Metadata-Version: 1.1
Name: focus-cfe
Version: 0.0.dev2
Summary: FOCUS is a python package for generating counterfactual explanations for a tree-based model
Home-page: UNKNOWN
Author: Kyosuke Morita
Author-email: kq441morita@gmail.com
License: UNKNOWN
Description: FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
        ==========================================================================
        
        **Deployment & Documentation & Stats & License**
        
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        -----
        
        This library is an implementation of `FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles <https://arxiv.org/abs/1911.12199>`_.
        
        FOCUS generates optimal distance counterfactual explanations to the original data for all the instances in tree‐based machine learning models.
        
        **FOCUS counterfactual explanation generation with 3 Lines of Code**\ :
        
        .. code-block:: python
        
            from focus import Focus
            # Initialize Focus instance with default values
            focus = Focus()
            # Generate counterfactual explanations for given tree model and features
            pertubed = focus.generate(tree_model, X)
        
        
        **Examples**\:
        
        - More comprehensive example can be found in the `examples folder <https://github.com/kyosek/focus/blob/master/examples/focus_example.py>`_.
        - Another example in a kaggle notebook can be found `here <https://www.kaggle.com/code/kyosukemorita/focus-cfe-example>`_.
        - Below demonstrates the comparison of before and after FOCUS was applied to feature set from the above example.
        
        .. image:: docs/plot.png
            :width: 200px
            :height: 100px
            :scale: 50 %
            :alt: Before and After FOCUS was applied to the features from above example.
        
        **Note**\:
        
        Currently FOCUS can be applied to scikit-learn `DecisionTreeClassifier`, `RandomForestClassifier` and `AdaBoostClassifier`.
        
        Installation
        ^^^^^^^^^^^^
        
        It is recommended to use **pip** or **conda** for installation. Please make sure
        **the latest version** is installed:
        
        .. code-block:: bash
        
           pip install focus-cfe            # normal install
           pip install --upgrade focus-cfe  # or update if needed
        
        .. code-block:: bash
        
           conda install -c conda-forge focus-cfe
        
Keywords: python,counterfactual explanation,binary classification,machine learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
