| import json | |
| import os | |
| from itertools import product | |
| from statistics import mean | |
| import pandas as pd | |
| from datasets import load_dataset | |
| def process(name, split, output): | |
| data = load_dataset("relbert/t_rex", name, split=split) | |
| df = data.to_pandas() | |
| df.pop('text') | |
| df.pop('title') | |
| df['pairs'] = [[i, j] for i, j in zip(df.pop('subject'), df.pop('object'))] | |
| rel_sim_data = [{ | |
| "relation_type": pred, | |
| "positives": g['pairs'].values.tolist(), | |
| "negatives": df[df.predicate != pred]['pairs'].values.tolist() | |
| } for pred, g in df.groupby("predicate") if len(g) >= 2] | |
| with open(output, "w") as f: | |
| f.write('\n'.join([json.dumps(i) for i in rel_sim_data])) | |
| parameters_min_e_freq = [1, 2, 3, 4] | |
| parameters_max_p_freq = [100, 50, 25, 10] | |
| os.makedirs("data", exist_ok=True) | |
| for min_e_freq, max_p_freq in product(parameters_min_e_freq, parameters_max_p_freq): | |
| for s in ['train', 'validation']: | |
| process( | |
| name=f"filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}", | |
| split=s, | |
| output=f"data/filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.{s}.jsonl") | |
| process( | |
| name=f"filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}", | |
| split='test', | |
| output=f"data/filter_unified.test.jsonl") | |
| stats = [] | |
| for min_e_freq, max_p_freq in product(parameters_min_e_freq, parameters_max_p_freq): | |
| stats_tmp = {"data": f"filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}"} | |
| for s in ['train', 'validation']: | |
| with open(f"data/filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.{s}.jsonl") as f: | |
| tmp = [json.loads(i) for i in f.read().split('\n') if len(i) > 0] | |
| stats_tmp[f'num of relation types ({s})'] = len(tmp) | |
| stats_tmp[f'average num of positive pairs ({s})'] = round(mean([len(i['positives']) for i in tmp])) | |
| stats_tmp[f'average num of negative pairs ({s})'] = round(mean([len(i['negatives']) for i in tmp])) | |
| stats.append(stats_tmp) | |
| df_stats = pd.DataFrame(stats) | |
| df_stats.index = df_stats.pop('data') | |
| print(df_stats.to_markdown()) | |
| stats_tmp = {} | |
| with open("data/filter_unified.test.jsonl") as f: | |
| tmp = [json.loads(i) for i in f.read().split('\n') if len(i) > 0] | |
| stats_tmp[f'num of relation types (test)'] = len(tmp) | |
| stats_tmp[f'average num of positive pairs (test)'] = round(mean([len(i['positives']) for i in tmp])) | |
| stats_tmp[f'average num of negative pairs (test)'] = round(mean([len(i['negatives']) for i in tmp])) | |
| df_stats_test = pd.DataFrame([stats_tmp]) | |
| print(df_stats_test.to_markdown(index=False)) | |