomegranate supports constraint graphs in an extremely easy to use manner. Lets say that we have a graph with three layers like the diagnostic model, and five variables in each layer. We can define the constraint graph as a networkx DiGraph, with the nodes being tuples containing the column ids of each variable belonging to that variable.\n", "\n", "In this case, we're saying that (0, 1, 2, 3, 4) is the first node, (5, 6, 7, 8, 9) is the second node, and (10, 11, 12, 13, 14) is the final node. Lets make nodes 1, 7, and 12 related, 11, 13, 14 related, and 3 and 5 related. In this case, where should be an edge from 1 to 7, and 7 to 12. 11, 13, and 14 are all a part of the same layer and so that connection should be ignored, and then there should be a connection from 3 to 5." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Constraint Graph\n" ] }, { "data": { "image/png": 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Iw4QAjtyHkbsEIAJ6go/sjzYKIYaRCHlXXu9nvSufL24eD4WMQ0NxgRIml5NY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\nbNgml4ctJiZCo62myCNH1HBB43A4UQP3KnI4nKiBCxqHw4kauKBxOJy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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Learned Bayesian Network\n" ] }, { "data": { "image/png": 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YYWTjlMH69alsG8WMGVREDQ79djgcCAsLQ3BwcLHzJyQkBCEhIeaduxwOGv53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\nIIoi6uvr339/8eLF3HTTTYZRSZIkSZ+Ky3TVq8mTJ1NRUUE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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "pomegranate time: 0.339829921722 ((), (), (), (), (), (3,), (), (1,), (), (), (), (), (7,), (), ())\n" ] } ], "source": [ "numpy.random.seed(6)\n", "\n", "X = numpy.random.randint(2, size=(200, 15))\n", "\n", "X[:,1] = X[:,7]\n", "X[:,12] = 1 - X[:,7]\n", "\n", "X[:,5] = X[:,3]\n", "\n", "X[:,13] = X[:,11]\n", "X[:,14] = X[:,11]\n", "\n", "a = networkx.DiGraph()\n", "b = tuple((0, 1, 2, 3, 4))\n", "c = tuple((5, 6, 7, 8, 9))\n", "d = tuple((10, 11, 12, 13, 14))\n", "\n", "a.add_edge(b, c)\n", "a.add_edge(c, d)\n", "print \"Constraint Graph\"\n", "plot_networkx(a)\n", "plt.show()\n", "\n", "print \"Learned Bayesian Network\"\n", "\n", "tic = time.time()\n", "model = BayesianNetwork.from_samples(X, algorithm='exact', constraint_graph=a)\n", "\n", "plt.figure(figsize=(16, 8))\n", "model.plot()\n", "plt.show()\n", "print \"pomegranate time: \", time.time() - tic, model.structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that reconstructed perfectly here. Lets see what would happen if we didn't use the exact algorithm." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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uJVi7lgeYa3VWY1MTk4O77tJ3wxMAdXV1eO2117Bo0SIcOXIESUlJ+L//+z9c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\nZmiacp08aecIJyW5fuYqAABRJZCpn3hCqq62wjSI+SM+Pl5FRUW6444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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "pomegranate time: 3.44430494308 ((), (), (), (), (), (3,), (), (1,), (), (), (), (), (7,), (11,), (13,))\n" ] } ], "source": [ "tic = time.time()\n", "model = BayesianNetwork.from_samples(X, algorithm='exact')\n", "plt.figure(figsize=(16, 8))\n", "model.plot()\n", "plt.show()\n", "print \"pomegranate time: \", time.time() - tic, model.structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like we got three desirable attributes by using a constraint graph. The first is that there was over an order of magnitude speed improvement in finding the optimal graph. The second is that we were able to remove some edges we didn't want in the final Bayesian network, such as those between 11, 13, and 14. We also removed the edge between 1 and 12 and 1 and 3, which are spurious given the model that we originally defined. The third desired attribute is that we can specify the direction of some of the edges and get a better causal model.\n", "\n", "Lets take a look at how big of a model we can learn given a three layer constraint graph like before. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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e+nclClc3OLPZzKhRo/j5558ZO3YsHTp0AGDnzp0sWLCAL774gg8++AAvL69y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\nzZgxQ5GRkVq/fr3ef/99lZaW6pFHHgl1WV3Wrl27tHr1alaBDJF33nl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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "constraint_times, times = [], []\n", "\n", "x = numpy.arange(1, 7)\n", "for i in x:\n", " symptoms = tuple(range(i))\n", " diseases = tuple(range(i, i*2))\n", " genetic = tuple(range(i*2, i*3))\n", "\n", " constraints = networkx.DiGraph()\n", " constraints.add_edge(genetic, diseases)\n", " constraints.add_edge(diseases, symptoms)\n", " \n", " X = numpy.random.randint(2, size=(2000, i*3))\n", " \n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X, algorithm='exact', constraint_graph=constraints)\n", " constraint_times.append( time.time() - tic )\n", " \n", " tic = time.time()\n", " model = BayesianNetwork.from_samples(X, algorithm='exact')\n", " times.append( time.time() - tic ) \n", "\n", "\n", "plt.figure(figsize=(14, 6))\n", "plt.title('Time To Learn Bayesian Network', fontsize=18)\n", "plt.xlabel(\"Number of Variables\", fontsize=14)\n", "plt.ylabel(\"Time (s)\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.plot( x*3, times, linewidth=3, color='c', label='Exact')\n", "plt.plot( x*3, constraint_times, linewidth=3, color='m', label='Constrained')\n", "plt.legend(loc=2, fontsize=16)\n", "plt.yscale('log')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like by including a constraint graph, we can far more quickly find the optimal Bayesian network. This can allow us to create Bayesian networks on dozens of variables as long as we can order them into layers.\n", "\n", "## Other Parameters\n", "\n", "There are three other parameters that are worth mentioning. The first is that a vector of weights can be passed in alongside a matrix of samples, with each entry in the vector corresponding to the weight of that sample. By default all weights are set to 1, but they can be any non-negative number.\n", "\n", "```\n", "model = BayesianNetwork.from_samples(X, weights=weights)\n", "```\n", "\n", "Next is the pseudocounts parameter that indicates the number added to the observations of each observed variable combination. This acts as a regularizer by smoothing over the counts and is typically used to prevent the model from saying it is impossible for a combination of variables to occur together just because they weren't observed in the data. Currently all learning algorithms support pseudocounts. By default this is set to 0.\n", "\n", "```\n", "model = BayesianNetwork.from_samples(X, pseudocount=1.7\n", "```\n", "\n", "Lastly, the maximum number of parents that each variable can have can be limited. This is sometimes referred to as the k-learn problem in literature. In practice this can dramatically speed up the BNSL task as the majority of time is spent determining the best parent sets. Currently this only affects the exact and greedy algorithm as in the Chow-Liu algorithm each variable only has a single parent.\n", "\n", "```\n", "model = BayesianNetwork.from_samples(X, algorithm='exact', max_parents=2)\n", "```\n", "\n", "Since exact inference uses minimum description length as a score function, the maximum number of parents is by default set to $\\log \\left(\\frac{n}{\\log(n)} \\right)$ with $n$ being the number of samples in the dataset.\n", "\n", "# Conclusions\n", "\n", "pomegranate currently supports exact BNSL through an a shortest path dynamic programming algorithm, an A\\* algorithm, a greedy algorithm, the Chow-Liu tree building algorithm, and a constraint-graph based algorithm which can significantly speed up structure learning if you have any prior knowledge of the interactions between variables.\n", "\n", "If you have any suggestions for how to improve pomegranate or ideas for algorithms to implement, feel free to open an issue on the issue tracker! I'd love to hear feedback." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 } Tutorial_5_Bayes_Classifiers.ipynb000066400000000000000000027045201420461712600321740ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/old{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jmschr/anaconda2/lib/python2.7/site-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['random', 'log']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] } ], "source": [ "from pomegranate import *\n", "import seaborn\n", "%pylab inline\n", "seaborn.set_style('whitegrid')\n", "numpy.set_printoptions(suppress=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Naive Bayes and Bayes Classifiers: A Tutorial\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com\n", "\n", "Bayes classifiers are some of the simplest machine learning models that exist, due to their intuitive probabilistic interpretation and simple fitting step. Each class is modeled as a probability distribution, and the data is interpreted as samples drawn from these underlying distributions. Fitting the model to data is as simple as calculating maximum likelihood parameters for the data that falls under each class, and making predictions is as simple as using Bayes' rule to determine which class is most likely given the distributions. Bayes' Rule is the following: \n", "\n", "\\begin{equation}\n", "P(M|D) = \\frac{P(D|M)P(M)}{P(D)}\n", "\\end{equation}\n", "\n", "where M stands for the model and D stands for the data. $P(M)$ is known as the prior, because it is the probability that a sample is of a certain class before you even know what the sample is. This is generally just the frequency of each class. Intuitively, it makes sense that you would want to model this, because if one class occurs 10x more than another class, it is more likely that a given sample will belong to that distribution. $P(D|M)$ is the likelihood, or the probability, of the data under a given model. Lastly, $P(M|D)$ is the posterior, which is the probability of each component of the model, or class, being the component which generated the data. It is called the posterior because the prior corresponds to probabilities before seeing data, and the posterior corresponds to probabilities after observing the data. In cases where the prior is uniform, the posterior is just equal to the normalized likelihoods. This equation forms the basis of most probabilistic modeling, with interesting priors allowing the user to inject sophisticated expert knowledge into the problem directly.\n", "\n", "Let's take a look at some single dimensional data in order to introduce these concepts more thoroughly." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.concatenate((numpy.random.normal(3, 1, 200), numpy.random.normal(10, 2, 1000)))\n", "y = numpy.concatenate((numpy.zeros(200), numpy.ones(1000)))\n", "\n", "x1 = X[:200]\n", "x2 = X[200:]\n", "\n", "plt.figure(figsize=(16, 5))\n", "plt.hist(x1, bins=25, color='m', edgecolor='m', label=\"Class A\")\n", "plt.hist(x2, bins=25, color='c', edgecolor='c', label=\"Class B\")\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.ylabel(\"Count\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data seems like it comes from two normal distributions, with the cyan class being more prevalent than the magenta class. A natural way to model this data would be to create a normal distribution for the cyan data, and another for the magenta distribution.\n", "\n", "Let's take a look at doing that. All we need to do is use the `from_samples` class method of the `NormalDistribution` class." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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t7dvRhfcPJ6xfGMV/KsZX6LM6joiIiIi0k1YXvw8//DDbtm07rSdbvHgx5eXl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\nAIdhMCwoiOyQELKDgxkREkJ2SAhDg4JwnqBFsYrffso0TZo+aaJ6WTV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DjTfeiNPpJCMj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\nERERERHp9/4f9DTfx+WZJ0gAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d1 = NormalDistribution.from_samples(x1)\n", "d2 = NormalDistribution.from_samples(x2)\n", "idxs = numpy.arange(0, 15, 0.1)\n", "\n", "p1 = map(d1.probability, idxs)\n", "p2 = map(d2.probability, idxs)\n", "\n", "plt.figure(figsize=(16, 5))\n", "plt.plot(idxs, p1, color='m'); plt.fill_between(idxs, 0, p1, facecolor='m', alpha=0.2)\n", "plt.plot(idxs, p2, color='c'); plt.fill_between(idxs, 0, p2, facecolor='c', alpha=0.2)\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.ylabel(\"Probability\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like some aspects of the data are captured well by doing things this way-- specifically the mean and variance of the normal distributions. This allows us to easily calculate $P(D|M)$ as the probability of a sample under either the cyan or magenta distributions using the normal (or Gaussian) probability density equation:\n", "\n", "\\begin{align}\n", "P(D|M) &= P(x|\\mu, \\sigma) \\\\\n", " &= \\frac{1}{\\sqrt{2\\pi\\sigma^{2}}} exp \\left(-\\frac{(x-u)^{2}}{2\\sigma^{2}} \\right) \n", "\\end{align}\n", "\n", "However, if we look at the original data, we see that the cyan distributions is both much wider than the purple distribution and much taller, as there were more samples from that class in general. If we reduce that data down to these two distributions, we lose the class imbalance. We want our prior to model this class imbalance, with the reasoning being that if we randomly draw a sample from the samples observed thus far, it is far more likely to be a cyan than a magenta sample. Let's take a look at this class imbalance exactly." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ayc7ONqmuK3qQ981UF//jTJPM60xeHmosWv+ag9FwAP46A2ip1atX4/vvv8eG\nDRvQs2dPk+b4+vq2eDtdQXZ29gO7by1hq9EAJ050dBsPDU8PD7ja2Bita+6Jy2g4yGQyqNVqg7HG\nx3K5vEm9TqfD3//+dxw7dgwpKSno16+f0eaIqPMxes7h6emJ8vJyVFRU6Mdyc3Mhl8vh7OzcpD4+\nPh6nT5/Gjz/+yGAg6sKMhoO7uzsUCgUSEhJQU1ODkpISJCcnIyIiAhKJBCEhIcjMzAQAnDp1Cmlp\nafjmm2/w+OOPm715IjIfk+45JCYmIjY2FgEBAbC2tkZYWBiUSiUAoKioCBqNBgCQlpYGjUaDMWPG\nGMz38/PD+vXr27h1IjInk8LBwcEBa9asES4rLCzU//zJJ5/gk08+aZvOiKhD8bMVRCTEcCAiIYYD\nEQkxHIhIiOFAREImvVrRWegadLh5/mZHt9Fi2otaaGw1Hd1Gizza71FIHpF0dBvUgbpUONw8fxMn\n3Lrme/NPoGv1/Vzhc7BxNf6+fHpw8bKCiIQYDkQkxHAgIiGGAxEJMRyISIjhQERCDAciEmI4EJEQ\nw4GIhBgORCTEcCAiIYYDEQkxHIhIiOFAREIMByISYjgQkRDDgYiEGA5EJMRwICIhhgMRCZkUDmVl\nZVAqlfD390dQUBDi4uJQV1fXbP3mzZsxaNAgrFy5ss0aJaL2ZVI4REVFwd7eHhkZGUhJSUFOTg6S\nkpKard27dy+eeOKJNm2UiNqX0XBQqVTIz89HdHQ0pFIpnJycEBkZidTUVGi12ib1zz77LDZu3Ahb\nW1uzNExE7cNoOOTl5cHR0REymUw/5uHhgerqahQXFzepj4qKwiOPPNK2XRJRuzP6pTZqtRpSqdRg\nzM7ODgBQVVUFFxeXNmsmOzv7nsu1F5ueqZB55J3Jg0VN296vvig40yTzOZOXhxqL1h9Dk77xSqfT\ntXoDLeHr63vP5RpbTZf75qiuysPTo82/8cpWowFO8Pi1F08PD7jaGD+GzT0pG40VmUwGtVptMNb4\nWC6Xm9IjEXVBRsPB09MT5eXlqKio0I/l5uZCLpfD2dnZrM0RUccxGg7u7u5QKBRISEhATU0NSkpK\nkJycjIiICEgkEoSEhCAzM7M9eiWidmTS3YrExERcv34dAQEBCA8PR2BgIJRKJQCgqKgIGs1fXy9/\n8uRJeHl5wcvLC/n5+UhOToaXlxdmzpxpvj0gIrMw6Yakg4MD1qxZI1xWWFio/9nPzw8qlaptOiOi\nDsXPVhDU7fV7AAAMNklEQVSREMOBiIQYDkQkxHAgIiGGAxEJMRyISIjhQERCDAciEmI4EJEQw4GI\nhBgORCTEcCAiIYYDEQkxHIhIiOFAREIMByISYjgQkRDDgYiEGA5EJMRwICIhhgMRCTEciEiI4UBE\nQgwHIhJiOBCREMOBiIRMCoeysjIolUr4+/sjKCgIcXFxqKurE9bu3bsXU6ZMgbe3NyZPnoz9+/e3\nacNE1D5MCoeoqCjY29sjIyMDKSkpyMnJQVJSUpO6goICREdHY968eTh+/Djmz5+PhQsX4uzZs23e\nOBGZl9FwUKlUyM/PR3R0NKRSKZycnBAZGYnU1FRotVqD2tTUVAwbNgyjR49G9+7dMWrUKAwZMgRb\nt2412w4QkXkYDYe8vDw4OjpCJpPpxzw8PFBdXY3i4uImtR4eHgZj7u7u/OZtoi6om7ECtVoNqVRq\nMGZnZwcAqKqqgouLi9Haqqoqk5rJzs42WmObZWvSuuj+/F7zO2D8cLRYli2PX3up+f33+zqERsMB\nAHQ6nckrbEnt3Xx9fVs1j4jMw+hlhUwmg1qtNhhrfCyXyw3G7e3thbX/WUdEnZ/RcPD09ER5eTkq\nKir0Y7m5uZDL5XB2dm5Se+bMGYMxlUqFQYMGtVG7RNRejIaDu7s7FAoFEhISUFNTg5KSEiQnJyMi\nIgISiQQhISHIzMwEALzyyivIzMxERkYG6urqsGfPHmRlZeGVV14x+44QUdsy6X0OiYmJuH79OgIC\nAhAeHo7AwEAolUoAQFFRETQaDQCgf//++PLLL/HFF1/Ax8cHq1evxsqVK/H000+bbw+IyCwkutbe\nQSSiB9pD/dmK+fPn49NPP+3oNszm9u3bGD9+PPbs2dPRrXQKneV4d5njonsAjRw5Uufu7q7z9PTU\neXp66nx8fHQvv/yy7l//+pe+JjU1VffCCy/obt++rZ8zaNAgXW1tbZP17d69W+fq6qpLSkpqt324\nl4yMDN2FCxdMqlWpVDo/Pz/dlStXzNxVx2nN8dbpdLqioiJddHS0btiwYTovLy/diBEjdH//+991\nV69eNXvPXeG4PLBnDjExMVCpVFCpVDhy5AgmTJiAyMhI/PHHH6ivr8fq1asxa9YsWFlZ6efY2NgI\nPyi2c+fOTvVybGJiIv7973+bVOvp6QkfHx9888035m2qg7X0eBcUFOCll16Cvb09tm3bhtOnT2Pt\n2rUoLS1FeHg4qqurzdpvVzguD2w43M3a2hrTp09H37598euvv+LgwYOoqanBhAkTDOqCgoKwfft2\ngzG1Wo2TJ0/iueeeMxj//vvvMXbsWHh7e2PMmDFIS0vTL2toaEBcXBy8vb0RGBiIn3/+GZMmTcKm\nTZsAAFqtFqtWrcKYMWMwaNAghIaG4tixY/r5wcHBSE1NxaxZs+Dt7Y2xY8fi+PHjAIAJEybg7Nmz\nmDdvHt59910AwNGjR/HSSy/Bx8cHw4cPxz/+8Q80NDTo1/fKK69g69atuH37dhv8Njs/U473xx9/\nDH9/f8TExOCJJ56AhYUFXF1d8dVXX2HYsGG4cuUKlixZor/x3igjIwPPPfcc6urqUFJSgsjISPj7\n+8PPzw+zZ8/G1atX9bVubm7Yt28fpk2bBoVCgcmTJ6OwsFC/vLMfl4ciHBo1NDSgW7duOHLkCAYP\nHgxLS0uD5aNGjcLp06dRXl6uH9uzZw+GDRsGa2tr/VhWVhaWL1+OL7/8EqdOnUJMTAzef/99XLhw\nAQDwww8/YPfu3fjxxx+Rnp6OX375BaWlpfr533//PbZv3461a9ciKysL06ZNw5w5cwzeQPbtt98i\nKioKmZmZ8PLywvLlywEAu3fvBgCsXLkSn376KW7duoW5c+ciNDQU2dnZSElJwa5du/DPf/5Tv67B\ngwejvr4ep0+fbsPfZufX3PGurKzEiRMn8OqrrzaZY2VlhY8//hhubm4ICwvDv/71L4O3/+/btw8h\nISGwsrLC0qVL0aNHDxw+fBgHDx5EbW2t/jg1WrduHT755BMcPXoUdnZ2WLlypX5ZZz8uD0U4aDQa\n/PDDD7h8+TJGjx6Nc+fOwdXVtUmdra0tRo4ciR07dujHdu7ciSlTphjU+fr64tixY/Dw8IBEIkFw\ncDAeffRR5OfnAwAOHTqECRMmwM3NDT169MB7772HGzdu6Odv3boVM2bMwDPPPANLS0u8/PLL6NOn\nD/bu3auvCQoKwsCBA2FlZYVRo0bh/Pnzwn2ztrbG4cOHMW3aNEgkEjz11FNQKBQGb0br0aMHevfu\njXPnzrXuF9jFGDveJSUlAIC+ffvecz2+vr7o3bs30tPTAQB1dXX47bffMHnyZADA2rVr8fHHH6N7\n9+6wtbVFcHBwkzcBTpw4EX379oWNjQ0CAwMNjmNnPy4mfbaiK1q2bJk+xa2treHm5oZ169bB2dkZ\narVa/+Gx/xQaGorPPvsMb7zxBi5duoR///vfCAwMNLgXcefOHXz11VfYu3cv/vzzTwB//cdp/AM4\nFRUVGD58uL6+T58+BvcsiouLER8fb/Aso9PpUFZWZjCn0aOPPnrPU8+9e/di48aNKC0tRUNDA+7c\nudMk0Ozt7VFZWdn8L6yLa83xvvvSS0QikSA0NBQ7d+5EREQEjh07BqlUqv8c0JkzZ/Dll1+ioKAA\ndXV10Gq1cHBwMFiHsePYmY/LAxsOMTExwtPGRhKJRDg+fPhwLFmyBL///jsOHTqE8ePHo1s3w1/T\n6tWrsWvXLnz11Vfw9PSEhYUF/Pz89Mu1Wm2TORYW/3eSZm1tjQ8//BDjx49vtr+76+/l2LFjiI2N\nxfLlyzFu3DhYWVlh7ty5wv3VPcBvaWnJ8XZxcYFEIsEff/wBJyene653ypQpWLVqFUpKSrBv3z5M\nmjQJEokE1dXVmDVrFsLDw5GcnAypVIrvvvsO3333ncF8Y8exMx+Xh+Ky4j/17NmzyQfEGj3yyCOY\nOHEi0tPTkZ6erj+FvJtKpUJwcDAGDhwICwsLlJSU4Pr16/rlcrkcly9f1j++fPmywWdTnnrqKYMb\nUwBw6dKlVu1Lbm4unJ2dMWnSJFhZWaGhoQEFBQVN6iorKw3+JsfD5D+Pt52dHYYMGYL169c3qa2v\nr8e0adNw6NAhAICzszMGDx6MPXv24JdfftH/f7hw4QJu3LiB119/Xf9nCvLy8lrcW2c+Lg9lOAwY\nMOCe13mhoaHYvXs36uvrMXDgwCbL+/Tpg4KCAmg0GhQVFSE+Ph4ODg76G5n+/v7YvXs3zp8/j9ra\nWnz22Wd47LHH9POnTZuGLVu2ICsrCw0NDfjll18wceJE/Q1NY7p3746LFy+itrYWzs7OqKiowKVL\nl1BZWYm4uDhIpVKDu+a1tbW4cuWK8D7Lw0B0vBcvXoy8vDy8+eabKC0thVarxdmzZ6FUKqHRaDB4\n8GB9bWhoKL7++ms4OTmhX79+AIAnn3wSFhYWyMnJwc2bN/HTTz+hqKgI1dXVuHXrlkl9dfbj8lCG\nw7Bhw5CVlYU7d+4Ilz/77LOQSqVNrtsbKZVKWFhYYOjQoViwYAFmzZqFl19+GcnJyfjxxx/xP//z\nP/D390dYWBimTp2K8ePHw87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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "magenta_prior = 1. * len(x1) / len(X)\n", "cyan_prior = 1. * len(x2) / len(X)\n", "\n", "plt.figure(figsize=(4, 6))\n", "plt.title(\"Prior Probabilities P(M)\", fontsize=14)\n", "plt.bar(0, magenta_prior, facecolor='m', edgecolor='m')\n", "plt.bar(1, cyan_prior, facecolor='c', edgecolor='c')\n", "plt.xticks([0, 1], ['P(Magenta)', 'P(Cyan)'], fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The prior $P(M)$ is a vector of probabilities over the classes that the model can predict, also known as components. In this case, if we draw a sample randomly from the data that we have, there is a ~83% chance that it will come from the cyan class and a ~17% chance that it will come from the magenta class.\n", "\n", "Let's multiply the probability densities we got before by this imbalance." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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XOp0OkZGREAQBBoOhbX9SUhLkcjmioqJ65sUQERFRv9QqingsJQVHS0owzNUV+8PD4cHC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\n/AxIDQkhvAN36NlxK5d2x+/xU/V5FeWflFO+qpyaDU33SMUGESMiiBk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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d1 = NormalDistribution.from_samples(x1)\n", "d2 = NormalDistribution.from_samples(x2)\n", "idxs = numpy.arange(0, 15, 0.1)\n", "\n", "p_magenta = numpy.array(map(d1.probability, idxs)) * magenta_prior\n", "p_cyan = numpy.array(map(d2.probability, idxs)) * cyan_prior\n", "\n", "plt.figure(figsize=(16, 5))\n", "plt.plot(idxs, p_magenta, color='m'); plt.fill_between(idxs, 0, p_magenta, facecolor='m', alpha=0.2)\n", "plt.plot(idxs, p_cyan, color='c'); plt.fill_between(idxs, 0, p_cyan, facecolor='c', alpha=0.2)\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.ylabel(\"P(M)P(D|M)\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This looks a lot more faithful to the original data, and actually corresponds to $P(M)P(D|M)$, the prior multiplied by the likelihood. However, these aren't actually probability distributions anymore, as they no longer integrate to 1. This is why the $P(M)P(D|M)$ term has to be normalized by the $P(D)$ term in Bayes' rule in order to get a probability distribution over the components. However, $P(D)$ is difficult to determine exactly-- what is the probability of the data? Well, we can sum over the classes to get that value, since $P(D) = \\sum_{i=1}^{c} P(D|M)P(M)$ for a problem with c classes. This translates into $P(D) = P(M=Cyan)P(D|M=Cyan) + P(M=Magenta)P(D|M=Magenta)$ for this specific problem, and those values can just be pulled from the unnormalized plots above.\n", "\n", "This gives us the full Bayes' rule, with the posterior $P(M|D)$ being the proportion of density of the above plot coming from each of the two distributions at any point on the line. Let's take a look at the posterior probabilities of the two classes on the same line." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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xhGw6HT/LyWGO2809FRW8Xl/PKr+fn+XksCgnB4tW29shCvGtdZn8RqNRbrjh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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "magenta_posterior = p_magenta / (p_magenta + p_cyan)\n", "cyan_posterior = p_cyan / (p_magenta + p_cyan)\n", "\n", "plt.figure(figsize=(16, 5))\n", "plt.subplot(211)\n", "plt.plot(idxs, p_magenta, color='m'); plt.fill_between(idxs, 0, p_magenta, facecolor='m', alpha=0.2)\n", "plt.plot(idxs, p_cyan, color='c'); plt.fill_between(idxs, 0, p_cyan, facecolor='c', alpha=0.2)\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.ylabel(\"P(M)P(D|M)\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "\n", "plt.subplot(212)\n", "plt.plot(idxs, magenta_posterior, color='m')\n", "plt.plot(idxs, cyan_posterior, color='c')\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.ylabel(\"P(M|D)\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The top plot shows the same densities as before, while the bottom plot shows the proportion of the density belonging to either class at that point. This proportion is known as the posterior $P(M|D)$, and can be interpreted as the probability of that point belonging to each class. This is one of the native benefits of probabilistic models, that instead of providing a hard class label for each sample, they can provide a soft label in the form of the probability of belonging to each class.\n", "\n", "We can implement all of this simply in pomegranate using the `NaiveBayes` class." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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RERF1lQ4nZHjrrbc6daDMzMwrDkNE5G12nz2LOrWI8T8A1aXVCJkbInUkIiIi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\nOlbAWbhwIfr37y91jIDz3nvvYfTo0Rg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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "idxs = idxs.reshape(idxs.shape[0], 1)\n", "X = X.reshape(X.shape[0], 1)\n", "\n", "model = NaiveBayes.from_samples(NormalDistribution, X, y)\n", "posteriors = model.predict_proba(idxs)\n", "\n", "plt.figure(figsize=(14, 4))\n", "plt.plot(idxs, posteriors[:,0], color='m')\n", "plt.plot(idxs, posteriors[:,1], color='c')\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.ylabel(\"P(M|D)\", fontsize=14)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like we're getting the same plots for the posteriors just through fitting the naive Bayes model directly to data. The predictions made will come directly from the posteriors in this plot, with cyan predictions happening whenever the cyan posterior is greater than the magenta posterior, and vice-versa." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Naive Bayes\n", "\n", "In the univariate setting, naive Bayes is identical to a general Bayes classifier. The divergence occurs in the multivariate setting, the naive Bayes model assumes independence of all features, while a Bayes classifier is more general and can support more complicated interactions or covariances between features. Let's take a look at what this means in terms of Bayes' rule. \n", "\n", "\\begin{align}\n", "P(M|D) &= \\frac{P(M)P(D|M)}{P(D)} \\\\\n", " &= \\frac{P(M)\\prod_{i=1}^{d}P(D_{i}|M_{i})}{P(D)}\n", "\\end{align}\n", "\n", "This looks fairly simple to compute, as we just need to pass each dimension into the appropriate distribution and then multiply the returned probabilities together. This simplicity is one of the reasons why naive Bayes is so widely used. Let's look closer at using this in pomegranate, starting off by generating two blobs of data that overlap a bit and inspecting them." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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RHX/bobrknH5t0pg0Xv97gnBcgPBIH8pvCOIT/yRi4zbgb3594WdivcBfvt2L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\n9ipnhcPY0tiY894W6++nzC+5FWqfmw+dOVPgKyt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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.concatenate([numpy.random.normal(3, 2, size=(150, 2)), numpy.random.normal(7, 1, size=(250, 2))])\n", "y = numpy.concatenate([numpy.zeros(150), numpy.ones(250)])\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c')\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m')\n", "plt.xlim(-2, 10)\n", "plt.ylim(-4, 12)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's fit our naive Bayes model to this data using pomegranate. We can use the `from_samples` class method, pass in the distribution that we want to model each dimension, and then the data. We choose to use `NormalDistribution` in this particular case, but any supported distribution would work equally well, such as `BernoulliDistribution` or `ExponentialDistribution`. To ensure we get the correct decision boundary, let's also plot the boundary recovered by sklearn." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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9vn79+jh48KDpqyUis5KRkYFRo0bh2LFjuHfvHgCgYcOGOHDgAFxdXWWujoyh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\ngOkk1qgOh7VhxgwZb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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "\n", "model = NaiveBayes.from_samples(NormalDistribution, X, y)\n", "clf = GaussianNB().fit(X, y)\n", "\n", "xx, yy = np.meshgrid(np.arange(-2, 10, 0.02), np.arange(-4, 12, 0.02))\n", "Z1 = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "Z2 = clf.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "\n", "plt.figure(figsize=(16, 8))\n", "plt.subplot(121)\n", "plt.title(\"pomegranate naive Bayes\", fontsize=16)\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c')\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m')\n", "plt.contour(xx, yy, Z1)\n", "plt.xlim(-2, 10)\n", "plt.ylim(-4, 12)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "\n", "plt.subplot(122)\n", "plt.title(\"sklearn naive Bayes\", fontsize=16)\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c')\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m')\n", "plt.contour(xx, yy, Z2)\n", "plt.xlim(-2, 10)\n", "plt.ylim(-4, 12)\n", "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Drawing the decision boundary helps to verify that we've produced a good result by cleanly splitting the two blobs from each other.\n", "\n", "Bayes' rule provides a great deal of flexibility in terms of what the actually likelihood functions are. For example, when considering a multivariate distribution, there is no need for each dimension to be modeled by the same distribution. In fact, each dimension can be modeled by a different distribution, as long as we can multiply the $P(D|M)$ terms together. \n", "\n", "Let's consider the example of some noisy signals that have been segmented. We know that they come from two underlying phenomena, the cyan phenomena and the magenta phenomena, and want to classify future segments. To do this, we have three features-- the mean signal of the segment, the standard deviation, and the duration. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jmschr/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:6: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n" ] }, { "data": { "image/png": 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9o9o/o/rz+9F3TF9iN0e/IyGn5NOLwW8sUmrEktVOtbziu4krFUriqQsTjT1K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X5qvSqWPRMRhQ4GvyFOr6Fg79RJQaisLt+u/ET4Vnq65O/EGY6RlAsfK3Rt0zWnqjcU4oLUS1KR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\nb6b6sJPe/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_signal(X, n):\n", " plt.figure(figsize=(16, 6))\n", " t_current = 0\n", " for i in range(n):\n", " mu, std, t = X[i]\n", " chunk = numpy.random.normal(mu, std, int(t))\n", " plt.plot(numpy.arange(t_current, t_current+t), chunk, c='cm'[i % 2])\n", " t_current += t\n", " \n", " plt.xticks(fontsize=14)\n", " plt.yticks(fontsize=14)\n", " plt.xlabel(\"Time (s)\", fontsize=14)\n", " plt.ylabel(\"Signal\", fontsize=14)\n", " plt.ylim(20, 40)\n", " plt.show()\n", "\n", "def create_signal(n):\n", " X, y = [], []\n", " for i in range(n):\n", " mu = numpy.random.normal(30.0, 0.4)\n", " std = numpy.random.lognormal(-0.1, 0.4)\n", " t = int(numpy.random.exponential(50)) + 1\n", " X.append([mu, std, int(t)])\n", " y.append(0)\n", "\n", " mu = numpy.random.normal(30.5, 0.8)\n", " std = numpy.random.lognormal(-0.3, 0.6)\n", " t = int(numpy.random.exponential(200)) + 1\n", " X.append([mu, std, int(t)])\n", " y.append(1)\n", " \n", " return numpy.array(X), numpy.array(y)\n", "\n", "X_train, y_train = create_signal(1000)\n", "X_test, y_test = create_signal(250)\n", "plot_signal(X_train, 20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can start by modeling each variable as Gaussians, like before, and see what accuracy we get." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gaussian Naive Bayes: 0.798\n", "sklearn Gaussian Naive Bayes: 0.798\n" ] } ], "source": [ "model = NaiveBayes.from_samples(NormalDistribution, X_train, y_train)\n", "print \"Gaussian Naive Bayes: \", (model.predict(X_test) == y_test).mean()\n", "\n", "clf = GaussianNB().fit(X_train, y_train)\n", "print \"sklearn Gaussian Naive Bayes: \", (clf.predict(X_test) == y_test).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We get identical values for sklearn and for pomegranate, which is good. However, let's take a look at the data itself to see whether a Gaussian distribution is the appropriate distribution for the data." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AUFmjrsz8+uuvKikp0dSpU1VQUKBZs2apuLjY0q3Mz89POTk59d6fyWRqTAyH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\n47wdNIdF4aVCebf1dshzV+j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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 4))\n", "plt.subplot(131)\n", "plt.title(\"Mean\")\n", "plt.hist(X_train[y_train == 0, 0], color='c', alpha=0.5, bins=25)\n", "plt.hist(X_train[y_train == 1, 0], color='m', alpha=0.5, bins=25)\n", "\n", "plt.subplot(132)\n", "plt.title(\"Standard Deviation\")\n", "plt.hist(X_train[y_train == 0, 1], color='c', alpha=0.5, bins=25)\n", "plt.hist(X_train[y_train == 1, 1], color='m', alpha=0.5, bins=25)\n", "\n", "plt.subplot(133)\n", "plt.title(\"Duration\")\n", "plt.hist(X_train[y_train == 0, 2], color='c', alpha=0.5, bins=25)\n", "plt.hist(X_train[y_train == 1, 2], color='m', alpha=0.5, bins=25)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, unsurprisingly (since you can see that I used non-Gaussian distributions to generate the data originally), it looks like only the mean follows a normal distribution, whereas the standard deviation seems to follow either a gamma or a log-normal distribution. We can take advantage of that by explicitly using these distributions instead of approximating them as normal distributions. pomegranate is flexible enough to allow for this, whereas sklearn currently is not." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gaussian Naive Bayes: 0.798\n", "sklearn Gaussian Naive Bayes: 0.798\n", "Heterogeneous Naive Bayes: 0.844\n" ] } ], "source": [ "model = NaiveBayes.from_samples(NormalDistribution, X_train, y_train)\n", "print \"Gaussian Naive Bayes: \", (model.predict(X_test) == y_test).mean()\n", "\n", "clf = GaussianNB().fit(X_train, y_train)\n", "print \"sklearn Gaussian Naive Bayes: \", (clf.predict(X_test) == y_test).mean()\n", "\n", "model = NaiveBayes.from_samples([NormalDistribution, LogNormalDistribution, ExponentialDistribution], X_train, y_train)\n", "print \"Heterogeneous Naive Bayes: \", (model.predict(X_test) == y_test).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like we're able to get a small improvement in accuracy just by using appropriate distributions for the features, without any type of data transformation or filtering. This certainly seems worthwhile if you can determine what the appropriate underlying distribution is.\n", "\n", "Next, there's obviously the issue of speed. Let's compare the speed of the pomegranate implementation and the sklearn implementation." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 2.45 ms per loop\n", "1000 loops, best of 3: 2.03 ms per loop\n", "100 loops, best of 3: 1.99 ms per loop\n" ] } ], "source": [ "%timeit GaussianNB().fit(X_train, y_train)\n", "%timeit NaiveBayes.from_samples(NormalDistribution, X_train, y_train)\n", "%timeit NaiveBayes.from_samples([NormalDistribution, LogNormalDistribution, ExponentialDistribution], X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks as if on this small dataset they're all taking approximately the same time. This is pretty much expected, as the fitting step is fairly simple and both implementations use C-level numerics for the calculations. We can give a more thorough treatment of the speed comparison on larger datasets. Let's look at the average time it takes to fit a model to data of increasing dimensionality across 25 runs." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pom_time, skl_time = [], []\n", "\n", "n1, n2 = 15000, 60000,\n", "for d in range(1, 101, 5): \n", " X = numpy.concatenate([numpy.random.normal(3, 2, size=(n1, d)), numpy.random.normal(7, 1, size=(n2, d))])\n", " y = numpy.concatenate([numpy.zeros(n1), numpy.ones(n2)])\n", "\n", " tic = time.time()\n", " for i in range(25):\n", " GaussianNB().fit(X, y)\n", " skl_time.append((time.time() - tic) / 25)\n", " \n", " tic = time.time()\n", " for i in range(25):\n", " NaiveBayes.from_samples(NormalDistribution, X, y)\n", " pom_time.append((time.time() - tic) / 25)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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fPkynTkcumEtKSsLf358uXbo0aXvfffcRHR3N1KlTGx8zmUw4Ozvj6upK7969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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 6))\n", "plt.plot(range(1, 101, 5), pom_time, color='c', label=\"pomegranate\")\n", "plt.plot(range(1, 101, 5), skl_time, color='m', label=\"sklearn\")\n", "plt.xticks(fontsize=14)\n", "plt.xlabel(\"Number of Dimensions\", fontsize=14)\n", "plt.yticks(fontsize=14)\n", "plt.ylabel(\"Time (s)\")\n", "plt.legend(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It appears as if the two implementations are basically the same speed. This is unsurprising given the simplicity of the calculations, and as mentioned before, the low level implementation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bayes Classifiers\n", "\n", "The natural generalization of the naive Bayes classifier is to allow any multivariate function take the place of $P(D|M)$ instead of it being the product of several univariate probability distributions. One immediate difference is that now instead of creating a Gaussian model with effectively a diagonal covariance matrix, you can now create one with a full covariance matrix. Let's see an example of that at work." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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KI8Y1pdzQ+od55RGpBO+//z7z589n586dhEIhrrvuOr7zne8EXdaQpkaCDBlB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tilt_a = [[-2, 0.5], [5, 2]]\n", "tilt_b = [[-1, 1.5], [3, 3]]\n", "\n", "X = numpy.concatenate((numpy.random.normal(4, 1, size=(250, 2)).dot(tilt_a), numpy.random.normal(3, 1, size=(800, 2)).dot(tilt_b)))\n", "y = numpy.concatenate((numpy.zeros(250), numpy.ones(800)))\n", "\n", "model_a = NaiveBayes.from_samples(NormalDistribution, X, y)\n", "model_b = BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y)\n", "\n", "xx, yy = np.meshgrid(np.arange(-5, 30, 0.02), np.arange(0, 25, 0.02))\n", "Z1 = model_a.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "Z2 = model_b.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "\n", "plt.figure(figsize=(18, 8))\n", "plt.subplot(121)\n", "plt.contour(xx, yy, Z1)\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c', alpha=0.3)\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m', alpha=0.3)\n", "plt.xlim(-5, 30)\n", "plt.ylim(0, 25)\n", "\n", "plt.subplot(122)\n", "plt.contour(xx, yy, Z2)\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c', alpha=0.3)\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m', alpha=0.3)\n", "plt.xlim(-5, 30)\n", "plt.ylim(0, 25)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like we are able to get a better boundary between the two blobs of data. The primary for this is because the data don't form spherical clusters, like you assume when you force a diagonal covariance matrix, but are tilted ellipsoids, that can be better modeled by a full covariance matrix. We can quantify this quickly by looking at performance on the training data." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "naive training accuracy: 0.9286\n", "bayes classifier training accuracy: 0.9657\n" ] } ], "source": [ "print \"naive training accuracy: {:4.4}\".format((model_a.predict(X) == y).mean())\n", "print \"bayes classifier training accuracy: {:4.4}\".format((model_b.predict(X) == y).mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like there is a significant boost. Naturally you'd want to evaluate the performance of the model on separate validation data, but for the purposes of demonstrating the effect of a full covariance matrix this should be sufficient.\n", "\n", "While using a full covariance matrix is certainly more complicated than using only the diagonal, there is no reason that the $P(D|M)$ has to even be a single simple distribution versus a full probabilistic model. After all, all probabilistic models, including general mixtures, hidden Markov models, and Bayesian networks, can calculate $P(D|M)$. Let's take a look at an example of using a mixture model instead of a single gaussian distribution." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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azDZaPAIAqAAmNxCugE40DAycsgO/z58Uj0ytrGQ8QmsNkwtEm8BCRxtN+lxj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oOPaKXxx20qQvPCVq98RJXUclxkaGyrjbGG0mpBESUR0pPkb8+vy0dUWSGltKkQe1M+kiJFL0iL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\n93msVmNQyta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pSDlrUW/+2ajp7GdaM5IkVLOMBIiEIBKCGjCVZTxYLpNzXdaFISXXZcD3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.empty(shape=(0, 2))\n", "X = numpy.concatenate((X, numpy.random.normal(4, 1, size=(200, 2)).dot([[-2, 0.5], [2, 0.5]])))\n", "X = numpy.concatenate((X, numpy.random.normal(3, 1, size=(350, 2)).dot([[-1, 2], [1, 0.8]])))\n", "X = numpy.concatenate((X, numpy.random.normal(7, 1, size=(700, 2)).dot([[-0.75, 0.8], [0.9, 1.5]])))\n", "X = numpy.concatenate((X, numpy.random.normal(6, 1, size=(120, 2)).dot([[-1.5, 1.2], [0.6, 1.2]])))\n", "y = numpy.concatenate((numpy.zeros(550), numpy.ones(820)))\n", "\n", "model_a = BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y)\n", "\n", "gmm_a = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X[y == 0])\n", "gmm_b = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X[y == 1])\n", "model_b = BayesClassifier([gmm_a, gmm_b], weights=numpy.array([1-y.mean(), y.mean()]))\n", "\n", "xx, yy = np.meshgrid(np.arange(-10, 10, 0.02), np.arange(0, 25, 0.02))\n", "Z1 = model_a.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "Z2 = model_b.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "centroids1 = numpy.array([distribution.mu for distribution in model_a.distributions])\n", "centroids2 = numpy.concatenate([[distribution.mu for distribution in component.distributions] for component in model_b.distributions])\n", "\n", "plt.figure(figsize=(18, 8))\n", "plt.subplot(121)\n", "plt.contour(xx, yy, Z1)\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c', alpha=0.3)\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m', alpha=0.3)\n", "plt.scatter(centroids1[:,0], centroids1[:,1], color='k', s=100)\n", "\n", "plt.subplot(122)\n", "plt.contour(xx, yy, Z2)\n", "plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c', alpha=0.3)\n", "plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m', alpha=0.3)\n", "plt.scatter(centroids2[:,0], centroids2[:,1], color='k', s=100)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Frequently in the real world you will end up with data that looks like the ones in the above plots, not neatly falling into any single simple distribution. Using a mixture here allowed us to model the various components separately and get a more sophisticated decision boundary that doesn't seem to be extremely overfit.\n", "\n", "If one wanted to use hidden Markov models to model sequences, all that needs to change is passing in `HiddenMarkovModel` objects instead of `GeneralMixtureModel` ones. Likewise, Bayesian networks are similarly supported and can be passed in just as easily. In fact, the most stacking one could reasonably do is to create a Bayes classifier that distinguishes mixtures of hidden Markov models with mixture emissions (GMM-HMM-GMMs) from each other!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/old/Tutorial_6_Markov_Chain.ipynb000066400000000000000000000161041420461712600312130ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Markov Chains\n", "\n", "author: Jacob Schreiber
\n", "contact: jmschreiber91@gmail.com\n", "\n", "Markov Chains are a simple model based on conditional probability, where a sequence is modelled as the product of conditional probabilities. A n-th order Markov chain looks back n emissions to base its conditional probability on. For example, a 3rd order Markov chain models $P(X_{t} | X_{t-1}, X_{t-2}, X_{t-3})$.\n", "\n", "However, a full Markov model needs to model the first observations, and the first n-1 observations. The first observation can't really be modelled well using $P(X_{t} | X_{t-1}, X_{t-2}, X_{t-3})$, but can be modelled by $P(X_{t})$. The second observation has to be modelled by $P(X_{t} | X_{t-1} )$. This means that these distributions have to be passed into the Markov chain as well. \n", "\n", "We can initialize a Markov chain easily enough by passing in a list of the distributions." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jmschr/anaconda/lib/python2.7/site-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['log', 'random']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] } ], "source": [ "from pomegranate import *\n", "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d1 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})\n", "d2 = ConditionalProbabilityTable([['A', 'A', 0.10],\n", " ['A', 'C', 0.50],\n", " ['A', 'G', 0.30],\n", " ['A', 'T', 0.10],\n", " ['C', 'A', 0.10],\n", " ['C', 'C', 0.40],\n", " ['C', 'T', 0.40],\n", " ['C', 'G', 0.10],\n", " ['G', 'A', 0.05],\n", " ['G', 'C', 0.45],\n", " ['G', 'G', 0.45],\n", " ['G', 'T', 0.05],\n", " ['T', 'A', 0.20],\n", " ['T', 'C', 0.30],\n", " ['T', 'G', 0.30],\n", " ['T', 'T', 0.20]], [d1])\n", "\n", "clf = MarkovChain([d1, d2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Markov chains have log probability, fit, summarize, and from summaries methods implemented. They do not have classification capabilities by themselves, but when combined with a Naive Bayes classifier can be used to do discrimination between multiple models (see the Naive Bayes tutorial notebook).\n", "\n", "Lets see the log probability of some data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-17.532789486599906" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.log_probability( list('CAGCATCAGT') ) " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-0.916290731874155" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.log_probability( list('C') )" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-38.55615991599665" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.log_probability( list('CACATCACGACTAATGATAAT') )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can fit the model to sequences which we pass in, and as expected, get better performance on sequences which we train on. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-9.49627091139\n", "-0.69314718056\n", "-25.2575143893\n" ] } ], "source": [ "clf.fit( map( list, ('CAGCATCAGT', 'C', 'ATATAGAGATAAGCT', 'GCGCAAGT', 'GCATTGC', 'CACATCACGACTAATGATAAT') ) )\n", "print clf.log_probability( list('CAGCATCAGT') ) \n", "print clf.log_probability( list('C') )\n", "print clf.log_probability( list('CACATCACGACTAATGATAAT') )" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " {\n", " \"A\" :0.16666666666666666,\n", " \"C\" :0.5,\n", " \"T\" :0.0,\n", " \"G\" :0.33333333333333331\n", " }\n", " ],\n", " \"name\" :\"DiscreteDistribution\"\n", "}\n" ] } ], "source": [ "print clf.distributions[0] " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\tA\t0.181818181818\n", "A\tC\t0.136363636364\n", "A\tG\t0.272727272727\n", "A\tT\t0.409090909091\n", "C\tA\t0.666666666667\n", "C\tC\t0.0\n", "C\tT\t0.166666666667\n", "C\tG\t0.166666666667\n", "G\tA\t0.333333333333\n", "G\tC\t0.5\n", "G\tG\t0.0\n", "G\tT\t0.166666666667\n", "T\tA\t0.5\n", "T\tC\t0.2\n", "T\tG\t0.2\n", "T\tT\t0.1\n" ] } ], "source": [ "print clf.distributions[1]" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/old/Tutorial_7_Parallelization.ipynb000066400000000000000000002437421420461712600320170ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pomegranate and parallelization\n", "\n", "pomegranate supports parallelization through a set of built in functions based off of joblib. All computationally intensive functions in pomegranate are implemented in cython with the global interpreter lock (GIL) released, allowing for multithreading to be used for efficient parallel processing. The following functions can be called for parallelization:\n", "\n", "1. fit\n", "2. summarize\n", "3. predict\n", "4. predict_proba\n", "5. predict_log_proba\n", "6. log_probability\n", "7. probability\n", "\n", "These functions can all be simply parallelized by passing in `n_jobs=X` to the method calls. This tutorial will demonstrate how to use those calls. First we'll look at a simple multivariate Gaussian mixture model, and compare its performance to sklearn. Then we'll look at a hidden Markov model with Gaussian emissions, and lastly we'll look at a mixture of Gaussian HMMs. These can all utilize the build-in parallelization that pomegranate has.\n", "\n", "Let's dive right in!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "from sklearn.mixture import GaussianMixture\n", "from pomegranate import *\n", "import seaborn, time\n", "seaborn.set_style('whitegrid')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def create_dataset(n_samples, n_dim, n_classes, alpha=1):\n", " \"\"\"Create a random dataset with n_samples in each class.\"\"\"\n", " \n", " X = numpy.concatenate([numpy.random.normal(i*alpha, 1, size=(n_samples, n_dim)) for i in range(n_classes)])\n", " y = numpy.concatenate([numpy.zeros(n_samples) + i for i in range(n_classes)])\n", " idx = numpy.arange(X.shape[0])\n", " numpy.random.shuffle(idx)\n", " return X[idx], y[idx]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. General Mixture Models\n", "\n", "pomegranate has a very efficient implementation of mixture models, particularly Gaussian mixture models. Lets take a look at how fast pomegranate is versus sklearn, and then see how much faster parallelization can get it to be." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sklearn GMM\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jmschr/anaconda2/lib/python2.7/site-packages/sklearn/mixture/base.py:237: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 37 s per loop\n", "\n", "pomegranate GMM\n", "1 loop, best of 3: 23.6 s per loop\n", "\n", "pomegranate GMM (4 jobs)\n", "1 loop, best of 3: 10.6 s per loop\n" ] } ], "source": [ "n, d, k = 1000000, 5, 3\n", "X, y = create_dataset(n, d, k)\n", "\n", "print \"sklearn GMM\"\n", "%timeit GaussianMixture(n_components=k, covariance_type='full', max_iter=15, tol=1e-10).fit(X)\n", "print \n", "print \"pomegranate GMM\"\n", "%timeit GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, k, X, max_iterations=15, stop_threshold=1e-10)\n", "print\n", "print \"pomegranate GMM (4 jobs)\"\n", "%timeit GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, k, X, n_jobs=4, max_iterations=15, stop_threshold=1e-10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like on a large dataset not only is pomegranate faster than sklearn at performing 15 iterations of EM on 3 million 5 dimensional datapoints with 3 clusters, but the parallelization is able to help in speeding things up. \n", "\n", "Lets now take a look at the time it takes to make predictions using GMMs. Lets fit the model to a small amount of data, and then predict a larger amount of data drawn from the same underlying distributions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "d, k = 25, 2\n", "X, y = create_dataset(1000, d, k)\n", "a = GaussianMixture(k, n_init=1, max_iter=25).fit(X)\n", "b = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, k, X, max_iterations=25)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sklearn GMM\n", "1 loop, best of 3: 2.12 s per loop\n", "\n", "pomegranate GMM\n", "1 loop, best of 3: 1.68 s per loop\n", "\n", "pomegranate GMM (4 jobs)\n", "1 loop, best of 3: 1.59 s per loop\n" ] } ], "source": [ "del X, y\n", "n = 1000000\n", "X, y = create_dataset(n, d, k)\n", "\n", "print \"sklearn GMM\"\n", "%timeit -n 1 a.predict_proba(X)\n", "print\n", "print \"pomegranate GMM\"\n", "%timeit -n 1 b.predict_proba(X)\n", "print\n", "print \"pomegranate GMM (4 jobs)\"\n", "%timeit -n 1 b.predict_proba(X, n_jobs=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like pomegranate can be slightly slower than sklearn when using a single processor, but that it can be parallelized to get faster performance. At the same time, predictions at this level happen so quickly (millions per second) that this may not be the most reliable test for parallelization.\n", "\n", "To ensure that we're getting the exact same results just faster, lets subtract the predictions from each other and make sure that the sum is equal to 0." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0\n" ] } ], "source": [ "print (b.predict_proba(X) - b.predict_proba(X, n_jobs=4)).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, no difference between the two.\n", "\n", "Lets now make sure that pomegranate and sklearn are learning basically the same thing. Lets fit both models to some 2 dimensional 2 component data and make sure that they both extract the underlying clusters by plotting them." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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VjqIO6c5RyWQODEEQBJEdSJsTIW0mrOBYyxQAOO+883Deeec5OSRB9BrivZ4n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\nAMA2DsuyLLtfpKWlxe6XAAD8Y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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d, k = 2, 2\n", "X, y = create_dataset(1000, d, k, alpha=2)\n", "a = GaussianMixture(k, n_init=1, max_iter=25).fit(X)\n", "b = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, k, X, max_iterations=25)\n", "\n", "y1, y2 = a.predict(X), b.predict(X)\n", "\n", "plt.figure(figsize=(16,6))\n", "plt.subplot(121)\n", "plt.title(\"sklearn clusters\", fontsize=14)\n", "plt.scatter(X[y1==0, 0], X[y1==0, 1], color='m', edgecolor='m')\n", "plt.scatter(X[y1==1, 0], X[y1==1, 1], color='c', edgecolor='c')\n", "\n", "plt.subplot(122)\n", "plt.title(\"pomegranate clusters\", fontsize=14)\n", "plt.scatter(X[y2==0, 0], X[y2==0, 1], color='m', edgecolor='m')\n", "plt.scatter(X[y2==1, 0], X[y2==1, 1], color='c', edgecolor='c')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like we're getting the same basic results for the two. The two algorithms are initialized a bit differently, and so it can be difficult to directly compare the results between them, but it looks like they're getting roughly the same results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Multivariate Gaussian HMM\n", "\n", "Now let's move on to training a hidden Markov model with multivariate Gaussian emissions with a diagonal covariance matrix. We'll randomly generate some Gaussian distributed numbers and use pomegranate with either one or four threads to fit our model to the data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pomegranate Gaussian HMM (1 job)\n", "1 loop, best of 1: 22.1 s per loop\n", "\n", "pomegranate Gaussian HMM (2 jobs)\n", "1 loop, best of 1: 12.9 s per loop\n", "\n", "pomegranate Gaussian HMM (2 jobs)\n", "1 loop, best of 1: 8.37 s per loop\n" ] } ], "source": [ "X = numpy.random.randn(1000, 500, 50)\n", "\n", "print \"pomegranate Gaussian HMM (1 job)\"\n", "%timeit -n 1 -r 1 HiddenMarkovModel.from_samples(NormalDistribution, 5, X, max_iterations=5)\n", "print\n", "print \"pomegranate Gaussian HMM (2 jobs)\"\n", "%timeit -n 1 -r 1 HiddenMarkovModel.from_samples(NormalDistribution, 5, X, max_iterations=5, n_jobs=2)\n", "print\n", "print \"pomegranate Gaussian HMM (2 jobs)\"\n", "%timeit -n 1 -r 1 HiddenMarkovModel.from_samples(NormalDistribution, 5, X, max_iterations=5, n_jobs=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All we had to do was pass in the n_jobs parameter to the fit function in order to get a speed improvement. It looks like we're getting a really good speed improvement, as well! This is mostly because the HMM algorithms perform a lot more operations than the other models, and so spend the vast majority of time with the GIL released. You may not notice as strong speedups when using a MultivariateGaussianDistribution because BLAS uses multithreaded operations already internally, even when only one job is specified.\n", "\n", "Now lets look at the prediction function to make sure the we're getting speedups there as well. You'll have to use a wrapper function to parallelize the predictions for a HMM because it returns an annotated sequence rather than a single value like a classic machine learning model might." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pomegranate Gaussian HMM (1 job)\n", "1 loop, best of 3: 3.22 s per loop\n", "\n", "pomegranate Gaussian HMM (2 jobs)\n", "1 loop, best of 3: 1.93 s per loop\n" ] } ], "source": [ "model = HiddenMarkovModel.from_samples(NormalDistribution, 5, X, max_iterations=2, verbose=False)\n", "\n", "print \"pomegranate Gaussian HMM (1 job)\"\n", "%timeit predict_proba(model, X)\n", "print\n", "print \"pomegranate Gaussian HMM (2 jobs)\"\n", "%timeit predict_proba(model, X, n_jobs=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, we're getting a really good speedup on that as well! Looks like the parallel processing is more efficient with a bigger, more complex model, than with a simple one. This can make sense, because all inference/training is more complex, and so there is more time with the GIL released compared to with the simpler operations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Mixture of Hidden Markov Models\n", "\n", "Let's stack another layer onto this model by making it a mixture of these hidden Markov models, instead of a single one. At this point we're sticking a multivariate Gaussian HMM into a mixture and we're going to train this big thing in parallel." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def create_model(mus):\n", " n = mus.shape[0]\n", " \n", " starts = numpy.zeros(n)\n", " starts[0] = 1.\n", " \n", " ends = numpy.zeros(n)\n", " ends[-1] = 0.5\n", " \n", " transition_matrix = numpy.zeros((n, n))\n", " distributions = []\n", " \n", " for i in range(n):\n", " transition_matrix[i, i] = 0.5\n", " \n", " if i < n - 1:\n", " transition_matrix[i, i+1] = 0.5\n", " \n", " distribution = IndependentComponentsDistribution([NormalDistribution(mu, 1) for mu in mus[i]])\n", " distributions.append(distribution)\n", " \n", " model = HiddenMarkovModel.from_matrix(transition_matrix, distributions, starts, ends)\n", " return model\n", " \n", "\n", "def create_mixture(mus):\n", " hmms = [create_model(mu) for mu in mus]\n", " return GeneralMixtureModel(hmms)\n", "\n", "n, d = 50, 10\n", "mus = [(numpy.random.randn(d, n)*0.2 + numpy.random.randn(n)*2).T for i in range(2)]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pomegranate Mixture of Gaussian HMMs (1 job)\n", "1 loop, best of 3: 3 s per loop\n", "\n", "pomegranate Mixture of Gaussian HMMs (2 jobs)\n", "1 loop, best of 3: 1.83 s per loop\n" ] } ], "source": [ "model = create_mixture(mus)\n", "X = numpy.random.randn(400, 150, d)\n", "\n", "print \"pomegranate Mixture of Gaussian HMMs (1 job)\"\n", "%timeit model.fit(X, max_iterations=5)\n", "print\n", "\n", "model = create_mixture(mus)\n", "print \"pomegranate Mixture of Gaussian HMMs (2 jobs)\"\n", "%timeit model.fit(X, max_iterations=5, n_jobs=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like we're getting a really nice speed improvement when training this complex model. Let's take a look now at the time it takes to do inference with it." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pomegranate Mixture of Gaussian HMMs (1 job)\n", "1 loop, best of 3: 598 ms per loop\n", "\n", "pomegranate Mixture of Gaussian HMMs (2 jobs)\n", "1 loop, best of 3: 411 ms per loop\n" ] } ], "source": [ "model = create_mixture(mus)\n", "\n", "print \"pomegranate Mixture of Gaussian HMMs (1 job)\"\n", "%timeit model.predict_proba(X)\n", "print\n", "\n", "model = create_mixture(mus)\n", "print \"pomegranate Mixture of Gaussian HMMs (2 jobs)\"\n", "%timeit model.predict_proba(X, n_jobs=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're getting a good speed improvement here too through parallelization." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusions\n", "\n", "Hopefully you'll find pomegranate useful in your work! Parallelization should allow you to train complex models faster than before. Keep in mind though that there is an overhead to using parallel processing, and so it's possible that on some smaller examples it does not work as well. In general, the bigger the dataset, the closer to a linear speedup you'll get with pomegranate.\n", "\n", "If you have any interesting examples of how you've used pomegranate in your work, I'd love to hear about them. In addition I'd like to hear any feedback you may have on features you'd like to see. Please shoot me an email. Good luck!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 1 } Tutorial_8_Semisupervised_Learning.ipynb000066400000000000000000034773451420461712600334500ustar00rootroot00000000000000pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/old{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Semi-supervised Learning in pomegranate\n", "\n", "Most classical machine learning algorithms either assume that an entire dataset is either labeled (supervised learning) or that there are no labels (unsupervised learning). However, frequently it is the case that some labeled data is present but there is a great deal of unlabeled data as well. A great example of this is that of computer vision where the internet is filled of pictures (mostly of cats) that could be useful, but you don't have the time or money to label them all in accordance with your specific task. Typically what ends up happening is that either the unlabeled data is discarded in favor of training a model solely on the labeled data, or that an unsupervised model is initialized with the labeled data and then set free on the unlabeled data. Neither method uses both sets of data in the optimization process.\n", "\n", "Semi-supervised learning is a method to incorporate both labeled and unlabeled data into the training task, typically yield better performing estimators than using the labeled data alone. There are many methods one could use for semisupervised learning, and scikit-learn has a good write-up on some of these techniques.\n", "\n", "pomegranate natively implements semi-supervised learning through the a merger of maximum-likelihood and expectation-maximization. As an overview, the models are initialized by first fitting to the labeled data directly using maximum-likelihood estimates. The models are then refined by running expectation-maximization (EM) on the unlabeled datasets and adding the sufficient statistics to those acquired from maximum-likelihood estimates on the labeled data. Under the hood both a supervised model and an unsupervised mixture model are created using the same underlying distribution objects. The summarize method is first called using the supervised method on the labeled data, and then the summarize method is called again using the unsupervised method on the unlabeled data. This causes the sufficient statistics to be updated appropriately given the results of first maximum-likelihood and then EM. This process continues until convergence in the EM step.\n", "\n", "Let's take a look!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "from pomegranate import *\n", "from sklearn.semi_supervised import LabelPropagation\n", "from sklearn.datasets import make_blobs\n", "import seaborn, time\n", "seaborn.set_style('whitegrid')\n", "numpy.random.seed(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first generate some data in the form of blobs that are close together. Generally one tends to have far more unlabeled data than labeled data, so let's say that a person only has 50 samples of labeled training data and 4950 unlabeled samples. In pomegranate you a sample can be specified as lacking a label by providing the integer -1 as the label, just like in scikit-learn. Let's also say there there is a bit of bias in the labeled samples to inject some noise into the problem, as otherwise Gaussian blobs are trivially modeled with even a few samples." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X, y = make_blobs(10000, 2, 3, cluster_std=2)\n", "x_min, x_max = X[:,0].min()-2, X[:,0].max()+2\n", "y_min, y_max = X[:,1].min()-2, X[:,1].max()+2\n", "\n", "X_train = X[:5000]\n", "y_train = y[:5000]\n", "\n", "# Set the majority of samples to unlabeled.\n", "y_train[numpy.random.choice(5000, size=4950, replace=False)] = -1\n", "\n", "# Inject noise into the problem\n", "X_train[y_train != -1] += 2.5\n", "\n", "X_test = X[5000:]\n", "y_test = y[5000:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's take a look at the data when we plot it." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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UoNdut4UTpQhvkCgTRI+Ish5T1rSBd14eogV4cnIS09PT1r+dgsr+RkI7GNg3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\n00ZfcC8AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 8))\n", "plt.scatter(X_train[y_train == -1, 0], X_train[y_train == -1, 1], color='0.6')\n", "plt.scatter(X_train[y_train == 0, 0], X_train[y_train == 0, 1], color='c')\n", "plt.scatter(X_train[y_train == 1, 0], X_train[y_train == 1, 1], color='m')\n", "plt.scatter(X_train[y_train == 2, 0], X_train[y_train == 2, 1], color='r')\n", "plt.xlim(x_min, x_max)\n", "plt.ylim(y_min, y_max)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The clusters of unlabeled data seem clear to us, and it doesn't seem like the labeled data is perfectly faithful to these clusters. This can typically happen in a semisupervised setting as well, as the data that is labeled is sometimes biased either because the labeled data was chosen as it was easy to label, or the data was chosen to be labeled in a biased maner.\n", "\n", "Now let's try fitting a simple naive Bayes classifier to this data and compare the results when using only the labeled data to when using both the labeled and unlabeled data together." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Supervised Learning Accuracy: 0.8706\n", "Semisupervised Learning Accuracy: 0.9274\n" ] } ], "source": [ "model_a = NaiveBayes.from_samples(NormalDistribution, X_train[y_train != -1], y_train[y_train != -1])\n", "print \"Supervised Learning Accuracy: {}\".format((model_a.predict(X_test) == y_test).mean())\n", "\n", "model_b = NaiveBayes.from_samples(NormalDistribution, X_train, y_train)\n", "print \"Semisupervised Learning Accuracy: {}\".format((model_b.predict(X_test) == y_test).mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems like we get a big bump in test set accuracy when we use semi-supervised learning. Let's visualize the data to get a better sense of what is happening here." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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eR6ckT8Z3GsHx4yedHZaoIFJTU4mLi8NikfuohBCivIQO6sETn76CTq+j4w0T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\nxzUxMU9CmLsq66irr9lqzff85HeOxh0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LT/bSjphj7QKrVvYigwTfcDE2I5n6pHL6fbA/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Ak52PZ9qGE5dj5kwiT0Sou7UCKXYYueFqAJRZmCBeSMo59y7ZtYuMVX7W2AIW+3y8MjExr6rl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\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d095eO0HTgAHWKShNnYPjYqkWYQsriRZb3LSUSiUiIiKwdetWfUvz9OnTjV6D4t2PZTIZ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Xr8DMfOtozn0UtemzkFGS+V/D5lGwkfo3iasCjRefG55N9xHWJhGfzv/6LMvgmh+/5GBVCs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\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bYB1PRro2Fhb5WGvz8cdYm2PvbEc48CJlt94IdmfO2jz31FqbC/UZxuNw2WW6YE0mB5WZ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\nMTcAP/iBXtp78cWjTwe+6y7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_contour(X, y, Z):\n", " plt.scatter(X[y == -1, 0], X[y == -1, 1], color='0.2', alpha=0.5, s=20)\n", " plt.scatter(X[y == 0, 0], X[y == 0, 1], color='c', s=20)\n", " plt.scatter(X[y == 1, 0], X[y == 1, 1], color='m', s=20)\n", " plt.scatter(X[y == 2, 0], X[y == 2, 1], color='r', s=20)\n", " plt.contour(xx, yy, Z)\n", " plt.xlim(x_min, x_max)\n", " plt.ylim(y_min, y_max)\n", " plt.xticks(fontsize=14)\n", " plt.yticks(fontsize=14)\n", "\n", "xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, 0.1), numpy.arange(y_min, y_max, 0.1))\n", "Z1 = model_a.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "Z2 = model_b.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "\n", "plt.figure(figsize=(16, 16))\n", "plt.subplot(221)\n", "plt.title(\"Training Data, Supervised Boundaries\", fontsize=16)\n", "plot_contour(X_train, y_train, Z1)\n", "\n", "plt.subplot(223)\n", "plt.title(\"Training Data, Semi-supervised Boundaries\", fontsize=16)\n", "plot_contour(X_train, y_train, Z2)\n", "\n", "plt.subplot(222)\n", "plt.title(\"Test Data, Supervised Boundaries\", fontsize=16)\n", "plot_contour(X_test, y_test, Z1)\n", "\n", "plt.subplot(224)\n", "plt.title(\"Test Data, Semi-supervised Boundaries\", fontsize=16)\n", "plot_contour(X_test, y_test, Z2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The contours plot the decision boundaries between the different classes with the left figures corresponding to the partially labeled training set and the right figures corresponding to the test set. We can see that the boundaries learning using only the labeled data look a bit weird when considering the unlabeled data, particularly in that it doesn't cleanly separate the cyan cluster from the other two. In addition, it seems like the boundary between the magenta and red clusters is a bit curved in an unrealistic way. We would not expect points that fell around (-18, -7) to actually come from the red class. Training the model in a semi-supervised manner cleaned up both of these concerns by learning better boundaries that are also flatter and more generalizable.\n", "\n", "Let's next compare the training times to see how much slower it is to do semi-supervised learning than it is to do simple supervised learning." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Supervised Learning: \n", "1000 loops, best of 3: 379 µs per loop\n", "\n", "Semi-supervised Learning: \n", "10 loops, best of 3: 31.8 ms per loop\n", "\n", "Label Propagation (sklearn): \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jmschr/anaconda2/lib/python2.7/site-packages/sklearn/semi_supervised/label_propagation.py:277: RuntimeWarning: invalid value encountered in divide\n", " self.label_distributions_ /= normalizer\n", "/home/jmschr/anaconda2/lib/python2.7/site-packages/sklearn/semi_supervised/label_propagation.py:288: ConvergenceWarning: max_iter=1000 was reached without convergence.\n", " category=ConvergenceWarning\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 50.9 s per loop\n" ] } ], "source": [ "print \"Supervised Learning: \"\n", "%timeit NaiveBayes.from_samples(NormalDistribution, X_train[y_train != -1], y_train[y_train != -1])\n", "print\n", "print \"Semi-supervised Learning: \"\n", "%timeit NaiveBayes.from_samples(NormalDistribution, X_train, y_train)\n", "print\n", "print \"Label Propagation (sklearn): \"\n", "%timeit LabelPropagation().fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is quite a bit slower to do semi-supervised learning than simple supervised learning in this example. This is expected as the simple supervised update for naive Bayes is a trivial MLE across each dimension whereas the semi-supervised case requires EM to converge to complete. However, it is still faster to do semi-supervised learning this setting to learn a naive Bayes classifier than it is to fit the label propagation estimator from sklearn. \n", "\n", "However, though it is widely used, the naive Bayes classifier is still a fairly simple model. One can construct a more complicated model that does not assume feature independence called a Bayes classifier that can also be trained using semi-supervised learning in pretty much the same manner. You can read more about the Bayes classifier in its tutorial in the tutorial folder. Let's move on to more complicated data and try to fit a mixture model Bayes classifier, comparing the performance between using only labeled data and using all data.\n", "\n", "First let's generate some more complicated, noisier data." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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Ll6gdE6Ncbt68iaWlJTKNksvliEajSKVSsFqtzSz1NlD9lyqyqSwqtmaGPTQ0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\nxf4pNEJOp9Ohr68PW1tbUCgUrJkHGh2wNBmHnAVtNtue3b7kSNjitl8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CGBgYQDAYxMTE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\njNxvR8olK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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.empty(shape=(0, 2))\n", "X = numpy.concatenate((X, numpy.random.normal(4, 1, size=(3000, 2)).dot([[-2, 0.5], [2, 0.5]])))\n", "X = numpy.concatenate((X, numpy.random.normal(3, 1, size=(6500, 2)).dot([[-1, 2], [1, 0.8]])))\n", "X = numpy.concatenate((X, numpy.random.normal(7, 1, size=(8000, 2)).dot([[-0.75, 0.8], [0.9, 1.5]])))\n", "X = numpy.concatenate((X, numpy.random.normal(6, 1, size=(2200, 2)).dot([[-1.5, 1.2], [0.6, 1.2]])))\n", "X = numpy.concatenate((X, numpy.random.normal(8, 1, size=(3500, 2)).dot([[-0.2, 0.8], [0.7, 0.8]])))\n", "X = numpy.concatenate((X, numpy.random.normal(9, 1, size=(6500, 2)).dot([[-0.0, 0.8], [0.5, 1.2]])))\n", "x_min, x_max = X[:,0].min()-2, X[:,0].max()+2\n", "y_min, y_max = X[:,1].min()-2, X[:,1].max()+2\n", "\n", "y = numpy.concatenate((numpy.zeros(9500), numpy.ones(10200), numpy.ones(10000)*2))\n", "idxs = numpy.arange(29700)\n", "numpy.random.shuffle(idxs)\n", "\n", "X = X[idxs]\n", "y = y[idxs]\n", "\n", "X_train, X_test = X[:25000], X[25000:]\n", "y_train, y_test = y[:25000], y[25000:]\n", "y_train[numpy.random.choice(25000, size=24920, replace=False)] = -1\n", "\n", "plt.scatter(X_train[y_train == -1, 0], X_train[y_train == -1, 1], color='0.6', s=1)\n", "plt.scatter(X_train[y_train == 0, 0], X_train[y_train == 0, 1], color='c', s=10)\n", "plt.scatter(X_train[y_train == 1, 0], X_train[y_train == 1, 1], color='m', s=10)\n", "plt.scatter(X_train[y_train == 2, 0], X_train[y_train == 2, 1], color='r', s=10)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's take a look at the accuracies that we get when training a model using just the labeled examples versus all of the examples in a semi-supervised manner." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Supervised Learning Accuracy: 0.936170212766\n", "Semisupervised Learning Accuracy: 0.984680851064\n" ] } ], "source": [ "d1 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 0], max_iterations=1)\n", "d2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 1], max_iterations=1)\n", "d3 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 2], max_iterations=1)\n", "model_a = BayesClassifier([d1, d2, d3]).fit(X_train[y_train != -1], y_train[y_train != -1])\n", "print \"Supervised Learning Accuracy: {}\".format((model_a.predict(X_test) == y_test).mean())\n", "\n", "d1 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 0], max_iterations=1)\n", "d2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 1], max_iterations=1)\n", "d3 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 2], max_iterations=1)\n", "model_b = BayesClassifier([d1, d2, d3])\n", "model_b.fit(X_train, y_train)\n", "print \"Semisupervised Learning Accuracy: {}\".format((model_b.predict(X_test) == y_test).mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the semi-supervised method performs better. Let's visualize the landscape in the same manner as before in order to see why this is the case." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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FF2natCk33HADDz30EFarlaysLIqKisqdl1OybRkZGeW+zvK2VcRisZS6Tv/+/enQoQO/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\nObchWbRoEUuWLEEURUpKSojFYui6bmybTHUNh8M0NjYyY8YMysrKchZ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i25rf9NhIJIIg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GYBJWx9jXhCiEbn/jXLOGtQDiGaIwSDQbq6usyZT6fTSSgUwuVymeTNIE+gzxomEglUVTWDTzRN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FZX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iov79Hx7hk6lCdGjepSfEY7z/cXF8dcGFDH6u76nIoMGpTGW1vXcHHeZVxnHMHe97aSNW5k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\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mE3rhlj4PV7/5P9z45YLYIdkd5mYiIipp2vTxaDy2B+7o83A56gRu/XrJque3WAF6+PBh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PEfSY75c3AsmDu5hebIkWPKKF9TTtHHizIWY6nE0kgHajaJu1TMtjEyqfpqR6GT0pkeea7DzWEE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\npHZN49fm+UeuzR7UxhPr2nzSCdCJYArN3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6mq8KbGTfRu7E079kKU5ckg9brlMkRKPX7z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IADyMMSMCS8W2520qGPbDcyda56Ne1IqZ0f/bta6LVa+ucmJrRjslj1FmFTA8Y489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/DzwdtUVeWrK75Fw5sHsUqD383Tgy4qv/IrZn36LFyZ7g92SBJLVnyMqQtOm4BZC6Kp/b/t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\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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, 0.1), numpy.arange(y_min, y_max, 0.1))\n", "Z1 = model_a.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "Z2 = model_b.predict(numpy.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "\n", "plt.figure(figsize=(16, 16))\n", "plt.subplot(221)\n", "plt.title(\"Training Data, Supervised Boundaries\", fontsize=16)\n", "plot_contour(X_train, y_train, Z1)\n", "\n", "plt.subplot(223)\n", "plt.title(\"Training Data, Semi-supervised Boundaries\", fontsize=16)\n", "plot_contour(X_train, y_train, Z2)\n", "\n", "plt.subplot(222)\n", "plt.title(\"Test Data, Supervised Boundaries\", fontsize=16)\n", "plot_contour(X_test, y_test, Z1)\n", "\n", "plt.subplot(224)\n", "plt.title(\"Test Data, Semi-supervised Boundaries\", fontsize=16)\n", "plot_contour(X_test, y_test, Z2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Immediately, one would notice that the decision boundaries when using semi-supervised learning are smoother than those when using only a few samples. This can be explained mostly because having more data can generally lead to smoother decision boundaries as the model does not overfit to spurious examples in the dataset. It appears that the majority of the correctly classified samples come from having a more accurate decision boundary for the magenta samples in the left cluster. When using only the labeled samples many of the magenta samples in this region get classified incorrectly as cyan samples. In contrast, when using all of the data these points are all classified correctly.\n", "\n", "Lastly, let's take a look at a time comparison in this more complicated example." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Supervised Learning: \n", "1000 loops, best of 3: 746 µs per loop\n", "\n", "Semi-supervised Learning: \n", "1 loop, best of 3: 243 ms per loop\n", "\n", "Label Propagation (sklearn): \n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"Label Propagation (sklearn): \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'timeit LabelPropagation().fit(X_train, y_train)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/home/jmschr/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m 2158\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2159\u001b[0m \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m 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138\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "d1 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 0], max_iterations=1)\n", "d2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 1], max_iterations=1)\n", "d3 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 2], max_iterations=1)\n", "model = BayesClassifier([d1, d2, d3])\n", "\n", "print \"Supervised Learning: \"\n", "%timeit model.fit(X_train[y_train != -1], y_train[y_train != -1])\n", "print\n", "\n", "d1 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 0], max_iterations=1)\n", "d2 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 1], max_iterations=1)\n", "d3 = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_train[y_train == 2], max_iterations=1)\n", "model = BayesClassifier([d1, d2, d3])\n", "\n", "print \"Semi-supervised Learning: \"\n", "%timeit model.fit(X_train, y_train)\n", "\n", "print\n", "print \"Label Propagation (sklearn): \"\n", "%timeit LabelPropagation().fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like the difference, while still large, is not as large as in the previous example, being only a ~40x difference instead of a ~1000x difference. This is likely because even without the unlabeled data the supervised model is performing EM to train each of the mixtures that are the components of the Bayes classifier. Again, it is faster to do semi-supervised learning in this manner for generative models than it is to perform LabelPropagation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "In the real world (ack) there are frequently situations where only a small fraction of the available data has useful labels. Semi-supervised learning provides a framework for leveraging both the labeled and unlabeled aspects of a dataset to learn a sophisticated estimator. In this case, semi-supervised learning plays well with probabilistic models as normal maximum likelihood estimates can be done on the labeled data and expectation-maximization can be run on the unlabeled data using the same distributions.\n", "\n", "This notebook has covered how to implement semi-supervised learning in pomegranate using both naive Bayes and a Bayes classifier. All one has to do is set the labels of unlabeled samples to -1 and pomegranate will take care of the rest. This can be particularly useful when encountering complex, noisy, data in the real world that aren't neat Gaussian blobs." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 } pomegranate-0652e955c400bc56df5661db3298a06854c7cce8/tutorials/old/Tutorial_9_Missing_Values.ipynb000066400000000000000000006462351420461712600316230ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Missing Value Support in pomegranate" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "from pomegranate import *\n", "import seaborn\n", "seaborn.set_style('whitegrid')\n", "numpy.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The majority of machine learning algorithms assume that they are operating on a fully observed data set. In contast, a great deal of data sets in the real world are missing some values. Sometimes, this missingness is missing at random (MAR), which means that there is no important pattern to the missingness, and sometimes the missingness itself can be interpreted as a feature. For example, in the Titanic data set, males were more likely to have missing records than females were, and those without children were more likely to have missing records. \n", "\n", "A common approach to bridging this gap is to impute the missing values and then treat the entire data set as observed. For continuous features this is commonly done by replacing the missing values with the mean or median of the column. For categorical variables it is commonly done by replacing the missing values with the most common category observed in that column. While these techniques are simple and allow for almost any ML algorithm to be run, they are frequently suboptimal. Consider the follow simple example of continuous data that is bimodally distributed:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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U1ar/jTpfX1/17t1bEyZMuOTnR4wYoSpVqmj58uWqX7++du/erXvvvdfpPV5e5e9J6OWv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\nAMDkGDMvZ5LTjyoti/FBXL3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.concatenate([numpy.random.normal(0, 1, size=(1000)), numpy.random.normal(6, 1, size=(1250))])\n", "\n", "plt.title(\"Bimodal Distribution\", fontsize=14)\n", "plt.hist(X, bins=numpy.arange(-3, 9, 0.1), alpha=0.6)\n", "plt.ylabel(\"Count\", fontsize=14)\n", "plt.yticks(fontsize=12)\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.yticks(fontsize=12)\n", "plt.vlines(numpy.mean(X), 0, 80, color='r', label=\"Mean\")\n", "plt.vlines(numpy.median(X), 0, 80, color='b', label=\"Median\")\n", "plt.legend(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data peaks around 0 and around 6. Replacing the missing values with ~3 will be inserting values into the data set that mostly don't exist in the observed values. The median is slightly better, but will still cause the imputed values to be in one of the two clusters. This has the effect of essentially increasing the variance of the appropriate distribution. Let's take a look at what the distribution looks like if we add 500 missing values and then impute them using the mean of the observed values." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jmschr/anaconda2/lib/python2.7/site-packages/numpy/lib/function_base.py:4016: RuntimeWarning: Invalid value encountered in median\n", " r = func(a, **kwargs)\n" ] }, { "data": { "image/png": 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lpKRY3afFbDarXbt2kqScnBxNnz5du3fv1qVLlyyv+fM1Zs2bN7c8/mOmoKCgoEq3qyJs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\n2qEy+2vUqJGys7Mtz7OystS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.concatenate([X, [numpy.nan]*500])\n", "X_imp = X.copy()\n", "X_imp[numpy.isnan(X_imp)] = numpy.mean(X_imp[~numpy.isnan(X_imp)])\n", "\n", "plt.title(\"Bimodal Distribution\", fontsize=14)\n", "plt.hist(X_imp, bins=numpy.arange(-3, 9, 0.1), alpha=0.6)\n", "plt.ylabel(\"Count\", fontsize=14)\n", "plt.yticks(fontsize=12)\n", "plt.xlabel(\"Value\", fontsize=14)\n", "plt.yticks(fontsize=12)\n", "plt.vlines(numpy.mean(X), 0, 80, color='r', label=\"Mean\")\n", "plt.vlines(numpy.median(X), 0, 80, color='b', label=\"Median\")\n", "plt.legend(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It doesn't appear to be that great. We can see the issue with increased variance by trying to fit a Gaussian mixture model to the data with the imputed values, versus fitting it to the data and ignoring missing values." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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/TJs2bdi3bx9vvfUWv/zyCwqFAjs7O/W2FhYWmJiYoFAoqFmzZqGva4zTfRpj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\nkzPp29fn4i/chlorMXf3NBLTp5yYPuWAOlYL8hzIPOLCsSN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b/KTNbntmmxZQnPXZZ5/h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\nuIUQQgghhGhDknALIYQQQgjRhv4PCxxueQgyA9MAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = numpy.arange(-3, 9, 0.1)\n", "model1 = GeneralMixtureModel.from_samples(NormalDistribution, 2, X_imp.reshape(X_imp.shape[0], 1))\n", "model2 = GeneralMixtureModel.from_samples(NormalDistribution, 2, X.reshape(X.shape[0], 1))\n", "p1 = model1.probability(x.reshape(x.shape[0], 1))\n", "p2 = model2.probability(x.reshape(x.shape[0], 1))\n", "\n", "plt.figure(figsize=(12, 3))\n", "plt.subplot(121)\n", "plt.title(\"Mean Impute Missing Values\", fontsize=14)\n", "plt.hist(X_imp, bins=x, alpha=0.6, density=True)\n", "plt.plot(x, p1, color='b')\n", "plt.ylabel(\"Count\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.xlabel(\"Value\", fontsize=14); plt.yticks(fontsize=12)\n", "\n", "plt.subplot(122)\n", "plt.title(\"Ignore Missing Values\", fontsize=14)\n", "plt.hist(X[~numpy.isnan(X)], bins=x, alpha=0.6, density=True)\n", "plt.plot(x, p2, color='b')\n", "plt.ylabel(\"Count\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.xlabel(\"Value\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we impute the missing values, it seems that one component is fit properly and one has drastically increased variance. In contrast, when we ignore the missing values, we fit a model that represents the underlying data much more faithfully.\n", "\n", "At this point, you may think that as long as the data comes from a single distribution it shouldn't matter if you do a mean imputation of the data. However, this has the effect of shrinking the variance inappropriately. Let's take a look quickly at data drawn from a single Gaussian." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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FF2hpabF48WL8/f354IMPOHLkCF27duXy5ct5ej2Rf9KoLiN0dHRwd3dn37597Nu3jx49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\njCiEEEKIQjJtGkREwNy5YG2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S4nTp2DMc4/Lpmo7zVnN0hN694dF9Uy6ezXnhO1Gy5Kqn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\nZsyYwXr1Cd2ilJerxVTy8iA3F86fhwsXrv5MTW1PUZE6C33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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = numpy.concatenate([numpy.random.normal(0, 1, size=(750)), [numpy.nan]*250])\n", "X_imp = X.copy()\n", "X_imp[numpy.isnan(X_imp)] = numpy.mean(X_imp[~numpy.isnan(X_imp)])\n", "\n", "x = numpy.arange(-3, 3, 0.1)\n", "d1 = NormalDistribution.from_samples(X_imp)\n", "d2 = NormalDistribution.from_samples(X)\n", "p1 = d1.probability(x.reshape(x.shape[0], 1))\n", "p2 = d2.probability(x.reshape(x.shape[0], 1))\n", "\n", "plt.figure(figsize=(12, 3))\n", "plt.subplot(121)\n", "plt.title(\"Mean Impute Missing Values\", fontsize=14)\n", "plt.hist(X_imp, bins=x, alpha=0.6, density=True, label=\"$\\sigma$ = {:4.4}\".format(d1.parameters[1]))\n", "plt.plot(x, p1, color='b')\n", "plt.ylabel(\"Count\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.xlabel(\"Value\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.legend(fontsize=14)\n", "\n", "plt.subplot(122)\n", "plt.title(\"Ignore Missing Values\", fontsize=14)\n", "plt.hist(X[~numpy.isnan(X)], bins=x, alpha=0.6, density=True, label=\"$\\sigma$ = {:4.4}\".format(d2.parameters[1]))\n", "plt.plot(x, p2, color='b')\n", "plt.ylabel(\"Count\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.xlabel(\"Value\", fontsize=14); plt.yticks(fontsize=12)\n", "plt.legend(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even when the data is all drawn from a single, Gaussian, distribution, it is not a great idea to do mean imputation. We can see that the standard deviation of the learned distribution is significantly smaller than the true standard deviation (of 1), whereas if the missing data is ignored the value is closer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This might all be intuitive for a single variable. However, the concept of only collecting sufficient statistics from values that are present in the data and ignoring the missing values can be used in much more complicated, and/or multivariate models. Let's take a look at how well one can estimate the covariance matrix of a multivariate Gaussian distribution using these two strategies. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n, d, steps = 1000, 10, 50\n", "diffs1 = numpy.zeros(int(steps*0.86))\n", "diffs2 = numpy.zeros(int(steps*0.86))\n", "\n", "X = numpy.random.normal(6, 3, size=(n, d))\n", "\n", "for k, size in enumerate(range(0, int(n*d*0.86), n*d / steps)):\n", " idxs = numpy.random.choice(numpy.arange(n*d), replace=False, size=size)\n", " i, j = idxs / d, idxs % d\n", "\n", " cov_true = numpy.cov(X, rowvar=False, bias=True)\n", " X_nan = X.copy()\n", " X_nan[i, j] = numpy.nan\n", "\n", " X_mean = X_nan.copy()\n", " for col in range(d):\n", " mask = numpy.isnan(X_mean[:,col])\n", " X_mean[mask, col] = X_mean[~mask, col].mean()\n", "\n", " diff = numpy.abs(numpy.cov(X_mean, rowvar=False, bias=True) - cov_true).sum()\n", " diffs1[k] = diff\n", "\n", " dist = MultivariateGaussianDistribution.from_samples(X_nan)\n", " diff = numpy.abs(numpy.array(dist.parameters[1]) - cov_true).sum()\n", " diffs2[k] = diff" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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Vq1czcOBAkpKScHZ21m/j6upKYmJiVYVZLTVv3hwHB4di1fIADRs2ZP78+axc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\nfA4t7Mz571M9cWp+66Vq04uyiEq7QFR6DGfTL5Bdklu9zdqsGUHu3ej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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.title(\"Error in Multivariate Gaussian Covariance Matrix\", fontsize=16)\n", "plt.plot(diffs1, label=\"Mean\")\n", "plt.plot(diffs2, label=\"Ignore\")\n", "\n", "plt.xlabel(\"Percentage Missing\", fontsize=14)\n", "plt.ylabel(\"L1 Errors\", fontsize=14)\n", "plt.xticks(range(0, 51, 10), numpy.arange(0, 5001, 1000) / 5000.)\n", "plt.xlim(0, 50)\n", "plt.legend(fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In even the simplest case of Gaussian distributed data with a diagonal covariance matrix, it is more accurate to use the ignoring strategy rather than imputing the mean. When the data set is mostly unobserved the mean imputation strategy tends to do better in this case, but only because there is so little data for the ignoring strategy to actually train on. The deflation of the variance benefits the mean imputation strategy because all of the off-diagonal elements should be 0, but are likely to be artificially high when there are only few examples of the pairs of the variables co-existing in the dataset. This weakness in the ignoring strategy also makes it more likely to encounter linear algebra errors, such as a non-invertable covariance matrix." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This long introduction is a way of saying that pomegranate uses a strategy of ignoring missing values instead of attempting to impute them, followed by fitting to the newly complete data set. There are other imputation strategies, such as those based on EM, that would be a natural fit with the types of probabilistic models implemented in pomegranate. While those have not yet been added, they would be a good addition that I hope to get to this year.\n", "\n", "Let's now take a look at how to use missing values in some pomegranate models!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Distributions\n", "\n", "We've seen some examples of fitting distributions to missing data. For univariate distributions, the missing values are simply ignored when fitting to the data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting only to observed values:\n", "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " -0.0844412750220139,\n", " 0.9166407920894343\n", " ],\n", " \"name\" :\"NormalDistribution\"\n", "}\n", "\n", "Fitting to observed and missing values:\n", "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " -0.0844412750220139,\n", " 0.9166407920894343\n", " ],\n", " \"name\" :\"NormalDistribution\"\n", "}\n" ] } ], "source": [ "X = numpy.random.randn(100)\n", "X_nan = numpy.concatenate([X, [numpy.nan]*100])\n", "\n", "print \"Fitting only to observed values:\"\n", "print NormalDistribution.from_samples(X)\n", "print \n", "print \"Fitting to observed and missing values:\"\n", "print NormalDistribution.from_samples(X_nan)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This may seem to be an obvious thing to do. However, it suggests a way for dealing with multivariate data being modeled with an IndependentComponentsDistribution when some of the features are missing. Specifically, treat each column independently, and update based on the observed values, regardless of if there is an unobserved value in the same sample but another column. For example:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " [\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " -0.08339536402140717,\n", " 0.9112109573110577\n", " ],\n", " \"name\" : \"NormalDistribution\"\n", " },\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " 0.048834959770246114,\n", " 0.9877919452098168\n", " ],\n", " \"name\" : \"NormalDistribution\"\n", " },\n", " {\n", " \"frozen\" : false,\n", " \"class\" : \"Distribution\",\n", " \"parameters\" : [\n", " 0.014464502073682939,\n", " 0.9392312251035693\n", " ],\n", " \"name\" : \"NormalDistribution\"\n", " }\n", " ],\n", " [\n", " 1.0,\n", " 1.0,\n", " 1.0\n", " ]\n", " ],\n", " \"name\" : \"IndependentComponentsDistribution\"\n", "}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = numpy.random.normal(0, 1, size=(500, 3))\n", "idxs = numpy.random.choice(1500, replace=False, size=500)\n", "i, j = idxs // 3, idxs % 3\n", "X[i, j] = numpy.nan\n", "\n", "d = IndependentComponentsDistribution.from_samples(X, distributions=[NormalDistribution]*3)\n", "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Easy. As we saw above, we can do the same to learn a multivariate Gaussian distribution in the presence of missing data. Again, we don't need to change anything about how we interact with the data, and there are no flags to toggle.\n", "\n", "The last aspect is that the probability of missing data under any univariate distribution is 1, for the purposes of downstream algorithms." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "NormalDistribution(1, 2).probability(numpy.nan)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In an IndependentComponentsDistribution, this just means that when multiplying together the probabilities of each feature to get the total probability, that some dimensions don't factor into the calculation." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.00015597177674513935" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.probability((numpy.nan, 2, 3))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.00015597177674513921" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.distributions[1].probability(2) * d.distributions[2].probability(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. K-Means Clustering\n", "\n", "K-means clustering mostly serves a helper role in initializing mixture models and hidden Markov models. However, it can still be used by itself if desired. In addition to having the same parallelization and out-of-core capabilities of the main models, it also supports missing values now." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = numpy.concatenate([numpy.random.normal(0, 1, size=(50, 2)), numpy.random.normal(3, 1, size=(75, 2))])\n", "X_nan = X.copy()\n", "\n", "idxs = numpy.random.choice(250, replace=False, size=50)\n", "i, j = idxs // 2, idxs % 2\n", "X_nan[i, j] = numpy.nan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like the other models, you don't need to change the method calls in order to handle missing data. You can fit a K-means model to data sets with missing values and make predictions on samples with missing values in the same way you would without missing values. The prediction step will assign samples to the nearest centroid in the dimensions that are observed, ignoring the missing values." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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tixxs2iQ98YSxCKYbOVVYeIScsuDCWwzATcmuWt9aY6PxmDvuML6vrjbO0JSU\nGEHgxtmgRGKv4fS1m5uNcbzV1dKOHUZHI/b4VEMhFpSLFnXclu6gBIAwCXJWpdL2mOZm6eab3c+q\n/PwoOWWBjgvgU7GqL3bEO9h/9VVpzx5nO2WnUg2E2KTEmNikRMl43lSl2rECABwX1KySkmt7e+nM\nKnIqvpSGijU1Nenzn/+8VqxY4VZ7gKzjRp32eBVIbr01tZKXqXBSbjMTk+fdXMcgk6jn7ww5BaTO\nrf2PH7PKiXRnFTkVX0odl5///Oc66aST3GoLkFXcqtMepkpZmZw8n+o6BplCPf/UkFOAc83NUmVl\nsSv7H6+zys2D6kxlVZByyq3PSSKOOy7btm3TO++8o8mTJ7vYHCB7VFUVu1Kn3Q+VstwKBCbPd+TW\n5yQbkVNAaioqpKVLI67sf7zKqnSc/CGr2nLzc5KI44tOd911l374wx9qZbzus4mampqkX8fJY/yA\ndmdW0Nrd1JSjdeuGmG5btqxJpaVblZ8ftf1ckcgQ1dfnd9gWiTRp9+6t2rfP3nPZFXu/m5uNA+t1\n63qqoaGrIpFDmjRpv8rL6xxf0h43rli1tRGT2xv0+ut1qTQ7qz8n2ShTOZXK47xGuzMrSO1uasrR\n448PkdQxW5zsf7zKqsrKYi1dejxTYgfVDQ0NqqhwnilklcHtz0kijg4tVq5cqZEjR6qkpCSpx40Z\nMyap+9fU1CT9GD+g3ZkVxHZv2yY1NJj/ITc05Kt379FJTRi87DLzCiSlpfkaP360w1a2FasCs3v3\nyy3PWV4uLV16/D719flaujRfkUjE8eTEhx+WIhGzSYkR5eV1DAm7+JwYghSIqchUTknB/GxJtDvT\ngtZuY/9jvs3p/ifTWTVq1Gi98IL5/TZsiGjw4IjjYVhklSEdnxOrnHLUcVm7dq127typtWvXavfu\n3erSpYt69+6tz372s06eDsg6RUVSJHLI9MyTk8vM6axA0r4KTCQyRJddZpSxtBqvvHChs3G5bpSp\nDAu3PyfZhJwCUhMbDlVb23Gb0/1PprNq6tTEw9OcVhUjqwzp+JxYcdRxqWp1KvXee+9V3759CQMg\nCQUF0qRJ+7V0accDUid12tO5A21f8rG+Pl+LFkn79qUvEKTUylSGhdufk2xCTgGpSce6V5nOqsWL\npe7dpQ8/7Hh/tw6qsz2rMr0+ms8LqwHhVV5ep0gk4uqZJ7d3oFZVYNaskYqLjTNb7YXtakBs6IEX\nZ9TS8TkBADsqK425IBs2RFzd/2Qyq+IJ28kfL3MqXZ8TMyl3XK6//no32gFknSBcZraqArNrlzRn\njvTb33bcFpZAiLdYWqorIydK7dvJAAAgAElEQVQjCJ8TvyOnAGfy8qSKijoNHhzx9f7HKqs++kia\nO1dauzacJ3/8klOZ+pxwxQXwmJ8vMycau7pokdSz5/Hxyn37Sp/7nDH/JQzSuTJysvz8OQEQbn7f\n/1hlVb9+0s9+Zvw/dlAtSdu3+7cjloxsy6mUFqAEEG6xsatmZs6UevQwdoyvvmpcfZGMSisjRgR/\nkUSvF0sDANiTKKsKCox//ftLN98cngV9szGnuOICwFL7KjCRSJNKS/PbXGa/9da2Q8a8POPjFjuL\npfn5DCQAZBM7WeWnqxNuyMac4ooL4IBbK8UHQWyOxZYt0ptvSo8/vlVVVcfHzto54xPE94uVkQEE\nWRD3u6lINav27g3e+5WNOUXHBUhCc7NxWTksl5mTERu72n4F3ERnfObPD+b7ZWfoAQD4TTbnlOQs\nq7Zvl0aODN77lY05xVAxIAlhu8zsBqtJkSecIC1efPz7oL1f6VwsDQDSgZwyZ5VV0ahRKVMK3vuV\nbTnFFRfApmycBGeH1RmfaNT89qC8X+2HHmzZojZDDwDAT8ip+KyyykxQ3q9syyk6LoBNdibBZavK\nSqmsTBowQMrNNb7OnSsdPGh+/6C9X7GhB2G87A4gPMgpa+2zqrg4/n2D9n5lS07RcQFsysZJcHaZ\nnfH52c+M0pNmsv39AoB08FNO+bE4QPuseuUVoxNjhpzyJzougE1+mgTnx0CQ2p7x8dP7BQDZwA/7\nXafFATKZa7GsKiz0/v1Ccui4AEkwGxJVVpa5SXBBCITW7rzTqNSSm2t8n5trfH/nnZltBwBkC69z\nKlYcoLZWOnbs+GT3igrz+3tdBS1eTt16qz9PEGY7Oi5AEryeBBeUQIh1lG68Udq0STp61Lj96FHj\n+xtvJBAAIB28zCknxQGSzTW3JMqpAQOCVx45G9BxARzwYhJcEAKhdUfpjDOkX/zC/H6/+IWxPciB\n4NfhegAgeZNTyRYH8KIKmt2c+vDDzHak0iGMOUXHBQiIIARC645SNHr8DFZ7R48a24MYCF4PawAA\nv0q2OIAXVdDs5lR7QSmPLIU7p+i4AAHh90Cw6iglEqRA8GpYAwD4XbLFATJdBS2VnApSeeQw5xQd\nFyAg/B4IVh2lRIISCCzuBgDWkikOkOkqaKnkVFDKI4c9p+i4AAHi50Cw6ijl5h7/ZyYogcDibgBg\nLdniAJmsgmYnp3r0MN8elPLIYc8pOi5AgPg5EKw6SvPmGe2dN898e1ACwU+LuwGAn9ktDpDJKmh2\ncmrnTm/LSacq7DmVoSKuANwUC4REYoGwcKFxlqWoKL0dhNiOvbra2PmXlBghUVlptGXRIqlzZ/Pt\nQRALvUWLOm4LSucLAPzIbq6lKlFOSZnNTbeFPafouABZIF2B0NjYdseeqKOU6Y5UOliFHgDAf9pn\nlZ0cylRHKh3CnFMMFQOQtESlFhMNEfBifQG3eL0IKQDAHqusCnIOJRLmnArBjwAg02KlFmNipRYl\nY+eYSPuzX0EU5LNxAJANUskqcsqfuOICwDZjFd6uWrHCfHuiUotuLooVxhWBAQCpSyWryCl/o+MC\nBFQmd4itd+SzZw91XGrRjUWxmpul664zAiVsKwIDQJhk+sDdjaxyK6fKy6XBg42cGjyYnHILHRcg\nYNw8G2RX6x15NJoT935WpRbdWBSruVkaO1a6/35p167wrQgMAGHgRU5JqWeVW4s3LlhgtGPHDiOn\nduwwvl+wwN7jER8dFyBg3DgblAyrHXl7VqUW3VgUq6xM2rTJfFsYVgQGgDDIdE5J7mSVGznV2Cj9\n9rfm2xYvJqdSRccFCBC3zgYlw2pHLkmdOtlboCvVRbEShVIYVgQGgKDzIqckd7LKjcUb331X+vBD\n820HDhjb4RwdFyBA3DgblCyrHXn//tKrr9ortWi1YrGdRbHq661/vqKi4K8IDABB50VOSe5kVao5\n5RYm9cdHxwUIEDfOBiXLakd+ySXSsGH2d+aVlcbZrgEDpNxce1dqYoqKjPCJ50tfstcOAgEA0seL\nnJLcy6pUckoyfvbOnc23de8unXZa/Mc2N0uVlcUZnxsUJHRcgABJ5myQmwforXfknTpFk96Rx6Sy\nKFZBgTRjhvm2ESPa1uo349VkUQDIJl7llOROVqW6eOOtt0pHjphvmzvXuvNUUSEtXRrJ6NygoKHj\nAgRMorNB6ThAb70jX7Fic8qr8Lq9YvH55yduS1VVccYniwJApvnhqrIXOSW5m1VOcspqfk+PHtKP\nfuTssRSfOY6OC2CTH8JASnw2KJ3VXAoKpOLiw56sItzYKK1aZb7tiSesfy+NjdK6dT1NtxEIAMLA\nT1eVvcwpybussprf89FH0p49zh5L8ZnjHHVcPv74Y5WVlelrX/uaSktLtWbNGrfbBfiGn8KgNbOz\nQWE+Y5PKTr2+Xmpo6OrosQgusgrZxIsSxIlkW06lMr/Hq7lBQeOo47JmzRoNGzZMjzzyiKqqqnTn\nnXe63S7AN/wYBvGE+YxNqoEQiRxy9FgEF1mFbBGkzkCYcyqVqmR+qWjmd446LtOmTdM111wjSaqv\nr1ckEnG1UYBfBCkMpPCdsWk9PC/VQJg0ab+jxyK4yCpkiyB1BsKcU1JqVckqK6XZsxscVzTLBjnR\naDTq9MGzZ8/W7t279cADD+jMM8+0vG9NTY3TlwE8U1fXRbNmDdOxYzkdtnXqFNWKFZtVXHzYg5bF\nV1lZrKVLOx6gzZ7doIqKOg9alLzmZmMy/bp1PdXQ0FWRyCFNmrRf3/pWne67r1jr1/fU7t1d1bv3\nIZ1//n6Vl9clnHwZe04njw2bMWPGeN2EjLKbVeQUgqqpKUelpUNUX5/fYVufPk16/PGtys93fLjn\nujDnVCxTmppytHdvZxUWHkn6vU/lsWERN6eiKdq6dWt0+vTp0WPHjlneb+PGjUk/t5PH+AHtzqx0\ntvujj6LRAQOiUanjvwEDjO1OpavdR45Eo2VlRvtyc42vZWXG7W6Itfujj6LRd95J7j2w+5iyMvP3\nvKwsuedxq91ecvtzEtS/81TZySqn701Q31PanVnpbnei/aZT6Wh3unMqGjXa7WVOpdLuIHKz3VbP\n5Wio2ObNm1X/yXXHwYMH6+jRo/r3v//tuFcF+FUQx5ymWoM+ETvFCtpfOk+mwIGd4XnJlKmMtaWp\nybhq5nYpZvgXWYVskurCiZmUiZxKtJBjunMqGX6pWhoEjj4iGzdu1K5du3TLLbdo7969amxs1Mkn\nn+x22wBfiO30q6uNscIlJUanxQ9h0NhojF0uKup4IB47QHdbVVWxli49/n2sWIFkvCcVFcZ7tWOH\nMY555kyjqMG995o/pqqq7fPbGatt5+dqbm7blkhkiC67zGhjtg0Ny1ZkFbJJrDOwcGH8XPCCFzkV\nW8gxJig51a+fNG5csR5+mJyKx9HbMnv2bN1yyy36j//4DzU1NenWW29Vp04sCYNWrPZUAePHMDDb\n2cU6U+nc2SVaD+XIEen++4/fFtvxd+9u/nzV1cb72vr9jE3crK3teP9kJm7GqsHF1Nfnxw0hhBNZ\nhURCFFUt0tUZSJaXOWV1NcTPOVVbK9XWRhSJkFPxONqD5+fn66c//al+97vfacWKFZoyZYrb7UJQ\n+XXRExf4aYiRVyWaE62HEi8sPvzQ/HazajfJDM+Ld3k9aNXgWmPIgHvIKsQT4qjyjXg5VVaW3n1c\noqsh5FTqvMwpTj3BXUFa9CSgrHZ2y5dLe/em77Wt1kMpKkq+5GbrM1Otd4SJxmonOugIUmnQGA6k\ngMwhqtLLKqd+8Yv07uOsyi2TU6nxQ07RcYF7gnz6IECsdna7dkkjR6ZvR2K1HsqXviT172/+uB49\nzG+fOVPq0qXjjrCiwtj5x5u4meigI4jrBMT7maqqir1uGhAqRFX6WeXU0aPp7SxaXQ3JRE4dPmx0\nbsrKsienMtnhp+MC9wTx9EEAWe3sJKPzks4dSXl5nelZpkWL4ofF178e/8yU1Y6w9fC82JmuvXvt\nVR0LUjU4qwOp9et7ciAFuIioSr9EOdVaOjqL8RZyTGdOte7cnHGGcWXJ6ucNU05VVx+v3Jlu1CyA\ne9yarQZLsZ1d6wl9ZswmFLrBqliBVQW2vLyOj0m0I1y40AiD1hM8i4qMzpmZ1tVc2rclEmlSaWm+\nL6rBtWd1ILV7d1fbFWoAJEZUpZ/dnJKSq8JlV16eVFFRp8GDIxnJqYKCjhPtjx5N/POatWXcuAZV\nVnZcnNNriTr8e/d2zkg7uOIC9wTt9EGAxcbW9u0b/z7pPnNoVqwgUW3+9o+xc+az/ZmueJ0Wqe1B\nR/u2PP74VlfXCXCT1dnJ3r0PcSAFuIioyozWc0A6dTKuYphJZ2cxUzll1blpzyqntmwxOlxBy6mS\nEqmw8EhG2kHHBe4K0gpYARbb2W3aFL/z4uWZQ7sV2BLtCHv2tB8GkvlBR6wt+flR+0+UYVYHUuef\nv58DKcBlRFX6tT4of+stad488/t51Vl0K6diE/7jdW7as8opP+/rE3X4M5WxdFzgrnQvh4s2Cgul\nr3zFfFsQzhwm2hHu328dBsXF4TnoiHcgVV5e53XTgNAhqjIndlAeK4UctM6inSt0Vp2b3Nxg/bxW\n/NDh508U6eGXFbBc4udFyqzG6waBVfsPH44/Fn3AAOmll4zOjR9/L8mKN3eopsbrlgHhFaao8nNO\nSf5czNmuRDlrNadn3jxpwYJg/bzx+OF3SMcFsODVyr/J8MOOJBVW7c/Lix8GM2caV5wKCzPb3nQL\n04EUgPQLQk61FsR9nJ2cTTTpP0y8/B2G7K0E3NW+SkisBKJk7MT8JIhh0Fq89gf9ihIApFOQciro\nrHI26CcRg4I5LkAcLFLmD4xFBwBz5JT/BGGifZDRcQHiYJEyfyEMAKAtcgrZho4LEIedEogAAHiF\nnEK2oeMCxOHlImWNjdK2bVzmBwDE5/VimmQVMo2OC2Ah0zXLm5ul8nJp6FBp4EDja3m5cXuYEHYA\n4A4v1tbIhqwip/yJjgtgIdMTw2PVYWprpWPHjleHqahIz+tlWjaEHQBkkhcFTMKcVeSUv9FxAWzI\nxMTwsFWHMTtb5UXYxdrR1JSTvhcBAI9lqoBJ2LMqEznV/jW5umMfHRfAJ8JSHSbe2aoDB+KH3R/+\nIO3d687rxwLgwIG27SgtHcJZMwBIUZiz6rrr0ptT7V9zyBBp1Cjja6wNlZXF5JQFVkIAfCJWHaa2\ntuO2VKvDNDZmbkGseIuh7dsXP+zq6qSRI6WvfMX5KsPtV48+4QTpww+Pb6+vz2dRNgBIUbqyKpM5\nJZln1f33x7+/GznV/jW3bzf+tW5DbW1EkQg5FQ9XXACfSEd1mHSO1TW7tG01hGDNGqm4OP7z7dqV\n2uX49pf3W3daWgviUAYA8Au3syrdc0qSzarc3PjPlUpOWb1me+RUfHRcAB9xuzpMOsbqWoWM1RCC\nXbukKVMSP7+THXYygRCkoQwA4EduZlW65pQ4zaqjRxM/t5OcsnrN9sip+Oi4AD7iZnWYdE2grKoq\njhsyiRZDW7TICDerKy9OdtjJBAKLsgFAatzKqnRO9HeaVf37S/Pnu59TVq/ZHjkVHx0XwIfcqA6T\njgmUjY3SunU9TbdVVxtfrYYQ9OhhhNsrr0h9+5rfz8kOO5lAyMSibACQDVLNqnRN9E8lqy65RPrZ\nz9zPKashdu2RU/HRcQFCKtHVDydnc+rrpYaGrqbbYiFjZwhBYaExwdGMkx22VSD06GG0o0+fprQv\nygYAsC8dOSW5k1Vu55TU8TX79zcm/Pfvf7wNs2c3kFMWqCoGhFTsYL51BZMYpzvdoiIpEjmk+vr8\nDttiIRMbQrBwoXWFmNiOubraCJKSEqNdTnfY8Z7vjjukPXuk3bu3avz40c6eHADgunTklOReVrmd\nU/Fes3VFtddfr1NeXsTZC2QBOi7IWpkuvegFt3e6BQXSpEn7tXRpxzBoHzKxIQTx2O3g2GX1fD16\nSPv2RZ0/OQB4gJxyxq2scjun4r1morzEcXRckHXar/fRr9/xnaSTSfB+lo6dbnl5nSKRiKudITd3\n2AQAgKAjp1J/Xjezilzxj5B9/IHE4i2QKIV3wSc3d7rpChkAgIGcSh1ZFU5MzkdWSWfpxWzjRuUz\nAEBb5JS7yKpwoeOCrJKu0osAALiBnALio+OCrJKu0otwX2OjtG0bZxcBZBdyKjjIqcyj42IHn8zQ\nsFrvgwWf/KG5WSovl4YOlQYONL6Wlxu3A7BAVoUCOeV/5JR3Upqcf/fdd6umpkbNzc2aN2+epk6d\n6la7/CGbynpkkXSUXoR7snFSKtIn9DklkVUhRE75GznlHcd7tA0bNujtt9/W73//e33wwQe69NJL\nwxcIfDJDiUoj/pVoUurChfyuYF9W5JREVoUQOeVf5JS3HA8VGzt2rBZ9smPs2bOnPv74Yx09etS1\nhnmOsh6hR6UR/2FSKtwU+pySyKqQI6f8h5zyluMrLrm5uSr45C9p2bJlOv/885Wbm+tawzxn55PJ\nakSAq2KTUmtrO25jUiqS5SSnampqHL2W08elqktdnYbt3Kkck23RHTu0+ZlndLi4OO7jvWp3qmh3\nZtHu45qachSJDFF9fX6HbZFIk3bv3qp9+6IpvQbvd3wpD3599tlntXz5cv36179OeF8nP5BXv7yc\npiYNiUSUb9J1bopEtHX3bkX37Yv7eD50qctpalLnvXt1pLBQ0fyOO4jW/NTuZNDujsaNK1ZtbcTk\n9ga9/npdSs/N+52dksmpMWPGJP38NTU1jh7nisGD4/b2c/r101kXXBD3dL2n7U6Br9rd2Gh7LJev\n2p0E2t3RZZe1HZ0ZU1qar/HjR6f03Lzf1pmXUsflb3/7mx544AH96le/Uvfu3RPeP9kfyPNfXpxP\nZn5pqUaPHx/3YZ632yHftDvJiaa+aXeS/NDuJDK3Rbrb/fDDUiRiNik1ory8jh0au/zwfjvhdruz\nrROUbE4FTqwEldlRFCWo0ifkBRFaZ4PXnORUulE8wTuO/7o+/PBD3X333frtb3+rk046yc02+Qef\nTG8EeKKpnR1s7D5NTWaDOzLDz5nLpFS4JStySiKrvBDgnJLiZ5VZNowbV6yHH858NpBTMON4cv5T\nTz2lDz74QOXl5ZozZ47mzJmj9957z822eS/2ydyyRXrzTeNrVZX3fzFhFtCJpnZqure/T2npEM/q\nvscyt7ZWOnbseOZWVGS+LfEwKRWpyoqcksiqTAtoTkmJs8osG5YujXiSDeQUzDjeq11++eW6/PLL\n3WyLf8U+mUi/gBZFsHPyrf196uvzPTlBRylHZIusyimJrMqUgOaUZJ1VCxf6JxvIKcTj+IoLkBax\nslJmfFpWys7JNz+doKOUIwCkIIA5JSXOoXff9U82kFOIh44L/CU20dSMTyea2tnB+mkn7NfMbWyU\ntm3z9SgLIP34Q/C/AOaUlDiHJP9kg19zSuJP1GvZ1XHh0xYMlZVSWZk0YICUm2t8LSvz7URTOztY\nP+2Evc7c9n+GduYHAaHHH0KwBCynpMQ5dNpp/umP+S2nJP5E/SI7Zu75uTQFOgpYuQ671Uj9VLHU\niyJE8f4Mjx2T7r33+P0CVpwHcEfAq1RlnYDllGQvq8yyYdy4BlVWOi9F75Sfcqqykj9Rv8iOo3Y+\nbcEUoImmdnaw7e8TiTSptDTfkxN0XmRuvD/DeEtruDkBs7FRqqvrosGDfX9sgWzETOTgClBOSYmz\nyiwbXn+9LqU1tJzyU04dOSKtXm3+GLf+RP2wVEIQhH+omJ9mRacLQ+A8Z6caafv7PP74Vs8rlmaq\nlKPVn+GHH5rf7sbcn9aX9mfNGsalffiTnybBpRNZ5Tm7lbP9VOY3mbak8hFLdLiYrj9RPy2VEATh\n77iEORAYcOk7dnawsfvk50cz1zAXxALBydkgqz/DeNyY+9N2HYAcX64DAPhqElw6kFW+46eOiRvM\nPmKVlcVJfcSscip2xcdMqn+i7deriS2VQE6ZC3/HJcyBEITVmRBojY3GWbnrrkvtbJDVn2G8oWKp\nzv3JhoutCAmvZyKnG1mFNCsrS33hTKucis11MZPKnyg5lbzwd1zCGgh82pFGrc9enXmmdP/9qZ0N\nsvoznDs3PcV5wnyxFSEUwCpVtpBVSKPmZuPE2i9+Yb49mY9YosPFRYvc/xMlp5KXHZPzvShNkW4B\nXrnXUmx2WgAqtHgtnW9V+wmKZpKdkGj1Z5iX5/4EzNjZs9rajtuCfrEVIRTAKlW2hDGryCnb0v1W\nVVQYJ9biSfYjliin3P4TJaeSF/4rLpL92WhB4tchcE5nxjEG2rZ0v1VWJ0hbS/ZsUKI/Q7fHXIf1\nYitCLmyTD8KUVeSUbZl4q+xkVbIfMTuHi27+iZJTycuOjktMmALBb5/2VPdSjIG2Ld1vld2J9E6P\nOTL5Z9h69E2nTtHQjL4BAiNMWUVO2ZaJt8pOVjn9iHmVU7m5Up8+TeSUhezquISNn8ZEp7KXYgy0\nbZl4q6xOkLYWhLNBrc+erVixORQXW4HACUNWkVO2Zeqtssqq3FzpK19pCMTBvx+XSvAzOi5BEO+S\ntl+GwKW6l2J2mm2ZeKusTpBKwTwbVFAgFRcf9n1HCwi0MGcVOWVbpt4qq6yaN0+66aa6QB38B3Wp\nhEyj4+Jndi9pez0ELtW9lF/HQPuQk7fKyVBusxOk8+dLb7zB2SA3sA4fQiUbsoqcsi1TOSXFv5iX\nqLgMbPBpUNFx8bOgjKdNdYfutzHQPpbMW5XKUG6zE6Q/+5k0aBBng1LB3F6EUjZkFTllW6ZySvLP\nxbxQ8XlQ0XHxqyCNp3Vjh+6nMdA+Z/etcuNYwusTpGETlOM7wLZsyipyyrZM5pREVrnK50FFx8Wv\ngjaeNtUdOqdNbLPzVgXpWCJb8DtBKGVTVpFTtpFTARWAXwodF78K2nhat3bonDaxzeqtCtqxRDbg\nd4JQysasIqdsc5pT27fbK8sPlwUgqOi4+FVQx9OyQ/cFq2OJE06QevXKbHsQvOM7wBayCg5Z7ROj\nUel//zez7YECEVR0XPyM8bSwYFXww+pY4sAB6dZb09s2dBTU4zsgIbIKcSTKqWnT4j/2qad8MTIp\nuwQgqOi4+BnjaWHCbsGPO+6Qunc3fw6fDFXNOhzfIZTIKrRjN6e+/e34z+GTkUnZx+dBxV4lCGKX\ntAEdL/gREyv4IRnHCjF79kgffWT+HLFA4GOVWbHju4ULjfe/qMgXJ7AAd5BV+ITdnCopMY6La2s7\nPodPRiZlH58HFVdcgABJpuBHAIaqZi2G1wMIq2RyKgAjk7KXT4OKjgsQIMkU/CAQAMBDPl15PN2S\nLUzl85FJ8Bk6LgieLA0DKfmrKAQCAGSYz1ceT7dkc4opUkgGHRcER5aHgZT8VRQCAQAyLM7K48Wt\nJ3eEmNOr/T4dmQSf4fAFwRFntl9xQ4P02GOeNSvTYldLqquNy+4lJUYYWF1FYc4sAGSAxQSPnuvX\nG9uz4MjcSU4BdtBxQTAQBi18XvADALKXxQSPrrt3Z005R3IK6cJQMQSDnTDIMlxWBwCfsZjgcah3\n76wr50hOwW10XBAMhAEAwO8sJnjsP/98juCBFNFxQTAQBgCAIIhTzrGuvNzrlgGBl1LH5a233tIX\nvvAFPfLII261B4iPMEBIZHFF74wjp5BxlHNECPg1pxx3XBobG/U///M/Ou+889xsDxAfYYCAo6J3\nZpFT8BQTPBBAfs8pxx2XLl266MEHH9Spp57qZnv8za/dz2xDGCCg4izvoIoKr1sWTuQUACTH7znl\n+FR1Xl6e8rLlTHdzs/Ebq66WduwwJonHCpJny3sAICUWFb1VXW2UDaUv7i4nOVVTU+PotZw+zjXN\nzSquqlLPdevUtaFBhyIR7Z80yRhKa/EeeN5uh2h3ZtHuzPKq3U1NOXr88SGS8jtsW7asSaWlW5Wf\nH437+Ey0O6NH3U5+ID986IorKxVZuvT4DZ90PxsaGlQXpwvqh3Y7kUq7c5qa1HnvXh0pLFQ0v+OH\nPp2y8f32Eu1OXl1dF+3cOUxSTodtO3ZE9cwzm1VcfNj0sUF9v4NozJgxST+mpqbG0eNcVV4utcqp\n/Pp65S9dqkgkYgypNeGLdjuQcrsbGz1ZXCRr32+P0O7kbdsmNTSYb2toyFfv3qPjLkPkZrutMi+j\nHZdkfyBffOgaG6UXXjDdFNmwQZHBgzvs+HzRbgcct9vjK1JZ9357jHY7M3iw8adRW9txW79+Obrg\ngrNMj6HcbjedoBDicp49jJ4ALMVWnjDLqZISf6w8QTnkRCwWPtTOnVm58GEHfh8QCfiARUVvzZzJ\ncSVSQE7ZQ1YBloKQU447Lps3b9acOXP0f//v/9WSJUs0Z84c7du3z822+YPFwoe+6X56KdGZPiaI\nAi3iVPRWZaXXLQsnckrkVAxZBdji95xyfG102LBhevjhh91siz/Fup+LFnXc5pfup5fsnOmLNyAS\nyDKxit4LF3oyxD7rkFMip2LIKsAWv+cUgzrtiHUzq6uNHVxJyfFxsX5gd6JhOiYkBmFAJOAzsYre\ngGv8nlMSWQUEiF9zijkudvh14UO7qwSlezWhSZPMb+dMHwBkhl9zSiKrALjGB3u0APFb9zM20TAm\nNtFQalv+0u79ktG+Okv37sbtH33UtlILACBz/JZTElkFwDVccQkquxMN0zUhsX11lg8/NP7953/6\n60wfAMA7ZBUAF9FxCSq75S/TUSbTKmDWrk3++QAA4URWAXARHZegslv+Mh1lMt0MmMZGY6lWSlEC\nQPiEJKuIKsAf6LgEld1VgtKxmpAbAZPuSZgAAO8FPKuIKsBfGNgZZHbLX7pdJtONNQPSMQkTAOA/\nAc6qeFHV+UijfrLAh4tcACFHxyXI7K4SlI7VhFIJmESTMH/wA2n/fgIBAMIgoFllFlW5atZPVKGv\n/KJa0Qd2KKd1ZTIm+bad/CsAABtESURBVANpx19ZGNgtf+lmmcxkA6b1gmJW4463b5dGjjTuQyAA\nQHj4PavaLXxpFlU/UYVu0CLp6Cc3MFoAyCjmuCA1sYCJFwRmA4Tvucc462UmGpV27TLKVsYCoaIi\nbc0HAGQBq6yKM5GlqFdzmyky3dSoS5SGks0AbKPj4ndBL2XSvoZ+ba10//3SySfbfw4CAQD8LchZ\nZZZTixap4NaKNvUCilSvErlcshlAUui4+FUYSplYzWX54ANp/nxpwAApN1cqLo7/PAQCAPhT0LMq\nwZzLyjsaVVZmRNW/OhWpPtflks0AkkLHxQ3pONMU5wxQoIZNWc1lqauTFiwwVi5+803plVeMZDBD\nIABA6siqjhKs9ZK3p15VVUZUbXqrQL3nuVyyGUBS6LikIl1nmhJV3QrKpXg7NfRj444LC92v4Q8A\nIKus2FzrJRZVnRdVquUSTG6u8bWszHnJZgBJoeOSijhnmoqrqlI7s+XmyvReSnZBsUoCAQBcR1bF\nl2xOxaqUxUYLbNlifE/lSyAj+EtzyuJM06eeeEIaPNgYDuWkpG/sDFBtbcdtQRs2lUwN/XTU8AeA\nbEZWJeZkrRc3SzYDsI0rLk5ZnGnK++gjaccO5+N9kz0D5Afxzto5OTuVqMQyAMAesqots6ziKgoQ\nGHRcnLIaF2sm2fG+QRk2ZXfsNJ0RAMg8sspgJ6vIKcD36Lg4ZXWmyUyy433TfQbIreoyQa8o4wdB\nXv8AgL8FMata7RNd2z2SVakhp+ATdFxS0f5MU//+Uvfu5vd1Ot7X7TNABw5Ic+ca45pTrS4Thooy\nXgr6+gcAgiEoWdVqnxgdOFDv9x6qx3qXa/AZzantHskq55qbVVxZSU7BN+i4pKL9maatW6UrrzS/\nr9fjfWOBUFwsLV6c2rjmmDBUlPFQcVUVZwABpF9QsqrVVZGcY8f0qQ9rddWHi3RXtML+7tHsygBZ\n5VxFhSJLl5JT8A06Lm5ofaapslINs2f7b7xvLBA+/NB8e3W1cpqarJ+jfSDYrH+fcUG4pN3YqJ7r\n1plv4wwggHTwc1ZZXBWZqWp1k7FPrK6WmppyOt7J6go2WeUMV6rgQ3Rc3JaXp7qKCn9VJ7Ha+cTs\n3KnOe/eab2tulq67zgiD1oHQpYu/KsoEaehVfb26NjSYb+MMIIB081tWWVwVKdFOFcnYJ+7cKe3d\n27njnazmsPit+llQsoorVfAhav2li59qvFvtfGJKSnSksLDj7c3N0tix0qZNx2+LBYLkrP59usSC\nK6Z1O6uqMt8eK0VFOhSJKN9sxx+k9Q8ABJtfsspiTZidKlG9jH1iSYlUWHik7R0OHJB+/Wvz562u\nNtYGI6uSF5Z1ehAqXHHJBnbKYc6cqWh+fsfby8radlpaq66WDh/2R/37oF3SLijQ/kmTzLd5PR8K\nAJxyOvzJ4qpItWbqYxn7xJkzpfz8aNs7fPvb8YdBx64M+GWtliBlld+uVAGi4xIcqYyFtdr5dO8e\nf1xzoiFmrS8Ve13/PoCXtOvKy4Ox/gEAJOLG8KdW1c+iubna22OAHupepps6VcbfPTY2Sn/9a/zn\nLC5ue2WArEqO3+ZCIesxVMzvmpuNy8rV1UYlsH79jl/eTuZMUfvL5MXF0uc+Z1ye7tHD/DH19dY7\n0aIi/1wqDuIl7dgZwIULjfe5qIgzWACCyY3hT632iTn19SosKtIVKtBkq91jfb20a1f85/zc5/y1\nXw1aVn0yFyoyeDA5BV/giovfubVollk5zN/8Jn6nRTJ2UP37x9/+pS/5ZwcW5EvaXp8BBIBUuD38\nqdU+MeHu0WoodPfubTtTfhDUrCKn4BN0XPwsHWNhk9n5WO1gR470XyC0X2SNS9oAkH5eDn+yyqkr\nr7Q+OecVsgpwjKFiXmhstHfJ1U4YpLsaTOshZjt2GG2eOdPotHhZ4tkMQ68AwD12s8rr4U9+qhhm\nB1kFOMYVl0xKdvKiHxbNOnxYuv566aWXpLfeMv797Gf+67S0xiVtAHAu2azyevhTXp7RCXjiCaMK\nptdr0thFVgFJc9xxWbhwoS6//HLNnj1br732mpttCq9k56t4GQbtg2vsWOnee41FJwEgIMgqB5zM\nrfRq+FPrrBoxQpoxQ7r5Zv8t5gjAFY46Li+++KK2b9+u3//+9/rRj36k//mf/3G7XeHjdL6KV2GQ\nKLhSKc8MABlAVjngNKu8WieFrAKyiqOOywsvvKAvfOELkqTPfOYzOnDggA4ePOhqw0LH6eRFL8LA\nKrhWrpSuuy61Wv0AkAFklQOpTrTP5PCnZLJq4EDje7IKCDRHR7979+7V0KFDW77/1Kc+pT179ujE\nE090rWGhk+rkxVgYZIJVcG3fLt1///HvY2e3jhwx5r4AgE84yaqamhpHr+X0cV5r3+6cpiYNiUSU\nb9JBaYpEtHX3bkX37ctU8+KqqalRl7o6Ddu5Uzkm26PbtyundVbt2iXdf78+evZZvbFkiWfzX8Ly\nOQkK2p1ZmWi3o7/caDTa4fucHLNdR1tOfqAw/fKKx41TxKTj0jBunOpefz0DrUqspqbGMriOdeqk\nTseOdbz9gQe051//Ul1FhSeBEKbPSRDQ7swKaru95iSrxowZk/Tr1NTUOHqc1+K2+7LLTMvd55eW\navT48RlombWWdg8eHPeEYE5urnT0aIfbT3jrLY1ZssSTE22h+5z4HO3OLDfbbZV5jo4wI5GI9u7d\n2/L9v/71LxUWFiZ8XLI/UOh+eQ8/LEUiHUo2RiorFfFB9ZM27Y4TXGadltjtkeXLFenb1/4qyS7x\nxefEbtnQVnzRbgdod2a53e5s6gQ5zaqsF5TywrECNmZripl0WlpUV0s/+Un2VfNykFOA3zia4zJ+\n/Hg9/fTTkqStW7fq1FNPZZiYHXbnq/hhMqFZUYD586X+/a0f53RhzKBKtmwogIwhqxwKQ1b17Rv/\nMfX16V0U02/IKYSIo9P8o0eP1tChQzV79mzl5OTotttuc7td4RZvvkpzs1EJJbbYY79+x89yZfqK\nTLwFsjp3Nj+7FZOphTH9IlbRJiY250fK+JUnAG2RVSkKclZJbedjttavX2bWQfMLcgoh4ngPU2FV\nzx3O+HHn0j64KiuNifi/+IX5pfhMLYzpB4nKhi5cyOV4wGNkVRoEIasWLZKef95YkLK9TCyK6Rfk\nFELG8QKUcJnT2vmZlpdnTGqcN898ezYFQqplQwEgaIKUVS+9dHzYWKdOmVsHzU/IKYQMHRe/CNrO\nZdEibxbG9JNYiWsz2XTlCUD2CFJWxU60vfWW8S9Ti2L6CTmFkKHj4hdB27l4tUqyn8Qq2pjJpitP\nALJH0LJKyuyimH5DTiFk6Lj4RVB3LtkcCJJ5RZtsu/IEIHsENauyGTmFEMmi0+MBEJTa+TjOqqIN\nAIQRWRUs5BRChI6Ln7Bzcc7rhbXilQ0FgLAhqxzJaWoy1r0hpwDHGCrmR9ky/MqNxcuam1VcWcnC\nWgCQadmQVS7llMrLNaS0lJwCUkTHBZnn5iq+FRWKLF1qrCNw7Njx9QRYuwEA4JTLOaVFi5RfX09O\nASmi44LMiy1elmpnIyjrCQAAgoWcAnyJjgsMjY3qUleX/p2omzvxIK0nAABIzSfDtnKamtL/OuQU\n4Et0XLJdq8vhw2bNSv/YWzd34kFcTwBA+LkxLwLHtRu2NaS0lJwCslSwOi6EgftaXQ7PycTYWzd3\n4qwnAMBP3JwXgePaDdvKr68np4AsFYyOC2GQHl6MvXV7J15ZqYbZs1lYC4D33JoXgeNCklMqK1NT\nnz7kFJCiYKzjEguDmFgYSEYteThj53J4Omq+u7l4WV6e6ioqFBk8mPUEAHgn0QH2woXsm5wISU6p\nqkpbS0s1undvcgpIgf+vuFCRI328GnsbW7xsyxbpzTeNr1VVxu1OZcN6AgD8KxsmYXsxXDtEORXN\nzyengBT5v+OSDWEgeRMIXo+9pbMBICzCMgnbLIu8HK5NTgFoxf8dl7CEgeS/QJBaxt5qwABFO3Vi\n7C0AOOH1AXaqrLLI67k7rXJKubnGXBFyCshK/u+4BD0MJH8HQqvL4ZtXrHBn2BYAZKN2B9iBOhEU\nL4vKyrwfrt1u2NbWxx8np4AsFYy/ejcnyXkhXnGBI0ek1avNH5PpyZwFBTpcXByMjiAA+FHsAHvh\nwmAVC7GaS7pqlbRrl/m2dE6ON/PJsK3ovn2ZeT0AvhOMjktQw0AKTiAAANwRmxcRFFZzSevrpT59\nzLMqaMO1AQSe/4eKtRbESXJ2AsEMgQAAyIREc0mDPlwbQGgEq+MSRAQCAMDPEs0ljc11CeLcHS95\nUS0UCLlgDBULqsZG46rKtGnS/fd33B6bp9O5c3Dn73gl9t5yVQoAnIvtS++4w/jeLIuCPFzbC7HC\nO9XV0o4dxsnLmTOlK64wv3/rPON9BSzRcUmHWBWx2E6rpEQaOVL64AOpro5ASIVJIBSPGyc9/LB5\nhRkCAQA6am5WcWWltGFD24PrV1+V9uwx32cGbe6OV+IU5CluaJAee+z47fE6OLFjAwAd8JeRBsVV\nVdLSpcdv2L7d+Dd/vrRgAYGQCpNAiNTWSpGI0QGMIRAAIL6KCkVa51Ss2qXUdl+K5FgU5Om5fr2x\nPZb/8SqOSvwOgDiY4+K2xkb1XLfOfNtTT3HmPxVWFdraryfg9fo4AOBXyexLkRyLgjxdd+82tkvG\ne7xihflz8DsA4qLj4rb6enVtaDDfFitxDGesKrS1fm/37pWWLTO/H4EAINvZ3ZcieRYFeQ717m1s\nb26WrruO3wHgAB0XtxUV6VAkYr6NEsepSVShrVcvY27RyJHSe++Z349AAJDtEu1LySnnLCq07T//\nfGN7RYX029/Gfw5+B0BcdFzcVlCg/ZMmmW+jxHFqEpXsvPVWYzhYvEU9JQIBABLtS8mp1FRWmpaP\nrisvtx6mF8PvAIiLWcpOWVSrqisvVyQSocRxOsTew1bvbcO4cYrccYc0fHjixxMIALJJvKyqrFRD\nQ4MiGzaQU26LVy20psZ6mJ4kff3r/A4AC3RckmWnWhUljtPH5L2te/11RfbssQ6Dvn2lr3yFQACQ\nHRJlVV6e6ioqFBk8mJxKF7NqobFherW1He/fv7+x5huVL4G4+OtIVjLlCylxnD7t31urMCgull55\nRSoszFjzAMBTdrOKnMqs2DC91r+bmEsuofMIJOB4jsuLL76o8847T2vWrHGzPf5GCUn/shqz/eUv\n02kBslBW5pREVvldnDkwjAgAEnN0xWXHjh36zW9+ozFjxrjdHn+zU0Iym85c+W1VepP5L4zZBrJT\n1uaURFa15reckhhODqTA0RWXXr166b777tOJJ57odnv8jRKShuZmo+zw0KHSwIHG1/Jy43YvxcJg\nyxbpzTeNr1VVjBcGslDW5pREVkn+zanWYsP06LQAtjk6ouvWrZvb7QgGq7Gp2VStKpl5Pl5gzDaQ\n9ZzmVE1NTUYfly7F48YpYjLnr2HcONW9/nrL935rt12J2l1cWanI0qXHb/gkpxoaGlRXUZHexlkI\n6/vtV7Q7szLR7oQdl2XLlmlZu1XIr7/+ek2cODHpF3PyA/nul3fFFSpuaFDP9evVdfduHerdW/vP\nP191V1xhlDr8hO/abVOiduc0NWnI448r32Rb07Jl2lpaqmi+2db0Cuv77Ve0O7OC2u5McTOnnAwt\nq6mp8d+QtIcflkzK8kcqKxX55Cq0L9ttQ8J2NzZKL7xguimyYYNRSc2DE42hfb99inZnlpvttsq8\nhB2X0tJSlZaWutKQZH8g3/7yHnusZdxsflGR8gsKFGm12bftTsBWu7dtkxoaTDflNzRodO/eGb/a\nEer324dod2a53e4wdoLczKnQyOZ5FMzxAULLcVWxrJetY1MZOw0AwZGNWUVOAaHlqOOydu1azZkz\nR3/72990zz336Morr3S7XfArq7LD2TTPB4CvkVNZjJwCQsvR5PzJkydr8uTJLjcFgUHZYQA+R05l\nOXIKCCXqxCJ52Tx2GgDgf+QUEEp0XOAcZYcBAH5GTgGhwuR8AAAAAL5HxyVMGhuNcsWNjV63BAAA\nc2QVAIfouIRBc7NUXi4NHSoNHGh8LS83bk8F4QIAcEs6soqcArIKHZcwqKiQFi2SamulY8eMr4sW\nGbc7ka6OEAAge7mZVeQUkJXouARdY6O0cqX5tupqZ2eh3O4IAQCym9tZRU4BWYmOS9DV1xs16s3s\n3GlsT0Y6OkIAgOzmYlblNDWRU0CWouMSdEVFUr9+5ttKSoztyXC7IwQAgItZ1XnvXnIKyFJ0XIKu\noMBYDdjMzJnJL7jldkcIAAAXs+pIYSE5BWQpOi5hUFkplZVJAwZIubnG17Iy4/Zkud0RAgBAci2r\novn55BSQpfK8bgBckJcnVVVJCxcal8iLilLbccdCpLrauOxeUmKEgZOOEAAAkrtZRU4BWYmOS5gU\nFEinn57687gRLo2N7nSiAADh4kZWudUJIquAQGGoGOKLhUsyO3Nq6wMAMsVJTklkFRBQXHGBu2K1\n9WNitfUl4+wYAABeI6uAQOKKC9zDGjAAAL8jq4DAouMC97AGDADA78gqILDouMA9rAEDAPA7sgoI\nLDoucA9rwAAA/I6sAgKLyflwF7X1AQB+R1YBgUTHBe5yezFMAADclpdn5NTVVxvfn3YaWQUEAEPF\n4D4W9AIA+FXrNVxGjJBmzJBuvpk1XIAAoOMC97CgFwDA72JruNTWSseOHV/DpaLC65YBSICOC9xD\nGAAA/Iw1XIBAo+MCdxAGAAC/Yw0XINDouMAdhAEAwO9YwwUINDoucAdhAADwO9ZwAQKNjgvcQRgA\nAIKgslIqK5MGDJByc42vZWWs4QIEAOu4wD0s6AUA8DvWGwMCi44L3EMYAACCoqBAOv10r1sBIAl0\nXOA+wgAAAAAuY44LAAAAAN9zdMWlublZt9xyi3bu3Knm5mZ997vf1dlnn+122wAAcIScAoDwcdRx\nqa6uVrdu3fS73/1Ob7/9tr7//e9r+fLlbrcNAABHyCkACB9HHZcvfelLmj59uiTplFNO0b59+1xt\nFAAAqSCnACB8HHVcOnfu3PL/xYsXt4QDAAB+QE4BQPjkRKPRqNUdli1bpmXLlrW57frrr9fEiRP1\n6KOP6q9//aseeOCBNiFhpqamJvXWAgBSNmbMGK+b4CpyCgDCJW5ORR16/PHHo1deeWW0qanJ1v03\nbtyY9Gs4eYwf0O7Mot2ZRbszy+12B/V9cCITOZXK47xGuzOLdmcW7c4sN9tt9VyOhort3LlTS5cu\n1SOPPKKuXbum0qECAMB15BQAhI+jjsuyZcu0b98+feMb32i57aGHHlKXLl1caxgAAE6RUwAQPo46\nLgsWLNCCBQvcbgsAAK4gpwAgfDp53QAAAAAASCRhVTG3UK0FAPwhbFXF3EJOAYA/xMupjHVcAAAA\nAMAphooBAAAA8D06LgAAAP+/vfsJafKP4wD+Hs6VYlhOnRYdxA4GEV06WOSfFIOIoGI6SgOjThpd\njLIdOgjBRERYqOCfCiwcG1FBFxEddEhkXWIW5CpKhpnLrOxxROLv1KBMfcbv93s+36fer9Oe57L3\n4bu99+H73UZEyuPgQkREREREyuPgQkREREREyuPgQkREREREylN6cPn+/TsuXbqEkydPorq6GqFQ\nSDqSbuPj4yguLsbo6Kh0FF2uXbuGmpoauFwuPH36VDqObi9evEBlZSUGBgakoySltbUVNTU1OHHi\nBIaGhqTj6LK4uIgLFy6gtrYWTqfTNGv7h3g8joqKCty9e1c6ii7hcBglJSWoq6tDXV0dWlpapCPR\nKthVxmFXGctsXcWeMpZET1n/92f4F+7fv4+0tDTcuXMHk5OTaG5uRiAQkI61rrdv3+LGjRum+a+E\n8fFxvHnzBj6fD5FIBM3NzfD7/dKx1qVpGlpaWlBcXCwdJSljY2OYnJyEz+fDx48fcezYMVRVVUnH\nWtfo6Ch27dqFc+fOIRqN4syZMygvL5eOpVtXVxc2b94sHUM3TdNw6NAhuN1u6Si0DnaVMdhVxjJj\nV7GnjCXRU0oPLkePHsWRI0cAAFlZWZifnxdOpE9OTg6uX79umg8cjx8/RmVlJQBgx44d+Pz5MxYW\nFpCRkSGcbG02mw09PT3o6emRjpKUvXv3Yvfu3QCAzMxMLC4uYmlpCSkpKcLJ1nb48OHE4+npaTgc\nDsE0yXn58iUikQjKysqko+j29etX6QikE7vKGOwqY5mxq9hTxpLoKaWPiqWmpmLDhg0AgFu3biWK\nQXVpaWlKv7B/FYvFsGXLlsS13W7H7OysYCJ9rFYrNm7cKB0jaSkpKUhPTwcA+P1+lJSUmGq9uFwu\nNDU14cqVK9JRdPN4PLh8+bJ0jKRomoYnT57g7NmzOHXqFMbGxqQj0SrYVcZgVxnLzF3FnjKGRE8p\ns+Pi9/tXbPmeP38eBw4cwO3btzExMYHu7m6hdKtbK7dZLC8vr7i2WCxCaf4ew8PDCAQC6O/vl46S\nlMHBQTx//hwXL17EgwcPlF8r9+7dw549e7B9+3bpKEkpKipCQ0MDKioq8Pr1a9TX12NoaAg2m006\n2l+NXSWHXSXDjF3FnjKGRE8pM7g4nU44nc4V9/1+P0ZGRtDZ2YnU1FSBZGtbLbeZOBwOxGKxxPX7\n9++RnZ0tmOjP9+jRI3R3d6O3txebNm2SjqNLOByG3W5Hfn4+du7ciaWlJczNzcFut0tHW1MwGMTU\n1BSCwSDevXsHm82GvLw87Nu3TzramgoLC1FYWAgAKCgoQHZ2NmZmZkxXbH8adpUcdpXxzNZV7Clj\nSfSUMoPL70xNTWFwcBADAwOJbXj67+3fvx9erxculwvPnj1Dbm6u8meGzezLly9obW3FzZs3TfUl\nvFAohGg0CrfbjVgsBk3Tfjq2oaqOjo7EY6/Xi23btilfBgAQCASgaRpOnz6N2dlZfPjwwVTntf8m\n7CpjsKuMZcauYk8ZS6KnLMu/7r0qpL29HQ8fPsTWrVsT9/r6+pQ/KhEMBtHX14dXr14hKysLOTk5\nym+xtrW1IRQKwWKx4OrVqygqKpKOtK5wOAyPx4NoNAqr1QqHwwGv16v8G6zP54PX60VBQUHinsfj\n+Wmdqygej8PtdmN6ehrxeByNjY04ePCgdKyk/CiE48ePS0dZ16dPn9DU1ARN0/Dt2zc0NjaitLRU\nOhb9BrvKOOwq45ixq9hTxpLoKaUHFyIiIiIiIkDxXxUjIiIiIiICOLgQEREREZEJcHAhIiIiIiLl\ncXAhIiIiIiLlcXAhIiIiIiLlcXAhIiIiIiLlcXAhIiIiIiLlcXAhIiIiIiLl/QOIvjsNmrXHpAAA\nAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model1 = Kmeans.from_samples(2, X)\n", "model2 = Kmeans.from_samples(2, X_nan)\n", "\n", "y1 = model1.predict(X)\n", "y2 = model2.predict(X_nan)\n", "\n", "plt.figure(figsize=(14, 6))\n", "plt.subplot(121)\n", "plt.title(\"Fit w/o Missing Values\", fontsize=16)\n", "plt.scatter(X[y1 == 0,0], X[y1 == 0,1], color='b')\n", "plt.scatter(X[y1 == 1,0], X[y1 == 1,1], color='r')\n", "\n", "plt.subplot(122)\n", "plt.title(\"Fit w/ Missing Values\", fontsize=16)\n", "plt.scatter(X[y2 == 0,0], X[y2 == 0,1], color='b')\n", "plt.scatter(X[y2 == 1,0], X[y2 == 1,1], color='r')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that there are some blue points in the red cluster on the right plot because those samples are entirely NaN. Any sample that is entirely NaN is assigned to cluster 0. Otherwise, it's still able to identify the two clusters even there there are many missing values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. General Mixture Models\n", "\n", "Missing value support for mixture models follows that of k-means fairly closely. Essentially, one passes in a data set containing missing values denoted as `numpy.nan` and they get used appropriately for the fit and predict steps. All methods automatically handle missing values appropriately without any additional flags or methods. \n", "\n", "Since training is an iterative procedure that involves calculating the probabilities of samples given each component, multivariate Gaussian mixtures will be much slower when handling missing values than they would be when using only observed values. This is because an inverse covariance matrix has to be calculated by subsetting the covariance matrix and inverting it based only on the observed dimensions for each sample. Each sample, then, needs its own matrix inversion. Since there is no single inverse covariance matrix, one also cannot use BLAS or a GPU to accelerate this step. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 34.8 ms per loop\n", "1 loop, best of 3: 22.3 s per loop\n" ] } ], "source": [ "X = numpy.concatenate([numpy.random.normal(0, 1, size=(1000, 10)), numpy.random.normal(2, 1, size=(1250, 10))])\n", "\n", "idxs = numpy.random.choice(22500, replace=False, size=5000)\n", "i, j = idxs // 10, idxs % 10\n", "\n", "X_nan = X.copy()\n", "X_nan[i, j] = numpy.nan\n", "\n", "%timeit GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X, max_iterations=10)\n", "%timeit GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X_nan, max_iterations=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, if one was modeling each dimension independently, there should be no hit at all!" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 3.49 ms per loop\n", "100 loops, best of 3: 3.69 ms per loop\n" ] } ], "source": [ "%timeit -n 100 GeneralMixtureModel.from_samples([NormalDistribution]*2, 2, X, max_iterations=10)\n", "%timeit -n 100 GeneralMixtureModel.from_samples([NormalDistribution]*2, 2, X_nan, max_iterations=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Naive Bayes / Bayes Classifiers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Support for these models mirrors what's been seen before. However, since the fitting step does not involve calculating probabilities of samples, it should be no slower to train them on data sets involving missing values than to train them on dense data sets." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 3.2 ms per loop\n", "100 loops, best of 3: 4 ms per loop\n" ] } ], "source": [ "y = numpy.concatenate([numpy.zeros(1000), numpy.ones(1250)])\n", "\n", "%timeit -n 100 BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y)\n", "%timeit -n 100 BayesClassifier.from_samples(MultivariateGaussianDistribution, X_nan, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since pomegranate also has semi-supervised learning built-in, this means that one can now fit Bayes classifiers on data sets with missingness in both the labels and in the values! Since semi-supervised learning does rely on EM, it will be slower to train multivariate Gaussian models with missing values than not to." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] Improvement: 50.9041578247\tTime (s): 1.117\n", "[2] Improvement: 0.0770505675373\tTime (s): 1.13\n", "Total Improvement: 50.9812083923\n", "Total Time (s): 3.5037\n" ] } ], "source": [ "idx = numpy.random.choice(2250, replace=False, size=750)\n", "y_nan = y.copy()\n", "y_nan[idx] = -1\n", "\n", "model = BayesClassifier.from_samples(MultivariateGaussianDistribution, X_nan, y_nan, verbose=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 17.4 ms per loop\n", "1 loop, best of 3: 3.31 s per loop\n" ] } ], "source": [ "%timeit BayesClassifier.from_samples(MultivariateGaussianDistribution, X, y_nan)\n", "%timeit BayesClassifier.from_samples(MultivariateGaussianDistribution, X_nan, y_nan)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Hidden Markov Models\n", "\n", "Hidden Markov models are slightly different from the others, in that they operate over sequences. This adds another level of complication to the model because the forward and backward algorithms are needed in order to identify the best component for each observation. Typically this involves calculating the probability of each observation given each state, and taking the sum of all paths through the model, multiplying the transition probability of each edge crossed by the emission probability of the state you transition to emitting the next character. If this is a univariate model and the character is missing, you ignore the emission probability and just multiply by the transition probability. This is easily done by having the probability of missing values be 1 under all univariate models." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "d1 = DiscreteDistribution({'A': 0.25, 'B': 0.75})\n", "d2 = DiscreteDistribution({'A': 0.67, 'B': 0.33})\n", "\n", "s1 = State(d1, name=\"s1\")\n", "s2 = State(d2, name=\"s2\")\n", "\n", "model = HiddenMarkovModel()\n", "model.add_states(s1, s2)\n", "model.add_transition(model.start, s1, 1.0)\n", "model.add_transition(s1, s1, 0.5)\n", "model.add_transition(s1, s2, 0.5)\n", "model.add_transition(s2, s2, 0.5)\n", "model.add_transition(s2, s1, 0.5)\n", "model.bake()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's run the forward algorithm on a simple sequence." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0. , 1. , 0. ],\n", " [ 0.25 , 0. , 0. , 0. ],\n", " [ 0.09375 , 0.04125 , 0. , 0. ],\n", " [ 0.016875 , 0.045225 , 0. , 0. ],\n", " [ 0.0077625, 0.0208035, 0. , 0. ]])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numpy.exp(model.forward(['A', 'B', 'A', 'A']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see what happens when we remove one of the characters." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0. , 1. , 0. ],\n", " [ 0.25 , 0. , 0. , 0. ],\n", " [ 0.125 , 0.125 , 0. , 0. ],\n", " [ 0.03125 , 0.08375 , 0. , 0. ],\n", " [ 0.014375, 0.038525, 0. , 0. ]])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numpy.exp(model.forward(['A', 'nan', 'A', 'A']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that initially the first character is aligned to s1 because there is a 100% chance of going from the start state to s1. The value is 0.25 because it is equal to the transition probability (1.0) multiplied by the emission probability (0.25). In the next step, you can see that the probability is equally diffused between two options, staying in the current state (transition probability of 0.5) and moving to s2 (also transition probability of 0.5). Since the character is missing, there is no emission probability to multiply by. \n", "\n", "If we want to decode the sequence we can call the same methods as before." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 0, 0, 1, 1]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict(['A', 'A', 'B', 'B', 'A', 'A'])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 0, 0, 0, 0, 1]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict(['A', 'nan', 'B', 'B', 'nan', 'A'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fitting is pretty much the same story as the previous models. Like the Bayes classifiers, one can now train a hidden Markov model in a supervised manner, having some observations in the sequence missing, but also labels on each observation. Labeled missing data can still be used to train the transition parameters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. Bayesian Networks\n", "\n", "Bayesian networks could previously handle missing data in the `predict` and `predict_proba` methods. In fact, handling missing data was the area in which they excelled. However, one couldn't calculate the probability of a sample containing missing values, fit a Bayesian network to incomplete data sets, or learn the structure of a network on incomplete data sets. The new missing value support allows all of these things (except Chow-Liu tree building on missing data sets)! To be clear, there is a very common iterative technique for fitting/learning models on incomplete data sets using an iterative EM based approach. This does not do that. This only ignores sufficient statistics from missing samples, and so is not an iterative approach.\n", "\n", "Learning the structure of a network on an incomplete data set should take a similar amount of time as learning it on a complete data set. If you are indicating missing values in numeric data sets, you will have to convert your data set to floats, whereas previously integers would be fine. If your data set is complete, there is no need." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 242 ms per loop\n", "100 loops, best of 3: 251 ms per loop\n" ] } ], "source": [ "X = numpy.random.randint(3, size=(500, 10)).astype('float64')\n", "\n", "idxs = numpy.random.choice(5000, replace=False, size=2000)\n", "i, j = idxs // 10, idxs % 10\n", "X_nan = X.copy()\n", "X_nan[i, j] = numpy.nan\n", "\n", "%timeit -n 100 BayesianNetwork.from_samples(X, algorithm='exact')\n", "%timeit -n 100 BayesianNetwork.from_samples(X_nan, algorithm='exact')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusions\n", "\n", "Missing value support has been added to pomegranate as of v0.9.0! A lot of care was taken to make the interface as simple to the end user while not compromising on speed. While multivariate Gaussian distributions, and the compositional models that are built on top of them, may be slower on incomplete data sets than on complete ones, everything else should take a similar amount of time!\n", "\n", "The implemented missing value support focuses on ignoring data that is missing. Another approach that works well is to use an EM based algorithm to impute the missing values based on the observed values and the model and fit to that complete data set. This works well in the framework of probabilistic modeling and is a natural addition to pomegranate that I hope to add in the coming year.\n", "\n", "As always, feedback and questions are always welcome!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }