HW01/HW01.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ML2021Spring - HW1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPuibV8rME8Y2Er3TVnCm93",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ga642381/ML2021-Spring/blob/main/HW01/HW01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mz0_QVkxCrX3"
      },
      "source": [
        "# **Homework 1: COVID-19 Cases Prediction (Regression)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZeZnPAiwDRWG"
      },
      "source": [
        "Author: Heng-Jui Chang\n",
        "\n",
        "Slides: https://github.com/ga642381/ML2021-Spring/blob/main/HW01/HW01.pdf  \n",
        "Videos (Mandarin): https://cool.ntu.edu.tw/courses/4793/modules/items/172854  \n",
        "https://cool.ntu.edu.tw/courses/4793/modules/items/172853  \n",
        "Video (English): https://cool.ntu.edu.tw/courses/4793/modules/items/176529\n",
        "\n",
        "\n",
        "Objectives:\n",
        "* Solve a regression problem with deep neural networks (DNN).\n",
        "* Understand basic DNN training tips.\n",
        "* Get familiar with PyTorch.\n",
        "\n",
        "If any questions, please contact the TAs via TA hours, NTU COOL, or email.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jx3x1nDkG-Uy"
      },
      "source": [
        "# **Download Data**\n",
        "\n",
        "\n",
        "If the Google drive links are dead, you can download data from [kaggle](https://www.kaggle.com/c/ml2021spring-hw1/data), and upload data manually to the workspace."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tMj55YDKG6ch",
        "outputId": "fc40ecc9-4756-48b1-d5c6-c169a8b453b2"
      },
      "source": [
        "tr_path = 'covid.train.csv'  # path to training data\n",
        "tt_path = 'covid.test.csv'   # path to testing data\n",
        "\n",
        "!gdown --id '19CCyCgJrUxtvgZF53vnctJiOJ23T5mqF' --output covid.train.csv\n",
        "!gdown --id '1CE240jLm2npU-tdz81-oVKEF3T2yfT1O' --output covid.test.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=19CCyCgJrUxtvgZF53vnctJiOJ23T5mqF\n",
            "To: /content/covid.train.csv\n",
            "100% 2.00M/2.00M [00:00<00:00, 31.7MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1CE240jLm2npU-tdz81-oVKEF3T2yfT1O\n",
            "To: /content/covid.test.csv\n",
            "100% 651k/651k [00:00<00:00, 10.2MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wS_4-77xHk44"
      },
      "source": [
        "# **Import Some Packages**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-onQd4JNA5H"
      },
      "source": [
        "# PyTorch\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "# For data preprocess\n",
        "import numpy as np\n",
        "import csv\n",
        "import os\n",
        "\n",
        "# For plotting\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import figure\n",
        "\n",
        "myseed = 42069  # set a random seed for reproducibility\n",
        "torch.backends.cudnn.deterministic = True\n",
        "torch.backends.cudnn.benchmark = False\n",
        "np.random.seed(myseed)\n",
        "torch.manual_seed(myseed)\n",
        "if torch.cuda.is_available():\n",
        "    torch.cuda.manual_seed_all(myseed)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BtE3b6JEH7rw"
      },
      "source": [
        "# **Some Utilities**\n",
        "\n",
        "You do not need to modify this part."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWMT3uf1NGQp"
      },
      "source": [
        "def get_device():\n",
        "    ''' Get device (if GPU is available, use GPU) '''\n",
        "    return 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "def plot_learning_curve(loss_record, title=''):\n",
        "    ''' Plot learning curve of your DNN (train & dev loss) '''\n",
        "    total_steps = len(loss_record['train'])\n",
        "    x_1 = range(total_steps)\n",
        "    x_2 = x_1[::len(loss_record['train']) // len(loss_record['dev'])]\n",
        "    figure(figsize=(6, 4))\n",
        "    plt.plot(x_1, loss_record['train'], c='tab:red', label='train')\n",
        "    plt.plot(x_2, loss_record['dev'], c='tab:cyan', label='dev')\n",
        "    plt.ylim(0.0, 5.)\n",
        "    plt.xlabel('Training steps')\n",
        "    plt.ylabel('MSE loss')\n",
        "    plt.title('Learning curve of {}'.format(title))\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def plot_pred(dv_set, model, device, lim=35., preds=None, targets=None):\n",
        "    ''' Plot prediction of your DNN '''\n",
        "    if preds is None or targets is None:\n",
        "        model.eval()\n",
        "        preds, targets = [], []\n",
        "        for x, y in dv_set:\n",
        "            x, y = x.to(device), y.to(device)\n",
        "            with torch.no_grad():\n",
        "                pred = model(x)\n",
        "                preds.append(pred.detach().cpu())\n",
        "                targets.append(y.detach().cpu())\n",
        "        preds = torch.cat(preds, dim=0).numpy()\n",
        "        targets = torch.cat(targets, dim=0).numpy()\n",
        "\n",
        "    figure(figsize=(5, 5))\n",
        "    plt.scatter(targets, preds, c='r', alpha=0.5)\n",
        "    plt.plot([-0.2, lim], [-0.2, lim], c='b')\n",
        "    plt.xlim(-0.2, lim)\n",
        "    plt.ylim(-0.2, lim)\n",
        "    plt.xlabel('ground truth value')\n",
        "    plt.ylabel('predicted value')\n",
        "    plt.title('Ground Truth v.s. Prediction')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39U_XFX6KOoj"
      },
      "source": [
        "# **Preprocess**\n",
        "\n",
        "We have three kinds of datasets:\n",
        "* `train`: for training\n",
        "* `dev`: for validation\n",
        "* `test`: for testing (w/o target value)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TQ-MdwpLL7Dt"
      },
      "source": [
        "## **Dataset**\n",
        "\n",
        "The `COVID19Dataset` below does:\n",
        "* read `.csv` files\n",
        "* extract features\n",
        "* split `covid.train.csv` into train/dev sets\n",
        "* normalize features\n",
        "\n",
        "Finishing `TODO` below might make you pass medium baseline."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0zlpIp9ANJRU"
      },
      "source": [
        "class COVID19Dataset(Dataset):\n",
        "    ''' Dataset for loading and preprocessing the COVID19 dataset '''\n",
        "    def __init__(self,\n",
        "                 path,\n",
        "                 mode='train',\n",
        "                 target_only=False):\n",
        "        self.mode = mode\n",
        "\n",
        "        # Read data into numpy arrays\n",
        "        with open(path, 'r') as fp:\n",
        "            data = list(csv.reader(fp))\n",
        "            data = np.array(data[1:])[:, 1:].astype(float)\n",
        "        \n",
        "        if not target_only:\n",
        "            feats = list(range(93))\n",
        "        else:\n",
        "            # TODO: Using 40 states & 2 tested_positive features (indices = 57 & 75)\n",
        "            pass\n",
        "\n",
        "        if mode == 'test':\n",
        "            # Testing data\n",
        "            # data: 893 x 93 (40 states + day 1 (18) + day 2 (18) + day 3 (17))\n",
        "            data = data[:, feats]\n",
        "            self.data = torch.FloatTensor(data)\n",
        "        else:\n",
        "            # Training data (train/dev sets)\n",
        "            # data: 2700 x 94 (40 states + day 1 (18) + day 2 (18) + day 3 (18))\n",
        "            target = data[:, -1]\n",
        "            data = data[:, feats]\n",
        "            \n",
        "            # Splitting training data into train & dev sets\n",
        "            if mode == 'train':\n",
        "                indices = [i for i in range(len(data)) if i % 10 != 0]\n",
        "            elif mode == 'dev':\n",
        "                indices = [i for i in range(len(data)) if i % 10 == 0]\n",
        "            \n",
        "            # Convert data into PyTorch tensors\n",
        "            self.data = torch.FloatTensor(data[indices])\n",
        "            self.target = torch.FloatTensor(target[indices])\n",
        "\n",
        "        # Normalize features (you may remove this part to see what will happen)\n",
        "        self.data[:, 40:] = \\\n",
        "            (self.data[:, 40:] - self.data[:, 40:].mean(dim=0, keepdim=True)) \\\n",
        "            / self.data[:, 40:].std(dim=0, keepdim=True)\n",
        "\n",
        "        self.dim = self.data.shape[1]\n",
        "\n",
        "        print('Finished reading the {} set of COVID19 Dataset ({} samples found, each dim = {})'\n",
        "              .format(mode, len(self.data), self.dim))\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        # Returns one sample at a time\n",
        "        if self.mode in ['train', 'dev']:\n",
        "            # For training\n",
        "            return self.data[index], self.target[index]\n",
        "        else:\n",
        "            # For testing (no target)\n",
        "            return self.data[index]\n",
        "\n",
        "    def __len__(self):\n",
        "        # Returns the size of the dataset\n",
        "        return len(self.data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlhTlkE7MDo3"
      },
      "source": [
        "## **DataLoader**\n",
        "\n",
        "A `DataLoader` loads data from a given `Dataset` into batches.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlhLk5t6MBX3"
      },
      "source": [
        "def prep_dataloader(path, mode, batch_size, n_jobs=0, target_only=False):\n",
        "    ''' Generates a dataset, then is put into a dataloader. '''\n",
        "    dataset = COVID19Dataset(path, mode=mode, target_only=target_only)  # Construct dataset\n",
        "    dataloader = DataLoader(\n",
        "        dataset, batch_size,\n",
        "        shuffle=(mode == 'train'), drop_last=False,\n",
        "        num_workers=n_jobs, pin_memory=True)                            # Construct dataloader\n",
        "    return dataloader"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SGuycwR0MeQB"
      },
      "source": [
        "# **Deep Neural Network**\n",
        "\n",
        "`NeuralNet` is an `nn.Module` designed for regression.\n",
        "The DNN consists of 2 fully-connected layers with ReLU activation.\n",
        "This module also included a function `cal_loss` for calculating loss.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49-uXYovOAI0"
      },
      "source": [
        "class NeuralNet(nn.Module):\n",
        "    ''' A simple fully-connected deep neural network '''\n",
        "    def __init__(self, input_dim):\n",
        "        super(NeuralNet, self).__init__()\n",
        "\n",
        "        # Define your neural network here\n",
        "        # TODO: How to modify this model to achieve better performance?\n",
        "        self.net = nn.Sequential(\n",
        "            nn.Linear(input_dim, 64),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(64, 1)\n",
        "        )\n",
        "\n",
        "        # Mean squared error loss\n",
        "        self.criterion = nn.MSELoss(reduction='mean')\n",
        "\n",
        "    def forward(self, x):\n",
        "        ''' Given input of size (batch_size x input_dim), compute output of the network '''\n",
        "        return self.net(x).squeeze(1)\n",
        "\n",
        "    def cal_loss(self, pred, target):\n",
        "        ''' Calculate loss '''\n",
        "        # TODO: you may implement L1/L2 regularization here\n",
        "        return self.criterion(pred, target)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvFWVjZ5Nvga"
      },
      "source": [
        "# **Train/Dev/Test**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MAM8QecJOyqn"
      },
      "source": [
        "## **Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOqcmYzMO7jB"
      },
      "source": [
        "def train(tr_set, dv_set, model, config, device):\n",
        "    ''' DNN training '''\n",
        "\n",
        "    n_epochs = config['n_epochs']  # Maximum number of epochs\n",
        "\n",
        "    # Setup optimizer\n",
        "    optimizer = getattr(torch.optim, config['optimizer'])(\n",
        "        model.parameters(), **config['optim_hparas'])\n",
        "\n",
        "    min_mse = 1000.\n",
        "    loss_record = {'train': [], 'dev': []}      # for recording training loss\n",
        "    early_stop_cnt = 0\n",
        "    epoch = 0\n",
        "    while epoch < n_epochs:\n",
        "        model.train()                           # set model to training mode\n",
        "        for x, y in tr_set:                     # iterate through the dataloader\n",
        "            optimizer.zero_grad()               # set gradient to zero\n",
        "            x, y = x.to(device), y.to(device)   # move data to device (cpu/cuda)\n",
        "            pred = model(x)                     # forward pass (compute output)\n",
        "            mse_loss = model.cal_loss(pred, y)  # compute loss\n",
        "            mse_loss.backward()                 # compute gradient (backpropagation)\n",
        "            optimizer.step()                    # update model with optimizer\n",
        "            loss_record['train'].append(mse_loss.detach().cpu().item())\n",
        "\n",
        "        # After each epoch, test your model on the validation (development) set.\n",
        "        dev_mse = dev(dv_set, model, device)\n",
        "        if dev_mse < min_mse:\n",
        "            # Save model if your model improved\n",
        "            min_mse = dev_mse\n",
        "            print('Saving model (epoch = {:4d}, loss = {:.4f})'\n",
        "                .format(epoch + 1, min_mse))\n",
        "            torch.save(model.state_dict(), config['save_path'])  # Save model to specified path\n",
        "            early_stop_cnt = 0\n",
        "        else:\n",
        "            early_stop_cnt += 1\n",
        "\n",
        "        epoch += 1\n",
        "        loss_record['dev'].append(dev_mse)\n",
        "        if early_stop_cnt > config['early_stop']:\n",
        "            # Stop training if your model stops improving for \"config['early_stop']\" epochs.\n",
        "            break\n",
        "\n",
        "    print('Finished training after {} epochs'.format(epoch))\n",
        "    return min_mse, loss_record"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0hSd4Bn3O2PL"
      },
      "source": [
        "## **Validation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yrxrD3YsN3U2"
      },
      "source": [
        "def dev(dv_set, model, device):\n",
        "    model.eval()                                # set model to evalutation mode\n",
        "    total_loss = 0\n",
        "    for x, y in dv_set:                         # iterate through the dataloader\n",
        "        x, y = x.to(device), y.to(device)       # move data to device (cpu/cuda)\n",
        "        with torch.no_grad():                   # disable gradient calculation\n",
        "            pred = model(x)                     # forward pass (compute output)\n",
        "            mse_loss = model.cal_loss(pred, y)  # compute loss\n",
        "        total_loss += mse_loss.detach().cpu().item() * len(x)  # accumulate loss\n",
        "    total_loss = total_loss / len(dv_set.dataset)              # compute averaged loss\n",
        "\n",
        "    return total_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0pdrhQAO41L"
      },
      "source": [
        "## **Testing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aSBMRFlYN5tB"
      },
      "source": [
        "def test(tt_set, model, device):\n",
        "    model.eval()                                # set model to evalutation mode\n",
        "    preds = []\n",
        "    for x in tt_set:                            # iterate through the dataloader\n",
        "        x = x.to(device)                        # move data to device (cpu/cuda)\n",
        "        with torch.no_grad():                   # disable gradient calculation\n",
        "            pred = model(x)                     # forward pass (compute output)\n",
        "            preds.append(pred.detach().cpu())   # collect prediction\n",
        "    preds = torch.cat(preds, dim=0).numpy()     # concatenate all predictions and convert to a numpy array\n",
        "    return preds"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SvckkF5dvf0j"
      },
      "source": [
        "# **Setup Hyper-parameters**\n",
        "\n",
        "`config` contains hyper-parameters for training and the path to save your model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NPXpdumwPjE7"
      },
      "source": [
        "device = get_device()                 # get the current available device ('cpu' or 'cuda')\n",
        "os.makedirs('models', exist_ok=True)  # The trained model will be saved to ./models/\n",
        "target_only = False                   # TODO: Using 40 states & 2 tested_positive features\n",
        "\n",
        "# TODO: How to tune these hyper-parameters to improve your model's performance?\n",
        "config = {\n",
        "    'n_epochs': 3000,                # maximum number of epochs\n",
        "    'batch_size': 270,               # mini-batch size for dataloader\n",
        "    'optimizer': 'SGD',              # optimization algorithm (optimizer in torch.optim)\n",
        "    'optim_hparas': {                # hyper-parameters for the optimizer (depends on which optimizer you are using)\n",
        "        'lr': 0.001,                 # learning rate of SGD\n",
        "        'momentum': 0.9              # momentum for SGD\n",
        "    },\n",
        "    'early_stop': 200,               # early stopping epochs (the number epochs since your model's last improvement)\n",
        "    'save_path': 'models/model.pth'  # your model will be saved here\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6j1eOV3TOH-j"
      },
      "source": [
        "# **Load data and model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eNrYBMmePLKm",
        "outputId": "fcd4f175-4f7e-4306-f33c-5f8285f11dce"
      },
      "source": [
        "tr_set = prep_dataloader(tr_path, 'train', config['batch_size'], target_only=target_only)\n",
        "dv_set = prep_dataloader(tr_path, 'dev', config['batch_size'], target_only=target_only)\n",
        "tt_set = prep_dataloader(tt_path, 'test', config['batch_size'], target_only=target_only)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Finished reading the train set of COVID19 Dataset (2430 samples found, each dim = 93)\n",
            "Finished reading the dev set of COVID19 Dataset (270 samples found, each dim = 93)\n",
            "Finished reading the test set of COVID19 Dataset (893 samples found, each dim = 93)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHylSirLP9oh"
      },
      "source": [
        "model = NeuralNet(tr_set.dataset.dim).to(device)  # Construct model and move to device"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sX2B_zgSOPTJ"
      },
      "source": [
        "# **Start Training!**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GrEbUxazQAAZ",
        "outputId": "f4f3bd74-2d97-4275-b69f-6609976b91f9"
      },
      "source": [
        "model_loss, model_loss_record = train(tr_set, dv_set, model, config, device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving model (epoch =    1, loss = 74.9742)\n",
            "Saving model (epoch =    2, loss = 50.5313)\n",
            "Saving model (epoch =    3, loss = 29.1148)\n",
            "Saving model (epoch =    4, loss = 15.8134)\n",
            "Saving model (epoch =    5, loss = 9.5430)\n",
            "Saving model (epoch =    6, loss = 6.8086)\n",
            "Saving model (epoch =    7, loss = 5.3892)\n",
            "Saving model (epoch =    8, loss = 4.5267)\n",
            "Saving model (epoch =    9, loss = 3.9454)\n",
            "Saving model (epoch =   10, loss = 3.5560)\n",
            "Saving model (epoch =   11, loss = 3.2303)\n",
            "Saving model (epoch =   12, loss = 2.9920)\n",
            "Saving model (epoch =   13, loss = 2.7737)\n",
            "Saving model (epoch =   14, loss = 2.6181)\n",
            "Saving model (epoch =   15, loss = 2.3987)\n",
            "Saving model (epoch =   16, loss = 2.2712)\n",
            "Saving model (epoch =   17, loss = 2.1349)\n",
            "Saving model (epoch =   18, loss = 2.0210)\n",
            "Saving model (epoch =   19, loss = 1.8848)\n",
            "Saving model (epoch =   20, loss = 1.7999)\n",
            "Saving model (epoch =   21, loss = 1.7510)\n",
            "Saving model (epoch =   22, loss = 1.6787)\n",
            "Saving model (epoch =   23, loss = 1.6450)\n",
            "Saving model (epoch =   24, loss = 1.6030)\n",
            "Saving model (epoch =   26, loss = 1.5052)\n",
            "Saving model (epoch =   27, loss = 1.4486)\n",
            "Saving model (epoch =   28, loss = 1.4069)\n",
            "Saving model (epoch =   29, loss = 1.3733)\n",
            "Saving model (epoch =   30, loss = 1.3533)\n",
            "Saving model (epoch =   31, loss = 1.3335)\n",
            "Saving model (epoch =   32, loss = 1.3011)\n",
            "Saving model (epoch =   33, loss = 1.2711)\n",
            "Saving model (epoch =   35, loss = 1.2331)\n",
            "Saving model (epoch =   36, loss = 1.2235)\n",
            "Saving model (epoch =   38, loss = 1.2180)\n",
            "Saving model (epoch =   39, loss = 1.2018)\n",
            "Saving model (epoch =   40, loss = 1.1651)\n",
            "Saving model (epoch =   42, loss = 1.1631)\n",
            "Saving model (epoch =   43, loss = 1.1394)\n",
            "Saving model (epoch =   46, loss = 1.1129)\n",
            "Saving model (epoch =   47, loss = 1.1107)\n",
            "Saving model (epoch =   49, loss = 1.1091)\n",
            "Saving model (epoch =   50, loss = 1.0838)\n",
            "Saving model (epoch =   52, loss = 1.0692)\n",
            "Saving model (epoch =   53, loss = 1.0681)\n",
            "Saving model (epoch =   55, loss = 1.0537)\n",
            "Saving model (epoch =   60, loss = 1.0457)\n",
            "Saving model (epoch =   61, loss = 1.0366)\n",
            "Saving model (epoch =   63, loss = 1.0359)\n",
            "Saving model (epoch =   64, loss = 1.0111)\n",
            "Saving model (epoch =   69, loss = 1.0072)\n",
            "Saving model (epoch =   72, loss = 0.9760)\n",
            "Saving model (epoch =   76, loss = 0.9672)\n",
            "Saving model (epoch =   79, loss = 0.9584)\n",
            "Saving model (epoch =   80, loss = 0.9526)\n",
            "Saving model (epoch =   82, loss = 0.9494)\n",
            "Saving model (epoch =   83, loss = 0.9426)\n",
            "Saving model (epoch =   88, loss = 0.9398)\n",
            "Saving model (epoch =   89, loss = 0.9223)\n",
            "Saving model (epoch =   95, loss = 0.9111)\n",
            "Saving model (epoch =   98, loss = 0.9034)\n",
            "Saving model (epoch =  101, loss = 0.9014)\n",
            "Saving model (epoch =  105, loss = 0.9011)\n",
            "Saving model (epoch =  106, loss = 0.8933)\n",
            "Saving model (epoch =  110, loss = 0.8893)\n",
            "Saving model (epoch =  117, loss = 0.8867)\n",
            "Saving model (epoch =  118, loss = 0.8867)\n",
            "Saving model (epoch =  121, loss = 0.8790)\n",
            "Saving model (epoch =  126, loss = 0.8642)\n",
            "Saving model (epoch =  130, loss = 0.8627)\n",
            "Saving model (epoch =  137, loss = 0.8616)\n",
            "Saving model (epoch =  139, loss = 0.8534)\n",
            "Saving model (epoch =  147, loss = 0.8467)\n",
            "Saving model (epoch =  154, loss = 0.8463)\n",
            "Saving model (epoch =  155, loss = 0.8408)\n",
            "Saving model (epoch =  167, loss = 0.8354)\n",
            "Saving model (epoch =  176, loss = 0.8314)\n",
            "Saving model (epoch =  191, loss = 0.8267)\n",
            "Saving model (epoch =  200, loss = 0.8212)\n",
            "Saving model (epoch =  226, loss = 0.8190)\n",
            "Saving model (epoch =  230, loss = 0.8144)\n",
            "Saving model (epoch =  244, loss = 0.8136)\n",
            "Saving model (epoch =  258, loss = 0.8095)\n",
            "Saving model (epoch =  269, loss = 0.8076)\n",
            "Saving model (epoch =  285, loss = 0.8064)\n",
            "Saving model (epoch =  330, loss = 0.8055)\n",
            "Saving model (epoch =  347, loss = 0.8053)\n",
            "Saving model (epoch =  359, loss = 0.7992)\n",
            "Saving model (epoch =  410, loss = 0.7989)\n",
            "Saving model (epoch =  442, loss = 0.7966)\n",
            "Saving model (epoch =  447, loss = 0.7966)\n",
            "Saving model (epoch =  576, loss = 0.7958)\n",
            "Saving model (epoch =  596, loss = 0.7929)\n",
            "Saving model (epoch =  600, loss = 0.7893)\n",
            "Saving model (epoch =  683, loss = 0.7825)\n",
            "Saving model (epoch =  878, loss = 0.7817)\n",
            "Saving model (epoch =  904, loss = 0.7794)\n",
            "Saving model (epoch =  931, loss = 0.7790)\n",
            "Saving model (epoch =  951, loss = 0.7781)\n",
            "Saving model (epoch =  965, loss = 0.7771)\n",
            "Saving model (epoch = 1018, loss = 0.7717)\n",
            "Saving model (epoch = 1168, loss = 0.7653)\n",
            "Saving model (epoch = 1267, loss = 0.7645)\n",
            "Saving model (epoch = 1428, loss = 0.7644)\n",
            "Saving model (epoch = 1461, loss = 0.7635)\n",
            "Saving model (epoch = 1484, loss = 0.7629)\n",
            "Saving model (epoch = 1493, loss = 0.7590)\n",
            "Finished training after 1694 epochs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "hsNO9nnXQBvP",
        "outputId": "1626def6-94c7-4a87-9447-d939f827c8eb"
      },
      "source": [
        "plot_learning_curve(model_loss_record, title='deep model')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "3iZTVn5WQFpX",
        "outputId": "a2d5e118-559d-45c6-b644-6792af54663d"
      },
      "source": [
        "del model\n",
        "model = NeuralNet(tr_set.dataset.dim).to(device)\n",
        "ckpt = torch.load(config['save_path'], map_location='cpu')  # Load your best model\n",
        "model.load_state_dict(ckpt)\n",
        "plot_pred(dv_set, model, device)  # Show prediction on the validation set"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQikz3IPiyPf"
      },
      "source": [
        "# **Testing**\n",
        "The predictions of your model on testing set will be stored at `pred.csv`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8cTuQjQQOon",
        "outputId": "6bc5de07-4c5a-4e87-9ae3-d09f539c5f2c"
      },
      "source": [
        "def save_pred(preds, file):\n",
        "    ''' Save predictions to specified file '''\n",
        "    print('Saving results to {}'.format(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",
        "preds = test(tt_set, model, device)  # predict COVID-19 cases with your model\n",
        "save_pred(preds, 'pred.csv')         # save prediction file to pred.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving results to pred.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nfrVxqJanGpE"
      },
      "source": [
        "# **Hints**\n",
        "\n",
        "## **Simple Baseline**\n",
        "* Run sample code\n",
        "\n",
        "## **Medium Baseline**\n",
        "* Feature selection: 40 states + 2 `tested_positive` (`TODO` in dataset)\n",
        "\n",
        "## **Strong Baseline**\n",
        "* Feature selection (what other features are useful?)\n",
        "* DNN architecture (layers? dimension? activation function?)\n",
        "* Training (mini-batch? optimizer? learning rate?)\n",
        "* L2 regularization\n",
        "* There are some mistakes in the sample code, can you find them?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tmCwXgpot3t"
      },
      "source": [
        "# **Reference**\n",
        "This code is completely written by Heng-Jui Chang @ NTUEE.  \n",
        "Copying or reusing this code is required to specify the original author. \n",
        "\n",
        "E.g.  \n",
        "Source: Heng-Jui Chang @ NTUEE (https://github.com/ga642381/ML2021-Spring/blob/main/HW01/HW01.ipynb)\n"
      ]
    }
  ]
}