Uploaded Test files

venv/Lib/site-packages/sklearn/covariance/tests/test_elliptic_envelope.py

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+"""
+Testing for Elliptic Envelope algorithm (sklearn.covariance.elliptic_envelope).
+"""
+
+import numpy as np
+import pytest
+
+from sklearn.covariance import EllipticEnvelope
+from sklearn.utils._testing import assert_almost_equal
+from sklearn.utils._testing import assert_array_almost_equal
+from sklearn.utils._testing import assert_array_equal
+from sklearn.exceptions import NotFittedError
+
+
+def test_elliptic_envelope():
+    rnd = np.random.RandomState(0)
+    X = rnd.randn(100, 10)
+    clf = EllipticEnvelope(contamination=0.1)
+    with pytest.raises(NotFittedError):
+        clf.predict(X)
+    with pytest.raises(NotFittedError):
+        clf.decision_function(X)
+    clf.fit(X)
+    y_pred = clf.predict(X)
+    scores = clf.score_samples(X)
+    decisions = clf.decision_function(X)
+
+    assert_array_almost_equal(
+        scores, -clf.mahalanobis(X))
+    assert_array_almost_equal(clf.mahalanobis(X), clf.dist_)
+    assert_almost_equal(clf.score(X, np.ones(100)),
+                        (100 - y_pred[y_pred == -1].size) / 100.)
+    assert(sum(y_pred == -1) == sum(decisions < 0))
+
+
+def test_score_samples():
+    X_train = [[1, 1], [1, 2], [2, 1]]
+    clf1 = EllipticEnvelope(contamination=0.2).fit(X_train)
+    clf2 = EllipticEnvelope().fit(X_train)
+    assert_array_equal(clf1.score_samples([[2., 2.]]),
+                       clf1.decision_function([[2., 2.]]) + clf1.offset_)
+    assert_array_equal(clf2.score_samples([[2., 2.]]),
+                       clf2.decision_function([[2., 2.]]) + clf2.offset_)
+    assert_array_equal(clf1.score_samples([[2., 2.]]),
+                       clf2.score_samples([[2., 2.]]))