wehub-resource-sync
131 lines
5.3 KiB
Python
131 lines
5.3 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Adapted from https://github.com/ymcui/Chinese-LLaMA-Alpaca and https://github.com/SJTU-LIT/ceval
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import argparse
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import json
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import os
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import time
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import pandas as pd
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from model_evaluator import ModelEvaluator
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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(
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f"{index/len(subject_list)} Inference starts at {run_date} on {args.model_name_or_path} with subject of {subject_name}!"
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)
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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(
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subject_name,
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val_df,
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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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)
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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] = {
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"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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}
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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_name_or_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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evaluator = ModelEvaluator(
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choices=choices, k=args.ntrain, model_name_or_path=args.model_name_or_path, 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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