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