docs/crepes_nb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "329c8c07",
   "metadata": {},
   "source": [
    "# More examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbf8fa8-100f-4721-81f0-987702d48411",
   "metadata": {},
   "source": [
    "## Importing packages\n",
    "\n",
    "In the examples below, we will be using the three main classes `ConformalClassifier`, `ConformalRegressor`, and `ConformalPredictiveSystem` from the `crepes` package, as an alternative to using the classes `WrapClassifier` and `WrapRegressor` in the same package, which was illustrated [here](https://crepes.readthedocs.io/en/latest/crepes_nb_wrap.html). In the examples, we will be using a helper class and functions from `crepes.extras` as well as `NumPy`, `pandas`, `matplotlib` and `sklearn`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bcb4a887-d50a-4e6f-9476-a8c38dc27b40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crepes v. 0.7.1\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "from crepes import (ConformalClassifier, \n",
    "                    ConformalRegressor, \n",
    "                    ConformalPredictiveSystem, \n",
    "                    __version__)\n",
    "\n",
    "from crepes.extras import hinge, margin, binning, DifficultyEstimator\n",
    "\n",
    "print(f\"crepes v. {__version__}\")\n",
    "\n",
    "np.random.seed(602211023)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcece726-d056-4bef-9d7a-0c5660c18ad3",
   "metadata": {},
   "source": [
    "## Conformal classifiers (CC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83facf0e-6b96-4278-984c-14ed9125247a",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Importing and splitting a classification dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84b745e3-6035-486b-9376-777ce3781216",
   "metadata": {},
   "source": [
    "Let us import a classification dataset from [www.openml.org](https://www.openml.org)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c6e86963-6481-4da0-968f-1399ccebb705",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = fetch_openml(name=\"gas-drift\", parser=\"auto\")\n",
    "\n",
    "X = dataset.data.values.astype(float)\n",
    "y = dataset.target.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f701e0c-e5f4-4c6e-8cc9-176a3bedc5f2",
   "metadata": {},
   "source": [
    "We now split the dataset into a training and a test set, and further split the training set into a proper training set and a calibration set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "085ecd58-bf24-49ad-bee5-b6e84ae19857",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)\n",
    "\n",
    "X_prop_train, X_cal, y_prop_train, y_cal = train_test_split(X_train, y_train,\n",
    "                                                            test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e16c3f-1d78-4f9e-9d7f-fc72d7005fd4",
   "metadata": {},
   "source": [
    "### Standard conformal classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bc63429-27e9-480b-b271-91af3471fded",
   "metadata": {},
   "source": [
    "Let us first create and fit a random forest in the usual way (using the proper training set only):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f45f72bb-8e22-40ac-8d63-08e3b087fc57",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=500, n_jobs=-1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_jobs=-1, n_estimators=500)\n",
    "\n",
    "rf.fit(X_prop_train, y_prop_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a0db5d-7a4b-4d1f-aa05-5ceb308c37d1",
   "metadata": {},
   "source": [
    "Let us also calculate non-conformity scores for the calibration set using the `hinge` function imported from `crepes.extras`, which takes predicted probabilities, class names and labels as input:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe40b0e2-c8ee-482f-98ee-36ebc657d1de",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas_cal = hinge(rf.predict_proba(X_cal), rf.classes_, y_cal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adb1031b-334b-4f2b-839e-80c9f4c41ee6",
   "metadata": {
    "tags": []
   },
   "source": [
    "Using the non-conformity scores, we create and fit a standard conformal classifier by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7180f6e9-19d5-4fb9-a8b4-8d19b6118c47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConformalClassifier(fitted=True, mondrian=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cc_std = ConformalClassifier()\n",
    "\n",
    "cc_std.fit(alphas_cal)\n",
    "\n",
    "display(cc_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1006efe8-00cb-47e4-8138-7a7e6827ac7d",
   "metadata": {
    "tags": []
   },
   "source": [
    "In order to make predictions for a test set, we need non-conformity scores for this too; note that class names and labels should not be provided in this case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "450b1e52-8e4c-4e78-92ff-5ee443b86b93",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas_test = hinge(rf.predict_proba(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b8ef821-b828-4cdf-9e78-bdd3b6c1736b",
   "metadata": {
    "tags": []
   },
   "source": [
    "We obtain p-values for the test set from the non-conformity scores by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "918aaeb5-17c2-41ba-b360-a3e4bd2712ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.11853998e-02, 6.81623163e-04, 7.51952414e-05, 7.22822111e-03,\n",
       "        1.83551541e-03, 1.25354912e-03],\n",
       "       [7.69995483e-04, 4.02198309e-01, 3.28246348e-03, 8.46337362e-04,\n",
       "        1.03880711e-03, 1.29555038e-03],\n",
       "       [1.65747617e-03, 1.16900791e-03, 2.02917868e-04, 5.02167131e-05,\n",
       "        7.65836044e-01, 1.70578620e-03],\n",
       "       ...,\n",
       "       [1.30852359e-03, 1.38763636e-03, 2.20624808e-03, 4.54321090e-03,\n",
       "        2.99559728e-03, 2.06218609e-01],\n",
       "       [1.89321540e-03, 1.75937091e-03, 1.64400289e-03, 6.98791143e-03,\n",
       "        2.22087512e-03, 1.16866012e-01],\n",
       "       [2.90605130e-03, 1.94900613e-03, 1.74838116e-03, 3.30855703e-03,\n",
       "        7.68918667e-02, 7.03350329e-03]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_values = cc_std.predict_p(alphas_test)\n",
    "\n",
    "display(p_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f762fb-4c9f-4c5b-ad9e-bc47054ce2ae",
   "metadata": {
    "tags": []
   },
   "source": [
    "We can also obtain predictions sets, i.e., binary vectors indicating the presence (1) or absence (0) of each class label, for some specified confidence level (the default is 0.95):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bbd6f7f2-c8dc-4eae-b7b2-6976bc75f6af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_std.predict_set(alphas_test, confidence=0.99)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab81b0b-845f-45a2-af6e-5c7061436c82",
   "metadata": {
    "tags": []
   },
   "source": [
    "We may also evaluate the predictions; here using all available metrics, which is the default:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "50d50dea-b07b-45f6-923c-d20bda7ab95e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.005463695183321349,\n",
       " 'avg_c': 1.0043134435657801,\n",
       " 'one_c': 0.9933860531991373,\n",
       " 'empty': 0.0011502516175413372,\n",
       " 'time_fit': 7.963180541992188e-05,\n",
       " 'time_evaluate': 0.47777247428894043}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_std.evaluate(alphas_test, rf.classes_, y_test, confidence=0.99)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f90b012-be41-474b-b17f-95d122180f8e",
   "metadata": {},
   "source": [
    "Alternatively, we could calculate non-conformity scores using the `margin` function, also imported from `crepes.extras`, which similar to `hinge`, takes predicted probabilities, class names and labels as input:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39d9045a-23d4-41ea-b95f-9ca93f50f2ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas_margin_cal = margin(rf.predict_proba(X_cal), rf.classes_, y_cal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba92a34d-29c4-4200-ae05-87b8c990685b",
   "metadata": {
    "tags": []
   },
   "source": [
    "Using the new non-conformity scores, we create and fit a standard conformal classifier by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e4ad2d2-9f7e-4ed1-861b-2528eceea2f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConformalClassifier(fitted=True, mondrian=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cc_margin_std = ConformalClassifier()\n",
    "\n",
    "cc_margin_std.fit(alphas_margin_cal)\n",
    "\n",
    "display(cc_margin_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f372586d-3787-42d5-94c4-5b6266dec290",
   "metadata": {
    "tags": []
   },
   "source": [
    "In order to make predictions for the test set, we again need the non-conformity scores for this too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75444649-f6c0-49c9-98e2-69db3f24b9a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas_margin_test = margin(rf.predict_proba(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24a1a7a4-5ee2-4c91-8762-813a2c25bdad",
   "metadata": {
    "tags": []
   },
   "source": [
    "Let us again evaluate the predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "28abae2e-7108-4272-8cbd-29334d4539dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.005176132278935985,\n",
       " 'avg_c': 1.0037383177570094,\n",
       " 'one_c': 0.9962616822429906,\n",
       " 'empty': 0.0,\n",
       " 'time_fit': 0.0001475811004638672,\n",
       " 'time_evaluate': 0.47726917266845703}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_margin_std.evaluate(alphas_margin_test, rf.classes_, y_test, confidence=0.99)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "821400bf-bbeb-45d6-81cc-09c6e5eee186",
   "metadata": {},
   "source": [
    "### Mondrian conformal classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21b9f68d-22c0-4029-b200-e1effb434326",
   "metadata": {},
   "source": [
    "To control the error level across different groups of objects of interest, we may use so-called Mondrian conformal classifiers.\n",
    "A Mondrian conformal classifier is formed by providing the names of the categories\n",
    "as an additional argument, named `bins`, for the `calibrate` method.\n",
    "\n",
    "We can form the Mondrian categories in any way we like, as long as we only\n",
    "use information that is available for both calibration and test instances;\n",
    "this means that we may not use the target values for this purpose, since these will \n",
    "typically not be available for the test instances. \n",
    "\n",
    "For illustration, we will use the predicted labels as the categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b3267179-9662-4dc0-8f06-2a6b6f62ff07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConformalClassifier(fitted=True, mondrian=True)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins_cal = rf.predict(X_cal)\n",
    "\n",
    "cc_mond = ConformalClassifier()\n",
    "\n",
    "cc_mond.fit(alphas_cal, bins_cal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9106e639-40bc-4fdb-9d2c-873dfececfef",
   "metadata": {
    "tags": []
   },
   "source": [
    "To obtain p-values for the test objects, we need to provide bins for these too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0808e91a-6fb3-4a9e-a25c-87d5309aad0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.10390226e-02, 5.07360123e-05, 1.62770516e-03, 5.50761506e-04,\n",
       "        1.77689397e-03, 4.53369715e-04],\n",
       "       [3.14745262e-03, 2.47274679e-01, 5.23513413e-03, 3.13117197e-03,\n",
       "        3.33529663e-03, 5.44493768e-03],\n",
       "       [3.07521523e-03, 3.37557858e-03, 5.02578402e-03, 6.70378162e-04,\n",
       "        6.98537297e-01, 3.94446883e-03],\n",
       "       ...,\n",
       "       [4.03402972e-03, 3.52563072e-03, 3.02038225e-03, 1.23638493e-02,\n",
       "        1.06774588e-02, 3.36265728e-01],\n",
       "       [2.55143708e-03, 3.49852664e-03, 3.52749722e-03, 1.05954947e-02,\n",
       "        3.85419529e-03, 1.74450162e-01],\n",
       "       [5.85086489e-03, 6.15385128e-03, 7.33465750e-03, 6.50366810e-03,\n",
       "        8.36859737e-02, 1.46611712e-02]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins_test = rf.predict(X_test)\n",
    "\n",
    "p_values = cc_mond.predict_p(alphas_test, bins_test)\n",
    "\n",
    "display(p_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b1a75e-2db5-4737-ab6b-5d095d324d8a",
   "metadata": {
    "tags": []
   },
   "source": [
    "Similarly, prediction sets are obtained by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6525c3e5-0f33-449a-859b-ce5e2808da9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_sets = cc_mond.predict_set(alphas_test, bins_test, confidence=0.8)\n",
    "\n",
    "display(prediction_sets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05351cb3-bc63-4a65-a282-88428411f21f",
   "metadata": {
    "tags": []
   },
   "source": [
    "Bins also have to be provided for the test objects when evaluating a Mondrian conformal classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8b65993-fb96-4c17-af20-12efbad88c82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.08914450035945365,\n",
       " 'avg_c': 0.9120057512580877,\n",
       " 'one_c': 0.9120057512580877,\n",
       " 'empty': 0.08799424874191229,\n",
       " 'time_fit': 0.0005381107330322266,\n",
       " 'time_evaluate': 0.39998531341552734}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_mond.evaluate(alphas_test, rf.classes_, y_test, bins_test, 0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f29300c-3e07-4f00-a4db-cbf0d70682ae",
   "metadata": {},
   "source": [
    "### Class-conditional conformal classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e440ac-5d2d-44f4-ade7-2052d5cc4c61",
   "metadata": {},
   "source": [
    "Class-conditional conformal classifiers is a special type of Mondrian conformal classifiers where the categories simply are defined by the class labels.\n",
    "The fitting is hence straightforward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63456505-ed51-4357-9dbe-3c189a717d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConformalClassifier(fitted=True, mondrian=True)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cc_class_cond = ConformalClassifier()\n",
    "\n",
    "cc_class_cond.fit(alphas_cal, y_cal)\n",
    "\n",
    "display(cc_class_cond)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bc47da2-0223-42bb-adeb-246851dc7676",
   "metadata": {},
   "source": [
    "However, the test objects need special treatment since we do not know to which categories they belong. Below we show how to obtain the prediction sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9f8b3a8d-aaf0-4a79-bfce-bf9292b148d3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 0]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_set = np.array([\n",
    "                cc_class_cond.predict_set(alphas_test,\n",
    "                                          np.full(len(alphas_test),\n",
    "                                                  rf.classes_[c]),\n",
    "                                          confidence=0.9)[:, c]\n",
    "    for c in range(len(rf.classes_))]).T\n",
    "\n",
    "display(prediction_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "961f94ef-5a10-4589-bb12-4a655818c903",
   "metadata": {},
   "source": [
    "For easy handling of class-conditional conformal classifiers, you are advised to consider using the class `WrapClassifier`, which is illustrated [here](https://crepes.readthedocs.io/en/latest/crepes_nb_wrap.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fa11c93-186a-4f00-93a0-ff7824cd86b9",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Conformal classifiers without a separate calibration set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "960d94fd-0bcd-4119-9cb3-eaf8675bab50",
   "metadata": {
    "tags": []
   },
   "source": [
    "For conformal classifiers that employ learners that use bagging, like random forests, we may consider an alternative strategy to dividing the original training set into a proper training and calibration set; we may use the out-of-bag (OOB) predictions, which allow us to use the full training set for both model building and calibration. It should be noted that this strategy does not come with the theoretical validity guarantee of the above (inductive) conformal classifiers, due to that calibration and test instances are not handled in exactly the same way. In practice, however, conformal classifiers based on out-of-bag predictions rarely fail to meet the coverage requirements."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72c4b096-36c0-4efd-8f52-8d201c74a5f5",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Standard conformal classifiers with out-of-bag calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec0ecb66-c588-4875-93bd-503a44e2693b",
   "metadata": {
    "tags": []
   },
   "source": [
    "Let us first generate a model from the full training set, making sure the learner has an attribute `oob_decision_function_`, which e.g. is the case for a `RandomForestClassifier` if `oob_score` is set to `True` when created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c88a4d32-c7f2-4778-b067-3fc283bd4688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=500, n_jobs=-1, oob_score=True)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=500, n_jobs=-1, oob_score=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=500, n_jobs=-1, oob_score=True)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_jobs=-1, n_estimators=500, oob_score=True)\n",
    "\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c9f2f1d-d09e-4c19-9486-f6e0baa88e25",
   "metadata": {},
   "source": [
    "We may now obtain a standard conformal regressor using OOB predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f37ae25f-0f48-414e-82ff-8238e5ed5b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConformalClassifier(fitted=True, mondrian=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alphas_oob = hinge(rf.oob_decision_function_, rf.classes_, y_train)\n",
    "\n",
    "cc_std_oob = ConformalClassifier()\n",
    "\n",
    "cc_std_oob.fit(alphas_oob)\n",
    "\n",
    "display(cc_std_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a109c4-bd2a-47f2-a82e-698c11d48505",
   "metadata": {},
   "source": [
    "... and use it to get prediction sets for the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e04dec43-0ca5-4433-8b12-fd151a4110d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 1, 0]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alphas_test = hinge(rf.predict_proba(X_test))\n",
    "\n",
    "prediction_sets_oob = cc_std_oob.predict_set(alphas_test)\n",
    "\n",
    "display(prediction_sets_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "700fb351-bb1a-47d7-bc66-f07692fceedc",
   "metadata": {},
   "source": [
    "#### Mondrian conformal classifiers with out-of-bag calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab8a6825-a918-4267-b703-dad7bc11f6a2",
   "metadata": {},
   "source": [
    "Using out-of-bag calibration works equally well for Mondrian conformal classifiers. Here we form two categories by binning the values of the first feature using a certain threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "63a0ecdf-f905-402f-9e7a-5ac1d4577243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConformalClassifier(fitted=True, mondrian=True)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins_oob = X_train[:,0] > 50000\n",
    "\n",
    "cc_mond_oob = ConformalClassifier()\n",
    "\n",
    "cc_mond_oob.fit(alphas_oob, bins_oob)\n",
    "\n",
    "display(cc_mond_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25aef2d0-713a-4944-abcc-1a2cbdb3e6cb",
   "metadata": {},
   "source": [
    "Prediction sets for the test objects are obtained in the following way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c2df9d29-83f6-44ab-b871-eb0b6e08a1e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 1, 0]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins_test = X_test[:,0] > 50000\n",
    "\n",
    "prediction_sets_mond_oob = cc_mond_oob.predict_set(alphas_test, bins=bins_test)\n",
    "\n",
    "display(prediction_sets_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db312591-7098-4f01-a939-4e41be4cf4da",
   "metadata": {},
   "source": [
    "We may of course evaluate the Mondrian conformal classifier using out-of-bag predictions too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bf15b071-673a-48dc-8790-beed23c3d1e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.045147375988497496,\n",
       " 'avg_c': 0.9560028756290438,\n",
       " 'one_c': 0.9560028756290438,\n",
       " 'empty': 0.043997124370956146,\n",
       " 'time_fit': 0.0004088878631591797,\n",
       " 'time_evaluate': 0.6689441204071045}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = cc_mond_oob.evaluate(alphas_test, rf.classes_, y_test, bins=bins_test)\n",
    "\n",
    "display(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc21f90",
   "metadata": {},
   "source": [
    "## Conformal regressors (CR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6f6511-5479-4728-af6a-d1faaf1564f4",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Importing and splitting a regression dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5718c5b-ff4f-45ec-821e-94052d91f092",
   "metadata": {},
   "source": [
    "Let us import a regression dataset from [www.openml.org](https://www.openml.org) and min-max normalize the targets; the latter is not really necessary, but useful, allowing to directly compare the size of a prediction interval to the whole target range, which becomes 1.0 in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "32685e4b-408c-4e94-b3c6-a55d120fe21e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = fetch_openml(name=\"house_sales\", version=3, parser=\"auto\")\n",
    "\n",
    "X = dataset.data.values.astype(float)\n",
    "y = dataset.target.values.astype(float)\n",
    "\n",
    "y = np.array([(y[i]-y.min())/(y.max()-y.min()) for i in range(len(y))])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4aaf3fc-2ec8-436b-a1a4-93c89e88c2a7",
   "metadata": {},
   "source": [
    "We now split the dataset into a training and a test set, and further split the training set into a proper training set and a calibration set. Let us fit a random forest to the proper training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "72a9bdb3-9a98-486d-9407-8e9e47eafd6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)\n",
    "\n",
    "X_prop_train, X_cal, y_prop_train, y_cal = train_test_split(X_train, y_train,\n",
    "                                                            test_size=0.25)\n",
    "\n",
    "learner_prop = RandomForestRegressor(n_jobs=-1, n_estimators=500, oob_score=True) \n",
    "\n",
    "learner_prop.fit(X_prop_train, y_prop_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11169c37",
   "metadata": {},
   "source": [
    "### Standard conformal regressors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa14a8fc-87e5-4bd3-9189-d4a3e587ce7a",
   "metadata": {},
   "source": [
    "Let us create a conformal regressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "af331cb9-25fb-474d-9555-26cbfc2377e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "cr_std = ConformalRegressor()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d93743da-4a58-49fe-882d-240c2deecb45",
   "metadata": {},
   "source": [
    "We may display the object, e.g., to see whether it has been fitted or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b6558046-d948-470f-9ace-68d205899f13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConformalRegressor(fitted=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(cr_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa740e9-b730-4fc4-92d2-f690b00272cd",
   "metadata": {},
   "source": [
    "We will use the residuals from the calibration set to fit the conformal regressor. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a3fa09d0-68ec-4831-8d99-fdddddf51b1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConformalRegressor(fitted=True, normalized=False, mondrian=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_cal = learner_prop.predict(X_cal)\n",
    "\n",
    "residuals_cal = y_cal - y_hat_cal\n",
    "\n",
    "cr_std.fit(residuals_cal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1fc47d2-dfb2-4d1f-b700-ff38cd6ee93f",
   "metadata": {},
   "source": [
    "We may now obtain prediction intervals from the point predictions for the test set; \n",
    "here using a confidence level of 99%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d82a747f-ec61-4693-b7bd-cbe11feec2dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.00132875,  0.12993958],\n",
       "       [ 0.09877535,  0.23004369],\n",
       "       [-0.0266666 ,  0.10460174],\n",
       "       ...,\n",
       "       [-0.02120372,  0.11006462],\n",
       "       [-0.02792417,  0.10334417],\n",
       "       [ 0.0286207 ,  0.15988904]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat_test = learner_prop.predict(X_test)\n",
    "\n",
    "intervals = cr_std.predict(y_hat_test, confidence=0.99)\n",
    "\n",
    "display(intervals)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eb51bc1-50a1-4212-a417-a20f659b941c",
   "metadata": {},
   "source": [
    "We may request that the intervals are cut to exclude impossible values, in this case below 0 and above 1; below we also use the default \n",
    "confidence level (95%), which further tightens the intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "297e612d-0f83-4e3f-a7da-ad0260d4bb8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.03531488, 0.09329595],\n",
       "       [0.13541899, 0.19340006],\n",
       "       [0.00997703, 0.06795811],\n",
       "       ...,\n",
       "       [0.01543991, 0.07342098],\n",
       "       [0.00871946, 0.06670054],\n",
       "       [0.06526433, 0.1232454 ]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervals_std = cr_std.predict(y_hat_test, y_min=0, y_max=1)\n",
    "\n",
    "display(intervals_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9be9c70c-5b36-40f9-acd6-3731a42bead0",
   "metadata": {},
   "source": [
    "### Normalized conformal regressors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3c02d09-5450-41af-bd7a-4cc67f21bacc",
   "metadata": {},
   "source": [
    "The above intervals are not normalized, i.e., they are all of the same size (at least before they are cut). We could make the intervals more informative through normalization using difficulty estimates; \n",
    "more difficult instances will be assigned wider intervals. We can use a `DifficultyEstimator`, as imported from `crepes.extras`,  for this purpose. It can be used to estimate the difficulty by using k-nearest neighbors in three different ways: i) by the (Euclidean) distances to the nearest neighbors, ii) by the standard deviation of the targets of the nearest neighbors, and iii) by the absolute errors of the k nearest neighbors. \n",
    "\n",
    "A small value (beta) is added to the estimates, which may be given through a (named) argument to the `fit` method; we will just use the default for this, i.e., `beta=0.01`. In order to make the beta value have the same effect across different estimators, we may opt for normalizing the difficulty estimates (using min-max scaling) by setting `scaler=True`. It should be noted that this comes with a computational cost; for estimators based on the k-nearest neighbor, a leave-one-out protocol is employed to find the minimum and maximum distances that are used by the scaler.\n",
    "\n",
    "We will first consider just using the first option (distances to the k-nearest neighbors) to produce normalized conformal regressors, using the default number of nearest neighbors, i.e., `k=25`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "147abc38-c44c-4078-b98c-a3c697b5c0d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DifficultyEstimator(fitted=True, type=knn, k=25, target=none, scaler=True, beta=0.01, oob=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "ConformalRegressor(fitted=True, normalized=True, mondrian=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "de_knn = DifficultyEstimator()\n",
    "\n",
    "de_knn.fit(X=X_prop_train, scaler=True)\n",
    "\n",
    "display(de_knn)\n",
    "\n",
    "sigmas_cal_knn_dist = de_knn.apply(X_cal)\n",
    "\n",
    "cr_norm_knn_dist = ConformalRegressor()\n",
    "\n",
    "cr_norm_knn_dist.fit(residuals_cal, sigmas=sigmas_cal_knn_dist)\n",
    "\n",
    "display(cr_norm_knn_dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d860d47-f7a2-4883-a554-c8cf88f57b7c",
   "metadata": {},
   "source": [
    "To generate prediction intervals for the test set, we need difficulty estimates for the latter too, which we get in the same way as for the calibration objects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "895e3436-f717-4adb-80a8-de023fa6e1f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04799009, 0.08062074],\n",
       "       [0.1159317 , 0.21288734],\n",
       "       [0.01369359, 0.06424155],\n",
       "       ...,\n",
       "       [0.        , 0.09011652],\n",
       "       [0.00946011, 0.06595989],\n",
       "       [0.06454369, 0.12396605]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmas_test_knn_dist = de_knn.apply(X_test)\n",
    "\n",
    "intervals_norm_knn_dist = cr_norm_knn_dist.predict(y_hat_test, \n",
    "                                                   sigmas=sigmas_test_knn_dist,\n",
    "                                                   y_min=0, y_max=1)\n",
    "\n",
    "display(intervals_norm_knn_dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fa9fb35-1646-422c-a8d1-6b27f8960685",
   "metadata": {},
   "source": [
    "Alternatively, we could estimate the difficulty using the standard deviation of the targets of the nearest neighbors; we specify this by providing the targets too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2393bc9e-80eb-4579-a4b9-df1a065fdc64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DifficultyEstimator(fitted=True, type=knn, k=25, target=labels, scaler=True, beta=0.01, oob=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "ConformalRegressor(fitted=True, normalized=True, mondrian=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "de_knn_std = DifficultyEstimator()\n",
    "\n",
    "de_knn_std.fit(X=X_prop_train, y=y_prop_train, scaler=True)\n",
    "\n",
    "display(de_knn_std)\n",
    "\n",
    "sigmas_cal_knn_std = de_knn_std.apply(X_cal)\n",
    "\n",
    "cr_norm_knn_std = ConformalRegressor()\n",
    "\n",
    "cr_norm_knn_std.fit(residuals_cal, sigmas=sigmas_cal_knn_std)\n",
    "\n",
    "display(cr_norm_knn_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84e9f1b-1a18-460d-82bc-7107391505c5",
   "metadata": {},
   "source": [
    "... and similarly for the test objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e173e02c-3359-4c82-b627-bce6d46fb449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.03622789, 0.09238294],\n",
       "       [0.08668564, 0.2421334 ],\n",
       "       [0.01728669, 0.06064845],\n",
       "       ...,\n",
       "       [0.        , 0.09222827],\n",
       "       [0.01741763, 0.05800237],\n",
       "       [0.07537787, 0.11313186]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmas_test_knn_std = de_knn_std.apply(X_test)\n",
    "\n",
    "intervals_norm_knn_std = cr_norm_knn_std.predict(y_hat_test, \n",
    "                                                 sigmas=sigmas_test_knn_std,\n",
    "                                                 y_min=0, y_max=1)\n",
    "\n",
    "display(intervals_norm_knn_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0915f18f-fb0a-41c1-8eb0-ca21c18db4e2",
   "metadata": {},
   "source": [
    "A third option is to use (absolute) residuals for the reference objects. For a model that overfits the training data, it can be a good idea to use a separate set of (reference) objects and labels from which the residuals could be calculated, rather than using the original training data. Since we in this case have trained a random forest, we opt for estimating the residuals by using the out-of-bag predictions for the training instances. (This was made possible by setting `oob_score=True` for the `RandomForestRegressor` above.)\n",
    "\n",
    "To inform the `fit` method that this is what we want to do, we provide a value for `residuals`, instead of `y` as we did above for the option to use the (standard deviation of) the targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0b35fac9-a4a7-4c22-b379-dd9080c55337",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DifficultyEstimator(fitted=True, type=knn, k=25, target=residuals, scaler=True, beta=0.01, oob=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "ConformalRegressor(fitted=True, normalized=True, mondrian=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oob_predictions = learner_prop.oob_prediction_\n",
    "\n",
    "residuals_oob = y_prop_train - oob_predictions\n",
    "\n",
    "de_knn_res = DifficultyEstimator()\n",
    "\n",
    "de_knn_res.fit(X=X_prop_train, residuals=residuals_oob, scaler=True)\n",
    "\n",
    "display(de_knn_res)\n",
    "\n",
    "sigmas_cal_knn_res = de_knn_res.apply(X_cal)\n",
    "\n",
    "cr_norm_knn_res = ConformalRegressor()\n",
    "\n",
    "cr_norm_knn_res.fit(residuals_cal, sigmas=sigmas_cal_knn_res)\n",
    "\n",
    "display(cr_norm_knn_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac8b3c2-9aed-4836-9d35-8055c5b270c0",
   "metadata": {},
   "source": [
    "... and again, the difficulty estimates are formed in the same way for the test objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c7111fb4-0cb4-447f-8114-68f88412c202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.00541009e-02, 1.08556730e-01],\n",
       "       [8.48834290e-02, 2.43935614e-01],\n",
       "       [1.52964556e-02, 6.26386841e-02],\n",
       "       ...,\n",
       "       [0.00000000e+00, 9.05429051e-02],\n",
       "       [2.30945538e-04, 7.51890555e-02],\n",
       "       [7.33599838e-02, 1.15149753e-01]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmas_test_knn_res = de_knn_res.apply(X_test)\n",
    "\n",
    "intervals_norm_knn_res = cr_norm_knn_res.predict(y_hat_test, \n",
    "                                                 sigmas=sigmas_test_knn_res,\n",
    "                                                 y_min=0, y_max=1)\n",
    "\n",
    "display(intervals_norm_knn_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17302b23-a35c-4a23-bee5-5f6a171f4793",
   "metadata": {},
   "source": [
    "In case we have trained an ensemble model, like a `RandomForestRegressor`, we could alternatively request `DifficultyEstimator` to estimate the difficulty by the variance of the predictions of the constituent models. This requires us to provide the trained model `learner` as input to `fit`, assuming that `learner.estimators_` is a collection of base models, each implementing the `predict` method; this holds e.g., for `RandomForestRegressor`. A set of objects (`X`) has to be provided only if we employ scaling (`scaler=True`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8f8b1536-e94b-4e37-84e1-6f6a51707a5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DifficultyEstimator(fitted=True, type=variance, scaler=True, beta=0.01, oob=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "ConformalRegressor(fitted=True, normalized=True, mondrian=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "de_var = DifficultyEstimator()\n",
    "\n",
    "de_var.fit(X=X_prop_train, learner=learner_prop, scaler=True)\n",
    "\n",
    "display(de_var)\n",
    "\n",
    "sigmas_cal_var = de_var.apply(X_cal)\n",
    "\n",
    "cr_norm_var = ConformalRegressor()\n",
    "\n",
    "cr_norm_var.fit(residuals_cal, sigmas=sigmas_cal_var)\n",
    "\n",
    "display(cr_norm_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b920a7f7-0da3-4ae9-a751-b0955d9c6a6f",
   "metadata": {},
   "source": [
    "The difficulty estimates for the test set are generated in the same way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e2771706-1143-437c-9588-aa65b52124ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04369085, 0.08491998],\n",
       "       [0.10756479, 0.22125425],\n",
       "       [0.02258562, 0.05534952],\n",
       "       ...,\n",
       "       [0.02913251, 0.05972839],\n",
       "       [0.02214613, 0.05327387],\n",
       "       [0.07527942, 0.11323032]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmas_test_var = de_var.apply(X_test)\n",
    "\n",
    "intervals_norm_var = cr_norm_var.predict(y_hat_test, \n",
    "                                         sigmas=sigmas_test_var, \n",
    "                                         y_min=0, y_max=1)\n",
    "\n",
    "display(intervals_norm_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dee70797-6a01-478a-b86e-6bd9d6f97287",
   "metadata": {},
   "source": [
    "### Mondrian conformal regressors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618d477f-0efb-4fea-be8d-98cdcd7f9adf",
   "metadata": {},
   "source": [
    "An alternative way of generating prediction intervals of varying size\n",
    "is to divide the object space into non-overlapping so-called Mondrian categories.\n",
    "A Mondrian conformal regressor is formed by providing the names of the categories\n",
    "as an additional argument, named `bins`, for the `fit` method.\n",
    "\n",
    "Here we employ the helper function `binning`, imported from `crepes.extras`, which given a list/array of values\n",
    "returns an array of the same length with the assigned bins. \n",
    "If the optional argument `bins` is an integer, the function will divide the values \n",
    "into equal-sized bins and return both the assigned bins and the bin boundaries. \n",
    "If `bins` instead is a set of bin boundaries, the function will just return the assigned bins.\n",
    "\n",
    "We can form the Mondrian categories in any way we like, as long as we only\n",
    "use information that is available for both calibration and test instances;\n",
    "this means that we may not use the target values for this purpose, since these will \n",
    "typically not be available for the test instances. \n",
    "We will form categories by binning of the difficulty estimates, here using the\n",
    "ones previously produced using the standard deviations of the nearest neighbor targets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "496c99ba-fc3b-4679-8210-b5fa965472a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConformalRegressor(fitted=True, normalized=False, mondrian=True)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins_cal, bin_thresholds = binning(sigmas_cal_var, bins=20)\n",
    "\n",
    "cr_mond = ConformalRegressor()\n",
    "\n",
    "cr_mond.fit(residuals_cal, bins=bins_cal)\n",
    "\n",
    "display(cr_mond)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d82697a1-bf6e-46df-afe9-d749b0099d20",
   "metadata": {},
   "source": [
    "Let us now obtain the categories for the test instances using the\n",
    "same Mondrian categorization, i.e., bin borders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "54012c36-ab14-4c31-9846-8d5490f9d7b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins_test = binning(sigmas_test_var, bins=bin_thresholds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a1fc9a-4b2e-420f-963d-4d007b7d38e2",
   "metadata": {},
   "source": [
    "... and now we can form prediction intervals for the test instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1ae5ec66-7920-4041-9b79-46fabd76fe96",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.03911358, 0.08949726],\n",
       "       [0.11455341, 0.21426563],\n",
       "       [0.02073375, 0.05720139],\n",
       "       ...,\n",
       "       [0.03185217, 0.05700872],\n",
       "       [0.02320901, 0.052211  ],\n",
       "       [0.06526433, 0.1232454 ]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervals_mond = cr_mond.predict(y_hat_test, bins=bins_test, y_min=0, y_max=1)\n",
    "\n",
    "display(intervals_mond)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e0394c-3a37-45d6-903d-7b17e2527a78",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Investigating the prediction intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c091d83d-2f25-41c6-b7ae-783fb853e317",
   "metadata": {},
   "source": [
    "Let us first put all the intervals in a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c93efef7-6510-40e3-bab5-e43591c1130a",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_intervals = {\n",
    "    \"Std CR\":intervals_std,\n",
    "    \"Norm CR knn dist\":intervals_norm_knn_dist,\n",
    "    \"Norm CR knn std\":intervals_norm_knn_std,\n",
    "    \"Norm CR knn res\":intervals_norm_knn_res,\n",
    "    \"Norm CR var\":intervals_norm_var,\n",
    "    \"Mond CR\":intervals_mond,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "427e3f54-ece8-47ac-9acc-557ed056c679",
   "metadata": {},
   "source": [
    "Let us see what fraction of the intervals that contain the true targets and how large the intervals are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fac1300f-ef37-49ab-bb4c-5674339c48fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Mean size</th>\n",
       "      <th>Median size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Std CR</th>\n",
       "      <td>0.9409</td>\n",
       "      <td>0.0568</td>\n",
       "      <td>0.0580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn dist</th>\n",
       "      <td>0.9444</td>\n",
       "      <td>0.0568</td>\n",
       "      <td>0.0466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn std</th>\n",
       "      <td>0.9548</td>\n",
       "      <td>0.0575</td>\n",
       "      <td>0.0459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn res</th>\n",
       "      <td>0.9510</td>\n",
       "      <td>0.0557</td>\n",
       "      <td>0.0439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR var</th>\n",
       "      <td>0.9424</td>\n",
       "      <td>0.0511</td>\n",
       "      <td>0.0337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond CR</th>\n",
       "      <td>0.9546</td>\n",
       "      <td>0.0532</td>\n",
       "      <td>0.0368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.9480</td>\n",
       "      <td>0.0552</td>\n",
       "      <td>0.0441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Coverage  Mean size  Median size\n",
       "Std CR              0.9409     0.0568       0.0580\n",
       "Norm CR knn dist    0.9444     0.0568       0.0466\n",
       "Norm CR knn std     0.9548     0.0575       0.0459\n",
       "Norm CR knn res     0.9510     0.0557       0.0439\n",
       "Norm CR var         0.9424     0.0511       0.0337\n",
       "Mond CR             0.9546     0.0532       0.0368\n",
       "Mean                0.9480     0.0552       0.0441"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coverages = []\n",
    "mean_sizes = []\n",
    "median_sizes = []\n",
    "\n",
    "for name in prediction_intervals.keys():\n",
    "    intervals = prediction_intervals[name]\n",
    "    coverages.append(np.sum([1 if (y_test[i]>=intervals[i,0] and \n",
    "                                   y_test[i]<=intervals[i,1]) else 0 \n",
    "                            for i in range(len(y_test))])/len(y_test))\n",
    "    mean_sizes.append((intervals[:,1]-intervals[:,0]).mean())\n",
    "    median_sizes.append(np.median((intervals[:,1]-intervals[:,0])))\n",
    "\n",
    "pred_int_df = pd.DataFrame({\"Coverage\":coverages, \n",
    "                            \"Mean size\":mean_sizes, \n",
    "                            \"Median size\":median_sizes}, \n",
    "                           index=list(prediction_intervals.keys()))\n",
    "\n",
    "pred_int_df.loc[\"Mean\"] = [pred_int_df[\"Coverage\"].mean(), \n",
    "                           pred_int_df[\"Mean size\"].mean(),\n",
    "                           pred_int_df[\"Median size\"].mean()]\n",
    "\n",
    "display(pred_int_df.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da6c765-b746-4ac0-ace2-12c35b8d59ee",
   "metadata": {},
   "source": [
    "Let us look at the distribution of the interval sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6cc02637-bd5b-4d8a-be42-64f3b77d9ac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interval_sizes = {}\n",
    "for name in prediction_intervals.keys():\n",
    "    interval_sizes[name] = prediction_intervals[name][:,1] \\\n",
    "    - prediction_intervals[name][:,0]\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.ylabel(\"CDF\")\n",
    "plt.xlabel(\"Interval sizes\")\n",
    "plt.xlim(0,interval_sizes[\"Mond CR\"].max()*1.25)\n",
    "\n",
    "colors = [\"b\",\"r\",\"g\",\"y\",\"k\",\"m\",\"c\",\"orange\"]\n",
    "\n",
    "for i, name in enumerate(interval_sizes.keys()):\n",
    "    if \"Std\" in name:\n",
    "        style = \"dotted\"\n",
    "    else:\n",
    "        style = \"solid\"\n",
    "    plt.plot(np.sort(interval_sizes[name]),\n",
    "             [i/len(interval_sizes[name])\n",
    "              for i in range(1,len(interval_sizes[name])+1)],\n",
    "             linestyle=style, c=colors[i], label=name)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65702de-437f-4a78-b273-c8b402ea2581",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Evaluating the conformal regressors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15b75494-9572-42d9-ba90-c8e8bdad36bc",
   "metadata": {},
   "source": [
    "Let us put the six above conformal regressors in a dictionary, together with the corresponding difficulty estimates for the test instances (if any)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "abcd8956-24ab-4bda-ab6e-aeb63e9b1c38",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_cr = {\n",
    "    \"Std CR\": (cr_std, []),\n",
    "    \"Norm CR knn dist\": (cr_norm_knn_dist, sigmas_test_knn_dist),\n",
    "    \"Norm CR knn std\": (cr_norm_knn_std, sigmas_test_knn_std),\n",
    "    \"Norm CR knn res\": (cr_norm_knn_res, sigmas_test_knn_res),\n",
    "    \"Norm CR var\" : (cr_norm_var, sigmas_test_var),\n",
    "    \"Mond CR\": (cr_mond, sigmas_test_var),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa72dd9c-6b88-4b2a-b4d9-9e14f89c593c",
   "metadata": {},
   "source": [
    "Let us evaluate them using three confidence levels on the test set.\n",
    "We could specify a subset of the metrics to use by the named\n",
    "`metrics` argument of the `evaluate` method; here we use all, \n",
    "which is the default.\n",
    "\n",
    "Note that the arguments `sigmas` and `bins` can always be provided,\n",
    "but they will be ignored by conformal regressors not using them,\n",
    "e.g., both arguments will be ignored by the standard conformal regressors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e29b7e5f-5d8d-40a5-808e-974883f75b60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Std CR</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Norm CR knn dist</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Norm CR knn std</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Norm CR knn res</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Norm CR var</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Mond CR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "      <th>0.99</th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "      <th>0.99</th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "      <th>0.99</th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "      <th>0.99</th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "      <th>0.99</th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "      <th>0.99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>error</th>\n",
       "      <td>0.1098</td>\n",
       "      <td>0.0591</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.1091</td>\n",
       "      <td>0.0556</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.1023</td>\n",
       "      <td>0.0452</td>\n",
       "      <td>0.0087</td>\n",
       "      <td>0.1056</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>0.0576</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.1014</td>\n",
       "      <td>0.0454</td>\n",
       "      <td>0.0068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_mean</th>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0568</td>\n",
       "      <td>0.1138</td>\n",
       "      <td>0.0431</td>\n",
       "      <td>0.0568</td>\n",
       "      <td>0.0953</td>\n",
       "      <td>0.0435</td>\n",
       "      <td>0.0575</td>\n",
       "      <td>0.0934</td>\n",
       "      <td>0.0420</td>\n",
       "      <td>0.0557</td>\n",
       "      <td>0.0923</td>\n",
       "      <td>0.0420</td>\n",
       "      <td>0.0511</td>\n",
       "      <td>0.0851</td>\n",
       "      <td>0.0407</td>\n",
       "      <td>0.0532</td>\n",
       "      <td>0.0920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_med</th>\n",
       "      <td>0.0396</td>\n",
       "      <td>0.0580</td>\n",
       "      <td>0.1165</td>\n",
       "      <td>0.0352</td>\n",
       "      <td>0.0466</td>\n",
       "      <td>0.0786</td>\n",
       "      <td>0.0346</td>\n",
       "      <td>0.0459</td>\n",
       "      <td>0.0757</td>\n",
       "      <td>0.0330</td>\n",
       "      <td>0.0439</td>\n",
       "      <td>0.0736</td>\n",
       "      <td>0.0274</td>\n",
       "      <td>0.0337</td>\n",
       "      <td>0.0586</td>\n",
       "      <td>0.0294</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.0658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_fit</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_evaluate</th>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Std CR                 Norm CR knn dist                  \\\n",
       "                 0.90    0.95    0.99             0.90    0.95    0.99   \n",
       "error          0.1098  0.0591  0.0116           0.1091  0.0556  0.0085   \n",
       "eff_mean       0.0394  0.0568  0.1138           0.0431  0.0568  0.0953   \n",
       "eff_med        0.0396  0.0580  0.1165           0.0352  0.0466  0.0786   \n",
       "time_fit       0.0001  0.0001  0.0001           0.0001  0.0001  0.0001   \n",
       "time_evaluate  0.0011  0.0003  0.0003           0.0004  0.0003  0.0003   \n",
       "\n",
       "              Norm CR knn std                 Norm CR knn res                  \\\n",
       "                         0.90    0.95    0.99            0.90    0.95    0.99   \n",
       "error                  0.1023  0.0452  0.0087          0.1056  0.0490  0.0074   \n",
       "eff_mean               0.0435  0.0575  0.0934          0.0420  0.0557  0.0923   \n",
       "eff_med                0.0346  0.0459  0.0757          0.0330  0.0439  0.0736   \n",
       "time_fit               0.0000  0.0000  0.0000          0.0001  0.0001  0.0001   \n",
       "time_evaluate          0.0003  0.0003  0.0002          0.0004  0.0003  0.0002   \n",
       "\n",
       "              Norm CR var                 Mond CR                  \n",
       "                     0.90    0.95    0.99    0.90    0.95    0.99  \n",
       "error              0.1030  0.0576  0.0084  0.1014  0.0454  0.0068  \n",
       "eff_mean           0.0420  0.0511  0.0851  0.0407  0.0532  0.0920  \n",
       "eff_med            0.0274  0.0337  0.0586  0.0294  0.0368  0.0658  \n",
       "time_fit           0.0000  0.0000  0.0000  0.0002  0.0002  0.0002  \n",
       "time_evaluate      0.0003  0.0002  0.0004  0.0011  0.0009  0.0011  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confidence_levels = [0.9,0.95,0.99]\n",
    "\n",
    "names = list(all_cr.keys())\n",
    "\n",
    "all_results = {}\n",
    "\n",
    "for confidence in confidence_levels:\n",
    "    for name in names:\n",
    "        all_results[(name,confidence)] = all_cr[name][0].evaluate(\n",
    "        y_hat_test, y_test, sigmas=all_cr[name][1],\n",
    "        bins=bins_test, confidence=confidence, \n",
    "        y_min=0, y_max=1)\n",
    "\n",
    "results_df = pd.DataFrame(columns=pd.MultiIndex.from_product(\n",
    "    [names,confidence_levels]), index=list(list(\n",
    "    all_results.values())[0].keys()))\n",
    "\n",
    "for key in all_results.keys():\n",
    "    results_df[key] = all_results[key].values()\n",
    "\n",
    "display(results_df.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63733c6a-d586-49f9-8e01-c02e386ddd99",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Conformal regressors without a separate calibration set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb701612-705e-446e-a96e-dacb20fd8366",
   "metadata": {
    "tags": []
   },
   "source": [
    "For conformal regressors that employ learners that use bagging, like random forests, we may consider an alternative strategy to dividing the original training set into a proper training and calibration set; we may use the out-of-bag (OOB) predictions, which allow us to use the full training set for both model building and calibration. It should be noted that this strategy does not come with the theoretical validity guarantee of the above (inductive) conformal regressors, due to that calibration and test instances are not handled in exactly the same way. In practice, however, conformal regressors based on out-of-bag predictions rarely do not meet the coverage requirements."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea422ac1-dbdc-47e9-bf00-8b2def1825c9",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Standard conformal regressors with out-of-bag calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0263047-86e8-425d-92c4-f148e5db6913",
   "metadata": {
    "tags": []
   },
   "source": [
    "Let us first generate a model from the full training set and then get the residuals using the OOB predictions; we rely on that the learner has an attribute `oob_prediction_`, which e.g. is the case for a `RandomForestRegressor` if `oob_score` is set to `True` when created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "772644f0-ce59-49a6-847d-c43fd9bb0374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True)</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 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 sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learner_full = RandomForestRegressor(n_jobs=-1, n_estimators=500, \n",
    "                                     oob_score=True)\n",
    "\n",
    "learner_full.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a18461b2-09e0-47b0-8138-02b452d0ff88",
   "metadata": {},
   "source": [
    "Now we can obtain the residuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "be432940-af35-478e-b49f-26746056b2cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "oob_predictions = learner_full.oob_prediction_\n",
    "\n",
    "residuals_oob = y_train - oob_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91abe570-276e-4a2f-8e2e-08b1da212e5d",
   "metadata": {},
   "source": [
    "We may now obtain a standard conformal regressor from these OOB residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "de8818b7-91b5-4516-a74b-7076b725d141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConformalRegressor(fitted=True, normalized=False, mondrian=False)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_std_oob = ConformalRegressor()\n",
    "\n",
    "cr_std_oob.fit(residuals_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab40dda6-0d81-4768-9a84-5893d09eb1be",
   "metadata": {},
   "source": [
    "... and apply it using the point predictions of the full model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ff0c3105-3141-4dfa-9377-65d24763156c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.03322223, 0.09448771],\n",
       "       [0.13330828, 0.19457376],\n",
       "       [0.01079954, 0.07206502],\n",
       "       ...,\n",
       "       [0.01466877, 0.07593424],\n",
       "       [0.0079914 , 0.06925687],\n",
       "       [0.05884236, 0.12010784]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat_full = learner_full.predict(X_test)\n",
    "\n",
    "intervals_std_oob = cr_std_oob.predict(y_hat_full, y_min=0, y_max=1)\n",
    "\n",
    "display(intervals_std_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c35965ec-0543-49d6-afef-c6d23cfc9d5e",
   "metadata": {},
   "source": [
    "#### Normalized conformal regressors with out-of-bag calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d87c0492-dea7-435d-b4b4-f9937182b91d",
   "metadata": {},
   "source": [
    "We may also generate normalized conformal regressors from the OOB predictions. The `DifficultyEstimator` can be used also for this purpose; for the k-nearest neighbor approaches, the difficulty of each object in the training set will be computed using a leave-one-out procedure, while for the variance-based approach the out-of-bag predictions will be employed. \n",
    "\n",
    "By setting `oob=True`, we inform the `fit` method that we may request difficulty estimates for the provided set of objects; these will be retrieved by not providing any objects when calling the `apply` method.\n",
    "\n",
    "Let us start with the k-nearest neighbor approach using distances only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d5ea97f8-1d9a-4025-b8d5-99ef2ec9433f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DifficultyEstimator(fitted=True, type=knn, k=25, target=none, scaler=True, beta=0.01, oob=True)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "ConformalRegressor(fitted=True, normalized=True, mondrian=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de_knn_dist_oob = DifficultyEstimator()\n",
    "\n",
    "de_knn_dist_oob.fit(X=X_train, scaler=True, oob=True)\n",
    "\n",
    "display(de_knn_dist_oob)\n",
    "\n",
    "sigmas_knn_dist_oob = de_knn_dist_oob.apply()\n",
    "\n",
    "cr_norm_knn_dist_oob = ConformalRegressor()\n",
    "\n",
    "cr_norm_knn_dist_oob.fit(residuals_oob, sigmas=sigmas_knn_dist_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6920423-279d-4ddc-982d-6fa422eea71f",
   "metadata": {},
   "source": [
    "In order to apply the normalized OOB regressors to the test set, we\n",
    "need to generate difficulty estimates for the latter too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0ecff050-2e20-450e-aa27-8df9a16886bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04653797, 0.08117197],\n",
       "       [0.11290619, 0.21497585],\n",
       "       [0.01719983, 0.06566472],\n",
       "       ...,\n",
       "       [0.        , 0.09412261],\n",
       "       [0.00993602, 0.06731225],\n",
       "       [0.05772396, 0.12122624]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmas_test_knn_dist_oob = de_knn_dist_oob.apply(X_test)\n",
    "\n",
    "intervals_norm_knn_dist_oob = cr_norm_knn_dist_oob.predict(\n",
    "    y_hat_full, sigmas=sigmas_test_knn_dist_oob, y_min=0, y_max=1)\n",
    "\n",
    "display(intervals_norm_knn_dist_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa3cb65-e631-4245-8993-f053eb7c02cf",
   "metadata": {},
   "source": [
    "For completeness, we will illustrate the use of out-of-bag calibration for the remaining approaches too. For k-nearest neighbors with labels, we do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "681e81b5-f626-4879-bcaa-3b405646d849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DifficultyEstimator(fitted=True, type=knn, k=25, target=labels, scaler=True, beta=0.01, oob=True)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[0.03604063, 0.09166931],\n",
       "       [0.09949362, 0.22838842],\n",
       "       [0.01677922, 0.06608533],\n",
       "       ...,\n",
       "       [0.        , 0.0994757 ],\n",
       "       [0.01183709, 0.06541118],\n",
       "       [0.07173485, 0.10721535]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "de_knn_std_oob = DifficultyEstimator()\n",
    "\n",
    "de_knn_std_oob.fit(X=X_train, y=y_train, scaler=True, oob=True)\n",
    "\n",
    "display(de_knn_std_oob)\n",
    "\n",
    "sigmas_knn_std_oob = de_knn_std_oob.apply()\n",
    "\n",
    "cr_norm_knn_std_oob = ConformalRegressor()\n",
    "\n",
    "cr_norm_knn_std_oob.fit(residuals=residuals_oob, sigmas=sigmas_knn_std_oob)\n",
    "\n",
    "sigmas_test_knn_std_oob = de_knn_std_oob.apply(X_test)\n",
    "\n",
    "intervals_norm_knn_std_oob = cr_norm_knn_std_oob.predict(\n",
    "    y_hat_full, sigmas=sigmas_test_knn_std_oob, y_min=0, y_max=1)\n",
    "\n",
    "display(intervals_norm_knn_std_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb3f08dc-5173-4043-91c5-c5bbda8de75e",
   "metadata": {},
   "source": [
    "A third option is to use k-nearest neighbors with (OOB) residuals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "81bd366b-64de-40e3-b0ec-904d9367be15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DifficultyEstimator(fitted=True, type=knn, k=25, target=residuals, scaler=True, beta=0.01, oob=True)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[0.02291667, 0.10479326],\n",
       "       [0.09427382, 0.23360822],\n",
       "       [0.01906763, 0.06379692],\n",
       "       ...,\n",
       "       [0.        , 0.09564689],\n",
       "       [0.        , 0.07791071],\n",
       "       [0.06679388, 0.11215631]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "de_knn_res_oob = DifficultyEstimator()\n",
    "\n",
    "de_knn_res_oob.fit(X=X_train, residuals=residuals_oob, scaler=True, oob=True)\n",
    "\n",
    "display(de_knn_res_oob)\n",
    "\n",
    "sigmas_knn_res_oob = de_knn_res_oob.apply()\n",
    "\n",
    "cr_norm_knn_res_oob = ConformalRegressor()\n",
    "\n",
    "cr_norm_knn_res_oob.fit(residuals_oob, sigmas=sigmas_knn_res_oob)\n",
    "\n",
    "sigmas_test_knn_res_oob = de_knn_res_oob.apply(X_test)\n",
    "\n",
    "intervals_norm_knn_res_oob = cr_norm_knn_res_oob.predict(\n",
    "    y_hat_full, sigmas=sigmas_test_knn_res_oob, y_min=0, y_max=1)\n",
    "\n",
    "display(intervals_norm_knn_res_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7667bd56-8b45-4b0f-8586-935659e6b1f5",
   "metadata": {},
   "source": [
    "A fourth and final option for the normalized conformal regressors is to use variance as a difficulty estimate. We then leave labels and residuals out, but provide an (ensemble) learner. In contrast to when `oob=False`, we are here required to provide the (full) training set, from which the variance of the out-of-bag predictions will be computed. When applied to the test set, the full ensemble model will not be used to obtain the difficulty estimates, but instead a subset of the constituent models is used, following what could be seen as post hoc assignment of each test instance to a bag.      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9344f245-7ed7-446d-b462-5b52a43d5795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DifficultyEstimator(fitted=True, type=variance, scaler=True, beta=0.01, oob=True)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[0.04172072, 0.08598921],\n",
       "       [0.10822068, 0.21966136],\n",
       "       [0.02401861, 0.05884594],\n",
       "       ...,\n",
       "       [0.02955233, 0.06105068],\n",
       "       [0.02264877, 0.0545995 ],\n",
       "       [0.06901595, 0.10993424]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "de_var_oob = DifficultyEstimator()\n",
    "\n",
    "de_var_oob.fit(X=X_train, learner=learner_full, scaler=True, oob=True)\n",
    "\n",
    "display(de_var_oob)\n",
    "\n",
    "sigmas_var_oob = de_var_oob.apply()\n",
    "\n",
    "cr_norm_var_oob = ConformalRegressor()\n",
    "\n",
    "cr_norm_var_oob.fit(residuals_oob, sigmas=sigmas_var_oob)\n",
    "\n",
    "sigmas_test_var_oob = de_var_oob.apply(X_test)\n",
    "\n",
    "intervals_norm_var_oob = cr_norm_var_oob.predict(y_hat_full, \n",
    "                                                 sigmas=sigmas_test_var_oob, \n",
    "                                                 y_min=0, y_max=1)\n",
    "\n",
    "display(intervals_norm_var_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943b41e1-ef45-4041-8042-791434fa7bf6",
   "metadata": {},
   "source": [
    "#### Mondrian conformal regressors with out-of-bag calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "224d4917-cd8b-433a-abc3-49fb96b3561d",
   "metadata": {},
   "source": [
    "We may form the categories using the difficulty estimates obtained from the OOB predictions. We here consider the difficulty estimates produced by the fourth above option (using variance) only. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7dde227d-112a-48ff-9d6b-76367b4f956e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConformalRegressor(fitted=True, normalized=False, mondrian=True)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins_oob, bin_thresholds_oob = binning(sigmas_var_oob, bins=20)\n",
    "\n",
    "cr_mond_oob = ConformalRegressor()\n",
    "\n",
    "cr_mond_oob.fit(residuals=residuals_oob, bins=bins_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "139631b1-2f60-4fb2-8e27-5466628c8a9c",
   "metadata": {},
   "source": [
    "... and assign the categories for the test instances, ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c91acfb0-abdc-4633-939c-92f28b71666c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins_test_oob = binning(sigmas_test_var_oob, bins=bin_thresholds_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aaf927a-dd60-485e-a850-e6aca91da270",
   "metadata": {},
   "source": [
    "... and finally generate the prediction intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "224d067f-4db3-4903-9f1b-5dd9f8d75cbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.03502864, 0.0926813 ],\n",
       "       [0.10798329, 0.21989876],\n",
       "       [0.02516001, 0.05770455],\n",
       "       ...,\n",
       "       [0.03415905, 0.05644395],\n",
       "       [0.02590607, 0.05134221],\n",
       "       [0.06336176, 0.11558844]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervals_mond_oob = cr_mond_oob.predict(y_hat_full,\n",
    "                                         bins=bins_test_oob,\n",
    "                                         y_min=0, y_max=1)\n",
    "\n",
    "display(intervals_mond_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de107bca-8a71-427b-96f3-fe79f1887d3d",
   "metadata": {},
   "source": [
    "### Investigating the OOB prediction intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "47c85f2d-edf7-441c-9372-c5ff0011f265",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_intervals = {\n",
    "    \"Std CR OOB\":intervals_std_oob,\n",
    "    \"Norm CR knn dist OOB\":intervals_norm_knn_dist_oob,\n",
    "    \"Norm CR knn std OOB\":intervals_norm_knn_std_oob,\n",
    "    \"Norm CR knn res OOB\":intervals_norm_knn_res_oob,\n",
    "    \"Norm CR var OOB\":intervals_norm_var_oob,\n",
    "    \"Mond CR OOB\":intervals_mond_oob,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0704cfac-b4a3-417a-bc94-72f6b4a1525f",
   "metadata": {},
   "source": [
    "Let us see what fraction of the intervals that contain the true targets and how large the intervals are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "342f0d39-d50e-466d-a944-469f55d3ecbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Mean size</th>\n",
       "      <th>Median size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Std CR OOB</th>\n",
       "      <td>0.9494</td>\n",
       "      <td>0.0597</td>\n",
       "      <td>0.0613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn dist OOB</th>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.0580</td>\n",
       "      <td>0.0473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn std OOB</th>\n",
       "      <td>0.9577</td>\n",
       "      <td>0.0599</td>\n",
       "      <td>0.0469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn res OOB</th>\n",
       "      <td>0.9479</td>\n",
       "      <td>0.0540</td>\n",
       "      <td>0.0418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR var OOB</th>\n",
       "      <td>0.9525</td>\n",
       "      <td>0.0522</td>\n",
       "      <td>0.0350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond CR OOB</th>\n",
       "      <td>0.9535</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.0352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.9518</td>\n",
       "      <td>0.0559</td>\n",
       "      <td>0.0446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Coverage  Mean size  Median size\n",
       "Std CR OOB              0.9494     0.0597       0.0613\n",
       "Norm CR knn dist OOB    0.9500     0.0580       0.0473\n",
       "Norm CR knn std OOB     0.9577     0.0599       0.0469\n",
       "Norm CR knn res OOB     0.9479     0.0540       0.0418\n",
       "Norm CR var OOB         0.9525     0.0522       0.0350\n",
       "Mond CR OOB             0.9535     0.0519       0.0352\n",
       "Mean                    0.9518     0.0559       0.0446"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coverages = []\n",
    "mean_sizes = []\n",
    "median_sizes = []\n",
    "\n",
    "for name in prediction_intervals.keys():\n",
    "    intervals = prediction_intervals[name]\n",
    "    coverages.append(np.sum([1 if (y_test[i]>=intervals[i,0] and \n",
    "                                   y_test[i]<=intervals[i,1]) else 0 \n",
    "                            for i in range(len(y_test))])/len(y_test))\n",
    "    mean_sizes.append((intervals[:,1]-intervals[:,0]).mean())\n",
    "    median_sizes.append(np.median((intervals[:,1]-intervals[:,0])))\n",
    "\n",
    "pred_int_df = pd.DataFrame({\"Coverage\":coverages, \n",
    "                            \"Mean size\":mean_sizes, \n",
    "                            \"Median size\":median_sizes}, \n",
    "                           index=list(prediction_intervals.keys()))\n",
    "\n",
    "pred_int_df.loc[\"Mean\"] = [pred_int_df[\"Coverage\"].mean(), \n",
    "                           pred_int_df[\"Mean size\"].mean(),\n",
    "                           pred_int_df[\"Median size\"].mean()]\n",
    "\n",
    "display(pred_int_df.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48ad1398-2cde-46a0-8c39-35b94c80c5d1",
   "metadata": {},
   "source": [
    "Let us look at the distribution of the interval sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1e20a1a1-c8ec-4268-9ff9-60cafed650af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interval_sizes = {}\n",
    "for name in prediction_intervals.keys():\n",
    "    interval_sizes[name] = prediction_intervals[name][:,1] \\\n",
    "    - prediction_intervals[name][:,0]\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.ylabel(\"CDF\")\n",
    "plt.xlabel(\"Interval sizes\")\n",
    "plt.xlim(0,interval_sizes[\"Mond CR OOB\"].max()*1.25)\n",
    "\n",
    "colors = [\"b\",\"r\",\"g\",\"y\",\"k\",\"m\",\"c\",\"orange\"]\n",
    "\n",
    "for i, name in enumerate(interval_sizes.keys()):\n",
    "    if \"Std\" in name:\n",
    "        style = \"dotted\"\n",
    "    else:\n",
    "        style = \"solid\"\n",
    "    plt.plot(np.sort(interval_sizes[name]),\n",
    "             [i/len(interval_sizes[name])\n",
    "              for i in range(1,len(interval_sizes[name])+1)],\n",
    "             linestyle=style, c=colors[i], label=name)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97bb33f0",
   "metadata": {},
   "source": [
    "## Conformal Predictive Systems (CPS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e208caf6",
   "metadata": {},
   "source": [
    "### Creating and fitting CPS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b5131e-e544-4945-876b-2dc5cbe370de",
   "metadata": {},
   "source": [
    "Let us create and fit standard and normalized conformal predictive systems, using the residuals from the calibration set (as obtained in the previous section), as well two conformal predictive systems using out-of-bag residuals; with and without normalization. As can be seen, the input for fitting conformal predictive systems is on the same format as for the conformal regressors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9286190d-1f9b-41a2-a1d1-1cba42f1ab0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cps_std = ConformalPredictiveSystem().fit(residuals_cal)\n",
    "\n",
    "cps_norm = ConformalPredictiveSystem().fit(residuals_cal,\n",
    "                                           sigmas=sigmas_cal_var)\n",
    "\n",
    "cps_std_oob = ConformalPredictiveSystem().fit(residuals_oob)\n",
    "\n",
    "cps_norm_oob = ConformalPredictiveSystem().fit(residuals_oob, \n",
    "                                               sigmas=sigmas_var_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "158f850a-b7e6-4c7e-a701-b7a6c22d03ed",
   "metadata": {},
   "source": [
    "Let us also create some Mondrian CPS, but in contrast to the Mondrian conformal regressors above, we here form the categories through binning of the predictions rather than binning of the difficulty estimates. We may use the latter, i.e., the sigmas, to obtain a normalized CPS for each category (bin)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a89db840-c6b8-47aa-8c18-b00eb199c5a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins_cal, bin_thresholds = binning(y_hat_cal, bins=5)\n",
    "\n",
    "cps_mond_std = ConformalPredictiveSystem().fit(residuals_cal,\n",
    "                                               bins=bins_cal)\n",
    "\n",
    "cps_mond_norm = ConformalPredictiveSystem().fit(residuals_cal,\n",
    "                                                sigmas=sigmas_cal_var,\n",
    "                                                bins=bins_cal)\n",
    "\n",
    "\n",
    "bins_oob, bin_thresholds_oob = binning(oob_predictions, bins=5)\n",
    "\n",
    "cps_mond_std_oob = ConformalPredictiveSystem().fit(residuals_oob,\n",
    "                                                   bins=bins_oob)\n",
    "\n",
    "cps_mond_norm_oob = ConformalPredictiveSystem().fit(residuals_oob,\n",
    "                                                    sigmas=sigmas_var_oob,\n",
    "                                                    bins=bins_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e541e40f-3085-4bdc-847f-11089c66a090",
   "metadata": {},
   "source": [
    "### Making predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d142f5b-8b79-4962-881d-66005cc6ec8c",
   "metadata": {},
   "source": [
    "For the normalized approaches, we already have the difficulty estimates which are needed for the test instances. \n",
    "For the Mondrian approaches, we also need to assign the new categories to the test instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "85d7f64d-9214-4908-8295-74f25bd9fbad",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins_test = binning(y_hat_test, bins=bin_thresholds)\n",
    "\n",
    "bins_test_oob = binning(y_hat_full, bins=bin_thresholds_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00406bed-b471-459a-9741-2a6fd111bff1",
   "metadata": {},
   "source": [
    "The output of the `predict` method of a `ConformalPredictiveSystem` will depend on how we specify the input. If we provide specific target values (using the parameter `y`), the method will output a p-value for each test instance, i.e., the probability that the true target is less than or equal to the provided values. The method assumes that either one value is provided for each test instance or that the same (single) value is provided for all test instances.\n",
    "\n",
    "Here we will obtain the p-values from `cps_mond_norm` for the true targets of the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "072d72f7-6c0e-46d7-b447-906863f56c70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.51524805, 0.43762558, 0.59879723, ..., 0.533513  , 0.5069589 ,\n",
       "       0.57852452])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_values = cps_mond_norm.predict(y_hat_test,\n",
    "                                 sigmas=sigmas_test_knn_res,\n",
    "                                 bins=bins_test,\n",
    "                                 y=y_test)\n",
    "\n",
    "display(p_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04ec480-1586-48b9-a8e3-8062f9924bdc",
   "metadata": {},
   "source": [
    "If we instead would like to get threshold values, with a specified probability that the true target is less than the threshold for each test instance, we may instead provide percentiles as input to the `predict` method. This is done through the parameter\n",
    "`lower_percentiles`, which denotes (one or more) percentiles for which a lower value\n",
    "will be selected in case a percentile lies between two values\n",
    "(similar to `interpolation=\"lower\"` in `numpy.percentile`), or using\n",
    "`higher_percentiles`, which denotes (one or more) percentiles for which a higher value\n",
    "will be selected in such cases (similar to `interpolation=\"higher\"` in `numpy.percentile`).\n",
    "\n",
    "Here we will obtain the lowest values from `cps_mond_norm`, such that the probability for the target values being less than these is at least 50%:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0440b9f7-6847-4637-bfcf-ecbce5de7cc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05766299, 0.143966  , 0.0355314 , ..., 0.04441064, 0.03226943,\n",
       "       0.08888349])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresholds = cps_mond_norm.predict(y_hat_test,\n",
    "                                   sigmas=sigmas_test_knn_res,\n",
    "                                   bins=bins_test,\n",
    "                                   higher_percentiles=50)\n",
    "\n",
    "display(thresholds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c85f5ff8-7c5d-444d-979c-b01404965fe9",
   "metadata": {},
   "source": [
    "We can also specify both target values and percentiles; the resulting p-values will be returned in the first column, while any values corresponding to the lower percentiles will be included in the subsequent columns, followed by columns containing the values corresponding to the higher percentiles. The following call hence results in an array with five columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1cda8888-2c35-45a5-9286-53ab3761483d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.5147695 , -0.1453888 , -0.10477296,  0.23667889,  0.30194424],\n",
       "       [ 0.43791624, -0.17658491, -0.13148684,  0.48930068,  0.57872646],\n",
       "       [ 0.59712769, -0.05399873, -0.03674741,  0.11435319,  0.15295928],\n",
       "       ...,\n",
       "       [ 0.53405861, -0.1773396 , -0.12559947,  0.23902838,  0.30665434],\n",
       "       [ 0.50738882, -0.10948583, -0.08217138,  0.1570699 ,  0.21819587],\n",
       "       [ 0.57831067,  0.00466114,  0.01651032,  0.17961758,  0.20311354]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = cps_mond_norm.predict(y_hat_test,\n",
    "                                sigmas=sigmas_test_knn_res,\n",
    "                                bins=bins_test,\n",
    "                                y=y_test,\n",
    "                                lower_percentiles=[2.5, 5],\n",
    "                                higher_percentiles=[95, 97.5])\n",
    "\n",
    "display(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aee1e061-bf68-4a1d-ade2-311a06c6b600",
   "metadata": {},
   "source": [
    "In addition to p-values and threshold values, we can request that the `predict` method returns the full conformal predictive distribution (CPD) for each test instance, as defined by the threshold values, by setting `return_cpds=True`. The format of the distributions vary with the type of conformal predictive system; for a standard and normalized CPS, the output is an array with a row for each test instance and a column for each calibration instance (residual), while for a Mondrian CPS, the default output is a vector containing one CPD per test instance (since the number of values may vary between categories). If the desired output instead is an array of distributions per category, where all distributions in a category have the same number of columns, which in turn depends on the number of calibration instances in the corresponding category, then `cpds_by_bins=True` may be specified. In case `return_cpds=True` is specified together with `y`, `lower_percentiles` or `higher_percentiles`, the output of `predict` will be a pair, with the first element holding the results of the above type and the second element will contain the CPDs. \n",
    "\n",
    "For the above Mondrian CPS, the following call to `predict` will result in a vector of distributions, with one element for each test instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "18a96b56-79cd-4ad6-b2ed-61b89ad93bef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of test instances: 10807\n",
      "Shape of cpds: (10807,)\n"
     ]
    }
   ],
   "source": [
    "cpds = cps_mond_norm.predict(y_hat_test,\n",
    "                             sigmas=sigmas_test_knn_res,\n",
    "                             bins=bins_test,\n",
    "                             return_cpds=True)\n",
    "\n",
    "print(f\"No. of test instances: {len(y_hat_test)}\")\n",
    "print(f\"Shape of cpds: {cpds.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3a119a-689a-4afc-be70-5d626900fb32",
   "metadata": {},
   "source": [
    "If we instead would prefer to represent these distributions by one array per category, we set `cpds_by_bins=True`, noting that it will be a bit trickier to associate a test instance to a specific distribution.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "cb029611-b2e8-4061-92bd-f4951e3c0f2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bin 0: 2062 test instances, 541 threshold values\n",
      "bin 1: 1968 test instances, 540 threshold values\n",
      "bin 2: 2242 test instances, 540 threshold values\n",
      "bin 3: 2193 test instances, 540 threshold values\n",
      "bin 4: 2342 test instances, 541 threshold values\n",
      "No. of test instances: 10807\n"
     ]
    }
   ],
   "source": [
    "cpds = cps_mond_norm.predict(y_hat_test,\n",
    "                             sigmas=sigmas_test_knn_res,\n",
    "                             bins=bins_test,\n",
    "                             return_cpds=True, \n",
    "                             cpds_by_bins=True)\n",
    "\n",
    "for i, cpd in enumerate(cpds):\n",
    "    print(f\"bin {i}: {cpd.shape[0]} test instances, {cpd.shape[1]} threshold values\")\n",
    "\n",
    "print(f\"No. of test instances: {sum([c.shape[0] for c in cpds])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2217d879-b8f4-4b2a-9507-be5fb0edd8c0",
   "metadata": {},
   "source": [
    "We may also plot the conformal predictive distribution for some test object. In case the calibration set is very large, you may consider plotting an approximation of the full distribution by using a grid of values for `lower_percentiles` or `higher_percentiles`, instead of setting `return_cpds=True`. For the Mondrian CPS, the size of the calibration set for each bin is reasonable in this case, so we may just use the distributions directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2c9200fb-2cbc-4d6a-a047-032509606b37",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpds = cps_mond_norm_oob.predict(y_hat_full,\n",
    "                                 bins=bins_test_oob,\n",
    "                                 sigmas=sigmas_test_var_oob,\n",
    "                                 return_cpds=True)\n",
    "\n",
    "test_index = np.random.randint(len(y_hat_full)) # A test object is randomly selected\n",
    "cpd = cpds[test_index]\n",
    "\n",
    "p = np.array([i/len(cpd) for i in range(len(cpd))])\n",
    "\n",
    "lower_index = np.where(p<=0.025)[0][-1]\n",
    "mid_index = np.where(p>=0.50)[0][0]\n",
    "upper_index = np.where(p>=0.975)[0][0]\n",
    "\n",
    "low_percentile = cpd[lower_index]\n",
    "median = cpd[mid_index]\n",
    "high_percentile = cpd[upper_index]\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot([y_hat_full[test_index],y_hat_full[test_index]],[0,1], color=\"tab:orange\")\n",
    "plt.plot([y_test[test_index],y_test[test_index]],[0,1], color=\"tab:red\")\n",
    "plt.xlabel(\"y\")\n",
    "plt.ylabel(\"Q(y)\")\n",
    "plt.ylim(0,1)\n",
    "\n",
    "plt.plot([median,median],[0,1],\"g--\")\n",
    "plt.plot([low_percentile,low_percentile],[0,1],\"y--\")\n",
    "plt.legend([\"ŷ\",\"target\",\"$y_{0.5}$\",\"[$y_{0.025}$,$y_{0.975}$]\"])\n",
    "plt.plot([high_percentile,high_percentile],[0,1],\"y--\")\n",
    "plt.plot(cpd,p, color=\"tab:blue\")\n",
    "rectangle = plt.Rectangle((low_percentile,0),\n",
    "                          abs(high_percentile-low_percentile),1, color=\"y\", \n",
    "                          alpha=0.05)\n",
    "plt.gca().add_patch(rectangle)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9702e595-7bfa-40b2-9fca-8725dc4fc8cf",
   "metadata": {},
   "source": [
    "### Analyzing the p-values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59fbdc53-3e36-4675-8443-238cbf58318f",
   "metadata": {},
   "source": [
    "Let us put all the generated CPS in a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9c211408-9610-47cd-82cf-f2dfe9f355fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_cps = {\"Std CPS\":cps_std,\n",
    "           \"Std OOB CPS\":cps_std_oob,\n",
    "           \"Norm CPS\":cps_norm,\n",
    "           \"Norm OOB CPS\":cps_norm_oob,\n",
    "           \"Mond CPS\":cps_mond_std,\n",
    "           \"Mond OOB CPS\":cps_mond_std_oob,\n",
    "           \"Mond norm CPS\":cps_mond_norm,\n",
    "           \"Mond norm OOB CPS\":cps_mond_norm_oob\n",
    "          }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5dc0027-edc0-43dd-856d-b5768a72a669",
   "metadata": {},
   "source": [
    "Now we will check if the p-values for the test targets seem to be uniformly distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a82b51a6-5e29-4e33-b8de-f20cdaba7964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(len(all_cps.keys())//2,2,figsize=(12,18))\n",
    "\n",
    "for i, name in enumerate(all_cps.keys()):\n",
    "\n",
    "    if \"OOB\" in name:\n",
    "        p_values = all_cps[name].predict(y_hat_full, \n",
    "                                         sigmas=sigmas_test_var_oob,\n",
    "                                         bins=bins_test_oob, \n",
    "                                         y=y_test)\n",
    "    else:\n",
    "        p_values = all_cps[name].predict(y_hat_test, \n",
    "                                         sigmas=sigmas_test_var, \n",
    "                                         bins=bins_test, \n",
    "                                         y=y_test)\n",
    "\n",
    "    plt.subplot(len(all_cps.keys())//2,2,i+1)\n",
    "\n",
    "    plt.scatter(np.sort(p_values),\n",
    "                [(i+1)/len(y_test) for i in range(len(y_test))],\n",
    "                label=name, c=\"y\", marker=\".\", alpha=0.1)\n",
    "\n",
    "    plt.plot([0,1],[0,1],\"r--\")\n",
    "    plt.legend()\n",
    "    plt.ylabel(\"fraction\")\n",
    "    plt.xlabel(\"p value\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "836d7327-4377-4177-8866-b0bac60202a0",
   "metadata": {},
   "source": [
    "### Investigating the coverage and size of extracted prediction intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2504a57-4a33-4c7f-9dff-e993392ad05d",
   "metadata": {},
   "source": [
    "Let us investigate the extracted prediction intervals at the 95% confidence level. \n",
    "This is done by a specifying percentiles corresponding to the interval endpoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "937eee95-9ae8-4072-873a-fa2f5f0cec5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Mean size</th>\n",
       "      <th>Median size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Std CPS</th>\n",
       "      <td>0.9406</td>\n",
       "      <td>0.0568</td>\n",
       "      <td>0.0580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std OOB CPS</th>\n",
       "      <td>0.9485</td>\n",
       "      <td>0.0609</td>\n",
       "      <td>0.0620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CPS</th>\n",
       "      <td>0.9437</td>\n",
       "      <td>0.0527</td>\n",
       "      <td>0.0347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm OOB CPS</th>\n",
       "      <td>0.9502</td>\n",
       "      <td>0.0531</td>\n",
       "      <td>0.0356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond CPS</th>\n",
       "      <td>0.9498</td>\n",
       "      <td>0.0585</td>\n",
       "      <td>0.0449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond OOB CPS</th>\n",
       "      <td>0.9513</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.0403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond norm CPS</th>\n",
       "      <td>0.9497</td>\n",
       "      <td>0.0549</td>\n",
       "      <td>0.0387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond norm OOB CPS</th>\n",
       "      <td>0.9522</td>\n",
       "      <td>0.0549</td>\n",
       "      <td>0.0358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.9482</td>\n",
       "      <td>0.0563</td>\n",
       "      <td>0.0437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Coverage  Mean size  Median size\n",
       "Std CPS              0.9406     0.0568       0.0580\n",
       "Std OOB CPS          0.9485     0.0609       0.0620\n",
       "Norm CPS             0.9437     0.0527       0.0347\n",
       "Norm OOB CPS         0.9502     0.0531       0.0356\n",
       "Mond CPS             0.9498     0.0585       0.0449\n",
       "Mond OOB CPS         0.9513     0.0588       0.0403\n",
       "Mond norm CPS        0.9497     0.0549       0.0387\n",
       "Mond norm OOB CPS    0.9522     0.0549       0.0358\n",
       "Mean                 0.9482     0.0563       0.0437"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_cps_intervals = {}\n",
    "\n",
    "coverages = []\n",
    "mean_sizes = []\n",
    "median_sizes = []\n",
    "\n",
    "for name in all_cps.keys():\n",
    "    if \"OOB\" in name:\n",
    "        intervals = all_cps[name].predict(y_hat_full, \n",
    "                                          sigmas=sigmas_test_var_oob, \n",
    "                                          bins=bins_test_oob,\n",
    "                                          lower_percentiles=2.5, \n",
    "                                          higher_percentiles=97.5,\n",
    "                                          y_min=0, y_max=1)\n",
    "    else:\n",
    "        intervals = all_cps[name].predict(y_hat_test, \n",
    "                                          sigmas=sigmas_test_var, \n",
    "                                          bins=bins_test,\n",
    "                                          lower_percentiles=2.5, \n",
    "                                          higher_percentiles=97.5,\n",
    "                                          y_min=0, y_max=1)\n",
    "    all_cps_intervals[name] = intervals\n",
    "    coverages.append(np.sum([1 if (y_test[i]>=intervals[i,0] and \n",
    "                                   y_test[i]<=intervals[i,1]) else 0\n",
    "                            for i in range(len(y_test))])/len(y_test))\n",
    "    mean_sizes.append((intervals[:,1]-intervals[:,0]).mean())\n",
    "    median_sizes.append(np.median((intervals[:,1]-intervals[:,0])))\n",
    "\n",
    "pred_int_df = pd.DataFrame({\"Coverage\":coverages, \n",
    "                            \"Mean size\":mean_sizes, \n",
    "                            \"Median size\":median_sizes}, \n",
    "                           index=list(all_cps_intervals.keys()))\n",
    "\n",
    "pred_int_df.loc[\"Mean\"] = [pred_int_df[\"Coverage\"].mean(), \n",
    "                           pred_int_df[\"Mean size\"].mean(),\n",
    "                           pred_int_df[\"Median size\"].mean()]\n",
    "\n",
    "display(pred_int_df.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04c31348-ba38-4b68-9bde-bb8882127522",
   "metadata": {},
   "source": [
    "### Investigating the distributions of extracted prediction intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47719178-2dde-43fa-b837-175ce8cb105b",
   "metadata": {},
   "source": [
    "Let us take a look at the distribution of the interval sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ad58bd5a-13d3-4ba6-9968-9c045c83b422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cps_interval_sizes = {}\n",
    "\n",
    "for name in all_cps_intervals.keys():\n",
    "    cps_interval_sizes[name] = \\\n",
    "    all_cps_intervals[name][:,1] - all_cps_intervals[name][:,0]\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.ylabel(\"CDF\")\n",
    "plt.xlabel(\"Interval sizes\")\n",
    "plt.xlim(0,cps_interval_sizes[\"Mond OOB CPS\"].max()*1.25)\n",
    "\n",
    "colors = [\"b\",\"r\",\"g\",\"y\",\"k\",\"m\", \"gray\", \"orange\"]\n",
    "\n",
    "for i, name in enumerate(cps_interval_sizes.keys()):\n",
    "    if \"Std\" in name:\n",
    "        style = \"dotted\"\n",
    "    else:\n",
    "        style = \"solid\"\n",
    "    plt.plot(np.sort(cps_interval_sizes[name]),\n",
    "             [i/len(cps_interval_sizes[name])\n",
    "              for i in range(1,len(cps_interval_sizes[name])+1)],\n",
    "             linestyle=style, c=colors[i], label=name)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "965d1c97-25da-4c9e-aaf8-bf8089501cd1",
   "metadata": {},
   "source": [
    "### Extracting medians"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8df1ea3-4ea8-40cf-ae37-798d0c343229",
   "metadata": {},
   "source": [
    "Let us take a look at the medians; they can be derived using either lower or higher interpolation,\n",
    "but ideally the differences should be small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ebea7b27-a9a1-4473-a20d-3b2b7fcaea79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Std CPS\n",
      "\tMean difference of the medians:    0.000006\n",
      "\tLargest difference of the medians: 0.000006\n",
      "Std OOB CPS\n",
      "\tMean difference of the medians:    0.000000\n",
      "\tLargest difference of the medians: 0.000000\n",
      "Norm CPS\n",
      "\tMean difference of the medians:    0.000000\n",
      "\tLargest difference of the medians: 0.000022\n",
      "Norm OOB CPS\n",
      "\tMean difference of the medians:    0.000002\n",
      "\tLargest difference of the medians: 0.000133\n",
      "Mond CPS\n",
      "\tMean difference of the medians:    0.000026\n",
      "\tLargest difference of the medians: 0.000095\n",
      "Mond OOB CPS\n",
      "\tMean difference of the medians:    0.000000\n",
      "\tLargest difference of the medians: 0.000001\n",
      "Mond norm CPS\n",
      "\tMean difference of the medians:    0.000013\n",
      "\tLargest difference of the medians: 0.000097\n",
      "Mond norm OOB CPS\n",
      "\tMean difference of the medians:    0.000000\n",
      "\tLargest difference of the medians: 0.000000\n"
     ]
    }
   ],
   "source": [
    "all_cps_medians = {}\n",
    "\n",
    "for name in all_cps.keys():\n",
    "    if \"OOB\" in name:\n",
    "        medians = all_cps[name].predict(y_hat_full, \n",
    "                                        sigmas=sigmas_test_var_oob, \n",
    "                                        bins=bins_test_oob,\n",
    "                                        lower_percentiles=50, \n",
    "                                        higher_percentiles=50)\n",
    "    else:\n",
    "        medians = all_cps[name].predict(y_hat_test, \n",
    "                                        sigmas=sigmas_test_var, \n",
    "                                        bins=bins_test,\n",
    "                                        lower_percentiles=50, \n",
    "                                        higher_percentiles=50)\n",
    "    all_cps_medians[name] = medians\n",
    "    print(name)\n",
    "    print(\"\\tMean difference of the medians:    {:.6f}\".format((medians[:,1]-medians[:,0]).mean()))\n",
    "    print(\"\\tLargest difference of the medians: {:.6f}\".format((medians[:,1]-medians[:,0]).max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca4f7b8-bdc2-4094-84a2-878bba4f8362",
   "metadata": {},
   "source": [
    "### Another view of the medians and prediction intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0c4e0a72-7f09-4590-bc5e-8d597ea1fbae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x2400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(len(all_cps_intervals.keys())//2,2,figsize=(12,24))\n",
    "\n",
    "sorted_prop_indexes = np.argsort(y_hat_test) \n",
    "\n",
    "sorted_full_indexes = np.argsort(y_hat_full) \n",
    "\n",
    "alpha=0.2\n",
    "\n",
    "for i, name in enumerate(all_cps_intervals.keys()):\n",
    "\n",
    "    plt.subplot(len(all_cps_intervals.keys())//2,2,i+1)\n",
    "    if \"OOB\" in name:\n",
    "        indexes = sorted_full_indexes\n",
    "        y_hat_ = y_hat_full\n",
    "    else:\n",
    "        indexes = sorted_prop_indexes\n",
    "        y_hat_ = y_hat_test\n",
    "\n",
    "    plt.title(name)\n",
    "    plt.plot(y_hat_[indexes], all_cps_intervals[name][indexes,0], \n",
    "             color=\"r\", alpha=alpha)\n",
    "    plt.plot(y_hat_[indexes], all_cps_intervals[name][indexes,1], \n",
    "             color=\"r\", alpha=alpha)\n",
    "    plt.scatter(y_hat_[indexes],y_test[indexes],\n",
    "                color=\"b\", marker=\"o\", alpha=alpha)\n",
    "    plt.scatter(y_hat_[indexes],y_hat_[indexes],\n",
    "                color=\"y\", marker=\".\", alpha=alpha)\n",
    "    plt.xlabel(\"predicted\")\n",
    "    plt.ylabel(\"endpoints\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef7766c8-5f32-46a2-9c73-83ce799bcd06",
   "metadata": {},
   "source": [
    "### Evaluating the CPS using a test set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbf00f7e-6eea-41cd-84d6-d4de4fe7277f",
   "metadata": {},
   "source": [
    "Let us evaluate the generated CPS using three confidence levels on the test set.\n",
    "We could specify a subset of the metrics to use by the\n",
    "`metrics` parameter of the `evaluate` method; here we use all metrics, \n",
    "which is the default\n",
    "\n",
    "Note that values for the parameters `sigmas` and `bins` can always be provided,\n",
    "but they will be ignored by CPS that have not been fitted with such values,\n",
    "e.g., both arguments will be ignored by the standard CPS.\n",
    "\n",
    "Note that CRPS takes some time to compute, in particular when the CPS have been fitted with \n",
    "larger calibration sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2775d1e6-86e2-4421-8531-dc97eb3d51ba",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Std CPS</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Norm CPS</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Mond CPS</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Mond norm CPS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "      <th>0.99</th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "      <th>0.99</th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "      <th>0.99</th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "      <th>0.99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>error</th>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.0594</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>0.1038</td>\n",
       "      <td>0.0563</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.1038</td>\n",
       "      <td>0.0502</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.1018</td>\n",
       "      <td>0.0503</td>\n",
       "      <td>0.0053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_mean</th>\n",
       "      <td>0.0393</td>\n",
       "      <td>0.0568</td>\n",
       "      <td>0.1160</td>\n",
       "      <td>0.0419</td>\n",
       "      <td>0.0527</td>\n",
       "      <td>0.0839</td>\n",
       "      <td>0.0420</td>\n",
       "      <td>0.0585</td>\n",
       "      <td>0.1068</td>\n",
       "      <td>0.0436</td>\n",
       "      <td>0.0549</td>\n",
       "      <td>0.1032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_med</th>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.0580</td>\n",
       "      <td>0.1187</td>\n",
       "      <td>0.0274</td>\n",
       "      <td>0.0347</td>\n",
       "      <td>0.0567</td>\n",
       "      <td>0.0339</td>\n",
       "      <td>0.0449</td>\n",
       "      <td>0.0834</td>\n",
       "      <td>0.0294</td>\n",
       "      <td>0.0387</td>\n",
       "      <td>0.0711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRPS</th>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_fit</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_evaluate</th>\n",
       "      <td>0.3443</td>\n",
       "      <td>0.3168</td>\n",
       "      <td>0.3169</td>\n",
       "      <td>0.3499</td>\n",
       "      <td>0.3574</td>\n",
       "      <td>0.3598</td>\n",
       "      <td>0.2073</td>\n",
       "      <td>0.2077</td>\n",
       "      <td>0.2142</td>\n",
       "      <td>0.2372</td>\n",
       "      <td>0.2388</td>\n",
       "      <td>0.2425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Std CPS                 Norm CPS                 Mond CPS  \\\n",
       "                 0.90    0.95    0.99     0.90    0.95    0.99     0.90   \n",
       "error          0.1100  0.0594  0.0111   0.1038  0.0563  0.0091   0.1038   \n",
       "eff_mean       0.0393  0.0568  0.1160   0.0419  0.0527  0.0839   0.0420   \n",
       "eff_med        0.0395  0.0580  0.1187   0.0274  0.0347  0.0567   0.0339   \n",
       "CRPS           0.0075  0.0075  0.0075   0.0069  0.0069  0.0069   0.0072   \n",
       "time_fit       0.0001  0.0001  0.0001   0.0001  0.0001  0.0001   0.0001   \n",
       "time_evaluate  0.3443  0.3168  0.3169   0.3499  0.3574  0.3598   0.2073   \n",
       "\n",
       "                              Mond norm CPS                  \n",
       "                 0.95    0.99          0.90    0.95    0.99  \n",
       "error          0.0502  0.0065        0.1018  0.0503  0.0053  \n",
       "eff_mean       0.0585  0.1068        0.0436  0.0549  0.1032  \n",
       "eff_med        0.0449  0.0834        0.0294  0.0387  0.0711  \n",
       "CRPS           0.0072  0.0072        0.0068  0.0068  0.0068  \n",
       "time_fit       0.0001  0.0001        0.0001  0.0001  0.0001  \n",
       "time_evaluate  0.2077  0.2142        0.2372  0.2388  0.2425  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Std OOB CPS</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Norm OOB CPS</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Mond OOB CPS</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Mond norm OOB CPS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "      <th>0.99</th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
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       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "      <th>0.99</th>\n",
       "      <th>0.90</th>\n",
       "      <th>0.95</th>\n",
       "      <th>0.99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>error</th>\n",
       "      <td>0.1008</td>\n",
       "      <td>0.0515</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0997</td>\n",
       "      <td>0.0498</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.0487</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0985</td>\n",
       "      <td>0.0478</td>\n",
       "      <td>0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_mean</th>\n",
       "      <td>0.0408</td>\n",
       "      <td>0.0609</td>\n",
       "      <td>0.1321</td>\n",
       "      <td>0.0413</td>\n",
       "      <td>0.0531</td>\n",
       "      <td>0.0835</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.1007</td>\n",
       "      <td>0.0439</td>\n",
       "      <td>0.0549</td>\n",
       "      <td>0.0890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_med</th>\n",
       "      <td>0.0409</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.1385</td>\n",
       "      <td>0.0274</td>\n",
       "      <td>0.0356</td>\n",
       "      <td>0.0571</td>\n",
       "      <td>0.0303</td>\n",
       "      <td>0.0403</td>\n",
       "      <td>0.0704</td>\n",
       "      <td>0.0276</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRPS</th>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_fit</th>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_evaluate</th>\n",
       "      <td>0.8434</td>\n",
       "      <td>0.7490</td>\n",
       "      <td>0.7360</td>\n",
       "      <td>0.8357</td>\n",
       "      <td>0.8133</td>\n",
       "      <td>0.8082</td>\n",
       "      <td>0.2910</td>\n",
       "      <td>0.2836</td>\n",
       "      <td>0.2830</td>\n",
       "      <td>0.3322</td>\n",
       "      <td>0.3249</td>\n",
       "      <td>0.3230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Std OOB CPS                 Norm OOB CPS                  \\\n",
       "                     0.90    0.95    0.99         0.90    0.95    0.99   \n",
       "error              0.1008  0.0515  0.0094       0.0997  0.0498  0.0090   \n",
       "eff_mean           0.0408  0.0609  0.1321       0.0413  0.0531  0.0835   \n",
       "eff_med            0.0409  0.0620  0.1385       0.0274  0.0356  0.0571   \n",
       "CRPS               0.0073  0.0073  0.0073       0.0068  0.0068  0.0068   \n",
       "time_fit           0.0002  0.0002  0.0002       0.0001  0.0001  0.0001   \n",
       "time_evaluate      0.8434  0.7490  0.7360       0.8357  0.8133  0.8082   \n",
       "\n",
       "              Mond OOB CPS                 Mond norm OOB CPS                  \n",
       "                      0.90    0.95    0.99              0.90    0.95    0.99  \n",
       "error               0.1000  0.0487  0.0079            0.0985  0.0478  0.0081  \n",
       "eff_mean            0.0425  0.0588  0.1007            0.0439  0.0549  0.0890  \n",
       "eff_med             0.0303  0.0403  0.0704            0.0276  0.0358  0.0579  \n",
       "CRPS                0.0070  0.0070  0.0070            0.0068  0.0068  0.0068  \n",
       "time_fit            0.0003  0.0003  0.0003            0.0004  0.0004  0.0004  \n",
       "time_evaluate       0.2910  0.2836  0.2830            0.3322  0.3249  0.3230  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confidence_levels = [0.9,0.95,0.99]\n",
    "\n",
    "names = np.array(list(all_cps.keys()))\n",
    "\n",
    "first_set = names[[\"OOB\" not in name for name in names]]\n",
    "second_set = names[[\"OOB\" in name for name in names]]\n",
    "\n",
    "for methods in [first_set, second_set]:\n",
    "    all_cps_results = {}\n",
    "    for confidence in confidence_levels:\n",
    "        for name in methods:\n",
    "            if \"OOB\" in name:\n",
    "                all_cps_results[(name,confidence)] = all_cps[name].evaluate(\n",
    "                    y_hat_full, y_test, sigmas=sigmas_test_var_oob, \n",
    "                    bins=bins_test_oob, confidence=confidence, \n",
    "                    y_min=0, y_max=1)\n",
    "            else:\n",
    "                all_cps_results[(name,confidence)] =  all_cps[name].evaluate(\n",
    "                    y_hat_test, y_test, sigmas=sigmas_test_var, \n",
    "                    bins=bins_test, confidence=confidence, y_min=0, y_max=1)\n",
    "\n",
    "    cps_results_df = pd.DataFrame(columns=pd.MultiIndex.from_product(\n",
    "        [methods,confidence_levels]), index=list(list(\n",
    "        all_cps_results.values())[0].keys()))\n",
    "\n",
    "    for key in all_cps_results.keys():\n",
    "        cps_results_df[key] = all_cps_results[key].values()\n",
    "\n",
    "    display(cps_results_df.round(4))"
   ]
  }
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