MelanomaDetection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MelanomaDetection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/koltaib/Melanoma_Detection/blob/main/MelanomaDetection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N8nXdzLiiilX"
      },
      "source": [
        "# This is a melanoma detection notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "756hKZwW1va2"
      },
      "source": [
        "**Prevent disconnecting**\n",
        "\n",
        "Set a javascript interval to click on the connect button every 60 seconds. Open developer-settings (in your web-browser) with Ctrl+Shift+I then click on console tab and type this on the console prompt. (for mac press Option+Command+I)\n",
        "\n",
        "```\n",
        "function ConnectButton(){\n",
        "    console.log(\"Connect pushed\"); \n",
        "    document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n",
        "}\n",
        "setInterval(ConnectButton,60000);\n",
        "```\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APq3E1QXn67g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16da03c4-1f89-4ab1-eb8c-3658dfe3070f"
      },
      "source": [
        "# Mount Google Drive\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29Yh34gLuAbb"
      },
      "source": [
        "# Bea\n",
        "\n",
        "root_path = '/content/drive/My Drive/Deep learning a gyakorlatban'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxFY7vXuuCeL"
      },
      "source": [
        "# Adrienn\n",
        "\n",
        "root_path = '/content/drive/My Drive/DL/Deep learning a gyakorlatban'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gclgzSs4uF_m"
      },
      "source": [
        "# Viktor\n",
        "\n",
        "root_path = '/content/drive/MyDrive/Egyetem/Deep learning a gyakorlatban'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gTVvG59qZger"
      },
      "source": [
        "# Set False if you just want to see the results.\r\n",
        "# Set True if you want to make changes in the project.\r\n",
        "developer = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mfouThcPnNzF"
      },
      "source": [
        "# Local variables\n",
        "\n",
        "# Path for metadata\n",
        "metadata = root_path + '/original_files/HAM10000_metadata.csv'\n",
        "\n",
        "# Datasets\n",
        "train_ds = root_path + '/data/new_datasets/train'\n",
        "test_ds = root_path + '/data/new_datasets/test'\n",
        "valid_ds = root_path + '/data/new_datasets/validation'\n",
        "\n",
        "# Checkpoint for effnet\n",
        "checkpoints_effnet = root_path + '/checkpoints/effnet/' + 'bestcheckpoint_{epoch:02d}_{val_loss:.3f}_{val_accuracy:.3f}.hdf5'\n",
        "best_weights_effnet = root_path + '/checkpoints/effnet/'\n",
        "\n",
        "# Checkpoint for inception\n",
        "checkpoints_inception = root_path + '/checkpoints/inception/' + 'bestcheckpoint_{epoch:02d}_{val_loss:.3f}_{val_accuracy:.3f}.hdf5'\n",
        "best_weights_inception = root_path + '/checkpoints/inception/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnkGYANFr8sq"
      },
      "source": [
        "# Import necessary libs\n",
        "\n",
        "import pandas as pd\n",
        "import os\n",
        "import shutil\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from tqdm import tqdm\n",
        "import cv2\n",
        "import gc\n",
        "from google.colab.patches import cv2_imshow"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KICHGw720DG5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "0bbf2b46-eba1-446d-e550-5566e57431ed"
      },
      "source": [
        "# Read csv file to metadata dataframe\n",
        "\n",
        "metadata = pd.read_csv(metadata)\n",
        "print(metadata.shape)\n",
        "metadata.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10015, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lesion_id</th>\n",
              "      <th>image_id</th>\n",
              "      <th>dx</th>\n",
              "      <th>dx_type</th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>localization</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>HAM_0000118</td>\n",
              "      <td>ISIC_0027419</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>HAM_0000118</td>\n",
              "      <td>ISIC_0025030</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>HAM_0002730</td>\n",
              "      <td>ISIC_0026769</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>HAM_0002730</td>\n",
              "      <td>ISIC_0025661</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>HAM_0001466</td>\n",
              "      <td>ISIC_0031633</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>75.0</td>\n",
              "      <td>male</td>\n",
              "      <td>ear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>HAM_0001466</td>\n",
              "      <td>ISIC_0027850</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>75.0</td>\n",
              "      <td>male</td>\n",
              "      <td>ear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>HAM_0002761</td>\n",
              "      <td>ISIC_0029176</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>60.0</td>\n",
              "      <td>male</td>\n",
              "      <td>face</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>HAM_0002761</td>\n",
              "      <td>ISIC_0029068</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>60.0</td>\n",
              "      <td>male</td>\n",
              "      <td>face</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>HAM_0005132</td>\n",
              "      <td>ISIC_0025837</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>70.0</td>\n",
              "      <td>female</td>\n",
              "      <td>back</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>HAM_0005132</td>\n",
              "      <td>ISIC_0025209</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>70.0</td>\n",
              "      <td>female</td>\n",
              "      <td>back</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     lesion_id      image_id   dx dx_type   age     sex localization\n",
              "0  HAM_0000118  ISIC_0027419  bkl   histo  80.0    male        scalp\n",
              "1  HAM_0000118  ISIC_0025030  bkl   histo  80.0    male        scalp\n",
              "2  HAM_0002730  ISIC_0026769  bkl   histo  80.0    male        scalp\n",
              "3  HAM_0002730  ISIC_0025661  bkl   histo  80.0    male        scalp\n",
              "4  HAM_0001466  ISIC_0031633  bkl   histo  75.0    male          ear\n",
              "5  HAM_0001466  ISIC_0027850  bkl   histo  75.0    male          ear\n",
              "6  HAM_0002761  ISIC_0029176  bkl   histo  60.0    male         face\n",
              "7  HAM_0002761  ISIC_0029068  bkl   histo  60.0    male         face\n",
              "8  HAM_0005132  ISIC_0025837  bkl   histo  70.0  female         back\n",
              "9  HAM_0005132  ISIC_0025209  bkl   histo  70.0  female         back"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ft7qOQPB9YTI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "3e565795-0644-4e6d-8811-2d94e031ef27"
      },
      "source": [
        "# Select melanoma records in metadata\n",
        "\n",
        "melanoma_data = metadata.loc[metadata['dx'] == 'mel']\n",
        "\n",
        "print(melanoma_data.shape)\n",
        "melanoma_data.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1113, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lesion_id</th>\n",
              "      <th>image_id</th>\n",
              "      <th>dx</th>\n",
              "      <th>dx_type</th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>localization</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1211</th>\n",
              "      <td>HAM_0000871</td>\n",
              "      <td>ISIC_0025964</td>\n",
              "      <td>mel</td>\n",
              "      <td>histo</td>\n",
              "      <td>40.0</td>\n",
              "      <td>female</td>\n",
              "      <td>chest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1212</th>\n",
              "      <td>HAM_0000871</td>\n",
              "      <td>ISIC_0030623</td>\n",
              "      <td>mel</td>\n",
              "      <td>histo</td>\n",
              "      <td>40.0</td>\n",
              "      <td>female</td>\n",
              "      <td>chest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1213</th>\n",
              "      <td>HAM_0000040</td>\n",
              "      <td>ISIC_0027190</td>\n",
              "      <td>mel</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>upper extremity</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        lesion_id      image_id   dx dx_type   age     sex     localization\n",
              "1211  HAM_0000871  ISIC_0025964  mel   histo  40.0  female            chest\n",
              "1212  HAM_0000871  ISIC_0030623  mel   histo  40.0  female            chest\n",
              "1213  HAM_0000040  ISIC_0027190  mel   histo  80.0    male  upper extremity"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "caiNdgRIbG1D"
      },
      "source": [
        "# Find and delete duplicated images\n",
        "if developer:\n",
        "    \n",
        "  sum = 0\n",
        "  dir = ''\n",
        "  for filename in tqdm(os.listdir(dir)):\n",
        "      if (filename.find(\"(\")) != -1:\n",
        "          os.remove(dir + \"/\" + filename)\n",
        "          sum += 1\n",
        "  sum"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2EGwOdP1JNJ1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcf4f017-0d9a-4291-cfed-1baee38ce36e"
      },
      "source": [
        "# Count melanoma datas in metadata\n",
        "\n",
        "sum = 0\n",
        "for ind in metadata.index:  \n",
        "     if metadata['dx'][ind] == 'mel':\n",
        "         sum = sum + 1\n",
        "print(sum)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1113\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bf9nLHbRKaJQ"
      },
      "source": [
        "# Separate melanoma jpg files from other\n",
        "if developer:\n",
        "    \n",
        "  for filename in os.listdir(others_images_path):\n",
        "      id = os.path.splitext(filename)[0]\n",
        "      for i in melanoma_data.index:\n",
        "          if melanoma_data['image_id'][i] == id:\n",
        "              shutil.move(others_images_path + '/' + filename, melanoma_images_path)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4TwS3b6HngR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d4036fa-f6ca-4bdf-ad21-a9c5d9ce524a"
      },
      "source": [
        "# Count jpg files in directory\n",
        "if developer:\n",
        "\n",
        "  dir = ''\n",
        "  sum = 0\n",
        "  for filename in os.listdir(dir):\n",
        "    if filename.endswith(\".jpg\"): \n",
        "        sum = sum + 1\n",
        "  print(sum)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3564\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWPgiOUITPOU"
      },
      "source": [
        "# Count melanoma jpg files in directory\n",
        "if developer:\n",
        "\n",
        "  dir = ''\n",
        "  sum = 0\n",
        "  for filename in os.listdir(dir):\n",
        "      id = os.path.splitext(filename)[0]\n",
        "      for i in melanoma_data.index:\n",
        "          if melanoma_data['image_id'][i] == id:\n",
        "              sum = sum + 1\n",
        "  print(sum)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ElhrItlxc5u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5be4075-0665-48bf-ac0a-6a46f27a4ae3"
      },
      "source": [
        "# Create balanced datasets with data augmentation\n",
        "if developer:\n",
        "\n",
        "  # To generate names for the images\n",
        "  def formatter(prefix, suffix):\n",
        "    if suffix < 1000:\n",
        "      prefix = prefix + '0'\n",
        "    if suffix < 100:\n",
        "      prefix = prefix + '0'\n",
        "    if suffix < 10:\n",
        "      prefix = prefix + '0'\n",
        "    return prefix + str(suffix) + \".jpg\"\n",
        "\n",
        "  # Melanoma images\n",
        "  dir = root_path + '/data/images/melanoma'\n",
        "  dst = root_path + '/data/new_images/melanoma/'\n",
        "  index = 0\n",
        "  for filename in tqdm(os.listdir(dir)):\n",
        "    src = dir + \"/\" + filename\n",
        "    if index < 111:\n",
        "       shutil.copy(src, dst + formatter(\"melanoma_\", index))\n",
        "    elif index < 222:\n",
        "       shutil.copy(src, dst + formatter(\"melanoma_\", index))\n",
        "    else:\n",
        "      # Rotate images\n",
        "       img = cv2.imread(src)\n",
        "       img_horizontal = cv2.flip(img, 0)\n",
        "       img_vertical = cv2.flip(img, 1)\n",
        "       img_horizontal_vertical = cv2.flip(img, -1)\n",
        "       # Save images\n",
        "       cv2.imwrite(dst + formatter(\"melanoma_\", index), img)\n",
        "       cv2.imwrite(dst + formatter(\"melanoma_\", index+1), img_horizontal)\n",
        "       cv2.imwrite(dst + formatter(\"melanoma_\", index+2), img_vertical)\n",
        "       cv2.imwrite(dst + formatter(\"melanoma_\", index+3), img_horizontal_vertical)\n",
        "       index += 3\n",
        "    index += 1\n",
        "\n",
        "  # Others images\n",
        "  dir = root_path + '/images/others'\n",
        "  prefix = root_path + '/data/new_images/others/'\n",
        "  index = 0\n",
        "  for filename in tqdm(os.listdir(dir)[:3786]):\n",
        "      src = dir + \"/\" + filename\n",
        "      shutil.copy(src, prefix + formatter(\"others_\", index))\n",
        "      index += 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 3786/3786 [20:02<00:00,  3.15it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3vj1cE_j1v3e",
        "outputId": "0e250793-7dd8-4133-c8c0-2aeca2db4262"
      },
      "source": [
        "# Separate melanoma images\n",
        "if developer:\n",
        "\n",
        "  dir = root_path + '/data/new_images/melanoma'\n",
        "  dst_root = root_path + '/data/new_datasets/'\n",
        "  index = 0\n",
        "\n",
        "  for filename in tqdm(os.listdir(dir)):\n",
        "    src = dir + \"/\" + filename\n",
        "    if index < 111:\n",
        "        shutil.copy(src, dst + \"test/melanoma/\")\n",
        "    elif index < 222:\n",
        "        shutil.copy(src, dst + \"validation/melanoma/\")\n",
        "    else:\n",
        "        shutil.copy(src, dst + \"train/melanoma/\")\n",
        "    index += 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 3786/3786 [00:44<00:00, 84.43it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TrSjN5Eb4BO9",
        "outputId": "70e8481d-4380-4f08-d958-27e295d89acf"
      },
      "source": [
        "# Separate others images\n",
        "if developer:\n",
        "    \n",
        "  dir = root_path + '/data/new_images/others'\n",
        "  dst_root = root_path + '/data/new_datasets/'\n",
        "  index = 0\n",
        "\n",
        "  for filename in tqdm(os.listdir(dir)):\n",
        "    src = dir + \"/\" + filename\n",
        "    if index < 111:\n",
        "        shutil.copy(src, dst + \"test/others/\")\n",
        "    elif index < 222:\n",
        "        shutil.copy(src, dst + \"validation/others/\")\n",
        "    else:\n",
        "        shutil.copy(src, dst + \"train/others/\")\n",
        "    index += 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 3786/3786 [01:01<00:00, 61.43it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZixlDE10q_n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3417a9f4-4bf7-4b2a-ba77-40dd48a87e03"
      },
      "source": [
        "# Check dx attribute\n",
        "\n",
        "print(\"dx:\")\n",
        "dx = metadata['dx']\n",
        "dx.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dx:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "nv       6705\n",
              "mel      1113\n",
              "bkl      1099\n",
              "bcc       514\n",
              "akiec     327\n",
              "vasc      142\n",
              "df        115\n",
              "Name: dx, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hh-JhQC95E3Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "09bb9d91-fd40-42a2-8d8a-3611c9c2aa7e"
      },
      "source": [
        "# Visualize dataset\n",
        "\n",
        "fig = plt.figure()\n",
        "sns.set(style='darkgrid', context='notebook', palette='gray')\n",
        "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
        "sns.countplot(dx, ax=ax[0])\n",
        "sns.countplot(dx_type, ax=ax[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2676d80a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7ViNGjFBmZqZSU1O1cuVKSfK5BgDA+Zw3JObm5kqSpk+froyMjCZvCACAQNCY4+OZgChJ5eXlCgoKksvlUn5+vvXdxuTkZM2bN08lJSUyxvhUCwsLa1CfAIALg9c/gfH9AdDj8dSqORx+uUgqAADNTmONjzNnztS2bdtkjNHLL7+swsJCRUVFyel0SpKcTqciIyNVWFgoY4xPNUIiAMAbXofEzz77THPnztW+fftUVVUlSTLGKCgoSJ9//nmTNQgAQHPWWOPjggULJEnr1q1TRkaGJk+e3CT9eoPflmwagfi7xQACV0P2OV6HxJSUFN1000364x//qDZt2vj8gAAAtCSNPT6OHDlSqamp6tq1q4qKiuR2u+V0OuV2u1VcXKzo6GgZY3yqecvbq5uifri6KeA79jn116RXNz3jm2++0aOPPqqgoKD6dQcAQAvW0PHxxIkTOn78uBXicnNz1blzZ4WHhysmJkbZ2dkaMWKEsrOzFRMTY00Z9bUGAMD5eB0Sb775Zn344YcaMGBAU/YDAEBAaej4WFFRocmTJ6uiokIOh0OdO3fW8uXLFRQUpDlz5iglJUXLli1Tp06dlJ6ebq3naw0AgPPxOiRWVVXp4Ycf1rXXXquLLrqoVo2rngIALlQNHR8vuugirV69+qy1nj17as2aNY1aAwDgfLwOib169VKvXr2ashcAAAIO4yMAoKXxOiQ+/PDDTdkHAAABifERANDSeB0St2/fXmft+uuvb5RmAAAINIyPAICWxuuQOHPmzFq3jx07ppqaGkVFRen9999v9MYAAAgEjI8AgJbG65CYm5tb67bb7dbzzz+v9u3bN3pTAAAECsZHAEBL4/B1RafTqQcffFAvv/xyY/YDAEBAY3wEAAQ6n0OiJG3bts3nHw8GAKClYnwEAAQyr6ebDho0qNaAV1FRoerqaqWlpTVJYwAABALGRwBAS+N1SFy0aFGt223bttVll12mDh06NHpTAAAECsZHAEBL43VIjI+PlyR5PB4dPXpUF110kRyOBs1WBQAg4DE+AgBaGq9HsfLyck2fPl1XXXWVBg4cqKuuukpPPPGEysrKmrI/AACaNcZHAEBL43VInD9/vioqKpSVlaV//vOfysrKUkVFhebPn9+U/QEA0KwxPgIAWhqvp5tu3bpVmzdvVtu2bSVJl112mf70pz/p5ptvbrLmAABo7hgfAQAtjddnEkNCQlRSUlJr2bFjxxQcHNzoTQEAECgYHwEALY3XZxLvuOMOTZgwQffee6+6deumw4cPa8WKFbrzzjubsj8AAJo1xkcAQEvjdUh86KGHFBUVpaysLBUXFysyMlITJ05kEAQAXNAYHwEALY3X000XLFigyy67TCtWrNDGjRu1YsUK9ezZUwsWLGjK/gAAaNYYHwEALY3XITE7O1v9+vWrtaxfv37Kzs5u9KYAAAgUjI8AgJbG65AYFBQkj8dTa5nb7f7BMgAALiSMjwCAlsbrkBgXF6clS5ZYg57H49HSpUsVFxfXZM0BANDcMT4CAFoary9cM3PmTP32t7/VjTfeqG7duqmwsFARERFavnx5U/YHAECzxvgIAGhpvA6JXbt21dq1a/XPf/5ThYWFio6O1lVXXSWHw7uTkb/73e906NAhORwOtWvXTrNnz1ZMTIwKCgqUkpKi0tJShYaGKj09XT169JAkn2sAAPhLQ8dHAACam3qNYA6HQ7GxsbrlllsUGxtbrwEwPT1d69ev17p16zRhwgTNmDFDkpSWlqaxY8cqJydHY8eOVWpqqrWOrzUAAPypIeMjAADNjd9GsY4dO1r/Li8vV1BQkFwul/Lz85WcnCxJSk5OVn5+vkpKSnyuAQAAAAB85/V008Ywc+ZMbdu2TcYYvfzyyyosLFRUVJScTqckyel0KjIyUoWFhTLG+FQLCwvz5yYBAAAAQIvi15B45oeF161bp4yMDE2ePNmfD/8D4eEdbH181C0iouP57wQAAACg0fk1JJ4xcuRIpaamqmvXrioqKpLb7ZbT6ZTb7VZxcbGio6NljPGpVh8uV7k8HtNEW9l8BGLgOnKkzO4WALQADkcQBwQBAKgnv3wn8cSJEyosLLRu5+bmqnPnzgoPD1dMTIyys7MlSdnZ2YqJiVFYWJjPNQAAAACA7/xyJrGiokKTJ09WRUWFHA6HOnfurOXLlysoKEhz5sxRSkqKli1bpk6dOik9Pd1az9caAAAAAMA3fgmJF110kVavXn3WWs+ePbVmzZpGrQEAAAAAfMMPOQEAAAAALIREAAAAAICFkAgAAAAAsBASAQAAAAAWQiIAAAAAwEJIBAAAAABYCIkAAAAAAAshEQAAAABgISQCAAAAACyERAAAbHTs2DHdf//9SkxM1LBhw/Twww+rpKREkrR3714NHz5ciYmJmjBhglwul7WerzUAAM6HkAgAgI2CgoI0ceJE5eTkKCsrS5dccokWL14sj8ejadOmKTU1VTk5OYqLi9PixYslyecaAADeICQCAGCj0NBQ9e/f37odGxurw4cPKy8vTyEhIYqLi5MkjRkzRu+++64k+VwDAMAbhEQAAJoJj8ejt956SwkJCSosLFS3bt2sWlhYmDwej0pLS32uAQDgjVZ2NwAAAE6bN2+e2rVrp3Hjxum9996zrY/w8A62PXZLFhHR0e4WAFxAGrLPISQCANAMpKen6+uvv9by5cvlcDgUHR2tw4cPW/WSkhI5HA6Fhob6XPOWy1Uuj8ec8z4Envo7cqTM7haAgMU+p/7Ot89xOILqPCjIdFMAAGz21FNPKS8vT88995yCg4MlSf369VNlZaV2794tSVq1apWSkpIaVAMAwBucSQQAwEb79+/XCy+8oB49emjMmDGSpIsvvljPPfecMjIylJaWpqqqKnXv3l2LFi2SJDkcDp9qAAB4g5AIAICNevfurX379p21ds011ygrK6tRawAAnA/TTQEAAAAAFkIiAAAAAMBCSAQAAAAAWAiJAAAAAAALIREAAAAAYCEkAgAAAAAshEQAAAAAgIWQCAAAAACwEBIBAAAAABZCIgAAAADAQkgEAAAAAFj8EhKPHTum+++/X4mJiRo2bJgefvhhlZSUSJL27t2r4cOHKzExURMmTJDL5bLW87UGAAAAAPCNX0JiUFCQJk6cqJycHGVlZemSSy7R4sWL5fF4NG3aNKWmpionJ0dxcXFavHixJPlcAwAAAAD4zi8hMTQ0VP3797dux8bG6vDhw8rLy1NISIji4uIkSWPGjNG7774rST7XAAAAAAC+8/t3Ej0ej9566y0lJCSosLBQ3bp1s2phYWHyeDwqLS31uQYAAAAA8F0rfz/gvHnz1K5dO40bN07vvfeevx++lvDwDrY+PuoWEdHR7hYAAACAC5JfQ2J6erq+/vprLV++XA6HQ9HR0Tp8+LBVLykpkcPhUGhoqM+1+nC5yuXxmIZvWDMXiIHryJEyu1sA0AI4HEEcEAQAoJ78Nt30qaeeUl5enp577jkFBwdLkvr166fKykrt3r1bkrRq1SolJSU1qAYAAAAA8J1fziTu379fL7zwgnr06KExY8ZIki6++GI999xzysjIUFpamqqqqtS9e3ctWrRIkuRwOHyqAQAAAAB855eQ2Lt3b+3bt++stWuuuUZZWVmNWgMAAAAA+MbvVzcFAAAAADRfhEQAAAAAgIWQCAAAAACwEBIBAAAAABZCIgAAAADAQkgEAAAAAFgIiQAAAAAACyERAAAAAGAhJAIAAAAALIREAAAAAICFkAgAAAAAsBASAQAAAAAWQiIAAAAAwEJIBAAAAABYCIkAAAAAAAshEQAAAABgISQCAAAAACyERAAAAACAhZAIAAAAALAQEgEAAAAAFkIiAAA2Sk9PV0JCgvr27at//etf1vKCggKNHj1aiYmJGj16tL766qsG1wAA8AYhEQAAGw0ePFhvvPGGunfvXmt5Wlqaxo4dq5ycHI0dO1apqakNrgEA4A1CIgAANoqLi1N0dHStZS6XS/n5+UpOTpYkJScnKz8/XyUlJT7XAADwViu7GwAAALUVFhYqKipKTqdTkuR0OhUZGanCwkIZY3yqhYWF2bY9AIDAQkgEAAC1hId3sLuFFikioqPdLQC4gDRkn0NIBACgmYmOjlZRUZHcbrecTqfcbreKi4sVHR0tY4xPtfpwucrl8Zhz3ofAU39HjpTZ3QIQsNjn1N/59jkOR1CdBwX5TiIAAM1MeHi4YmJilJ2dLUnKzs5WTEyMwsLCfK4BAOAtziQCAGCj+fPna9OmTTp69Kh+85vfKDQ0VBs2bNCcOXOUkpKiZcuWqVOnTkpPT7fW8bUGAIA3CIkAANho1qxZmjVr1g+W9+zZU2vWrDnrOr7WAADwBiERaIY6hrZRm9at7W7Da5U1NSorrbS7DQC4IHTsGKw2bULsbiNgVFZWqays2u42gIDil5CYnp6unJwcffPNN8rKylKfPn0kSQUFBUpJSVFpaalCQ0OVnp6uHj16NKjWEGGd28gZHDgfzN3VNSr5jg/mLVGb1q019J3/srsNr224bYrKxHsRAPyhTZsQ3XrrrXa3ETA2btxISATqyS8hcfDgwRo/frx+/etf11qelpamsWPHasSIEcrMzFRqaqpWrlzZoFpDOINb68jzrzf47/hLxEPjJD6YAwAAAGhEfrm6aVxc3A8uv+1yuZSfn6/k5GRJUnJysvLz81VSUuJzDQAAAADQMLZ9J7GwsFBRUVFyOp2SJKfTqcjISBUWFsoY41ONS3wDAAAAQMNc0BeuqevHIwNJS/1h0Za6XS0ZrxkAAEDLYFtIjI6OVlFRkdxut5xOp9xut4qLixUdHS1jjE+1+nK5yuXxGOt2IH7IPXKk7Lz3aanb1ZLxmgGNw+EIahEHBAEA8Ce/fCfxbMLDwxUTE6Ps7GxJUnZ2tmJiYhQWFuZzDQAAAADQMH45kzh//nxt2rRJR48e1W9+8xuFhoZqw4YNmjNnjlJSUrRs2TJ16tRJ6enp1jq+1gAAAAAAvvNLSJw1a5ZmzZr1g+U9e/bUmjVrzrqOrzUAAAAAgO9sm24KAAAAAGh+CIkAAAAAAAshEQAAAABgISQCAAAAACyERAAAAACAhZAIAAAAALAQEgEAAAAAFkIiAAAAAMBCSAQAAAAAWAiJAAAAAAALIREAAAAAYCEkAgAAAAAshEQAAAAAgIWQCAAAAACwtLK7AQBoCUJD26t168A47lZT41Fp6Qm72wAAAM0UIREAGkHr1g799ztH7G7DK/fcFmF3CwAAoBkLjMPeAAAAAAC/ICQCAAAAACyERAAAAACAhZAIAAAAALAQEgEAAAAAFq5uCgCoU1jndnIGO+1uw2vuardKvjtpdxsAAAQ0QiIAoE7OYKe+XfS13W14reu0H9ndAgAAAY/ppgAAAAAACyERAAAAAGAhJAIAAAAALIREAAAAAICFkAgAAAAAsHB1UwB+1TG0rdq0DoxdT2XNKZWVVtjdBgAAgF8Fxie1OhQUFCglJUWlpaUKDQ1Venq6evToYXdb8KPOoa0V3LqN3W14pbqmUt+V1tjdhu3atG6lEf+bY3cbXsm8I1FldjcB+IDxEQDQEAEdEtPS0jR27FiNGDFCmZmZSk1N1cqVK+1uC34U3LqN0lcl2t2GV54YkyOJkAig6TE+Ak2jU6cQhYQE291GwKiqqtbx41V2twEfBGxIdLlcys/P16uvvipJSk5O1rx581RSUqKwsDCbuwMAwB6Mj0DTCQkJ1iOPPGJ3GwHjmWeekURIDEQBGxILCwsVFRUlp9MpSXI6nYqMjFRhYaHXg6DDEfTDZR3bN2qfTe1s23A2IR0im7iTxuXtdklSp/ZRTdhJ46rPdkW269SEnTS++m1bYEwRluq3Xe3bBc61wOqzXc5OzibspPF9f9vqs50tRVONj2fTvXt3n/u8EDXm+zEyMrDGdbs15nPPwZb6acznvmPHjo32ty4E53vuz1UPMsaYxm7IH/Ly8vTEE09ow4YN1rJbb71VixYt0pVXXmljZwAA2IfxEQDQUIFz2Ps/REdHq6ioSG63W5LkdrtVXFys6OhomzsDAMA+jI8AgIYK2JAYHh6umJgYZWdnS5Kys7MVExPDFAAAwNbyuDIAABULSURBVAWN8REA0FABO91Ukg4cOKCUlBQdP35cnTp1Unp6ui6//HK72wIAwFaMjwCAhgjokAgAAAAAaFwBO90UAAAAAND4CIkAAAAAAAshEQAAAABgISQCAAAAACyERAAAAACAhZDYSPr27asTJ07Uq5aSkqLXX3+9qVuDD868Zps3b9Ytt9yikSNH6ssvv7S7rSb3zjvv6JFHHrG7jQvGufYbgW7Hjh267bbbzlq7//779e9//9vPHSEQnev/yIgRI1RZWVnnusePH9dLL73UVK0BtnvzzTeVlJSkkSNHqry8vFH/9tKlS5Went6ofxOBhZAInMOqVav0yCOPaN26dfzGGNBIXnrpJV166aV2t4EAl5mZqTZt2tRZP378uF5++WU/dgT412uvvaaMjAytW7dOHTp0sLudZsmbg/3fP6h56NAh9e/f358tNlut7G6gJXnllVf0/vvvq7KyUo899pgSExNr1T0ejxYuXKijR49q4cKFNnVZf3379tWjjz6q9957T6WlpZo+fboSExO1bNkylZaWasaMGZKkY8eOKSkpSX/961/Vrl07m7uun02bNumpp55SSEiIhgwZIklasmSJ/v73v6ugoEBvvvmmXnvtNZu7PLe+fftqypQp2rx5s0pLSzV//nx99NFH2rp1q06dOqUlS5aoZ8+ekqS1a9fqzTfflNvtVocOHTRnzpxmFYLrem+lp6fr+eefV1VVldxutx588EENHTpUkvTss88qOztbISEhCgoK0sqVK9WpUyft2bNHGRkZ1tmI6dOn68Ybb7Rt2/5TXfuNuvo+cOCAFixYoCNHjkiSJkyYoFGjRtnWvyQ9/vjjKigoUE1NjS699FL98Y9/rFU/fvy4Hn74YSUkJOjee+9VQkKCli9frj59+qi4uFjz58/X4cOHVVVVpaFDh+rBBx+UpGa5rfC/11577Qfjj3R6n/ePf/xDbdu21dy5c/Xxxx8rODhY7dq106pVqzR37lyVlZVpxIgRatu2rVatWqWvv/5aqampKikpUatWrfToo49q4MCBNm9h0zrbvqRTp05asGCBTp48qXbt2mnmzJm66qqrdOjQId1+++0aM2aMtmzZooqKCi1YsEBxcXFyuVx6/PHH5XK5JEnXX3+9tY9+8cUXtWnTJrndbkVFRWnevHmKiIjQ0qVLVVBQoLKyMh08eFCXXnqplixZorZt22rz5s1asmSJHA6H3G63Zs+erf79+9faP0iybvfq1eusr3OgaazXY8qUKTp48KCmT5+uK6+8Un/+85+1bt06vfLKK5KkSy+9VHPnzlV4eLgk6YUXXlB2draCgoLUrl07vfnmm3K5XHrsscd04sQJVVVVadCgQZo+fbptz01TOHOw/5ZbbrG7lcBj0Cj69Oljli5daowx5sCBAyY+Pt4cPXrUqrlcLjNp0iSzcOFC4/F4jDHGPPHEE+a1116zrWdv9enTx+pz9+7d5sYbbzTGGPPNN9+Yn//856ampsYYY8zKlStNSkqKbX366siRIyY+Pt4cOHDAGGPMiy++aPr06WPKy8vNuHHjTG5urs0deqdPnz7m9ddfN8YYs3HjRhMbG2v1/uKLL5rHH3/cGGPMrl27zP3332+qqqqMMcZ88MEHZvTo0cYYY95++20zadIkG7qvra73VmlpqTl16pQx5vTrNmDAAFNaWmqOHTtmrr32WlNRUWGMMaasrMzU1NSYY8eOmRtuuMH8/e9/N8YYc+rUKVNaWmrPRp1FXfuNuvquqakxQ4YMMRs3brT+RklJiS29f5/L5bL+/dRTT5lFixaZjz/+2IwaNcocOnTIjBo1yvzlL3+x7nPTTTeZffv2GWOMuffee83OnTuNMcZUVVWZu+66y3z44YfNdlvhX3WNP2dq5eXl5rPPPjNJSUnG7XYbY4z1f/zgwYMmPj6+1t+74447zOrVq40xxuzfv9/Ex8fXev+2NGfblxw5csQMGjTIfPTRR8YYY7Zt22YGDRpkqqqqzMGDB02fPn2ssSMzM9MaH1599VUze/Zs62+feZ7XrVtnZs2aZT3/b7zxhnnssceMMcY888wz5uabbzbfffed8Xg85je/+Y35n//5H2OMMcOGDTP/+Mc/rL7KysqMMbX3D9+/XdfrHEga8/UwpvZztW/fPvPzn//cFBUVGWOMefrpp83kyZONMca888475le/+pX1HJ/Zl1ZWVpry8nJjjDHV1dXm7rvvNlu2bDHGnH7tFi5c2KTPR1NbsGCBiY2NNQkJCWbcuHFmy5YtZsSIESY5OdmMHz/efPXVV8YYY41Xxvxwv1HXOo8++qg1Pr344ovmmmuusT6f3HLLLebLL7+ss6+63uNn/r1o0SIzatQo88tf/tLWnMCZxEZ05513SpIuv/xyXXHFFdq7d68GDx4sSZo4caKGDh2q++67z84WfXbrrbdKkmJjY1VcXKyqqip169ZNvXr10pYtWzR48GCtXbtWf/jDH2zutP4++eQTXXHFFdaZtNGjR2vx4sU2d+WbM0fKrrzySknSTTfdJEnq16+f3nvvPUlSbm6uvvjiC+v9aozR8ePHbei2bnW9t0pKSjRjxgx9/fXXcjqd+u6771RQUKCf/OQnuvTSS62zbb/4xS/UoUMH7d27Vz179tQ111wjSXI6nercubPNW1fb2fYbTqfzrH3v379fp06dqnVEtEuXLrb0/X2ZmZnKyspSTU2NTp48qR49emjAgAE6cuSIxo8fr/T0dMXFxf1gvZMnT2rnzp0qKSmxlp04cUIHDhxQZGRks9xW+N/Zxp+QkBCrfskll+jUqVOaOXOm+vfvb+33/lN5ebk+//xz3X777ZKkXr16KSYmRnv37lVCQkLTb4gNzrYPdLlcat26ta6//npJ0g033KDWrVuroKBA7du3V7t27aznMDY21vpe2tVXX60VK1YoPT1d8fHx1oyM3Nxc5eXlWWf5z8xQOePGG29Up06dJElXXXWV9X3k6667Tn/60580ZMgQDRw40DpzWBdvX+fmrDFfj/+0Y8cODRo0SJGRkZKkMWPGaMSIEZKkv/71r7rrrrus1+XMvtTtdisjI0N79uyRMUZHjx7VF1980WLOrs+YMUOff/65JkyYoKuuukpDhw7V66+/rl69emnNmjWaOnWq1qxZU+f6LpdL06dPP+s6119/vbZv365bbrlFH3/8sXr37q1PP/1U3bp108mTJ3XZZZf53LfL5dI777yjo0ePauTIkYqLi9OPf/xjn/+er/hOop/0799fW7duVUVFhd2t+OTMgOx0OiVJp06dkiSNGjVK69at0759+1RWVnbWD4LwnzOvk8PhUHBwsLXc4XBYr5kxRrfffrsyMzOVmZmp9evX64MPPrCj3XM623trzpw5io+PV1ZWljIzM9W1a1dVVVXJ6XRq9erVGjdunL799lvddttt+uKLL+zehAvC7t279dZbb+nll19WVlaWpkyZourqaklS586dddlll+lvf/vbWdf1eDwKCgrS//7v/1rvx82bN2v8+PH+3AQ0c3WNP2d07NhRGzZs0K233qp9+/Zp6NCh1hRl1F9dY8dPf/pTrV27Vv369VNmZqb1/9QYo4ceesj6P5ydnV1rGuj3A73T6ZTb7ZZ0+gP8vHnz1Lp1a02ePFmrV6+27uPxeKx1qqqqJF24r3Ndr0djePXVV3X8+HGtWbNGWVlZ+uUvf2k93y3NJ598oh//+Mfq1auXJOn222/X559/fs4L/pxrneuuu07bt29XdXW1vv32W/3qV7/SRx99pI8++qjB32m84447JEkXXXSRfvGLX2jnzp0N+nu+IiQ2orfffluS9NVXXyk/P1+xsbFW7eGHH9YNN9yg++67r9GvQGWnIUOGaNeuXXr11Vc1atQoBQUF2d1SvcXGxio/P19fffWVJJ3zqFJLkJCQoMzMTH377beSTh9JzMvLs7mrHzrbe6usrEzdu3dXUFCQtm3bpq+//lrS6TMEJSUlio+P1yOPPKI+ffpo//79io2N1YEDB7Rnzx5Jp7f1u+++s3OzfuBs+426+r7sssvUqlUr/eUvf7HWP3bsmC19n3H8+HF16NBBoaGhqq6utrZHOv3hZtmyZfq///s/zZ8/X8aYWut26NBB1157rV588UVrWWFhoY4cOdIstxXNU0lJiSoqKjRgwABNnTpVHTt21MGDB9WhQwdVVlZaH6o7dOigmJgYrV27VtLp77x+8cUXtcbqluZs+5Lw8HDV1NTo448/liRt375dp06dOu+ZjzPP6dChQ/WHP/xBn332mTwejxISEvTmm29a+9bq6mqvDtJ9+eWX6tu3r+655x4NHz5cn376qaTT36U78+/t27fr6NGjkup+nQNJY74e/6l///7asmWLFZxXr16tG264QdLpWUVvvfWW9fnzzL60rKxMERERCgkJUVFRkd5///1G2c4LwSWXXCKPx6MNGzYoNjbWOrP48ccfW2eF61LXgZDmhummjcjtdmvkyJGqqKio9WXhMx544AG1adNG9957b4u54lrbtm01ePBgvfPOOwG7cwkPD9e8efP04IMPqk2bNtaFa1qqn/3sZ5oyZYoeeughud1u1dTUKCkpSf369bO7tVrO9t56/PHH9eSTT2rp0qX6yU9+or59+0o6HRInTZqkyspKGWN0xRVXaMiQIQoJCdHSpUu1cOFCnTx5Ug6HQ0888YQ1cDYHde036up72bJlmjt3rpYtW6agoCBNmDBBI0eOtK3/AQMGaP369UpMTFSXLl0UFxdnfcCTTgfFZ555RtOmTdPs2bM1d+7cWusvXrxYf/rTnzRs2DBJUvv27bVgwQJFREQ0u21F81RYWKjZs2fr1KlTcrvdGjhwoGJjY+VwODRs2DANGzZMnTt31qpVq7R48WKlpqZqxYoVatWqlTIyMhQWFmb3JjSZ0NDQs+5LnnnmmVoXSlmyZEmtM1Zns3PnTq1YsUIOh0Mej0dPPvmkHA6HRo4cqdLSUo0bN07S6TOLd91113mnx/35z3+2vjpw5sItkjR58mTrJ8Kuu+46devWTVLdr3MgaczX4z/16dNHU6dO1YQJEySdDjFn9rcjR45UUVGRRo8erVatWqldu3Z64403dPfdd2vy5MlKTk5WVFTUecNNIIuNjdWMGTN04MAB9ezZU2vXrtUVV1xxzqvCnm+d6667TkuXLtXjjz+u6OholZaWqqCgQI8++ug5ezlzIOTHP/5xrQMhZ6xdu1bXXnutSkpKtGXLFt19990NfwJ8EGT+89AuAAAAAAS4u+++WxMmTNBNN92kv/3tb3r66ad16tQphYWFae7cufrRj36kHTt2KD09Xe+88451RdkdO3ZIUp3rSFJ2dramTp2qbdu2KTw8XHPmzNH27duVk5Nzzp7++c9/KiUlRa1bt9Z1112nTZs26YUXXlCfPn2UkJCgoUOHatu2bSorK9M999xjHYDxN0IiAAAAANjsP38Cxk58JxEAAAAAYOFMIgAAAAA0ki1btuipp576wfLHHntMgwYNsqGj+iMkAgAAAAAsTDcFAAAAAFgIicAFICUlRU8//bTdbQAAACAAEBIBAAAQkDgICjQNQiIAAAAuOIcOHVLfvn116tQpu1sBmh1CItAC5efna9SoUfrpT3+qKVOmqKqqSpL04osv6s4777QGxDfffFNDhw616gAAAAAhEWhhqqur9fvf/14jRozQzp07lZSUpE2bNkmSJk6cqODgYD3//PP66quv9PTTT2vRokUKCQmxuWsAAM6vMQ+Cjhs3TpL0s5/9TD/96U+1c+dOxcfHa9++fdZ9XC6Xrr76apWUlGjHjh0aOHCgli9frv79+yshIUHr16+37ltdXa309HT94he/0A033KDU1FRVVlY2xdMANDlCItDCfPLJJ6qpqdE999yj1q1bKykpST/5yU8kSQ6HQ+np6Xrttdf00EMPaeLEibriiits7hgAgPNr7IOgr7/+uiRp165d2rNnj+Lj43XrrbfWCn7Z2dm6/vrrFRYWJkk6evSojh07pq1bt2rhwoVKTU3Vl19+KUlavHixCgoKtG7dOm3atEnFxcV67rnnmurpAJoUIRFoYYqLixUVFaWgoCBrWbdu3ax/X3zxxerfv7+++eYb/frXv7ajRQAA6s0fB0FHjRqlDRs26MzPiGdmZmr48OG17jN58mQFBwcrPj5egwYN0l/+8hcZY7R69WrNmDFDoaGh6tChg377299qw4YNDd9wwAat7G4AQOOKiIhQUVGRjDFWUDx8+LAuueQSSdIHH3ygPXv26Prrr1dGRobmzp1rZ7sAAHjF24OgW7Zs8fkg6NVXX602bdpox44dioiI0L///W8NHjzYqnfq1Ent2rWr9fjFxcUqKSlRRUWFbrvtNqtmjJHH4/GpD8BunEkEWpjY2Fi1atVKK1euVE1NjTZt2qRPP/1UklRSUqJZs2ZpwYIFWrhwoXJzc7VlyxabOwYA4Py+fxD0jMOHD1v//s+DoOfz/bD5faNGjdL69eu1fv16JSYm1pqyevz4cZ08edK6XVhYqMjISHXp0kVt2rTRhg0btHv3bu3evVt///vftWfPHl82FbAdIRFoYYKDg7V06VKtXbtW8fHx2rhxo26++WZJUmpqqhISEjRo0CB16dJFCxYs0MyZM3Xs2DGbuwYA4Nwa+yBoWFiYHA6HDh48WGv58OHDtXnzZq1fv14jR478wXpLly5VdXW1du/erQ8++EBJSUlyOBy688479cc//lEul0uSVFRUpK1btzbS1gP+FWS+fzgGAAAAaKY+/fRTzZ49W19//bUGDRokSfrRj36kAwcOKCwszPoKxZYtWzRz5kxlZWWpS5cudf69JUuW6K233tKpU6f08ssvKzY2VpJ077336t///rfef/9964zjjh07NG3aNN11111asWKF2rZtqylTplhBsqqqSs8995w2bNigY8eOKSoqSnfddZfGjx/flE8J0CQIiQAAAMD3/OEPf1BkZKQeffRRa9mZkPi3v/3Nxs4A/+DCNQAAAMD/d+jQIb333ntau3at3a0AtiEkAgAAoEVav3690tLSfrC8W7duZ/15iv/6r//Sf//3f+uBBx6wrgoOXIiYbgoAAAAAsHB1UwAAAACAhZAIAAAAALAQEgEAAAAAFkIiAAAAAMBCSAQAAAAAWAiJAAAAAADL/wPEQwUKtRg4VQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIn5Z_jILDC5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7c97431-0332-4023-b42b-420744da1c88"
      },
      "source": [
        "# Import necessary libs\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D,Dense,Dropout\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.regularizers import l2\n",
        "from keras import optimizers\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "!pip install -U efficientnet\n",
        "from keras import layers\n",
        "import efficientnet.keras as efn\n",
        "from keras import regularizers\n",
        "from keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
        "from keras import applications"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: efficientnet in /usr/local/lib/python3.6/dist-packages (1.1.1)\n",
            "Requirement already satisfied, skipping upgrade: scikit-image in /usr/local/lib/python3.6/dist-packages (from efficientnet) (0.16.2)\n",
            "Requirement already satisfied, skipping upgrade: keras-applications<=1.0.8,>=1.0.7 in /usr/local/lib/python3.6/dist-packages (from efficientnet) (1.0.8)\n",
            "Requirement already satisfied, skipping upgrade: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (3.2.2)\n",
            "Requirement already satisfied, skipping upgrade: imageio>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (2.4.1)\n",
            "Requirement already satisfied, skipping upgrade: pillow>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (7.0.0)\n",
            "Requirement already satisfied, skipping upgrade: scipy>=0.19.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: PyWavelets>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (1.1.1)\n",
            "Requirement already satisfied, skipping upgrade: networkx>=2.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (2.5)\n",
            "Requirement already satisfied, skipping upgrade: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (2.10.0)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.18.5)\n",
            "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (1.3.1)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.4.7)\n",
            "Requirement already satisfied, skipping upgrade: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.0->scikit-image->efficientnet) (4.4.2)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from h5py->keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGfM4ClTQnw4"
      },
      "source": [
        "# Set 0 if you want to use EfficientNet based model.\r\n",
        "# Set 1 if you want to use InceptionV3 based mofel.\r\n",
        "model_id = 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYvr_imgaiQw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7549d42-f0f6-4e94-a733-e5e065099ff7"
      },
      "source": [
        "#-------------------------------------------------------------------------------------------------------------\n",
        "#-------------------------------------------------------------------------------------------------------------\n",
        "#------------------------------------------- LOAD DATA -------------------------------------------------------\n",
        "#-------------------------------------------------------------------------------------------------------------\n",
        "#-------------------------------------------------------------------------------------------------------------\n",
        "# Load data in batches with an ImageDataGenerator\n",
        "\n",
        "datagen_train = ImageDataGenerator(\n",
        "                    featurewise_center=False, \n",
        "                    samplewise_center=False,\n",
        "                    featurewise_std_normalization=False, \n",
        "                    samplewise_std_normalization=False,\n",
        "                    zca_whitening=False, \n",
        "                    rotation_range=40,\n",
        "                    width_shift_range=0.1,\n",
        "                    height_shift_range=0.1,\n",
        "                    brightness_range=None, \n",
        "                    shear_range=0.2,\n",
        "                    zoom_range=0.2,\n",
        "                    channel_shift_range=0.0, \n",
        "                    fill_mode='nearest', \n",
        "                    cval=0.0, \n",
        "                    horizontal_flip=False,\n",
        "                    vertical_flip=False,\n",
        "                    rescale=1.0/255.0, \n",
        "                    preprocessing_function=None,\n",
        "                    data_format=None, \n",
        "                    validation_split=0.0, \n",
        "                    dtype=None)\n",
        "\n",
        "datagen_test = ImageDataGenerator(\n",
        "                    rescale=1.0/255.0)\n",
        "\n",
        "# Shape of the images (lxl)\n",
        "if model_id is 0:\n",
        "  l = 224\n",
        "else:\n",
        "  l = 299\n",
        "\n",
        "# Iterators for each dataset\n",
        "train_it = datagen_train.flow_from_directory(train_ds, class_mode='binary', batch_size=16, target_size= (l, l))\n",
        "val_it = datagen_test.flow_from_directory(valid_ds, class_mode='binary', batch_size=16, target_size= (l, l))\n",
        "test_it = datagen_test.flow_from_directory(test_ds, class_mode='binary',batch_size=16, target_size=(l, l))\n",
        "\n",
        "batchX, batchy = train_it.next()\n",
        "print('Batch shape=%s, min=%.3f, max=%.3f' % (batchX.shape, batchX.min(), batchX.max()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 7128 images belonging to 2 classes.\n",
            "Found 222 images belonging to 2 classes.\n",
            "Found 222 images belonging to 2 classes.\n",
            "Batch shape=(16, 299, 299, 3), min=0.000, max=1.000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rLEc56G9V5T3"
      },
      "source": [
        "# Build the model\r\n",
        "\r\n",
        "if model_id == 0: \r\n",
        "  base_model = efn.EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\r\n",
        "else: \r\n",
        "  base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(299, 299, 3))\r\n",
        "\r\n",
        "model = base_model.output\r\n",
        "model = GlobalAveragePooling2D()(model)\r\n",
        "model = Dense(128, activation='relu')(model)\r\n",
        "predictions = Dense(1, activation='sigmoid')(model)\r\n",
        "model = Model(inputs=base_model.input, outputs=predictions)\r\n",
        "\r\n",
        "# Unfreeze the layers\r\n",
        "for layer in model.layers[0:]:\r\n",
        "layer.trainable = True\r\n",
        "\r\n",
        "# Set learning rate\r\n",
        "if model_id == 0:\r\n",
        "  my_lr = 0.0001\r\n",
        "else:\r\n",
        "  my_lr = 0.00001\r\n",
        "\r\n",
        "optimizer=optimizers.Adam(lr=my_lr)\r\n",
        "model.compile(optimizer=optimizer,loss=\"binary_crossentropy\", metrics=['accuracy']) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9DHpwcDEL9cG"
      },
      "source": [
        "# Visualizing the model \n",
        "\n",
        "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkDGXui9IDfw"
      },
      "source": [
        "# Callback functions\n",
        "\n",
        "# Earlystop\n",
        "earlystop_callback = EarlyStopping(monitor = 'val_loss',\n",
        "                          min_delta = 0,\n",
        "                          patience = 3,\n",
        "                          verbose = 1,\n",
        "                          restore_best_weights = True)\n",
        "\n",
        "# Save the most accurate model's weights\n",
        "if model_id == 0: cp = checkpoints_effnet\n",
        "else: cp = checkpoints_inception\n",
        "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "                          filepath=cp,\n",
        "                          save_weights_only=True,\n",
        "                          monitor='val_accuracy',\n",
        "                          mode='max',\n",
        "                          save_best_only=True)\n",
        "\n",
        "# Modify LR during train\n",
        "lr_callback = ReduceLROnPlateau(monitor='val_accuracy', factor=0.2, patience=3, min_lr=0.000005)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xR812y0bH-pb"
      },
      "source": [
        "# Train the model\n",
        "\n",
        "my_callbacks = [\n",
        "                earlystop_callback,\n",
        "                checkpoint_callback,\n",
        "                # lr_callback\n",
        "]\n",
        "\n",
        "h = model.fit(train_it, epochs = 50, validation_data = val_it, callbacks = my_callbacks, shuffle = True, steps_per_epoch = 100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NXGpXhbHLj6P",
        "outputId": "f1d901b1-2e5a-40b6-8f2d-47411e67fa2b"
      },
      "source": [
        "# Load the model\n",
        "\n",
        "if model_id == 0:\n",
        "  dir = best_weights_effnet\n",
        "else:\n",
        "  dir = best_weights_inception\n",
        "\n",
        "if len(os.listdir(dir)) == 0:\n",
        "  print(\"There are no previous checkpoints. Make sure you use the correct directory.\")\n",
        "else:\n",
        "  filename = os.listdir(dir)[-1]\n",
        "  best_weight = dir + filename\n",
        "  print(best_weight)\n",
        "  model.load_weights(best_weight)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/Egyetem/Deep learning a gyakorlatban/checkpoints/effnet/bestcheckpoint_10_0.375_0.847.hdf5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3eIdxIRMj75",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3432db62-cdbe-4f0e-d356-57d73c2780be"
      },
      "source": [
        "# Evaluate the model \n",
        "\n",
        "results = model.evaluate(test_it, batch_size=16)\n",
        "print(\"Test loss: {:.4f}\".format(results[0]))\n",
        "print(\"Test accuracy: {:.4f}\".format(results[1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "14/14 [==============================] - 3s 183ms/step - loss: 0.3056 - accuracy: 0.8874\n",
            "Test loss: 0.3056\n",
            "Test accuracy: 0.8874\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDcssmwhMJ83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "0d1de615-4527-4ac3-dc76-6fa1cf31c468"
      },
      "source": [
        "# Plotting train loss and validation loss\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(h.history['loss'],color=\"olive\", label='loss')\n",
        "plt.plot(h.history['val_loss'], color=\"plum\", label='validation loss')\n",
        "plt.legend(loc='best')\n",
        "plt.title(label=\"Loss and Validation Loss\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss and Validation Loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ziKr28gMPxk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "68d4dda6-a999-4578-feda-3747c2744edb"
      },
      "source": [
        "# Plotting train accuracy and validation accuracy\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(h.history['accuracy'],color=\"mediumspringgreen\", label='accuracy')\n",
        "plt.plot(h.history['val_accuracy'], color=\"black\", label='validation accuracy')\n",
        "plt.legend(loc='best')\n",
        "plt.title(label=\"Accuracy and Validation Accuracy\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy and Validation Accuracy')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}