Metadata-Version: 1.2
Name: trelawney
Version: 0.1.2
Summary: Generic Interpretability package
Home-page: https://github.com/skanderkam/trelawney
Author: Skander Kamoun
Author-email: skander.kam2@gmail.com
License: MIT license
Description: =========
        trelawney
        =========
        
        
        .. image:: https://img.shields.io/pypi/v/trelawney.svg
                :target: https://pypi.python.org/pypi/trelawney
        
        .. image:: https://img.shields.io/travis/skanderkam/trelawney.svg
                :target: https://travis-ci.org/skanderkam/trelawney
        
        .. image:: https://readthedocs.org/projects/trelawney/badge/?version=latest
                :target: https://trelawney.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        .. image:: https://img.shields.io/github/license/skanderkam/trelawney
                :alt: MIT License
        
        
        
        Trelawney is a general interpretability package that aims at providing a common api to use most of the modern
        interpretability methods to shed light on sklearn compatible models (support for Keras and XGBoost are tested).
        
        Trelawney will try to provide you with two kind of explanation when possible:
        
        - global explanation of the model that highlights the most importance features the model uses to make its
          predictions globally
        - local explanation of the model that will try to shed light on why a specific model made a specific prediction
        
        The Trelawney package is build around:
        
        - some model specific explainers that use the inner workings of some types of models to explain them:
           - `LogRegExplainer` that uses the weights of the your logistic regression to produce global and local explanations of
             your model
           - `TreeExplainer` that uses the path of your tree (single tree model only) to produce explanations of the model
        
        - Some model agnostic explainers that should work with all models:
           - `LimeExplainer` that uses the Lime_ package to create local explanations only (the local nature of Lime prohibits
             it from generating global explanations of a model
           - `ShapExplainer` that uses the SHAP_ package to create local and global explanations of your model
           - `SurrogateExplainer` that creates a general surogate of your model (fitted on the output of your model) using an
             explainable model (`DecisionTreeClassifier`,`LogisticRegression` for now). The explainer will then use the
             internals of the surrogate model to explain your black box model as well as informing you on how well the surrogate
             model explains the black box one
        
        Quick Tutorial (30s to Trelawney):
        ----------------------------------
        
        Here is an example of how to use a Trelawney explainer
        
        >>> model = LogisticRegression().fit(X, y)
        >>> # creating and fiting the explainer
        >>> explainer = ShapExplainer()
        >>> explainer.fit(model, X, y)
        >>> # explaining observation
        >>> explanation =  explainer.explain_local(X_expain)
        [
            {'var_1': 0.1, 'var_2': -0.07, ...},
            ...
            {'var_1': 0.23, 'var_2': -0.15, ...} ,
        ]
        >>> explanation =  explainer.graph_local_explanation(X_expain.iloc[:1, :])
        
        .. image:: http://drive.google.com/uc?export=view&id=1a1kdH8mjGdKiiF_JHR56L2-JeaRStwr2
           :width: 400
           :alt: Local Explanation Graph
        
        >>> explanation =  explainer.feature_importance(X_expain)
        {'var_1': 0.5, 'var_2': 0.2, ...} ,
        >>> explanation =  explainer.graph_feature_importance(X_expain)
        
        
        .. image:: http://drive.google.com/uc?export=view&id=1R2NFEU0bcZYpeiFsLZDKYfPkjHz-cHJ_
           :width: 400
           :alt: Local Explanation Graph
        
        FAQ
        ---
        
           Why should I use Trelawney rather than Lime_ and SHAP_
        
        while you can definitally use the Lime and SHAP packages directly (they will give you more control over how to use their
        packages), they are very specialized packages with different APIs, graphs and vocabulary. Trelawnaey offers you a
        unified API, representation and vocabulary for all state of the art explanation methods so that you don't lose time
        adapting to each new method but just change a class and Trelawney will adapt to you.
        
        Comming Soon
        ------------
        
        * Regressor Support (PR welcome)
        * Image and text Support (PR welcome)
        
        
        Credits
        -------
        
        This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
        
        .. _Cookiecutter: https://github.com/audreyr/cookiecutter
        .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
        .. _SHAP: https://github.com/slundberg/shap
        .. _Lime: https://github.com/marcotcr/lime
        
        
        =======
        History
        =======
        
        0.1.0 (2019-10-02)
        ------------------
        
        * First release on PyPI.
        
Keywords: trelawney
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.5, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*
