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.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/trust_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_trust_classification.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_trust_classification.py:


TrustReport for Classification
==============================

Simple example on how to use the TrustReport class to analyze the explanations
produced by ClassificationTrustee from a RandomForestClassifier from scikit-learn.
Notice that using the method `TrustReport.load()`, one can load a previously
generated report saved using `trust_report.save()`.

.. GENERATED FROM PYTHON SOURCE LINES 10-58

.. code-block:: Python


    import os

    # importing required libraries
    # importing Scikit-learn library and datasets package
    from sklearn import datasets
    from sklearn.ensemble import RandomForestClassifier

    from trustee.report.trust import TrustReport

    OUTPUT_PATH = "out/"
    REPORT_PATH = f"{OUTPUT_PATH}/report/trust_report.obj"

    if os.path.exists(REPORT_PATH):
        print(f"Loading trust report from {REPORT_PATH}...")
        trust_report = TrustReport.load(REPORT_PATH)
        print("Done!")
    else:
        # Loading the iris plants dataset (classification)
        iris = datasets.load_iris()
        # dividing the datasets into two parts i.e. training datasets and test datasets
        X, y = datasets.load_iris(return_X_y=True, as_frame=True)

        # creating a RF classifier
        clf = RandomForestClassifier(n_estimators=100)

        # The trust report (can) fit and explain the classifier
        trust_report = TrustReport(
            clf,
            X=X,
            y=y,
            max_iter=5,
            num_pruning_iter=5,
            train_size=0.7,
            trustee_num_iter=10,
            trustee_num_stability_iter=5,
            trustee_sample_size=0.3,
            analyze_branches=True,
            analyze_stability=True,
            top_k=10,
            verbose=True,
            class_names=iris.target_names,
            feature_names=iris.feature_names,
            is_classify=True,
        )

    print(trust_report)
    trust_report.save(OUTPUT_PATH)


.. _sphx_glr_download_auto_examples_trust_classification.py:

.. only:: html

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

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

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

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

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

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

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


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