| import json |
| import argparse |
| from nltk.translate.bleu_score import sentence_bleu |
|
|
|
|
| parser = argparse.ArgumentParser() |
| parser.add_argument("--category", required=True, type=str, choices=["chemistry", "physics"]) |
| parser.add_argument("--file", required=True, type=str) |
|
|
| args = parser.parse_args() |
| with open(args.file, 'r') as reader: |
| data = json.load(reader) |
|
|
|
|
| def extract_float(pred_str): |
| flag = False |
| answer_str = "" |
| for s in pred_str: |
| if (s >= "0" and s <= "9") or s == ".": |
| answer_str += s |
| if flag == False: |
| flag = True |
| else: |
| if flag == True: |
| break |
| if len(answer_str) == 0 or answer_str == ".": |
| return 0 |
| if answer_str[-1] == ".": |
| answer_str = answer_str[:-1] |
| |
| return float(answer_str) |
|
|
|
|
| def split_IUPAC_name(name_str): |
| special_strs = [",", "[", "]", "-", "(", ")"] |
| name_list = [name_str] |
| for special_str in special_strs: |
| new_name_list = [] |
| for name in name_list: |
| name_split = name.split(special_str) |
| name_split = [s for s in name_split if len(s) != 0] |
| new_name_list += name_split |
| name_list = new_name_list.copy() |
| |
| return name_list |
|
|
|
|
| if args.category == "chemistry": |
| bleu_scores = [] |
| mse_scores = [] |
| acc_cnt = 0 |
| for d in data: |
| if f"{d['answer'][0]}".lower() in d["pred"]: |
| acc_cnt += 1 |
| if "What is the SMILES expression of " in d["question"]: |
| answer = [a for a in d["answer"][0].lower()] |
| pred_split = d["pred"].split(" ") |
| max_bleu = 0 |
| for pred in pred_split: |
| pred = [a for a in pred.lower()] |
| reference = [answer] |
| score = sentence_bleu(reference, pred, weights=(0.25, 0.25, 0.25, 0.25)) |
| if score > max_bleu: |
| max_bleu = score |
| bleu_scores.append(max_bleu) |
| elif "What is the molecular formula of" in d["question"]: |
| answer = [a for a in d["answer"][0].lower()] |
| pred_split = d["pred"].split(" ") |
| max_bleu = 0 |
| for pred in pred_split: |
| pred = [a for a in pred.lower()] |
| reference = [answer] |
| score = sentence_bleu(reference, pred, weights=(0.25, 0.25, 0.25, 0.25)) |
| if score > max_bleu: |
| max_bleu = score |
| bleu_scores.append(max_bleu) |
| elif "What is the molecular weight of " in d["question"]: |
| answer = float(d["answer"][0]) |
| min_mse = 1e10 |
| pred_split = d["pred"].split(" ") |
| for pred in pred_split: |
| pred = extract_float(pred) |
| if pred == 0: |
| continue |
| mse = (pred - answer) ** 2 |
| if mse < min_mse: |
| min_mse = mse |
| if min_mse != 1e10: |
| mse_scores.append(min_mse) |
| elif "How many atoms are there in" in d["question"]: |
| answer = float(d["answer"][0]) |
| min_mse = 1e10 |
| pred_split = d["pred"].split(" ") |
| for pred in pred_split: |
| pred = extract_float(pred) |
| if pred == 0: |
| continue |
| mse = (pred - answer) ** 2 |
| if mse < min_mse: |
| min_mse = mse |
| if min_mse != 1e10: |
| mse_scores.append(min_mse) |
| elif "What is the name of" in d["question"]: |
| answer = split_IUPAC_name(d["answer"][0].strip().lower()) |
| pred_split = d["pred"].split(" ") |
| max_bleu = 0 |
| for pred in pred_split: |
| pred = split_IUPAC_name(pred.strip().lower()) |
| reference = [answer] |
| score = sentence_bleu(reference, pred, weights=(0.25, 0.25, 0.25, 0.25)) |
| if score > max_bleu: |
| max_bleu = score |
| bleu_scores.append(max_bleu) |
|
|
| print("blue: ", sum(bleu_scores) / len(bleu_scores)) |
| print("mse: ", sum(mse_scores) / len(mse_scores)) |
| print("EM: ", acc_cnt / len(data)) |
| else: |
| acc_cnt = 0 |
| for d in data: |
| if len(d["pred"]) == 0: |
| continue |
| if d["answer"][0].lower() == d["pred"][0].lower(): |
| acc_cnt += 1 |
| print(acc_cnt/len(data)) |
|
|
