class TestOCSVM(unittest.TestCase):
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.6
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test,
contamination=self.contamination, random_state=42)
self.clf = OCSVM()
self.clf.fit(self.X_train)
def test_sklearn_estimator(self):
check_estimator(self.clf)
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, 'support_') and
self.clf.support_ is not None)
assert_true(hasattr(self.clf, 'support_vectors_') and
self.clf.support_vectors_ is not None)
assert_true(hasattr(self.clf, 'dual_coef_') and
self.clf.dual_coef_ is not None)
assert_true(hasattr(self.clf, 'intercept_') and
self.clf.intercept_ is not None)
# only available for linear kernel
# if not hasattr(self.clf, 'coef_') or self.clf.coef_ is None:
# self.assertRaises(AttributeError, 'coef_ is not set')
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_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_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), 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 tearDown(self):
pass
class TestOCSVM(unittest.TestCase):
def setUp(self):
self.n_train = 200
self.n_test = 100
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,
contamination=self.contamination,
random_state=42)
self.clf = OCSVM()
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, 'support_')
and self.clf.support_ is not None)
assert (hasattr(self.clf, 'support_vectors_')
and self.clf.support_vectors_ is not None)
assert (hasattr(self.clf, 'dual_coef_')
and self.clf.dual_coef_ is not None)
assert (hasattr(self.clf, 'intercept_')
and self.clf.intercept_ is not None)
# only available for linear kernel
# if not hasattr(self.clf, 'coef_') or self.clf.coef_ is None:
# self.assertRaises(AttributeError, 'coef_ is not set')
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_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_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=3.5)
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=3.5)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def tearDown(self):
pass
def getDetectors(self, parameterdict):
detectors = []
windowSize = [
value for key, value in parameterdict.items()
if "windowsize_" in key.lower()
][0]
featureset = [
value for key, value in parameterdict.items()
if "featureset_" in key.lower()
][0]
detectortype = [
value for key, value in parameterdict.items()
if "type_" in key.lower()
][0]
detectorlist = detectortype.split()
features = self.featureGenerator.getSlidingWindowFeaturesEvents(
int(windowSize), int(featureset))
X = features[0]
y = features[1]
for detector in detectorlist:
if (detector.rstrip() == "OCSVM"):
kernel = [
value for key, value in parameterdict.items()
if "kernel_" in key.lower()
][0]
nu = [
value for key, value in parameterdict.items()
if "nu_" in key.lower()
][0]
clf = OCSVM(kernel=kernel, nu=nu, max_iter=100)
if (detector.rstrip() == "IForest"):
num_estimators = [
value for key, value in parameterdict.items()
if "num_estimators_" in key.lower()
][0]
max_samples = [
value for key, value in parameterdict.items()
if "max_samples_" in key.lower()
][0]
clf = IForest(n_estimators=int(num_estimators),
max_samples=int(max_samples))
if (detector.rstrip() == "PCA"):
clf = PCA()
if (detector.rstrip() == "LOF"):
n_neighbors = [
value for key, value in parameterdict.items()
if "lof_n_neighbors_" in key.lower()
][0]
clf = LOF(n_neighbors=int(n_neighbors))
if (detector.rstrip() == "KNN"):
n_neighbors = [
value for key, value in parameterdict.items()
if "knn_n_neighbors_" in key.lower()
][0]
clf = KNN(n_neighbors=int(n_neighbors))
with HiddenPrints():
clf.fit_predict_score(X, y, scoring='roc_auc_score')
detectors.append(clf)
return detectors, detectorlist, y, X