<jupyter_start><jupyter_text>Stack<jupyter_code>import pandas as pd<jupyter_output><empty_output><jupyter_text>Args<jupyter_code>...<jupyter_output><empty_output><jupyter_text>Read<jupyter_code>mb = pd.read_csv('submission.csv').proba
mb1 = pd.read_csv('my_best1.csv').proba
mb2 = pd.read_csv('my_best2.csv').proba
mb3 = pd.read_csv('my_best3.csv').proba
db1 = pd.read_csv('denis_best1.csv').proba
db2 = pd.read_csv('denis_best2.csv').proba
db3 = pd.read_csv('denis_best3.csv').proba<jupyter_output><empty_output><jupyter_text>Submission<jupyter_code>def make_submission(proba, path):
    sdf = pd.DataFrame(list(range(100000)), columns=['ID'])
    sdf['proba'] = proba
    sdf.to_csv(path, index=False)
make_submission((mb + db1 + db2) / 3, 'final_sub.csv')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16751269035532995, 'fraction_numerical': 0.0266497461928934, 'mean_word_length': 8.506024096385541, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 17, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '611080', 'license': 'MIT', 'n_tokens_mistral': 348, 'n_tokens_neox': 340, 'n_words': 31, 'path': 'stack.ipynb', 'repo_name': 'stasbel/AIHack2018', 'size': '788'}