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

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

.. _sphx_glr_auto_examples_plot_classifier_memory_bounded.py:


=============================
Memory bounded DL85Classifier
=============================

.. GENERATED FROM PYTHON SOURCE LINES 7-34




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

 .. code-block:: none

    ######################################################################
    #                   DL8.5 memory bounded classifier                  #
    ######################################################################
    Model building...
    Model built. Duration of building = 0.9935
    Confusion Matrix below
    [[ 26   8]
     [  9 120]]
    Accuracy DL8.5 on training set = 0.886
    Accuracy DL8.5 on test set = 0.8957






|

.. 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 pydl85 import DL85Classifier, Cache_Type, Wipe_Type
    import time

    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 memory bounded classifier                  #\n"
          "######################################################################")
    clf = DL85Classifier(max_depth=4, cache_type=Cache_Type.Cache_TrieItemset, maxcachesize=5000, wipe_factor=0.4, wipe_type=Wipe_Type.Reuses)
    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))



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

   **Total running time of the script:** ( 0 minutes  1.036 seconds)


.. _sphx_glr_download_auto_examples_plot_classifier_memory_bounded.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_memory_bounded.py <plot_classifier_memory_bounded.py>`

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

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


.. only:: html

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

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