From 40992a5ca8c10ca1371d46869f9c696e6cc6700b Mon Sep 17 00:00:00 2001 From: hwy893747147 Date: Sat, 10 Apr 2021 11:25:27 -0500 Subject: [PATCH] initial push --- ...500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt | 20000 +++++++++++++++++++ examples/sk_examples/DeepLog_test.py | 53 + examples/sk_examples/IsolationForest_test.py | 44 + examples/sk_examples/MatrixProfile_test.py | 37 + examples/sk_examples/Telemanom_test.py | 54 + 5 files changed, 20188 insertions(+) create mode 100644 examples/sk_examples/500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt create mode 100644 examples/sk_examples/DeepLog_test.py create mode 100644 examples/sk_examples/IsolationForest_test.py create mode 100644 examples/sk_examples/MatrixProfile_test.py create mode 100644 examples/sk_examples/Telemanom_test.py diff --git a/examples/sk_examples/500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt b/examples/sk_examples/500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt new file mode 100644 index 0000000..f965afb --- 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7.6923000e-02 + 7.6923000e-02 + 7.6923000e-02 + 7.6923000e-02 + 7.6923000e-02 + 7.6923000e-02 + 5.9829000e-02 + 5.9829000e-02 + 9.4017000e-02 + 1.2820500e-01 + 1.4529900e-01 + 1.6239300e-01 + 1.6239300e-01 + 1.7948700e-01 + 2.1367500e-01 + 2.3076900e-01 + 2.3076900e-01 + 2.3076900e-01 + 2.1367500e-01 + 1.9658100e-01 + 2.1367500e-01 + 2.1367500e-01 + 2.1367500e-01 + 1.9658100e-01 + 1.7948700e-01 + 1.7948700e-01 + 1.9658100e-01 + 2.1367500e-01 + 2.6495700e-01 + 2.9914500e-01 + 3.3333300e-01 + 3.8461500e-01 + 4.0170900e-01 + 4.0170900e-01 + 3.5042700e-01 + 2.4786300e-01 + 1.4529900e-01 + 5.9829000e-02 + 2.5641000e-02 + 8.5469500e-03 + 2.5641000e-02 + 5.9829000e-02 diff --git a/examples/sk_examples/DeepLog_test.py b/examples/sk_examples/DeepLog_test.py new file mode 100644 index 0000000..2a24bab --- /dev/null +++ b/examples/sk_examples/DeepLog_test.py @@ -0,0 +1,53 @@ +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() diff --git a/examples/sk_examples/IsolationForest_test.py b/examples/sk_examples/IsolationForest_test.py new file mode 100644 index 0000000..4c507fe --- /dev/null +++ b/examples/sk_examples/IsolationForest_test.py @@ -0,0 +1,44 @@ +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)) \ No newline at end of file diff --git a/examples/sk_examples/MatrixProfile_test.py b/examples/sk_examples/MatrixProfile_test.py new file mode 100644 index 0000000..2cb8108 --- /dev/null +++ b/examples/sk_examples/MatrixProfile_test.py @@ -0,0 +1,37 @@ +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)) \ No newline at end of file diff --git a/examples/sk_examples/Telemanom_test.py b/examples/sk_examples/Telemanom_test.py new file mode 100644 index 0000000..92bd90f --- /dev/null +++ b/examples/sk_examples/Telemanom_test.py @@ -0,0 +1,54 @@ +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()