initial push

examples/sk_examples/DeepLog_test.py

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+import numpy as np
+from tods.tods_skinterface.primitiveSKI.detection_algorithm.DeepLog_skinterface import DeepLogSKI
+from sklearn.metrics import precision_recall_curve
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import classification_report
+import matplotlib.pyplot as plt
+from sklearn import metrics
+
+#prepare the data
+data = np.loadtxt("./500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt")
+
+X_train = np.expand_dims(data[:10000], axis=1)
+X_test = np.expand_dims(data[10000:], axis=1)
+
+transformer = DeepLogSKI()
+transformer.fit(X_train)
+
+prediction_labels_train = transformer.predict(X_train)
+
+prediction_labels = transformer.predict(X_test)
+prediction_score = transformer.predict_score(X_test)
+
+print("Primitive: ", transformer.primitive)
+print("Prediction Labels\n", prediction_labels)
+print("Prediction Score\n", prediction_score)
+
+# y_true = prediction_labels_train[:1000]
+# y_pred = prediction_labels[:1000]
+y_true = prediction_labels_train
+y_pred = prediction_labels
+
+print('Accuracy Score: ', accuracy_score(y_true, y_pred))
+
+confusion_matrix(y_true, y_pred)
+
+print(classification_report(y_true, y_pred))
+
+precision, recall, thresholds = precision_recall_curve(y_true, y_pred)
+f1_scores = 2*recall*precision/(recall+precision)
+
+print('Best threshold: ', thresholds[np.argmax(f1_scores)])
+print('Best F1-Score: ', np.max(f1_scores))
+
+fpr, tpr, threshold = metrics.roc_curve(y_true, y_pred)
+roc_auc = metrics.auc(fpr, tpr)
+
+plt.title('ROC')
+plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
+plt.legend(loc = 'lower right')
+plt.ylabel('True Positive Rate')
+plt.xlabel('False Positive Rate')
+plt.show()

examples/sk_examples/IsolationForest_test.py

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+import numpy as np
+from tods.tods_skinterface.primitiveSKI.detection_algorithm.IsolationForest_skinterface import IsolationForestSKI
+from sklearn.metrics import precision_recall_curve
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import classification_report
+#prepare the data
+
+data = np.loadtxt("./500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt")
+
+# print("shape:", data.shape)
+# print("datatype of data:",data.dtype)
+# print("First 5 rows:\n", data[:5])
+
+X_train = np.expand_dims(data[:10000], axis=1)
+X_test = np.expand_dims(data[10000:], axis=1)
+
+# print("First 5 rows train:\n", X_train[:5])
+# print("First 5 rows test:\n", X_test[:5])
+
+transformer = IsolationForestSKI()
+transformer.fit(X_train)
+prediction_labels_train = transformer.predict(X_train)
+prediction_labels = transformer.predict(X_test)
+prediction_score = transformer.predict_score(X_test)
+
+print("Primitive: ", transformer.primitive)
+print("Prediction Labels\n", prediction_labels)
+print("Prediction Score\n", prediction_score)
+
+y_true = prediction_labels_train
+y_pred = prediction_labels
+
+print('Accuracy Score: ', accuracy_score(y_true, y_pred))
+
+confusion_matrix(y_true, y_pred)
+
+print(classification_report(y_true, y_pred))
+
+precision, recall, thresholds = precision_recall_curve(y_true, y_pred)
+f1_scores = 2*recall*precision/(recall+precision)
+
+print('Best threshold: ', thresholds[np.argmax(f1_scores)])
+print('Best F1-Score: ', np.max(f1_scores))

examples/sk_examples/MatrixProfile_test.py

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+import numpy as np
+from tods.tods_skinterface.primitiveSKI.detection_algorithm.MatrixProfile_skinterface import MatrixProfileSKI
+from sklearn.metrics import precision_recall_curve
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import classification_report
+#prepare the data
+
+data = np.loadtxt("./500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt")
+
+X_train = np.expand_dims(data[:10000], axis=1)
+X_test = np.expand_dims(data[10000:], axis=1)
+
+transformer = MatrixProfileSKI()
+transformer.fit(X_train)
+prediction_labels_train = transformer.predict(X_train)
+prediction_labels = transformer.predict(X_test)
+prediction_score = transformer.predict_score(X_test)
+
+print("Primitive: ", transformer.primitive)
+print("Prediction Labels\n", prediction_labels)
+print("Prediction Score\n", prediction_score)
+
+y_true = prediction_labels_train
+y_pred = prediction_labels
+
+print('Accuracy Score: ', accuracy_score(y_true, y_pred))
+
+confusion_matrix(y_true, y_pred)
+
+print(classification_report(y_true, y_pred))
+
+precision, recall, thresholds = precision_recall_curve(y_true, y_pred)
+f1_scores = 2*recall*precision/(recall+precision)
+
+print('Best threshold: ', thresholds[np.argmax(f1_scores)])
+print('Best F1-Score: ', np.max(f1_scores))

examples/sk_examples/Telemanom_test.py

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+import numpy as np
+from tods.tods_skinterface.primitiveSKI.detection_algorithm.Telemanom_skinterface import TelemanomSKI
+from sklearn.metrics import precision_recall_curve
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import classification_report
+import matplotlib.pyplot as plt
+from sklearn import metrics
+#prepare the data
+
+data = np.loadtxt("./500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt")
+# print("shape:", data.shape)
+# print("datatype of data:",data.dtype)
+# print("First 5 rows:\n", data[:5])
+
+X_train = np.expand_dims(data[:10000], axis=1)
+X_test = np.expand_dims(data[10000:], axis=1)
+
+# print("First 5 rows train:\n", X_train[:5])
+# print("First 5 rows test:\n", X_test[:5])
+
+transformer = TelemanomSKI(l_s= 2, n_predictions= 1)
+transformer.fit(X_train)
+prediction_labels_train = transformer.predict(X_train)
+prediction_labels = transformer.predict(X_test)
+prediction_score = transformer.predict_score(X_test)
+
+print("Primitive: ", transformer.primitive)
+print("Prediction Labels\n", prediction_labels)
+print("Prediction Score\n", prediction_score)
+y_true = prediction_labels_train
+y_pred = prediction_labels
+
+print('Accuracy Score: ', accuracy_score(y_true, y_pred))
+
+confusion_matrix(y_true, y_pred)
+
+print(classification_report(y_true, y_pred))
+
+precision, recall, thresholds = precision_recall_curve(y_true, y_pred)
+f1_scores = 2*recall*precision/(recall+precision)
+
+print('Best threshold: ', thresholds[np.argmax(f1_scores)])
+print('Best F1-Score: ', np.max(f1_scores))
+
+fpr, tpr, threshold = metrics.roc_curve(y_true, y_pred)
+roc_auc = metrics.auc(fpr, tpr)
+
+plt.title('ROC')
+plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
+plt.legend(loc = 'lower right')
+plt.ylabel('True Positive Rate')
+plt.xlabel('False Positive Rate')
+plt.show()