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

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

.. _sphx_glr_auto_examples__general_plot_nonparametric_clustering.py:


==========================
Non parametric clustering
==========================

This example illustrates how we can run nonparametric clustering using GEMINI.
The specificity of this model is that the decision of model is not dependent on the position of the inputs, but
only on the parameters associated to the input. Consequently, this model cannot be used for unseen samples as
it will always return the same predictions.

.. GENERATED FROM PYTHON SOURCE LINES 11-19

.. code-block:: Python


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

    from gemclus import data
    from gemclus.nonparametric import CategoricalMMD








.. GENERATED FROM PYTHON SOURCE LINES 20-22

Draw samples from a GMM
--------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 24-32

.. code-block:: Python


    # Generate samples on that are simple to separate
    N = 100  # Number of nodes in the graph
    # GMM parameters
    means = np.array([[1, -1], [1, 1], [-1, -1], [-1, 1]])*2
    covariances = [np.eye(2)*0.5]*4
    X, y = data.draw_gmm(N, means, covariances, np.ones(4) / 4, random_state=1789)








.. GENERATED FROM PYTHON SOURCE LINES 33-37

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 39-43

.. code-block:: Python


    model = CategoricalMMD(n_clusters=4, ovo=True, random_state=0, learning_rate=1e-2)
    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(




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

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.. 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_nonparametric_clustering_001.png
   :alt: plot nonparametric clustering
   :srcset: /auto_examples/_general/images/sphx_glr_plot_nonparametric_clustering_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Final ARI score: 0.975
    GEMINI score is 3.328





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

   **Total running time of the script:** (0 minutes 0.289 seconds)


.. _sphx_glr_download_auto_examples__general_plot_nonparametric_clustering.py:

.. only:: html

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

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

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

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

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

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

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


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