baseline.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "b1b836e9-d9e8-4476-a8ec-11960eb7468f",
      "metadata": {
        "tags": [],
        "id": "b1b836e9-d9e8-4476-a8ec-11960eb7468f"
      },
      "source": [
        "# Baseline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Imports and Installation"
      ],
      "metadata": {
        "id": "4BM0GBZVPrwy"
      },
      "id": "4BM0GBZVPrwy"
    },
    {
      "cell_type": "code",
      "source": [
        "!{sys.executable} -m pip install plotly\n",
        "!{sys.executable} -m pip install chart_studio\n",
        "!{sys.executable} -m pip install kaleido\n",
        "!{sys.executable} -m pip install torch torchvision"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RcNywA5uWv6F",
        "outputId": "af4bd199-ff65-491d-9aa7-99442869c690"
      },
      "id": "RcNywA5uWv6F",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting kaleido\n",
            "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 79.9 MB 147 kB/s \n",
            "\u001b[?25hInstalling collected packages: kaleido\n",
            "Successfully installed kaleido-0.2.1\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision) (2.10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import sys\n",
        "import csv\n",
        "import torch\n",
        "import torchvision\n",
        "from torch import nn\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "from sklearn import metrics\n",
        "import plotly\n",
        "import matplotlib.pyplot as plt\n",
        "%config InlineBackend.figure_formats = ['svg']\n",
        "%matplotlib inline\n",
        "params = {\"ytick.color\" : \"w\",\n",
        "          \"xtick.color\" : \"w\",\n",
        "          \"axes.labelcolor\" : \"w\",\n",
        "          'axes.titlecolor': 'w',\n",
        "          \"axes.edgecolor\" : \"w\"}\n",
        "plt.rcParams.update(params)\n",
        "import plotly.graph_objects as go\n",
        "import plotly.offline as offline"
      ],
      "metadata": {
        "id": "GjEN2513PrZm"
      },
      "id": "GjEN2513PrZm",
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "fd74fe47-b889-4a92-a2a5-041c6a694b34",
      "metadata": {
        "tags": [],
        "id": "fd74fe47-b889-4a92-a2a5-041c6a694b34"
      },
      "source": [
        "# Dataset\n",
        "Our dataset is going to be based off of the PNG representation of the flight, from a top down perspective.\n",
        "\n",
        "Images will be saved to `{path}_img` folder.\n",
        "\n",
        "You can modify this `Dataset` class to fit your needs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "fef1b54e-505c-4d33-b0e9-2392f3a544ef",
      "metadata": {
        "id": "fef1b54e-505c-4d33-b0e9-2392f3a544ef"
      },
      "outputs": [],
      "source": [
        "class FlightsAsImageDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, path, size=128, make=True):\n",
        "        \"\"\"\n",
        "        DESC\n",
        "        ---\n",
        "        Initializes a Dataset class for the maneuver-id data. Reads the data\n",
        "        from .tsv files convert all flights to graphs as a png file. The graphs are \n",
        "        based on 'xEast' and 'yNorth' and represent a top-down view of the flight.\n",
        "        ---\n",
        "        INPUTS\n",
        "        ---\n",
        "        path - folder path that includes the two subdirectories called\n",
        "        'good' and 'bad'. These directories should include only .tsv files\n",
        "        size - the height and width of the output .png\n",
        "        make - whether or not to generate the image (false allows you to skip \n",
        "        making the image if it already exists)\n",
        "        ---\n",
        "        RETURN\n",
        "        ---\n",
        "        __FlightsAsImageDataset instance\n",
        "        \"\"\"\n",
        "\n",
        "        self.all_labels = [folder for folder in os.listdir(path)]\n",
        "        self.images = []\n",
        "        self.labels = []\n",
        "        self.size = size\n",
        "        self.png_path = 'flights/imgs/'\n",
        "        self.good_paths = os.listdir('flights/imgs/good/')\n",
        "        self.bad_paths = os.listdir('flights/imgs/bad/')\n",
        "        i = 1\n",
        "        for label in self.all_labels:\n",
        "            for f in os.listdir(os.path.join(path, label)):\n",
        "                if f.startswith('.') or '.tsv' not in f:\n",
        "                    continue\n",
        "                if i%100==0:\n",
        "                  print(i, \"files completed\")\n",
        "                i+=1\n",
        "                new_path = self.convert_to_png(os.path.join(path, label, f), label, make)\n",
        "                self.images.append(new_path)\n",
        "                self.labels.append(label)\n",
        "            \n",
        "    def __len__(self):\n",
        "        return len(self.images)\n",
        "    \n",
        "    def __getitem__(self, idx):\n",
        "        path = self.images[idx]\n",
        "        img = Image.open(path).convert('RGB')\n",
        "        array = np.array(img)\n",
        "        label = self.labels[idx]\n",
        "        if label == \"good\":\n",
        "            label = 1\n",
        "        else:\n",
        "            label = 0\n",
        "        label = torch.tensor(label, dtype=torch.float32)\n",
        "        tensor = torchvision.transforms.ToTensor()(img)\n",
        "        return tensor, label.long()\n",
        "    \n",
        "    def convert_to_png(self, full_path, label, make):\n",
        "        folder = os.path.dirname(full_path)\n",
        "        file_name = os.path.basename(full_path)\n",
        "        os.makedirs(folder, exist_ok=True)\n",
        "        png_name = file_name.replace('.tsv', '.png')\n",
        "        check_path = os.path.join(self.png_path+label, png_name)\n",
        "\n",
        "        if check_path[18:] in self.good_paths or check_path[17:] in self.bad_paths:\n",
        "          #print(\"Already exists, returning\")\n",
        "          return check_path\n",
        "      \n",
        "        \n",
        "        new_path = check_path\n",
        "        \n",
        "        if make:\n",
        "            data = self.load(full_path)\n",
        "\n",
        "            y = [row[\"yNorth (m)\"] for row in data]\n",
        "            x = [row[\"xEast (m)\"] for row in data]\n",
        "            plot = go.Scatter(\n",
        "                x=x, \n",
        "                y=y,\n",
        "                marker=dict(\n",
        "                    size=2,\n",
        "                    color=\"black\"\n",
        "                ),\n",
        "                line=dict(\n",
        "                    color='black',\n",
        "                    width=2\n",
        "                )\n",
        "            )\n",
        "            fig = go.Figure(data=plot)\n",
        "            fig.update_layout(\n",
        "                margin=go.layout.Margin(\n",
        "                    l=0, #left margin\n",
        "                    r=0, #right margin\n",
        "                    b=0, #bottom margin\n",
        "                    t=0, #top margin\n",
        "                ),\n",
        "                height=self.size,\n",
        "                width=self.size,\n",
        "                scene=dict(\n",
        "                    camera=dict(\n",
        "                        up=dict(\n",
        "                            x=0,\n",
        "                            y=0,\n",
        "                            z=1\n",
        "                        ),\n",
        "                        eye=dict(\n",
        "                            x=0,\n",
        "                            y=1.0707,\n",
        "                            z=1,\n",
        "                        )\n",
        "                    ),\n",
        "                    aspectratio = dict( x=1, y=1, z=0.7 ),\n",
        "                    aspectmode = 'manual'\n",
        "                ),\n",
        "            )\n",
        "            fig.update_xaxes(showticklabels=False, showgrid=False)\n",
        "            fig.update_yaxes(showticklabels=False, showgrid=False)\n",
        "            fig.write_image(new_path)\n",
        "        return new_path\n",
        "    \n",
        "    @staticmethod\n",
        "    def clean_row(row):\n",
        "        output = {}\n",
        "        for header in row:\n",
        "            value = row[header]\n",
        "            try:\n",
        "                output[header.strip()] = float(value)\n",
        "            except:\n",
        "                return False\n",
        "        return output\n",
        "        \n",
        "    def load(self, full_path):\n",
        "        output = []\n",
        "        with open(full_path) as incoming:\n",
        "            reader = csv.DictReader(incoming, delimiter='\\t')\n",
        "            for row in reader:\n",
        "                cleaned_row = self.clean_row(row)\n",
        "                if cleaned_row:\n",
        "                    output.append(cleaned_row)\n",
        "        return output\n",
        "        "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "14cb10cc-c043-4456-87eb-f51b3d4a2887",
      "metadata": {
        "id": "14cb10cc-c043-4456-87eb-f51b3d4a2887",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a98b360-9e29-44d0-a97a-481d101871e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Size of dataset 6659\n"
          ]
        }
      ],
      "source": [
        "train_path = 'flights/tsv/'\n",
        "dataset = FlightsAsImageDataset(train_path)\n",
        "print(\"Size of dataset\", len(dataset))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train, Val, Test Split"
      ],
      "metadata": {
        "id": "BJdiQBFzePju"
      },
      "id": "BJdiQBFzePju"
    },
    {
      "cell_type": "code",
      "source": [
        "train_percentage = 0.50\n",
        "val_percentage   = 0.25\n",
        "# test percentage is inferred\n",
        "batch_size = 1\n",
        "N = len(dataset) \n",
        "train_size = int(N * train_percentage)\n",
        "val_size = int(N * val_percentage)\n",
        "test_size = N - train_size - val_size\n",
        "train_data, val_data, test_data = torch.utils.data.random_split(dataset, [train_size, val_size, test_size], generator=torch.Generator().manual_seed(42))\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(train_data, batch_size = batch_size, shuffle=True)\n",
        "val_loader = torch.utils.data.DataLoader(val_data, batch_size = batch_size, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(test_data, batch_size = batch_size, shuffle=False)"
      ],
      "metadata": {
        "id": "MEZ9l-mqcfSX"
      },
      "id": "MEZ9l-mqcfSX",
      "execution_count": 236,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "tags": [],
        "id": "220355c7-61f8-4728-9806-438bfc7b87e4"
      },
      "source": [
        "# Making A Model\n",
        "Now we'll write a convolutional neural network (CNN) based on graphs of the `xEast` and `yNorth` fields (it will visualize as though we were looking at the flight path from a top-down perspective) to see if it can classify the flights in accordance with the labled data.\n",
        "\n",
        "---\n",
        "\n",
        "**Caveat**\n",
        "\n",
        "In order to accomplish this part of the notebook, you will have have to have access to the dataset.\n",
        "\n",
        "---"
      ],
      "id": "220355c7-61f8-4728-9806-438bfc7b87e4"
    },
    {
      "cell_type": "markdown",
      "id": "d547e39b-c2f3-4b59-8c3e-d2b94bbed600",
      "metadata": {
        "id": "d547e39b-c2f3-4b59-8c3e-d2b94bbed600"
      },
      "source": [
        "## Building The Model\n",
        "Here we'll build a CNN to train on our dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 258,
      "id": "71d948b1-3fcf-40fb-a637-abfbbfe3f292",
      "metadata": {
        "id": "71d948b1-3fcf-40fb-a637-abfbbfe3f292"
      },
      "outputs": [],
      "source": [
        "# 3rd party\n",
        "from torch import nn\n",
        "\n",
        "class CNN(nn.Module):\n",
        "    \"\"\"\n",
        "    A basic convolutional neural network\n",
        "    \"\"\"\n",
        "    def __init__(self):\n",
        "        \n",
        "        super(CNN, self).__init__()\n",
        "        \n",
        "        # reused\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.flatten = torch.flatten\n",
        "        self.relu = nn.ReLU()\n",
        "        \n",
        "        # layer one\n",
        "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
        "        \n",
        "        # layer two\n",
        "        self.conv2 = nn.Conv2d(6, 12, 5)\n",
        "        \n",
        "        # connected layers\n",
        "        self.linear1 = nn.Linear(10092, 512)\n",
        "        self.linear2 = nn.Linear(512, 128)\n",
        "        self.linear3 = nn.Linear(128, 1)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        # first set of layers\n",
        "        #print(x.shape)\n",
        "        x = self.conv1(x)\n",
        "        #print(x.shape)\n",
        "        x = self.relu(x)\n",
        "        #print(x.shape)\n",
        "        x = self.pool(x)\n",
        "        \n",
        "        \n",
        "        # second set of layers\n",
        "        x = self.conv2(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.pool(x)\n",
        "        # connected\n",
        "        x = torch.flatten(x)\n",
        "        x = self.linear1(x)\n",
        "        x = self.relu(x)\n",
        "        \n",
        "        x = self.linear2(x)\n",
        "        x = self.relu(x)\n",
        "        \n",
        "        x = self.linear3(x)\n",
        "        \n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 259,
      "id": "67b28b73-4949-4ffa-86f8-91360dc09e0f",
      "metadata": {
        "id": "67b28b73-4949-4ffa-86f8-91360dc09e0f",
        "outputId": "de0bdf2c-7ec9-49ca-bc29-b169290450e1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using cuda device\n"
          ]
        }
      ],
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "print(f'Using {device} device')\n",
        "model = CNN().to(device)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iGhS9OOLoW7D",
        "outputId": "5170a3a9-ea2a-4269-8964-6b0ec7e56a58"
      },
      "id": "iGhS9OOLoW7D",
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CNN(\n",
            "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (relu): ReLU()\n",
            "  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
            "  (conv2): Conv2d(6, 12, kernel_size=(5, 5), stride=(1, 1))\n",
            "  (linear1): Linear(in_features=10092, out_features=512, bias=True)\n",
            "  (linear2): Linear(in_features=512, out_features=128, bias=True)\n",
            "  (linear3): Linear(in_features=128, out_features=1, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b42aad9f-80e3-4f0b-b572-eb5f4046448c",
      "metadata": {
        "id": "b42aad9f-80e3-4f0b-b572-eb5f4046448c"
      },
      "source": [
        "## Training the Model\n",
        "Here we'll do 5 passes at training the model, saving the best models."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train_one_epoch(model, data, loss, optimizer):\n",
        "    \"\"\"\n",
        "    DESC\n",
        "    ---\n",
        "    Trains the given model for 1 epoch on the training data, \n",
        "    computes the loss and gradients with criterion and optimizer.\n",
        "    ---\n",
        "    INPUTS\n",
        "    ---\n",
        "    model - CNN model\n",
        "    data - the train dataloader \n",
        "    criterion - loss function such as CrossEntropyLoss\n",
        "    optimizer - optimizer module such as SGD or AdamW\n",
        "    ---\n",
        "    RETURN\n",
        "    ---\n",
        "    None\n",
        "    \"\"\"\n",
        "    total_loss = 0.\n",
        "    model.train()\n",
        "    i = 0\n",
        "    for img, label in data:\n",
        "        pred_label = model(img.to(device))\n",
        "        l = loss(pred_label, label.float().to(device))\n",
        "        l.backward()\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        total_loss += l.item()\n",
        "        i += 1\n",
        "        # if i % 500 == 0:\n",
        "        #     print(f'train: img {i} avg loss - {total_loss / i}')\n",
        "    return total_loss / len(data)\n",
        "\n",
        "\n",
        "def test_one_epoch(model, data, loss):\n",
        "    \"\"\"\n",
        "    DESC\n",
        "    ---\n",
        "    Evaluates the given model for 1 epoch on the validation data\n",
        "    computes the loss.\n",
        "    ---\n",
        "    INPUTS\n",
        "    ---\n",
        "    model - CNN model\n",
        "    data - the validation dataloader \n",
        "    criterion - loss function such as CrossEntropyLoss\n",
        "    optimizer - optimizer module such as SGD or AdamW\n",
        "    ---\n",
        "    RETURN\n",
        "    ---\n",
        "    Loss - validation loss\n",
        "    \"\"\"\n",
        "    total_loss = 0.\n",
        "    model.eval()\n",
        "    i = 0\n",
        "    for img, label in data:\n",
        "        pred_label = model(img.to(device))\n",
        "        l = loss(pred_label, label.to(device))\n",
        "        total_loss += l.item()\n",
        "        i += 1\n",
        "        # if i % 100 == 0:\n",
        "        #     print(f'validate: img {i} avg loss - {total_loss / i}')\n",
        "    return total_loss / len(data)\n"
      ],
      "metadata": {
        "id": "Qu7HqxYNhgfE"
      },
      "id": "Qu7HqxYNhgfE",
      "execution_count": 261,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 262,
      "id": "9e6865cc-9ef5-4d5f-ba31-b8281b9c7b75",
      "metadata": {
        "id": "9e6865cc-9ef5-4d5f-ba31-b8281b9c7b75"
      },
      "outputs": [],
      "source": [
        "epochs = 50\n",
        "learning_rate = 0.01\n",
        "\n",
        "loss = nn.MSELoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_loss = None\n",
        "print(f'training size - {len(train_loader)} validate size - {len(val_loader)}')\n",
        "for epoch in range(epochs):\n",
        "    train_loss = train_one_epoch(model, train_loader, loss, optimizer)\n",
        "    val_loss = test_one_epoch(model, val_loader, loss)\n",
        "    print(f'{epoch + 1}: train - {train_loss:.7f} test - {val_loss:.7f}')\n",
        "    if best_loss is None or val_loss < best_loss:\n",
        "        best_loss = val_loss\n",
        "        torch.save(model.state_dict(), 'best_model.pth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aYBPdGgV8dgN",
        "outputId": "4a35a267-d5b2-4e2c-b8a1-b75853422a12"
      },
      "id": "aYBPdGgV8dgN",
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training size - 3329 validate size - 1664\n",
            "1: train - 0.0905706 test - 0.0631910\n",
            "2: train - 0.0413787 test - 0.0369113\n",
            "3: train - 0.0276606 test - 0.0237726\n",
            "4: train - 0.0192708 test - 0.0222371\n",
            "5: train - 0.0154438 test - 0.0203805\n",
            "6: train - 0.0138717 test - 0.0201998\n",
            "7: train - 0.0123564 test - 0.0217214\n",
            "8: train - 0.0093991 test - 0.0204195\n",
            "9: train - 0.0085865 test - 0.0210661\n",
            "10: train - 0.0074925 test - 0.0201289\n",
            "11: train - 0.0072284 test - 0.0207292\n",
            "12: train - 0.0059765 test - 0.0195103\n",
            "13: train - 0.0062520 test - 0.0195607\n",
            "14: train - 0.0054384 test - 0.0199980\n",
            "15: train - 0.0045644 test - 0.0211382\n",
            "16: train - 0.0044975 test - 0.0191085\n",
            "17: train - 0.0046182 test - 0.0185351\n",
            "18: train - 0.0041038 test - 0.0187096\n",
            "19: train - 0.0038716 test - 0.0187719\n",
            "20: train - 0.0038811 test - 0.0198259\n",
            "21: train - 0.0038700 test - 0.0191306\n",
            "22: train - 0.0037393 test - 0.0187319\n",
            "23: train - 0.0035810 test - 0.0183399\n",
            "24: train - 0.0035021 test - 0.0185116\n",
            "25: train - 0.0038177 test - 0.0180941\n",
            "26: train - 0.0034578 test - 0.0188463\n",
            "27: train - 0.0036427 test - 0.0185514\n",
            "28: train - 0.0032527 test - 0.0186549\n",
            "29: train - 0.0032756 test - 0.0183201\n",
            "30: train - 0.0034348 test - 0.0181179\n",
            "31: train - 0.0031369 test - 0.0182395\n",
            "32: train - 0.0033957 test - 0.0182413\n",
            "33: train - 0.0030112 test - 0.0221458\n",
            "34: train - 0.0029943 test - 0.0182763\n",
            "35: train - 0.0029905 test - 0.0183857\n",
            "36: train - 0.0029380 test - 0.0182797\n",
            "37: train - 0.0028354 test - 0.0182872\n",
            "38: train - 0.0028323 test - 0.0180160\n",
            "39: train - 0.0030070 test - 0.0199141\n",
            "40: train - 0.0030723 test - 0.0180813\n",
            "41: train - 0.0028497 test - 0.0196850\n",
            "42: train - 0.0027730 test - 0.0203439\n",
            "43: train - 0.0030490 test - 0.0179631\n",
            "44: train - 0.0026845 test - 0.0181820\n",
            "45: train - 0.0026014 test - 0.0180392\n",
            "46: train - 0.0026151 test - 0.0183258\n",
            "47: train - 0.0031289 test - 0.0179165\n",
            "48: train - 0.0026759 test - 0.0185993\n",
            "49: train - 0.0025866 test - 0.0180900\n",
            "50: train - 0.0024939 test - 0.0180751\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9b3d68ae-3aad-433d-8634-6302daa6aaf0",
      "metadata": {
        "id": "9b3d68ae-3aad-433d-8634-6302daa6aaf0"
      },
      "source": [
        "# Model Evaluation\n",
        "Next we'll check the overall accuracy of the model on our testing data."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_state_dict(torch.load('best_model.pth'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zXy7knOq8kQk",
        "outputId": "5f6acc63-e038-4c1b-83f7-0e63d5016707"
      },
      "id": "zXy7knOq8kQk",
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correct = 0\n",
        "# Regression\n",
        "for img, label in test_loader:\n",
        "    model.eval()\n",
        "    pred = model(img.to(device))\n",
        "    rounded_pred = round(pred.item())\n",
        "    if rounded_pred == label:\n",
        "        correct += 1\n",
        "print(f'{(correct / len(test_loader)) * 100:.4f}% correct')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3AIOZUqEgUgz",
        "outputId": "7d31dbbf-b612-478f-8514-523b26e31cdd"
      },
      "id": "3AIOZUqEgUgz",
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "97.7191% correct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "edbfc760-5303-4007-a5ba-6cd7db04e3c1",
      "metadata": {
        "id": "edbfc760-5303-4007-a5ba-6cd7db04e3c1"
      },
      "source": [
        "Now we'll load up 5 random flights and see how our model did."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Precision, Recall, F1 Score and Confusion Matrix"
      ],
      "metadata": {
        "id": "yHk0WmXFMLdU"
      },
      "id": "yHk0WmXFMLdU"
    },
    {
      "cell_type": "code",
      "source": [
        "y_true = []\n",
        "y_pred = []\n",
        "#y_pred_prob =[]\n",
        "for img, label in test_loader:\n",
        "    model.eval()\n",
        "    pred = model(img.to(device))\n",
        "    #print(pred.item(), prob.item())\n",
        "    #prob = nn.functional.softmax(pred)\n",
        "    #pred = torch.argmax(prob)\n",
        "    #y_pred_prob.append(prob.cpu().detach().numpy()[1])\n",
        "    y_pred.append(round(pred.item()))\n",
        "    y_true.append(label.item())"
      ],
      "metadata": {
        "id": "IWkKvCZ2DUzo"
      },
      "id": "IWkKvCZ2DUzo",
      "execution_count": 266,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_true, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D3S4hEkQMKpT",
        "outputId": "5c633629-92e6-4b00-b89f-2fd9fe847ca2"
      },
      "id": "D3S4hEkQMKpT",
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.86      0.91       226\n",
            "           1       0.98      1.00      0.99      1440\n",
            "\n",
            "    accuracy                           0.98      1666\n",
            "   macro avg       0.97      0.93      0.95      1666\n",
            "weighted avg       0.98      0.98      0.98      1666\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_true, y_pred)\n",
        "tp, fp = cm[0]\n",
        "fn, tn = cm[1]\n",
        "fpr = fp / (tn + fp)\n",
        "tpr = tp / (tp + fn)\n",
        "fnr = fn / (tp + fn)\n",
        "ax= plt.subplot()\n",
        "sns.heatmap(cm, annot=True, fmt='g', ax=ax);  #annot=True to annotate cells, ftm='g' to disable scientific notation\n",
        "\n",
        "# labels, title and ticks\n",
        "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
        "ax.set_title('Confusion matrix '); \n",
        "ax.xaxis.set_ticklabels(['Bad', 'Good']); ax.yaxis.set_ticklabels(['Bad', 'Good']);\n",
        "plt.show()\n",
        "print(\"TPR:\", tpr, \"FPR:\", fpr, \"FNR:\", fnr, \"TNR:\", (1-fpr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "-r93NwuoNIMp",
        "outputId": "2fd65df6-c05b-440e-9e10-ad20281ceb7c"
      },
      "id": "-r93NwuoNIMp",
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
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            "TPR: 0.97 FPR: 0.021828103683492497 FNR: 0.03 TNR: 0.9781718963165075\n"
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