| from itertools import product | |
| import pandas as pd | |
| from datasets import load_dataset | |
| def get_stats(name): | |
| relation = [] | |
| size = [] | |
| data = load_dataset("relbert/t_rex", name) | |
| splits = data.keys() | |
| for split in splits: | |
| df = data[split].to_pandas() | |
| size.append({ | |
| "number of pairs": len(df), | |
| "number of unique relation types": len(df["relation"].unique()) | |
| }) | |
| relation.append(df.groupby('relation')['head'].count().to_dict()) | |
| relation = pd.DataFrame(relation, index=[f"number of pairs ({s})" for s in splits]).T | |
| relation = relation.fillna(0).astype(int) | |
| size = pd.DataFrame(size, index=splits).T | |
| return relation, size | |
| df_relation, df_size = get_stats("filter_unified.min_entity_4_max_predicate_10") | |
| print(f"\n- Number of instances (`filter_unified.min_entity_4_max_predicate_10`) \n\n {df_size.to_markdown()}") | |
| print(f"\n- Number of pairs in each relation type (`filter_unified.min_entity_4_max_predicate_10`) \n\n {df_relation.to_markdown()}") | |
| parameters_min_e_freq = [1, 2, 3, 4] | |
| parameters_max_p_freq = [100, 50, 25, 10] | |
| df_size_list = [] | |
| for e, p in product(parameters_min_e_freq, parameters_max_p_freq): | |
| _, df_size = get_stats(f"filter_unified.min_entity_{e}_max_predicate_{p}") | |
| df_size.pop("test") | |
| df_size.columns = [f"min_entity_{e}_max_predicate_{p} ({c})" for c in df_size.columns] | |
| df_size_list.append(df_size) | |
| df_size_list = pd.concat([i.T for i in df_size_list]) | |
| print(df_size_list.to_markdown()) |