李宏毅机器学习作业1——预测COVID-19人数

目录

数据集

导包

辅助函数

设定种子

划分数据集

模型

特征选择

训练函数

配置参数

Dataloader 

开始训练

预测

预测函数

输出结果

解答

训练函数

模型

特征选择

超参数设置

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数据集

训练集中给出美国某些州五天COVID-19的感染人数（及相关特征数据），测试集中给出前四天的相关数据，预测第五天的感染人数。 下载地址：ML2022Spring-hw1 | Kaggle

特征包括：
● States (37, 独热编码)
● COVID-like illness (4)
        ○ cli、ili …
● Behavior Indicators (8)
        ○ wearing_mask、travel_outside_state …
● Mental Health Indicators (3)
        ○ anxious、depressed …
● Tested Positive Cases (1)
        ○ tested_positive (this is what we want to predict)

训练集有2699行， 118列 (id + 37 states + 16 features x 5 days)
测试集有1078，117列 (without last day's positive rate)

导包

# Numerical Operations
import math
import numpy as np
from sklearn.model_selection import train_test_split

# Reading/Writing Data
import pandas as pd
import os
import csv

# For Progress Bar
from tqdm import tqdm
from d2l import torch as d2l

# Pytorch
import torch 
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader, random_split, TensorDataset

# For plotting learning curve
from torch.utils.tensorboard import SummaryWriter

辅助函数

设定种子

我的理解是，模型初始化和验证集划分都要用到seed，这里固定下来

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)

划分数据集

可以使用random_split函数

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)

也可以采取train_test_split函数

def train_valid_split(data_set, valid_ratio, seed):
    '''Split provided training data into training set and validation set'''    
    train_set, valid_set =  train_test_split(data_set, test_size=valid_ratio, random_state=seed)
    return np.array(train_set), np.array(valid_set)

模型

整个作业最重要的就是模型和特征选择，还有超参数设置。这里放原代码

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, 16),
            nn.ReLU(),
            nn.Linear(16, 8),
            nn.ReLU(),
            nn.Linear(8, 1)
        )

    def forward(self, x):
        x = self.layers(x)
        x = x.squeeze(1) # (B, 1) -> (B)
        return x

特征选择

对原代码进行了改动，因为使用了TensorDataset，它的自变量应该是tensor，所以要把train_data, valid_data, test_data变为tensor格式

def select_feat(train_data, valid_data, test_data, select_all=True):
    '''Selects useful features to perform regression'''  
    
    train_data = torch.FloatTensor(train_data)
    valid_data = torch.FloatTensor(valid_data)
    test_data = torch.FloatTensor(test_data)
    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

    if select_all:
        feat_idx = list(range(raw_x_train.shape[1]))
    else:
        feat_idx = [0,1,2,3] # TODO: Select suitable feature columns.

    return raw_x_train[:,feat_idx], raw_x_valid[:,feat_idx], raw_x_test[:,feat_idx], y_train, y_valid

训练函数

以下是原代码给出的模板，使用了tqdm来呈现训练过程

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) 

    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()})

        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.')
            return

配置参数

config contains hyper-parameters for training and the path to save your model.

device = 'cuda' if torch.cuda.is_available() else 'cpu'
config = {
    'seed': 5201314,      # Your seed number, you can pick your lucky number. :)
    'select_all': True,   # Whether to use all features.
    'valid_ratio': 0.2,   # validation_size = train_size * valid_ratio
    'n_epochs': 3000,     # Number of epochs.            
    'batch_size': 256, 
    'learning_rate': 1e-5,              
    '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.
}

Dataloader 

使用TensorDataset来包装数据集，而不是像原代码自定义了一个

# 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('./covid.train.csv').values, pd.read_csv('./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, False)

# Print out the number of features.
print(f'number of features: {x_train.shape[1]}')

train_dataset, valid_dataset, test_dataset = TensorDataset(x_train, y_train), \
        TensorDataset(x_valid, y_valid), \
        TensorDataset(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)

开始训练

使用nn.DataParallel函数进行并行计算。我在kaggle上使用GPU T4， 选择使用并行计算，能看到两块GPU都被占用了，但是计算速度比不使用并行计算还要慢一半，奇怪了。有没有大神解答一下

devices = d2l.try_all_gpus()
model = My_Model(input_dim=x_train.shape[1]) # put your model and data on the same computation device.
model = nn.DataParallel(model, device_ids = devices).to(devices[0])
trainer(train_loader, valid_loader, model, config)

预测

预测函数

def predict(test_loader, model, devices):
    model.eval() # Set your model to evaluation mode.
    preds = []
    for x in tqdm(test_loader):
        x = x[0].to(devices[0])                      
        with torch.no_grad():                   
            pred = model(x)                     
            preds.append(pred.detach().cpu())   
    preds = torch.cat(preds, dim=0).numpy()  
    return preds

输出结果

def save_pred(preds, file):
    ''' Save predictions to specified file '''
    with open(file, 'w') as f:
        f.write('id,tested_positive' + '\n')        
        for i, p in enumerate(preds):
            f.write(str(i) + ',' + str(p) + '\n')

model = My_Model(input_dim=x_train.shape[1]).to(devices[0])
model = nn.DataParallel(model, device_ids = devices).to(devices[0])
model.load_state_dict(torch.load(config['save_path']))
preds = predict(test_loader, model, devices) 
save_pred(preds, 'pred.csv')  

解答

训练函数

使用了《动手学深度学习》这本书中的d2l工具包，在训练过程画出loss。注意这里使用了adam函数，在optimizer中加入L2正则，并且使用CosineAnnealingWarmRestarts函数调整学习率。

def trainer(train_loader, valid_loader, model, config, devices):    
    
    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.Adam(model.parameters(), lr=config['learning_rate'], weight_decay=config['weight_decay']) 
    scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 
                                        T_0=2, T_mult=2, eta_min=config['learning_rate']/50)
    
    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

    legend = ['train loss']
    if valid_loader is not None:
        legend.append('valid loss')        
    animator = d2l.Animator(xlabel='epoch', xlim=[1, n_epochs], ylim=[0.5, 2.5], legend=legend)
      
    for epoch in range(n_epochs):
        model.train() # Set your model to train mode.
        loss_record = []

        for x, y in train_loader:
            optimizer.zero_grad()               # Set gradient to zero.
            x, y = x.to(devices[0]), y.to(devices[0])  # 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())
            
        scheduler.step()
            
        mean_train_loss = sum(loss_record)/len(loss_record)
        writer.add_scalar('Loss/train', mean_train_loss, step)
        animator.add(epoch + 1, (mean_train_loss, None)) 
               
            
        if valid_loader is not None:
            model.eval() # Set your model to evaluation mode.
            loss_record = []
            for x, y in valid_loader:
                x, y = x.to(devices[0]), y.to(devices[0])
                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)
            writer.add_scalar('Loss/valid', mean_valid_loss, step)
            animator.add(epoch +1, (None, mean_valid_loss))

            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.')
                return

模型

加深了一层

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, 16),
            nn.ReLU(),
            nn.Linear(16, 8),
            nn.ReLU(),
            nn.Linear(8, 4),
            nn.ReLU(),
            nn.Linear(4, 1)
        )

    def forward(self, x):
        x = self.layers(x)
        x = x.squeeze(1) # (B, 1) -> (B)
        return x

特征选择

本来以为，根据表征学习的概念，什么特征重要，什么特征不重要，机器可以自己学出来，所以特征选择并不重要。在不断地调试模型过程中，发现这种想法大大地错误，特征选择对结果影响很大。可能的原因是，数据量不够大，导致模型不够复杂，所以达不到表征学习的效果。换句话说，这个任务还是在机器学习的范畴，还没到深度学习的领域，所以特征很重要。

下面选择前四天的患病数作为特征。

def select_feat(train_data, valid_data, test_data, select_all=True):
    '''Selects useful features to perform regression'''  
    
    train_data = torch.FloatTensor(train_data)
    valid_data = torch.FloatTensor(valid_data)
    test_data = torch.FloatTensor(test_data)
    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

    if select_all:
        feat_idx = list(range(raw_x_train.shape[1]))
    else:
        feat_idx = [53, 69, 85, 101]  # TODO: Select suitable feature columns.

    return raw_x_train[:,feat_idx], raw_x_valid[:,feat_idx], raw_x_test[:,feat_idx], y_train, y_valid

超参数设置

学习率非常重要，大体试几个数即可确定范围：在有的学习率下，迭代了200步，loss还是好几十；在合适的学习率下，迭代了不到50步，loss就降到了个位数。后者是想要的

config = {
    'seed': 5201314,      # Your seed number, you can pick your lucky number. :)
    'select_all': True,   # Whether to use all features.
    'valid_ratio': 0.2,   # validation_size = train_size * valid_ratio
    'n_epochs': 3000,     # Number of epochs.            
    'batch_size': 256, 
    'learning_rate': 1e-3,  
    'weight_decay': 1e-4,
    'early_stop': 900,    # If model has not improved for this many consecutive epochs, stop training.     
    'save_path': './models/model.ckpt',  # Your model will be saved here.
}

通过Strong Baseline 

其实步调整模型层数也行，结果如下图，几乎一模一样

模型设置和特征选择可以更加复杂，结果也会更好，可以参考：

李宏毅2022机器学习HW2解析_机器学习手艺人的博客-CSDN博客_李宏毅机器学习作业2