李宏毅2023机器学习作业1--homework1

一、前期准备

下载训练数据和测试数据

# dropbox link
!wget -O covid_train.csv https://www.dropbox.com/s/lmy1riadzoy0ahw/covid.train.csv?dl=0
!wget -O covid_test.csv https://www.dropbox.com/s/zalbw42lu4nmhr2/covid.test.csv?dl=0

导入包

# Numerical Operations
import math
import numpy as np        # numpy操作数据，增加删除查找修改# Reading/Writing Data
import pandas as pd       # pandas读取csv文件
import os                 # 进行文件夹操作
import csv# For Progress Bar
from tqdm import tqdm     # 可视化# Pytorch
import torch              # pytorch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader, random_split# For plotting learning curve
from torch.utils.tensorboard import SummaryWriter

定义一些功能函数

def same_seed(seed):'''Fixes random number generator seeds for reproducibility.'''torch.backends.cudnn.deterministic = Truetorch.backends.cudnn.benchmark = Falsenp.random.seed(seed)torch.manual_seed(seed)if torch.cuda.is_available():torch.cuda.manual_seed_all(seed)# 划分训练数据集和验证数据集
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_sizetrain_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)

配置项

device = 'cuda' if torch.cuda.is_available() else 'cpu'
config = {'seed': 5201314,      # 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': 5000,     # Number of epochs.'batch_size': 256,'learning_rate': 1e-5,'early_stop': 600,    # If model has not improved for this many consecutive epochs, stop training.'save_path': './models/model.ckpt'  # Your model will be saved here.
}

二、创建数据

创建Dataset

class COVID19Dataset(Dataset):'''x: Features.y: Targets, if none, do prediction.'''def __init__(self, x, y=None):if y is None:self.y = yelse: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)

特征选择

删除了belife和mental 的特征，belife和mental都是心理上精神上的特征，感觉可能和阳性率的偏差较大，就删去了这两类的特征

def select_feat(train_data, valid_data, test_data, select_all=True):'''Selects useful features to perform regression'''# [:,-1]第一个维度选择所有，选取所有行，第二个维度选择-1，-1是倒数第一个元素，也就是标签labely_train, y_valid = train_data[:,-1], valid_data[:,-1]   # 选择标签元素# [:,:-1]第一个维度选择所有，所有行，第二个维度从开始元素到倒数第一个元素（不包含倒数第一个元素）raw_x_train, raw_x_valid, raw_x_test = train_data[:,:-1], valid_data[:,:-1], test_dataif select_all:feat_idx = list(range(raw_x_train.shape[1]))else:# feat_idx = list(range(35, raw_x_train.shape[1])) # TODO: Select suitable feature columns."""删除了belife和mental 的特征[0, 38, 39, 46, 51, 56, 57, 64, 69, 74, 75, 82, 87]是belife和mental所在列"""del_col = [0, 38, 39, 46, 51, 56, 57, 64, 69, 74, 75, 82, 87]  raw_x_train = np.delete(raw_x_train, del_col, axis=1) # numpy数组增删查改方法raw_x_valid = np.delete(raw_x_valid, del_col, axis=1)raw_x_test = np.delete(raw_x_test, del_col, axis=1)return raw_x_train, raw_x_valid, raw_x_test, y_train, y_validreturn raw_x_train[:,feat_idx], raw_x_valid[:,feat_idx], raw_x_test[:,feat_idx], y_train, y_valid

 创建 Dataloader

读取文件，设置训练，验证和测试数据集

# Set seed for reproducibility
same_seed(config['seed'])# train_data size: 3009 x 89 (35 states + 18 features x 3 days)  
# train_data共3009条数据，每条数据89个维度
# test_data size: 997 x 88 (without last day's positive rate)
# test_data共997条数据，每条数据88个维度，没有最后一天的最后一列数据positive rate# pands读取csv数据
train_data, test_data = pd.read_csv('./covid_train.csv').values, pd.read_csv('./covid_test.csv').values     # train_valid_split切分训练集和验证集
train_data, valid_data = train_valid_split(train_data, config['valid_ratio'], config['seed'])# Print out the data size.打印数据尺寸
print(f"""train_data size: {train_data.shape}
valid_data size: {valid_data.shape}
test_data size: {test_data.shape}""")# Select features 选择特征
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. 打印特征数
print(f'number of features: {x_train.shape[1]}')# 生成dataset
train_dataset, valid_dataset, test_dataset = COVID19Dataset(x_train, y_train), \COVID19Dataset(x_valid, y_valid), \COVID19Dataset(x_test)# Pytorch data loader loads pytorch dataset into batches.
# pytorch的dataloder加载dataset
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)

 三、创建神经网络模型

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

四、模型训练和模型测试

模型训练

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.# 如果没有models文件夹，创建名称为models的文件夹，保存模型if not os.path.isdir('./models'):    os.mkdir('./models') # Create directory of saving models.# math.inf为无限大n_epochs, best_loss, step, early_stop_count = config['n_epochs'], math.inf, 0, 0for epoch in range(n_epochs):model.train() # Set your model to train mode.loss_record = []    # 记录损失# 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)                     # 数据传入模型model，生成预测值predloss = criterion(pred, y)           # 预测值pred和真实值y计算损失loss  loss.backward()                     # Compute gradient(backpropagation).optimizer.step()                    # Update parameters.step += 1loss_record.append(loss.detach().item())   # 当前步骤的loss加到loss_record[]# 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)      # 计算训练集上平均损失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)      # 计算验证集上平均损失     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)# 保存验证集上平均损失最小的模型if mean_valid_loss < best_loss:         best_loss = mean_valid_losstorch.save(model.state_dict(), config['save_path']) # Save your best modelprint('Saving model with loss {:.3f}...'.format(best_loss))early_stop_count = 0else:early_stop_count += 1# 设置早停early_stop_count# 如果early_stop_count次数，验证集上的平均损失没有变化，模型性能没有提升，停止训练if early_stop_count >= config['early_stop']:   print('\nModel is not improving, so we halt the training session.')return

模型测试

# 测试数据集的预测
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():   # 关闭梯度pred = model(x)preds.append(pred.detach().cpu())preds = torch.cat(preds, dim=0).numpy()return preds

 

五、训练模型

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)

六、测试模型，生成预测值

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])model = My_Model(input_dim=x_train.shape[1]).to(device)
model.load_state_dict(torch.load(config['save_path']))    # 加载模型
preds = predict(test_loader, model, device)               # 生成预测结果preds
save_pred(preds, 'pred.csv')                              # 保存preds到pred.csv   

tensorboard可视化训练和验证损失图像

%reload_ext tensorboard
%tensorboard --logdir=./runs/

参考：

李宏毅_机器学习_作业1（详解）_COVID-19 Cases Prediction (Regression)-物联沃-IOTWORD物联网

【深度学习】2023李宏毅homework1作业一代码详解_李宏毅作业1-CSDN博客

np.delete详解-CSDN博客