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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_mlearning_clustering_model_plot_kmeans.py>`
        to download the full example code

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

.. _sphx_glr_examples_mlearning_clustering_model_plot_kmeans.py:


K-means
=======

Load Iris dataset and create clusters.

.. GENERATED FROM PYTHON SOURCE LINES 29-31

Import
------

.. GENERATED FROM PYTHON SOURCE LINES 31-43

.. code-block:: default

    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo import create_benchmark_dataset
    from gemseo.datasets.dataset import Dataset
    from gemseo.mlearning import create_clustering_model
    from gemseo.post.dataset.scatter_plot_matrix import ScatterMatrix
    from numpy import array

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 44-47

Create dataset
--------------
We import the Iris benchmark dataset through the API.

.. GENERATED FROM PYTHON SOURCE LINES 47-56

.. code-block:: default

    iris = create_benchmark_dataset("IrisDataset")

    # Extract inputs as a new dataset
    data = iris.get_view(group_names=iris.PARAMETER_GROUP).to_numpy()
    variables = iris.get_variable_names(iris.PARAMETER_GROUP)
    print(variables)

    dataset = Dataset.from_array(data, variables)





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

 .. code-block:: none

    ['petal_length', 'petal_width', 'sepal_length', 'sepal_width']




.. GENERATED FROM PYTHON SOURCE LINES 57-61

Create clustering model
-----------------------
We know that there are three classes of Iris plants.
We will thus try to identify three clusters.

.. GENERATED FROM PYTHON SOURCE LINES 61-65

.. code-block:: default

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





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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.0.1/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
      warnings.warn(
    KMeans(n_clusters=3, random_state=0, var_names=None)
       based on the scikit-learn library
       built from 150 learning samples




.. GENERATED FROM PYTHON SOURCE LINES 66-69

Predict output
--------------
Once it is built, we can use it for prediction.

.. GENERATED FROM PYTHON SOURCE LINES 69-78

.. code-block:: default

    input_value = {
        "sepal_length": array([4.5]),
        "sepal_width": array([3.0]),
        "petal_length": array([1.0]),
        "petal_width": array([0.2]),
    }
    output_value = model.predict(input_value)
    print(output_value)





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

 .. code-block:: none

    0




.. GENERATED FROM PYTHON SOURCE LINES 79-82

Plot clusters
-------------
Show cluster labels

.. GENERATED FROM PYTHON SOURCE LINES 82-84

.. code-block:: default

    dataset.add_variable("km_specy", model.labels.reshape((-1, 1)), "labels")
    ScatterMatrix(dataset, kde=True, classifier="km_specy").execute(save=False, show=True)



.. image-sg:: /examples/mlearning/clustering_model/images/sphx_glr_plot_kmeans_001.png
   :alt: plot kmeans
   :srcset: /examples/mlearning/clustering_model/images/sphx_glr_plot_kmeans_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.0.1/lib/python3.9/site-packages/gemseo/post/dataset/scatter_plot_matrix.py:137: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared.
      sub_axes = scatter_matrix(

    [<Figure size 640x480 with 16 Axes>]




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

   **Total running time of the script:** ( 0 minutes  0.825 seconds)


.. _sphx_glr_download_examples_mlearning_clustering_model_plot_kmeans.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

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