assignment10_project.ipynb

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        "<a href=\"https://colab.research.google.com/github/SiyuChen1/C-LearnSummary/blob/master/assignment10_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Assignment 10: X-Ray Pneumonia Detection\n",
        "\n",
        "In this assignment, you are tasked with developing your own classifier for pneumonia in X-ray images. You will go through to the complete ML development cycle from loading and preprocessing your data to evaluating your models.\n",
        "\n",
        "Download and extract the X-Ray dataset from Ilias to the same directory as your Jupyter notebook. The data is already split into a training, validation, and testing set. The dataset originates from the paper [Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning\n",
        "](https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5).\n",
        "\n",
        "You may use any packages, we encountered during the exercises (numpy, matplotlib, scikit-learn, scikit-image, pandas, pytorch) as well as the Python standard library.\n",
        "\n",
        "You should (at least) address the following points in your development process:\n",
        "\n",
        "- The dataset is imbalanced. Do at least one of the following:\n",
        "    - Augment your dataset by including rotated, flipped, or brightened images. This will also improve the generalization capabilities of your model.\n",
        "    - or: Modify your objective function by weighting the classes differently.\n",
        "- Optimize the hyperparameters of your models using grid-search or random-search on the validation set.\n",
        "- Consider at least two classes of models, e.g. CNN and SVM. At least one of your model classes should be some type of neural network implemented in PyTorch.\n",
        "- After the hyperparameter optimization, select the best-performing models of each class. Evaluate these models on the testing data and visualize your results.\n",
        "\n",
        "\n",
        "*Note*: You have 2 weeks to complete this assignment. The assignment is optional for B.Sc.Data Science students, who have an alternative easier assignment 10 on clustering. If you already have successfully submitted 7 assignments, you do not need to submit this assignment.\n",
        "Presenting this assignment in a video counts as **2 video presentations**. Please indicate if you wish to present at the top of the notebook."
      ]
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    {
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      },
      "source": [
        "- Siyu Chen 3494095\n",
        "\n",
        "**I am glad to present this project.**"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "## Step 1: download data set\n",
        "\n",
        "Make directionary to store the total data set, download the data set and unpack the data set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CZlY3MILsler",
        "colab_type": "text"
      },
      "source": [
        "1.  download and unpack data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d767760b-527e-4bae-9355-1c2332e9ce74"
      },
      "source": [
        "!mkdir data\n",
        "!gdown https://ipvs.informatik.uni-stuttgart.de/cloud/s/XjwFgNdaRjPHkTp/download\n",
        "!mv download data\n",
        "!cd data && mv download download.zip && unzip download.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "   creating: chest_xray/val/\n",
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            "   creating: chest_xray/val/PNEUMONIA/\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rF7kXfzksfuB",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "2. check the data set\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGBYBRoYfRby",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "ba079373-d270-45e0-99cb-e084b5f51a9b"
      },
      "source": [
        "!cd data && pwd && cd chest_xray && ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/data\n",
            "chest_xray  __MACOSX  test  train  val\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eBR_16FhtNmn",
        "colab_type": "text"
      },
      "source": [
        "## Step 2: Using Pytorch to creat CNN "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jCCl5f62tZHA",
        "colab_type": "text"
      },
      "source": [
        "1. Import necessary libraries "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AcD6qC8KqTdD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import torch.utils.data as data\n",
        "from skimage import io, transform\n",
        "from skimage.color import rgb2gray\n",
        "from torchvision import transforms, utils\n",
        "from PIL import Image\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s1EKvMhhtwMb",
        "colab_type": "text"
      },
      "source": [
        "2. Check CUDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mx3kEr18X71a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e93883a2-08f5-493b-df22-d9de1e161404"
      },
      "source": [
        "# The default device name is 'cpu'. \n",
        "# You can access your GPU with the name 'cuda' \n",
        "# or if you have multiple GPUs indexed like 'cuda:0', 'cuda:1', ...\n",
        "device_name = 'cuda'\n",
        "device = torch.device(device_name)\n",
        "device"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lZvkolAPt0Kp",
        "colab_type": "text"
      },
      "source": [
        "3. Build X-Ray data set as a subclass of `data.Dataset `"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAH68obYsKGA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class XRayDataset(data.Dataset):\n",
        "    \"\"\"X-Ray dataset.\"\"\"\n",
        "\n",
        "    def __init__(self, dir_path, transform=None):\n",
        "        def load_data(directory):\n",
        "            directories = [d for d in os.listdir(directory)\n",
        "                        if os.path.isdir(os.path.join(directory, d))]\n",
        "            labels = []\n",
        "            file_names_all = []\n",
        "            for d in directories:\n",
        "                label_directory = os.path.join(directory, d)\n",
        "                for f in os.listdir(label_directory):\n",
        "                    if f.endswith(\".jpeg\"):\n",
        "                        file_names_all.append(os.path.join(label_directory, f))\n",
        "                    if 'NORMAL' == d:\n",
        "                        labels.append(0)\n",
        "                    else:\n",
        "                        labels.append(1)\n",
        "            return file_names_all, labels\n",
        "        self.file_names, self.labels = load_data(dir_path)\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        if torch.is_tensor(idx):\n",
        "            idx = idx.tolist()\n",
        "\n",
        "        img_name = self.file_names[idx]\n",
        "        # image = io.imread(img_name)\n",
        "        image = Image.open(img_name)\n",
        "\n",
        "        if self.transform:\n",
        "            image = self.transform(image)\n",
        "\n",
        "        return {'x': image, 'y': self.labels[idx]}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5q1DmVvuAWQ",
        "colab_type": "text"
      },
      "source": [
        "4. Transform functions "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nzooO8v29PA_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Resize(object):\n",
        "    \"\"\"\n",
        "        Convert a RGB image to a grey image.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self,output_size):\n",
        "        assert isinstance(output_size, (int, tuple))\n",
        "        self.output_size = output_size\n",
        "\n",
        "    def __call__(self, image):\n",
        "        # print('input',image.shape)\n",
        "\n",
        "        if isinstance(self.output_size, int) :\n",
        "            self.output_size = (self.output_size, self.output_size) \n",
        "        \n",
        "        # print('outputsize',self.output_size)\n",
        "        img = transform.resize(image, self.output_size)\n",
        "        # print('real',img.shape)\n",
        "        return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kvXLwAep9Nij",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ToGrey(object):\n",
        "    \"\"\"\n",
        "        Convert a RGB image to a grey image.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        pass\n",
        "\n",
        "    def __call__(self, image):\n",
        "\n",
        "        if len(image.shape) == 3:\n",
        "            print('enter')\n",
        "            image = rgb2gray(image)\n",
        "        # plt.imshow(image)\n",
        "        return image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1TlyiL3uJZc0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from skimage import exposure\n",
        "\n",
        "class Brightness(object):\n",
        "    \"\"\"\n",
        "        change the brightness of the image\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, gamma):\n",
        "        self.gamma = gamma\n",
        "\n",
        "    def __call__(self, image):\n",
        "        img = exposure.adjust_gamma(image, self.gamma)\n",
        "        return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vx_AKRI1A5Xl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class RotateImage(object):\n",
        "    \"\"\"\n",
        "        Convert a RGB image to a grey image.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, angle):\n",
        "        self.angle = angle\n",
        "\n",
        "    def __call__(self, image):\n",
        "        # print('before ratation',image.shape)\n",
        "        img = transform.rotate(image, self.angle) \n",
        "        # print('after rotation',img.shape)\n",
        "        # plt.imshow(img)\n",
        "        return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_wXTy42_ySw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ToTensor(object):\n",
        "    \"\"\"Convert ndarrays in sample to Tensors.\"\"\"\n",
        "    def __init__(self):\n",
        "        pass\n",
        "\n",
        "    def __call__(self, image):\n",
        "        # print('numpy',image.shape)\n",
        "        img = np.expand_dims(image, axis=0)\n",
        "        # print('torch',image.shape)\n",
        "        return torch.from_numpy(image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rN01J9Vz0NZL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class RandomCrop(object):\n",
        "    \"\"\"Crop randomly the image in a sample.\n",
        "\n",
        "    Args:\n",
        "        output_size (tuple or int): Desired output size. If int, square crop\n",
        "            is made.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, output_size):\n",
        "        assert isinstance(output_size, (int, tuple))\n",
        "        if isinstance(output_size, int):\n",
        "            self.output_size = (output_size, output_size)\n",
        "        else:\n",
        "            assert len(output_size) == 2\n",
        "            self.output_size = output_size\n",
        "\n",
        "    def __call__(self, image):\n",
        "\n",
        "        h, w = image.shape[:2]\n",
        "        new_h, new_w = self.output_size\n",
        "\n",
        "        top = np.random.randint(0, h - new_h)\n",
        "        left = np.random.randint(0, w - new_w)\n",
        "\n",
        "        image = image[top: top + new_h,\n",
        "                      left: left + new_w]\n",
        "\n",
        "        return image\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqQ0xQqcuFQg",
        "colab_type": "text"
      },
      "source": [
        "5. Visualise the data augmentation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ceXPRp6Dxv2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "a87007a9-d191-483b-8662-d4d9c893a3e4"
      },
      "source": [
        "fig, axes = plt.subplots(nrows=2, ncols=3)\n",
        "\n",
        "ax = axes.ravel()\n",
        "\n",
        "resize_image_size = (512,512)\n",
        "angle = 3\n",
        "gamma = 0.3\n",
        "# composed_1 = transforms.Compose([ToGrey()])\n",
        "# composed_2 = transforms.Compose([ToGrey(), Resize(resize_image_size)])\n",
        "# composed_3 = transforms.Compose([ToGrey(), Resize(resize_image_size),RotateImage(angle)])\n",
        "# composed_4 = transforms.Compose([ToGrey(), Resize(resize_image_size),Brightness(gamma)])\n",
        "composed_1 = transforms.Compose([transforms.Grayscale()])\n",
        "composed_2 = transforms.Compose([transforms.Grayscale(), transforms.Resize(resize_image_size)])\n",
        "composed_3 = transforms.Compose([transforms.Grayscale(), transforms.Resize(resize_image_size), transforms.RandomRotation(angle)])\n",
        "composed_4 = transforms.Compose([transforms.Grayscale(), transforms.Resize(resize_image_size), transforms.ColorJitter(gamma)])\n",
        "composed_5 = transforms.Compose([transforms.Grayscale(), transforms.Resize(resize_image_size), transforms.RandomHorizontalFlip()])\n",
        "composed_6 = transforms.Compose([transforms.Grayscale(), transforms.Resize(resize_image_size), transforms.RandomErasing()])\n",
        "composed_list = [composed_1,composed_2,composed_3,composed_4, composed_5, composed_5]\n",
        "title_list = ['greyscale image','resize ' + str(resize_image_size),'rotate ' + str(angle),'gamma ' + str(gamma),'RandomHorizontalFlip','RandomErasing']\n",
        "\n",
        "for i in range(6):\n",
        "    xray_dataset = XRayDataset(dir_path='./data/chest_xray/train/',transform=composed_list[i])\n",
        "    x_data = xray_dataset[0]['x']\n",
        "    # print(np.array(x_data))\n",
        "    # print(np.min(x_data), np.max(x_data))\n",
        "    ax[i].imshow(x_data,cmap='gray')\n",
        "    ax[i].set_title(title_list[i])\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PhozM9r2uWBx",
        "colab_type": "text"
      },
      "source": [
        "6. Create CNN using `nn.Module`\n",
        "\n",
        "-  Two Modules: `class MyAlexNet(nn.Module):` and `class MyMiniAlexNet(nn.Module):`\n",
        "- `class MyAlexNet(nn.Module):` has more parameters and is deeper than `class MyMiniAlexNet(nn.Module):`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTuMrLO5TaqG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MyMiniAlexNet(nn.Module):\n",
        "    def __init__(self, output_dimension):\n",
        "        super().__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 16, (11,11),stride=2)\n",
        "        self.pool1 = nn.MaxPool2d((3,3), stride=2)\n",
        "        self.conv2 = nn.Conv2d(16, 32, (5,5), stride=4)\n",
        "        self.pool2 = nn.MaxPool2d((3,3), stride=2)\n",
        "        self.conv3 = nn.Conv2d(32, 4, (3,3), stride=2)\n",
        "        self.fc1 = nn.Linear(4*7*7, 2*7)\n",
        "        self.dropout = nn.Dropout(p=0.2)\n",
        "        self.fc2 = nn.Linear(2*7, output_dimension)\n",
        "        self.softmax = nn.Softmax(dim=1)\n",
        "        \n",
        "    def forward(self, X, training=False, feature_extra=False):\n",
        "        # print(X.shape)\n",
        "        X = self.conv1(X)\n",
        "        # print(X.shape)\n",
        "        X = self.pool1(X)\n",
        "        # print(X.shape)\n",
        "        X = self.conv2(X)\n",
        "        # print(X.shape)\n",
        "        X = self.pool2(X)\n",
        "        # print(X.shape)\n",
        "        X = self.conv3(X)\n",
        "        # print(X.shape)\n",
        "        X = torch.flatten(X, start_dim=1)\n",
        "        # print(X.shape)\n",
        "        X = F.relu(self.fc1(X))\n",
        "        if training:\n",
        "            X = self.dropout(X)\n",
        "        if feature_extra:\n",
        "            return X\n",
        "        X = self.softmax(self.fc2(X))\n",
        "        return X"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSx5U04uoQld",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MyAlexNet(nn.Module):\n",
        "    def __init__(self, output_dimension):\n",
        "        super().__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 8, (11,11),stride=2)\n",
        "        self.pool1 = nn.MaxPool2d((3,3), stride=2)\n",
        "        self.conv2 = nn.Conv2d(8, 16, (5,5), padding=2)\n",
        "        self.pool2 = nn.MaxPool2d((3,3), stride=2)\n",
        "        self.conv3 = nn.Conv2d(16, 32, (3,3), padding=1)\n",
        "        self.conv4 = nn.Conv2d(32, 8, (3,3), padding=1)\n",
        "        self.pool3 = nn.MaxPool2d((3,3), stride=2)\n",
        "        self.conv5 = nn.Conv2d(8, 2, (3,3), padding=1)\n",
        "        self.fc1 = nn.Linear(1800, 256)\n",
        "        self.dropout = nn.Dropout(p=0.2)\n",
        "        self.fc2 = nn.Linear(256, 32)\n",
        "        self.fc3 = nn.Linear(32, output_dimension)\n",
        "        self.softmax = nn.Softmax(dim=1)\n",
        "        \n",
        "    def forward(self, X, training=False, feature_extra=False):\n",
        "        # print(X.shape)\n",
        "        X = self.conv1(X)\n",
        "        # print(X.shape)\n",
        "        X = self.pool1(X)\n",
        "        # print(X.shape)\n",
        "        X = self.conv2(X)\n",
        "        # print(X.shape)\n",
        "        X = self.pool2(X)\n",
        "        # print(X.shape)\n",
        "        X = self.conv3(X)\n",
        "        # print(X.shape)\n",
        "        X = self.conv4(X)\n",
        "        # print(X.shape)\n",
        "        X = self.pool3(X)\n",
        "        # print(X.shape)\n",
        "        X = self.conv5(X)\n",
        "        # print(X.shape)\n",
        "        X = torch.flatten(X, start_dim=1)\n",
        "        # print(X.shape)\n",
        "        X = F.relu(self.fc1(X))\n",
        "        if training:\n",
        "            X = self.dropout(X)\n",
        "        X = F.relu(self.fc2(X))\n",
        "        if feature_extra:\n",
        "            return X\n",
        "        X = self.softmax(self.fc3(X))\n",
        "        # print(X.shape)\n",
        "        return X"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8owbWTy6uggi",
        "colab_type": "text"
      },
      "source": [
        "7. Compute class weight for training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7gHnDt2azq-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "75e6a200-ac92-4078-dc62-2107e26a18be"
      },
      "source": [
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "def load_train_dataset_y():\n",
        "    import os \n",
        "    def load_data(directory):\n",
        "        directories = [d for d in os.listdir(directory)\n",
        "                       if os.path.isdir(os.path.join(directory, d))]\n",
        "        labels = []\n",
        "        for d in directories:\n",
        "            label_directory = os.path.join(directory, d)\n",
        "            file_names = [os.path.join(label_directory, f)\n",
        "                          for f in os.listdir(label_directory)\n",
        "                          if f.endswith(\".jpeg\")]\n",
        "            for f in file_names:\n",
        "                if d == \"NORMAL\":\n",
        "                    labels.append(0)\n",
        "                else:\n",
        "                    labels.append(1)\n",
        "        labels = np.array(labels)\n",
        "        return labels\n",
        "            \n",
        "    y_train = load_data('./data/chest_xray/train/')\n",
        "    return y_train\n",
        "\n",
        "y_train = load_train_dataset_y()\n",
        "print(np.bincount(y_train))\n",
        "class_weights = compute_class_weight(class_weight='balanced',classes=np.unique(y_train), y=y_train)\n",
        "print(class_weights)\n",
        "\n",
        "# mean_list = []\n",
        "\n",
        "# composed_compute = transforms.Compose([transforms.Grayscale(), transforms.Resize(resize_image_size)])\n",
        "\n",
        "# data_train_compute = XRayDataset(dir_path='./data/chest_xray/train/',transform=composed_compute)\n",
        "\n",
        "# for id in range(len(data_train_compute)):\n",
        "#     mean_list.append(np.mean(xray_dataset[i]['x']))\n",
        "\n",
        "# print(mean_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1341 3875]\n",
            "[1.9448173  0.67303226]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IZ_6ZgdAumBt",
        "colab_type": "text"
      },
      "source": [
        "8. Configure Google Colab to store the trained parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LCUT8Vr3grWr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "4e0e6eba-0433-4d96-d94b-ce08202e362c"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DIpLtqWBhH89",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "f7983cf9-d3bc-4afa-8861-9dd8c0e0d39b"
      },
      "source": [
        "!ls /content/gdrive/My\\ Drive"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " 001948974414.doc_02_01_20_10_29_07.pdf   Europass\n",
            " Aufenthaltstitel\t\t\t  foo.txt\n",
            " classifier_20.pth\t\t\t 'Machine Learning'\n",
            " classifier_epoches_25_big_lr.pth\t  MyCode\n",
            " classifier_epoches_25.pth\t\t  sim_classifier.pth\n",
            " classifier.pth\t\t\t\t 'Uni Stuttgart'\n",
            "'Colab Notebooks'\t\t\t 'Wohnen mit Hilfe'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MzA-CrTju2DE",
        "colab_type": "text"
      },
      "source": [
        "9. Train CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZnQXZ-8XZ-A8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "resize_image_size = (512,512)\n",
        "angle = 2\n",
        "gamma = 0.5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpjDXfNkYeQ3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "outputId": "6425317d-2f2a-4801-c7ee-70270d329b41"
      },
      "source": [
        "myalexnet = MyAlexNet(2).to(device)\n",
        "\n",
        "composed_train = transforms.Compose([transforms.Grayscale(), transforms.Resize(resize_image_size), transforms.RandomRotation(angle), \n",
        "                               transforms.ColorJitter(gamma),transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.RandomErasing()])\n",
        "\n",
        "composed_val = transforms.Compose([transforms.Grayscale(), transforms.Resize(resize_image_size)])\n",
        "\n",
        "data_train = XRayDataset(dir_path='./data/chest_xray/train/',transform=composed_train)\n",
        "data_val = XRayDataset(dir_path='./data/chest_xray/val/',transform=composed_val)\n",
        "\n",
        "epochs = 20\n",
        "report_every_epoch = 1\n",
        "batch_size = 512\n",
        "learning_rate = 1e-3\n",
        "\n",
        "losses = []\n",
        "\n",
        "weights = torch.FloatTensor(class_weights).cuda()\n",
        "criterion = nn.CrossEntropyLoss(weight=weights)\n",
        "\n",
        "optimizer = optim.Adam(myalexnet.parameters(), lr=learning_rate)\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    data_loader = data.DataLoader(data_train, batch_size=batch_size, shuffle=True)\n",
        "    epoch_loss = 0.0\n",
        "    \n",
        "    for batch in data_loader:\n",
        "        myalexnet.zero_grad()\n",
        "        \n",
        "        # Change! #\n",
        "        yhat = myalexnet.forward(batch['x'].to(device),training=True)\n",
        "        batch_loss = criterion(yhat, batch['y'].to(device))\n",
        "        \n",
        "        epoch_loss += batch_loss.item()\n",
        "        batch_loss.backward()\n",
        "        optimizer.step()\n",
        "    losses.append(epoch_loss)\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        # for sample in data_val:\n",
        "        #     print(np.array(sample['y']))\n",
        "        x = np.array([np.expand_dims(np.array(sample['x']),axis=0) for sample in data_val])\n",
        "        # print(x.shape)\n",
        "        y = np.array([sample['y'] for sample in data_val])\n",
        "        # Change! #\n",
        "        x = torch.from_numpy(x).float().to(device)\n",
        "        y = torch.from_numpy(y).to(device)\n",
        "        \n",
        "        yhat = myalexnet.forward(x)\n",
        "        error_val = criterion(yhat, y).item()\n",
        "        \n",
        "    if ((epoch + 1) % report_every_epoch) == 0 or epoch == 0:\n",
        "        print(f'Epoch {epoch+1}/{epochs}\\n- Loss: {epoch_loss:.03f} - Vali: {error_val:.03f}')\n",
        "\n",
        "# model_save_name = 'sim_classifier.pth'\n",
        "model_save_name = 'classifier_epoches_20.pth'\n",
        "path = F\"/content/gdrive/My Drive/{model_save_name}\" \n",
        "# To save just take your trained module and call the torch.save function.\n",
        "torch.save(myalexnet.state_dict(), path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "- Loss: 7.480 - Vali: 0.878\n",
            "Epoch 2/20\n",
            "- Loss: 7.067 - Vali: 0.778\n",
            "Epoch 3/20\n",
            "- Loss: 5.912 - Vali: 0.535\n",
            "Epoch 4/20\n",
            "- Loss: 6.000 - Vali: 0.778\n",
            "Epoch 5/20\n",
            "- Loss: 5.348 - Vali: 0.470\n",
            "Epoch 6/20\n",
            "- Loss: 5.014 - Vali: 0.470\n",
            "Epoch 7/20\n",
            "- Loss: 4.942 - Vali: 0.531\n",
            "Epoch 8/20\n",
            "- Loss: 4.873 - Vali: 0.378\n",
            "Epoch 9/20\n",
            "- Loss: 4.690 - Vali: 0.438\n",
            "Epoch 10/20\n",
            "- Loss: 4.711 - Vali: 0.378\n",
            "Epoch 11/20\n",
            "- Loss: 4.696 - Vali: 0.470\n",
            "Epoch 12/20\n",
            "- Loss: 4.562 - Vali: 0.499\n",
            "Epoch 13/20\n",
            "- Loss: 4.565 - Vali: 0.378\n",
            "Epoch 14/20\n",
            "- Loss: 4.547 - Vali: 0.438\n",
            "Epoch 15/20\n",
            "- Loss: 4.418 - Vali: 0.531\n",
            "Epoch 16/20\n",
            "- Loss: 4.380 - Vali: 0.378\n",
            "Epoch 17/20\n",
            "- Loss: 4.454 - Vali: 0.410\n",
            "Epoch 18/20\n",
            "- Loss: 4.456 - Vali: 0.410\n",
            "Epoch 19/20\n",
            "- Loss: 4.577 - Vali: 0.410\n",
            "Epoch 20/20\n",
            "- Loss: 4.435 - Vali: 0.531\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BY2cl78N0QDQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "6c6ad80e-0056-4275-feab-83b55b5fe92c"
      },
      "source": [
        "myminialexnet = MyMiniAlexNet(2).to(device)\n",
        "\n",
        "composed_train = transforms.Compose([transforms.Grayscale(), transforms.Resize(resize_image_size), transforms.RandomRotation(angle), \n",
        "                               transforms.ColorJitter(gamma),transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.RandomErasing()])\n",
        "\n",
        "composed_val = transforms.Compose([transforms.Grayscale(), transforms.Resize(resize_image_size)])\n",
        "\n",
        "data_train = XRayDataset(dir_path='./data/chest_xray/train/',transform=composed_train)\n",
        "data_val = XRayDataset(dir_path='./data/chest_xray/val/',transform=composed_val)\n",
        "\n",
        "epochs = 20\n",
        "report_every_epoch = 1\n",
        "batch_size = 512\n",
        "learning_rate = 1e-3\n",
        "\n",
        "losses = []\n",
        "\n",
        "weights = torch.FloatTensor(class_weights).cuda()\n",
        "criterion = nn.CrossEntropyLoss(weight=weights)\n",
        "\n",
        "optimizer = optim.Adam(myminialexnet.parameters(), lr=learning_rate)\n",
        "\n",
        "print('Training process of MiniAlexNet:')\n",
        "for epoch in range(epochs):\n",
        "    data_loader = data.DataLoader(data_train, batch_size=batch_size, shuffle=True)\n",
        "    epoch_loss = 0.0\n",
        "    \n",
        "    for batch in data_loader:\n",
        "        myminialexnet.zero_grad()\n",
        "        \n",
        "        # Change! #\n",
        "        yhat = myminialexnet.forward(batch['x'].to(device),training=True)\n",
        "        batch_loss = criterion(yhat, batch['y'].to(device))\n",
        "        \n",
        "        epoch_loss += batch_loss.item()\n",
        "        batch_loss.backward()\n",
        "        optimizer.step()\n",
        "    losses.append(epoch_loss)\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        # for sample in data_val:\n",
        "        #     print(np.array(sample['y']))\n",
        "        x = np.array([np.expand_dims(np.array(sample['x']),axis=0) for sample in data_val])\n",
        "        # print(x.shape)\n",
        "        y = np.array([sample['y'] for sample in data_val])\n",
        "        # Change! #\n",
        "        x = torch.from_numpy(x).float().to(device)\n",
        "        y = torch.from_numpy(y).to(device)\n",
        "        \n",
        "        yhat = myminialexnet.forward(x)\n",
        "        error_val = criterion(yhat, y).item()\n",
        "        \n",
        "    if ((epoch + 1) % report_every_epoch) == 0 or epoch == 0:\n",
        "        print(f'Epoch {epoch+1}/{epochs}\\n- Loss: {epoch_loss:.03f} - Vali: {error_val:.03f}')\n",
        "\n",
        "# model_save_name = 'sim_classifier.pth'\n",
        "model_save_name = 'mini_classifier_epoches_20.pth'\n",
        "path = F\"/content/gdrive/My Drive/{model_save_name}\" \n",
        "# To save just take your trained module and call the torch.save function.\n",
        "torch.save(myminialexnet.state_dict(), path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training process of MiniAlexNet:\n",
            "Epoch 1/20\n",
            "- Loss: 7.633 - Vali: 0.570\n",
            "Epoch 2/20\n",
            "- Loss: 7.215 - Vali: 0.778\n",
            "Epoch 3/20\n",
            "- Loss: 6.503 - Vali: 0.778\n",
            "Epoch 4/20\n",
            "- Loss: 5.929 - Vali: 0.545\n",
            "Epoch 5/20\n",
            "- Loss: 5.693 - Vali: 0.438\n",
            "Epoch 6/20\n",
            "- Loss: 5.518 - Vali: 0.499\n",
            "Epoch 7/20\n",
            "- Loss: 5.310 - Vali: 0.410\n",
            "Epoch 8/20\n",
            "- Loss: 5.207 - Vali: 0.410\n",
            "Epoch 9/20\n",
            "- Loss: 5.146 - Vali: 0.442\n",
            "Epoch 10/20\n",
            "- Loss: 4.965 - Vali: 0.406\n",
            "Epoch 11/20\n",
            "- Loss: 4.971 - Vali: 0.313\n",
            "Epoch 12/20\n",
            "- Loss: 4.984 - Vali: 0.406\n",
            "Epoch 13/20\n",
            "- Loss: 4.762 - Vali: 0.313\n",
            "Epoch 14/20\n",
            "- Loss: 4.795 - Vali: 0.406\n",
            "Epoch 15/20\n",
            "- Loss: 4.651 - Vali: 0.406\n",
            "Epoch 16/20\n",
            "- Loss: 4.840 - Vali: 0.313\n",
            "Epoch 17/20\n",
            "- Loss: 4.660 - Vali: 0.313\n",
            "Epoch 18/20\n",
            "- Loss: 4.638 - Vali: 0.313\n",
            "Epoch 19/20\n",
            "- Loss: 4.666 - Vali: 0.313\n",
            "Epoch 20/20\n",
            "- Loss: 4.673 - Vali: 0.406\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DyYzJdiivG5o",
        "colab_type": "text"
      },
      "source": [
        "10. Evaluate CNN model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0m8Gc0jSN16",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.cuda.empty_cache()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kc7LUy_PwQAr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "6b0c1468-22e0-4683-a91b-c1f307478442"
      },
      "source": [
        "composed_test = transforms.Compose([transforms.Grayscale(), transforms.Resize(resize_image_size)])\n",
        "\n",
        "data_test = XRayDataset(dir_path='./data/chest_xray/test/',transform=composed_test)\n",
        "\n",
        "# To load you first need to initialize your module with the exact same hyperparameters.\n",
        "myalexnet = MyAlexNet(2).to(device)\n",
        "# Then you call the load_state_dict method and torch.load function. \n",
        "# To make sure it loads the weights to the correct device, we specify this with map_location.\n",
        "myalexnet.load_state_dict(torch.load('/content/gdrive/My Drive/classifier_epoches_20.pth', map_location=device_name))\n",
        "\n",
        "with torch.no_grad():\n",
        "    # for sample in data_val:\n",
        "    #     print(np.array(sample['y']))\n",
        "    x = np.array([np.expand_dims(np.array(sample['x']),axis=0) for sample in data_test])\n",
        "    # print(x.shape)\n",
        "    y = np.array([sample['y'] for sample in data_test])\n",
        "    # Change! #\n",
        "    x = torch.from_numpy(x).float().to(device)\n",
        "    y_cpu = y\n",
        "    y = torch.from_numpy(y).to(device)\n",
        "    # y_cpu = y.cpu().numpy()\n",
        "    # print(y_cpu)\n",
        "    \n",
        "    yhat = myalexnet.forward(x)\n",
        "    # print(yhat)\n",
        "    yhat_cpu = np.argmax(yhat.cpu().numpy(), axis = 1)\n",
        "    # print(yhat_cpu)\n",
        "\n",
        "    print('Prediction on test data set using AlexNet:')\n",
        "    from sklearn.metrics import f1_score, confusion_matrix, recall_score, precision_score\n",
        "    print('F1-Score with micro average:',f1_score(y_cpu, yhat_cpu,average='micro'))\n",
        "    print('Recall score:',recall_score(y_cpu, yhat_cpu))\n",
        "    print('Precision score:',precision_score(y_cpu, yhat_cpu))\n",
        "    print('Number of correct classification:',np.sum(confusion_matrix(y_cpu, yhat_cpu).diagonal()),'/',len(y_cpu))\n",
        "        "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction on test data set using AlexNet:\n",
            "F1-Score with micro average: 0.7451923076923076\n",
            "Recall score: 0.958974358974359\n",
            "Precision score: 0.723404255319149\n",
            "Number of correct classification: 465 / 624\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RR_9RTPlbMyW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "6fb638c8-eae8-4517-c3fb-b23f1ef87e83"
      },
      "source": [
        "# To load you first need to initialize your module with the exact same hyperparameters.\n",
        "myminialexnet = MyMiniAlexNet(2).to(device)\n",
        "# Then you call the load_state_dict method and torch.load function. \n",
        "# To make sure it loads the weights to the correct device, we specify this with map_location.\n",
        "myminialexnet.load_state_dict(torch.load('/content/gdrive/My Drive/mini_classifier_epoches_20.pth', map_location=device_name))\n",
        "\n",
        "with torch.no_grad():\n",
        "    # for sample in data_val:\n",
        "    #     print(np.array(sample['y']))\n",
        "    x = np.array([np.expand_dims(np.array(sample['x']),axis=0) for sample in data_test])\n",
        "    # print(x.shape)\n",
        "    y = np.array([sample['y'] for sample in data_test])\n",
        "    # Change! #\n",
        "    x = torch.from_numpy(x).float().to(device)\n",
        "    y_cpu = y\n",
        "    y = torch.from_numpy(y).to(device)\n",
        "    # y_cpu = y.cpu().numpy()\n",
        "    # print(y_cpu)\n",
        "    \n",
        "    yhat = myminialexnet.forward(x)\n",
        "    # print(yhat)\n",
        "    yhat_cpu = np.argmax(yhat.cpu().numpy(), axis = 1)\n",
        "    # print(yhat_cpu)\n",
        "\n",
        "    print('Prediction on test data set using MiniAlexNet:')\n",
        "    from sklearn.metrics import f1_score, confusion_matrix, recall_score, precision_score\n",
        "    print('F1-Score with micro average:',f1_score(y_cpu, yhat_cpu,average='micro'))\n",
        "    print('Recall score:',recall_score(y_cpu, yhat_cpu))\n",
        "    print('Precision score:',precision_score(y_cpu, yhat_cpu))\n",
        "    print('Number of correct classification:',np.sum(confusion_matrix(y_cpu, yhat_cpu).diagonal()),'/',len(y_cpu))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction on test data set using MiniAlexNet:\n",
            "F1-Score with micro average: 0.7612179487179487\n",
            "Recall score: 0.9717948717948718\n",
            "Precision score: 0.7330754352030948\n",
            "Number of correct classification: 475 / 624\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NiAgfWIUvPbM",
        "colab_type": "text"
      },
      "source": [
        "11. Free cuda memory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKG3Y6YYk4Do",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6e9785f4-4dbe-48b9-d777-d0ddc91c5c3e"
      },
      "source": [
        "torch.cuda.empty_cache()\n",
        "torch.cuda.memory_stats(device=device)\n",
        "torch.cuda.memory_allocated(device=device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "664141824"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LClgs2t-vUdi",
        "colab_type": "text"
      },
      "source": [
        "## Step 3: Extract features using pretrained CNN model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AavjPUA6vfFV",
        "colab_type": "text"
      },
      "source": [
        "1. Extract feature using **MyMiniAlexNet** for training data set and test data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lVklx0N4cHjH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "860c742d-dec6-4b59-cd2b-7f5ce496e845"
      },
      "source": [
        "resize_image_size = (512, 512)\n",
        "composed_train = transforms.Compose([transforms.Grayscale(), transforms.Resize(resize_image_size), transforms.ToTensor()])\n",
        "\n",
        "data_train = XRayDataset(dir_path='./data/chest_xray/train/',transform=composed_train)\n",
        "data_test = XRayDataset(dir_path='./data/chest_xray/test/',transform=composed_train)\n",
        "\n",
        "batch_size = 9\n",
        "\n",
        "# To load you first need to initialize your module with the exact same hyperparameters.\n",
        "myminialexnet = MyMiniAlexNet(2).to(device)\n",
        "# Then you call the load_state_dict method and torch.load function. \n",
        "# To make sure it loads the weights to the correct device, we specify this with map_location.\n",
        "myminialexnet.load_state_dict(torch.load('/content/gdrive/My Drive/mini_classifier_epoches_20.pth', map_location=device_name))\n",
        "\n",
        "train_data_loader = data.DataLoader(data_train, batch_size=batch_size)\n",
        "test_data_loader = data.DataLoader(data_test, batch_size=batch_size)\n",
        "\n",
        "feature_train_np_list = []\n",
        "y_train_np_list = []\n",
        "feature_test_np_list = []\n",
        "y_test_np_list = []\n",
        "\n",
        "with torch.no_grad():\n",
        "    for batch in train_data_loader:\n",
        "        feature_train = myminialexnet.forward(batch['x'].to(device), feature_extra=True)\n",
        "        feature_train_np = feature_train.cpu().detach().numpy()\n",
        "        feature_train_np_list.append(feature_train_np)\n",
        "        y_train_np_list.append(batch['y'].numpy())\n",
        "\n",
        "    for batch in test_data_loader:\n",
        "        feature_test = myminialexnet.forward(batch['x'].to(device), feature_extra=True)\n",
        "        feature_test_np = feature_test.cpu().detach().numpy()\n",
        "        feature_test_np_list.append(feature_test_np)\n",
        "        y_test_np_list.append(batch['y'].numpy())\n",
        "\n",
        "# print(y_train_np_list)\n",
        "# print(y_test_np_list)\n",
        "\n",
        "feature_train_np = feature_train_np_list[0]\n",
        "y_train_np = y_train_np_list[0]\n",
        "for i in range(len(feature_train_np_list)-1):\n",
        "    feature_train_np = np.vstack((feature_train_np, feature_train_np_list[i+1]))\n",
        "    y_train_np = np.hstack((y_train_np, y_train_np_list[i+1]))\n",
        "\n",
        "feature_test_np = feature_test_np_list[0]\n",
        "y_test_np = y_test_np_list[0]\n",
        "for i in range(len(feature_test_np_list)-1):\n",
        "    feature_test_np = np.vstack((feature_test_np, feature_test_np_list[i+1]))\n",
        "    y_test_np = np.hstack((y_test_np, y_test_np_list[i+1]))\n",
        "\n",
        "print('Training data set shape:')\n",
        "print(feature_train_np.shape)\n",
        "print('Test data set shape:')\n",
        "print(feature_test_np.shape)\n",
        "\n",
        "# print(y_train_np.shape)\n",
        "\n",
        "# with torch.no_grad():\n",
        "#     # for sample in data_val:\n",
        "#     #     print(np.array(sample['y']))\n",
        "#     x = np.array([np.expand_dims(np.array(sample['x']),axis=0) for sample in data_train])\n",
        "#     y_train = np.array([sample['y'] for sample in data_train])\n",
        "#     # print(x.shape)\n",
        "#     # Change! #\n",
        "#     x = torch.from_numpy(x).float().to(device)\n",
        "    \n",
        "#     feature_train = myminialexnet.forward(x, feature_extra=True)\n",
        "#     feature_train_np = feature_train.cpu().numpy()\n",
        "\n",
        "#     x_test = np.array([np.expand_dims(np.array(sample['x']),axis=0) for sample in data_test])\n",
        "#     y_test = np.array([sample['y'] for sample in data_test])\n",
        "#     # print(x.shape)\n",
        "#     # Change! #\n",
        "#     x_test = torch.from_numpy(x_test).float().to(device)\n",
        "    \n",
        "#     feature_test = myminialexnet.forward(x_test, feature_extra=True)\n",
        "#     feature_test_np = feature_test.cpu().numpy()\n",
        "\n",
        "# print('Input training data shape:')\n",
        "# print(feature_train_np.shape)\n",
        "# print(y_train.shape)\n",
        "\n",
        "# print('Input test data shape:')\n",
        "# print(feature_test_np.shape)\n",
        "# print(y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data set shape:\n",
            "(5216, 14)\n",
            "Test data set shape:\n",
            "(624, 14)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXCqvIUivy2I",
        "colab_type": "text"
      },
      "source": [
        "## Step 4: SVM classifier on features which are extracted using CNN model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJ8hOJFwdza8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "1db33489-a1b6-4ffc-aa93-88535227db1e"
      },
      "source": [
        "from sklearn import svm\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import make_scorer\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import precision_score\n",
        "\n",
        "param_grid = {'C': [0.01, 0.1, 1, 5, 10], 'kernel':['rbf','poly'],'class_weight':['balanced'],'degree':[3,4,5]}\n",
        "search = GridSearchCV(svm.SVC(), param_grid)\n",
        "search.fit(feature_train_np, y_train_np)\n",
        "print('Best parameter:')\n",
        "print(search.best_params_)\n",
        "\n",
        "y_pred = search.predict(feature_test_np)\n",
        "# Model Accuracy: how often is the classifier correct?\n",
        "print(\"Accuracy:\",accuracy_score(y_test_np, y_pred))\n",
        "\n",
        "# Model Precision: what percentage of positive tuples are labeled as such?\n",
        "print(\"Precision:\",precision_score(y_test_np, y_pred))\n",
        "\n",
        "# Model Recall: what percentage of positive tuples are labelled as such?\n",
        "print(\"Recall:\",recall_score(y_test_np, y_pred))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best parameter:\n",
            "{'C': 0.1, 'class_weight': 'balanced', 'degree': 3, 'kernel': 'poly'}\n",
            "Accuracy: 0.7612179487179487\n",
            "Precision: 0.7348927875243665\n",
            "Recall: 0.9666666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NhPntaojDPqn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "random_id = np.random.choice(len(y_train_np) ,1000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BzpUvi3yDGZH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "0c7d253d-0930-4de4-eb6b-9b2b30cba4ac"
      },
      "source": [
        "print('Using {} samples which are random selected'.format(1000))\n",
        "param_grid = {'C': [0.01, 0.1, 1, 5, 10], 'kernel':['rbf','poly'],'class_weight':['balanced'],'degree':[3,4,5]}\n",
        "search = GridSearchCV(svm.SVC(), param_grid)\n",
        "search.fit(feature_train_np[random_id], y_train_np[random_id])\n",
        "print('Best parameter:')\n",
        "print(search.best_params_)\n",
        "\n",
        "y_pred = search.predict(feature_test_np)\n",
        "# Model Accuracy: how often is the classifier correct?\n",
        "print(\"Accuracy:\",accuracy_score(y_test_np, y_pred))\n",
        "\n",
        "# Model Precision: what percentage of positive tuples are labeled as such?\n",
        "print(\"Precision:\",precision_score(y_test_np, y_pred))\n",
        "\n",
        "# Model Recall: what percentage of positive tuples are labelled as such?\n",
        "print(\"Recall:\",recall_score(y_test_np, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using 1000 samples which are random selected\n",
            "Best parameter:\n",
            "{'C': 1, 'class_weight': 'balanced', 'degree': 4, 'kernel': 'poly'}\n",
            "Accuracy: 0.75\n",
            "Precision: 0.7232824427480916\n",
            "Recall: 0.9717948717948718\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YNzz8K-xwFeW",
        "colab_type": "text"
      },
      "source": [
        "## Step 5: RandomForest classifier on features which are extracted using CNN model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OrYyVPnkpTtz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "3db0ca77-c827-40b8-abde-e0b428bd3d70"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "param_grid = {'n_estimators': [70, 80, 90, 100, 110, 120], 'criterion':['gini','entropy'],'max_features':['auto','log2'],'class_weight':['balanced']}\n",
        "search_rm = GridSearchCV(RandomForestClassifier(), param_grid)\n",
        "search_rm.fit(feature_train_np, y_train_np)\n",
        "print('Best parameter:')\n",
        "print(search_rm.best_params_)\n",
        "\n",
        "y_pred = search_rm.predict(feature_test_np)\n",
        "# Model Accuracy: how often is the classifier correct?\n",
        "print(\"Accuracy:\",accuracy_score(y_test_np, y_pred))\n",
        "\n",
        "# Model Precision: what percentage of positive tuples are labeled as such?\n",
        "print(\"Precision:\",precision_score(y_test_np, y_pred))\n",
        "\n",
        "# Model Recall: what percentage of positive tuples are labelled as such?\n",
        "print(\"Recall:\",recall_score(y_test_np, y_pred))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best parameter:\n",
            "{'class_weight': 'balanced', 'criterion': 'entropy', 'max_features': 'auto', 'n_estimators': 110}\n",
            "Accuracy: 0.7307692307692307\n",
            "Precision: 0.7055555555555556\n",
            "Recall: 0.9769230769230769\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}