# 結婚の有無・東京大阪勤務かどうかでグループ分け
class partner_capital_group(Feature):
def create_features(self):
whole = pd.concat([train, test], axis=0)
whole['is_tokyo'] = whole['area'].apply(lambda x: 1
if x == '東京都' else 0)
whole['is_osaka'] = whole['area'].apply(lambda x: 1
if x == '大阪府' else 0)
whole['is_capital'] = whole['is_tokyo'] + whole['is_osaka']
whole['partner_capital_group_str'] = whole['partner'].astype(
str) + '_' + whole['is_capital'].astype(str)
le = {'0_0': 0, '0_1': 1, '1_0': 2, '1_1': 3}
whole[self.__class__.
__name__] = whole['partner_capital_group_str'].map(le)
self.train[self.__class__.__name__] = whole[
self.__class__.__name__].values[:len_train]
self.test[self.__class__.__name__] = whole[
self.__class__.__name__].values[len_train:]
if __name__ == '__main__':
args = get_arguments()
train = pd.read_feather('../data/input/train_data.feather')
test = pd.read_feather('../data/input/test_data.feather')
len_train = len(train)
generate_features(globals(), args.force)
if x == 'male' else 0)
def add_meta(self):
self.meta_dict['memo'] = 'sex'
self.meta_dict['num_or_cat'] = 'cat'
self.meta_dict['date'] = '{0:%Y-%m-%d %H:%M:%S}'.format(self.now)
if __name__ == '__main__':
# log
logger, sh, fh = preparation_logger()
# do
args = Util.get_arguments()
train = Util.load_train_data()
test = Util.load_test_data()
# test mode?
if args.debug:
fh.setLevel(logging.ERROR) # file書き出ししないという意思表示
sh.setLevel(logging.DEBUG) # stream handler を infoからdebugへ
logger.info('*******************************')
logger.info('********** test mode **********')
logger.info('*******************************')
logger.info('-------------------- start')
logger.debug(f'\n-train\n {train.head()}')
logger.debug(f'\n-test\n {test.head()}')
generate_features(globals(), args.force, args.debug)
logger.info('-------------------- end')
if len(col) != 0:
return
writer = csv.writer(f)
writer.writerow([col_name, desc])
if __name__ == '__main__':
# CSVのヘッダーを書き込み
create_memo('特徴量', 'メモ')
if 'ipykernel' in sys.modules:
# pklファイルを上書きする際はTrueに書き換え
overwrite_ok = True
else:
overwrite_ok = get_arguments().force
train = pd.read_csv(RAW_DATA_DIR_NAME + 'train.csv')
test = pd.read_csv(RAW_DATA_DIR_NAME + 'test.csv')
# globals()でtrain,testのdictionaryを渡す
generate_features(globals(), overwrite_ok)
# 特徴量メモをソートする
feature_df = pd.read_csv(feature_memo_path)
feature_df = feature_df.sort_values('特徴量')
feature_df.to_csv(feature_memo_path, index=False)
# %%
