{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ofNokKz6XDRq"
   },
   "source": [
    "# SVR\n",
    "\n",
    "[![Slides](https://img.shields.io/badge/🦌-ReHLine-blueviolet)](https://rehline-python.readthedocs.io/en/latest/)\n",
    "\n",
    "SVR (Support Vector Regression) solves the following optimization problem:\n",
    "\n",
    "$$\n",
    "\\min_{\\beta \\in \\mathbb{R}^d}\n",
    "\\sum_{i=1}^n \\left(\\left|y_i-\\mathbf{x}_i^\\top \\beta\\right|-\\epsilon\\right)_+\n",
    "+\\frac{\\lambda}{2}\\|\\beta\\|^2\n",
    "$$\n",
    "\n",
    "where $\\mathbf{x}_i \\in \\mathbb{R}^d$ is a feature vector, and $y_i \\in \\mathbb{R}$ is a continuous response variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": "NjjjqDBE4pkX"
   },
   "outputs": [],
   "source": [
    "## install rehline\n",
    "%pip install rehline -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "sIbawm0_3iq9"
   },
   "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": "eq-Bqnt14Xcf"
   },
   "outputs": [],
   "source": [
    "# Simulate data\n",
    "np.random.seed(42)\n",
    "scaler_svr = StandardScaler()\n",
    "\n",
    "n, d = 10000, 5\n",
    "X, y = make_regression(n_samples=n, n_features=d, noise=1.0)\n",
    "X = scaler_svr.fit_transform(X)\n",
    "y = y / y.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "HgExVVV54a58",
    "outputId": "e3bd1fc8-f4c4-49e9-83df-3b3ccfceb0cc"
   },
   "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;epsilon&#x27;: 0.1, &#x27;name&#x27;: &#x27;svr&#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;epsilon&#x27;: 0.1, &#x27;name&#x27;: &#x27;svr&#x27;})</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## solve SVR via `plq_Ridge_Regressor`\n",
    "from rehline import plq_Ridge_Regressor\n",
    "\n",
    "clf = plq_Ridge_Regressor(loss={\"name\": \"svr\", \"epsilon\": 0.1}, 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": "URF-gXRN45jb",
    "outputId": "22f332e5-b060-42eb-9ba2-12a27da51789"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot SVR 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",
    "svr_sample = clf.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"svr\": svr_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": "2g1sDxDyWyxd"
   },
   "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": "3-rs8zHc3vVb"
   },
   "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": "6RXuMsWQ5aNh"
   },
   "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": "Iq5LJkIj5b49",
    "outputId": "c0b65856-fbed-4093-f3df-09e7d9f766e3"
   },
   "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(loss={&#x27;epsilon&#x27;: 0.1, &#x27;name&#x27;: &#x27;svr&#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;,\n",
       "                 plq_Ridge_Regressor(loss={&#x27;epsilon&#x27;: 0.1, &#x27;name&#x27;: &#x27;svr&#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;epsilon&#x27;: 0.1, &#x27;name&#x27;: &#x27;svr&#x27;})</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('reg',\n",
       "                 plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'}))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## solve SVR via `plq_Ridge_Regressor` with Pipeline\n",
    "from rehline import plq_Ridge_Regressor\n",
    "\n",
    "pipe = Pipeline(\n",
    "    [(\"scaler\", StandardScaler()), (\"reg\", plq_Ridge_Regressor(loss={\"name\": \"svr\", \"epsilon\": 0.1}, C=1.0))]\n",
    ")\n",
    "pipe.fit(X=X, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "id": "tLkX5wvV5qPJ",
    "outputId": "d52de4ef-0ff2-4983-8a26-5e7eb8af9606"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot SVR 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",
    "svr_sample = pipe.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"svr\": svr_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": "ioNCrEIpW6n4"
   },
   "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": "GHXOhTMeW9zG",
    "outputId": "b54d15c3-1cef-41e1-ee18-363fccf82626"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:{'reg__C': 1.0}\n",
      "Best CV Score: 0.0009\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\": [0.1, 1.0, 10.0]}\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_svr = GridSearchCV(pipe, param_grid, cv=5, scoring=mse_scorer)\n",
    "\n",
    "grid_svr.fit(X, y)\n",
    "\n",
    "# Print the best parameters and scores\n",
    "print(f\"Best params:{grid_svr.best_params_}\")\n",
    "print(f\"Best CV Score: {-grid_svr.best_score_:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "pUUVwcTFXv-_",
    "outputId": "687b8526-e77f-4309-e5c1-b0c41b56fc5f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot SVR results\n",
    "n_sample = 200\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "svr_sample = grid_svr.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"svr\": svr_sample})\n",
    "df = df.melt(id_vars=\"x0\")\n",
    "\n",
    "sns.scatterplot(data=df, x=\"x0\", y=\"value\", hue=\"variable\").set_title(\"SVR(C=1.0)\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}