<jupyter_start><jupyter_text>When you call the function, it will return the feature vector X and the label vector y and save them as features.csv and output.csv<jupyter_code>from Data_Parsing import simple_features_generator

relative_path = '../Hololens_data/'
game_files=[
           relative_path + '5.json',
           relative_path + '6.json',
           relative_path + '7.json',
           relative_path + '8.json',
           relative_path + '9.json',
           relative_path + '10.json']

X,y = simple_features_generator(game_files, 1)


y<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09844559585492228, 'fraction_numerical': 0.013816925734024179, 'mean_word_length': 3.264705882352941, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '657863', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 184, 'n_tokens_neox': 180, 'n_words': 46, 'path': 'Hololens_project/Kate Library/Using the Parser Example.ipynb', 'repo_name': 'netomenoci/data-analysis', 'size': '579'}