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import os
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import pandas as pd
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import numpy as np
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import argparse
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import datasets
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import torch
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import re
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from thefuzz import process
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from typing import List
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from tqdm import tqdm
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from transformers.trainer_utils import set_seed
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'''
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wget https://huggingface.co/datasets/ceval/ceval-exam/resolve/main/ceval-exam.zip
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mkdir data/ceval
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mv ceval-exam.zip data/ceval
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cd data/ceval; unzip ceval-exam.zip
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cd ../../
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pip install thefuzz
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python eval/evaluate_chat_ceval.py -d data/ceval
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'''
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def load_models_tokenizer(args):
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers.generation import GenerationConfig
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tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(args.checkpoint_path, device_map="auto", trust_remote_code=True, bf16=True, use_flash_attn=True).eval()
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model.generation_config = GenerationConfig.from_pretrained(args.checkpoint_path, trust_remote_code=True)
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model.generation_config.do_sample = False # use greedy decoding
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return model, tokenizer
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def process_before_extraction(gen, question, choice_dict):
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# Example Prompt:
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# 关于传输层的面向连接服务的特性是____。
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# A. 既不保证可靠，也不保证按序交付
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# B. 不保证可靠，但保证按序交付
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# C. 保证可靠，但不保证按序交付
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# D. 既保证可靠，也保证按序交付
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# Example Model Output：
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# 关于传输层的面向连接服务的特性是既保证可靠，也保证按序交付
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# Processed Output:
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# 答案是D
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question_split = question.rstrip("。").split("。")[-1].split("_")
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# replacing the question
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if len(question_split[0].strip()) > 4:
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gen = gen.replace(question_split[0], "答案是")
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if len(question_split[-1].strip()) > 4:
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gen = gen.replace(question_split[-1], "")
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# replace the choice by letter in the generated sentence
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# from longest one to shortest one
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for key, val in sorted(choice_dict.items(), key=lambda x: len(x[1]), reverse=True):
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gen = gen.replace(val.rstrip("。"), key)
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return gen
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def count_substr(gen, pattern):
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return len(re.findall(pattern, gen))
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def extract_choice(gen, prompt, choice_list):
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# 答案是A | 选项是A | 应该选A选项
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res = re.search(r"(?:(?:选|选择|选定)|(?:(?:答案|选项)(?![^ABCD]{0,10}?(?:不|非)[^ABCD]{0,10}?(?:是|为|：|:|】))[^ABCD]{0,10}?(?:是|为|：|:|】))[^ABCD]{0,10}?)(A|B|C|D)(?:选项)?(?:\)|。|\.|，|,|．|、|A|B|C|D|$)", gen)
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# A选项正确 | A选项符合题意
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if res is None:
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res = re.search(r"(A|B|C|D)(?:选?项)?(?![^ABCD]{0,4}?(?:不|非)[^ABCD]{0,4}?(?:正确|对|符合))[^ABCD]{0,4}?(?:正确|对|符合)", gen)
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# 直接输出 A
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if res is None:
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res = re.search(r"^(A|B|C|D)(?:。|\.|，|,|．|$)", gen)
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# 获取第一个出现的字母
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if res is None:
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res = re.search(r"(?<![a-zA-Z])(A|B|C|D)(?![a-zA-Z=])", gen)
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if res is None:
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return choices[choice_list.index(process.extractOne(gen, choice_list)[0])]
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else:
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return res.group(1)
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def format_example(line):
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example = line['question'] + "\n\n"
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for choice in choices:
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example += f'{choice}. {line[f"{choice}"]}\n'
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return example
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def extract_answer(response, row):
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prompt = row['question']
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gen = process_before_extraction(response, prompt, {choice: row[choice] for choice in choices})
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if not isinstance(prompt, str):
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prompt = prompt[0]
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pred = extract_choice(gen, prompt, [row[choice] for choice in choices])
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return pred
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@torch.no_grad()
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def eval_subject(
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model,
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tokenizer,
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subject_name,
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test_df,
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save_result_dir=None,
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overwrite=False,
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**kwargs
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):
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result_path = os.path.join(save_result_dir, f'{subject_name}_result.csv')
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if not overwrite and os.path.exists(result_path):
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print(f"{result_path} existed, skip!")
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score = []
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for (_, datarow), (_, resultrow) in zip(test_df.iterrows(), pd.read_csv(result_path).iterrows()):
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pred = extract_answer(resultrow['model_response'], datarow)
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correct = 1 if pred == datarow['answer'] else 0
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score.append(correct)
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correct_ratio = 100 * sum(score) / len(score)
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return correct_ratio
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responses = []
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result = []
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score = []
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for _, row in tqdm(test_df.iterrows(), total=len(test_df)):
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question = format_example(row)
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response, history = model.chat(
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tokenizer,
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question,
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history=None,
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)
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print(question)
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print(response)
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pred = extract_answer(response, row)
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print(pred)
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print("======================")
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if 'answer' in row:
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correct = 1 if pred == row['answer'] else 0
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score.append(correct)
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if args.debug: print(f'{question} pred: {pred} ref: {row["answer"]}')
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responses.append(response)
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result.append(pred)
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if score:
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correct_ratio = 100 * sum(score) / len(score)
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if args.debug: print(subject_name, correct_ratio)
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else:
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correct_ratio = 0
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if save_result_dir:
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test_df['model_response'] = responses
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test_df['model_output'] = result
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if score:
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test_df["correctness"] = score
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os.makedirs(save_result_dir, exist_ok=True)
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test_df.to_csv(result_path, encoding="utf-8", index=False)
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return correct_ratio
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def cal_ceval(res):
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acc_sum_dict = dict()
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acc_norm_sum_dict = dict()
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cnt_dict = dict()
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acc_sum = 0.
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cnt = 0
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hard_cnt = 0
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hard_acc_sum = 0.
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for tt in res.keys():
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name = tt.split('-')[-1]
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acc_sum += float(res[tt])
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cnt += 1
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class_ = TASK_NAME_MAPPING[name][2]
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if class_ not in acc_sum_dict:
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acc_sum_dict[class_] = 0.
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acc_norm_sum_dict[class_] = 0.
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cnt_dict[class_] = 0.
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if name in hard_list:
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hard_cnt += 1
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hard_acc_sum += float(res[tt])
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acc_sum_dict[class_] += float(res[tt])
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cnt_dict[class_] += 1
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print('\n\n\n')
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for k in ['STEM', 'Social Science', 'Humanities', 'Other']:
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if k in cnt_dict:
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print('%s acc: %.2f ' % (
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k, acc_sum_dict[k] / cnt_dict[k]))
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if hard_cnt > 0:
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print('Hard acc:%.2f ' % (hard_acc_sum / hard_cnt))
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print('AVERAGE acc:%.2f ' % (acc_sum / cnt))
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TASK_NAME_MAPPING = {
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"computer_network": ["Computer Network", "\u8ba1\u7b97\u673a\u7f51\u7edc", "STEM"],
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"operating_system": ["Operating System", "\u64cd\u4f5c\u7cfb\u7edf", "STEM"],
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"computer_architecture": ["Computer Architecture", "\u8ba1\u7b97\u673a\u7ec4\u6210", "STEM"],
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"college_programming": ["College Programming", "\u5927\u5b66\u7f16\u7a0b", "STEM"],
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"college_physics": ["College Physics", "\u5927\u5b66\u7269\u7406", "STEM"],
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"college_chemistry": ["College Chemistry", "\u5927\u5b66\u5316\u5b66", "STEM"],
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"advanced_mathematics": ["Advanced Mathematics", "\u9ad8\u7b49\u6570\u5b66", "STEM"],
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"probability_and_statistics": ["Probability and Statistics", "\u6982\u7387\u7edf\u8ba1", "STEM"],
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"discrete_mathematics": ["Discrete Mathematics", "\u79bb\u6563\u6570\u5b66", "STEM"],
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"electrical_engineer": ["Electrical Engineer", "\u6ce8\u518c\u7535\u6c14\u5de5\u7a0b\u5e08", "STEM"],
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"metrology_engineer": ["Metrology Engineer", "\u6ce8\u518c\u8ba1\u91cf\u5e08", "STEM"],
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"high_school_mathematics": ["High School Mathematics", "\u9ad8\u4e2d\u6570\u5b66", "STEM"],
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"high_school_physics": ["High School Physics", "\u9ad8\u4e2d\u7269\u7406", "STEM"],
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"high_school_chemistry": ["High School Chemistry", "\u9ad8\u4e2d\u5316\u5b66", "STEM"],
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"high_school_biology": ["High School Biology", "\u9ad8\u4e2d\u751f\u7269", "STEM"],
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"middle_school_mathematics": ["Middle School Mathematics", "\u521d\u4e2d\u6570\u5b66", "STEM"],
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"middle_school_biology": ["Middle School Biology", "\u521d\u4e2d\u751f\u7269", "STEM"],
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"middle_school_physics": ["Middle School Physics", "\u521d\u4e2d\u7269\u7406", "STEM"],
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"middle_school_chemistry": ["Middle School Chemistry", "\u521d\u4e2d\u5316\u5b66", "STEM"],
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"veterinary_medicine": ["Veterinary Medicine", "\u517d\u533b\u5b66", "STEM"],
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"college_economics": ["College Economics", "\u5927\u5b66\u7ecf\u6d4e\u5b66", "Social Science"],
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"business_administration": ["Business Administration", "\u5de5\u5546\u7ba1\u7406", "Social Science"],
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"marxism": ["Marxism", "\u9a6c\u514b\u601d\u4e3b\u4e49\u57fa\u672c\u539f\u7406", "Social Science"],
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"mao_zedong_thought": ["Mao Zedong Thought", "\u6bdb\u6cfd\u4e1c\u601d\u60f3\u548c\u4e2d\u56fd\u7279\u8272\u793e\u4f1a\u4e3b\u4e49\u7406\u8bba\u4f53\u7cfb\u6982\u8bba", "Social Science"],
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"education_science": ["Education Science", "\u6559\u80b2\u5b66", "Social Science"],
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"teacher_qualification": ["Teacher Qualification", "\u6559\u5e08\u8d44\u683c", "Social Science"],
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"high_school_politics": ["High School Politics", "\u9ad8\u4e2d\u653f\u6cbb", "Social Science"],
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"high_school_geography": ["High School Geography", "\u9ad8\u4e2d\u5730\u7406", "Social Science"],
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"middle_school_politics": ["Middle School Politics", "\u521d\u4e2d\u653f\u6cbb", "Social Science"],
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"middle_school_geography": ["Middle School Geography", "\u521d\u4e2d\u5730\u7406", "Social Science"],
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"modern_chinese_history": ["Modern Chinese History", "\u8fd1\u4ee3\u53f2\u7eb2\u8981", "Humanities"],
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"ideological_and_moral_cultivation": ["Ideological and Moral Cultivation", "\u601d\u60f3\u9053\u5fb7\u4fee\u517b\u4e0e\u6cd5\u5f8b\u57fa\u7840", "Humanities"],
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"logic": ["Logic", "\u903b\u8f91\u5b66", "Humanities"],
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"law": ["Law", "\u6cd5\u5b66", "Humanities"],
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"chinese_language_and_literature": ["Chinese Language and Literature", "\u4e2d\u56fd\u8bed\u8a00\u6587\u5b66", "Humanities"],
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"art_studies": ["Art Studies", "\u827a\u672f\u5b66", "Humanities"],
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"professional_tour_guide": ["Professional Tour Guide", "\u5bfc\u6e38\u8d44\u683c", "Humanities"],
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"legal_professional": ["Legal Professional", "\u6cd5\u5f8b\u804c\u4e1a\u8d44\u683c", "Humanities"],
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"high_school_chinese": ["High School Chinese", "\u9ad8\u4e2d\u8bed\u6587", "Humanities"],
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"high_school_history": ["High School History", "\u9ad8\u4e2d\u5386\u53f2", "Humanities"],
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"middle_school_history": ["Middle School History", "\u521d\u4e2d\u5386\u53f2", "Humanities"],
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"civil_servant": ["Civil Servant", "\u516c\u52a1\u5458", "Other"],
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"sports_science": ["Sports Science", "\u4f53\u80b2\u5b66", "Other"],
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"plant_protection": ["Plant Protection", "\u690d\u7269\u4fdd\u62a4", "Other"],
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"basic_medicine": ["Basic Medicine", "\u57fa\u7840\u533b\u5b66", "Other"],
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"clinical_medicine": ["Clinical Medicine", "\u4e34\u5e8a\u533b\u5b66", "Other"],
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"urban_and_rural_planner": ["Urban and Rural Planner", "\u6ce8\u518c\u57ce\u4e61\u89c4\u5212\u5e08", "Other"],
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"accountant": ["Accountant", "\u6ce8\u518c\u4f1a\u8ba1\u5e08", "Other"],
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"fire_engineer": ["Fire Engineer", "\u6ce8\u518c\u6d88\u9632\u5de5\u7a0b\u5e08", "Other"],
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"environmental_impact_assessment_engineer": ["Environmental Impact Assessment Engineer", "\u73af\u5883\u5f71\u54cd\u8bc4\u4ef7\u5de5\u7a0b\u5e08", "Other"],
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"tax_accountant": ["Tax Accountant", "\u7a0e\u52a1\u5e08", "Other"],
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"physician": ["Physician", "\u533b\u5e08\u8d44\u683c", "Other"]
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}
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hard_list = ['advanced_mathematics', 'discrete_mathematics', 'probability_and_statistics', 'college_physics', 'college_chemistry', 'high_school_mathematics', 'high_school_physics', 'high_school_chemistry']
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choices = ["A", "B", "C", "D"]
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def main(args):
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print("loading model weights")
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if args.checkpoint_path:
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model, tokenizer = load_models_tokenizer(args)
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else:
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model, tokenizer = None, None
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print("model loaded")
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dev_result = {}
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for subject_name in tqdm(TASK_NAME_MAPPING.keys()):
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val_file_path = os.path.join(args.eval_data_path, 'val', f'{subject_name}_val.csv')
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# dev_file_path = os.path.join(args.eval_data_path, 'dev', f'{subject_name}_dev.csv')
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# test_file_path = os.path.join(args.eval_data_path, 'test', f'{subject_name}_test.csv')
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val_df = pd.read_csv(val_file_path)
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# dev_df = pd.read_csv(dev_file_path)
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# test_df = pd.read_csv(test_file_path)
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score = eval_subject(model, tokenizer, subject_name, val_df,
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save_result_dir=f"outs_chat/ceval_eval_result", overwrite=args.overwrite)
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dev_result[subject_name] = score
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cal_ceval(dev_result)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Test HF checkpoint.')
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parser.add_argument('-c', '--checkpoint-path', type=str, help='Checkpoint path', default="Qwen/Qwen-7B-Chat")
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parser.add_argument('-s', '--seed', type=int, default=1234, help='Random seed')
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"""Provide extra arguments required for tasks."""
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group = parser.add_argument_group(title='Evaluation options')
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group.add_argument('-d', '--eval_data_path', type=str, required=True,
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help='Path to eval data')
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group.add_argument("--debug", action='store_true', default=False,
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help='Print infos.')
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group.add_argument("--overwrite", action='store_true', default=False,
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help='Overwrite existed results')
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args = parser.parse_args()
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set_seed(args.seed)
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main(args) |