feature_engineering.py

import pandas as pd
import numpy as np

# df = pd.read_csv('data/churn_train.csv')

def build_y(df, delta_days='30 days'):
    today = df['last_trip_date'].max()
    delta = pd.Timedelta(delta_days)
    df['churn?'] = (df['last_trip_date'] < (today-delta)) *1
    y = df['churn?']
    return y

def fill_cont_nans(df, col_list=['avg_rating_by_driver', 'avg_rating_of_driver'], grouper=['city', 'luxury_car_user']):
    for col in col_list:
        df[col].fillna(df.groupby(grouper)[col].transform('median'), inplace=True)
    return df

# def fill_categ_nans(df, col_list=['phone']):
#     for col in col_list:
#         # value = df[col].mode().values.flatten()
#         # # print(value[0])
#         # df[col] = df[col].fillna(value[0], inplace=True)
#         df[col] = df[col].fillna('iPhone', inplace=True)
#     return df

def fill_categ_nans(df, col_list=['phone']):
    for col in col_list:
        value = df[col].mode().values.flatten()
        # df.loc[df['phone'].isnull(), 'phone'] = value[0]
        df.loc[df[col].isnull(), col] = value[0]
    return df


def dummify(df, col_list=['city', 'phone', 'luxury_car_user']):
    for col in col_list:
        dummies = pd.get_dummies(df[col],prefix=col)
        df[dummies.columns] = dummies
    return df

def logify(df, col_list=['avg_dist', 'avg_rating_by_driver', 'avg_rating_of_driver']):
    for col in col_list:
        df[col+'_log'] = np.log(df[col]+1)
    return df

def feature_creation(df, delta_days):
    # Create user lifespan
    today = df['last_trip_date'].max()
    delta = pd.Timedelta(delta_days)
    df['user_lifespan'] = ((today-delta)-df['signup_date']).dt.days
    # Create dummy for if a user rated a driver or not
    df['user_rated_driver'] = (df['avg_rating_of_driver'].isnull() == 0) *1
    return df

def interactify(df, interacter1=['user_rated_driver'], interacter2=['avg_rating_of_driver']):
    # print(type(df["user_rated_driver"]))
    for col1, col2 in zip(interacter1, interacter2):
        df[col1+'_'+col2] = df[col1] * df[col2]
    return df

# def feature_engineering(df):
#     data = fill_cont_nans(df, ['avg_rating_by_driver', 'avg_rating_of_driver'], ['city', 'luxury_car_user'])
#     data = fill_categ_nans(data, ['phone'])
#     # print(df.phone.value_counts())
#     data = dummify(data, ['city', 'phone', 'luxury_car_user'])
#     # print(df.columns)
#     logify(df, ['avg_dist', 'avg_rating_by_driver', 'avg_rating_of_driver'])
#     data = feature_creation(data, '30 days')
#     data = interactify(data, interacter1=['user_rated_driver'], interacter2=['avg_rating_of_driver'])
#     # print(df.columns)
#     return df

def build_X(df, model='GradientBoostingRegressor', delta_days='30 days'):
    data = fill_cont_nans(df, ['avg_rating_by_driver', 'avg_rating_of_driver'], ['city', 'luxury_car_user'])
    data = fill_categ_nans(data, ['phone'])
    # print(df.phone.value_counts())
    data = dummify(data, ['city', 'phone', 'luxury_car_user'])
    # print(df.columns)
    logify(df, ['avg_dist', 'avg_rating_by_driver', 'avg_rating_of_driver'])
    data = feature_creation(data, delta_days)
    data = interactify(data, interacter1=['user_rated_driver'], interacter2=['avg_rating_of_driver'])
    
    cols_to_keep = ['avg_dist_log', 'avg_rating_by_driver_log', 'avg_rating_of_driver_log', 'avg_surge',
       'surge_pct',
       'trips_in_first_30_days', 'weekday_pct',
        "city_King's Landing",
       'city_Winterfell', 'phone_iPhone',
        'luxury_car_user_True', 'user_lifespan', 'user_rated_driver',
       'user_rated_driver_avg_rating_of_driver']
    cols_to_keep_nonparam = ['city_Astapor', 'phone_Android',
                        'luxury_car_user_False',
                        'avg_dist', 'avg_rating_by_driver', 'avg_rating_of_driver']

    if model == 'logisticModel':
        X = data[cols_to_keep]
    else:
        X = data[cols_to_keep+cols_to_keep_nonparam]
    return X