scripts/06_analyze_fl_analyze.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9c2bdc37",
   "metadata": {},
   "source": [
    "### NC Model Applied to FL Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f832baff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ethnicolr in /opt/miniconda3/envs/jupyter/lib/python3.9/site-packages (0.7.0)\n",
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      "Requirement already satisfied: wrapt~=1.12.1 in /opt/miniconda3/envs/jupyter/lib/python3.9/site-packages (from tensorflow==2.5.0->ethnicolr) (1.12.1)\n",
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      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /opt/miniconda3/envs/jupyter/lib/python3.9/site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow==2.5.0->ethnicolr) (4.2.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /opt/miniconda3/envs/jupyter/lib/python3.9/site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow==2.5.0->ethnicolr) (0.2.8)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /opt/miniconda3/envs/jupyter/lib/python3.9/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow==2.5.0->ethnicolr) (1.3.0)\n",
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      "Requirement already satisfied: idna<3,>=2.5 in /opt/miniconda3/envs/jupyter/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow==2.5.0->ethnicolr) (2.10)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/miniconda3/envs/jupyter/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow==2.5.0->ethnicolr) (1.26.5)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/miniconda3/envs/jupyter/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow==2.5.0->ethnicolr) (2021.5.30)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in /opt/miniconda3/envs/jupyter/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow==2.5.0->ethnicolr) (4.0.0)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/miniconda3/envs/jupyter/lib/python3.9/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow==2.5.0->ethnicolr) (3.1.1)\n",
      "\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install ethnicolr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48db6dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5190c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "fl_data = pd.read_csv('../data/fl_reg_name_race.csv.gz', compression='gzip', \n",
    "                                 header=0, sep=',', quotechar='\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4da15b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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>name_last</th>\n",
       "      <th>name_first</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Walker</td>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Palmer</td>\n",
       "      <td>Alton</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mc Cleod</td>\n",
       "      <td>Alicia</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Dale</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Walker</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name_last name_first      race\n",
       "0       Walker  Elizabeth  nh_white\n",
       "1       Palmer      Alton  nh_white\n",
       "2     Mc Cleod     Alicia  nh_black\n",
       "3  Scarborough       Dale  nh_white\n",
       "4       Walker     Daniel  nh_white"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fl_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "90cb956e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>name_last</th>\n",
       "      <th>name_first</th>\n",
       "      <th>fl_race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Walker</td>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Palmer</td>\n",
       "      <td>Alton</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mc Cleod</td>\n",
       "      <td>Alicia</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Dale</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Walker</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name_last name_first   fl_race\n",
       "0       Walker  Elizabeth  nh_white\n",
       "1       Palmer      Alton  nh_white\n",
       "2     Mc Cleod     Alicia  nh_black\n",
       "3  Scarborough       Dale  nh_white\n",
       "4       Walker     Daniel  nh_white"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fl_data.rename(columns={'race': 'fl_race'}, inplace=True)\n",
    "fl_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "194a2996",
   "metadata": {},
   "outputs": [],
   "source": [
    "fl_samp = fl_data.sample(n = 10000, random_state = 31415)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "28ed0b91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 1s\n"
     ]
    }
   ],
   "source": [
    "from ethnicolr import pred_nc_reg_name\n",
    "preds = pred_nc_reg_name(fl_samp, lname_col = \"name_last\", fname_col = \"name_first\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6b86df98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>name_last</th>\n",
       "      <th>name_first</th>\n",
       "      <th>fl_race</th>\n",
       "      <th>race</th>\n",
       "      <th>HL+A</th>\n",
       "      <th>HL+B</th>\n",
       "      <th>HL+I</th>\n",
       "      <th>HL+M</th>\n",
       "      <th>HL+O</th>\n",
       "      <th>HL+W</th>\n",
       "      <th>NL+A</th>\n",
       "      <th>NL+B</th>\n",
       "      <th>NL+I</th>\n",
       "      <th>NL+M</th>\n",
       "      <th>NL+O</th>\n",
       "      <th>NL+W</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>757560</th>\n",
       "      <td>Oldenburg</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>NL+W</td>\n",
       "      <td>6.027722e-09</td>\n",
       "      <td>6.098673e-12</td>\n",
       "      <td>1.571013e-09</td>\n",
       "      <td>3.826774e-08</td>\n",
       "      <td>0.137427</td>\n",
       "      <td>0.299829</td>\n",
       "      <td>0.013250</td>\n",
       "      <td>0.049568</td>\n",
       "      <td>7.611386e-04</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>0.171335</td>\n",
       "      <td>0.327559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7949174</th>\n",
       "      <td>BOLLINGER</td>\n",
       "      <td>VIRGINIA</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>NL+I</td>\n",
       "      <td>1.605675e-06</td>\n",
       "      <td>9.508462e-08</td>\n",
       "      <td>1.755933e-07</td>\n",
       "      <td>1.269503e-10</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.002651</td>\n",
       "      <td>0.000699</td>\n",
       "      <td>0.136859</td>\n",
       "      <td>7.947510e-01</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.015746</td>\n",
       "      <td>0.048802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952706</th>\n",
       "      <td>Blanco</td>\n",
       "      <td>Enrique</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>HL+O</td>\n",
       "      <td>1.682701e-12</td>\n",
       "      <td>2.484461e-02</td>\n",
       "      <td>5.479284e-16</td>\n",
       "      <td>8.073048e-11</td>\n",
       "      <td>0.653203</td>\n",
       "      <td>0.210335</td>\n",
       "      <td>0.005093</td>\n",
       "      <td>0.008122</td>\n",
       "      <td>9.161754e-08</td>\n",
       "      <td>0.049161</td>\n",
       "      <td>0.038115</td>\n",
       "      <td>0.011126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5649972</th>\n",
       "      <td>McMurtry</td>\n",
       "      <td>Shea</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>NL+B</td>\n",
       "      <td>6.887608e-12</td>\n",
       "      <td>2.754199e-08</td>\n",
       "      <td>9.820796e-08</td>\n",
       "      <td>3.417462e-06</td>\n",
       "      <td>0.002718</td>\n",
       "      <td>0.001321</td>\n",
       "      <td>0.000825</td>\n",
       "      <td>0.699702</td>\n",
       "      <td>7.514443e-03</td>\n",
       "      <td>0.014814</td>\n",
       "      <td>0.031897</td>\n",
       "      <td>0.241206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8036160</th>\n",
       "      <td>Yukus</td>\n",
       "      <td>John</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>NL+A</td>\n",
       "      <td>9.330448e-11</td>\n",
       "      <td>9.119455e-11</td>\n",
       "      <td>7.943597e-10</td>\n",
       "      <td>5.297330e-07</td>\n",
       "      <td>0.012541</td>\n",
       "      <td>0.016837</td>\n",
       "      <td>0.565154</td>\n",
       "      <td>0.030571</td>\n",
       "      <td>9.481369e-07</td>\n",
       "      <td>0.003193</td>\n",
       "      <td>0.212010</td>\n",
       "      <td>0.159694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2849522</th>\n",
       "      <td>Nelson</td>\n",
       "      <td>Steven</td>\n",
       "      <td>nh_black</td>\n",
       "      <td>HL+W</td>\n",
       "      <td>1.910448e-09</td>\n",
       "      <td>5.061355e-03</td>\n",
       "      <td>1.070835e-03</td>\n",
       "      <td>1.642348e-04</td>\n",
       "      <td>0.286262</td>\n",
       "      <td>0.348349</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>0.053558</td>\n",
       "      <td>1.361813e-02</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>0.131855</td>\n",
       "      <td>0.159450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4185395</th>\n",
       "      <td>Hill Hausman</td>\n",
       "      <td>Vicki</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>NL+W</td>\n",
       "      <td>2.469423e-11</td>\n",
       "      <td>1.765721e-13</td>\n",
       "      <td>4.090911e-12</td>\n",
       "      <td>1.145996e-05</td>\n",
       "      <td>0.003438</td>\n",
       "      <td>0.121431</td>\n",
       "      <td>0.000836</td>\n",
       "      <td>0.119120</td>\n",
       "      <td>3.982964e-03</td>\n",
       "      <td>0.020424</td>\n",
       "      <td>0.054761</td>\n",
       "      <td>0.675997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11962715</th>\n",
       "      <td>Awtrey</td>\n",
       "      <td>Cynthia</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>NL+W</td>\n",
       "      <td>1.671661e-07</td>\n",
       "      <td>5.599681e-13</td>\n",
       "      <td>3.660668e-09</td>\n",
       "      <td>4.818694e-04</td>\n",
       "      <td>0.021302</td>\n",
       "      <td>0.007244</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>0.309827</td>\n",
       "      <td>3.536879e-02</td>\n",
       "      <td>0.001705</td>\n",
       "      <td>0.291285</td>\n",
       "      <td>0.331958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3667369</th>\n",
       "      <td>Garza</td>\n",
       "      <td>Maria</td>\n",
       "      <td>hispanic</td>\n",
       "      <td>HL+W</td>\n",
       "      <td>1.401596e-08</td>\n",
       "      <td>3.298952e-08</td>\n",
       "      <td>1.768033e-07</td>\n",
       "      <td>2.819802e-02</td>\n",
       "      <td>0.418315</td>\n",
       "      <td>0.431341</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>6.667362e-07</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.118751</td>\n",
       "      <td>0.002837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11376177</th>\n",
       "      <td>LOVE</td>\n",
       "      <td>WILLIAM</td>\n",
       "      <td>nh_white</td>\n",
       "      <td>NL+M</td>\n",
       "      <td>8.392744e-12</td>\n",
       "      <td>3.217062e-07</td>\n",
       "      <td>5.106748e-11</td>\n",
       "      <td>6.399214e-06</td>\n",
       "      <td>0.003612</td>\n",
       "      <td>0.024831</td>\n",
       "      <td>0.002348</td>\n",
       "      <td>0.202332</td>\n",
       "      <td>1.497838e-01</td>\n",
       "      <td>0.349678</td>\n",
       "      <td>0.046807</td>\n",
       "      <td>0.220602</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name_last name_first   fl_race  race          HL+A          HL+B  \\\n",
       "757560       Oldenburg      James  nh_white  NL+W  6.027722e-09  6.098673e-12   \n",
       "7949174      BOLLINGER   VIRGINIA  nh_white  NL+I  1.605675e-06  9.508462e-08   \n",
       "1952706         Blanco    Enrique  hispanic  HL+O  1.682701e-12  2.484461e-02   \n",
       "5649972       McMurtry       Shea  nh_white  NL+B  6.887608e-12  2.754199e-08   \n",
       "8036160          Yukus       John  nh_white  NL+A  9.330448e-11  9.119455e-11   \n",
       "...                ...        ...       ...   ...           ...           ...   \n",
       "2849522         Nelson     Steven  nh_black  HL+W  1.910448e-09  5.061355e-03   \n",
       "4185395   Hill Hausman      Vicki  nh_white  NL+W  2.469423e-11  1.765721e-13   \n",
       "11962715        Awtrey    Cynthia  nh_white  NL+W  1.671661e-07  5.599681e-13   \n",
       "3667369          Garza      Maria  hispanic  HL+W  1.401596e-08  3.298952e-08   \n",
       "11376177          LOVE    WILLIAM  nh_white  NL+M  8.392744e-12  3.217062e-07   \n",
       "\n",
       "                  HL+I          HL+M      HL+O      HL+W      NL+A      NL+B  \\\n",
       "757560    1.571013e-09  3.826774e-08  0.137427  0.299829  0.013250  0.049568   \n",
       "7949174   1.755933e-07  1.269503e-10  0.000424  0.002651  0.000699  0.136859   \n",
       "1952706   5.479284e-16  8.073048e-11  0.653203  0.210335  0.005093  0.008122   \n",
       "5649972   9.820796e-08  3.417462e-06  0.002718  0.001321  0.000825  0.699702   \n",
       "8036160   7.943597e-10  5.297330e-07  0.012541  0.016837  0.565154  0.030571   \n",
       "...                ...           ...       ...       ...       ...       ...   \n",
       "2849522   1.070835e-03  1.642348e-04  0.286262  0.348349  0.000416  0.053558   \n",
       "4185395   4.090911e-12  1.145996e-05  0.003438  0.121431  0.000836  0.119120   \n",
       "11962715  3.660668e-09  4.818694e-04  0.021302  0.007244  0.000829  0.309827   \n",
       "3667369   1.768033e-07  2.819802e-02  0.418315  0.431341  0.000156  0.000394   \n",
       "11376177  5.106748e-11  6.399214e-06  0.003612  0.024831  0.002348  0.202332   \n",
       "\n",
       "                  NL+I      NL+M      NL+O      NL+W  \n",
       "757560    7.611386e-04  0.000270  0.171335  0.327559  \n",
       "7949174   7.947510e-01  0.000066  0.015746  0.048802  \n",
       "1952706   9.161754e-08  0.049161  0.038115  0.011126  \n",
       "5649972   7.514443e-03  0.014814  0.031897  0.241206  \n",
       "8036160   9.481369e-07  0.003193  0.212010  0.159694  \n",
       "...                ...       ...       ...       ...  \n",
       "2849522   1.361813e-02  0.000196  0.131855  0.159450  \n",
       "4185395   3.982964e-03  0.020424  0.054761  0.675997  \n",
       "11962715  3.536879e-02  0.001705  0.291285  0.331958  \n",
       "3667369   6.667362e-07  0.000007  0.118751  0.002837  \n",
       "11376177  1.497838e-01  0.349678  0.046807  0.220602  \n",
       "\n",
       "[10000 rows x 16 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2ac3833",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>HL+A</th>\n",
       "      <th>HL+B</th>\n",
       "      <th>HL+I</th>\n",
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       "      <th>fl_race</th>\n",
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       "  <tbody>\n",
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       "      <th>asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>76</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic</th>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>99</td>\n",
       "      <td>732</td>\n",
       "      <td>376</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "      <td>34</td>\n",
       "      <td>106</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multi_racial</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native_indian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nh_black</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>56</td>\n",
       "      <td>74</td>\n",
       "      <td>536</td>\n",
       "      <td>93</td>\n",
       "      <td>144</td>\n",
       "      <td>96</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nh_white</th>\n",
       "      <td>4</td>\n",
       "      <td>86</td>\n",
       "      <td>7</td>\n",
       "      <td>181</td>\n",
       "      <td>309</td>\n",
       "      <td>394</td>\n",
       "      <td>322</td>\n",
       "      <td>1101</td>\n",
       "      <td>431</td>\n",
       "      <td>591</td>\n",
       "      <td>624</td>\n",
       "      <td>2325</td>\n",
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       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>27</td>\n",
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       "      <td>17</td>\n",
       "      <td>22</td>\n",
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       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
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       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "      <td>7</td>\n",
       "      <td>37</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race           HL+A  HL+B  HL+I  HL+M  HL+O  HL+W  NL+A  NL+B  NL+I  NL+M  \\\n",
       "fl_race                                                                     \n",
       "asian             0     0     1     4     8     6    76    14     5     6   \n",
       "hispanic          8    24     8    99   732   376    56    49    16    34   \n",
       "multi_racial      0     2     0     2     4     5     7    10     7     6   \n",
       "native_indian     0     0     0     1     1     1     1    11     1     2   \n",
       "nh_black          0    45     0    34    35    56    74   536    93   144   \n",
       "nh_white          4    86     7   181   309   394   322  1101   431   591   \n",
       "other             0     3     0     2    27     8    17    22    10    15   \n",
       "unknown           1     6     2     7    37    28    20    38     7    17   \n",
       "\n",
       "race           NL+O  NL+W  \n",
       "fl_race                    \n",
       "asian            32    13  \n",
       "hispanic        106    75  \n",
       "multi_racial     11    18  \n",
       "native_indian     2    10  \n",
       "nh_black         96   256  \n",
       "nh_white        624  2325  \n",
       "other            28    30  \n",
       "unknown          21    60  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(preds['fl_race'], preds['race'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "87b89bf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds['ethnic_code'] = preds['race'].str.split(\"+\").str[0]\n",
    "preds['race_code']  = preds['race'].str.split(\"+\").str[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af8de587",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race_code</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>I</th>\n",
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       "      <th>hispanic</th>\n",
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       "      <th>nh_black</th>\n",
       "      <td>74</td>\n",
       "      <td>581</td>\n",
       "      <td>93</td>\n",
       "      <td>178</td>\n",
       "      <td>131</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nh_white</th>\n",
       "      <td>326</td>\n",
       "      <td>1187</td>\n",
       "      <td>438</td>\n",
       "      <td>772</td>\n",
       "      <td>933</td>\n",
       "      <td>2719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>55</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>21</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>58</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race_code        A     B    I    M    O     W\n",
       "fl_race                                      \n",
       "asian           76    14    6   10   40    19\n",
       "hispanic        64    73   24  133  838   451\n",
       "multi_racial     7    12    7    8   15    23\n",
       "native_indian    1    11    1    3    3    11\n",
       "nh_black        74   581   93  178  131   312\n",
       "nh_white       326  1187  438  772  933  2719\n",
       "other           17    25   10   17   55    38\n",
       "unknown         21    44    9   24   58    88"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(preds['fl_race'], preds['race_code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "12f312df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ethnic_code</th>\n",
       "      <th>HL</th>\n",
       "      <th>NL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fl_race</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>asian</th>\n",
       "      <td>19</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic</th>\n",
       "      <td>1247</td>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multi_racial</th>\n",
       "      <td>13</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native_indian</th>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nh_black</th>\n",
       "      <td>170</td>\n",
       "      <td>1199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nh_white</th>\n",
       "      <td>981</td>\n",
       "      <td>5394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>40</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>81</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ethnic_code      HL    NL\n",
       "fl_race                  \n",
       "asian            19   146\n",
       "hispanic       1247   336\n",
       "multi_racial     13    59\n",
       "native_indian     3    27\n",
       "nh_black        170  1199\n",
       "nh_white        981  5394\n",
       "other            40   122\n",
       "unknown          81   163"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(preds['fl_race'], preds['ethnic_code'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abbbc3d4",
   "metadata": {},
   "source": [
    "### Comparison # 1:\n",
    "Accuracy using Known-Known:\n",
    "\n",
    "(race_code == 'B') & (ethnic_code == 'NL') ==> nh_black<br>\n",
    "(race_code == 'W') & (ethnic_code == 'NL') ==> nh_white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b4b271a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7744, 19)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds['nc_race'] = preds['race']\n",
    "preds.loc[(preds.race_code=='B') & (preds.ethnic_code=='NL'), 'nc_race'] = 'nh_black'\n",
    "preds.loc[(preds.race_code=='W') & (preds.ethnic_code=='NL'), 'nc_race'] = 'nh_white'\n",
    "\n",
    "comp_1 = preds[preds.fl_race.isin([\"nh_black\", \"nh_white\"])]\n",
    "comp_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "627d8ada",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nc_race   HL+A  HL+B  HL+I  HL+M  HL+O  HL+W  NL+A  NL+I  NL+M  NL+O  \\\n",
      "fl_race                                                                \n",
      "nh_black     0    45     0    34    35    56    74    93   144    96   \n",
      "nh_white     4    86     7   181   309   394   322   431   591   624   \n",
      "\n",
      "nc_race   nh_black  nh_white  \n",
      "fl_race                       \n",
      "nh_black       536       256  \n",
      "nh_white      1101      2325  \n",
      "Total N 7744\n",
      "NH Black: 0.39\n",
      "NH White: 0.36\n",
      "Accuracy: 0.37\n"
     ]
    }
   ],
   "source": [
    "cf = pd.crosstab(comp_1['fl_race'], comp_1['nc_race'])\n",
    "print(cf)\n",
    "\n",
    "# Total\n",
    "print(\"Total N\", cf.values.sum())\n",
    "\n",
    "# TP/Total \n",
    "print(\"NH Black:\",(cf.values[0,10]/cf.values[0,].sum()).round(2))\n",
    "print(\"NH White:\", (cf.values[1,11]/cf.values[1,].sum()).round(2))\n",
    "print(\"Accuracy:\", ((cf.values[0,10] + cf.values[1,11])/cf.values.sum()).round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "326f41fa",
   "metadata": {},
   "source": [
    "### Comparison #2: Low FP\n",
    "\n",
    "(race_code == 'B') & (ethnic_code == 'NL') ==> nh_black<br>\n",
    "(race_code == 'W') & (ethnic_code == 'NL') ==> nh_white<br>\n",
    "((race_code == 'W') & (ethnic_code == 'HL')) | ((race_code == 'B') & (ethnic_code == 'HL')) ==> hispanic<br>\n",
    "(race_code == 'A') & (ethnic_code == 'NL') ==> asian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4572063f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9492, 20)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds['nc_race_low_fp'] = preds.race\n",
    "preds.loc[(preds.race_code=='B') & (preds.ethnic_code=='NL'), 'nc_race_low_fp'] = 'nh_black'\n",
    "preds.loc[(preds.race_code=='W') & (preds.ethnic_code=='NL'), 'nc_race_low_fp'] = 'nh_white'\n",
    "preds.loc[((preds.race_code == 'W') & (preds.ethnic_code == 'HL')) |\n",
    "        ((preds.race_code == 'B') & (preds.ethnic_code == 'HL')), 'nc_race_low_fp'] = 'hispanic'\n",
    "preds.loc[(preds.race_code == 'A') & (preds.ethnic_code == 'NL'), 'nc_race_low_fp'] = 'asian'\n",
    "preds\n",
    "\n",
    "comp_2 = preds[preds.fl_race.isin([\"nh_black\", \"nh_white\", \"hispanic\", \"asian\"])]\n",
    "comp_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e5ead105",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nc_race_low_fp  HL+A  HL+I  HL+M  HL+O  NL+I  NL+M  NL+O  asian  hispanic  \\\n",
      "fl_race                                                                     \n",
      "asian              0     1     4     8     5     6    32     76         6   \n",
      "hispanic           8     8    99   732    16    34   106     56       400   \n",
      "nh_black           0     0    34    35    93   144    96     74       101   \n",
      "nh_white           4     7   181   309   431   591   624    322       480   \n",
      "\n",
      "nc_race_low_fp  nh_black  nh_white  \n",
      "fl_race                             \n",
      "asian                 14        13  \n",
      "hispanic              49        75  \n",
      "nh_black             536       256  \n",
      "nh_white            1101      2325  \n",
      "Total N 9492\n",
      "Asian: 0.46\n",
      "Hispanic: 0.25\n",
      "NH Black: 0.39\n",
      "NH White: 0.36\n",
      "Accuracy: 0.35\n"
     ]
    }
   ],
   "source": [
    "cf = pd.crosstab(comp_2['fl_race'], comp_2['nc_race_low_fp'])\n",
    "print(cf)\n",
    "\n",
    "# Total\n",
    "print(\"Total N\", cf.values.sum())\n",
    "\n",
    "# TP/Total \n",
    "print(\"Asian:\",(cf.values[0,7]/cf.values[0,].sum()).round(2))\n",
    "print(\"Hispanic:\",(cf.values[1,8]/cf.values[1,].sum()).round(2))\n",
    "print(\"NH Black:\",(cf.values[2,9]/cf.values[2,].sum()).round(2))\n",
    "print(\"NH White:\", (cf.values[3,10]/cf.values[3,].sum()).round(2))\n",
    "print(\"Accuracy:\", ((cf.values[0,7] + cf.values[1,8] + cf.values[2,9] + cf.values[3,10])/cf.values.sum()).round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86bf9682",
   "metadata": {},
   "source": [
    "### Comparison #3: Low FN\n",
    "(race_code == 'B') & (ethnic_code == 'NL') ==> nh_black<br>\n",
    "(race_code == 'W') & (ethnic_code == 'NL') ==> nh_white<br>\n",
    "ethnic_code == 'HL' ==> hispanic<br>\n",
    "(race_code == 'A') & (ethnic_code == 'NL') ==> asian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2a9c7af7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9492, 21)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds['nc_race_low_fn'] = preds.race\n",
    "preds.loc[(preds.race_code=='B') & (preds.ethnic_code=='NL'), 'nc_race_low_fn'] = 'nh_black'\n",
    "preds.loc[(preds.race_code=='W') & (preds.ethnic_code=='NL'), 'nc_race_low_fn'] = 'nh_white'\n",
    "preds.loc[(preds.ethnic_code=='HL'), 'nc_race_low_fn'] = 'hispanic'\n",
    "preds.loc[(preds.race_code=='A') & (preds.ethnic_code=='NL'), 'nc_race_low_fn'] = 'asian'\n",
    "preds\n",
    "\n",
    "comp_3 = preds[preds.fl_race.isin([\"nh_black\", \"nh_white\", \"hispanic\", \"asian\"])]\n",
    "comp_3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2fb145ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nc_race_low_fn  NL+I  NL+M  NL+O  asian  hispanic  nh_black  nh_white\n",
      "fl_race                                                              \n",
      "asian              5     6    32     76        19        14        13\n",
      "hispanic          16    34   106     56      1247        49        75\n",
      "nh_black          93   144    96     74       170       536       256\n",
      "nh_white         431   591   624    322       981      1101      2325\n",
      "Total N 9492\n",
      "Asian: 0.46\n",
      "Hispanic: 0.79\n",
      "NH Black: 0.39\n",
      "NH White: 0.36\n",
      "Accuracy: 0.44\n"
     ]
    }
   ],
   "source": [
    "cf = pd.crosstab(comp_3['fl_race'], comp_3['nc_race_low_fn'])\n",
    "print(cf)\n",
    "\n",
    "# Total\n",
    "print(\"Total N\", cf.values.sum())\n",
    "# TP/Total \n",
    "print(\"Asian:\",(cf.values[0,3]/cf.values[0,].sum()).round(2))\n",
    "print(\"Hispanic:\",(cf.values[1,4]/cf.values[1,].sum()).round(2))\n",
    "print(\"NH Black:\",(cf.values[2,5]/cf.values[2,].sum()).round(2))\n",
    "print(\"NH White:\", (cf.values[3,6]/cf.values[3,].sum()).round(2))\n",
    "print(\"Accuracy:\", ((cf.values[0,3] + cf.values[1,4] + cf.values[2,5] + cf.values[3,6])/cf.values.sum()).round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c32b1df",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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