diff --git a/HW01/wuxuejing-HW1.ipynb b/HW01/wuxuejing-HW1.ipynb new file mode 100644 index 00000000..cb97418c --- /dev/null +++ b/HW01/wuxuejing-HW1.ipynb @@ -0,0 +1,22361 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "guE34D3Fj2R9" + }, + "source": [ + "# **Homework 1: COVID-19 Cases Prediction (Regression)**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V57zhcTp1Xxb" + }, + "source": [ + "Objectives:\n", + "* Solve a regression problem with deep neural networks (DNN).\n", + "* Understand basic DNN training tips.\n", + "* Familiarize yourself with PyTorch.\n", + "\n", + "If you have any questions, please contact the TAs via TA hours, NTU COOL, or email to mlta-2022-spring@googlegroups.com" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Tm2aXcb-j9Fc" + }, + "source": [ + "# Download data\n", + "If the Google Drive links below do not work, you can download data from [Kaggle](https://www.kaggle.com/c/ml2022spring-hw1/data), and upload data manually to the workspace." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YPmfl-awlKZA", + "outputId": "71e77d1b-5543-4b3e-dc70-604bd7952f2b" + }, + "source": [ + "!gdown --id '1kLSW_-cW2Huj7bh84YTdimGBOJaODiOS' --output covid.train.csv\n", + "!gdown --id '1iiI5qROrAhZn-o4FPqsE97bMzDEFvIdg' --output covid.test.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "igqIMEgu64-F" + }, + "source": [ + "# Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "xybQNYCXYu13" + }, + "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" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fTAVqRfc2KK3" + }, + "source": [ + "# Some Utility Functions\n", + "\n", + "You do not need to modify this part." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "RbrcpfYN2I-H" + }, + "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],\n", + " 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": { + "id": "IqO3lTm78nNO" + }, + "source": [ + "# Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "-mjaJM0wprMs" + }, + "outputs": [], + "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", + "\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": { + "id": "m73ooU75CL_j" + }, + "source": [ + "# Neural Network Model\n", + "Try out different model architectures by modifying the class below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "Qn97_WvvrEkG" + }, + "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", + " #input_dim is the num of features we selected\n", + " self.layers = nn.Sequential(\n", + " nn.Linear(input_dim, 64),\n", + " nn.LeakyReLU(0.2),\n", + " nn.BatchNorm1d(64),\n", + " nn.Dropout(0.2),\n", + " \n", + " nn.Linear(64, 16),\n", + " nn.LeakyReLU(0.2),\n", + " #nn.BatchNorm1d(10),\n", + " nn.Dropout(0.1),\n", + " \n", + " nn.Linear(16, 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": { + "id": "x5-LKF6R8xeq" + }, + "source": [ + "# Feature Selection\n", + "Choose features you deem useful by modifying the function below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 24 Best feature score \n", + "[90072.43401367 42336.37370139 26889.70377033 18870.55811361\n", + " 11290.79919656 10849.62638725 10420.334481 10365.26105926\n", + " 10055.85024148 9859.62690961 9636.4254885 9330.74236337\n", + " 9180.16305651 8703.90128488 7857.10815311 7840.26399997\n", + " 7669.80626316 7634.02832221 7471.14168359 7427.81602428\n", + " 7379.57519499 7350.43226583 7200.10012107 7189.72334257]\n", + "\n", + "Top 24 Best feature index \n", + "[101 85 69 53 104 88 105 72 89 56 73 40 57 41 103 102 87 86\n", + " 71 70 55 54 39 38]\n", + "\n", + "Top 24 Best feature name\n", + "Index(['tested_positive.3', 'tested_positive.2', 'tested_positive.1',\n", + " 'tested_positive', 'hh_cmnty_cli.4', 'hh_cmnty_cli.3',\n", + " 'nohh_cmnty_cli.4', 'hh_cmnty_cli.2', 'nohh_cmnty_cli.3',\n", + " 'hh_cmnty_cli.1', 'nohh_cmnty_cli.2', 'hh_cmnty_cli',\n", + " 'nohh_cmnty_cli.1', 'nohh_cmnty_cli', 'ili.4', 'cli.4', 'ili.3',\n", + " 'cli.3', 'ili.2', 'cli.2', 'ili.1', 'cli.1', 'ili', 'cli'],\n", + " dtype='object')\n", + "[38, 39, 40, 41, 53, 54, 55, 56, 57, 69, 70, 71, 72, 73, 85, 86, 87, 88, 89, 101, 102, 103, 104, 105]\n", + "Index(['cli', 'ili', 'hh_cmnty_cli', 'nohh_cmnty_cli', 'tested_positive',\n", + " 'cli.1', 'ili.1', 'hh_cmnty_cli.1', 'nohh_cmnty_cli.1',\n", + " 'tested_positive.1', 'cli.2', 'ili.2', 'hh_cmnty_cli.2',\n", + " 'nohh_cmnty_cli.2', 'tested_positive.2', 'cli.3', 'ili.3',\n", + " 'hh_cmnty_cli.3', 'nohh_cmnty_cli.3', 'tested_positive.3', 'cli.4',\n", + " 'ili.4', 'hh_cmnty_cli.4', 'nohh_cmnty_cli.4'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "\n", + "features = pd.read_csv('./covid.train.csv')\n", + "x_data, y_data = features.iloc[:, 0:117], features.iloc[:, 117]\n", + "\n", + "#try choose your k best features\n", + "k = 24\n", + "selector = SelectKBest(score_func=f_regression, k=k)\n", + "result = selector.fit(x_data, y_data)\n", + "\n", + "#result.scores_ inclues scores for each features\n", + "#np.argsort sort scores in ascending order by index, we reverse it to make it descending.\n", + "idx = np.argsort(result.scores_)[::-1]\n", + "print(f'Top {k} Best feature score ')\n", + "print(result.scores_[idx[:k]])\n", + "\n", + "print(f'\\nTop {k} Best feature index ')\n", + "print(idx[:k])\n", + "\n", + "print(f'\\nTop {k} Best feature name')\n", + "print(x_data.columns[idx[:k]])\n", + "\n", + "selected_idx = list(np.sort(idx[:k]))\n", + "print(selected_idx)\n", + "print(x_data.columns[selected_idx])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "0FEnKRaIIeKp" + }, + "outputs": [], + "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 = selected_idx\n", + " # 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" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kADIPNQ2Ih5X" + }, + "source": [ + "# Training Loop" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "k4Rq8_TztAhq" + }, + "outputs": [], + "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=0.9) \n", + " optimizer = torch.optim.Adam(model.parameters(), lr=config['learning_rate']*50,\n", + " weight_decay=1e-3)\n", + " scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, \n", + " T_0=2, T_mult=2, eta_min=config['learning_rate'])\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", + " train_losses = []\n", + " val_losses = []\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", + " scheduler.step() \n", + "\n", + " mean_train_loss = sum(loss_record)/len(loss_record)\n", + " train_losses.append(mean_train_loss)\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", + " val_losses.append(mean_valid_loss)\n", + " print(f'Epoch [{epoch+1}/{n_epochs}]: Train loss: {mean_train_loss:.4f}, Valid loss: {mean_valid_loss:.4f}')\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", + " break\n", + " return train_losses, val_losses" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0pgkOh2e9UjE" + }, + "source": [ + "# Configurations\n", + "`config` contains hyper-parameters for training and the path to save your model." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "QoWPUahCtoT6" + }, + "outputs": [], + "source": [ + "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "config = {\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': 500, # 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", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lrS-aJJh9XkW" + }, + "source": [ + "# Dataloader\n", + "Read data from files and set up training, validation, and testing sets. You do not need to modify this part." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2jc7ZfDot2t9", + "outputId": "62d291db-61c8-424d-ec87-2cfce6dd330d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train_data size: (2160, 118) \n", + "valid_data size: (539, 118) \n", + "test_data size: (1078, 117)\n", + "train_data size: (2160, 24) \n", + "valid_data size: (539, 24) \n", + "test_data size: (1078, 24)\n", + "number of features: 24\n" + ] + } + ], + "source": [ + "# Set seed for reproducibility\n", + "same_seed(config['seed'])\n", + "\n", + "\n", + "# train_data size: 2699 x 118 (id + 37 states + 16 features x 5 days) \n", + "# test_data size: 1078 x 117 (without last day's positive rate)\n", + "train_data, test_data = pd.read_csv('./covid.train.csv').values, pd.read_csv('./covid.test.csv').values\n", + "train_data, valid_data = train_valid_split(train_data, config['valid_ratio'], config['seed'])\n", + "\n", + "# Print out the data size.\n", + "print(f\"\"\"train_data size: {train_data.shape} \n", + "valid_data size: {valid_data.shape} \n", + "test_data size: {test_data.shape}\"\"\")\n", + "\n", + "# Select features\n", + "x_train, x_valid, x_test, y_train, y_valid = select_feat(train_data, valid_data, test_data, False)\n", + "\n", + "\n", + "#normalization\n", + "x_min, x_max = x_train.min(axis=0), x_train.max(axis=0)\n", + "x_train = (x_train - x_min) / (x_max - x_min)\n", + "x_valid = (x_valid - x_min) / (x_max - x_min)\n", + "x_test = (x_test - x_min) / (x_max - x_min)\n", + "\n", + "print(f\"\"\"train_data size: {x_train.shape} \n", + "valid_data size: {x_valid.shape} \n", + "test_data size: {x_test.shape}\"\"\")\n", + "# Print out the number of features.\n", + "print(f'number of features: {x_train.shape[1]}')\n", + "\n", + "train_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.\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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0OBYgjCA-YwD" + }, + "source": [ + "# Start training!" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YdttVRkAfu2t", + "outputId": "864a1214-e69e-4197-dda0-ce2f6fe07d3c", + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [1/5000]: 100%|██████████| 9/9 [00:00<00:00, 18.52it/s, loss=135]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/5000]: Train loss: 132.7794, Valid loss: 137.9565\n", + "Saving model with loss 137.956...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [2/5000]: 100%|██████████| 9/9 [00:00<00:00, 35.52it/s, loss=121]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [2/5000]: Train loss: 127.6153, Valid loss: 158.6411\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [3/5000]: 100%|██████████| 9/9 [00:00<00:00, 39.91it/s, loss=124]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [3/5000]: Train loss: 124.7711, Valid loss: 145.2899\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [4/5000]: 100%|██████████| 9/9 [00:00<00:00, 42.90it/s, loss=144]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [4/5000]: Train loss: 122.5533, Valid loss: 139.7529\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [5/5000]: 100%|██████████| 9/9 [00:00<00:00, 46.61it/s, loss=129]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [5/5000]: Train loss: 118.7962, Valid loss: 118.4478\n", + "Saving model with loss 118.448...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [6/5000]: 100%|██████████| 9/9 [00:00<00:00, 35.73it/s, loss=157]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [6/5000]: Train loss: 119.1472, Valid loss: 130.4736\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [7/5000]: 100%|██████████| 9/9 [00:00<00:00, 44.98it/s, loss=115]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [7/5000]: Train loss: 114.5344, Valid loss: 123.4781\n" + ] + }, + { + "name": "stderr", + "output_type": 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39.79it/s, loss=2.55]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1540/5000]: Train loss: 2.6592, Valid loss: 1.1042\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [1541/5000]: 100%|██████████| 9/9 [00:00<00:00, 62.57it/s, loss=2.2] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1541/5000]: Train loss: 2.6832, Valid loss: 1.1308\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [1542/5000]: 100%|██████████| 9/9 [00:00<00:00, 44.64it/s, loss=1.93]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1542/5000]: Train loss: 2.2980, Valid loss: 1.2611\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [1543/5000]: 100%|██████████| 9/9 [00:00<00:00, 50.30it/s, loss=1.74]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1543/5000]: Train loss: 2.3524, Valid loss: 1.0757\n", + "\n", + "Model is not improving, so we halt the training session.\n" + ] + } + ], + "source": [ + "model = My_Model(input_dim=x_train.shape[1]).to(device) \n", + "#put your model and data on the same computation device.\n", + "train_losses, val_losses = trainer(train_loader, valid_loader, model, config, device)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Val Loss')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "plt.figure(figsize=(10, 10))\n", + "plt.subplot(211)\n", + "plt.plot(train_losses[1000:])\n", + "plt.title('Traing Loss')\n", + "\n", + "plt.subplot(212)\n", + "plt.plot(val_losses[1000:])\n", + "plt.title('Val Loss')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yhAHGqC9-woK" + }, + "source": [ + "# Testing\n", + "The predictions of your model on testing set will be stored at `pred.csv`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Q5eVdpbvAlAe", + "outputId": "53d0a7b8-20ed-4f83-8a9b-d860f16aed31" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:00<00:00, 231.70it/s]\n" + ] + } + ], + "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", + "\n", + "model = My_Model(input_dim=x_train.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, 'pred1.csv') " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IJ_k5rY0GvSV" + }, + "source": [ + "# Reference\n", + "This notebook uses code written by Heng-Jui Chang @ NTUEE (https://github.com/ga642381/ML2021-Spring/blob/main/HW01/HW01.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "ML2022Spring - HW1.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}