def setUp(self):
self.n_train = 6000
self.n_test = 1000
self.n_features = 300
self.contamination = 0.1
self.roc_floor = 0.5
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=self.n_features,
contamination=self.contamination,
random_state=42)
self.clf = DeepSVDD(epochs=10,
hidden_neurons=[64, 32],
contamination=self.contamination,
random_state=2021)
self.clf_ae = DeepSVDD(epochs=5,
use_ae=True,
output_activation='relu',
hidden_neurons=[16, 8, 4],
contamination=self.contamination,
preprocessing=False)
self.clf.fit(self.X_train)
self.clf_ae.fit(self.X_train)
n_test = 2000 # number of testing points
n_features = 300 # number of features
use_ae = False # hyperparameter for use ae architecture instead of simple NN
random_state = 10 # if C is set to None use random_state
# Generate sample data
X_train, X_test, y_train, y_test = \
generate_data(n_train=n_train,
n_test=n_test,
n_features=n_features,
contamination=contamination,
random_state=42)
# train DeepSVDD detector (Without-AE)
clf_name = 'DeepSVDD'
clf = DeepSVDD(use_ae=use_ae, epochs=5, contamination=contamination,
random_state=random_state)
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 TestDeepSVDD(unittest.TestCase):
def setUp(self):
self.n_train = 6000
self.n_test = 1000
self.n_features = 300
self.contamination = 0.1
self.roc_floor = 0.5
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=self.n_features,
contamination=self.contamination,
random_state=42)
self.clf = DeepSVDD(epochs=10,
hidden_neurons=[64, 32],
contamination=self.contamination,
random_state=2021)
self.clf_ae = DeepSVDD(epochs=5,
use_ae=True,
output_activation='relu',
hidden_neurons=[16, 8, 4],
contamination=self.contamination,
preprocessing=False)
self.clf.fit(self.X_train)
self.clf_ae.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):
clone_clf = clone(self.clf)
clone_clf = clone(self.clf_ae)
def tearDown(self):
pass