{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0cedfd8f",
   "metadata": {},
   "source": [
    "Overview of the different settings possible\n",
    "===========================================\n",
    "\n",
    "1) Choosing `type_tree`\n",
    "\t- 'PMQRT'        : train quantile regression trees (multi-quantile support). Use when you want direct quantile estimates.\n",
    "\t- 'RT'           : standard regression tree with squared-error splitting (uses `RegressionTreeQuadratic`).\n",
    "\t- 'CRPS'         : specialized tree optimized for CRPS (Continuous Ranked Probability Score) available in `CRPSRT/RegressionTree.py`.\n",
    "\n",
    "\n",
    "2) Choosing `type_conformal`\n",
    "    - 'None'   : in case you don't plan to use conformal prediction methods\n",
    "    - 'split'  : if you want to use split conformal prediction\n",
    "\n",
    "2) Conformalization `nested_set` options\n",
    "\t- 'CQR' / 'CQR-m' / 'CQR-r' : variants of Conformalized Quantile Regression implemented in `Conformalisation_CQR.py`. They require you pass `nominal_quantiles` to `conformalize` (see example below).\n",
    "\t- 'distributional' : distributional conformalization implemented in `Conformalisation_distributional.py`. It uses a list of nested low quantiles (param `list_distri_low_quantiles`) and supports group coverage.\n",
    "\n",
    "3) Group coverage\n",
    "\t- Set `group_coverage=True` when instantiating `UQ` to enable group-aware conformalization.\n",
    "\n",
    "4) Choose aggregation type\n",
    "    - 'vr'      : corresponds to quantile bagging: simple average of quantile obtained from single trees in the forest\n",
    "    - 'vr-avg'  : corresponds to distributional bagging: aggregates conditional CDF from all trees before querying the desired quantile\n",
    "\n",
    "4) Parameters:\n",
    "    - 'nTrees'                   : number of trees to train\n",
    "    - 'treeID2quantiles_train'   : dict mapping tree IDs to quantiles to train for (used in PMQRT)\n",
    "    - 'max_depth'                : maximum depth passed to tree constructors\n",
    "    - 'min_samples_split'        : minimum samples to split passed to tree constructors\n",
    "    - 'IG_biais_correction'      : whether tree fit uses leave-one-out or Mallows correction of information gains\n",
    "    - 'max_depth_group'          : maximum depth for group coverage trees\n",
    "    - 'list_distri_low_quantiles': list of lower quantiles for distributional conformal prediction\n",
    "\n",
    "5) Main methods:\n",
    "    - Use `uq.train_trees(X_train, y_train)` to train a forest\n",
    "\n",
    "    a) Query quantiles  \n",
    "\t    - Use `uq.get_quantile_estimate(trees, X_test, quantiles)` to query raw quantiles aggregated across trees.\n",
    "        - Use `uq.compute_width_coverage(sample2predset, y_test)` to compute width and coverage if `sample2predset` contains confidence intervals for each test sample.\n",
    "    \n",
    "    b) Conformal prediction\n",
    "\t    - Use `uq.conformalize(trees, X_calib, y_calib, alpha, nominal_quantiles=[2*alpha])` to conformalize the your forest. Note that `nominal_quantiles` is only needed if you trained PMQRTs.\n",
    "        - Use `uq.predict_conformal_set(trees, X_test)` to predict the conformal sets on the test samples.\n",
    "        - Use `uq.compute_width_coverage(sample2predset, y_test)` to get the width and coverage of your predictions if `sample2predset` contains the conformalized sets. Note that if you used PMQRTs, `sample2predset` actually contains different conformal sets based on the different nominal_quantiles you considered when conformalizing the method. In our example, you specify a single nominal quantile and those we would get access to the conformal sets on the test set using `sample2predset|0]`. Therefore, one should use `uq.compute_width_coverage(sample2predset[0], y_test)`. \n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}