Multi_Class_LBP_.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1ec580fziIccr76qwnITArZXtKHibURC1",
      "authorship_tag": "ABX9TyPoOVBuqpKmsZtESsVXGlpo",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RuthKassahun/Skin-Lesion-Classification/blob/main/Multi_Class_LBP_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LBP Multi class\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "uZZBnPHNLLVZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Libraries"
      ],
      "metadata": {
        "id": "VEyKRvtkLVv6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "C6XWF4DqCFRw"
      },
      "outputs": [],
      "source": [
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "import cv2\n",
        "import os\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "from skimage.filters import sobel\n",
        "from skimage.feature import greycomatrix, greycoprops\n",
        "from skimage.measure import shannon_entropy\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from skimage.feature import local_binary_pattern\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler\n",
        "from sklearn import metrics\n",
        "from pathlib import Path\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb\n",
        "from PIL import Image, ImageOps\n",
        "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
        "from sklearn.metrics import accuracy_score, auc, roc_curve, roc_auc_score, recall_score, confusion_matrix\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Data"
      ],
      "metadata": {
        "id": "6fxJw8o3LfvZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SIZE = 224"
      ],
      "metadata": {
        "id": "XxVgvMwHCrME"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_lbp(arr):\n",
        "  # LBP function params\n",
        "  radius = 3\n",
        "  n_points = 8 * radius\n",
        "  n_bins = n_points + 2\n",
        "  lbp = local_binary_pattern(arr,n_points,radius,'uniform')\n",
        "  lbp = lbp.ravel()\n",
        "  #feature_len = int(lbp.max() + 1)\n",
        "  feature = np.zeros(n_bins)\n",
        "  for i in lbp:\n",
        "    feature[int(i)] += 1 # Feature\n",
        "  feature /= np.linalg.norm(feature, ord=1) # Normalizing feature\n",
        "  return feature"
      ],
      "metadata": {
        "id": "oGuoxk8GIz_K"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_images = []\n",
        "train_labels = [] \n",
        "hu_features = []\n",
        "#for directory_path in glob.glob(\"cell_images/train/*\"):\n",
        "for directory_path in glob.glob(\"/content/drive/MyDrive/Multiclass/multi_train/*\"):\n",
        "    label = directory_path.split(\"/\")[-1]\n",
        "    #print(label)\n",
        "    for img_path in glob.glob(os.path.join(directory_path, \"*.jpg\")):\n",
        "        img = cv2.imread(img_path,0) #Reading color images\n",
        "        img = cv2.resize(img, (SIZE, SIZE)) #Resize images\n",
        "        arr = np.array(img) # convert the image to numpy array\n",
        "        feature = compute_lbp(arr)\n",
        "        train_images.append(feature)\n",
        "        train_labels.append(label)"
      ],
      "metadata": {
        "id": "LMC30n42p6zS"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.unique(train_labels))"
      ],
      "metadata": {
        "id": "u-QOj9ELwCdk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b939df4d-701a-42ca-bb6b-886fc863f66e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['bcc' 'mel' 'scc']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_images = []\n",
        "test_labels = []\n",
        "hu_features_test = []\n",
        "\n",
        "#for directory_path in glob.glob(\"cell_images/test/*\"): \n",
        "for directory_path in glob.glob(\"/content/drive/MyDrive/Multiclass/multi_val/*\"):\n",
        "    test_label = directory_path.split(\"/\")[-1]\n",
        "    for img_path in glob.glob(os.path.join(directory_path, \"*.jpg\")):\n",
        "        img = cv2.imread(img_path,0) #Reading color images\n",
        "        img = cv2.resize(img, (SIZE, SIZE)) #Resize images\n",
        "        arr = np.array(img) # convert the image to numpy array\n",
        "        feature = compute_lbp(arr)\n",
        "        test_images.append(feature)\n",
        "        test_labels.append(test_label)\n"
      ],
      "metadata": {
        "id": "OmGYCrkIqj9i"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Label Encoding"
      ],
      "metadata": {
        "id": "CerVmwjfLvEs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "le.fit(test_labels)\n",
        "test_labels_encoded = le.transform(test_labels)\n",
        "le.fit(train_labels)\n",
        "train_labels_encoded = le.transform(train_labels)"
      ],
      "metadata": {
        "id": "Nx-a7VFwrE3R"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, y_train, x_test, y_test = train_images, train_labels_encoded, test_images, test_labels_encoded"
      ],
      "metadata": {
        "id": "L5aVP0sVLJva"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install imblearn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmIduAqep55m",
        "outputId": "1437c526-33c6-4dac-917a-a2c00c281696"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: imblearn in /usr/local/lib/python3.7/dist-packages (0.0)\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.7/dist-packages (from imblearn) (0.8.1)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn->imblearn) (1.7.3)\n",
            "Requirement already satisfied: scikit-learn>=0.24 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn->imblearn) (1.0.2)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn->imblearn) (1.21.6)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn->imblearn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (3.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from imblearn.under_sampling import RandomUnderSampler"
      ],
      "metadata": {
        "id": "FAfcFFm9p_Q8"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "over=RandomOverSampler(random_state=0,sampling_strategy='minority')\n",
        "X_res,Y_res=over.fit_resample(x_train,y_train)"
      ],
      "metadata": {
        "id": "T65A0HIxqDFd"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(sum(Y_res==0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_OgRUl8rM4w",
        "outputId": "74d7fb7d-19d4-4381-deb4-401b006feed7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1993\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "over=RandomOverSampler(random_state=0,sampling_strategy='minority')\n",
        "X_result,Y_result=over.fit_resample(X_res,Y_res)"
      ],
      "metadata": {
        "id": "LywaTOBvv0sF"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(sum(Y_result==0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ArLPdt2GwEHf",
        "outputId": "8174dd5a-cc22-4ec5-c338-6a64a2f3210d"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2713\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SVM"
      ],
      "metadata": {
        "id": "mRt5SBmbMCBH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipe1 = Pipeline([\n",
        "                 (\"scale\", StandardScaler()),\n",
        "                 (\"classifier\", SVC(class_weight='balanced', probability=True, kernel= 'rbf', gamma=0.01, C=100)) #radial basis function kernel\n",
        "                 ])"
      ],
      "metadata": {
        "id": "8NeblTA2AdM3"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe1.fit(X_result,Y_result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jt-exYkgAeb4",
        "outputId": "01943b64-7b79-44c2-b830-e772f59c26fb"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('scale', StandardScaler()),\n",
              "                ('classifier',\n",
              "                 SVC(C=100, class_weight='balanced', gamma=0.01,\n",
              "                     probability=True))])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test set evaluation\n",
        "prediction = pipe1.predict(x_test)\n",
        "# Visualization\n",
        "cmat = metrics.confusion_matrix(prediction,test_labels_encoded)\n",
        "s = sns.heatmap(cmat,annot=True,xticklabels=label, yticklabels=label)\n",
        "s.set(xlabel='Predicted', ylabel='Actual')\n",
        "acc = cmat.trace() / cmat.sum()\n",
        "print('Accuracy: {0:5.2f}%'.format(acc*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "_Ycj94-_CCiT",
        "outputId": "60749588-f05e-4e02-d5d6-8ccf22e1583c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 56.22%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import cohen_kappa_score\n",
        "kappa = cohen_kappa_score(prediction, test_labels_encoded)\n",
        "print(kappa)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JnB-T1Vk7qw8",
        "outputId": "795c762b-d1cf-4d4b-c3f5-ef4c2ad53412"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.3028564207267227\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest"
      ],
      "metadata": {
        "id": "0hRhX6FrMG3L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipe2 = Pipeline([\n",
        "                 (\"scale\", StandardScaler()),\n",
        "                 (\"classifier\", RandomForestClassifier(n_estimators = 100, random_state = 42))\n",
        "                 ])"
      ],
      "metadata": {
        "id": "KX2wmJx8DZrO"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe2.fit(X_result,Y_result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s9Ho7JfnDi5W",
        "outputId": "c673fcc7-dd3b-4c28-e3c4-9f19d63fcaaf"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('scale', StandardScaler()),\n",
              "                ('classifier', RandomForestClassifier(random_state=42))])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test set evaluation\n",
        "prediction = pipe2.predict(x_test)\n",
        "# Visualization\n",
        "cmat = metrics.confusion_matrix(prediction,test_labels_encoded)\n",
        "s = sns.heatmap(cmat,annot=True,xticklabels=label, yticklabels=label)\n",
        "s.set(xlabel='Predicted', ylabel='Actual')\n",
        "acc = cmat.trace() / cmat.sum()\n",
        "print('Accuracy: {0:5.2f}%'.format(acc*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "cz3LJigzDnda",
        "outputId": "34f53d88-ebe8-49b3-8f4c-88e3cf8065be"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 65.91%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kappa = cohen_kappa_score(prediction, test_labels_encoded)\n",
        "print(kappa)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AR0awIlR-hCR",
        "outputId": "464d386f-208c-4282-f1f8-78976cfa7b27"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.35598678971284015\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## KNN"
      ],
      "metadata": {
        "id": "QxrLlQ3uMPr4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipe3 = Pipeline([\n",
        "                 (\"scale\",StandardScaler()),\n",
        "                 (\"classifier\", KNeighborsClassifier(n_neighbors=7))\n",
        "                 ])"
      ],
      "metadata": {
        "id": "XBC_zxYbEj4M"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe3.fit(X_result,Y_result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fX78m5oMEeIF",
        "outputId": "810c61d0-a715-494f-b740-aa279351f6c2"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('scale', StandardScaler()),\n",
              "                ('classifier', KNeighborsClassifier(n_neighbors=7))])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test set evaluation\n",
        "prediction = pipe3.predict(x_test)\n",
        "# Visualization\n",
        "cmat = metrics.confusion_matrix(prediction,test_labels_encoded)\n",
        "s = sns.heatmap(cmat,annot=True,xticklabels=label, yticklabels=label)\n",
        "s.set(xlabel='Predicted', ylabel='Actual')\n",
        "acc = cmat.trace() / cmat.sum()\n",
        "print('Accuracy: {0:5.2f}%'.format(acc*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "LAhKGaG4Eyoh",
        "outputId": "7068ef79-c569-4619-f8c4-52ea0f5e69b1"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 52.99%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kappa = cohen_kappa_score(prediction, test_labels_encoded)\n",
        "print(kappa)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjjqFKYb_IKT",
        "outputId": "1f7d3da8-681c-4246-b30c-8e36090a9f11"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2422161721317373\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## AdaBoost"
      ],
      "metadata": {
        "id": "lWYVd2MoMW7f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = AdaBoostClassifier(n_estimators=200, random_state=0)\n",
        "clf.fit(X_result,Y_result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z8HWAHCWFBT6",
        "outputId": "4efaf43a-7127-4cfc-f851-97921f3fd8e4"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(n_estimators=200, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test set evaluation\n",
        "prediction = clf.predict(x_test)\n",
        "# Visualization\n",
        "cmat = confusion_matrix(prediction,test_labels_encoded)\n",
        "s = sns.heatmap(cmat,annot=True,xticklabels=label, yticklabels=label)\n",
        "s.set(xlabel='Predicted', ylabel='Actual')\n",
        "acc = cmat.trace() / cmat.sum()\n",
        "print('Accuracy: {0:5.2f}%'.format(acc*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "dHSeEpDxFOxA",
        "outputId": "d20975c2-148a-4fc9-e7ca-6e8d642ae494"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 51.18%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kappa = cohen_kappa_score(prediction, test_labels_encoded)\n",
        "print(kappa)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sl1PpGK7_aqT",
        "outputId": "c323275e-448c-40fb-c446-96891771be0c"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.19424160062872486\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## XGB"
      ],
      "metadata": {
        "id": "QfIx7G5NMdo7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mod1 = xgb.XGBClassifier(booster='dart',learning_rate=0.1,objective='multi:softmax')\n",
        "train_arr = np.array(X_result)\n",
        "test_arr = np.array(Y_result)\n",
        "mod1.fit(train_arr, test_arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6l_-aSAGbrJ",
        "outputId": "727050e4-03fd-4b37-f821-ddcd49a3e4ab"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(booster='dart', objective='multi:softprob')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = mod1.predict(x_test)\n",
        "print(\"Accuracy = \", accuracy_score(test_labels_encoded, prediction))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "haEaOdTlH2YX",
        "outputId": "5bae4ea2-dd51-4a41-fdea-c519a956b758"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy =  0.5393700787401575\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(test_labels_encoded, prediction)\n",
        "sns.heatmap(cm, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "tN55xdyAISLT",
        "outputId": "ef31ced3-fdd0-40dd-c6ae-7a266a9669c8"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa58236e50>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kappa = cohen_kappa_score(prediction, test_labels_encoded)\n",
        "print(kappa)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJ7hNmo0Brm7",
        "outputId": "ead5a41e-7a38-4f23-88eb-c2bd83775d95"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2517282916099298\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "skf = StratifiedKFold(n_splits = 5)"
      ],
      "metadata": {
        "id": "8b7SqgzuzmfO"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining a dictionary\n",
        "model_params = {\n",
        "    'svm': {\n",
        "        'model': SVC(gamma='auto'),\n",
        "        'params' : {\n",
        "            'C': [0.1, 1, 5, 10, 100], #Regularization parameter.\n",
        "            'kernel': ['rbf','linear','sigmoid','poly'], \n",
        "            'gamma':[0.00001,0.0001,0.001,0.01,0.1,1]\n",
        "        }  \n",
        "    },\n",
        "    'random_forest': {\n",
        "        'model': RandomForestClassifier(),\n",
        "        'params' : {\n",
        "            'n_estimators': [30,50,70,100] #Trees: may lead to overfitting if n_estimators is too much\n",
        "        }\n",
        "    },\n",
        "    'KNN' : {\n",
        "        'model': KNeighborsClassifier(),\n",
        "        'params': {\n",
        "            'n_neighbors': [1, 5, 9, 15, 19, 23, 27, 31, 35, 39]\n",
        "        }\n",
        "    }\n",
        "}"
      ],
      "metadata": {
        "id": "eBtuCxPZztYB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores = []\n",
        "\n",
        "for model_name, mp in model_params.items():\n",
        "    grid =  GridSearchCV(estimator=mp['model'], \n",
        "                         param_grid=mp['params'], \n",
        "                         cv=skf, n_jobs=6, #five fold cross validation\n",
        "                         return_train_score=False)\n",
        "    \n",
        "    grid.fit(X_result,Y_result)\n",
        "    \n",
        "    scores.append({\n",
        "        'model': model_name,\n",
        "        'best_score': grid.best_score_,\n",
        "        'best_params': grid.best_params_\n",
        "    })\n",
        "\n",
        "df = pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
        "\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qgYYVxv9zw5b",
        "outputId": "8d66c651-bb13-4b62-cb7c-078359b6a557"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           model  best_score                              best_params\n",
            "0            svm    0.339972  {'C': 10, 'gamma': 1, 'kernel': 'poly'}\n",
            "1  random_forest    0.652664                    {'n_estimators': 100}\n",
            "2            KNN    0.745926                       {'n_neighbors': 1}\n"
          ]
        }
      ]
    }
  ]
}