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"""
Testing for Elliptic Envelope algorithm (sklearn.covariance.elliptic_envelope).
"""
import numpy as np
from sklearn.covariance import EllipticEnvelope
from sklearn.utils.testing import assert_raises
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, assert_warns_message
from sklearn.exceptions import NotFittedError
def test_elliptic_envelope():
rnd = np.random.RandomState(0)
X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
assert_raises(NotFittedError, clf.predict, X)
assert_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.]]))
def test_raw_values_deprecation():
X = [[0.0], [1.0]]
clf = EllipticEnvelope().fit(X)
assert_warns_message(DeprecationWarning,
"raw_values parameter is deprecated in 0.20 and will"
" be removed in 0.22.",
clf.decision_function, X, raw_values=True)
def test_threshold_deprecation():
X = [[0.0], [1.0]]
clf = EllipticEnvelope().fit(X)
assert_warns_message(DeprecationWarning,
"threshold_ attribute is deprecated in 0.20 and will"
" be removed in 0.22.",
getattr, clf, "threshold_")
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