def load_saved_attributes():
global model
model = XGBRegressor()
booster = Booster()
booster.load_model('./ny_taxi_fare')
model._Booster = booster
def load_saved_attributes():
global host_response_time_values
global neighbourhood_values
global property_type_values
global room_type_values
global cancellation_policy_values
global model
with open("columns.json", "r") as f:
resp = json.load(f)
host_response_time_values = resp["host_response_time"]
neighbourhood_values = resp["neighbourhood"]
property_type_values = resp["property_type"]
room_type_values = resp["room_type"]
cancellation_policy_values = resp["cancellation_policy"]
model = XGBRegressor()
booster = Booster()
booster.load_model('airbnb_price_predictor')
model._Booster = booster
def sklearn_regressor(booster, params, num_round):
reg = XGBRegressor(n_estimators=num_round, missing=-999,
**_complete_params(params))
reg._Booster = booster
return reg
