{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MFDBSDOzXDVt"
   },
   "source": [
    "# Ridge Quantile Regression\n",
    "\n",
    "[![Slides](https://img.shields.io/badge/🦌-ReHLine-blueviolet)](https://rehline-python.readthedocs.io/en/latest/)\n",
    "\n",
    "The regularized quantile regression solves the following optimization problem:\n",
    "\n",
    "$$\n",
    "\\min_{\\mathbf{\\beta} \\in \\mathbb{R}^d} C \\sum_{i=1}^n \\rho_\\kappa (y_i - \\mathbf{x}_i^\\top \\mathbf{\\beta}) + \\frac{1}{2} \\|\\mathbf{\\beta}\\|^2,\n",
    "$$\n",
    "\n",
    "where $\\rho_\\kappa(u) = u \\cdot (\\kappa - \\mathbf{1}(u < 0))$ is the check loss, $\\mathbf{x}_i \\in \\mathbb{R}^d$ is a feature vector, $y_i \\in \\mathbb{R}$ is the response variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note.** Since the check 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": "rTzmChto1ltC"
   },
   "outputs": [],
   "source": [
    "## install rehline\n",
    "%pip install rehline -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "BWLs6_gP1YHE"
   },
   "outputs": [],
   "source": [
    "## simulate data\n",
    "import numpy as np\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "n, d = 10000, 5\n",
    "X, y = make_regression(n_samples=n, n_features=d, noise=1.0, random_state=42)\n",
    "X = scaler.fit_transform(X)\n",
    "y = y / y.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "L8KI3Odl4l_Z",
    "outputId": "1d6664a6-aef8-4bc4-dbda-9e07a1eee914"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>plq_Ridge_Regressor(C=0.001, loss={&#x27;name&#x27;: &#x27;QR&#x27;, &#x27;qt&#x27;: 0.95})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>plq_Ridge_Regressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>plq_Ridge_Regressor(C=0.001, loss={&#x27;name&#x27;: &#x27;QR&#x27;, &#x27;qt&#x27;: 0.95})</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "plq_Ridge_Regressor(C=0.001, loss={'name': 'QR', 'qt': 0.95})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## solve QR with different `qt` via `plq_Ridge_Regressor`\n",
    "from rehline import plq_Ridge_Regressor\n",
    "\n",
    "clf5 = plq_Ridge_Regressor(loss={\"name\": \"QR\", \"qt\": 0.05}, C=10.0 / n)\n",
    "clf5.fit(X=X, y=y)\n",
    "\n",
    "clf95 = plq_Ridge_Regressor(loss={\"name\": \"QR\", \"qt\": 0.95}, C=10.0 / n)\n",
    "clf95.fit(X=X, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "id": "WR-5hLZe4oEE",
    "outputId": "c83dface-a593-4d27-d6bf-108ee75e09fb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot QR results\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", \"is_categorical_dtype\")\n",
    "\n",
    "n_sample = 50\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "q05_sample = clf5.predict(X_sample)\n",
    "q95_sample = clf95.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"q05\": q05_sample, \"q95\": q95_sample})\n",
    "df = df.melt(id_vars=\"x0\")\n",
    "\n",
    "sns.scatterplot(data=df, x=\"x0\", y=\"value\", hue=\"variable\").set_title(\"Ridge Quantile Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t7sHr3zkZaxi"
   },
   "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": 5,
   "metadata": {
    "id": "eezf3egK8S32"
   },
   "outputs": [],
   "source": [
    "## simulate data\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "n, d = 10000, 5\n",
    "X, y = make_regression(n_samples=n, n_features=d, noise=1.0, random_state=42)\n",
    "y = y / y.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 155
    },
    "id": "t99I5I5E8s0S",
    "outputId": "c764a030-3b39-4ebd-8f45-ebe7d11a6873"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;reg&#x27;,\n",
       "                 plq_Ridge_Regressor(C=0.001,\n",
       "                                     loss={&#x27;name&#x27;: &#x27;QR&#x27;, &#x27;qt&#x27;: 0.95}))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;reg&#x27;,\n",
       "                 plq_Ridge_Regressor(C=0.001,\n",
       "                                     loss={&#x27;name&#x27;: &#x27;QR&#x27;, &#x27;qt&#x27;: 0.95}))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>plq_Ridge_Regressor</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>plq_Ridge_Regressor(C=0.001, loss={&#x27;name&#x27;: &#x27;QR&#x27;, &#x27;qt&#x27;: 0.95})</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('reg',\n",
       "                 plq_Ridge_Regressor(C=0.001,\n",
       "                                     loss={'name': 'QR', 'qt': 0.95}))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## solve QR with different `qt` via `plq_Ridge_Regressor`\n",
    "from rehline import plq_Ridge_Regressor\n",
    "\n",
    "pipe5 = Pipeline(\n",
    "    [(\"scaler\", StandardScaler()), (\"reg\", plq_Ridge_Regressor(loss={\"name\": \"QR\", \"qt\": 0.05}, C=10.0 / n))]\n",
    ")\n",
    "pipe5.fit(X=X, y=y)\n",
    "\n",
    "pipe95 = Pipeline(\n",
    "    [(\"scaler\", StandardScaler()), (\"reg\", plq_Ridge_Regressor(loss={\"name\": \"QR\", \"qt\": 0.95}, C=10.0 / n))]\n",
    ")\n",
    "pipe95.fit(X=X, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "id": "D0n0ZqKC8uoj",
    "outputId": "84e05d15-7649-4f83-e4cb-1fe839dcf1f3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot QR results\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", \"is_categorical_dtype\")\n",
    "\n",
    "n_sample = 50\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "q05_sample = pipe5.predict(X_sample)\n",
    "q95_sample = pipe95.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"q05\": q05_sample, \"q95\": q95_sample})\n",
    "df = df.melt(id_vars=\"x0\")\n",
    "\n",
    "sns.scatterplot(data=df, x=\"x0\", y=\"value\", hue=\"variable\").set_title(\"Ridge Quantile Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NjwuUVoGhQnp"
   },
   "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": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BIai5NqMii3j",
    "outputId": "0144dd7d-ec6d-44cf-9191-59d6764121d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters (qt=0.05): {'reg__C': 0.01}\n",
      "Best CV Score (qt=0.05): 0.0008\n",
      "Best Parameters (qt=0.95): {'reg__C': 0.01}\n",
      "Best CV Score (qt=0.95): 0.0008\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "from sklearn.metrics import make_scorer, mean_pinball_loss\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\": [100.0 / n, 10.0 / n, 1.0 / n]}\n",
    "\n",
    "# Use negative pinball\n",
    "scorer05 = make_scorer(mean_pinball_loss, alpha=0.05, greater_is_better=False)\n",
    "scorer95 = make_scorer(mean_pinball_loss, alpha=0.95, greater_is_better=False)\n",
    "\n",
    "# Create the GridSearchCV objects\n",
    "grid_search5 = GridSearchCV(pipe5, param_grid, cv=5, scoring=scorer05)\n",
    "grid_search95 = GridSearchCV(pipe95, param_grid, cv=5, scoring=scorer95)\n",
    "\n",
    "grid_search5.fit(X, y)\n",
    "grid_search95.fit(X, y)\n",
    "\n",
    "# Print the best parameters and scores\n",
    "print(f\"Best Parameters (qt=0.05): {grid_search5.best_params_}\")\n",
    "print(f\"Best CV Score (qt=0.05): {-grid_search5.best_score_:.4f}\")\n",
    "\n",
    "print(f\"Best Parameters (qt=0.95): {grid_search95.best_params_}\")\n",
    "print(f\"Best CV Score (qt=0.95): {-grid_search95.best_score_:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "id": "mv4sgOk8h5Ww",
    "outputId": "5bdad76c-fa09-408b-f98a-a76dfd3d15f0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", \"is_categorical_dtype\")\n",
    "warnings.filterwarnings(\"ignore\", \"use_inf_as_na\")\n",
    "\n",
    "n_sample = 50\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "q05_sample = grid_search5.predict(X_sample)\n",
    "q95_sample = grid_search95.predict(X_sample)\n",
    "\n",
    "df = pd.DataFrame({\"x0\": X_sample[:, 0], \"real_y\": y_sample, \"q05_pred\": q05_sample, \"q95_pred\": q95_sample})\n",
    "df = df.melt(id_vars=\"x0\")\n",
    "\n",
    "sns.scatterplot(data=df, x=\"x0\", y=\"value\", hue=\"variable\").set_title(\"Ridge Quantile Regression(C=100.0/n)\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}