def _combine(self, scores):
"""
Helper method to wrap the PyOD ensembler.
Args:
scores (np.float array of shape (num_anomaly_detectors, )) : List of scores from multiple anomaly detectors.
Returns:
float: Resulting anomaly score.
"""
return median(scores)
k = k_list[i]
clf = KNN(n_neighbors=k, method='largest')
clf.fit(X_train_norm)
train_scores[:, i] = clf.decision_scores_
test_scores[:, i] = clf.decision_function(X_test_norm)
# Decision scores have to be normalized before combination
train_scores_norm, test_scores_norm = standardizer(train_scores,
test_scores)
# Combination by average
y_by_average = average(test_scores_norm)
evaluate_print('Combination by Average', y_test, y_by_average)
# Combination by max
y_by_maximization = maximization(test_scores_norm)
evaluate_print('Combination by Maximization', y_test, y_by_maximization)
# Combination by median
y_by_median = median(test_scores_norm)
evaluate_print('Combination by Median', y_test, y_by_median)
# Combination by aom
y_by_aom = aom(test_scores_norm, n_buckets=5)
evaluate_print('Combination by AOM', y_test, y_by_aom)
# Combination by moa
y_by_moa = moa(test_scores_norm, n_buckets=5)
evaluate_print('Combination by MOA', y_test, y_by_moa)
def test_median(self):
score = median(self.median_scores)
assert_allclose(score, np.array([2, 2, 4]))
k = k_list[i]
clf = KNN(n_neighbors=k, method='largest')
clf.fit(X_train_norm)
train_scores[:, i] = clf.decision_scores_
test_scores[:, i] = clf.decision_function(X_test_norm)
# Decision scores have to be normalized before combination
train_scores_norm, test_scores_norm = standardizer(train_scores,
test_scores)
# Combination by average
y_by_average = average(test_scores_norm)
evaluate_print('Combination by Average', y_test, y_by_average)
# Combination by max
y_by_maximization = maximization(test_scores_norm)
evaluate_print('Combination by Maximization', y_test, y_by_maximization)
# Combination by max
y_by_maximization = median(test_scores_norm)
evaluate_print('Combination by Median', y_test, y_by_maximization)
# Combination by aom
y_by_aom = aom(test_scores_norm, n_buckets=5)
evaluate_print('Combination by AOM', y_test, y_by_aom)
# Combination by moa
y_by_moa = moa(test_scores_norm, n_buckets=5)
evaluate_print('Combination by MOA', y_test, y_by_moa)