.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_lcpl_explainer.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_plot_lcpl_explainer.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_lcpl_explainer.py:


=========================================
Explaining Local Classifier Per Level
=========================================

A minimalist example showing how to use HiClass Explainer to obtain SHAP values of LCPL model.
A detailed summary of the Explainer class has been given at Algorithms Overview Section for :ref:`Hierarchical Explainability`.
SHAP values are calculated based on a synthetic platypus diseases dataset that can be downloaded `here <https://gist.githubusercontent.com/ashishpatel16/9306f8ed3ed101e7ddcb519776bcbd80/raw/3f225c3f80dd8cbb1b6252f6c372a054ec968705/platypus_diseases.csv>`_.

.. GENERATED FROM PYTHON SOURCE LINES 11-60



.. image-sg:: /auto_examples/images/sphx_glr_plot_lcpl_explainer_001.png
   :alt: plot lcpl explainer
   :srcset: /auto_examples/images/sphx_glr_plot_lcpl_explainer_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    <xarray.Dataset>
    Dimensions:          (class: 15, level: 3, sample: 246, feature: 9)
    Coordinates:
      * class            (class) <U16 'Allergy' 'Bee Allergy' ... 'Respiratory'
      * level            (level) int64 0 1 2
    Dimensions without coordinates: sample, feature
    Data variables:
        node             (sample, level) object 'Respiratory' ... 'Milk Allergy'
        predicted_class  (sample, level) object 'Respiratory' ... 'Milk Allergy'
        predict_proba    (sample, level, class) float64 0.16 nan nan ... 0.02 nan
        classes          (sample, level, class) object 'Allergy' nan ... nan
        shap_values      (level, class, sample, feature) float64 0.01697 ... nan






|

.. code-block:: default

    from sklearn.ensemble import RandomForestClassifier
    from hiclass import LocalClassifierPerLevel, Explainer
    import shap
    from hiclass.datasets import load_platypus

    # Load train and test splits
    X_train, X_test, Y_train, Y_test = load_platypus()

    # Use random forest classifiers for every level
    rfc = RandomForestClassifier()
    classifier = LocalClassifierPerLevel(local_classifier=rfc, replace_classifiers=False)

    # Train local classifiers per level
    classifier.fit(X_train, Y_train)

    # Define Explainer
    explainer = Explainer(classifier, data=X_train, mode="tree")
    explanations = explainer.explain(X_test.values)
    print(explanations)

    # Let's filter the Shapley values corresponding to the Covid (level 1)
    # and 'Respiratory' (level 0)

    covid_idx = classifier.predict(X_test)[:, 1] == "Covid"

    shap_filter_covid = {"level": 1, "class": "Covid", "sample": covid_idx}
    shap_filter_resp = {"level": 0, "class": "Respiratory", "sample": covid_idx}
    shap_val_covid = explanations.sel(**shap_filter_covid)
    shap_val_resp = explanations.sel(**shap_filter_resp)


    # This code snippet demonstrates how to visually compare the mean absolute SHAP values for 'Covid' vs. 'Respiratory' diseases.

    # Feature names for the X-axis
    feature_names = X_train.columns.values

    # SHAP values for 'Covid'
    shap_values_covid = shap_val_covid.shap_values.values

    # SHAP values for 'Respiratory'
    shap_values_resp = shap_val_resp.shap_values.values

    shap.summary_plot(
        [shap_values_covid, shap_values_resp],
        features=X_test.iloc[covid_idx],
        feature_names=X_train.columns.values,
        plot_type="bar",
        class_names=["Covid", "Respiratory"],
    )


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  50.910 seconds)


.. _sphx_glr_download_auto_examples_plot_lcpl_explainer.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_lcpl_explainer.py <plot_lcpl_explainer.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_lcpl_explainer.ipynb <plot_lcpl_explainer.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_