multi_class_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOg7wuiinoH+4UYQdDgkyBF",
      "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/ericyoc/ml_portfolio/blob/main/multi_class_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "hz6XepqDzsjL"
      },
      "outputs": [],
      "source": [
        "#multi-class classiciation\n",
        "# softmax regression\n",
        "# one vs all\n",
        "# one vs one\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import datasets\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Surpress warnings:\n",
        "def warn(*args, **kwargs):\n",
        "    pass\n",
        "import warnings\n",
        "warnings.warn = warn"
      ],
      "metadata": {
        "id": "p5UabWIqzzd5"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_colors = \"ryb\"\n",
        "plot_step = 0.02"
      ],
      "metadata": {
        "id": "o_sTMXmsz1JH"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def decision_boundary (X,y,model,iris, two=None):\n",
        "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "    xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n",
        "                         np.arange(y_min, y_max, plot_step))\n",
        "    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n",
        "\n",
        "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "    Z = Z.reshape(xx.shape)\n",
        "    cs = plt.contourf(xx, yy, Z,cmap=plt.cm.RdYlBu)\n",
        "\n",
        "    if two:\n",
        "        cs = plt.contourf(xx, yy, Z,cmap=plt.cm.RdYlBu)\n",
        "        for i, color in zip(np.unique(y), plot_colors):\n",
        "\n",
        "            idx = np.where( y== i)\n",
        "            plt.scatter(X[idx, 0], X[idx, 1], label=y,cmap=plt.cm.RdYlBu, s=15)\n",
        "        plt.show()\n",
        "\n",
        "    else:\n",
        "        set_={0,1,2}\n",
        "        print(set_)\n",
        "        for i, color in zip(range(3), plot_colors):\n",
        "            idx = np.where( y== i)\n",
        "            if np.any(idx):\n",
        "\n",
        "                set_.remove(i)\n",
        "\n",
        "                plt.scatter(X[idx, 0], X[idx, 1], label=y,cmap=plt.cm.RdYlBu, edgecolor='black', s=15)\n",
        "\n",
        "\n",
        "        for  i in set_:\n",
        "            idx = np.where( iris.target== i)\n",
        "            plt.scatter(X[idx, 0], X[idx, 1], marker='x',color='black')\n",
        "\n",
        "        plt.show()"
      ],
      "metadata": {
        "id": "vhcqDs4Az2vm"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_probability_array(X,probability_array):\n",
        "\n",
        "    plot_array=np.zeros((X.shape[0],30))\n",
        "    col_start=0\n",
        "    ones=np.ones((X.shape[0],30))\n",
        "    for class_,col_end in enumerate([10,20,30]):\n",
        "        plot_array[:,col_start:col_end]= np.repeat(probability_array[:,class_].reshape(-1,1), 10,axis=1)\n",
        "        col_start=col_end\n",
        "    plt.imshow(plot_array)\n",
        "    plt.xticks([])\n",
        "    plt.ylabel(\"samples\")\n",
        "    plt.xlabel(\"probability of 3 classes\")\n",
        "    plt.colorbar()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "NRHGKfMAz5Jz"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def main():\n",
        "    # load dataset\n",
        "    pair = [1, 3]\n",
        "    iris = datasets.load_iris()\n",
        "    X = iris.data[:, pair]\n",
        "    y = iris.target\n",
        "    np.unique(y)\n",
        "\n",
        "    # plot dataset\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdYlBu)\n",
        "    plt.xlabel(\"sepal width (cm)\")\n",
        "    plt.ylabel(\"petal width\")\n",
        "    plt.title(\"Figure 1: Scatter plot of the dataset\")\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\"\"\n",
        "    Figure 1 explanation:\n",
        "    The scatter plot of the dataset is important to visualize the distribution of the data points and observe any\n",
        "    potential patterns or clusters based on the target classes. It helps in understanding the separability of the\n",
        "    classes and the complexity of the classification task.\n",
        "    \"\"\")\n",
        "\n",
        "    print(\"\\n\" * 3)  # Add spacing between the explanation and the next chart\n",
        "\n",
        "    # softmax regression\n",
        "    lr = LogisticRegression(random_state=0).fit(X, y)\n",
        "\n",
        "    # generate the probability\n",
        "    probability = lr.predict_proba(X)\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.title(\"Figure 2: Probability plot of softmax regression\")\n",
        "    plot_probability_array(X, probability)\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\"\"\n",
        "    Figure 2 explanation:\n",
        "    The probability plot of softmax regression visualizes the predicted probabilities of each class for each data point.\n",
        "    It helps in understanding how confident the classifier is in its predictions and how well it distinguishes\n",
        "    between different classes.\n",
        "    \"\"\")\n",
        "\n",
        "    print(\"\\n\" * 3)  # Add spacing between the explanation and the next chart\n",
        "\n",
        "    # svm\n",
        "    model = SVC(kernel='linear', gamma=.5, probability=True)\n",
        "    model.fit(X, y)\n",
        "\n",
        "    # plot decision boundary\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.title(\"Figure 3: Decision boundary of SVM\")\n",
        "    decision_boundary(X, y, model, iris)\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\"\"\n",
        "    Figure 3 explanation:\n",
        "    The decision boundary plot of SVM shows how the classifier separates the different classes in the feature space.\n",
        "    It helps in understanding the decision regions and the effectiveness of the classifier in distinguishing between classes.\n",
        "    \"\"\")\n",
        "\n",
        "    print(\"\\n\" * 3)  # Add spacing between the explanation and the next chart\n",
        "\n",
        "    # one vs All (one vs Rest)\n",
        "    # train each classifier\n",
        "    dummy_class = y.max() + 1\n",
        "    my_models = []\n",
        "    for i, class_ in enumerate(np.unique(y)):\n",
        "        select = (y == class_)\n",
        "        temp_y = np.zeros(y.shape)\n",
        "        temp_y[y == class_] = class_\n",
        "        temp_y[y != class_] = dummy_class\n",
        "        model = SVC(kernel='linear', gamma=.5, probability=True)\n",
        "        my_models.append(model.fit(X, temp_y))\n",
        "\n",
        "        plt.figure(figsize=(8, 6))\n",
        "        plt.title(f\"Figure {4+i}: Decision boundary of One vs All - Class {class_}\")\n",
        "        decision_boundary(X, temp_y, model, iris)\n",
        "        plt.show()\n",
        "\n",
        "        print(f\"\"\"\n",
        "        Figure {4+i} explanation:\n",
        "        The decision boundary plot of One vs All for Class {class_} visualizes how the individual classifier distinguishes\n",
        "        this class from the rest. It helps in understanding the effectiveness of the classifier in separating its respective\n",
        "        class from the others.\n",
        "        \"\"\")\n",
        "\n",
        "        print(\"\\n\" * 3)  # Add spacing between the explanation and the next chart\n",
        "\n",
        "    # calculate the probability\n",
        "    probability_array = np.zeros((X.shape[0], 3))\n",
        "    for j, model in enumerate(my_models):\n",
        "        real_class = np.where(np.array(model.classes_) != 3)[0]\n",
        "        probability_array[:, j] = model.predict_proba(X)[:, real_class][:, 0]\n",
        "\n",
        "    # plot probability\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.title(\"Figure 7: Probability plot of One vs All\")\n",
        "    plot_probability_array(X, probability_array)\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\"\"\n",
        "    Figure 7 explanation:\n",
        "    The probability plot of One vs All classification visualizes the predicted probabilities of each class for each data point\n",
        "    using the individual classifiers. It helps in understanding how confident each classifier is in its predictions and how well\n",
        "    they distinguish between classes.\n",
        "    \"\"\")\n",
        "\n",
        "    print(\"\\n\" * 3)  # Add spacing between the explanation and the next chart\n",
        "\n",
        "    # one vs one\n",
        "    classes_ = set(np.unique(y))\n",
        "    pairs = []\n",
        "    left_overs = classes_.copy()\n",
        "    my_models = []\n",
        "    figure_count = 8\n",
        "    for i, class_ in enumerate(classes_):\n",
        "        left_overs.remove(class_)\n",
        "        for j, second_class in enumerate(left_overs):\n",
        "            pairs.append(str(class_) + ' and ' + str(second_class))\n",
        "            print(\"class {} vs class {} \".format(class_, second_class))\n",
        "            temp_y = np.zeros(y.shape)\n",
        "            select = np.logical_or(y == class_, y == second_class)\n",
        "            model = SVC(kernel='linear', gamma=.5, probability=True)\n",
        "            model.fit(X[select, :], y[select])\n",
        "            my_models.append(model)\n",
        "\n",
        "            plt.figure(figsize=(8, 6))\n",
        "            plt.title(f\"Figure {figure_count}: Decision boundary of One vs One - Class {class_} vs {second_class}\")\n",
        "            decision_boundary(X[select, :], y[select], model, iris, two=True)\n",
        "            plt.show()\n",
        "\n",
        "            print(f\"\"\"\n",
        "            Figure {figure_count} explanation:\n",
        "            The decision boundary plot of One vs One for Class {class_} vs Class {second_class} visualizes how the individual\n",
        "            classifier distinguishes between these two classes. It helps in understanding the effectiveness of the classifier\n",
        "            in separating the two classes in this pair.\n",
        "            \"\"\")\n",
        "\n",
        "            print(\"\\n\" * 3)  # Add spacing between the explanation and the next chart\n",
        "            figure_count += 1\n",
        "\n",
        "    print(pairs)\n",
        "\n",
        "    majority_vote_array = np.zeros((X.shape[0], 3))\n",
        "    majority_vote_dict = {}\n",
        "    for j, (model, pair) in enumerate(zip(my_models, pairs)):\n",
        "        majority_vote_dict[pair] = model.predict(X)\n",
        "        majority_vote_array[:, j] = model.predict(X)\n",
        "\n",
        "    print(pd.DataFrame(majority_vote_dict).head(10))\n",
        "\n",
        "    print(\"\"\"\n",
        "    Table explanation:\n",
        "    The table shows the predictions of each classifier for each pair of classes. It helps in understanding how each classifier\n",
        "    votes for each data point and visualizes the majority voting process used in One vs One classification.\n",
        "    \"\"\")"
      ],
      "metadata": {
        "id": "VlIUh84Tz6dU"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hn7GOYUAz_0I",
        "outputId": "5b6149ea-82da-4691-f62f-4c8cb684d283"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    Figure 1 explanation:\n",
            "    The scatter plot of the dataset is important to visualize the distribution of the data points and observe any\n",
            "    potential patterns or clusters based on the target classes. It helps in understanding the separability of the\n",
            "    classes and the complexity of the classification task.\n",
            "    \n",
            "\n",
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    Figure 2 explanation:\n",
            "    The probability plot of softmax regression visualizes the predicted probabilities of each class for each data point.\n",
            "    It helps in understanding how confident the classifier is in its predictions and how well it distinguishes\n",
            "    between different classes.\n",
            "    \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "{0, 1, 2}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    Figure 3 explanation:\n",
            "    The decision boundary plot of SVM shows how the classifier separates the different classes in the feature space.\n",
            "    It helps in understanding the decision regions and the effectiveness of the classifier in distinguishing between classes.\n",
            "    \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "{0, 1, 2}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "        Figure 4 explanation:\n",
            "        The decision boundary plot of One vs All for Class 0 visualizes how the individual classifier distinguishes\n",
            "        this class from the rest. It helps in understanding the effectiveness of the classifier in separating its respective\n",
            "        class from the others.\n",
            "        \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "{0, 1, 2}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "        Figure 5 explanation:\n",
            "        The decision boundary plot of One vs All for Class 1 visualizes how the individual classifier distinguishes\n",
            "        this class from the rest. It helps in understanding the effectiveness of the classifier in separating its respective\n",
            "        class from the others.\n",
            "        \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "{0, 1, 2}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "        Figure 6 explanation:\n",
            "        The decision boundary plot of One vs All for Class 2 visualizes how the individual classifier distinguishes\n",
            "        this class from the rest. It helps in understanding the effectiveness of the classifier in separating its respective\n",
            "        class from the others.\n",
            "        \n",
            "\n",
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    Figure 7 explanation:\n",
            "    The probability plot of One vs All classification visualizes the predicted probabilities of each class for each data point\n",
            "    using the individual classifiers. It helps in understanding how confident each classifier is in its predictions and how well\n",
            "    they distinguish between classes.\n",
            "    \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "class 0 vs class 1 \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "            Figure 8 explanation:\n",
            "            The decision boundary plot of One vs One for Class 0 vs Class 1 visualizes how the individual\n",
            "            classifier distinguishes between these two classes. It helps in understanding the effectiveness of the classifier\n",
            "            in separating the two classes in this pair.\n",
            "            \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "class 0 vs class 2 \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "            Figure 9 explanation:\n",
            "            The decision boundary plot of One vs One for Class 0 vs Class 2 visualizes how the individual\n",
            "            classifier distinguishes between these two classes. It helps in understanding the effectiveness of the classifier\n",
            "            in separating the two classes in this pair.\n",
            "            \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "class 1 vs class 2 \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "            Figure 10 explanation:\n",
            "            The decision boundary plot of One vs One for Class 1 vs Class 2 visualizes how the individual\n",
            "            classifier distinguishes between these two classes. It helps in understanding the effectiveness of the classifier\n",
            "            in separating the two classes in this pair.\n",
            "            \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "['0 and 1', '0 and 2', '1 and 2']\n",
            "   0 and 1  0 and 2  1 and 2\n",
            "0        0        0        1\n",
            "1        0        0        1\n",
            "2        0        0        1\n",
            "3        0        0        1\n",
            "4        0        0        1\n",
            "5        0        0        1\n",
            "6        0        0        1\n",
            "7        0        0        1\n",
            "8        0        0        1\n",
            "9        0        0        1\n",
            "\n",
            "    Table explanation:\n",
            "    The table shows the predictions of each classifier for each pair of classes. It helps in understanding how each classifier\n",
            "    votes for each data point and visualizes the majority voting process used in One vs One classification.\n",
            "    \n"
          ]
        }
      ]
    }
  ]
}