.

v0_unsupervised.py

@@ -0,0 +1,279 @@
+# Csar Fdez, UdL, 2025
+#  Unsupervised classification. Uses tslearn
+#  https://tslearn.readthedocs.io/en/stable/index.html
+
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import datetime
+import numpy as np
+import keras
+import os.path
+from optparse import OptionParser
+import copy
+import pickle
+from tslearn.clustering import TimeSeriesKMeans
+from collections import Counter
+
+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)
+
+(options, args) = parser.parse_args()
+
+
+# data files arrays. Index:
+# 0.  No failure
+# 1.  Blocked evaporator
+# 2.   Full Blocked condenser
+# 3.   Partial Blocked condenser
+# 4   Fan condenser not working
+# 5.  Open door
+
+
+NumberOfFailures=4  # So far, we have only data for the first 4 types of failures
+datafiles=[]
+for i in range(NumberOfFailures+1):
+    datafiles.append([])
+
+# Next set of ddata corresponds to Freezer, SP=-26
+datafiles[0]=['2024-08-07_5_','2024-08-08_5_','2025-01-25_5_','2025-01-26_5_','2025-01-27_5_','2025-01-28_5_'] 
+datafiles[1]=['2024-12-11_5_', '2024-12-12_5_','2024-12-13_5_','2024-12-14_5_','2024-12-15_5_'] 
+#datafiles[1]=['2024-12-17_5_','2024-12-16_5_','2024-12-11_5_', '2024-12-12_5_','2024-12-13_5_','2024-12-14_5_','2024-12-15_5_'] #   This have transitions
+datafiles[2]=['2024-12-18_5_','2024-12-19_5_'] 
+datafiles[3]=['2024-12-21_5_','2024-12-22_5_','2024-12-23_5_','2024-12-24_5_','2024-12-25_5_','2024-12-26_5_'] 
+datafiles[4]=['2024-12-28_5_','2024-12-29_5_','2024-12-30_5_','2024-12-31_5_','2025-01-01_5_'] 
+#datafiles[4]=['2024-12-27_5_','2024-12-28_5_','2024-12-29_5_','2024-12-30_5_','2024-12-31_5_','2025-01-01_5_']  #   This have transitions
+
+#datafiles[4]=[] 
+
+# Features suggested by Xavier
+# Care with 'tc s3' because on datafiles[0] is always nulll
+# Seems to be incoropored in new tests
+
+#r1s5 supply air flow temperature
+#r1s1 inlet evaporator temperature
+#r1s4 condenser outlet
+
+# VAriables r1s4 and pa1 apiii  may not exists in cloud controlers
+
+
+features=['r1 s1','r1 s4','r1 s5','pa1 apiii']
+features=['r1 s1','r1 s4','r1 s5']
+featureNames={}
+featureNames['r1 s1']='$T_{evap}$'
+featureNames['r1 s4']='$T_{cond}$'
+featureNames['r1 s5']='$T_{air}$'
+featureNames['pa1 apiii']='$P_{elec}$'
+
+unitNames={}
+unitNames['r1 s1']='$(^{o}C)$'
+unitNames['r1 s4']='$(^{o}C)$'
+unitNames['r1 s5']='$(^{o}C)$'
+unitNames['pa1 apiii']='$(W)$'
+
+
+#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=[]
+for i in range(NumberOfFailures+1):
+    df_list.append([])
+
+for i in range(NumberOfFailures+1):
+    dftemp=[]
+    for f in datafiles[i]:
+        print("                 ", f)
+        #df1 = pd.read_csv('./data/'+f+'.csv', parse_dates=['datetime'], dayfirst=True, index_col='datetime')
+        df1 = pd.read_csv('./data/'+f+'.csv')
+        dftemp.append(df1)
+    df_list[i]=pd.concat(dftemp)
+
+
+# subsampled to 5'  =  30 * 10"
+# We consider smaples every 5' because in production, we will only have data at this frequency
+subsamplingrate=30
+
+dataframe=[]
+for i in range(NumberOfFailures+1):
+    dataframe.append([])
+
+for i in range(NumberOfFailures+1):
+    datalength=df_list[i].shape[0]
+    dataframe[i]=df_list[i].iloc[range(0,datalength,subsamplingrate)][features]
+    dataframe[i].reset_index(inplace=True,drop=True)
+    dataframe[i].dropna(inplace=True)
+
+
+# Train data is first 2/3 of data
+# Test data is: last 1/3 of data 
+dataTrain=[]
+dataTest=[]
+for i in range(NumberOfFailures+1):
+    dataTrain.append(dataframe[i].values[0:int(dataframe[i].shape[0]*2/3),:])
+    dataTest.append(dataframe[i].values[int(dataframe[i].shape[0]*2/3):,:])
+
+# Calculate means and stdev
+a=dataTrain[0]
+for i in range(1,NumberOfFailures+1):
+    a=np.vstack((a,dataTrain[i]))
+
+means=a.mean(axis=0) 
+stdevs=a.std(axis=0)
+def normalize2(train,test):
+    return( (train-means)/stdevs, (test-means)/stdevs )
+
+dataTrainNorm=[]
+dataTestNorm=[]
+for i in range(NumberOfFailures+1):
+    dataTrainNorm.append([])
+    dataTestNorm.append([])
+
+for i in range(NumberOfFailures+1):
+    (dataTrainNorm[i],dataTestNorm[i])=normalize2(dataTrain[i],dataTest[i])
+
+def plotData():    
+    fig, axes = plt.subplots(
+        nrows=NumberOfFailures+1, ncols=2, figsize=(15, 20), dpi=80, facecolor="w", edgecolor="k",sharex=True
+    )
+    for i in range(NumberOfFailures+1):
+        axes[i][0].plot(np.concatenate((dataTrainNorm[i][:,0],dataTestNorm[i][:,0])),label="Fail "+str(i)+",  feature 0")
+        axes[i][1].plot(np.concatenate((dataTrainNorm[i][:,1],dataTestNorm[i][:,1])),label="Fail "+str(i)+",  feature 1")
+    #axes[1].legend()
+    #axes[0].set_ylabel(features[0])
+    #axes[1].set_ylabel(features[1])
+    plt.show()
+
+#plotData()
+
+def create_sequences(values, time_steps):
+    output = []
+    for i in range(len(values) - time_steps + 1):
+        output.append(values[i : (i + time_steps)])
+    return np.stack(output)
+
+def listToString(l):
+    r=''
+    for i in l:
+        r+=str(i)
+    return(r.replace(' ',''))
+
+timesteps=int(options.timesteps)
+x_train=[]
+for i in range(NumberOfFailures+1):
+    x_train.append(create_sequences(dataTrainNorm[i],timesteps))
+
+xtrain=x_train[0]
+for i in range(1,NumberOfFailures+1):
+    xtrain=np.vstack((xtrain,x_train[i]))
+
+km = TimeSeriesKMeans(n_clusters=NumberOfFailures+1, metric="dtw")
+modelpath="model_co_unsupervised_"+str(timesteps)+listToString(features)+".pk"
+if options.train:
+    km.fit(xtrain)
+    km.to_pickle(modelpath)
+else:
+    km.from_pickle(modelpath)
+    km.fit_predict(xtrain)
+
+colorline=['violet','lightcoral','cyan','lime','grey']
+colordot=['darkviolet','red','blue','green','black']
+
+
+Ranges=[]
+r=0
+for i in range(NumberOfFailures+1):
+    Ranges.append([r,r+x_train[i].shape[0]])
+    r+=x_train[i].shape[0]
+# Drop the last TIME_STEPS for plotting
+Ranges[NumberOfFailures][1]=Ranges[NumberOfFailures][1]
+
+featuresToPlot=features
+indexesToPlot=[]
+for i in featuresToPlot:
+    indexesToPlot.append(features.index(i))
+
+labels=[]    #   Labels are assigned randomly by classifer
+for i in range(NumberOfFailures+1):
+    b=Counter(km.labels_[Ranges[i][0]:Ranges[i][1]])
+    labels.append(b.most_common(1)[0][0])
+
+
+def plot(data,ranges,labels):
+    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):
+        init=0
+        end=ranges[0][1]
+        for j in range(NumberOfFailures+1):
+            axes[i].plot(range(init,end),data[ranges[j][0]:ranges[j][1],0,indexesToPlot[i]]*stdevs[i]+means[i],label="Class "+str(j), color=colorline[j],linewidth=1)
+            if j<NumberOfFailures:
+                init=end
+                end+=(ranges[j+1][1]-ranges[j+1][0])
+
+        x=[]
+        y=[]
+        for j in range(NumberOfFailures+1):
+            x.append([])
+            y.append([])
+
+        for j in range(NumberOfFailures+1):
+            for k in range(ranges[j][0],ranges[j][1]):
+                print(j,k)
+                x[labels.index(km.labels_[k])].append(k)
+                y[labels.index(km.labels_[k])].append(data[k,0,indexesToPlot[i]]*stdevs[i]+means[i])
+
+        for j in range(NumberOfFailures+1):
+            axes[i].plot(x[j],y[j] ,color=colordot[j],marker='.',linewidth=0,label="Class type "+str(j) )
+
+
+        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()
+        
+   
+
+#plot(xtrain,Ranges,labels)
+
+
+# Try with test data
+
+x_test=[]
+for i in range(NumberOfFailures+1):
+    x_test.append(create_sequences(dataTestNorm[i],timesteps))
+
+xtest=x_test[0]
+for i in range(1,NumberOfFailures+1):
+    xtest=np.vstack((xtest,x_test[i]))
+
+Ranges=[]
+r=0
+for i in range(NumberOfFailures+1):
+    Ranges.append([r,r+x_test[i].shape[0]])
+    r+=x_test[i].shape[0]
+# Drop the last TIME_STEPS for plotting
+Ranges[NumberOfFailures][1]=Ranges[NumberOfFailures][1]
+
+
+km.fit_predict(xtest)
+
+labels=[]    #   Labels are assigned randomly by classifer
+for i in range(NumberOfFailures+1):
+    b=Counter(km.labels_[Ranges[i][0]:Ranges[i][1]])
+    labels.append(b.most_common(1)[0][0])
+
+plot(xtest,Ranges,labels)
+

v1_unsupervised.py

@@ -0,0 +1,329 @@
+# Csar Fdez, UdL, 2025
+#  Unsupervised classification. Uses tslearn
+#  https://tslearn.readthedocs.io/en/stable/index.html
+
+# Be careful with v0_unsupervised  and all versions for supervised.
+# because dataTrain is not stacke before create_sequences,  so, 
+#  the sets are not aligned in time
+
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import datetime
+import numpy as np
+import os.path
+from optparse import OptionParser
+import copy
+import pickle
+from tslearn.clustering import TimeSeriesKMeans
+from collections import Counter
+
+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)
+
+(options, args) = parser.parse_args()
+
+
+# data files arrays. Index:
+# 0.  No failure
+# 1.  Blocked evaporator
+# 2.   Full Blocked condenser
+# 3.   Partial Blocked condenser
+# 4   Fan condenser not working
+# 5.  Open door
+
+
+NumberOfFailures=4  # So far, we have only data for the first 4 types of failures
+datafiles=[]
+for i in range(NumberOfFailures+1):
+    datafiles.append([])
+
+# Next set of ddata corresponds to Freezer, SP=-26
+datafiles[0]=['2024-08-07_5_','2024-08-08_5_','2025-01-25_5_','2025-01-26_5_','2025-01-27_5_','2025-01-28_5_'] 
+datafiles[1]=['2024-12-11_5_', '2024-12-12_5_','2024-12-13_5_','2024-12-14_5_','2024-12-15_5_'] 
+#datafiles[1]=['2024-12-17_5_','2024-12-16_5_','2024-12-11_5_', '2024-12-12_5_','2024-12-13_5_','2024-12-14_5_','2024-12-15_5_'] #   This have transitions
+datafiles[2]=['2024-12-18_5_','2024-12-19_5_'] 
+datafiles[3]=['2024-12-21_5_','2024-12-22_5_','2024-12-23_5_','2024-12-24_5_','2024-12-25_5_','2024-12-26_5_'] 
+datafiles[4]=['2024-12-28_5_','2024-12-29_5_','2024-12-30_5_','2024-12-31_5_','2025-01-01_5_'] 
+#datafiles[4]=['2024-12-27_5_','2024-12-28_5_','2024-12-29_5_','2024-12-30_5_','2024-12-31_5_','2025-01-01_5_']  #   This have transitions
+
+#datafiles[4]=[] 
+
+# Features suggested by Xavier
+# Care with 'tc s3' because on datafiles[0] is always nulll
+# Seems to be incoropored in new tests
+
+#r1s5 supply air flow temperature
+#r1s1 inlet evaporator temperature
+#r1s4 condenser outlet
+
+# VAriables r1s4 and pa1 apiii  may not exists in cloud controlers
+
+
+features=['r1 s1','r1 s4','r1 s5','pa1 apiii']
+features=['r1 s1','r1 s4','r1 s5']
+featureNames={}
+featureNames['r1 s1']='$T_{evap}$'
+featureNames['r1 s4']='$T_{cond}$'
+featureNames['r1 s5']='$T_{air}$'
+featureNames['pa1 apiii']='$P_{elec}$'
+
+unitNames={}
+unitNames['r1 s1']='$(^{o}C)$'
+unitNames['r1 s4']='$(^{o}C)$'
+unitNames['r1 s5']='$(^{o}C)$'
+unitNames['pa1 apiii']='$(W)$'
+
+
+#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=[]
+for i in range(NumberOfFailures+1):
+    df_list.append([])
+
+for i in range(NumberOfFailures+1):
+    dftemp=[]
+    for f in datafiles[i]:
+        print("                 ", f)
+        #df1 = pd.read_csv('./data/'+f+'.csv', parse_dates=['datetime'], dayfirst=True, index_col='datetime')
+        df1 = pd.read_csv('./data/'+f+'.csv')
+        dftemp.append(df1)
+    df_list[i]=pd.concat(dftemp)
+
+
+# subsampled to 5'  =  30 * 10"
+# We consider smaples every 5' because in production, we will only have data at this frequency
+subsamplingrate=30
+
+dataframe=[]
+for i in range(NumberOfFailures+1):
+    dataframe.append([])
+
+for i in range(NumberOfFailures+1):
+    datalength=df_list[i].shape[0]
+    dataframe[i]=df_list[i].iloc[range(0,datalength,subsamplingrate)][features]
+    dataframe[i].reset_index(inplace=True,drop=True)
+    dataframe[i].dropna(inplace=True)
+
+
+# Train data is first 2/3 of data
+# Test data is: last 1/3 of data 
+dataTrain=[]
+dataTest=[]
+for i in range(NumberOfFailures+1):
+    dataTrain.append(dataframe[i].values[0:int(dataframe[i].shape[0]*2/3),:])
+    dataTest.append(dataframe[i].values[int(dataframe[i].shape[0]*2/3):,:])
+
+# Calculate means and stdev
+a=dataTrain[0]
+for i in range(1,NumberOfFailures+1):
+    a=np.vstack((a,dataTrain[i]))
+
+means=a.mean(axis=0) 
+stdevs=a.std(axis=0)
+def normalize2(train,test):
+    return( (train-means)/stdevs, (test-means)/stdevs )
+
+dataTrainNorm=[]
+dataTestNorm=[]
+for i in range(NumberOfFailures+1):
+    dataTrainNorm.append([])
+    dataTestNorm.append([])
+
+for i in range(NumberOfFailures+1):
+    (dataTrainNorm[i],dataTestNorm[i])=normalize2(dataTrain[i],dataTest[i])
+
+def plotData():    
+    fig, axes = plt.subplots(
+        nrows=NumberOfFailures+1, ncols=2, figsize=(15, 20), dpi=80, facecolor="w", edgecolor="k",sharex=True
+    )
+    for i in range(NumberOfFailures+1):
+        axes[i][0].plot(np.concatenate((dataTrainNorm[i][:,0],dataTestNorm[i][:,0])),label="Fail "+str(i)+",  feature 0")
+        axes[i][1].plot(np.concatenate((dataTrainNorm[i][:,1],dataTestNorm[i][:,1])),label="Fail "+str(i)+",  feature 1")
+    #axes[1].legend()
+    #axes[0].set_ylabel(features[0])
+    #axes[1].set_ylabel(features[1])
+    plt.show()
+
+#plotData()
+
+def create_sequences(values, time_steps):
+    output = []
+    for i in range(len(values) - time_steps ):
+        output.append(values[i : (i + time_steps)])
+    return np.stack(output)
+
+def listToString(l):
+    r=''
+    for i in l:
+        r+=str(i)
+    return(r.replace(' ',''))
+
+timesteps=int(options.timesteps)
+
+X=dataTrainNorm[0]
+for i in range(1,NumberOfFailures+1):
+    X=np.vstack((X,dataTrainNorm[i]))
+
+xtrain=create_sequences(X,timesteps)
+
+km = TimeSeriesKMeans(n_clusters=NumberOfFailures+1, metric="dtw")
+modelpath="model_v1_unsupervised_"+str(timesteps)+listToString(features)+".pk"
+if options.train:
+    km.fit(xtrain)
+    km.to_pickle(modelpath)
+else:
+    km.from_pickle(modelpath)
+    #km.fit_predict(xtrain)
+
+colorline=['violet','lightcoral','cyan','lime','grey']
+colordot=['darkviolet','red','blue','green','black']
+
+
+
+
+
+featuresToPlot=features
+indexesToPlot=[]
+for i in featuresToPlot:
+    indexesToPlot.append(features.index(i))
+
+
+def plot(data,ranges):
+
+    km.fit_predict(data)
+# Expand data to plot with the timesteps samples of last sample
+    datatoplot=data[:,0,:]
+    datatoplot=np.vstack((datatoplot,data[ranges[len(ranges)-1][1],:,:]))
+
+    labels=[]    #   Labels are assigned randomly by classifer
+    for i in range(len(ranges)):
+        b=Counter(km.labels_[ranges[i][0]:ranges[i][1]])
+        labels.append(b.most_common(1)[0][0])
+
+    print("\n\n\n\LABELS: ",labels,"\n\n")
+
+    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):
+        init=0
+        end=ranges[0][1]
+        labelsplotted=[]
+        for j in range(len(ranges)):
+            if j==(len(ranges)-1): # Plot the last timesteps
+                classtype=labels.index(labels[j])
+                if classtype in labelsplotted:
+                    axes[i].plot(range(init,end+timesteps),datatoplot[ranges[j][0]:ranges[j][1]+timesteps,indexesToPlot[i]]*stdevs[i]+means[i], color=colorline[classtype],linewidth=1)
+                else:
+                    axes[i].plot(range(init,end+timesteps),datatoplot[ranges[j][0]:ranges[j][1]+timesteps,indexesToPlot[i]]*stdevs[i]+means[i], label="Class: "+str(classtype), color=colorline[classtype],linewidth=1)
+                    labelsplotted.append(classtype)
+            else:
+                classtype=labels.index(labels[j])
+                if classtype in labelsplotted:
+                    axes[i].plot(range(init,end),datatoplot[ranges[j][0]:ranges[j][1],indexesToPlot[i]]*stdevs[i]+means[i], color=colorline[classtype],linewidth=1)
+                else:
+                    axes[i].plot(range(init,end),datatoplot[ranges[j][0]:ranges[j][1],indexesToPlot[i]]*stdevs[i]+means[i], label="Class: "+str(classtype), color=colorline[classtype],linewidth=1)
+                    labelsplotted.append(classtype)
+            init=end
+            if j<(len(ranges)-1):
+                end+=(ranges[j+1][1]-ranges[j+1][0])
+        x=[]
+        y=[]
+        for j in range(len(ranges)):
+            x.append([])
+            y.append([])
+
+        for j in range(len(ranges)):
+            for k in range(ranges[j][0],ranges[j][1]):
+                try:  # Idont know why sometimes fails index !!!!
+                    x[labels.index(km.labels_[k])].append(k+timesteps)
+                    y[labels.index(km.labels_[k])].append(datatoplot[k+timesteps,indexesToPlot[i]]*stdevs[i]+means[i])
+                except:
+                    x[0].append(k+timesteps)
+                    y[0].append(datatoplot[k+timesteps,indexesToPlot[i]]*stdevs[i]+means[i])
+
+        labelsplotted=[]
+        for j in range(len(ranges)):
+            classtype=labels.index(labels[j])
+            if classtype in labelsplotted:
+                axes[i].plot(x[j],y[j] ,color=colordot[labels.index(labels[j])],marker='.',linewidth=0)
+            else:
+                axes[i].plot(x[j],y[j] ,color=colordot[labels.index(labels[j])],marker='.',linewidth=0,label="Class type "+str(j) )
+                labelsplotted.append(classtype)
+
+        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()
+        
+   
+'''
+Ranges=[]
+r=0
+for i in range(NumberOfFailures+1):
+    Ranges.append([r,r+dataTrainNorm[i].shape[0]])
+    r+=dataTrainNorm[i].shape[0]
+# Drop the last TIME_STEPS for plotting
+Ranges[NumberOfFailures][1]=Ranges[NumberOfFailures][1]-timesteps-1
+
+plot(xtrain,Ranges)
+'''
+# Try with test data
+X=dataTestNorm[0]
+Ranges=[[0,dataTestNorm[0].shape[0]]]
+r=dataTestNorm[0].shape[0]
+for i in range(1,NumberOfFailures+1):
+    X=np.vstack((X,dataTestNorm[i]))
+    Ranges.append([r,r+dataTestNorm[i].shape[0]])
+    r+=dataTestNorm[i].shape[0]
+
+X=np.vstack((X,dataTestNorm[0]))  # We add a last segment of no fail data
+Ranges.append([r,r+dataTestNorm[0].shape[0]])
+Ranges[len(Ranges)-1][1]=Ranges[len(Ranges)-1][1]-timesteps-1
+
+xtest=create_sequences(X,timesteps)
+
+
+
+km.fit_predict(xtest)
+plot(xtest,Ranges)
+
+exit(0)
+def anomalyMetric(labels,ranges):
+    # FP, TP: false/true positive
+    # TN, FN: true/false negative
+    # Sensitivity (recall): probab failure detection if data is fail: TP/(TP+FN)
+    # Precision: Rate of positive results:  TP/(TP+FP)  
+    # F1-score: predictive performance measure: 2*Precision*Sensitity/(Precision+Sensitity)
+
+    lab=[]    #   Labels are assigned randomly by classifer
+    TP=[]
+    for i in range(NumberOfFailures+1):
+        TP.append([])
+        b=Counter(labels[ranges[i][0]:ranges[i][1]])
+        lab.append(b.most_common(1)[0][0])
+
+    print(lab)
+    #for i in range(NumberOfFailures+1):
+
+
+
+
+anomalyMetric(km.labels_,Ranges)
+
+
+

v3_class.py

@@ -0,0 +1,475 @@
+# Csar Fdez, UdL, 2025
+# Changes from v1:   Normalization 
+# IN v1, each failure type has its own normalization pars (mean and stdevs)
+# In v2, mean and stdev is the same for all data
+# v3.py trains the models looping in TIME_STEPS (4,8,12,16,20,24,....) finding the optimal Threshold factor
+
+#  Derived from v3.py with code from v1_multifailure.py
+#  This code don't train for multiple time steps !!
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import datetime
+import numpy as np
+import keras
+import os.path
+from keras import layers
+from optparse import OptionParser
+import copy
+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)
+
+(options, args) = parser.parse_args()
+
+
+# data files arrays. Index:
+# 0.  No failure
+# 1.  Blocked evaporator
+# 2.   Full Blocked condenser
+# 3.   Partial Blocked condenser
+# 4   Fan condenser not working
+# 5.  Open door
+
+
+NumberOfFailures=4  # So far, we have only data for the first 4 types of failures
+datafiles=[]
+for i in range(NumberOfFailures+1):
+    datafiles.append([])
+
+# Next set of ddata corresponds to Freezer, SP=-26
+datafiles[0]=['2024-08-07_5_','2024-08-08_5_','2025-01-25_5_','2025-01-26_5_','2025-01-27_5_','2025-01-28_5_'] 
+datafiles[1]=['2024-12-11_5_', '2024-12-12_5_','2024-12-13_5_','2024-12-14_5_','2024-12-15_5_'] 
+#datafiles[1]=['2024-12-17_5_','2024-12-16_5_','2024-12-11_5_', '2024-12-12_5_','2024-12-13_5_','2024-12-14_5_','2024-12-15_5_'] #   This have transitions
+datafiles[2]=['2024-12-18_5_','2024-12-19_5_'] 
+datafiles[3]=['2024-12-21_5_','2024-12-22_5_','2024-12-23_5_','2024-12-24_5_','2024-12-25_5_','2024-12-26_5_'] 
+datafiles[4]=['2024-12-28_5_','2024-12-29_5_','2024-12-30_5_','2024-12-31_5_','2025-01-01_5_'] 
+#datafiles[4]=['2024-12-27_5_','2024-12-28_5_','2024-12-29_5_','2024-12-30_5_','2024-12-31_5_','2025-01-01_5_']  #   This have transitions
+
+#datafiles[4]=[] 
+
+# Features suggested by Xavier
+# Care with 'tc s3' because on datafiles[0] is always nulll
+# Seems to be incoropored in new tests
+
+#r1s5 supply air flow temperature
+#r1s1 inlet evaporator temperature
+#r1s4 condenser outlet
+
+# VAriables r1s4 and pa1 apiii  may not exists in cloud controlers
+
+
+features=['r1 s1','r1 s4','r1 s5','pa1 apiii']
+features=['r1 s1','r1 s4','r1 s5']
+featureNames={}
+featureNames['r1 s1']='$T_{evap}$'
+featureNames['r1 s4']='$T_{cond}$'
+featureNames['r1 s5']='$T_{air}$'
+featureNames['pa1 apiii']='$P_{elec}$'
+
+unitNames={}
+unitNames['r1 s1']='$(^{o}C)$'
+unitNames['r1 s4']='$(^{o}C)$'
+unitNames['r1 s5']='$(^{o}C)$'
+unitNames['pa1 apiii']='$(W)$'
+
+
+#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=[]
+for i in range(NumberOfFailures+1):
+    df_list.append([])
+
+for i in range(NumberOfFailures+1):
+    dftemp=[]
+    for f in datafiles[i]:
+        print("                 ", f)
+        #df1 = pd.read_csv('./data/'+f+'.csv', parse_dates=['datetime'], dayfirst=True, index_col='datetime')
+        df1 = pd.read_csv('./data/'+f+'.csv')
+        dftemp.append(df1)
+    df_list[i]=pd.concat(dftemp)
+
+
+# subsampled to 5'  =  30 * 10"
+# We consider smaples every 5' because in production, we will only have data at this frequency
+subsamplingrate=30
+
+dataframe=[]
+for i in range(NumberOfFailures+1):
+    dataframe.append([])
+
+for i in range(NumberOfFailures+1):
+    datalength=df_list[i].shape[0]
+    dataframe[i]=df_list[i].iloc[range(0,datalength,subsamplingrate)][features]
+    dataframe[i].reset_index(inplace=True,drop=True)
+    dataframe[i].dropna(inplace=True)
+
+
+# Train data is first 2/3 of data
+# Test data is: last 1/3 of data 
+dataTrain=[]
+dataTest=[]
+for i in range(NumberOfFailures+1):
+    dataTrain.append(dataframe[i].values[0:int(dataframe[i].shape[0]*2/3),:])
+    dataTest.append(dataframe[i].values[int(dataframe[i].shape[0]*2/3):,:])
+
+# Calculate means and stdev
+a=dataTrain[0]
+for i in range(1,NumberOfFailures+1):
+    a=np.vstack((a,dataTrain[i]))
+
+means=a.mean(axis=0) 
+stdevs=a.std(axis=0)
+def normalize2(train,test):
+    return( (train-means)/stdevs, (test-means)/stdevs )
+
+dataTrainNorm=[]
+dataTestNorm=[]
+for i in range(NumberOfFailures+1):
+    dataTrainNorm.append([])
+    dataTestNorm.append([])
+
+for i in range(NumberOfFailures+1):
+    (dataTrainNorm[i],dataTestNorm[i])=normalize2(dataTrain[i],dataTest[i])
+
+def plotData():    
+    fig, axes = plt.subplots(
+        nrows=NumberOfFailures+1, ncols=2, figsize=(15, 20), dpi=80, facecolor="w", edgecolor="k",sharex=True
+    )
+    for i in range(NumberOfFailures+1):
+        axes[i][0].plot(np.concatenate((dataTrainNorm[i][:,0],dataTestNorm[i][:,0])),label="Fail "+str(i)+",  feature 0")
+        axes[i][1].plot(np.concatenate((dataTrainNorm[i][:,1],dataTestNorm[i][:,1])),label="Fail "+str(i)+",  feature 1")
+    #axes[1].legend()
+    #axes[0].set_ylabel(features[0])
+    #axes[1].set_ylabel(features[1])
+    plt.show()
+
+#plotData()
+#exit(0)
+
+
+NumFilters=64
+KernelSize=7
+DropOut=0.2
+ThresholdFactor=1.4
+def create_sequences(values, time_steps):
+    output = []
+    for i in range(len(values) - time_steps + 1):
+        output.append(values[i : (i + time_steps)])
+    return np.stack(output)
+
+def AtLeastOneTrue(x):
+    for i in range(NumFeatures):
+        if x[i]:
+            return True
+    return False
+
+def anomalyMetric(th,ts,testList):  # first of list is non failure data
+    # FP, TP: false/true positive
+    # TN, FN: true/false negative
+    # Sensitivity (recall): probab failure detection if data is fail: TP/(TP+FN)
+    # Specificity: true negative ratio given  data is OK: TN/(TN+FP)
+    # Accuracy: Rate of correct predictions:  (TN+TP)/(TN+TP+FP+FN)
+    # Precision: Rate of positive results:  TP/(TP+FP)  
+    # F1-score: predictive performance measure: 2*Precision*Sensitity/(Precision+Sensitity)
+    # F2-score: predictive performance measure:  2*Specificity*Sensitity/(Specificity+Sensitity)
+
+    x_test = create_sequences(testList[0],ts)
+    x_test_pred = model.predict(x_test)
+    test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
+    anomalies = test_mae_loss > th
+    count=0
+    for i in range(anomalies.shape[0]):
+        if AtLeastOneTrue(anomalies[i]):
+            count+=1
+    FP=count
+    TN=anomalies.shape[0]-count
+    count=0
+    TP=np.zeros((NumberOfFailures))
+    FN=np.zeros((NumberOfFailures))
+    Sensitivity=np.zeros((NumberOfFailures))
+    Precision=np.zeros((NumberOfFailures))
+    for i in range(1,len(testList)):
+        x_test = create_sequences(testList[i],ts)
+        x_test_pred = model.predict(x_test)
+        test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
+        anomalies = test_mae_loss > th
+        count=0
+        for j in range(anomalies.shape[0]):
+            if AtLeastOneTrue(anomalies[j]):
+                count+=1
+        TP[i-1] = count
+        FN[i-1] = anomalies.shape[0]-count
+        Sensitivity[i-1]=TP[i-1]/(TP[i-1]+FN[i-1])
+        Precision[i-1]=TP[i-1]/(TP[i-1]+FP)
+
+    GlobalSensitivity=TP.sum()/(TP.sum()+FN.sum())
+    Specificity=TN/(TN+FP)
+    Accuracy=(TN+TP.sum())/(TN+TP.sum()+FP+FN.sum())
+    GlobalPrecision=TP.sum()/(TP.sum()+FP)
+    F1Score= 2*GlobalPrecision*GlobalSensitivity/(GlobalPrecision+GlobalSensitivity)
+    F2Score = 2*Specificity*GlobalSensitivity/(Specificity+GlobalSensitivity)
+
+    print("Sensitivity: ",Sensitivity)
+    print("Global Sensitivity: ",GlobalSensitivity)
+    #print("Precision: ",Precision)
+    #print("Global Precision: ",GlobalPrecision)
+    print("Specifity: ",Specificity)
+    #print("Accuracy: ",Accuracy)
+    #print("F1Score: ",F1Score)
+    print("F2Score: ",F2Score)
+    #print("FP: ",FP)
+    #return Sensitivity+Specifity
+    return F2Score
+
+def listToString(l):
+    r=''
+    for i in l:
+        r+=str(i)
+    return(r.replace(' ',''))
+
+threshold={} 
+
+fname='threshold_class'+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 =[]
+es_callback =[]
+path_checkpoint=[]
+
+timesteps=int(options.timesteps)
+x_train=[]
+for i in range(NumberOfFailures+1):
+    x_train.append(create_sequences(dataTrainNorm[i],timesteps))
+    model.append([])
+    model[i] = keras.Sequential(
+        [
+            layers.Input(shape=(x_train[i].shape[1], x_train[i].shape[2])),
+            layers.Conv1D(
+                filters=NumFilters,
+                kernel_size=KernelSize,
+                padding="same",
+                strides=2,
+                activation="relu",
+            ),
+            layers.Dropout(rate=DropOut),
+            layers.Conv1D(
+                filters=int(NumFilters/2),
+                kernel_size=KernelSize,
+                padding="same",
+                strides=2,
+                activation="relu",
+            ),
+            layers.Conv1DTranspose(
+                filters=int(NumFilters/2),
+                kernel_size=KernelSize,
+                padding="same",
+                strides=2,
+                activation="relu",
+            ),
+            layers.Dropout(rate=DropOut),
+            layers.Conv1DTranspose(
+                filters=NumFilters,
+                kernel_size=KernelSize,
+                padding="same",
+                strides=2,
+                activation="relu",
+            ),
+            layers.Conv1DTranspose(filters=x_train[i].shape[2], kernel_size=KernelSize, padding="same"),
+        ]
+    )
+    model[i].compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss="mse")
+    model[i].summary()
+    path_checkpoint.append("model_class_v3_"+str(i)+"_"+str(timesteps)+listToString(features)+"_checkpoint.weights.h5")
+    es_callback.append(keras.callbacks.EarlyStopping(monitor="val_loss", min_delta=0, patience=15))
+    modelckpt_callback.append(keras.callbacks.ModelCheckpoint( monitor="val_loss", filepath=path_checkpoint[i], verbose=1, save_weights_only=True, save_best_only=True,))
+
+
+if options.train:
+    history=[]    
+    for i in range(NumberOfFailures+1):
+        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'+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'+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]]
+d=np.vstack((datalist))
+
+x_test = create_sequences(d,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]])
+    r+=datalist[i].shape[0]
+# Drop the last TIME_STEPS for plotting
+testRanges[NumberOfFailures][1]=testRanges[NumberOfFailures][1]-int(options.timesteps)
+
+# Let's plot some features
+
+colorline=['violet','lightcoral','cyan','lime','grey']
+colordot=['darkviolet','red','blue','green','black']
+
+#featuresToPlot=['r1 s1','r1 s2','r1 s3','pa1 apiii']
+featuresToPlot=features
+
+indexesToPlot=[]
+for i in featuresToPlot:
+    indexesToPlot.append(features.index(i))
+
+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+int(options.timesteps))<x_test.shape[0]:
+                x.append(k+int(options.timesteps))
+                y.append(x_train[k+int(options.timesteps),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),xtrain[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),xtrain[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()
+
+
+#plotData3()
+
+
+#   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)
+
+
+def plotData4():
+    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):
+
+
+        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", color='black')
+        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])
+
+        for j in range(NumberOfFailures):
+            x=[]
+            y=[]
+            for k in anomalous_data_type[j]:
+                if (k+int(options.timesteps))<x_test.shape[0]:
+                    x.append(k+int(options.timesteps))
+                    y.append(x_test[k+int(options.timesteps),0,indexesToPlot[i]]*stdevs[i]+means[i])
+            axes[i].plot(x,y ,color=colordot[j],marker='.',linewidth=0,label="Fail detect  type "+str(j+1) )
+
+
+
+        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()
+
+
+plotData4()
+
+##   It remains to implemenent anomaly metrics for each failure type
+
+exit(0)
+
+