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Affinity Propagation Clustering Algorithm in Scikit-learn

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Version

In [1]:
import sklearn
sklearn.__version__
Out[1]:
'0.18'

Imports

This tutorial imports AffinityPropagation and make_blobs.

In [2]:
print(__doc__)

import plotly.plotly as py
import plotly.graph_objs as go

from sklearn.cluster import AffinityPropagation
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
Automatically created module for IPython interactive environment

Calculations

Generate sample data

In [3]:
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

In [4]:
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'))
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

In [5]:
colors = ['blue','green','red','cyan','magenta']
data = []
for k, col in zip(range(n_clusters_), colors):
    class_members = labels == k
    cluster_center = X[cluster_centers_indices[k]]
    trace1 = go.Scatter(x=X[class_members, 0], 
                        y=X[class_members, 1],
                        showlegend=False,
                        mode='markers', marker=dict(color=col,
                                                   size=10))
    
    trace2 = go.Scatter(x=[cluster_center[0]], 
                        y=[cluster_center[1]], 
                        showlegend=False,
                        mode='markers', marker=dict(color=col,
                                                    size=14))
    data.append(trace1)
    data.append(trace2)
    for x in X[class_members]:
        trace3 = go.Scatter(x = [cluster_center[0], x[0]], 
                            y=[cluster_center[1], x[1]],
                            showlegend=False,
                            mode='lines', line=dict(color=col,
                                                    width=2))
        data.append(trace3)

layout = go.Layout(title='Estimated number of clusters: %d' % n_clusters_,
                   xaxis=dict(zeroline=False),
                   yaxis=dict(zeroline=False))
fig = go.Figure(data=data, layout=layout)

py.iplot(fig)
Out[5]:

Reference

Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Science Feb. 2007

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