def feature_bagging(X_train, X_test, Y_train, Y_test): from pyod.models.feature_bagging import FeatureBagging model = FeatureBagging(random_state=1) model.fit(X_train) pred = model.predict(X_test) acc = np.sum(pred == Y_test) / X_test.shape[0] print(acc) return (acc * 100)
def getOulierFeatureBagging(dataset): ''' @brief Function that executes Feature Bagging 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 without verbose fb = FeatureBagging(verbose=0) # Fits the data and obtains labels fb.fit(dataset) # Return labels return fb.labels_
class TestFeatureBagging(unittest.TestCase): def setUp(self): self.n_train = 200 self.n_test = 100 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 = FeatureBagging(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, 'estimators_') and self.clf.estimators_ is not None) assert (hasattr(self.clf, 'estimators_features_') and self.clf.estimators_features_ 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_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_socres = self.clf.decision_function(self.X_test) pred_ranks = self.clf._predict_rank(self.X_test) # assert the order is reserved assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=3) 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=3) assert_array_less(pred_ranks, 1.01) assert_array_less(-0.1, pred_ranks) 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, y_train, X_test, y_test = \ generate_data(n_train=n_train, n_test=n_test, n_features=2, contamination=contamination, random_state=42) # train ABOD detector clf_name = 'FeatureBagging' clf = FeatureBagging() 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)
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, y_train, X_test, y_test = \ generate_data(n_train=n_train, n_test=n_test, n_features=2, contamination=contamination, random_state=42) # train Feature Bagging detector clf_name = 'FeatureBagging' clf = FeatureBagging() 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)
s = df.loc[:, model] s_argmax = s[s == s.max()].index.values max_index_list[i]=s_argmax s_max_list[i]=s.max() print(max_index_list) final_predict=pd.DataFrame(columns=['model_name','max_roc','acc','recall']) #FB # 读取roc值最高的benchmark数据并训练模型 dff = pd.read_csv(df.iat[int(max_index_list[0]), 1], encoding='utf-8') x = dff.loc[:, ('R', 'G', 'B')] y = dff.loc[:, 'original.label'] X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=random_state) X_train_norm, X_test_norm = standardizer(X_train, X_test) clf=FeatureBagging() clf.fit(X_train_norm, y_train) y_pred = clf.predict(x_orignal) final_predict=final_predict.append(pd.DataFrame( {'model_name':['FB'],'max_roc':[s_max_list[0]],'acc':[accuracy_score(y_orignal, y_pred)],'recall':[recall_score(y_orignal, y_pred, average='macro')]}),ignore_index=True) print('{model_name} Max_ROC:{roc}, acc:{acc}, ''recall_score: {recall}'.format( model_name='FB', roc=s_max_list[0], acc=accuracy_score(y_orignal, y_pred),recall=recall_score(y_orignal, y_pred, average='macro'))) #IForest dff = pd.read_csv(df.iat[int(max_index_list[1]), 1], encoding='utf-8') x = dff.loc[:, ('R', 'G', 'B')] y = dff.loc[:, 'original.label'] X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=random_state) X_train_norm, X_test_norm = standardizer(X_train, X_test) clf=IForest() clf.fit(X_train_norm, y_train) y_pred = clf.predict(x_orignal)
class TestFeatureBagging(unittest.TestCase): def setUp(self): self.n_train = 100 self.n_test = 50 self.contamination = 0.1 self.roc_floor = 0.6 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 = FeatureBagging(contamination=self.contamination) self.clf.fit(self.X_train) # TODO: failed due to sklearn uses 2 feature examples. # def test_sklearn_estimator(self): # check_estimator(self.clf) def test_parameters(self): assert_true(hasattr(self.clf, 'decision_scores_') and self.clf.decision_scores_ is not None) assert_true(hasattr(self.clf, 'labels_') and self.clf.labels_ is not None) assert_true(hasattr(self.clf, 'threshold_') and self.clf.threshold_ is not None) assert_true(hasattr(self.clf, '_mu') and self.clf._mu is not None) assert_true(hasattr(self.clf, '_sigma') and self.clf._sigma is not None) assert_true(hasattr(self.clf, 'estimators_') and self.clf.estimators_ is not None) assert_true(hasattr(self.clf, 'estimators_features_') and self.clf.estimators_features_ 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_socres = self.clf.decision_function(self.X_test) pred_ranks = self.clf._predict_rank(self.X_test) # assert the order is reserved assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=3) 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=3) assert_array_less(pred_ranks, 1.01) assert_array_less(-0.1, pred_ranks) def tearDown(self): pass
class TestFeatureBagging(unittest.TestCase): def setUp(self): self.n_train = 100 self.n_test = 50 self.contamination = 0.1 self.roc_floor = 0.6 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) self.clf = FeatureBagging(contamination=self.contamination) self.clf.fit(self.X_train) def test_sklearn_estimator(self): check_estimator(self.clf) def test_parameters(self): if not hasattr(self.clf, 'decision_scores_') or self.clf.decision_scores_ is None: self.assertRaises(AttributeError, 'decision_scores_ is not set') if not hasattr(self.clf, 'labels_') or self.clf.labels_ is None: self.assertRaises(AttributeError, 'labels_ is not set') if not hasattr(self.clf, 'threshold_') or self.clf.threshold_ is None: self.assertRaises(AttributeError, 'threshold_ is not set') if not hasattr(self.clf, '_mu') or self.clf._mu is None: self.assertRaises(AttributeError, '_mu is not set') if not hasattr(self.clf, '_sigma') or self.clf._sigma is None: self.assertRaises(AttributeError, '_sigma is not set') if not hasattr(self.clf, 'estimators_') or self.clf.estimators_ is None: self.assertRaises(AttributeError, 'estimators_ is not set') if not hasattr(self.clf, 'estimators_features_') or self.clf.estimators_features_ is None: self.assertRaises(AttributeError, 'estimators_features_ is not set') 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_evaluate(self): self.clf.fit_predict_evaluate(self.X_test, self.y_test) def tearDown(self): pass