| import pandas as pd | |
| from datasets import load_dataset | |
| data = load_dataset('relbert/conceptnet_high_confidence') | |
| stats = [] | |
| for k in data.keys(): | |
| for i in data[k]: | |
| stats.append({'relation_type': i['relation_type'], 'split': k, 'positives': len(i['positives']), 'negatives': len(i['negatives'])}) | |
| df = pd.DataFrame(stats) | |
| df_train = df[df['split'] == 'train'] | |
| df_valid = df[df['split'] == 'validation'] | |
| stats = [] | |
| for r in df['relation_type'].unique(): | |
| _df_t = df_train[df_train['relation_type'] == r] | |
| _df_v = df_valid[df_valid['relation_type'] == r] | |
| stats.append({ | |
| 'relation_type': r, | |
| 'positive (train)': 0 if len(_df_t) == 0 else _df_t['positives'].values[0], | |
| 'negative (train)': 0 if len(_df_t) == 0 else _df_t['negatives'].values[0], | |
| 'positive (validation)': 0 if len(_df_v) == 0 else _df_v['positives'].values[0], | |
| 'negative (validation)': 0 if len(_df_v) == 0 else _df_v['negatives'].values[0], | |
| }) | |
| df = pd.DataFrame(stats).sort_values(by=['relation_type']) | |
| df.index = df.pop('relation_type') | |
| sum_pairs = df.sum(0) | |
| df = df.T | |
| df['SUM'] = sum_pairs | |
| df = df.T | |
| df.to_csv('stats.csv') | |
| with open('stats.md', 'w') as f: | |
| f.write(df.to_markdown()) | |