.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_binary_policies.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_plot_binary_policies.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_binary_policies.py:


===========================
Binary Training Policies
===========================

The siblings policy is used by default on the local classifier per node, but the remaining ones can be selected with the parameter :literal:`binary_policy`, for example:

.. tabs::

    .. code-tab:: python
        :caption: Exclusive

        rf = RandomForestClassifier()
        classifier = LocalClassifierPerNode(local_classifier=rf, binary_policy="exclusive")

    .. code-tab:: python
        :caption: Less exclusive

        rf = RandomForestClassifier()
        classifier = LocalClassifierPerNode(local_classifier=rf, binary_policy="less_exclusive")

    .. code-tab:: python
        :caption: Less inclusive

        rf = RandomForestClassifier()
        classifier = LocalClassifierPerNode(local_classifier=rf, binary_policy="less_inclusive")

    .. code-tab:: python
        :caption: Inclusive

        rf = RandomForestClassifier()
        classifier = LocalClassifierPerNode(local_classifier=rf, binary_policy="inclusive")

    .. code-tab:: python
        :caption: Siblings

        rf = RandomForestClassifier()
        classifier = LocalClassifierPerNode(local_classifier=rf, binary_policy="siblings")

    .. code-tab:: python
        :caption: Exclusive siblings

        rf = RandomForestClassifier()
        classifier = LocalClassifierPerNode(local_classifier=rf, binary_policy="exclusive_siblings")

In the code below, the inclusive policy is selected.
However, the code can be easily updated by replacing lines 20-21 with the examples shown in the tabs above.

.. seealso::

   Mathematical definition on the different policies is given at :ref:`Training Policies`.

.. GENERATED FROM PYTHON SOURCE LINES 54-79




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

 Out:

 .. code-block:: none

    [['Animal' 'Mammal' 'Sheep']
     ['Animal' 'Mammal' 'Sheep']
     ['Animal' 'Mammal' 'Sheep']
     ['Animal' 'Mammal' 'Sheep']]






|

.. code-block:: default

    from sklearn.ensemble import RandomForestClassifier

    from hiclass import LocalClassifierPerNode

    # Define data
    X_train = [[1], [2], [3], [4]]
    X_test = [[4], [3], [2], [1]]
    Y_train = [
        ["Animal", "Mammal", "Sheep"],
        ["Animal", "Mammal", "Cow"],
        ["Animal", "Reptile", "Snake"],
        ["Animal", "Reptile", "Lizard"],
    ]

    # Use random forest classifiers for every node
    # And inclusive policy to select training examples for binary classifiers.
    rf = RandomForestClassifier()
    classifier = LocalClassifierPerNode(local_classifier=rf, binary_policy="inclusive")

    # Train local classifier per node
    classifier.fit(X_train, Y_train)

    # Predict
    predictions = classifier.predict(X_test)
    print(predictions)


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

   **Total running time of the script:** ( 0 minutes  0.010 seconds)


.. _sphx_glr_download_auto_examples_plot_binary_policies.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_binary_policies.py <plot_binary_policies.py>`



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

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


.. only:: html

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

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