def trainAllModel(self, modelName):
idforData = ''.join(choice(ascii_uppercase) for i in range(12))
saveStatus = logFolder + idforData + '/'
kerasUtilities.checkCreatePath(saveStatus)
statusfileLocation = saveStatus + 'status.txt'
pID = 0
tempRunMemory = {
'idforData': idforData,
'status': 'Execution Failed' if pID == -1 else 'In Progress',
'createdOn': str(datetime.datetime.now()),
'type': 'NNModel',
'pid': pID
}
tempRunMemory['taskName'] = modelName
RUNNING_TASK_MEMORY.append(tempRunMemory)
with open(statusfileLocation, 'w') as filetosave:
json.dump(tempRunMemory, filetosave)
import time
time.sleep(5)
trainVieClassObj = TrainingViewModels()
train_prc = Thread(target=trainVieClassObj.trainModel,
args=(
modelName,
statusfileLocation,
))
train_prc.start()
pID = train_prc.ident
tempRunMemory = {
'idforData': idforData,
'status': 'Execution Failed' if pID == -1 else 'In Progress',
'createdOn': str(datetime.datetime.now()),
'type': 'NNModel',
'pid': pID
}
tempRunMemory['taskName'] = modelName
RUNNING_TASK_MEMORY.append(tempRunMemory)
with open(statusfileLocation, 'w') as filetosave:
json.dump(tempRunMemory, filetosave)
return JsonResponse(tempRunMemory)
def predictFolderDataInBatch(self, pmmlstroragepointer, filpath, numFile):
from threading import Thread
idforData = ''.join(choice(ascii_uppercase) for i in range(12))
saveStatus = './logs/' + idforData + '/'
self.checkCreatePath(saveStatus)
statusfileLocation = saveStatus + 'status.txt'
with open(statusfileLocation, 'w') as filetosave:
json.dump({}, filetosave)
prc = Thread(target=self.predictFolderdata,
args=(pmmlstroragepointer, filpath, statusfileLocation))
prc.start()
pID = prc.ident
tempRunMemory = {
'idforData':
idforData,
'status':
'Scoring Failed' if pID == -1 else 'In Progress',
'createdOn':
str(datetime.datetime.now()),
'type':
'BatchScore',
'pid':
pID,
'newPMMLFileName':
pmmlstroragepointer,
'information': [{
'property': 'No of files to be scored',
'value': numFile
}]
}
RUNNING_TASK_MEMORY.append(tempRunMemory)
with open(statusfileLocation, 'w') as filetosave:
json.dump(tempRunMemory, filetosave)
return tempRunMemory
def trainNeuralNetworkModels(requests):
def getValueFromReq(keyVal, bodyVal):
# print ('requests',requests.body)
try:
# print (requests.POST.get(keyVal))
return bodyVal[keyVal]
except:
return ''
# pmmlFile=requests.POST.get('filePath')
bodyVal = json.loads(requests.body)
# print (bodyVal)
# print ('came heer 2nd',bodyVal)
pmmlFile = getValueFromReq('filePath', bodyVal)
tensorboardUrl = getValueFromReq('tensorboardUrl', bodyVal)
tensorboardLogFolder = getValueFromReq('tensorboardLogFolder', bodyVal)
hyperParaUser = {}
hyperParaUser['batchSize'] = getValueFromReq('batchSize', bodyVal)
hyperParaUser['optimizer'] = getValueFromReq('optimizer', bodyVal)
hyperParaUser['loss'] = getValueFromReq('loss', bodyVal)
hyperParaUser['metrics'] = getValueFromReq('metrics', bodyVal)
hyperParaUser['epoch'] = getValueFromReq('epoch', bodyVal)
hyperParaUser['problemType'] = getValueFromReq('problemType', bodyVal)
hyperParaUser['testSize'] = getValueFromReq('testSize', bodyVal)
hyperParaUser['learningRate'] = getValueFromReq(
'learningRate', bodyVal)
# hyperParaUser['']=getValueFromReq('',requests)
# hyperParaUser['']=getValueFromReq('',requests)
# print ('>>>>>>>>PPPPPPPPPPPPPPPP ',pmmlFile,tensorboardUrl,tensorboardLogFolder,hyperParaUser)
idforData = int(time.time())
idforData = str(idforData) + '_NN'
saveStatus = logFolder + idforData + '/'
kerasUtilities.checkCreatePath(saveStatus)
statusfileLocation = saveStatus + 'status.txt'
data_details = {}
data_details['tensorboardUrl'] = tensorboardUrl
data_details['idforData'] = idforData
data_details['status'] = 'In Progress'
fObjScrpt = pathlib.Path(pmmlFile)
print('fObjScrpt.name.replace(fObjScrpt.suffix,'
')', fObjScrpt.name.replace(fObjScrpt.suffix, ''))
data_details['taskName'] = fObjScrpt.name.replace(fObjScrpt.suffix, '')
data_details['createdOn'] = str(datetime.datetime.now())
data_details['type'] = 'NNProject'
data_details['problem_type'] = hyperParaUser['problemType']
data_details["newPMMLFileName"] = pmmlFile
nntrainer = mergeTrainingNN.NeuralNetworkModelTrainer()
pID = nntrainer.train(idforData, pmmlFile, tensorboardLogFolder,
hyperParaUser, pmmlFile)
data_details['pID'] = str(pID)
saveStatus = logFolder + idforData + '/'
kerasUtilities.checkCreatePath(saveStatus)
# statusfileLocation=saveStatus+'status.txt'
with open(statusfileLocation, 'w') as filetosave:
json.dump(data_details, filetosave)
if pID == -1:
# data_details['status']='In Progress'
kerasUtilities.updateStatusOfTraining(statusfileLocation,
'Training Failed')
else:
pass
runTemp = [i['idforData'] for i in RUNNING_TASK_MEMORY]
if data_details['idforData'] not in runTemp:
# print ('PPPPPPPPPPPPPPPPPPPP Saved to runningTask')
tempRunMemory = data_details
RUNNING_TASK_MEMORY.append(tempRunMemory)
else:
pass
print('P' * 200)
print('data_details', data_details)
return JsonResponse(data_details, status=202)
def trainMRCNN(userInput):
# userInput=requests.body
# userInput=json.loads(userInput)
# print (userInput)
pmmlFile = userInput['filePath']
try:
dataFolder = userInput['dataFolder']
except:
print('Get Data folder')
try:
tensorboardLogFolder = userInput['tensorboardLogFolder']
except:
tensorboardLogFolder = target_path = './logs/' + ''.join(
choice(ascii_uppercase) for i in range(12)) + '/'
# print ('tensorboardLogFolder',tensorboardLogFolder)
kerasUtilities.checkCreatePath(tensorboardLogFolder)
# batchSize=userInput['batchSize']
epoch = userInput['epoch']
stepsPerEpoch = userInput['stepPerEpoch']
# learningRate=userInput['learningRate']
try:
tensorboardUrl = userInput['tensorboardUrl']
except:
tensorboardUrl = ''
# idforData=pmmlFile.split('/')[-1].replace('.pmml','')
idforData = os.path.basename(pmmlFile).replace('.pmml', '') + '_MRCNN'
saveStatus = logFolder + idforData + '/'
kerasUtilities.checkCreatePath(saveStatus)
statusfileLocation = saveStatus + 'status.txt'
# print("status file generated")
data_details = {}
data_details['pmmlFile'] = idforData
data_details['dataFolder'] = dataFolder
data_details['fileName'] = pmmlFile
data_details['tensorboardLogFolder'] = tensorboardLogFolder
data_details['tensorboardUrl'] = tensorboardUrl
# data_details['batchSize']=batchSize
data_details['epoch'] = epoch
data_details['stepsPerEpoch'] = stepsPerEpoch
# data_details['learningRate']=learningRate
data_details['idforData'] = idforData
data_details['status'] = 'Building Architecture'
with open(statusfileLocation, 'w') as filetosave:
json.dump(data_details, filetosave)
objtrainer = trainMaskRCNN.ObjectDetetctionModels()
prc = Process(target=objtrainer.train, args=(pmmlFile,dataFolder,statusfileLocation,idforData,epoch,\
tensorboardLogFolder,stepsPerEpoch))
prc.start()
pID = prc.ident
data_details['pID'] = str(pID)
if pID == -1:
kerasUtilities.updateStatusOfTraining(statusfileLocation,
'Training Failed')
else:
with open(statusfileLocation, 'w') as filetosave:
json.dump(data_details, filetosave)
runTemp = [i['idforData'] for i in RUNNING_TASK_MEMORY]
if data_details['idforData'] not in runTemp:
# print ('PPPPPPPPPPPPPPPPPPPP Saved to runningTask')
tempRunMemory = {
'idforData': idforData,
'status': 'Training Failed' if pID == -1 else 'In Progress',
'createdOn': str(datetime.datetime.now()),
'type': 'ObjectDetectionProject',
'pid': pID,
'newPMMLFileName': idforData + '.pmml'
}
tempRunMemory['taskName'] = tempRunMemory['newPMMLFileName']
RUNNING_TASK_MEMORY.append(tempRunMemory)
else:
pass
return JsonResponse(data_details, status=202)
def autoAnomalyModel(userInput):
global DATA_MEMORY_OBJS_SKLEARN
# userInput=requests.body
# userInput=json.loads(userInput)
paramToTrainModel = userInput['data']
idforData = userInput['idforData']
data = DATA_MEMORY_OBJS_SKLEARN[idforData]
dataPath = userInput['filePath']
try:
targetVar = userInput['target_variable']
except:
targetVar = None
try:
problem_type = userInput['problem_type']
except:
problem_type = None
algorithms = userInput['parameters']['algorithm']
try:
newPMMLFileName = userInput['newPMMLFileName']
if not newPMMLFileName.endswith('.pmml'):
newPMMLFileName = newPMMLFileName + '.pmml'
except:
newPMMLFileName = idforData + '.pmml'
projectName = idforData
projectPath = logFolder + projectName
dataFolder = projectPath + '/dataFolder/'
try:
os.makedirs(projectPath)
os.mkdir(dataFolder)
except Exception as e:
print('>>>>>>>>>>>>>>>>', str(e))
autoMLLock = Lock()
trainer = trainAutoMLV2.AnomalyTrainer(algorithms=algorithms,
problemType=problem_type)
train_prc = Process(target=trainer.trainAnomalyModel,
args=(data, logFolder, newPMMLFileName, autoMLLock,
userInput))
# train_prc = Process(target=trainAutoMLV2.mainTrainAutoML,args=(data,paramToTrainModel,targetVar,idforData,problem_type,logFolder,newPMMLFileName))
train_prc.start()
pID = train_prc.ident
statusFile = dataFolder + 'status' + '.txt'
# sFileText=sFile.read()
# data_details=json.loads(sFileText)
data_details = {}
data_details['pID'] = str(pID)
data_details['status'] = 'In Progress'
data_details['newPMMLFileName'] = newPMMLFileName
data_details['targetVar'] = targetVar
data_details['problem_type'] = problem_type
data_details['idforData'] = idforData
data_details['shape'] = data.shape
import pathlib
fVar = pathlib.Path(dataPath)
data_details['taskName'] = fVar.name.replace(
fVar.suffix, '') #newPMMLFileName.split('/')[-1]
autoMLLock.acquire()
with open(statusFile, 'w') as filetosave:
json.dump(data_details, filetosave)
autoMLLock.release()
tempRunMemory = {
'idforData': projectName,
'status': 'In Progress',
'type': 'AutoMLProject',
'pid': pID,
'createdOn': str(datetime.datetime.now()),
'newPMMLFileName': newPMMLFileName.split('/')[-1]
}
tempRunMemory['taskName'] = data_details['taskName']
print('>>>>>>>>>>>>>>>>>>>>AutoML', tempRunMemory)
RUNNING_TASK_MEMORY.append(tempRunMemory)
# print ('RUNNING_TASK_MEMORY >>>>>>>>>',RUNNING_TASK_MEMORY)
return JsonResponse(data_details, status=202)
def autoMLtrainModel(userInput):
global DATA_MEMORY_OBJS_SKLEARN
# userInput=requests.body
# userInput=json.loads(userInput)
paramToTrainModel = userInput['data']
idforData = userInput['idforData']
# print (DATA_MEMORY_OBJS_SKLEARN)
data = DATA_MEMORY_OBJS_SKLEARN[idforData]
dataPath = userInput['filePath']
targetVar = userInput['target_variable']
problem_type = userInput['problem_type']
# algorithms=userInput['parameters']['algorithm']
try:
algorithms = userInput['parameters']['algorithm']
if algorithms[0] == 'All':
raise Exception("")
except:
if problem_type == 'Regression':
algorithms=['ExtraTreeRegressor','GradientBoostingRegressor','DecisionTreeRegressor','LinearSVR',\
'RandomForestRegressor','XGBRegressor','KNeighborsRegressor','LinearRegression','LGBMRegressor']
else:
algorithms=['DecisionTreeClassifier','ExtraTreesClassifier','RandomForestClassifier','GradientBoostingClassifier',\
'KNeighborsClassifier','LinearSVC','LogisticRegression','XGBClassifier','LGBMClassifier']
try:
newPMMLFileName = userInput['newPMMLFileName']
if not newPMMLFileName.endswith('.pmml'):
newPMMLFileName = newPMMLFileName + '.pmml'
except:
newPMMLFileName = idforData + '.pmml'
projectName = idforData
projectPath = logFolder + projectName
dataFolder = projectPath + '/dataFolder/'
tpotFolder = projectPath + '/tpotFolder/'
try:
os.makedirs(projectPath)
os.mkdir(dataFolder)
os.mkdir(tpotFolder)
except Exception as e:
print('>>>>>>>>>>>>>>>>', str(e))
autoMLLock = Lock()
trainer = trainAutoMLV2.AutoMLTrainer(algorithms=algorithms,
problemType=problem_type)
train_prc = Process(target=trainer.trainModel,
args=(data, logFolder, newPMMLFileName, autoMLLock,
userInput))
# train_prc = Process(target=trainAutoMLV2.mainTrainAutoML,args=(data,paramToTrainModel,targetVar,idforData,problem_type,logFolder,newPMMLFileName))
train_prc.start()
pID = train_prc.ident
statusFile = dataFolder + 'status' + '.txt'
# sFileText=sFile.read()
# data_details=json.loads(sFileText)
data_details = {}
data_details['pID'] = str(pID)
data_details['status'] = 'In Progress'
data_details['newPMMLFileName'] = newPMMLFileName
data_details['targetVar'] = targetVar
data_details['problem_type'] = problem_type
data_details['idforData'] = idforData
data_details['shape'] = data.shape
import pathlib
fVar = pathlib.Path(dataPath)
data_details['taskName'] = fVar.name.replace(
fVar.suffix, '') #newPMMLFileName.split('/')[-1]
autoMLLock.acquire()
with open(statusFile, 'w') as filetosave:
json.dump(data_details, filetosave)
autoMLLock.release()
tempRunMemory = {
'idforData': projectName,
'status': 'In Progress',
'type': 'AutoMLProject',
'pid': pID,
'createdOn': str(datetime.datetime.now()),
'newPMMLFileName': newPMMLFileName.split('/')[-1]
}
tempRunMemory['taskName'] = data_details['taskName']
# print ('>>>>>>>>>>>>>>>>>>>>AutoML',tempRunMemory)
RUNNING_TASK_MEMORY.append(tempRunMemory)
# print ('RUNNING_TASK_MEMORY >>>>>>>>>',RUNNING_TASK_MEMORY)
return JsonResponse(data_details, status=202)
def executeCode(filePath, params):
def updateStatusOfExecution(filePath, updatedStatus, info_dict):
with open(filePath, 'r') as sFile:
sFileText = sFile.read()
data_details = json.loads(sFileText)
data_details['status'] = updatedStatus
data_details['information'] = []
for key, val in info_dict.items():
data_details['information'].append({
'property': key,
'value': val
})
if updatedStatus == 'Complete':
data_details['completedOn'] = str(datetime.datetime.now())
with open(filePath, 'w') as filetosave:
json.dump(data_details, filetosave)
return 'Success'
def monitorThread(filePath, args, statusfileLocation):
print(args)
args = [str(a) for a in args]
popen = subprocess.Popen([sys.executable, filePath] + args,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
output, error = popen.communicate()
if output:
output = output.decode('utf-8')
info = {'Output': output}
updateStatusOfExecution(statusfileLocation,
'Complete',
info_dict=info)
if error:
error = error.decode('utf-8')
info = {'Error': error}
updateStatusOfExecution(statusfileLocation,
'Execution Failed',
info_dict=info)
idforData = ''.join(choice(ascii_uppercase) for i in range(12))
saveStatus = logFolder + idforData + '/'
kerasUtilities.checkCreatePath(saveStatus)
statusfileLocation = saveStatus + 'status.txt'
with open(statusfileLocation, 'w') as filetosave:
json.dump({}, filetosave)
# print ('>>>>>>>>>>>>>>>>',params)
import threading
pp = threading.Thread(target=monitorThread,
args=(filePath, params, statusfileLocation))
pp.start()
pID = pp.ident
import pathlib
fullPath = pathlib.Path(filePath)
fileName = fullPath.name.replace('.py', '')
tempRunMemory = {
'idforData': idforData,
'status': 'Execution Failed' if pID == -1 else 'In Progress',
'createdOn': str(datetime.datetime.now()),
'type': 'Code',
'pid': pID,
'newPMMLFileName': fileName.split('/')[-1],
'information': [{
'property': 'Parameters',
'value': params
}]
}
tempRunMemory['taskName'] = tempRunMemory['newPMMLFileName']
RUNNING_TASK_MEMORY.append(tempRunMemory)
with open(statusfileLocation, 'w') as filetosave:
json.dump(tempRunMemory, filetosave)
return JsonResponse(tempRunMemory, status=200)
def trainNeuralNetworkModels(userInput):
# userInput=requests.body
# userInput=json.loads(userInput)
# print (userInput)
pmmlFile = userInput['filePath']
try:
dataFolder = userInput['dataFolder']
except:
dataFolder = './logs/' + ''.join(
choice(ascii_uppercase) for i in range(12)) + '/'
kerasUtilities.checkCreatePath(dataFolder)
try:
fileName = userInput['filePath']
except:
fileName = userInput['filePath']
try:
tensorboardLogFolder = userInput['tensorboardLogFolder']
print('Log folder came correct')
except:
print('Log folder has some issue')
tensorboardLogFolder = target_path = './logs/' + ''.join(
choice(ascii_uppercase) for i in range(12)) + '/'
# print ('tensorboardLogFolder',tensorboardLogFolder)
kerasUtilities.checkCreatePath(tensorboardLogFolder)
lossType = userInput['loss']
listOfMetrics = userInput['metrics']
batchSize = userInput['batchSize']
epoch = userInput['epoch']
stepsPerEpoch = userInput['stepPerEpoch']
problemType = userInput['problemType']
testSize = userInput['testSize']
scriptOutput = userInput['scriptOutput']
optimizerName = userInput['optimizer']
learningRate = userInput['learningRate']
try:
tensorboardUrl = userInput['tensorboardUrl']
except:
tensorboardUrl = ''
# idforData=pmmlFile.split('/')[-1].replace('.pmml','')
idforData = os.path.basename(pmmlFile).replace('.pmml', '')
saveStatus = logFolder + idforData + '/'
kerasUtilities.checkCreatePath(saveStatus)
statusfileLocation = saveStatus + 'status.txt'
# print("status file generated")
data_details = {}
data_details['pmmlFile'] = idforData
data_details['dataFolder'] = dataFolder
data_details['fileName'] = fileName
data_details['tensorboardLogFolder'] = tensorboardLogFolder
data_details['tensorboardUrl'] = tensorboardUrl
data_details['lossType'] = lossType
data_details['listOfMetrics'] = listOfMetrics
data_details['batchSize'] = batchSize
data_details['epoch'] = epoch
data_details['stepsPerEpoch'] = stepsPerEpoch
data_details['problemType'] = problemType
data_details['testSize'] = testSize
data_details['scriptOutput'] = scriptOutput
data_details['optimizerName'] = optimizerName
data_details['learningRate'] = learningRate
data_details['idforData'] = idforData
data_details['status'] = 'In Progress'
fObjScrpt = pathlib.Path(fileName)
data_details['taskName'] = fObjScrpt.name
with open(statusfileLocation, 'w') as filetosave:
json.dump(data_details, filetosave)
nntrainer = mergeTrainingNN.NeuralNetworkModelTrainer()
pID = nntrainer.train(pmmlFile,dataFolder,fileName,tensorboardLogFolder,lossType,listOfMetrics,batchSize,\
epoch,stepsPerEpoch,idforData,testSize,problemType,scriptOutput,optimizerName,learningRate)
data_details['pID'] = str(pID)
saveStatus = logFolder + idforData + '/'
kerasUtilities.checkCreatePath(saveStatus)
statusfileLocation = saveStatus + 'status.txt'
if pID == -1:
kerasUtilities.updateStatusOfTraining(statusfileLocation,
'Training Failed')
else:
with open(statusfileLocation, 'w') as filetosave:
json.dump(data_details, filetosave)
runTemp = [i['idforData'] for i in RUNNING_TASK_MEMORY]
if data_details['idforData'] not in runTemp:
# print ('PPPPPPPPPPPPPPPPPPPP Saved to runningTask')
tempRunMemory = {
'idforData': idforData,
'status': 'Training Failed' if pID == -1 else 'In Progress',
'createdOn': str(datetime.datetime.now()),
'type': 'NNProject',
'pid': pID,
'newPMMLFileName': fileName.split('/')[-1]
}
tempRunMemory['taskName'] = data_details['taskName']
RUNNING_TASK_MEMORY.append(tempRunMemory)
else:
pass
return JsonResponse(data_details, status=202)