add_new_analogy_2.py

import json
import os
from itertools import combinations
from random import seed, randint, shuffle

import pandas as pd
from datasets import load_dataset


def get_stats(filename):
    with open(filename) as f:
        _data = [json.loads(i) for i in f.read().splitlines()]
    return len(_data), list(set([len(i['choice']) for i in _data])), len(list(set([i['prefix'] for i in _data])))


def create_analogy(_data):
    analogy_data = []
    seed(12)
    for i in _data:
        source = []
        target = []
        for s, t in zip(i['source'], i['target']):
            if s not in source and t not in target:
                source.append(s)
                target.append(t)
        assert len(source) == len(target), f"{len(source)} != {len(target)}"
        all_combinations = list(combinations(range(len(source)), 2))
        for n, (q_h_id, q_t_id) in enumerate(all_combinations):
            choice = [[target[x], target[y]] for m, (x, y) in enumerate(all_combinations) if m != n]
            answer_id = randint(0, len(source) - 1)
            choice = choice[:answer_id] + [[target[q_h_id], target[q_t_id]]] + choice[answer_id:]
            assert choice[answer_id] == [target[q_h_id], target[q_t_id]]
            analogy_data.append({
                "stem": [source[q_h_id], source[q_t_id]],
                "choice": choice,
                "answer": answer_id,
                "prefix": i["type"]
            })
    return analogy_data

data = load_dataset("relbert/scientific_and_creative_analogy", split='test')
data = create_analogy(data)
data_m = [i for i in data if i['prefix'] == 'metaphor']
data_s = [i for i in data if i['prefix'] != 'metaphor']
seed(12)
shuffle(data_m)
shuffle(data_s)
validation = data_s[:int(0.1 * len(data_s))] + data_m[:int(0.1 * len(data_m))]
test = data_s[int(0.1 * len(data_s)):] + data_m[int(0.1 * len(data_m)):]
os.makedirs("dataset/scan", exist_ok=True)
with open("dataset/scan/valid.jsonl", "w") as f:
    f.write("\n".join([json.dumps(i) for i in validation]))

with open("dataset/scan/test.jsonl", "w") as f:
    f.write("\n".join([json.dumps(i) for i in test]))

t_size, t_num_choice, t_relation_type = get_stats("dataset/scan/test.jsonl")
v_size, v_num_choice, v_relation_type = get_stats("dataset/scan/valid.jsonl")
stat = [{
    "name": "`scan`",
    "Size (valid/test)": f"{v_size}/{t_size}",
    "Num of choice (valid/test)": f"{','.join([str(n) for n in v_num_choice])}/{','.join([str(n) for n in t_num_choice])}",
    "Num of relation group (valid/test)": f"{v_relation_type}/{t_relation_type}",
    "Original Reference": "[relbert/scientific_and_creative_analogy](https://huggingface.co/datasets/relbert/scientific_and_creative_analogy)"
}]
print(pd.DataFrame(stat).to_markdown(index=False))