.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_classifier_dl85.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_dl85.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_classifier_dl85.py:


======================
Default DL85Classifier
======================

.. GENERATED FROM PYTHON SOURCE LINES 7-71




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

 .. code-block:: none

    ######################################################################
    #                      DL8.5 default classifier                      #
    ######################################################################
    Model building...
    Model built. Duration of building = 0.0039
    Confusion Matrix below
    [[  9  25]
     [  0 129]]
    Accuracy DL8.5 on training set = 0.8243
    Accuracy DL8.5 on test set = 0.8466 



    ##############################################################
    #     DL8.5 classifier : Manual cross-validation (5-fold)    #
    ##############################################################
    Model building...
    Model built. Duration of building = 0.025
    Average accuracy on training set = 0.8319
    Average accuracy on test set = 0.8227 



    ##############################################################
    #   DL8.5 classifier : Automatic cross-validation (5-fold)   #
    ##############################################################
    Model building...
    Model built. Duration of building = 0.0275
    Average accuracy on test set = 0.8239






|

.. 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
    from sklearn.model_selection import KFold
    from sklearn.model_selection import cross_val_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                      #\n"
          "######################################################################")
    clf = DL85Classifier(max_depth=2, time_limit=600, desc=True)
    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), "\n\n\n")


    print("##############################################################\n"
          "#     DL8.5 classifier : Manual cross-validation (5-fold)    #\n"
          "##############################################################")
    kf = KFold(n_splits=5, random_state=42, shuffle=True)
    training_accuracies = []
    test_accuracies = []
    start = time.perf_counter()
    print("Model building...")
    for train_index, test_index in kf.split(X):
        data_train = X[train_index]
        target_train = y[train_index]
        data_test = X[test_index]
        target_test = y[test_index]
        clf = DL85Classifier(max_depth=2, time_limit=600)
        clf.fit(data_train, target_train)
        preds = clf.predict(data_test)
        training_accuracies.append(clf.accuracy_)
        test_accuracies.append(accuracy_score(target_test, preds))
    duration = time.perf_counter() - start
    print("Model built. Duration of building =", round(duration, 4))
    print("Average accuracy on training set =", round(np.mean(training_accuracies), 4))
    print("Average accuracy on test set =", round(np.mean(test_accuracies), 4), "\n\n\n")


    print("##############################################################\n"
          "#   DL8.5 classifier : Automatic cross-validation (5-fold)   #\n"
          "##############################################################")
    clf = DL85Classifier(max_depth=2, time_limit=600)
    start = time.perf_counter()
    print("Model building...")
    scores = cross_val_score(clf, X, y, cv=5)
    duration = time.perf_counter() - start
    print("Model built. Duration of building =", round(duration, 4))
    print("Average accuracy on test set =", round(np.mean(scores), 4))


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

   **Total running time of the script:** ( 0 minutes  0.100 seconds)


.. _sphx_glr_download_auto_examples_plot_classifier_dl85.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_dl85.py <plot_classifier_dl85.py>`

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

      :download:`Download Jupyter notebook: plot_classifier_dl85.ipynb <plot_classifier_dl85.ipynb>`


.. only:: html

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

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