4_logistic_regression.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNRpyvyNQvnOAMYkUvzHaj7",
      "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/aseddin/F23_ECE4715/blob/main/4_logistic_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "# Logistic Regression\n",
        "\n",
        "Logistic regression is a classification model designed to assess the likelihood of an input instance belonging to a specific class. Unlike linear regression, which predicts actual values, logistic regression focuses on estimating probabilities.\n",
        "\n",
        "In a logistic regression model, the weighted sum of input features, including a bias term, is calculated. This sum is then passed through a function that transforms it into a probability, represented by a number ranging between 0 and 1.\n",
        "\n",
        "$$\n",
        "\\hat{p} = h_\\theta({\\bf X}) = \\sigma({\\bf\\Theta^T X})\n",
        "$$\n",
        "\n",
        "## Sigmoid (Logistic) Function $\\sigma(.)$\n",
        "\n",
        "A sigmoid function is an S-shaped function that maps the entire real axis to a value between 0 and 1.\n",
        "\n",
        "$$\n",
        "\\sigma(t) = \\frac{1}{1 + e^{-t}}\n",
        "$$\n",
        "\n",
        "Or\n",
        "$$\n",
        "\\sigma(t) = \\frac{e^{t}}{1 + e^{t}}\n",
        "$$\n",
        "\n",
        "$t$ has several names (log-odds, logit, pre-activation)\n",
        "\n",
        "$$\n",
        "t = \\sigma^{-1}(\\hat{p})=\\text{logit}(\\hat{p}) = \\text{log}\\frac{\\hat{p}}{1 - \\hat{p}}\n",
        "$$\n",
        "\n",
        "The derivative\n",
        "\n",
        "$$\n",
        "\\frac{d}{dt}\\sigma(t) = \\sigma(t) (1 - \\sigma(t))\n",
        "$$\n",
        "\n",
        "## Class Prediction\n",
        "\n",
        "After estimating the probability ($\\hat{p}$) of belonging to a specific class, predictions can be made by comparing this estimate with a threshold, commonly set at 50%.\n",
        "\n",
        "$$\n",
        "\\hat{y} =\n",
        "\\begin{cases}\n",
        "0 & \\hat{p} < 0.5\\\\\n",
        "1 & \\hat{p} \\geq 0.5\\\\\n",
        "\\end{cases}\n",
        "$$"
      ],
      "metadata": {
        "id": "KsiP188Oq_uR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "9URH9Errq2Eu"
      },
      "outputs": [],
      "source": [
        "# code to set things up\n",
        "\n",
        "# importing common libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Things related to figures and plots\n",
        "# make figures look nicer\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rc('font', size=14)\n",
        "plt.rc('axes', labelsize=14, titlesize=14)\n",
        "plt.rc('legend', fontsize=14)\n",
        "plt.rc('xtick', labelsize=10)\n",
        "plt.rc('ytick', labelsize=10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the sigmoid function\n",
        "\n",
        "lim = 6\n",
        "t = np.linspace(-lim, lim, 100)\n",
        "sig = 1 / (1 + np.exp(-t))\n",
        "\n",
        "plt.figure(figsize=(8, 3))\n",
        "plt.plot(t, sig, \"b-\", label=\"$\\sigma(t) = \\dfrac{1}{1 + e^{-t}}$\")\n",
        "\n",
        "plt.plot([0, 0], [0, 1], \"k-\", linewidth=2)\n",
        "plt.plot([-lim, lim], [0, 0], \"k-\", linewidth=2)\n",
        "\n",
        "plt.plot([-lim, lim], [1, 1], \"k:\", linewidth=2)\n",
        "plt.plot([-lim, lim], [0.5, 0.5], \"k:\", linewidth=2)\n",
        "\n",
        "plt.axis([-lim, lim, -0.1, 1.1])\n",
        "plt.gca().set_yticks([0, 0.25, 0.5, 0.75, 1])\n",
        "plt.xlabel(\"$t$\")\n",
        "plt.ylabel(\"$\\hat{p}$\", rotation=0)\n",
        "plt.grid()\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "FwPwY-_utFoV",
        "outputId": "0f4b5ea1-6cc6-4945-e126-15c70cb66782"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's important to recognize that with a 50% threshold, if $\\sigma(t) < 0.5$, then $t < 0$, and if $\\sigma(t) \\geq 0.5$, then $t \\geq 0$. Therefore, making prediction is simply done by figuring out whether the logit $t$ is positive or negative. Nonetheless, it is advisable to perform the logistic regression computation, especially when considering thresholds other than 50%, as specific scenarios may require different threshold values."
      ],
      "metadata": {
        "id": "5qizlLtAxBur"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training and Cost Function\n",
        "\n",
        "The goal of training is to determine a weight vector ${\\bf\\Theta}$ that enables the model to estimate high probabilities for positive classes ($y=1$) and low probabilities for negative classes ($y=0$). The following cost function captures this concept for a single training instance:\n",
        "\n",
        "$$\n",
        "c({\\bf\\Theta}) =\n",
        "\\begin{cases}\n",
        "-\\text{log}(\\hat{p}) & y = 1 \\\\\n",
        "-\\text{log}(1 - \\hat{p}) & y = 0 \\\\\n",
        "\\end{cases}\n",
        "$$\n",
        "\n",
        "For a positive class $(y=1)$\n",
        "  * When $\\hat{p}$ approches 0, the cost function will be very large\n",
        "  * When $\\hat{p}$ approaches 1, the cost function becomes 0\n",
        "\n",
        "For a negative class $(y=0)$\n",
        "  * When $\\hat{p}$ approaches 0, the cost function becomes 0\n",
        "  * When $\\hat{p}$ approaches 1, the cost function will be very large\n",
        "\n",
        "The cost function over the whole training set (all training instances) is an average cost and is called _log loss_:\n",
        "$$\n",
        "J( {\\bf\\Theta}) = -\\frac{1}{m} \\sum_{i=1}^{m}[y^{(i)}log(\\hat{p}^{(i)}) + (1 - y^{(i)})log(1 - \\hat{p}^{(i)})]\n",
        "$$\n",
        "\n",
        "There is no known closed-form equation to find ${\\bf\\Theta}$ that minimizes $J({\\bf\\Theta})$ (i.e. there is no equivalent to the Normal Equation for Linear Regression). However, this cost function is convex, so gradient descent should be able to find a good ${\\bf\\Theta}$.\n",
        "\n",
        "The partial derivative of the cost function with regards to $\\theta_j$\n",
        "$$\n",
        "\\frac{\\partial}{\\partial \\theta_j}J({\\bf\\Theta}) = \\frac{1}{m}\\sum_{i=1}^{m}(\\sigma({\\bf\\Theta}^T {\\bf X}^{(i)}) - y^{(i)})x_j^{(i)}\n",
        "$$\n",
        "\n",
        "This is similar to the partial derivative for linear regression:\n",
        "\n",
        "$$\n",
        "\\frac{\\partial}{\\partial \\theta_j}\\text{MSE}({\\bf\\Theta}) = \\frac{2}{m}\n",
        "\\sum_{i=1}^{m} ({\\bf\\Theta}^T {\\bf X}^{(i)} - y^{(i)})x_j^{(i)}\n",
        "$$\n",
        "\n",
        "You can build a gradient vector and employ optimization techniques such as Batch Gradient Descent, Stochastic Gradient Descent, or Mini-Batch Gradient Descent to iteratively refine the model parameters. Additionally, $\\ell_2$ or $\\ell_1$ regularization can be applied.\n"
      ],
      "metadata": {
        "id": "0bP-uY9Wx4LM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load the Iris plants dataset\n",
        "\n",
        "We will employ the iris dataset to demonstrate logistic regression and look at the decision boundaries."
      ],
      "metadata": {
        "id": "VxKOkIXk1wsH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "\n",
        "iris = load_iris(as_frame=True)"
      ],
      "metadata": {
        "id": "BhqarbCxw4Jx"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(iris)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "JINa9nTQ2IN2",
        "outputId": "8966d280-8a82-4c33-ab70-e704ffed1444"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.utils._bunch.Bunch"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
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              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>sklearn.utils._bunch.Bunch</b><br/>def __init__(**kwargs)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/sklearn/utils/_bunch.py</a>Container object exposing keys as attributes.\n",
              "\n",
              "Bunch objects are sometimes used as an output for functions and methods.\n",
              "They extend dictionaries by enabling values to be accessed by key,\n",
              "`bunch[&quot;value_key&quot;]`, or by an attribute, `bunch.value_key`.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "&gt;&gt;&gt; from sklearn.utils import Bunch\n",
              "&gt;&gt;&gt; b = Bunch(a=1, b=2)\n",
              "&gt;&gt;&gt; b[&#x27;b&#x27;]\n",
              "2\n",
              "&gt;&gt;&gt; b.b\n",
              "2\n",
              "&gt;&gt;&gt; b.a = 3\n",
              "&gt;&gt;&gt; b[&#x27;a&#x27;]\n",
              "3\n",
              "&gt;&gt;&gt; b.c = 6\n",
              "&gt;&gt;&gt; b[&#x27;c&#x27;]\n",
              "6</pre>\n",
              "      <script>\n",
              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
              "        for (const element of document.querySelectorAll('.filepath')) {\n",
              "          element.style.display = 'block'\n",
              "          element.onclick = (event) => {\n",
              "            event.preventDefault();\n",
              "            event.stopPropagation();\n",
              "            google.colab.files.view(element.textContent, 4);\n",
              "          };\n",
              "        }\n",
              "      }\n",
              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4GDtuGsd2Lf0",
        "outputId": "cec04b8c-29d5-42fb-c9b1-a9bd93c89e79"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(iris.DESCR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PrXRVoCC2Yww",
        "outputId": "a7cb6fe8-bb59-44f8-8aa5-8984918eab30"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _iris_dataset:\n",
            "\n",
            "Iris plants dataset\n",
            "--------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            ":Number of Instances: 150 (50 in each of three classes)\n",
            ":Number of Attributes: 4 numeric, predictive attributes and the class\n",
            ":Attribute Information:\n",
            "    - sepal length in cm\n",
            "    - sepal width in cm\n",
            "    - petal length in cm\n",
            "    - petal width in cm\n",
            "    - class:\n",
            "            - Iris-Setosa\n",
            "            - Iris-Versicolour\n",
            "            - Iris-Virginica\n",
            "\n",
            ":Summary Statistics:\n",
            "\n",
            "============== ==== ==== ======= ===== ====================\n",
            "                Min  Max   Mean    SD   Class Correlation\n",
            "============== ==== ==== ======= ===== ====================\n",
            "sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
            "sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
            "petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
            "petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
            "============== ==== ==== ======= ===== ====================\n",
            "\n",
            ":Missing Attribute Values: None\n",
            ":Class Distribution: 33.3% for each of 3 classes.\n",
            ":Creator: R.A. Fisher\n",
            ":Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
            ":Date: July, 1988\n",
            "\n",
            "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
            "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
            "Machine Learning Repository, which has two wrong data points.\n",
            "\n",
            "This is perhaps the best known database to be found in the\n",
            "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
            "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
            "data set contains 3 classes of 50 instances each, where each class refers to a\n",
            "type of iris plant.  One class is linearly separable from the other 2; the\n",
            "latter are NOT linearly separable from each other.\n",
            "\n",
            ".. dropdown:: References\n",
            "\n",
            "  - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
            "    Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
            "    Mathematical Statistics\" (John Wiley, NY, 1950).\n",
            "  - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
            "    (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
            "  - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
            "    Structure and Classification Rule for Recognition in Partially Exposed\n",
            "    Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
            "    Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
            "  - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
            "    on Information Theory, May 1972, 431-433.\n",
            "  - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
            "    conceptual clustering system finds 3 classes in the data.\n",
            "  - Many, many more ...\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.target.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fJNWgcI2Qk7",
        "outputId": "7af3191e-b5b1-40b5-96fa-fff16672661b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.target_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVwV5s-k2Wln",
        "outputId": "187a91f0-e552-47ad-b358-593ce612b693"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.feature_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "px-IZw-b2rsP",
        "outputId": "28dbe8d9-8681-46cf-c2be-fa27779b31e6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sepal length (cm)',\n",
              " 'sepal width (cm)',\n",
              " 'petal length (cm)',\n",
              " 'petal width (cm)']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.data"
      ],
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        "colab": {
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          "height": 424
        },
        "id": "cvfAUa3n2Nrp",
        "outputId": "edf705e2-9898-4b1e-9d61-85d36bfa415a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
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              "4                  5.0               3.6                1.4               0.2\n",
              "..                 ...               ...                ...               ...\n",
              "145                6.7               3.0                5.2               2.3\n",
              "146                6.3               2.5                5.0               1.9\n",
              "147                6.5               3.0                5.2               2.0\n",
              "148                6.2               3.4                5.4               2.3\n",
              "149                5.9               3.0                5.1               1.8\n",
              "\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"iris\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.435866284936698,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594667,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split the data into train/test datasets\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    iris.data, iris.target, test_size=0.2, random_state=42, stratify=iris.target\n",
        ")"
      ],
      "metadata": {
        "id": "aBjgcm-G197q"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Single Class / Single Feature\n",
        "\n",
        "We will train a logistic regression classifier using the **petal width** feature only and for the **virginica** class only (i.e. binary classification using one feature)"
      ],
      "metadata": {
        "id": "4v_9M73M3d4F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Single feature (petal width (cm))\n",
        "X_train_pw = X_train[[\"petal width (cm)\"]].values\n",
        "X_test_pw = X_test[[\"petal width (cm)\"]].values\n",
        "\n",
        "# Binary classification (Virginica VS not Virginica)\n",
        "y_train_virginica = (y_train == 2).astype(np.int8)\n",
        "y_test_virginica = (y_test == 2).astype(np.int8)"
      ],
      "metadata": {
        "id": "PNeKvx9k3WZI"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a Logistic Regression model\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "log_reg = LogisticRegression(penalty='l2', random_state=42)\n",
        "log_reg.fit(X_train_pw, y_train_virginica)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "k1Vrt7hP31Qg",
        "outputId": "c7cc9b56-63ef-4b82-8f32-54a34aacf050"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the decision boundary and estimated probabilities\n",
        "\n",
        "X_new = np.linspace(0, 3, 1000).reshape(-1, 1)\n",
        "y_proba = log_reg.predict_proba(X_new)\n",
        "decision_boundary = X_new[y_proba[:, 1] >= 0.5][0, 0]\n",
        "\n",
        "plt.figure(figsize=(9, 4))\n",
        "plt.plot(X_new, y_proba[:, 0], \"b--\", linewidth=2, label=\"Not Virginica Proba\")\n",
        "plt.plot(X_new, y_proba[:, 1], \"g-\", linewidth=2, label=\"Virginica Porba\")\n",
        "plt.plot([decision_boundary, decision_boundary], [0, 1], \"k:\", linewidth=2, label=f\"Decision Boundary = {decision_boundary:.2f}\")\n",
        "\n",
        "plt.plot(X_train_pw[y_train_virginica == 0], y_train_virginica[y_train_virginica==0], \"bs\")\n",
        "plt.plot(X_train_pw[y_train_virginica == 1], y_train_virginica[y_train_virginica==1], \"g^\")\n",
        "plt.xlabel(\"Petal Width (cm)\")\n",
        "plt.ylabel(\"Probability\")\n",
        "plt.legend(loc=\"center left\")\n",
        "plt.axis([0, 3, -0.02, 1.02])\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "wpqcn0d75O7X",
        "outputId": "286b8932-3635-4a07-cf5b-e4fe079a9b4b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The blue squares represent \"Not virginical flowers\". The green triangles represent Virginica flowers. The dotted line represent the decision boundary. Flowers with petal width greater than the decision boundary will be classified as Virginica flowers; whereas, flowers with petal width less than the decision boundary will be classified as not Virginica.\n",
        "\n",
        "The model seem to be confident that flowers with petal width greater than 2 are Virginica (Virginica proba > 80%)\n",
        "\n",
        "The model seem to be confident that flowers with petal widths less than 1.25 are not Virginica (Not Virginica proba > 80%)"
      ],
      "metadata": {
        "id": "q9Lm-5yo30qG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# the point at which both \"Virginica\" and \"Not Virginica\" have 50% probability\n",
        "decision_boundary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ahs2gBW_6Lqy",
        "outputId": "308c0697-4827-49ad-814f-673c88621184"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.6666666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict the classes for 2 flowers\n",
        "\n",
        "log_reg.predict([[1.7], [1.5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oPKP3kHB6Ozs",
        "outputId": "58641d99-9186-42de-95ce-add631872a17"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0], dtype=int8)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict the class probabilites for 2 flowers\n",
        "log_reg.predict_proba([[1.7], [1.5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJCrZcWc6QAC",
        "outputId": "564f4cd8-f668-4908-a748-c6a1aea1a99a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.46472526, 0.53527474],\n",
              "       [0.66163414, 0.33836586]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Single Class / Multiple Features\n",
        "\n",
        "We now use 2 features: **Petal Width** and **Petal Length** to train a logistic regression classifier for the **virginica** class only (i.e. binary classification using multiple features)"
      ],
      "metadata": {
        "id": "0_8swEgW7WF7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2 features (petal width (cm)) & (petal length (cm))\n",
        "X_train_pwl = X_train[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
        "X_test_pwl = X_test[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
        "\n",
        "# Binary classification (Virginica VS not Virginica)\n",
        "y_train_virginica = (y_train == 2).astype(np.int8)\n",
        "y_test_virginica = (y_test == 2).astype(np.int8)\n",
        "\n",
        "# Train a logistic regression\n",
        "log_reg = LogisticRegression(penalty=\"l2\", C=2, random_state=42)\n",
        "log_reg.fit(X_train_pwl, y_train_virginica)\n",
        "\n",
        "# contour plot for the probabilities\n",
        "x0, x1 = np.meshgrid(\n",
        "    np.linspace(2.9, 7, 500).reshape(-1, 1),\n",
        "    np.linspace(0.8, 2.7, 200).reshape(-1, 1)\n",
        ")\n",
        "X_new = np.c_[x0.ravel(), x1.ravel()]\n",
        "y_proba = log_reg.predict_proba(X_new)\n",
        "zz = y_proba[:, 1].reshape(x0.shape) # virginica class\n",
        "\n",
        "# for the decision boundary\n",
        "left_right = np.array([2.9, 7])\n",
        "boundary = -((log_reg.coef_[0, 0] * left_right + log_reg.intercept_[0])/log_reg.coef_[0, 1])\n",
        "\n",
        "plt.figure(figsize=(10, 4))\n",
        "plt.plot(X_train_pwl[y_train_virginica==0, 0], X_train_pwl[y_train_virginica == 0, 1], \"bs\")\n",
        "plt.plot(X_train_pwl[y_train_virginica==1, 0], X_train_pwl[y_train_virginica == 1, 1], \"g^\")\n",
        "contour = plt.contour(x0, x1, zz, cmap=plt.cm.brg)\n",
        "plt.clabel(contour, inline=1)\n",
        "plt.plot(left_right, boundary, \"k--\", linewidth=3)\n",
        "plt.text(3.7, 1.5, \"Not Virginica\", color=\"b\", ha=\"right\")\n",
        "plt.text(6.5, 2.4, \"Virginica\", color=\"g\", ha=\"left\")\n",
        "plt.xlabel(\"Petal length\")\n",
        "plt.ylabel(\"Petal width\")\n",
        "plt.axis([2.9, 7, 0.8, 2.7])\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "TGAmsfRq6S19",
        "outputId": "76d57754-4bbe-4c72-fea6-9e15c0bff9e8"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that regularization for a `LogisticRegression` model is controlled by the hyperparameter `C`, which is the inverse of the regularization parameter `alpha` $\\alpha$ in other linear models (i.e. `LinearRegression`). In other words a higher `C` value corresponds to less regularization."
      ],
      "metadata": {
        "id": "PHoyr6ig_jQ7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Softmax Regression\n",
        "\n",
        "Also named **Multinomial Logistic Regression**, is an extension of the binary logistic regression to support multiple classes. Without having to traing multiple binary classifiers (i.e. without OvO or OvR methods)\n",
        "\n",
        "\n",
        "## Estimating probabilities and predictions:\n",
        "1. For each instance ${\\bf x}$ compute a weighted sum score for each class $k$:\n",
        "$$\n",
        "s_k({\\bf x}) = ({\\bf \\theta}^{(k)})^T {\\bf x}\n",
        "$$\n",
        "  Note that each class has its own parameter vector ${\\bf \\theta}^{(k)}$ and we can store the parameter vectors as rows in a parameter matrix ${\\bf \\Theta}$\n",
        "\n",
        "2. Estimate the probability of each class by applying the _softmax function_ (aka _normalized exponential_) to each weighted sum score:\n",
        "$$\n",
        "\\hat{p}_k = \\sigma(s({\\bf x}))_k = \\frac{e^{s_k({\\bf x})}}{\\sum_{j=1}^{K} e^{s_j({\\bf x})}}\n",
        "$$\n",
        "Where:\n",
        "  * $K$ is the number of classes\n",
        "  * $s({\\bf x})$ is a vector containing the weighted sum scores of each class for the instance ${\\bf x}$\n",
        "  * $\\sigma(s({\\bf x}))_k$ is the estimated probability that instance ${\\bf x}$ belongs to class $k$\n",
        "\n",
        "3. The class with highest estimated probability will be the predicted class:\n",
        "$$\n",
        "\\DeclareMathOperator*{\\argmax}{argmax}\n",
        "\\hat{y} = \\argmax_k \\hat{p}_k\n",
        "$$\n",
        "\n",
        "## Training and Cost Function:\n",
        "\n",
        "The objective is to have a model that estimates a high probability of the target class and low probability for the other classes.\n",
        "\n",
        "Cross entropy is a good cost function and minimizing it should achieve the training objective:\n",
        "\n",
        "$$\n",
        "J({\\bf \\Theta}) = -\\frac{1}{m} \\sum_{i=1}^{m} \\sum_{k=1}^{K}\n",
        "y^{(i)}_k \\log(\\hat{p}^{(i)}_k)\n",
        "$$\n",
        "\n",
        "Where:\n",
        "  * $y^{(i)}_k$ is the target probability the $i^{th}$ instance belongs to class $k$. (generally it is equal to 1 if the instance belongs to the class or 0 if it does not)\n",
        "\n",
        "The gradient vector for this cost function with regards to class $k$'s parameter vector ${\\bf \\theta}^{(k)}$:\n",
        "$$\n",
        "\\nabla_{{\\bf \\theta}^{(k)}} J({\\bf \\Theta}) = \\frac{1}{m} \\sum_{i=1}^{m} (\\hat{p}^{(i)}_k - y^{(i)}_k) {\\bf x}^{(i)}\n",
        "$$\n",
        "\n",
        "${\\bf \\Theta}$ that minimizes $J({\\bf \\Theta})$ can be found using gradient descent.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "DdduceJZAS9D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Multiple Class / Multiple Features\n",
        "\n",
        "We now use 2 features: **Petal Width** and **Petal Length** to train a Multinomial classifier (using the three different flower classes)\n",
        "\n",
        "`LogisticRegression` uses softmax regression automatically when it is trained no more than two classes."
      ],
      "metadata": {
        "id": "204e83fQGkz3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2 features (petal width (cm)) & (petal length (cm))\n",
        "X_train_pwl = X_train[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
        "X_test_pwl = X_test[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
        "\n",
        "# Train a multinomial (multiclass) logistic regression (softmax regression)\n",
        "softmax_reg = LogisticRegression(penalty=\"l2\", C=30, solver='lbfgs', random_state=42)\n",
        "softmax_reg.fit(X_train_pwl, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "Ht9eCWHDAVCV",
        "outputId": "e397ad59-b61e-4660-ae46-96df51652817"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=30, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=30, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=30, random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_instance = [[5, 2]]\n",
        "softmax_reg.predict_proba(test_instance).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vkn4pKssG4IO",
        "outputId": "6ebff216-fbc1-4185-a18f-c41ebb9f6b83"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.  , 0.03, 0.97]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "softmax_reg.predict(test_instance)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_jO-kEzkHAOT",
        "outputId": "d35f2aaa-e276-4b92-ce27-f66b78d4262b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the decision boundaries\n",
        "X = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
        "y = iris.target.values\n",
        "\n",
        "x0, x1 = np.meshgrid(np.linspace(0, 8, 500).reshape(-1, 1),\n",
        "                     np.linspace(0, 3.5, 200).reshape(-1, 1))\n",
        "X_new = np.c_[x0.ravel(), x1.ravel()]\n",
        "\n",
        "y_proba = softmax_reg.predict_proba(X_new)\n",
        "y_predict = softmax_reg.predict(X_new)\n",
        "\n",
        "# Uncomment one of the following 3 lines to see the relevant probabilities\n",
        "#zz1 = y_proba[:, 0].reshape(x0.shape) # Setosa probabilities\n",
        "zz1 = y_proba[:, 1].reshape(x0.shape) # Versicolor probabilities\n",
        "#zz1 = y_proba[:, 2].reshape(x0.shape) # Virginica probabilities\n",
        "\n",
        "zz = y_predict.reshape(x0.shape)\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "plt.plot(X[y == 2, 0], X[y == 2, 1], \"y^\", label=\"Virginica\")\n",
        "plt.plot(X[y == 1, 0], X[y == 1, 1], \"bs\", label=\"Versicolor\")\n",
        "plt.plot(X[y == 0, 0], X[y == 0, 1], \"mo\", label=\"Setosa\")\n",
        "\n",
        "plt.contourf(x0, x1, zz, alpha=0.5)\n",
        "\n",
        "contour = plt.contour(x0, x1, zz1, cmap=\"hot\")\n",
        "plt.clabel(contour, inline=1)\n",
        "plt.xlabel(\"Petal length\")\n",
        "plt.ylabel(\"Petal width\")\n",
        "plt.legend(loc=\"center left\")\n",
        "plt.axis([0.5, 7, 0, 3.5])\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "D3abBvk6HD2z",
        "outputId": "57744b4e-3ff1-4259-f308-78b3f2798203"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The decision regions and boundaries are shown by the background colors.\n",
        "\n",
        "The decision boundaries are linear between each pair of classes.\n",
        "\n",
        "The probabilities for a specific class (selected by uncommenting its relevant line) is represented by the labeled curved lines.\n"
      ],
      "metadata": {
        "id": "mLx5SpW0Ikqo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "y_train_pred = softmax_reg.predict(X_train_pwl)\n",
        "y_test_pred = softmax_reg.predict(X_test_pwl)\n",
        "\n",
        "\n",
        "print(\"Train Scores\")\n",
        "print(classification_report(y_train, y_train_pred, target_names=iris[\"target_names\"]))\n",
        "\n",
        "print(\"----------------------\")\n",
        "\n",
        "print(\"Test Scores\")\n",
        "print(classification_report(y_test, y_test_pred, target_names=iris[\"target_names\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CrrE9jBUHFps",
        "outputId": "e39f435e-1eb7-4622-ff1e-0f30cf732aac"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Scores\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      setosa       1.00      1.00      1.00        40\n",
            "  versicolor       0.93      0.95      0.94        40\n",
            "   virginica       0.95      0.93      0.94        40\n",
            "\n",
            "    accuracy                           0.96       120\n",
            "   macro avg       0.96      0.96      0.96       120\n",
            "weighted avg       0.96      0.96      0.96       120\n",
            "\n",
            "----------------------\n",
            "Test Scores\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      setosa       1.00      1.00      1.00        10\n",
            "  versicolor       1.00      0.90      0.95        10\n",
            "   virginica       0.91      1.00      0.95        10\n",
            "\n",
            "    accuracy                           0.97        30\n",
            "   macro avg       0.97      0.97      0.97        30\n",
            "weighted avg       0.97      0.97      0.97        30\n",
            "\n"
          ]
        }
      ]
    }
  ]
}