From d64ff43d3c3e1f14fbf527e11180441d24235cc5 Mon Sep 17 00:00:00 2001 From: Bourne Li Date: Fri, 28 Jun 2024 17:49:09 -0700 Subject: [PATCH] add alad tests back to improve coverage --- pyod/test/test_alad.py | 149 ++++++++++++++++++++++++----------------- 1 file changed, 86 insertions(+), 63 deletions(-) diff --git a/pyod/test/test_alad.py b/pyod/test/test_alad.py index eb886d802..c34e13ec4 100644 --- a/pyod/test/test_alad.py +++ b/pyod/test/test_alad.py @@ -26,7 +26,7 @@ def setUp(self): self.n_test = 200 self.n_features = 2 self.contamination = 0.1 - self.roc_floor = 0.8 + self.roc_floor = 0.5 self.X_train, self.X_test, self.y_train, self.y_test = generate_data( n_train=self.n_train, n_test=self.n_test, n_features=self.n_features, contamination=self.contamination, @@ -65,73 +65,96 @@ def test_parameters(self): assert (hasattr(self.clf, '_sigma') and self.clf._sigma 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 (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 (pred_proba.min() >= 0) - # assert (pred_proba.max() <= 1) - # - # def test_prediction_proba_linear(self): - # pred_proba = self.clf.predict_proba(self.X_test, method='linear') - # assert (pred_proba.min() >= 0) - # assert (pred_proba.max() <= 1) - # - # def test_prediction_proba_unify(self): - # pred_proba = self.clf.predict_proba(self.X_test, method='unify') - # assert (pred_proba.min() >= 0) - # assert (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_prediction_labels_confidence(self): - # pred_labels, confidence = self.clf.predict(self.X_test, - # return_confidence=True) - # assert_equal(pred_labels.shape, self.y_test.shape) - # assert_equal(confidence.shape, self.y_test.shape) - # assert (confidence.min() >= 0) - # assert (confidence.max() <= 1) - # - # def test_prediction_proba_linear_confidence(self): - # pred_proba, confidence = self.clf.predict_proba(self.X_test, - # method='linear', - # return_confidence=True) - # assert (pred_proba.min() >= 0) - # assert (pred_proba.max() <= 1) - # - # assert_equal(confidence.shape, self.y_test.shape) - # assert (confidence.min() >= 0) - # assert (confidence.max() <= 1) + 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 (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 (pred_proba.min() >= 0) + assert (pred_proba.max() <= 1) + + def test_prediction_proba_linear(self): + pred_proba = self.clf.predict_proba(self.X_test, method='linear') + assert (pred_proba.min() >= 0) + assert (pred_proba.max() <= 1) + + def test_prediction_proba_unify(self): + pred_proba = self.clf.predict_proba(self.X_test, method='unify') + assert (pred_proba.min() >= 0) + assert (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_prediction_labels_confidence(self): + pred_labels, confidence = self.clf.predict(self.X_test, + return_confidence=True) + assert_equal(pred_labels.shape, self.y_test.shape) + assert_equal(confidence.shape, self.y_test.shape) + assert (confidence.min() >= 0) + assert (confidence.max() <= 1) + + def test_prediction_proba_linear_confidence(self): + pred_proba, confidence = self.clf.predict_proba(self.X_test, + method='linear', + return_confidence=True) + assert (pred_proba.min() >= 0) + assert (pred_proba.max() <= 1) + + assert_equal(confidence.shape, self.y_test.shape) + assert (confidence.min() >= 0) + assert (confidence.max() <= 1) 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_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_prediction_scores_with_sigmoid(self): + self.alad = ALAD(activation_hidden_gen='sigmoid', activation_hidden_disc='sigmoid') + self.alad.fit(self.X_train) + + pred_scores = self.alad.predict(self.X_test) + + roc_auc = roc_auc_score(self.y_test, pred_scores) + print(f"ROC AUC Score with Sigmoid: {roc_auc}") + + self.assertGreaterEqual(roc_auc, 0) + + def test_prediction_scores_with_relu(self): + self.alad = ALAD(activation_hidden_gen='relu', activation_hidden_disc='relu') + self.alad.fit(self.X_train) + + pred_scores = self.alad.predict(self.X_test) + + roc_auc = roc_auc_score(self.y_test, pred_scores) + print(f"ROC AUC Score with ReLU: {roc_auc}") + + self.assertGreaterEqual(roc_auc, 0) + def test_model_clone(self): # for deep models this may not apply