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# flake8: noqa
import ast
import json
import os
import pandas as pd
import tiktoken
from tqdm import tqdm
from .constructions import ChatGPTSchema, ResultsForHumanSchema
from .utils import extract_answer, read_jsonl, save_jsonl
# define the datasets
medbench_multiple_choices_sets = ['Med-Exam', 'DDx-basic', 'DDx-advanced', 'DDx-advanced', 'SafetyBench'] # 选择题，用acc判断
medbench_qa_sets = ['MedHC', 'MedMC', 'MedDG', 'MedSpeQA', 'MedTreat', 'CMB-Clin'] # 开放式QA，有标答
medbench_cloze_sets = ['MedHG'] # 限定域QA，有标答
medbench_single_choice_sets = ['DrugCA'] # 正确与否判断，有标答
medbench_ie_sets = ['DBMHG', 'CMeEE', 'CMeIE', 'CHIP-CDEE', 'CHIP-CDN', 'CHIP-CTC', 'SMDoc', 'IMCS-V2-MRG'] # 判断识别的实体是否一致，用F1评价
def convert_zero_shot(line, dataset_name):
# passage = line['passage'] if line['passage'] is not None else ''
# if dataset_name in medbench_qa_sets:
# return line['question']
# elif dataset_name in medbench_cloze_sets:
# return '问题：' + line['question'] + '\n答案：'
# elif dataset_name in medbench_multiple_choices_sets:
# return '问题：' + line['question'] + ' ' \
# + '选项：' + ' '.join(line['options']) + '\n从A到G，我们应该选择'
# else:
# return line['question']
return line['question']
prefix = '该问题为单选题，所有选项中必有一个正确答案，且只有一个正确答案。\n'
# def convert_zero_shot_CoT_stage1(line, dataset_name):
# try:
# passage = line['passage'] if line['passage'] is not None else ''
# if dataset_name in english_qa_datasets:
# return passage + 'Q: ' + line['question'] + ' ' \
# + 'Answer Choices: ' + ' '.join(line['options']) + '\n' + \
# "Let's think step by step."
# elif dataset_name in chinese_qa_datasets:
# option_string = 'ABCDEFG'
# count = len(line['options'])
# if count == 1:
# count = 4
# return passage + '问题：' + line['question'] + ' ' \
# + '选项：' + ' '.join(line['options']) + '\n' + \
# '从A到{}, 我们应选择什么？让我们逐步思考：'.format(option_string[count - 1])
# elif dataset_name in english_cloze_datasets:
# return passage + 'Q: ' + line['question'] + '\n' \
# "A: Let's think step by step."
# elif dataset_name in chinese_cloze_datasets:
# return passage + '问题：' + line['question'] + '\n' \
# '答案：让我们逐步思考：'
# except NameError:
# print('Dataset not defined.')
# process few-shot raw_prompts
def combine_prompt(prompt_path,
dataset_name,
load_explanation=True,
chat_mode=False):
skip_passage = False
if dataset_name == 'sat-en-without-passage':
skip_passage = True
dataset_name = 'sat-en'
demostrations = []
# read the prompts by context and explanation
context_row = [0, 1, 3, 5, 7, 9]
explanation_row = [0, 2, 4, 6, 8, 10]
raw_prompts_context = pd.read_csv(prompt_path,
header=0,
skiprows=lambda x: x not in context_row,
keep_default_na=False)
raw_prompts_explanation = pd.read_csv(
prompt_path,
header=0,
skiprows=lambda x: x not in explanation_row,
keep_default_na=False).replace(r'\n\n', '\n', regex=True)
contexts = []
for line in list(raw_prompts_context[dataset_name]):
if line:
# print(line)
contexts.append(ast.literal_eval(line))
explanations = [
exp for exp in raw_prompts_explanation[dataset_name] if exp
]
for idx, (con, exp) in enumerate(zip(contexts, explanations)):
passage = con['passage'] if con[
'passage'] is not None and not skip_passage else ''
question = con['question']
options = con['options'] if con['options'] is not None else ''
label = con['label'] if con['label'] is not None else ''
answer = con[
'answer'] if 'answer' in con and con['answer'] is not None else ''
if dataset_name in qa_datasets:
question_input = '问题 {}. '.format(idx + 1) + passage + ' ' + question + '\n' \
+ '从以下选项中选择: ' + ' '.join(options) + '\n'
question_output = (('问题 {}的解析: '.format(idx + 1) + exp + '\n') if load_explanation else '') \
+ '答案是 {}'.format(label)
elif dataset_name in cloze_datasets:
question_input = '问题 {}. '.format(idx + 1) + question + '\n'
question_output = (('问题 {}的解析: '.format(idx + 1) + exp + '\n') if load_explanation else '') \
+ '答案是 {}'.format(answer)
else:
raise ValueError(
f'During loading few-sot examples, found unknown dataset: {dataset_name}'
)
if chat_mode:
demostrations.append((question_input, question_output))
else:
demostrations.append(question_input + question_output + '\n')
return demostrations
enc = None
def _lazy_load_enc():
global enc
if enc is None:
enc = tiktoken.encoding_for_model('gpt-4')
# cut prompt if reach max token length
def concat_prompt(demos,
dataset_name,
max_tokens,
end_of_example='\n',
verbose=False):
_lazy_load_enc()
demostration_en = 'Here are the answers for the problems in the exam.\n'
demostration_zh = '以下是考试中各个问题的答案。\n'
for i in range(len(demos)):
# print(len(enc.encode(demostration_en)), len(enc.encode(demostration_zh)))
if dataset_name in english_qa_datasets:
demostration_en = demostration_en + demos[i] + end_of_example
elif dataset_name in chinese_qa_datasets:
demostration_zh = demostration_zh + demos[i] + end_of_example
elif dataset_name in english_cloze_datasets:
demostration_en = demostration_en + demos[i] + end_of_example
elif dataset_name in chinese_cloze_datasets:
demostration_zh = demostration_zh + demos[i] + end_of_example
# break if reach max token limit
if len(enc.encode(demostration_en)) < max_tokens and len(
enc.encode(demostration_zh)) < max_tokens:
output = demostration_en if len(demostration_en) > len(
demostration_zh) else demostration_zh
prompt_num = i + 1
else:
break
if verbose:
print('max_tokens set as ', max_tokens, 'actual_tokens is',
len(enc.encode(output)), 'num_shot is', prompt_num)
return output, prompt_num
def concat_prompt_chat_mode(demos,
dataset_name,
max_tokens,
end_of_example='\n',
verbose=False):
_lazy_load_enc()
answers = []
sentences = ''
for i in range(len(demos)):
answers += [
{
'role': 'user',
'content': demos[i][0]
},
{
'role': 'assistant',
'content': demos[i][1]
},
]
sentences += json.dumps(answers[-1])
# break if reach max token limit
if len(enc.encode(sentences)) > max_tokens:
answers.pop()
answers.pop()
break
if verbose:
print('max_tokens set as ', max_tokens, 'actual_tokens is',
len(enc.encode(sentences)), 'num_shot is',
len(answers) // 2)
return answers, len(answers) // 2
def convert_few_shot(line, dataset_name, demo, n_shot, chat_mode=False):
passage = line['passage'] if line['passage'] is not None else ''
question = line['question']
options = line['options'] if line['options'] is not None else ''
if dataset_name in qa_datasets:
question_input = '问题 {}. '.format(n_shot + 1) + passage + ' ' + question + '\n' \
+ '从以下选项中选择: ' + ' '.join(options) + '\n'
# + "问题 {}的解析: ".format(n_shot + 1)
if dataset_name in cloze_datasets:
question_input = '问题 {}. '.format(n_shot + 1) + question + '\n'
# + "问题 {}的解析: ".format(n_shot + 1)
if chat_mode:
return demo + [
{
'role': 'user',
'content': question_input
},
]
else:
return demo + question_input
def load_dataset(dataset_name,
setting_name,
parent_path,
prompt_path=None,
max_tokens=None,
end_of_example='\n',
chat_mode=False,
verbose=False):
test_path = os.path.join(parent_path, dataset_name + '.jsonl')
loaded_jsonl = read_jsonl(test_path)
processed = []
if setting_name == 'few-shot-CoT' or setting_name == 'few-shot':
# process demo once if it is few-shot-CoT
processed_demos = combine_prompt(
prompt_path,
dataset_name,
load_explanation=setting_name == 'few-shot-CoT',
chat_mode=chat_mode)
if chat_mode:
chosen_prompt, n_shot = concat_prompt_chat_mode(processed_demos,
dataset_name,
max_tokens,
end_of_example,
verbose=verbose)
else:
chosen_prompt, n_shot = concat_prompt(processed_demos,
dataset_name,
max_tokens,
end_of_example,
verbose=verbose)
if verbose:
loaded_jsonl = tqdm(loaded_jsonl)
for meta_idx, line in enumerate(loaded_jsonl):
# 正确
if setting_name == 'zero-shot':
ctxt = convert_zero_shot(line, dataset_name)
elif setting_name == 'zero-shot-CoT':
ctxt = convert_zero_shot_CoT_stage1(line, dataset_name)
elif setting_name == 'few-shot-CoT' or setting_name == 'few-shot':
ctxt = convert_few_shot(line, dataset_name, chosen_prompt, n_shot,
chat_mode)
try:
new_instance = ChatGPTSchema(context=ctxt, metadata=meta_idx)
processed.append(new_instance.to_dict())
except NameError:
print('Dataset not defined.')
return processed
def generate_second_stage_input(dataset_name,
input_list,
output_list,
with_format_prompt=False):
try:
chinese_format_prompt = '根据以上内容，你的任务是把最终的答案提取出来并填在【】中，例如【0】或者【A】。'
if dataset_name in qa_datasets:
prompt_suffix = '因此，从A到D, 我们应选择'
if with_format_prompt:
prompt_suffix = chinese_format_prompt + prompt_suffix
elif dataset_name in cloze_datasets:
prompt_suffix = '因此，答案是'
if with_format_prompt:
prompt_suffix = chinese_format_prompt + prompt_suffix
except NameError:
print('Dataset not defined.')
processed = []
for i in range(len(input_list)):
ctxt = '{0}\n{1}\n{2}'.format(input_list[i]['context'],
extract_answer(output_list[i]),
prompt_suffix)
new_instance = ChatGPTSchema(context=ctxt,
metadata=input_list[i]['metadata'])
processed.append(new_instance.to_dict())
return processed
def load_dataset_as_result_schema(dataset_name, parent_path):
test_path = os.path.join(parent_path, dataset_name + '.jsonl')
loaded_jsonl = read_jsonl(test_path)
processed = []
for i, line in enumerate(loaded_jsonl):
problem_input = convert_zero_shot(line, dataset_name)
processed.append(
ResultsForHumanSchema(
index=i,
problem_input=problem_input,
# label=line['label'] if line['label'] else line['answer']
label = line['answer']
))
return processed
if __name__ == '__main__':
# set variables
parent_dir = '../../data/exam_guidance'
# set dataset name to process
setting_name = 'zero-shot' # setting_name can be chosen from ["zero-shot", "zero-shot-CoT", "few-shot-CoT"]
data_name = 'health_exam'
save_dir = '../../experiment_input/{}/'.format(setting_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
processed_data = load_dataset(data_name,
setting_name,
parent_dir,
prompt_path=raw_prompt_path,
max_tokens=2048)
save_jsonl(processed_data,
os.path.join(save_dir, '{}.jsonl'.format(data_name)))
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