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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))
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