{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1d51e31",
   "metadata": {
    "papermill": {
     "duration": 0.052042,
     "end_time": "2021-09-18T10:51:56.464794",
     "exception": false,
     "start_time": "2021-09-18T10:51:56.412752",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Bayesian Hierarchical Stacking: Well Switching Case Study\n",
    "\n",
    " <figure>\n",
    "  <img src=\"https://i.imgur.com/CiUmZKx.jpeg\" width=\"700px\"/>\n",
    "  <figcaption>Photo by Belinda Fewings, https://unsplash.com/photos/6p-KtXCBGNw.</figcaption>\n",
    "</figure> \n",
    "\n",
    "## Table of Contents\n",
    "\n",
    "* [Intro](#intro)\n",
    "* [1. Exploratory Data Analysis](#1)\n",
    "* [2. Prepare 6 Different Models](#2)\n",
    "    * [2.1 Feature Engineering](#2.1)\n",
    "    * [2.2 Training](#2.2)\n",
    "* [3. Bayesian Hierarchical Stacking](#3)\n",
    "    * [3.1 Prepare stacking datasets](#3.1)\n",
    "    * [3.2 Define stacking model](#3.2)\n",
    "* [4. Evaluate on test set](#4)\n",
    "    * [4.1 Stack predictions](#4.1)\n",
    "    * [4.2 Compare methods](#4.2)\n",
    "* [Conclusion](#conclusion)\n",
    "* [References](#references)\n",
    "\n",
    "## Intro <a class=\"anchor\" id=\"1\"></a>\n",
    "\n",
    "Suppose you have just fit 6 models to a dataset, and need to choose which one to use to make predictions on your test set. How do you choose which one to use? A couple of common tactics are:\n",
    "- choose the best model based on cross-validation;\n",
    "- average the models, using weights based on cross-validation scores.\n",
    "\n",
    "In the paper [Bayesian hierarchical stacking: Some models are (somewhere) useful](https://arxiv.org/abs/2101.08954), a new technique is introduced: average models based on weights which are allowed to vary across according to the input data, based on a hierarchical structure.\n",
    "\n",
    "\n",
    "Here, we'll implement the first case study from that paper - readers are nonetheless encouraged to look at the original paper to find other cases studies, as well as theoretical results. Code from the article (in R / Stan) can be found [here](https://github.com/yao-yl/hierarchical-stacking-code)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2d43427d-0ac3-4383-8441-375164cbecb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q numpyro@git+https://github.com/pyro-ppl/numpyro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a71e927",
   "metadata": {
    "papermill": {
     "duration": 4.069199,
     "end_time": "2021-09-18T10:52:00.594720",
     "exception": false,
     "start_time": "2021-09-18T10:51:56.525521",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from IPython.display import set_matplotlib_formats\n",
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.interpolate import BSpline\n",
    "import seaborn as sns\n",
    "\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "\n",
    "import numpyro\n",
    "import numpyro.distributions as dist\n",
    "\n",
    "plt.style.use(\"seaborn\")\n",
    "if \"NUMPYRO_SPHINXBUILD\" in os.environ:\n",
    "    set_matplotlib_formats(\"svg\")\n",
    "\n",
    "numpyro.set_host_device_count(4)\n",
    "assert numpyro.__version__.startswith(\"0.10.1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "227f2ff1-63f3-4529-89ba-4c92fc7bb518",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255e8d79",
   "metadata": {
    "papermill": {
     "duration": 0.043256,
     "end_time": "2021-09-18T10:52:00.780796",
     "exception": false,
     "start_time": "2021-09-18T10:52:00.737540",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 1. Exploratory Data Analysis <a class=\"anchor\" id=\"1\"></a>\n",
    "\n",
    "The data we have to work with looks at households in Bangladesh, some of which were affected by high levels of arsenic in their water. Would affected households want to switch to a neighbour's well?\n",
    "\n",
    "We'll split the data into a train and test set, and then we'll train six different models to try to predict whether households would switch wells. Then, we'll see how we can stack them when predicting on the test set!\n",
    "\n",
    "But first, let's load it in and visualise it! Each row represents a household, and the features we have available to us are:\n",
    "\n",
    "- switch: whether a household switched to another well;\n",
    "- arsenic: level of arsenic in drinking water;\n",
    "- educ: level of education of \"head of household\";\n",
    "- dist100: distance to nearest safe-drinking well;\n",
    "- assoc: whether the household participates in any community activities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01d1703b",
   "metadata": {
    "papermill": {
     "duration": 0.078754,
     "end_time": "2021-09-18T10:52:00.905455",
     "exception": false,
     "start_time": "2021-09-18T10:52:00.826701",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "wells = pd.read_csv(\n",
    "    \"http://stat.columbia.edu/~gelman/arm/examples/arsenic/wells.dat\", sep=\" \"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2bf6c000-cb9a-4c81-898f-5ac4cdd1020a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>switch</th>\n",
       "      <th>arsenic</th>\n",
       "      <th>dist</th>\n",
       "      <th>assoc</th>\n",
       "      <th>educ</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.36</td>\n",
       "      <td>16.826000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.71</td>\n",
       "      <td>47.321999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2.07</td>\n",
       "      <td>20.966999</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.15</td>\n",
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       "      <td>0</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1.10</td>\n",
       "      <td>40.874001</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
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      "text/plain": [
       "   switch  arsenic       dist  assoc  educ\n",
       "1       1     2.36  16.826000      0     0\n",
       "2       1     0.71  47.321999      0     0\n",
       "3       0     2.07  20.966999      0    10\n",
       "4       1     1.15  21.486000      0    12\n",
       "5       1     1.10  40.874001      1    14"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wells.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5dec77a2",
   "metadata": {
    "papermill": {
     "duration": 1.122344,
     "end_time": "2021-09-18T10:52:02.072825",
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    },
    "tags": []
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(12, 6))\n",
    "fig.suptitle(\"Target variable plotted against various predictors\")\n",
    "sns.scatterplot(data=wells, x=\"arsenic\", y=\"switch\", ax=ax[0][0])\n",
    "sns.scatterplot(data=wells, x=\"dist\", y=\"switch\", ax=ax[0][1])\n",
    "sns.barplot(\n",
    "    data=wells.groupby(\"assoc\")[\"switch\"].mean().reset_index(),\n",
    "    x=\"assoc\",\n",
    "    y=\"switch\",\n",
    "    ax=ax[1][0],\n",
    ")\n",
    "ax[1][0].set_ylabel(\"Proportion switch\")\n",
    "sns.barplot(\n",
    "    data=wells.groupby(\"educ\")[\"switch\"].mean().reset_index(),\n",
    "    x=\"educ\",\n",
    "    y=\"switch\",\n",
    "    ax=ax[1][1],\n",
    ")\n",
    "ax[1][1].set_ylabel(\"Proportion switch\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c9daff",
   "metadata": {
    "papermill": {
     "duration": 0.046834,
     "end_time": "2021-09-18T10:52:02.167845",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.121011",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we'll choose 200 observations to be part of our train set, and 1500 to be part of our test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e6b41da0",
   "metadata": {
    "papermill": {
     "duration": 0.058671,
     "end_time": "2021-09-18T10:52:02.274078",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.215407",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "train_id = wells.sample(n=200).index\n",
    "test_id = wells.loc[~wells.index.isin(train_id)].sample(n=1500).index\n",
    "y_train = wells.loc[train_id, \"switch\"].to_numpy()\n",
    "y_test = wells.loc[test_id, \"switch\"].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01c56e27",
   "metadata": {
    "papermill": {
     "duration": 0.047031,
     "end_time": "2021-09-18T10:52:02.368998",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.321967",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2. Prepare 6 different candidate models <a class=\"anchor\" id=\"2\"></a>\n",
    "\n",
    "### 2.1 Feature Engineering <a class=\"anchor\" id=\"2.1\"></a>\n",
    "\n",
    "First, let's add a few new columns:\n",
    "- `edu0`: whether `educ` is `0`,\n",
    "- `edu1`: whether `educ` is between `1` and `5`,\n",
    "- `edu2`: whether `educ` is between `6` and `11`,\n",
    "- `edu3`: whether `educ` is between `12` and `17`,\n",
    "- `logarsenic`: natural logarithm of `arsenic`,\n",
    "- `assoc_half`: half of `assoc`,\n",
    "- `as_square`: natural logarithm of `arsenic`, squared,\n",
    "- `as_third`: natural logarithm of `arsenic`, cubed,\n",
    "- `dist100`: `dist` divided by `100`,\n",
    " - `intercept`: just a columns of `1`s.\n",
    "\n",
    "We're going to start by fitting 6 different models to our train set:\n",
    "\n",
    "- logistic regression using `intercept`, `arsenic`, `assoc`, `edu1`, `edu2`, and `edu3`;\n",
    "- same as above, but with `logarsenic` instead of `arsenic`;\n",
    "- same as the first one, but with square and cubic features as well;\n",
    "- same as the first one, but with spline features derived from `logarsenic` as well;\n",
    "- same as the first one, but with spline features derived from `dist100` as well;\n",
    "- same as the first one, but with `educ` instead of the binary `edu` variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa79c0ee-54b9-458d-9f97-c9e91ae83e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "wells[\"edu0\"] = wells[\"educ\"].isin(np.arange(0, 1)).astype(int)\n",
    "wells[\"edu1\"] = wells[\"educ\"].isin(np.arange(1, 6)).astype(int)\n",
    "wells[\"edu2\"] = wells[\"educ\"].isin(np.arange(6, 12)).astype(int)\n",
    "wells[\"edu3\"] = wells[\"educ\"].isin(np.arange(12, 18)).astype(int)\n",
    "wells[\"logarsenic\"] = np.log(wells[\"arsenic\"])\n",
    "wells[\"assoc_half\"] = wells[\"assoc\"] / 2.0\n",
    "wells[\"as_square\"] = wells[\"logarsenic\"] ** 2\n",
    "wells[\"as_third\"] = wells[\"logarsenic\"] ** 3\n",
    "wells[\"dist100\"] = wells[\"dist\"] / 100.0\n",
    "wells[\"intercept\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6726d0fa",
   "metadata": {
    "papermill": {
     "duration": 0.062523,
     "end_time": "2021-09-18T10:52:02.478421",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.415898",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def bs(x, knots, degree):\n",
    "    \"\"\"\n",
    "    Generate the B-spline basis matrix for a polynomial spline.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x\n",
    "        predictor variable.\n",
    "    knots\n",
    "        locations of internal breakpoints (not padded).\n",
    "    degree\n",
    "        degree of the piecewise polynomial.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    pd.DataFrame\n",
    "        Spline basis matrix.\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    This mirrors ``bs`` from splines package in R.\n",
    "    \"\"\"\n",
    "    padded_knots = np.hstack(\n",
    "        [[x.min()] * (degree + 1), knots, [x.max()] * (degree + 1)]\n",
    "    )\n",
    "    return pd.DataFrame(\n",
    "        BSpline(padded_knots, np.eye(len(padded_knots) - degree - 1), degree)(x)[:, 1:],\n",
    "        index=x.index,\n",
    "    )\n",
    "\n",
    "\n",
    "knots = np.quantile(wells.loc[train_id, \"logarsenic\"], np.linspace(0.1, 0.9, num=10))\n",
    "spline_arsenic = bs(wells[\"logarsenic\"], knots=knots, degree=3)\n",
    "knots = np.quantile(wells.loc[train_id, \"dist100\"], np.linspace(0.1, 0.9, num=10))\n",
    "spline_dist = bs(wells[\"dist100\"], knots=knots, degree=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "064a3de6",
   "metadata": {
    "papermill": {
     "duration": 0.081958,
     "end_time": "2021-09-18T10:52:02.608879",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.526921",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "features_0 = [\"intercept\", \"dist100\", \"arsenic\", \"assoc\", \"edu1\", \"edu2\", \"edu3\"]\n",
    "features_1 = [\"intercept\", \"dist100\", \"logarsenic\", \"assoc\", \"edu1\", \"edu2\", \"edu3\"]\n",
    "features_2 = [\n",
    "    \"intercept\",\n",
    "    \"dist100\",\n",
    "    \"arsenic\",\n",
    "    \"as_third\",\n",
    "    \"as_square\",\n",
    "    \"assoc\",\n",
    "    \"edu1\",\n",
    "    \"edu2\",\n",
    "    \"edu3\",\n",
    "]\n",
    "features_3 = [\"intercept\", \"dist100\", \"assoc\", \"edu1\", \"edu2\", \"edu3\"]\n",
    "features_4 = [\"intercept\", \"logarsenic\", \"assoc\", \"edu1\", \"edu2\", \"edu3\"]\n",
    "features_5 = [\"intercept\", \"dist100\", \"logarsenic\", \"assoc\", \"educ\"]\n",
    "\n",
    "X0 = wells.loc[train_id, features_0].to_numpy()\n",
    "X1 = wells.loc[train_id, features_1].to_numpy()\n",
    "X2 = wells.loc[train_id, features_2].to_numpy()\n",
    "X3 = (\n",
    "    pd.concat([wells.loc[:, features_3], spline_arsenic], axis=1)\n",
    "    .loc[train_id]\n",
    "    .to_numpy()\n",
    ")\n",
    "X4 = pd.concat([wells.loc[:, features_4], spline_dist], axis=1).loc[train_id].to_numpy()\n",
    "X5 = wells.loc[train_id, features_5].to_numpy()\n",
    "\n",
    "X0_test = wells.loc[test_id, features_0].to_numpy()\n",
    "X1_test = wells.loc[test_id, features_1].to_numpy()\n",
    "X2_test = wells.loc[test_id, features_2].to_numpy()\n",
    "X3_test = (\n",
    "    pd.concat([wells.loc[:, features_3], spline_arsenic], axis=1)\n",
    "    .loc[test_id]\n",
    "    .to_numpy()\n",
    ")\n",
    "X4_test = (\n",
    "    pd.concat([wells.loc[:, features_4], spline_dist], axis=1).loc[test_id].to_numpy()\n",
    ")\n",
    "X5_test = wells.loc[test_id, features_5].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64fa1b43",
   "metadata": {
    "papermill": {
     "duration": 0.055757,
     "end_time": "2021-09-18T10:52:02.713347",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.657590",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_x_list = [X0, X1, X2, X3, X4, X5]\n",
    "test_x_list = [X0_test, X1_test, X2_test, X3_test, X4_test, X5_test]\n",
    "K = len(train_x_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d1a65d",
   "metadata": {
    "papermill": {
     "duration": 0.049466,
     "end_time": "2021-09-18T10:52:02.811950",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.762484",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 2.2 Training <a class=\"anchor\" id=\"2.2\"></a>\n",
    "\n",
    "Each model will be trained in the same way - with a Bernoulli likelihood and a logit link function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c070567f",
   "metadata": {
    "papermill": {
     "duration": 0.056796,
     "end_time": "2021-09-18T10:52:02.917713",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.860917",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def logistic(x, y=None):\n",
    "    beta = numpyro.sample(\"beta\", dist.Normal(0, 3).expand([x.shape[1]]))\n",
    "    logits = numpyro.deterministic(\"logits\", jnp.matmul(x, beta))\n",
    "\n",
    "    numpyro.sample(\n",
    "        \"obs\",\n",
    "        dist.Bernoulli(logits=logits),\n",
    "        obs=y,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b29ed6c2",
   "metadata": {
    "papermill": {
     "duration": 820.388941,
     "end_time": "2021-09-18T11:05:43.355092",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.966151",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fit_list = []\n",
    "for k in range(K):\n",
    "    sampler = numpyro.infer.NUTS(logistic)\n",
    "    mcmc = numpyro.infer.MCMC(\n",
    "        sampler, num_chains=4, num_samples=1000, num_warmup=1000, progress_bar=False\n",
    "    )\n",
    "    rng_key = jax.random.fold_in(jax.random.PRNGKey(13), k)\n",
    "    mcmc.run(rng_key, x=train_x_list[k], y=y_train)\n",
    "    fit_list.append(mcmc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2ac5012",
   "metadata": {
    "papermill": {
     "duration": 0.051074,
     "end_time": "2021-09-18T11:05:43.479751",
     "exception": false,
     "start_time": "2021-09-18T11:05:43.428677",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 2.3 Estimate leave-one-out cross-validated score for each training point <a class=\"anchor\" id=\"2.3\"></a>\n",
    "\n",
    "Rather than refitting each model 100 times, we will estimate the leave-one-out cross-validated score using [LOO](https://arxiv.org/abs/2001.00980)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0dfe6166",
   "metadata": {
    "papermill": {
     "duration": 14.787853,
     "end_time": "2021-09-18T11:05:58.318434",
     "exception": false,
     "start_time": "2021-09-18T11:05:43.530581",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def find_point_wise_loo_score(fit):\n",
    "    return az.loo(az.from_numpyro(fit), pointwise=True, scale=\"log\").loo_i.values\n",
    "\n",
    "\n",
    "lpd_point = np.vstack([find_point_wise_loo_score(fit) for fit in fit_list]).T\n",
    "exp_lpd_point = np.exp(lpd_point)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f7a74a",
   "metadata": {
    "papermill": {
     "duration": 0.051972,
     "end_time": "2021-09-18T11:05:58.422802",
     "exception": false,
     "start_time": "2021-09-18T11:05:58.370830",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3. Bayesian Hierarchical Stacking <a class=\"anchor\" id=\"3\"></a>\n",
    "\n",
    "### 3.1 Prepare stacking datasets <a class=\"anchor\" id=\"3.1\"></a>\n",
    "\n",
    "To determine how the stacking weights should vary across training and test sets, we will need to create \"stacking datasets\" which include all the features which we want the stacking weights to depend on. How should such features be included? For discrete features, this is easy, we just one-hot-encode them. But for continuous features, we need a trick. In Equation (16), the authors recommend the following: if you have a continuous feature `f`, then replace it with the following two features:\n",
    "\n",
    "- `f_l`: `f` minus the median of `f`, clipped above at 0;\n",
    "- `f_r`: `f` minus the median of `f`, clipped below at 0;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8450ac11",
   "metadata": {
    "papermill": {
     "duration": 0.078407,
     "end_time": "2021-09-18T11:05:58.566113",
     "exception": false,
     "start_time": "2021-09-18T11:05:58.487706",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dist100_median = wells.loc[wells.index[train_id], \"dist100\"].median()\n",
    "logarsenic_median = wells.loc[wells.index[train_id], \"logarsenic\"].median()\n",
    "wells[\"dist100_l\"] = (wells[\"dist100\"] - dist100_median).clip(upper=0)\n",
    "wells[\"dist100_r\"] = (wells[\"dist100\"] - dist100_median).clip(lower=0)\n",
    "wells[\"logarsenic_l\"] = (wells[\"logarsenic\"] - logarsenic_median).clip(upper=0)\n",
    "wells[\"logarsenic_r\"] = (wells[\"logarsenic\"] - logarsenic_median).clip(lower=0)\n",
    "\n",
    "stacking_features = [\n",
    "    \"edu0\",\n",
    "    \"edu1\",\n",
    "    \"edu2\",\n",
    "    \"edu3\",\n",
    "    \"assoc_half\",\n",
    "    \"dist100_l\",\n",
    "    \"dist100_r\",\n",
    "    \"logarsenic_l\",\n",
    "    \"logarsenic_r\",\n",
    "]\n",
    "X_stacking_train = wells.loc[train_id, stacking_features].to_numpy()\n",
    "X_stacking_test = wells.loc[test_id, stacking_features].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb323c68",
   "metadata": {
    "papermill": {
     "duration": 0.052318,
     "end_time": "2021-09-18T11:05:58.671602",
     "exception": false,
     "start_time": "2021-09-18T11:05:58.619284",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.2 Define stacking model <a class=\"anchor\" id=\"3.2\"></a>\n",
    "\n",
    "What we seek to find is a matrix of weights $W$ with which to multiply the models' predictions. Let's define a matrix $Pred$ such that $Pred_{i,k}$ represents the prediction made for point $i$ by model $k$. Then the final prediction for point $i$ will then be:\n",
    "\n",
    "$$ \\sum_k W_{i, k}Pred_{i,k} $$\n",
    "\n",
    "Such a matrix $W$ would be required to have each column sum to $1$. Hence, we calculate each row $W_i$ of $W$ as:\n",
    "\n",
    "$$ W_i = \\text{softmax}(X\\_\\text{stacking}_i \\cdot \\beta), $$\n",
    "\n",
    "where $\\beta$ is a matrix whose values we seek to determine. For the discrete features, $\\beta$ is given a hierarchical structure over the possible inputs. Continuous features, on the other hand, get no hierarchical structure in this case study and just vary according to the input values.\n",
    "\n",
    "Notice how, for the discrete features, a [non-centered parametrisation is used](https://twiecki.io/blog/2017/02/08/bayesian-hierchical-non-centered/). Also note that we only need to estimate `K-1` columns of $\\beta$, because the weights `W_{i, k}` will have to sum to `1` for each `i`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f2203a8c",
   "metadata": {
    "papermill": {
     "duration": 0.075301,
     "end_time": "2021-09-18T11:05:58.799743",
     "exception": false,
     "start_time": "2021-09-18T11:05:58.724442",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def stacking(\n",
    "    X,\n",
    "    d_discrete,\n",
    "    X_test,\n",
    "    exp_lpd_point,\n",
    "    tau_mu,\n",
    "    tau_sigma,\n",
    "    *,\n",
    "    test,\n",
    "):\n",
    "    \"\"\"\n",
    "    Get weights with which to stack candidate models' predictions.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    X\n",
    "        Training stacking matrix: features on which stacking weights should depend, for the\n",
    "        training set.\n",
    "    d_discrete\n",
    "        Number of discrete features in `X` and `X_test`. The first `d_discrete` features\n",
    "        from these matrices should be the discrete ones, with the continuous ones coming\n",
    "        after them.\n",
    "    X_test\n",
    "        Test stacking matrix: features on which stacking weights should depend, for the\n",
    "        testing set.\n",
    "    exp_lpd_point\n",
    "        LOO score evaluated at each point in the training set, for each candidate model.\n",
    "    tau_mu\n",
    "        Hyperprior for mean of `beta`, for discrete features.\n",
    "    tau_sigma\n",
    "        Hyperprior for standard deviation of `beta`, for continuous features.\n",
    "    test\n",
    "        Whether to calculate stacking weights for test set.\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    Naming of variables mirrors what's used in the original paper.\n",
    "    \"\"\"\n",
    "    N = X.shape[0]\n",
    "    d = X.shape[1]\n",
    "    N_test = X_test.shape[0]\n",
    "    K = lpd_point.shape[1]  # number of candidate models\n",
    "\n",
    "    with numpyro.plate(\"Candidate models\", K - 1, dim=-2):\n",
    "        # mean effect of discrete features on stacking weights\n",
    "        mu = numpyro.sample(\"mu\", dist.Normal(0, tau_mu))\n",
    "        # standard deviation effect of discrete features on stacking weights\n",
    "        sigma = numpyro.sample(\"sigma\", dist.HalfNormal(scale=tau_sigma))\n",
    "        with numpyro.plate(\"Discrete features\", d_discrete, dim=-1):\n",
    "            # effect of discrete features on stacking weights\n",
    "            tau = numpyro.sample(\"tau\", dist.Normal(0, 1))\n",
    "        with numpyro.plate(\"Continuous features\", d - d_discrete, dim=-1):\n",
    "            # effect of continuous features on stacking weights\n",
    "            beta_con = numpyro.sample(\"beta_con\", dist.Normal(0, 1))\n",
    "\n",
    "    # effects of features on stacking weights\n",
    "    beta = numpyro.deterministic(\n",
    "        \"beta\", jnp.hstack([(sigma.squeeze() * tau.T + mu.squeeze()).T, beta_con])\n",
    "    )\n",
    "    assert beta.shape == (K - 1, d)\n",
    "\n",
    "    # stacking weights (in unconstrained space)\n",
    "    f = jnp.hstack([X @ beta.T, jnp.zeros((N, 1))])\n",
    "    assert f.shape == (N, K)\n",
    "\n",
    "    # log probability of LOO training scores weighted by stacking weights.\n",
    "    log_w = jax.nn.log_softmax(f, axis=1)\n",
    "    # stacking weights (constrained to sum to 1)\n",
    "    numpyro.deterministic(\"w\", jnp.exp(log_w))\n",
    "    logp = jax.nn.logsumexp(lpd_point + log_w, axis=1)\n",
    "    numpyro.factor(\"logp\", jnp.sum(logp))\n",
    "\n",
    "    if test:\n",
    "        # test set stacking weights (in unconstrained space)\n",
    "        f_test = jnp.hstack([X_test @ beta.T, jnp.zeros((N_test, 1))])\n",
    "        # test set stacking weights (constrained to sum to 1)\n",
    "        w_test = numpyro.deterministic(\"w_test\", jax.nn.softmax(f_test, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9827977d",
   "metadata": {
    "papermill": {
     "duration": 296.084187,
     "end_time": "2021-09-18T11:10:54.936288",
     "exception": false,
     "start_time": "2021-09-18T11:05:58.852101",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sampler = numpyro.infer.NUTS(stacking)\n",
    "mcmc = numpyro.infer.MCMC(\n",
    "    sampler, num_chains=4, num_samples=1000, num_warmup=1000, progress_bar=False\n",
    ")\n",
    "mcmc.run(\n",
    "    jax.random.PRNGKey(17),\n",
    "    X=X_stacking_train,\n",
    "    d_discrete=4,\n",
    "    X_test=X_stacking_test,\n",
    "    exp_lpd_point=exp_lpd_point,\n",
    "    tau_mu=1.0,\n",
    "    tau_sigma=0.5,\n",
    "    test=True,\n",
    ")\n",
    "trace = mcmc.get_samples()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ede764",
   "metadata": {
    "papermill": {
     "duration": 0.052553,
     "end_time": "2021-09-18T11:10:55.042375",
     "exception": false,
     "start_time": "2021-09-18T11:10:54.989822",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can now extract the weights with which to weight the different models from the posterior, and then visualise how they vary across the training set.\n",
    "\n",
    "Let's compare them with what the weights would've been if we'd just used fixed stacking weights (computed using ArviZ - see [their docs](https://arviz-devs.github.io/arviz/api/generated/arviz.compare.html) for details)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "812117cb",
   "metadata": {
    "papermill": {
     "duration": 2.523295,
     "end_time": "2021-09-18T11:10:57.979955",
     "exception": false,
     "start_time": "2021-09-18T11:10:55.456660",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 6), sharey=True)\n",
    "training_stacking_weights = trace[\"w\"].mean(axis=0)\n",
    "sns.scatterplot(data=pd.DataFrame(training_stacking_weights), ax=ax[0])\n",
    "fixed_weights = (\n",
    "    az.compare({idx: fit for idx, fit in enumerate(fit_list)}, method=\"stacking\")\n",
    "    .sort_index()[\"weight\"]\n",
    "    .to_numpy()\n",
    ")\n",
    "fixed_weights_df = pd.DataFrame(\n",
    "    np.repeat(\n",
    "        fixed_weights[jnp.newaxis, :],\n",
    "        len(X_stacking_train),\n",
    "        axis=0,\n",
    "    )\n",
    ")\n",
    "sns.scatterplot(data=fixed_weights_df, ax=ax[1])\n",
    "ax[0].set_title(\"Training weights from Bayesian Hierarchical stacking\")\n",
    "ax[1].set_title(\"Fixed weights stacking\")\n",
    "ax[0].set_xlabel(\"Index\")\n",
    "ax[1].set_xlabel(\"Index\")\n",
    "fig.suptitle(\n",
    "    \"Bayesian Hierarchical Stacking weights can vary according to the input\",\n",
    "    fontsize=18,\n",
    ")\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60e0c01",
   "metadata": {
    "papermill": {
     "duration": 0.065143,
     "end_time": "2021-09-18T11:10:58.110931",
     "exception": false,
     "start_time": "2021-09-18T11:10:58.045788",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 4. Evaluate on test set <a class=\"anchor\" id=\"4\"></a>\n",
    "\n",
    "### 4.1 Stack predictions <a class=\"anchor\" id=\"4.1\"></a>\n",
    "\n",
    "Now, for each model, let's evaluate the log predictive density for each point in the test set. Once we have predictions for each model, we need to think about how to combine them, such that for each test point, we get a single prediction.\n",
    "\n",
    "We decided we'd do this in three ways:\n",
    "- Bayesian Hierarchical Stacking (`bhs_pred`);\n",
    "- choosing the model with the best training set LOO score (`model_selection_preds`);\n",
    "- fixed-weights stacking (`fixed_weights_preds`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ce86bd9e-b2c6-4947-9675-92c925b6088d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for each candidate model, extract the posterior predictive logits\n",
    "train_preds = []\n",
    "for k in range(K):\n",
    "    predictive = numpyro.infer.Predictive(logistic, fit_list[k].get_samples())\n",
    "    rng_key = jax.random.fold_in(jax.random.PRNGKey(19), k)\n",
    "    train_pred = predictive(rng_key, x=train_x_list[k])[\"logits\"]\n",
    "    train_preds.append(train_pred.mean(axis=0))\n",
    "# reshape, so we have (N, K)\n",
    "train_preds = np.vstack(train_preds).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5b686b7c",
   "metadata": {
    "papermill": {
     "duration": 0.54285,
     "end_time": "2021-09-18T11:10:59.694998",
     "exception": false,
     "start_time": "2021-09-18T11:10:59.152148",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# same as previous cell, but for test set\n",
    "test_preds = []\n",
    "for k in range(K):\n",
    "    predictive = numpyro.infer.Predictive(logistic, fit_list[k].get_samples())\n",
    "    rng_key = jax.random.fold_in(jax.random.PRNGKey(20), k)\n",
    "    test_pred = predictive(rng_key, x=test_x_list[k])[\"logits\"]\n",
    "    test_preds.append(test_pred.mean(axis=0))\n",
    "test_preds = np.vstack(test_preds).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "436f8789",
   "metadata": {
    "papermill": {
     "duration": 0.145066,
     "end_time": "2021-09-18T11:11:00.042707",
     "exception": false,
     "start_time": "2021-09-18T11:10:59.897641",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# get the stacking weights for the test set\n",
    "test_stacking_weights = trace[\"w_test\"].mean(axis=0)\n",
    "# get predictions using the stacking weights\n",
    "bhs_predictions = (test_stacking_weights * test_preds).sum(axis=1)\n",
    "# get predictions using only the model with the best LOO score\n",
    "model_selection_preds = test_preds[:, lpd_point.sum(axis=0).argmax()]\n",
    "# get predictions using fixed stacking weights, dependent on the LOO score\n",
    "fixed_weights_preds = (fixed_weights * test_preds).sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76233762",
   "metadata": {
    "papermill": {
     "duration": 0.064289,
     "end_time": "2021-09-18T11:11:00.170538",
     "exception": false,
     "start_time": "2021-09-18T11:11:00.106249",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.2 Compare methods <a class=\"anchor\" id=\"4.2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2d889c2",
   "metadata": {
    "papermill": {
     "duration": 0.06178,
     "end_time": "2021-09-18T11:11:00.293209",
     "exception": false,
     "start_time": "2021-09-18T11:11:00.231429",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's compare the negative log predictive density scores on the test set (note - lower is better):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "33e15689",
   "metadata": {
    "papermill": {
     "duration": 0.463508,
     "end_time": "2021-09-18T11:11:00.819086",
     "exception": false,
     "start_time": "2021-09-18T11:11:00.355578",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "neg_log_pred_densities = np.vstack(\n",
    "    [\n",
    "        -dist.Bernoulli(logits=bhs_predictions).log_prob(y_test),\n",
    "        -dist.Bernoulli(logits=model_selection_preds).log_prob(y_test),\n",
    "        -dist.Bernoulli(logits=fixed_weights_preds).log_prob(y_test),\n",
    "    ]\n",
    ").T\n",
    "neg_log_pred_density = pd.DataFrame(\n",
    "    neg_log_pred_densities,\n",
    "    columns=[\n",
    "        \"Bayesian Hierarchical Stacking\",\n",
    "        \"Model selection\",\n",
    "        \"Fixed stacking weights\",\n",
    "    ],\n",
    ")\n",
    "sns.barplot(\n",
    "    data=neg_log_pred_density.reindex(\n",
    "        columns=neg_log_pred_density.mean(axis=0).sort_values(ascending=False).index\n",
    "    ),\n",
    "    orient=\"h\",\n",
    "    ax=ax,\n",
    ")\n",
    "ax.set_title(\n",
    "    \"Bayesian Hierarchical Stacking performs best here\", fontdict={\"fontsize\": 18}\n",
    ")\n",
    "ax.set_xlabel(\"Negative mean log predictive density (lower is better)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc7517ce",
   "metadata": {
    "papermill": {
     "duration": 0.066707,
     "end_time": "2021-09-18T11:11:01.178051",
     "exception": false,
     "start_time": "2021-09-18T11:11:01.111344",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "So, in this dataset, with this particular train-test split, Bayesian Hierarchical Stacking does indeed bring a small gain compared with model selection and compared with fixed-weight stacking.\n",
    "\n",
    "### 4.3 Does this prove that Bayesian Hierarchical Stacking works? <a class=\"anchor\" id=\"4.3\"></a>\n",
    "\n",
    "No, a single train-test split doesn't prove anything. Check the original paper for results with varying training set sizes, repeated with different train-test splits, in which they show that Bayesian Hierarchical Stacking consistently outperforms model selection and fixed-weight stacking.\n",
    "\n",
    "The goal of this notebook was just to show how to implement this technique in NumPyro."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29cf8140",
   "metadata": {
    "papermill": {
     "duration": 0.066367,
     "end_time": "2021-09-18T11:11:01.310721",
     "exception": false,
     "start_time": "2021-09-18T11:11:01.244354",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Conclusion <a class=\"anchor\" id=\"conclusion\"></a>\n",
    "\n",
    "We've seen how Bayesian Hierarchical Stacking can help us average models with input-dependent weights, in a manner which doesn't overfit. We only implemented the first case study from the paper, but readers are encouraged to check out the other two as well. Also check the paper for theoretical results and results from more experiments.\n",
    "\n",
    "## References\n",
    "\n",
    "1. Yuling Yao, Gregor Pirš, Aki Vehtari, Andrew Gelman (2021). [Bayesian hierarchical stacking: Some models are (somewhere) useful](https://arxiv.org/abs/2101.08954)\n",
    "2. Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari (2020). [Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data](https://arxiv.org/abs/2001.00980)\n",
    "3. https://github.com/yao-yl/hierarchical-stacking-code.\n",
    "4. Thomas Wiecki (2017). [Why hierarchical models are awesome, tricky, and Bayesian](https://twiecki.io/blog/2017/02/08/bayesian-hierchical-non-centered/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32694722",
   "metadata": {
    "papermill": {
     "duration": 0.068268,
     "end_time": "2021-09-18T11:11:03.218672",
     "exception": false,
     "start_time": "2021-09-18T11:11:03.150404",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
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