# 引入必要模块 # 数据操作 import pandas as pd import numpy as np import math import csv import random import os from monai.utils import set_determinism from torch.utils.data import Dataset, DataLoader # 进度条 from tqdm import tqdm # 特征选择 from sklearn.feature_selection import SelectKBest, f_regression # pytorch import torch import torch.nn as nn # 绘制图像 from torch.utils.tensorboard import SummaryWriter # set random seed def split_valid_set(data_set, valid_ratio, seed): np.random.seed(seed) indices = np.random.permutation(len(data_set)) valid_size = int(len(data_set) * valid_ratio) valid_idx = indices[:valid_size] train_idx = indices[valid_size:] return data_set[train_idx], data_set[valid_idx] def select_features(train_data,valid_data,test_data,select_all=True): # choose label y_train=train_data[:,-1] y_valid=valid_data[:,-1] # choose all features except label raw_x_train=train_data[:,:-1] raw_x_valid=valid_data[:,:-1] raw_x_test=test_data if select_all: feat_idx=list(range(raw_x_train.shape[1])) all_to_remove = {0} feat_idx = [x for x in feat_idx if x not in all_to_remove] else: # Feature selection feat_idx = [34, 35, 36, 43, 46, 47, 51, 52, 53, 54, 61, 64, 65, 69, 70, 71, 72, 79, 82, 83] return raw_x_train[:,feat_idx],raw_x_valid[:,feat_idx],raw_x_test[:,feat_idx],y_train,y_valid class COVID19Dataset(Dataset): def __init__(self, features,target=None): if target is None: # predict self.target=target else: # train self.target = torch.FloatTensor(target) self.features = torch.FloatTensor(features) def __getitem__(self,idx): # idx means index of sample if self.target is None: return self.features[idx] else: return self.features[idx],self.target[idx] def __len__(self): return len(self.features) # define model class MyModel(nn.Module): def __init__(self, input_dim): super(MyModel, self).__init__() hidden_dims = config['layer'] # 如 [128, 64, 32] layers = [] dims = [input_dim] + hidden_dims for in_dim, out_dim in zip(dims[:-1], dims[1:]): layers.append(nn.Linear(in_dim, out_dim)) layers.append(nn.GELU()) # 更先进的激活函数 # 可选：加入 Dropout 或 BatchNorm 以提升泛化能力 # layers.append(nn.Dropout(0.2)) layers.append(nn.Linear(dims[-1], 1)) # 输出层：无激活 self.layers = nn.Sequential(*layers) def forward(self, x): x = self.layers(x) return x.squeeze(1) # 输出 (B) # Set CFG device=torch.device('cuda' if torch.cuda.is_available() else 'cpu') config={ 'layer': [128, 64, 32], 'k':20, 'seed': 3407, 'select_all': False, 'normalize': True, 'batch_size': 128, 'lr': 0.00035099698616997447, 'n_epochs': 10000, 'valid_ratio': 0.2, 'early_stop': 400, 'save_path': './models/model.ckpt', 'beta1': 0.95, # β₁ 'eps': 1e-7, # ϵ } # train def train(train_loader,valid_loader,model,config,device): criterion=nn.MSELoss() optimizer = torch.optim.Adam( model.parameters(), lr=config['lr'], betas=(config['beta1'], 0.999), # 调整 β₁ eps=config['eps'], # 调整 ϵ # weight_decay=0 # 可选：是否加入 L2 正则化 ) writer=SummaryWriter() best_loss=math.inf early_stop_count=0 step=0 for epoch in range(config['n_epochs']): loss_record=[] model.train() train_bar=tqdm(train_loader) for x,y in train_bar: x,y=x.to(device),y.to(device) optimizer.zero_grad() outputs=model(x) loss=criterion(outputs,y) loss.backward() optimizer.step() loss_record.append(loss.detach().item()) step+=1 # validate model.eval() valid_loss_record=[] valid_bar=tqdm(valid_loader) for x,y in valid_bar: x,y=x.to(device),y.to(device) with torch.no_grad(): outputs=model(x) loss=criterion(outputs,y) valid_loss_record.append(loss.detach().item()) valid_loss=np.mean(valid_loss_record) writer.add_scalar('valid_loss',valid_loss,step) # save best model if valid_loss<best_loss: best_loss=valid_loss torch.save(model.state_dict(),config['save_path']) early_stop_count=0 print('Saving model with loss {:.3f}...'.format(best_loss)) else: early_stop_count+=1 if early_stop_count>=config['early_stop']: return best_loss return best_loss # set same seed set_determinism(seed=config['seed']) # 读取数据（保留 DataFrame 或 NumPy） train_data = pd.read_csv('./covid_train.csv').values test_data = pd.read_csv('./covid_test.csv').values # 切分数据（不转成 Subset） train_data, valid_data = split_valid_set(train_data, config['valid_ratio'], config['seed']) # 检查标签是否有 NaN if np.isnan(train_data[:, -1]).sum() > 0 or np.isnan(valid_data[:, -1]).sum() > 0: raise ValueError("Labels contain NaN values. Please check your data.") # Normalization with Min-Max scaling 可以加一个点 if config['normalize']: train_min = np.min(train_data[:, 35:-1], axis=0) # 计算每列最小值 train_max = np.max(train_data[:, 35:-1], axis=0) # 计算每列最大值 # 防止除以0的情况，可以加一个很小的数 epsilon = 1e-8 train_range = train_max - train_min + epsilon # 归一化训练集 train_data[:, 35:-1] = (train_data[:, 35:-1] - train_min) / train_range # 用相同的min和range归一化验证集 valid_data[:, 35:-1] = (valid_data[:, 35:-1] - train_min) / train_range # 用相同的min和range归一化测试集 test_data[:, 35:] = (test_data[:, 35:] - train_min) / train_range x_train,x_valid,x_test,y_train,y_valid=select_features(train_data,valid_data,test_data,config['select_all']) # convert to tensor train_dataset=COVID19Dataset(x_train,y_train) valid_dataset=COVID19Dataset(x_valid,y_valid) test_dataset=COVID19Dataset(x_test) # pin memory能保存数据到GPU的高速缓存中，能加快数据加载速度,shuffle=True表示每个epoch打乱数据 train_loader=DataLoader(train_dataset,batch_size=config['batch_size'],shuffle=True,pin_memory=True) valid_loader=DataLoader(valid_dataset,batch_size=config['batch_size'],shuffle=True,pin_memory=True) test_loader=DataLoader(test_dataset,batch_size=config['batch_size'],shuffle=False,pin_memory=True) # create model model=MyModel(input_dim=x_train.shape[1]).to(device) # train print('Training model...') best_loss=train(train_loader,valid_loader,model,config,device) print(f'Best validation loss: {best_loss:.3f}') def predict(test_loader,model,device): model.eval() preds=[] with torch.no_grad(): for x in tqdm(test_loader): x=x.to(device) outputs=model(x) # 把数据从GPU转移到CPU，才能转换为numpy数组 preds.append(outputs.detach().cpu()) preds = torch.cat(preds, dim=0).numpy() return preds # predict predictions=predict(test_loader,model,device) # save predictions def save_pred(preds, file): ''' Save predictions to specified file ''' with open(file, 'w') as fp: writer = csv.writer(fp) writer.writerow(['id', 'tested_positive']) for i, p in enumerate(preds): writer.writerow([i, p]) save_pred(predictions, './predictions.csv') print('Predictions saved in ./predictions.csv')
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