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Repository: Hoper-J/HUNG-YI_LEE_Machine-Learning_Homework
Branch: master
Commit: b441f875bc84
Files: 47
Total size: 187.7 MB
Directory structure:
gitextract_thjx82pk/
├── HW01/
│ ├── HW01.ipynb
│ ├── HW01_Sample_Code.ipynb
│ ├── README.md
│ ├── covid_test.csv
│ └── covid_train.csv
├── HW02/
│ ├── HW02.ipynb
│ ├── HW02_Sample_Code.ipynb
│ └── README.md
├── HW03/
│ ├── HW03_0.85333.ipynb
│ ├── HW03_Medium_0.70533.ipynb
│ ├── HW03_Sample_Code.ipynb
│ ├── HW03_Strong_0.81400.ipynb
│ ├── Q1/
│ │ ├── README.md
│ │ └── plot_transforms.ipynb
│ └── README.md
├── HW04/
│ ├── HW04_Boss_0.95125.ipynb
│ ├── HW04_Medium_0.83300.ipynb
│ ├── HW04_Sample_Code.ipynb
│ ├── HW04_Strong_0.89700.ipynb
│ └── README.md
├── HW05/
│ ├── README.md
│ ├── en/
│ │ ├── HW05_Boss_29.69.ipynb
│ │ ├── HW05_Gradescope.ipynb
│ │ ├── HW05_Medium_18.83.ipynb
│ │ ├── HW05_Simple_16.63.ipynb
│ │ ├── HW05_Strong_24.60.ipynb
│ │ └── HW05_sample_code.ipynb
│ └── zh/
│ ├── HW05_Boss_zh_29.72.ipynb
│ ├── HW05_Gradescope_zh.ipynb
│ ├── HW05_Medium_zh_18.9.ipynb
│ ├── HW05_Simple_zh_16.63.ipynb
│ ├── HW05_Strong-zh_24.91.ipynb
│ └── HW05_sample_code_zh.ipynb
├── HW06/
│ ├── HW06_Boss_0.835.ipynb
│ ├── HW06_Medium_0.536.ipynb
│ ├── HW06_Sample_Code.ipynb
│ ├── HW06_Simple_0.459.ipynb
│ ├── HW06_Strong_0.542.ipynb
│ └── README.md
├── HW07/
│ ├── HW07_Boss_0.84335.ipynb
│ ├── HW07_Medium_0.70090.ipynb
│ ├── HW07_Sample_Code.ipynb
│ ├── HW07_Sample_Code_zh.ipynb
│ ├── HW07_Simple_0.54426.ipynb
│ ├── HW07_Strong_0.79001.ipynb
│ └── README.md
└── README.md
================================================
FILE CONTENTS
================================================
================================================
FILE: HW01/HW01.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Numerical Operations\n",
"import math\n",
"import numpy as np\n",
"\n",
"# Reading/Writing Data\n",
"import pandas as pd\n",
"import os\n",
"import csv\n",
"\n",
"# For Progress Bar\n",
"from tqdm import tqdm\n",
"\n",
"# Pytorch\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.utils.data import Dataset, DataLoader, random_split\n",
"\n",
"# Matplotlib\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Optuna\n",
"import optuna\n",
"\n",
"# For plotting learning curve\n",
"from torch.utils.tensorboard import SummaryWriter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Some Utility Functions\n",
"\n",
"You do not need to modify this part."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def same_seed(seed):\n",
" '''Fixes random number generator seeds for reproducibility.'''\n",
" torch.backends.cudnn.deterministic = True\n",
" torch.backends.cudnn.benchmark = False\n",
" np.random.seed(seed)\n",
" torch.manual_seed(seed)\n",
" if torch.cuda.is_available():\n",
" torch.cuda.manual_seed_all(seed)\n",
"\n",
"def train_valid_split(data_set, valid_ratio, seed):\n",
" '''Split provided training data into training set and validation set'''\n",
" valid_set_size = int(valid_ratio * len(data_set))\n",
" train_set_size = len(data_set) - valid_set_size\n",
" train_set, valid_set = random_split(data_set, [train_set_size, valid_set_size], generator=torch.Generator().manual_seed(seed))\n",
" return np.array(train_set), np.array(valid_set)\n",
"\n",
"def predict(test_loader, model, device):\n",
" model.eval() # Set your model to evaluation mode.\n",
" preds = []\n",
" for x in tqdm(test_loader):\n",
" x = x.to(device)\n",
" with torch.no_grad():\n",
" pred = model(x)\n",
" preds.append(pred.detach().cpu())\n",
" preds = torch.cat(preds, dim=0).numpy()\n",
" return preds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"class CovidDataset(Dataset):\n",
" def __init__(self, x, y=None):\n",
" if y is None:\n",
" self.y = y\n",
" else:\n",
" self.y = torch.FloatTensor(y)\n",
" self.x = torch.FloatTensor(x)\n",
" \n",
" def __getitem__(self, idx):\n",
" if self.y is None:\n",
" return self.x[idx]\n",
" else:\n",
" return self.x[idx], self.y[idx]\n",
" \n",
" def __len__(self):\n",
" return len(self.x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Neural Network Model\n",
"\n",
"Try out different model architectures by modifying the class below. (You could tune config['layer'] to try)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"class My_Model(nn.Module):\n",
" def __init__(self, input_dim):\n",
" super(My_Model, self).__init__()\n",
" # TODO: modify model's structure, be aware of dimensions.\n",
" self.layers = nn.Sequential(\n",
" nn.Linear(input_dim, config['layer'][0]),\n",
" nn.ReLU(),\n",
" nn.Linear(config['layer'][0], config['layer'][1]),\n",
" nn.ReLU(),\n",
" nn.Linear(config['layer'][1], 1)\n",
" )\n",
"\n",
" def forward(self, x):\n",
" x = self.layers(x)\n",
" x = x.squeeze(1) # (B, 1) -> (B)\n",
" return x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature Selection\n",
"\n",
"Choose features you deem useful by modifying the function below."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_selection import SelectKBest, f_regression\n",
"\n",
"def select_feat(train_data, valid_data, test_data, no_select_all=True):\n",
" '''Selects useful features to perform regression'''\n",
" global config\n",
" y_train, y_valid = train_data[:,-1], valid_data[:,-1]\n",
" raw_x_train, raw_x_valid, raw_x_test = train_data[:,:-1], valid_data[:,:-1], test_data\n",
"\n",
" if not no_select_all:\n",
" feat_idx = list(range(raw_x_train.shape[1]))\n",
" else:\n",
" # Feature selection\n",
" k = config['k']\n",
" selector = SelectKBest(score_func=f_regression, k=k)\n",
" result = selector.fit(train_data[:, :-1], train_data[:,-1])\n",
" idx = np.argsort(result.scores_)[::-1]\n",
" feat_idx = list(np.sort(idx[:k]))\n",
"\n",
" return raw_x_train[:,feat_idx], raw_x_valid[:,feat_idx], raw_x_test[:,feat_idx], y_train, y_valid"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Training Loop"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def trainer(train_loader, valid_loader, model, config, device):\n",
" \n",
" # Define your loss function, do not modify this.\n",
" criterion = nn.MSELoss(reduction='mean') \n",
" \n",
" # Define your optimization algorithm.\n",
" if config['optim'] == 'SGD':\n",
" if config['no_momentum']:\n",
" optimizer = torch.optim.SGD(model.parameters(), lr=config['learning_rate'], weight_decay=config['weight_decay']) \n",
" else:\n",
" optimizer = torch.optim.SGD(model.parameters(), lr=config['learning_rate'], momentum=config['momentum'], weight_decay=config['weight_decay']) \n",
" elif config['optim'] == 'Adam':\n",
" optimizer = torch.optim.Adam(model.parameters(), lr=config['learning_rate'], weight_decay=config['weight_decay'])\n",
" \n",
" # Writer of tensoboard.\n",
" writer = SummaryWriter() \n",
"\n",
" \n",
" if not os.path.isdir('./models'):\n",
" os.mkdir('./models') # Create directory of saving models.\n",
"\n",
" n_epochs, best_loss, step, early_stop_count = config['n_epochs'], math.inf, 0, 0\n",
"\n",
" for epoch in range(n_epochs):\n",
" model.train() # Set your model to train mode.\n",
" loss_record = []\n",
" \n",
" # 如果你在kaggle上运行，可以注释掉大部分的打印函数，并将train_pbar注释掉，令 x,y in train_loader，因为kaggle上打印太多可能会报错。\n",
" # tqdm is a package to visualize your training progress.\n",
" #train_pbar = tqdm(train_loader, position=0, leave=True)\n",
" #for x, y in train_pbar:\n",
" for x, y in train_loader:\n",
" optimizer.zero_grad() # Set gradient to zero.\n",
" x, y = x.to(device), y.to(device) # Move your data to device.\n",
" pred = model(x)\n",
" loss = criterion(pred, y)\n",
" loss.backward() # Compute gradient(backpropagation).\n",
" optimizer.step() # Update parameters.\n",
" step += 1\n",
" loss_record.append(loss.detach().item())\n",
"\n",
" # Display current epoch number and loss on tqdm progress bar.\n",
" #train_pbar.set_description(f'Epoch [{epoch+1}/{n_epochs}]')\n",
" #train_pbar.set_postfix({'loss': loss.detach().item()})\n",
"\n",
" mean_train_loss = sum(loss_record)/len(loss_record)\n",
"\n",
" model.eval() # Set your model to evaluation mode.\n",
" loss_record = []\n",
" for x, y in valid_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" with torch.no_grad():\n",
" pred = model(x)\n",
" loss = criterion(pred, y)\n",
"\n",
" loss_record.append(loss.item())\n",
"\n",
" mean_valid_loss = sum(loss_record)/len(loss_record) \n",
" \n",
" #if epoch % 100 == 0:\n",
" # print(f'Epoch [{epoch+1}/{n_epochs}]: Train loss: {mean_train_loss:.4f}, Valid loss: {mean_valid_loss:.4f}')\n",
"\n",
" if not config['no_tensorboard']:\n",
" writer.add_scalar('Loss/train', mean_train_loss, step)\n",
" writer.add_scalar('Loss/valid', mean_valid_loss, step)\n",
"\n",
" if mean_valid_loss < best_loss:\n",
" best_loss = mean_valid_loss\n",
" \n",
" # 一轮实验中保存 K 折交叉验证中单折表现最好的模型\n",
" if len(valid_scores):\n",
" if best_loss < min(valid_scores):\n",
" torch.save(model.state_dict(), config['save_path']) # Save your best model\n",
" #print(f'Epoch [{epoch+1}/{n_epochs}]: Train loss: {mean_train_loss:.4f}, Valid loss: {mean_valid_loss:.4f}')\n",
" print('Saving model with loss {:.3f}...'.format(best_loss))\n",
" else:\n",
" torch.save(model.state_dict(), config['save_path']) # Save your best model\n",
" #print(f'Epoch [{epoch+1}/{n_epochs}]: Train loss: {mean_train_loss:.4f}, Valid loss: {mean_valid_loss:.4f}')\n",
" print('Saving model with loss {:.3f}...'.format(best_loss))\n",
" \n",
" early_stop_count = 0\n",
" else:\n",
" early_stop_count += 1\n",
"\n",
" if early_stop_count >= config['early_stop']:\n",
" print('Best loss {:.3f}...'.format(best_loss))\n",
" print('\\nModel is not improving, so we halt the training session.')\n",
" break\n",
" return best_loss"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Save predictions"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def save_pred(preds, file):\n",
" ''' Save predictions to specified file '''\n",
" with open(file, 'w') as fp:\n",
" writer = csv.writer(fp)\n",
" writer.writerow(['id', 'tested_positive'])\n",
" for i, p in enumerate(preds):\n",
" writer.writerow([i, p])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Start training!\n",
"\n",
"config contains hyper-parameters for training and the path to save your model.\n",
"\n",
"`objective()` is used for automatic parameter tuning, but you could set `AUTO_TUNE_PARAM` `False` to avoid it."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You could set AUTO_TUNE_PARAM True to tune parameters automatically.\n",
"AUTO_TUNE_PARAM: False\n",
"hyper-parameter: \n",
" optimizer: SGD,\n",
" lr: 1e-05, \n",
" batch_size: 256, \n",
" k: 16, \n",
" layer: [16, 16]\n",
"Saving model with loss 323.558...\n",
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"Best loss 0.834...\n",
"\n",
"Model is not improving, so we halt the training session.\n",
"valid_scores: [0.8343698779741923]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████| 4/4 [00:00<00:00, 2854.24it/s]\n"
]
}
],
"source": [
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"\n",
"config = {\n",
" 'seed': 5201314, # Your seed number, you can pick your lucky number. :)\n",
" 'k': 16, # Select k features\n",
" 'layer': [16, 16],\n",
" 'optim': 'SGD',\n",
" 'momentum': 0.7,\n",
" 'valid_ratio': 0.2, # validation_size = train_size * valid_ratio\n",
" 'n_epochs': 10000, # Number of epochs.\n",
" 'batch_size': 256,\n",
" 'learning_rate': 1e-5,\n",
" 'weight_decay': 1e-5,\n",
" 'early_stop': 600, # If model has not improved for this many consecutive epochs, stop training.\n",
" 'save_path': './models/model.ckpt', # Your model will be saved here.\n",
" 'no_select_all': True, # Whether to use all features.\n",
" 'no_momentum': True, # Whether to use momentum\n",
" 'no_normal': True, # Whether to normalize data\n",
" 'no_k_cross': False, # Whether to use K-fold cross validation\n",
" 'no_save': False, # Whether to save model parameters\n",
" 'no_tensorboard': False, # Whether to write tensorboard\n",
"} \n",
"\n",
"# 设置 k-fold 中的 k，这里是根据 valid_ratio 设定的\n",
"k = int(1 / config['valid_ratio'])\n",
"\n",
" # Set seed for reproducibility\n",
"same_seed(config['seed'])\n",
"\n",
"training_data, test_data = pd.read_csv('./covid_train.csv').values, pd.read_csv('./covid_test.csv').values\n",
" \n",
"num_valid_samples = len(training_data) // k\n",
"np.random.shuffle(training_data)\n",
"valid_scores = [] # 记录 valid_loss\n",
"\n",
"def objective(trial):\n",
" if trial != None:\n",
" print('\\nNew trial here')\n",
" # 定义需要调优的超参数空间\n",
" config['learning_rate'] = trial.suggest_float('lr', 1e-6, 1e-3)\n",
" config['batch_size'] = trial.suggest_categorical('batch_size', [128])\n",
" config['k'] = trial.suggest_int('k_feats', 16, 32)\n",
" config['layer'][0] = config['k']\n",
" \n",
" # 打印所需的超参数\n",
" print(f'''hyper-parameter: \n",
" optimizer: {config['optim']},\n",
" lr: {config['learning_rate']}, \n",
" batch_size: {config['batch_size']}, \n",
" k: {config['k']}, \n",
" layer: {config['layer']}''')\n",
" \n",
" global valid_scores\n",
" # 每次 trial 初始化 valid_scores，可以不初始化，通过 trial * k + fold 来访问当前 trial 的 valid_score，\n",
" # 这样可以让 trainer() 保存 trials 中最好的模型参数，但这并不意味着该参数对应的 k-fold validation loss 最低。\n",
" valid_scores = []\n",
"\n",
" for fold in range(k):\n",
" # Data split\n",
" valid_data = training_data[num_valid_samples * fold:\n",
" num_valid_samples * (fold + 1)]\n",
" train_data = np.concatenate((\n",
" training_data[:num_valid_samples * fold],\n",
" training_data[num_valid_samples * (fold + 1):]))\n",
"\n",
" # Normalization\n",
" if not config['no_normal']:\n",
" train_mean = np.mean(train_data[:, 35:-1], axis=0) # 前 35 列为 one-hot vector，我并没有对他们做 normalization，可以自行设置\n",
" train_std = np.std(train_data[:, 35:-1], axis=0)\n",
" train_data[:, 35:-1] -= train_mean\n",
" train_data[:, 35:-1] /= train_std\n",
" valid_data[:, 35:-1] -= train_mean\n",
" valid_data[:, 35:-1] /= train_std\n",
" test_data[:, 35:] -= train_mean\n",
" test_data[:, 35:] /= train_std\n",
"\n",
" x_train, x_valid, x_test, y_train, y_valid = select_feat(train_data, valid_data, test_data, config['no_select_all'])\n",
" \n",
" train_dataset, valid_dataset, test_dataset = CovidDataset(x_train, y_train), \\\n",
" CovidDataset(x_valid, y_valid), \\\n",
" CovidDataset(x_test)\n",
"\n",
" # Pytorch data loader loads pytorch dataset into batches.\n",
" train_loader = DataLoader(train_dataset, batch_size=config['batch_size'], shuffle=True, pin_memory=True)\n",
" valid_loader = DataLoader(valid_dataset, batch_size=config['batch_size'], shuffle=True, pin_memory=True)\n",
" test_loader = DataLoader(test_dataset, batch_size=config['batch_size'], shuffle=False, pin_memory=True)\n",
" \n",
" model = My_Model(input_dim=x_train.shape[1]).to(device) # put your model and data on the same computation device.\n",
" valid_score = trainer(train_loader, valid_loader, model, config, device)\n",
" valid_scores.append(valid_score)\n",
" \n",
" if not config['no_k_cross']:\n",
" break\n",
" \n",
" if valid_score > 2:\n",
" print(f'在第{fold+1}折上欠拟合') # 提前终止，减少计算资源\n",
" break \n",
" \n",
" print(f'valid_scores: {valid_scores}')\n",
" \n",
" if trial != None:\n",
" return np.average(valid_scores)\n",
" else:\n",
" return x_test, test_loader\n",
"\n",
"\n",
"AUTO_TUNE_PARAM = False # Whether to tune parameters automatically\n",
"\n",
"if AUTO_TUNE_PARAM:\n",
" # 使用Optuna库进行超参数搜索\n",
" n_trials = 10 # 设置试验数量\n",
" print(f'AUTO_TUNE_PARAM: {AUTO_TUNE_PARAM}\\nn_trials: {n_trials}')\n",
" study = optuna.create_study(direction='minimize')\n",
" study.optimize(objective, n_trials=n_trials)\n",
"\n",
" # 输出最优的超参数组合和性能指标\n",
" print('Best hyperparameters: {}'.format(study.best_params))\n",
" print('Best performance: {:.4f}'.format(study.best_value))\n",
"else:\n",
" # 注意，只有非自动调参时才进行了predict，节省一下计算资源\n",
" print(f'You could set AUTO_TUNE_PARAM True to tune parameters automatically.\\nAUTO_TUNE_PARAM: {AUTO_TUNE_PARAM}')\n",
" x_test, test_loader = objective(None)\n",
" model = My_Model(input_dim=x_test.shape[1]).to(device)\n",
" model.load_state_dict(torch.load(config['save_path']))\n",
" preds = predict(test_loader, model, device)\n",
" save_pred(preds, 'submission.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot learning curves with `tensorboard` (optional)\n",
"\n",
"`tensorboard` is a tool that allows you to visualize your training progress.\n",
"\n",
"If this block does not display your learning curve, please wait for few minutes, and re-run this block. It might take some time to load your logging information. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [
{
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"\n",
" <iframe id=\"tensorboard-frame-9fab987f7fe73d17\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
" </iframe>\n",
" <script>\n",
" (function() {\n",
" const frame = document.getElementById(\"tensorboard-frame-9fab987f7fe73d17\");\n",
" const url = new URL(\"/\", window.location);\n",
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"source": [
"%reload_ext tensorboard\n",
"%tensorboard --logdir=./runs/"
]
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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================================================
FILE: HW01/HW01_Sample_Code.ipynb
================================================
{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **Homework 1: COVID-19 Cases Prediction (Regression)**","metadata":{"id":"guE34D3Fj2R9"}},{"cell_type":"markdown","source":"Objectives:\n* Solve a regression problem with deep neural networks (DNN).\n* Understand basic DNN training tips.\n* Familiarize yourself with PyTorch.\n\nIf you have any questions, please contact the TAs via TA hours, NTU COOL, or email to mlta-2023-spring@googlegroups.com","metadata":{"id":"V57zhcTp1Xxb"}},{"cell_type":"code","source":"!nvidia-smi","metadata":{"id":"GUATI4ONArv_","execution":{"iopub.status.busy":"2023-02-12T07:30:52.741854Z","iopub.execute_input":"2023-02-12T07:30:52.742902Z","iopub.status.idle":"2023-02-12T07:30:53.95358Z","shell.execute_reply.started":"2023-02-12T07:30:52.742777Z","shell.execute_reply":"2023-02-12T07:30:53.95213Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Download data\nIf the Google Drive links below do not work, you can use the dropbox link below or download data from [Kaggle](https://www.kaggle.com/t/a339b77fa5214978bfb8dde62d3151fe), and upload data manually to the workspace.","metadata":{"id":"Tm2aXcb-j9Fc"}},{"cell_type":"code","source":"# google drive link\n# !pip install gdown\n# !gdown --id '1BjXalPZxq9mybPKNjF3h5L3NcF7XKTS-' --output covid_train.csv\n# !gdown --id '1B55t74Jg2E5FCsKCsUEkPKIuqaY7UIi1' --output covid_test.csv\n\n# dropbox link\n!wget -O covid_train.csv https://www.dropbox.com/s/lmy1riadzoy0ahw/covid.train.csv?dl=0\n!wget -O covid_test.csv https://www.dropbox.com/s/zalbw42lu4nmhr2/covid.test.csv?dl=0","metadata":{"id":"YPmfl-awlKZA","execution":{"iopub.status.busy":"2023-02-12T07:30:53.956273Z","iopub.execute_input":"2023-02-12T07:30:53.956956Z","iopub.status.idle":"2023-02-12T07:31:15.495174Z","shell.execute_reply.started":"2023-02-12T07:30:53.956913Z","shell.execute_reply":"2023-02-12T07:31:15.493786Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Import packages","metadata":{"id":"igqIMEgu64-F"}},{"cell_type":"code","source":"# Numerical Operations\nimport math\nimport numpy as np\n\n# Reading/Writing Data\nimport pandas as pd\nimport os\nimport csv\n\n# For Progress Bar\nfrom tqdm import tqdm\n\n# Pytorch\nimport torch \nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader, random_split\n\n# For plotting learning curve\nfrom torch.utils.tensorboard import SummaryWriter","metadata":{"id":"xybQNYCXYu13","execution":{"iopub.status.busy":"2023-02-12T07:31:16.478205Z","iopub.execute_input":"2023-02-12T07:31:16.478628Z","iopub.status.idle":"2023-02-12T07:31:18.240062Z","shell.execute_reply.started":"2023-02-12T07:31:16.478584Z","shell.execute_reply":"2023-02-12T07:31:18.239091Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Some Utility Functions\n\nYou do not need to modify this part.","metadata":{"id":"fTAVqRfc2KK3"}},{"cell_type":"code","source":"def same_seed(seed): \n '''Fixes random number generator seeds for reproducibility.'''\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n np.random.seed(seed)\n torch.manual_seed(seed)\n if torch.cuda.is_available():\n torch.cuda.manual_seed_all(seed)\n\ndef train_valid_split(data_set, valid_ratio, seed):\n '''Split provided training data into training set and validation set'''\n valid_set_size = int(valid_ratio * len(data_set)) \n train_set_size = len(data_set) - valid_set_size\n train_set, valid_set = random_split(data_set, [train_set_size, valid_set_size], generator=torch.Generator().manual_seed(seed))\n return np.array(train_set), np.array(valid_set)\n\ndef predict(test_loader, model, device):\n model.eval() # Set your model to evaluation mode.\n preds = []\n for x in tqdm(test_loader):\n x = x.to(device) \n with torch.no_grad(): \n pred = model(x) \n preds.append(pred.detach().cpu()) \n preds = torch.cat(preds, dim=0).numpy() \n return preds","metadata":{"id":"RbrcpfYN2I-H","execution":{"iopub.status.busy":"2023-02-12T07:31:18.241353Z","iopub.execute_input":"2023-02-12T07:31:18.241964Z","iopub.status.idle":"2023-02-12T07:31:18.251864Z","shell.execute_reply.started":"2023-02-12T07:31:18.241928Z","shell.execute_reply":"2023-02-12T07:31:18.250938Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Dataset","metadata":{"id":"IqO3lTm78nNO"}},{"cell_type":"code","source":"class COVID19Dataset(Dataset):\n '''\n x: Features.\n y: Targets, if none, do prediction.\n '''\n def __init__(self, x, y=None):\n if y is None:\n self.y = y\n else:\n self.y = torch.FloatTensor(y)\n self.x = torch.FloatTensor(x)\n\n def __getitem__(self, idx):\n if self.y is None:\n return self.x[idx]\n else:\n return self.x[idx], self.y[idx]\n\n def __len__(self):\n return len(self.x)","metadata":{"id":"-mjaJM0wprMs","execution":{"iopub.status.busy":"2023-02-12T07:31:18.253412Z","iopub.execute_input":"2023-02-12T07:31:18.254031Z","iopub.status.idle":"2023-02-12T07:31:18.262072Z","shell.execute_reply.started":"2023-02-12T07:31:18.253994Z","shell.execute_reply":"2023-02-12T07:31:18.261108Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Neural Network Model\nTry out different model architectures by modifying the class below.","metadata":{"id":"m73ooU75CL_j"}},{"cell_type":"code","source":"class My_Model(nn.Module):\n def __init__(self, input_dim):\n super(My_Model, self).__init__()\n # TODO: modify model's structure, be aware of dimensions. \n self.layers = nn.Sequential(\n nn.Linear(input_dim, 16),\n nn.ReLU(),\n nn.Linear(16, 8),\n nn.ReLU(),\n nn.Linear(8, 1)\n )\n\n def forward(self, x):\n x = self.layers(x)\n x = x.squeeze(1) # (B, 1) -> (B)\n return x","metadata":{"id":"Qn97_WvvrEkG","execution":{"iopub.status.busy":"2023-02-12T07:31:18.263546Z","iopub.execute_input":"2023-02-12T07:31:18.263886Z","iopub.status.idle":"2023-02-12T07:31:18.274148Z","shell.execute_reply.started":"2023-02-12T07:31:18.263852Z","shell.execute_reply":"2023-02-12T07:31:18.273078Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Feature Selection\nChoose features you deem useful by modifying the function below.","metadata":{"id":"x5-LKF6R8xeq"}},{"cell_type":"code","source":"def select_feat(train_data, valid_data, test_data, select_all=True):\n '''Selects useful features to perform regression'''\n y_train, y_valid = train_data[:,-1], valid_data[:,-1]\n raw_x_train, raw_x_valid, raw_x_test = train_data[:,:-1], valid_data[:,:-1], test_data\n\n if select_all:\n feat_idx = list(range(raw_x_train.shape[1]))\n else:\n feat_idx = list(range(35, raw_x_train.shape[1])) # TODO: Select suitable feature columns.\n \n return raw_x_train[:,feat_idx], raw_x_valid[:,feat_idx], raw_x_test[:,feat_idx], y_train, y_valid","metadata":{"id":"0FEnKRaIIeKp","execution":{"iopub.status.busy":"2023-02-12T07:31:18.277047Z","iopub.execute_input":"2023-02-12T07:31:18.277589Z","iopub.status.idle":"2023-02-12T07:31:18.284837Z","shell.execute_reply.started":"2023-02-12T07:31:18.277554Z","shell.execute_reply":"2023-02-12T07:31:18.283918Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Training Loop","metadata":{"id":"kADIPNQ2Ih5X"}},{"cell_type":"code","source":"def trainer(train_loader, valid_loader, model, config, device):\n\n criterion = nn.MSELoss(reduction='mean') # Define your loss function, do not modify this.\n\n # Define your optimization algorithm. \n # TODO: Please check https://pytorch.org/docs/stable/optim.html to get more available algorithms.\n # TODO: L2 regularization (optimizer(weight decay...) or implement by your self).\n optimizer = torch.optim.SGD(model.parameters(), lr=config['learning_rate'], momentum=0.7) \n writer = SummaryWriter() # Writer of tensoboard.\n\n if not os.path.isdir('./models'):\n os.mkdir('./models') # Create directory of saving models.\n\n n_epochs, best_loss, step, early_stop_count = config['n_epochs'], math.inf, 0, 0\n\n for epoch in range(n_epochs):\n model.train() # Set your model to train mode.\n loss_record = []\n\n # tqdm is a package to visualize your training progress.\n train_pbar = tqdm(train_loader, position=0, leave=True)\n\n for x, y in train_pbar:\n optimizer.zero_grad() # Set gradient to zero.\n x, y = x.to(device), y.to(device) # Move your data to device. \n pred = model(x) \n loss = criterion(pred, y)\n loss.backward() # Compute gradient(backpropagation).\n optimizer.step() # Update parameters.\n step += 1\n loss_record.append(loss.detach().item())\n \n # Display current epoch number and loss on tqdm progress bar.\n train_pbar.set_description(f'Epoch [{epoch+1}/{n_epochs}]')\n train_pbar.set_postfix({'loss': loss.detach().item()})\n\n mean_train_loss = sum(loss_record)/len(loss_record)\n writer.add_scalar('Loss/train', mean_train_loss, step)\n\n model.eval() # Set your model to evaluation mode.\n loss_record = []\n for x, y in valid_loader:\n x, y = x.to(device), y.to(device)\n with torch.no_grad():\n pred = model(x)\n loss = criterion(pred, y)\n\n loss_record.append(loss.item())\n \n mean_valid_loss = sum(loss_record)/len(loss_record)\n print(f'Epoch [{epoch+1}/{n_epochs}]: Train loss: {mean_train_loss:.4f}, Valid loss: {mean_valid_loss:.4f}')\n writer.add_scalar('Loss/valid', mean_valid_loss, step)\n\n if mean_valid_loss < best_loss:\n best_loss = mean_valid_loss\n torch.save(model.state_dict(), config['save_path']) # Save your best model\n print('Saving model with loss {:.3f}...'.format(best_loss))\n early_stop_count = 0\n else: \n early_stop_count += 1\n\n if early_stop_count >= config['early_stop']:\n print('\\nModel is not improving, so we halt the training session.')\n return","metadata":{"id":"k4Rq8_TztAhq","execution":{"iopub.status.busy":"2023-02-12T07:31:18.286341Z","iopub.execute_input":"2023-02-12T07:31:18.286701Z","iopub.status.idle":"2023-02-12T07:31:18.301211Z","shell.execute_reply.started":"2023-02-12T07:31:18.286646Z","shell.execute_reply":"2023-02-12T07:31:18.300217Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Configurations\n`config` contains hyper-parameters for training and the path to save your model.","metadata":{"id":"0pgkOh2e9UjE"}},{"cell_type":"code","source":"device = 'cuda' if torch.cuda.is_available() else 'cpu'\nconfig = {\n 'seed': 5201314, # Your seed number, you can pick your lucky number. :)\n 'select_all': True, # Whether to use all features.\n 'valid_ratio': 0.2, # validation_size = train_size * valid_ratio\n 'n_epochs': 5000, # Number of epochs. \n 'batch_size': 256, \n 'learning_rate': 1e-5, \n 'early_stop': 600, # If model has not improved for this many consecutive epochs, stop training. \n 'save_path': './models/model.ckpt' # Your model will be saved here.\n}","metadata":{"id":"QoWPUahCtoT6","execution":{"iopub.status.busy":"2023-02-12T07:31:18.304983Z","iopub.execute_input":"2023-02-12T07:31:18.305345Z","iopub.status.idle":"2023-02-12T07:31:18.361404Z","shell.execute_reply.started":"2023-02-12T07:31:18.305313Z","shell.execute_reply":"2023-02-12T07:31:18.36028Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Dataloader\nRead data from files and set up training, validation, and testing sets. You do not need to modify this part.","metadata":{"id":"lrS-aJJh9XkW"}},{"cell_type":"code","source":"# Set seed for reproducibility\nsame_seed(config['seed'])\n\n\n# train_data size: 3009 x 89 (35 states + 18 features x 3 days) \n# test_data size: 997 x 88 (without last day's positive rate)\ntrain_data, test_data = pd.read_csv('./covid_train.csv').values, pd.read_csv('./covid_test.csv').values\ntrain_data, valid_data = train_valid_split(train_data, config['valid_ratio'], config['seed'])\n\n# Print out the data size.\nprint(f\"\"\"train_data size: {train_data.shape} \nvalid_data size: {valid_data.shape} \ntest_data size: {test_data.shape}\"\"\")\n\n# Select features\nx_train, x_valid, x_test, y_train, y_valid = select_feat(train_data, valid_data, test_data, config['select_all'])\n\n# Print out the number of features.\nprint(f'number of features: {x_train.shape[1]}')\n\ntrain_dataset, valid_dataset, test_dataset = COVID19Dataset(x_train, y_train), \\\n COVID19Dataset(x_valid, y_valid), \\\n COVID19Dataset(x_test)\n\n# Pytorch data loader loads pytorch dataset into batches.\ntrain_loader = DataLoader(train_dataset, batch_size=config['batch_size'], shuffle=True, pin_memory=True)\nvalid_loader = DataLoader(valid_dataset, batch_size=config['batch_size'], shuffle=True, pin_memory=True)\ntest_loader = DataLoader(test_dataset, batch_size=config['batch_size'], shuffle=False, pin_memory=True)","metadata":{"id":"2jc7ZfDot2t9","execution":{"iopub.status.busy":"2023-02-12T07:31:18.363178Z","iopub.execute_input":"2023-02-12T07:31:18.363561Z","iopub.status.idle":"2023-02-12T07:31:18.439675Z","shell.execute_reply.started":"2023-02-12T07:31:18.363526Z","shell.execute_reply":"2023-02-12T07:31:18.43872Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Start training!","metadata":{"id":"0OBYgjCA-YwD"}},{"cell_type":"code","source":"model = My_Model(input_dim=x_train.shape[1]).to(device) # put your model and data on the same computation device.\ntrainer(train_loader, valid_loader, model, config, device)","metadata":{"id":"YdttVRkAfu2t","execution":{"iopub.status.busy":"2023-02-12T07:31:18.441083Z","iopub.execute_input":"2023-02-12T07:31:18.441534Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Testing\nThe predictions of your model on testing set will be stored at `pred.csv`.","metadata":{"id":"yhAHGqC9-woK"}},{"cell_type":"code","source":"def save_pred(preds, file):\n ''' Save predictions to specified file '''\n with open(file, 'w') as fp:\n writer = csv.writer(fp)\n writer.writerow(['id', 'tested_positive'])\n for i, p in enumerate(preds):\n writer.writerow([i, p])\n\nmodel = My_Model(input_dim=x_train.shape[1]).to(device)\nmodel.load_state_dict(torch.load(config['save_path']))\npreds = predict(test_loader, model, device) \nsave_pred(preds, 'pred.csv') ","metadata":{"id":"Q5eVdpbvAlAe","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Download\n\nRun this block to download the `pred.csv` by clicking.","metadata":{"id":"T_N-wBvVahc7"}},{"cell_type":"code","source":"from IPython.display import FileLink\nFileLink(r'pred.csv')","metadata":{"id":"PmMnwrHeavJv","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Reference\nThis notebook uses code written by Heng-Jui Chang @ NTUEE (https://github.com/ga642381/ML2021-Spring/blob/main/HW01/HW01.ipynb)","metadata":{"id":"IJ_k5rY0GvSV"}}]}
================================================
FILE: HW01/README.md
================================================
> ML2023Spring - HW01 相关信息：
>
> [课程主页](https://speech.ee.ntu.edu.tw/~hylee/ml/2023-spring.php)
>
> [课程视频](https://www.bilibili.com/video/BV1TD4y137mP/?spm_id_from=333.337.search-card.all.click&vd_source=436107f586d66ab4fcf756c76eb96c35)
>
> [Kaggle link](https://www.kaggle.com/t/a339b77fa5214978bfb8dde62d3151fe)
>
> [Sample code](https://colab.research.google.com/drive/1BESEu-l3qrGRULoATuXnWasUNuUlVF1Z?fbclid=IwAR1FrjUsp4rTy5PPFV-aWq6IG_Z44mFT4VH5e1lIhlekFl7fAvxGRCTCyR0#scrollTo=QoWPUahCtoT6)
>
> [HW01 视频]( https://www.bilibili.com/video/BV1TD4y137mP/?p=14&share_source=copy_web&vd_source=e46571d631061853c8f9eead71bdb390) 可以在做作业之前看一部分，我摸索完才发现视频有讲 Data Feature :(
>
> [HW01 PDF](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2023-course-data/HW01.pdf)
>
>
>P.S. 即便 kaggle 上的时间已经截止，你仍然可以在上面提交和查看分数。但需要注意的是：在 kaggle 截止日期前你应该选择两个结果进行最后的Private评分。
> 每年的数据集size和feature并不完全相同，但基本一致，过去的代码仍可用于新一年的 Homework
# 目录
* [任务目标（回归）](#任务目标回归)
* [性能指标（Metric）](#性能指标metric)
* [数据解析](#数据解析)
* [数据下载](#数据下载)
* [Sample code 主体部分解析](#sample-code-主体部分解析)
* [Some Utility Functions](#some-utility-functions)
* [Dataset](#dataset)
* [Neural Network Model](#neural-network-model)
* [Feature Selection](#feature-selection)
* [Training Loop](#training-loop)
* [Baselines](#baselines)
* [参考链接](#参考链接)
# 任务目标（回归）：
- COVID-19 daily cases prediction: COVID-19 每天的病例预测
- 训练/测试数据大小：3009/997（每一年的homework 可能不同）
# 性能指标（Metric）
- 均方误差 Mean Squared Error (MSE)
# 数据解析
- covid_train.txt: 训练数据
- covid_test.txt: 测试数据
数据大体分为三个部分：id, states: 病例对应的地区, 以及其他数据
- id: sample 对应的序号。
- states: 对 sample 来说该项为 one-hot vector。从整个数据集上来看，每个地区的 sample 数量是均匀的，可以使用`pd.read_csv('./covid_train.csv').iloc[:,1:34].sum()`来查看，地区 sample 数量为 88/89。
- 其他数据: 这一部分最终应用在助教所给的 sample code 中的 select_feat。
- Covid-like illness (5) 新冠症状
- cli, ili ...
- Behavier indicators (5) 行为表现
- wearing_mask、travel_outside_state ... 是否戴口罩，出去旅游 ...
- Belief indicators (2) 是否相信某种行为对防疫有效
- belief_mask_effective, belief_distancing_effective. 相信戴口罩有效，相信保持距离有效。
- Mental indicator (2) 心理表现
- worried_catch_covid, worried_finance. 担心得到covid，担心经济状况
- Environmental indicators (3) 环境表现
- other_masked_public, other_distanced_public ... 周围的人是否大部分戴口罩，周围的人是否大部分保持距离 ...
- Tested Positive Cases (1) 检测阳性病例，该项为模型的预测目标
- **tested_positive (this is what we want to predict)** 单位为百分比，指有多少比例的人
## 数据下载
> To use the Kaggle API, sign up for a Kaggle account at [https://www.kaggle.com](https://www.kaggle.com/). Then go to the 'Account' tab of your user profile (`https://www.kaggle.com/<username>/account`) and select 'Create API Token'. This will trigger the download of `kaggle.json`, a file containing your API credentials. Place this file in the location `~/.kaggle/kaggle.json` (on Windows in the location `C:\Users\<Windows-username>\.kaggle\kaggle.json` - you can check the exact location, sans drive, with `echo %HOMEPATH%`). You can define a shell environment variable `KAGGLE_CONFIG_DIR` to change this location to `$KAGGLE_CONFIG_DIR/kaggle.json` (on Windows it will be `%KAGGLE_CONFIG_DIR%\kaggle.json`).
>
> -\- [Official Kaggle API](https://github.com/Kaggle/kaggle-api)
`gdown` 的链接总是挂，可以考虑使用 `kaggle` 的 `api`，流程非常简单，替换<username>为你自己的用户名，`https://www.kaggle.com/<username>/account`，然后点击 `Create New API Token`，将下载下来的文件放去应该放的位置：
- Mac 和 Linux 放在 `~/.kaggle`
- Windows 放在 `C:\Users\<Windows-username>\.kaggle`
```bash
pip install kaggle
# 你需要先在 Kaggle -> Account -> Create New API Token 中下载 kaggle.json
# mv kaggle.json ~/.kaggle/kaggle.json
kaggle competitions download -c ml2023spring-hw1
unzip ml2023spring-hw1
```
# Sample code 主体部分解析
## Some Utility Functions
```python
def same_seed(seed):
'''Fixes random number generator seeds for reproducibility.'''
# 使用确定的卷积算法 (A bool that, if True, causes cuDNN to only use deterministic convolution algorithms.)
torch.backends.cudnn.deterministic = True
# 不对多个卷积算法进行基准测试和选择最优 (A bool that, if True, causes cuDNN to benchmark multiple convolution algorithms and select the fastest.)
torch.backends.cudnn.benchmark = False
# 设置随机数种子
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):
# 用于评估模型（验证/测试）
model.eval() # Set your model to evaluation mode.
preds = []
for x in tqdm(test_loader):
# device (int, optional): if specified, all parameters will be copied to that device）
x = x.to(device) # 将数据 copy 到 device
with torch.no_grad(): # 禁用梯度计算，以减少消耗
pred = model(x)
preds.append(pred.detach().cpu()) # detach() 创建一个不在计算图中的新张量，值相同
preds = torch.cat(preds, dim=0).numpy() # 连接 preds
return preds
```
## Dataset
```python
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)
'''meth:`__getitem__`, supporting fetching a data sample for a given key.'''
def __getitem__(self, idx): # 自定义 dataset 的 idx 对应的 sample
if self.y is None:
return self.x[idx]
else:
return self.x[idx], self.y[idx]
def __len__(self):
return len(self.x)
```
\_\_getitem\_\_()实际应用于 dataloader 中，详细可见下图（图源自 [PyTorch Tutorial PDF](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2023-course-data/environment.pdf)）
## Neural Network Model
这部分我做了简单的修改，以便于后续调参
```python
class My_Model(nn.Module):
def __init__(self, input_dim):
super(My_Model, self).__init__()
# TODO: modify model's structure in hyper-parameter: 'config', be aware of dimensions.
self.layers = nn.Sequential(
nn.Linear(input_dim, config['layer'][0]),
nn.ReLU(),
nn.Linear(config['layer'][0], config['layer'][1]),
nn.ReLU(),
nn.Linear(config['layer'][1], 1)
)
def forward(self, x):
x = self.layers(x)
x = x.squeeze(1) # (B, 1) -> (B)
return x
```
## Feature Selection
这部分可以使用 sklearn.feature_selection.SelectKBest 来进行特征选择。
具体代码如下（你可能需要传入 config）：
```python
from sklearn.feature_selection import SelectKBest, f_regression
k = config['k'] # 所要选择的特征数量
selector = SelectKBest(score_func=f_regression, k=k)
result = selector.fit(train_data[:, :-1], train_data[:,-1])
idx = np.argsort(result.scores_)[::-1]
feat_idx = list(np.sort(idx[:k]))
```
## Training Loop
```python
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=config['momentum']) # 设置 optimizer 为SGD
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 = [] # 初始化空列表，用于记录训练误差
# tqdm is a package to visualize your training progress.
train_pbar = tqdm(train_loader, position=0, leave=True) # 让训练进度显示出来，可以去除这一行，然后将下面的 train_pbar 改成 train_loader（目的是尽量减少 jupyter notebook 的打印，因为如果这段代码在 kaggle 执行，在一定的输出后会报错: IOPub message rate exceeded...）
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) # 等价于 model.forward(x)
loss = criterion(pred, y) # 计算 pred 和 y 的均方误差
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 = [] # 初始化空列表，用于记录验证误差
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
```
# Baselines
根据作业 PDF 中的提示：
- Simple Baseline (1.96993)
- 运行所给的 sample code。
- Medium Baseline (1.15678)
- 特征选择，简单手动的选择你认为关联性较大的特征。
- Strong Baseline (0.92619)
- 尝试不同的优化器（如：Adam）。
- 应用 L2 正则化（SGD/Adam ... 优化器参数中的 weight_decay)
- Boss Baseline (0.81456)
- 尝试更好的特征选择，可以使用 sklearn.feature_selection.SelectKBest。
- 尝试不同的模型架构（调整 my_module.layers）
- 调整其他超参数
# 参考链接
1. [PyTorch: What is the difference between tensor.cuda() and tensor.to(torch.device("cuda:0"))?](https://stackoverflow.com/questions/62907815/pytorch-what-is-the-difference-between-tensor-cuda-and-tensor-totorch-device)
1. [PyTorch Tutorial PDF](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2023-course-data/environment.pdf)
================================================
FILE: HW01/covid_test.csv
================================================
id,AL,AZ,CA,CO,CT,FL,GA,IL,IN,IA,KS,KY,LA,ME,MD,MA,MI,MN,MO,NJ,NM,NY,NC,OH,OK,OR,PA,SC,TN,TX,VA,WA,WV,WI,cli,ili,wnohh_cmnty_cli,wbelief_masking_effective,wbelief_distancing_effective,wcovid_vaccinated_friends,wlarge_event_indoors,wothers_masked_public,wothers_distanced_public,wshop_indoors,wrestaurant_indoors,wworried_catch_covid,hh_cmnty_cli,nohh_cmnty_cli,wearing_mask_7d,public_transit,worried_finances,tested_positive,cli,ili,wnohh_cmnty_cli,wbelief_masking_effective,wbelief_distancing_effective,wcovid_vaccinated_friends,wlarge_event_indoors,wothers_masked_public,wothers_distanced_public,wshop_indoors,wrestaurant_indoors,wworried_catch_covid,hh_cmnty_cli,nohh_cmnty_cli,wearing_mask_7d,public_transit,worried_finances,tested_positive,cli,ili,wnohh_cmnty_cli,wbelief_masking_effective,wbelief_distancing_effective,wcovid_vaccinated_friends,wlarge_event_indoors,wothers_masked_public,wothers_distanced_public,wshop_indoors,wrestaurant_indoors,wworried_catch_covid,hh_cmnty_cli,nohh_cmnty_cli,wearing_mask_7d,public_transit,worried_finances
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gitextract_thjx82pk/ ├── HW01/ │ ├── HW01.ipynb │ ├── HW01_Sample_Code.ipynb │ ├── README.md │ ├── covid_test.csv │ └── covid_train.csv ├── HW02/ │ ├── HW02.ipynb │ ├── HW02_Sample_Code.ipynb │ └── README.md ├── HW03/ │ ├── HW03_0.85333.ipynb │ ├── HW03_Medium_0.70533.ipynb │ ├── HW03_Sample_Code.ipynb │ ├── HW03_Strong_0.81400.ipynb │ ├── Q1/ │ │ ├── README.md │ │ └── plot_transforms.ipynb │ └── README.md ├── HW04/ │ ├── HW04_Boss_0.95125.ipynb │ ├── HW04_Medium_0.83300.ipynb │ ├── HW04_Sample_Code.ipynb │ ├── HW04_Strong_0.89700.ipynb │ └── README.md ├── HW05/ │ ├── README.md │ ├── en/ │ │ ├── HW05_Boss_29.69.ipynb │ │ ├── HW05_Gradescope.ipynb │ │ ├── HW05_Medium_18.83.ipynb │ │ ├── HW05_Simple_16.63.ipynb │ │ ├── HW05_Strong_24.60.ipynb │ │ └── HW05_sample_code.ipynb │ └── zh/ │ ├── HW05_Boss_zh_29.72.ipynb │ ├── HW05_Gradescope_zh.ipynb │ ├── HW05_Medium_zh_18.9.ipynb │ ├── HW05_Simple_zh_16.63.ipynb │ ├── HW05_Strong-zh_24.91.ipynb │ └── HW05_sample_code_zh.ipynb ├── HW06/ │ ├── HW06_Boss_0.835.ipynb │ ├── HW06_Medium_0.536.ipynb │ ├── HW06_Sample_Code.ipynb │ ├── HW06_Simple_0.459.ipynb │ ├── HW06_Strong_0.542.ipynb │ └── README.md ├── HW07/ │ ├── HW07_Boss_0.84335.ipynb │ ├── HW07_Medium_0.70090.ipynb │ ├── HW07_Sample_Code.ipynb │ ├── HW07_Sample_Code_zh.ipynb │ ├── HW07_Simple_0.54426.ipynb │ ├── HW07_Strong_0.79001.ipynb │ └── README.md └── README.md
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"preview": "> ML2023Spring - HW01 相关信息：\n> \n> [课程主页](https://speech.ee.ntu.edu.tw/~hylee/ml/2023-spring.php)\n> \n> [课程视频](https:/"
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"preview": "plot_transforms 文件是 torchvision 官方文档提供的示例代码, 尝试运行它, 可以对变换有更多的理解，我增加了一些注释和小部分扩展的函数演示。\n"
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"path": "HW07/HW07_Strong_0.79001.ipynb",
"chars": 211628,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"xvSGDbExff_I\"\n },\n \"source\": [\n \"# **Ho"
},
{
"path": "HW07/README.md",
"chars": 23157,
"preview": "> ML2023Spring - HW7 相关信息：\n>\n> [课程主页](https://speech.ee.ntu.edu.tw/~hylee/ml/2023-spring.php)\n>\n> [课程视频](https://www."
},
{
"path": "README.md",
"chars": 2431,
"preview": "# 李宏毅2023机器学习作业思路和代码分享\n\n这里是我个人的 code 分享，所有的 code 最终均能达到 Boss baseline，希望能给你带来帮助。代码会一直更新到课程结束。\n\n当前代码版本对应的是 2023 年春，其中 HW0"
}
]
// ... and 3 more files (download for full content)
About this extraction
This page contains the full source code of the Hoper-J/HUNG-YI_LEE_Machine-Learning_Homework GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 47 files (187.7 MB), approximately 3.8M tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.