def getOutlierMCD(dataset): ''' @brief Function that executes MCD 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 mcd = MCD() # Fits the data and obtains labels mcd.fit(dataset) # Return labels return mcd.labels_
class TestMCD(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 = MCD(contamination=self.contamination, random_state=42) 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, 'raw_location_') and self.clf.raw_location_ is not None) assert (hasattr(self.clf, 'raw_covariance_') and self.clf.raw_covariance_ is not None) assert (hasattr(self.clf, 'raw_support_') and self.clf.raw_support_ is not None) assert (hasattr(self.clf, 'location_') and self.clf.location_ is not None) assert (hasattr(self.clf, 'covariance_') and self.clf.covariance_ is not None) assert (hasattr(self.clf, 'precision_') and self.clf.precision_ is not None) assert (hasattr(self.clf, 'support_') and self.clf.support_ 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=2.5) 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=2.5) 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 LOF detector clf_name = 'MCD' clf = MCD() 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)
class TestMCD(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 = MCD(contamination=self.contamination, random_state=42) self.clf.fit(self.X_train) 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, 'raw_location_') and self.clf.raw_location_ is not None) assert_true(hasattr(self.clf, 'raw_covariance_') and self.clf.raw_covariance_ is not None) assert_true(hasattr(self.clf, 'raw_support_') and self.clf.raw_support_ is not None) assert_true(hasattr(self.clf, 'location_') and self.clf.location_ is not None) assert_true(hasattr(self.clf, 'covariance_') and self.clf.covariance_ is not None) assert_true(hasattr(self.clf, 'precision_') and self.clf.precision_ is not None) assert_true(hasattr(self.clf, 'support_') and self.clf.support_ 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=2) 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=2) assert_array_less(pred_ranks, 1.01) assert_array_less(-0.1, pred_ranks) def tearDown(self): pass
from pyod.models.pca import PCA from pyod.models.lof import LOF from pyod.models.cblof import CBLOF from pyod.models.mcd import MCD from pyod.models.lscp import LSCP # from pyod.models.auto_encoder import AutoEncoder clf_knn = KNN() clf_pca = PCA() clf_mcd = MCD() clf_lof = LOF() clf_cblof = CBLOF() # clf_lscp = LSCP([clf_knn, clf_pca, clf_mcd ]) # clf_ae = AutoEncoder(epochs=50) clf_mcd.fit(encodings_train) clf_pca.fit(encodings_train) clf_knn.fit(encodings_train) clf_lof.fit(encodings_train) clf_cblof.fit(encodings_train) # clf_lscp.fit(encodings_train) # clf_ae.fit(encodings_train) anomaly_scores_mcd = clf_mcd.decision_function(encodings_train) anomaly_scores_pca = clf_pca.decision_function(encodings_train) anomaly_scores_knn = clf_knn.decision_function(encodings_train) anomaly_scores_lof = clf_lof.decision_function(encodings_train) anomaly_scores_cblof = clf_cblof.decision_function(encodings_train) # anomaly_scores_lscp = clf_lscp.decision_function(encodings_train) # anomaly_scores_ae = clf_ae.predict_proba(encodings_train)
a.add_argument("--cluster", help="Save cluster data to specified file []", type=str, required=False) args = a.parse_args() mongo = pymongo.MongoClient(args.db, args.port, username=args.dbuser, password=str(args.dbpassword)) db = mongo[args.dbname] for matching_control, df in every_control_dataframe(db, args): print("Found {} hits for {}".format(len(df), matching_control)) df = df.set_index('xref') deduped = df.drop_duplicates() deduped.to_csv('/tmp/crap.txt', sep='\t') if len( deduped ) > 15: # require at least 15 different vectors to be suitable for analysis mcd = MCD() mcd.fit(deduped) labels = mcd.predict(df) print(labels) else: print( "ERROR! Not enough de-duped datapoints ({}) for analysis - check /tmp/crap.txt for details" .format(len(deduped))) exit(0)