.

v4.py

@@ -71,7 +71,7 @@ datafiles[4]=['2024-12-28_5_','2024-12-29_5_','2024-12-30_5_','2024-12-31_5_','2
 
 features=['r1 s1','r1 s4','r1 s5','pa1 apiii']
 features=['r1 s1','r1 s4','r1 s5']
-features=['r1 s1','r1 s5']
+#features=['r1 s1','r1 s5']
 featureNames={}
 featureNames['r1 s1']='$T_{evap}$'
 featureNames['r1 s4']='$T_{cond}$'

v4_class.py

@@ -22,7 +22,8 @@ import pickle
 parser = OptionParser()
 parser.add_option("-t", "--train", dest="train", help="Trains the models (false)", default=False, action="store_true")
 parser.add_option("-n", "--timesteps", dest="timesteps", help="TIME STEPS ", default=12)
-parser.add_option("-f", "--thresholdfactor", dest="TF", help="Threshold Factor ", default=1.4)
+#parser.add_option("-f", "--thresholdfactor", dest="TF", help="Threshold Factor ", default=1.4)
+# threshold makes no sense when classifying, becaues we apply many models and decide class for the less MSE
 
 (options, args) = parser.parse_args()
 
@@ -236,23 +237,6 @@ def listToString(l):
         r+=str(i)
     return(r.replace(' ',''))
 
-threshold={} 
-
-fname='threshold_class_v4_'+listToString(features)+'.pk'
-if os.path.isfile(fname):  # Checks if it's a file and exists
-    print("File ",fname," exists. Loading it!")
-    file = open(fname, 'rb')
-    threshold=pickle.load(file)
-    file.close()
-    if not int(options.timesteps) in threshold.keys():
-        threshold[int(options.timesteps)]=[]
-        for i in range(NumberOfFailures+1):
-            threshold[int(options.timesteps)].append(0) # Initzialize
-else:
-    threshold[int(options.timesteps)]=[]
-    for i in range(NumberOfFailures+1):
-        threshold[int(options.timesteps)].append(0) # Initzialize
-
 
 model=[]
 modelckpt_callback =[]
@@ -313,44 +297,11 @@ if options.train:
         history.append(model[i].fit( x_train[i], x_train[i], epochs=400, batch_size=128, validation_split=0.3, callbacks=[  es_callback[i], modelckpt_callback[i]      ],))
 
         x_train_pred=model[i].predict(x_train[i])
-        train_mae_loss=np.mean(np.abs(x_train_pred - x_train[i]), axis=1)
-        threshold[timesteps][i]=np.max(train_mae_loss,axis=0)
-
-    file = open('threshold_class_v4_'+listToString(features)+'.pk', 'wb')
-    pickle.dump(threshold, file)
-    file.close()
 else:
     for i in range(NumberOfFailures+1):
         model[i].load_weights(path_checkpoint[i])
 
-file = open('threshold_class_v4_'+listToString(features)+'.pk', 'rb')
-threshold=pickle.load(file)
-file.close()
-#print(threshold)   
 
-#  1st scenario. Detect only anomaly.  Later, we will classiffy it
-# Test data=  testnormal + testfail1 + testtail2 + testfail3 + testfail4 + testnormal
-datalist=[dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4]]
-
-x_test=create_sequences(datalist[0],int(options.timesteps))
-for i in range(1,len(datalist)):
-    x_test=np.vstack((x_test,create_sequences(datalist[i],int(options.timesteps))))
-
-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[int(options.timesteps)][0]*float(options.TF)
-anomalous_data_indices = []
-for i in range(anomalies.shape[0]):
-    if AtLeastOneTrue(anomalies[i]):
-    #if anomalies[i][0] or anomalies[i][1] or anomalies[i][2] or anomalies[i][3]:
-        anomalous_data_indices.append(i)
-
-# Define ranges for plotting in different colors
-testRanges=[]
-r=0
-for i in range(len(datalist)):
-    testRanges.append([r,r+datalist[i].shape[0]-int(options.timesteps)])
-    r+=datalist[i].shape[0]-int(options.timesteps)
 
 # Let's plot some features
 
@@ -366,63 +317,28 @@ for i in featuresToPlot:
 
 
 
-def plotData3():
-    NumFeaturesToPlot=len(indexesToPlot)
-    plt.rcParams.update({'font.size': 16})
-    fig, axes = plt.subplots(
-        nrows=NumFeaturesToPlot, ncols=1, figsize=(15, 10), dpi=80, facecolor="w", edgecolor="k",sharex=True
-    )
-    for i in range(NumFeaturesToPlot):
-        x=[]
-        y=[]
-        for k in anomalous_data_indices:
-            if (k)<x_test.shape[0]:
-                x.append(k)
-                y.append(x_test[k,0,indexesToPlot[i]]*stdevs[i]+means[i])
-        axes[i].plot(x,y ,color='black',marker='.',linewidth=0,label="Fail detection" )
-
-        init=0
-        end=testRanges[0][1]
-        axes[i].plot(range(init,end),x_test[testRanges[0][0]:testRanges[0][1],0,indexesToPlot[i]]*stdevs[i]+means[i],label="No fail")
-        init=end
-        end+=(testRanges[1][1]-testRanges[1][0])
-        for j in range(1,NumberOfFailures+1):
-            axes[i].plot(range(init,end),x_test[testRanges[j][0]:testRanges[j][1],0,indexesToPlot[i]]*stdevs[i]+means[i],label="Fail type "+str(j), color=colorline[j-1],linewidth=1)
-            if j<NumberOfFailures:
-                init=end
-                end+=(testRanges[j+1][1]-testRanges[j+1][0])
-
-        if i==(NumFeatures-1):
-            axes[i].legend(loc='right')
-        s=''
-        s+=featureNames[features[indexesToPlot[i]]]
-        s+=' '+unitNames[features[indexesToPlot[i]]]
-        axes[i].set_ylabel(s)
-        axes[i].grid()
-    axes[NumFeaturesToPlot-1].set_xlabel("Sample number")
-    plt.show()
+#   2nd scenario. Go over anomalies and classify it by less error
+datalist=[dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4]]
+x_test=create_sequences(datalist[0],int(options.timesteps))
+for i in range(1,len(datalist)):
+    x_test=np.vstack((x_test,create_sequences(datalist[i],int(options.timesteps))))
 
-anomalyMetric(threshold[int(options.timesteps)][0]*float(options.TF), int(options.timesteps),datalist)
-plotData3()
-exit(0)
+# Define ranges for plotting in different colors
+testRanges=[]
+r=0
+for i in range(len(datalist)):
+    testRanges.append([r,r+datalist[i].shape[0]-int(options.timesteps)])
+    r+=datalist[i].shape[0]-int(options.timesteps)
 
-#   2nd scenario. Go over anomalies and classify it by less error
 
-anomalous_data_type=[]
 x_test_predict=[]
 for m in range(1,NumberOfFailures+1):
     x_test_predict.append(model[m].predict(x_test))
 
-anomalous_data_type={}
-for i in range(1,NumberOfFailures+1):
-    anomalous_data_type[i-1]=[]
-
-for i in anomalous_data_indices:
-    error=[]
-    for m in range(1,NumberOfFailures+1):
-        error.append(np.mean(np.mean(np.abs(x_test_predict[m-1][i:i+1,:,:]-x_test[i:i+1,:,:]),axis=1)))
-    anomalous_data_type[np.argmin(error)].append(i)
+x_test_predict=np.array((x_test_predict))
 
+print(x_test_predict)
+exit(0)
 
 def plotData4():
     NumFeaturesToPlot=len(indexesToPlot)