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