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TestChatGLM.py
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TestChatGLM.py
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import argparse
import os
import numpy as np
import pandas as pd
import time
import re
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("ChatGLM_6B", trust_remote_code=True)
model = AutoModel.from_pretrained("ChatGLM_6B", trust_remote_code=True).half().cuda()
choices = ["A", "B", "C", "D"]
def find_valid_substrings(s):
# 匹配长度为1到4的、不包含重复字符的子串
pattern = r'[ABCD]{1,4}'
substrings = re.findall(pattern, s)
# 过滤出不包含重复字符的子串
valid_substrings = [substring for substring in substrings if len(substring) == len(set(substring))]
return valid_substrings
def format_subject(subject):
l = subject.split("_")
s = ""
for entry in l:
s += " " + entry
return s
def format_example(df, idx, include_answer=True):
prompt = df.iloc[idx, 0]
k = df.shape[1] - 2
for j in range(k):
#prompt += "\n{}. {}".format(choices[j], df.iloc[idx, j+1])
try:
prompt += "\n{}. {}".format(choices[j], df.iloc[idx, j+1].replace(" ．", "").replace("A、", "").replace("B、", "").replace("C、", "").replace("D、", "").replace("A.", "").replace("B.", "").replace("C.", "").replace("D.", "").replace("A", "").replace("B", "").replace("C", "").replace("D", "").replace("Ａ、", "").replace("Ｂ、", "").replace("Ｃ、", "").replace("Ｄ、", "").strip())
except Exception as e:
prompt += "\n{}. {}".format(choices[j], df.iloc[idx, j+1])
#prompt += "\nAnswer:"
prompt += "\n正确答案的序号是："
if include_answer:
prompt += " {}\n\n".format(df.iloc[idx, k + 1])
return prompt
# 生成 prompt
# 不提示专业科目，让模型直接理解题目
def gen_prompt(train_df, k=-1):
prompt = "请阅读以下选择题并给出正确选项，不要解释原因。请只给出答案的序号。\n"
if k == -1:
k = train_df.shape[0]
for i in range(k):
prompt += format_example(train_df, i)
return prompt
# chatglm
def plain_chat(prompt):
response, history = model.chat(tokenizer, prompt, history=[])
return response
def read_file_lines(file):
with open(file, 'r', errors='ignore', encoding='utf8') as f:
lines = f.readlines()
return lines
def writelines_to_file(file, lines):
with open(file, 'w', encoding='utf8') as f:
f.writelines(lines)
def writetext_to_file(file, contents):
with open(file, 'w', encoding='utf8') as f:
f.write(contents)
def eval(args, subject, dev_df, test_df):
logfile = "chatglmlogfile0512"
cors = []
#labels = []
preds = []
for i in range(test_df.shape[0]):
# get prompt and make sure it fits
k = args.ntrain
prompt_end = format_example(test_df, i, include_answer=False)
train_prompt = gen_prompt(dev_df, k)
prompt = train_prompt + prompt_end
#print("train_prompt:", train_prompt)
print("题目：", prompt)
with open(logfile, 'a', encoding='utf8') as f:
f.write(prompt+"\n")
try:
label = test_df.iloc[i, test_df.shape[1]-1]
# A B C D 特殊处理
label = label.replace(" ", "").replace("Ａ", "A").replace("Ｂ", "B").replace("Ｃ", "C").replace("Ｄ", "D")
label = label.replace("\u3000", "").replace(",", "")
print("正确答案:", label)
with open(logfile, 'a', encoding='utf8') as f:
f.write("正确答案:"+label+"\n")
except Exception as e:
print(e)
break
while True:
try:
time.sleep(1)
pred = plain_chat(prompt)
pred = pred.replace("、", "").replace(".", "").replace(",", "").replace(";", "").replace("，", "")
try:
# 识别答案pattern
pred = find_valid_substrings(pred)[0]
except Exception as e:
print(e)
pred = "未成功回答"
print("模型预测答案:", pred)
with open(logfile, 'a', encoding='utf8') as f:
f.write("模型预测答案:"+pred+"\n")
break
except Exception as e:
print(e)
print("pausing")
time.sleep(10)
continue
try:
cor = pred == label
print("是否答对：", cor)
with open(logfile, 'a', encoding='utf8') as f:
f.write("是否答对："+str(cor)+"\n")
cors.append(cor)
preds.append(pred+"|||"+label+"|||"+str(cor))
#labels.append(label)
except Exception as e:
print(e)
acc = np.mean(cors)
acc_info = "Average accuracy {:.3f} - {}".format(acc, subject)
print(acc_info)
preds.append(acc_info)
preds = [x+"\n" for x in preds]
return preds
def main(args):
subjects = sorted([f.split(".xlsx")[0] for f in os.listdir(os.path.join(args.data_dir, "test")) if ".xlsx" in f])
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
print("subjects:", subjects)
print("args", args)
for subject in subjects:
if subject != "医疗":
dev_df = pd.read_excel(os.path.join(args.data_dir, "dev", subject + ".xlsx"), header=0)[:args.ntrain]
test_df = pd.read_excel(os.path.join(args.data_dir, "test", subject + ".xlsx"), header=0)
preds = eval(args, subject, dev_df, test_df)
writelines_to_file(os.path.join(args.save_dir, subject), preds)
else:
dev_df = pd.read_excel(os.path.join(args.data_dir, "dev", subject + ".xlsx"), header=0)[:args.ntrain]
f = pd.ExcelFile(os.path.join(args.data_dir, "test", subject + ".xlsx"))
sheet_list = f.sheet_names
for sheet in sheet_list:
test_df = pd.read_excel(os.path.join(args.data_dir, "test", subject + ".xlsx"), header=0, sheet_name=sheet)
preds = eval(args, sheet, dev_df, test_df)
writelines_to_file(os.path.join(args.save_dir, sheet), preds)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--ntrain", "-k", type=int, default=5)
parser.add_argument("--data_dir", "-d", type=str, default="data")
parser.add_argument("--save_dir", "-s", type=str, default="results")
args = parser.parse_args()
main(args)