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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(global_random_seed):
rnd = np.random.RandomState(global_random_seed)
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) / 100.0
)
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([[2.0, 2.0]]),
clf1.decision_function([[2.0, 2.0]]) + clf1.offset_,
)
assert_array_equal(
clf2.score_samples([[2.0, 2.0]]),
clf2.decision_function([[2.0, 2.0]]) + clf2.offset_,
)
assert_array_equal(
clf1.score_samples([[2.0, 2.0]]), clf2.score_samples([[2.0, 2.0]])
)
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