def test_6_trainNNModel(self):
logging.info("Test Case : Train NN model.")
filePath = 'testUseCase/supportdata/irisNN.pmml'
logFolder = 'testUseCase/supportdata/logs'
payload = {
"batchSize": 15,
"epoch": 10,
"stepPerEpoch": 10,
"learningRate": 0.001,
"loss": "categorical_crossentropy",
"metrics": ["accuracy"],
"optimizer": "Adam",
"testSize": 0.3,
"scriptOutput": "NA",
"problemType": "classification",
"filePath": os.path.abspath(filePath),
"tensorboardLogFolder": os.path.abspath(logFolder),
"tensorboardUrl": '',
'dataFolder': ''
}
result = Training.trainNeuralNetworkModels(payload)
result = json.loads(result.__dict__['_container'][0])
self.assertEqual(result['pmmlFile'],
filePath.split('/')[-1].replace('.pmml', ''))
self.assertEqual(result['idforData'],
filePath.split('/')[-1].replace('.pmml', ''))
self.assertEqual(result['status'], 'In Progress')
self.assertEqual('pID' in result, True)
Utility.deleteTaskfromMemory(result['idforData'])
logging.info("PASSED")
def get(self, requests):
try:
filePath = requests.GET['filePath']
if not filePath:
raise Exception("Invalid Request Parameter")
except:
return JsonResponse({'error': 'Invalid Request Parameter'},
status=400)
return Utility.downloadPMML(filePath)
def test_1_runningTasksList(self):
logging.info("Test Case : Get all running tasks.")
result = Utility.runningTaskList()
self.assertEqual(result.status_code, 200)
self.assertEqual(
'runningTask' in json.loads(result.__dict__['_container'][0]),
True)
self.assertEqual(
len(json.loads(result.__dict__['_container'][0])['runningTask']),
0)
logging.info("PASSED")
def post(self, requests):
try:
oldFile = requests.POST.get('oldFilePath')
if not oldFile:
raise Exception("Invalid Request Parameter")
except:
return JsonResponse({'error': 'Invalid Request Parameter'},
status=400)
try:
newFile = requests.POST.get('newFilePath')
except:
newFile = None
return Utility.convertZMKtoZS(oldFile, newFile)
def test_3_deleteARunningTask(self):
logging.info("Test Case : Deleting a running tasks.")
idforData = 'id'
result = Utility.deleteTaskfromMemory(idforData)
self.assertEqual(
'idforData' in json.loads(result.__dict__['_container'][0]), True)
self.assertEqual(
'message' in json.loads(result.__dict__['_container'][0]), True)
self.assertEqual(
json.loads(result.__dict__['_container'][0])['idforData'], 'id')
self.assertEqual(
json.loads(result.__dict__['_container'][0])['message'],
'Something went wrong. Please contact Admin')
logging.info("PASSED")
def test_3_downloadPMMLFile(self):
logging.info("Test Case : Test download file for pmml file.")
with open('testUseCase/supportdata/test.pmml', 'w') as ff:
ff.write('<?xml version="1.0" encoding="UTF-8"?>')
filePath = os.path.abspath('testUseCase/supportdata/test.pmml')
result = Utility.downloadPMML(filePath)
self.assertEqual(result.status_code, 200)
self.assertEqual(result.__dict__['_container'][0].decode('utf-8'),
'<?xml version="1.0" encoding="UTF-8"?>')
self.assertEqual(result.__dict__['_headers']['content-type'][1],
'application/xml')
os.remove('testUseCase/supportdata/test.pmml')
logging.info("PASSED")
def test_2_downloadCSVFile(self):
logging.info("Test Case : Test download file for csv file.")
with open('testUseCase/supportdata/test.csv', 'w') as ff:
ff.write('This,is,a,Test,File')
filePath = os.path.abspath('testUseCase/supportdata/test.csv')
result = Utility.downloadPMML(filePath)
self.assertEqual(result.status_code, 200)
self.assertEqual(result.__dict__['_container'][0].decode('utf-8'),
'This,is,a,Test,File')
self.assertEqual(result.__dict__['_headers']['content-type'][1],
'text/csv')
os.remove('testUseCase/supportdata/test.csv')
logging.info("PASSED")
def test_1_downloadJSONFile(self):
logging.info("Test Case : Test download file for json file.")
content = {'content': 'This is a Test File'}
with open('testUseCase/supportdata/test.json', 'w') as ff:
ff.write(json.dumps(content))
filePath = os.path.abspath('testUseCase/supportdata/test.json')
result = Utility.downloadPMML(filePath)
self.assertEqual(result.status_code, 200)
self.assertEqual(
json.loads(result.__dict__['_container'][0])['content'],
'This is a Test File')
self.assertEqual(result.__dict__['_headers']['content-type'][1],
'application/json')
os.remove('testUseCase/supportdata/test.json')
logging.info("PASSED")
def test_4_CodeExecution(self):
logging.info("Test Case : Test Code execution.")
filePath = os.path.abspath(
'testUseCase/supportdata/testScriptSimple.py')
result = CodeUtilityClass.executeCode(filePath, [])
import json
self.assertEqual(
json.loads(result.__dict__['_container'][0])['status'],
"In Progress")
self.assertEqual(result.status_code, 200)
import time
time.sleep(3)
idForCheck = json.loads(result.__dict__['_container'][0])['idforData']
result2 = Utility().runningTaskList()
jLoad = json.loads(result2.__dict__['_container'][0])['runningTask']
# print(jLoad)#=result2.__dict__['_container']
for en in jLoad:
if en['idforData'] == idForCheck:
checkval = True
break
self.assertEqual(checkval, True)
logging.info("PASSED")
def delete(self, requests, id_for_task):
return Utility.deleteTaskfromMemory(id_for_task)
def get(self, requests):
return Utility().runningTaskList()
def get(self, requests, taskName, idForData):
return Utility().taskUpdateByTaskNameIdForData(taskName, idForData)
def get(self, requests, taskName):
return Utility().taskUpdateByTaskName(taskName)
def test_5_AutoMLTrain(self):
logging.info(
"Test Case : Perform preprocessing and train AutoML model.")
filePath = os.path.abspath(
'testUseCase/supportdata/mpg_data_example2.csv')
result = Training.autoMLdataprocess(filePath)
tempa = json.loads(result.__dict__['_container'][0])
newPMMLFileName = 'xyz.pmml'
target_variable = 'mpg'
true = True
false = False
dataPreprocessingsteps = {
"data": [{
"position": 1,
"variable": "mpg",
"dtype": "float64",
"missing_val": 0,
"changedataType": "Continuous",
"imputation_method": "None",
"data_transformation_step": "None",
"use_for_model": true
}, {
"position": 2,
"variable": "cylinders",
"dtype": "int64",
"missing_val": 0,
"changedataType": "Continuous",
"imputation_method": "None",
"data_transformation_step": "None",
"use_for_model": true
}, {
"position": 3,
"variable": "displacement",
"dtype": "float64",
"missing_val": 0,
"changedataType": "Continuous",
"imputation_method": "None",
"data_transformation_step": "None",
"use_for_model": true
}, {
"position": 4,
"variable": "horsepower",
"dtype": "float64",
"missing_val": 6,
"changedataType": "Continuous",
"imputation_method": "Mean",
"data_transformation_step": "None",
"use_for_model": true
}, {
"position": 5,
"variable": "weight",
"dtype": "int64",
"missing_val": 0,
"changedataType": "Continuous",
"imputation_method": "None",
"data_transformation_step": "None",
"use_for_model": true
}, {
"position": 6,
"variable": "acceleration",
"dtype": "float64",
"missing_val": 0,
"changedataType": "Continuous",
"imputation_method": "None",
"data_transformation_step": "None",
"use_for_model": true
}, {
"position": 7,
"variable": "model year",
"dtype": "int64",
"missing_val": 0,
"changedataType": "Categorical",
"imputation_method": "None",
"data_transformation_step": "None",
"use_for_model": true
}, {
"position": 8,
"variable": "origin",
"dtype": "int64",
"missing_val": 0,
"changedataType": "Categorical",
"imputation_method": "None",
"data_transformation_step": "One Hot Encoding",
"use_for_model": true
}, {
"position": 9,
"variable": "car name",
"dtype": "object",
"missing_val": 0,
"changedataType": "Categorical",
"imputation_method": "None",
"data_transformation_step": "None",
"use_for_model": false
}],
"problem_type":
"Regression",
"target_variable":
target_variable,
"idforData":
tempa['idforData'],
'newPMMLFileName':
newPMMLFileName,
'filePath':
filePath,
"parameters": []
}
result2 = Training.autoMLtrainModel(dataPreprocessingsteps)
result2 = json.loads(result2.__dict__['_container'][0])
self.assertEqual('pID' in result2, True)
self.assertEqual('status' in result2, True)
self.assertEqual('newPMMLFileName' in result2, True)
self.assertEqual('targetVar' in result2, True)
self.assertEqual('problem_type' in result2, True)
self.assertEqual('idforData' in result2, True)
self.assertEqual(result2['status'], 'In Progress')
self.assertEqual(result2['newPMMLFileName'], newPMMLFileName)
self.assertEqual(result2['targetVar'], target_variable)
self.assertEqual(result2['idforData'], tempa['idforData'])
result = Utility.runningTaskList()
self.assertEqual(result.status_code, 200)
self.assertEqual(
'runningTask' in json.loads(result.__dict__['_container'][0]),
True)
self.assertEqual(
len(json.loads(result.__dict__['_container'][0])['runningTask']),
1)
idforData = tempa['idforData']
result = Training.statusOfModel(idforData)
self.assertEqual(result.status_code, 200)
result = json.loads(result.__dict__['_container'][0])
self.assertEqual('pID' in result, True)
self.assertEqual('status' in result, True)
self.assertEqual('idforData' in result, True)
idforData = tempa['idforData']
result = Utility.deleteTaskfromMemory(idforData)
self.assertEqual(
'idforData' in json.loads(result.__dict__['_container'][0]), True)
self.assertEqual(
'message' in json.loads(result.__dict__['_container'][0]), True)
self.assertEqual(
json.loads(result.__dict__['_container'][0])['idforData'],
tempa['idforData'])
self.assertEqual(
json.loads(result.__dict__['_container'][0])['message'],
'Deleted successfully')
result = Utility.runningTaskList()
self.assertEqual(result.status_code, 200)
self.assertEqual(
'runningTask' in json.loads(result.__dict__['_container'][0]),
True)
self.assertEqual(
len(json.loads(result.__dict__['_container'][0])['runningTask']),
0)
logging.info("PASSED")
