def __init__(self, path):
self.raw_data = Prediction_Data_validation(path)
self.dataTransform = dataTransformPredict()
self.dBOperation = dBOperation()
self.file_object = open("Prediction_Logs/Prediction_Log.txt", 'a+')
self.log_writer = logger.App_Logger()
def __init__(self):
self.log_writer = logger.App_Logger()
self.file_object = open("Training_Logs/ModelTrainingLog.txt",'a+')
def __init__(self,path):
self.raw_data = Raw_Data_validation(path)
self.dataTransform = dataTransform()
self.dBOperation = dBOperation()
self.file_object = open("Training_Logs/Training_Main_Log.txt", 'a+')
self.log_writer = logger.App_Logger()
def __init__(self):
self.file_object = 'wafer_log'
self.log_writer = logger.App_Logger()
self.pred_data_val = Prediction_Data_validation()
def __init__(self,path):
self.file_object = open("Prediction_Logs/Prediction_Log.txt", 'a+')
self.log_writer = logger.App_Logger()
self.pred_data_val = Prediction_Data_validation(path)
def __init__(self):
self.file_object = open("Prediction_Logs/Prediction_Log.txt", 'a+')
self.log_writer = logger.App_Logger()
def predictNewRouteClient():
try:
sqfeet = int(request.form['sqfeet'])
beds = int(request.form['beds'])
baths = float(request.form['baths'])
is_cats_allowed = request.form['cats_allowed']
if (is_cats_allowed == 'Yes'):
cats_allowed = 1
else:
cats_allowed = 0
is_smoking_allowed = request.form['smoking_allowed']
if (is_smoking_allowed == 'Yes'):
smoking_allowed = 1
else:
smoking_allowed = 0
is_wheelchair_access = request.form['wheelchair_access']
if (is_wheelchair_access == 'Yes'):
wheelchair_access = 1
else:
wheelchair_access = 0
is_electric_vehicle_charge = request.form['electric_vehicle_charge']
if (is_electric_vehicle_charge == 'Yes'):
electric_vehicle_charge = 1
else:
electric_vehicle_charge = 0
is_comes_furnished = request.form['comes_furnished']
if (is_comes_furnished == 'Yes'):
comes_furnished = 1
else:
comes_furnished = 0
lat = float(request.form['lat'])
long = float(request.form['long'])
is_parking_options = request.form['parking_options']
if (is_parking_options == 'carport'):
parking_options_1 = 1
parking_options_2 = 0
parking_options_3 = 0
parking_options_4 = 0
parking_options_5 = 0
parking_options_6 = 0
elif (is_parking_options == 'detached garage'):
parking_options_1 = 0
parking_options_2 = 1
parking_options_3 = 0
parking_options_4 = 0
parking_options_5 = 0
parking_options_6 = 0
elif (is_parking_options == 'no parking'):
parking_options_1 = 0
parking_options_2 = 0
parking_options_3 = 1
parking_options_4 = 0
parking_options_5 = 0
parking_options_6 = 0
elif (is_parking_options == 'off-street parking'):
parking_options_1 = 0
parking_options_2 = 0
parking_options_3 = 0
parking_options_4 = 1
parking_options_5 = 0
parking_options_6 = 0
elif (is_parking_options == 'street parking'):
parking_options_1 = 0
parking_options_2 = 0
parking_options_3 = 0
parking_options_4 = 0
parking_options_5 = 1
parking_options_6 = 0
elif (is_parking_options == 'valet parking'):
parking_options_1 = 0
parking_options_2 = 0
parking_options_3 = 0
parking_options_4 = 0
parking_options_5 = 0
parking_options_6 = 1
else:
parking_options_1 = 0
parking_options_2 = 0
parking_options_3 = 0
parking_options_4 = 0
parking_options_5 = 0
parking_options_6 = 0
is_laundry_options = request.form['laundry_options']
if (is_laundry_options == 'w/d in unit'):
laundry_options_1 = 0
laundry_options_2 = 0
laundry_options_3 = 0
laundry_options_4 = 1
elif (is_laundry_options == 'w/d hookups'):
laundry_options_1 = 0
laundry_options_2 = 0
laundry_options_3 = 1
laundry_options_4 = 0
elif (is_laundry_options == 'laundry on site'):
laundry_options_1 = 1
laundry_options_2 = 0
laundry_options_3 = 0
laundry_options_4 = 0
elif (is_laundry_options == 'no laundry on site'):
laundry_options_1 = 0
laundry_options_2 = 1
laundry_options_3 = 0
laundry_options_4 = 0
else:
laundry_options_1 = 0
laundry_options_2 = 0
laundry_options_3 = 0
laundry_options_4 = 0
filename = "models/KMeans/KMeans.sav"
loaded_model = pickle.load(open(
filename, 'rb')) # loading the model file from the storage
# predictions using the loaded model file
clusters = loaded_model.predict([[
sqfeet, beds, baths, cats_allowed, smoking_allowed,
wheelchair_access, electric_vehicle_charge, comes_furnished, lat,
long, laundry_options_1, laundry_options_2, laundry_options_3,
laundry_options_4, parking_options_1, parking_options_2,
parking_options_3, parking_options_4, parking_options_5,
parking_options_6
]])
file_object = open("Prediction_Logs/Prediction_Log_single.txt", 'a+')
log_writer = logger.App_Logger()
file_loader = file_methods.File_Operation(file_object, log_writer)
model_name = file_loader.find_correct_model_file(clusters[0])
model = file_loader.load_model(model_name)
scalar = StandardScaler()
X_scaled = scalar.fit_transform([[
sqfeet, beds, baths, cats_allowed, smoking_allowed,
wheelchair_access, electric_vehicle_charge, comes_furnished, lat,
long, laundry_options_1, laundry_options_2, laundry_options_3,
laundry_options_4, parking_options_1, parking_options_2,
parking_options_3, parking_options_4, parking_options_5,
parking_options_6
]])
result = model.predict(X_scaled)
log_writer.log(file_object, 'End of Prediction')
file_object.close()
return render_template(
'results.html',
prediction='Your House rent prediction is {} USD'.format(
round(result[0], 2)))
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 __init__(self):
self.logger = logger.App_Logger()
error_file = open("Preprocessing_log/preprocessing_error_log.txt",
'a+')
"""
Method Name: get_data
Description: This method reads the data from source.
Output: A pandas DataFrame.
On Failure: Raise Exception
Written By: Piyush
Version: 1.0
Revisions: None
"""
self.logger_object.log(self.file_object,'Entered the get_data method of the Data_Getter class')
try:
self.data= pd.read_csv(self.training_file) # reading the data file
self.data['Output'] = self.data['Output'].map({-1:0, 1:1})
self.logger_object.log(self.file_object,'Data Load Successful.Exited the get_data method of the Data_Getter class')
return self.data
except Exception as e:
self.logger_object.log(self.file_object,'Exception occured in get_data method of the Data_Getter class. Exception message: '+str(e))
self.logger_object.log(self.file_object,
'Data Load Unsuccessful.Exited the get_data method of the Data_Getter class')
raise Exception()
if __name__ == "__main__":
from application_logging import logger
log_writer = logger.App_Logger()
file_object = open("Training_Logs/ModelTrainingLog.txt", 'a+')
dataload = Data_Getter(file_object, log_writer)
data = dataload.get_data()
def __init__(self):
self.log_writer = logger.App_Logger()
#self.file_object = open(rootProjPath+"\\Training_Logs\\ModelTrainingLog.txt", 'a+')
self.file_object = open("Training_Logs/ModelTrainingLog.txt", 'a+')
def __init__(self):
self.raw_data = Raw_Data_validation() # done
self.dataTransform = dataTransform() # done
self.dBOperation = dBOperation() # may be not required
# self.file_object = open("Training_Logs/Training_Main_Log.txt", 'a+')
self.log_writer = logger.App_Logger()
def __init__(self, path):
self.Directory = path
self.schema_path = 'schema_training.json'
self.logger = logger.App_Logger()
