class TestXGBOD(unittest.TestCase):
def setUp(self):
# Define data file and read X and y
# Generate some data if the source data is missing
this_directory = path.abspath(path.dirname(__file__))
mat_file = 'pima.mat'
try:
mat = loadmat(path.join(*[this_directory, 'data', mat_file]))
except TypeError:
print('{data_file} does not exist. Use generated data'.format(
data_file=mat_file))
X, y = generate_data(train_only=True) # load data
except IOError:
print('{data_file} does not exist. Use generated data'.format(
data_file=mat_file))
X, y = generate_data(train_only=True) # load data
else:
X = mat['X']
y = mat['y'].ravel()
X, y = check_X_y(X, y)
self.X_train, self.X_test, self.y_train, self.y_test = \
train_test_split(X, y, test_size=0.4, random_state=42)
self.clf = XGBOD(random_state=42)
self.clf.fit(self.X_train, self.y_train)
self.roc_floor = 0.8
def test_parameters(self):
assert_true(hasattr(self.clf, 'clf_') and
self.clf.decision_scores_ is not None)
assert_true(hasattr(self.clf, '_scalar') and
self.clf.labels_ is not None)
assert_true(hasattr(self.clf, 'n_detector_') and
self.clf.labels_ is not None)
assert_true(hasattr(self.clf, 'X_train_add_') and
self.clf.labels_ is not None)
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)
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)
# check performance
assert_greater(roc_auc_score(self.y_test, pred_proba), self.roc_floor)
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train, self.y_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)
print(pred_ranks)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=4)
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=4)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def tearDown(self):
pass
class TestXGBOD(unittest.TestCase):
def setUp(self):
# Define data file and read X and y
# Generate some data if the source data is missing
this_directory = path.abspath(path.dirname(__file__))
mat_file = 'pima.mat'
try:
mat = loadmat(path.join(*[this_directory, 'data', mat_file]))
except TypeError:
print('{data_file} does not exist. Use generated data'.format(
data_file=mat_file))
X, y = generate_data(train_only=True) # load data
except IOError:
print('{data_file} does not exist. Use generated data'.format(
data_file=mat_file))
X, y = generate_data(train_only=True) # load data
else:
X = mat['X']
y = mat['y'].ravel()
X, y = check_X_y(X, y)
self.X_train, self.X_test, self.y_train, self.y_test = \
train_test_split(X, y, test_size=0.4, random_state=42)
self.clf = XGBOD(random_state=42)
self.clf.fit(self.X_train, self.y_train)
self.roc_floor = 0.75
def test_parameters(self):
assert (hasattr(self.clf, 'clf_')
and self.clf.decision_scores_ is not None)
assert (hasattr(self.clf, '_scalar') and self.clf.labels_ is not None)
assert (hasattr(self.clf, 'n_detector_')
and self.clf.labels_ is not None)
assert (hasattr(self.clf, 'X_train_add_')
and self.clf.labels_ is not None)
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)
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)
# check performance
assert_greater(roc_auc_score(self.y_test, pred_proba), self.roc_floor)
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train, self.y_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)
print(pred_ranks)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), rtol=4)
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), rtol=4)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def tearDown(self):
pass
except IOError:
print('{data_file} does not exist. Use generated data'.format(
data_file=mat_file))
X, y = generate_data(train_only=True) # load data
else:
X = mat['X']
y = mat['y'].ravel()
X, y = check_X_y(X, y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4,
random_state=42)
# train XGBOD detector
clf_name = 'XGBOD'
clf = XGBOD(random_state=42)
clf.fit(X_train, y_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)