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

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

.. _sphx_glr_auto_examples_plot_empty_levels.py:


==========================
Different Number of Levels
==========================

HiClass supports different number of levels in the hierarchy.
For this example, we will train a local classifier per node
with a hierarchy similar to the following image:

.. figure:: ../algorithms/local_classifier_per_node.svg
   :align: center

.. GENERATED FROM PYTHON SOURCE LINES 14-43




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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/hiclass/envs/v5.0.0/lib/python3.12/site-packages/sklearn/base.py:474: FutureWarning: `BaseEstimator._validate_data` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation.validate_data` instead. This function becomes public and is part of the scikit-learn developer API.
      warnings.warn(
    [['Mammal' 'Wolf' 'Dog']
     ['Mammal' 'Cat' '']
     ['Reptile' 'Lizard' '']
     ['Reptile' 'Snake' '']
     ['Bird' '' '']]






|

.. code-block:: Python

    import numpy as np
    from sklearn.linear_model import LogisticRegression

    from hiclass import LocalClassifierPerNode

    # Define data
    X_train = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
    X_test = [[9, 10], [7, 8], [5, 6], [3, 4], [1, 2]]
    Y_train = np.array(
        [
            ["Bird"],
            ["Reptile", "Snake"],
            ["Reptile", "Lizard"],
            ["Mammal", "Cat"],
            ["Mammal", "Wolf", "Dog"],
        ],
        dtype=object,
    )

    # Use random forest classifiers for every node
    rf = LogisticRegression()
    classifier = LocalClassifierPerNode(local_classifier=rf)

    # 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 0.023 seconds)


.. _sphx_glr_download_auto_examples_plot_empty_levels.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_empty_levels.zip <plot_empty_levels.zip>`


.. only:: html

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

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