.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_classifier_user_2.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_user_2.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_classifier_user_2.py:


========================================================================
DL8.5 classifier : user specific error function based on transactions ID
========================================================================
PyDL8.5 allows users to write their own error function. This example shows how 
to write an error function based on transaction identifiers. PyDL8.5 will determine
these transaction identifiers based on the occurrences of an itemset in the
training data. 

The error function is called very often, and calculating an error score based
on tids can be time consuming. For classification tasks, it is highly recommended
not to write an error function in Python that traverses the tids. 
check the plot_classifier_user_1.py example for a more efficient user-written
error function in classification settings.

.. GENERATED FROM PYTHON SOURCE LINES 16-51




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

 .. code-block:: none

    ########################################################################################
    #      DL8.5 classifier : user specific error function based on transactions ids       #
    ########################################################################################
    Model building...
    Model built. Duration of building = 0.6875
    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 classifier : user specific error function based on transactions ids       #\n"
          "########################################################################################")


    # return the error and the majority class
    def error(tids, y):
        classes, supports = np.unique(y.take(list(tids)), return_counts=True)
        maxindex = np.argmax(supports)
        return sum(supports) - supports[maxindex], classes[maxindex]


    clf = DL85Classifier(max_depth=2, error_function=lambda tids: error(tids, y_train), time_limit=600)
    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  0.730 seconds)


.. _sphx_glr_download_auto_examples_plot_classifier_user_2.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_user_2.py <plot_classifier_user_2.py>`

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

      :download:`Download Jupyter notebook: plot_classifier_user_2.ipynb <plot_classifier_user_2.ipynb>`


.. only:: html

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

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