def run(self, period, mlName):
print("starting prediction for "+period+" days"+ " with "+mlName)
dataFrame = getDatafromDb(period)
print(dataFrame)
myframe = dataFrame.copy()
dataFrame = processDbDataForLrTestInput(dataFrame)
X_test, y_test = prepareData(dataFrame[['CpuUsage']], lag_start=3, lag_end=25)
print("y_test")
print(y_test)
#print(y_test.timebucket)
if(mlName == "LR"):
predictedData = linearRegression(X_test)
elif(mlName == "RF"):
predictedData = randomForest(X_test)
elif (mlName == "ARIMA"):
dataFrame = getDatafromDb(-1)
dataFrame = processDbDataForLrTestInput(dataFrame)
X_train, y_train = prepareData(dataFrame[['CpuUsage']], lag_start=3, lag_end=25)
history = [x for x in y_train]
predictedData = arimaPrediction(y_test, history)
else:
print("Invalid Model"+ mlName)
return
# write into promql
writeDataToDb(mlName, myframe['timebucket'], myframe['CpuUsage'], predictedData)
print("predicted")
print(predictedData)
print(mlName + " Model completed prediction for "+ period)
def buildRandomForestMNodel():
dataFrame = getDatafromDb(-1)
dataFrame = processDbDataForLrTestInput(dataFrame)
X_train, y_train = prepareData(dataFrame[['CpuUsage']],
lag_start=3,
lag_end=25)
model = RandomForestRegressor()
model.fit(X_train, y_train)
return model
def buildLinearRegressionModel():
dataFrame = getDatafromDb(-1)
dataFrame = processDbDataForLrTestInput(dataFrame)
X_train, y_train = prepareData(dataFrame[['CpuUsage']],
lag_start=3,
lag_end=25)
model = LinearRegression()
model.fit(X_train, y_train)
return model
def buildArimaModel():
dataFrame = getDatafromDb(-1)
dataFrame = processDbDataForLrTestInput(dataFrame)
X_train, y_train = prepareData(dataFrame[['CpuUsage']],
lag_start=3,
lag_end=25)
history = [x for x in y_train]
model = ARIMA(history, order=(5, 1, 0))
#for t in range(len(y_train)):
# model = ARIMA(history, order=(5, 1, 0))
# model_fit = model.fit(disp=0)
# output = model_fit.forecast()
# yhat = output[0]
# predictions.append(yhat[0])
# obs = y_train[t]
# history.append(obs)
# print('predicted=%f, expected=%f' % (yhat, obs))
return model
p.start() # Start the execution
def run(self, mlName):
print("Building Model ", mlName)
if(mlName == "LR"):
model = buildLinearRegressionModel()
name = settings.ML_LR_MODEL
elif(mlName == "RF"):
model = buildRandomForestMNodel()
name = settings.ML_RF_MODEL
elif (mlName == "ARIMA"):
model = buildArimaModel()
name = settings.ML_ARIMA_MODEL
else:
print("Invalid Building Model", mlName, "completed")
return
saveModel(model, name)
print("Building Model", mlName, "completed")
if __name__ == "__main__":
dataFrame = getDatafromDb(1)
dataFrame = processDbDataForLrTestInput(dataFrame)
print(dataFrame)
X_test, y_test = prepareData(dataFrame[['CpuUsage']], lag_start=3, lag_end=25)
predictedData = linearRegression(X_test)
print(y_test)
print(predictedData)
error = mean_absolute_percentage_error(predictedData, y_test)
print("Mean Absolute Error: {0:.2f}%".format(error))