| from ...smp import * |
| from ...utils import can_infer |
| import timeout_decorator |
| try: |
| from latex2sympy2 import latex2sympy |
| except Exception as e: |
| logging.critical(f'{type(e)}: {e}') |
| logging.critical('Please install latex2sympy2 by running "pip install latex2sympy2"') |
|
|
|
|
| FAIL_MSG = 'Failed to obtain answer via API.' |
|
|
|
|
| @timeout_decorator.timeout(30, use_signals=False) |
| def is_equal(asw: str, gt_asw: str) -> bool: |
| if not isinstance(asw, str) != str or not isinstance(gt_asw, str): |
| print('Warning: input is not string') |
| print(asw, gt_asw) |
| asw = str(asw).lower().strip() |
| gt_asw = str(gt_asw).lower().strip() |
| if gt_asw == asw: |
| return True |
| try: |
| a = eval(gt_asw) |
| b = eval(asw) |
| if abs(a - b) < 1e-6: |
| return True |
| except: |
| pass |
| try: |
| a = latex2sympy(gt_asw) |
| b = latex2sympy(asw) |
| if abs(eval(str(a)) - eval(str(b))) < 1e-6: |
| return True |
| if abs(a - b) < 1e-6: |
| return True |
| except: |
| pass |
| return False |
|
|
|
|
| def get_gpt4_ICE(): |
| example_1 = """ |
| Hint: Please answer the question and provide the final answer at the end.\n |
| Question: Which number is missing?\n |
| Model response: The number missing in the sequence is 14.\n |
| Extracted answer: 14 |
| """ |
|
|
| example_2 = """ |
| Hint: Please answer the question and provide the final answer at the end.\n |
| Question: What is the fraction of females facing the camera?\n |
| Model response: The fraction of females facing the camera is 0.6, |
| which means that six out of ten females in the group are facing the camera.\n |
| Extracted answer: 0.6 |
| """ |
|
|
| example_3 = """ |
| Hint: Please answer the question and provide the final answer at the end.\n |
| Question: How much money does Luca need to buy a sour apple candy and a butter-scotch candy? (Unit: $)\n |
| Model response: Luca needs $1.45 to buy a sour apple candy and a butterscotch candy.\n |
| Extracted answer: 1.45 |
| """ |
|
|
| example_4 = """ |
| Hint: Please answer the question and provide the final answer at the end.\n |
| Question: Between which two years does the line graph saw its maximum peak?\n |
| Model response: The line graph saw its maximum peak between 2007 and 2008.\n |
| Extracted answer: [2007, 2008] |
| """ |
|
|
| example_5 = """ |
| Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end.\n |
| Question: What fraction of the shape is blue?\n |
| Choices: (A) 3/11 (B) 8/11 (C) 6/11 (D) 3/5\n |
| Model response: The correct answer is (B) 8/11.\n |
| Extracted answer: B |
| """ |
|
|
| return [example_1, example_2, example_3, example_4, example_5] |
|
|
|
|
| def build_mathv_gpt4_prompt(line): |
| task_description = """ |
| Please read the following example. |
| Then extract the answer from the model response and type it at the end of the prompt.\n |
| """ |
| question = line['question'] |
| prediction = str(line['prediction']) |
| prompt = task_description |
| examples = get_gpt4_ICE() |
| for example in examples: |
| prompt += example + '\n' |
| prompt += question + '\n' |
| prompt += 'Model respone: ' + prediction |
| prompt += 'Extracted answer:' |
| return prompt |
|
|
|
|
| def list_to_dict(lst): |
| return {chr(65 + i): val for i, val in enumerate(lst)} |
|
|
|
|
| def post_check(line, prefetch=False): |
| res = None |
| ans = line['answer'] |
| response = line['prediction'] if prefetch else line['res'] |
| try: |
| if len(eval(line['choices'])) > 0: |
| ans = line['answer'] |
| choices = list_to_dict(eval(line['choices'])) |
| res = can_infer(response, choices) |
| if prefetch: |
| return res |
| else: |
| res = str(response) |
| ans = str(ans) |
| except ValueError: |
| pass |
|
|
| try: |
| if is_equal(res, ans): |
| return res if prefetch else True |
| else: |
| return False |
| except Exception as err: |
| logging.warning(f'{type(err)}: {err}') |
| return False |
|
|
|
|
| def MATH_V_auxeval(model, line): |
| prompt = build_mathv_gpt4_prompt(line) |
| log = '' |
| retry = 5 |
| if post_check(line, prefetch=True): |
| res = post_check(line, prefetch=True) |
| return dict(log='Prefetch succeed', res=res) |
| for i in range(retry): |
| prediction = line['prediction'] |
| res = model.generate(prompt, temperature=i * 0.5) |
|
|
| if FAIL_MSG in res: |
| log += f'Try {i}: output is {prediction}, failed to parse.\n' |
| else: |
| log += 'Succeed' |
| return dict(log=log, res=res) |
| log += 'All 5 retries failed.\n' |
| return dict(log=log, res='') |
|
|
|
|
| def MATH_V_acc(result_file): |
| data = load(result_file) |
| tot = defaultdict(lambda: 0) |
| fetch = defaultdict(lambda: 0) |
| hit = defaultdict(lambda: 0) |
| lt = len(data) |
| from tqdm import tqdm |
| for i in tqdm(range(lt)): |
| item = data.iloc[i] |
| cate = item['category'] |
| tot['Overall'] += 1 |
| tot[cate] += 1 |
| if item['log'] == 'Prefetch succeed': |
| fetch['Overall'] += 1 |
| fetch[cate] += 1 |
| if post_check(item, prefetch=False): |
| hit['Overall'] += 1 |
| hit[cate] += 1 |
|
|
| res = defaultdict(list) |
| for k in tot.keys(): |
| res['Subject'].append(k) |
| res['tot'].append(tot[k]) |
| res['prefetch'].append(fetch[k]) |
| res['hit'].append(hit[k]) |
| res['prefetch_rate'].append(fetch[k] / tot[k] * 100) |
| res['acc'].append(hit[k] / tot[k] * 100) |
| res = pd.DataFrame(res).sort_values('Subject', ignore_index=True) |
| return res |
|
|
