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

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

.. _sphx_glr_auto_examples_plot_model_persistence.py:


=====================
Model Persistence
=====================

HiClass is fully compatible with Pickle.
Pickle can be used to easily store machine learning models on disk.
In this example, we demonstrate how to use pickle to store and load trained classifiers.

.. GENERATED FROM PYTHON SOURCE LINES 11-46




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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/hiclass/envs/v5.0.1/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(
    [['Animal' 'Reptile' 'Lizard']
     ['Animal' 'Reptile' 'Snake']
     ['Animal' 'Mammal' 'Cow']
     ['Animal' 'Mammal' 'Sheep']]






|

.. code-block:: Python

    import pickle

    from sklearn.linear_model import LogisticRegression

    from hiclass import LocalClassifierPerLevel

    # Define data
    X_train = [[1, 2], [3, 4], [5, 6], [7, 8]]
    X_test = [[7, 8], [5, 6], [3, 4], [1, 2]]
    Y_train = [
        ["Animal", "Mammal", "Sheep"],
        ["Animal", "Mammal", "Cow"],
        ["Animal", "Reptile", "Snake"],
        ["Animal", "Reptile", "Lizard"],
    ]

    # Use Logistic Regression classifiers for every level in the hierarchy
    lr = LogisticRegression()
    classifier = LocalClassifierPerLevel(local_classifier=lr)

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

    # Save the model to disk
    filename = "trained_model.sav"
    pickle.dump(classifier, open(filename, "wb"))

    # Some time in the future...

    # Load the model from disk
    loaded_model = pickle.load(open(filename, "rb"))

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


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

   **Total running time of the script:** (0 minutes 0.013 seconds)


.. _sphx_glr_download_auto_examples_plot_model_persistence.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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