def batch_prediction_route_client():
"""
* method: batch_prediction_route_client
* description: method to call batch prediction route
* return: none
*
*
* Parameters
* None
"""
try:
config = Config()
# get run id
run_id = config.get_run_id()
data_path = config.prediction_data_path
# prediction object initialization
predictModel = PredictModel(run_id, data_path)
# prediction the model
predictModel.batch_predict_from_model()
return Response("Prediction successfull! and its RunID is : " +
str(run_id))
except ValueError:
return Response("Error Occurred! %s" % ValueError)
except KeyError:
return Response("Error Occurred! %s" % KeyError)
except Exception as e:
return Response("Error Occurred! %s" % e)
def single_prediction_route_client():
try:
config = Config()
#get run id
run_id = config.get_run_id()
data_path = config.prediction_data_path
print('Test')
if request.method == 'POST':
satisfaction_level = request.form['satisfaction_level']
last_evaluation = request.form["last_evaluation"]
number_project = request.form["number_project"]
average_montly_hours = request.form["average_montly_hours"]
time_spend_company = request.form["time_spend_company"]
work_accident = request.form["work_accident"]
promotion_last_5years = request.form["promotion_last_5years"]
salary = request.form["salary"]
data = pd.DataFrame(data=[[
0, satisfaction_level, last_evaluation, number_project,
average_montly_hours, time_spend_company, work_accident,
promotion_last_5years, salary
]],
columns=[
'empid', 'satisfaction_level',
'last_evaluation', 'number_project',
'average_montly_hours',
'time_spend_company', 'Work_accident',
'promotion_last_5years', 'salary'
])
# using dictionary to convert specific columns
convert_dict = {
'empid': int,
'satisfaction_level': float,
'last_evaluation': float,
'number_project': int,
'average_montly_hours': int,
'time_spend_company': int,
'Work_accident': int,
'promotion_last_5years': int,
'salary': object
}
data = data.astype(convert_dict)
# object initialization
predictModel = PredictModel(run_id, data_path)
# prediction the model
output = predictModel.single_predict_from_model(data)
print('output : ' + str(output))
return Response("Predicted Output is : " + str(output))
except ValueError:
return Response("Error Occurred! %s" % ValueError)
except KeyError:
return Response("Error Occurred! %s" % KeyError)
except Exception as e:
return Response("Error Occurred! %s" % e)
def batch_prediction_route_client():
try:
config = Config()
run_id = config.get_run_id()
data_path = config.prediction_data_path
predictModel = PredictModel(run_id, data_path)
predictModel.batch_predict_from_model()
return Response("Prediction sucessfull with RunID : " + str(run_id))
except ValueError:
return Response("Error Occured! %s" % ValueError)
except KeyError:
return Reponse("Error Occured! %s" % KeyError)
except Exception as e:
return Response("Error Occured! %s" % e)
def batch_prediction_route_client():
try:
config = Config()
#get run id
run_id = config.get_run_id()
data_path = config.prediction_data_path
#prediction object initialization
predictModel=PredictModel(run_id, data_path)
#prediction the model
predictModel.batch_predict_from_model()
return Response("Prediction successfull! and its RunID is : "+str(run_id))
except ValueError:
return Response("Error Occurred! %s" % ValueError)
except KeyError:
return Response("Error Occurred! %s" % KeyError)
except Exception as e:
return Response("Error Occurred! %s" % e)