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('head'), df.pop('tail'))] rel_sim_data = [{ "relation_type": pred, "positives": g['pairs'].values.tolist(), "negatives": [] } for pred, g in df.groupby("relation") 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))