def _score_combination(self, scores):
"""Internal function for combining univarite scores.
"""
# combine by different approaches
if self.method == 'average':
return average(scores, estimator_weights=self.weights)
if self.method == 'maximization':
return maximization(scores)
if self.method == 'median':
return median(scores)
X_test,
n_estimators,
# rp_flags[starts[i]:starts[i + 1]],
jl_transformers,
approx_flags[starts[i]:starts[i + 1]],
verbose=True) for i in range(n_jobs))
print('Orig decision_function time:', time.time() - start)
print()
# unfold and generate the label matrix
predicted_scores_orig = np.zeros([X_test.shape[0], n_estimators])
for i in range(n_jobs):
predicted_scores_orig[:, starts[i]:starts[i + 1]] = np.asarray(
all_results_scores[i]).T
##########################################################################
predicted_scores = standardizer(predicted_scores)
predicted_scores_orig = standardizer(predicted_scores_orig)
evaluate_print('orig', y_test, average(predicted_scores_orig))
evaluate_print('new', y_test, average(predicted_scores))
evaluate_print('orig max', y_test, maximization(predicted_scores_orig))
evaluate_print('new max', y_test, maximization(predicted_scores))
evaluate_print('orig aom', y_test, aom(predicted_scores_orig))
evaluate_print('new aom', y_test, aom(predicted_scores))
evaluate_print('orig moa', y_test, moa(predicted_scores_orig))
evaluate_print('new moa', y_test, moa(predicted_scores))
train_test_split(X, y, test_size=0.4, random_state=42)
contamination = y.sum() / len(y)
base_estimators = get_estimators_small(contamination)
model = SUOD(base_estimators=base_estimators,
n_jobs=6,
bps_flag=True,
contamination=contamination,
approx_flag_global=True)
model.fit(X_train) # fit all models with X
model.approximate(X_train) # conduct model approximation if it is enabled
predicted_labels = model.predict(X_test) # predict labels
predicted_scores = model.decision_function(X_test) # predict scores
predicted_probs = model.predict_proba(X_test) # predict scores
###########################################################################
# compared with other approaches
evaluate_print('majority vote', y_test, majority_vote(predicted_labels))
evaluate_print('average', y_test, average(predicted_scores))
evaluate_print('maximization', y_test, maximization(predicted_scores))
clf = LOF()
clf.fit(X_train)
evaluate_print('LOF', y_test, clf.decision_function(X_test))
clf = IForest()
clf.fit(X_train)
evaluate_print('IForest', y_test, clf.decision_function(X_test))
def test_weighted_average(self):
score = average(self.scores, self.weights)
assert_allclose(score, np.array([1.75, 3.75, 5.75]))
def test_average(self):
score = average(self.scores)
assert_allclose(score, np.array([1.5, 3.5, 5.5]))
