{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "192f9035-a727-482e-9e19-edd6379a11f1",
   "metadata": {},
   "source": [
    "# Conformal classification using CV+ in a [`Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64f29941-60c1-4f93-bc00-6cd2c8ad20dd",
   "metadata": {},
   "source": [
    "This tutorial demonstrates how to use `CoverForestClassifier` in a scikit-learn pipeline for CV+ conformal classification on the breast cancer dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a1480a5a-a9e6-4e6e-a4fc-7f139bfa4be3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import tree\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from coverforest import CoverForestClassifier\n",
    "from coverforest.metrics import average_set_size_loss, classification_coverage_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14abb88b-19fb-41b0-88c2-3e8411ac9549",
   "metadata": {},
   "source": [
    "Load the dataset and split it into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a22d7730-643b-491c-ba0a-61f02df2790f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = load_breast_cancer(return_X_y=True, as_frame=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f00091c-c344-4059-b084-fb3f92869f7b",
   "metadata": {},
   "source": [
    "## Making a pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be1a9971-81df-48f5-97be-aff809806491",
   "metadata": {},
   "source": [
    "We'll create a scikit-learn pipeline that normalizes the features first before fitting with `CoverForestClassifier`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6628ee7-4fc0-4eae-88cb-7675cb36d7a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(\n",
    "    [\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\n",
    "            \"clf\",\n",
    "            CoverForestClassifier(\n",
    "                n_estimators=100, method=\"cv\", cv=10, random_state=0, verbose=1\n",
    "            ),\n",
    "        ),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d230a0c3-9848-4bf2-b3db-94ffd7e8a615",
   "metadata": {},
   "source": [
    "We'll now fit the pipeline on the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "743a90f8-c03e-49e8-950a-388181763411",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching regularization parameters...\n",
      "Fitting with k = 1 and lambda = 0.1.\n"
     ]
    },
    {
     "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>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;clf&#x27;,\n",
       "                 CoverForestClassifier(cv=10, n_estimators=100, random_state=0,\n",
       "                                       verbose=1))])</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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" 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;clf&#x27;,\n",
       "                 CoverForestClassifier(cv=10, n_estimators=100, random_state=0,\n",
       "                                       verbose=1))])</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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" 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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>CoverForestClassifier</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>CoverForestClassifier(cv=10, n_estimators=100, random_state=0, verbose=1)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('clf',\n",
       "                 CoverForestClassifier(cv=10, n_estimators=100, random_state=0,\n",
       "                                       verbose=1))])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c801e49-252b-4131-b346-549c655e59c3",
   "metadata": {},
   "source": [
    "At the default setting, the `fit()` method performs a hyperparameter search before the actual fitting, which as a result yields `k = 1` and `lambda = 0.1`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e36cea0-4421-4c5f-ae4a-542ba0a9be0c",
   "metadata": {},
   "source": [
    "## Inspecting individual forest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa8fca1b-0bac-4210-9072-e10afc96df1c",
   "metadata": {},
   "source": [
    "In constrast to scikit-learn's `RandomForestClassifier`, each subestimator in a `CoverForestClassifier(method='cv')` is a `FastRandomForestClassifier`, a modification of `RandomForestClassifier` that skips all input checks for faster training times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb0727a3-e1e4-460a-ab3a-799f7a400aec",
   "metadata": {},
   "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>FastRandomForestClassifier(random_state=1162135467)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>FastRandomForestClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>FastRandomForestClassifier(random_state=1162135467)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "FastRandomForestClassifier(random_state=1162135467)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_forests = pipe[\"clf\"].estimators_\n",
    "first_forest = all_forests[0]\n",
    "\n",
    "first_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23d43233-cb40-4645-afc2-d3fbf99e699c",
   "metadata": {},
   "source": [
    "This class retains all `RandomForestClassifier`'s post-train attributes and methods.\n",
    "\n",
    "For example, we can plot individual trees in each forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c1ef2bc-e61c-4ff8-ba73-825ab3db8b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "first_tree = first_forest.estimators_[0]\n",
    "tree.plot_tree(\n",
    "    first_tree,\n",
    "    feature_names=pipe[\"scaler\"].get_feature_names_out(),\n",
    "    max_depth=2,\n",
    "    proportion=True,\n",
    "    fontsize=9,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0c0a3d6-bc12-413e-a2cc-2afba79f8d07",
   "metadata": {},
   "source": [
    "As another example, we plot the feature importances in each forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "83a92f80-11d9-4331-a641-801d8274e639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = pipe[\"scaler\"].get_feature_names_out()\n",
    "importances = first_forest.feature_importances_\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(names, importances)\n",
    "ax.set_ylabel(\"Feature importances\")\n",
    "ax.tick_params(axis=\"x\", labelrotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e281e7-2092-41e3-82ed-0c082bdedcbd",
   "metadata": {},
   "source": [
    "Let's visualize the variable importances across all forests by plotting the means with 1-sd error bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "695569bb-61fd-41cf-b0c2-c8e165b89bcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = pipe[\"scaler\"].get_feature_names_out()\n",
    "imp_all = [all_forests[i].feature_importances_ for i in range(len(all_forests))]\n",
    "imp_mean = np.mean(imp_all, axis=0)\n",
    "imp_std = np.std(imp_all, axis=0)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(names, imp_mean, yerr=imp_std)\n",
    "ax.set_ylabel(\"Feature importances\")\n",
    "ax.tick_params(axis=\"x\", labelrotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fc266c0-1875-4d4b-97fb-1b1d9f4bb2ad",
   "metadata": {},
   "source": [
    "## Making set predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e44f90c-1661-4f05-aa87-6590d3e0c114",
   "metadata": {},
   "source": [
    "Now we'll make predictions with 95% coverage ($\\alpha=0.05$). This will return both the point predictions and the set predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "72c51177-3140-490e-af06-0ad4d100a18d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred, y_sets = pipe.predict(X_test, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e45418ec-3564-436a-9cee-6a33f312fe14",
   "metadata": {},
   "source": [
    "Here, `y_pred` is an array of point predictions, which are the single most likely classes predicted by the model. `y_sets` is a list that contains set predictions.\n",
    "\n",
    "Let's compare between the true classes, the single predictions and the set predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b4a593bf-36ec-40bf-8dcd-832f1e3c30fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: 1, Pred: 0, Set: [0 1]\n",
      "True: 1, Pred: 1, Set: [1]\n",
      "True: 1, Pred: 1, Set: [1]\n",
      "True: 1, Pred: 0, Set: [0 1]\n",
      "True: 1, Pred: 0, Set: [0 1]\n",
      "True: 0, Pred: 0, Set: [0 1]\n",
      "True: 1, Pred: 1, Set: [1]\n",
      "True: 0, Pred: 0, Set: [0]\n",
      "True: 0, Pred: 0, Set: [0]\n",
      "True: 0, Pred: 0, Set: [0]\n"
     ]
    }
   ],
   "source": [
    "for i in range(10, 20):\n",
    "    print(f\"True: {y_test.iloc[i]}, Pred: {y_pred[i]}, Set: {y_sets[i]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad2362c5-f854-4353-be28-b6aa4095c0e3",
   "metadata": {},
   "source": [
    "All test samples with incorrection have both classes in the prediction sets, indicating that the model outputs both classes when they are uncertain about their single predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc7e1ebc-67e0-4f29-b5dd-53d21c6b8dbe",
   "metadata": {},
   "source": [
    "## Evaluations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe10748-51a6-41ee-b8bc-d825184ddc15",
   "metadata": {},
   "source": [
    "The `coverforest.metrics` module provides two metrics for evaluation of set predictions.\n",
    "\n",
    "- `average_set_size_loss` calculates the average set size. Small values indicate that our model is quite confident in its predictions.\n",
    "- `classification_coverage_score` calculates the *empirical coverage`, that is, the proportion of sets that contain the true classes. Higher coverage scores are better, especially when making risky decisions.  The standard guaranteed lower bound on the coverage probability is $1-2\\alpha$. Nonetheless, in practice, a good conformal algorithm should have empirical coverage more than $1-\\alpha$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46895faf-6a8f-4af6-9e74-b9340cfbe540",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average set size: 1.105263157894737\n",
      "Empirical coverage: 0.9824561403508771\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average set size: {average_set_size_loss(y_test, y_sets)}\")\n",
    "print(f\"Empirical coverage: {classification_coverage_score(y_test, y_sets)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e233f3e-119a-42d1-a5ab-2eb20bf0c0b5",
   "metadata": {},
   "source": [
    "In this particular example, the empirical coverage is above the specified coverage level of 0.982. The average set size of 1.105 indicates that the random forest can output a single label with high confidence."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6564c937-dd4e-4851-9d28-8724a873778e",
   "metadata": {},
   "source": [
    "## Comparison with Jackknife+-after-bootstrap and split conformal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9b3c4b4-b262-4ef9-8a15-4ccf06a3c3de",
   "metadata": {},
   "source": [
    "Let's try the remaining two conformal classification methods, namely the Jackknife+-after-bootstrap and split conformal methods and see how they perform compared to CV+."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a22671c-0f88-4015-ac0f-d5d46380d6af",
   "metadata": {},
   "source": [
    "### Jackknife+-after-bootstrap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c45d6915-78b8-49df-9428-fa9ba017bf79",
   "metadata": {},
   "source": [
    "The model is exactly the same as a regular random forest, but now it keeps track of which samples haven't been trained by each tree for out-of-bag score calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "67d31f26-dde5-4137-b5a2-1d4e3e407d8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_b = Pipeline(\n",
    "    [\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\n",
    "            \"clf_b\",\n",
    "            CoverForestClassifier(n_estimators=400, method=\"bootstrap\", random_state=0),\n",
    "        ),\n",
    "    ]\n",
    ")\n",
    "pipe_b.fit(X_train, y_train)\n",
    "y_b_pred, y_b_sets = pipe_b.predict(X_test, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0c251b4a-8902-4baa-a092-abb5b2a891fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average set size: 1.105263157894737\n",
      "Empirical coverage: 0.9824561403508771\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average set size: {average_set_size_loss(y_test, y_b_sets)}\")\n",
    "print(f\"Empirical coverage: {classification_coverage_score(y_test, y_b_sets)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c954f8e2-e7f7-4dbc-8ba0-f3c8909f1347",
   "metadata": {},
   "source": [
    "### Split conformal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ddef0a50-4ac0-4c20-8590-b0a69d2447eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_sp = Pipeline(\n",
    "    [\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\n",
    "            \"clf_sp\",\n",
    "            CoverForestClassifier(n_estimators=400, method=\"split\", random_state=0),\n",
    "        ),\n",
    "    ]\n",
    ")\n",
    "pipe_sp.fit(X_train, y_train)\n",
    "y_sp_pred, y_sp_sets = pipe_sp.predict(X_test, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "21648766-8241-4f9f-8a50-d3a7c7d4ab78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average set size: 1.105263157894737\n",
      "Empirical coverage: 0.9649122807017544\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average set size: {average_set_size_loss(y_test, y_sp_sets)}\")\n",
    "print(f\"Empirical coverage: {classification_coverage_score(y_test, y_sp_sets)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05bc1305-10a9-4099-925b-54e4014ebc5d",
   "metadata": {},
   "source": [
    "We can see that the split conformal provides a slightly smaller coverage than the other two methods and a larger average set size."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68bc007b-a748-4b0b-b8be-fd980a1e186d",
   "metadata": {},
   "source": [
    "## Final notes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2f11600-186f-495b-afae-4c57e7d5883d",
   "metadata": {},
   "source": [
    "1. The methods used for constructing prediction sets are different variations of the adaptive prediction set (APS) method introduced in {footcite:t}`Romano2020`.\n",
    "2. The CV+ method is the only one with random forests as the sub-estimators\n",
    "3. The CV+ method also comes with additional parameters, namely:\n",
    "   - `k_init='auto'` and `lambda_init='auto'`: Initial regularization parameters\n",
    "   - `allow_empty_sets=True`: Whether empty prediction sets are allowed\n",
    "   - `randomized=True`: Whether to include one more class to the set prediction at random.\n",
    "\n",
    "   It is recommended that:\n",
    "   - If many of the set predictions are empty, it is recommended to increase the target coverage rate (decrease $\\alpha$) until most sets are nonempty.\n",
    "   - When `allow_empty_sets=False`, an additional class is added to all prediction sets, not only the empty ones, due to the exchangeability requirement of conformal prediction. This leads to larger prediction sets overall, so this option should only be used sparingly.\n",
    "4. In the case of Jackknife+-after-bootstrap, the coverage guarantee in {footcite:t}`Kim2020` relies on resampling the forest's tree count from $\\operatorname{Binomial}(B (1-1/n)^m)$, where:\n",
    "   - $B$ is the initial number of trees\n",
    "   - $n$ is the number of training samples\n",
    "   - $m$ is the number of bootstrap samples.\n",
    "\n",
    "   The `fit()` method in `CoverForestClassifier` automatically sets $B= \\text{n_estimators}/(1-1/n)^m$ so that, on average, the number of trees to be fitted is $\\text{n_estimators}$, with standard deviation of $\\sqrt{\\text{n_estimators}*(1- (1-1/n)^m)}$.\n",
    "\n",
    "   `CoverForestClassifier` also provides an option not to resample the number of sub-estimators by initiating  with `resample_n_estimators=False`.\n",
    "5. Random forest leverages parallel computation by processing trees concurrently. Use the `n_jobs` parameter in `fit()` and `predict()` to control CPU usage (`n_jobs=-1` uses all cores).\n",
    "7. For prediction, conformity score calculations require a memory array of size `(n_train × n_test × n_classes)`. To optimize performance with high `n_jobs` values, split large test sets into smaller batches."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65ad7501-b87b-4f34-8e08-b4e801f90d0f",
   "metadata": {},
   "source": [
    "```{bibliography} references.bib\n",
    "```"
   ]
  }
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