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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_classification.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_classification.py:


ClassificationTrustee
=====================

Simple example on how to use the ClassificationTrustee class to extract
a decision tree from a RandomForestClassifier from scikit-learn.

.. GENERATED FROM PYTHON SOURCE LINES 8-91

.. code-block:: Python


    # importing required libraries
    # importing Scikit-learn library and datasets package
    import graphviz

    from sklearn import tree
    from sklearn import datasets
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import classification_report


    from trustee import ClassificationTrustee

    # Loading the iris plants dataset (classification)
    iris = datasets.load_iris()
    X, y = datasets.load_iris(return_X_y=True)

    # Spliting arrays or matrices into random train and test subsets
    # i.e. 70 % training dataset and 30 % test datasets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)

    # creating a RF classifier
    clf = RandomForestClassifier(n_estimators=100)
    # Training the model on the training dataset
    # fit function is used to train the model using the training sets as parameters
    clf.fit(X_train, y_train)
    # performing predictions on the test dataset
    y_pred = clf.predict(X_test)

    # Evaluate model accuracy
    print("Model classification report:")
    print(classification_report(y_test, y_pred))

    # Initialize Trustee and fit for classification models
    trustee = ClassificationTrustee(expert=clf)
    trustee.fit(X_train, y_train, num_iter=50, num_stability_iter=10, samples_size=0.3, verbose=True)

    # Get the best explanation from Trustee
    dt, pruned_dt, agreement, reward = trustee.explain()
    print(f"Model explanation training (agreement, fidelity): ({agreement}, {reward})")
    print(f"Model Explanation size: {dt.tree_.node_count}")
    print(f"Top-k Prunned Model explanation size: {pruned_dt.tree_.node_count}")

    # Use explanations to make predictions
    dt_y_pred = dt.predict(X_test)
    pruned_dt_y_pred = pruned_dt.predict(X_test)

    # Evaluate accuracy and fidelity of explanations
    print("Model explanation global fidelity report:")
    print(classification_report(y_pred, dt_y_pred))
    print("Top-k Model explanation global fidelity report:")
    print(classification_report(y_pred, pruned_dt_y_pred))

    print("Model explanation score report:")
    print(classification_report(y_test, dt_y_pred))
    print("Top-k Model explanation score report:")
    print(classification_report(y_test, pruned_dt_y_pred))


    # Output decision tree to pdf
    dot_data = tree.export_graphviz(
        dt,
        class_names=iris.target_names,
        feature_names=iris.feature_names,
        filled=True,
        rounded=True,
        special_characters=True,
    )
    graph = graphviz.Source(dot_data)
    graph.render("dt_explanation")

    # Output pruned decision tree to pdf
    dot_data = tree.export_graphviz(
        pruned_dt,
        class_names=iris.target_names,
        feature_names=iris.feature_names,
        filled=True,
        rounded=True,
        special_characters=True,
    )
    graph = graphviz.Source(dot_data)
    graph.render("pruned_dt_explation")


.. _sphx_glr_download_auto_examples_classification.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: classification.zip <classification.zip>`


.. only:: html

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

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