feat: zhengquanwei028-HW1~HW2~HW3

HW01/zhengquanwei028-HW01.py

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+# HW1
+# Numerical Operations
+import math
+import numpy as np
+
+# Reading/Writing Data
+import pandas as pd
+import os
+import csv
+os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
+# For Progress Bar
+from tqdm import tqdm
+
+# Pytorch
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader, random_split
+from torch.optim.lr_scheduler import CosineAnnealingLR,CosineAnnealingWarmRestarts,StepLR, ReduceLROnPlateau
+
+# For plotting learning curve
+from torch.utils.tensorboard import SummaryWriter
+
+# 功能函数
+def same_seed(seed):
+    '''Fixes random number generator seeds for reproducibility.'''
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+
+
+def train_valid_split(data_set, valid_ratio, seed):
+    '''Split provided training data into training set and validation set'''
+    valid_set_size = int(valid_ratio * len(data_set))
+    train_set_size = len(data_set) - valid_set_size
+    train_set, valid_set = random_split(data_set, [train_set_size, valid_set_size], generator=torch.Generator().manual_seed(seed))
+    return np.array(train_set), np.array(valid_set)
+
+
+def predict(test_loader, model, device):
+    model.eval() # Set your model to evaluation mode.
+    preds = []
+    for x in tqdm(test_loader):
+        x = x.to(device)
+        with torch.no_grad():
+            pred = model(x)
+            preds.append(pred.detach().cpu())
+    preds = torch.cat(preds, dim=0).numpy()
+    return preds
+
+
+## 数据
+
+class COVID19Dataset(Dataset):
+    '''
+    x: Features.
+    y: Targets, if none, do prediction.
+    '''
+    def __init__(self, x, y=None):
+        if y is None:
+            self.y = y
+        else:
+            self.y = torch.FloatTensor(y)
+        self.x = torch.FloatTensor(x)
+
+
+    def __getitem__(self, idx):
+        if self.y is None:
+            return self.x[idx]
+        else:
+            return self.x[idx], self.y[idx]
+
+    def __len__(self):
+        return len(self.x)
+
+
+## 神经网络
+
+class My_Model(nn.Module):
+    def __init__(self, input_dim):
+        super(My_Model, self).__init__()
+        # TODO: modify model's structure, be aware of dimensions.
+        self.layers = nn.Sequential(
+            nn.Linear(input_dim, 64),
+            nn.Dropout(p=0.2),
+            nn.ReLU(),
+            nn.Linear(64, 8),
+            nn.Dropout(p=0.1),
+            nn.LeakyReLU(0.6),
+            nn.Linear(8, 1),
+        )
+
+    def forward(self, x):
+        # x = self.dropout(x)
+        # print('dropout 输出', x)
+        x = self.layers(x)
+        x = x.squeeze(1) # (B, 1) -> (B)
+        return x
+
+config = {
+    'seed': 404,      # Your seed number, you can pick your lucky number. :)
+    'select_all': False,   # Whether to use all features.
+    'valid_ratio': 0.2,   # validation_size = train_size * valid_ratio
+    'n_epochs': 3000,     # Number of epochs.
+    'batch_size': 512,
+    'learning_rate': 1e-3,
+    # 'weight_decay': 1e-3,
+    'weight_decay': 0,
+    'early_stop': 400,    # If model has not improved for this many consecutive epochs, stop training.
+    'save_path': './models/model.ckpt'  # Your model will be saved here.
+}
+
+def select_feat(train_data, valid_data, test_data, select_all=True):
+    '''Selects useful features to perform regression'''
+    y_train, y_valid = train_data[:, -1], valid_data[:, -1]
+    raw_x_train, raw_x_valid, raw_x_test = train_data[:, :-1], valid_data[:, :-1], test_data
+    print('select_feats', raw_x_train[0])
+    print('select_feat default idx', raw_x_train.shape[1])
+    all_feats = 'id,AL,AK,AZ,AR,CA,CO,CT,FL,GA,ID,IL,IN,IA,KS,KY,LA,MD,MA,MI,MN,MS,MO,NE,NV,NJ,NM,NY,NC,OH,OK,OR,RI,SC,TX,UT,VA,WA,cli,ili,hh_cmnty_cli,nohh_cmnty_cli,wearing_mask,travel_outside_state,work_outside_home,shop,restaurant,spent_time,large_event,public_transit,anxious,depressed,worried_finances,tested_positive,cli,ili,hh_cmnty_cli,nohh_cmnty_cli,wearing_mask,travel_outside_state,work_outside_home,shop,restaurant,spent_time,large_event,public_transit,anxious,depressed,worried_finances,tested_positive,cli,ili,hh_cmnty_cli,nohh_cmnty_cli,wearing_mask,travel_outside_state,work_outside_home,shop,restaurant,spent_time,large_event,public_transit,anxious,depressed,worried_finances,tested_positive,cli,ili,hh_cmnty_cli,nohh_cmnty_cli,wearing_mask,travel_outside_state,work_outside_home,shop,restaurant,spent_time,large_event,public_transit,anxious,depressed,worried_finances,tested_positive,cli,ili,hh_cmnty_cli,nohh_cmnty_cli,wearing_mask,travel_outside_state,work_outside_home,shop,restaurant,spent_time,large_event,public_transit,anxious,depressed,worried_finances'.split(',')
+    # states_feats = all_feats[1:38]
+    # illness = np.array(['cli','ili','hh_cmnty_cli','nohh_cmnty_cli','tested_positive'])
+    # valid_feats = 'AL,AK,AZ,AR,CA,CO,CT,FL,GA,ID,IL,IN,IA,KS,KY,LA,MD,MA,MI,MN,MS,MO,NE,NV,NJ,NM,NY,NC,OH,OK,OR,RI,SC,TX,UT,VA,WA,cli,ili,hh_cmnty_cli,nohh_cmnty_cli,tested_positive'.split(',')
+    valid_feats = 'cli,ili,hh_cmnty_cli,nohh_cmnty_cli,tested_positive'.split(',')
+
+    valid_idx = []
+    for i in list(range(len(all_feats))):
+        x = all_feats[i]
+        if x in valid_feats:
+            valid_idx.append(i)
+    print(valid_idx)
+    if select_all:
+        feat_idx = list(range(raw_x_train.shape[1]))
+    else:
+        # feat_idx = [0, 1, 2, 3, 4]  # TODO: Select suitable feature columns.
+        feat_idx = valid_idx
+    return raw_x_train[:, feat_idx], raw_x_valid[:, feat_idx], raw_x_test[:, feat_idx], y_train, y_valid
+
+
+def trainer(train_loader, valid_loader, model, config, device):
+    criterion = nn.MSELoss(reduction='mean')  # Define your loss function, do not modify this.
+    # Define your optimization algorithm.
+    # TODO: Please check https://pytorch.org/docs/stable/optim.html to get more available algorithms.
+    # TODO: L2 regularization (optimizer(weight decay...) or implement by your self).
+
+    # optimizer = torch.optim.SGD(model.parameters(), lr=config['learning_rate'], momentum=0.9, weight_decay=config['weight_decay'])
+    # params_dict = [{
+    #     'params': model.layers[0].parameters(),
+    #     'lr': 0.001,
+    # }, {
+    #     'params': model.layers[1].parameters(),
+    #     'lr': 0.01,
+    #     # 'weight_decay': config['weight_decay'],
+    # }]
+    optimizer = torch.optim.Adam(model.parameters(), lr=config['learning_rate'],weight_decay=config['weight_decay'], amsgrad=False)
+    # optimizer = torch.optim.Adam(params_dict)
+    # 余弦退火
+    # scheduler = CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=1, verbose=True)
+    # scheduler = ReduceLROnPlateau(optimizer, 'min', verbose=True, min_lr=1e-7)
+    # scheduler = CosineAnnealingLR(optimizer, T_max=50, eta_min=1e-7, verbose=True)
+    writer = SummaryWriter()  # Writer of tensoboard.
+
+    if not os.path.isdir('./models'):
+        os.mkdir('./models')  # Create directory of saving models.
+
+    n_epochs, best_loss, step, early_stop_count = config['n_epochs'], math.inf, 0, 0
+
+    for epoch in range(n_epochs):
+        model.train()  # Set your model to train mode.
+        loss_record = []
+
+        # tqdm is a package to visualize your training progress.
+        train_pbar = tqdm(train_loader, position=0, leave=True)
+
+        for x, y in train_pbar:
+            optimizer.zero_grad()  # Set gradient to zero.
+            x, y = x.to(device), y.to(device)  # Move your data to device.
+            pred = model(x)
+            loss = criterion(pred, y)
+            loss.backward()  # Compute gradient(backpropagation).
+            optimizer.step()  # Update parameters.
+            step += 1
+            loss_record.append(loss.detach().item())
+
+            # Display current epoch number and loss on tqdm progress bar.
+            train_pbar.set_description(f'Epoch [{epoch + 1}/{n_epochs}]')
+            train_pbar.set_postfix({'loss': loss.detach().item()})
+        #
+        # scheduler.step(epoch)
+        mean_train_loss = sum(loss_record) / len(loss_record)
+        writer.add_scalar('Loss/train', mean_train_loss, step)
+
+        model.eval()  # Set your model to evaluation mode.
+        loss_record = []
+        for x, y in valid_loader:
+            x, y = x.to(device), y.to(device)
+            with torch.no_grad():
+                pred = model(x)
+                loss = criterion(pred, y)
+            loss_record.append(loss.item())
+
+        mean_valid_loss = sum(loss_record) / len(loss_record)
+        print(f'Epoch [{epoch + 1}/{n_epochs}]: Train loss: {mean_train_loss:.4f}, Valid loss: {mean_valid_loss:.4f}')
+        writer.add_scalar('Loss/valid', mean_valid_loss, step)
+
+        if mean_valid_loss < best_loss:
+            best_loss = mean_valid_loss
+            torch.save(model.state_dict(), config['save_path'])  # Save your best model
+            print('Saving model with loss {:.3f}...'.format(best_loss))
+            early_stop_count = 0
+        else:
+            early_stop_count += 1
+
+        if early_stop_count >= config['early_stop']:
+            print('\nModel is not improving, so we halt the training session.')
+            print('\n Mean valid loss', mean_valid_loss)
+            print('\n Best loss', best_loss)
+            return
+
+
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+
+# Set seed for reproducibility
+same_seed(config['seed'])
+
+
+# train_data size: 2699 x 118 (id + 37 states + 16 features x 5 days)
+# test_data size: 1078 x 117 (without last day's positive rate)
+train_data, test_data = pd.read_csv('./data/covid.train.csv').values, pd.read_csv('./data/covid.test.csv').values
+train_data, valid_data = train_valid_split(train_data, config['valid_ratio'], config['seed'])
+
+# Print out the data size.
+print(f"""train_data size: {train_data.shape} 
+valid_data size: {valid_data.shape} 
+test_data size: {test_data.shape}""")
+
+# Select features
+x_train, x_valid, x_test, y_train, y_valid = select_feat(train_data, valid_data, test_data, config['select_all'])
+
+# Print out the number of features.
+print(f'number of features: {x_train.shape[1]}')
+
+# train_dataset, valid_dataset, test_dataset = COVID19Dataset(x_train, y_train), \
+#                                             COVID19Dataset(x_valid, y_valid), \
+#                                             COVID19Dataset(x_test)
+train_dataset = COVID19Dataset(x_train, y_train)
+valid_dataset = COVID19Dataset(x_valid, y_valid)
+test_dataset = COVID19Dataset(x_test)
+
+
+# Pytorch data loader loads pytorch dataset into batches.
+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)
+
+
+model = My_Model(input_dim=x_train.shape[1]).to(device) # put your model and data on the same computation device.
+trainer(train_loader, valid_loader, model, config, device)
+
+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])
+
+# 预测测试集
+model.load_state_dict(torch.load(config['save_path']))
+preds = predict(test_loader, model, device)
+save_pred(preds, 'pred.csv')
+
+