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

.. only:: html

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

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

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

.. _sphx_glr_examples_mlearning_clustering_model_plot_clustering_api.py:


API
===

Here are some examples of the machine learning API
applied to clustering models.

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Import
------

.. GENERATED FROM PYTHON SOURCE LINES 34-46

.. code-block:: default

    from __future__ import division, unicode_literals

    from gemseo.api import configure_logger, load_dataset
    from gemseo.mlearning.api import (
        create_clustering_model,
        get_clustering_models,
        get_clustering_options,
    )

    configure_logger()






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

 Out:

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 47-49

Get available clustering models
-------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 49-51

.. code-block:: default

    print(get_clustering_models())





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

 Out:

 .. code-block:: none

    ['GaussianMixture', 'KMeans', 'MLPredictiveClusteringAlgo']




.. GENERATED FROM PYTHON SOURCE LINES 52-54

Get clustering model options
----------------------------

.. GENERATED FROM PYTHON SOURCE LINES 54-56

.. code-block:: default

    print(get_clustering_options("GaussianMixture"))





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

 Out:

 .. code-block:: none

    {'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'properties': {'transformer': {'type': 'null'}, 'var_names': {'type': 'null'}, 'n_components': {'description': 'The number of components of the Gaussian mixture.', 'type': 'integer'}}, 'required': ['n_components']}




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Create clustering model
-----------------------

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.. code-block:: default

    iris = load_dataset("IrisDataset")

    model = create_clustering_model("KMeans", data=iris, n_clusters=3)
    model.learn()

    print(model)




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

 Out:

 .. code-block:: none

    KMeans(n_clusters=3, random_state=0, var_names=None)
       built from 150 learning samples





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

   **Total running time of the script:** ( 0 minutes  0.060 seconds)


.. _sphx_glr_download_examples_mlearning_clustering_model_plot_clustering_api.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_clustering_api.py <plot_clustering_api.py>`



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

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


.. only:: html

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

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