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

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

.. _sphx_glr_auto_examples__general_plot_kernel_kmeans.py:


====================================
Kernel KMeans clustering with GEMINI
====================================

Since the MMD GEMINI objective is equivalent in OvO mode to a kernel KMeans objective, we can use it
with the nonparametric model that directly associates a cluster to each sample. The overall model
would thus behave as a kernel KMeans algorithm. However, its training is done by gradient descent.

.. GENERATED FROM PYTHON SOURCE LINES 10-17

.. code-block:: Python


    import numpy as np
    from matplotlib import pyplot as plt
    from sklearn import metrics, datasets

    from gemclus.nonparametric import CategoricalMMD








.. GENERATED FROM PYTHON SOURCE LINES 18-20

Draw samples from a circular dataset
--------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 22-36

.. code-block:: Python


    # We start by generating samples distributed on two circles
    X, y = datasets.make_circles(n_samples=200, noise=0.05, factor=0.05, random_state=0)

    # then normalise the data
    X = (X - np.mean(X, 0)) / np.std(X, ddof=0)

    # Have a look at it
    plt.scatter(X[:, 0], X[:, 1], c=y)
    plt.axis("off")
    plt.ylim((-3, 3))
    plt.ylim((-3, 3))
    plt.show()




.. image-sg:: /auto_examples/_general/images/sphx_glr_plot_kernel_kmeans_001.png
   :alt: plot kernel kmeans
   :srcset: /auto_examples/_general/images/sphx_glr_plot_kernel_kmeans_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 37-41

Train the model
--------------------------------------------------------------
Create the Non parametric GEMINI clustering model and call the .fit method to optimise the cluster
assignment of the nodes

.. GENERATED FROM PYTHON SOURCE LINES 43-47

.. code-block:: Python


    model = CategoricalMMD(n_clusters=2, random_state=0, kernel="rbf")
    y_pred = model.fit_predict(X)





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

 .. code-block:: none

    /home/circleci/.local/lib/python3.10/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(




.. GENERATED FROM PYTHON SOURCE LINES 48-50

Final Clustering
-----------------

.. GENERATED FROM PYTHON SOURCE LINES 52-59

.. code-block:: Python


    plt.scatter(X[:, 0], X[:, 1], c=y_pred)
    plt.show()

    ari_score = metrics.adjusted_rand_score(y, y_pred)
    gemini_score = model.score(X)
    print(f"Final ARI score: {ari_score:.3f}")
    print(f"GEMINI score is {gemini_score:.3f}")


.. image-sg:: /auto_examples/_general/images/sphx_glr_plot_kernel_kmeans_002.png
   :alt: plot kernel kmeans
   :srcset: /auto_examples/_general/images/sphx_glr_plot_kernel_kmeans_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Final ARI score: 1.000
    GEMINI score is 0.330





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

   **Total running time of the script:** (0 minutes 10.002 seconds)


.. _sphx_glr_download_auto_examples__general_plot_kernel_kmeans.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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