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توضیحات

Feature Importance Analysis of Models

ویژگی	مقدار
سیستم عامل	-
نام فایل	anamod-0.1.4
نام	anamod
نسخه کتابخانه	0.1.4
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Akshay Sood
ایمیل نویسنده	sood.iitd@gmail.com
آدرس صفحه اصلی	https://github.com/cloudbopper/anamod
آدرس اینترنتی	https://pypi.org/project/anamod/
مجوز	MIT

======== anamod ======== .. image:: https://app.travis-ci.com/cloudbopper/anamod.svg :target: https://app.travis-ci.com/github/cloudbopper/anamod :alt: Build status .. image:: https://readthedocs.org/projects/anamod/badge/?version=latest :target: https://anamod.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status .. image:: https://codecov.io/gh/cloudbopper/anamod/branch/master/graph/badge.svg?token=5XLhbjehGp :target: https://codecov.io/gh/cloudbopper/anamod :alt: Code Coverage .. image:: https://badge.fury.io/py/anamod.svg :target: https://pypi.org/project/anamod/ :alt: PyPI - Package Version .. image:: https://img.shields.io/pypi/pyversions/anamod :target: https://pypi.org/project/anamod/ :alt: PyPI - Python Version -------- Overview -------- ``anamod`` is a python library that implements model-agnostic algorithms for the feature importance analysis of trained black-box models. It is designed to serve the larger goal of interpretable machine learning by using different abstractions over features to interpret models. At a high level, ``anamod`` implements the following algorithms: * Given a learned model and a hierarchy over features, (i) it tests feature groups, in addition to base features, and tries to determine the level of resolution at which important features can be determined, (ii) uses hypothesis testing to rigorously assess the effect of each feature on the model's loss, (iii) employs a hierarchical approach to control the false discovery rate when testing feature groups and individual base features for importance, and (iv) uses hypothesis testing to identify important interactions among features and feature groups. More details may be found in the following paper:: Lee, Kyubin, Akshay Sood, and Mark Craven. 2019. “Understanding Learned Models by Identifying Important Features at the Right Resolution.” In Proceedings of the AAAI Conference on Artificial Intelligence, 33:4155–63. https://doi.org/10.1609/aaai.v33i01.33014155. * Given a learned temporal or sequence model, it identifies its important features, windows as well as its dependence on temporal ordering. More details may be found in the following paper:: Sood, Akshay, and Mark Craven. “Feature Importance Explanations for Temporal Black-Box Models.” ArXiv:2102.11934 [Cs, Stat], February 23, 2021. http://arxiv.org/abs/2102.11934. ``anamod`` supersedes the library ``mihifepe``, based on the first paper (https://github.com/Craven-Biostat-Lab/mihifepe). ``mihifepe`` is maintained for legacy reasons but will not receive further updates. ``anamod`` uses the library ``synmod`` to generate synthetic data, including time-series data, to test and validate the algorithms (https://github.com/cloudbopper/synmod). ----- Usage ----- See detailed API documentation here_. Here are some examples of how the package may be used: Analyzing a scikit-learn binary classification model:: # Train a model from sklearn.linear_model import LogisticRegression from sklearn import datasets model = LogisticRegression() dataset = datasets.load_breast_cancer() X, y, feature_names = (dataset.data, dataset.target, dataset.feature_names) model.fit(X, y) # Analyze the model import anamod output_dir = "example_sklearn_classifier" model.predict = lambda X: model.predict_proba(X)[:, 1] # To return a vector of probabilities when model.predict is called analyzer = anamod.ModelAnalyzer(model, X, y, feature_names=feature_names, output_dir=output_dir) features = analyzer.analyze() # Show list of important features sorted in decreasing order of importance score, along with importance score and model coefficient from pprint import pprint important_features = sorted([feature for feature in features if feature.important], key=lambda feature: feature.importance_score, reverse=True) pprint([(feature.name, feature.importance_score, model.coef_[0][feature.idx[0]]) for feature in important_features]) Analyzing a scikit-learn regression model:: # Train a model from sklearn.linear_model import Ridge from sklearn import datasets model = Ridge(alpha=1e-2) dataset = datasets.load_diabetes() X, y, feature_names = (dataset.data, dataset.target, dataset.feature_names) model.fit(X, y) # Analyze the model import anamod output_dir = "example_sklearn_regressor" analyzer = anamod.ModelAnalyzer(model, X, y, feature_names=feature_names, output_dir=output_dir) features = analyzer.analyze() # Show list of important features sorted in decreasing order of importance score, along with importance score and model coefficient from pprint import pprint important_features = sorted([feature for feature in features if feature.important], key=lambda feature: feature.importance_score, reverse=True) pprint([(feature.name, feature.importance_score, model.coef_[feature.idx[0]]) for feature in important_features]) The outputs can be visualized in other ways as well. To show a table indicating feature importance:: import subprocess subprocess.run(["open", f"{output_dir}/feature_importance.csv"], check=True) .. image:: https://github.com/cloudbopper/anamod/blob/master/docs/images/sklearn-table.png?raw=true To visualize the feature importance hierarchy (since no hierarchy is provided in this case, a flat hierarchy is automatically created):: subprocess.run(["open", f"{output_dir}/feature_importance_hierarchy.png"], check=True) .. image:: https://github.com/cloudbopper/anamod/blob/master/docs/images/sklearn-tree.png?raw=true Analyzing a synthentic model with a hierarchy generated using hierarchical clustering:: # Generate synthetic data and model import synmod output_dir = "example_synthetic_non_temporal" num_features = 10 synthesized_features, X, model = synmod.synthesize(output_dir=output_dir, num_instances=100, seed=100, num_features=num_features, fraction_relevant_features=0.5, synthesis_type="static", model_type="regressor") y = model.predict(X, labels=True) # Generate hierarchy using hierarchical clustering from types import SimpleNamespace from anamod.simulation import simulation args = SimpleNamespace(hierarchy_type="cluster_from_data", contiguous_node_names=True, num_features=num_features) feature_hierarchy, _ = simulation.gen_hierarchy(args, X) # Analyze the model from anamod import ModelAnalyzer analyzer = ModelAnalyzer(model, X, y, feature_hierarchy=feature_hierarchy, output_dir=output_dir) features = analyzer.analyze() # Visualize feature importance hierarchy import subprocess subprocess.run(["open", f"{output_dir}/feature_importance_hierarchy.png"], check=True) .. image:: https://github.com/cloudbopper/anamod/blob/master/docs/images/synthetic-tree.png?raw=true Analyzing a synthetic temporal model:: # Generate synthetic data and model import synmod output_dir = "example_synthetic_temporal" num_features = 10 synthesized_features, X, model = synmod.synthesize(output_dir=output_dir, num_instances=100, seed=100, num_features=num_features, fraction_relevant_features=0.5, synthesis_type="temporal", sequence_length=20, model_type="regressor") y = model.predict(X, labels=True) # Analyze the model from anamod import TemporalModelAnalyzer analyzer = TemporalModelAnalyzer(model, X, y, output_dir=output_dir) features = analyzer.analyze() # Visualize feature importance for temporal windows import subprocess subprocess.run(["open", f"{output_dir}/feature_importance_windows.png"], check=True) .. image:: https://github.com/cloudbopper/anamod/blob/master/docs/images/synthetic-windows.png?raw=true The package supports parallelization using HTCondor_, which can significantly improve running time for large models. If HTCondor is available on your system, you can enable it by providing the "condor" keyword argument. The python package ``htcondor`` must be installed (see Installation). Additional condor options may be viewed in the API documentation:: analyzer = anamod.ModelAnalyzer(model, X, y, condor=True) .. _here: https://anamod.readthedocs.io/en/latest/usage.html .. _HTCondor: https://research.cs.wisc.edu/htcondor/ ------------ Installation ------------ The recommended installation method is via `virtual environments`_ and pip_. In addition, you also need graphviz_ installed on your system to visualize feature importance hierarchies. To install the latest stable release:: pip install anamod Or to install the latest development version from GitHub:: pip install git+https://github.com/cloudbopper/anamod.git@master#egg=anamod If HTCondor is available on your platform, install the ``htcondor`` PyPi package using pip. To enable it, see Usage:: pip install htcondor .. _pip: https://pip.pypa.io/ .. _virtual environments: https://docs.python.org/3/tutorial/venv.html .. _graphviz: https://www.graphviz.org/ ----------- Development ----------- Collaborations and contributions are welcome. If you are interested in helping with development, please take a look at https://anamod.readthedocs.io/en/latest/contributing.html. ------- License ------- ``anamod`` is free, open source software, released under the MIT license. See LICENSE_ for details. .. _LICENSE: https://github.com/cloudbopper/anamod/blob/master/LICENSE ------- Contact ------- `Akshay Sood`_ .. _Akshay Sood: https://github.com/cloudbopper ========= Changelog =========

نیازمندی

مقدار	نام
-	anytree
-	cloudpickle
-	h5py
-	matplotlib
>=1.19.0	numpy
-	scikit-learn
-	scipy
-	seaborn
-	sympy
-	synmod
-	xxhash

زبان مورد نیاز

مقدار	نام
>= 3.6	Python

نحوه نصب

نصب پکیج whl anamod-0.1.4:

pip install anamod-0.1.4.whl

نصب پکیج tar.gz anamod-0.1.4:

pip install anamod-0.1.4.tar.gz