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# Authors: Nicolas Goix <nicolas.goix@telecom-paristech.fr>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
from math import sqrt
import numpy as np
from sklearn import neighbors
import pytest
from numpy.testing import assert_array_equal
from sklearn import metrics
from sklearn.metrics import roc_auc_score
from sklearn.utils import check_random_state
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_warns_message
from sklearn.utils._testing import assert_raises
from sklearn.utils._testing import assert_raises_regex
from sklearn.utils.estimator_checks import check_estimator
from sklearn.utils.estimator_checks import check_outlier_corruption
from sklearn.datasets import load_iris
# load the iris dataset
# and randomly permute it
rng = check_random_state(0)
iris = load_iris()
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
def test_lof():
# Toy sample (the last two samples are outliers):
X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [5, 3], [-4, 2]]
# Test LocalOutlierFactor:
clf = neighbors.LocalOutlierFactor(n_neighbors=5)
score = clf.fit(X).negative_outlier_factor_
assert_array_equal(clf._fit_X, X)
# Assert largest outlier score is smaller than smallest inlier score:
assert np.min(score[:-2]) > np.max(score[-2:])
# Assert predict() works:
clf = neighbors.LocalOutlierFactor(contamination=0.25,
n_neighbors=5).fit(X)
assert_array_equal(clf._predict(), 6 * [1] + 2 * [-1])
assert_array_equal(clf.fit_predict(X), 6 * [1] + 2 * [-1])
def test_lof_performance():
# Generate train/test data
rng = check_random_state(2)
X = 0.3 * rng.randn(120, 2)
X_train = X[:100]
# Generate some abnormal novel observations
X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
X_test = np.r_[X[100:], X_outliers]
y_test = np.array([0] * 20 + [1] * 20)
# fit the model for novelty detection
clf = neighbors.LocalOutlierFactor(novelty=True).fit(X_train)
# predict scores (the lower, the more normal)
y_pred = -clf.decision_function(X_test)
# check that roc_auc is good
assert roc_auc_score(y_test, y_pred) > .99
def test_lof_values():
# toy samples:
X_train = [[1, 1], [1, 2], [2, 1]]
clf1 = neighbors.LocalOutlierFactor(n_neighbors=2,
contamination=0.1,
novelty=True).fit(X_train)
clf2 = neighbors.LocalOutlierFactor(n_neighbors=2,
novelty=True).fit(X_train)
s_0 = 2. * sqrt(2.) / (1. + sqrt(2.))
s_1 = (1. + sqrt(2)) * (1. / (4. * sqrt(2.)) + 1. / (2. + 2. * sqrt(2)))
# check predict()
assert_array_almost_equal(-clf1.negative_outlier_factor_, [s_0, s_1, s_1])
assert_array_almost_equal(-clf2.negative_outlier_factor_, [s_0, s_1, s_1])
# check predict(one sample not in train)
assert_array_almost_equal(-clf1.score_samples([[2., 2.]]), [s_0])
assert_array_almost_equal(-clf2.score_samples([[2., 2.]]), [s_0])
# check predict(one sample already in train)
assert_array_almost_equal(-clf1.score_samples([[1., 1.]]), [s_1])
assert_array_almost_equal(-clf2.score_samples([[1., 1.]]), [s_1])
def test_lof_precomputed(random_state=42):
"""Tests LOF with a distance matrix."""
# Note: smaller samples may result in spurious test success
rng = np.random.RandomState(random_state)
X = rng.random_sample((10, 4))
Y = rng.random_sample((3, 4))
DXX = metrics.pairwise_distances(X, metric='euclidean')
DYX = metrics.pairwise_distances(Y, X, metric='euclidean')
# As a feature matrix (n_samples by n_features)
lof_X = neighbors.LocalOutlierFactor(n_neighbors=3, novelty=True)
lof_X.fit(X)
pred_X_X = lof_X._predict()
pred_X_Y = lof_X.predict(Y)
# As a dense distance matrix (n_samples by n_samples)
lof_D = neighbors.LocalOutlierFactor(n_neighbors=3, algorithm='brute',
metric='precomputed', novelty=True)
lof_D.fit(DXX)
pred_D_X = lof_D._predict()
pred_D_Y = lof_D.predict(DYX)
assert_array_almost_equal(pred_X_X, pred_D_X)
assert_array_almost_equal(pred_X_Y, pred_D_Y)
def test_n_neighbors_attribute():
X = iris.data
clf = neighbors.LocalOutlierFactor(n_neighbors=500).fit(X)
assert clf.n_neighbors_ == X.shape[0] - 1
clf = neighbors.LocalOutlierFactor(n_neighbors=500)
assert_warns_message(UserWarning,
"n_neighbors will be set to (n_samples - 1)",
clf.fit, X)
assert clf.n_neighbors_ == X.shape[0] - 1
def test_score_samples():
X_train = [[1, 1], [1, 2], [2, 1]]
clf1 = neighbors.LocalOutlierFactor(n_neighbors=2,
contamination=0.1,
novelty=True).fit(X_train)
clf2 = neighbors.LocalOutlierFactor(n_neighbors=2,
novelty=True).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_contamination():
X = [[1, 1], [1, 0]]
clf = neighbors.LocalOutlierFactor(contamination=0.6)
assert_raises(ValueError, clf.fit, X)
def test_novelty_errors():
X = iris.data
# check errors for novelty=False
clf = neighbors.LocalOutlierFactor()
clf.fit(X)
# predict, decision_function and score_samples raise ValueError
for method in ['predict', 'decision_function', 'score_samples']:
msg = ('{} is not available when novelty=False'.format(method))
assert_raises_regex(AttributeError, msg, getattr, clf, method)
# check errors for novelty=True
clf = neighbors.LocalOutlierFactor(novelty=True)
msg = 'fit_predict is not available when novelty=True'
assert_raises_regex(AttributeError, msg, getattr, clf, 'fit_predict')
def test_novelty_training_scores():
# check that the scores of the training samples are still accessible
# when novelty=True through the negative_outlier_factor_ attribute
X = iris.data
# fit with novelty=False
clf_1 = neighbors.LocalOutlierFactor()
clf_1.fit(X)
scores_1 = clf_1.negative_outlier_factor_
# fit with novelty=True
clf_2 = neighbors.LocalOutlierFactor(novelty=True)
clf_2.fit(X)
scores_2 = clf_2.negative_outlier_factor_
assert_array_almost_equal(scores_1, scores_2)
def test_hasattr_prediction():
# check availability of prediction methods depending on novelty value.
X = [[1, 1], [1, 2], [2, 1]]
# when novelty=True
clf = neighbors.LocalOutlierFactor(novelty=True)
clf.fit(X)
assert hasattr(clf, 'predict')
assert hasattr(clf, 'decision_function')
assert hasattr(clf, 'score_samples')
assert not hasattr(clf, 'fit_predict')
# when novelty=False
clf = neighbors.LocalOutlierFactor(novelty=False)
clf.fit(X)
assert hasattr(clf, 'fit_predict')
assert not hasattr(clf, 'predict')
assert not hasattr(clf, 'decision_function')
assert not hasattr(clf, 'score_samples')
def test_novelty_true_common_tests():
# the common tests are run for the default LOF (novelty=False).
# here we run these common tests for LOF when novelty=True
check_estimator(neighbors.LocalOutlierFactor(novelty=True))
@pytest.mark.parametrize('expected_outliers', [30, 53])
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)
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