Purposed_Method_Evaluation.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Purposed Method Evaluation.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmdVvnMI7CcI"
      },
      "source": [
        "# Purposed Method Evaluation for Breast cancer detection\n",
        "\n",
        "*    on [BreaKHis](https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis) dataseet "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zMNH3RI87S9e"
      },
      "source": [
        "## import libraries and connect to drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C4F-cAEZ8UKG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "072f3674-1f45-4a0d-acea-a6443b8cd3b3"
      },
      "source": [
        "!pip install catboost\n",
        "\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import time\n",
        "import glob\n",
        "import os\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "from IPython.display import HTML, display, clear_output\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, balanced_accuracy_score, f1_score, confusion_matrix, roc_auc_score, auc, roc_curve, precision_recall_curve\n",
        "from lightgbm import LGBMClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from catboost import CatBoostClassifier\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import tensorflow.keras.applications as k_apps\n",
        "print(tf.version)\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "\n",
        "size = (460,700)\n",
        "working_dir = 'drive/MyDrive/article-97'\n",
        "InceptionResNetV2 = {\n",
        "    'model': k_apps.inception_resnet_v2.InceptionResNetV2, 'preprocessor': k_apps.inception_resnet_v2.preprocess_input, 'activation': 'conv_7b_ac',\n",
        "}\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting catboost\n",
            "  Downloading catboost-1.0.4-cp37-none-manylinux1_x86_64.whl (76.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 76.1 MB 1.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (5.5.0)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.21.5)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.3.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (3.0.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.2)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (8.0.1)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-1.0.4\n",
            "<module 'tensorflow._api.v2.version' from '/usr/local/lib/python3.7/dist-packages/tensorflow/_api/v2/version/__init__.py'>\n",
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yaEWsU3dnC8u"
      },
      "source": [
        "## Reading images and extracting features "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_seed = 0\n",
        "np.random.seed(random_seed)\n",
        "random.seed(random_seed)\n",
        "tf.random.set_seed(random_seed)\n",
        "\n",
        "\n",
        "X = {\"breast_40\":[], \"breast_100\":[], \"breast_200\":[], \"breast_400\":[]}\n",
        "y = {\"breast_40\":[], \"breast_100\":[], \"breast_200\":[], \"breast_400\":[]}\n",
        "\n",
        "\n",
        "def get_features(mag):\n",
        "  pre_extracted_file = f'{working_dir}/extracted_features/InceptionResNetV2-{mag}.txt'\n",
        "  if os.path.isfile(pre_extracted_file):\n",
        "    print(f'[ {mag} pre-extracted features file was FOUND] : {pre_extracted_file}')\n",
        "    with open(pre_extracted_file , 'r') as f:\n",
        "      l = f.readline()\n",
        "      while l != '':\n",
        "          X[mag].append([float(x) for x in l.split()])\n",
        "          l = f.readline()\n",
        "      X[mag] = np.array(X[mag])\n",
        "      y[mag] = X[mag][:, -3]\n",
        "      X[mag] = X[mag][:, :-3]\n",
        "\n",
        "  else:\n",
        "    print(f'[ {mag} pre-extracted features file NOT FOUND]')\n",
        "\n",
        "    model = InceptionResNetV2['model'](input_shape=size+(3,), weights=None, pooling='max')\n",
        "    preprocess_input = InceptionResNetV2['preprocessor']\n",
        "    print(f'[start reading images and extracting features] \\nfeture extractor: {featureExt}, {model} \\npreprocessor    : {preprocess_input}\\n')\n",
        "\n",
        "    paths = glob.glob(f'{working_dir}/tf_files/{mag}/*/*')\n",
        "\n",
        "    out = display(HTML(\"<progress style='width: 50%'/>\"), display_id=True)\n",
        "    n = len(paths)\n",
        "    for i in range(n):\n",
        "        img = image.load_img(str(paths[i]), target_size=size)\n",
        "        img = image.img_to_array(img)\n",
        "        img = preprocess_input(img) \n",
        "        img = np.expand_dims(img, axis=0)\n",
        "        features = model.predict(img)\n",
        "\n",
        "        X[mag].append(features[0])\n",
        "        y[mag].append(1 if paths[i].split('/')[-1].split('_')[1] == 'M' else 0)\n",
        "\n",
        "        out.update(HTML(f\"[{i+1}/{n} images are processed] <br><progress value={i+1} max={n} , style='width: 60%'/></progress>  [{(i+1)/n*100:.2f}]%\"))\n",
        "\n",
        "    # convert to np.array\n",
        "    X[mag] = np.array(X)\n",
        "    y[mag] = np.array(y[mag])\n",
        "\n",
        "  print(f\"==> available features on {mag} :   X : {X[mag].shape}   y : {y[mag].shape}\\n\")\n",
        "\n",
        "get_features(\"breast_40\")\n",
        "get_features(\"breast_100\")\n",
        "get_features(\"breast_200\")\n",
        "get_features(\"breast_400\")\n"
      ],
      "metadata": {
        "id": "3Mq9Zyxtj_Mk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92a9f741-b962-4cc1-e3db-f7c1ca77b7f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ breast_40 pre-extracted features file was FOUND] : drive/MyDrive/article-97/extracted_features/InceptionResNetV2-breast_40.txt\n",
            "==> available features on breast_40 :   X : (1995, 1000)   y : (1995,)\n",
            "\n",
            "[ breast_100 pre-extracted features file was FOUND] : drive/MyDrive/article-97/extracted_features/InceptionResNetV2-breast_100.txt\n",
            "==> available features on breast_100 :   X : (2081, 1000)   y : (2081,)\n",
            "\n",
            "[ breast_200 pre-extracted features file was FOUND] : drive/MyDrive/article-97/extracted_features/InceptionResNetV2-breast_200.txt\n",
            "==> available features on breast_200 :   X : (2013, 1000)   y : (2013,)\n",
            "\n",
            "[ breast_400 pre-extracted features file was FOUND] : drive/MyDrive/article-97/extracted_features/InceptionResNetV2-breast_400.txt\n",
            "==> available features on breast_400 :   X : (1820, 1000)   y : (1820,)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Classification"
      ],
      "metadata": {
        "id": "Jo1zHMOA5RZ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def eval(X, y, rs):\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = rs)\n",
        "  c_probas = []\n",
        "  clfs = [XGBClassifier(learning_rate = 0.34, silent = True),\n",
        "          LGBMClassifier(learning_rate = 0.11, silent = True),\n",
        "          CatBoostClassifier(l2_leaf_reg=3, border_count=128, iterations=1000, depth=7, logging_level='Silent')]\n",
        "\n",
        "  for c in clfs:\n",
        "    c.fit(X_train, y_train)\n",
        "    c_probas.append(c.predict_proba(X_test))\n",
        "\n",
        "  all_probas = ((c_probas[0] + c_probas[1] + c_probas[2]) / 3)[:, 1]\n",
        "  y_pred = all_probas > 0.5\n",
        "\n",
        "  fpr, tpr, _ = roc_curve(y_test, all_probas)\n",
        "  pre, rec, _ = precision_recall_curve(y_test, all_probas)\n",
        "  tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
        "  return [accuracy_score(y_test, y_pred),\n",
        "          balanced_accuracy_score(y_test, y_pred),\n",
        "          f1_score(y_test, y_pred),\n",
        "          auc(fpr, tpr), #ROC AUC\n",
        "          (tn, fp, fn, tp),\n",
        "          (fpr, tpr),\n",
        "          auc(rec, pre), #PRC AUC\n",
        "          (pre, rec)\n",
        "          ]\n",
        "\n",
        "e__40 = eval(X['breast_40'],  y['breast_40'])\n",
        "e_100 = eval(X['breast_100'], y['breast_100'])\n",
        "e_200 = eval(X['breast_200'], y['breast_200'])\n",
        "e_400 = eval(X['breast_400'], y['breast_400'])"
      ],
      "metadata": {
        "id": "zj115Q9wsWFm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(f'''\n",
        "• features extrated by InceptionResNetV2\n",
        "• classification by ensemble of XGBClassifier, LGBMClassifier and CatBoostClassifier\n",
        "\n",
        "                            40×        100×        200×        400×\n",
        "   - accuracy         :   {e__40[0]*100:.2f}       {e_100[0]*100:.2f}       {e_200[0]*100:.2f}       {e_400[0]*100:.2f}\n",
        "   - balanced accuracy:   {e__40[1]*100:.2f}       {e_100[1]*100:.2f}       {e_200[1]*100:.2f}       {e_400[1]*100:.2f}\n",
        "   - F1_score         :   {e__40[2]*100:.2f}       {e_100[2]*100:.2f}       {e_200[2]*100:.2f}       {e_400[2]*100:.2f}\n",
        "   - ROC AUC          :   {e__40[3]:.3f}       {e_100[3]:.3f}       {e_200[3]:.3f}       {e_400[3]:.3f}\n",
        "   - PRC AUC          :   {e__40[6]:.3f}       {e_100[6]:.3f}       {e_200[6]:.3f}       {e_400[6]:.3f}\n",
        "\n",
        "   - Confusion matrix :   40×: | {e__40[4][0]} {e__40[4][1]} |    100×: | {e_100[4][0]} {e_100[4][1]} |    200×: | {e_200[4][0]} {e_200[4][1]} |    400×: | {e_400[4][0]} {e_400[4][1]} |\n",
        "                               | {e__40[4][2]}  {e__40[4][3]} |          | {e_100[4][2]} {e_100[4][3]} |          | {e_200[4][2]}  {e_200[4][3]} |          | {e_400[4][2]}  {e_400[4][3]} |\n",
        "''')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kx8AXPoGl4Cb",
        "outputId": "2f7d6946-aee2-4d0e-d85f-53353f08b48a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "• features extrated by InceptionResNetV2\n",
            "• classification by ensemble of XGBClassifier, LGBMClassifier and CatBoostClassifier\n",
            "\n",
            "                            40×        100×        200×        400×\n",
            "   - accuracy         :   96.83       95.84       97.02       96.15\n",
            "   - balanced accuracy:   95.64       94.90       95.89       95.15\n",
            "   - F1_score         :   97.76       97.03       97.87       97.11\n",
            "   - ROC AUC          :   0.992       0.992       0.993       0.993\n",
            "   - PRC AUC          :   0.997       0.997       0.997       0.996\n",
            "\n",
            "   - Confusion matrix :   40×: | 165 13 |    100×: | 174 14 |    200×: | 172 13 |    400×: | 172 15 |\n",
            "                               | 6  415 |          | 12 425 |          | 5  414 |          | 6  353 |\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matplotlib.rcdefaults()\n",
        "plt.rcParams.update({'xtick.labelsize':10, \n",
        "                     'ytick.labelsize':10,\n",
        "                     'legend.fontsize':10,\n",
        "                     'axes.labelsize' :18})"
      ],
      "metadata": {
        "id": "oMYLGhCSZFk8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (8, 8))\n",
        "\n",
        "plt.plot(e__40[5][0], e__40[5][1], color=\"#AB47BC\", lw=2, label= f\"ROC IRv2-CXL   40×   (AUC = {e__40[3]:.4f})\")\n",
        "plt.plot(e_100[5][0], e_100[5][1], color=\"#FFEB3B\", lw=2, label= f\"ROC IRv2-CXL 100×   (AUC = {e_100[3]:.4f})\")\n",
        "plt.plot(e_200[5][0], e_200[5][1], color=\"#43A047\", lw=2, label= f\"ROC IRv2-CXL 200×   (AUC = {e_200[3]:.4f})\")\n",
        "plt.plot(e_400[5][0], e_400[5][1], color=\"#F44336\", lw=2, label= f\"ROC IRv2-CXL 400×   (AUC = {e_400[3]:.4f})\")\n",
        "plt.plot([0, 1], [0, 1], color=\"navy\", lw=1, linestyle=\"--\")\n",
        "\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "\n",
        "plt.xlabel(\"False Positive Rate\", labelpad = 15)\n",
        "plt.ylabel(\"True Positive Rate\", labelpad = 15)\n",
        "plt.legend(loc=\"lower center\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "3jSh8t6Byp1b",
        "outputId": "657240a1-ccc5-4672-e856-c5be36804b39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (8, 8))\n",
        "\n",
        "plt.plot(e__40[7][0], e__40[7][1], color=\"#AB47BC\", lw=2, label= f\"ROC IRv2-CXL   40×   (AUC = {e__40[6]:.4f})\")\n",
        "plt.plot(e_100[7][0], e_100[7][1], color=\"#FFEB3B\", lw=2, label= f\"ROC IRv2-CXL 100×   (AUC = {e_100[6]:.4f})\")\n",
        "plt.plot(e_200[7][0], e_200[7][1], color=\"#43A047\", lw=2, label= f\"ROC IRv2-CXL 200×   (AUC = {e_200[6]:.4f})\")\n",
        "plt.plot(e_400[7][0], e_400[7][1], color=\"#F44336\", lw=2, label= f\"ROC IRv2-CXL 400×   (AUC = {e_400[6]:.4f})\")\n",
        "plt.plot([0, 1], [0,0], color=\"navy\", lw=1, linestyle=\"--\")\n",
        "\n",
        "plt.xlim([-0.05, 1.05])\n",
        "plt.ylim([-0.05, 1.05])\n",
        "\n",
        "plt.xlabel(\"Recall\", labelpad = 15)\n",
        "plt.ylabel(\"Precision\", labelpad = 15)\n",
        "plt.legend(loc=\"center\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "A_yynC-XkBSu",
        "outputId": "b95de905-615b-440a-9d1b-a9ca9a5aa6b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}