2025-AKO/v1.py

# Csar Fdez, UdL, 2025
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import numpy as np
import keras
import os.path
import pickle
from keras import layers
from optparse import OptionParser
import copy


parser = OptionParser()
parser.add_option("-t", "--train", dest="train", help="Trains the models (false)", default=False, action="store_true")

(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=5
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_'] 
datafiles[1]=['2024-12-11_5_', '2024-12-12_5_','2024-12-13_5_','2024-12-14_5_','2024-12-15_5_'] 
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]=[] 

# 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']

#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):,:])


def normalize2(train,test):
    # merges train and test
    means=[]
    stdevs=[]
    for i in range(NumFeatures):
        means.append(train[:,i].mean())
        stdevs.append(train[:,i].std())
    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=0.9
TIME_STEPS = 48  # This is a trade off among better performance (high) and better response delay (low)
def create_sequences(values, time_steps=TIME_STEPS):
    output = []
    for i in range(len(values) - time_steps + 1):
        output.append(values[i : (i + time_steps)])
    return np.stack(output)

x_train=[]
for i in range(NumberOfFailures+1):
    x_train.append(create_sequences(dataTrainNorm[i]))


# Reused code from v1_multifailure for only one model. No classification
#for i in range(NumberOfFailures+1):
model = keras.Sequential(
    [
        layers.Input(shape=(x_train[0].shape[1], x_train[0].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.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss="mse")
model.summary()
path_checkpoint="model_noclass_v1_checkpoint.weights.h5"
es_callback=keras.callbacks.EarlyStopping(monitor="val_loss", min_delta=0, patience=15)
modelckpt_callback=keras.callbacks.ModelCheckpoint( monitor="val_loss", filepath=path_checkpoint, verbose=1, save_weights_only=True, save_best_only=True,)


if options.train:
    history=model.fit( x_train[0], x_train[0], epochs=400, batch_size=128, validation_split=0.3, callbacks=[  es_callback, modelckpt_callback      ],)
else:
    model.load_weights(path_checkpoint)


x_train_pred=model.predict(x_train[0])
train_mae_loss=np.mean(np.abs(x_train_pred - x_train[0]), axis=1)
threshold=np.max(train_mae_loss,axis=0)
thresholdOrig=copy.deepcopy(threshold)

print("Threshold : ",threshold)
threshold=threshold*ThresholdFactor
# Threshold is enlarged because, otherwise, for subsamples at 5' have many false positives


#  1st scenario. Detect only anomaly.  Later, we will classiffy it
# Test data=  testnormal + testfail1 + testtail2 + testfail3 + testfail4 + testnormal
#d=np.vstack((dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4],dataTestNorm[0]))
d=np.vstack((dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4]))

x_test = create_sequences(d)
x_test_pred = model.predict(x_test)
test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)


# Define ranges for plotting in different colors
testRanges=[]
r=dataTestNorm[0].shape[0]
testRanges.append([0,r])
for i in range(1,NumberOfFailures+1):
    rnext=r+dataTestNorm[i].shape[0]
    testRanges.append([r,rnext] )
    r=rnext

# Drop the last TIME_STEPS for plotting
testRanges[NumberOfFailures][1]=testRanges[NumberOfFailures][1]-TIME_STEPS


def AtLeastOneTrue(x):
    for i in range(NumFeatures):
        if x[i]:
            return True
    return False

anomalies = test_mae_loss > threshold
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)

#print(anomalous_data_indices)


# 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):
        init=0
        end=testRanges[0][1]
        axes[i].plot(range(init,end),x_test[testRanges[0][0]:testRanges[0][1],0,indexesToPlot[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]],label="fail type "+str(j), color=colorline[j-1])
            if j<NumberOfFailures:
                init=end
                end+=(testRanges[j+1][1]-testRanges[j+1][0])
        x=[]
        y=[]
        for k in anomalous_data_indices:
            if (k+TIME_STEPS)<x_test.shape[0]:
                x.append(k+TIME_STEPS)
                y.append(x_test[k+TIME_STEPS,0,indexesToPlot[i]])
        axes[i].plot(x,y ,color='grey',marker='.',linewidth=0,label="fail detection" )

        if i==0:
            axes[i].legend(bbox_to_anchor=(0.9, 0.4))
        axes[i].set_ylabel(features[indexesToPlot[i]])
        axes[i].grid()
    axes[NumFeaturesToPlot-1].set_xlabel("Sample number")
    plt.show()


def anomalyMetric(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)  
    # F-score: predictive performance measure: 2*Precision*Sensitity/(Precision+Sensitity)

    x_test = create_sequences(testList[0])
    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 > threshold
    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])
        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 > threshold
        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)
    FScore= 2*GlobalPrecision*GlobalSensitivity/(GlobalPrecision+GlobalSensitivity)

    print("Sensitivity: ",Sensitivity)
    print("Global Sensitivity: ",GlobalSensitivity)
    print("Precision: ",Precision)
    print("Global Precision: ",GlobalPrecision)
    print("Specifity: ",Specificity)
    print("Accuracy: ",Accuracy)
    print("FScore: ",FScore)
    print("FP: ",FP)
    #return Sensitivity+Specifity
    return FScore

anomalyMetric([dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4]])


def plotFScore():
    global threshold
    res=[]
    # plots FSCroe as a function of Threshold  Factor
    tf=0.3
    while tf<1.5:
        threshold=thresholdOrig*tf
        r=anomalyMetric([dataTestNorm[0],dataTestNorm[1],dataTestNorm[2],dataTestNorm[3],dataTestNorm[4]])
        res.append([tf,r])
        tf+=0.05

    print(res)
    ar=np.array((res))
    plt.rcParams.update({'font.size': 16})
    fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(14, 10), dpi=80, facecolor="w", edgecolor="k")
    axes.plot(ar[:,0],ar[:,1],label="normal train",linewidth=4)
    axes.set_xlabel("Threshold factor")
    axes.set_ylabel("F-Score")
    plt.grid()
    plt.show()

#plotFScore()
plotData3()