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test_Normal_with_exception.py
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test_Normal_with_exception.py
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# !/usr/bin/env python3
# _*_ coding:utf-8 _*_
"""
@File : test.py
@Project : SelfPolish
@Time : 2023/5/30
@Author : Zhiheng Xi
"""
from ProblemMethod import *
from openai_utils import *
from utils import *
import os
import argparse
import numpy as np
import traceback
import time
import openai
def main(args):
keys = [
"Your Key."
]
# os.environ['http_proxy'] = '127.0.0.1:7890'
# os.environ['https_proxy'] = '127.0.0.1:7890'
openai.api_key=keys[0]
# data
args.dataset_path = get_dataset_path(args.dataset, args.file_type, ori_path="./datasets")
questions, answers = data_reader(dataset=args.dataset,dataset_path=args.dataset_path)
qa_pairs = [(questions[idx], answers[idx]) for idx in range(len(questions))]
print("loading dataset complete. altogether", len(qa_pairs), "questions")
print("top 10 quesitons: ", questions[: 10])
print("top 10 answers: ", answers[: 10])
if args.num_test == -1:
qa_pairs_test = qa_pairs
else:
np.random.seed(args.seed)
rand_indices = np.random.choice(len(qa_pairs), args.num_test, replace=False)
qa_pairs_test = [qa_pairs[i] for i in rand_indices]
print("qa_pairs_test_len:", len(qa_pairs_test))
# set logs
set_log(args)
# answer few shot cot
if args.method == "few_shot":
cur_answer_method = AnswerFewShot(
answer_method=args.method,
eng=args.eng,
few_shot_prompt_path="prompt/my_prompts/standard/standard_base_{}.txt".format(args.dataset)
)
elif args.method=="few_shot_cot":
cur_answer_method = AnswerFewShotCoT(
answer_method=args.method,
eng=args.eng,
few_shot_prompt_path="prompt/my_prompts/cot/cot_base_{}.txt".format(args.dataset)
)
elif args.method=="least_to_most":
cur_answer_method=AnswerFewShotLtM(
answer_method = args.method,
eng = args.eng,
problem_reducing_prompt_path="prompt/my_prompts/least_to_most/problem_reducing_{}.txt".format(args.dataset),
problem_solving_prompt_path="prompt/my_prompts/least_to_most/problem_solving_{}.txt".format(args.dataset)
)
# problem side
if args.method2=="Normal":
normal_method = ProblemNormal(
problem_method=args.method2,
answer_method=cur_answer_method,
eng=args.eng
)
count = 0
original_correct = 0.0
i = 0
while i < len(qa_pairs_test):
question = qa_pairs_test[i][0]
answer = qa_pairs_test[i][1]
print()
print("*************************************************************")
print("*************************************************************")
time.sleep(1.5)
original_correctness=False
try:
original_correctness,original_answer,_,_ = normal_method.forward(
# question="James wants to swim across a 20-mile lake. He can swim at a pace of 2 miles per hour. He swims 60% of the distance and then rests on an island for half as long as his swimming time. He then finishes the remaining distance while going half the speed. How long does it take him to swim across the lake?",
# question="Melanie is a door-to-door saleswoman. She sold a third of her vacuum cleaners at the green house, 2 more to the red house, and half of what was left at the orange house. If Melanie has 5 vacuum cleaners left, how many did she start with?",
question= question,
# answer="17",
# answer="18",
answer=answer,
direct_answer_trigger_for_fewshot=args.direct_answer_trigger_for_fewshot,
dataset=args.dataset,
prompt_index=args.prompt_index
)
if original_correctness:
original_correct+=1
print("all/correct: {}/{}".format(i+1, original_correct))
i += 1
except Exception as e:
print(repr(e))
traceback.print_exc()
if "RateLimitError" in repr(e) or "APIConnectionError" in repr(e) or "AuthenticationError" in repr(e):
print("Exception of Open AI API Key!")
else:
print("all/original correct: {}/{}".format(i+1, original_correct))
i += 1
continue
pass
def set_log(args):
args.output_csv_path = "./results/{}_{}_{}_{}_num_test_{}_seed_{}_max_tokens_{}_file_type_{}_prompt_index{}.csv".format(
args.method2, args.method, args.eng, args.dataset, args.num_test,
args.seed, args.max_tokens, args.file_type, args.prompt_index
)
if not os.path.exists(args.output_csv_path):
with open(args.output_csv_path, 'a+', encoding='utf-8') as f:
f.write("")
make_print_to_file("./logs/{}_{}_{}_{}_num_test_{}_seed_{}_max_tokens_{}_file_type_{}.log".format(
args.method2, args.method, args.eng, args.dataset, args.num_test,
args.seed, args.max_tokens, args.file_type
))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--eng", default="text-davinci-003", type=str, help="engine")
parser.add_argument("--dataset", default="gsm8k", type=str,
help="the dataset name")
parser.add_argument("--num_test", default=200, type=int, help="number of samples tested. -1 if on all test samples")
parser.add_argument("--seed", default=1357, type=int, help="random seed")
parser.add_argument("--temp", default=0.0, type=float, help="temperature for generation")
parser.add_argument("--max_tokens", default=1024, type=int, help="max # of tokens for generation")
parser.add_argument("--suffix", default="", type=str, help="")
parser.add_argument("--direct_answer_trigger_for_fewshot", default="The answer is", type=str,
help="used for extract answer")
parser.add_argument("--method", default="few_shot", type=str,
help="we use prompt so that the method is few-shot")
parser.add_argument("--method2", default="Normal", type=str,
help="SP Self-Polish；Normal")
parser.add_argument("--file_type", default="test", type=str)
parser.add_argument("--prompt_index", default="0", type=str, help="choose the best prompt set for the dataset")
args = parser.parse_args()
main(args)