.. _sphx_glr_auto_examples_cluster_plot_affinity_propagation.py:


=================================================
Demo of affinity propagation clustering algorithm
=================================================

Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007



.. code-block:: python

    print(__doc__)

    from sklearn.cluster import AffinityPropagation
    from sklearn import metrics
    from sklearn.datasets.samples_generator import make_blobs







Generate sample data


.. code-block:: python

    centers = [[1, 1], [-1, -1], [1, -1]]
    X, labels_true = make_blobs(n_samples=300, centers=centers, cluster_std=0.5,
                                random_state=0)







Compute Affinity Propagation


.. code-block:: python

    af = AffinityPropagation(preference=-50).fit(X)
    cluster_centers_indices = af.cluster_centers_indices_
    labels = af.labels_

    n_clusters_ = len(cluster_centers_indices)

    print('Estimated number of clusters: %d' % n_clusters_)
    print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
    print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
    print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
    print("Adjusted Rand Index: %0.3f"
          % metrics.adjusted_rand_score(labels_true, labels))
    print("Adjusted Mutual Information: %0.3f"
          % metrics.adjusted_mutual_info_score(labels_true, labels))
    print("Silhouette Coefficient: %0.3f"
          % metrics.silhouette_score(X, labels, metric='sqeuclidean'))





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

 Out::

      Estimated number of clusters: 3
    Homogeneity: 0.872
    Completeness: 0.872
    V-measure: 0.872
    Adjusted Rand Index: 0.912
    Adjusted Mutual Information: 0.871
    Silhouette Coefficient: 0.753


Plot result


.. code-block:: python

    import matplotlib.pyplot as plt
    from itertools import cycle

    plt.close('all')
    plt.figure(1)
    plt.clf()

    colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
    for k, col in zip(range(n_clusters_), colors):
        class_members = labels == k
        cluster_center = X[cluster_centers_indices[k]]
        plt.plot(X[class_members, 0], X[class_members, 1], col + '.')
        plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
                 markeredgecolor='k', markersize=14)
        for x in X[class_members]:
            plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)

    plt.title('Estimated number of clusters: %d' % n_clusters_)
    plt.show()



.. image:: /auto_examples/cluster/images/sphx_glr_plot_affinity_propagation_001.png
    :align: center




**Total running time of the script:**
(0 minutes 0.700 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`plot_affinity_propagation.py <plot_affinity_propagation.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_affinity_propagation.ipynb <plot_affinity_propagation.ipynb>`