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.. "examples/mlearning/clustering_model/plot_clustering_api.py"
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    .. note::
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        :ref:`Go to the end <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
------

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.. code-block:: Python

    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo import create_benchmark_dataset
    from gemseo.mlearning import create_clustering_model
    from gemseo.mlearning import get_clustering_models
    from gemseo.mlearning import get_clustering_options

    configure_logger()






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 .. code-block:: none


    <RootLogger root (INFO)>



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

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.. code-block:: Python

    get_clustering_models()





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 .. code-block:: none


    ['GaussianMixture', 'KMeans']



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

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.. code-block:: Python

    get_clustering_options("GaussianMixture")





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 .. code-block:: none

    +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
    |            Name           |                                        Description                                         |            Type           |
    +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
    |        n_components       |                     The number of components of the gaussian mixture.                      |          integer          |
    |        random_state       |  The random state passed to the random number generator. use an integer for reproducible   |          integer          |
    |                           |                                          results.                                          |                           |
    |        transformer        |           The strategies to transform the variables. the values are instances of           |           object          |
    |                           |  :class:`.transformer` while the keys are the names of either the variables or the groups  |                           |
    |                           | of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression algorithms. |                           |
    |                           | if a group is specified, the :class:`.transformer` will be applied to all the variables of |                           |
    |                           |             this group. if :attr:`.identity`, do not transform the variables.              |                           |
    |         var_names         | The names of the variables. if ``none``, consider all variables mentioned in the learning  |            null           |
    |                           |                                          dataset.                                          |                           |
    +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 13:55:15: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 13:55:15: |            Name           |                                        Description                                         |            Type           |
        INFO - 13:55:15: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+
        INFO - 13:55:15: |        n_components       |                     The number of components of the gaussian mixture.                      |          integer          |
        INFO - 13:55:15: |        random_state       |  The random state passed to the random number generator. use an integer for reproducible   |          integer          |
        INFO - 13:55:15: |                           |                                          results.                                          |                           |
        INFO - 13:55:15: |        transformer        |           The strategies to transform the variables. the values are instances of           |           object          |
        INFO - 13:55:15: |                           |  :class:`.transformer` while the keys are the names of either the variables or the groups  |                           |
        INFO - 13:55:15: |                           | of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression algorithms. |                           |
        INFO - 13:55:15: |                           | if a group is specified, the :class:`.transformer` will be applied to all the variables of |                           |
        INFO - 13:55:15: |                           |             this group. if :attr:`.identity`, do not transform the variables.              |                           |
        INFO - 13:55:15: |         var_names         | The names of the variables. if ``none``, consider all variables mentioned in the learning  |            null           |
        INFO - 13:55:15: |                           |                                          dataset.                                          |                           |
        INFO - 13:55:15: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+

    {'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'properties': {'transformer': {'description': 'The strategies to transform the variables. The values are instances of :class:`.Transformer` while the keys are the names of either the variables or the groups of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression algorithms. If a group is specified, the :class:`.Transformer` will be applied to all the variables of this group. If :attr:`.IDENTITY`, do not transform the variables.', 'type': 'object'}, 'var_names': {'description': 'The names of the variables. If ``None``, consider all variables mentioned in the learning dataset.', 'type': 'null'}, 'n_components': {'description': 'The number of components of the Gaussian mixture.', 'type': 'integer'}, 'random_state': {'description': 'The random state passed to the random number generator. Use an integer for reproducible results.', 'type': 'integer'}}}



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

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.. code-block:: Python

    iris = create_benchmark_dataset("IrisDataset")

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





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div style='background-color: #fafae2; padding: 10px;'>KMeans(n_clusters=3, random_state=0, var_names=None)<br/><ul><li>based on the scikit-learn library</li><li>built from 150 learning samples</li></ul></div>
    </div>
    <br />
    <br />


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   **Total running time of the script:** (0 minutes 0.145 seconds)


.. _sphx_glr_download_examples_mlearning_clustering_model_plot_clustering_api.py:

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