{
 "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. 0.9.0\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 scipy.stats import kstest\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)\n",
    "np.set_printoptions(legacy='1.25')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b070d3d4-db50-4975-9ddd-fca275aca150",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Conformal classifiers"
   ]
  },
  {
   "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": "f566ed0d-4c76-4945-82d5-586bbc086797",
   "metadata": {},
   "source": [
    "If we want only the p-values for the correct labels, we need to provide these and set `all_classes=False`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "581869ef-7247-4f90-9710-338b9a06d8bb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.07138628, 0.40106441, 0.75784407, ..., 0.20672283, 0.11784216,\n",
       "       0.07747337])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_values = rf.predict_p(X_test, y_test, all_classes=False)\n",
    "display(p_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6b569be-e402-4152-9c55-11e341072414",
   "metadata": {},
   "source": [
    "Let us take a look at how the p-values are distributed. From the visual inspection they may appear be approximaly uniformly distributed; this is however not guaranteed, since the p-values are not independent (see further below for semi-online conformal classifiers, for which this indeed holds). It is hence not very surprising if the Kolmogorov-Smirnov test allows us to reject that the p-values are sampled from a uniform distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e03157d-95a2-476c-b362-1723c7faa5f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.0010132423204891204\n"
     ]
    }
   ],
   "source": [
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "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": 12,
   "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": 13,
   "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": 14,
   "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": 15,
   "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": 16,
   "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": 17,
   "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": 18,
   "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": 19,
   "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",
       " 'ks_test': 0.0012658471604212744,\n",
       " 'time_fit': 1.9073486328125e-06,\n",
       " 'time_evaluate': 0.36807727813720703}"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "id": "887e89ca-47db-4770-8d0f-c8ee13b53dad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.0846872753414809,\n",
       " 'avg_c': 0.9164629762760604,\n",
       " 'one_c': 0.9164629762760604,\n",
       " 'empty': 0.08353702372393962,\n",
       " 'ks_test': 0.0014393522574334269,\n",
       " 'time_fit': 1.9073486328125e-06,\n",
       " 'time_evaluate': 0.2785508632659912}"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "id": "235fca55-2522-41de-ac89-6aed74596b9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.08439971243709565,\n",
       " 'avg_c': 0.9167505391804457,\n",
       " 'one_c': 0.9167505391804457,\n",
       " 'empty': 0.08324946081955428,\n",
       " 'ks_test': 0.001590942736191051,\n",
       " 'time_fit': 1.1920928955078125e-06,\n",
       " 'time_evaluate': 0.38449645042419434}"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "id": "50541f80-7a21-4997-98ed-c8292c8b2e21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.08914450035945365,\n",
       " 'avg_c': 0.9120057512580877,\n",
       " 'one_c': 0.9120057512580877,\n",
       " 'empty': 0.08799424874191229,\n",
       " 'ks_test': 5.593471621486094e-05,\n",
       " 'time_fit': 7.152557373046875e-07,\n",
       " 'time_evaluate': 0.29114413261413574}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_mond.evaluate(X_test, y_test, confidence=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "acea5ed5-31aa-43f7-bc22-59333fb6b608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.08339324227174694,\n",
       " 'avg_c': 0.9176132278936018,\n",
       " 'one_c': 0.9176132278936018,\n",
       " 'empty': 0.08238677210639828,\n",
       " 'ks_test': 9.245027662987211e-05,\n",
       " 'time_fit': 1.1920928955078125e-06,\n",
       " 'time_evaluate': 0.25664424896240234}"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "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",
       " 'ks_test': 7.429100238891395e-05,\n",
       " 'time_fit': 7.152557373046875e-07,\n",
       " 'time_evaluate': 1.0580544471740723}"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "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",
       " 'ks_test': 3.0946574531302674e-10,\n",
       " 'time_fit': 1.9073486328125e-06,\n",
       " 'time_evaluate': 0.08918213844299316}"
      ]
     },
     "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",
       " 'ks_test': 1.9363640550744776e-56,\n",
       " 'time_fit': 1.9073486328125e-06,\n",
       " 'time_evaluate': 0.08379364013671875}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 3:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.08343409915356714,\n",
       " 'avg_c': 0.9226118500604595,\n",
       " 'one_c': 0.9226118500604595,\n",
       " 'empty': 0.0773881499395405,\n",
       " 'ks_test': 0.0024108210160489504,\n",
       " 'time_fit': 1.9073486328125e-06,\n",
       " 'time_evaluate': 0.06382465362548828}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 4:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.15583075335397312,\n",
       " 'avg_c': 0.8441692466460269,\n",
       " 'one_c': 0.8441692466460269,\n",
       " 'empty': 0.15583075335397317,\n",
       " 'ks_test': 1.1504056113176992e-89,\n",
       " 'time_fit': 1.9073486328125e-06,\n",
       " 'time_evaluate': 0.06989574432373047}"
      ]
     },
     "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",
       " 'ks_test': 5.592538443391781e-21,\n",
       " 'time_fit': 1.9073486328125e-06,\n",
       " 'time_evaluate': 0.08673357963562012}"
      ]
     },
     "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",
       " 'ks_test': 4.88787792970183e-46,\n",
       " 'time_fit': 1.9073486328125e-06,\n",
       " 'time_evaluate': 0.0678706169128418}"
      ]
     },
     "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": 26,
   "id": "e0d00931-43b8-4301-a33c-5a5ecffe0e13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 1:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.09105058365758756,\n",
       " 'avg_c': 0.9089494163424124,\n",
       " 'one_c': 0.9089494163424124,\n",
       " 'empty': 0.09105058365758754,\n",
       " 'ks_test': 0.0001858581690487567,\n",
       " 'time_fit': 7.152557373046875e-07,\n",
       " 'time_evaluate': 0.23185038566589355}"
      ]
     },
     "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",
       " 'ks_test': 0.07821095302063885,\n",
       " 'time_fit': 7.152557373046875e-07,\n",
       " 'time_evaluate': 0.24954438209533691}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 3:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.10157194679564696,\n",
       " 'avg_c': 0.9032648125755743,\n",
       " 'one_c': 0.9032648125755743,\n",
       " 'empty': 0.09673518742442563,\n",
       " 'ks_test': 0.04995278806404546,\n",
       " 'time_fit': 7.152557373046875e-07,\n",
       " 'time_evaluate': 0.16086578369140625}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 4:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.09907120743034059,\n",
       " 'avg_c': 0.9009287925696594,\n",
       " 'one_c': 0.9009287925696594,\n",
       " 'empty': 0.09907120743034056,\n",
       " 'ks_test': 0.012866228112669556,\n",
       " 'time_fit': 7.152557373046875e-07,\n",
       " 'time_evaluate': 0.1813945770263672}"
      ]
     },
     "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",
       " 'ks_test': 5.088808613816955e-05,\n",
       " 'time_fit': 7.152557373046875e-07,\n",
       " 'time_evaluate': 0.2648732662200928}"
      ]
     },
     "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",
       " 'ks_test': 0.0008464549521329398,\n",
       " 'time_fit': 7.152557373046875e-07,\n",
       " 'time_evaluate': 0.18083763122558594}"
      ]
     },
     "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": [
    "### Semi-online conformal classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee293a8b-7c7d-48bb-89d9-33ab59010e72",
   "metadata": {
    "tags": []
   },
   "source": [
    "The above conformal classifiers are fitted once using the provided calibration set. In case we are receiving the correct label for each test object immediately after making a prediction, we may consider the option of employing *online calibration*, i.e., continuously updating the calibration set. This is done by setting `online=True` when calling the methods `predict_p`, `predict_set`, and `evaluate`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed267ec-94dc-4dca-af3f-97fd009ae5dc",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Online calibration with an already calibrated conformal classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e9c9b3-a34f-4e06-b606-e385103cfc92",
   "metadata": {},
   "source": [
    "Here we will obtain p-values computed in an online fashion for the above fitted standard conformal classifier. Note that in addition to the test objects, we also need to provide the correct labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aae227bb-9a7a-4d4c-af8c-c8c73fd23471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.12382545e-02, 1.42401659e-03, 4.90159428e-04, 7.45847495e-03,\n",
       "        2.18309515e-03, 1.98037996e-03],\n",
       "       [1.12347260e-04, 4.07636586e-01, 3.05823930e-03, 1.02150784e-03,\n",
       "        1.10784632e-04, 1.54268756e-03],\n",
       "       [1.52584633e-03, 3.80601569e-04, 1.98480542e-04, 5.65052745e-04,\n",
       "        7.41303433e-01, 1.14888673e-03],\n",
       "       ...,\n",
       "       [2.68831282e-04, 3.10807302e-04, 5.62649546e-04, 2.17152755e-03,\n",
       "        9.63450138e-04, 1.97410403e-01],\n",
       "       [4.61553549e-04, 5.72752086e-04, 4.44550165e-04, 2.89724023e-03,\n",
       "        4.94855748e-04, 1.10570207e-01],\n",
       "       [9.82250766e-04, 4.94254144e-04, 4.92979994e-04, 9.92940707e-04,\n",
       "        6.65843559e-02, 3.27146481e-03]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.predict_p(X_test, y_test, online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ef598619-a900-4df9-81a9-0a9956a99256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.26757071e-02, 3.51253841e-04, 4.35781175e-03, 8.55582874e-03,\n",
       "        4.32614054e-03, 2.92515154e-03],\n",
       "       [1.21060677e-04, 2.35660361e-01, 1.18911734e-02, 3.82175834e-03,\n",
       "        2.64861356e-03, 2.57595924e-03],\n",
       "       [8.68279739e-04, 1.99223121e-03, 1.45128000e-03, 1.67018875e-03,\n",
       "        7.32038060e-01, 1.80246458e-03],\n",
       "       ...,\n",
       "       [6.05436328e-04, 4.19731028e-04, 3.86819905e-03, 3.63961131e-03,\n",
       "        8.34501791e-04, 2.95091236e-01],\n",
       "       [6.18334268e-05, 4.88607026e-04, 2.27753474e-03, 3.37836609e-03,\n",
       "        6.51029990e-04, 1.38137766e-01],\n",
       "       [3.53629711e-04, 6.23145846e-05, 3.61793911e-03, 2.25112369e-03,\n",
       "        6.79539731e-02, 2.04515808e-03]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_class_cond.predict_p(X_test, y_test, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fabcc20-6a8d-4c5d-b82f-58efd9a517d8",
   "metadata": {},
   "source": [
    "Similarly, we can obtain prediction sets based on the p-values computed online, which are compared to the specified level of confidence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b814b952-285f-455b-959b-ee5472508a74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_mond.predict_set(X_test, y_test, confidence=0.99, online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c04ae3db-20aa-49bc-9fa7-40e29bc110d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 1, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_class_cond.predict_set(X_test, y_test, confidence=0.99, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1bd50e3-76e9-407a-b640-86c0764102f0",
   "metadata": {},
   "source": [
    "By default, the test objects and labels are sequentially added to the existing calibration set, i.e., the one used when calibrating the conformal classifier. If we would like the original calibration set to be ignored, we can set the `warm_start` option to `False`. Note that few (if any) class labels will be excluded from the initial prediction sets, before we have a sufficiently large calibration set to allow for excluding labels at the specified level of confidence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1215667c-97f3-4363-9a13-e3e4e62ccfb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1],\n",
       "       ...,\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_mond.predict_set(X_test, y_test, confidence=0.99,\n",
    "                    online=True, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9bdb1948-f480-45fd-bc9e-a818211d51e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1],\n",
       "       ...,\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_class_cond.predict_set(X_test, y_test, confidence=0.99, \n",
    "                          online=True, warm_start=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f70a030b-15df-404a-9a8f-b3796b1521ad",
   "metadata": {},
   "source": [
    "Let us take a look at how the p-values for the correct labels are distributed. From both the visual inspection and the Kolmogorov-Smirnov test, we cannot rule out that the p-values are sampled from a uniform distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "60b58d9b-ba60-4aa0-bc50-78e5e2e334c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.27047607586651834\n"
     ]
    }
   ],
   "source": [
    "p_values = rf_class_cond.predict_p(X_test, y_test, all_classes=False, \n",
    "                                    online=True, warm_start=False)\n",
    "\n",
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42032f48-89f8-4fc0-93a8-b4dab17b57e7",
   "metadata": {},
   "source": [
    "We may also evaluate the conformal classifiers using online calibration, by specifying `online=True` for the `evaluate` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f0e329f7-0190-47cd-ac24-6474a247f5ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.008051761322789397,\n",
       " 'avg_c': 1.0166786484543493,\n",
       " 'one_c': 0.9794392523364486,\n",
       " 'empty': 0.004313443565780014,\n",
       " 'ks_test': 0.15766280695981605,\n",
       " 'time_fit': 7.152557373046875e-07,\n",
       " 'time_evaluate': 0.33994340896606445}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_mond.evaluate(X_test, y_test, confidence=0.99, online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d259129f-ed09-45ae-8d5c-393ce4ab5c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.007620416966211407,\n",
       " 'avg_c': 1.025017972681524,\n",
       " 'one_c': 0.9676491732566499,\n",
       " 'empty': 0.00402588066139468,\n",
       " 'ks_test': 0.08904358923516276,\n",
       " 'time_fit': 7.152557373046875e-07,\n",
       " 'time_evaluate': 0.29168057441711426}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_class_cond.evaluate(X_test, y_test, confidence=0.99, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e53c5b58-1869-4251-ab12-6ab8c157e8ad",
   "metadata": {},
   "source": [
    "Again, we may consider ignoring the original calibration set by setting `warm_start=False`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1198cf9b-3edb-48fc-b73e-e67720ffe9c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.009489575844715992,\n",
       " 'avg_c': 1.2153846153846153,\n",
       " 'one_c': 0.9145938173975557,\n",
       " 'empty': 0.005463695183321351,\n",
       " 'ks_test': 0.8488051963934213,\n",
       " 'time_fit': 7.152557373046875e-07,\n",
       " 'time_evaluate': 0.33046674728393555}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_mond.evaluate(X_test, y_test, confidence=0.99, online=True, warm_start=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e59a167-cfbf-4645-8768-bbae39180a17",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Online calibration without an initial calibration set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ba799e2-d967-4036-8c82-b9a26206bb3f",
   "metadata": {},
   "source": [
    "Since the calibration set is incrementally extended during online calibration, we may consider starting with an empty calibration set; this allows us to use the full training set when fitting the underlying model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "987845d8-7376-43ad-93e3-1438f9be6eb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_full = WrapClassifier(RandomForestClassifier(n_jobs=-1, n_estimators=500))\n",
    "\n",
    "rf_full.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a6ca6a-c7ba-4ff9-b288-27f771f541f5",
   "metadata": {},
   "source": [
    "Let us first initialize the conformal classifier with an empty calibration set, i.e., not specifying any objects or labels for the `calibrate` method (we may still provide a non-conformity function and Mondrian categorizer):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "91f2ed63-c7ce-4977-ab13-1d7c3674c4ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapClassifier(learner=RandomForestClassifier(n_estimators=500, n_jobs=-1), calibrated=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_full.calibrate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f126b62f-f23f-45de-af02-9b5a6f5c8738",
   "metadata": {},
   "source": [
    "We may now obtain prediction sets while sequentially adding test objects and labels to the calibration set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "54c5edac-e04d-4b72-8163-9280aecec7c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1, 1],\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_online = rf_full.predict_set(X_test, y_test, confidence=0.99, online=True)\n",
    "\n",
    "display(prediction_sets_online)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "800fe91b-a8d8-435e-bed9-e7c90ff12b2d",
   "metadata": {},
   "source": [
    "Let us take a look at how the p-values for the correct labels are distributed. From both the visual inspection and the Kolmogorov-Smirnov test, we cannot rule out that the p-values are sampled from a uniform distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ad1a19e8-a34e-4a47-b55b-abffc48cc073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.9138435032150545\n"
     ]
    }
   ],
   "source": [
    "p_values = rf_full.predict_p(X_test, y_test, all_classes=False, online=True)\n",
    "\n",
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e22a41-a344-4ab8-8e45-abfb2628c800",
   "metadata": {},
   "source": [
    "We may also evaluate the conformal classifier using online calibration, by specifying `online=True` for the `evaluate` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3a516ab4-0acf-40fc-b4ce-3ae6f92dc2c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.010064701653486718,\n",
       " 'avg_c': 1.0290438533429187,\n",
       " 'one_c': 0.9813084112149533,\n",
       " 'empty': 0.006901509705248023,\n",
       " 'ks_test': 0.8529608925186685,\n",
       " 'time_fit': 4.76837158203125e-07,\n",
       " 'time_evaluate': 0.32596683502197266}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_full.evaluate(X_test, y_test, confidence=0.99, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c38fa1cc-c915-4922-bb15-d686c49840ca",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Out-of-bag calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06d66df4-ec70-4dd0-b065-fcb936354c8e",
   "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": 42,
   "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": 43,
   "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": 44,
   "id": "99d9d88c-98ae-42c7-9771-d48a48d7ef6d",
   "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_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": 45,
   "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": 46,
   "id": "9985f056-493c-46bf-8496-a782463b457d",
   "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_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": 47,
   "id": "25b6a9a3-cb0a-4e68-a488-4ecdf8295e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.007620416966211407,\n",
       " 'avg_c': 1.0005751258087707,\n",
       " 'one_c': 0.99137311286844,\n",
       " 'empty': 0.00402588066139468,\n",
       " 'ks_test': 3.5417834161346386e-20,\n",
       " 'time_fit': 4.76837158203125e-07,\n",
       " 'time_evaluate': 0.27462220191955566}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = rf.evaluate(X_test, y_test, confidence=0.99)\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": 48,
   "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": 49,
   "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": 50,
   "id": "0308a1fc-161f-49f4-b9ba-ef814b12f180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.006326383896477328,\n",
       " 'avg_c': 1.0063263838964773,\n",
       " 'one_c': 0.987922358015816,\n",
       " 'empty': 0.002875629043853343,\n",
       " 'ks_test': 4.660862606055584e-22,\n",
       " 'time_fit': 2.384185791015625e-07,\n",
       " 'time_evaluate': 1.1810693740844727}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = rf.evaluate(X_test, y_test, confidence=0.99)\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": 51,
   "id": "6d9ddaf2-5229-43de-80a2-512e4a54b05a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 1:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.007003891050583633,\n",
       " 'avg_c': 0.9937743190661479,\n",
       " 'one_c': 0.9937743190661479,\n",
       " 'empty': 0.0062256809338521405,\n",
       " 'ks_test': 6.965109765115711e-10,\n",
       " 'time_fit': 2.384185791015625e-07,\n",
       " 'time_evaluate': 0.2629227638244629}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 2:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.008152173913043459,\n",
       " 'avg_c': 1.0264945652173914,\n",
       " 'one_c': 0.9694293478260869,\n",
       " 'empty': 0.0020380434782608695,\n",
       " 'ks_test': 3.818481342223841e-11,\n",
       " 'time_fit': 2.384185791015625e-07,\n",
       " 'time_evaluate': 0.2851731777191162}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 3:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.0048367593712213,\n",
       " 'avg_c': 1.0060459492140266,\n",
       " 'one_c': 0.9939540507859734,\n",
       " 'empty': 0.0,\n",
       " 'ks_test': 2.542875384301272e-09,\n",
       " 'time_fit': 2.384185791015625e-07,\n",
       " 'time_evaluate': 0.17826128005981445}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 4:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.006191950464396245,\n",
       " 'avg_c': 1.001031991744066,\n",
       " 'one_c': 0.9907120743034056,\n",
       " 'empty': 0.0041279669762641896,\n",
       " 'ks_test': 3.929046208555481e-05,\n",
       " 'time_fit': 2.384185791015625e-07,\n",
       " 'time_evaluate': 0.202775239944458}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 5:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.0026791694574681557,\n",
       " 'avg_c': 1.0053583389149363,\n",
       " 'one_c': 0.9906229068988613,\n",
       " 'empty': 0.0020093770931011385,\n",
       " 'ks_test': 1.035676366633335e-13,\n",
       " 'time_fit': 2.384185791015625e-07,\n",
       " 'time_evaluate': 0.2880847454071045}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class 6:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'error': 0.00990099009900991,\n",
       " 'avg_c': 0.9955995599559956,\n",
       " 'one_c': 0.9933993399339934,\n",
       " 'empty': 0.005500550055005501,\n",
       " 'ks_test': 0.043879710873837574,\n",
       " 'time_fit': 2.384185791015625e-07,\n",
       " 'time_evaluate': 0.19328784942626953}"
      ]
     },
     "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=0.99))\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc21f90",
   "metadata": {},
   "source": [
    "## Conformal regressors"
   ]
  },
  {
   "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": 52,
   "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": 53,
   "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": 54,
   "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": 55,
   "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": 56,
   "id": "0b6b778f-1280-4d7a-9443-b3ad84bc902a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00432563,  0.1509834 ],\n",
       "       [-0.02212541,  0.12453236],\n",
       "       [ 0.00705543,  0.15371321],\n",
       "       ...,\n",
       "       [-0.03614123,  0.11051654],\n",
       "       [-0.00408622,  0.14257155],\n",
       "       [-0.046295  ,  0.10036277]])"
      ]
     },
     "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": 57,
   "id": "297e612d-0f83-4e3f-a7da-ad0260d4bb8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04431751, 0.11099151],\n",
       "       [0.01786648, 0.08454048],\n",
       "       [0.04704732, 0.11372132],\n",
       "       ...,\n",
       "       [0.00385065, 0.07052465],\n",
       "       [0.03590566, 0.10257966],\n",
       "       [0.        , 0.06037088]])"
      ]
     },
     "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": "1372532d-38a7-4b29-b514-cf70f3f193c2",
   "metadata": {},
   "source": [
    "If we want to obtain the p-values for the correct labels, we can use `predict_p`, and in addition to the point predictions also provide the correct labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "561a78b2-a7cb-48b1-ad67-10dcdf8d2969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.58226226, 0.29829358, 0.42156858, ..., 0.13607924, 0.5801904 ,\n",
       "       0.6795197 ])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_values = rf.predict_p(X_test, y_test)\n",
    "display(p_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa5a596-f6a8-4e79-aec3-1e1aa4055dc3",
   "metadata": {},
   "source": [
    "Let us take a look at how the p-values are distributed. From the visual inspection they may appear be approximaly uniformly distributed; this is however not guaranteed, since the p-values are not independent (see further below for semi-online conformal regressors, for which this indeed holds). It is hence not very surprising if the Kolmogorov-Smirnov test allows us to reject that the p-values are sampled from a uniform distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "015c2cbd-0d66-4c8b-bb3d-9f021d47500d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.011247230215991305\n"
     ]
    }
   ],
   "source": [
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "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": 60,
   "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": 61,
   "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": 62,
   "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": 63,
   "id": "895e3436-f717-4adb-80a8-de023fa6e1f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.06030177, 0.09500725],\n",
       "       [0.02350499, 0.07890197],\n",
       "       [0.05643085, 0.10433779],\n",
       "       ...,\n",
       "       [0.00218195, 0.07219336],\n",
       "       [0.01759727, 0.12088805],\n",
       "       [0.        , 0.06153991]])"
      ]
     },
     "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": 64,
   "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": 65,
   "id": "e173e02c-3359-4c82-b627-bce6d46fb449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05751344, 0.09779558],\n",
       "       [0.02813811, 0.07426884],\n",
       "       [0.05944059, 0.10132805],\n",
       "       ...,\n",
       "       [0.01194057, 0.06243473],\n",
       "       [0.05001645, 0.08846887],\n",
       "       [0.01819191, 0.03587586]])"
      ]
     },
     "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": 66,
   "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": 67,
   "id": "c7111fb4-0cb4-447f-8114-68f88412c202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05024389, 0.10506514],\n",
       "       [0.03399668, 0.06841028],\n",
       "       [0.06025162, 0.10051702],\n",
       "       ...,\n",
       "       [0.00827465, 0.06610065],\n",
       "       [0.05932399, 0.07916133],\n",
       "       [0.00708803, 0.04697973]])"
      ]
     },
     "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": 68,
   "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": 69,
   "id": "e2771706-1143-437c-9588-aa65b52124ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05858419, 0.09672483],\n",
       "       [0.03407071, 0.06833625],\n",
       "       [0.03113766, 0.12963098],\n",
       "       ...,\n",
       "       [0.02119189, 0.05318341],\n",
       "       [0.04789142, 0.0905939 ],\n",
       "       [0.01277108, 0.04129668]])"
      ]
     },
     "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": "2b82fb61-f6c4-436a-8355-95f1ca87c10e",
   "metadata": {},
   "source": [
    "Let us also take a look at how the p-values are distributed. Again, they appear to be approximaly uniformly distributed, but this is not guaranteed, and with the Kolmogorov-Smirnov test one may be able to rule out that the p-values are sampled from a uniform distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fd2a2c1a-033b-4565-9d73-13180984b987",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PEADYjxPxl3THR+uVkp4lSarr7aF3HmyuNv7MEl3WFNfnNxMkAABsUnjUOY36cpdZgt7o2US9W9eWm7OTxclQmlCEAAA2Z+Xus3puYaQkybu8qxY80U4Nq1W0OBVKI4oQAMCmfL3rjJ5ftEuSFOR3g2YPbC1PdxdrQ6HUoggBAGzC5bQMvfvDQc3ZdEKS5OnuTAnCv6IIAQDKvIzMLA35YqfWH/pdktS67g2aO7CNyrNWGP4F/4UAAMq8D388bJagF25vqKG33njV5ZqAv6MIAQDKtCNxyfr4lyOSskvQM13qW5wIZQlzigMAyqylEafV7f31krIvh1GCkF8UIQBAmbQs4rRGL9ltbr92TxML06Cs4tIYAKBMiUm8ote+3qc1UeckSfWrVtCXT7VX5fKuFidDWUQRAgCUGSnpmer633W6kp5pji0fGswj8igwihAAoEw4e+GKur631lwy4/3ezXVfy5o8HYZCoQgBAEq1rCxDMzYc09vfHzDH3r6/me5vVcvCVLAVFCEAQKm190yiHpq+xbwUVq9KeQ3vFqB7m9ewOBlsBUUIAFDqGIah8d9Eae7mE+bYU53r6cXbb5KTI5fCUHQoQgCAUiUuKUVPfRGhyOgLkiSfCm6aO7C1mtb0sjYYbBJFCABQqgydv9MsQd0aVdX0RwPl7MS0dygeFCEAQKlwKTVD7cJ+UnJKhiRp4gPN1DuoNk+FoVhRhAAAlruYmqEHp202S1CvFjXUp3Udi1PBHlCEAACWmrH+qCb/fETJKRlydXbU5Eda6vYm1ayOBTtBEQIAWGbhtmj936q/5geaGRqkzgFVLEwEe0MRAgCUuJnrj+n98EPm/EAd6ntr+qOBqshSGShhFCEAQImauvaI3ll90NyuWamc5g1sw5NhsARFCABQIv64lKZnF0Zq45F4SVKzml7670PN1bBaRYuTwZ5RhAAAxcowDC3afkphq/Yr6X9Phd1UraKWDw2WC2eBYDGKEACg2GRmGRr31R4t3HbKHJv4QDP1almTEoRSgSIEACgWWVmGuvx3raLPX5Yk1ansocVPtVN1r3IWJwP+QhECABS535NTNWDONrME3RJQRfMGtmaWaJQ6FCEAQJGKTUxRh4k/KzPLkCR99HAL9WxR0+JUQN4oQgCAIrNmX6zGfxNllqBZ/YN0WyNfi1MBV0cRAgAU2o9R5/Tq13t1NjFFkuToIH0xqK2C6/tYnAy4NooQAKBQPttyQq9+vc/cvqlaRc0d2EbVvNwtTAVcH4oQAKBAjsQla/C8HTqRkH1DtHd5V03p10rt6nlbnAy4fhQhAECBjF2x1yxB1TzdtXp4J1XycLU4FZA/FCEAQL59FXlGW4+flyS991BzPRBYy+JEQMFQhAAA1y3xcrpavRluPhXWvHYlShDKNOY3BwBctxeW7jZL0E3VKuqLQW0sTgQUDmeEAADX5avIM1oTdU5S9qPxHRvwaDzKPooQAOBfTVt7VBNXH5Ak9WhWnRIEm0ERAgBcVXpmloYt2Kkf9mWfCerWyFfv9W5ucSqg6FCEAAB5mrvpuGZvOmEunNqsppc+eSxQTo4snArbQRECAOQStmq/Pll/zNwe2T1Az3atz+rxsDkUIQCAJOlKWqY+/PGQvtl91lwzrFF1T30+qI18KrhZnA4oHhQhAIBO/3FZg+ft0IHYZHPs7pur66OHW3IpDDaNIgQAdm77ifPqO/NXpWdmzw90f6uaGhXSUDUrlbM4GVD8KEIAYKcMw9C7PxzU1LVHzbFPQ4PUrbGvhamAkkURAgA7NXfziRwl6Ifht6hhtYoWJgJKHkUIAOxQZPQfmvBtlKTspTK+HNJenu4uFqcCSh5FCADsTGaWoVFLdsswJJ8Krvrm2Y5ycWLpSdgn/ssHADvz0/5zOvb7JUnSp/1bU4Jg1/ivHwDsyO5TFzR0/k5JUp+g2mpRu5K1gQCLcWkMAOxAUkq6Xlr6m77fGytJqlLRTWPvbmRxKsB6FCEAsHGX0zLUa8om83KYTwVXzR3YmpujAVGEAMDmDZyz3SxBI7sHaEjnG+XqzJ0RgEQRAgCbtnh7tLYePy9JevXuxnq8o7/FiYDShf8lAAAb9cO+WL20bI8kqY1/ZQ3sUNfaQEApxBkhALAxaRlZev2bfVqwNVqS1PWmqprxWKAcHFg8FfgnihAA2JDI6D/0/KJdij5/WZIU4FtBHz3cQs7MFQTkiSIEADYgK8tQnxlbtP3EH+bYg4G19H/3NePGaOAaKEIAUIalZWTpre+iNG/LyRzjy54OVqDfDRalAsoOihAAlGH/XXMwRwnq0ay6Jj3SUk6O3A8EXA+KEACUQSsiT+vTDce172ySJKlqRTeteKaDalYqZ3EyoGyhCAFAGbN852mN/HK3uR3kd4NmDWgtr3LMFA3kl+V30E2dOlX+/v5yd3dXYGCgNmzYcM39U1NTNXbsWPn5+cnNzU033nijZs+eXUJpAcBaUWeTzBLkU8FN3z7bUUufDqYEAQVk6RmhxYsXa/jw4Zo6dao6dOigTz75RHfeeaeioqJUp06dPF/Tu3dvnTt3TrNmzVL9+vUVFxenjIyMEk4OACUrK8vQgm3RGvfVXkmSq5OjNr3cRW7OThYnA8o2B8MwDKvevG3btmrVqpWmTZtmjjVq1Ei9evVSWFhYrv1Xr16thx9+WMeOHVPlypUL9J5JSUny8vJSYmKiPD09C5wdAEqCYRj6Yd85fRB+SAfPJZvjXwxqq44NfCxMBpSs4vr8tuzSWFpamiIiIhQSEpJjPCQkRJs3b87zNStXrlRQUJDeeecd1axZUwEBARo9erSuXLly1fdJTU1VUlJSji8AKAuO/n5Rd3y4QUO+iDBLkFc5F60YGkwJAoqIZZfG4uPjlZmZKV9f3xzjvr6+io2NzfM1x44d08aNG+Xu7q4VK1YoPj5eQ4cO1fnz5696n1BYWJjGjx9f5PkBoDh9+9tZjVi8S+mZ2SftG1StoPE9myj4RgoQUJQsf2rsn2vfGIZx1fVwsrKy5ODgoPnz58vLy0uS9P777+vBBx/UlClTVK5c7sdGx4wZo5EjR5rbSUlJql27dhH+BABQdKITLmt55Gl9+ONhSdkF6MOHW6hJDS+LkwG2ybIi5OPjIycnp1xnf+Li4nKdJfpT9erVVbNmTbMESdn3FBmGodOnT6tBgwa5XuPm5iY3N7eiDQ8ARcwwDA2Ys13rDv1ujtWvWkHfPdeJJTKAYmTZ3y5XV1cFBgYqPDw8x3h4eLiCg4PzfE2HDh109uxZXbx40Rw7dOiQHB0dVatWrWLNCwDF5ejvF9Vx4i85StDTt96ob5/tSAkCipmlf8NGjhypTz/9VLNnz9b+/fs1YsQIRUdHa8iQIZKyL2uFhoaa+/ft21fe3t4aOHCgoqKitH79er3wwgt6/PHH87wsBgCl3aXUDD3x2Q6duZD90MdTnevpxNs99NIdN8ndhUfjgeJm6T1Cffr0UUJCgiZMmKCYmBg1bdpUq1atkp+fnyQpJiZG0dHR5v4VKlRQeHi4nn32WQUFBcnb21u9e/fWm2++adWPAAAF9uuxBI1YvEsxiSmSpAVPtOVmaKCEWTqPkBWYRwiAlQzD0Le/xeiDHw/p2O+XzPFZ/YN0W6O8748EUHyf35Y/NQYA9mRN1Dk9uzDS3K7m6a5xdzeiBAEWoQgBQAnZeDheT30eIUkq5+KkyY+0VLfGFCDAShQhAChmszYe13trDupyWqY5tmbELapd2cPCVAAkihAAFBvDMPTmd/s1a+Nxc6xpTU998liQalbiSVegNKAIAUAxWLUnRm9+G6Wz/3sirF29ypr4wM3y8y5vcTIAf0cRAoAitnh7tF5atsfcfuWum/TkLTdamAjA1VCEAKAIRUb/YZYgV2dHffZ4G7Wr521xKgBXQxECgCKQnpml98MPafq6o+bYr2NuU+XyrhamAvBvKEIAUEhX0jLV79NftTP6gqTsFeM/eSyQEgSUARQhACiEy2kZunvSRh2Lz54l+tmu9fVMl/qsEwaUERQhACiAK2mZej/8oGZu+OvR+Pceaq4HAmtZmApAflGEACCfDsYm64nPdij6/GVzbELPJpQgoAyiCAHAddp16oLe+DZKESf/kCS5OTvqudsaaHAnf7k5cykMKIsoQgBwHWZtPK43vo0yt50cHfTD8FtU14cJEoGyjCIEANeQlWXov2sOauravx6Ln9qvle5sWk0ODg4WJgNQFBzzs/Ps2bOVmppaXFkAoFSJS0rRfdM2myWoRe1KOvTmnbqrWXVKEGAj8lWEnnjiCSUmJprbNWrU0IkTJ4o6EwBYLiU9Uw9M36zdpy5IkgZ39Neyp4Pl6pyvX5sASrl8XRozDCPHdnJysrKysoo0EABY7UhcsgbN26FT56/I1clRb93XVA8F1bY6FoBiwD1CAPA/hmFo4bZTemVF9lphTo4O+iQ0UF0aVrU4GYDikq8i5ODgkOO6+D+3AaCs+n5PjMZ9tVcJl9LMsS+faq9AvxssTAWguOX70lhAQIBZfi5evKiWLVvK0THnNfPz588XXUIAKEYp6Zl6flGkfth3zhxrXfcGvXZPEzWt6WVhMgAlIV9FaM6cOcWVAwBKVFJKur7cfkpT1x7V+f+dBbq1YRWNDmlIAQLsSL6KUP/+/YsrBwCUmM1H4jX4sx26nJZpjk3o2USh7etaFwqAJQp0s7RhGIqIiNCJEyfk4OAgf39/tWzZkvuFAJR60QmXNWDOdqVlZj/xem/zGnrlrkaq5uVucTIAVsh3Efrll180aNAgnTx50nyc/s8yNHv2bN1yyy1FHhIAioJhGHp6foTSMrPk6+mmb5/tpCoV3ayOBcBC+ZoZ7MiRI7r77rtVt25dLV++XPv371dUVJSWLFmiWrVq6a677tKxY8eKKysAFMq4r/Zq39kkSdLcgW0oQQDkYPxzlsRrGDZsmPbv36+ffvop1/cMw1C3bt3UuHFjTZ48uUhDFqWkpCR5eXkpMTFRnp6eVscBUAJ2nbqg0Ut260jcRUnSc7c10MjuARanApAfxfX5na8zQmvXrtXw4cPz/J6Dg4OGDx+uX375pShyAUChGYah99ccVK8pm8wSdE/zGnr+tgYWJwNQWuTrHqHo6Gg1a9bsqt9v2rSpTp48WehQAFBYszce14c/HlJSSoYkqYaXu8bd3Vh3NatucTIApUm+itDFixfl4eFx1e97eHjo8uXLhQ4FAIWxYGu0JnwbZW7f36qm3n2wuZwcebIVQE75fmosKipKsbGxeX4vPj6+0IEAoDAm/3RY74UfkiQ1qu6ppUPaq7wbyyoCyFu+fzvcdtttuVahl7LvETIMg7mEAFgiM8vQ84si9e1vMZKkJjU8tXJYR84CAbimfBWh48ePF1cOACiwkwmX1PuTLTqXlCop+0zQiqEdKEEA/lW+ipCfn19x5QCAAjkYm6yHpm82b4p+o2cTPcZSGQCuU74enz98+LAeeeQRJSUl5fpeYmKi+vbty4SKAErMkbhkPfi/ElTe1UkLnmhLCQKQL/kqQu+++65q166d50RGXl5eql27tt59990iCwcAeUlKSdfUtUfU7f31Sv7fmaDVw29R8I0+FicDUNbk69LY+vXr9fnnn1/1+71791bfvn0LHQoAruaD8EP66KfDOca+GdZRtStffWoPALiafBWhkydPqmrVqlf9vo+Pj06dOlXoUACQlwnfRGn2pr8e2hjRLUADguvKy8PFwlQAyrJ8FSEvLy8dPXr0qjdNHzlyhPW7ABS59Mwszdt8wixBQX43aO7jbVSB+YEAFFK+fovccsstmjx5srp27Zrn9ydNmqROnToVSTAAkKSMzCy1mhCu5NTse4F8Pd305VPt5cij8QCKQL5ulh4zZoy+//57Pfjgg9q2bZsSExOVmJiorVu36oEHHtAPP/ygMWPGFFdWAHZm75lE3fvxJrMENa7uqeVDO1CCABSZfJ0RatmypZYuXarHH39cK1asyPE9b29vffnll2rVqlWRBgRgn2ISr+jhGb/q4v9K0LgejTS4Uz2LUwGwNfm+wH733Xfr5MmTWr16tY4cOSLDMBQQEKCQkJBrLsgKANfr9+RUDZyzXRdTs+cHmtk/iEfjARSLfF0au+uuu5SYmKhy5crpvvvuU1pamp588kn16tVLHh4eSkhIUOPGjYsrKwAbl5SSrsHzdqj1Wz/qQGyyHB2kRU+2pwQBKDb5KkI//PCDUlNTze2JEyfq/Pnz5nZGRoYOHjxYdOkA2I3Ve2PU4e2f9eP+c5IkD1cnzR/cTs1qeVmcDIAty9elsX+uOp/XKvQAkF+r98ZoyBc7ze3RIQEa1rWBhYkA2Asm4QBgqbikFD09P7sEOTs66KdRneXnXd7iVADsRb6KkIODgxwcHHKNAUB+GIah1XtjtWBbtDYcjjfHVw7rSAkCUKLyfWlswIABcnNzkySlpKRoyJAhKl8++xfX3+8fAoCr+fjnI3ov/FCOsa+e6aDGNZiZHkDJylcR6t+/f47tRx99NNc+oaGhhUsEwKaNXrJbSyNOS5I6NfBRv7Z+6npTVbk65+vZDQAoEvkqQnPmzCmuHADswLpDv5sl6LabqmpmaBCzRAOwFDdLAygRX+86o9FLdkvKXjT10/5B3GMIwHIUIQDFbszy37Rw2ylJkk8FV0oQgFKDIgSg2MRfTFXfmb/q0LmLkrLPBE16pKUqebhanAwAslGEABSL5TtP67WV+5Sckr1o6v2taur93i2sDQUA/0ARAlCkth5L0MvL9+h4/CVJkpOjg5659UYN7VLf4mQAkBtFCECRSEnP1Gtf79PiHafMseAbvTUjNEgV3PhVA6B04rcTgELJyMzSW6v2a9G2U7qSnilJ8vV006SHW6ptPW+L0wHAtVGEABRYRmaWHpu1TVuOJUiSvMu7ql/bOhrRPYCnwgCUCRQhAAX22sp9ZgkKbe+nl+64SeW5DAagDOE3FoB8i01M0Vur9uub3WclSW/0bKLH2te1NhQAFABFCEC+nP7jsjpO/MXc7npTVUoQgDKLIgTguhmGoZeX7TG333ngZvVuXdvCRABQOJYv9zx16lT5+/vL3d1dgYGB2rBhw3W9btOmTXJ2dlaLFi2KNyAA04z1x7TxSLwkafIjLSlBAMo8S4vQ4sWLNXz4cI0dO1aRkZHq1KmT7rzzTkVHR1/zdYmJiQoNDdVtt91WQkkBhEedU9j3ByRJ9atW0D3Na1icCAAKz8EwDMOqN2/btq1atWqladOmmWONGjVSr169FBYWdtXXPfzww2rQoIGcnJz01VdfadeuXdf9nklJSfLy8lJiYqI8PT0LEx+wGxNXH9C0tUclSRXdnLX2hVvlXcHN4lQA7ElxfX5bdkYoLS1NERERCgkJyTEeEhKizZs3X/V1c+bM0dGjR/Xaa69d1/ukpqYqKSkpxxeA6xOXlKKnv4gwS1Ab/8qaN6gNJQiAzbDsZun4+HhlZmbK19c3x7ivr69iY2PzfM3hw4f18ssva8OGDXJ2vr7oYWFhGj9+fKHzAvbm8y0n9J+v95nb/dv7aXzPphYmAoCiZ/nN0v+cfdYwjDxnpM3MzFTfvn01fvx4BQQEXPefP2bMGCUmJppfp06d+vcXAXbu+z0xOUpQKCUIgI2y7IyQj4+PnJyccp39iYuLy3WWSJKSk5O1Y8cORUZGatiwYZKkrKwsGYYhZ2dnrVmzRl27ds31Ojc3N7m5cRofuF7/PBN04I075O7iZGEiACg+lhUhV1dXBQYGKjw8XPfdd585Hh4erp49e+ba39PTU3v27MkxNnXqVP38889aunSp/P39iz0zYMtS0jP19vcHNHfzCXNs6yu3UYIA2DRLJ1QcOXKkHnvsMQUFBal9+/aaMWOGoqOjNWTIEEnZl7XOnDmjzz77TI6OjmraNOep+apVq8rd3T3XOIDrl5KeqQ9/PKzp646aY+4ujtr4Ulf5cFM0ABtnaRHq06ePEhISNGHCBMXExKhp06ZatWqV/Pz8JEkxMTH/OqcQgIK7mJqhuz7aoOjzlyVJDg5S78DamvjgzRYnA4CSYek8QlZgHiHgLwPmbNPag79Lkh7v4K9xPRrJ0TH3wwoAYLXi+vxmrTHADhmGoU83HDdL0Mt33qQhnW+0OBUAlDyKEGBnDp9L1qB5O8zLYZ0a+GhwRx42AGCfKEKAHVm4LVpjlv/19OU9zWvo3QdvlrOT5VOKAYAlKEKAnfj7/EAV3Z01/dFAdajvY3EqALAWRQiwcRsPx2vCt/t06NxFSdKtDatozoDWec7gDgD2hiIE2Kg/LqXpvfCD+uLXv6ag6FDfW7P6U4IA4E8UIcAGzdt8QmHf71dKepYkydPdWQueaKemNb0sTgYApQtFCLAxE1cf0LS12bNE+1Rw1cAO/nqiUz25OnNDNAD8E0UIsBGJV9L17MJIrT+UPTdQPZ/yWvFMB3mVc7E4GQCUXhQhwAZEnU1SnxlblJySIUlq619Zi55sx71AAPAvKEJAGZackq7+s7dpZ/QFc2xQx+ylMihBAPDvKEJAGfXT/nMaNG+HuV3OxUkzQ4PUsQFzAwHA9aIIAWVQSnqmXv7bDNFDb71RL9zekLNAAJBPFCGgjPlp/zm9tOw3xV9MkyT9OPIW1a9a0eJUAFA2UYSAMsIwDK3cfVbPL9pljn3QpzklCAAKgSIElBFvfbdfn248LkmqUtFNnz3eRo2qe1qcCgDKNooQUAZcSs3QkojTkrIXTF0xNFi1bvCwOBUAlH0UIaCUS7ySrjs+XK/EK+mqXN5VG1/qIg9X/uoCQFFgzn2gFDsSd1G9p29RTGKKXJ0d9V7v5pQgAChC/EYFSqG9ZxL18c9HtHpfrDk2d0BrBddnjiAAKEoUIaAUOfb7Rd09eaMup2XmGJ/+aCtKEAAUA4oQUEocjE3W7R+uN7fr+ZTXHU2raUT3ALk4cRUbAIoDRQgoJV5c9pv5z+891FwPBNayMA0A2AeKEGCxtIwsTf75sHafuiBJmvRIS93bvIa1oQDATlCEAAv9eixBwxbsNJfLuCWgCiUIAEoQRQiwwNZjCXrzu/3acybRHBvVPUBP3FLPwlQAYH8oQkAJ+3TDMb353X5z+8Yq5TUzNEj1qlSwMBUA2CeKEFBC4i+matyKvTnmBprxWKC6N/aVg4ODhckAwH5RhIASkHAxVbd/sF4Jl7LvBfJ0d9baF7qocnlXi5MBgH2jCAHFLC0jSwPmbDdL0IDguhrboxFzAwFAKUARAorRjhPn9eisrUpJz5IkzRnQWl1uqmpxKgDAnyhCQDEwDENh3x/QjPXHzLGp/VpRggCglKEIAcXgmQU7tWpP9k3RlTxctPjJ9mpYraLFqQAA/0QRAopQRmaWXl6+xyxBo0MCNPTW+nJ05KkwACiNKEJAETh1/rJmbTyuuZtPmGM9mlXXsK4NrAsFAPhXFCGgEBKvpOvt7/dr4bZTOca73lRVHz3cwppQAIDrRhECCmjz0Xg9/cVOJV5JlyTVuqGcHgyspYHB/vLycLE4HQDgelCEgHyKS07RiMW7tOlIgiTJwUF67e7Geqx9XTlxLxAAlCkUIeA6GIahTUcS9J+v9+p4/CVzvFMDH4Xd30y1bvCwMB0AoKAoQsA17D2TqCU7TmlN1DnFJKaY4xXdnDX10Vbq1KCKhekAAIVFEQLykJKeqbe+26/Pfz2ZY7xpTU8NvbW+bm9SjctgAGADKELA35xLStH4b/aZ8wBJUvPaldS8lpdC29dV/aoVLEwHAChqFCFA0vYT5/X+mkOKOPmH0jKzzPHh3Rro+dsayMGBsz8AYIsoQrB7Y1fs0fyt0TnG3r6/mfq0rk0BAgAbRxGCXXt/zUGzBHmVc9HI7gF6ILCWKrjxVwMA7AG/7WGXUtIz9einW7Xj5B+SspfDmPRIS26ABgA7QxGC3fni15Ma99Vec/v+VjX13kPNuQwGAHaIIgS7cebCFfWfvU1H4i6aY4+2q6PX72lCCQIAO0URgs0zDEOLtp/SmOV7zLGba3lp7sA2qlze1cJkAACrUYRg047+flGvfb1PG4/ES5I83Z31SNs6eiGkoZydHC1OBwCwGkUINinhYqoem7VNUTFJ5linBj6aGRokdxcnC5MBAEoTihBszsmES+r+wXqlZWRPjFjRzVnPd2ugR9rUoQQBAHKgCMGmbDwcr0dnbTW3P3q4he5tXoOboQEAeaIIwSYYhqH5W6NzPBa/YHBbBdf3sTAVAKC0owihzDuZcEn9Pt2q039ckSRV93LX54PaskAqAOBfUYRQpl1MzVCfT35VbFKKpOzH4j8f1FZe5VwsTgYAKAsoQiiTMrMMvbj0Ny3bedocm9CziULb17UuFACgzKEIoUxJTknXzPXHNOnnI+aYk6ODpvZrpdubVLMwGQCgLKIIoUxISknXiEW79NOBuBzj/dv76Zku9VXV092iZACAsowihFLvzIUrGjR3uw7EJptj43o00v2tarFEBgCgUChCKNUOxCbpjg83mNuPd/DXc7fVVyUPChAAoPAoQiiVklLSNW/TCX3w4yFzbGZokLo39rUwFQDA1lCEUOpsP3FeD03fkmPsp1GddWMV5gUCABQty5ffnjp1qvz9/eXu7q7AwEBt2LDhqvsuX75c3bt3V5UqVeTp6an27dvrhx9+KMG0KG4f/3w4RwnqcXN17RjXjRIEACgWlhahxYsXa/jw4Ro7dqwiIyPVqVMn3XnnnYqOjs5z//Xr16t79+5atWqVIiIi1KVLF91zzz2KjIws4eQoDl9uP6X/rvnrUthXz3TQlL6t5FPBzcJUAABb5mAYhmHVm7dt21atWrXStGnTzLFGjRqpV69eCgsLu64/o0mTJurTp49effXV69o/KSlJXl5eSkxMlKenZ4Fyo+h9FXlGo5bsVmaWoZqVyunrYR0oQAAAU3F9flt2j1BaWpoiIiL08ssv5xgPCQnR5s2br+vPyMrKUnJysipXrnzVfVJTU5WammpuJyUlFSwwisXx+Et69NOtOnMhe50wP28PrRzWkSUyAAAlwrJLY/Hx8crMzJSvb86ngHx9fRUbG3tdf8Z7772nS5cuqXfv3lfdJywsTF5eXuZX7dq1C5UbRWdZxGl1+e9aswTVqeyhr4Z2oAQBAEqM5TdLOzg45Ng2DCPXWF4WLlyo119/XYsXL1bVqlWvut+YMWOUmJhofp06darQmVF487ee1Kglu83tKX1bad0Lt+oGJkgEAJQgyy6N+fj4yMnJKdfZn7i4uFxnif5p8eLFGjRokJYsWaJu3bpdc183Nze5uXGvSWmQlWVo1d4YffdbjL7fm33ca91QTuN6NNYdTVknDABQ8iwrQq6urgoMDFR4eLjuu+8+czw8PFw9e/a86usWLlyoxx9/XAsXLlSPHj1KIiqKwLmkFA2at117z/x1j1Zb/8qa93gbubs4WZgMAGDPLJ1QceTIkXrssccUFBSk9u3ba8aMGYqOjtaQIUMkZV/WOnPmjD777DNJ2SUoNDRUH330kdq1a2eeTSpXrpy8vLws+znw7z788bBZgm6u5aUezarryVvqXddlUAAAioulRahPnz5KSEjQhAkTFBMTo6ZNm2rVqlXy8/OTJMXExOSYU+iTTz5RRkaGnnnmGT3zzDPmeP/+/TV37tySjo/rdDA2WYu3Zx/H/7uvmfq2rWNxIgAAslk6j5AVmEeo5D00fbO2n/hDbfwra/GT7TgLBADIt+L6/Lb8qTHYtlV7YrT9xB+SpLfvb0YJAgCUKiy6imKx48R5vbVqvyKjL0iSOgdUUT3WCwMAlDIUIRS59MwsPT1/p35Pzp7Ru3ktL714R0OLUwEAkBtFCEXm9B+XtWjbKX2x9aQuXE6XJH35VHu18b/6EigAAFiJIoRCWbLjlL7cccq8D+jvXr+nMSUIAFCqUYSQb4Zh6EBsst78LkqbjiTk+F4NL3fVquyhfm3r6N7mNSxKCADA9aEI4bplZhlaezBOzy6M1OW0THM80O8GPRhYSx3r+6h2ZQ8LEwIAkD8UIVyX4/GX1G/mrzqbmGKONa7uqac611PPFjUtTAYAQMFRhPCvvtx+Sq+s2KOMrOy5N9v6V9bztzVQcH0fi5MBAFA4FCFc04n4S3px2W/m9voXuqiON5e/AAC2gSKEPGVlGZrwbZTmbj5hjm0Z01XVvcpZFwoAgCJGEUIuO6P/0OB5O3T+Upo59t1zHSlBAACbQxGCaceJ83p87nYlpWSYY4M7+mtUSEOVc3WyMBkAAMWDIgRlZhl69eu9mr812hxrXN1TE3o2UVBdJkQEANguipCdS7ySriGfR2jLseyJEdvX89aI7gHMCA0AsAsUITuVmWXohSW7tTzyjDk2/t4m6h9c17pQAACUMIqQnUlJz9SSiNOa+ssRxfxtcsQpfVupx83VLUwGAEDJowjZkQuX09Th7Z916X/LYzg6SENvra/nuzWQi5OjxekAACh5FCE7kZqRqYdn/GqWoFsCquitXk1ZGwwAYNcoQnYgNjFFt7231ixBo7oH6NnbGlicCgAA61GEbNz6Q7/ryc93KCU9S5I08YFm6tO6jsWpAAAoHShCNurC5TQ9v2iX1h363RxbOayDbq5VybpQAACUMhQhG/Rj1DkNXbBTaRnZZ4E8XJ206Ml2lCAAAP6BImRDMjKz9PWusxq1ZLc59kavpnqsnZ+FqQAAKL0oQjbiu99i9MyCnea2q5Ojto/rJq9yLhamAgCgdKMIlXGGYSh09jZtOBxvjt3bvIZevKMhJQgAgH9BESrDfjt9Qfd+vMnc9vV009rRXVgpHgCA60QRKqNSMzI1fPEuc3tgh7p67Z4m1gUCAKAMogiVQRdTM9Tnky069vslSdK3z3ZU05peFqcCAKDsoQiVMSnpmXpw2mYdiE2WlP1UGCUIAICCoQiVEZlZhh6Z+au2HT9vjs0Z0FpdbqpqYSoAAMo2ilAZEHHyDw2et11/XE43xyY/0pISBABAIVGESrH0zCw9NH2Ldp26IElydJBGdg/Q4E715O7Ck2EAABQWRaiUMgxDg+btMEuQp7uzZoYGqW09b2uDAQBgQyhCpdSB2GSt/9+Cqa/f01gDOvhbnAgAANtDESpl0jOz9PmWk5rwbZQkqUkNT0oQAADFhCJUiqw/9LtCZ2/LMfbcbQ0sSgMAgO2jCJUSy3ee1sgv/1o1vnNAFb374M2q6uluYSoAAGwbRchie88k6qOfDis86pwkydnRQete7KKalcpZnAwAANtHEbJIZpah/rO3aeOR+BzjG17qoupelCAAAEoCRaiEZWRmaerao3o//JA55ursqDd7NdX9LWvK2cnRwnQAANgXilAJycjM0hvfRmnelpM5xvu2raP/u6+ZRakAALBvFKESkHglXQ9O26zDcRfNsU4NfDT5kZaq5OFqYTIAAOwbRagYxV9MVc+PN+nMhSvmWI9m1fV/9zeTVzkXC5MBAACJIlRsEq+k66nPI3KUoPH3NlH/4LrWhQIAADlQhIrBb6cv6N6PN5nbw7rU16iQADk4OFiYCgAA/BNFqAhdTsvQ6yv36csdp82xCT2bKLR9XetCAQCAq6IIFZH4i6l6aPoWHY+/JEmq5umuLwa3Uf2qFS1OBgAAroYiVARS0jPV5d21Sk7NkJS9Ptjw2xrI0ZFLYQAAlGYUoULKzDL06td7zRL0QZ/muq9lLYtTAQCA60ERKoSU9EwNWxCpH/dnrxMWdn8zShAAAGUIRaiAvoo8o9dW7lPilXRJ0hu9muqRNnUsTgUAAPKDIlQAW48laPjiXeb27AFB6nqTr3WBAABAgVCE8iEjM0ufrD+md384KEmq6Oasn0ffqioV3SxOBgAACoIidJ0uXE5Tm//7SWkZWebYnIGtKUEAAJRhFKHr8NvpC3po+hazBD15Sz092tZPdbw9LE4GAAAKgyJ0DWcuXNFLS3/TxiPxkiR3F0dN6dtKtzXifiAAAGwBRegqNh2JV79Pt5rbjg7SsqeD1aSGl4WpAABAUaII/cM/b4iWpNfuaayBHfwtTAUAAIoDRegfwr4/oFkbj0uSAnwraMXQDirvxr8mAABskaPVAUqTGeuPmiXo7pur68un2lOCAACwYXzKK3upjOcXReqHfdlLZXSs76PJj7SUgwOLpgIAYMvsvgitO/S7+s/eZm5X8nDRp/2DKEEAANgByy+NTZ06Vf7+/nJ3d1dgYKA2bNhwzf3XrVunwMBAubu7q169epo+fXqB3vdEwiX1nLIpRwka1NFf217pJncXpwL9mQAAoGyxtAgtXrxYw4cP19ixYxUZGalOnTrpzjvvVHR0dJ77Hz9+XHfddZc6deqkyMhIvfLKK3ruuee0bNmyfL93/1lbtfvUBUlSDS93bX3lNv3n7sZydba8GwIAgBLiYBiGYdWbt23bVq1atdK0adPMsUaNGqlXr14KCwvLtf9LL72klStXav/+/ebYkCFDtHv3bm3ZsuW63jMpKUleXl6qPfxLObp56I1eTfVo2zpcCgMAoBT78/M7MTFRnp6eRfbnWnaPUFpamiIiIvTyyy/nGA8JCdHmzZvzfM2WLVsUEhKSY+z222/XrFmzlJ6eLhcXl1yvSU1NVWpqqrmdmJgoScpKvaypj7bSLQ1uUHJycmF/HAAAUIySkpIkSUV9/sayIhQfH6/MzEz5+uZcrsLX11exsbF5viY2NjbP/TMyMhQfH6/q1avnek1YWJjGjx+fa/zMtAG6Z1quYQAAUIolJCTIy6voVnmw/Kmxf16SMgzjmpep8to/r/E/jRkzRiNHjjS3L1y4ID8/P0VHRxfpv0gUTFJSkmrXrq1Tp04V6alO5B/HovTgWJQeHIvSIzExUXXq1FHlypWL9M+1rAj5+PjIyckp19mfuLi4XGd9/lStWrU893d2dpa3t3eer3Fzc5Obm1uucS8vL/6jLkU8PT05HqUEx6L04FiUHhyL0sPRsWgfarLsESlXV1cFBgYqPDw8x3h4eLiCg4PzfE379u1z7b9mzRoFBQXleX8QAADAtVj6rPjIkSP16aefavbs2dq/f79GjBih6OhoDRkyRFL2Za3Q0FBz/yFDhujkyZMaOXKk9u/fr9mzZ2vWrFkaPXq0VT8CAAAowyy9R6hPnz5KSEjQhAkTFBMTo6ZNm2rVqlXy8/OTJMXExOSYU8jf31+rVq3SiBEjNGXKFNWoUUOTJk3SAw88cN3v6ebmptdeey3Py2UoeRyP0oNjUXpwLEoPjkXpUVzHwtJ5hAAAAKzENMoAAMBuUYQAAIDdoggBAAC7RRECAAB2yyaL0NSpU+Xv7y93d3cFBgZqw4YN19x/3bp1CgwMlLu7u+rVq6fp06eXUFLbl59jsXz5cnXv3l1VqlSRp6en2rdvrx9++KEE09q+/P7d+NOmTZvk7OysFi1aFG9AO5LfY5GamqqxY8fKz89Pbm5uuvHGGzV79uwSSmvb8nss5s+fr+bNm8vDw0PVq1fXwIEDlZCQUEJpbdf69et1zz33qEaNGnJwcNBXX331r68pks9vw8YsWrTIcHFxMWbOnGlERUUZzz//vFG+fHnj5MmTee5/7Ngxw8PDw3j++eeNqKgoY+bMmYaLi4uxdOnSEk5ue/J7LJ5//nlj4sSJxrZt24xDhw4ZY8aMMVxcXIydO3eWcHLblN/j8acLFy4Y9erVM0JCQozmzZuXTFgbV5Bjce+99xpt27Y1wsPDjePHjxtbt241Nm3aVIKpbVN+j8WGDRsMR0dH46OPPjKOHTtmbNiwwWjSpInRq1evEk5ue1atWmWMHTvWWLZsmSHJWLFixTX3L6rPb5srQm3atDGGDBmSY+ymm24yXn755Tz3f/HFF42bbropx9hTTz1ltGvXrtgy2ov8Hou8NG7c2Bg/fnxRR7NLBT0effr0McaNG2e89tprFKEikt9j8f333xteXl5GQkJCScSzK/k9Fu+++65Rr169HGOTJk0yatWqVWwZ7dH1FKGi+vy2qUtjaWlpioiIUEhISI7xkJAQbd68Oc/XbNmyJdf+t99+u3bs2KH09PRiy2rrCnIs/ikrK0vJyclFvsCePSro8ZgzZ46OHj2q1157rbgj2o2CHIuVK1cqKChI77zzjmrWrKmAgACNHj1aV65cKYnINqsgxyI4OFinT5/WqlWrZBiGzp07p6VLl6pHjx4lERl/U1Sf35avPl+U4uPjlZmZmWvRVl9f31yLtf4pNjY2z/0zMjIUHx+v6tWrF1teW1aQY/FP7733ni5duqTevXsXR0S7UpDjcfjwYb388svasGGDnJ1t6leFpQpyLI4dO6aNGzfK3d1dK1asUHx8vIYOHarz589zn1AhFORYBAcHa/78+erTp49SUlKUkZGhe++9V5MnTy6JyPibovr8tqkzQn9ycHDIsW0YRq6xf9s/r3HkX36PxZ8WLlyo119/XYsXL1bVqlWLK57dud7jkZmZqb59+2r8+PEKCAgoqXh2JT9/N7KysuTg4KD58+erTZs2uuuuu/T+++9r7ty5nBUqAvk5FlFRUXruuef06quvKiIiQqtXr9bx48fNNTJRsori89um/jfPx8dHTk5OuZp8XFxcrtb4p2rVquW5v7Ozs7y9vYstq60ryLH40+LFizVo0CAtWbJE3bp1K86YdiO/xyM5OVk7duxQZGSkhg0bJin7w9gwDDk7O2vNmjXq2rVriWS3NQX5u1G9enXVrFlTXl5e5lijRo1kGIZOnz6tBg0aFGtmW1WQYxEWFqYOHTrohRdekCTdfPPNKl++vDp16qQ333yTqwglqKg+v23qjJCrq6sCAwMVHh6eYzw8PFzBwcF5vqZ9+/a59l+zZo2CgoLk4uJSbFltXUGOhZR9JmjAgAFasGAB19yLUH6Ph6enp/bs2aNdu3aZX0OGDFHDhg21a9cutW3btqSi25yC/N3o0KGDzp49q4sXL5pjhw4dkqOjo2rVqlWseW1ZQY7F5cuX5eiY86PTyclJ0l9nI1AyiuzzO1+3VpcBfz4KOWvWLCMqKsoYPny4Ub58eePEiROGYRjGyy+/bDz22GPm/n8+fjdixAgjKirKmDVrFo/PF5H8HosFCxYYzs7OxpQpU4yYmBjz68KFC1b9CDYlv8fjn3hqrOjk91gkJycbtWrVMh588EFj3759xrp164wGDRoYgwcPtupHsBn5PRZz5swxnJ2djalTpxpHjx41Nm7caAQFBRlt2rSx6kewGcnJyUZkZKQRGRlpSDLef/99IzIy0pzKoLg+v22uCBmGYUyZMsXw8/MzXF1djVatWhnr1q0zv9e/f3+jc+fOOfZfu3at0bJlS8PV1dWoW7euMW3atBJObLvycyw6d+5sSMr11b9//5IPbqPy+3fj7yhCRSu/x2L//v1Gt27djHLlyhm1atUyRo4caVy+fLmEU9um/B6LSZMmGY0bNzbKlStnVK9e3ejXr59x+vTpEk5te3755ZdrfgYU1+e3g2FwLg8AANgnm7pHCAAAID8oQgAAwG5RhAAAgN2iCAEAALtFEQIAAHaLIgQAAOwWRQgAANgtihAAm/f666+rRYsWVscAUApRhAAAgN2iCAEAALtFEQJQom699VYNGzZMw4YNU6VKleTt7a1x48bluXJ3YmKiypUrp9WrV+cYX758ucqXL2+uxv7SSy8pICBAHh4eqlevnv7zn/8oPT39mhmGDx+eY6xXr14aMGCAuZ2WlqYXX3xRNWvWVPny5dW2bVutXbu2wD83gNKJIgSgxM2bN0/Ozs7aunWrJk2apA8++ECffvpprv28vLzUo0cPzZ8/P8f4ggUL1LNnT1WoUEGSVLFiRc2dO1dRUVH66KOPNHPmTH3wwQeFyjhw4EBt2rRJixYt0m+//aaHHnpId9xxhw4fPlyoPxdA6eJsdQAA9qd27dr64IMP5ODgoIYNG2rPnj364IMP9MQTT+Tat1+/fgoNDdXly5fl4eGhpKQkfffdd1q2bJm5z7hx48x/rlu3rkaNGqXFixfrxRdfLFC+o0ePauHChTp9+rRq1KghSRo9erRWr16tOXPm6P/+7/8K9OcCKH04IwSgxLVr104ODg7mdvv27XX48GG99dZbqlChgvkVHR2tHj16yNnZWStXrpQkLVu2TBUrVlRISIj5+qVLl6pjx46qVq2aKlSooP/85z+Kjo4ucL6dO3fKMAwFBATkyLNu3TodPXq04D84gFKHM0IASo0hQ4aoT58+5naNGjXk7OysBx98UAsWLNDDDz+sBQsWqE+fPnJ2zv719euvv+rhhx/W+PHjdfvtt8vLy0uLFi3Se++9d9X3cXR0zHVP0t/vKcrKypKTk5MiIiLk5OSUY78/L8cBsA0UIQAl7tdff8213aBBA3l7e8vb2zvX/v369VNISIj27dunX375RW+88Yb5vU2bNsnPz09jx441x06ePHnN969SpYpiYmLM7czMTO3du1ddunSRJLVs2VKZmZmKi4tTp06dCvQzAigbuDQGoMSdOnVKI0eO1MGDB7Vw4UJNnjxZzz///FX379y5s3x9fdWvXz/VrVtX7dq1M79Xv359RUdHa9GiRTp69KgmTZqkFStWXPP9u3btqu+++07fffedDhw4oKFDh+rChQvm9wMCAsx7k5YvX67jx49r+/btmjhxolatWlXonx9A6UERAlDiQkNDdeXKFbVp00bPPPOMnn32WT355JNX3d/BwUGPPPKIdu/erX79+uX4Xs+ePTVixAgNGzZMLVq00ObNm/Wf//znmu//+OOPq3///goNDVXnzp3l7+9vng3605w5cxQaGqpRo0apYcOGuvfee7V161bVrl274D84gFLHwchr8g4AKCa33nqrWrRooQ8//NDqKADAGSEAAGC/KEIAAMBucWkMAADYLc4IAQAAu0URAgAAdosiBAAA7BZFCAAA2C2KEAAAsFsUIQAAYLcoQgAAwG5RhAAAgN2iCAEAALv1/wncZhyHu+dRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.00019458318494431685\n"
     ]
    }
   ],
   "source": [
    "p_values = rf_norm_var.predict_p(X_test, y_test)\n",
    "\n",
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "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": 71,
   "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": 72,
   "id": "1ae5ec66-7920-4041-9b79-46fabd76fe96",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05467909, 0.10062993],\n",
       "       [0.03241103, 0.06999593],\n",
       "       [0.03447095, 0.12629769],\n",
       "       ...,\n",
       "       [0.02014533, 0.05422997],\n",
       "       [0.04138478, 0.09710054],\n",
       "       [0.0117465 , 0.04232127]])"
      ]
     },
     "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": "ce304a48-7b32-4c7c-aeea-c77311ebab6d",
   "metadata": {},
   "source": [
    "Let us also take a look at how the p-values are distributed. Again, they may appear to be approximaly uniformly distributed, but this is not guaranteed, and with the Kolmogorov-Smirnov test one may be able to rule out that the p-values are sampled from a uniform distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e528fe7c-f236-4057-bc97-5d57353b0388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.00036458705973426564\n"
     ]
    }
   ],
   "source": [
    "p_values = rf_mond.predict_p(X_test, y_test)\n",
    "\n",
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "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": 74,
   "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": 75,
   "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>0.9553</td>\n",
       "      <td>0.0647</td>\n",
       "      <td>0.0667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn dist</th>\n",
       "      <td>0.9550</td>\n",
       "      <td>0.0615</td>\n",
       "      <td>0.0513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn std</th>\n",
       "      <td>0.9497</td>\n",
       "      <td>0.0601</td>\n",
       "      <td>0.0471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn res</th>\n",
       "      <td>0.9510</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>0.0447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR var</th>\n",
       "      <td>0.9458</td>\n",
       "      <td>0.0579</td>\n",
       "      <td>0.0322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond CR</th>\n",
       "      <td>0.9576</td>\n",
       "      <td>0.0569</td>\n",
       "      <td>0.0376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.9524</td>\n",
       "      <td>0.0597</td>\n",
       "      <td>0.0466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Coverage  Mean size  Median size\n",
       "Std CR              0.9553     0.0647       0.0667\n",
       "Norm CR knn dist    0.9550     0.0615       0.0513\n",
       "Norm CR knn std     0.9497     0.0601       0.0471\n",
       "Norm CR knn res     0.9510     0.0570       0.0447\n",
       "Norm CR var         0.9458     0.0579       0.0322\n",
       "Mond CR             0.9576     0.0569       0.0376\n",
       "Mean                0.9524     0.0597       0.0466"
      ]
     },
     "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": 76,
   "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(\"ECDF\")\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": 77,
   "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": 78,
   "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>0.1000</td>\n",
       "      <td>0.0496</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.1026</td>\n",
       "      <td>0.0495</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.0515</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.1011</td>\n",
       "      <td>0.0498</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0997</td>\n",
       "      <td>0.0527</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0989</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.0090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_mean</th>\n",
       "      <td>0.0420</td>\n",
       "      <td>0.0618</td>\n",
       "      <td>0.1220</td>\n",
       "      <td>0.0442</td>\n",
       "      <td>0.0594</td>\n",
       "      <td>0.0969</td>\n",
       "      <td>0.0454</td>\n",
       "      <td>0.0597</td>\n",
       "      <td>0.0969</td>\n",
       "      <td>0.0429</td>\n",
       "      <td>0.0568</td>\n",
       "      <td>0.0957</td>\n",
       "      <td>0.0474</td>\n",
       "      <td>0.0583</td>\n",
       "      <td>0.0845</td>\n",
       "      <td>0.0414</td>\n",
       "      <td>0.0530</td>\n",
       "      <td>0.0925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_med</th>\n",
       "      <td>0.0422</td>\n",
       "      <td>0.0632</td>\n",
       "      <td>0.1224</td>\n",
       "      <td>0.0366</td>\n",
       "      <td>0.0494</td>\n",
       "      <td>0.0807</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>0.0772</td>\n",
       "      <td>0.0335</td>\n",
       "      <td>0.0445</td>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.0259</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>0.0496</td>\n",
       "      <td>0.0276</td>\n",
       "      <td>0.0350</td>\n",
       "      <td>0.0576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ks_test</th>\n",
       "      <td>0.5164</td>\n",
       "      <td>0.5164</td>\n",
       "      <td>0.5164</td>\n",
       "      <td>0.8766</td>\n",
       "      <td>0.8766</td>\n",
       "      <td>0.8766</td>\n",
       "      <td>0.8060</td>\n",
       "      <td>0.8060</td>\n",
       "      <td>0.8060</td>\n",
       "      <td>0.8111</td>\n",
       "      <td>0.8111</td>\n",
       "      <td>0.8111</td>\n",
       "      <td>0.3080</td>\n",
       "      <td>0.3080</td>\n",
       "      <td>0.3080</td>\n",
       "      <td>0.1259</td>\n",
       "      <td>0.1259</td>\n",
       "      <td>0.1259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_fit</th>\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>0.0000</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>0.0610</td>\n",
       "      <td>0.0610</td>\n",
       "      <td>0.0612</td>\n",
       "      <td>0.0724</td>\n",
       "      <td>0.0669</td>\n",
       "      <td>0.0802</td>\n",
       "      <td>0.0717</td>\n",
       "      <td>0.0713</td>\n",
       "      <td>0.0703</td>\n",
       "      <td>0.0651</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.0698</td>\n",
       "      <td>0.0636</td>\n",
       "      <td>0.0633</td>\n",
       "      <td>0.0636</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0397</td>\n",
       "      <td>0.0397</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          0.1000  0.0496  0.0102           0.1026  0.0495  0.0088   \n",
       "eff_mean       0.0420  0.0618  0.1220           0.0442  0.0594  0.0969   \n",
       "eff_med        0.0422  0.0632  0.1224           0.0366  0.0494  0.0807   \n",
       "ks_test        0.5164  0.5164  0.5164           0.8766  0.8766  0.8766   \n",
       "time_fit       0.0000  0.0000  0.0000           0.0001  0.0001  0.0001   \n",
       "time_evaluate  0.0610  0.0610  0.0612           0.0724  0.0669  0.0802   \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                  0.1010  0.0515  0.0084          0.1011  0.0498  0.0094   \n",
       "eff_mean               0.0454  0.0597  0.0969          0.0429  0.0568  0.0957   \n",
       "eff_med                0.0353  0.0467  0.0772          0.0335  0.0445  0.0762   \n",
       "ks_test                0.8060  0.8060  0.8060          0.8111  0.8111  0.8111   \n",
       "time_fit               0.0000  0.0000  0.0000          0.0000  0.0000  0.0000   \n",
       "time_evaluate          0.0717  0.0713  0.0703          0.0651  0.0900  0.0698   \n",
       "\n",
       "              Norm CR var                 Mond CR                  \n",
       "                     0.90    0.95    0.99    0.90    0.95    0.99  \n",
       "error              0.0997  0.0527  0.0105  0.0989  0.0485  0.0090  \n",
       "eff_mean           0.0474  0.0583  0.0845  0.0414  0.0530  0.0925  \n",
       "eff_med            0.0259  0.0324  0.0496  0.0276  0.0350  0.0576  \n",
       "ks_test            0.3080  0.3080  0.3080  0.1259  0.1259  0.1259  \n",
       "time_fit           0.0000  0.0000  0.0000  0.0002  0.0002  0.0002  \n",
       "time_evaluate      0.0636  0.0633  0.0636  0.0400  0.0397  0.0397  "
      ]
     },
     "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": "7993f92a-b4c6-49af-9ba6-10821e37213c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Semi-online conformal regressors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd435376-c256-47b6-ae74-563533ae828e",
   "metadata": {
    "tags": []
   },
   "source": [
    "Similar to semi-online conformal classifiers, we may consider employing *online calibration* also for conformal regressors, i.e., continuously updating the calibration set after making each prediction. This is achieved by setting `online=True` when calling the methods `predict_p`, `predict_int` and `evaluate`. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf886ef4-ccae-4d16-8592-720c74053d2e",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Online calibration with calibrated conformal regressors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2703584-69da-42de-826c-2c27bf84b229",
   "metadata": {},
   "source": [
    "Here we will obtain p-values computed in an online fashion for some of the above calibrated conformal regressors. Note that in addition to the test objects, we also need to provide the correct labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "19b0edf3-5a80-4ccb-b2af-e184f1652f4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.58198376, 0.29809299, 0.42178719, ..., 0.13470887, 0.57332177,\n",
       "       0.67954364])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.5055890820835738\n"
     ]
    }
   ],
   "source": [
    "p_values = rf.predict_p(X_test, y_test, online=True)\n",
    "\n",
    "display(p_values)\n",
    "\n",
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a0103e81-b8e3-47d9-a848-7940c268909d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.42868216, 0.3064395 , 0.37436168, ..., 0.19469389, 0.76232103,\n",
       "       0.75206161])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.8751126653645003\n"
     ]
    }
   ],
   "source": [
    "p_values = rf_norm_knn_dist.predict_p(X_test, y_test, online=True)\n",
    "\n",
    "display(p_values)\n",
    "\n",
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8e8d5f75-96cb-4104-a2e6-8ced3438a210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.66062829, 0.30086454, 0.74103721, ..., 0.04789733, 0.69831211,\n",
       "       0.60161052])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.23618933740362713\n"
     ]
    }
   ],
   "source": [
    "p_values = rf_mond.predict_p(X_test, y_test, online=True)\n",
    "\n",
    "display(p_values)\n",
    "\n",
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c327a00-4284-4a5c-9f71-c918a771570d",
   "metadata": {},
   "source": [
    "Similarly, we can obtain prediction intervals using online calibration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4d3b4c8e-feb4-4cde-8bd9-a868b4fcd479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.04431751,  0.11099151],\n",
       "       [ 0.01786648,  0.08454048],\n",
       "       [ 0.04704732,  0.11372132],\n",
       "       ...,\n",
       "       [ 0.0054869 ,  0.06888841],\n",
       "       [ 0.03754191,  0.10094342],\n",
       "       [-0.00466687,  0.05873464]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.predict_int(X_test, y_test, online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "036cda43-c0c9-4d89-8ed5-2b272c986d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.06030177,  0.09500725],\n",
       "       [ 0.02350499,  0.07890197],\n",
       "       [ 0.05643085,  0.10433779],\n",
       "       ...,\n",
       "       [ 0.0033792 ,  0.0709961 ],\n",
       "       [ 0.01936363,  0.11912169],\n",
       "       [-0.00629198,  0.06035975]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_norm_knn_dist.predict_int(X_test, y_test, online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3d44060d-074a-4c70-ab48-bd0f010b390c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05467909, 0.10062993],\n",
       "       [0.03241103, 0.06999593],\n",
       "       [0.03447095, 0.12629769],\n",
       "       ...,\n",
       "       [0.02078338, 0.05359192],\n",
       "       [0.0442988 , 0.09418652],\n",
       "       [0.01263441, 0.04143335]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_mond.predict_int(X_test, y_test, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be0d93a-ddd2-4a85-a176-240b23ab0140",
   "metadata": {},
   "source": [
    "By default, the test objects and labels are sequentially added to the existing calibration set, i.e., the one used when fitting the conformal regressor. We may set the `warm_start` option to `False`, if we would like to ignore the original calibration set. Note that the initial predictions tend to be very (maximally) wide, before we have a sufficiently large calibration set to allow for providing tighter intervals at the specified level of confidence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "bab86f5a-0627-4884-aee6-93bc9a78be33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       ...,\n",
       "       [0.        , 0.10942014],\n",
       "       [0.        , 0.14147515],\n",
       "       [0.        , 0.09926637]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.predict_int(X_test, y_test, confidence=0.99, y_min=0, y_max=1, \n",
    "               online=True, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "69a566f2-9cdb-4806-a80a-4bd59cb58df2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       ...,\n",
       "       [0.        , 0.09462578],\n",
       "       [0.        , 0.15398354],\n",
       "       [0.        , 0.08365213]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_norm_knn_dist.predict_int(X_test, y_test, confidence=0.99, \n",
    "                             y_min=0, y_max=1, \n",
    "                             online=True, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9b31d768-be8b-41d1-bc93-9107e55bff6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.        , 1.        ],\n",
       "       ...,\n",
       "       [0.01020721, 0.06416809],\n",
       "       [0.03216402, 0.1063213 ],\n",
       "       [0.00412457, 0.0499432 ]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_mond.predict_int(X_test, y_test, confidence=0.99,\n",
    "                    y_min=0, y_max=1, \n",
    "                    online=True, warm_start=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07163cd9-e195-4e12-b092-40e4395b27e6",
   "metadata": {},
   "source": [
    "We may also evaluate the conformal regressors using online calibration, by specifying `online=True` for the `evaluate` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "cb2ded53-e560-4c5e-9951-3cac6556c540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.010178587952253126,\n",
       " 'eff_mean': 0.12200597236093809,\n",
       " 'eff_med': 0.12243605455737835,\n",
       " 'ks_test': 0.516378414590601,\n",
       " 'time_fit': 3.790855407714844e-05,\n",
       " 'time_evaluate': 0.061490535736083984}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.evaluate(X_test, y_test, confidence=0.99, y_min=0, y_max=1, online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "efd3f4fc-75d1-471a-bfe0-e91cf4f4e122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.008790598686036821,\n",
       " 'eff_mean': 0.0969143763897544,\n",
       " 'eff_med': 0.08066956341773926,\n",
       " 'ks_test': 0.8766458617832448,\n",
       " 'time_fit': 8.702278137207031e-05,\n",
       " 'time_evaluate': 0.06917285919189453}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_norm_knn_dist.evaluate(X_test, y_test, confidence=0.99, y_min=0, y_max=1, \n",
    "                          online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "aed271c2-7a27-4546-bf8b-596cbb5a7965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.008975663921532373,\n",
       " 'eff_mean': 0.092468876423132,\n",
       " 'eff_med': 0.05764718032786891,\n",
       " 'ks_test': 0.1258970152170158,\n",
       " 'time_fit': 0.00016379356384277344,\n",
       " 'time_evaluate': 0.04006457328796387}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_mond.evaluate(X_test, y_test, confidence=0.99, y_min=0, y_max=1, \n",
    "                 online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c5231a-3990-4aa1-835e-1eda075a7e09",
   "metadata": {},
   "source": [
    "Again, we may consider ignoring the original calibration set by setting `warm_start=False`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ab228df2-eab2-4b6e-946b-3e0106002a21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.006477283242342979,\n",
       " 'eff_mean': 0.10174169713301602,\n",
       " 'eff_med': 0.08480375077638828,\n",
       " 'ks_test': 0.11767146471046397,\n",
       " 'time_fit': 8.702278137207031e-05,\n",
       " 'time_evaluate': 0.03881978988647461}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_norm_knn_dist.evaluate(X_test, y_test, confidence=0.99, y_min=0, y_max=1, \n",
    "                          online=True, warm_start=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655817bc-c6a2-4cdd-a96a-d2fc876fa91c",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Online calibration without an initial calibration set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e55153-b9b1-49ee-b2a4-3c87e7357985",
   "metadata": {},
   "source": [
    "Since the calibration set is incrementally extended during online calibration, we may consider starting with an empty calibration set; this allows us to use the full training set when fitting the underlying model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b6f3d53c-d183-442f-a5a9-48810bbf8f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_full = WrapRegressor(RandomForestRegressor(n_jobs=-1, n_estimators=500,\n",
    "                                              oob_score=True))\n",
    "\n",
    "rf_full.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2fdb7c-6f7c-4606-aa49-4707ff7adfb0",
   "metadata": {},
   "source": [
    "Let us first initialize the conformal regressor with an empty calibration set, i.e., not specifying any objects or labels for the `calibrate` method (as we will see further below, we may still provide other parameters such as difficulty estimator, Mondrian categorizer, etc.):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "6462a530-409c-49b9-9869-bca5c4b834f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=False)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_full.calibrate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d5c0e5d-0e25-4cb6-8a39-9834fea072e8",
   "metadata": {},
   "source": [
    "We may now obtain prediction intervals while sequentially updating the calibration set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a460610e-d639-41f9-85b3-1bfb66fb2ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       ...,\n",
       "       [0.0178809 , 0.05935522],\n",
       "       [0.05063494, 0.09210926],\n",
       "       [0.00682944, 0.04830376]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_full.predict_int(X_test, y_test, confidence=0.9, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f862131-1867-4479-88e4-5d2789b3087a",
   "metadata": {},
   "source": [
    "If we provide also a difficulty estimator to `calibrate`, we will obtain a normalized conformal regressor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "764e1ccf-a927-44d6-9a52-516dc0306ab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=False)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de_var = DifficultyEstimator()\n",
    "de_var.fit(X=X_train, learner=rf_full.learner, scaler=True)\n",
    "\n",
    "rf_full.calibrate(de=de_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a83a97-a7db-46df-9816-8b07bb9bc723",
   "metadata": {},
   "source": [
    "Now we get the intervals in the usual way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "83951c88-43b6-48d1-9257-50d556a62414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       ...,\n",
       "       [0.02401128, 0.05322483],\n",
       "       [0.05327082, 0.08947339],\n",
       "       [0.0131407 , 0.04199251]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_full.predict_int(X_test, y_test, confidence=0.9, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe8fe5e3-7506-44e8-a6b7-064cf94bb244",
   "metadata": {},
   "source": [
    "We get a Mondrian regressor by providing a Mondrian categorizer to `calibrate`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "7b7da6a4-b1d9-47b1-85cd-f476a0ccfeb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True), calibrated=False)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mc = MondrianCategorizer()\n",
    "mc.fit(X_cal, de=de_var, no_bins=10)\n",
    "\n",
    "rf_full.calibrate(mc=mc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b16c32a2-9bf8-49bd-902f-1edccb612f8b",
   "metadata": {},
   "source": [
    "We use the same call again to get the intervals from the Mondrian regressor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "3cdd38ff-2a7f-4f37-8e19-8424d8f69837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       ...,\n",
       "       [0.02483413, 0.05240198],\n",
       "       [0.04927617, 0.09346803],\n",
       "       [0.01378268, 0.04135053]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_full.predict_int(X_test, y_test, confidence=0.9, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b35cd2-d437-459b-bb12-3fedfb6f6032",
   "metadata": {},
   "source": [
    "Let us investigate the coverage and prediction interval size for each category (here limiting the lower and upper bounds of the intervals):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "1c53eddd-267b-4cf0-aa40-5ea2bfbace25",
   "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>Number</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>178</td>\n",
       "      <td>0.9213</td>\n",
       "      <td>0.0657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>493</td>\n",
       "      <td>0.9047</td>\n",
       "      <td>0.0337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>643</td>\n",
       "      <td>0.9160</td>\n",
       "      <td>0.0325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>800</td>\n",
       "      <td>0.9138</td>\n",
       "      <td>0.0306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>930</td>\n",
       "      <td>0.9032</td>\n",
       "      <td>0.0299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1229</td>\n",
       "      <td>0.9056</td>\n",
       "      <td>0.0302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1211</td>\n",
       "      <td>0.9075</td>\n",
       "      <td>0.0360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1614</td>\n",
       "      <td>0.8934</td>\n",
       "      <td>0.0381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.9054</td>\n",
       "      <td>0.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1691</td>\n",
       "      <td>0.8965</td>\n",
       "      <td>0.1130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number  Coverage    Size\n",
       "0     178    0.9213  0.0657\n",
       "1     493    0.9047  0.0337\n",
       "2     643    0.9160  0.0325\n",
       "3     800    0.9138  0.0306\n",
       "4     930    0.9032  0.0299\n",
       "5    1229    0.9056  0.0302\n",
       "6    1211    0.9075  0.0360\n",
       "7    1614    0.8934  0.0381\n",
       "8    2018    0.9054  0.0500\n",
       "9    1691    0.8965  0.1130"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_intervals = rf_full.predict_int(X_test, y_test, y_min=0, \n",
    "                                           y_max=1, confidence=0.9, online=True)\n",
    "number = []\n",
    "coverage = []\n",
    "size = []\n",
    "\n",
    "bins_test = mc.apply(X_test)\n",
    "\n",
    "for c in range(len(mc.bin_thresholds)-1):\n",
    "    selection = bins_test == c\n",
    "    number.append(np.sum(selection))\n",
    "    coverage.append(np.sum(\n",
    "        (prediction_intervals[selection,0] <= y_test[selection]) & \\\n",
    "        (y_test[selection] <= prediction_intervals[selection,1]))/np.sum(selection))\n",
    "    size.append(np.sum(prediction_intervals[selection,1]-\\\n",
    "                       prediction_intervals[selection,0])/np.sum(selection))\n",
    "\n",
    "df = pd.DataFrame({\"Number\":number, \"Coverage\":coverage, \"Size\":size})\n",
    "df.round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09873a68-0c34-4de3-bc33-ff2008f9ef71",
   "metadata": {},
   "source": [
    "Let us investigate also the p-values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "23fa9472-2021-40bb-b3c7-4609a429443f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.65972588, 0.13878553, 0.87155344, ..., 0.06198063, 0.5857074 ,\n",
       "       0.61964411])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.27460617882907834\n"
     ]
    }
   ],
   "source": [
    "p_values = rf_full.predict_p(X_test, y_test, online=True)\n",
    "\n",
    "display(p_values)\n",
    "\n",
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb5ee68b-98d7-4598-b242-6512d024b02c",
   "metadata": {},
   "source": [
    "We may also evaluate the conformal regressor using online calibration, by specifying `online=True` for the `evaluate` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "7c432636-e4f4-4c99-b3c0-fcffe518c1a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.008235402979550277,\n",
       " 'eff_mean': 0.17411042132140828,\n",
       " 'eff_med': 0.06522391501639344,\n",
       " 'ks_test': 0.061994601898437085,\n",
       " 'time_fit': 7.867813110351562e-06,\n",
       " 'time_evaluate': 0.04030299186706543}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_full.evaluate(X_test, y_test, confidence=0.99, y_min=0, y_max=1, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63733c6a-d586-49f9-8e01-c02e386ddd99",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Out-of-bag calibration"
   ]
  },
  {
   "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": 102,
   "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": 103,
   "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": 104,
   "id": "ff0c3105-3141-4dfa-9377-65d24763156c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04266303, 0.10568848],\n",
       "       [0.01798263, 0.08100809],\n",
       "       [0.04546086, 0.10848631],\n",
       "       ...,\n",
       "       [0.00708747, 0.07011292],\n",
       "       [0.03904191, 0.10206736],\n",
       "       [0.        , 0.05911262]])"
      ]
     },
     "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": 105,
   "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": 106,
   "id": "0ecff050-2e20-450e-aa27-8df9a16886bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05573182, 0.09261969],\n",
       "       [0.0183875 , 0.08060322],\n",
       "       [0.04998982, 0.10395736],\n",
       "       ...,\n",
       "       [0.        , 0.07982535],\n",
       "       [0.01039599, 0.13071329],\n",
       "       [0.        , 0.06170583]])"
      ]
     },
     "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": 107,
   "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([[0.05331876, 0.09503276],\n",
       "       [0.02445888, 0.07453184],\n",
       "       [0.05440063, 0.09954654],\n",
       "       ...,\n",
       "       [0.01273307, 0.06446732],\n",
       "       [0.04964271, 0.09146657],\n",
       "       [0.01963824, 0.03556155]])"
      ]
     },
     "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": 108,
   "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([[0.0527948 , 0.09555671],\n",
       "       [0.03636704, 0.06262368],\n",
       "       [0.06107297, 0.09287421],\n",
       "       ...,\n",
       "       [0.01433576, 0.06286463],\n",
       "       [0.05793811, 0.08317117],\n",
       "       [0.00844388, 0.04675591]])"
      ]
     },
     "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": 109,
   "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([[0.05497132, 0.09338019],\n",
       "       [0.03009186, 0.06889886],\n",
       "       [0.05537115, 0.09857603],\n",
       "       ...,\n",
       "       [0.01980171, 0.05739868],\n",
       "       [0.04727714, 0.09383214],\n",
       "       [0.00907835, 0.04612144]])"
      ]
     },
     "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": 110,
   "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": 111,
   "id": "224d067f-4db3-4903-9f1b-5dd9f8d75cbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05479904, 0.09355247],\n",
       "       [0.03011864, 0.06887208],\n",
       "       [0.05064936, 0.10329782],\n",
       "       ...,\n",
       "       [0.02101589, 0.05618451],\n",
       "       [0.03805054, 0.10305874],\n",
       "       [0.01102719, 0.0441726 ]])"
      ]
     },
     "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": 112,
   "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": 113,
   "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>0.9519</td>\n",
       "      <td>0.0614</td>\n",
       "      <td>0.0630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn dist OOB</th>\n",
       "      <td>0.9672</td>\n",
       "      <td>0.0672</td>\n",
       "      <td>0.0554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn std OOB</th>\n",
       "      <td>0.9480</td>\n",
       "      <td>0.0562</td>\n",
       "      <td>0.0439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR knn res OOB</th>\n",
       "      <td>0.9361</td>\n",
       "      <td>0.0492</td>\n",
       "      <td>0.0387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CR var OOB</th>\n",
       "      <td>0.9477</td>\n",
       "      <td>0.0493</td>\n",
       "      <td>0.0375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond CR OOB</th>\n",
       "      <td>0.9491</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.0331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.0558</td>\n",
       "      <td>0.0453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Coverage  Mean size  Median size\n",
       "Std CR OOB              0.9519     0.0614       0.0630\n",
       "Norm CR knn dist OOB    0.9672     0.0672       0.0554\n",
       "Norm CR knn std OOB     0.9480     0.0562       0.0439\n",
       "Norm CR knn res OOB     0.9361     0.0492       0.0387\n",
       "Norm CR var OOB         0.9477     0.0493       0.0375\n",
       "Mond CR OOB             0.9491     0.0518       0.0331\n",
       "Mean                    0.9500     0.0558       0.0453"
      ]
     },
     "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": 114,
   "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(\"ECDF\")\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e208caf6",
   "metadata": {},
   "source": [
    "### Creating and fitting conformal predictive systems"
   ]
  },
  {
   "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": 115,
   "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_knn_std, \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": 116,
   "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_knn_std, 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_p` and `predict_int` methods, which work as for conformal regressors. There are also some methods that are specific to conformal predictive systems; `predict_percentiles` and `predict_cpds` that are used for obtaining percentiles and conformal predictive distributions, respectively. In addition, there is also a method called `predict_cps`, for which the output will depend on how we specify the input and which can be used for getting all of the above by just one method call.\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": 117,
   "id": "072d72f7-6c0e-46d7-b447-906863f56c70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75174948, 0.88777475, 0.21298863, ..., 0.96174913, 0.29422676,\n",
       "       0.25497542])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_values = cps_mond_norm.predict_p(X_test, y_test, seed=123)\n",
    "\n",
    "display(p_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e03b554-fe98-4b50-b489-d3ce7b473a6a",
   "metadata": {},
   "source": [
    "Let us take a look at how the p-values are distributed. From the visual inspection they may appear be approximaly uniformly distributed; this is however not guaranteed, since the p-values are not independent (see further below for semi-online conformal predictive systems, for which this indeed holds). It is hence not very surprising if the Kolmogorov-Smirnov test allows us to reject that the p-values are sampled from a uniform distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "2625e226-5d03-4718-99d4-e53eb6ce4a2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.10638623750175547\n"
     ]
    }
   ],
   "source": [
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dbd7cfe-dc2b-4afc-8ffb-8eabc8c03a91",
   "metadata": {},
   "source": [
    "We can get the same result through `predict_cps` in the following way: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "f5e58b8e-a505-4557-9d7a-630af398cee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75174948, 0.88777475, 0.21298863, ..., 0.96174913, 0.29422676,\n",
       "       0.25497542])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_values = cps_mond_norm.predict_cps(X_test, y=y_test, seed=123)\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 request these through `predict_percentiles` or by providing `lower_percentiles` and/or `upper_percentiles` as input to the `predict_cps` method. These denote (one or more) percentiles for which a lower (upper) value will be selected in case a percentile lies between two values (similar to `interpolation=\"lower\"` and `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%, first using `predict_percentiles`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "0440b9f7-6847-4637-bfcf-ecbce5de7cc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.07711538, 0.05053219, 0.0798237 , ..., 0.03700809, 0.06872801,\n",
       "       0.02668173])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresholds = cps_mond_norm.predict_percentiles(X_test, higher_percentiles=50)\n",
    "\n",
    "display(thresholds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d82e5bb-4729-49f5-a9b3-1a5cec64c25a",
   "metadata": {},
   "source": [
    "Again, the same result can be obtained using the `predict_cps` method: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "4b38a463-df34-40aa-a574-b7e0e7d3e9f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.07711538, 0.05053219, 0.0798237 , ..., 0.03700809, 0.06872801,\n",
       "       0.02668173])"
      ]
     },
     "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": [
    "For the `predict_cps` method, 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": 122,
   "id": "1cda8888-2c35-45a5-9286-53ab3761483d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.7518413 , 0.05975328, 0.063534  , 0.09171462, 0.0964323 ],\n",
       "       [0.88746581, 0.03004174, 0.03559306, 0.06619671, 0.07237199],\n",
       "       [0.21348939, 0.06176969, 0.06570108, 0.09500475, 0.09991044],\n",
       "       ...,\n",
       "       [0.96298644, 0.01194057, 0.01964534, 0.05314692, 0.05759479],\n",
       "       [0.29505613, 0.05215455, 0.05576354, 0.08266412, 0.08716751],\n",
       "       [0.25575831, 0.01432424, 0.01743067, 0.03249756, 0.0345924 ]])"
      ]
     },
     "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 conformal predictive system returns the full conformal predictive distribution (CPD) for each test instance, as defined by the threshold values, either by the `predict_cpds` method or by setting `return_cpds=True` for the `predict_cps` method . 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 (for both methods). 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_cpds` will result in a vector of distributions, with one element for each test instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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_cpds(X_test)\n",
    "\n",
    "print(f\"No. of test instances: {len(X_test)}\")\n",
    "print(f\"Shape of cpds: {cpds.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23934295-174e-4157-9af9-bb58bf88bd96",
   "metadata": {},
   "source": [
    "The same result can be obtained with the following call to `predict_cps`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "e05e1f99-17da-4ac7-bc74-f2556131301f",
   "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(X_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": 125,
   "id": "73238353-903c-4d57-9a5b-d52d19fd6a9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bin 0: 2020 test instances, 541 threshold values\n",
      "bin 1: 2375 test instances, 540 threshold values\n",
      "bin 2: 2177 test instances, 540 threshold values\n",
      "bin 3: 2114 test instances, 540 threshold values\n",
      "bin 4: 2121 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": 126,
   "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": "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": 127,
   "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>0.9548</td>\n",
       "      <td>0.0662</td>\n",
       "      <td>0.0674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std OOB CPS</th>\n",
       "      <td>0.9538</td>\n",
       "      <td>0.0631</td>\n",
       "      <td>0.0643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm CPS</th>\n",
       "      <td>0.9506</td>\n",
       "      <td>0.0599</td>\n",
       "      <td>0.0470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norm OOB CPS</th>\n",
       "      <td>0.9489</td>\n",
       "      <td>0.0505</td>\n",
       "      <td>0.0384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond CPS</th>\n",
       "      <td>0.9493</td>\n",
       "      <td>0.0596</td>\n",
       "      <td>0.0452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond OOB CPS</th>\n",
       "      <td>0.9455</td>\n",
       "      <td>0.0564</td>\n",
       "      <td>0.0388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond norm CPS</th>\n",
       "      <td>0.9536</td>\n",
       "      <td>0.0627</td>\n",
       "      <td>0.0444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mond norm OOB CPS</th>\n",
       "      <td>0.9473</td>\n",
       "      <td>0.0520</td>\n",
       "      <td>0.0360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.9505</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.0477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Coverage  Mean size  Median size\n",
       "Std CPS              0.9548     0.0662       0.0674\n",
       "Std OOB CPS          0.9538     0.0631       0.0643\n",
       "Norm CPS             0.9506     0.0599       0.0470\n",
       "Norm OOB CPS         0.9489     0.0505       0.0384\n",
       "Mond CPS             0.9493     0.0596       0.0452\n",
       "Mond OOB CPS         0.9455     0.0564       0.0388\n",
       "Mond norm CPS        0.9536     0.0627       0.0444\n",
       "Mond norm OOB CPS    0.9473     0.0520       0.0360\n",
       "Mean                 0.9505     0.0588       0.0477"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "          }\n",
    "\n",
    "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": 128,
   "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(\"ECDF\")\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": 129,
   "id": "ebea7b27-a9a1-4473-a20d-3b2b7fcaea79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Std CPS\n",
      "\tMean difference of the medians:    0.000006\n",
      "\tLargest difference of the medians: 0.000006\n",
      "Std OOB CPS\n",
      "\tMean difference of the medians:    0.000002\n",
      "\tLargest difference of the medians: 0.000002\n",
      "Norm CPS\n",
      "\tMean difference of the medians:    0.000023\n",
      "\tLargest difference of the medians: 0.000185\n",
      "Norm OOB CPS\n",
      "\tMean difference of the medians:    0.000001\n",
      "\tLargest difference of the medians: 0.000038\n",
      "Mond CPS\n",
      "\tMean difference of the medians:    0.000007\n",
      "\tLargest difference of the medians: 0.000024\n",
      "Mond OOB CPS\n",
      "\tMean difference of the medians:    0.000012\n",
      "\tLargest difference of the medians: 0.000045\n",
      "Mond norm CPS\n",
      "\tMean difference of the medians:    0.000018\n",
      "\tLargest difference of the medians: 0.000320\n",
      "Mond norm OOB CPS\n",
      "\tMean difference of the medians:    0.000011\n",
      "\tLargest difference of the medians: 0.000795\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": 130,
   "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": 131,
   "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>0.0998</td>\n",
       "      <td>0.0489</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.1013</td>\n",
       "      <td>0.0512</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.0501</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.1012</td>\n",
       "      <td>0.0488</td>\n",
       "      <td>0.0082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_mean</th>\n",
       "      <td>0.0422</td>\n",
       "      <td>0.0628</td>\n",
       "      <td>0.1296</td>\n",
       "      <td>0.0451</td>\n",
       "      <td>0.0592</td>\n",
       "      <td>0.0972</td>\n",
       "      <td>0.0429</td>\n",
       "      <td>0.0592</td>\n",
       "      <td>0.1117</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>0.0613</td>\n",
       "      <td>0.1042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_med</th>\n",
       "      <td>0.0424</td>\n",
       "      <td>0.0639</td>\n",
       "      <td>0.1330</td>\n",
       "      <td>0.0352</td>\n",
       "      <td>0.0464</td>\n",
       "      <td>0.0773</td>\n",
       "      <td>0.0301</td>\n",
       "      <td>0.0415</td>\n",
       "      <td>0.0694</td>\n",
       "      <td>0.0338</td>\n",
       "      <td>0.0440</td>\n",
       "      <td>0.0728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_fit</th>\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>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ks_test</th>\n",
       "      <td>0.5752</td>\n",
       "      <td>0.5752</td>\n",
       "      <td>0.5752</td>\n",
       "      <td>0.6970</td>\n",
       "      <td>0.6970</td>\n",
       "      <td>0.6970</td>\n",
       "      <td>0.6117</td>\n",
       "      <td>0.6117</td>\n",
       "      <td>0.6117</td>\n",
       "      <td>0.5664</td>\n",
       "      <td>0.5664</td>\n",
       "      <td>0.5664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_evaluate</th>\n",
       "      <td>0.0528</td>\n",
       "      <td>0.0529</td>\n",
       "      <td>0.0530</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0592</td>\n",
       "      <td>0.0596</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>0.0374</td>\n",
       "      <td>0.0364</td>\n",
       "      <td>0.0390</td>\n",
       "      <td>0.0422</td>\n",
       "      <td>0.0444</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          0.0998  0.0489  0.0098   0.1013  0.0512  0.0081   0.1020   \n",
       "eff_mean       0.0422  0.0628  0.1296   0.0451  0.0592  0.0972   0.0429   \n",
       "eff_med        0.0424  0.0639  0.1330   0.0352  0.0464  0.0773   0.0301   \n",
       "time_fit       0.0000  0.0000  0.0000   0.0000  0.0000  0.0000   0.0001   \n",
       "ks_test        0.5752  0.5752  0.5752   0.6970  0.6970  0.6970   0.6117   \n",
       "time_evaluate  0.0528  0.0529  0.0530   0.0620  0.0592  0.0596   0.0359   \n",
       "\n",
       "                              Mond norm CPS                  \n",
       "                 0.95    0.99          0.90    0.95    0.99  \n",
       "error          0.0501  0.0098        0.1012  0.0488  0.0082  \n",
       "eff_mean       0.0592  0.1117        0.0467  0.0613  0.1042  \n",
       "eff_med        0.0415  0.0694        0.0338  0.0440  0.0728  \n",
       "time_fit       0.0001  0.0001        0.0002  0.0002  0.0002  \n",
       "ks_test        0.6117  0.6117        0.5664  0.5664  0.5664  \n",
       "time_evaluate  0.0374  0.0364        0.0390  0.0422  0.0444  "
      ]
     },
     "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>0.0995</td>\n",
       "      <td>0.0482</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.1043</td>\n",
       "      <td>0.0517</td>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.0530</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>0.0511</td>\n",
       "      <td>0.0104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_mean</th>\n",
       "      <td>0.0414</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.1278</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>0.0504</td>\n",
       "      <td>0.0790</td>\n",
       "      <td>0.0420</td>\n",
       "      <td>0.0567</td>\n",
       "      <td>0.0961</td>\n",
       "      <td>0.0415</td>\n",
       "      <td>0.0522</td>\n",
       "      <td>0.0794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eff_med</th>\n",
       "      <td>0.0416</td>\n",
       "      <td>0.0630</td>\n",
       "      <td>0.1325</td>\n",
       "      <td>0.0291</td>\n",
       "      <td>0.0383</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>0.0296</td>\n",
       "      <td>0.0388</td>\n",
       "      <td>0.0668</td>\n",
       "      <td>0.0281</td>\n",
       "      <td>0.0361</td>\n",
       "      <td>0.0609</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>ks_test</th>\n",
       "      <td>0.2726</td>\n",
       "      <td>0.2726</td>\n",
       "      <td>0.2726</td>\n",
       "      <td>0.2117</td>\n",
       "      <td>0.2117</td>\n",
       "      <td>0.2117</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.1952</td>\n",
       "      <td>0.1952</td>\n",
       "      <td>0.1952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_evaluate</th>\n",
       "      <td>0.0758</td>\n",
       "      <td>0.0758</td>\n",
       "      <td>0.0760</td>\n",
       "      <td>0.0776</td>\n",
       "      <td>0.0769</td>\n",
       "      <td>0.0776</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.0422</td>\n",
       "      <td>0.0420</td>\n",
       "      <td>0.0425</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              0.0995  0.0482  0.0099       0.1043  0.0517  0.0096   \n",
       "eff_mean           0.0414  0.0620  0.1278       0.0385  0.0504  0.0790   \n",
       "eff_med            0.0416  0.0630  0.1325       0.0291  0.0383  0.0608   \n",
       "time_fit           0.0001  0.0001  0.0001       0.0001  0.0001  0.0001   \n",
       "ks_test            0.2726  0.2726  0.2726       0.2117  0.2117  0.2117   \n",
       "time_evaluate      0.0758  0.0758  0.0760       0.0776  0.0769  0.0776   \n",
       "\n",
       "              Mond OOB CPS                 Mond norm OOB CPS                  \n",
       "                      0.90    0.95    0.99              0.90    0.95    0.99  \n",
       "error               0.1035  0.0530  0.0101            0.1045  0.0511  0.0104  \n",
       "eff_mean            0.0420  0.0567  0.0961            0.0415  0.0522  0.0794  \n",
       "eff_med             0.0296  0.0388  0.0668            0.0281  0.0361  0.0609  \n",
       "time_fit            0.0003  0.0003  0.0003            0.0004  0.0004  0.0004  \n",
       "ks_test             0.2218  0.2218  0.2218            0.1952  0.1952  0.1952  \n",
       "time_evaluate       0.0398  0.0398  0.0395            0.0422  0.0420  0.0425  "
      ]
     },
     "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))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b57912-70d4-42e9-959d-edc6a71dc7e5",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Semi-online conformal predictive systems"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dadc300-22b8-46bf-b30c-cbda08f4532a",
   "metadata": {
    "tags": []
   },
   "source": [
    "Similar to semi-online conformal regressors and classifiers, we may consider employing *online calibration* also for conformal predictive systems, i.e., continuously updating the calibration set after making each prediction. This is achieved by setting `online=True` when calling the methods `predict_p`, `predict_int`, `predict_percentiles`, `predict_cpds`,  and `evaluate`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "781d2378-e860-42ff-9a7c-f3b4efa1e23b",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Online calibration with calibrated conformal predictive systems"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a1aad7-bbc7-429f-891a-1afcf276fe30",
   "metadata": {},
   "source": [
    "Here we will obtain p-values computed in an online fashion for some of the above calibrated conformal predictive systems. Note that in addition to the test objects, we also need to provide the correct labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b2998cf9-de32-4737-9931-f36acb9f2587",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Std CPS\n",
      "KS-test: 0.5962803463185213\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Norm CPS\n",
      "KS-test: 0.7102578812667508\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mond CPS\n",
      "KS-test: 0.5552349214807512\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mond norm CPS\n",
      "KS-test: 0.6179750177751291\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for cps in first_set:\n",
    "    print(cps)\n",
    "    p_values = all_cps[cps].predict_p(X_test, y_test, online=True)\n",
    "    print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")\n",
    "    plt.ecdf(p_values)\n",
    "    plt.xlim(0,1)\n",
    "    plt.xlabel(\"p-value\")\n",
    "    plt.ylabel(\"ECDF\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bede2cc-ec84-4cdb-9ee2-a77bed3cedc2",
   "metadata": {},
   "source": [
    "Similarly, we can obtain prediction intervals using online calibration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "9ca97bc7-9623-4145-9824-b61e5c31556c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.04787515,  0.11526344],\n",
       "       [ 0.02142412,  0.08881241],\n",
       "       [ 0.05060496,  0.11799325],\n",
       "       ...,\n",
       "       [ 0.00737904,  0.07128304],\n",
       "       [ 0.03943405,  0.10333805],\n",
       "       [-0.00277473,  0.06112927]])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_std.predict_int(X_test, y_test, online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "e739221f-e3c8-4a25-a5f1-ade88906ef21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05604126, 0.09637615],\n",
       "       [0.02645218, 0.07264332],\n",
       "       [0.05790973, 0.09985205],\n",
       "       ...,\n",
       "       [0.01109987, 0.06127579],\n",
       "       [0.04937624, 0.08758631],\n",
       "       [0.01789748, 0.03546998]])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_norm.predict_int(X_test, y_test, online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "765dc980-5264-4a1e-bb16-fee8b762d80b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05308445, 0.10671735],\n",
       "       [0.03205985, 0.07723537],\n",
       "       [0.05581426, 0.10944716],\n",
       "       ...,\n",
       "       [0.02155846, 0.05330625],\n",
       "       [0.04449723, 0.09811713],\n",
       "       [0.01394607, 0.0382353 ]])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_mond_std.predict_int(X_test, y_test, online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "934e725c-e1d4-4c07-8981-f95844bfafb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05975328, 0.0964323 ],\n",
       "       [0.03004174, 0.07237199],\n",
       "       [0.06176969, 0.09991044],\n",
       "       ...,\n",
       "       [0.01161363, 0.0582614 ],\n",
       "       [0.05184458, 0.08812224],\n",
       "       [0.01579513, 0.03496522]])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_mond_norm.predict_int(X_test, y_test, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370304b0-75dc-4a59-a7a8-77e5330e88f7",
   "metadata": {},
   "source": [
    "We can also get (lower and higher) percentiles using online calibration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d1d71e0a-4330-4fb1-a29b-77065647c3a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04787515, 0.07123022, 0.07708466, 0.08224582, 0.11526344],\n",
       "       [0.02142412, 0.04479082, 0.05063363, 0.05577722, 0.08881241],\n",
       "       [0.05060496, 0.07397166, 0.07982421, 0.08497562, 0.11799325],\n",
       "       ...,\n",
       "       [0.00737904, 0.03091324, 0.03665321, 0.04169801, 0.07128304],\n",
       "       [0.03943405, 0.06296883, 0.06870822, 0.07375302, 0.10333805],\n",
       "       [0.        , 0.02076005, 0.02649944, 0.03154424, 0.06112927]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_std.predict_percentiles(X_test, y_test, \n",
    "                            lower_percentiles = [2.5, 25],\n",
    "                            higher_percentiles = [50, 75, 97.5],\n",
    "                            y_min=0, y_max=1, online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "5e7fe4a7-2331-48e8-a17c-9e54eef1af78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05604126, 0.07197938, 0.07715928, 0.08170414, 0.09637615],\n",
       "       [0.02645218, 0.04470513, 0.05063634, 0.05584107, 0.07264332],\n",
       "       [0.05790973, 0.07448371, 0.07988571, 0.08460066, 0.09985205],\n",
       "       ...,\n",
       "       [0.01109987, 0.0302245 , 0.03654571, 0.04226749, 0.06127579],\n",
       "       [0.04937624, 0.06394102, 0.06875381, 0.07311107, 0.08758631],\n",
       "       [0.01789748, 0.0245957 , 0.02680906, 0.02881293, 0.03546998]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_norm.predict_percentiles(X_test, y_test, \n",
    "                             lower_percentiles = [2.5, 25],\n",
    "                             higher_percentiles = [50, 75, 97.5],\n",
    "                             y_min=0, y_max=1, online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "736f399d-ffa1-43b9-b912-68f2852509c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05308445, 0.069342  , 0.07671758, 0.08403515, 0.10671735],\n",
       "       [0.03205985, 0.04515901, 0.05051634, 0.05615443, 0.07723537],\n",
       "       [0.05581426, 0.07207181, 0.07944739, 0.08676496, 0.10944716],\n",
       "       ...,\n",
       "       [0.02155846, 0.03300713, 0.03694911, 0.04072134, 0.05330625],\n",
       "       [0.04449723, 0.06135048, 0.06825908, 0.07542132, 0.09811713],\n",
       "       [0.01394607, 0.02320803, 0.02676756, 0.02946943, 0.0382353 ]])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_mond_std.predict_percentiles(X_test, y_test, \n",
    "                                 lower_percentiles = [2.5, 25],\n",
    "                                 higher_percentiles = [50, 75, 97.5],\n",
    "                                 y_min=0, y_max=1, online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "1eacb242-f797-4691-8308-a2144a4ed37f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05975328, 0.07202332, 0.07711538, 0.08183104, 0.0964323 ],\n",
       "       [0.03004174, 0.04544445, 0.05053219, 0.05500278, 0.07237199],\n",
       "       [0.06176969, 0.07452872, 0.0798237 , 0.08486889, 0.09991044],\n",
       "       ...,\n",
       "       [0.01161363, 0.03123941, 0.0368722 , 0.04191775, 0.0582614 ],\n",
       "       [0.05184458, 0.06409599, 0.06867258, 0.07303321, 0.08812224],\n",
       "       [0.01579513, 0.02400104, 0.02682975, 0.02901168, 0.03496522]])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_mond_norm.predict_percentiles(X_test, y_test,\n",
    "                                  lower_percentiles = [2.5, 25],\n",
    "                                  higher_percentiles = [50, 75, 97.5],\n",
    "                                  y_min=0, y_max=1, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2210a61f-7a13-4323-a88b-23dea558392c",
   "metadata": {},
   "source": [
    "We can also obtain the full conformal predictive distributions (cpds) using online calibration. For standard and normalized conformal predictive systems, each cpd will contain one more value than the preceding, since the calibration set is growing by one element for each prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "42ae06b9-68c9-445f-95df-63dbf40cd675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpds = cps_norm.predict_cpds(X_test, y_test, online=True)\n",
    "plt.plot(np.arange(len(cpds)), [len(cpds[i]) for i in range(len(cpds))])\n",
    "plt.xlabel(\"#test objects\")\n",
    "plt.ylabel(\"calibration set size\")\n",
    "plt.ylim(0)\n",
    "plt.xlim(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a49dcfe4-d19a-413d-9d99-e40f424bb54b",
   "metadata": {},
   "source": [
    "For Mondrian conformal predictive systems, the calibration set for one of the categories will be extended with one element for each prediction; the size of the predicted cpd is dependent on which category the test object falls in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "4e41c66f-0189-4753-828a-007cf72d6ade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpds = cps_mond_norm.predict_cpds(X_test, y_test, online=True)\n",
    "plt.plot(np.arange(len(cpds)), [len(cpds[i]) for i in range(len(cpds))])\n",
    "plt.xlabel(\"#test objects\")\n",
    "plt.ylabel(\"calibration set size\")\n",
    "plt.ylim(0)\n",
    "plt.xlim(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc5d472f-143f-4ca0-9894-b812698d16f0",
   "metadata": {},
   "source": [
    "By default, the test objects and labels are sequentially added to the existing calibration set, i.e., the one used when fitting the conformal regressor. We may set the `warm_start` option to `False`, if we would like to ignore the original calibration set. Note that the initial predictions tend to be very (maximally) wide, before we have a sufficiently large calibration set to allow for providing tighter intervals at the specified level of confidence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "c9f217ce-ce4a-4c12-8a5f-539fc0e54d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[       -inf,         inf],\n",
       "       [       -inf,         inf],\n",
       "       [       -inf,         inf],\n",
       "       ...,\n",
       "       [ 0.00728605,  0.07035561],\n",
       "       [ 0.03934106,  0.10241062],\n",
       "       [-0.00286772,  0.06020184]])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_std.predict_int(X_test, y_test, online=True, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "1742ac24-c460-46cc-8e13-18007ba6acc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       ...,\n",
       "       [0.01123974, 0.06145531],\n",
       "       [0.04948275, 0.08772302],\n",
       "       [0.01794646, 0.03553285]])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_norm.predict_int(X_test, y_test, online=True, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "ee6279db-ee9f-440a-9549-1d2099e30793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       ...,\n",
       "       [0.02124808, 0.05362491],\n",
       "       [0.0442988 , 0.09821379],\n",
       "       [0.01379224, 0.0381352 ]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_mond_std.predict_int(X_test, y_test, online=True, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "6b6904cb-8cd9-4adc-bf14-0e26fdf814b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       ...,\n",
       "       [0.01161026, 0.05861562],\n",
       "       [0.05153434, 0.08833175],\n",
       "       [0.01584102, 0.03514241]])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_mond_norm.predict_int(X_test, y_test, online=True, warm_start=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d834e0a3-ae2a-411c-8d98-a0b5f61c6845",
   "metadata": {},
   "source": [
    "Similarly, we can request percentiles while disabling `warm_start` for online calibration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "8990f09d-ef41-4a29-96b9-804d14be9f40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 1.        , 1.        , 1.        ],\n",
       "       [0.        , 0.        , 0.05551618, 1.        , 1.        ],\n",
       "       [0.        , 0.        , 0.09015789, 1.        , 1.        ],\n",
       "       ...,\n",
       "       [0.00728605, 0.03097585, 0.03666226, 0.04169251, 0.07035561],\n",
       "       [0.03934106, 0.06303086, 0.06871727, 0.07374803, 0.10241062],\n",
       "       [0.        , 0.02082208, 0.0265085 , 0.03153925, 0.06020184]])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_std.predict_percentiles(X_test, y_test, \n",
    "                            lower_percentiles = [2.5, 25],\n",
    "                            higher_percentiles = [50, 75, 97.5],\n",
    "                            y_min=0, y_max=1, \n",
    "                            online=True, warm_start=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dee90124-363f-40a2-8d06-1cbdce3a369a",
   "metadata": {},
   "source": [
    "When disabling `warm_start`, we can observe that size of the cpd (= size of the calibration set) is initially zero:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "22907558-99a6-4e4d-aff5-452f6abfbfc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpds = cps_norm.predict_cpds(X_test, y_test, online=True, warm_start=False)\n",
    "plt.plot(np.arange(len(cpds)), [len(cpds[i]) for i in range(len(cpds))])\n",
    "plt.xlabel(\"#test objects\")\n",
    "plt.ylabel(\"calibration set size\")\n",
    "plt.ylim(0)\n",
    "plt.xlim(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ba4ac02-cec8-49c7-bbfa-2929f4edd20e",
   "metadata": {},
   "source": [
    "Similarly for Mondrian conformal predictive systems:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "4f126043-5af9-4cf5-aef7-3528e6720280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpds = cps_mond_norm.predict_cpds(X_test, y_test, online=True, warm_start=False)\n",
    "plt.plot(np.arange(len(cpds)), [len(cpds[i]) for i in range(len(cpds))])\n",
    "plt.xlabel(\"#test objects\")\n",
    "plt.ylabel(\"calibration set size\")\n",
    "plt.ylim(0)\n",
    "plt.xlim(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda2f420-a5bd-4a80-8b30-159eb6ffdc83",
   "metadata": {},
   "source": [
    "We may also evaluate the conformal predictive systems using online calibration, by specifying `online=True` for the `evaluate` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "c0c329f5-111f-444b-8992-fbf865c50912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.009808457481262134,\n",
       " 'eff_mean': 0.12957579238578523,\n",
       " 'eff_med': 0.13303423186885244,\n",
       " 'time_fit': 3.5762786865234375e-05,\n",
       " 'ks_test': 0.5751564855532147,\n",
       " 'time_evaluate': 0.05411887168884277}"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_std.evaluate(X_test, y_test, confidence=0.99, y_min=0, y_max=1, \n",
    "                 online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "1228257b-5557-4023-960a-241f973235a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.008050337744054725,\n",
       " 'eff_mean': 0.09718410114669336,\n",
       " 'eff_med': 0.07732296021173327,\n",
       " 'time_fit': 3.504753112792969e-05,\n",
       " 'ks_test': 0.6969522935470824,\n",
       " 'time_evaluate': 0.05935978889465332}"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_norm.evaluate(X_test, y_test, confidence=0.99, y_min=0, y_max=1, \n",
    "                  online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "b93e0450-555b-4df8-b5bb-a2d8d218175f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.009808457481262134,\n",
       " 'eff_mean': 0.11171693610561466,\n",
       " 'eff_med': 0.06939848891803274,\n",
       " 'time_fit': 0.00011849403381347656,\n",
       " 'ks_test': 0.6116867409848821,\n",
       " 'time_evaluate': 0.036829471588134766}"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_mond_std.evaluate(X_test, y_test, confidence=0.99, y_min=0, y_max=1, \n",
    "                      online=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "fa25c3f4-d549-46ed-b5ba-7555f7983eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.008235402979550277,\n",
       " 'eff_mean': 0.10420182711656334,\n",
       " 'eff_med': 0.07276971379532547,\n",
       " 'time_fit': 0.00016427040100097656,\n",
       " 'ks_test': 0.5664371560997961,\n",
       " 'time_evaluate': 0.041620492935180664}"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_mond_norm.evaluate(X_test, y_test, confidence=0.99, y_min=0, y_max=1, \n",
    "                       online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd42b6f-3f38-4090-b166-e334428c3c48",
   "metadata": {},
   "source": [
    "Again, we may consider ignoring the original calibration set by setting `warm_start=False`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "e3f637e7-fbde-453c-aa99-d6befb499573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.005274359211622115,\n",
       " 'eff_mean': 0.12011788694232398,\n",
       " 'eff_med': 0.08528677361644477,\n",
       " 'time_fit': 0.00016427040100097656,\n",
       " 'CRPS': 0.007030363971560899,\n",
       " 'ks_test': 0.04559119572615944,\n",
       " 'time_evaluate': 0.16329216957092285}"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps_mond_norm.evaluate(X_test, y_test, confidence=0.99, y_min=0, y_max=1, \n",
    "                       online=True, warm_start=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea3e7ed-bd95-4d31-90a6-9a8c1486aed9",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Online calibration without an initial calibration set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d325da60-72e8-4e34-9260-171bae497cf0",
   "metadata": {},
   "source": [
    "Since the calibration set is incrementally extended during online calibration, we may consider starting with an empty calibration set; this allows us to use the full training set when fitting the underlying model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "2440cfc6-6623-40c8-9ddf-bd1b96926b59",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_full = WrapRegressor(RandomForestRegressor(n_jobs=-1, n_estimators=500))\n",
    "\n",
    "rf_full.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "796f89e4-6cc4-4ea5-8d3a-41b895d9fa7a",
   "metadata": {},
   "source": [
    "Let us first initialize the conformal predictive system with an empty calibration set, i.e., not specifying any objects or labels for the `calibrate` method, but just setting `cps=True` (as we will see further below, we may additionally provide parameters such as difficulty estimator, Mondrian categorizer, etc.):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "e6035dc1-ded4-4c35-8193-aaf71eb7c7f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1), calibrated=False)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_full.calibrate(cps=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f109d9-1870-48e1-ad1e-b6af1cd0a88f",
   "metadata": {},
   "source": [
    "We may now obtain prediction intervals while sequentially updating the calibration set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "bc1b5e15-c050-49a9-b492-76b61e43510d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       ...,\n",
       "       [0.01785395, 0.05922435],\n",
       "       [0.0493198 , 0.0906902 ],\n",
       "       [0.00719896, 0.04856936]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_full.predict_int(X_test, y_test, confidence=0.9, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "207b82f4-636b-4526-b543-ccc69da690d5",
   "metadata": {},
   "source": [
    "Let us investigate also the p-values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "17f1dc66-8adb-4c91-a672-ade80b849d4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.1428238 , 0.0259196 , 0.74812542, ..., 0.07030082, 0.71222233,\n",
       "       0.66332484])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.1934153416570974\n"
     ]
    }
   ],
   "source": [
    "p_values = rf_full.predict_p(X_test, y_test, online=True)\n",
    "\n",
    "display(p_values)\n",
    "\n",
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e29184f-7877-4842-b4a1-f5882ff0c6a8",
   "metadata": {},
   "source": [
    "If we provide also a difficulty estimator to `calibrate`, we will obtain a normalized conformal predictive system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "82334b4e-5f73-4f4b-9aa2-2696c0acc85b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1), calibrated=False)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de_var = DifficultyEstimator()\n",
    "de_var.fit(X=X_train, learner=rf_full.learner, scaler=True)\n",
    "\n",
    "rf_full.calibrate(de=de_var, cps=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bf626f0-bf1f-4ac7-a17a-ca85c7e2c1aa",
   "metadata": {},
   "source": [
    "Now we get the intervals in the usual way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "96638f7d-0b0a-446e-966b-5311496e9207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       ...,\n",
       "       [0.02426831, 0.05333176],\n",
       "       [0.052767  , 0.08801168],\n",
       "       [0.01374864, 0.04253788]])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_full.predict_int(X_test, y_test, confidence=0.9, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acefdf9e-5426-4074-b3e2-439d1ff848c8",
   "metadata": {},
   "source": [
    "Let us investigate also the p-values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "0af46cc6-13da-4f8f-b83f-836c77da6133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.10247823, 0.26073051, 0.98338702, ..., 0.0424165 , 0.69378292,\n",
       "       0.67831493])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.1016922037284812\n"
     ]
    }
   ],
   "source": [
    "p_values = rf_full.predict_p(X_test, y_test, online=True)\n",
    "\n",
    "display(p_values)\n",
    "\n",
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "086ad158-312d-4690-9648-e4fb77a1aed5",
   "metadata": {},
   "source": [
    "We get a Mondrian conformal predictive system by providing a Mondrian categorizer to `calibrate`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "1d6d7711-fdf7-4f20-b8de-9f26d666d82e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WrapRegressor(learner=RandomForestRegressor(n_estimators=500, n_jobs=-1), calibrated=False)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mc = MondrianCategorizer()\n",
    "mc.fit(X_cal, de=de_var, no_bins=10)\n",
    "\n",
    "rf_full.calibrate(mc=mc, cps=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e458ee96-4664-4f7d-ad0a-d1122713718f",
   "metadata": {},
   "source": [
    "We use the same call again to get the intervals from the Mondrian conformal predictive system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "415ad844-41c0-45e2-b579-60d97c0e70f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       [      -inf,        inf],\n",
       "       ...,\n",
       "       [0.02510731, 0.05171442],\n",
       "       [0.04995927, 0.0952166 ],\n",
       "       [0.01445232, 0.0411108 ]])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_full.predict_int(X_test, y_test, confidence=0.9, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1464d6be-1dbb-4cea-8bb4-68bff5a247c7",
   "metadata": {},
   "source": [
    "Let us investigate the coverage and prediction interval size for each category (here limiting the lower and upper bounds of the intervals):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "85c1547a-f227-4f51-8f4c-1b03685c053f",
   "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>Number</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>147</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>537</td>\n",
       "      <td>0.9963</td>\n",
       "      <td>0.3891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>679</td>\n",
       "      <td>0.9926</td>\n",
       "      <td>0.3142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>733</td>\n",
       "      <td>0.9945</td>\n",
       "      <td>0.2959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>983</td>\n",
       "      <td>0.9858</td>\n",
       "      <td>0.2265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1148</td>\n",
       "      <td>0.9939</td>\n",
       "      <td>0.2152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1283</td>\n",
       "      <td>0.9883</td>\n",
       "      <td>0.1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1634</td>\n",
       "      <td>0.9920</td>\n",
       "      <td>0.1835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1969</td>\n",
       "      <td>0.9924</td>\n",
       "      <td>0.1872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1694</td>\n",
       "      <td>0.9965</td>\n",
       "      <td>0.3670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number  Coverage    Size\n",
       "0     147    1.0000  1.0000\n",
       "1     537    0.9963  0.3891\n",
       "2     679    0.9926  0.3142\n",
       "3     733    0.9945  0.2959\n",
       "4     983    0.9858  0.2265\n",
       "5    1148    0.9939  0.2152\n",
       "6    1283    0.9883  0.1985\n",
       "7    1634    0.9920  0.1835\n",
       "8    1969    0.9924  0.1872\n",
       "9    1694    0.9965  0.3670"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_intervals = rf_full.predict_int(X_test, y_test, y_min=0, \n",
    "                                           y_max=1, confidence=0.99,\n",
    "                                           online=True)\n",
    "number = []\n",
    "coverage = []\n",
    "size = []\n",
    "\n",
    "bins_test = mc.apply(X_test)\n",
    "\n",
    "for c in range(len(mc.bin_thresholds)-1):\n",
    "    selection = bins_test == c\n",
    "    number.append(np.sum(selection))\n",
    "    coverage.append(np.sum(\n",
    "        (prediction_intervals[selection,0] <= y_test[selection])& \\\n",
    "        (y_test[selection] <= prediction_intervals[selection,1]))/np.sum(selection))\n",
    "    size.append(np.sum(prediction_intervals[selection,1]-\\\n",
    "                       prediction_intervals[selection,0])/np.sum(selection))\n",
    "\n",
    "df = pd.DataFrame({\"Number\":number, \"Coverage\":coverage, \"Size\":size})\n",
    "df.round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3dad7e7-5f19-4847-a457-f8a3ed355d8b",
   "metadata": {},
   "source": [
    "Let us investigate also the p-values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "2e16f1d0-bcb8-4d2f-b8e0-e26853be979e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.53214714, 0.43778192, 0.03993967, ..., 0.02242697, 0.63007498,\n",
       "       0.67977321])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS-test: 0.10544072348439648\n"
     ]
    }
   ],
   "source": [
    "p_values = rf_full.predict_p(X_test, y_test, online=True)\n",
    "\n",
    "display(p_values)\n",
    "\n",
    "plt.ecdf(p_values)\n",
    "plt.xlim(0,1)\n",
    "plt.xlabel(\"p-value\")\n",
    "plt.ylabel(\"ECDF\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"KS-test: {kstest(p_values, \"uniform\").pvalue}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6113a79e-170b-4275-82fb-64da74b7acb4",
   "metadata": {},
   "source": [
    "Similarly, we can also request percentiles using online calibration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "0172c405-f88b-4c00-acf4-9dabb0d2423c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 1.        , 1.        , 1.        ],\n",
       "       [0.        , 0.        , 1.        , 1.        , 1.        ],\n",
       "       [0.        , 0.        , 1.        , 1.        , 1.        ],\n",
       "       ...,\n",
       "       [0.0224026 , 0.03304831, 0.03768926, 0.04268077, 0.05384568],\n",
       "       [0.04447137, 0.06129608, 0.06858073, 0.07719714, 0.10112838],\n",
       "       [0.01174761, 0.02239332, 0.02704832, 0.03206945, 0.04329931]])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_full.predict_percentiles(X_test, y_test, \n",
    "                            lower_percentiles = [2.5, 25],\n",
    "                            higher_percentiles = [50, 75, 97.5],\n",
    "                            y_min=0, y_max=1, online=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c6fa52c-e0ca-41b0-a429-03a3f5063a67",
   "metadata": {},
   "source": [
    "We can of course also request the cpds; here we take a close look at those belonging to category 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "fce687a9-5af6-49fa-9294-f1e4955fc86e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([array([], dtype=float64), array([0.03971966]),\n",
       "       array([0.03241696, 0.04000547]), ...,\n",
       "       array([-0.00564027,  0.00130014,  0.00366736, ...,  0.0589863 ,\n",
       "               0.06150993,  0.06468008])                              ,\n",
       "       array([0.01863575, 0.02557616, 0.02794338, ..., 0.08326232, 0.08578595,\n",
       "              0.0889561 ])                                                    ,\n",
       "       array([-0.00226413,  0.00467628,  0.0070435 , ...,  0.06236244,\n",
       "               0.06488607,  0.06805622])                              ],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpds = rf_full.predict_cpds(X_test, y_test, online=True)\n",
    "\n",
    "cpds[bins_test == 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7539a6a2-4e81-4ab3-bee0-413bede6e6a9",
   "metadata": {},
   "source": [
    "We may also evaluate the conformal predictive system using online calibration, by specifying `online=True` for the `evaluate` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "3a99254a-33d2-46dc-9d93-01ef4e8dc655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': 0.007495142037568292,\n",
       " 'eff_mean': 0.25916496104626785,\n",
       " 'eff_med': 0.07428075960655744,\n",
       " 'time_fit': 5.7220458984375e-06,\n",
       " 'ks_test': 0.006618064524288634,\n",
       " 'time_evaluate': 0.040779829025268555}"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_full.evaluate(X_test, y_test, confidence=0.99, y_min=0, y_max=1, online=True)"
   ]
  }
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