120 lines
4 KiB
Python
120 lines
4 KiB
Python
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# Authors: Nicolas Goix <nicolas.goix@telecom-paristech.fr>
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# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
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# License: BSD 3 clause
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from math import sqrt
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import numpy as np
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from sklearn import neighbors
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from numpy.testing import assert_array_equal
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from sklearn import metrics
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from sklearn.metrics import roc_auc_score
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from sklearn.utils import check_random_state
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from sklearn.utils.testing import assert_greater
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_warns_message
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from sklearn.datasets import load_iris
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# load the iris dataset
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# and randomly permute it
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rng = check_random_state(0)
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iris = load_iris()
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perm = rng.permutation(iris.target.size)
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iris.data = iris.data[perm]
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iris.target = iris.target[perm]
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def test_lof():
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# Toy sample (the last two samples are outliers):
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X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [5, 3], [-4, 2]]
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# Test LocalOutlierFactor:
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clf = neighbors.LocalOutlierFactor(n_neighbors=5)
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score = clf.fit(X).negative_outlier_factor_
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assert_array_equal(clf._fit_X, X)
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# Assert largest outlier score is smaller than smallest inlier score:
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assert_greater(np.min(score[:-2]), np.max(score[-2:]))
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# Assert predict() works:
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clf = neighbors.LocalOutlierFactor(contamination=0.25,
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n_neighbors=5).fit(X)
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assert_array_equal(clf._predict(), 6 * [1] + 2 * [-1])
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def test_lof_performance():
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# Generate train/test data
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rng = check_random_state(2)
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X = 0.3 * rng.randn(120, 2)
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X_train = X[:100]
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# Generate some abnormal novel observations
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X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
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X_test = np.r_[X[100:], X_outliers]
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y_test = np.array([0] * 20 + [1] * 20)
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# fit the model
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clf = neighbors.LocalOutlierFactor().fit(X_train)
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# predict scores (the lower, the more normal)
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y_pred = -clf._decision_function(X_test)
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# check that roc_auc is good
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assert_greater(roc_auc_score(y_test, y_pred), .99)
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def test_lof_values():
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# toy samples:
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X_train = [[1, 1], [1, 2], [2, 1]]
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clf = neighbors.LocalOutlierFactor(n_neighbors=2).fit(X_train)
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s_0 = 2. * sqrt(2.) / (1. + sqrt(2.))
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s_1 = (1. + sqrt(2)) * (1. / (4. * sqrt(2.)) + 1. / (2. + 2. * sqrt(2)))
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# check predict()
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assert_array_almost_equal(-clf.negative_outlier_factor_, [s_0, s_1, s_1])
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# check predict(one sample not in train)
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assert_array_almost_equal(-clf._decision_function([[2., 2.]]), [s_0])
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# # check predict(one sample already in train)
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assert_array_almost_equal(-clf._decision_function([[1., 1.]]), [s_1])
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def test_lof_precomputed(random_state=42):
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"""Tests LOF with a distance matrix."""
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# Note: smaller samples may result in spurious test success
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rng = np.random.RandomState(random_state)
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X = rng.random_sample((10, 4))
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Y = rng.random_sample((3, 4))
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DXX = metrics.pairwise_distances(X, metric='euclidean')
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DYX = metrics.pairwise_distances(Y, X, metric='euclidean')
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# As a feature matrix (n_samples by n_features)
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lof_X = neighbors.LocalOutlierFactor(n_neighbors=3)
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lof_X.fit(X)
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pred_X_X = lof_X._predict()
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pred_X_Y = lof_X._predict(Y)
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# As a dense distance matrix (n_samples by n_samples)
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lof_D = neighbors.LocalOutlierFactor(n_neighbors=3, algorithm='brute',
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metric='precomputed')
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lof_D.fit(DXX)
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pred_D_X = lof_D._predict()
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pred_D_Y = lof_D._predict(DYX)
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assert_array_almost_equal(pred_X_X, pred_D_X)
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assert_array_almost_equal(pred_X_Y, pred_D_Y)
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def test_n_neighbors_attribute():
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X = iris.data
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clf = neighbors.LocalOutlierFactor(n_neighbors=500).fit(X)
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assert_equal(clf.n_neighbors_, X.shape[0] - 1)
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clf = neighbors.LocalOutlierFactor(n_neighbors=500)
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assert_warns_message(UserWarning,
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"n_neighbors will be set to (n_samples - 1)",
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clf.fit, X)
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assert_equal(clf.n_neighbors_, X.shape[0] - 1)
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