# -*- coding: utf-8 -*- from __future__ import division from __future__ import print_function import os import sys import unittest # noinspection PyProtectedMember from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_greater_equal from sklearn.utils.testing import assert_less_equal from sklearn.utils.testing import assert_raises # temporary solution for relative imports in case pyod is not installed # if pyod is installed, no need to use the following line sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) from pyod.models.so_gaal import SO_GAAL from pyod.utils.data import generate_data from pyod.utils.data import evaluate_print from sklearn.metrics import roc_auc_score class TestSO_GAAL(unittest.TestCase): """ Notes: GAN may yield unstable results, so the test is design for running models only, without any performance check. """ def setUp(self): self.n_train = 1000 self.n_test = 200 self.n_features = 2 self.contamination = 0.1 # GAN may yield unstable results; turning performance check off # 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, n_features=self.n_features, contamination=self.contamination, random_state=42) self.clf = SO_GAAL(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, '_mu') and self.clf._mu is not None) assert(hasattr(self.clf, '_sigma') and self.clf._sigma is not None) assert(hasattr(self.clf, 'discriminator') and self.clf.discriminator 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 tearDown(self): pass if __name__ == '__main__': unittest.main()