{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l4fAl3V0MysB"
   },
   "source": [
    "# Huber Regression\n",
    "\n",
    "[![Slides](https://img.shields.io/badge/🦌-ReHLine-blueviolet)](https://rehline-python.readthedocs.io/en/latest/)\n",
    "\n",
    "The regularized Huber regression solves the following optimization problem:\n",
    "\n",
    "$$\n",
    "\\min_{\\beta \\in \\mathbb{R}^d}\n",
    "C \\sum_{i=1}^n H_{\\tau}(y_i - x_i^\\top \\beta)\n",
    "+ \\frac{\\lambda}{2}\\|\\beta\\|_2^2,\n",
    "$$\n",
    "\n",
    "where $H_{\\tau}(\\cdot)$ is the Huber loss with parameter $\\tau$:\n",
    "\n",
    "$$\n",
    "H_{\\tau}(z)=\n",
    "\\begin{cases}\n",
    "\\frac{z^2}{2}, & |z| \\le \\tau, \\\\\n",
    "\\tau\\left(|z|-\\frac{\\tau}{2}\\right), & |z| > \\tau.\n",
    "\\end{cases}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0zndTa8DOfT6"
   },
   "source": [
    "> **Note.** Since the Huber 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": "8MeXCyHX0hSB"
   },
   "outputs": [],
   "source": [
    "## install rehline\n",
    "%pip install rehline -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "l1yoptXqUxlc"
   },
   "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": "96X3ttm4Em6O"
   },
   "outputs": [],
   "source": [
    "## simulate data\n",
    "np.random.seed(42)\n",
    "scaler_huber = StandardScaler()\n",
    "\n",
    "n, d = 10000, 5\n",
    "X, y = make_regression(n_samples=n, n_features=d, noise=10.0)\n",
    "X = scaler_huber.fit_transform(X)\n",
    "y = y / y.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "vSCgN97C0ei6",
    "outputId": "6acc8a4d-52b5-4017-b6b7-1d7356c2318a"
   },
   "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(C=0.001, loss={&#x27;name&#x27;: &#x27;huber&#x27;, &#x27;tau&#x27;: 5.0})</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(C=0.001, loss={&#x27;name&#x27;: &#x27;huber&#x27;, &#x27;tau&#x27;: 5.0})</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "plq_Ridge_Regressor(C=0.001, loss={'name': 'huber', 'tau': 5.0})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## solve Huber Regression with different `tau` via `plq_Ridge_Regressor`\n",
    "from rehline import plq_Ridge_Regressor\n",
    "\n",
    "clf_tau01 = plq_Ridge_Regressor(loss={\"name\": \"huber\", \"tau\": 0.1}, C=10.0 / n)\n",
    "clf_tau01.fit(X=X, y=y)\n",
    "clf_tau5 = plq_Ridge_Regressor(loss={\"name\": \"huber\", \"tau\": 5.0}, C=10.0 / n)\n",
    "clf_tau5.fit(X=X, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "-lHj744W0gFS",
    "outputId": "f54a7a46-b2fc-46c7-9e5d-73912a8db40b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot Huber Regression results\n",
    "warnings.filterwarnings(\"ignore\", \"is_categorical_dtype\")\n",
    "\n",
    "n_sample = 50\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "tau01_sample = clf_tau01.predict(X_sample)\n",
    "tau5_sample = clf_tau5.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"tau0.1\": tau01_sample, \"tau5.0\": tau5_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": "sluWAu-_MBxY"
   },
   "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": "HsIykQjgXxJ8"
   },
   "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": "XgGUDZnNEDnU"
   },
   "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=10.0)\n",
    "y = y / y.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 155
    },
    "id": "dsos8G2nEQqX",
    "outputId": "2eb58d33-aab2-4f4f-b01a-ab46c52354db"
   },
   "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;,\n",
       "                 plq_Ridge_Regressor(C=0.001,\n",
       "                                     loss={&#x27;name&#x27;: &#x27;huber&#x27;, &#x27;tau&#x27;: 5.0}))])</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;,\n",
       "                 plq_Ridge_Regressor(C=0.001,\n",
       "                                     loss={&#x27;name&#x27;: &#x27;huber&#x27;, &#x27;tau&#x27;: 5.0}))])</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(C=0.001, loss={&#x27;name&#x27;: &#x27;huber&#x27;, &#x27;tau&#x27;: 5.0})</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('reg',\n",
       "                 plq_Ridge_Regressor(C=0.001,\n",
       "                                     loss={'name': 'huber', 'tau': 5.0}))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## solve Huber Regression with different 'tau' via 'plq_Ridge_Regressor'\n",
    "from rehline import plq_Ridge_Regressor\n",
    "\n",
    "pipe_tau01 = Pipeline(\n",
    "    [(\"scaler\", StandardScaler()), (\"reg\", plq_Ridge_Regressor(loss={\"name\": \"huber\", \"tau\": 0.1}, C=10.0 / n))]\n",
    ")\n",
    "pipe_tau01.fit(X, y)\n",
    "\n",
    "pipe_tau5 = Pipeline(\n",
    "    [(\"scaler\", StandardScaler()), (\"reg\", plq_Ridge_Regressor(loss={\"name\": \"huber\", \"tau\": 5.0}, C=10.0 / n))]\n",
    ")\n",
    "pipe_tau5.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "jSJn1AaSEVfg",
    "outputId": "ad872f94-0b63-4763-f634-b3c987683d8e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot Huber Regression results\n",
    "warnings.filterwarnings(\"ignore\", \"is_categorical_dtype\")\n",
    "\n",
    "n_sample = 50\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "tau01_sample = pipe_tau01.predict(X_sample)\n",
    "tau5_sample = pipe_tau5.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"tau0.1\": tau01_sample, \"tau5.0\": tau5_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": "A4gZ5oAWMFGf"
   },
   "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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R4quF91YMH-W",
    "outputId": "15b30639-ee35-4846-c7a7-d2a686326057"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters (tau=0.1): {'reg__C': 0.01}\n",
      "Best CV Score (tau=0.1): 0.0057\n",
      "Best Parameters (tau=5.0): {'reg__C': 0.01}\n",
      "Best CV Score (tau=5.0): 0.0056\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "from sklearn.metrics import make_scorer, mean_squared_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\": [1.0 / n, 10.0 / n, 100.0 / n]}\n",
    "\n",
    "# Use mse to evaluate the performances\n",
    "mse_scorer = make_scorer(mean_squared_error, greater_is_better=False)\n",
    "\n",
    "# Create the GridSearchCV objects\n",
    "grid_tau01 = GridSearchCV(pipe_tau01, param_grid, cv=5, scoring=mse_scorer)\n",
    "grid_tau5 = GridSearchCV(pipe_tau5, param_grid, cv=5, scoring=mse_scorer)\n",
    "\n",
    "grid_tau01.fit(X, y)\n",
    "grid_tau5.fit(X, y)\n",
    "\n",
    "# Print the best parameters and scores\n",
    "print(f\"Best Parameters (tau=0.1): {grid_tau01.best_params_}\")\n",
    "print(f\"Best CV Score (tau=0.1): {-grid_tau01.best_score_:.4f}\")\n",
    "\n",
    "print(f\"Best Parameters (tau=5.0): {grid_tau5.best_params_}\")\n",
    "print(f\"Best CV Score (tau=5.0): {-grid_tau5.best_score_:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "id": "ETVczzhqMurc",
    "outputId": "9494e16a-a4cc-4973-9ea3-bd41003cfbc1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot Huber Regression results\n",
    "warnings.filterwarnings(\"ignore\", \"is_categorical_dtype\")\n",
    "\n",
    "n_sample = 50\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "tau01_sample = grid_tau01.predict(X_sample)\n",
    "tau5_sample = grid_tau5.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"tau0.1\": tau01_sample, \"tau5.0\": tau5_sample})\n",
    "df = df.melt(id_vars=\"x0\")\n",
    "\n",
    "sns.scatterplot(data=df, x=\"x0\", y=\"value\", hue=\"variable\").set_title(\n",
    "    \"Huber Regression(C tau0.1=100.0/n, C tau5.0=100.0/n)\"\n",
    ")\n",
    "plt.show()"
   ]
  }
 ],
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