.

plotFScore_v5.py

 import matplotlib.pyplot as plt
 import numpy as np
 
-F={}
-for i in range(4,29,4):
+F={}   # FScore in stationary phase
+      # first index:  time series length, second index: features considered 
+for i in range(4,27,4):
     F[i]=np.ones(6)
-for i in range(4,29,4):
-    for j in range(6):
-        F[i][j]=np.random.uniform(0.8,1)
+#for i in range(4,27,4):
+#    for j in range(6):
+#        F[i][j]=np.random.uniform(0.8,1)
 
+F[4]= [0.99,0.99,0.97,0.96,0.98,0.9  ]
+F[8]= [1,1,0.99,0.98,0.99,0.96  ]
+F[12]=[1,1,0.99,0.96,0.99, 0.65 ]
+F[16]=[1,1,1,0.98,0.99 ,0.67 ]
+F[20]=[1,1,1,0.98 ,1, 0.67 ]
+F[24]=[1,1,1,1,1, 0.66  ]
+
+
+plt.rcParams.update({'font.size': 16})
+fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(15, 6), dpi=80, facecolor="w", edgecolor="k",sharex=True)
+width=0.1
+for i in range(4,27,4):
+    axes.bar(np.arange(0,6)+(i/4-3)*width , F[i], width*0.8, label="$ns=$"+str(i), align="center")
+axes.set_ylim(0.6,1.01)
+axes.legend(loc='center right')
+axes.set_ylabel("F1-Score")
+axes.set_xticks([0,1,2,3,4,5],['$T_{evap},T_{cond}$ \n $T_{air},P_{elec}$','$T_{evap},T_{cond},T_{air}$','$T_{evap},T_{air},P_{elec}$','$T_{air},P_{elec}$','$T_{evap},T_{air}$','$T_{air}$'])
+plt.grid()
+plt.show()
 
 # https://stackoverflow.com/questions/10369681/how-to-plot-bar-graphs-with-same-x-coordinates-side-by-side-dodged
 

v5_class.py

 # 5.  Open door
 
 
-NumberOfFailures=3  # So far, we have only data for the first 4 types of failures
+NumberOfFailures=4  # So far, we have only data for the first 4 types of failures
 datafiles=[[],[]]   # 0 for train,  1 for test
 for i in range(NumberOfFailures+1):
     datafiles[0].append([])
 datafiles[0][1]=['2024-12-11_5_', '2024-12-12_5_','2024-12-13_5_'] 
 datafiles[0][2]=['2024-12-18_5_','2024-12-21_5_','2024-12-22_5_','2024-12-23_5_','2024-12-24_5_'] 
 datafiles[0][3]=['2024-12-28_5_','2024-12-29_5_','2024-12-30_5_'] 
-#datafiles[0][4]=['2025-02-13_5_']
+datafiles[0][4]=['2025-02-13_5_','2025-02-14_5_']
 
 if options.transition:
     datafiles[1][0]=['2025-01-27_5_','2025-01-28_5_'] 
     datafiles[1][1]=['2024-12-14_5_','2024-12-15_5_','2024-12-16_5_']  # with TRANSITION
     datafiles[1][2]=['2024-12-17_5_','2024-12-19_5_','2024-12-25_5_','2024-12-26_5_'] # with TRANSITION
     datafiles[1][3]=['2024-12-27_5_','2024-12-31_5_','2025-01-01_5_'] # with TRANSITION
-    #datafiles[1][4]=['2025-02-12_5_','2025-02-13_5_']
+    datafiles[1][4]=['2025-02-12_5_','2025-02-15_5_','2025-02-16_5_']
 
 else:
     datafiles[1][0]=['2025-01-27_5_','2025-01-28_5_'] 
     datafiles[1][1]=['2024-12-14_5_','2024-12-15_5_'] 
     datafiles[1][2]=['2024-12-19_5_','2024-12-25_5_','2024-12-26_5_'] 
     datafiles[1][3]=['2024-12-31_5_','2025-01-01_5_'] 
-    #datafiles[1][4]=['2025-02-13_5_']
+    datafiles[1][4]=['2025-02-15_5_','2025-02-16_5_']
  
 
 #datafiles[0][4]=['2025-02-05_5_'] 
 features=['r1 s5']
 # Feature combination suggested by AKO
 #features=['r1 s1','r1 s4','r1 s5','pa1 apiii']
-#features=['r1 s1','r1 s4','r1 s5']
+features=['r1 s1','r1 s4','r1 s5']
 #features=['r1 s1','r1 s5','pa1 apiii']
 #features=['r1 s5','pa1 apiii']
 #features=['r1 s1','r1 s5']
-features=['r1 s5']
+#features=['r1 s5']
 
 
 
 
 
 #   2nd scenario. Go over anomalies and classify it by less error
-datalist=[dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3]]
-#datalist=[dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4]]
+#datalist=[dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3]]
+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))))
     plotData()
     exit(0)
 
-testClasses=[0,1,2,3]
+testClasses=[0,1,2,3,4]
 
 if not len(testClasses)==len(testRanges):
     print("ERROR:  testClasses and testRanges must have same length")
         s+=' '+unitNames[features[indexesToPlot[i]]]
         if NumFeaturesToPlot==1:
             axes.set_ylabel(s)
+            axes.set_xlabel('Sample number')
             axes.grid()
         else:
             axes[i].set_ylabel(s)
+            axes[NumFeaturesToPlot-1].set_xlabel('Sample number')
             axes[i].grid()
 
     for j in range(NumberOfFailures+1):
                     FN[classes[k]]+=1
 
     for i in range(NumberOfFailures+1):
-        Sensitivity[i]=TP[i]/(TP[i]+FN[i])
+        if (TP[i]+FN[i])>0:
+            Sensitivity[i]=TP[i]/(TP[i]+FN[i])
+        else:
+            Sensitivity[i]=0
         Precision[i]=TP[i]/(TP[i]+FP[i])
     S=Sensitivity.mean()
     P=Precision.mean()
     print("F1-Score: ",F1)
 
 anomalyMetric(classes,testRanges,testClasses)
-#plotData4()
+plotData4()
 exit(0)
 # Compute delay until correct detection for a list of ranges (when transition data exists)
 def computeDelay(l,classes,testRanges,testClasses):