{
 "cells": [
  {
   "cell_type": "markdown",
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     "end_time": "2020-09-17T22:29:16.183136",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "# User Profile Bayesian Analysis\n",
    "\n",
    "This notebook analyzes the results of the Bayesian analysis of the user profile data.\n",
    "\n",
    "We split our Bayesian analyses into three steps:\n",
    "\n",
    "1. Exploratory analysis of data + preparation for sampling (notebook)\n",
    "2. Sampling (Python script)\n",
    "3. Analysis of sampling results (notebook)\n",
    "\n",
    "This is to enable the sampling process and its results to be managed and cached by DVC, and to avoid re-running the Bayesian sampling process when we only want to change how we look at its results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "end_time": "2020-09-17T22:29:16.219133",
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     "start_time": "2020-09-17T22:29:16.202105",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:16.257105Z",
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     "shell.execute_reply": "2020-09-17T22:29:16.274106Z"
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     "exception": false,
     "start_time": "2020-09-17T22:29:16.235134",
     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "model = 'profile'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:16.311133Z",
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     "shell.execute_reply": "2020-09-17T22:29:16.320103Z"
    },
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     "end_time": "2020-09-17T22:29:16.321139",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:16.355134Z",
     "iopub.status.busy": "2020-09-17T22:29:16.354133Z",
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     "shell.execute_reply": "2020-09-17T22:29:17.998075Z"
    },
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     "end_time": "2020-09-17T22:29:17.999104",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.special as sps\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import plotnine as p\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.nonparametric.kde import KDEUnivariate\n",
    "import zarr\n",
    "from IPython.display import display, Markdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:18.032102Z",
     "iopub.status.busy": "2020-09-17T22:29:18.032102Z",
     "iopub.status.idle": "2020-09-17T22:29:19.083057Z",
     "shell.execute_reply": "2020-09-17T22:29:19.082056Z"
    },
    "papermill": {
     "duration": 1.069952,
     "end_time": "2020-09-17T22:29:19.083057",
     "exception": false,
     "start_time": "2020-09-17T22:29:18.013105",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from bookgender.config import rng_seed\n",
    "from lenskit.util import init_rng\n",
    "import bookgender.datatools as dt\n",
    "from bookgender.nbutils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:19.117089Z",
     "iopub.status.busy": "2020-09-17T22:29:19.117089Z",
     "iopub.status.idle": "2020-09-17T22:29:19.130064Z",
     "shell.execute_reply": "2020-09-17T22:29:19.130064Z"
    },
    "papermill": {
     "duration": 0.032002,
     "end_time": "2020-09-17T22:29:19.130064",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.098062",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeedSequence(\n",
       "    entropy=261868553827208103807548308384201786360,\n",
       "    spawn_key=(2851018176,),\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = init_rng(rng_seed(), 'ProfileModelAnalysis', propagate=True)\n",
    "rng = np.random.default_rng(seed)\n",
    "seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:19.165079Z",
     "iopub.status.busy": "2020-09-17T22:29:19.163095Z",
     "iopub.status.idle": "2020-09-17T22:29:19.176053Z",
     "shell.execute_reply": "2020-09-17T22:29:19.177052Z"
    },
    "papermill": {
     "duration": 0.031998,
     "end_time": "2020-09-17T22:29:19.177052",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.145054",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'profile'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if model.startswith('profile'):\n",
    "    fig_key = model\n",
    "else:\n",
    "    fig_key = 'profile-' + model\n",
    "fig_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:19.212082Z",
     "iopub.status.busy": "2020-09-17T22:29:19.211052Z",
     "iopub.status.idle": "2020-09-17T22:29:19.222051Z",
     "shell.execute_reply": "2020-09-17T22:29:19.223082Z"
    },
    "papermill": {
     "duration": 0.031029,
     "end_time": "2020-09-17T22:29:19.223082",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.192053",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using figure dir figures\\PMA-profile\n"
     ]
    }
   ],
   "source": [
    "fig_dir = init_figs(f'PMA-{fig_key}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.018006,
     "end_time": "2020-09-17T22:29:19.257057",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.239051",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:19.292056Z",
     "iopub.status.busy": "2020-09-17T22:29:19.290081Z",
     "iopub.status.idle": "2020-09-17T22:29:19.300051Z",
     "shell.execute_reply": "2020-09-17T22:29:19.300051Z"
    },
    "papermill": {
     "duration": 0.027959,
     "end_time": "2020-09-17T22:29:19.300051",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.272092",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AZ', 'BX-E', 'BX-I', 'GR-E', 'GR-I']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets = list(dt.datasets.keys())\n",
    "datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014979,
     "end_time": "2020-09-17T22:29:19.332086",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.317107",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We need to load the sampling results for each data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:19.369080Z",
     "iopub.status.busy": "2020-09-17T22:29:19.369080Z",
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     "shell.execute_reply": "2020-09-17T22:29:19.475089Z"
    },
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     "duration": 0.127027,
     "end_time": "2020-09-17T22:29:19.475089",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.348062",
     "status": "completed"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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></th>\n",
       "      <th>Mean</th>\n",
       "      <th>MCSE</th>\n",
       "      <th>StdDev</th>\n",
       "      <th>5%</th>\n",
       "      <th>50%</th>\n",
       "      <th>95%</th>\n",
       "      <th>N_Eff</th>\n",
       "      <th>N_Eff/s</th>\n",
       "      <th>R_hat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AZ</th>\n",
       "      <th>lp__</th>\n",
       "      <td>-38486.600000</td>\n",
       "      <td>1.205860</td>\n",
       "      <td>59.937900</td>\n",
       "      <td>-38585.400000</td>\n",
       "      <td>-38486.600000</td>\n",
       "      <td>-38389.500000</td>\n",
       "      <td>2470.63</td>\n",
       "      <td>7.02802</td>\n",
       "      <td>1.001160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu</th>\n",
       "      <td>-0.514324</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.030539</td>\n",
       "      <td>-0.564679</td>\n",
       "      <td>-0.514312</td>\n",
       "      <td>-0.464619</td>\n",
       "      <td>16390.50</td>\n",
       "      <td>46.62480</td>\n",
       "      <td>0.999683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>1.875070</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>0.030105</td>\n",
       "      <td>1.825890</td>\n",
       "      <td>1.874820</td>\n",
       "      <td>1.925420</td>\n",
       "      <td>4846.20</td>\n",
       "      <td>13.78560</td>\n",
       "      <td>1.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nTheta[1]</th>\n",
       "      <td>1.054400</td>\n",
       "      <td>0.006098</td>\n",
       "      <td>0.927009</td>\n",
       "      <td>-0.392912</td>\n",
       "      <td>1.019800</td>\n",
       "      <td>2.638920</td>\n",
       "      <td>23110.10</td>\n",
       "      <td>65.73970</td>\n",
       "      <td>0.999805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nTheta[2]</th>\n",
       "      <td>-0.934858</td>\n",
       "      <td>0.003410</td>\n",
       "      <td>0.487180</td>\n",
       "      <td>-1.760500</td>\n",
       "      <td>-0.920706</td>\n",
       "      <td>-0.159354</td>\n",
       "      <td>20405.70</td>\n",
       "      <td>58.04650</td>\n",
       "      <td>0.999767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Mean      MCSE     StdDev            5%           50%  \\\n",
       "Set name                                                                       \n",
       "AZ  lp__      -38486.600000  1.205860  59.937900 -38585.400000 -38486.600000   \n",
       "    mu            -0.514324  0.000239   0.030539     -0.564679     -0.514312   \n",
       "    sigma          1.875070  0.000432   0.030105      1.825890      1.874820   \n",
       "    nTheta[1]      1.054400  0.006098   0.927009     -0.392912      1.019800   \n",
       "    nTheta[2]     -0.934858  0.003410   0.487180     -1.760500     -0.920706   \n",
       "\n",
       "                        95%     N_Eff   N_Eff/s     R_hat  \n",
       "Set name                                                   \n",
       "AZ  lp__      -38389.500000   2470.63   7.02802  1.001160  \n",
       "    mu            -0.464619  16390.50  46.62480  0.999683  \n",
       "    sigma          1.925420   4846.20  13.78560  1.000050  \n",
       "    nTheta[1]      2.638920  23110.10  65.73970  0.999805  \n",
       "    nTheta[2]     -0.159354  20405.70  58.04650  0.999767  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = {}\n",
    "summary = {}\n",
    "for ds in datasets:\n",
    "    _zf = zarr.ZipStore(f'data/{ds}/inference/{model}/samples.zarr', mode='r')\n",
    "    _c = zarr.LRUStoreCache(_zf, 2**30)\n",
    "    samples[ds] = zarr.group(_c)\n",
    "    summary[ds] = pd.read_csv(f'data/{ds}/inference/{model}-summary.csv', index_col='name')\n",
    "summary = pd.concat(summary, names=['Set'])\n",
    "summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.015996,
     "end_time": "2020-09-17T22:29:19.507079",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.491083",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Extract and verify sample size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:19.548074Z",
     "iopub.status.busy": "2020-09-17T22:29:19.548074Z",
     "iopub.status.idle": "2020-09-17T22:29:19.553047Z",
     "shell.execute_reply": "2020-09-17T22:29:19.553047Z"
    },
    "papermill": {
     "duration": 0.027986,
     "end_time": "2020-09-17T22:29:19.553047",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.525061",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = len(samples['AZ']['lp__'])\n",
    "assert all(len(s['lp__']) == sample_size for s in samples.values())\n",
    "sample_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.017001,
     "end_time": "2020-09-17T22:29:19.586046",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.569045",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Quality Checks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.01603,
     "end_time": "2020-09-17T22:29:19.617075",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.601045",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Do we have parameters with bad mixing? (high $\\hat{R}$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:19.653047Z",
     "iopub.status.busy": "2020-09-17T22:29:19.652078Z",
     "iopub.status.idle": "2020-09-17T22:29:19.674061Z",
     "shell.execute_reply": "2020-09-17T22:29:19.674061Z"
    },
    "papermill": {
     "duration": 0.040983,
     "end_time": "2020-09-17T22:29:19.674061",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.633078",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>MCSE</th>\n",
       "      <th>StdDev</th>\n",
       "      <th>5%</th>\n",
       "      <th>50%</th>\n",
       "      <th>95%</th>\n",
       "      <th>N_Eff</th>\n",
       "      <th>N_Eff/s</th>\n",
       "      <th>R_hat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BX-E</th>\n",
       "      <th>log_lik[4647]</th>\n",
       "      <td>-1.64209</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>0.514282</td>\n",
       "      <td>-2.68315</td>\n",
       "      <td>-1.44387</td>\n",
       "      <td>-1.26870</td>\n",
       "      <td>3112.65</td>\n",
       "      <td>9.33244</td>\n",
       "      <td>1.00506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">GR-I</th>\n",
       "      <th>log_lik[1010]</th>\n",
       "      <td>-3.83992</td>\n",
       "      <td>0.011997</td>\n",
       "      <td>0.699917</td>\n",
       "      <td>-5.23830</td>\n",
       "      <td>-3.56766</td>\n",
       "      <td>-3.33978</td>\n",
       "      <td>3403.51</td>\n",
       "      <td>10.82510</td>\n",
       "      <td>1.00484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log_lik[4433]</th>\n",
       "      <td>-2.40830</td>\n",
       "      <td>0.012284</td>\n",
       "      <td>0.658181</td>\n",
       "      <td>-3.79130</td>\n",
       "      <td>-2.14496</td>\n",
       "      <td>-1.93683</td>\n",
       "      <td>2870.91</td>\n",
       "      <td>9.13111</td>\n",
       "      <td>1.00481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-I</th>\n",
       "      <th>log_lik[360]</th>\n",
       "      <td>-2.12231</td>\n",
       "      <td>0.010487</td>\n",
       "      <td>0.598602</td>\n",
       "      <td>-3.32954</td>\n",
       "      <td>-1.89253</td>\n",
       "      <td>-1.70068</td>\n",
       "      <td>3257.91</td>\n",
       "      <td>9.59960</td>\n",
       "      <td>1.00471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-I</th>\n",
       "      <th>log_lik[2111]</th>\n",
       "      <td>-3.54390</td>\n",
       "      <td>0.015330</td>\n",
       "      <td>0.734541</td>\n",
       "      <td>-5.02787</td>\n",
       "      <td>-3.25711</td>\n",
       "      <td>-3.03061</td>\n",
       "      <td>2295.88</td>\n",
       "      <td>7.30219</td>\n",
       "      <td>1.00453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Mean      MCSE    StdDev       5%      50%      95%  \\\n",
       "Set  name                                                                    \n",
       "BX-E log_lik[4647] -1.64209  0.009218  0.514282 -2.68315 -1.44387 -1.26870   \n",
       "GR-I log_lik[1010] -3.83992  0.011997  0.699917 -5.23830 -3.56766 -3.33978   \n",
       "     log_lik[4433] -2.40830  0.012284  0.658181 -3.79130 -2.14496 -1.93683   \n",
       "BX-I log_lik[360]  -2.12231  0.010487  0.598602 -3.32954 -1.89253 -1.70068   \n",
       "GR-I log_lik[2111] -3.54390  0.015330  0.734541 -5.02787 -3.25711 -3.03061   \n",
       "\n",
       "                      N_Eff   N_Eff/s    R_hat  \n",
       "Set  name                                       \n",
       "BX-E log_lik[4647]  3112.65   9.33244  1.00506  \n",
       "GR-I log_lik[1010]  3403.51  10.82510  1.00484  \n",
       "     log_lik[4433]  2870.91   9.13111  1.00481  \n",
       "BX-I log_lik[360]   3257.91   9.59960  1.00471  \n",
       "GR-I log_lik[2111]  2295.88   7.30219  1.00453  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary.sort_values('R_hat', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.01602,
     "end_time": "2020-09-17T22:29:19.707074",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.691054",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's compute LPPD and WAIC for each of our models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:19.744045Z",
     "iopub.status.busy": "2020-09-17T22:29:19.742075Z",
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     "shell.execute_reply": "2020-09-17T22:29:19.767946Z"
    },
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     "end_time": "2020-09-17T22:29:19.767946",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.724044",
     "status": "completed"
    },
    "tags": []
   },
   "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>lppd</th>\n",
       "      <th>pWAIC</th>\n",
       "      <th>WAIC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>-6413.234228</td>\n",
       "      <td>2258.789672</td>\n",
       "      <td>17344.047799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-E</th>\n",
       "      <td>-8210.900013</td>\n",
       "      <td>2298.205891</td>\n",
       "      <td>21018.211808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-I</th>\n",
       "      <td>-8837.921196</td>\n",
       "      <td>2296.897944</td>\n",
       "      <td>22269.638279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-E</th>\n",
       "      <td>-11181.420201</td>\n",
       "      <td>2488.399088</td>\n",
       "      <td>27339.638579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-I</th>\n",
       "      <td>-12605.109108</td>\n",
       "      <td>2521.594828</td>\n",
       "      <td>30253.407871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              lppd        pWAIC          WAIC\n",
       "Set                                          \n",
       "AZ    -6413.234228  2258.789672  17344.047799\n",
       "BX-E  -8210.900013  2298.205891  21018.211808\n",
       "BX-I  -8837.921196  2296.897944  22269.638279\n",
       "GR-E -11181.420201  2488.399088  27339.638579\n",
       "GR-I -12605.109108  2521.594828  30253.407871"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def ll_stats(ds):\n",
    "    ll_exp = samples[ds]['ll_exp']\n",
    "    ll_var = samples[ds]['ll_var']\n",
    "    lppd = np.sum(ll_exp)\n",
    "    pwaic = np.sum(ll_var)\n",
    "    return pd.Series({'lppd': lppd, 'pWAIC': pwaic, 'WAIC': -2 * (lppd - pwaic)})\n",
    "pd.Series(datasets).apply(ll_stats).assign(Set=datasets).set_index('Set')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.017016,
     "end_time": "2020-09-17T22:29:19.801986",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.784970",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Extract and Summarize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.016998,
     "end_time": "2020-09-17T22:29:19.834975",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.817977",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Extract sample data in a form usable for plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:19.871973Z",
     "iopub.status.busy": "2020-09-17T22:29:19.870973Z",
     "iopub.status.idle": "2020-09-17T22:29:19.906949Z",
     "shell.execute_reply": "2020-09-17T22:29:19.906949Z"
    },
    "papermill": {
     "duration": 0.055975,
     "end_time": "2020-09-17T22:29:19.906949",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.850974",
     "status": "completed"
    },
    "tags": []
   },
   "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></th>\n",
       "      <th>Mu</th>\n",
       "      <th>Sigma</th>\n",
       "      <th>Theta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AZ</th>\n",
       "      <th>0</th>\n",
       "      <td>-0.517427</td>\n",
       "      <td>1.89591</td>\n",
       "      <td>0.074957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.549932</td>\n",
       "      <td>1.88656</td>\n",
       "      <td>0.195367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.494122</td>\n",
       "      <td>1.86783</td>\n",
       "      <td>0.692173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.558269</td>\n",
       "      <td>1.89490</td>\n",
       "      <td>0.170902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.460593</td>\n",
       "      <td>1.86343</td>\n",
       "      <td>0.347322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">GR-I</th>\n",
       "      <th>9995</th>\n",
       "      <td>-0.277604</td>\n",
       "      <td>1.43525</td>\n",
       "      <td>0.055015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>-0.256107</td>\n",
       "      <td>1.47247</td>\n",
       "      <td>0.337325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>-0.258977</td>\n",
       "      <td>1.42790</td>\n",
       "      <td>0.602984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>-0.249830</td>\n",
       "      <td>1.44898</td>\n",
       "      <td>0.621380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>-0.257523</td>\n",
       "      <td>1.44148</td>\n",
       "      <td>0.880777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Mu    Sigma     Theta\n",
       "Set                                   \n",
       "AZ   0    -0.517427  1.89591  0.074957\n",
       "     1    -0.549932  1.88656  0.195367\n",
       "     2    -0.494122  1.86783  0.692173\n",
       "     3    -0.558269  1.89490  0.170902\n",
       "     4    -0.460593  1.86343  0.347322\n",
       "...             ...      ...       ...\n",
       "GR-I 9995 -0.277604  1.43525  0.055015\n",
       "     9996 -0.256107  1.47247  0.337325\n",
       "     9997 -0.258977  1.42790  0.602984\n",
       "     9998 -0.249830  1.44898  0.621380\n",
       "     9999 -0.257523  1.44148  0.880777\n",
       "\n",
       "[50000 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samp_df = pd.concat(dict((ds, pd.DataFrame({\n",
    "    'Mu': samples[ds]['mu'],\n",
    "    'Sigma': samples[ds]['sigma'],\n",
    "    'Theta': samples[ds]['thetaP']\n",
    "})) for ds in datasets), names=['Set']).copy()\n",
    "samp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.021975,
     "end_time": "2020-09-17T22:29:19.950943",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.928968",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We also want to show observed distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:19.995964Z",
     "iopub.status.busy": "2020-09-17T22:29:19.994954Z",
     "iopub.status.idle": "2020-09-17T22:29:20.017941Z",
     "shell.execute_reply": "2020-09-17T22:29:20.017941Z"
    },
    "papermill": {
     "duration": 0.045997,
     "end_time": "2020-09-17T22:29:20.017941",
     "exception": false,
     "start_time": "2020-09-17T22:29:19.971944",
     "status": "completed"
    },
    "tags": []
   },
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>linked</th>\n",
       "      <th>ambiguous</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "      <th>dcknown</th>\n",
       "      <th>dcyes</th>\n",
       "      <th>PropDC</th>\n",
       "      <th>Known</th>\n",
       "      <th>PropFemale</th>\n",
       "      <th>PropKnown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th>user</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AZ</th>\n",
       "      <th>529</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>21</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1810</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2781</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2863</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count  linked  ambiguous  male  female  dcknown  dcyes    PropDC  \\\n",
       "Set user                                                                     \n",
       "AZ  529       8       8          2     1       4        8      3  0.375000   \n",
       "    1723     25      24          3    15       6       25     14  0.560000   \n",
       "    1810     14       6          0     6       0        8      1  0.125000   \n",
       "    2781      8       8          1     5       1        8      5  0.625000   \n",
       "    2863      6       6          0     6       0        6      4  0.666667   \n",
       "\n",
       "          Known  PropFemale  PropKnown  \n",
       "Set user                                \n",
       "AZ  529       5    0.800000   0.625000  \n",
       "    1723     21    0.285714   0.840000  \n",
       "    1810      6    0.000000   0.428571  \n",
       "    2781      6    0.166667   0.750000  \n",
       "    2863      6    0.000000   1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles = pd.read_pickle('data/profile-data.pkl')\n",
    "profiles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.021001,
     "end_time": "2020-09-17T22:29:20.061939",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.040938",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "For each sample, we want to sample a predicted observation.  We do this by:\n",
    "\n",
    "1. Sample $n'$ from the observed profile sizes\n",
    "2. Sample $y' \\sim \\mathrm{Binomial}(n', \\theta')$\n",
    "3. Compute $y'/n'$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:20.102981Z",
     "iopub.status.busy": "2020-09-17T22:29:20.101968Z",
     "iopub.status.idle": "2020-09-17T22:29:20.125940Z",
     "shell.execute_reply": "2020-09-17T22:29:20.124973Z"
    },
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     "duration": 0.044985,
     "end_time": "2020-09-17T22:29:20.125940",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.080955",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Set       \n",
       "AZ    0         6\n",
       "      1         5\n",
       "      2         9\n",
       "      3         8\n",
       "      4        22\n",
       "             ... \n",
       "GR-I  9995    124\n",
       "      9996     27\n",
       "      9997    338\n",
       "      9998     33\n",
       "      9999     37\n",
       "Length: 50000, dtype: int32"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def _sample_ns(df):\n",
    "    known = profiles.loc[df.name, 'Known']\n",
    "    # return known.sample(len(df), replace=True).reset_index(drop=True)\n",
    "    return pd.Series(rng.choice(known, len(df)))\n",
    "samp_df.groupby('Set').apply(_sample_ns).stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:20.165015Z",
     "iopub.status.busy": "2020-09-17T22:29:20.165015Z",
     "iopub.status.idle": "2020-09-17T22:29:20.205016Z",
     "shell.execute_reply": "2020-09-17T22:29:20.205016Z"
    },
    "papermill": {
     "duration": 0.061072,
     "end_time": "2020-09-17T22:29:20.205016",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.143944",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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></th>\n",
       "      <th>Mu</th>\n",
       "      <th>Sigma</th>\n",
       "      <th>Theta</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
       "      <th>PropFemale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AZ</th>\n",
       "      <th>0</th>\n",
       "      <td>-0.517427</td>\n",
       "      <td>1.89591</td>\n",
       "      <td>0.074957</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.549932</td>\n",
       "      <td>1.88656</td>\n",
       "      <td>0.195367</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.494122</td>\n",
       "      <td>1.86783</td>\n",
       "      <td>0.692173</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>0.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.558269</td>\n",
       "      <td>1.89490</td>\n",
       "      <td>0.170902</td>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.460593</td>\n",
       "      <td>1.86343</td>\n",
       "      <td>0.347322</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Mu    Sigma     Theta   N   Y  PropFemale\n",
       "Set                                                   \n",
       "AZ  0 -0.517427  1.89591  0.074957   5   1    0.200000\n",
       "    1 -0.549932  1.88656  0.195367  17   3    0.176471\n",
       "    2 -0.494122  1.86783  0.692173  17  13    0.764706\n",
       "    3 -0.558269  1.89490  0.170902  44   6    0.136364\n",
       "    4 -0.460593  1.86343  0.347322  11   3    0.272727"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samp_df['N'] = samp_df.groupby('Set').apply(_sample_ns).stack()\n",
    "samp_df['Y'] = rng.binomial(samp_df['N'], samp_df['Theta'], len(samp_df))\n",
    "samp_df['PropFemale'] = samp_df['Y'] / samp_df['N']\n",
    "samp_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.018953,
     "end_time": "2020-09-17T22:29:20.244013",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.225060",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Summarize Statistics and Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.01896,
     "end_time": "2020-09-17T22:29:20.282011",
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     "start_time": "2020-09-17T22:29:20.263051",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We want to report summary statistics of the various parameters.  We will start with observed ratios:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:20.324044Z",
     "iopub.status.busy": "2020-09-17T22:29:20.324044Z",
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     "shell.execute_reply": "2020-09-17T22:29:20.342047Z"
    },
    "papermill": {
     "duration": 0.04103,
     "end_time": "2020-09-17T22:29:20.343042",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.302012",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Set</th>\n",
       "      <th>AZ</th>\n",
       "      <th>BX-E</th>\n",
       "      <th>BX-I</th>\n",
       "      <th>GR-E</th>\n",
       "      <th>GR-I</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.414445</td>\n",
       "      <td>0.418886</td>\n",
       "      <td>0.407030</td>\n",
       "      <td>0.446998</td>\n",
       "      <td>0.450201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.329436</td>\n",
       "      <td>0.267354</td>\n",
       "      <td>0.254304</td>\n",
       "      <td>0.276480</td>\n",
       "      <td>0.269127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Set         AZ      BX-E      BX-I      GR-E      GR-I\n",
       "mean  0.414445  0.418886  0.407030  0.446998  0.450201\n",
       "std   0.329436  0.267354  0.254304  0.276480  0.269127"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles.groupby('Set')['PropFemale'].agg(['mean', 'std']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:20.382041Z",
     "iopub.status.busy": "2020-09-17T22:29:20.381043Z",
     "iopub.status.idle": "2020-09-17T22:29:20.389041Z",
     "shell.execute_reply": "2020-09-17T22:29:20.389041Z"
    },
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     "duration": 0.029027,
     "end_time": "2020-09-17T22:29:20.389041",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.360014",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrr}\n",
      "\\toprule\n",
      "Set &    AZ &  BX-E &  BX-I &  GR-E &  GR-I \\\\\n",
      "\\midrule\n",
      "mean & 0.414 & 0.419 & 0.407 & 0.447 & 0.450 \\\\\n",
      "std  & 0.329 & 0.267 & 0.254 & 0.276 & 0.269 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(profiles.groupby('Set')['PropFemale'].agg(['mean', 'std']).T.to_latex(float_format='%.3f'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.018012,
     "end_time": "2020-09-17T22:29:20.425048",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.407036",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "What about our est. mean log odds ($\\mu$) and variance ($\\sigma$)?  We also want to the posterior mean tendancy ($\\theta$) and its variance.\n",
    "\n",
    "It's important to distinguish between the *estimated variance of users* ($\\sigma$) and the *variance of the estimate* (e.g. the variance of $\\mu$). Since $\\theta$ is a sample of a random unseen user, its variance should be comparable to the variance of the underlying users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:20.464039Z",
     "iopub.status.busy": "2020-09-17T22:29:20.464039Z",
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     "shell.execute_reply": "2020-09-17T22:29:20.483034Z"
    },
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     "duration": 0.039993,
     "end_time": "2020-09-17T22:29:20.483034",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.443041",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>Set</th>\n",
       "      <th>AZ</th>\n",
       "      <th>BX-E</th>\n",
       "      <th>BX-I</th>\n",
       "      <th>GR-E</th>\n",
       "      <th>GR-I</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Mu</th>\n",
       "      <th>mean</th>\n",
       "      <td>-0.514324</td>\n",
       "      <td>-0.399788</td>\n",
       "      <td>-0.443699</td>\n",
       "      <td>-0.273188</td>\n",
       "      <td>-0.254082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.030539</td>\n",
       "      <td>0.019782</td>\n",
       "      <td>0.018357</td>\n",
       "      <td>0.022253</td>\n",
       "      <td>0.021304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sigma</th>\n",
       "      <th>mean</th>\n",
       "      <td>1.875065</td>\n",
       "      <td>1.185700</td>\n",
       "      <td>1.083546</td>\n",
       "      <td>1.498579</td>\n",
       "      <td>1.444677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.030105</td>\n",
       "      <td>0.018620</td>\n",
       "      <td>0.016934</td>\n",
       "      <td>0.018377</td>\n",
       "      <td>0.016772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Theta</th>\n",
       "      <th>mean</th>\n",
       "      <td>0.422618</td>\n",
       "      <td>0.422504</td>\n",
       "      <td>0.412017</td>\n",
       "      <td>0.451731</td>\n",
       "      <td>0.452796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.300345</td>\n",
       "      <td>0.230205</td>\n",
       "      <td>0.214590</td>\n",
       "      <td>0.267270</td>\n",
       "      <td>0.261241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Set               AZ      BX-E      BX-I      GR-E      GR-I\n",
       "Mu    mean -0.514324 -0.399788 -0.443699 -0.273188 -0.254082\n",
       "      std   0.030539  0.019782  0.018357  0.022253  0.021304\n",
       "Sigma mean  1.875065  1.185700  1.083546  1.498579  1.444677\n",
       "      std   0.030105  0.018620  0.016934  0.018377  0.016772\n",
       "Theta mean  0.422618  0.422504  0.412017  0.451731  0.452796\n",
       "      std   0.300345  0.230205  0.214590  0.267270  0.261241"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samp_df.groupby('Set')[['Mu', 'Sigma', 'Theta']].agg(['mean', 'std']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.02197,
     "end_time": "2020-09-17T22:29:20.523009",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.501039",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Find the 95% confidence intervals of $\\mu$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:20.563008Z",
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     "duration": 0.051,
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     "start_time": "2020-09-17T22:29:20.541008",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Low</th>\n",
       "      <th>High</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>-0.575250</td>\n",
       "      <td>-0.455224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-E</th>\n",
       "      <td>-0.439076</td>\n",
       "      <td>-0.361210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-I</th>\n",
       "      <td>-0.479702</td>\n",
       "      <td>-0.407404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-E</th>\n",
       "      <td>-0.316602</td>\n",
       "      <td>-0.229702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-I</th>\n",
       "      <td>-0.295727</td>\n",
       "      <td>-0.212234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Low      High\n",
       "Set                     \n",
       "AZ   -0.575250 -0.455224\n",
       "BX-E -0.439076 -0.361210\n",
       "BX-I -0.479702 -0.407404\n",
       "GR-E -0.316602 -0.229702\n",
       "GR-I -0.295727 -0.212234"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samp_df.groupby('Set')['Mu'].apply(lambda s: {\n",
    "    'Low': s.quantile(0.025),\n",
    "    'High': s.quantile(0.975)\n",
    "}).unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.018016,
     "end_time": "2020-09-17T22:29:20.629037",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.611021",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's look at the STAN summary output for these parameters too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:20.669005Z",
     "iopub.status.busy": "2020-09-17T22:29:20.669005Z",
     "iopub.status.idle": "2020-09-17T22:29:20.684007Z",
     "shell.execute_reply": "2020-09-17T22:29:20.684007Z"
    },
    "papermill": {
     "duration": 0.036964,
     "end_time": "2020-09-17T22:29:20.684007",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.647043",
     "status": "completed"
    },
    "tags": []
   },
   "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>Set</th>\n",
       "      <th>AZ</th>\n",
       "      <th>BX-E</th>\n",
       "      <th>BX-I</th>\n",
       "      <th>GR-E</th>\n",
       "      <th>GR-I</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>-0.514324</td>\n",
       "      <td>-0.399788</td>\n",
       "      <td>-0.443699</td>\n",
       "      <td>-0.273188</td>\n",
       "      <td>-0.254082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StdDev</th>\n",
       "      <td>0.030539</td>\n",
       "      <td>0.019782</td>\n",
       "      <td>0.018357</td>\n",
       "      <td>0.022253</td>\n",
       "      <td>0.021304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5%</th>\n",
       "      <td>-0.564679</td>\n",
       "      <td>-0.432862</td>\n",
       "      <td>-0.473772</td>\n",
       "      <td>-0.309588</td>\n",
       "      <td>-0.288754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>-0.464619</td>\n",
       "      <td>-0.367577</td>\n",
       "      <td>-0.413480</td>\n",
       "      <td>-0.236570</td>\n",
       "      <td>-0.219111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Set           AZ      BX-E      BX-I      GR-E      GR-I\n",
       "Mean   -0.514324 -0.399788 -0.443699 -0.273188 -0.254082\n",
       "StdDev  0.030539  0.019782  0.018357  0.022253  0.021304\n",
       "5%     -0.564679 -0.432862 -0.473772 -0.309588 -0.288754\n",
       "95%    -0.464619 -0.367577 -0.413480 -0.236570 -0.219111"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary.swaplevel().loc['mu', ['Mean', 'StdDev', '5%', '95%']].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:20.727037Z",
     "iopub.status.busy": "2020-09-17T22:29:20.727037Z",
     "iopub.status.idle": "2020-09-17T22:29:20.746035Z",
     "shell.execute_reply": "2020-09-17T22:29:20.745004Z"
    },
    "papermill": {
     "duration": 0.04103,
     "end_time": "2020-09-17T22:29:20.746035",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.705005",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Set</th>\n",
       "      <th>AZ</th>\n",
       "      <th>BX-E</th>\n",
       "      <th>BX-I</th>\n",
       "      <th>GR-E</th>\n",
       "      <th>GR-I</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>1.875070</td>\n",
       "      <td>1.185700</td>\n",
       "      <td>1.083550</td>\n",
       "      <td>1.498580</td>\n",
       "      <td>1.444680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StdDev</th>\n",
       "      <td>0.030105</td>\n",
       "      <td>0.018621</td>\n",
       "      <td>0.016934</td>\n",
       "      <td>0.018377</td>\n",
       "      <td>0.016772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5%</th>\n",
       "      <td>1.825890</td>\n",
       "      <td>1.155360</td>\n",
       "      <td>1.055850</td>\n",
       "      <td>1.468210</td>\n",
       "      <td>1.417360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>1.925420</td>\n",
       "      <td>1.216610</td>\n",
       "      <td>1.111790</td>\n",
       "      <td>1.529110</td>\n",
       "      <td>1.472570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Set           AZ      BX-E      BX-I      GR-E      GR-I\n",
       "Mean    1.875070  1.185700  1.083550  1.498580  1.444680\n",
       "StdDev  0.030105  0.018621  0.016934  0.018377  0.016772\n",
       "5%      1.825890  1.155360  1.055850  1.468210  1.417360\n",
       "95%     1.925420  1.216610  1.111790  1.529110  1.472570"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary.swaplevel().loc['sigma', ['Mean', 'StdDev', '5%', '95%']].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:20.787005Z",
     "iopub.status.busy": "2020-09-17T22:29:20.787005Z",
     "iopub.status.idle": "2020-09-17T22:29:20.808066Z",
     "shell.execute_reply": "2020-09-17T22:29:20.808066Z"
    },
    "papermill": {
     "duration": 0.044003,
     "end_time": "2020-09-17T22:29:20.809039",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.765036",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Set</th>\n",
       "      <th>AZ</th>\n",
       "      <th>BX-E</th>\n",
       "      <th>BX-I</th>\n",
       "      <th>GR-E</th>\n",
       "      <th>GR-I</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.422618</td>\n",
       "      <td>0.422504</td>\n",
       "      <td>0.412017</td>\n",
       "      <td>0.451731</td>\n",
       "      <td>0.452796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StdDev</th>\n",
       "      <td>0.300345</td>\n",
       "      <td>0.230205</td>\n",
       "      <td>0.214590</td>\n",
       "      <td>0.267270</td>\n",
       "      <td>0.261241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5%</th>\n",
       "      <td>0.027185</td>\n",
       "      <td>0.085766</td>\n",
       "      <td>0.096002</td>\n",
       "      <td>0.062607</td>\n",
       "      <td>0.068967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>0.926785</td>\n",
       "      <td>0.821006</td>\n",
       "      <td>0.790900</td>\n",
       "      <td>0.899438</td>\n",
       "      <td>0.892961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Set           AZ      BX-E      BX-I      GR-E      GR-I\n",
       "Mean    0.422618  0.422504  0.412017  0.451731  0.452796\n",
       "StdDev  0.300345  0.230205  0.214590  0.267270  0.261241\n",
       "5%      0.027185  0.085766  0.096002  0.062607  0.068967\n",
       "95%     0.926785  0.821006  0.790900  0.899438  0.892961"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary.swaplevel().loc['thetaP', ['Mean', 'StdDev', '5%', '95%']].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.022017,
     "end_time": "2020-09-17T22:29:20.853022",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.831005",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Plot Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.023976,
     "end_time": "2020-09-17T22:29:20.897003",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.873027",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Stack simulated proportions from two sources:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:20.950000Z",
     "iopub.status.busy": "2020-09-17T22:29:20.950000Z",
     "iopub.status.idle": "2020-09-17T22:29:20.976014Z",
     "shell.execute_reply": "2020-09-17T22:29:20.977003Z"
    },
    "papermill": {
     "duration": 0.056973,
     "end_time": "2020-09-17T22:29:20.977003",
     "exception": false,
     "start_time": "2020-09-17T22:29:20.920030",
     "status": "completed"
    },
    "tags": []
   },
   "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>Set</th>\n",
       "      <th>Method</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Theta</td>\n",
       "      <td>0.074957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AZ</td>\n",
       "      <td>PropFemale</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Theta</td>\n",
       "      <td>0.195367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>PropFemale</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Theta</td>\n",
       "      <td>0.692173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Set      Method     value\n",
       "0  AZ       Theta  0.074957\n",
       "1  AZ  PropFemale  0.200000\n",
       "2  AZ       Theta  0.195367\n",
       "3  AZ  PropFemale  0.176471\n",
       "4  AZ       Theta  0.692173"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulated_prop = pd.DataFrame({'value': samp_df[['Theta', 'PropFemale']].stack()})\n",
    "simulated_prop.reset_index([0,2], inplace=True)\n",
    "simulated_prop['Set'] = simulated_prop['Set'].astype('category')\n",
    "simulated_prop.rename(columns={'level_2': 'Method'}, inplace=True)\n",
    "simulated_prop.reset_index(drop=True, inplace=True)\n",
    "simulated_prop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:21.032031Z",
     "iopub.status.busy": "2020-09-17T22:29:21.031034Z",
     "iopub.status.idle": "2020-09-17T22:29:21.068030Z",
     "shell.execute_reply": "2020-09-17T22:29:21.068030Z"
    },
    "papermill": {
     "duration": 0.068028,
     "end_time": "2020-09-17T22:29:21.068030",
     "exception": false,
     "start_time": "2020-09-17T22:29:21.000002",
     "status": "completed"
    },
    "tags": []
   },
   "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>Set</th>\n",
       "      <th>Method</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Observed</td>\n",
       "      <td>0.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Observed</td>\n",
       "      <td>0.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Observed</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Observed</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Observed</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Set    Method     value\n",
       "0  AZ  Observed  0.071429\n",
       "1  AZ  Observed  0.411765\n",
       "2  AZ  Observed  0.285714\n",
       "3  AZ  Observed  0.142857\n",
       "4  AZ  Observed  1.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_prop = profiles.groupby('Set').apply(lambda df: df['PropFemale'].sample(sample_size, replace=True))\n",
    "obs_prop.name = 'value'\n",
    "obs_prop = obs_prop.reset_index(0)\n",
    "obs_prop.reset_index(drop=True, inplace=True)\n",
    "obs_prop['Method'] = 'Observed'\n",
    "obs_prop = obs_prop[['Set', 'Method', 'value']]\n",
    "obs_prop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:21.115997Z",
     "iopub.status.busy": "2020-09-17T22:29:21.115997Z",
     "iopub.status.idle": "2020-09-17T22:29:21.144030Z",
     "shell.execute_reply": "2020-09-17T22:29:21.142999Z"
    },
    "papermill": {
     "duration": 0.053031,
     "end_time": "2020-09-17T22:29:21.144030",
     "exception": false,
     "start_time": "2020-09-17T22:29:21.090999",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Set</th>\n",
       "      <th>Method</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Smoothed</td>\n",
       "      <td>0.074957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Predicted y/n</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Smoothed</td>\n",
       "      <td>0.195367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Predicted y/n</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Smoothed</td>\n",
       "      <td>0.692173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Set         Method     value\n",
       "0  AZ       Smoothed  0.074957\n",
       "1  AZ  Predicted y/n  0.200000\n",
       "2  AZ       Smoothed  0.195367\n",
       "3  AZ  Predicted y/n  0.176471\n",
       "4  AZ       Smoothed  0.692173"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_prop = pd.concat([simulated_prop, obs_prop], ignore_index=True)\n",
    "all_prop['Method'] = all_prop['Method'].astype('category')\n",
    "all_prop['Method'].cat.reorder_categories(['Theta', 'PropFemale', 'Observed'], inplace=True)\n",
    "all_prop['Method'].cat.rename_categories({'Theta': 'Smoothed', 'PropFemale': 'Predicted y/n', 'Observed': 'Observed y/n'}, inplace=True)\n",
    "all_prop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.021005,
     "end_time": "2020-09-17T22:29:21.185032",
     "exception": false,
     "start_time": "2020-09-17T22:29:21.164027",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We want the mean for each method to show:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:21.236030Z",
     "iopub.status.busy": "2020-09-17T22:29:21.236030Z",
     "iopub.status.idle": "2020-09-17T22:29:21.254996Z",
     "shell.execute_reply": "2020-09-17T22:29:21.256044Z"
    },
    "papermill": {
     "duration": 0.050015,
     "end_time": "2020-09-17T22:29:21.256044",
     "exception": false,
     "start_time": "2020-09-17T22:29:21.206029",
     "status": "completed"
    },
    "tags": []
   },
   "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>Set</th>\n",
       "      <th>Method</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Smoothed</td>\n",
       "      <td>0.422618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Predicted y/n</td>\n",
       "      <td>0.422601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Observed y/n</td>\n",
       "      <td>0.415003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BX-E</td>\n",
       "      <td>Smoothed</td>\n",
       "      <td>0.422504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BX-E</td>\n",
       "      <td>Predicted y/n</td>\n",
       "      <td>0.422523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BX-E</td>\n",
       "      <td>Observed y/n</td>\n",
       "      <td>0.420246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BX-I</td>\n",
       "      <td>Smoothed</td>\n",
       "      <td>0.412017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BX-I</td>\n",
       "      <td>Predicted y/n</td>\n",
       "      <td>0.411594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BX-I</td>\n",
       "      <td>Observed y/n</td>\n",
       "      <td>0.404499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GR-E</td>\n",
       "      <td>Smoothed</td>\n",
       "      <td>0.451731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GR-E</td>\n",
       "      <td>Predicted y/n</td>\n",
       "      <td>0.453571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GR-E</td>\n",
       "      <td>Observed y/n</td>\n",
       "      <td>0.446901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GR-I</td>\n",
       "      <td>Smoothed</td>\n",
       "      <td>0.452796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GR-I</td>\n",
       "      <td>Predicted y/n</td>\n",
       "      <td>0.451868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GR-I</td>\n",
       "      <td>Observed y/n</td>\n",
       "      <td>0.448502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Set         Method     value\n",
       "0     AZ       Smoothed  0.422618\n",
       "1     AZ  Predicted y/n  0.422601\n",
       "2     AZ   Observed y/n  0.415003\n",
       "3   BX-E       Smoothed  0.422504\n",
       "4   BX-E  Predicted y/n  0.422523\n",
       "5   BX-E   Observed y/n  0.420246\n",
       "6   BX-I       Smoothed  0.412017\n",
       "7   BX-I  Predicted y/n  0.411594\n",
       "8   BX-I   Observed y/n  0.404499\n",
       "9   GR-E       Smoothed  0.451731\n",
       "10  GR-E  Predicted y/n  0.453571\n",
       "11  GR-E   Observed y/n  0.446901\n",
       "12  GR-I       Smoothed  0.452796\n",
       "13  GR-I  Predicted y/n  0.451868\n",
       "14  GR-I   Observed y/n  0.448502"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_means = all_prop.groupby(['Set', 'Method'])['value'].mean().reset_index()\n",
    "prop_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.021025,
     "end_time": "2020-09-17T22:29:21.297025",
     "exception": false,
     "start_time": "2020-09-17T22:29:21.276000",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now finally we can view densities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:21.342995Z",
     "iopub.status.busy": "2020-09-17T22:29:21.342995Z",
     "iopub.status.idle": "2020-09-17T22:29:22.358978Z",
     "shell.execute_reply": "2020-09-17T22:29:22.357978Z"
    },
    "papermill": {
     "duration": 1.039944,
     "end_time": "2020-09-17T22:29:22.358978",
     "exception": false,
     "start_time": "2020-09-17T22:29:21.319034",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 822.625x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(all_prop.assign(hist=all_prop.Method == 'Observed y/n'), row='Set', hue='Method',\n",
    "                     sharey=False, height=2, aspect=5)\n",
    "for (r, c, h), df in grid.facet_data():\n",
    "    if h == 0:\n",
    "        sns.distplot(df['value'], kde=False, norm_hist=True, ax=grid.axes[r, c])\n",
    "grid.map(sns.kdeplot, 'value', clip=(0,1)).add_legend()\n",
    "plt.xlim(0, 1)\n",
    "# plt.savefig(fig_dir / 'dist.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:22.418006Z",
     "iopub.status.busy": "2020-09-17T22:29:22.417007Z",
     "iopub.status.idle": "2020-09-17T22:31:17.893108Z",
     "shell.execute_reply": "2020-09-17T22:31:17.892109Z"
    },
    "papermill": {
     "duration": 115.510131,
     "end_time": "2020-09-17T22:31:17.893108",
     "exception": false,
     "start_time": "2020-09-17T22:29:22.382977",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:729: PlotnineWarning: Saving 8 x 6 in image.\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:730: PlotnineWarning: Filename: figures\\PMA-profile\\profile-dist.pdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:729: PlotnineWarning: Saving 8 x 6 in image.\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:730: PlotnineWarning: Filename: figures\\PMA-profile\\profile-dist.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223371864104079948)>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bookgender.nbutils import make_plot\n",
    "make_plot(all_prop, p.aes('value'),\n",
    "          p.geom_histogram(p.aes(y='..density..'), data=obs_prop,\n",
    "                           binwidth=0.05, fill='#CCCCCC'),\n",
    "          p.geom_line(p.aes(color='Method', linetype='Method'),\n",
    "                      stat='density', bw='scott', adjust=0.5),\n",
    "          p.geom_vline(p.aes(xintercept='value', color='Method', linetype='Method'),\n",
    "                       data=prop_means, show_legend=False),\n",
    "          p.facet_grid('Set ~', scales='free_y'),\n",
    "          p.scale_color_brewer('qual', 'Dark2'),\n",
    "          p.xlab(\"Profile Proportion of Female Authors\"),\n",
    "          p.ylab(\"Density\"),\n",
    "          legend_position='top', legend_title=p.element_blank(),\n",
    "          file='profile-dist', width=8, height=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.032999,
     "end_time": "2020-09-17T22:31:17.956076",
     "exception": false,
     "start_time": "2020-09-17T22:31:17.923077",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Robustness of Individual Inferences\n",
    "\n",
    "The profiling process infers a $g(\\theta_u)$ value for each user.  What is the variance of those estimates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:31:18.024076Z",
     "iopub.status.busy": "2020-09-17T22:31:18.023075Z",
     "iopub.status.idle": "2020-09-17T22:31:47.856589Z",
     "shell.execute_reply": "2020-09-17T22:31:47.857589Z"
    },
    "papermill": {
     "duration": 29.871482,
     "end_time": "2020-09-17T22:31:47.857589",
     "exception": false,
     "start_time": "2020-09-17T22:31:17.986107",
     "status": "completed"
    },
    "tags": []
   },
   "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></th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>lo</th>\n",
       "      <th>hi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th>uidx</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AZ</th>\n",
       "      <th>0</th>\n",
       "      <td>1.054401</td>\n",
       "      <td>0.859259</td>\n",
       "      <td>-0.700571</td>\n",
       "      <td>3.002711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.934858</td>\n",
       "      <td>0.237320</td>\n",
       "      <td>-1.930807</td>\n",
       "      <td>-0.018967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.654415</td>\n",
       "      <td>1.442534</td>\n",
       "      <td>-5.297968</td>\n",
       "      <td>-0.577858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.526691</td>\n",
       "      <td>0.928144</td>\n",
       "      <td>-3.603834</td>\n",
       "      <td>0.212650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.666763</td>\n",
       "      <td>1.462858</td>\n",
       "      <td>-5.313519</td>\n",
       "      <td>-0.560131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">GR-I</th>\n",
       "      <th>4995</th>\n",
       "      <td>1.067249</td>\n",
       "      <td>0.002733</td>\n",
       "      <td>0.965943</td>\n",
       "      <td>1.169451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>-1.541715</td>\n",
       "      <td>0.614372</td>\n",
       "      <td>-3.199276</td>\n",
       "      <td>-0.101126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>-0.918005</td>\n",
       "      <td>0.050949</td>\n",
       "      <td>-1.365600</td>\n",
       "      <td>-0.494744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>1.510707</td>\n",
       "      <td>0.035612</td>\n",
       "      <td>1.153674</td>\n",
       "      <td>1.886884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>-2.026441</td>\n",
       "      <td>0.028055</td>\n",
       "      <td>-2.370763</td>\n",
       "      <td>-1.703400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean       var        lo        hi\n",
       "Set  uidx                                        \n",
       "AZ   0     1.054401  0.859259 -0.700571  3.002711\n",
       "     1    -0.934858  0.237320 -1.930807 -0.018967\n",
       "     2    -2.654415  1.442534 -5.297968 -0.577858\n",
       "     3    -1.526691  0.928144 -3.603834  0.212650\n",
       "     4    -2.666763  1.462858 -5.313519 -0.560131\n",
       "...             ...       ...       ...       ...\n",
       "GR-I 4995  1.067249  0.002733  0.965943  1.169451\n",
       "     4996 -1.541715  0.614372 -3.199276 -0.101126\n",
       "     4997 -0.918005  0.050949 -1.365600 -0.494744\n",
       "     4998  1.510707  0.035612  1.153674  1.886884\n",
       "     4999 -2.026441  0.028055 -2.370763 -1.703400\n",
       "\n",
       "[25000 rows x 4 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def u_stats(ds):\n",
    "    ths = samples[ds]['nTheta'][...].T\n",
    "    return pd.DataFrame({\n",
    "        'mean': np.mean(ths, axis=1),\n",
    "        'var': np.var(ths, axis=1),\n",
    "        'lo': np.quantile(ths, 0.025, axis=1),\n",
    "        'hi': np.quantile(ths, 0.975, axis=1)\n",
    "    })\n",
    "user_biases = pd.concat(dict(\n",
    "    (ds, u_stats(ds)) for ds in datasets\n",
    "), names=['Set', 'uidx'])\n",
    "user_biases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:31:47.920816Z",
     "iopub.status.busy": "2020-09-17T22:31:47.920816Z",
     "iopub.status.idle": "2020-09-17T22:31:49.483784Z",
     "shell.execute_reply": "2020-09-17T22:31:49.482785Z"
    },
    "papermill": {
     "duration": 1.599197,
     "end_time": "2020-09-17T22:31:49.483784",
     "exception": false,
     "start_time": "2020-09-17T22:31:47.884587",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\stats\\stat_bin.py:93: PlotnineWarning: 'stat_bin()' using 'bins = 85'. Pick better value with 'binwidth'.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223371864103765860)>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(p.ggplot(user_biases.reset_index(), p.aes('mean'))\n",
    " + p.geom_histogram()\n",
    " + p.facet_grid('Set ~'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:31:49.541784Z",
     "iopub.status.busy": "2020-09-17T22:31:49.541784Z",
     "iopub.status.idle": "2020-09-17T22:31:50.700764Z",
     "shell.execute_reply": "2020-09-17T22:31:50.699764Z"
    },
    "papermill": {
     "duration": 1.188979,
     "end_time": "2020-09-17T22:31:50.700764",
     "exception": false,
     "start_time": "2020-09-17T22:31:49.511785",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\stats\\stat_bin.py:93: PlotnineWarning: 'stat_bin()' using 'bins = 54'. Pick better value with 'binwidth'.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223371864104392608)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(p.ggplot(user_biases.reset_index(), p.aes('var'))\n",
    " + p.geom_histogram()\n",
    " + p.facet_grid('Set ~'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.029992,
     "end_time": "2020-09-17T22:31:50.759765",
     "exception": false,
     "start_time": "2020-09-17T22:31:50.729773",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This indicates quite a few $\\theta_u$ values have high posterior variance, so we really do want to use the entire distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.028006,
     "end_time": "2020-09-17T22:31:50.815793",
     "exception": false,
     "start_time": "2020-09-17T22:31:50.787787",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  },
  "papermill": {
   "duration": 157.780183,
   "end_time": "2020-09-17T22:31:52.375209",
   "environment_variables": {},
   "exception": null,
   "input_path": "ProfileModelAnalysis.ipynb",
   "output_path": "ProfileModelAnalysis.temp.ipynb",
   "parameters": {},
   "start_time": "2020-09-17T22:29:14.595026",
   "version": "1.0.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}