44 lines
1.7 KiB
Python
44 lines
1.7 KiB
Python
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"""
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Tests for sklearn.cluster._feature_agglomeration
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"""
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# Authors: Sergul Aydore 2017
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import numpy as np
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from sklearn.cluster import FeatureAgglomeration
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from sklearn.utils._testing import assert_no_warnings
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from sklearn.utils._testing import assert_array_almost_equal
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def test_feature_agglomeration():
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n_clusters = 1
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X = np.array([0, 0, 1]).reshape(1, 3) # (n_samples, n_features)
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agglo_mean = FeatureAgglomeration(n_clusters=n_clusters,
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pooling_func=np.mean)
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agglo_median = FeatureAgglomeration(n_clusters=n_clusters,
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pooling_func=np.median)
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assert_no_warnings(agglo_mean.fit, X)
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assert_no_warnings(agglo_median.fit, X)
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assert np.size(np.unique(agglo_mean.labels_)) == n_clusters
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assert np.size(np.unique(agglo_median.labels_)) == n_clusters
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assert np.size(agglo_mean.labels_) == X.shape[1]
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assert np.size(agglo_median.labels_) == X.shape[1]
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# Test transform
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Xt_mean = agglo_mean.transform(X)
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Xt_median = agglo_median.transform(X)
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assert Xt_mean.shape[1] == n_clusters
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assert Xt_median.shape[1] == n_clusters
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assert Xt_mean == np.array([1 / 3.])
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assert Xt_median == np.array([0.])
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# Test inverse transform
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X_full_mean = agglo_mean.inverse_transform(Xt_mean)
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X_full_median = agglo_median.inverse_transform(Xt_median)
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assert np.unique(X_full_mean[0]).size == n_clusters
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assert np.unique(X_full_median[0]).size == n_clusters
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assert_array_almost_equal(agglo_mean.transform(X_full_mean),
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Xt_mean)
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assert_array_almost_equal(agglo_median.transform(X_full_median),
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Xt_median)
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