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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MahdiEsrafili/image_segmentation/blob/master/skin_cancer_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwmIiVtjmVv3"
      },
      "source": [
        "! pip install -q kaggle"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGgsI7HMmXUF"
      },
      "source": [
        "! mkdir ~/.kaggle\r\n",
        "% cp /content/drive/MyDrive/kaggle.json ~/.kaggle/\r\n",
        "! chmod 600 ~/.kaggle/kaggle.json\r\n",
        "!kaggle datasets download -d nodoubttome/skin-cancer9-classesisic\r\n",
        "!unzip -q skin-cancer9-classesisic.zip -d data/"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMlURGiOmlIT"
      },
      "source": [
        "import cv2\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import os\r\n",
        "import numpy as np\r\n",
        "from PIL import Image\r\n",
        "from sklearn.metrics import classification_report\r\n",
        "import random\r\n",
        "%matplotlib inline"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "hubjX_A2mvMa",
        "outputId": "7f5651ad-aaa7-4f8b-95e4-68ea24af4c12"
      },
      "source": [
        "path = '/content/data/Skin cancer ISIC The International Skin Imaging Collaboration'\r\n",
        "branches = ['Test', 'Train']\r\n",
        "distributions = dict()\r\n",
        "for branch in branches:\r\n",
        "  diseases = dict()\r\n",
        "  for disease in os.listdir(os.path.join(path, branch)):\r\n",
        "    diseases[disease] = len(os.listdir(os.path.join(path, branch, disease)))\r\n",
        "  distributions[branch] = diseases\r\n",
        "\r\n",
        "plt.figure(figsize=(16, 5))\r\n",
        "plt.bar(distributions['Train'].keys(), distributions['Train'].values(), color='r')\r\n",
        "plt.bar(distributions['Test'].keys(), distributions['Test'].values(), color='b')\r\n",
        "plt.xticks(rotation = 90, fontsize=15) # Rotates X-Axis Ticks by 45-degrees\r\n",
        "plt.show()\r\n"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1152x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0RgQFaXenOjh",
        "outputId": "afe3715b-6bf1-43ae-e430-62ef4620352d"
      },
      "source": [
        "distributions"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Test': {'actinic keratosis': 16,\n",
              "  'basal cell carcinoma': 16,\n",
              "  'dermatofibroma': 16,\n",
              "  'melanoma': 16,\n",
              "  'nevus': 16,\n",
              "  'pigmented benign keratosis': 16,\n",
              "  'seborrheic keratosis': 3,\n",
              "  'squamous cell carcinoma': 16,\n",
              "  'vascular lesion': 3},\n",
              " 'Train': {'actinic keratosis': 114,\n",
              "  'basal cell carcinoma': 376,\n",
              "  'dermatofibroma': 95,\n",
              "  'melanoma': 438,\n",
              "  'nevus': 357,\n",
              "  'pigmented benign keratosis': 462,\n",
              "  'seborrheic keratosis': 77,\n",
              "  'squamous cell carcinoma': 181,\n",
              "  'vascular lesion': 139}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6KuTvEt-aax2"
      },
      "source": [
        "We can infer from distribution that data is unbalanced, so we duplicate the images using transforms to make disribution equal."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-eu7rHDx9Lv"
      },
      "source": [
        "import torchvision\r\n",
        "import torch\r\n",
        "from torchvision import transforms as T\r\n",
        "from torch import nn\r\n",
        "from torch.utils.data import DataLoader\r\n",
        "from tqdm.notebook import tqdm"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLFGR7D0yUa4"
      },
      "source": [
        "train_transforms = T.Compose([\r\n",
        "                        T.Resize((224, 224)),\r\n",
        "                        T.RandomVerticalFlip(0.3),\r\n",
        "                        T.RandomHorizontalFlip(0.3),\r\n",
        "                        T.ToTensor(),\r\n",
        "                        T.Normalize(mean = [0.485, 0.456, 0.406],\r\n",
        "                                    std = [0.229, 0.224, 0.225])\r\n",
        "])\r\n",
        "\r\n",
        "test_transforms = T.Compose([\r\n",
        "                        T.Resize((224, 224)),\r\n",
        "                        T.ToTensor(),\r\n",
        "                        T.Normalize(mean = [0.485, 0.456, 0.406],\r\n",
        "                                    std = [0.229, 0.224, 0.225])\r\n",
        "])"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_MJwgUHa-J7"
      },
      "source": [
        "# augmentation part"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Al1vrm-5bCD7",
        "outputId": "b07de1e1-1c20-455e-aa69-96349ad388ea"
      },
      "source": [
        "max(list(distributions['Train'].values()))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "462"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KclkHl12bOco",
        "outputId": "108118ca-a7f2-4210-a0e3-fa5ec5641503"
      },
      "source": [
        "def augment(path, transforms, max_number=462):\r\n",
        "  images = os.listdir(path)\r\n",
        "  count = len(images)\r\n",
        "  diff = max_number - count\r\n",
        "  for i in range(diff):\r\n",
        "    img_id = random.sample(images, 1)[0]\r\n",
        "    img_path = os.path.join(path, img_id)\r\n",
        "    img = Image.open(img_path)\r\n",
        "    img = transforms(img)\r\n",
        "    img.save(os.path.join(path, f'aug{random.randint(0, 1e5)}_{img_id}'))\r\n",
        "  print(f'augmentation {path} done with {diff} samples')\r\n",
        "\r\n",
        "aug_transforms = T.Compose([\r\n",
        "                            T.RandomRotation(45),\r\n",
        "                            T.RandomVerticalFlip(),\r\n",
        "                            T.RandomHorizontalFlip()\r\n",
        "])\r\n",
        "\r\n",
        "path = '/content/data/Skin cancer ISIC The International Skin Imaging Collaboration/Train'\r\n",
        "train_dirs = os.listdir(path)\r\n",
        "for d in train_dirs:\r\n",
        "   augment(os.path.join(path, d), aug_transforms)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "augmentation /content/data/Skin cancer ISIC The International Skin Imaging Collaboration/Train/dermatofibroma done with 367 samples\n",
            "augmentation /content/data/Skin cancer ISIC The International Skin Imaging Collaboration/Train/nevus done with 105 samples\n",
            "augmentation /content/data/Skin cancer ISIC The International Skin Imaging Collaboration/Train/vascular lesion done with 323 samples\n",
            "augmentation /content/data/Skin cancer ISIC The International Skin Imaging Collaboration/Train/actinic keratosis done with 348 samples\n",
            "augmentation /content/data/Skin cancer ISIC The International Skin Imaging Collaboration/Train/seborrheic keratosis done with 385 samples\n",
            "augmentation /content/data/Skin cancer ISIC The International Skin Imaging Collaboration/Train/basal cell carcinoma done with 86 samples\n",
            "augmentation /content/data/Skin cancer ISIC The International Skin Imaging Collaboration/Train/melanoma done with 24 samples\n",
            "augmentation /content/data/Skin cancer ISIC The International Skin Imaging Collaboration/Train/squamous cell carcinoma done with 281 samples\n",
            "augmentation /content/data/Skin cancer ISIC The International Skin Imaging Collaboration/Train/pigmented benign keratosis done with 0 samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "vTXRKh4Ri6Wo",
        "outputId": "291a2cda-116b-4d24-ecac-6a9bea18b608"
      },
      "source": [
        "path = '/content/data/Skin cancer ISIC The International Skin Imaging Collaboration/Train'\r\n",
        "distributions = dict()\r\n",
        "for disease in os.listdir(path):\r\n",
        "  distributions[disease] = len(os.listdir(os.path.join(path, disease)))\r\n",
        "\r\n",
        "plt.figure(figsize=(16, 5))\r\n",
        "plt.bar(distributions.keys(), distributions.values(), color='r')\r\n",
        "plt.xticks(rotation = 90, fontsize=15) # Rotates X-Axis Ticks by 45-degrees\r\n",
        "plt.show()\r\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9k_h0c3jjato",
        "outputId": "71887855-7225-4e29-c482-2cac81ded482"
      },
      "source": [
        "distributions"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'actinic keratosis': 462,\n",
              " 'basal cell carcinoma': 462,\n",
              " 'dermatofibroma': 462,\n",
              " 'melanoma': 462,\n",
              " 'nevus': 462,\n",
              " 'pigmented benign keratosis': 462,\n",
              " 'seborrheic keratosis': 462,\n",
              " 'squamous cell carcinoma': 462,\n",
              " 'vascular lesion': 462}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "zP3Hkd4_krjr",
        "outputId": "1105b194-6128-4565-b6ee-a77cfea158c4"
      },
      "source": [
        "path"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/data/Skin cancer ISIC The International Skin Imaging Collaboration/Train'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_qjkAYpzUqw"
      },
      "source": [
        "path = '/content/data/Skin cancer ISIC The International Skin Imaging Collaboration'\r\n",
        "train_dataset = torchvision.datasets.ImageFolder(os.path.join(path, 'Train'), \r\n",
        "                                                 transform=train_transforms)\r\n",
        "test_dataset = torchvision.datasets.ImageFolder(os.path.join(path, 'Test'), \r\n",
        "                                                 transform=test_transforms)\r\n",
        "\r\n",
        "batch_size = 32\r\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\r\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\r\n"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTchfrAjDyAb"
      },
      "source": [
        "# from functools import reduce\r\n",
        "# reduce(lambda x,y: x+y, distributions['Train'].values())"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDSeMSuJDexC"
      },
      "source": [
        "# total_samples = 2239\r\n",
        "# weights_named = {k:v/total_samples for (k,v) in distributions['Train'].items()}\r\n",
        "# weights_named"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTOO1vfQDKN3"
      },
      "source": [
        "# weights = dict()\r\n",
        "# for k, v in train_loader.dataset.class_to_idx.items():\r\n",
        "#   weights[v] = weights_named[k]\r\n",
        "# weights"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YHwXZiQH1a2p",
        "outputId": "64ad7305-c880-474a-abe8-e0b89787ae1a"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n",
        "device"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYkSniqL1gFd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "720911ad78ba483ba74a3c5fde9be533",
            "877ec102521f41b8b98e46e560990572",
            "b7702d658a4048c8a66eae004d7967e6",
            "2d944832d8244cac82aadfc51eb0adb6",
            "2f4405fea11c4f6aa91171da9522875e",
            "69f5626e802249e090dfe209e1fc1c91",
            "9cd5154f8ca74bbb99077d4f8474af05",
            "d524fc8dcb0c4779b0b4eaad8310374a"
          ]
        },
        "outputId": "d1e3b26f-cce2-4017-d390-c4e5707614cd"
      },
      "source": [
        "model = torchvision.models.resnet18(pretrained=True, progress=True)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-5c106cde.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "720911ad78ba483ba74a3c5fde9be533",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=46827520.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWL7Np5Q1oYM"
      },
      "source": [
        "num_classes = 9\r\n",
        "model.fc = nn.Linear(model.fc.in_features, num_classes)\r\n",
        "torch.nn.init.xavier_uniform_(model.fc.weight);\r\n",
        "model = model.to(device)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lv-0OeTq1qXD"
      },
      "source": [
        "# param_group = True\r\n",
        "lr = 1e-3\r\n",
        "# if param_group:\r\n",
        "#     params_1x = [param for name,param in model.named_parameters()\r\n",
        "#                 if name not in ['fc.weight','fc.bias']]\r\n",
        "#     optimizer = torch.optim.SGD([{'params':params_1x},\r\n",
        "#                                {'params':model.fc.parameters(), 'lr':0.001}],\r\n",
        "#                              lr=lr, weight_decay=0.001)\r\n",
        "# else:\r\n",
        "#     optimizer = torch.optim.SGD(model.parameters(), lr=lr, weight_decay=0.001)\r\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\r\n",
        "# loss_weights = torch.tensor(list(weights.values()))\r\n",
        "# criterion = nn.CrossEntropyLoss(weight=loss_weights).to(device)\r\n",
        "criterion = nn.CrossEntropyLoss().to(device)\r\n",
        "lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-QGv3su4E18"
      },
      "source": [
        "def train(model, trainset, criterion, optimizer, lr_scheduler, num_epochs=10, batch_size=32):\r\n",
        "  loss_history = list()\r\n",
        "  for epoch in range(num_epochs):\r\n",
        "    model.train()\r\n",
        "    train_size = int(0.9*len(trainset))\r\n",
        "    valid_size = len(trainset) - train_size\r\n",
        "    train_ds, valid_ds = torch.utils.data.random_split(trainset, [train_size, valid_size],\r\n",
        "                 generator=torch.Generator().manual_seed(42))\r\n",
        "    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\r\n",
        "    valid_loader = DataLoader(valid_ds, batch_size=batch_size, shuffle=True)\r\n",
        "    pbar = tqdm(train_loader)\r\n",
        "    epoch_losses = np.array([])\r\n",
        "    train_corrects = 0\r\n",
        "    train_img_passed = 0\r\n",
        "    for images, labels in pbar:\r\n",
        "      images = images.to(device)\r\n",
        "      labels = labels.to(device)\r\n",
        "      optimizer.zero_grad()\r\n",
        "      out = model(images)\r\n",
        "      loss = criterion(out, labels)\r\n",
        "      loss.backward()\r\n",
        "      optimizer.step()\r\n",
        "      pred = out.argmax(dim=1).cpu().numpy()\r\n",
        "      train_corrects += (pred==labels.cpu().numpy()).sum()\r\n",
        "      train_img_passed += pred.shape[0]\r\n",
        "      train_accuracy = train_corrects/train_img_passed\r\n",
        "      epoch_losses = np.append(epoch_losses, loss.item())\r\n",
        "      pbar.set_description(f'epoch {epoch+1}/{num_epochs},\\\r\n",
        "                            accuracy={train_accuracy:.4f}, error={epoch_losses.mean():.4f} ')\r\n",
        "    loss_history.append(epoch_losses.mean())\r\n",
        "    lr_scheduler.step()\r\n",
        "    with torch.no_grad():\r\n",
        "      model.eval()\r\n",
        "      valid_corrects = 0\r\n",
        "      valid_loss = np.array([])\r\n",
        "      for images, labels in valid_loader:\r\n",
        "        images = images.to(device)\r\n",
        "        labels = labels.to(device)\r\n",
        "        optimizer.zero_grad()\r\n",
        "        out = model(images)\r\n",
        "        pred = out.argmax(dim=1).cpu().numpy()\r\n",
        "        valid_corrects += (pred==labels.cpu().numpy()).sum()\r\n",
        "        loss = criterion(out, labels)\r\n",
        "        valid_loss = np.append(valid_loss, loss.item())\r\n",
        "      valid_accuracy = valid_corrects/len(valid_ds)\r\n",
        "      valid_loss = valid_loss.mean()\r\n",
        "      print(f'val accuracy = {valid_accuracy:.4f}, val loss ={valid_loss:.4f}')\r\n",
        "  return model, loss_history\r\n"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
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        "colab": {
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          "height": 1000,
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          "text": [
            "\n",
            "val accuracy = 0.8317, val loss =0.4909\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4HalGJlJVqAD"
      },
      "source": [
        "with open('/content/model.pt', 'wb') as f:\r\n",
        "  torch.save(model , f)\r\n",
        "!cp /content/model.pt /content/drive/MyDrive/skin_cancer/"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfdAhaT6M_xt"
      },
      "source": [
        "\r\n",
        "def test(model, dataloaders, criterion):\r\n",
        "  with torch.no_grad():\r\n",
        "    all_preds = np.array([])\r\n",
        "    ground_truth = np.array([])\r\n",
        "    losses = 0\r\n",
        "    model.eval()\r\n",
        "    for (images, labels) in dataloaders:\r\n",
        "      images = images.to(device)\r\n",
        "      labels = labels.to(device)\r\n",
        "      preds = model(images)\r\n",
        "      loss = criterion(preds, labels)\r\n",
        "      preds = preds.argmax(axis=1)\r\n",
        "      all_preds = np.append(all_preds, preds.cpu().numpy().astype(int))\r\n",
        "      ground_truth = np.append(ground_truth, labels.cpu().numpy().astype(int))\r\n",
        "      losses +=  loss.item()\r\n",
        "  print(losses/ground_truth.shape[0])\r\n",
        "  target_names = [k for k,v in train_loader.dataset.class_to_idx.items()]\r\n",
        "  print(classification_report(ground_truth.astype(np.int16), all_preds.astype(np.int16), target_names=target_names))"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eFcjwbR7NRvQ",
        "outputId": "624f79aa-4dc5-4a77-823b-a9f16cdcafa9"
      },
      "source": [
        "test(model, test_loader, criterion)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.08878846542309907\n",
            "                            precision    recall  f1-score   support\n",
            "\n",
            "         actinic keratosis       0.00      0.00      0.00        16\n",
            "      basal cell carcinoma       0.64      0.88      0.74        16\n",
            "            dermatofibroma       0.83      0.62      0.71        16\n",
            "                  melanoma       0.11      0.06      0.08        16\n",
            "                     nevus       0.37      0.94      0.53        16\n",
            "pigmented benign keratosis       0.65      0.81      0.72        16\n",
            "      seborrheic keratosis       0.00      0.00      0.00         3\n",
            "   squamous cell carcinoma       0.80      0.50      0.62        16\n",
            "           vascular lesion       1.00      1.00      1.00         3\n",
            "\n",
            "                  accuracy                           0.54       118\n",
            "                 macro avg       0.49      0.53      0.49       118\n",
            "              weighted avg       0.49      0.54      0.49       118\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_GtyGn3Ngf1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDePovlKNvus"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}