{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Compare Regression Losses with GridSearchCV\n",
        "\n",
        "Due to its compatibility with the scikit-learn API, `GridSearchCV` can be used to compare `Huber`, `MAE`, and `MSE` losses under a unified pipeline, identify the best loss, and select its optimal hyperparameter."
      ],
      "metadata": {
        "id": "_NvZXRY3oIW6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O_SFIipUcSyD"
      },
      "outputs": [],
      "source": [
        "## install rehline\n",
        "%pip install rehline -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## simulate data\n",
        "from sklearn.datasets import make_regression\n",
        "\n",
        "X, y = make_regression(n_samples=10000, n_features=5, noise=30.0, random_state=42)\n",
        "y = y / y.std()\n",
        "y[-40:] += 20"
      ],
      "metadata": {
        "id": "mLPT_rZskyrr"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## compare Huber, MAE and MSE via GridSearchCV\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from rehline import plq_Ridge_Regressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "pipe = Pipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('reg', plq_Ridge_Regressor(loss={'name': 'mse'}))\n",
        "])\n",
        "\n",
        "# Define the parameter grid to search\n",
        "param_grid = {\n",
        "    'reg__C': [0.1, 1.0, 10.0],\n",
        "    'reg__loss': [{'name': 'huber', 'tau': 1.0}, {'name': 'mae'}, {'name': 'mse'}]\n",
        "}\n",
        "\n",
        "# Create the GridSearchCV object\n",
        "grid_search = GridSearchCV(pipe, param_grid, cv=5)\n",
        "grid_search.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
        },
        "id": "cd-YVK8UpDD0",
        "outputId": "d3d158df-1329-42a3-e402-1ab30ce693e9"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
              "                                       ('reg',\n",
              "                                        plq_Ridge_Regressor(loss={'name': 'mse'}))]),\n",
              "             param_grid={'reg__C': [0.1, 1.0, 10.0],\n",
              "                         'reg__loss': [{'name': 'huber', 'tau': 1.0},\n",
              "                                       {'name': 'mae'}, {'name': 'mse'}]})"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
              "  text-align: center;\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>GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
              "                                       (&#x27;reg&#x27;,\n",
              "                                        plq_Ridge_Regressor(loss={&#x27;name&#x27;: &#x27;mse&#x27;}))]),\n",
              "             param_grid={&#x27;reg__C&#x27;: [0.1, 1.0, 10.0],\n",
              "                         &#x27;reg__loss&#x27;: [{&#x27;name&#x27;: &#x27;huber&#x27;, &#x27;tau&#x27;: 1.0},\n",
              "                                       {&#x27;name&#x27;: &#x27;mae&#x27;}, {&#x27;name&#x27;: &#x27;mse&#x27;}]})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
              "                                       (&#x27;reg&#x27;,\n",
              "                                        plq_Ridge_Regressor(loss={&#x27;name&#x27;: &#x27;mse&#x27;}))]),\n",
              "             param_grid={&#x27;reg__C&#x27;: [0.1, 1.0, 10.0],\n",
              "                         &#x27;reg__loss&#x27;: [{&#x27;name&#x27;: &#x27;huber&#x27;, &#x27;tau&#x27;: 1.0},\n",
              "                                       {&#x27;name&#x27;: &#x27;mae&#x27;}, {&#x27;name&#x27;: &#x27;mse&#x27;}]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: Pipeline</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
              "                (&#x27;reg&#x27;, plq_Ridge_Regressor(loss={&#x27;name&#x27;: &#x27;mae&#x27;}))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>plq_Ridge_Regressor</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>plq_Ridge_Regressor(loss={&#x27;name&#x27;: &#x27;mae&#x27;})</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the best loss function and score\n",
        "print(f\"Overall Best Params: {grid_search.best_params_}\")\n",
        "print(f\"Overall Best Score:  {grid_search.best_score_:.5f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cHQEgKt8pMKr",
        "outputId": "79033a37-e379-45e9-8717-0c28369fb346"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overall Best Params: {'reg__C': 1.0, 'reg__loss': {'name': 'mae'}}\n",
            "Overall Best Score:  0.77874\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Plot the best model\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "warnings.filterwarnings(\"ignore\", \"is_categorical_dtype\")\n",
        "warnings.filterwarnings(\"ignore\", \"use_inf_as_na\")\n",
        "\n",
        "n_sample = 50\n",
        "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
        "grid_sample = grid_search.predict(X_sample)\n",
        "\n",
        "df = pd.DataFrame({'x0': X_sample[:, 0], 'real_y': y_sample, 'mae': grid_sample})\n",
        "df = df.melt(id_vars='x0')\n",
        "\n",
        "sns.scatterplot(data=df, x='x0', y='value', hue='variable').set_title(\"MAE(C=1.0)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "M9d0eBU0o_bu",
        "outputId": "3b48f730-18d1-4aee-d496-ea57e4533698"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Compare Huber & MAE & MSE"
      ],
      "metadata": {
        "id": "bhsLjyUNpxHd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print best results per loss\n",
        "import pandas as pd\n",
        "df = pd.DataFrame(grid_search.cv_results_)\n",
        "df['Loss'] = df['param_reg__loss'].apply(lambda x: x['name'])\n",
        "df = df.sort_values('mean_test_score', ascending=False)\n",
        "best = df.drop_duplicates(subset=['Loss'])\n",
        "table = best[['Loss', 'param_reg__C', 'mean_test_score']].rename(\n",
        "    columns={'param_reg__C': 'Best C', 'mean_test_score': 'CV Score'}\n",
        ")\n",
        "\n",
        "print(table.to_string(index=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IgxOOM3_pIwW",
        "outputId": "3e8366b7-3018-432d-e9f6-eebb76ea9c56"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Loss  Best C  CV Score\n",
            "  mae     1.0  0.778740\n",
            "huber     0.1  0.778691\n",
            "  mse     0.1  0.769240\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "import matplotlib.pyplot as plt\n",
        "warnings.filterwarnings(\"ignore\", \"is_categorical_dtype\")\n",
        "warnings.filterwarnings(\"ignore\", \"use_inf_as_na\")\n",
        "\n",
        "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
        "n_sample = 50\n",
        "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
        "\n",
        "for i in range(len(best)):\n",
        "    loss = best['Loss'].iloc[i]\n",
        "    c = best['param_reg__C'].iloc[i]\n",
        "\n",
        "    pipe = Pipeline([\n",
        "        ('scaler', StandardScaler()),\n",
        "        ('reg', plq_Ridge_Regressor(loss={'name': loss}, C=c))\n",
        "    ])\n",
        "    pipe.fit(X, y)\n",
        "\n",
        "    pred_y = pipe.predict(X_sample)\n",
        "    df = pd.DataFrame({'x0': X_sample[:,0], 'real_y': y_sample, 'pred': pred_y})\n",
        "    df = df.melt(id_vars='x0')\n",
        "\n",
        "    sns.scatterplot(data=df, x='x0', y='value', hue='variable', ax=axes[i], alpha=0.8).set_title(f\"{loss} (C={c})\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "id": "SrjMX-b9pVus",
        "outputId": "3d99d719-1408-4e72-946f-8f4da9f67723"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 3 Axes>"
            ],
            "image/png": 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}