<jupyter_start><jupyter_text>Alpha<jupyter_code>print(df_tuning_results['alpha'].unique())
print("{}: {}".format(0, len(df_tuning_results[df_tuning_results['alpha']==0])))
print("{}: {}".format(0.1, len(df_tuning_results[df_tuning_results['alpha']==0.1])))
print("{}: {}".format(1, len(df_tuning_results[df_tuning_results['alpha']==1])))<jupyter_output>[0.  0.1 1. ]
0: 32
0.1: 21
1: 21<jupyter_text>Drop Out Rate<jupyter_code>print(df_tuning_results['corruption'].unique())
print("{}: {}".format(0.2, len(df_tuning_results[df_tuning_results['corruption']==0.2])))
print("{}: {}".format(0.3, len(df_tuning_results[df_tuning_results['corruption']==0.3])))
print("{}: {}".format(0.4, len(df_tuning_results[df_tuning_results['corruption']==0.4])))
print("{}: {}".format(0.5, len(df_tuning_results[df_tuning_results['corruption']==0.5])))<jupyter_output>[0.2 0.3 0.4]
0.2: 32
0.3: 21
0.4: 21
0.5: 0<jupyter_text>Key Dimension<jupyter_code>print(df_tuning_results['key_dimension'].unique())
print("{}: {}".format(3, len(df_tuning_results[df_tuning_results['key_dimension']==3])))
print("{}: {}".format(4, len(df_tuning_results[df_tuning_results['key_dimension']==4])))
print("{}: {}".format(5, len(df_tuning_results[df_tuning_results['key_dimension']==5])))<jupyter_output>[5 4 3]
3: 64
4: 5
5: 5<jupyter_text>Lambda<jupyter_code>print(df_tuning_results['lambda'].unique())
print("{}: {}".format(1e-3, len(df_tuning_results[df_tuning_results['lambda']==1e-3])))
print("{}: {}".format(1e-4, len(df_tuning_results[df_tuning_results['lambda']==1e-4])))
print("{}: {}".format(1e-5, len(df_tuning_results[df_tuning_results['lambda']==1e-5])))
print("{}: {}".format(1e-6, len(df_tuning_results[df_tuning_results['lambda']==1e-6])))
print("{}: {}".format(1e-7, len(df_tuning_results[df_tuning_results['lambda']==1e-7])))<jupyter_output>[0.001]
0.001: 74
0.0001: 0
1e-05: 0
1e-06: 0
1e-07: 0<jupyter_text>Learning Rate<jupyter_code>print(df_tuning_results['learning_rate'].unique())
print("{}: {}".format(5e-5, len(df_tuning_results[df_tuning_results['learning_rate']==5e-5])))
print("{}: {}".format(1e-4, len(df_tuning_results[df_tuning_results['learning_rate']==1e-4])))
print("{}: {}".format(5e-4, len(df_tuning_results[df_tuning_results['learning_rate']==5e-4])))<jupyter_output>[5.e-05 1.e-04]
5e-05: 73
0.0001: 1
0.0005: 0<jupyter_text>Mode Dimension<jupyter_code>print(df_tuning_results['mode_dimension'].unique())
print("{}: {}".format(1, len(df_tuning_results[df_tuning_results['mode_dimension']==1])))
print("{}: {}".format(3, len(df_tuning_results[df_tuning_results['mode_dimension']==3])))
print("{}: {}".format(5, len(df_tuning_results[df_tuning_results['mode_dimension']==5])))<jupyter_output>[5 3 1]
1: 33
3: 30
5: 11<jupyter_text>Rank<jupyter_code>print(df_tuning_results['rank'].unique())
print("{}: {}".format(40, len(df_tuning_results[df_tuning_results['rank']==40])))
print("{}: {}".format(60, len(df_tuning_results[df_tuning_results['rank']==60])))
print("{}: {}".format(80, len(df_tuning_results[df_tuning_results['rank']==80])))
print("{}: {}".format(100, len(df_tuning_results[df_tuning_results['rank']==100])))
sort_tuning_results(df=df_tuning_results, metric='NDCG')
sort_tuning_results(df=df_tuning_results, metric='Precision@10')
sort_tuning_results(df=df_tuning_results, metric='R-Precision')
sort_tuning_results(df=df_tuning_results, metric='Recall@10')
# metric = 'NDCG'
# df_tuning_results[metric+'_Score'] = df_tuning_results[metric].map(lambda x: ast.literal_eval(x)[0])
# df_tuning_results.sort_values(by=metric+'_Score', ascending=False)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.23119777158774374, 'fraction_numerical': 0.05543175487465181, 'mean_word_length': 13.900414937759336, 'pattern_counts': {'":': 0, '<': 23, '<?xml version=': 0, '>': 23, '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': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '97347', 'license': 'MIT', 'n_tokens_mistral': 1621, 'n_tokens_neox': 1525, 'n_words': 148, 'path': 'Amazon Video Games Tuning Results Analysis.ipynb', 'repo_name': 'wuga214/MultiModesPreferenceEstimation', 'size': '3590'}