.

v1_multifailure.py

@@ -40,8 +40,12 @@ datafiles[4]=['2024-12-28_5_','2024-12-29_5_','2024-12-30_5_','2024-12-31_5_','2
 #datafiles[4]=[] 
 
 # Features suggested by Xavier
+# Care with 'tc s3' because on datafiles[0] is always nulll
+# Seems to be incoropored in new tests
+
 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','tc s3']
+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']
+
 NumFeatures=len(features)
 
 df_list=[]
@@ -191,7 +195,7 @@ if options.train:
                 axes[i][j].plot(history[r].history["loss"], label="Training Loss")
                 axes[i][j].plot(history[r].history["val_loss"], label="Val Loss")
                 axes[i][j].legend()
-    plt.show()
+    #plt.show()
 else:
     for i in range(NumberOfFailures+1):
         model[i].load_weights(path_checkpoint[i])
@@ -307,6 +311,8 @@ colorline=['violet','lightcoral','cyan','lime','grey']
 colordot=['darkviolet','red','blue','green','black']
 
 featuresToPlot=['r1 s1','r1 s3','r1 s5','r2 s3','r2 s4','pa1 apiii','tc s1','tc s2','tc s3']
+featuresToPlot=features
+
 indexesToPlot=[]
 for i in featuresToPlot:
     indexesToPlot.append(features.index(i))
@@ -344,5 +350,39 @@ def plotData3():
     plt.show()
 
 
-plotData3()
 
+
+def anomalyMetric(testList):  # first of list is non failure data
+    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)
+    anomalies = test_mae_loss > threshold[0]
+    count=0
+    for i in range(anomalies.shape[0]):
+        if AtLeastOneTrue(anomalies[i]):
+            count+=1
+    FP=count/anomalies.shape[0]
+    count=0
+    for i in range(anomalies.shape[0]):
+        if AtLeastOneTrue(anomalies[i]):
+            count+=1
+    FP=count/anomalies.shape[0]
+    TN=np.zeros((NumberOfFailures))
+    for i in range(1,len(testList)):
+        x_test = create_sequences(testList[i])
+        x_test_pred = model[0].predict(x_test)
+        test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
+        anomalies = test_mae_loss > threshold[0]
+        count=0
+        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()
+
+
+
+anomalyMetric([dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4]])
+plotData3()