Metadata-Version: 2.1
Name: anamod
Version: 0.1.2
Summary: Feature Importance Analysis of Models
Home-page: https://github.com/cloudbopper/anamod
Author: Akshay Sood
Author-email: sood.iitd@gmail.com
License: MIT
Description: ========
        anamod
        ========
        
        .. image:: https://img.shields.io/travis/cloudbopper/anamod.svg
                :target: https://travis-ci.com/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
        
        --------
        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::
        
            [Under review]
        
        ``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:: images/sklearn-table.png
        
        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:: images/sklearn-tree.png
        
        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:: images/synthetic-tree.png
        
        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=10, 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:: images/synthetic-windows.png
        
        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
        =========
        
Keywords: anamod
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >= 3.6
Description-Content-Type: text/x-rst
