hafizio/data-mining-final-exam-zoo-dataset.ipynb

## data-mining-final-exam-zoo-dataset.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "data-mining-final-exam-zoo-dataset.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "13DpX1yD1KXP-H2CRCcfcTg9ghvzs_zr0",
      "authorship_tag": "ABX9TyPVKnTtv6+uNcCjjb14l1+A",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/hafizio/4c67e97f1fd3d7d8f78f96db0e16a1de/data-mining-final-exam-zoo-dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7RTjrPlYINb"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns; sns.set()\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from numpy import mean\n",
        "from numpy import std\n",
        "\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7eVuZI2YJa59",
        "outputId": "6d6dcc02-876b-4ad6-b4b9-4b3aee396c18"
      },
      "source": [
        "zoo_df = pd.read_csv('/content/drive/MyDrive/WQD7005 - Data Mining/FINAL EXAM/zoo.csv')\n",
        "\n",
        "zoo_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 101 entries, 0 to 100\n",
            "Data columns (total 18 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   Animal name  101 non-null    object\n",
            " 1   Hair         101 non-null    int64 \n",
            " 2   Feathers     101 non-null    int64 \n",
            " 3   Eggs         101 non-null    int64 \n",
            " 4   Milk         101 non-null    int64 \n",
            " 5   Airborne     101 non-null    int64 \n",
            " 6   Aquatic      101 non-null    int64 \n",
            " 7   Predator     101 non-null    int64 \n",
            " 8   Toothed      101 non-null    int64 \n",
            " 9   Backbone     101 non-null    int64 \n",
            " 10  Breathes     101 non-null    int64 \n",
            " 11  Venomous     101 non-null    int64 \n",
            " 12  Fins         101 non-null    int64 \n",
            " 13  Legs         101 non-null    int64 \n",
            " 14  Tail         101 non-null    int64 \n",
            " 15  Domestic     101 non-null    int64 \n",
            " 16  Catsize      101 non-null    int64 \n",
            " 17  Type         101 non-null    int64 \n",
            "dtypes: int64(17), object(1)\n",
            "memory usage: 14.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Q2g1NRMZYR8U",
        "outputId": "d70765fc-498d-4ad2-a4e3-642d2e110b78"
      },
      "source": [
        "zoo_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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>Animal name</th>\n",
              "      <th>Hair</th>\n",
              "      <th>Feathers</th>\n",
              "      <th>Eggs</th>\n",
              "      <th>Milk</th>\n",
              "      <th>Airborne</th>\n",
              "      <th>Aquatic</th>\n",
              "      <th>Predator</th>\n",
              "      <th>Toothed</th>\n",
              "      <th>Backbone</th>\n",
              "      <th>Breathes</th>\n",
              "      <th>Venomous</th>\n",
              "      <th>Fins</th>\n",
              "      <th>Legs</th>\n",
              "      <th>Tail</th>\n",
              "      <th>Domestic</th>\n",
              "      <th>Catsize</th>\n",
              "      <th>Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>aardvark</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>antelope</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>bass</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>bear</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>boar</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Animal name  Hair  Feathers  Eggs  Milk  ...  Legs  Tail  Domestic  Catsize  Type\n",
              "0    aardvark     1         0     0     1  ...     4     0         0        1     1\n",
              "1    antelope     1         0     0     1  ...     4     1         0        1     1\n",
              "2        bass     0         0     1     0  ...     0     1         0        0     4\n",
              "3        bear     1         0     0     1  ...     4     0         0        1     1\n",
              "4        boar     1         0     0     1  ...     4     1         0        1     1\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "UIw15-u6Yb5T",
        "outputId": "ed341891-6443-420b-921e-c27462c9e011"
      },
      "source": [
        "correlation_matrix = zoo_df.corr()\n",
        "sns.heatmap(correlation_matrix, annot = True)\n",
        "plt.rcParams['figure.figsize'] = (200,100)\n",
        "plt.show()\n",
        "print(correlation_matrix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "              Hair  Feathers      Eggs  ...  Domestic   Catsize      Type\n",
            "Hair      1.000000 -0.427851 -0.817382  ...  0.207208  0.455020 -0.562384\n",
            "Feathers -0.427851  1.000000  0.419248  ...  0.031586 -0.135934 -0.197520\n",
            "Eggs     -0.817382  0.419248  1.000000  ... -0.155610 -0.514650  0.661825\n",
            "Milk      0.878503 -0.410761 -0.938848  ...  0.163928  0.574906 -0.723683\n",
            "Airborne -0.198431  0.656553  0.376646  ...  0.063274 -0.349768  0.022677\n",
            "Aquatic  -0.473554 -0.058552  0.376244  ... -0.224308 -0.111866  0.326639\n",
            "Predator -0.154769 -0.104430  0.011605  ... -0.309794  0.144790  0.061179\n",
            "Toothed   0.492531 -0.613631 -0.642150  ...  0.069430  0.344010 -0.471527\n",
            "Backbone  0.191681  0.231403 -0.340420  ...  0.101733  0.356976 -0.828845\n",
            "Breathes  0.441149  0.254588 -0.382777  ...  0.124068  0.204125 -0.519308\n",
            "Venomous -0.104245 -0.145739  0.098689  ... -0.003252 -0.183748  0.321476\n",
            "Fins     -0.280313 -0.223541  0.164796  ... -0.093887  0.031705  0.099430\n",
            "Legs      0.394009 -0.206686 -0.224918  ...  0.073931  0.068791  0.131693\n",
            "Tail      0.048973  0.292569 -0.221090  ...  0.023434  0.243277 -0.631830\n",
            "Domestic  0.207208  0.031586 -0.155610  ...  1.000000  0.020073 -0.181043\n",
            "Catsize   0.455020 -0.135934 -0.514650  ...  0.020073  1.000000 -0.521030\n",
            "Type     -0.562384 -0.197520  0.661825  ... -0.181043 -0.521030  1.000000\n",
            "\n",
            "[17 rows x 17 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xa4IW9c7Yq4-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bba557d-d770-4049-aa68-5284ca8f9050"
      },
      "source": [
        "train_x = zoo_df.drop(['Animal name', 'Type'], axis = 1)\n",
        "train_x.head()\n",
        "print(train_x.columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Hair', 'Feathers', 'Eggs', 'Milk', 'Airborne', 'Aquatic', 'Predator',\n",
            "       'Toothed', 'Backbone', 'Breathes', 'Venomous', 'Fins', 'Legs', 'Tail',\n",
            "       'Domestic', 'Catsize'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PmN7GckHk2HJ",
        "outputId": "0de5c424-c278-494b-af4b-5c81c15d3778"
      },
      "source": [
        "from sklearn.model_selection import train_test_split, cross_val_score, KFold, StratifiedKFold\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, plot_confusion_matrix, roc_curve, roc_auc_score, auc\n",
        "from sklearn.preprocessing import label_binarize\n",
        "from itertools import cycle\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "train_x_set = pd.get_dummies(train_x, drop_first = True)\n",
        "\n",
        "data = np.array(train_x_set)\n",
        "print(data)\n",
        "\n",
        "target = np.array(zoo_df['Type'])\n",
        "class_names = np.unique(target)\n",
        "print(class_names)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1 0 0 ... 0 0 1]\n",
            " [1 0 0 ... 1 0 1]\n",
            " [0 0 1 ... 1 0 0]\n",
            " ...\n",
            " [1 0 0 ... 1 0 1]\n",
            " [0 0 1 ... 0 0 0]\n",
            " [0 1 1 ... 1 0 0]]\n",
            "[1 2 3 4 5 6 7]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zX6CEWjumBr4",
        "outputId": "59bcf7d9-09aa-4c63-8842-12a88f359442"
      },
      "source": [
        "# Multinomial Naive Bayes\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "\n",
        "# Using train/test split\n",
        "x_train, x_test, y_train, y_test = train_test_split(data, target, test_size = 0.3, random_state = 1)\n",
        "classifier = MultinomialNB()\n",
        "classifier.fit(x_train, y_train)\n",
        "\n",
        "y_prediction = classifier.predict(x_test)\n",
        "\n",
        "# Confusion Matrix\n",
        "classifier_confusion_matrix = confusion_matrix(y_test, y_prediction)\n",
        "FP = classifier_confusion_matrix.sum(axis=0) - np.diag(classifier_confusion_matrix)\n",
        "FN = classifier_confusion_matrix.sum(axis=1) - np.diag(classifier_confusion_matrix)\n",
        "TP = np.diag(classifier_confusion_matrix)\n",
        "TN = classifier_confusion_matrix.sum() - (FP + FN + TP)\n",
        "FP = FP.astype(float)\n",
        "FN = FN.astype(float)\n",
        "TP = TP.astype(float)\n",
        "TN = TN.astype(float)\n",
        "\n",
        "# Sensitivity, hit rate, recall, or true positive rate\n",
        "TPR = TP/(TP+FN)\n",
        "# Specificity or true negative rate\n",
        "TNR = TN/(TN+FP) \n",
        "# Precision or positive predictive value\n",
        "PPV = TP/(TP+FP)\n",
        "# Negative predictive value\n",
        "NPV = TN/(TN+FN)\n",
        "# Fall out or false positive rate\n",
        "FPR = FP/(FP+TN)\n",
        "# False negative rate\n",
        "FNR = FN/(TP+FN)\n",
        "# False discovery rate\n",
        "FDR = FP/(TP+FP)\n",
        "# Overall accuracy for each class\n",
        "ACC = (TP+TN)/(TP+FP+FN+TN)\n",
        "\n",
        "# Using 10-fold cross-validation\n",
        "cv = KFold(n_splits = 10, random_state = 42)\n",
        "cv = StratifiedKFold(n_splits = 10, random_state = 42)\n",
        "\n",
        "classifier_cross_validation_score = cross_val_score(classifier, data, target, cv = cv, scoring = 'accuracy', n_jobs = -1)\n",
        "cross_validation_score = \"%0.2f accuracy with a Standard Deviation of %0.2f\" %(mean(classifier_cross_validation_score), std(classifier_cross_validation_score))\n",
        "\n",
        "# Print Results\n",
        "print('Multinomial Naive Bayes (Train/Test Split) - Classification Report:\\n\\n', classification_report(y_test, y_prediction))\n",
        "print('\\n')\n",
        "print('Multinomial Naive Bayes (Train/Test Split) - Confusion Matrix:\\n', classifier_confusion_matrix)\n",
        "print('\\n')\n",
        "print('Multinomial Naive Bayes (Train/Test Split) - Mean FPR:\\n', mean(FPR))\n",
        "print('\\n')\n",
        "print('Multinomial Naive Bayes (Train/Test Split) - Accuracy:\\n', accuracy_score(y_test, y_prediction))\n",
        "print('\\n')\n",
        "print('Multinomial Naive Bayes (10-Fold Stratified Cross Validation) - Cross Validation Score:\\n\\n', cross_validation_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Multinomial Naive Bayes (Train/Test Split) - Classification Report:\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       1.00      0.93      0.97        15\n",
            "           2       0.83      1.00      0.91         5\n",
            "           3       0.00      0.00      0.00         1\n",
            "           4       0.60      1.00      0.75         3\n",
            "           5       1.00      1.00      1.00         1\n",
            "           6       1.00      1.00      1.00         3\n",
            "           7       1.00      0.67      0.80         3\n",
            "\n",
            "    accuracy                           0.90        31\n",
            "   macro avg       0.78      0.80      0.77        31\n",
            "weighted avg       0.90      0.90      0.89        31\n",
            "\n",
            "\n",
            "\n",
            "Multinomial Naive Bayes (Train/Test Split) - Confusion Matrix:\n",
            " [[14  0  0  1  0  0  0]\n",
            " [ 0  5  0  0  0  0  0]\n",
            " [ 0  0  0  1  0  0  0]\n",
            " [ 0  0  0  3  0  0  0]\n",
            " [ 0  0  0  0  1  0  0]\n",
            " [ 0  0  0  0  0  3  0]\n",
            " [ 0  1  0  0  0  0  2]]\n",
            "\n",
            "\n",
            "Multinomial Naive Bayes (Train/Test Split) - Mean FPR:\n",
            " 0.015698587127158554\n",
            "\n",
            "\n",
            "Multinomial Naive Bayes (Train/Test Split) - Accuracy:\n",
            " 0.9032258064516129\n",
            "\n",
            "\n",
            "Multinomial Naive Bayes (10-Fold Stratified Cross Validation) - Cross Validation Score:\n",
            "\n",
            " 0.89 accuracy with a Standard Deviation of 0.09\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "RCWVATUZEt0R",
        "outputId": "ce146661-fadb-4faf-92ec-1ea519b33fb2"
      },
      "source": [
        "# MNM - AUC\n",
        "y_bin = label_binarize(target, classes = class_names)\n",
        "n_classes = y_bin.shape[1]\n",
        "x_train, x_test, y_train, y_test = train_test_split(data, y_bin, test_size = 0.5, random_state = 0)\n",
        "\n",
        "rest_classifier = OneVsRestClassifier(MultinomialNB())\n",
        "y_score = rest_classifier.fit(x_train, y_train).predict_proba(x_test)\n",
        "\n",
        "# Plotting and estimation of FPR, TPR\n",
        "\n",
        "fpr = dict()\n",
        "tpr = dict()\n",
        "roc_auc = dict()\n",
        "\n",
        "\n",
        "for i in range(n_classes):\n",
        "    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n",
        "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "roc_auc_scores = list(roc_auc.values())\n",
        "print(roc_auc_scores)\n",
        "\n",
        "colors = cycle(['blue', 'red', 'green', 'pink', 'orange', 'purple', 'black'])\n",
        "\n",
        "for i, color in zip(range(n_classes), colors):\n",
        "    plt.plot(fpr[i], tpr[i], color=color, lw=1.5, label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i+1, roc_auc[i]))\n",
        "\n",
        "print('Multinomial Naive Bayes - Mean AUC-ROC Score:\\n\\n', mean(roc_auc_scores))\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k-', lw=1.5)\n",
        "plt.xlim([-0.05, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Multinomial Naive Bayes - AUC-ORC for multi-class data')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.0, 1.0, 0.5797872340425533, 1.0, 1.0, 1.0, 0.9722222222222223]\n",
            "Multinomial Naive Bayes - Mean AUC-ROC Score:\n",
            "\n",
            " 0.936001350894968\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "abcFoeffmyZH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7b73e21-59db-46b1-8c45-225f0a0fcbdb"
      },
      "source": [
        "# Support Vector Machine Classifier\n",
        "from sklearn import svm\n",
        "\n",
        "# Using train/test split\n",
        "x_train, x_test, y_train, y_test = train_test_split(data, target, test_size = 0.3, random_state = 1)\n",
        "\n",
        "classifier = svm.SVC()\n",
        "classifier.fit(x_train, y_train)\n",
        "\n",
        "y_prediction = classifier.predict(x_test)\n",
        "\n",
        "# Confusion Matrix\n",
        "classifier_confusion_matrix = confusion_matrix(y_test, y_prediction)\n",
        "FP = classifier_confusion_matrix.sum(axis=0) - np.diag(classifier_confusion_matrix)\n",
        "FN = classifier_confusion_matrix.sum(axis=1) - np.diag(classifier_confusion_matrix)\n",
        "TP = np.diag(classifier_confusion_matrix)\n",
        "TN = classifier_confusion_matrix.sum() - (FP + FN + TP)\n",
        "FP = FP.astype(float)\n",
        "FN = FN.astype(float)\n",
        "TP = TP.astype(float)\n",
        "TN = TN.astype(float)\n",
        "\n",
        "# Sensitivity, hit rate, recall, or true positive rate\n",
        "TPR = TP/(TP+FN)\n",
        "# Specificity or true negative rate\n",
        "TNR = TN/(TN+FP) \n",
        "# Precision or positive predictive value\n",
        "PPV = TP/(TP+FP)\n",
        "# Negative predictive value\n",
        "NPV = TN/(TN+FN)\n",
        "# Fall out or false positive rate\n",
        "FPR = FP/(FP+TN)\n",
        "# False negative rate\n",
        "FNR = FN/(TP+FN)\n",
        "# False discovery rate\n",
        "FDR = FP/(TP+FP)\n",
        "# Overall accuracy for each class\n",
        "ACC = (TP+TN)/(TP+FP+FN+TN)\n",
        "\n",
        "# Using 10-fold cross-validation\n",
        "cv = StratifiedKFold(n_splits = 10, random_state = 42)\n",
        "classifier_cross_validation_score = cross_val_score(classifier, data, target, cv = cv, scoring = 'accuracy', n_jobs = -1)\n",
        "\n",
        "cross_validation_score = \"%0.2f accuracy with a Standard Deviation of %0.2f\" %(classifier_cross_validation_score.mean(), classifier_cross_validation_score.std())\n",
        "\n",
        "print('Support Vector Machine Classifier (Train/Test Split) - Classification Report:\\n\\n', classification_report(y_test, y_prediction))\n",
        "print('\\n')\n",
        "print('Support Vector Machine Classifier (Train/Test Split) - Confusion Matrix:\\n\\n', classifier_confusion_matrix)\n",
        "print('\\n')\n",
        "print('Support Vector Machine Classifier (Train/Test Split) - Mean FPR:\\n', mean(FPR))\n",
        "print('\\n')\n",
        "print('Support Vector Machine Classifier (Train/Test Split) - Accuracy:\\n\\n', accuracy_score(y_test, y_prediction))\n",
        "print('\\n')\n",
        "print('Support Vector Machine Classifier (10-Fold Stratified Cross Validation) - Cross Validation Score:\\n\\n', cross_validation_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Support Vector Machine Classifier (Train/Test Split) - Classification Report:\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       1.00      1.00      1.00        15\n",
            "           2       1.00      1.00      1.00         5\n",
            "           3       0.00      0.00      0.00         1\n",
            "           4       0.75      1.00      0.86         3\n",
            "           5       1.00      1.00      1.00         1\n",
            "           6       1.00      1.00      1.00         3\n",
            "           7       1.00      1.00      1.00         3\n",
            "\n",
            "    accuracy                           0.97        31\n",
            "   macro avg       0.82      0.86      0.84        31\n",
            "weighted avg       0.94      0.97      0.95        31\n",
            "\n",
            "\n",
            "\n",
            "Support Vector Machine Classifier (Train/Test Split) - Confusion Matrix:\n",
            "\n",
            " [[15  0  0  0  0  0  0]\n",
            " [ 0  5  0  0  0  0  0]\n",
            " [ 0  0  0  1  0  0  0]\n",
            " [ 0  0  0  3  0  0  0]\n",
            " [ 0  0  0  0  1  0  0]\n",
            " [ 0  0  0  0  0  3  0]\n",
            " [ 0  0  0  0  0  0  3]]\n",
            "\n",
            "\n",
            "Support Vector Machine Classifier (Train/Test Split) - Mean FPR:\n",
            " 0.00510204081632653\n",
            "\n",
            "\n",
            "Support Vector Machine Classifier (Train/Test Split) - Accuracy:\n",
            "\n",
            " 0.967741935483871\n",
            "\n",
            "\n",
            "Support Vector Machine Classifier (10-Fold Stratified Cross Validation) - Cross Validation Score:\n",
            "\n",
            " 0.95 accuracy with a Standard Deviation of 0.05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ih2jlUny-s1p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "outputId": "bb46dbef-5761-429e-9b10-f31f2245a19b"
      },
      "source": [
        "# SVM - AUC\n",
        "y_bin = label_binarize(target, classes = class_names)\n",
        "n_classes = y_bin.shape[1]\n",
        "x_train, x_test, y_train, y_test = train_test_split(data, y_bin, test_size = 0.5, random_state = 0)\n",
        "\n",
        "rest_classifier = OneVsRestClassifier(svm.SVC(kernel = 'linear', probability = True, random_state = 0))\n",
        "y_score = rest_classifier.fit(x_train, y_train).decision_function(x_test)\n",
        "\n",
        "# Plotting and estimation of FPR, TPR\n",
        "\n",
        "fpr = dict()\n",
        "tpr = dict()\n",
        "roc_auc = dict()\n",
        "\n",
        "for i in range(n_classes):\n",
        "    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n",
        "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "roc_auc_scores = list(roc_auc.values())\n",
        "print(roc_auc_scores)\n",
        "\n",
        "colors = cycle(['blue', 'red', 'green', 'pink', 'orange', 'purple', 'black'])\n",
        "\n",
        "for i, color in zip(range(n_classes), colors):\n",
        "    plt.plot(fpr[i], tpr[i], color=color, lw=1.5, label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i+1, roc_auc[i]))\n",
        "\n",
        "print('Support Vector Machine Classifier - Mean AUC-ROC Score:\\n\\n', mean(roc_auc_scores))\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k-', lw=1.5)\n",
        "plt.xlim([-0.05, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Support Vector Machine Classifier - AUC-ORC for multi-class data')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.0, 1.0, 0.5478723404255319, 1.0, 1.0, 1.0, 1.0]\n",
            "Support Vector Machine Classifier - Mean AUC-ROC Score:\n",
            "\n",
            " 0.9354103343465046\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQR7f5az3yxv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4215beff-9677-4fcc-9f23-9dade1718ed3"
      },
      "source": [
        "# Random Forest\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Using train/test split\n",
        "x_train, x_test, y_train, y_test = train_test_split(data, target, test_size = 0.3, random_state = 1)\n",
        "\n",
        "classifier = RandomForestClassifier()\n",
        "classifier.fit(x_train, y_train)\n",
        "\n",
        "y_prediction = classifier.predict(x_test)\n",
        "\n",
        "# Confusion Matrix\n",
        "classifier_confusion_matrix = confusion_matrix(y_test, y_prediction)\n",
        "FP = classifier_confusion_matrix.sum(axis=0) - np.diag(classifier_confusion_matrix)\n",
        "FN = classifier_confusion_matrix.sum(axis=1) - np.diag(classifier_confusion_matrix)\n",
        "TP = np.diag(classifier_confusion_matrix)\n",
        "TN = classifier_confusion_matrix.sum() - (FP + FN + TP)\n",
        "FP = FP.astype(float)\n",
        "FN = FN.astype(float)\n",
        "TP = TP.astype(float)\n",
        "TN = TN.astype(float)\n",
        "\n",
        "# Sensitivity, hit rate, recall, or true positive rate\n",
        "TPR = TP/(TP+FN)\n",
        "# Specificity or true negative rate\n",
        "TNR = TN/(TN+FP) \n",
        "# Precision or positive predictive value\n",
        "PPV = TP/(TP+FP)\n",
        "# Negative predictive value\n",
        "NPV = TN/(TN+FN)\n",
        "# Fall out or false positive rate\n",
        "FPR = FP/(FP+TN)\n",
        "# False negative rate\n",
        "FNR = FN/(TP+FN)\n",
        "# False discovery rate\n",
        "FDR = FP/(TP+FP)\n",
        "# Overall accuracy for each class\n",
        "ACC = (TP+TN)/(TP+FP+FN+TN)\n",
        "\n",
        "# Using 10-fold cross-validation\n",
        "cv = StratifiedKFold(n_splits = 10, random_state = 42)\n",
        "classifier_cross_validation_score = cross_val_score(classifier, data, target, cv = cv, scoring = 'accuracy', n_jobs = -1)\n",
        "\n",
        "cross_validation_score = \"%0.2f accuracy with a Standard Deviation of %0.2f\" %(classifier_cross_validation_score.mean(), classifier_cross_validation_score.std())\n",
        "\n",
        "print('Random Forest Classifier (Train/Test Split) - Classification Report:\\n\\n', classification_report(y_test, y_prediction))\n",
        "print('\\n')\n",
        "print('Random Forest Classifier (Train/Test Split)  - Confusion Matrix:\\n', classifier_confusion_matrix)\n",
        "print('\\n')\n",
        "print('Random Forest Classifier (Train/Test Split) - Mean FPR:\\n', mean(FPR))\n",
        "print('\\n')\n",
        "print('Random Forest Classifier (Train/Test Split) - Accuracy:\\n', accuracy_score(y_test, y_prediction))\n",
        "print('\\n')\n",
        "print('Random Forest Classifier (10-Fold Stratified Cross Validation) - Cross Validation Score:\\n\\n', cross_validation_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random Forest Classifier (Train/Test Split) - Classification Report:\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       1.00      1.00      1.00        15\n",
            "           2       1.00      1.00      1.00         5\n",
            "           3       0.50      1.00      0.67         1\n",
            "           4       1.00      1.00      1.00         3\n",
            "           5       0.00      0.00      0.00         1\n",
            "           6       1.00      1.00      1.00         3\n",
            "           7       1.00      1.00      1.00         3\n",
            "\n",
            "    accuracy                           0.97        31\n",
            "   macro avg       0.79      0.86      0.81        31\n",
            "weighted avg       0.95      0.97      0.96        31\n",
            "\n",
            "\n",
            "\n",
            "Random Forest Classifier (Train/Test Split)  - Confusion Matrix:\n",
            " [[15  0  0  0  0  0  0]\n",
            " [ 0  5  0  0  0  0  0]\n",
            " [ 0  0  1  0  0  0  0]\n",
            " [ 0  0  0  3  0  0  0]\n",
            " [ 0  0  1  0  0  0  0]\n",
            " [ 0  0  0  0  0  3  0]\n",
            " [ 0  0  0  0  0  0  3]]\n",
            "\n",
            "\n",
            "Random Forest Classifier (Train/Test Split) - Mean FPR:\n",
            " 0.0047619047619047615\n",
            "\n",
            "\n",
            "Random Forest Classifier (Train/Test Split) - Accuracy:\n",
            " 0.967741935483871\n",
            "\n",
            "\n",
            "Random Forest Classifier (10-Fold Stratified Cross Validation) - Cross Validation Score:\n",
            "\n",
            " 0.96 accuracy with a Standard Deviation of 0.05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "r7HM5sZOGDQG",
        "outputId": "00cb223c-ee61-42af-f9ae-8279f8bd30ff"
      },
      "source": [
        "# RF - AUC\n",
        "y_bin = label_binarize(target, classes = class_names)\n",
        "n_classes = y_bin.shape[1]\n",
        "x_train, x_test, y_train, y_test = train_test_split(data, y_bin, test_size = 0.5, random_state = 0)\n",
        "\n",
        "rest_classifier = OneVsRestClassifier(RandomForestClassifier())\n",
        "y_score = rest_classifier.fit(x_train, y_train).predict_proba(x_test)\n",
        "\n",
        "# Plotting and estimation of FPR, TPR\n",
        "\n",
        "fpr = dict()\n",
        "tpr = dict()\n",
        "roc_auc = dict()\n",
        "\n",
        "for i in range(n_classes):\n",
        "    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n",
        "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "roc_auc_scores = list(roc_auc.values())\n",
        "print(roc_auc_scores)\n",
        "\n",
        "colors = cycle(['blue', 'red', 'green', 'pink', 'orange', 'purple', 'black'])\n",
        "\n",
        "for i, color in zip(range(n_classes), colors):\n",
        "    plt.plot(fpr[i], tpr[i], color=color, lw=1.5, label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i+1, roc_auc[i]))\n",
        "\n",
        "print('Random Forest Classifier - Mean AUC-ROC Score:\\n\\n', mean(roc_auc_scores))\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k-', lw=1.5)\n",
        "plt.xlim([-0.05, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Random Forest Classifier - ORC for multi-class data')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.0, 1.0, 0.4281914893617021, 1.0, 1.0, 1.0, 1.0]\n",
            "Random Forest Classifier - Mean AUC-ROC Score:\n",
            "\n",
            " 0.9183130699088146\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vBEZyhICGwpl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}