Chapter_6_classifier_calibration.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Classifier Calibraiton  for binary classification\n",
        "\n",
        "The goal of this notebook is to help you understand classifier calibration, including metrics used to measured classifier calibration and visualize the results."
      ],
      "metadata": {
        "id": "oovLX_2MyBKW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Required Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.calibration import calibration_curve\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import log_loss, brier_score_loss"
      ],
      "metadata": {
        "id": "ir84xSb1yOsF"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Generation\n",
        "X, y = datasets.make_classification(n_samples=2000, n_features=20, random_state=42)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)"
      ],
      "metadata": {
        "id": "66hF7AAVyeYW"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Models\n",
        "models = {\n",
        "    \"Logistic Regression\": LogisticRegression(),\n",
        "    \"Support Vector Machine\": SVC(kernel='linear', probability=True, random_state=42),\n",
        "    \"Random Forest\": RandomForestClassifier(random_state=42),\n",
        "    \"Naive Bayes\": GaussianNB()\n",
        "}\n",
        "\n"
      ],
      "metadata": {
        "id": "jgV_bz0Yyi2d"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training and Predictions\n",
        "predictions = {}\n",
        "for name, model in models.items():\n",
        "    model.fit(X_train, y_train)\n",
        "    predictions[name] = model.predict_proba(X_test)[:, 1]"
      ],
      "metadata": {
        "id": "jrPlqQ1Xyjcr"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute ECE using calibration_curve\n",
        "def compute_ece(y_true, y_prob, n_bins=10):\n",
        "    true_frequencies, predicted_probabilities = calibration_curve(y_true, y_prob, n_bins=n_bins, strategy='uniform')\n",
        "    bin_edges = np.linspace(0, 1, n_bins+1)\n",
        "    bin_width = 1.0 / n_bins\n",
        "    bin_centers = np.linspace(bin_width/2, 1.0 - bin_width/2, n_bins)\n",
        "    weights, _ = np.histogram(y_prob, bins=bin_edges, range=(0, 1))\n",
        "    ece = np.sum(weights * np.abs(predicted_probabilities - bin_centers)) / len(y_prob)\n",
        "    return ece"
      ],
      "metadata": {
        "id": "OwFpKmLfyrPD"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Metrics\n",
        "results = {\n",
        "    \"Model\": [],\n",
        "    \"Log Loss\": [],\n",
        "    \"Brier Loss\": [],\n",
        "    \"ECE\": []\n",
        "}"
      ],
      "metadata": {
        "id": "Xh4HlRHVyvBP"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for name, preds in predictions.items():\n",
        "    results[\"Model\"].append(name)\n",
        "    results[\"Log Loss\"].append(log_loss(y_test, preds))\n",
        "    results[\"Brier Loss\"].append(brier_score_loss(y_test, preds))\n",
        "    results[\"ECE\"].append(compute_ece(y_test, preds))\n",
        "\n",
        "results_df = pd.DataFrame(results)"
      ],
      "metadata": {
        "id": "BrjcCmBNz2R8"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calibration Plot\n",
        "colors = ['blue', 'orange', 'green', 'red']\n",
        "plt.figure(figsize=(12, 9))\n",
        "plt.plot([0, 1], [0, 1], \"k--\", label=\"Perfectly calibrated\", color='gray')\n",
        "for color, (name, preds) in zip(colors, predictions.items()):\n",
        "    frac, mean = calibration_curve(y_test, preds, n_bins=10)\n",
        "    plt.plot(mean, frac, \"s-\", label=name, color=color)\n",
        "plt.xlabel(\"Mean predicted probability\")\n",
        "plt.ylabel(\"Fraction of positives\")\n",
        "plt.title(\"Calibration Plot\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "id": "qFecSbt6zbim",
        "outputId": "3ca48bba-2d9f-40f2-a818-e3dadb43f758"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-50d4eb9320e7>:4: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k--\" (-> color='k'). The keyword argument will take precedence.\n",
            "  plt.plot([0, 1], [0, 1], \"k--\", label=\"Perfectly calibrated\", color='gray')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the results table\n",
        "results_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "NdxkTEOK0IQy",
        "outputId": "cc91b4f5-d043-4b72-a1b0-b0bb9fe9c094"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Model  Log Loss  Brier Loss       ECE\n",
              "0     Logistic Regression  0.292348    0.086274  0.017236\n",
              "1  Support Vector Machine  0.291903    0.086721  0.012468\n",
              "2           Random Forest  0.324370    0.076712  0.012507\n",
              "3             Naive Bayes  0.388189    0.089296  0.036086"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-637dfaf4-d4c5-43e3-944c-d2a9733c5da7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Log Loss</th>\n",
              "      <th>Brier Loss</th>\n",
              "      <th>ECE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.292348</td>\n",
              "      <td>0.086274</td>\n",
              "      <td>0.017236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Support Vector Machine</td>\n",
              "      <td>0.291903</td>\n",
              "      <td>0.086721</td>\n",
              "      <td>0.012468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.324370</td>\n",
              "      <td>0.076712</td>\n",
              "      <td>0.012507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.388189</td>\n",
              "      <td>0.089296</td>\n",
              "      <td>0.036086</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-637dfaf4-d4c5-43e3-944c-d2a9733c5da7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-637dfaf4-d4c5-43e3-944c-d2a9733c5da7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-637dfaf4-d4c5-43e3-944c-d2a9733c5da7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6453e1b9-3e31-4a61-834e-14d4e7a49baa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6453e1b9-3e31-4a61-834e-14d4e7a49baa')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6453e1b9-3e31-4a61-834e-14d4e7a49baa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    }
  ]
}