{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k8JuVt-Lddja"
   },
   "source": [
    "# MSE\n",
    "[![Slides](https://img.shields.io/badge/🦌-ReHLine-blueviolet)](https://rehline-python.readthedocs.io/en/latest/)\n",
    "\n",
    "The regularized MSE regression solves the following optimization problem:\n",
    "\n",
    "$$\n",
    "\\min_{\\mathbf{\\beta} \\in \\mathbb{R}^d}\n",
    "C \\sum_{i=1}^n (y_i - \\mathbf{x}_i^\\top \\mathbf{\\beta})^2\n",
    "+ \\frac{1}{2}\\|\\mathbf{\\beta}\\|_2^2,\n",
    "$$\n",
    "\n",
    "where $\\mathbf{x}_i \\in \\mathbb{R}^d$ is a feature vector, and $y_i \\in \\mathbb{R}$ is the response variable.\n",
    "\n",
    "> **Note.** Since the squared 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": "cY_04uxlIp6C"
   },
   "outputs": [],
   "source": [
    "## install rehline\n",
    "%pip install rehline -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "6A65T6tzF3Lj"
   },
   "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": "iH3AockWGKbl"
   },
   "outputs": [],
   "source": [
    "# Simulate data\n",
    "np.random.seed(42)\n",
    "scaler_mse = StandardScaler()\n",
    "\n",
    "n, d = 10000, 5\n",
    "X, y = make_regression(n_samples=n, n_features=d, noise=5.0)\n",
    "X = scaler_mse.fit_transform(X)\n",
    "y = y / y.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "1OGXUMlKGL3U",
    "outputId": "be99ca6b-ec7a-41ac-d252-fa24ea46f411"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>plq_Ridge_Regressor(loss={&#x27;name&#x27;: &#x27;mse&#x27;})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>plq_Ridge_Regressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>plq_Ridge_Regressor(loss={&#x27;name&#x27;: &#x27;mse&#x27;})</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "plq_Ridge_Regressor(loss={'name': 'mse'})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## solve MSE Regression via `plq_Ridge_Regressor`\n",
    "from rehline import plq_Ridge_Regressor\n",
    "\n",
    "clf = plq_Ridge_Regressor(loss={\"name\": \"mse\"}, 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": "EPp4wl_8GNA7",
    "outputId": "1193f1d0-5b02-4aeb-ab03-d20bf277d7b4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot MSE 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",
    "mse_sample = clf.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"mse\": mse_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": "E8DWsUeJdvs6"
   },
   "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": "Vb4eJISeAU5O"
   },
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "Uy9loVbWCw8A"
   },
   "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=5.0)\n",
    "y = y / y.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 155
    },
    "id": "3oUouDgWC3ao",
    "outputId": "d220bcb9-f95f-494a-a487-52bf267f51e7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;reg&#x27;, plq_Ridge_Regressor(loss={&#x27;name&#x27;: &#x27;mse&#x27;}))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;reg&#x27;, plq_Ridge_Regressor(loss={&#x27;name&#x27;: &#x27;mse&#x27;}))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>plq_Ridge_Regressor</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>plq_Ridge_Regressor(loss={&#x27;name&#x27;: &#x27;mse&#x27;})</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('reg', plq_Ridge_Regressor(loss={'name': 'mse'}))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## solve MSE Regression via `plq_Ridge_Regressor`\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from rehline import plq_Ridge_Regressor\n",
    "\n",
    "pipe = Pipeline([(\"scaler\", StandardScaler()), (\"reg\", plq_Ridge_Regressor(loss={\"name\": \"mse\"}, C=1.0))])\n",
    "pipe.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "id": "JjNnypf6DI9L",
    "outputId": "d1bc822b-46a8-4842-d035-12dd10e75274"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot MSE 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",
    "mse_sample = pipe.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"mse\": mse_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": "kq2Ko61cdz2R"
   },
   "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": "S3oYy6JOd3j5",
    "outputId": "8468d1b4-5716-4d0e-8b11-109cf3929242"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params:{'reg__C': 1.0}\n",
      "Best CV Score: 0.0014\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_mse = GridSearchCV(pipe, param_grid, cv=5, scoring=mse_scorer)\n",
    "\n",
    "grid_mse.fit(X, y)\n",
    "\n",
    "# Print the best parameters and scores\n",
    "print(f\"Best params:{grid_mse.best_params_}\")\n",
    "print(f\"Best CV Score: {-grid_mse.best_score_:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "obGMZc_yeB6N",
    "outputId": "11dfbc45-0774-4ef3-957e-cde7ece033a1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot MSE results\n",
    "n_sample = 200\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "mae_sample = grid_mse.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"mse\": mse_sample})\n",
    "df = df.melt(id_vars=\"x0\")\n",
    "\n",
    "sns.scatterplot(data=df, x=\"x0\", y=\"value\", hue=\"variable\").set_title(\"MSE(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
}