Uploaded Test files

venv/Lib/site-packages/sklearn/cluster/tests/test_feature_agglomeration.py

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