def test_check_univariate(self):
with assert_raises(ValueError):
MAD().fit(X=[[0.0, 0.0],
[0.0, 0.0]])
with assert_raises(ValueError):
MAD().decision_function(X=[[0.0, 0.0],
[0.0, 0.0]])
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.8
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test, n_features=1,
contamination=self.contamination, random_state=42)
self.clf = MAD()
self.clf.fit(self.X_train)
def test_parameters(self):
assert (hasattr(self.clf, 'decision_scores_')
and self.clf.decision_scores_ is not None)
assert (hasattr(self.clf, 'labels_') and self.clf.labels_ is not None)
assert (hasattr(self.clf, 'threshold_')
and self.clf.threshold_ is not None)
with assert_raises(TypeError):
MAD(threshold='str')
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.8
# generate data and fit model without missing or infinite values:
self.X_train, self.X_test, self.y_train, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test, n_features=1,
contamination=self.contamination, random_state=42)
self.clf = MAD()
self.clf.fit(self.X_train)
# generate data and fit model with missing value:
self.X_train_nan, self.X_test_nan, self.y_train_nan, self.y_test_nan = generate_data(
n_train=self.n_train, n_test=self.n_test, n_features=1,
contamination=self.contamination, random_state=42,
n_nan=1)
self.clf_nan = MAD()
self.clf_nan.fit(self.X_train_nan)
# generate data and fit model with infinite value:
self.X_train_inf, self.X_test_inf, self.y_train_inf, self.y_test_inf = generate_data(
n_train=self.n_train, n_test=self.n_test, n_features=1,
contamination=self.contamination, random_state=42,
n_inf=1)
self.clf_inf = MAD()
self.clf_inf.fit(self.X_train_inf)
def pyod_anomaly_detection(type, contamination):
X_train, y_train, X_test, y_test = data(type=type,
contamination=contamination)
if type == 'MAD':
# train MAD detector
clf_name = 'MAD'
clf = MAD(threshold=3.5)
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
# visualize the results
# making dimensions = 2 for visualising purpose only. By repeating same data each dimension.
visualize(clf_name,
np.hstack((X_train, X_train)),
y_train,
np.hstack((X_test, X_test)),
y_test,
y_train_pred,
y_test_pred,
show_figure=True,
save_figure=False)
elif type == 'ABOD':
# train ABOD detector
clf_name = 'ABOD'
clf = ABOD()
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
# visualize the results
visualize(clf_name,
X_train,
y_train,
X_test,
y_test,
y_train_pred,
y_test_pred,
show_figure=True,
save_figure=False)
elif type == 'AutoEncoder':
# train AutoEncoder detector
clf_name = 'AutoEncoder'
clf = AutoEncoder(epochs=30, contamination=contamination)
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
class TestMAD(unittest.TestCase):
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.8
# generate data and fit model without missing or infinite values:
self.X_train, self.X_test, self.y_train, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test, n_features=1,
contamination=self.contamination, random_state=42)
self.clf = MAD()
self.clf.fit(self.X_train)
# generate data and fit model with missing value:
self.X_train_nan, self.X_test_nan, self.y_train_nan, self.y_test_nan = generate_data(
n_train=self.n_train, n_test=self.n_test, n_features=1,
contamination=self.contamination, random_state=42,
n_nan=1)
self.clf_nan = MAD()
self.clf_nan.fit(self.X_train_nan)
# generate data and fit model with infinite value:
self.X_train_inf, self.X_test_inf, self.y_train_inf, self.y_test_inf = generate_data(
n_train=self.n_train, n_test=self.n_test, n_features=1,
contamination=self.contamination, random_state=42,
n_inf=1)
self.clf_inf = MAD()
self.clf_inf.fit(self.X_train_inf)
def test_parameters(self):
assert (hasattr(self.clf, 'decision_scores_') and
self.clf.decision_scores_ is not None)
assert (hasattr(self.clf, 'labels_') and
self.clf.labels_ is not None)
assert (hasattr(self.clf, 'threshold_') and
self.clf.threshold_ is not None)
with assert_raises(TypeError):
MAD(threshold='str')
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert (roc_auc_score(self.y_test, pred_scores) >= self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, method='linear')
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, method='unify')
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, method='something')
def test_prediction_labels_confidence(self):
pred_labels, confidence = self.clf.predict(self.X_test,
return_confidence=True)
assert_equal(pred_labels.shape, self.y_test.shape)
assert_equal(confidence.shape, self.y_test.shape)
assert (confidence.min() >= 0)
assert (confidence.max() <= 1)
def test_prediction_proba_linear_confidence(self):
pred_proba, confidence = self.clf.predict_proba(self.X_test,
method='linear',
return_confidence=True)
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
assert_equal(confidence.shape, self.y_test.shape)
assert (confidence.min() >= 0)
assert (confidence.max() <= 1)
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train)
assert_equal(pred_labels.shape, self.y_train.shape)
def test_fit_predict_with_nan(self):
pred_labels = self.clf_nan.fit_predict(self.X_train_nan)
assert_equal(pred_labels.shape, self.y_train_nan.shape)
def test_fit_predict_with_inf(self):
pred_labels = self.clf_inf.fit_predict(self.X_train_inf)
assert_equal(pred_labels.shape, self.y_train_inf.shape)
def test_fit_predict_score(self):
self.clf.fit_predict_score(self.X_test, self.y_test)
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='roc_auc_score')
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='prc_n_score')
with assert_raises(NotImplementedError):
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='something')
def test_predict_rank(self):
pred_scores = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test)
print(pred_ranks)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_scores), atol=2)
assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_with_nan(self):
pred_scores = self.clf_nan.decision_function(self.X_test_nan)
pred_ranks = self.clf_nan._predict_rank(self.X_test_nan)
print(pred_ranks)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_scores), atol=2)
assert_array_less(pred_ranks, self.X_train_nan.shape[0] + 1)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_with_inf(self):
pred_scores = self.clf_inf.decision_function(self.X_test_inf)
pred_ranks = self.clf_inf._predict_rank(self.X_test_inf)
print(pred_ranks)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_scores), atol=2)
assert_array_less(pred_ranks, self.X_train_inf.shape[0] + 1)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_normalized(self):
pred_scores = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_scores), atol=2)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_normalized_with_nan(self):
pred_scores = self.clf_nan.decision_function(self.X_test_nan)
pred_ranks = self.clf_nan._predict_rank(self.X_test_nan, normalized=True)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_scores), atol=2)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_normalized_with_inf(self):
pred_scores = self.clf_inf.decision_function(self.X_test_inf)
pred_ranks = self.clf_inf._predict_rank(self.X_test_inf, normalized=True)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_scores), atol=2)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def test_check_univariate(self):
with assert_raises(ValueError):
MAD().fit(X=[[0.0, 0.0],
[0.0, 0.0]])
with assert_raises(ValueError):
MAD().decision_function(X=[[0.0, 0.0],
[0.0, 0.0]])
def test_detect_anomaly(self):
X_test = [[10000]]
score = self.clf.decision_function(X_test)
anomaly = self.clf.predict(X_test)
self.assertGreaterEqual(score[0], self.clf.threshold_)
self.assertEqual(anomaly[0], 1)
def test_detect_anomaly_with_nan(self):
X_test = [[10000]]
score = self.clf_nan.decision_function(X_test)
anomaly = self.clf_nan.predict(X_test)
self.assertGreaterEqual(score[0], self.clf_nan.threshold_)
self.assertEqual(anomaly[0], 1)
def test_detect_anomaly_with_inf(self):
X_test = [[10000]]
score = self.clf_inf.decision_function(X_test)
anomaly = self.clf_inf.predict(X_test)
self.assertGreaterEqual(score[0], self.clf_inf.threshold_)
self.assertEqual(anomaly[0], 1)
# todo: fix clone issue
def test_model_clone(self):
pass
# clone_clf = clone(self.clf)
def tearDown(self):
pass
class TestMAD(unittest.TestCase):
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.8
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test, n_features=1,
contamination=self.contamination, random_state=42)
self.clf = MAD()
self.clf.fit(self.X_train)
def test_parameters(self):
assert (hasattr(self.clf, 'decision_scores_') and
self.clf.decision_scores_ is not None)
assert (hasattr(self.clf, 'labels_') and
self.clf.labels_ is not None)
assert (hasattr(self.clf, 'threshold_') and
self.clf.threshold_ is not None)
with assert_raises(TypeError):
MAD(threshold='str')
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert_greater(roc_auc_score(self.y_test, pred_scores), self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, method='linear')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, method='unify')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, method='something')
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train)
assert_equal(pred_labels.shape, self.y_train.shape)
def test_fit_predict_score(self):
self.clf.fit_predict_score(self.X_test, self.y_test)
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='roc_auc_score')
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='prc_n_score')
with assert_raises(NotImplementedError):
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='something')
def test_predict_rank(self):
pred_scores = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test)
print(pred_ranks)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_scores), atol=2)
assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_normalized(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def test_check_univariate(self):
with assert_raises(ValueError):
MAD().fit(X=[[0.0, 0.0],
[0.0, 0.0]])
with assert_raises(ValueError):
MAD().decision_function(X=[[0.0, 0.0],
[0.0, 0.0]])
def tearDown(self):
pass
if __name__ == "__main__":
contamination = 0.1 # percentage of outliers
n_train = 200 # number of training points
n_test = 100 # number of testing points
# Generate sample data
X_train, y_train, X_test, y_test = \
generate_data(n_train=n_train,
n_test=n_test,
n_features=1,
contamination=contamination,
random_state=42)
# train MAD detector
clf_name = 'MAD'
clf = MAD(threshold=3.5)
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)