Metadata-Version: 1.1
Name: focus-cfe
Version: 0.0.dev3
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.
        
        **Limitations**\:
        
        - Currently, FOCUS can only be applied to scikit-learn `DecisionTreeClassifier`, `RandomForestClassifier` and `AdaBoostClassifier`.
        - While categorical features may be included in the feature set, it is important to note that the interpretation of changes in categorical features, such as transitioning from age 40 to 20, may not provide meaningful insights.
        - The input features should be scaled to the range of 0 and 1 before applying FOCUS. Therefore, it is necessary to transform the features prior to using FOCUS. However, this scaling process may introduce some additional complexity when interpreting the features after applying FOCUS.
        
        ^^^^^^^^^^^^
        
        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
