Untitled
unknown
plain_text
24 days ago
1.4 kB
2
Indexable
Never
import preloop from sklearn.model_selection import train_test_split from sqlalchemy import create_engine import pandas as pd username = 'postgres' password = 'postgres' database = 'postgres' hostname = 'localhost:5432' table_name = 'phone' phone_datasource = f'postgresql://{username}:{password}@{hostname}/{database}' phone_datasource_preloop_object = Datasource(name='phone', connection_string=phone_datasource) preloop.create_datasource(phone_datasource_preloop_object) engine = create_engine(connection_string) query = 'SELECT battery_power, blue, clock_speed, dual_sim, fc, four_g, ram, price_range FROM ' + table_name data = pd.read_sql(query, engine) scaler_transformer = StandardScaler() minmax_transformer = MinMax() phone_feature_transform = scaler_transformer(data) phone_target_transform = minmax_transformer(data) phone_feature = data.drop('price_range', axis=1) if not isinstance(phone_feature, pandas.DataFrame): raise Exception('Feature must be a pandas dataframe') phone_feature_preloop_object = Feature(name=phone, transforms=['StandardScaler'], is_target=False) preloop.create_feature(phone_feature_preloop_object) phone_target = data['price_range'] if not isinstance(phone_target, pandas.DataFrame): raise Exception('Feature must be a pandas dataframe') phone_target_preloop_object = Feature(name=phone, transforms=['MinMax'], is_target=True) preloop.create_feature(phone_target_preloop_object)
Leave a Comment