<jupyter_start><jupyter_text>Averaging 3 models from notebooks: XGB+LGBM+LSTMhttps://www.kaggle.com/vladlee/tps-jan2022-xgb-optunahttps://www.kaggle.com/vladlee/tps-jan2022-lgbm-optunahttps://www.kaggle.com/vladlee/tps-jan-2022-lstm<jupyter_code>import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
lstm = pd.read_csv('../input/tps-jan-2022-lstm/submission.csv')
lgbm = pd.read_csv('../input/tps-jan2022-lgbm-optuna/submission.csv')
xgbm = pd.read_csv('../input/tps-jan2022-xgb-optuna/submission.csv')
submission = pd.read_csv('../input/tabular-playground-series-jan-2022/sample_submission.csv')<jupyter_output><empty_output><jupyter_text>Let's try AmbrosM solution :)https://www.kaggle.com/ambrosm/scaling-is-better-than-blending/notebook<jupyter_code>submission.num_sold = np.mean([lstm.num_sold, lgbm.num_sold, xgbm.num_sold], axis=0)
submission['num_sold'] = submission['num_sold']*0.99
submission.to_csv('submission.csv', index=False, float_format='%.6f')
submission.head(20)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16573556797020483, 'fraction_numerical': 0.0335195530726257, 'mean_word_length': 6.312925170068027, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 4, 'lorem ipsum': 0, 'www.': 4, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '76855', 'license': 'MIT', 'n_tokens_mistral': 469, 'n_tokens_neox': 440, 'n_words': 46, 'path': 'tabular-playground/jan-22/tps-jan-2022-lgb-xgb-lstm.ipynb', 'repo_name': 'vladlee098/kaggle', 'size': '1074'}