def cof(X):
contamination_factor = 0.1
k = 20
clf = COF(contamination=contamination_factor, n_neighbors=k)
clf.fit(X)
label = clf.labels_
score = clf.decision_scores_
threshold = clf.threshold_
writeLabel(label)
return
def test_check_parameters(self):
with assert_raises(ValueError):
COF(contamination=0.1, n_neighbors=-1)
with assert_raises(ValueError):
COF(contamination=10., n_neighbors=5)
with assert_raises(TypeError):
COF(contamination=0.1, n_neighbors='not int')
with assert_raises(TypeError):
COF(contamination='not float', n_neighbors=5)
cof_ = COF(contamination=0.1, n_neighbors=10000)
cof_.fit(self.X_train)
assert self.X_train.shape[0] > cof_.n_neighbors_
def getOutlierCOF(dataset):
'''
@brief Function that executes COF algorithm on the dataset and obtains the
labels of the dataset indicating which instance is an inlier (0) or outlier (1)
@param dataset Dataset on which to try the algorithm
@return It returns a list of labels 0 means inlier, 1 means outlier
'''
# Initializating the model
cof = COF()
# Fits the data and obtains labels
cof.fit(dataset)
# Return labels
return cof.labels_
class TestCOF(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,
contamination=self.contamination,
random_state=42)
self.clf = COF(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, 'n_neighbors_')
and self.clf.n_neighbors_ 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 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_parameters(self):
with assert_raises(ValueError):
COF(contamination=0.1, n_neighbors=-1)
with assert_raises(ValueError):
COF(contamination=10., n_neighbors=5)
with assert_raises(TypeError):
COF(contamination=0.1, n_neighbors='not int')
with assert_raises(TypeError):
COF(contamination='not float', n_neighbors=5)
cof_ = COF(contamination=0.1, n_neighbors=10000)
cof_.fit(self.X_train)
assert self.X_train.shape[0] > cof_.n_neighbors_
def tearDown(self):
pass
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, X_test, y_train, y_test = \
generate_data(n_train=n_train,
n_test=n_test,
n_features=2,
contamination=contamination,
random_state=42)
# train COF detector
clf_name = 'COF'
clf = COF(n_neighbors=30)
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)
