{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hin-2xH6bNex"
   },
   "source": [
    "# MAE\n",
    "[![Slides](https://img.shields.io/badge/🦌-ReHLine-blueviolet)](https://rehline-python.readthedocs.io/en/latest/)\n",
    "\n",
    "The regularized MAE regression solves the following optimization problem:\n",
    "\n",
    "$$\n",
    "\\min_{\\mathbf{\\beta} \\in \\mathbb{R}^d}\n",
    "C \\sum_{i=1}^n |y_i - \\mathbf{x}_i^\\top \\mathbf{\\beta}|\n",
    "+ \\frac{1}{2}\\|\\mathbf{\\beta}\\|_2^2,\n",
    "$$\n",
    "\n",
    "where $\\mathbf{x}_i \\in \\mathbb{R}^d$ is a feature vector, and $y_i \\in \\mathbb{R}$ is the response variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note.** Since the absolute loss is a plq function, we can optimize it using `rehline.plq_Ridge_Regressor`. \n",
    "> Moreover, this wrapper adapts the `plqERM_Ridge` into a regressor, compatible with the scikit-learn API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "D62H7LVT-hpG"
   },
   "outputs": [],
   "source": [
    "## install rehline\n",
    "%pip install rehline -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "tHPsW2Eq6lN9"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "M8dZ_mRK9YDK"
   },
   "outputs": [],
   "source": [
    "# Simulate data\n",
    "np.random.seed(42)\n",
    "scaler_mae = StandardScaler()\n",
    "\n",
    "n, d = 10000, 5\n",
    "X, y = make_regression(n_samples=n, n_features=d, noise=1.0)\n",
    "X = scaler_mae.fit_transform(X)\n",
    "y = y / y.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "9JP5VwfV9aCY",
    "outputId": "be0e31b9-a9f4-42db-e409-7794fc50bbe9"
   },
   "outputs": [
    {
     "data": {
      "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>plq_Ridge_Regressor(loss={&#x27;name&#x27;: &#x27;mae&#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\"><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\"><div><div>plq_Ridge_Regressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></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>"
      ],
      "text/plain": [
       "plq_Ridge_Regressor(loss={'name': 'mae'})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## solve MAE Regression via `plq_Ridge_Regressor`\n",
    "from rehline import plq_Ridge_Regressor\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "clf = plq_Ridge_Regressor(loss={\"name\": \"mae\"}, C=1.0)\n",
    "clf.fit(X=X, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "id": "JC3sBscd9bWU",
    "outputId": "25d1c2cf-347a-46df-f4e0-67883c07bd24"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot MAE results\n",
    "warnings.filterwarnings(\"ignore\", \"is_categorical_dtype\")\n",
    "\n",
    "n_sample = 200\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "mae_sample = clf.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"mae\": mae_sample})\n",
    "df = df.melt(id_vars=\"x0\")\n",
    "\n",
    "sns.scatterplot(data=df, x=\"x0\", y=\"value\", hue=\"variable\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jncslkFzacmJ"
   },
   "source": [
    "## With Pipeline\n",
    "`plq_Ridge_Regressor` can be integrated into a scikit-learn Pipeline to streamline preprocessing including scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "Y-nBBodB6xEB"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "wbCrxMoaD6zd"
   },
   "outputs": [],
   "source": [
    "# Simulate data\n",
    "np.random.seed(42)\n",
    "\n",
    "n, d = 10000, 5\n",
    "X, y = make_regression(n_samples=n, n_features=d, noise=1.0)\n",
    "y = y / y.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 155
    },
    "id": "W8QLXxxOD8AO",
    "outputId": "2aca0c56-6e39-46e4-dc14-45bb30a8214b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\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-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: 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-2 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-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: 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-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>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;reg&#x27;, plq_Ridge_Regressor(loss={&#x27;name&#x27;: &#x27;mae&#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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></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-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>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('reg', plq_Ridge_Regressor(loss={'name': 'mae'}))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## solve MAE Regression via `plq_Ridge_Regressor`\n",
    "from rehline import plq_Ridge_Regressor\n",
    "\n",
    "pipe = Pipeline([(\"scaler\", StandardScaler()), (\"reg\", plq_Ridge_Regressor(loss={\"name\": \"mae\"}, C=1.0))])\n",
    "pipe.fit(X=X, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "id": "f-I1H7joD9Mm",
    "outputId": "20d280f6-f29a-40ac-b6c3-9351d0a963bc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot MAE results\n",
    "warnings.filterwarnings(\"ignore\", \"is_categorical_dtype\")\n",
    "\n",
    "n_sample = 200\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "mae_sample = pipe.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"mae\": mae_sample})\n",
    "df = df.melt(id_vars=\"x0\")\n",
    "\n",
    "sns.scatterplot(data=df, x=\"x0\", y=\"value\", hue=\"variable\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DtRbz-jQahoi"
   },
   "source": [
    "## Hyperparameter Tuning with GridSearchCV\n",
    "\n",
    "Due to its compatibility with the scikit-learn API, `GridSearchCV` can be applied to determine the optimal hyperparameters for the ReHLine model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q88jOVj9akJO",
    "outputId": "666d460a-4e6e-4166-a7f6-cf794dce4bbd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:{'reg__C': 0.1}\n",
      "Best CV Score: 0.0059\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "from sklearn.metrics import make_scorer, mean_absolute_error\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Define the parameter grid to search\n",
    "param_grid = {\"reg__C\": [0.1, 1.0, 10.0]}\n",
    "\n",
    "# Use mae to evaluate the performances\n",
    "mae_scorer = make_scorer(mean_absolute_error, greater_is_better=False)\n",
    "\n",
    "# Create the GridSearchCV objects\n",
    "grid_mae = GridSearchCV(pipe, param_grid, cv=5, scoring=mae_scorer)\n",
    "\n",
    "grid_mae.fit(X, y)\n",
    "\n",
    "# Print the best parameters and scores\n",
    "print(f\"Best params:{grid_mae.best_params_}\")\n",
    "print(f\"Best CV Score: {-grid_mae.best_score_:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "pAMIglM1a8zx",
    "outputId": "7ec7fe65-ff86-46ac-9e51-34d58e779080"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot MAE results\n",
    "n_sample = 200\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "mae_sample = grid_mae.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"mae\": mae_sample})\n",
    "df = df.melt(id_vars=\"x0\")\n",
    "\n",
    "sns.scatterplot(data=df, x=\"x0\", y=\"value\", hue=\"variable\").set_title(\"MAE(C=0.1)\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}