.

v1_multifailure.py

 features=['r1 s1','r1 s4','r1 s5','pa1 apiii']
 features=['r1 s1','r1 s2','r1 s3','r1 s4','r1 s5','r1 s6','r1 s7','r1 s8','r1 s9','r1 s10','r2 s1','r2 s2','r2 s3','r2 s4','r2 s5','r2 s6','r2 s7','r2 s8','r2 s9','pa1 apiii','tc s1','tc s2']
 
+#features=['r2 s2', 'tc s1','r1 s10','r1 s6','r2 s8']
+
 NumFeatures=len(features)
 
 df_list=[]
     #axes[1].legend()
     #axes[0].set_ylabel(features[0])
     #axes[1].set_ylabel(features[1])
-    plt.show()
+    #plt.show()
 
 #plotData()
 
 
 
 def anomalyMetric(testList):  # first of list is non failure data
+    # FP, TP: false/true positive
+    # TN, FN: true/false negative
+    # Sensitivity: probab of fail detection if data is fail
+    # Specificity: prob of no fail detection if data is well
     x_test = create_sequences(testList[0])
     x_test_pred = model[0].predict(x_test)
     test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
     for i in range(anomalies.shape[0]):
         if AtLeastOneTrue(anomalies[i]):
             count+=1
-    FP=count/anomalies.shape[0]
+    FP=count
+    TN=anomalies.shape[0]-1
     count=0
-    for i in range(anomalies.shape[0]):
-        if AtLeastOneTrue(anomalies[i]):
-            count+=1
-    FP=count/anomalies.shape[0]
-    TN=np.zeros((NumberOfFailures))
+    TP=np.zeros((NumberOfFailures))
+    FN=np.zeros((NumberOfFailures))
     for i in range(1,len(testList)):
         x_test = create_sequences(testList[i])
         x_test_pred = model[0].predict(x_test)
         for j in range(anomalies.shape[0]):
             if AtLeastOneTrue(anomalies[j]):
                 count+=1
-        TN[i-1] = count/anomalies.shape[0]
-    print("FP: ",FP)
-    print("TN: ",TN)
-    return (1-FP)*NumberOfFailures+TN.sum()
+        TP[i-1] = count
+        FN[i-1] = anomalies.shape[0]-count
+    Sensitivity=TP.sum()/(TP.sum()+FN.sum())
+    Specifity=TN/(TN+FP)
+    print("Sensitivity: ",Sensitivity)
+    print("Specifity: ",Specifity)
+    return Sensitivity+Specifity