homework1.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1V2xetM3GFCCtbrbaWKk_yZ2XDVcONAA1",
      "authorship_tag": "ABX9TyOw5K+0sX6us3o4TGvdfX/A",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/codedog1229/MachineLearning/blob/main/homework1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pQeLX_wyBDx",
        "outputId": "06914272-e95f-4d02-ff80-cc7af94c3331"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "7HgLWL-uze3m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0e7da2b-6364-4c85-f175-a35fc102a344"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu May  4 06:16:20 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   46C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#下载资料\n",
        "!gdown --id '1kLSW_-cW2Huj7bh84YTdimGBOJaODiOS' --output covid.train.csv\n",
        "!gdown --id '1iiI5qROrAhZn-o4FPqsE97bMzDEFvIdg' --output covid.test.csv"
      ],
      "metadata": {
        "id": "FmEWDM-WZ7-0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#导包\n",
        "# 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",
        "# For plotting learning curve\n",
        "from torch.utils.tensorboard import SummaryWriter"
      ],
      "metadata": {
        "id": "kuTZJpKNbt8G"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Some Utility Functions\n",
        "\n",
        "You do not need to modify this part."
      ],
      "metadata": {
        "id": "sZrCQw01cDco"
      }
    },
    {
      "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",
        "\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"
      ],
      "metadata": {
        "id": "oTmCo0vWcEb2"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Dataset**"
      ],
      "metadata": {
        "id": "DGdj5Rp3cOii"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class COVID19Dataset(Dataset):\n",
        "  '''\n",
        "  X:Features.\n",
        "  Y:Target,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": "SC5LfFyScJEa"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Neural Network Model**\n",
        "Try out different model architectures by modifying the class below."
      ],
      "metadata": {
        "id": "XreruICXeAnv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class My_Model(nn.Module):\n",
        "  def __init__(self,input_dim):\n",
        "    super(My_Model,self).__init__()\n",
        "    # TODO：修改模型的结构，注意维度。\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)\n",
        "    return x"
      ],
      "metadata": {
        "id": "R4sF4mhqd_aJ"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Selection\n",
        "Choose features you deem useful by modifying the function below."
      ],
      "metadata": {
        "id": "iTwI-knqkQlm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def select_feat(train_data, valid_data, test_data, select_all=True):\n",
        "  '''Select useful features to perform regression'''\n",
        "  #最后一行为y值\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",
        "  if(select_all):\n",
        "    feat_idx = list(range(raw_x_train.shape[1]))\n",
        "  else:\n",
        "    feat_idx = [0,1,2,3,4] # TODO: Select suitable feature columns.\n",
        "  return raw_x_train[:,feat_idx],raw_x_valid[:,feat_idx],raw_x_test[:,feat_idx],y_train,y_valid"
      ],
      "metadata": {
        "id": "Qna_fPWMfF48"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training Loop"
      ],
      "metadata": {
        "id": "0q9soYvIllQv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Configurations\n",
        "#`config` contains hyper-parameters for training and the path to save your model."
      ],
      "metadata": {
        "id": "Ho-3O-42rVip"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def trainer(train_loader, valid_loader, model, config, device):\n",
        "  #定义Loss函数\n",
        "  criterion = nn.MSELoss(reduction='mean')\n",
        "  #定义优化器\n",
        "  optimizer = torch.optim.SGD(model.parameters(),lr=config['learning_rate'],momentum=0.9)\n",
        "  #SummaryWriter 类是您记录数据以供 TensorBoard 使用和可视化的主要入口\n",
        "  writer = SummaryWriter()\n",
        "\n",
        "  if not os.path.isdir('./models'):\n",
        "    os.mkdir('./models') #创建一个文件夹用来保存模型\n",
        "  \n",
        "  n_epochs = config['n_epochs']\n",
        "  best_loss = math.inf\n",
        "  step = 0\n",
        "  early_stop_count = 0\n",
        "\n",
        "  for epoch in range(n_epochs):\n",
        "    model.train()#将模型设置为训练模式\n",
        "    loss_record = []\n",
        "    #tqdm 是一个可视化训练进度的软件包。\n",
        "    train_pbar = tqdm(train_loader, position=0, leave=True)\n",
        "\n",
        "    for x,y in train_pbar:\n",
        "      optimizer.zero_grad()#设置梯度为0\n",
        "      x,y = x.to(device),y.to(device)#将张量放进对应的设备\n",
        "      pred = model(x)\n",
        "      #计算loss\n",
        "      loss = criterion(pred,y)\n",
        "      #反向传播计算梯度\n",
        "      loss.backward()\n",
        "      #更新参数\n",
        "      optimizer.step()\n",
        "      step += 1\n",
        "      loss_record.append(loss.detach().item())\n",
        "\n",
        "      #展示当前的进度和loss\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()#将模型设置为验证模式\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",
        "    #判断\n",
        "    if mean_valid_loss<best_loss:\n",
        "      best_loss = mean_valid_loss\n",
        "      torch.save(model.state_dict(),config['save_path']) #保存你最好的模型\n",
        "      print('Saving model with loss{:.3f}...'.format(best_loss))\n",
        "      early_stop_count = 0\n",
        "    else: \n",
        "      early_stop_count += 1                                               \n",
        "    #提早结束训练\n",
        "    if early_stop_count >= config['early_stop']:\n",
        "      print('\\nModel is not improving, so we halt the training session.')\n",
        "      return"
      ],
      "metadata": {
        "id": "LZtHt_PPlhR5"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Configurations\n",
        "`config` contains hyper-parameters for training and the path to save your model."
      ],
      "metadata": {
        "id": "C3ko9MXatUiO"
      }
    },
    {
      "cell_type": "code",
      "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': 3000,     # Number of epochs.            \n",
        "    'batch_size': 256, \n",
        "    'learning_rate': 1e-5,              \n",
        "    'early_stop': 400,    # 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": "zzmtKzn7tYgM"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataloader\n",
        "Read data from files and set up training, validation, and testing sets. You do not need to modify this part."
      ],
      "metadata": {
        "id": "mYzRjPNLth7p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd '/content/drive/MyDrive/ML2023/homework1'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1JTzvcmWtfIF",
        "outputId": "411c554f-0165-41d5-a411-30a9a978715d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/ML2023/homework1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set seed for reproducibility\n",
        "same_seed(config['seed'])\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",
        "# 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}\"\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "If5uLeQPt0aN",
        "outputId": "0f3a6920-49b0-40dc-ed67-ffcab6e1989a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_data size: (2408, 89) \n",
            "valid_data size: (601, 89) \n",
            "test_data size: (997, 88)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select features\n",
        "x_train, x_valid, x_test, y_train, y_valid = select_feat(train_data, valid_data, test_data, config['select_all'])\n",
        "# Print out the number of features.\n",
        "print(f'number of features: {x_train.shape[1]}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nk6djdLwuK4A",
        "outputId": "a9bfd038-b29f-457e-e598-74fab02d1706"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of features: 88\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset, valid_dataset, test_dataset = COVID19Dataset(x_train, y_train),COVID19Dataset(x_valid, y_valid),COVID19Dataset(x_test)"
      ],
      "metadata": {
        "id": "HinuthcGua6Q"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 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)"
      ],
      "metadata": {
        "id": "5rkIkjj5umFV"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Start Training!"
      ],
      "metadata": {
        "id": "yCF7Cm9Fu0Lh"
      }
    },
    {
      "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.\n",
        "trainer(train_loader, valid_loader, model, config, device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AhWVpQ96uxnT",
        "outputId": "ad8ae62f-de19-47b4-e313-56f425b1c730"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[1/3000]: 100%|██████████| 10/10 [00:02<00:00,  3.56it/s, loss=290]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/3000]: Train loss: 334.8469, Valid loss: 280.7511\n",
            "Saving model with loss280.751...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[2/3000]: 100%|██████████| 10/10 [00:00<00:00, 125.45it/s, loss=175]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [2/3000]: Train loss: 141.2223, Valid loss: 139.2577\n",
            "Saving model with loss139.258...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[3/3000]: 100%|██████████| 10/10 [00:00<00:00, 135.15it/s, loss=176]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [3/3000]: Train loss: 131.5447, Valid loss: 123.0023\n",
            "Saving model with loss123.002...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[4/3000]: 100%|██████████| 10/10 [00:00<00:00, 134.24it/s, loss=95.3]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [4/3000]: Train loss: 112.7463, Valid loss: 112.7983\n",
            "Saving model with loss112.798...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[5/3000]: 100%|██████████| 10/10 [00:00<00:00, 112.19it/s, loss=64.8]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [5/3000]: Train loss: 97.7331, Valid loss: 95.4611\n",
            "Saving model with loss95.461...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[6/3000]: 100%|██████████| 10/10 [00:00<00:00, 119.18it/s, loss=88.2]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [6/3000]: Train loss: 92.2806, Valid loss: 87.0581\n",
            "Saving model with loss87.058...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [379/3000]: Train loss: 169.7051, Valid loss: 172.4048\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[380/3000]: 100%|██████████| 10/10 [00:00<00:00, 116.50it/s, loss=212]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [380/3000]: Train loss: 172.7068, Valid loss: 171.3645\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[381/3000]: 100%|██████████| 10/10 [00:00<00:00, 115.73it/s, loss=154]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [381/3000]: Train loss: 168.9940, Valid loss: 167.7235\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[382/3000]: 100%|██████████| 10/10 [00:00<00:00, 126.52it/s, loss=156]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [382/3000]: Train loss: 168.8632, Valid loss: 168.3986\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[383/3000]: 100%|██████████| 10/10 [00:00<00:00, 102.82it/s, loss=154]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [383/3000]: Train loss: 168.4602, Valid loss: 166.9975\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[384/3000]: 100%|██████████| 10/10 [00:00<00:00, 112.54it/s, loss=184]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [384/3000]: Train loss: 169.9613, Valid loss: 170.3080\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[385/3000]: 100%|██████████| 10/10 [00:00<00:00, 121.10it/s, loss=185]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [385/3000]: Train loss: 169.7868, Valid loss: 172.8916\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[386/3000]: 100%|██████████| 10/10 [00:00<00:00, 119.49it/s, loss=209]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [386/3000]: Train loss: 170.9330, Valid loss: 169.9438\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[387/3000]: 100%|██████████| 10/10 [00:00<00:00, 110.24it/s, loss=134]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [387/3000]: Train loss: 166.2021, Valid loss: 169.0721\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[388/3000]: 100%|██████████| 10/10 [00:00<00:00, 122.11it/s, loss=181]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [388/3000]: Train loss: 168.7258, Valid loss: 169.1561\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[389/3000]: 100%|██████████| 10/10 [00:00<00:00, 110.26it/s, loss=174]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [389/3000]: Train loss: 168.0528, Valid loss: 166.7307\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[390/3000]: 100%|██████████| 10/10 [00:00<00:00, 119.10it/s, loss=160]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [390/3000]: Train loss: 166.9644, Valid loss: 166.3765\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[391/3000]: 100%|██████████| 10/10 [00:00<00:00, 103.94it/s, loss=162]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [391/3000]: Train loss: 166.8586, Valid loss: 166.5341\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[392/3000]: 100%|██████████| 10/10 [00:00<00:00, 115.79it/s, loss=180]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [392/3000]: Train loss: 167.6298, Valid loss: 166.5335\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[393/3000]: 100%|██████████| 10/10 [00:00<00:00, 116.16it/s, loss=135]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [393/3000]: Train loss: 164.7277, Valid loss: 166.0271\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[394/3000]: 100%|██████████| 10/10 [00:00<00:00, 120.18it/s, loss=163]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [394/3000]: Train loss: 166.1307, Valid loss: 166.9543\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[395/3000]: 100%|██████████| 10/10 [00:00<00:00, 119.49it/s, loss=153]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [395/3000]: Train loss: 165.2743, Valid loss: 165.1598\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[396/3000]: 100%|██████████| 10/10 [00:00<00:00, 116.85it/s, loss=154]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [396/3000]: Train loss: 165.0889, Valid loss: 167.3655\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[397/3000]: 100%|██████████| 10/10 [00:00<00:00, 108.90it/s, loss=154]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [397/3000]: Train loss: 164.8668, Valid loss: 165.1603\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[398/3000]: 100%|██████████| 10/10 [00:00<00:00, 83.28it/s, loss=182]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [398/3000]: Train loss: 166.2700, Valid loss: 165.7523\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[399/3000]: 100%|██████████| 10/10 [00:00<00:00, 111.54it/s, loss=159]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [399/3000]: Train loss: 164.6209, Valid loss: 162.2541\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[400/3000]: 100%|██████████| 10/10 [00:00<00:00, 115.39it/s, loss=186]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [400/3000]: Train loss: 165.9696, Valid loss: 165.5838\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[401/3000]: 100%|██████████| 10/10 [00:00<00:00, 101.42it/s, loss=212]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [401/3000]: Train loss: 167.2821, Valid loss: 165.7864\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[402/3000]: 100%|██████████| 10/10 [00:00<00:00, 24.38it/s, loss=163]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [402/3000]: Train loss: 164.1158, Valid loss: 163.8810\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[403/3000]: 100%|██████████| 10/10 [00:00<00:00, 118.03it/s, loss=133]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [403/3000]: Train loss: 162.0693, Valid loss: 161.6318\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[404/3000]: 100%|██████████| 10/10 [00:00<00:00, 110.35it/s, loss=150]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [404/3000]: Train loss: 162.8810, Valid loss: 164.0960\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[405/3000]: 100%|██████████| 10/10 [00:00<00:00, 93.89it/s, loss=118]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [405/3000]: Train loss: 160.7108, Valid loss: 162.0581\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[406/3000]: 100%|██████████| 10/10 [00:00<00:00, 118.79it/s, loss=139]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [406/3000]: Train loss: 161.7237, Valid loss: 161.7436\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[407/3000]: 100%|██████████| 10/10 [00:00<00:00, 123.52it/s, loss=132]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [407/3000]: Train loss: 161.0835, Valid loss: 161.1971\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[408/3000]: 100%|██████████| 10/10 [00:00<00:00, 124.61it/s, loss=175]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [408/3000]: Train loss: 163.4009, Valid loss: 161.6485\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[409/3000]: 100%|██████████| 10/10 [00:00<00:00, 125.43it/s, loss=149]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [409/3000]: Train loss: 161.6247, Valid loss: 162.6371\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[410/3000]: 100%|██████████| 10/10 [00:00<00:00, 121.04it/s, loss=169]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [410/3000]: Train loss: 162.5494, Valid loss: 161.0844\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[411/3000]: 100%|██████████| 10/10 [00:00<00:00, 125.98it/s, loss=167]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [411/3000]: Train loss: 162.1618, Valid loss: 162.0833\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[412/3000]: 100%|██████████| 10/10 [00:00<00:00, 125.16it/s, loss=183]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [412/3000]: Train loss: 162.8856, Valid loss: 163.2824\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[413/3000]: 100%|██████████| 10/10 [00:00<00:00, 126.30it/s, loss=238]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [413/3000]: Train loss: 165.9393, Valid loss: 163.6004\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[414/3000]: 100%|██████████| 10/10 [00:00<00:00, 129.47it/s, loss=156]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [414/3000]: Train loss: 160.8165, Valid loss: 159.0551\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[415/3000]: 100%|██████████| 10/10 [00:00<00:00, 124.96it/s, loss=161]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [415/3000]: Train loss: 160.8875, Valid loss: 160.5346\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[416/3000]: 100%|██████████| 10/10 [00:00<00:00, 133.85it/s, loss=139]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [416/3000]: Train loss: 159.3614, Valid loss: 158.0025\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[417/3000]: 100%|██████████| 10/10 [00:00<00:00, 127.08it/s, loss=143]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [417/3000]: Train loss: 159.3378, Valid loss: 160.3110\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[418/3000]: 100%|██████████| 10/10 [00:00<00:00, 138.60it/s, loss=190]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [418/3000]: Train loss: 161.8896, Valid loss: 160.5605\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[419/3000]: 100%|██████████| 10/10 [00:00<00:00, 124.12it/s, loss=133]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [419/3000]: Train loss: 158.2634, Valid loss: 156.6134\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[420/3000]: 100%|██████████| 10/10 [00:00<00:00, 139.36it/s, loss=155]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [420/3000]: Train loss: 159.3668, Valid loss: 158.2871\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[421/3000]: 100%|██████████| 10/10 [00:00<00:00, 115.06it/s, loss=120]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [421/3000]: Train loss: 157.0874, Valid loss: 156.5578\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch[422/3000]: 100%|██████████| 10/10 [00:00<00:00, 140.59it/s, loss=164]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [422/3000]: Train loss: 159.4523, Valid loss: 160.0338\n",
            "\n",
            "Model is not improving, so we halt the training session.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing\n",
        "The predictions of your model on testing set will be stored at pred.csv."
      ],
      "metadata": {
        "id": "IVwkLC4evlze"
      }
    },
    {
      "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",
        "\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, 'pred.csv')         "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xE7ZQcU7vlLZ",
        "outputId": "7f3cea59-4e00-4334-c213-cc1504523bce"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4/4 [00:00<00:00, 476.65it/s]\n"
          ]
        }
      ]
    }
  ]
}