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

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

.. _sphx_glr_auto_examples_plot_classifier_iterative_python.py:


===============================================
DL8.5 classifier : python side iterative search
===============================================
Iterative search is the idea that the algorithm starts with finding an optimal 
shallow tree, and then uses the quality of this tree to bound the quality of 
deeper trees. This class shows how to perform this type of search by repeatedly 
calling the DL8.5 algorithm. A second implementation is illustrated in 
plot_classifier_iterative_c_plus.py, and uses C++.

.. GENERATED FROM PYTHON SOURCE LINES 11-42




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

 .. code-block:: none

    ###########################################################################
    #      DL8.5 default classifier using python-based iterative search       #
    ###########################################################################
    Model built. Duration of building = 0.0055
    Confusion Matrix below
    [[  9  25]
     [  0 129]]
    Accuracy DL8.5 on training set = 0.8243
    Accuracy DL8.5 on test set = 0.8466






|

.. code-block:: default

    import numpy as np
    from sklearn.metrics import confusion_matrix
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import accuracy_score
    import time
    from pydl85 import DL85Classifier

    dataset = np.genfromtxt("../datasets/anneal.txt", delimiter=' ')
    X, y = dataset[:, 1:], dataset[:, 0]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)


    print("###########################################################################\n"
          "#      DL8.5 default classifier using python-based iterative search       #\n"
          "###########################################################################")
    start = time.perf_counter()
    error = 0  # default max error value expressing no bound
    clf = None
    remaining_time = 600
    for i in range(1, 3):  # max depth = 2
        clf = DL85Classifier(max_depth=i, max_error=error, time_limit=remaining_time)
        clf.fit(X_train, y_train)
        error = clf.error_
        remaining_time -= clf.runtime_
    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))


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

   **Total running time of the script:** ( 0 minutes  0.048 seconds)


.. _sphx_glr_download_auto_examples_plot_classifier_iterative_python.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_iterative_python.py <plot_classifier_iterative_python.py>`

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

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


.. only:: html

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

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