81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
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"""
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Testing for Clustering methods
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"""
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import numpy as np
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_raises
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from sklearn.cluster.affinity_propagation_ import AffinityPropagation
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from sklearn.cluster.affinity_propagation_ import affinity_propagation
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from sklearn.datasets.samples_generator import make_blobs
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from sklearn.metrics import euclidean_distances
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n_clusters = 3
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centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10
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X, _ = make_blobs(n_samples=60, n_features=2, centers=centers,
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cluster_std=0.4, shuffle=True, random_state=0)
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def test_affinity_propagation():
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# Affinity Propagation algorithm
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# Compute similarities
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S = -euclidean_distances(X, squared=True)
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preference = np.median(S) * 10
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# Compute Affinity Propagation
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cluster_centers_indices, labels = affinity_propagation(
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S, preference=preference)
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n_clusters_ = len(cluster_centers_indices)
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assert_equal(n_clusters, n_clusters_)
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af = AffinityPropagation(preference=preference, affinity="precomputed")
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labels_precomputed = af.fit(S).labels_
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af = AffinityPropagation(preference=preference, verbose=True)
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labels = af.fit(X).labels_
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assert_array_equal(labels, labels_precomputed)
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cluster_centers_indices = af.cluster_centers_indices_
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n_clusters_ = len(cluster_centers_indices)
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assert_equal(np.unique(labels).size, n_clusters_)
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assert_equal(n_clusters, n_clusters_)
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# Test also with no copy
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_, labels_no_copy = affinity_propagation(S, preference=preference,
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copy=False)
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assert_array_equal(labels, labels_no_copy)
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# Test input validation
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assert_raises(ValueError, affinity_propagation, S[:, :-1])
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assert_raises(ValueError, affinity_propagation, S, damping=0)
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af = AffinityPropagation(affinity="unknown")
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assert_raises(ValueError, af.fit, X)
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def test_affinity_propagation_predict():
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# Test AffinityPropagation.predict
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af = AffinityPropagation(affinity="euclidean")
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labels = af.fit_predict(X)
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labels2 = af.predict(X)
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assert_array_equal(labels, labels2)
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def test_affinity_propagation_predict_error():
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# Test exception in AffinityPropagation.predict
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# Not fitted.
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af = AffinityPropagation(affinity="euclidean")
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assert_raises(ValueError, af.predict, X)
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# Predict not supported when affinity="precomputed".
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S = np.dot(X, X.T)
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af = AffinityPropagation(affinity="precomputed")
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af.fit(S)
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assert_raises(ValueError, af.predict, X)
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