tods/examples/sk_examples/Telemanom_test.py

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("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()