reg_target_encoding

from sklearn.model_selection import KFold

def target_encoder_kfold(train_data, test_data, cols_encode, target, folds=10):
    """
    Mean regularized target encoding based on kfold
    """
    kf = KFold(n_splits=folds, random_state=1)
    for col in cols_encode:
        global_mean = train_data[target].mean()
        for train_index, test_index in kf.split(train_data):
            mean_target = train_data.iloc[train_index].groupby(col)[target].mean()
            train_data.loc[test_index, col + "_mean_enc"] = train_data.loc[test_index, col].map(mean_target)
        train_data[col + "_mean_enc"].fillna(global_mean, inplace=True)

        # making test encoding using full training data
        col_mean = train_data.groupby(col)[target].mean()
        test_data[col + "_mean_enc"] = test_data[col].map(col_mean)
        test_data[col + "_mean_enc"].fillna(global_mean, inplace=True)