81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
"""
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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 warnings
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import numpy as np
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import pytest
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from numpy.testing import assert_array_equal
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from sklearn.cluster import FeatureAgglomeration
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from sklearn.datasets import make_blobs
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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, pooling_func=np.mean)
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agglo_median = FeatureAgglomeration(n_clusters=n_clusters, pooling_func=np.median)
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agglo_mean.fit(X)
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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 / 3.0])
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assert Xt_median == np.array([0.0])
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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), Xt_mean)
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assert_array_almost_equal(agglo_median.transform(X_full_median), Xt_median)
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def test_feature_agglomeration_feature_names_out():
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"""Check `get_feature_names_out` for `FeatureAgglomeration`."""
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X, _ = make_blobs(n_features=6, random_state=0)
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agglo = FeatureAgglomeration(n_clusters=3)
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agglo.fit(X)
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n_clusters = agglo.n_clusters_
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names_out = agglo.get_feature_names_out()
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assert_array_equal(
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[f"featureagglomeration{i}" for i in range(n_clusters)], names_out
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)
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# TODO(1.5): remove this test
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def test_inverse_transform_Xred_deprecation():
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X = np.array([0, 0, 1]).reshape(1, 3) # (n_samples, n_features)
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est = FeatureAgglomeration(n_clusters=1, pooling_func=np.mean)
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est.fit(X)
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Xt = est.transform(X)
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with pytest.raises(TypeError, match="Missing required positional argument"):
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est.inverse_transform()
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with pytest.raises(ValueError, match="Please provide only"):
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est.inverse_transform(Xt=Xt, Xred=Xt)
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with warnings.catch_warnings(record=True):
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warnings.simplefilter("error")
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est.inverse_transform(Xt)
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with pytest.warns(FutureWarning, match="Input argument `Xred` was renamed to `Xt`"):
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est.inverse_transform(Xred=Xt)
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