wehub-resource-sync
116 lines
4.7 KiB
Python
116 lines
4.7 KiB
Python
# This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
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import os
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import argparse
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import pandas as pd
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import torch
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import json
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from llama_evaluator import Llama_Evaluator
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import time
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choices = ["A", "B", "C", "D"]
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def main(args, evaluator,take):
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assert os.path.exists("subject_mapping.json"), "subject_mapping.json not found!"
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with open("subject_mapping.json") as f:
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subject_mapping = json.load(f)
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filenames = os.listdir("data/val")
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subject_list = [val_file.replace("_val.csv","") for val_file in filenames]
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accuracy, summary = {}, {}
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run_date=time.strftime('%Y-%m-%d_%H-%M-%S',time.localtime(time.time()))
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output_dir = args.output_dir
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save_result_dir=os.path.join(output_dir,f"take{take}")
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if not os.path.exists(save_result_dir):
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os.makedirs(save_result_dir,exist_ok=True)
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all_answers = {}
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for index,subject_name in enumerate(subject_list):
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print(f"{index/len(subject_list)} Inference starts at {run_date} on {args.model_path} with subject of {subject_name}!")
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val_file_path=os.path.join('data/val',f'{subject_name}_val.csv')
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dev_file_path=os.path.join('data/dev',f'{subject_name}_dev.csv')
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test_file_path=os.path.join('data/test',f'{subject_name}_test.csv')
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val_df=pd.read_csv(val_file_path) if args.do_test is False else pd.read_csv(test_file_path)
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dev_df=pd.read_csv(dev_file_path) if args.few_shot else None
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correct_ratio, answers = evaluator.eval_subject(subject_name, val_df, dev_df,
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save_result_dir=save_result_dir if args.do_save_csv else None,
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few_shot=args.few_shot,
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cot=args.cot,
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with_prompt=args.with_prompt,
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constrained_decoding=args.constrained_decoding,
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do_test=args.do_test)
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print(f"Subject: {subject_name}")
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print(f"Acc: {correct_ratio}")
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accuracy[subject_name] = correct_ratio
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summary[subject_name] = {"score":correct_ratio,
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"num":len(val_df),
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"correct":correct_ratio*len(val_df)/100}
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all_answers[subject_name] = answers
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json.dump(all_answers,open(save_result_dir+'/submission.json','w'),ensure_ascii=False,indent=4)
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print("Accuracy:")
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for k, v in accuracy.items():
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print(k, ": ", v)
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total_num = 0
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total_correct = 0
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summary['grouped'] = {
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"STEM": {"correct": 0.0, "num": 0},
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"Social Science": {"correct": 0.0, "num": 0},
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"Humanities": {"correct": 0.0, "num": 0},
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"Other": {"correct": 0.0, "num": 0}
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}
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for subj, info in subject_mapping.items():
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group = info[2]
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summary['grouped'][group]["num"] += summary[subj]['num']
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summary['grouped'][group]["correct"] += summary[subj]['correct']
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for group, info in summary['grouped'].items():
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info['score'] = info["correct"] / info["num"]
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total_num += info["num"]
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total_correct += info["correct"]
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summary['All'] = {"score": total_correct / total_num, "num": total_num, "correct": total_correct}
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json.dump(summary,open(save_result_dir+'/summary.json','w'),ensure_ascii=False,indent=2)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_path", type=str)
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parser.add_argument("--cot",choices=["False","True"], default="False")
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parser.add_argument("--few_shot", choices=["False","True"], default="True")
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parser.add_argument("--ntrain", "-k", type=int, default=5)
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parser.add_argument("--with_prompt", choices=["False","True"], default="False")
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parser.add_argument("--constrained_decoding", choices=["False","True"], default="True")
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parser.add_argument("--temperature",type=float,default=0.2)
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parser.add_argument("--n_times", default=1,type=int)
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parser.add_argument("--do_save_csv", choices=["False","True"], default="False")
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parser.add_argument("--output_dir", type=str)
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parser.add_argument("--do_test", choices=["False","True"], default="False")
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args = parser.parse_args()
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args.cot = args.cot == "True"
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args.few_shot = args.few_shot == "True"
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args.with_prompt = args.with_prompt == "True"
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args.constrained_decoding = args.constrained_decoding == "True"
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args.do_test = args.do_test == "True"
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args.do_save_csv = args.do_save_csv == "True"
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if args.constrained_decoding is True:
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args.n_times=max(args.n_times,1)
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print(args)
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device = torch.device(0)
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print(device)
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evaluator=Llama_Evaluator(
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choices=choices,
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k=args.ntrain,
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model_path=args.model_path,
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device=device,
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temperature = args.temperature
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)
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for i in range(args.n_times):
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main(args,evaluator=evaluator,take=i)
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