def test_check_outlier_corruption():
# should raise AssertionError
decision = np.array([0., 1., 1.5, 2.])
assert_raises(AssertionError, check_outlier_corruption, 1, 2, decision)
# should pass
decision = np.array([0., 1., 1., 2.])
check_outlier_corruption(1, 2, decision)
def test_check_outlier_corruption():
# should raise AssertionError
decision = np.array([0., 1., 1.5, 2.])
assert_raises(AssertionError, check_outlier_corruption, 1, 2, decision)
# should pass
decision = np.array([0., 1., 1., 2.])
check_outlier_corruption(1, 2, decision)
def test_check_outlier_corruption():
# should raise AssertionError
decision = np.array([0.0, 1.0, 1.5, 2.0])
with raises(AssertionError):
check_outlier_corruption(1, 2, decision)
# should pass
decision = np.array([0.0, 1.0, 1.0, 2.0])
check_outlier_corruption(1, 2, decision)
def test_predicted_outlier_number(expected_outliers):
# the number of predicted outliers should be equal to the number of
# expected outliers unless there are ties in the abnormality scores.
X = iris.data
n_samples = X.shape[0]
contamination = float(expected_outliers) / n_samples
clf = neighbors.LocalOutlierFactor(contamination=contamination)
y_pred = clf.fit_predict(X)
num_outliers = np.sum(y_pred != 1)
if num_outliers != expected_outliers:
y_dec = clf.negative_outlier_factor_
check_outlier_corruption(num_outliers, expected_outliers, y_dec)
def test_predicted_outlier_number():
# the number of predicted outliers should be equal to the number of
# expected outliers unless there are ties in the abnormality scores.
X = iris.data
n_samples = X.shape[0]
expected_outliers = 30
contamination = float(expected_outliers)/n_samples
clf = neighbors.LocalOutlierFactor(contamination=contamination)
y_pred = clf.fit_predict(X)
num_outliers = np.sum(y_pred != 1)
if num_outliers != expected_outliers:
y_dec = clf.negative_outlier_factor_
check_outlier_corruption(num_outliers, expected_outliers, y_dec)
