.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_compas.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_plot_compas.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_compas.py:


========================================
Default DL85Classifier on COMPAS dataset
========================================

.. GENERATED FROM PYTHON SOURCE LINES 7-48




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

 .. code-block:: none

    ######################################################################
    #                      DL8.5 default classifier                      #
    ######################################################################
    Model building...
    Model built. Duration of building = 0.2881
    Confusion Matrix below
    [[543 246]
     [204 450]]
    Accuracy DL8.5 on training set = 0.6801
    Accuracy DL8.5 on test set = 0.6881
    Serialized json tree: {'feat': 2, 'left': {'feat': 1, 'left': {'feat': 0, 'left': {'value': 1, 'error': 40}, 'right': {'feat': 8, 'left': {'value': 1, 'error': 142}, 'right': {'value': 0, 'error': 35}}}, 'right': {'feat': 10, 'left': {'feat': 24, 'left': {'value': 1, 'error': 9}, 'right': {'value': 0, 'error': 61}}, 'right': {'feat': 22, 'left': {'value': 0, 'error': 64}, 'right': {'value': 1, 'error': 140}}}}, 'right': {'feat': 23, 'left': {'feat': 26, 'left': {'feat': 18, 'left': {'value': 0, 'error': 193}, 'right': {'value': 1, 'error': 8}}, 'right': {'feat': 5, 'left': {'value': 0, 'error': 251}, 'right': {'value': 1, 'error': 113}}}, 'right': {'feat': 4, 'left': {'feat': 22, 'left': {'value': 0, 'error': 191}, 'right': {'value': 1, 'error': 288}}, 'right': {'feat': 25, 'left': {'value': 1, 'error': 151}, 'right': {'value': 0, 'error': 160}}}}, 'proba': [0.5118696932940565, 0.4881303067059435]}






|

.. code-block:: default

    import time
    import graphviz
    import numpy as np
    from sklearn.metrics import confusion_matrix
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import accuracy_score

    from pydl85 import DL85Classifier, Cache_Type

    dataset = np.genfromtxt("../datasets/compas.csv", delimiter=',', skip_header=1)
    X, y = dataset[:, :-1], dataset[:, -1]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # read the column names
    with open("../datasets/compas.csv", 'r') as f:
        col_names = f.readline().strip().split(',')
        col_names = col_names[:-1]


    print("######################################################################\n"
          "#                      DL8.5 default classifier                      #\n"
          "######################################################################")
    clf = DL85Classifier(max_depth=4, cache_type=Cache_Type.Cache_HashCover)
    start = time.perf_counter()
    print("Model building...")
    clf.fit(X_train, y_train)
    duration = time.perf_counter() - start
    print("Model built. Duration of building =", round(duration, 4))
    y_pred = clf.predict(X_test)
    print("Confusion Matrix below")
    print(confusion_matrix(y_test, y_pred))
    print("Accuracy DL8.5 on training set =", round(clf.accuracy_, 4))
    print("Accuracy DL8.5 on test set =", round(accuracy_score(y_test, y_pred), 4))


    # print the tree
    print("Serialized json tree:", clf.tree_)
    dot = clf.export_graphviz(feature_names=col_names, class_names=["No Recidivism", "Recidivism"])
    # uncomment the following lines to save the tree as a png file
    # graph = graphviz.Source(dot, format="png")
    # graph.render("plots/compas_odt")


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

   **Total running time of the script:** ( 0 minutes  0.411 seconds)


.. _sphx_glr_download_auto_examples_plot_compas.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

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