docs/crepes_nb_wrap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "329c8c07",
   "metadata": {},
   "source": [
    "# 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 classes `WrapClassifier` and `WrapRegressor` from the `crepes` package. These classes rely on the classes `ConformalClassifier`, `ConformalRegressor`, and `ConformalPredictiveSystem` from the same package, which however will not be explicitly interfaced in the examples here; see [More examples](https://crepes.readthedocs.io/en/latest/crepes_nb.html) for how to obtain the same results as in this section by using these classes instead. The examples below also use helper classes and functions from `crepes.extras`, as well as the `NumPy`, `pandas`, `matplotlib` and `sklearn` libraries."
   ]
  },
  {
   "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 WrapClassifier, WrapRegressor, __version__\n",
    "\n",
    "from crepes.extras import margin, DifficultyEstimator, MondrianCategorizer\n",
    "\n",
    "print(f\"crepes v. {__version__}\")\n",
    "\n",
    "np.random.seed(602211023)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b070d3d4-db50-4975-9ddd-fca275aca150",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Conformal classifiers (CC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dea79f9-8c35-4fc5-89b7-407d5b7ce518",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Importing and splitting a classification dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe8532f-3ac5-47bd-8211-5f6b71580f6f",
   "metadata": {},
   "source": [
    "Let us import a classification dataset from [www.openml.org](https://www.openml.org)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "18a4f2f5-edf5-4e3a-b8a7-446fcf67cdbe",
   "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": "87ea0bf9-51f1-4346-a3ab-c95cdcb18900",
   "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": "4c0a167d-1c3d-44c1-b44e-fea4b9a7631c",
   "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": "6169251e-bdf7-428c-a917-618dca42607b",
   "metadata": {},
   "source": [
    "### Standard conformal classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e0907d2-4587-4263-bc74-276605ec421e",
   "metadata": {},
   "source": [
    "Let us create a standard conformal classifier through a `WrapClassifier` object, using a random forest that is fitted in the usual way (using the proper training set only), before or after being wrapped:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "edd755db-8c84-424d-9d1b-56a8c1146b3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapClassifier(learner=RandomForestClassifier(n_estimators=500, n_jobs=-1), calibrated=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = WrapClassifier(RandomForestClassifier(n_jobs=-1, n_estimators=500))\n",
    "\n",
    "rf.fit(X_prop_train, y_prop_train)\n",
    "\n",
    "display(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bb6a930-a8a7-4e4a-96b3-d5d3862db025",
   "metadata": {},
   "source": [
    "We will use the the calibration set to calibrate the conformal regressor; note that the `calibrate` method has been added to the wrapped learner, in addition to the standard `fit`and `predict` methods. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dcc24159-2104-40a6-a5de-2cd348395214",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapClassifier(learner=RandomForestClassifier(n_estimators=500, n_jobs=-1), calibrated=True, predictor=ConformalClassifier(fitted=True, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf.calibrate(X_cal, y_cal)\n",
    "\n",
    "display(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d8bd000-6c8d-42d5-b7d1-ec05f90905fc",
   "metadata": {},
   "source": [
    "We may now obtain prediction sets for the test set using the new method `predict_set`; \n",
    "here using the default confidence level (95%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d56a5cdc-041a-4f7b-bd38-abac300ba5f7",
   "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]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_sets = rf.predict_set(X_test)\n",
    "\n",
    "display(prediction_sets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404a83ca-bd3c-458b-b963-c5db7e69d1bd",
   "metadata": {},
   "source": [
    "For each object in the test set, we get a prediction set, i.e., a binary vector indicating the presence (1) or absence (0) of each class label. The columns are ordered according to `classes_` of the underlying learner:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8b71430-7b89-47b8-964e-b33a4f4d2c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '2', '3', '4', '5', '6'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.learner.classes_ "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86eeb76b-06f0-4920-bd0b-b9d27f754681",
   "metadata": {},
   "source": [
    "We may specify any confidence level that we are interested in, e.g., 99%. By increasing the confidence level, we can expect to see fewer labels getting rejected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb012652-e091-4b91-89eb-ac672c20d72d",
   "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]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_sets = rf.predict_set(X_test, confidence=0.99)\n",
    "\n",
    "display(prediction_sets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18056bb0-eab3-4675-9ce9-a9f9a20579c1",
   "metadata": {},
   "source": [
    "We may also get the p-values (without specifying any confidence level) by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5456f07b-dd8e-4a68-919c-665f7e1330a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.10084131e-02, 3.76337863e-04, 1.61785289e-03, 6.99925993e-03,\n",
       "        1.82050558e-03, 1.20915094e-03],\n",
       "       [5.95503603e-05, 4.07393350e-01, 3.24214611e-03, 1.40552735e-03,\n",
       "        1.11896218e-03, 1.64649805e-03],\n",
       "       [6.17609499e-05, 1.58977591e-04, 1.06765869e-03, 1.54249374e-03,\n",
       "        7.75056630e-01, 1.62044175e-03],\n",
       "       ...,\n",
       "       [1.72268866e-03, 1.52546900e-03, 1.96725794e-03, 4.32482019e-03,\n",
       "        2.89983912e-03, 2.07303634e-01],\n",
       "       [1.77148180e-03, 1.97424580e-03, 1.70943625e-03, 7.11470437e-03,\n",
       "        1.92482034e-03, 1.17051738e-01],\n",
       "       [3.17064994e-03, 2.04908710e-03, 1.77211302e-03, 2.87698579e-03,\n",
       "        7.78019930e-02, 7.36559535e-03]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_values = rf.predict_p(X_test)\n",
    "\n",
    "display(p_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3024c6f-5916-4135-9f7c-f41c83f2194c",
   "metadata": {},
   "source": [
    "By default, the conformal classifiers obtained using `WrapClassifier` will compute non-conformity scores using the `hinge` function defined in `crepes.extras`. We could alternatively generate a conformal classifier using the `margin` function, which we imported above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d836c5c-0ab1-4230-84b8-6aa91dc2714a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapClassifier(learner=RandomForestClassifier(n_estimators=500, n_jobs=-1), calibrated=True, predictor=ConformalClassifier(fitted=True, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_margin = WrapClassifier(rf.learner)\n",
    "\n",
    "rf_margin.calibrate(X_cal, y_cal, nc=margin)\n",
    "\n",
    "display(rf_margin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad344429-f758-4975-a60b-d800ca72e554",
   "metadata": {},
   "source": [
    "### Mondrian conformal classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51de019b-8591-4de3-ad02-6a8083448e59",
   "metadata": {},
   "source": [
    "To control the error level across different groups of objects of interest, we may use so-called Mondrian conformal classifiers. A Mondrian conformal classifier if formed by providing a function or a `MondrianCategorizer` (defined in `crepes.extras`) as an additional argument, named `mc`, for the `calibrate` method. \n",
    "\n",
    "For illustration, we will use the predicted labels of the underlying model to form the categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b01703f7-bd2e-4a51-968c-8d0d32a63fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapClassifier(learner=RandomForestClassifier(n_estimators=500, n_jobs=-1), calibrated=True, predictor=ConformalClassifier(fitted=True, mondrian=True))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_mond = WrapClassifier(rf.learner)\n",
    "rf_mond.calibrate(X_cal, y_cal, mc=rf_mond.predict)\n",
    "\n",
    "display(rf_mond)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5926cb9-374f-446e-900c-c32b62cf1585",
   "metadata": {},
   "source": [
    "We may now form prediction sets for the test objects (using the same categorization of the test objects under the hood):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a04fa58-3fab-458a-8460-9c145ed03289",
   "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, 1, 0]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_sets_mond = rf_mond.predict_set(X_test)\n",
    "\n",
    "display(prediction_sets_mond)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c50dec14-816a-40e6-9f50-5ca08ed5f78c",
   "metadata": {},
   "source": [
    "We may also form the categories using a `MondrianCategorizer`, which may be fitted in several different ways. Below we show how to form categories by (equal-sized) binning of the first feature value, using five bins (instead of the default which is 10); note that we need objects to get the threshold values for the categories (bins):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "09341cc1-a49e-45f6-8638-261f0151f5ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MondrianCategorizer(fitted=True, f=get_values, no_bins=5)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "WrapClassifier(learner=RandomForestClassifier(n_estimators=500, n_jobs=-1), calibrated=True, predictor=ConformalClassifier(fitted=True, mondrian=True))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_values(X):\n",
    "    return X[:,0]\n",
    "\n",
    "mc_scoring = MondrianCategorizer()\n",
    "mc_scoring.fit(X_cal, f=get_values, no_bins=5)\n",
    "\n",
    "display(mc_scoring)\n",
    "rf_mond_scoring = WrapClassifier(rf.learner)\n",
    "rf_mond_scoring.calibrate(X_cal, y_cal, mc=mc_scoring)\n",
    "\n",
    "display(rf_mond_scoring)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42cee490-568f-4ab4-918f-a9f01f6fa389",
   "metadata": {},
   "source": [
    "We may now form prediction sets for the test objects, again using the same Mondrian categorizer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "308b2ad7-d321-4829-86ef-255c0c708952",
   "metadata": {
    "tags": []
   },
   "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]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_sets_mond_scoring = rf_mond_scoring.predict_set(X_test)\n",
    "\n",
    "display(prediction_sets_mond_scoring)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19822c00-c7fc-4d80-93c5-7dc8968658ef",
   "metadata": {},
   "source": [
    "### Class-conditional conformal classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6fbe00-de85-4bae-8a2c-a35cb1c09da9",
   "metadata": {},
   "source": [
    "Class-conditional conformal classifiers is a special type of Mondrian conformal classifiers where the categories are defined by actual (and not predicted, as above) class labels. The test objects need special treatment since we do not know to which categories they belong; a non-conformity score (p-value) is generated for each possible class label. Since this is a common type of conformal classifier, and it is a bit tricky to handle the test objects in the above way, the `calibrate` method has a specific option (`class_cond`) to enable it, as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c0147837-04a1-4ef5-92e1-d1016e5faf1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapClassifier(learner=RandomForestClassifier(n_estimators=500, n_jobs=-1), calibrated=True, predictor=ConformalClassifier(fitted=True, mondrian=True))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_class_cond = WrapClassifier(rf.learner)\n",
    "\n",
    "rf_class_cond.calibrate(X_cal, y_cal, class_cond=True)\n",
    "\n",
    "display(rf_class_cond)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efee1155-ba10-4ccd-875e-c76c026851be",
   "metadata": {},
   "source": [
    "The prediction sets for the test objects are generated in the usual way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "169ee50d-501d-4d72-ab7a-5e0912eca409",
   "metadata": {
    "tags": []
   },
   "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]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_sets_class_cond = rf_class_cond.predict_set(X_test)\n",
    "\n",
    "display(prediction_sets_class_cond)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca64d935-5b7a-42f1-844d-75e7753535da",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Evaluating the conformal classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4061d90-ca97-46d4-a64a-813922736afb",
   "metadata": {},
   "source": [
    "Since we have access to the correct class labels for the test set, we can investigate the predictive performance of the conformal classifiers.\n",
    "Let us start with the standard conformal classifier (which uses the default `hinge` function to compute non-conformity scores):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b05a4b78-9b33-4c4a-97ba-b26bdbbbff6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.0404025880661395,\n",
       " 'avg_c': 0.9610352264557872,\n",
       " 'one_c': 0.9610352264557872,\n",
       " 'empty': 0.038964773544212794,\n",
       " 'time_fit': 7.319450378417969e-05,\n",
       " 'time_evaluate': 0.5599856376647949}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e2da78a-b0ef-40a6-b961-3bd22a82984a",
   "metadata": {},
   "source": [
    "Above we used the default (95%) confidence level, but we could specify some other:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "887e89ca-47db-4770-8d0f-c8ee13b53dad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.0851186196980589,\n",
       " 'avg_c': 0.9160316319194823,\n",
       " 'one_c': 0.9160316319194823,\n",
       " 'empty': 0.08396836808051761,\n",
       " 'time_fit': 7.319450378417969e-05,\n",
       " 'time_evaluate': 0.5954310894012451}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.evaluate(X_test, y_test, confidence=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a74a97dc-391c-4c17-a34f-e414e277cfff",
   "metadata": {},
   "source": [
    "Let us also evaluate the standard conformal classifier that uses the `margin` function to compute non-conformity scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "235fca55-2522-41de-ac89-6aed74596b9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.08425593098490292,\n",
       " 'avg_c': 0.9168943206326384,\n",
       " 'one_c': 0.9168943206326384,\n",
       " 'empty': 0.08310567936736162,\n",
       " 'time_fit': 6.580352783203125e-05,\n",
       " 'time_evaluate': 0.7449934482574463}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_margin.evaluate(X_test, y_test, confidence=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c36f9fcb-b455-424f-ad7a-0dea5361e161",
   "metadata": {},
   "source": [
    "The evaluation of the two Mondrian conformal classifiers (for which we used two different Mondrian categorizers) is done in the same way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "50541f80-7a21-4997-98ed-c8292c8b2e21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.08885693745506829,\n",
       " 'avg_c': 0.9122933141624731,\n",
       " 'one_c': 0.9122933141624731,\n",
       " 'empty': 0.08770668583752696,\n",
       " 'time_fit': 0.0006122589111328125,\n",
       " 'time_evaluate': 0.5700712203979492}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_mond.evaluate(X_test, y_test, confidence=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "acea5ed5-31aa-43f7-bc22-59333fb6b608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.08281811646297632,\n",
       " 'avg_c': 0.9181883537023724,\n",
       " 'one_c': 0.9181883537023724,\n",
       " 'empty': 0.0818116462976276,\n",
       " 'time_fit': 8.225440979003906e-05,\n",
       " 'time_evaluate': 0.49215149879455566}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_mond_scoring.evaluate(X_test, y_test, confidence=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9281894-2c39-4dd8-a11c-416708b8cdde",
   "metadata": {},
   "source": [
    "Let us also evaluate the class-conditional conformal classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2d6d9e29-6061-4e69-a89d-caa500ed6a9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.09144500359453633,\n",
       " 'avg_c': 0.9095614665708124,\n",
       " 'one_c': 0.9095614665708124,\n",
       " 'empty': 0.09043853342918763,\n",
       " 'time_fit': 0.0006628036499023438,\n",
       " 'time_evaluate': 2.2370712757110596}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_class_cond.evaluate(X_test, y_test, confidence=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bc5791f-6af1-4638-bd6e-f20555ce0d7c",
   "metadata": {},
   "source": [
    "We may evaluate the conformal classifiers on each class separately. For the standard conformal classifier, we get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aec8e74e-5167-482a-a72c-29aa9ac6c624",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 1:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.06692607003891049,\n",
       " 'avg_c': 0.9330739299610895,\n",
       " 'one_c': 0.9330739299610895,\n",
       " 'empty': 0.0669260700389105,\n",
       " 'time_fit': 7.319450378417969e-05,\n",
       " 'time_evaluate': 0.15456843376159668}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 2:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.05842391304347827,\n",
       " 'avg_c': 0.9429347826086957,\n",
       " 'one_c': 0.9429347826086957,\n",
       " 'empty': 0.057065217391304345,\n",
       " 'time_fit': 7.319450378417969e-05,\n",
       " 'time_evaluate': 0.16882014274597168}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 3:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.08222490931076176,\n",
       " 'avg_c': 0.9238210399032648,\n",
       " 'one_c': 0.9238210399032648,\n",
       " 'empty': 0.0761789600967352,\n",
       " 'time_fit': 7.319450378417969e-05,\n",
       " 'time_evaluate': 0.1242516040802002}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 4:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.1568627450980392,\n",
       " 'avg_c': 0.8431372549019608,\n",
       " 'one_c': 0.8431372549019608,\n",
       " 'empty': 0.1568627450980392,\n",
       " 'time_fit': 7.319450378417969e-05,\n",
       " 'time_evaluate': 0.13395428657531738}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 5:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.07702612190221036,\n",
       " 'avg_c': 0.9229738780977896,\n",
       " 'one_c': 0.9229738780977896,\n",
       " 'empty': 0.07702612190221031,\n",
       " 'time_fit': 7.319450378417969e-05,\n",
       " 'time_evaluate': 0.17065668106079102}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 6:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.09020902090209026,\n",
       " 'avg_c': 0.9108910891089109,\n",
       " 'one_c': 0.9108910891089109,\n",
       " 'empty': 0.0891089108910891,\n",
       " 'time_fit': 7.319450378417969e-05,\n",
       " 'time_evaluate': 0.12988018989562988}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for c in rf.learner.classes_:\n",
    "    print(f\"Class {c}:\")\n",
    "    display(rf.evaluate(X_test[y_test == c], y_test[y_test == c], confidence=0.9))\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3753f088-9091-4c3d-a718-c2dc16f733c1",
   "metadata": {},
   "source": [
    "As can be observed above, a standard conformal classifier may significantly exceed the error level when considering each class separately. This contrasts to the class-condition conformal classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e0d00931-43b8-4301-a33c-5a5ecffe0e13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 1:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.08949416342412453,\n",
       " 'avg_c': 0.9105058365758755,\n",
       " 'one_c': 0.9105058365758755,\n",
       " 'empty': 0.08949416342412451,\n",
       " 'time_fit': 0.0006628036499023438,\n",
       " 'time_evaluate': 0.4692251682281494}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 2:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.10733695652173914,\n",
       " 'avg_c': 0.8940217391304348,\n",
       " 'one_c': 0.8940217391304348,\n",
       " 'empty': 0.10597826086956522,\n",
       " 'time_fit': 0.0006628036499023438,\n",
       " 'time_evaluate': 0.5211074352264404}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 3:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.09794437726723093,\n",
       " 'avg_c': 0.9068923821039904,\n",
       " 'one_c': 0.9068923821039904,\n",
       " 'empty': 0.09310761789600967,\n",
       " 'time_fit': 0.0006628036499023438,\n",
       " 'time_evaluate': 0.3187546730041504}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 4:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.0980392156862745,\n",
       " 'avg_c': 0.9019607843137255,\n",
       " 'one_c': 0.9019607843137255,\n",
       " 'empty': 0.09803921568627451,\n",
       " 'time_fit': 0.0006628036499023438,\n",
       " 'time_evaluate': 0.363037109375}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 5:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.08975217682518422,\n",
       " 'avg_c': 0.9102478231748158,\n",
       " 'one_c': 0.9102478231748158,\n",
       " 'empty': 0.08975217682518419,\n",
       " 'time_fit': 0.0006628036499023438,\n",
       " 'time_evaluate': 0.5433154106140137}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 6:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.05280528052805278,\n",
       " 'avg_c': 0.9482948294829483,\n",
       " 'one_c': 0.9482948294829483,\n",
       " 'empty': 0.0517051705170517,\n",
       " 'time_fit': 0.0006628036499023438,\n",
       " 'time_evaluate': 0.3486359119415283}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for c in rf.learner.classes_:\n",
    "    print(f\"Class {c}:\")\n",
    "    display(rf_class_cond.evaluate(X_test[y_test == c], y_test[y_test == c], confidence=0.9))\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "767a168c-4aaa-4b66-96a1-6f4a9940e58d",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Conformal classifiers without a separate calibration set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5768cf1e-6740-47fe-98ee-037ee79227db",
   "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": "f057ccce-b74c-417e-8942-848103970387",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Standard conformal classifiers with out-of-bag calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "252d9d73-207c-4b71-a470-585c089525a9",
   "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": 25,
   "id": "083b1545-9475-44d3-b14e-e78ce04807d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "learner_full = RandomForestClassifier(n_jobs=-1, n_estimators=500, oob_score=True)\n",
    "\n",
    "rf = WrapClassifier(learner_full)\n",
    "\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcfe1555-4d23-4e0c-b569-58dd5e4cd330",
   "metadata": {},
   "source": [
    "We may now obtain a standard conformal classifier using OOB predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3c17536d-274b-4517-bb0a-6e80fef724fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapClassifier(learner=RandomForestClassifier(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalClassifier(fitted=True, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf.calibrate(X_train, y_train, oob=True)\n",
    "\n",
    "display(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "050b4876-7fb0-4e70-a577-dcb562cfb7a3",
   "metadata": {},
   "source": [
    "... and use it to get prediction sets for the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "99d9d88c-98ae-42c7-9771-d48a48d7ef6d",
   "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": [
    "prediction_sets_oob = rf.predict_set(X_test)\n",
    "\n",
    "display(prediction_sets_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ac05d3b-519a-4335-b493-1b2ba3cced4f",
   "metadata": {},
   "source": [
    "#### Mondrian conformal classifiers with out-of-bag calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "018152a0-d6f4-47b2-ba83-ecf7d34fa6de",
   "metadata": {},
   "source": [
    "Using out-of-bag calibration works equally well for Mondrian conformal classifiers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f53a1a5d-2da3-4e11-a0a4-ac3fe7af92e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapClassifier(learner=RandomForestClassifier(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalClassifier(fitted=True, mondrian=True))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf.calibrate(X_train, y_train, mc=mc_scoring, oob=True)\n",
    "\n",
    "display(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e106a138-f907-4e8c-b58f-f59a5fff8896",
   "metadata": {},
   "source": [
    "Prediction sets for the test objects are obtained in the usual way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9985f056-493c-46bf-8496-a782463b457d",
   "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": [
    "prediction_sets_mond_oob = rf.predict_set(X_test)\n",
    "\n",
    "display(prediction_sets_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b52bc170-00ec-444b-8521-8466f626888c",
   "metadata": {},
   "source": [
    "We may of course evaluate the Mondrian conformal classifier using out-of-bag predictions too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "25b6a9a3-cb0a-4e68-a488-4ecdf8295e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.04299065420560744,\n",
       " 'avg_c': 0.9581595974119339,\n",
       " 'one_c': 0.9581595974119339,\n",
       " 'empty': 0.041840402588066136,\n",
       " 'time_fit': 0.0002086162567138672,\n",
       " 'time_evaluate': 0.5787811279296875}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = rf.evaluate(X_test, y_test)\n",
    "\n",
    "display(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dee55d1b-882c-429c-9b0c-e565c47a3d94",
   "metadata": {},
   "source": [
    "#### Class-conditional conformal classifiers with out-of-bag calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce427f8-64d0-46a8-ab95-816df79dcc1a",
   "metadata": {},
   "source": [
    "Unsurprisingly, since a class-conditional conformal classifier is a special type of Mondrian conformal classifier, we can use out-of-bag calibration for them too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4b43b491-94b0-45b7-bdec-21257b609655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapClassifier(learner=RandomForestClassifier(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalClassifier(fitted=True, mondrian=True))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf.calibrate(X_train, y_train, class_cond=True, oob=True)\n",
    "\n",
    "display(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bda4aa74-a659-4544-b1e9-a729201a3edd",
   "metadata": {},
   "source": [
    "Prediction sets for the test objects are obtained in the usual way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c9b8e442-8896-4292-9b3d-1ba05fdeb866",
   "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": [
    "prediction_sets_class_cond_oob = rf.predict_set(X_test)\n",
    "\n",
    "display(prediction_sets_class_cond_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "796c7b3e-a48a-42fa-b937-1a0a902d6fca",
   "metadata": {},
   "source": [
    "Finally, for completeness, we show that a class-conditional conformal classifier formed using out-of-bag calibration may be evaluated too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0308a1fc-161f-49f4-b9ba-ef814b12f180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.047735442127965544,\n",
       " 'avg_c': 0.9534148094895758,\n",
       " 'one_c': 0.9534148094895758,\n",
       " 'empty': 0.04658519051042415,\n",
       " 'time_fit': 0.0015447139739990234,\n",
       " 'time_evaluate': 2.7635488510131836}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = rf.evaluate(X_test, y_test)\n",
    "\n",
    "display(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a135407-e55d-4b9e-b124-ca6b8f4a7359",
   "metadata": {},
   "source": [
    "Let us also check the performance for the classes separately:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6d9ddaf2-5229-43de-80a2-512e4a54b05a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 1:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.04280155642023342,\n",
       " 'avg_c': 0.9571984435797666,\n",
       " 'one_c': 0.9571984435797666,\n",
       " 'empty': 0.042801556420233464,\n",
       " 'time_fit': 0.0015447139739990234,\n",
       " 'time_evaluate': 0.5539159774780273}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 2:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.05842391304347827,\n",
       " 'avg_c': 0.9429347826086957,\n",
       " 'one_c': 0.9429347826086957,\n",
       " 'empty': 0.057065217391304345,\n",
       " 'time_fit': 0.0015447139739990234,\n",
       " 'time_evaluate': 0.6227080821990967}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 3:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.053204353083434075,\n",
       " 'avg_c': 0.9528415961305925,\n",
       " 'one_c': 0.9528415961305925,\n",
       " 'empty': 0.0471584038694075,\n",
       " 'time_fit': 0.0015447139739990234,\n",
       " 'time_evaluate': 0.3709239959716797}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 4:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.04127966976264186,\n",
       " 'avg_c': 0.9587203302373581,\n",
       " 'one_c': 0.9587203302373581,\n",
       " 'empty': 0.0412796697626419,\n",
       " 'time_fit': 0.0015447139739990234,\n",
       " 'time_evaluate': 0.4219629764556885}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 5:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.05291359678499663,\n",
       " 'avg_c': 0.9470864032150034,\n",
       " 'one_c': 0.9470864032150034,\n",
       " 'empty': 0.05291359678499665,\n",
       " 'time_fit': 0.0015447139739990234,\n",
       " 'time_evaluate': 0.6317543983459473}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 6:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.030803080308030806,\n",
       " 'avg_c': 0.9702970297029703,\n",
       " 'one_c': 0.9702970297029703,\n",
       " 'empty': 0.0297029702970297,\n",
       " 'time_fit': 0.0015447139739990234,\n",
       " 'time_evaluate': 0.40610599517822266}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for c in rf.learner.classes_:\n",
    "    print(f\"Class {c}:\")\n",
    "    display(rf.evaluate(X_test[y_test == c], y_test[y_test == c]))\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc21f90",
   "metadata": {},
   "source": [
    "## Conformal regressors (CR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba861d00-2bd2-450c-b55c-60c1eceab588",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Importing and splitting a regression dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e86931a-46e5-4524-9b19-af9975ad6ae7",
   "metadata": {},
   "source": [
    "Let us import a 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": 35,
   "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": "550e01be-9db1-4ed4-b7a2-ba7ecf68c44c",
   "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": 36,
   "id": "72a9bdb3-9a98-486d-9407-8e9e47eafd6b",
   "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": "11169c37",
   "metadata": {},
   "source": [
    "### Standard conformal regressors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa14a8fc-87e5-4bd3-9189-d4a3e587ce7a",
   "metadata": {},
   "source": [
    "Let us create a conformal regressor through a `WrapRegressor` object, using a random forest that is fitted in the usual way (using the proper training set only), before or after being wrapped:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f8b04519-d4db-4db5-ae44-8156132fa8f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = WrapRegressor(RandomForestRegressor(n_jobs=-1, n_estimators=500, oob_score=True))\n",
    "\n",
    "rf.fit(X_prop_train, y_prop_train)\n",
    "\n",
    "display(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6620b9d0-84bd-4ac2-b755-98aa90384150",
   "metadata": {},
   "source": [
    "We will use the the calibration set to calibrate the conformal regressor; note that the `calibrate` method has been added to the wrapped learner, in addition to the standard `fit`and `predict` methods. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3fdfd8b7-3c6d-46c8-bb54-267b7f95c39e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalRegressor(fitted=True, normalized=False, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf.calibrate(X_cal, y_cal)\n",
    "\n",
    "display(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1fc47d2-dfb2-4d1f-b700-ff38cd6ee93f",
   "metadata": {},
   "source": [
    "We may now obtain prediction intervals for the test set using the new method `predict_int`; \n",
    "here using a confidence level of 99%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0b6b778f-1280-4d7a-9443-b3ad84bc902a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02480588,  0.11222436],\n",
       "       [ 0.02322554,  0.16025577],\n",
       "       [ 0.11993764,  0.25696788],\n",
       "       ...,\n",
       "       [-0.03801077,  0.09901947],\n",
       "       [-0.03292067,  0.10410957],\n",
       "       [-0.01228703,  0.12474321]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervals = rf.predict_int(X_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": 40,
   "id": "297e612d-0f83-4e3f-a7da-ad0260d4bb8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01504779, 0.07237069],\n",
       "       [0.0630792 , 0.12040211],\n",
       "       [0.15979131, 0.21711421],\n",
       "       ...,\n",
       "       [0.0018429 , 0.0591658 ],\n",
       "       [0.006933  , 0.0642559 ],\n",
       "       [0.02756664, 0.08488954]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervals_std = rf.predict_int(X_test, y_min=0, y_max=1)\n",
    "\n",
    "display(intervals_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4855b9e-e590-438d-ba0b-a4089368de1b",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Accessing the wrapped learner "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c2724d-2cde-4360-ae2e-8fda116339ab",
   "metadata": {
    "tags": []
   },
   "source": [
    "As shown above, we have access directly to some methods of the wrapped learner, e.g., through the `fit` and `predict` methods. We can also access the wrapped learner directly by the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ded009cb-9771-4c44-976f-51d3d782ef9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "learner_prop = rf.learner"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30012a47-209d-4565-83fb-bfdd66cd7f9e",
   "metadata": {},
   "source": [
    "This may be useful, e.g., in case a fitted wrapped learner is to be re-used in another conformal regressor, as we will see below."
   ]
  },
  {
   "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": 42,
   "id": "eb3df21d-344e-40e0-8ccd-4ec5e55baca1",
   "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"
    }
   ],
   "source": [
    "de_knn = DifficultyEstimator()\n",
    "\n",
    "de_knn.fit(X=X_prop_train, scaler=True)\n",
    "\n",
    "display(de_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a755a34f-4d79-4977-91ea-4dbf1a9cc79d",
   "metadata": {},
   "source": [
    "We create a new (normalized) conformal regressor re-using the underlying (already fitted) learner of the previous conformal regressor, and calibrate it using the calibration objects and labels together the difficulty estimator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fb93971a-bd20-4fb3-96bd-0508ce8ec558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalRegressor(fitted=True, normalized=True, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_norm_knn_dist = WrapRegressor(learner_prop)\n",
    "\n",
    "rf_norm_knn_dist.calibrate(X_cal, y_cal, de=de_knn)\n",
    "\n",
    "display(rf_norm_knn_dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d860d47-f7a2-4883-a554-c8cf88f57b7c",
   "metadata": {},
   "source": [
    "To obtain prediction intervals, we just have to provide test objects to the `predict_int` method, as the difficulty estimates will be computed by the incorporated difficulty estimator: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "895e3436-f717-4adb-80a8-de023fa6e1f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02918549, 0.058233  ],\n",
       "       [0.03223885, 0.15124246],\n",
       "       [0.12829173, 0.24861379],\n",
       "       ...,\n",
       "       [0.        , 0.06581393],\n",
       "       [0.02467083, 0.04651807],\n",
       "       [0.040169  , 0.07228718]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervals_norm_knn_dist = rf_norm_knn_dist.predict_int(X_test, 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 to the `fit` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=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",
    "rf_norm_knn_std = WrapRegressor(learner_prop)\n",
    "\n",
    "rf_norm_knn_std.calibrate(X_cal, y_cal, de=de_knn_std)\n",
    "\n",
    "display(rf_norm_knn_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84e9f1b-1a18-460d-82bc-7107391505c5",
   "metadata": {},
   "source": [
    "We obtain the prediction intervals in the same way as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e173e02c-3359-4c82-b627-bce6d46fb449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02360122, 0.06381727],\n",
       "       [0.03871464, 0.14476667],\n",
       "       [0.06067669, 0.31622884],\n",
       "       ...,\n",
       "       [0.01415255, 0.04685615],\n",
       "       [0.01832372, 0.05286518],\n",
       "       [0.03686104, 0.07559513]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervals_norm_knn_std = rf_norm_knn_std.predict_int(X_test, 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, where the latter are stored in `oob_prediction_` of the underlying lerner (as a consequence of 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": 47,
   "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": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalRegressor(fitted=True, normalized=True, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals_prop_oob = y_prop_train - rf.learner.oob_prediction_\n",
    "\n",
    "de_knn_res = DifficultyEstimator()\n",
    "\n",
    "de_knn_res.fit(X=X_prop_train, residuals=residuals_prop_oob, scaler=True)\n",
    "\n",
    "display(de_knn_res)\n",
    "\n",
    "rf_norm_knn_res = WrapRegressor(learner_prop)\n",
    "\n",
    "rf_norm_knn_res.calibrate(X_cal, y_cal, de=de_knn_res)\n",
    "\n",
    "display(rf_norm_knn_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac8b3c2-9aed-4836-9d35-8055c5b270c0",
   "metadata": {},
   "source": [
    "Let us also generate the prediction intervals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c7111fb4-0cb4-447f-8114-68f88412c202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02880321, 0.05861528],\n",
       "       [0.03673212, 0.14674919],\n",
       "       [0.10185175, 0.27505377],\n",
       "       ...,\n",
       "       [0.01564583, 0.04536288],\n",
       "       [0.00789758, 0.06329132],\n",
       "       [0.04170032, 0.07075585]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervals_norm_knn_res = rf_norm_knn_res.predict_int(X_test, 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": 49,
   "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": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=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",
    "rf_norm_var = WrapRegressor(learner_prop)\n",
    "\n",
    "rf_norm_var.calibrate(X_cal, y_cal, de=de_var)\n",
    "\n",
    "display(rf_norm_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b920a7f7-0da3-4ae9-a751-b0955d9c6a6f",
   "metadata": {},
   "source": [
    "The prediction intervals are obtained in the usual way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e2771706-1143-437c-9588-aa65b52124ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02629049, 0.06112799],\n",
       "       [0.06984789, 0.11363342],\n",
       "       [0.08313428, 0.29377125],\n",
       "       ...,\n",
       "       [0.01343322, 0.04757549],\n",
       "       [0.01877718, 0.05241171],\n",
       "       [0.03842964, 0.07402653]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervals_norm_var = rf_norm_var.predict_int(X_test, 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 a function or a `MondrianCategorizer` (defined in `crepes.extras`) as an additional argument, named `mc`, for the `calibrate` method. \n",
    "\n",
    "Here we employ a `MondrianCategorizer`; it may be fitted in several different ways, and below we show how to form categories by binning of the difficulty estimates into 20 bins, using one of the previously fitted difficulty estimators. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "496c99ba-fc3b-4679-8210-b5fa965472a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MondrianCategorizer(fitted=True, de=DifficultyEstimator(fitted=True, type=variance, scaler=True, beta=0.01, oob=False), no_bins=20)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalRegressor(fitted=True, normalized=False, mondrian=True))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mc = MondrianCategorizer()\n",
    "\n",
    "mc.fit(X_cal, de=de_var, no_bins=20)\n",
    "\n",
    "display(mc)\n",
    "\n",
    "rf_mond = WrapRegressor(learner_prop)\n",
    "\n",
    "rf_mond.calibrate(X_cal, y_cal, mc=mc)\n",
    "\n",
    "display(rf_mond)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a1fc9a-4b2e-420f-963d-4d007b7d38e2",
   "metadata": {},
   "source": [
    "Prediction intervals for the test instances are obtained in the usual way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1ae5ec66-7920-4041-9b79-46fabd76fe96",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0292439 , 0.05817458],\n",
       "       [0.05808033, 0.12540098],\n",
       "       [0.05210677, 0.32479875],\n",
       "       ...,\n",
       "       [0.01812271, 0.04288599],\n",
       "       [0.02385246, 0.04733643],\n",
       "       [0.04100056, 0.07145562]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervals_mond = rf_mond.predict_int(X_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": 53,
   "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": 54,
   "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>0.9396</td>\n",
       "      <td>0.0562</td>\n",
       "      <td>0.0573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn dist</th>\n",
       "      <td>0.9446</td>\n",
       "      <td>0.0558</td>\n",
       "      <td>0.0462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn std</th>\n",
       "      <td>0.9446</td>\n",
       "      <td>0.0555</td>\n",
       "      <td>0.0442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn res</th>\n",
       "      <td>0.9424</td>\n",
       "      <td>0.0512</td>\n",
       "      <td>0.0418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR var</th>\n",
       "      <td>0.9466</td>\n",
       "      <td>0.0509</td>\n",
       "      <td>0.0370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond CR</th>\n",
       "      <td>0.9548</td>\n",
       "      <td>0.0544</td>\n",
       "      <td>0.0446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.9454</td>\n",
       "      <td>0.0540</td>\n",
       "      <td>0.0452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Coverage  Mean size  Median size\n",
       "Std CR              0.9396     0.0562       0.0573\n",
       "Norm CR knn dist    0.9446     0.0558       0.0462\n",
       "Norm CR knn std     0.9446     0.0555       0.0442\n",
       "Norm CR knn res     0.9424     0.0512       0.0418\n",
       "Norm CR var         0.9466     0.0509       0.0370\n",
       "Mond CR             0.9548     0.0544       0.0446\n",
       "Mean                0.9454     0.0540       0.0452"
      ]
     },
     "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": 55,
   "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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "abcd8956-24ab-4bda-ab6e-aeb63e9b1c38",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_methods = {\n",
    "    \"Std CR\": rf,\n",
    "    \"Norm CR knn dist\": rf_norm_knn_dist,\n",
    "    \"Norm CR knn std\": rf_norm_knn_std,\n",
    "    \"Norm CR knn res\": rf_norm_knn_res,\n",
    "    \"Norm CR var\" : rf_norm_var,\n",
    "    \"Mond CR\": rf_mond\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>0.1090</td>\n",
       "      <td>0.0604</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.1121</td>\n",
       "      <td>0.0554</td>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.1063</td>\n",
       "      <td>0.0554</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>0.1146</td>\n",
       "      <td>0.0576</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.1059</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.0901</td>\n",
       "      <td>0.0452</td>\n",
       "      <td>0.0102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_mean</th>\n",
       "      <td>0.0393</td>\n",
       "      <td>0.0562</td>\n",
       "      <td>0.1177</td>\n",
       "      <td>0.0421</td>\n",
       "      <td>0.0558</td>\n",
       "      <td>0.0930</td>\n",
       "      <td>0.0424</td>\n",
       "      <td>0.0555</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>0.0388</td>\n",
       "      <td>0.0512</td>\n",
       "      <td>0.0815</td>\n",
       "      <td>0.0399</td>\n",
       "      <td>0.0509</td>\n",
       "      <td>0.0744</td>\n",
       "      <td>0.0422</td>\n",
       "      <td>0.0544</td>\n",
       "      <td>0.0885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_med</th>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0573</td>\n",
       "      <td>0.1196</td>\n",
       "      <td>0.0346</td>\n",
       "      <td>0.0462</td>\n",
       "      <td>0.0775</td>\n",
       "      <td>0.0336</td>\n",
       "      <td>0.0442</td>\n",
       "      <td>0.0746</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>0.0418</td>\n",
       "      <td>0.0667</td>\n",
       "      <td>0.0287</td>\n",
       "      <td>0.0370</td>\n",
       "      <td>0.0555</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>0.0446</td>\n",
       "      <td>0.0644</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>0.0000</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>0.0000</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>0.0005</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0003</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>0.0003</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0007</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          0.1090  0.0604  0.0110           0.1121  0.0554  0.0092   \n",
       "eff_mean       0.0393  0.0562  0.1177           0.0421  0.0558  0.0930   \n",
       "eff_med        0.0394  0.0573  0.1196           0.0346  0.0462  0.0775   \n",
       "time_fit       0.0001  0.0001  0.0001           0.0000  0.0000  0.0000   \n",
       "time_evaluate  0.0005  0.0005  0.0006           0.0003  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                  0.1063  0.0554  0.0097          0.1146  0.0576  0.0132   \n",
       "eff_mean               0.0424  0.0555  0.0920          0.0388  0.0512  0.0815   \n",
       "eff_med                0.0336  0.0442  0.0746          0.0316  0.0418  0.0667   \n",
       "time_fit               0.0001  0.0001  0.0001          0.0000  0.0000  0.0000   \n",
       "time_evaluate          0.0003  0.0003  0.0003          0.0003  0.0003  0.0003   \n",
       "\n",
       "              Norm CR var                 Mond CR                  \n",
       "                     0.90    0.95    0.99    0.90    0.95    0.99  \n",
       "error              0.1059  0.0534  0.0132  0.0901  0.0452  0.0102  \n",
       "eff_mean           0.0399  0.0509  0.0744  0.0422  0.0544  0.0885  \n",
       "eff_med            0.0287  0.0370  0.0555  0.0325  0.0446  0.0644  \n",
       "time_fit           0.0000  0.0000  0.0000  0.0002  0.0002  0.0002  \n",
       "time_evaluate      0.0002  0.0003  0.0003  0.0007  0.0007  0.0007  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confidence_levels = [0.9,0.95,0.99]\n",
    "\n",
    "names = list(all_methods.keys())\n",
    "\n",
    "all_results = {}\n",
    "\n",
    "for confidence in confidence_levels:\n",
    "    for name in names:\n",
    "        all_results[(name,confidence)] = all_methods[name].evaluate(\n",
    "            X_test, y=y_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 fail to 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, making sure 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": 58,
   "id": "772644f0-ce59-49a6-847d-c43fd9bb0374",
   "metadata": {},
   "outputs": [],
   "source": [
    "learner_full = RandomForestRegressor(n_jobs=-1, n_estimators=500, oob_score=True)\n",
    "\n",
    "rf = WrapRegressor(learner_full)\n",
    "\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91abe570-276e-4a2f-8e2e-08b1da212e5d",
   "metadata": {},
   "source": [
    "We may now obtain a standard conformal regressor using OOB predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3838aff6-ca49-4ffc-b17d-9217fa742811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalRegressor(fitted=True, normalized=False, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf.calibrate(X_train, y_train, oob=True)\n",
    "\n",
    "display(rf)"
   ]
  },
  {
   "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": 60,
   "id": "ff0c3105-3141-4dfa-9377-65d24763156c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.014075  , 0.07431506],\n",
       "       [0.06207528, 0.12231534],\n",
       "       [0.15408189, 0.21432195],\n",
       "       ...,\n",
       "       [0.00152192, 0.06176198],\n",
       "       [0.00424339, 0.06448345],\n",
       "       [0.02453305, 0.08477311]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervals_std_oob = rf.predict_int(X_test, 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": 61,
   "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": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalRegressor(fitted=True, normalized=True, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "rf.calibrate(X_train, y_train, de=de_knn_dist_oob, oob=True)\n",
    "\n",
    "display(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6920423-279d-4ddc-982d-6fa422eea71f",
   "metadata": {},
   "source": [
    "We may then apply the normalized conformal OOB regressor to the test set, as usual:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0ecff050-2e20-450e-aa27-8df9a16886bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02924781, 0.05914225],\n",
       "       [0.0221347 , 0.16225591],\n",
       "       [0.1127133 , 0.25569054],\n",
       "       ...,\n",
       "       [0.        , 0.07321792],\n",
       "       [0.02298409, 0.04574276],\n",
       "       [0.03401029, 0.07529588]])"
      ]
     },
     "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 = rf.predict_int(X_test, 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": 63,
   "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": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalRegressor(fitted=True, normalized=True, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[0.02461306, 0.063777  ],\n",
       "       [0.03290581, 0.1514848 ],\n",
       "       [0.04613955, 0.32226428],\n",
       "       ...,\n",
       "       [0.0172473 , 0.04603661],\n",
       "       [0.02050422, 0.04822263],\n",
       "       [0.03418165, 0.07512452]])"
      ]
     },
     "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",
    "rf.calibrate(X_train, y_train, de=de_knn_std_oob, oob=True)\n",
    "\n",
    "display(rf)\n",
    "\n",
    "intervals_norm_knn_std_oob = rf.predict_int(X_test, 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": 64,
   "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": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalRegressor(fitted=True, normalized=True, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[0.03083778, 0.05755228],\n",
       "       [0.04356691, 0.1408237 ],\n",
       "       [0.09054843, 0.27785541],\n",
       "       ...,\n",
       "       [0.01837638, 0.04490753],\n",
       "       [0.01348057, 0.05524627],\n",
       "       [0.0360182 , 0.07328797]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals_oob = y_train - learner_full.oob_prediction_\n",
    "\n",
    "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",
    "rf.calibrate(X_train, y_train, de=de_knn_res_oob, oob=True)\n",
    "\n",
    "display(rf)\n",
    "\n",
    "intervals_norm_knn_res_oob = rf.predict_int(X_test, 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": 65,
   "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": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalRegressor(fitted=True, normalized=True, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[0.02660388, 0.06178618],\n",
       "       [0.06903951, 0.1153511 ],\n",
       "       [0.09592814, 0.2724757 ],\n",
       "       ...,\n",
       "       [0.01404707, 0.04923684],\n",
       "       [0.01725774, 0.05146911],\n",
       "       [0.03725377, 0.0720524 ]])"
      ]
     },
     "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",
    "rf.calibrate(X_train, y_train, de=de_var_oob, oob=True)\n",
    "\n",
    "display(rf)\n",
    "\n",
    "intervals_norm_var_oob = rf.predict_int(X_test, 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 estimator 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": 66,
   "id": "7dde227d-112a-48ff-9d6b-76367b4f956e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MondrianCategorizer(fitted=True, de=DifficultyEstimator(fitted=True, type=variance, scaler=True, beta=0.01, oob=True), no_bins=20)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalRegressor(fitted=True, normalized=False, mondrian=True))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mc_oob = MondrianCategorizer()\n",
    "\n",
    "mc_oob.fit(de=de_var_oob, oob=True, no_bins=20)\n",
    "\n",
    "display(mc_oob)\n",
    "\n",
    "rf.calibrate(X_train, y_train, mc=mc_oob, oob=True)\n",
    "\n",
    "display(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aaf927a-dd60-485e-a850-e6aca91da270",
   "metadata": {},
   "source": [
    "Let us generate prediction intervals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "224d067f-4db3-4903-9f1b-5dd9f8d75cbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.03176902, 0.05662104],\n",
       "       [0.06427517, 0.12011544],\n",
       "       [0.06909957, 0.29930426],\n",
       "       ...,\n",
       "       [0.01921595, 0.04406796],\n",
       "       [0.02342536, 0.04530149],\n",
       "       [0.04138186, 0.06792431]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervals_mond_oob = rf.predict_int(X_test, 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": 68,
   "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": 69,
   "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>0.9452</td>\n",
       "      <td>0.0589</td>\n",
       "      <td>0.0602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn dist OOB</th>\n",
       "      <td>0.9616</td>\n",
       "      <td>0.0631</td>\n",
       "      <td>0.0521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn std OOB</th>\n",
       "      <td>0.9457</td>\n",
       "      <td>0.0574</td>\n",
       "      <td>0.0447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn res OOB</th>\n",
       "      <td>0.9357</td>\n",
       "      <td>0.0495</td>\n",
       "      <td>0.0390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR var OOB</th>\n",
       "      <td>0.9498</td>\n",
       "      <td>0.0508</td>\n",
       "      <td>0.0373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond CR OOB</th>\n",
       "      <td>0.9480</td>\n",
       "      <td>0.0502</td>\n",
       "      <td>0.0371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.9477</td>\n",
       "      <td>0.0550</td>\n",
       "      <td>0.0451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Coverage  Mean size  Median size\n",
       "Std CR OOB              0.9452     0.0589       0.0602\n",
       "Norm CR knn dist OOB    0.9616     0.0631       0.0521\n",
       "Norm CR knn std OOB     0.9457     0.0574       0.0447\n",
       "Norm CR knn res OOB     0.9357     0.0495       0.0390\n",
       "Norm CR var OOB         0.9498     0.0508       0.0373\n",
       "Mond CR OOB             0.9480     0.0502       0.0371\n",
       "Mean                    0.9477     0.0550       0.0451"
      ]
     },
     "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": 70,
   "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": [
    "Conformal predictive systems are created through the `WrapRegressor` class in the same way as conformal regressors. We specify that a conformal predictive system, rather than a conformal regressor, should be formed by providing `cps=True` when calling the `calibrate` method (default is `cps=False`).\n",
    "\n",
    "Let us start by generating standard and normalized conformal predictive systems (CPS), using the previously fitted `learner_prop` and difficulty estimator `de_var`, as well two conformal predictive systems relying on out-of-bag predictions, using the previously fitted `learner_full` with and without normalization, in the former case using the difficulty estimator `de_var_oob`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1cc02a22-34d5-47d2-9f4b-df7f4d70efdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalPredictiveSystem(fitted=True, normalized=False, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalPredictiveSystem(fitted=True, normalized=True, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalPredictiveSystem(fitted=True, normalized=False, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalPredictiveSystem(fitted=True, normalized=True, mondrian=False))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cps_std = WrapRegressor(learner_prop)\n",
    "\n",
    "cps_std.calibrate(X_cal, y_cal, cps=True)\n",
    "\n",
    "display(cps_std)\n",
    "\n",
    "cps_norm = WrapRegressor(learner_prop).calibrate(X_cal, y_cal, de=de_var, \n",
    "                                                 cps=True)\n",
    "display(cps_norm)\n",
    "\n",
    "cps_std_oob = WrapRegressor(learner_full).calibrate(X_train, y_train, oob=True,\n",
    "                                                    cps=True)\n",
    "display(cps_std_oob)\n",
    "\n",
    "cps_norm_oob = WrapRegressor(learner_full).calibrate(X_train, y_train, \n",
    "                                                     de=de_var_oob, oob=True, cps=True)\n",
    "display(cps_norm_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 also show how we still may use the latter to obtain a normalized CPS for each category (bin)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a89db840-c6b8-47aa-8c18-b00eb199c5a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalPredictiveSystem(fitted=True, normalized=False, mondrian=True))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalPredictiveSystem(fitted=True, normalized=True, mondrian=True))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "MondrianCategorizer(fitted=True, learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), no_bins=5)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalPredictiveSystem(fitted=True, normalized=False, mondrian=True))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=True, predictor=ConformalPredictiveSystem(fitted=True, normalized=True, mondrian=True))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mc_p = MondrianCategorizer()\n",
    "\n",
    "mc_p.fit(X_cal, f=learner_prop.predict, no_bins=5)\n",
    "\n",
    "cps_mond_std = WrapRegressor(learner_prop)\n",
    "cps_mond_std.calibrate(X_cal, y_cal, mc=mc_p, cps=True)\n",
    "\n",
    "display(cps_mond_std)\n",
    "\n",
    "cps_mond_norm = WrapRegressor(learner_prop)\n",
    "cps_mond_norm.calibrate(X_cal, y_cal, de=de_var, mc=mc_p, cps=True)\n",
    "\n",
    "display(cps_mond_norm)\n",
    "\n",
    "mc_p_oob = MondrianCategorizer()\n",
    "mc_p_oob.fit(learner=learner_full, oob=True, no_bins=5)\n",
    "\n",
    "display(mc_p_oob)\n",
    "\n",
    "cps_mond_std_oob = WrapRegressor(learner_full)\n",
    "cps_mond_std_oob.calibrate(X_train, y_train, mc=mc_p_oob, oob=True, cps=True)\n",
    "\n",
    "display(cps_mond_std_oob)\n",
    "\n",
    "cps_mond_norm_oob = WrapRegressor(learner_full)\n",
    "cps_mond_norm_oob.calibrate(X_train, y_train, de=de_var_oob, mc=mc_p_oob, oob=True, cps=True)\n",
    "\n",
    "display(cps_mond_norm_oob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e541e40f-3085-4bdc-847f-11089c66a090",
   "metadata": {},
   "source": [
    "### Making predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00406bed-b471-459a-9741-2a6fd111bff1",
   "metadata": {},
   "source": [
    "We still have access to the usual `predict` method, to obtain point predictions of the wrapped learner, as well as the `predict_int` method, which works as for conformal regressors. In addition, there is now a method called `predict_cps`, for which the output 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, which is uniformly distributed for the true target. 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": 73,
   "id": "072d72f7-6c0e-46d7-b447-906863f56c70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.88077001, 0.99779574, 0.62521848, ..., 0.3122575 , 0.46325028,\n",
       "       0.62568664])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_values = cps_mond_norm.predict_cps(X_test, 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, such that the probability for 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": 74,
   "id": "0440b9f7-6847-4637-bfcf-ecbce5de7cc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04376101, 0.09166666, 0.18809677, ..., 0.03055509, 0.03564443,\n",
       "       0.05597935])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresholds = cps_mond_norm.predict_cps(X_test, 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": 75,
   "id": "1cda8888-2c35-45a5-9286-53ab3761483d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.8814577 , 0.02895864, 0.03221248, 0.05524699, 0.05843707],\n",
       "       [0.997684  , 0.07112827, 0.07479626, 0.11889625, 0.12454269],\n",
       "       [0.62472366, 0.08929374, 0.10693915, 0.31908888, 0.34625197],\n",
       "       ...,\n",
       "       [0.31082629, 0.01604812, 0.01923703, 0.04181185, 0.04493827],\n",
       "       [0.46318852, 0.0213532 , 0.02449468, 0.04673379, 0.04981371],\n",
       "       [0.6251371 , 0.03885546, 0.0416997 , 0.07079439, 0.075614  ]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = cps_mond_norm.predict_cps(X_test, 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_cps` 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_cps` will result in a vector of distributions, with one element for each test instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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_cps(X_test, return_cpds=True)\n",
    "\n",
    "print(f\"No. of test instances: {len(y_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": 77,
   "id": "cb029611-b2e8-4061-92bd-f4951e3c0f2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bin 0: 2116 test instances, 541 threshold values\n",
      "bin 1: 2191 test instances, 540 threshold values\n",
      "bin 2: 2078 test instances, 540 threshold values\n",
      "bin 3: 2192 test instances, 540 threshold values\n",
      "bin 4: 2230 test instances, 541 threshold values\n",
      "No. of test instances: 10807\n"
     ]
    }
   ],
   "source": [
    "cpds = cps_mond_norm.predict_cps(X_test, return_cpds=True, 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": 78,
   "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_cps(X_test, return_cpds=True)\n",
    "\n",
    "test_index = np.random.randint(len(X_test)) # 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",
    "\n",
    "y_hat = cps_mond_norm_oob.predict(X_test[test_index][None,:])\n",
    "\n",
    "plt.plot([y_hat,y_hat],[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": 79,
   "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": 80,
   "id": "3543362f-2fef-4e65-a6c8-3a809dfd5cab",
   "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",
    "    p_values = all_cps[name].predict_cps(X_test, 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": 81,
   "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>0.9413</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.0595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std OOB CPS</th>\n",
       "      <td>0.9464</td>\n",
       "      <td>0.0599</td>\n",
       "      <td>0.0610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CPS</th>\n",
       "      <td>0.9445</td>\n",
       "      <td>0.0521</td>\n",
       "      <td>0.0378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm OOB CPS</th>\n",
       "      <td>0.9493</td>\n",
       "      <td>0.0515</td>\n",
       "      <td>0.0378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond CPS</th>\n",
       "      <td>0.9496</td>\n",
       "      <td>0.0613</td>\n",
       "      <td>0.0425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond OOB CPS</th>\n",
       "      <td>0.9469</td>\n",
       "      <td>0.0566</td>\n",
       "      <td>0.0416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond norm CPS</th>\n",
       "      <td>0.9484</td>\n",
       "      <td>0.0541</td>\n",
       "      <td>0.0385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond norm OOB CPS</th>\n",
       "      <td>0.9466</td>\n",
       "      <td>0.0533</td>\n",
       "      <td>0.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.9466</td>\n",
       "      <td>0.0560</td>\n",
       "      <td>0.0446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Coverage  Mean size  Median size\n",
       "Std CPS              0.9413     0.0588       0.0595\n",
       "Std OOB CPS          0.9464     0.0599       0.0610\n",
       "Norm CPS             0.9445     0.0521       0.0378\n",
       "Norm OOB CPS         0.9493     0.0515       0.0378\n",
       "Mond CPS             0.9496     0.0613       0.0425\n",
       "Mond OOB CPS         0.9469     0.0566       0.0416\n",
       "Mond norm CPS        0.9484     0.0541       0.0385\n",
       "Mond norm OOB CPS    0.9466     0.0533       0.0381\n",
       "Mean                 0.9466     0.0560       0.0446"
      ]
     },
     "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",
    "    intervals = all_cps[name].predict_int(X_test, 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": 82,
   "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": 83,
   "id": "ebea7b27-a9a1-4473-a20d-3b2b7fcaea79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Std CPS\n",
      "\tMean difference of the medians:    0.000004\n",
      "\tLargest difference of the medians: 0.000004\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:    0.000013\n",
      "\tLargest difference of the medians: 0.000767\n",
      "Norm OOB CPS\n",
      "\tMean difference of the medians:    0.000001\n",
      "\tLargest difference of the medians: 0.000071\n",
      "Mond CPS\n",
      "\tMean difference of the medians:    0.000018\n",
      "\tLargest difference of the medians: 0.000062\n",
      "Mond OOB CPS\n",
      "\tMean difference of the medians:    0.000003\n",
      "\tLargest difference of the medians: 0.000013\n",
      "Mond norm CPS\n",
      "\tMean difference of the medians:    0.000023\n",
      "\tLargest difference of the medians: 0.000185\n",
      "Mond norm OOB CPS\n",
      "\tMean difference of the medians:    0.000002\n",
      "\tLargest difference of the medians: 0.000012\n"
     ]
    }
   ],
   "source": [
    "all_cps_medians = {}\n",
    "\n",
    "for name in all_cps.keys():\n",
    "    medians = all_cps[name].predict_cps(X_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": 84,
   "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": [
    "y_hat_test = learner_prop.predict(X_test)\n",
    "y_hat_full = learner_full.predict(X_test)\n",
    "\n",
    "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 `bins` can always be provided, but they will be ignored by CPS that \n",
    "have not been fitted with such, i.e., the CPS is not of the Mondrian type.\n",
    "\n",
    "Note that CRPS takes some time to compute, in particular when the CPS have been \n",
    "fitted with larger calibration sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "2775d1e6-86e2-4421-8531-dc97eb3d51ba",
   "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 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>0.1073</td>\n",
       "      <td>0.0587</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.1069</td>\n",
       "      <td>0.0555</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>0.1088</td>\n",
       "      <td>0.0504</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.0516</td>\n",
       "      <td>0.0109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_mean</th>\n",
       "      <td>0.0402</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.1425</td>\n",
       "      <td>0.0401</td>\n",
       "      <td>0.0521</td>\n",
       "      <td>0.0820</td>\n",
       "      <td>0.0405</td>\n",
       "      <td>0.0613</td>\n",
       "      <td>0.1049</td>\n",
       "      <td>0.0435</td>\n",
       "      <td>0.0541</td>\n",
       "      <td>0.0865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_med</th>\n",
       "      <td>0.0403</td>\n",
       "      <td>0.0595</td>\n",
       "      <td>0.1504</td>\n",
       "      <td>0.0288</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0609</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>0.0757</td>\n",
       "      <td>0.0303</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>0.0666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRPS</th>\n",
       "      <td>0.0076</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.0070</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>0.0000</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",
       "      <td>0.0003</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_evaluate</th>\n",
       "      <td>0.3342</td>\n",
       "      <td>0.3317</td>\n",
       "      <td>0.3339</td>\n",
       "      <td>0.3519</td>\n",
       "      <td>0.3756</td>\n",
       "      <td>0.3721</td>\n",
       "      <td>0.2108</td>\n",
       "      <td>0.2132</td>\n",
       "      <td>0.2121</td>\n",
       "      <td>0.2434</td>\n",
       "      <td>0.2386</td>\n",
       "      <td>0.2457</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          0.1073  0.0587  0.0094   0.1069  0.0555  0.0097   0.1088   \n",
       "eff_mean       0.0402  0.0588  0.1425   0.0401  0.0521  0.0820   0.0405   \n",
       "eff_med        0.0403  0.0595  0.1504   0.0288  0.0378  0.0609   0.0322   \n",
       "CRPS           0.0076  0.0076  0.0076   0.0071  0.0071  0.0071   0.0073   \n",
       "time_fit       0.0001  0.0001  0.0001   0.0000  0.0000  0.0000   0.0002   \n",
       "time_evaluate  0.3342  0.3317  0.3339   0.3519  0.3756  0.3721   0.2108   \n",
       "\n",
       "                              Mond norm CPS                  \n",
       "                 0.95    0.99          0.90    0.95    0.99  \n",
       "error          0.0504  0.0089        0.1010  0.0516  0.0109  \n",
       "eff_mean       0.0613  0.1049        0.0435  0.0541  0.0865  \n",
       "eff_med        0.0425  0.0757        0.0303  0.0385  0.0666  \n",
       "CRPS           0.0073  0.0073        0.0070  0.0070  0.0070  \n",
       "time_fit       0.0002  0.0002        0.0003  0.0003  0.0003  \n",
       "time_evaluate  0.2132  0.2121        0.2434  0.2386  0.2457  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <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>0.1034</td>\n",
       "      <td>0.0536</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.1011</td>\n",
       "      <td>0.0507</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.0531</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0979</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>0.0096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_mean</th>\n",
       "      <td>0.0408</td>\n",
       "      <td>0.0599</td>\n",
       "      <td>0.1279</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0515</td>\n",
       "      <td>0.0785</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>0.0566</td>\n",
       "      <td>0.0985</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>0.0533</td>\n",
       "      <td>0.0811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_med</th>\n",
       "      <td>0.0409</td>\n",
       "      <td>0.0610</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>0.0291</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.0313</td>\n",
       "      <td>0.0416</td>\n",
       "      <td>0.0724</td>\n",
       "      <td>0.0301</td>\n",
       "      <td>0.0381</td>\n",
       "      <td>0.0602</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>0.0070</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.0070</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>0.0003</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>0.8767</td>\n",
       "      <td>0.7397</td>\n",
       "      <td>0.7338</td>\n",
       "      <td>0.8308</td>\n",
       "      <td>0.8158</td>\n",
       "      <td>0.8187</td>\n",
       "      <td>0.2859</td>\n",
       "      <td>0.2840</td>\n",
       "      <td>0.2834</td>\n",
       "      <td>0.3244</td>\n",
       "      <td>0.3241</td>\n",
       "      <td>0.3234</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              0.1034  0.0536  0.0099       0.1011  0.0507  0.0108   \n",
       "eff_mean           0.0408  0.0599  0.1279       0.0400  0.0515  0.0785   \n",
       "eff_med            0.0409  0.0610  0.1343       0.0291  0.0378  0.0588   \n",
       "CRPS               0.0075  0.0075  0.0075       0.0070  0.0070  0.0070   \n",
       "time_fit           0.0001  0.0001  0.0001       0.0001  0.0001  0.0001   \n",
       "time_evaluate      0.8767  0.7397  0.7338       0.8308  0.8158  0.8187   \n",
       "\n",
       "              Mond OOB CPS                 Mond norm OOB CPS                  \n",
       "                      0.90    0.95    0.99              0.90    0.95    0.99  \n",
       "error               0.1010  0.0531  0.0099            0.0979  0.0534  0.0096  \n",
       "eff_mean            0.0425  0.0566  0.0985            0.0425  0.0533  0.0811  \n",
       "eff_med             0.0313  0.0416  0.0724            0.0301  0.0381  0.0602  \n",
       "CRPS                0.0071  0.0071  0.0071            0.0070  0.0070  0.0070  \n",
       "time_fit            0.0003  0.0003  0.0003            0.0004  0.0004  0.0004  \n",
       "time_evaluate       0.2859  0.2840  0.2834            0.3244  0.3241  0.3234  "
      ]
     },
     "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",
    "            all_cps_results[(name,confidence)] = all_cps[name].evaluate(\n",
    "                    X_test, y=y_test, confidence=confidence, \n",
    "                    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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