def autoencoder_outlier_detection(X_train, X_test, **kwargs):
detector = AutoEncoder(**kwargs)
detector.fit(X_train)
prob = detector.predict_proba(X_test)[:, -1]
if isinstance(X_test, pd.DataFrame):
return pd.Series(prob, name='outlier', index=X_test.index)
return pd.Series(prob, name='outlier')
class TestAutoEncoder(unittest.TestCase):
def setUp(self):
self.n_train = 6000
self.n_test = 1000
self.n_features = 300
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=self.n_features,
contamination=self.contamination,
random_state=42)
self.clf = AutoEncoder(epochs=5, contamination=self.contamination)
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)
assert (hasattr(self.clf, '_mu') and self.clf._mu is not None)
assert (hasattr(self.clf, '_sigma') and self.clf._sigma is not None)
assert (hasattr(self.clf, 'model_') and self.clf.model_ is not None)
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_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_model_clone(self):
# for deep models this may not apply
clone_clf = clone(self.clf)
def tearDown(self):
pass
IF.fit(X_train)
sklearn_score_anomalies = IF.decision_function(X_test)
original_paper_score = [-1 * s + 0.5 for s in sklearn_score_anomalies]
aucs_if_ws[r] = evaluate.AUC(original_paper_score, y_test)
auc_if_ws = np.mean(aucs_if_ws)
# --- T2 --- #
y_pred_proba_hot = Hoteliing_SPC_proba(X_train, X_test)
auc_hot_ws = evaluate.AUC(y_pred_proba_hot, y_test)
# --- AutoEncoder --- #
aucs_ae_ws = np.zeros(num_of_experiments)
for r in range(num_of_experiments):
AE = AutoEncoder(hidden_neurons=[64, 6, 6, 64], random_state=r)
AE.fit(X_train)
ae_pred_proba = AE.predict_proba(X_test)[:, 1]
aucs_ae_ws[r] = evaluate.AUC(ae_pred_proba, y_test)
auc_ae_ws = np.mean(aucs_ae_ws)
# --- one-class-SVM --- #
clf = svm.OneClassSVM(kernel="rbf")
clf.fit(X_train)
sklearn_score_anomalies = clf.decision_function(X_test)
original_paper_score = [-1 * s + 0.5 for s in sklearn_score_anomalies]
auc_svm_ws = evaluate.AUC(original_paper_score, y_test)
# --- LOF --- #
lof = LocalOutlierFactor(novelty=True)
lof.fit(X_train)
sklearn_score_anomalies = lof.decision_function(X_test)
original_paper_score = [-1 * s + 0.5 for s in sklearn_score_anomalies]
class TestAutoEncoder(unittest.TestCase):
def setUp(self):
self.n_train = 6000
self.n_test = 1000
self.n_features = 300
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=self.n_features,
contamination=self.contamination,
random_state=42)
self.clf = AutoEncoder(epochs=5, contamination=self.contamination)
self.clf.fit(self.X_train)
def test_sklearn_estimator(self):
# TODO: fix estimator check for AutoEncoder
# check_estimator(self.clf)
pass
def test_parameters(self):
assert_true(
hasattr(self.clf, 'decision_scores_')
and self.clf.decision_scores_ is not None)
assert_true(
hasattr(self.clf, 'labels_') and self.clf.labels_ is not None)
assert_true(
hasattr(self.clf, 'threshold_')
and self.clf.threshold_ is not None)
assert_true(hasattr(self.clf, '_mu') and self.clf._mu is not None)
assert_true(
hasattr(self.clf, '_sigma') and self.clf._sigma is not None)
assert_true(
hasattr(self.clf, 'model_') and self.clf.model_ is not None)
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 tearDown(self):
pass