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

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

.. _sphx_glr_auto_examples_plot_classifier_export_tree.py:


==================================
Default DL85Classifier export tree
==================================

.. GENERATED FROM PYTHON SOURCE LINES 7-43




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

 .. code-block:: none

    ######################################################################
    #                      DL8.5 default classifier                      #
    ######################################################################
    Model built in 0.002 seconds
    Confusion Matrix below
     [[  9  25]
     [  0 129]]
    Accuracy on training set = 0.8243
    Accuracy on test set = 0.8466
    Serialized json tree: {'feat': 5, 'left': {'feat': 32, 'left': {'value': 1, 'error': 44}, 'right': {'value': 0, 'error': 2}}, 'right': {'feat': 46, 'left': {'value': 1, 'error': 68}, 'right': {'value': 0, 'error': 0}}, 'proba': [0.2357473035439137, 0.7642526964560863]}






|

.. code-block:: default

    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


    print("######################################################################\n"
          "#                      DL8.5 default classifier                      #\n"
          "######################################################################")

    # read the dataset and split into features and targets
    dataset = np.genfromtxt("../datasets/anneal.txt", delimiter=' ')
    X, y = dataset[:, 1:], dataset[:, 0]
    # split the dataset into training and test sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

    clf = DL85Classifier(max_depth=2, min_sup=1)
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)

    # show results
    print("Model built in", round(clf.runtime_, 4), "seconds")
    print("Confusion Matrix below\n", confusion_matrix(y_test, y_pred))
    print("Accuracy on training set =", round(clf.accuracy_, 4))
    print("Accuracy on test set =", round(accuracy_score(y_test, y_pred), 4))

    # print the tree
    print("Serialized json tree:", clf.tree_)
    dot = clf.export_graphviz()
    # uncomment the following lines to save the tree as a png file
    # graph = graphviz.Source(dot, format="png")
    # graph.render("plots/anneal_odt")



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

   **Total running time of the script:** ( 0 minutes  0.057 seconds)


.. _sphx_glr_download_auto_examples_plot_classifier_export_tree.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_classifier_export_tree.py <plot_classifier_export_tree.py>`

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

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


.. only:: html

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

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