def test_07_scoreCsvDataWithSKL(self):
logging.info("Test Case : Score csv data with SKL model.")
filePath = os.path.abspath('testUseCase/supportdata/irisSKL.pmml')
result = Scoring.loadModelfile(filePath)
modelName = 'irisSKL'
filePath = os.path.abspath('testUseCase/supportdata/iris_test.csv')
result = Scoring.predicttestdata(filePath, modelName)
result = json.loads(result.__dict__['_container'][0])
self.assertEqual('result' in result, True)
self.assertEqual(result['result'].endswith('.csv'), True)
logging.info("PASSED")
def test_09_scoreJsonDataWithNN(self):
logging.info("Test Case : Score json data with NN model.")
filePath = os.path.abspath('testUseCase/supportdata/irisNN.pmml')
result = Scoring.loadModelfile(filePath)
modelName = 'irisNN'
filePath = os.path.abspath('testUseCase/supportdata/iris_test.json')
result = Scoring.predicttestdata(filePath, modelName)
result = json.loads(result.__dict__['_container'][0])
self.assertEqual('result' in result, True)
self.assertEqual(result['result'].endswith('.txt'), True)
logging.info("PASSED")
def test_04_unloadModelForError(self):
logging.info("Test Case : Remove a loaded model from memory. (1)")
modelName = 'id'
result = Scoring.removeModelfromMemory(modelName)
self.assertEqual(result.status_code, 500)
self.assertEqual(
'message' in json.loads(result.__dict__['_container'][0]), True)
self.assertEqual(
json.loads(result.__dict__['_container'][0])['message'],
'Not able to locate, make sure the model was loaded')
result = Scoring.getListOfModelinMemory()
self.assertEqual(len(json.loads(result.__dict__['_container'][0])), 1)
logging.info("PASSED")
def test_03_loadModelForError(self):
logging.info("Test Case : Load a model into memory. (2)")
filePath = os.path.abspath('testUseCase/supportdata/errorPmml.pmml')
result = Scoring.loadModelfile(filePath)
self.assertEqual(result.status_code, 500)
self.assertEqual(
json.loads(result.__dict__['_container'][0])['keytoModel'], None)
self.assertEqual(
json.loads(result.__dict__['_container'][0])['message'],
'Model loading failed, please contact Admin')
result = Scoring.getListOfModelinMemory()
self.assertEqual(len(json.loads(result.__dict__['_container'][0])), 1)
logging.info("PASSED")
def test_05_unloadModelForCorrectness(self):
logging.info("Test Case : Remove a loaded model from memory. (2)")
modelName = 'NewTrialModel'
result = Scoring.removeModelfromMemory(modelName)
self.assertEqual(result.status_code, 200)
self.assertEqual(
'message' in json.loads(result.__dict__['_container'][0]), True)
self.assertEqual(
json.loads(result.__dict__['_container'][0])['message'],
'Model unloaded successfully, now it will not be available for predictions.'
)
result = Scoring.getListOfModelinMemory()
self.assertEqual(len(json.loads(result.__dict__['_container'][0])), 0)
logging.info("PASSED")
def test_11_scoreSignleRecordWithSKL(self):
logging.info("Test Case : Score single record with SKL model.")
filePath = os.path.abspath('testUseCase/supportdata/irisSKL.pmml')
result = Scoring.loadModelfile(filePath)
modelName = 'irisSKL'
data = json.loads(
'{"sepal_length":4,"sepal_width":5,"petal_length":3,"petal_width":5}'
)
result = Scoring.predicttestdata(None, modelName, data)
result = json.loads(result.__dict__['_container'][0])
self.assertEqual('result' in result, True)
self.assertEqual(result['result'].endswith('.txt'), True)
logging.info("PASSED")
def test_02_loadModelForCorrectness(self):
logging.info("Test Case : Load a model into memory. (1)")
filePath = os.path.abspath(
'testUseCase/supportdata/NewTrialModel.pmml')
result = Scoring.loadModelfile(filePath)
self.assertEqual(result.status_code, 200)
self.assertEqual(
json.loads(result.__dict__['_container'][0])['keytoModel'],
'NewTrialModel')
self.assertEqual(
json.loads(result.__dict__['_container'][0])['message'],
'Model loaded successfully')
result = Scoring.getListOfModelinMemory()
self.assertEqual(len(json.loads(result.__dict__['_container'][0])), 1)
logging.info("PASSED")
def post(self,requests,modelName):
try:
filePath=requests.POST.get('filePath')
if not filePath:
raise Exception("Invalid Request Parameter")
except:
return JsonResponse({'error':'Invalid Request Parameter'},status=400)
return Scoring.predicttestdata(filePath,modelName)
def get(self,requests,modelName):
try:
jsonData = json.loads(requests.GET['jsonRecord'])
if not jsonData:
raise Exception("Invalid Request Parameter")
except:
return JsonResponse({'error':'Invalid Request Parameter'},status=400)
return Scoring.predicttestdata(None,modelName,jsonData)
def post(self,requests):
try:
filePath=requests.POST.get('filePath')
idfordata=requests.POST.get('idforData')
if not filePath:
raise Exception("Invalid Request Parameter")
except:
return JsonResponse({'error':'Invalid Request Parameter'},status=400)
print('filpath >>>>>>>>>>>>>>>> ',filePath)
return Scoring.loadModelfile(filePath,idfordata)
def test_02_loadModelForCorrectness(self):
logging.info("Test Case : Load a model into memory. (1)")
filePath = os.path.abspath('testUseCase/supportData2/mpgSKModel.pmml')
# result = Scoring.loadModelfile(filePath)
result = NewModelOperations().loadExecutionModel(filePath)
self.assertEqual(result.status_code, 200)
self.assertEqual(
json.loads(result.__dict__['_container'][0])['keytoModel'],
'mpgSKModel')
self.assertEqual(
json.loads(result.__dict__['_container'][0])['message'],
'Model Loaded Successfully')
result = Scoring.getListOfModelinMemory()
# print ('>>>>>>','result >>>>>>>>>',result.__dict__['_container'])
self.assertEqual(len(json.loads(result.__dict__['_container'][0])), 1)
logging.info("PASSED")
def delete(self, requests, modelName):
print('>>>>>>>>>>>>>>', modelName)
return Scoring().removeModelfromMemory(modelName)
def get(self, requests):
return Scoring.getListOfModelinMemory()
def test_01_listOfLoadedModels(self):
logging.info("Test Case : Get list of loaded models.")
result = Scoring.getListOfModelinMemory()
self.assertEqual(len(json.loads(result.__dict__['_container'][0])), 0)
logging.info("PASSED")