wehub-resource-sync
213 lines
7.5 KiB
Python
213 lines
7.5 KiB
Python
import argparse
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import collections
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import json
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import sys
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from concurrent.futures import ThreadPoolExecutor, as_completed
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import os
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from pebble import ProcessPool
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import re
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from tqdm import tqdm
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sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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from data.qwen25math.grader import math_equal
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from data.qwen25math.parser import extract_answer, strip_string, STRIP_EXCEPTIONS
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def extract_content_from_tag(pred: str):
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# Regular expression pattern to match the content between <answer> and </answer>
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pattern = r'<answer>(.*?)</answer>'
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# Use re.DOTALL to allow matching newlines within the tags
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match = re.search(pattern, pred, re.DOTALL)
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if match:
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return match.group(1).strip() # Strip removes extra spaces or newlines
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return pred
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def majority_voting_predict(preds):
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if isinstance(preds, str):
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return preds
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preds = [pred for pred in preds if pred]
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if len(preds) == 0:
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return ""
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assert isinstance(preds, list)
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if isinstance(preds[0], list):
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tmp = []
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for pred in preds:
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tmp.append(str(sorted(pred)))
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pred = collections.Counter(tmp).most_common(1)[0][0]
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pred = eval(pred)
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elif isinstance(preds[0], str):
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pred = collections.Counter(preds).most_common(1)[0][0]
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else:
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# raise ValueError(f"Unknown type {type(preds[0])}")
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print(f"Unknown type {type(preds[0])}")
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pred = ""
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return pred
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def _annotate(param):
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return param[0], math_equal(param[-2], param[-1])
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--input_file", type=str)
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parser.add_argument("--num_workers", type=int, default=16)
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parser.add_argument("--sub_category", type=str, default=None)
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parser.add_argument("--label_field", type=str, default="label")
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parser.add_argument("--response_field", type=str, default="response")
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args = parser.parse_args()
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if args.input_file.endswith(".json"):
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data = json.load(open(args.input_file))
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else:
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data = [json.loads(line) for line in open(args.input_file).readlines()]
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if args.sub_category is not None:
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print(args.sub_category)
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sub_categories = set(list(args.sub_category.split(",")))
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data = [item for item in data if any([sub_category in item["data_topic"] for sub_category in sub_categories])]
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_mp_inputs = []
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for i, item in enumerate(data):
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response = item[args.response_field]
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if isinstance(response, str):
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response = extract_content_from_tag(response)
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pred_clean = extract_answer(response, data_name="math")
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pred_clean = strip_string(pred_clean, skip_unit="math" in STRIP_EXCEPTIONS)
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if pred_clean is None:
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pred_clean = ""
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sc_pred = pred_clean
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elif isinstance(response, list):
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pred_clean = []
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for resp in response:
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resp = extract_content_from_tag(resp)
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tmp_pred_clean = extract_answer(resp, data_name="math")
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tmp_pred_clean = strip_string(tmp_pred_clean, skip_unit="math" in STRIP_EXCEPTIONS)
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if tmp_pred_clean is None:
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tmp_pred_clean = ""
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pred_clean.append(tmp_pred_clean)
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sc_pred = majority_voting_predict(pred_clean)
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else:
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raise ValueError(f"Unknown type of response: {type(response)}")
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item["pred"] = pred_clean
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item["sc_pred"] = sc_pred
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if not isinstance(item["pred"], list):
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preds = [item["pred"]]
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else:
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preds = item["pred"]
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# if "college_math" in item["data_topic"]:
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# item[args.label_field] = item[args.label_field].replace("$", "").strip()
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#
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# data_name = item["data_topic"].split(".")[0]
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# if data_name not in STRIP_EXCEPTIONS:
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# item[args.label_field] = strip_string(item[args.label_field], skip_unit=data_name == "carp_en")
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# else:
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# # gt_ans = (
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# # gt_ans.replace("\\neq", "\\ne")
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# # .replace("\\leq", "\\le")
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# # .replace("\\geq", "\\ge")
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# # )
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# raise NotImplementedError()
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item[args.label_field] = strip_string(item[args.label_field], skip_unit=False)
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for j, pred in enumerate(preds):
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_mp_inputs.append(((i, j), pred, str(item[args.label_field])))
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pbar = tqdm(_mp_inputs, total=len(_mp_inputs), desc="Submitting eval task", dynamic_ncols=True)
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outputs = collections.defaultdict(dict)
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timeout_cnt = 0
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with ProcessPool(max_workers=1) as pool:
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future = pool.map(_annotate, pbar, timeout=3)
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iterator = future.result()
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with tqdm(total=len(_mp_inputs), desc="Evaluate") as progress_bar:
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while True:
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try:
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idx, result = next(iterator)
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# scores.append(result)
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outputs[idx[0]][idx[1]] = result
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except StopIteration:
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break
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except TimeoutError as error:
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print(error)
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# outputs[idx[0]][idx[1]] = False
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timeout_cnt += 1
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except Exception as error:
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print(error)
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# exit()
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progress_bar.update(1)
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for i, item in enumerate(data):
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if not isinstance(item["pred"], list):
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preds = [item["pred"]]
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else:
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preds = item["pred"]
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if i not in outputs:
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all_res = [False] * len(preds)
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else:
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all_res = outputs[i]
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for j, pred in enumerate(preds):
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if j not in all_res:
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all_res[j] = False
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assert len(all_res) == len(preds)
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pred2res = {pred: all_res[j] for j, pred in enumerate(preds)}
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sc_res = pred2res[item["sc_pred"]]
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item["res"] = [pred2res[pred] for pred in preds]
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item["sc_res"] = sc_res
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if not isinstance(item["pred"], list):
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assert len(item["res"]) == 1
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item["res"] = item["res"][0]
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cnt = 0
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pass_at_k = 0
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sc = 0
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acc_data_topic = collections.Counter()
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cnt_data_topic = collections.Counter()
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for item in data:
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if not isinstance(item["res"], list):
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res = [item["res"]]
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else:
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res = item["res"]
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if res[0]:
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cnt += 1
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# if "data_topic" in item:
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# if "." in item["data_topic"]:
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# item["data_topic"] = item["data_topic"].split(".")[0]
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# acc_data_topic[item["data_topic"]] += int(res[0])
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# cnt_data_topic[item["data_topic"]] += 1
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if any(res):
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pass_at_k += 1
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if item["sc_res"]:
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sc += 1
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output_file = args.input_file.replace(".json", ".sympy_eval.json")
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assert pass_at_k <= len(data)
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json.dump(data, open(output_file, "w"), indent=2)
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if len(data) == 0:
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metrics = {"acc": 0, "pass@k": 0, "maj@k": 0, "correct": 0, "total": 0}
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else:
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metrics = {"acc": cnt / len(data), "pass@k": pass_at_k / len(data), "maj@k": sc / len(data),
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"correct": cnt, "total": len(data)}
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if len(acc_data_topic) > 0:
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for key in acc_data_topic:
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metrics[f"acc_{key}"] = acc_data_topic[key] / cnt_data_topic[key]
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json.dump(metrics, open(output_file.replace(".json", ".metrics.json"), "w"), indent=2)
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print(json.dumps(metrics, indent=2))
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if __name__ == '__main__':
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main()
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