python实现类似wandb.sweep搜参功能_和wandb类似的

import argparse
from argparse import Namespace
from wandb_train import main
from pprint import pprint
import wandb
import random
import os
import csv
 
 
if __name__ == "__main__":
    config_dataset_name=['assist2009','assist2012','assist2017','nips_task34']
    config_dropout=[0.1, 0.2, 0.3,0.4]
    config_emb_size=[64,128,256]
    config_num_attn_heads=[1,2,4,8]
    config_n_blocks=[1,2,4,8]
    config_learning_rate=[0.1,0.01,0.001,0.003,0.005]
 
    file_path = './result/'+config_dataset_name[3]+'_saint_2.csv'
    if os.path.exists(file_path):
        print("文件存在")
    else:
        file_name='./result/'+config_dataset_name[3]+'_saint_2.csv'
        data = ['best_auc','best_acc','best_epoch', 'dataset_name', 'dropout','emb_size','learning_rate','num_attn_heads','n_blocks']
        with open(file_name, 'w', newline='') as file:
            writer = csv.writer(file)
            writer.writerow(data)
 
    for i in range(900):
        random.seed(i)
        drop_num=random.randint(0,len(config_dropout)-1)
        emb_num=random.randint(0,len(config_emb_size)-1)
        heads_num=random.randint(0,len(config_num_attn_heads)-1)
        blocks_num=random.randint(0,len(config_n_blocks)-1)
        learning_num=random.randint(0,len(config_learning_rate)-1)
 
 
        parser = argparse.ArgumentParser()
        parser.add_argument("--dataset_name", type=str, default=config_dataset_name[3])
        parser.add_argument("--model_name", type=str, default="saint")
        parser.add_argument("--emb_type", type=str, default="qid")
        parser.add_argument("--save_dir", type=str, default="saved_model")
        # parser.add_argument("--learning_rate", type=float, default=1e-5)
        parser.add_argument("--seed", type=int, default=42)
        parser.add_argument("--fold", type=int, default=0)
 
        parser.add_argument("--dropout", type=float, default=config_dropout[drop_num])
        parser.add_argument("--emb_size", type=int, default=config_emb_size[emb_num])
        parser.add_argument("--learning_rate", type=float, default=config_learning_rate[learning_num])
        parser.add_argument("--num_attn_heads", type=int, default=config_num_attn_heads[heads_num])
        parser.add_argument("--n_blocks", type=int, default=config_n_blocks[blocks_num])
        parser.add_argument("--use_wandb", type=int, default=0)
        parser.add_argument("--add_uuid", type=int, default=1)
 
    
        args = parser.parse_args()
        params = vars(args)
 
        main(params)
    # main(params)

best_auc,best_acc, best_epoch = train_model(model, train_loader, valid_loader, num_epochs, opt, ckpt_path, None, None, save_model)
    
    
    data = [best_auc,best_acc,best_epoch, params["dataset_name"], params["dropout"],params["emb_size"],params["learning_rate"],params["num_attn_heads"],params["n_blocks"]]
    file_path = './result/'+params["dataset_name"]+'_saint_2.csv'
    with open(file_path, 'a', newline='') as file:
        writer = csv.writer(file)
        writer.writerow(data)