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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)