scripts/03_metaphone_lstm_fl_voter_name.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soundnames: Learning from sequence of sounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: Metaphone in /opt/venv37/lib/python3.7/site-packages (0.6)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install Metaphone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('ARKTKT', '')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from metaphone import doublemetaphone\n",
    "doublemetaphone(\"architect\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('PJTR', 'PHTR')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doublemetaphone(\"bajador\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "NGRAMS = (2, 3)\n",
    "SAMPLE = 1000000\n",
    "EPOCHS = 15\n",
    "\n",
    "# Florida voter\n",
    "df = pd.read_csv('/opt/data/fl_voter/fl_voter_name_gender_race.csv', usecols=['last_name', 'first_name', 'race'], nrows=SAMPLE * 1.5)\n",
    "df.dropna(subset=['first_name', 'last_name'], inplace=True)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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afJzex6J3/XUs2jo39e6Hgutg7AVPMrxYQ0Nfba8653iRod6xkQwv1J3vXjxdztox9ro8PxhYV+8xc3trLrjtLsvnJcv5HHOf13hKr9tc9wP2VZ3uT8b3PmhG4689Nsq7NZ/vlW7vd7XD95Pz1D2kvVPtneJ1OTlTfv7rg9c3YTp3LuRM+e1f6ZTjXsdzpqRu17p0hHfVwJHWcknYEZySDY8OGT62ltDWQ8Z58XfkkHEba7FFrnqtcViJx9eYr73TuG21UFc+vYve+etYNHVu6n3u4lL1zNFKp9teeaV/7oByP2v8vFB3cj1OWndZU2PnqTQez6m4ux631+Vq7Lb7IeNYSJbzuU6f97Fsdr1+cZGezj63RetTOZH5HIZLc0B5uJR2b7q6P+q+9v3yiFZ7sNtD7oOvV12814uL6/V97nI11hoyxOPrzNfeadw2r8uVh/dH9UB0Ln9uPHdOVD1ztNLptlde2eB1FF+63mMvvke10OPkWHaymXMe5ug9nlNxdz1ur8vV2G3PmZJaPbcX37WmvB9pLZeEvcuRqtc3IxvZN67byuZXr00rWf6hJn7n9U2P3kfr0HnvBUK6O7mVRn65DLfdx07jtnmhBhenXTKWniV0ovGDQ5jKR7eTwlj9RL5shN5tl0/Q0Lvsbh262rvujg/96t0ebK/y/N1luxzGUzHwmHleeF7PHTKuV3Y6n+v0eY2n9C7ZD66zw9FxyTgH4pIc0D5ZQvcKGt8r7CHfL24r+1u99oXsW7KH3A+X7TXAaerY96B0d3yVRn65HLfdx07jtnldBhenXTKWniV0osmZMv//OzYVA4+Z54XntcaeH37G1Xnnc50+r/GU3iX7wXVKztIcCd21zxfVi5HWcq0pPnsnlm8e31ReiNkIPodddVO5PPiglXyXUddFp3VtQnjovQiLpisokufy4XWbJMdlyX7onP9v//Z//kaW04leMr1YIcN72eLvc+POJi9GkiX8fM71O7ay1eV5XGWD+4lctw0dPqcx8+KpsdMcD3bxrl54uTzPG59XXOGrdnoMRKd1p+lyAt1uO/ZJr+MkG90up/Mc0tjxdnzdHsnyd+xzW7Tu/md87YNkr/gqz/eSvVau7xXfG+xr9eS9z6HH96D4XR580Po+dLmsq9e89pzPaex7XzTsT62h0/evaJDBuuYkx2V5nXL+nCk/8APHuVoobHmoxbyrVwyQo97zxucVV/g8HprzWIlO607T5QS63Xbsk17PBdnodjmd55DGnsu+b9weyfJ37HNbtO7+Z3zfM0W5OtJaLgk7SjL55vHN4htB9mrD+YbUXH3g941VaerGq+t6l+4On47W56Rfj8/VsTa6F5K6zvsSOi9CU/Z2892cYyv7oPH4yLb6jr3ei1/2+1wd1/jWdb1jA/0SHi+aS+g7vczJj7lYSZfnGkVc9jpOGi+xRXxTdFv8F2Cwz3U4XuCc/r4HzWjsu/06KutZPt97nAmS6Xkve3Om/HyG5kz5GROvkTWHOCuW9jlTUlufrW9n4te+GGktl4QdxfmpQ0b21Y/G+qEoP5zGDymXS1HxAuR8rKt3GR1GU3wqaNB3xa0eCFWO7JV92MJHpmT6POvqnQbd3ovG3+fGUxcX8dTLmNNKh96dRr6hq9KK3jF2Pq3xuAxkdbhCr95jAI/6zgbop/RXbF0PY9nosXEezz+N3fZqj+el7K25IX3dxUW0nf2OXWef21J1O24Z55BdmgPK0aW0e9P53vNaI711byn/fY/ID6dxfpdLDfD943ysq3cZne9TfF7PfM8iW3XE5VU5std98/rk88hT7zQum7FoGD/qvc7JNqevdctppUPvTuP8lVb0jrHzaY3HZWBLhyv06j0G8KjvbIB+Sn/F1vUwlo0eG+fx/NPYba/2eF7KXslFB33OlNR3z+k9xsq1kdZySdgeRkbmsTZC4nyceNRDJnvlOLFJLJ6PRWrN8xieIQ8T5+PEOWfKcWJxhr37DhtVL0Zay5Xic4+ET5yPE+ccMseJxTsK+NV1ptbcI78T5+PEOWfKcWJx9fo+6p/qxUhruVJ87pHwifNx4pxD5jixGC3C4ZuOYWrNNDZXypvE+ThxzplynFhcaY9v6YvqxUhruVJ87pHwifM94rxloYms5MxIDqTW3CNvEud7xHmkBoQnuVFzQPVipLVcKT73SLDE+R5xrsUi74n7q3MgteYeOZc43yPOr64f0XfNvFK9GGktV4rPNZOkbv7E+R5xrnHPe+L+6hxIrblHziXO94jzq+tH9F0zr1QvRlrLleJzzSSpmz9xvkeca9zznri/OgdSa+6Rc4nzPeL86voRfdfMK9WLkdZypfhcM0nq5k+c7xHnGve8J+6vzoHUmnvkXOJ8jzi/un5E3zXzSvVipLVcKT7XTJK6+RPne8S5xj3vifurcyC15h45lzjfI86vrh/Rd828Ur0YaS1Xis81k6Ru/sT5HnGucc974v7qHEituUfOJc73iPOr60f0XTOvVC9GWsuV4nPNJKmbP3G+R5xr3POeuL86B1Jr7pFzifM94vzq+hF918wr1YuR1nKl+FwzSermT5zvEeca97wn7q/OgdSae+Rc4nyPOL+6fkTfNfNK9WKktVwpPtdMkrr5E+d7xLnGPe+J+6tzILXmHjmXON8jzq+uH9F3zbxSvRhpLVeKzzWTpG7+xPkeca5xz3vi/uocSK25R84lzveI86vrR/RdM69UL0Zay5Xic80kqZs/cb5HnGvc8564vzoHUmvukXOJ8z3i/Or6EX3XzCvVi5HWcqX4XDNJ6uZPnO8R5xr3vCfur86B1Jp75FzifI84v7p+RN8180r1YqS1XBKWJxgkB5IDyYHkQHIgOZAcSA4kB5IDe+TApheXEWHhORcCSsK0IBAEgsDeCKTW7I3wMeQnzseIQ6wIAmdAYLRetF+uo8LOAFRs/IFA4vwDi4yCQBDYD4HUmv2wPZLkxPlI0YgtQeDYCIzWi1xcjh3XXa0bTZpdjYrwIBAELodAas3lQto6lDi3sGQyCASBBoHRepGLSwPmXaZGk+Yu+MTPIBAEtkEgtWYbHI8uJXE+eoRiXxA4DgKj9SIXl+PE8OWWjCbNyw2NwiAQBE6NQGrNqcO32PjEeTFUIQwCt0dgtF7k4nLj1BlNmhtDFteDQBAYQCC1ZgC0E7IkzicMWkwOAm9CYLRe5OLypoAdQe1o0hzB9rvY8OXLl+//afLPnz/fxeVv8lW5Kd+3aFvL28Kmu8lIrblHxF8V5w8fPnz7+PHjPUCNl0HgogiM1otcXC6SEL/88ss3FfM1bSRpvn79+uv/x88RPqb5sP/06dMa13eh1UEqTP0RXs80/ANrdDwr9xmbtuBVvoJTlbf1RWNredXeR+/KzS19VQ7UvQ6e5Mkjm169Lv/Tjo8Ae4V89V459qgtjTN7otYx6l1Xz+GRDUsuLtDzAwjvj3x4Zh28Ovu7fftI1xI/kSHdnV6tc244ba0hrL2zP3odeyc2V9S9tF5U39vTZFRYFZ731yGgmK0tRCNx9oNtqki+zutv33+Vlx9HsEV+c7Hbyh4Ocj5IOYDqgf9KzLfQhR+KHR8WyCXH6jzra/ut5a3Vr1yQn3rqx9+Ibd0H0NEPfPmednwEyEf1I21pnKf0kMfdWeYf8T6espN9Rx3hfYp+i3n2ufpao7t9+0jnEj+RIZ1T5w711mk7jFl/V0/8R/PvXXZH7xgCS+tFld6eJqPCqvC874+AYsWzthCNxFkFUM8rDoH90dtew94Xl+0tfr1EMOLjpR62zPPB8ayFW8tbaw97hd75R2wb+QByne8Yj9Sad9h5d53ko/qRtjTO1IC69zlfJMc//KHn4r/mgx4/uv3H2la97EZP59vaM3qNn+jufBGuS2PT8WcuCOyBwGhO5uKyRzTeIHPkY2Zt0vjhoY9K8dcDjkLLASgaijX8mtNTP0ydx/kEJ7w6uDgYJNfnHXYKNbr0rsYvOsxXPaLpfBBdtdf1McYeP7SYc9vRDx99tQ8/wBnfoWcdeepHdSPTe+Ls8n0de/FRdJqba85T/REfeQDevLsNGtdW16GBH3niQy95oZhXfvcDHCTLMSe3qy3+ji4wcrnVNrBxW1xHZ6fscTnIcH+xR3LxWXPui9bcNni26CU77fgIkEfqR9qaOCuXPbelT/ydDcyR0+LzvBefHtYli32gfVfzXLSumz2KHL2PNPGL13UjRza4Ts3P6cUW7+fsQjf6vMd/5sCPd/DtdGEj9QsarxUdv+i84T+1FDnipSHH41j1is954E1/PgRqjiz14LeZ9UeuUWFLlYZuewQoCmskr41zLca1+Em35iRX9tA0xzxzFEPeke0Fy33y4uVFi3kvotIvfd4kS7JF57SiwT7osdUPCeyTvrmGPc7LnOxy290/yQSTioHzQYMN+MU7utDPu8sQbdUNv/ccIm4z+iVXDVwkf2kTvtKvJl+rbegFB73jDzrE7zo17mhkZ5WHD+6XbMInt4tcwU7pwS7RuS/YVnv0aZ4xuqpt0odO5EiHHloXuyqnw6PSVLmySXxVP3qf6SU37fgIkCOKF4/n3iMP1sS57gX2mPJQOa51GrS8yybpchrlrebYW/W9ykBWrSWar7KhfdQ7n8aSTav7dkqv88hPf0dW17vuul51uVxi7nWNGEgOuHkeECtqhWR4LMRXdfLu/lTdU+/okVzZ5rZUX/N+HgSUsyOt5RoVNmJAeLZBQMVg7WZeG2fp8KJDQXMPZEO1AzoOFNF7gdK8bPHiJBqKo/opmjpfDyu3bWpceWS/++m2yO65hj1exJmr/rneKRowQC9YztnguTAl13VPyRIO7gd0Pr9EDnzqsQd/NKfYux7PDef1sdOAUcUXeqfFXtcPXe09D9BR+ZbEo9K4v25b1c87Ngs7NY8vNFVO1Vn5qkzkiE9+b93W1pqt9UfeOAKK3dL4LaWTNXVPKSfJPR+L1vdi9z4lT/awb7o9UfeN5KixP/74uriTPulRq7J93+K7em91vvrttHUs3Y8eeFwuuIATNPSs807v/jDn/Zz/0HEeCG+1yuN2wpP+OggoX0dayzUqbMSA8GyDwKMi0mlZE+daYCSPIqtiQ+sKTVf4vEAx7gqnbFRR6/RLZ51fggM8ku0P+jsf4KHA4m/toePw6myEB7+Fo49ZV18xnsPSfZEPathT7Z7Sh274RFebbEC+5Eqv6Je0jr76NGWb+8dY+KjxTs+81pCn3NC6r7nN2IYM9eJRq3GAD545/6t/zoNtbhPYux0ao6PL8Sqns1cypFtNMvANX9QjB12+9sxYutPOiUCXS1OerI2z6LU/1JSPjNGpPGQ/kLuiXVKjfZ+Jp+5D5qhleqehX/2a5v6Iz+30fYttnWzJwFfn72h9rur2NenWOs3lsue1rqfi0eEmOfjgtQIZ3oOh+48d6kVL3LFFPF3cnS/j8yOg2I+0lmtU2IgB4dkGgamiMCd9TZwpUuKpj3TTvCAy1xU+L1BzsqVL61NFrM53+rFDPQWcQqk59FOAOxlVj8v0MXQunznp8dZhgA3QcYCKVq1iSSxYF43nwhLd6PK+6vU1t6Fi53TdWNhic+3xwXGRDPR5nlUa0THncrt5j43WwUh8HLKa9zyYwmOJ/9gvmTTp0jw2o3dJfnp8kVflaN7tZ538mouDbIMO+c/2kpl2TgTYH7V+dd6sjbPnsniVpzTeyV32iNY9t6Gvdta92e1D9pt0dY/rRM9cLxleX9x297WzBbkuo/MTuto7X13DT+Y7udCAA35M2er4QiMZNPddc+4/NOrdbueZqrnOm/G5EVDsR1rLNSpsxIDwbIPAVFGYk74mzl2hk2wKFh86HR00bosXKMbIcDrG9VCamp/DYUqGF2DJ7XyY4sUOeujkM425evDjtwp0tQHeWrwdS+eHXr1jsES38zKe4tO6bBBGalN2I8f76ouvKRdlt1r1S2uOZ0fjsjTGLslyeeDnsfsnP1UAACAASURBVIBWPnvzPJiyfYrX5aDT5+Cjl/wpzKHBPo8vMt1H5pxPPI6h3sEb+j37NbVmTzsiez0CU3nZSVobZ3KU/CXHJZucVd5Sb9Dpe5O5aieykdntw0428kb6rlZhq/zBj2qb65IMravB6+tT4043tNLtsXkkV+vQd7hJrs93uompapua+49d6p3XeWo8nSfjayBAjq31JheXtYgdlH6qKMyZuzRppj7aJJu1uULrBQ57ugKluak2VcTqPLo4rFwetlY99RDpinrV43J9DJ3soDEHRsw7BlO21Xn8k4xqN3I9F5bohq/2HQ6i0Tz+TdlQZendba/rrMlex2WJ/aIXnTfHzeWJhkNcNGrodn6N3X+X53RL/J+Sr/3HI/lLdXh8saX6yLzkox9/tYbd0O3dL601e9sR+esRmMqtTtLaOJPz2mt6vClHmVcOe/O9yXytFeQ4tYF9AL16fIPG10bG7DfnRYfW8BG/teaNefZq56fT+7jTzTo1j3eXK5xqc6x87HTIqLhDg9/40tUt0brdlUdr4ku7JgKK70hruUaFjRgQnm0QmCoKc9KXxrkeAFUmBUzzPoauK3y1QEFDkXNe0U4Vx25efskOb5LPoeCFEN/Ew+HV+dDpcfmMoZM+GnP1gKgYyC6PCfZqTrRq4KQx6y4XGfi/VDe2eo996Hb9kqsGfrw7fx13uEKDL9KFXs2piQ9/9M66cBEN7+ppjhPryBONePXIbtadX/q0Tq64fehQv8R/t8V50Ysf2IVOl4+tPueYI8t9FC26HT/Nq0nm1PwfSTbrpCvt+AgoHzyHlGOKnefknBcjcRaPHuWqN/ac1nxvikZ2VpuwlXpY9+bUHpGszm7Ns8eQVW10ezXu/NC8bNWaZNKY4129aNwv9q/TTI2ndIu+6nL86hp24GtnA3iQK5LnvoG1bIJGepwGP9xu+ODhnZjC08lhLf15EFDsR1rLNSpsxIDwPIeAYtU9FNw56UvjLDovplUmhU3FRgWl0rLufBQkCpTWmHN/KJ71UELW1LzsmJPDmgoiRRjMOh+m9GCHeg4AZKsX3xQv/joGbrfkaU1yRKtWsUQGOkUvPslRW6P7O0P5B/qRr95bxc7XfIyd+OFrjCVbdkPruLh+YVBpsMPpkFtpNQ8uopeeyq91zwNwqPbDJ/qpVmPmdMQbX90u2Sb5nQ7PNY07H6UHuyWjay4H7LClox+dk+y04yNArpIL5OBSy0fiTA7WvSWd2FH3l+9NbGPvkOvdvnH/JIPm8+h0e9in0E/14pWs2tiHrlM0VW/Hi+4p2eiaWwdjaCVTczTXUeVUG7Wup8aEefhrTZK+6r/0Q69x5dEc2Ln8PWoUWKR/HQKK6UhruUaFjRgQnvchkDi/D/toDgJ3QiC15h7RvmKc64XoHpH84SUXlx8zGQWBbRAYrRe5uGyD/ymljCbNKZ2N0UEgCLwNgdSat0H/UsVXjDN/BXgpkAdSlovLgYJxMVNG60UuLhdLhDXujCbNGh2hDQJBIAik1twjB64YZ/0rTt2/wnWPiP7419nu4m/8fB0Co/UiF5fXxehwmkaT5nCOxKAgEAQOjUBqzaHDs5lxifNmUEZQELg8AqP1IheXy6fGtIOjSTMtMStBIAgEgZ8RSK35GZMrziTOV4xqfAoC+yAwWi9ycdknHqeQOpo0p3AuRgaBIHAYBFJrDhOKXQ1JnHeFN8KDwKUQGK0XkxcXCcwTDJIDyYHkQHIgOZAcSA4kB5IDyYGtc2DkJjZ5cRkRFp5zIaAETAsCQSAI7I1Aas3eCB9DfuJ8jDjEiiBwBgRG60X75Toq7AxAxcYfCCTOP7DIKAgEgf0QSK3ZD9sjSU6cjxSN2BIEjo3AaL3IxeXYcd3VutGk2dWoCA8CQeByCKTWXC6krUOJcwtLJoNAEGgQGK0Xubg0YN5lajRp7oJP/AwCQWAbBFJrtsHx6FIS56NHKPYFgeMgMFovcnE5Tgxfbslo0rzc0CgMAkHg1Aik1pw6fIuNT5wXQxXCIHB7BEbrRS4uN06d0aS5MWRxPQgEgQEEUmsGQDshS+J8wqDF5CDwJgRG60UuLm8K2BHUjibNEWy/qw2//PLLb/4z5e/C4cuXL9/t+Pz580tM+Pjx47cPHz68RFeUbI9Aas32mB5R4qvjTD38+vXrEeGITUEgCMwgMFovcnGZAfUMS/qYU/B5Pn36tNjskaTRAYGutR+tsk28+uh9VXN7sXvEb7eXj3aXp/Ea7F3e1JjYsi68pcfx0we95kYP7s6XJX7AtzYH8GVtn4vLWsSORa8cTTsPArVuav8taSNx5lwQL48uJLVprdamXFwqSnkPAudBYKReyLv2NBkVdh64rmEpH614w2Gz5yHDx3N3iGDHVM8B5R/eU7RbzHOo1UMQO+r8Up3dRzvYb/lXgXpx6T7eyYHRiws+g8lSOR0GyNqj73zfQ09k7oNAzpR9cN1DKnVzpE6vjbPoKw+1tM7rPReXPSIemUHgPQjUPb7UilxcliJ1QLruw3vNB+hI0ugDUg96DgjLd5P4sO4wEgH2L/1Qdz+RXf/awKWuzjvvM2Owf0bGFO9aPKYwmJL/7HwuLs8i+F7+kVrzXovvqZ19rX6krYmz9vQUPZcX1SWaaP1d81yyRuo4ctMHgSDwHgSm9v8ja3JxeYTQydbXFPK1ScNhIh0ccPUjHRrJ5uGvENU23qFTDy2w610HHJcCaB8drByKUwcadnIQ+oc7Oqpv2DTlO/PyC/nIou/sZo1e/Gr+sc5fX6BRL/uwG9vUV6xEO4UDfMipdK6PsXjwFYw6naL3hj/wIg8ZTssaPbxOQ4yhATfRkFseB193ORnvj4BilHZ8BLSn9Iy2pXGmBlB/O33UpD/84Q+/niXsdfXaz90+11onN/WiQzlzQeB9CCytF9XC9jQZFVaF5/31CFCcl2heG2c/JCSfSwW6+EisH4hcRiq/3jta6NFRD6IqB/3ei+fRASw96OKQ5N1l1TGHbv3gZl72CYsqC7tFpwZ9PWSJi+x3GRpXn7AbG3lHh+ZlZ9UBPT18fnGpuItW+kWD7WDQ6RAtvsBb4yI+zWEvOeR+oqti4e/wkU9g7frxNf3rEUgcXo/5iEbFiVqgMY/XhTm5S+PM/mTfdzKpDdQYbHNa5LjeWgtEr1qReuHIZRwE3o+A79s11uTisgatg9PygecffXMmr00ayXXZHHDoqAcN8/QcMnOHYKXRYeM6JQs/OdCQ7718q3y+rrHWwaD6Umn9fUo/tk8dxvVAlX4/TF0H9vl6h4XbXeVXeXPvyCE2+Cifusb6XAzIB/Do/K02Y0fV6bzgjK3Qihe8pmigTf9aBNhnr9UabWsQYC/WWLEn2cdzMivvFC37c04mNYYaJNmyxRtyai3wWjlFk3rhSGYcBF6PwNJ6US3LxaUicuJ3JcGaRFhDy6HGISKYOFj4eIUGO9T7gdIdIB2P8/kBRGjgcVtYo5cMfezONa2LTo3DeY6eteq35pmrBys6pIcHu/Ve6dGh3j/W9d5h4XbXi4LLejRGjscLe+nlIw1/iT3z0HoPX/XHecCh81F0zquxntrwXz50uVbp8/46BJQPacdGYK6uKn7s0TkvlsaZ/Ult6GRSY+bqJXK8bklW6kWHaOaCwLEQWFovqtXtaTIqrArP++sQ0Aef4jZ3EFRr1sSZA0I89akfkZVWtqkxzyEjPsnyA7HSdB+ycwcsPkputYs1esnGNj7cWZvrOVArDv4Rz0e0aGhut49Zr70fvlrrsHC7K3ZV3tw7cogNtO4H/moNDPAZfsccXnKy+oMOyRW/mo9ZV++8wgFbul4+PIOF6814GwQUp7RjIzBXk7ra03mzNM7sT2pDJ4v6QY2RbOoE9MipdSv1AoTSB4HjIrC0XlQP2tNkVFgVnvfXIKAirZjNHQKdJWviPHVw8cFaDw70sS7b/JCZOiSdRjI6vVO86FQPJj7nY2RwEGKn00yN5Yuw40Dt6PzgZB2d8lFNMtAPjfdVRoeF281BvzYPpBM5snGqERvpqRh0vlR7qj/ocV4fs67eeTXWM9ewdc6fOf6sbYuA4pp2fASm9l9XezpvlsaZ+kEt7GTVmtTZNrXPUy86RDMXBI6FwNJ6Ua1uT5NRYVV43vdHQAVa8Rr5WF0aZw6Z7kOdNR0g3SHkH69+yMBXZTqN0OsOzHoB6FBG/tTFoB6KvHey6hyyq+1Ot8Ru0eiZan74iqaT6XZjVxeHKR3MI4cPffnGGBrka83HU/EQnedm9Qe5opF+tSkanydH4O96aKoPHW3m9kdgaa3Z35JomENA+6zGamp/d3Iqb0fDHLq6PYpO6oJ4vE4gY2qfp16AUPogcFwE1tQL9yIXF0fjZOO5wr/ElaVJM3U4oIMPauj0UUvrDhAOKunXOg1+zUODbGjUc6iJfq4hzw8/0fOR7hcP5ubkseYf7czVHnn4gc3yDbuRU+3jMuPYSX6HBXrQz7vHQOOqA3p6+LCXS8cURtjOumzDbsmEX/5iS/UH3aLBPuSCkWjEJxqXX99dlsbEHn9YT/8eBBSvtOMjwP5jP8pi9t8S69fGWfR6fJ9SK32/Y0edm9rntdZIR+WVTOydkrPE59AEgSAwhgD7by13e5qMClurPPTPIaA4TT0q3I/a0jiLbk4eH7069Bhjlx8W9XDggIJW65VG/FU3fKJd0pDvfeXD7jrfvXO489He0WhOdqNTfnR2MwedeuSK3/HrsOjsBkOXOWUjvjitZKrNyYEPW0VfZXB5Ea1a9ef7ZPNLKnzIE3/H6/g6reRiu/BNez8Cik/aORCoNclr0CMPRuLMXmUPq9dc1yoNvHWfp1506GUuCBwLgZF6IQ/a02RU2LEgiTWPEEicHyGU9SAQBLZAILVmCxSPLyNxPn6MYmEQOAoCo/UiF5ejRPANdowmzRtMjcogEAROjEBqzYmDt8L0xHkFWCENAjdHYLRe5OJy48QZTZobQxbXg0AQGEAgtWYAtBOyJM4nDFpMDgJvQmC0XuTi8qaAHUHtaNIcwfbYEASCwHkQSK05T6yesTRxfga98AaBeyEwWi9ycblXnvzG29Gk+Y2QvASBIBAEHiCQWvMAoIssJ84XCWTcCAIvQGC0XuTi8oLgHFXFaNIc1Z/YFQSCwDERSK05Zly2tipx3hrRyAsC10VgtF7k4nLdnHjo2WjSPBQcgiAQBIKAIZBaY2BceJg4Xzi4cS0IbIzAaL3IxWXjQJxJ3GjSnMnH2BoEgsD7EUiteX8MXmFB4vwKlKMjCFwDgdF6kYvLNeI/5MVo0gwpC1MQCAK3RSC15h6hT5zvEed4GQS2QGC0XkxeXCQwTzBIDiQHkgPJgeRAciA5kBxIDiQHts6BkQvQ5MVlRFh4zoWAEjAtCASBILA3Aqk1eyN8DPmJ8zHiECuCwBkQGK0X7ZfrqLAzABUbfyCQOP/AIqMgEAT2QyC1Zj9sjyQ5cT5SNGJLEDg2AqP1IheXY8d1V+tGk2ZXoyI8CASByyGQWnO5kLYOJc4tLJkMAkGgQWC0XuTi0oB5l6nRpLkLPvEzCASBbRBIrdkGx6NLSZyPHqHYFwSOg8BovcjF5TgxfLklo0nzckOjMAgEgVMjkFpz6vAtNj5xXgxVCIPA7REYrRe5uNw4dUaT5saQxfUgEAQGEEitGQDthCyJ8wmDFpODwJsQGK0Xubi8KWBHUDuaNHO2S+anT5/mSFatffz48duHDx9W8YQ4CASBYyGwR605loexRggcIc5XPTN++eWX7/h+/fo1yRYELoHAaL3IxeXk4ddHvYLPs+Yjf03S6DBAh/ea96a1I15cVOyxWwdAbcKt+lJp6vtSX9Hd6ZVM6SZu0K61pdq2x7viKp+/fPmyh/jIvDACypu0YyNA7aFO1p4aNefFmjhzpkzVOurNEr1uk+Q5T30XLZeAz58/O+vLxmA95XtnCDaLNy0IXAGBNfXC/W1Pk1FhLjjj/RHoCrJi50V7zoo1ce50dcVXMo9+cen8FmZrDhHhutRXcMrFZS4bs3ZlBLo9d2V/r+ab4rekPq6Js+SJXk/3YwhrS88zMK9nVX0XHZeAXFxALX0QeD0Ca+qFW5eLi6NxsnH3IUxBXvKrzJqk6Yq/4OLwATrJPPLFhV/xKnZHubiAY/ogcCUE1tSaK/l9BV84U7rLRfVvTZw5U1R765khndTkPS4u1e4zvBOHJWf7GfyJjUFgTb1wtHJxcTQuMObDfElxW5M0HDIVormLC39pkB5/ugPQ1zXmYtHphXbNr2XYIrlg5L5wSPocvqEPm5DFPD3rLkNj6KfWpdsPZ8nzg1x86KBHFvgIU9bUOzYdv+uTjfgvPpfjsUKO/KFVveJ1HujS3xsB5UXaORFQ7FRnlrQ1caZ2UXO8rqgeqd5AI91TdZQaRM1zHsmRTf5oHZ2PapXXPGRoDn7m6CtG2Ma6enRqTJ3HN6dzWsl1W6qevAeBMyKgHB9pLdeosBEDwrMdAhRJFbglbU2c/TBAdqdPMr0Y1w9kii/FGxnwIBvbql7Nswbtkp6DQfoZu04+3JGld7e943Ff4et6eKfiUnW5XPCSDJoww3bhI3r1NA5VDnLJqLqrTr27Xsmquqfe0SMe2ea2YFP6eyOg3Eo7HwLseer1Iw/WxNlru/ioUdQv6XKaqTrKGUIdcp4qA/vR8cgv/K9+iZ8ajEzpdTp4sUt0Xh9Fiwz55ueNaOHHRt79LEB3+iBwRgR8v6yxvz1NRoWtURza7RBQ8VPM9KwpamviTFFGD73mvWmeYuzzjOvhUw8Z6Oh9nY9r1tb0VW89BCQbXzjUKpZ1/pGv2IduMOt6P7RcrmzS+1RzfJzG/fF5xnP+Q1M/CCqP2wlP+iDQITCXwx195o6BgOJGXVxi0Zo4e+2itkiH5jlDnIY6KlpvtU45D/K8vmqOWs6lwOX5GLvqWeA0jKvMR/Xx0Xr1d40t2JQ+CBwZgTX1wv1ov4hGhbngjN+DgAp0LdJTlqyJcz0MkCldkkNh15hDBxrxat4fDp+OHj716F3jl/MzroeA5l235EuXmuzvMESGDqjK/31i4h/w4XMlq765XRxWYIeNyAAf3unlg3ho2IAces2ruf+VB7uxRTz1YwGe9EGgQ8BzsVvP3PEQYL8/+rh3y9fE2WsX9Ym6RV3qaKhH6K21yHlEU981Vy8Zqn/URPXUfzDAHnTSOw9j2VNtgt570a85Kx/Z4rIzDgJnQEB7YKS1XKPCRgwIz7YIUJD5uJ6TvibOXfGXbAo0h4kXY2xxPRxQovfxlJ3SK34e6RtpnS4/CPzDfcrXKsN9nbOp8lVa6eag1FonV+tgoB68p2zlA0DywNAPSfddNO4/9lW7nYfYjsYDHenvgYByNu1cCChmqh1r2po419pFzfI65TS1HmEXZxBnnvOIpr5rbmn98pqHPvXYKtk0l+lj1msvrPAVesev+jtlS5Wb9yBwFgQ839fY3J4mo8LWKA7tPgjUIj6nZU2cu+Iv2RRXCrBkMu54oFcRVnP6zlaXIVo9krG2Vb3wo98/3DmUoKFHhg4ZNXhZn+rhw+dKJ916aI/kah16xwd+9cxP6a6HoPuPnMrrPGvyDHnp74uAcjbtPAiw19f+MLEmztQoUKGmuE6nqfWo8lGXnUc09V1zXBRcF/K8Bwfp9tbVaJeJL9jkvIxdRmdj9XfKFuSlDwJnQ2BNvXDf2tNkVJgLznhfBFTUVMhqo7g9KsjiWxPnrrBKBgUaW7wYL/kYFg0f4dUXvbteCvkcfSdDc/BiJ3TgJbulS80PIOh8XrLU3Fenq+Mp3dBVDFxutVc8jomPkaceGcSnHqD4jS+yAf+RU+12Hta4pMKTPgh0CCgf086DgOJV68ES69fEeap2uZ5KI/m1JlKvqXGVp75LPjyPzkmvedhF7ZuyQzKhmauP8oX1tfUXW9IHgTMjsKZeuJ/taTIqzAVnvC8CXWFkToV6SVsT5674o88vEl6M+cuF6NSgFw1Fnw9rCjh2I7Pq5cBxH9GDTGR4j+6ORrpkk8vUHDa47W5ntc31+XhOt+iqLsewrlVZnQ3gIVq16psw0JweaKTH/Rdf1QUfPLzzwQBPlfPdiPzj1ggo19LOgQD7+tFHfefNmjh3tavKrDT1nfNAeqlDlQZ/qFvSAd8jHzte8de6jDzZgUx4sUt8skH2qYmW86TWbGqvaCRHDXnux/eF/CMInBQB5fdIa7lGhY0YEJ5xBPjoV7x4KHJLpK6Js4otOryn8KJPaz7nfCr2FGS3kzmXS7Gvh5D0UMAlT018jLGj9uhwvdBw6HCgMO+2S0fH6zZ365I1p1vr9RCUTMfQdVQ7qo3Q4oPrZ0126tG7bMOG6n+1u/KID+yQ7TK/C84/gsAf92iAOD4CnCm1Fiy1XPt/aZOOR3W7oxEP9UZ1EpvnzgzJgUdj6haXjCmbu5oHLfLUy45OJnNOS82FD3luo3xcUn/hTR8EzoiA9sBIa7lGhY0YEJ73IXCFONdD631ovkdzd7C/x5JoDQLTCFyh1kx7lxUQSJxBIn0QCAKPEBitF7m4PEL2wuujSXMkSPSL2KNf7Y5k79a25OKyNaKRtwcCV6g1e+ByNZmJ89UiGn+CwH4IjNaLXFz2i8nhJY8mzZEc06VFl5e7tlxc7hr5c/l9hVpzLsTfY23i/B7cozUInBGB0XqRi8sZo72RzaNJs5H6iAkCQeAmCKTW3CPQifM94hwvg8AWCIzWi1xctkD/pDJGk+ak7sbsIBAE3oRAas2bgH+x2sT5xYBHXRA4MQKj9SIXlxMH/VnTR5PmWb3hDwJB4F4IpNbcI96J8z3iHC+DwBYIjNaLXFy2QP+kMkaT5qTuxuwgEATehEBqzZuAf7HaxPnFgEddEDgxAqP1IheXEwf9WdNHk+ZZveEPAkHgXgik1twj3onzPeIcL4PAFgiM1otcXLZA/6QyRpPmpO7G7CAQBN6EQGrNm4B/sdrE+cWAR10QODECo/UiF5cTB/1Z00eT5lm94Q8CQeBeCKTW3CPeifM94hwvg8AWCIzWi1xctkD/pDJGk+ak7sbsIBAE3oRAas2bgH+x2sT5xYBHXRA4MQKj9WLy4iKBeYJBciA5kBxIDiQHkgPJgeRAciA5sHUOjNy7Ji8uI8LCcy4ElIBpQSAIBIG9EUit2RvhY8hPnI8Rh1gRBM6AwGi9aL9cR4WdAajY+AOBxPkHFhkFgSCwHwKpNftheyTJifORohFbgsCxERitF7m4HDuuu1o3mjS7GhXhQSAIXA6B1JrLhbR1KHFuYclkEAgCDQKj9SIXlwbMu0yNJs1d8ImfQSAIbINAas02OB5dSuJ89AjFviBwHARG60UuLseJ4cstGU2alxsahUEgCJwagdSaU4dvsfGJ82KoQhgEbo/AaL3IxeXGqTOaNDeGLK4HgSAwgEBqzQBoJ2RJnE8YtJgcBN6EwGi9yMXlTQE7gtrRpDmC7bEhCASB8yCQWnOeWD1j6Tvj/OnTp+//Fw7P2B/eIBAEXofAaL3IxeV1Mdpd0y+//PK9cKtf0tYkzcePH799+PChFYveL1++fF8XrWR//fq1pX/XpOyTXTrg0oJAEHgdAmtqzeusiqYOgc+fP3+vk4rZ2nq5Js46T3RWdI1LyJozBJ5OXuaCQBA4HgJr6oVbn4uLo3HisR82ubj0gczFpccls0FgbwRGD6i97Yr83yJQf4TSxUGxm7pg/Jb723faOjf1novLFDKZDwL3QGD0XMjF5QL5weHCL07vvrhcANK4EASCwIYIjB5QG5oQUQsQUJzqX6T5UYy/qM+JWRPnXFzmkMxaELg+AmvqhaORi4ujcdIxhw0XmHdfXLhAOZyysT5a90NRBxk09Rc+5r0XL22JnCl8+FfbkF11oyN9EAgCYwhob6UdGwH+Iu11VRZP1c3OmzVxXnNxkVydK/xFiFot22jducOZIr4lZ0SVhR6/zOl8kFxvnW5slY2sgyVyl57VrivjIHAVBLQPRlrLNSpsxIDwPIeACihFlKK4tBiuiXNXrLGcAs0vchRp1qXHC7/mJU/2cpiIRu80vfsFotoKH4cs750cdHf4iB780C29+MJc+iAQBMYRqPt3XFI490Jg6uIifYofdXRO/5o4q+56jXe5nCGcCZKrx882aOCr75IvHmQsOSMkSzZVP/SOrZx3yBUPujiPNCd7OFuwjXetz+Gt9bQgcHUE6j5b6m8uLkuROiAdxRDTug9z1rp+TdJQzMUz9fCx73ZRnP3AcVs4TOBlrTscWKOXHdKlNiVHdnNYVHyW6EBX+iAQBMYRWFNrxrWE8xkEan10WV5rfb6O18RZdZnLQJXDGcLloNNfaz48ksV5Bb/mKj06/YzgvKrnkc8zljw13qXf/ZF/nE9uG3rVd375esZB4MoIrKkXjkMuLo7GicbdR/fcwdO5tiZpvLhXWdhCsa9FWnr8gU5ypg4TDgMOB9Ei12VxMEzJgUf8FZ85n6qPeQ8CQWAcgTW1ZlxLOJ9FQDVRsfIPfuo7tXZOx5o4P3txqWcEtR4fqp1Lzgh8rbx6l29aZwwempNO5Guds4bzC9u+M9s/5jAwsgyDwCURWFMvHIBcXByNE40phAr81PPInTVJM/eRT7HnQoJtrp+i7rZqnXl44fFDibF4/UDVO4fHlBy3hcOEwyeHBminDwL7IrCm1uxrSaQ/QoCaSa3mnbo5x78mznP1F53o8lrPHOdCvRxgN/PQLzkjql541bsNOg/1qMkPdIlGY3RxXk3JncPAdWccBK6IgPbLSGu5RoWNGBCe7RCoH+aPJK+J87MXF7eFi44X+HpxofBrfqroy36tqTm963Leis+cTy4j4yAQBJ5DYE2teU5TuLdGgAtCrdGdnjVx9o//Ksvrtta81kOLXar9as6jC4F43OYlZwRnExcOdKmXPC5vyOJMgV4+yQ7RyQaab24tkAAABF5JREFU28ac+lxcHI2M74bAmnrh2OTi4micfEwRpbg+cmdN0sx95FPsOSS8SKvAU9Sxxw8cDgB4oXEZU7plv+jUlsip+KCj2ocN6YNAENgGgTW1ZhuNkbIVAnMXjKpjTZypv1WG3usHvdd66P0c0VyVJx63Z8kZUWVWXZxTnCUVG3Ro3s/hahtyq5/Mpw8Cd0DA9+caf3NxWYPWwWkppl4w50xekzRTlwfJf3RxkR4VdJoXcQo9B4JoODzwo8oXjWTqkSy1To7mXVeHj2T4L2PwuD3fFeQfQSAIDCOgfZZ2PgRU99fEbg0t9Vg6vFGzvQZLLrUeWs4Jzhb46jr1fckZId7OZ+mvdkqu5tGPXs3pcfurbdDm4gIS6e+IgPbJSGu5RoWNGBCe7RDgIOCD/5HkNXFW0eYAqHLrxaIWadaljwcZHCbM09fDAJmsi19jzashxw8LzcOn8RQ+HEDIRuZ3wflHEAgCTyOgvZV2fAS4DIzWwpE41/orGarV3jRX6zK2clZ4rYeXc0H8jOfOCPiQJb5Ot+igqbZ2Fx9okU+fiwtIpL8jAtpbI63lGhU2YkB43ofAEeI8dZi8D5VoDgJBYGsEjlBrtvYp8n5GIHH+GZPMBIEg0CMwWi9ycenxvMXsaNJsCU4uLluiGVlB4JgIHKHWHBOZa1mVOF8rnvEmCOyJwGi9yMVlz6gcXPZo0mzpVi4uW6IZWUHgmAgcodYcE5lrWZU4Xyue8SYI7InAaL3IxWXPqBxc9mjSHNytmBcEgsDBEEitOVhAdjIncd4J2IgNAhdEYLRe5OJywWRY6tJo0iyVH7ogEASCgBBIrblHHiTO94hzvAwCWyAwWi9ycdkC/ZPKGE2ak7obs4NAEHgTAqk1bwL+xWoT5xcDHnVB4MQIjNaLXFxOHPRnTR9Nmmf1hj8IBIF7IZBac494J873iHO8DAJbIDBaL3Jx2QL9k8oYTZqTuhuzg0AQeBMCqTVvAv7FahPnFwMedUHgxAiM1otcXE4c9GdNH02aZ/WGPwgEgXshkFpzj3gnzveIc7wMAlsgMFovcnHZAv2TyhhNmpO6G7ODQBB4EwKpNW8C/sVqE+cXAx51QeDECIzWi8mLiwTmCQbJgeRAciA5kBxIDiQHkgPJgeTA1jkwcu9qLy4jgsITBIJAEAgCQSAIBIEgEASCQBDYC4FcXPZCNnKDQBAIAkEgCASBIBAEgkAQ2AyBXFw2gzKCgkAQCAJBIAgEgSAQBIJAENgLgVxc9kI2coNAEAgCQSAIBIEgEASCQBDYDIFcXDaDMoKCQBAIAkEgCASBIBAEgkAQ2AuBXFz2QjZyg0AQCAJBIAgEgSAQBIJAENgMgVxcNoMygoJAEAgCQSAIBIEgEASCQBDYC4FcXPZCNnKDQBAIAkEgCASBIBAEgkAQ2AyBXFw2gzKCgkAQCAJBIAgEgSAQBIJAENgLgVxc9kI2coNAEAgCQSAIBIEgEASCQBDYDIH/B6CS1Fs+/BxjAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmap = {1: 'aian',\n",
    "        2: 'asian',\n",
    "        3: 'nh_black',\n",
    "        4: 'hispanic',\n",
    "        5: 'nh_white',\n",
    "        6: 'other',\n",
    "        7: 'multi_racial',\n",
    "        9: 'unknown'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_name</th>\n",
       "      <th>first_name</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Collier</td>\n",
       "      <td>Seth</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Martin</td>\n",
       "      <td>Robert</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Turner</td>\n",
       "      <td>Sharon</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Wood</td>\n",
       "      <td>Lilianna</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Planck</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Burdge</td>\n",
       "      <td>Jeffrey</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Zofnas</td>\n",
       "      <td>Scott</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Fogarty</td>\n",
       "      <td>Kjaristy</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Evans</td>\n",
       "      <td>Claretha</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lake</td>\n",
       "      <td>James</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Paule</td>\n",
       "      <td>Joan</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>McCartney</td>\n",
       "      <td>Wayne</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Estores</td>\n",
       "      <td>David</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Bolton</td>\n",
       "      <td>Joe</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Fairchild</td>\n",
       "      <td>Mark</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Kung</td>\n",
       "      <td>Michael</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Katz</td>\n",
       "      <td>Robyn</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Fletcher</td>\n",
       "      <td>Omar</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Feathers</td>\n",
       "      <td>Ronda</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Arduine</td>\n",
       "      <td>Steven</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Potter</td>\n",
       "      <td>Tanda</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Long</td>\n",
       "      <td>Marcus</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Dreaden</td>\n",
       "      <td>Jenny</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nadeau</td>\n",
       "      <td>Donna</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Baker</td>\n",
       "      <td>Antonio</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499970</th>\n",
       "      <td>Bauer</td>\n",
       "      <td>Martha</td>\n",
       "      <td>multi_racial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499971</th>\n",
       "      <td>Williams</td>\n",
       "      <td>Candace</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499972</th>\n",
       "      <td>Taffe</td>\n",
       "      <td>Frederick</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499973</th>\n",
       "      <td>Chiera</td>\n",
       "      <td>Susan</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499974</th>\n",
       "      <td>Corday</td>\n",
       "      <td>Debbie</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499975</th>\n",
       "      <td>Villegas</td>\n",
       "      <td>Claire</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499976</th>\n",
       "      <td>Narcius</td>\n",
       "      <td>Idiana</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499977</th>\n",
       "      <td>Harrigan</td>\n",
       "      <td>Fritzcel</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499978</th>\n",
       "      <td>Griffin</td>\n",
       "      <td>Redonda</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499979</th>\n",
       "      <td>Salas</td>\n",
       "      <td>Luis</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499980</th>\n",
       "      <td>Gabel</td>\n",
       "      <td>Christine</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499981</th>\n",
       "      <td>Machado</td>\n",
       "      <td>Jasmine</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499982</th>\n",
       "      <td>Bushong</td>\n",
       "      <td>Gary</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499983</th>\n",
       "      <td>Phillips</td>\n",
       "      <td>Tiffany</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499984</th>\n",
       "      <td>Santana</td>\n",
       "      <td>Mark</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499985</th>\n",
       "      <td>Cadet Ortiz</td>\n",
       "      <td>Keedal-Lyn</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499986</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>Samantha</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499987</th>\n",
       "      <td>Digerolamo</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499988</th>\n",
       "      <td>Mazagwu</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499989</th>\n",
       "      <td>Fidlin</td>\n",
       "      <td>Olga</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499990</th>\n",
       "      <td>Estrella</td>\n",
       "      <td>Zoila</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499991</th>\n",
       "      <td>Gil</td>\n",
       "      <td>Ricky</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499992</th>\n",
       "      <td>Stephens</td>\n",
       "      <td>Alisa</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499993</th>\n",
       "      <td>McCarthy</td>\n",
       "      <td>Jeannette</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499994</th>\n",
       "      <td>Goldwyn</td>\n",
       "      <td>David</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499995</th>\n",
       "      <td>Castillo</td>\n",
       "      <td>Ammy</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499996</th>\n",
       "      <td>Garcia</td>\n",
       "      <td>Maria</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499997</th>\n",
       "      <td>Pearson</td>\n",
       "      <td>George</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499998</th>\n",
       "      <td>Clark</td>\n",
       "      <td>Timothy</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499999</th>\n",
       "      <td>Gittleman</td>\n",
       "      <td>Lawrence</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1499992 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           last_name  first_name          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\n",
       "5            Collier        Seth      nh_black\n",
       "6             Martin      Robert      nh_white\n",
       "7             Turner      Sharon      nh_black\n",
       "8               Wood    Lilianna      hispanic\n",
       "9             Planck     Barbara      nh_white\n",
       "10            Burdge     Jeffrey      nh_white\n",
       "11            Zofnas       Scott      nh_white\n",
       "12           Fogarty    Kjaristy      nh_white\n",
       "13             Evans    Claretha      nh_black\n",
       "14              Lake       James      nh_white\n",
       "15             Paule        Joan      nh_white\n",
       "16         McCartney       Wayne      nh_white\n",
       "17           Estores       David         asian\n",
       "18            Bolton         Joe      nh_white\n",
       "19         Fairchild        Mark      nh_white\n",
       "20              Kung     Michael         asian\n",
       "21              Katz       Robyn      nh_white\n",
       "22          Fletcher        Omar      nh_black\n",
       "23          Feathers       Ronda      nh_black\n",
       "24           Arduine      Steven      nh_white\n",
       "25            Potter       Tanda      nh_white\n",
       "26              Long      Marcus      nh_black\n",
       "27           Dreaden       Jenny      nh_white\n",
       "28            Nadeau       Donna      nh_white\n",
       "29             Baker     Antonio      nh_black\n",
       "...              ...         ...           ...\n",
       "1499970        Bauer      Martha  multi_racial\n",
       "1499971     Williams     Candace      nh_white\n",
       "1499972        Taffe   Frederick      nh_black\n",
       "1499973       Chiera       Susan      nh_white\n",
       "1499974       Corday      Debbie      nh_white\n",
       "1499975     Villegas      Claire      hispanic\n",
       "1499976      Narcius      Idiana      nh_black\n",
       "1499977     Harrigan    Fritzcel      nh_black\n",
       "1499978      Griffin     Redonda      nh_white\n",
       "1499979        Salas        Luis      hispanic\n",
       "1499980        Gabel   Christine      nh_white\n",
       "1499981      Machado     Jasmine      hispanic\n",
       "1499982      Bushong        Gary      nh_white\n",
       "1499983     Phillips     Tiffany      nh_white\n",
       "1499984      Santana        Mark      hispanic\n",
       "1499985  Cadet Ortiz  Keedal-Lyn      hispanic\n",
       "1499986        Isaac    Samantha      nh_black\n",
       "1499987   Digerolamo     Anthony      nh_white\n",
       "1499988      Mazagwu      Joseph      nh_black\n",
       "1499989       Fidlin        Olga      nh_white\n",
       "1499990     Estrella       Zoila      hispanic\n",
       "1499991          Gil       Ricky      nh_white\n",
       "1499992     Stephens       Alisa      nh_black\n",
       "1499993     McCarthy   Jeannette      nh_white\n",
       "1499994      Goldwyn       David      nh_white\n",
       "1499995     Castillo        Ammy      hispanic\n",
       "1499996       Garcia       Maria      hispanic\n",
       "1499997      Pearson      George      nh_white\n",
       "1499998        Clark     Timothy      nh_black\n",
       "1499999    Gittleman    Lawrence      nh_white\n",
       "\n",
       "[1499992 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['race'] = df.race.apply(lambda c: rmap[c])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race\n",
       "aian              5532\n",
       "asian            32964\n",
       "hispanic        150627\n",
       "multi_racial     12914\n",
       "nh_black        220858\n",
       "nh_white        791986\n",
       "other            40999\n",
       "unknown          22918\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['race']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>862323</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623089</th>\n",
       "      <td>Harris</td>\n",
       "      <td>Earlene</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1261984</th>\n",
       "      <td>Watt</td>\n",
       "      <td>Cordel</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165654</th>\n",
       "      <td>Truax</td>\n",
       "      <td>Timothy</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997727</th>\n",
       "      <td>Solivan Velazquez</td>\n",
       "      <td>Fredesvinda</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100393</th>\n",
       "      <td>Bailes</td>\n",
       "      <td>Rosa</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1134253</th>\n",
       "      <td>Ingram</td>\n",
       "      <td>Willie</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1485277</th>\n",
       "      <td>Innocent</td>\n",
       "      <td>Petrus</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910232</th>\n",
       "      <td>Charbonneau</td>\n",
       "      <td>Luisa</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17129</th>\n",
       "      <td>Bouwkamp</td>\n",
       "      <td>Kaitlin</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355761</th>\n",
       "      <td>Bedford</td>\n",
       "      <td>Cazz</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208294</th>\n",
       "      <td>Kirkland</td>\n",
       "      <td>Minnie</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808282</th>\n",
       "      <td>Boyles</td>\n",
       "      <td>Marjorie</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524196</th>\n",
       "      <td>Bucko</td>\n",
       "      <td>Ryszard</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208678</th>\n",
       "      <td>Jenkins</td>\n",
       "      <td>Sheila</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811891</th>\n",
       "      <td>Hamilton</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371040</th>\n",
       "      <td>Bowman</td>\n",
       "      <td>Elke</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896526</th>\n",
       "      <td>Abakunchik</td>\n",
       "      <td>Oleg</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690386</th>\n",
       "      <td>Kenny</td>\n",
       "      <td>Michele</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97228</th>\n",
       "      <td>Ellerbe</td>\n",
       "      <td>Linda</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384010</th>\n",
       "      <td>Charles</td>\n",
       "      <td>Lisa</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588585</th>\n",
       "      <td>Diberardino</td>\n",
       "      <td>William</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030946</th>\n",
       "      <td>Militello</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230560</th>\n",
       "      <td>TOLIVER</td>\n",
       "      <td>LANITA</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780556</th>\n",
       "      <td>Top</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387241</th>\n",
       "      <td>Waz</td>\n",
       "      <td>Zygmunt</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106104</th>\n",
       "      <td>Brogan</td>\n",
       "      <td>Stacy</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585954</th>\n",
       "      <td>Owens</td>\n",
       "      <td>Laquaisha</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005894</th>\n",
       "      <td>Barnfield</td>\n",
       "      <td>Yesenia</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232516</th>\n",
       "      <td>Wong</td>\n",
       "      <td>Sau Fung</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113140</th>\n",
       "      <td>Littles</td>\n",
       "      <td>Cora</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872991</th>\n",
       "      <td>Drutz</td>\n",
       "      <td>Madison</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150117</th>\n",
       "      <td>Obas</td>\n",
       "      <td>Lauriane</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417242</th>\n",
       "      <td>Lefebvre</td>\n",
       "      <td>Mary Jo</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396071</th>\n",
       "      <td>Izzo</td>\n",
       "      <td>Ann</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237255</th>\n",
       "      <td>UPHOFF</td>\n",
       "      <td>MIRANDA</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200486</th>\n",
       "      <td>Krayer</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192158</th>\n",
       "      <td>Howell</td>\n",
       "      <td>Ellise</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424804</th>\n",
       "      <td>Rumbaugh</td>\n",
       "      <td>Karsten</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124593</th>\n",
       "      <td>Halpern</td>\n",
       "      <td>Jean</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057238</th>\n",
       "      <td>Dahan</td>\n",
       "      <td>David</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485564</th>\n",
       "      <td>Szekeres</td>\n",
       "      <td>Arlene</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575245</th>\n",
       "      <td>Clarke</td>\n",
       "      <td>Orrett</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334645</th>\n",
       "      <td>Germaine</td>\n",
       "      <td>Janice</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794652</th>\n",
       "      <td>Gutowski</td>\n",
       "      <td>Michael</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168551</th>\n",
       "      <td>Hodge</td>\n",
       "      <td>Crystal</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119103</th>\n",
       "      <td>Stills</td>\n",
       "      <td>Joshua</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63182</th>\n",
       "      <td>Greenman</td>\n",
       "      <td>Shellie</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303207</th>\n",
       "      <td>Tevzadze</td>\n",
       "      <td>Nani</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81347</th>\n",
       "      <td>Waite</td>\n",
       "      <td>Arthur</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547130</th>\n",
       "      <td>Espinosa</td>\n",
       "      <td>William</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1473937</th>\n",
       "      <td>Lovins</td>\n",
       "      <td>Ali</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278513</th>\n",
       "      <td>BROXTON</td>\n",
       "      <td>ALEXANDER</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110088</th>\n",
       "      <td>Patel</td>\n",
       "      <td>Pranavkumar</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621819</th>\n",
       "      <td>Mochel</td>\n",
       "      <td>David</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285550</th>\n",
       "      <td>RHINEHART</td>\n",
       "      <td>GEOFFREY</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176930</th>\n",
       "      <td>Simmons</td>\n",
       "      <td>Bobbie</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454902</th>\n",
       "      <td>Sousa</td>\n",
       "      <td>Nicole</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319751</th>\n",
       "      <td>Seay</td>\n",
       "      <td>Deborah</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946156</th>\n",
       "      <td>Weidman</td>\n",
       "      <td>Allen</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 last_name   first_name      race\n",
       "862323              Jordan       Joseph  nh_black\n",
       "623089              Harris      Earlene  nh_black\n",
       "1261984               Watt       Cordel  nh_black\n",
       "1165654              Truax      Timothy  nh_white\n",
       "997727   Solivan Velazquez  Fredesvinda  hispanic\n",
       "100393              Bailes         Rosa  nh_white\n",
       "1134253             Ingram       Willie  nh_black\n",
       "1485277           Innocent       Petrus  nh_black\n",
       "910232         Charbonneau        Luisa  nh_white\n",
       "17129             Bouwkamp      Kaitlin  nh_white\n",
       "355761             Bedford         Cazz  nh_white\n",
       "208294            Kirkland       Minnie  nh_white\n",
       "808282              Boyles     Marjorie  nh_white\n",
       "524196               Bucko      Ryszard  nh_white\n",
       "1208678            Jenkins       Sheila  nh_black\n",
       "811891            Hamilton      Barbara  nh_black\n",
       "371040              Bowman         Elke  nh_white\n",
       "896526          Abakunchik         Oleg  nh_white\n",
       "690386               Kenny      Michele  nh_white\n",
       "97228              Ellerbe        Linda  nh_black\n",
       "384010             Charles         Lisa  nh_white\n",
       "588585         Diberardino      William  nh_white\n",
       "1030946          Militello       Thomas  nh_white\n",
       "230560             TOLIVER       LANITA  nh_black\n",
       "780556                 Top      Barbara  nh_white\n",
       "1387241                Waz      Zygmunt  nh_white\n",
       "106104              Brogan        Stacy  nh_white\n",
       "585954               Owens    Laquaisha  nh_black\n",
       "1005894          Barnfield      Yesenia  hispanic\n",
       "1232516               Wong     Sau Fung     asian\n",
       "...                    ...          ...       ...\n",
       "113140             Littles         Cora  nh_black\n",
       "872991               Drutz      Madison  nh_white\n",
       "1150117               Obas     Lauriane  nh_black\n",
       "417242            Lefebvre      Mary Jo  nh_white\n",
       "1396071               Izzo          Ann  nh_white\n",
       "237255              UPHOFF      MIRANDA  nh_white\n",
       "1200486             Krayer      Anthony  nh_white\n",
       "192158              Howell       Ellise  nh_white\n",
       "424804            Rumbaugh      Karsten  nh_white\n",
       "124593             Halpern         Jean  nh_white\n",
       "1057238              Dahan        David  nh_white\n",
       "485564            Szekeres       Arlene  nh_white\n",
       "575245              Clarke       Orrett  nh_black\n",
       "1334645           Germaine       Janice  nh_black\n",
       "794652            Gutowski      Michael  nh_white\n",
       "168551               Hodge      Crystal  nh_white\n",
       "119103              Stills       Joshua  nh_black\n",
       "63182             Greenman      Shellie  nh_white\n",
       "1303207           Tevzadze         Nani  nh_white\n",
       "81347                Waite       Arthur  nh_white\n",
       "547130            Espinosa      William  hispanic\n",
       "1473937             Lovins          Ali  nh_white\n",
       "278513             BROXTON    ALEXANDER  nh_black\n",
       "110088               Patel  Pranavkumar     asian\n",
       "621819              Mochel        David  nh_white\n",
       "285550           RHINEHART     GEOFFREY  nh_white\n",
       "176930             Simmons       Bobbie  nh_black\n",
       "454902               Sousa       Nicole  nh_white\n",
       "1319751               Seay      Deborah  nh_black\n",
       "946156             Weidman        Allen  nh_white\n",
       "\n",
       "[1000000 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf = df[df.race.isin(['asian', 'hispanic', 'nh_black', 'nh_white'])].sample(SAMPLE, random_state=21)\n",
    "sdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>asian</th>\n",
       "      <td>27592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic</th>\n",
       "      <td>125973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nh_black</th>\n",
       "      <td>184701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nh_white</th>\n",
       "      <td>661734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          first_name\n",
       "race                \n",
       "asian          27592\n",
       "hispanic      125973\n",
       "nh_black      184701\n",
       "nh_white      661734"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Additional features\n",
    "sdf['first_name'] = sdf.first_name.str.title()\n",
    "sdf['last_name'] = sdf.last_name.str.title()\n",
    "\n",
    "sdf.groupby('race').agg({'first_name': 'count'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metaphone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>862323</th>\n",
       "      <td>JRTN</td>\n",
       "      <td>JSF</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623089</th>\n",
       "      <td>HRS</td>\n",
       "      <td>ARLN</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1261984</th>\n",
       "      <td>AT</td>\n",
       "      <td>KRTL</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165654</th>\n",
       "      <td>TRKS</td>\n",
       "      <td>TM0</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997727</th>\n",
       "      <td>SLFNFLSKS</td>\n",
       "      <td>FRTSFNT</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100393</th>\n",
       "      <td>PLS</td>\n",
       "      <td>RS</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1134253</th>\n",
       "      <td>ANKRM</td>\n",
       "      <td>AL</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1485277</th>\n",
       "      <td>ANSNT</td>\n",
       "      <td>PTRS</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910232</th>\n",
       "      <td>XRPN</td>\n",
       "      <td>LS</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17129</th>\n",
       "      <td>PKMP</td>\n",
       "      <td>KTLN</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355761</th>\n",
       "      <td>PTFRT</td>\n",
       "      <td>KS</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208294</th>\n",
       "      <td>KRKLNT</td>\n",
       "      <td>MN</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808282</th>\n",
       "      <td>PLS</td>\n",
       "      <td>MRJR</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524196</th>\n",
       "      <td>PK</td>\n",
       "      <td>RSRT</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208678</th>\n",
       "      <td>JNKNS</td>\n",
       "      <td>XL</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811891</th>\n",
       "      <td>HMLTN</td>\n",
       "      <td>PRPR</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371040</th>\n",
       "      <td>PMN</td>\n",
       "      <td>ALK</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896526</th>\n",
       "      <td>APKNXK</td>\n",
       "      <td>ALK</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690386</th>\n",
       "      <td>KN</td>\n",
       "      <td>MXL</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97228</th>\n",
       "      <td>ALRP</td>\n",
       "      <td>LNT</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384010</th>\n",
       "      <td>XRLS</td>\n",
       "      <td>LS</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588585</th>\n",
       "      <td>TPRRTN</td>\n",
       "      <td>ALM</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030946</th>\n",
       "      <td>MLTL</td>\n",
       "      <td>TMS</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230560</th>\n",
       "      <td>TLFR</td>\n",
       "      <td>LNT</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780556</th>\n",
       "      <td>TP</td>\n",
       "      <td>PRPR</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387241</th>\n",
       "      <td>AS</td>\n",
       "      <td>SKMNT</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106104</th>\n",
       "      <td>PRKN</td>\n",
       "      <td>STS</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585954</th>\n",
       "      <td>ANS</td>\n",
       "      <td>LKX</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005894</th>\n",
       "      <td>PRNFLT</td>\n",
       "      <td>ASN</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232516</th>\n",
       "      <td>ANK</td>\n",
       "      <td>SFNK</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113140</th>\n",
       "      <td>LTLS</td>\n",
       "      <td>KR</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872991</th>\n",
       "      <td>TRTS</td>\n",
       "      <td>MTSN</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150117</th>\n",
       "      <td>APS</td>\n",
       "      <td>LRN</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417242</th>\n",
       "      <td>LFPFR</td>\n",
       "      <td>MRJ</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396071</th>\n",
       "      <td>AS</td>\n",
       "      <td>AN</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237255</th>\n",
       "      <td>AFF</td>\n",
       "      <td>MRNT</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200486</th>\n",
       "      <td>KRR</td>\n",
       "      <td>AN0N</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192158</th>\n",
       "      <td>HL</td>\n",
       "      <td>ALS</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424804</th>\n",
       "      <td>RMP</td>\n",
       "      <td>KRSTN</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124593</th>\n",
       "      <td>HLPRN</td>\n",
       "      <td>JN</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057238</th>\n",
       "      <td>THN</td>\n",
       "      <td>TFT</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485564</th>\n",
       "      <td>SKRS</td>\n",
       "      <td>ARLN</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575245</th>\n",
       "      <td>KLRK</td>\n",
       "      <td>ART</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334645</th>\n",
       "      <td>KRMN</td>\n",
       "      <td>JNS</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794652</th>\n",
       "      <td>KTSK</td>\n",
       "      <td>MKL</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168551</th>\n",
       "      <td>HJ</td>\n",
       "      <td>KRSTL</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119103</th>\n",
       "      <td>STLS</td>\n",
       "      <td>JX</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63182</th>\n",
       "      <td>KRNMN</td>\n",
       "      <td>XL</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303207</th>\n",
       "      <td>TFSTS</td>\n",
       "      <td>NN</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81347</th>\n",
       "      <td>AT</td>\n",
       "      <td>AR0R</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547130</th>\n",
       "      <td>ASPNS</td>\n",
       "      <td>ALM</td>\n",
       "      <td>hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1473937</th>\n",
       "      <td>LFNS</td>\n",
       "      <td>AL</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278513</th>\n",
       "      <td>PRKSTN</td>\n",
       "      <td>ALKSNTR</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110088</th>\n",
       "      <td>PTL</td>\n",
       "      <td>PRNFKMR</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621819</th>\n",
       "      <td>MXL</td>\n",
       "      <td>TFT</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285550</th>\n",
       "      <td>RNHRT</td>\n",
       "      <td>JFR</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176930</th>\n",
       "      <td>SMNS</td>\n",
       "      <td>PP</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454902</th>\n",
       "      <td>SS</td>\n",
       "      <td>NKL</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319751</th>\n",
       "      <td>S</td>\n",
       "      <td>TPR</td>\n",
       "      <td>nh_black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946156</th>\n",
       "      <td>ATMN</td>\n",
       "      <td>ALN</td>\n",
       "      <td>nh_white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         last_name first_name      race\n",
       "862323        JRTN        JSF  nh_black\n",
       "623089         HRS       ARLN  nh_black\n",
       "1261984         AT       KRTL  nh_black\n",
       "1165654       TRKS        TM0  nh_white\n",
       "997727   SLFNFLSKS    FRTSFNT  hispanic\n",
       "100393         PLS         RS  nh_white\n",
       "1134253      ANKRM         AL  nh_black\n",
       "1485277      ANSNT       PTRS  nh_black\n",
       "910232        XRPN         LS  nh_white\n",
       "17129         PKMP       KTLN  nh_white\n",
       "355761       PTFRT         KS  nh_white\n",
       "208294      KRKLNT         MN  nh_white\n",
       "808282         PLS       MRJR  nh_white\n",
       "524196          PK       RSRT  nh_white\n",
       "1208678      JNKNS         XL  nh_black\n",
       "811891       HMLTN       PRPR  nh_black\n",
       "371040         PMN        ALK  nh_white\n",
       "896526      APKNXK        ALK  nh_white\n",
       "690386          KN        MXL  nh_white\n",
       "97228         ALRP        LNT  nh_black\n",
       "384010        XRLS         LS  nh_white\n",
       "588585      TPRRTN        ALM  nh_white\n",
       "1030946       MLTL        TMS  nh_white\n",
       "230560        TLFR        LNT  nh_black\n",
       "780556          TP       PRPR  nh_white\n",
       "1387241         AS      SKMNT  nh_white\n",
       "106104        PRKN        STS  nh_white\n",
       "585954         ANS        LKX  nh_black\n",
       "1005894     PRNFLT        ASN  hispanic\n",
       "1232516        ANK       SFNK     asian\n",
       "...            ...        ...       ...\n",
       "113140        LTLS         KR  nh_black\n",
       "872991        TRTS       MTSN  nh_white\n",
       "1150117        APS        LRN  nh_black\n",
       "417242       LFPFR        MRJ  nh_white\n",
       "1396071         AS         AN  nh_white\n",
       "237255         AFF       MRNT  nh_white\n",
       "1200486        KRR       AN0N  nh_white\n",
       "192158          HL        ALS  nh_white\n",
       "424804         RMP      KRSTN  nh_white\n",
       "124593       HLPRN         JN  nh_white\n",
       "1057238        THN        TFT  nh_white\n",
       "485564        SKRS       ARLN  nh_white\n",
       "575245        KLRK        ART  nh_black\n",
       "1334645       KRMN        JNS  nh_black\n",
       "794652        KTSK        MKL  nh_white\n",
       "168551          HJ      KRSTL  nh_white\n",
       "119103        STLS         JX  nh_black\n",
       "63182        KRNMN         XL  nh_white\n",
       "1303207      TFSTS         NN  nh_white\n",
       "81347           AT       AR0R  nh_white\n",
       "547130       ASPNS        ALM  hispanic\n",
       "1473937       LFNS         AL  nh_white\n",
       "278513      PRKSTN    ALKSNTR  nh_black\n",
       "110088         PTL    PRNFKMR     asian\n",
       "621819         MXL        TFT  nh_white\n",
       "285550       RNHRT        JFR  nh_white\n",
       "176930        SMNS         PP  nh_black\n",
       "454902          SS        NKL  nh_white\n",
       "1319751          S        TPR  nh_black\n",
       "946156        ATMN        ALN  nh_white\n",
       "\n",
       "[1000000 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf['first_name'] = sdf.first_name.apply(lambda c: doublemetaphone(c)[0])\n",
    "sdf['last_name'] = sdf.last_name.apply(lambda c: doublemetaphone(c)[0])\n",
    "sdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing the input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_words = 2606\n"
     ]
    }
   ],
   "source": [
    "# concat last name and first name\n",
    "sdf['last_name_first_name'] = sdf['last_name'] + ' ' + sdf['first_name']\n",
    "\n",
    "# build n-gram list\n",
    "vect = CountVectorizer(analyzer='char', max_df=0.3, min_df=10, ngram_range=NGRAMS, lowercase=False) \n",
    "a = vect.fit_transform(sdf.last_name_first_name)\n",
    "vocab = vect.vocabulary_\n",
    "\n",
    "# sort n-gram by freq (highest -> lowest)\n",
    "words = []\n",
    "for b in vocab:\n",
    "    c = vocab[b]\n",
    "    #print(b, c, a[:, c].sum())\n",
    "    words.append((a[:, c].sum(), b))\n",
    "    #break\n",
    "words = sorted(words, reverse=True)\n",
    "words_list = [w[1] for w in words]\n",
    "num_words = len(words_list)\n",
    "print(\"num_words = %d\" % num_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'JR': 916,\n",
       " 'RT': 2055,\n",
       " 'TN': 2374,\n",
       " 'N ': 1523,\n",
       " ' J': 53,\n",
       " 'JS': 930,\n",
       " 'SF': 2108,\n",
       " 'JRT': 928,\n",
       " 'RTN': 2064,\n",
       " 'TN ': 2375,\n",
       " 'N J': 1528,\n",
       " ' JS': 64,\n",
       " 'JSF': 933,\n",
       " 'HR': 765,\n",
       " 'RS': 2041,\n",
       " 'S ': 2083,\n",
       " ' A': 10,\n",
       " 'AR': 438,\n",
       " 'RL': 1968,\n",
       " 'LN': 1251,\n",
       " 'HRS': 777,\n",
       " 'RS ': 2042,\n",
       " 'S A': 2085,\n",
       " ' AR': 21,\n",
       " 'ARL': 445,\n",
       " 'RLN': 1977,\n",
       " 'AT': 468,\n",
       " 'T ': 2274,\n",
       " ' K': 67,\n",
       " 'KR': 1092,\n",
       " 'TL': 2345,\n",
       " 'AT ': 469,\n",
       " 'T K': 2280,\n",
       " ' KR': 77,\n",
       " 'KRT': 1105,\n",
       " 'RTL': 2062,\n",
       " 'TR': 2403,\n",
       " 'RK': 1953,\n",
       " 'KS': 1107,\n",
       " ' T': 165,\n",
       " 'TM': 2360,\n",
       " 'M0': 1352,\n",
       " 'TRK': 2409,\n",
       " 'RKS': 1965,\n",
       " 'KS ': 1108,\n",
       " 'S T': 2096,\n",
       " ' TM': 172,\n",
       " 'TM0': 2362,\n",
       " 'SL': 2159,\n",
       " 'LF': 1171,\n",
       " 'FN': 576,\n",
       " 'NF': 1547,\n",
       " 'FL': 552,\n",
       " 'LS': 1296,\n",
       " 'SK': 2144,\n",
       " ' F': 25,\n",
       " 'FR': 599,\n",
       " 'TS': 2418,\n",
       " 'NT': 1683,\n",
       " 'SLF': 2162,\n",
       " 'LFN': 1180,\n",
       " 'FNF': 579,\n",
       " 'NFL': 1552,\n",
       " 'FLS': 564,\n",
       " 'LSK': 1301,\n",
       " 'SKS': 2156,\n",
       " 'S F': 2086,\n",
       " ' FR': 35,\n",
       " 'FRT': 612,\n",
       " 'RTS': 2067,\n",
       " 'TSF': 2421,\n",
       " 'SFN': 2116,\n",
       " 'FNT': 588,\n",
       " 'PL': 1780,\n",
       " ' R': 137,\n",
       " 'PLS': 1792,\n",
       " 'LS ': 1297,\n",
       " 'S R': 2094,\n",
       " ' RS': 148,\n",
       " 'AN': 408,\n",
       " 'NK': 1581,\n",
       " 'RM': 1983,\n",
       " 'M ': 1337,\n",
       " 'AL': 378,\n",
       " 'ANK': 414,\n",
       " 'NKR': 1591,\n",
       " 'KRM': 1100,\n",
       " 'RM ': 1984,\n",
       " 'M A': 1339,\n",
       " ' AL': 17,\n",
       " 'NS': 1669,\n",
       " 'SN': 2189,\n",
       " ' P': 123,\n",
       " 'PT': 1861,\n",
       " 'ANS': 420,\n",
       " 'NSN': 1677,\n",
       " 'SNT': 2202,\n",
       " 'NT ': 1684,\n",
       " 'T P': 2284,\n",
       " ' PT': 135,\n",
       " 'PTR': 1871,\n",
       " 'TRS': 2415,\n",
       " 'XR': 2560,\n",
       " 'RP': 2013,\n",
       " 'PN': 1805,\n",
       " ' L': 81,\n",
       " 'XRP': 2569,\n",
       " 'RPN': 2021,\n",
       " 'PN ': 1806,\n",
       " 'N L': 1530,\n",
       " ' LS': 92,\n",
       " 'PK': 1766,\n",
       " 'KM': 1047,\n",
       " 'MP': 1453,\n",
       " 'P ': 1710,\n",
       " 'KT': 1121,\n",
       " 'PKM': 1773,\n",
       " 'KMP': 1057,\n",
       " 'MP ': 1454,\n",
       " 'P K': 1716,\n",
       " ' KT': 79,\n",
       " 'KTL': 1127,\n",
       " 'TLN': 2354,\n",
       " 'TF': 2295,\n",
       " 'PTF': 1863,\n",
       " 'TFR': 2304,\n",
       " 'RT ': 2056,\n",
       " ' KS': 78,\n",
       " 'KL': 1032,\n",
       " ' M': 95,\n",
       " 'MN': 1438,\n",
       " 'KRK': 1098,\n",
       " 'RKL': 1960,\n",
       " 'KLN': 1041,\n",
       " 'LNT': 1264,\n",
       " 'T M': 2282,\n",
       " ' MN': 103,\n",
       " 'MR': 1467,\n",
       " 'RJ': 1940,\n",
       " 'S M': 2091,\n",
       " ' MR': 105,\n",
       " 'MRJ': 1472,\n",
       " 'RJR': 1949,\n",
       " 'K ': 964,\n",
       " 'SR': 2219,\n",
       " 'PK ': 1767,\n",
       " 'K R': 975,\n",
       " 'RSR': 2051,\n",
       " 'SRT': 2232,\n",
       " 'JN': 893,\n",
       " 'KN': 1062,\n",
       " ' X': 179,\n",
       " 'XL': 2509,\n",
       " 'JNK': 898,\n",
       " 'NKN': 1589,\n",
       " 'KNS': 1074,\n",
       " 'NS ': 1670,\n",
       " 'S X': 2097,\n",
       " ' XL': 184,\n",
       " 'HM': 727,\n",
       " 'ML': 1410,\n",
       " 'LT': 1310,\n",
       " 'PR': 1833,\n",
       " 'HML': 731,\n",
       " 'MLT': 1423,\n",
       " 'LTN': 1319,\n",
       " 'N P': 1533,\n",
       " ' PR': 133,\n",
       " 'PRP': 1843,\n",
       " 'RPR': 2023,\n",
       " 'PM': 1795,\n",
       " 'LK': 1207,\n",
       " 'PMN': 1800,\n",
       " 'MN ': 1439,\n",
       " 'N A': 1525,\n",
       " 'ALK': 384,\n",
       " 'AP': 423,\n",
       " 'NX': 1698,\n",
       " 'XK': 2496,\n",
       " 'APK': 429,\n",
       " 'PKN': 1774,\n",
       " 'KNX': 1076,\n",
       " 'NXK': 1701,\n",
       " 'XK ': 2497,\n",
       " 'K A': 966,\n",
       " 'MX': 1511,\n",
       " 'KN ': 1063,\n",
       " 'N M': 1531,\n",
       " ' MX': 108,\n",
       " 'MXL': 1515,\n",
       " 'LR': 1281,\n",
       " 'ALR': 389,\n",
       " 'LRP': 1291,\n",
       " 'RP ': 2014,\n",
       " 'P L': 1717,\n",
       " ' LN': 89,\n",
       " 'XRL': 2566,\n",
       " 'RLS': 1980,\n",
       " 'S L': 2090,\n",
       " 'TP': 2389,\n",
       " 'RR': 2027,\n",
       " 'LM': 1236,\n",
       " 'TPR': 2399,\n",
       " 'PRR': 1844,\n",
       " 'RRT': 2039,\n",
       " 'ALM': 386,\n",
       " 'L ': 1147,\n",
       " 'MS': 1482,\n",
       " 'LTL': 1317,\n",
       " 'TL ': 2346,\n",
       " 'L T': 1160,\n",
       " 'TMS': 2371,\n",
       " 'R ': 1887,\n",
       " 'TLF': 2348,\n",
       " 'LFR': 1182,\n",
       " 'FR ': 600,\n",
       " 'R L': 1894,\n",
       " 'TP ': 2390,\n",
       " 'P P': 1720,\n",
       " 'AS': 453,\n",
       " ' S': 151,\n",
       " 'AS ': 454,\n",
       " 'S S': 2095,\n",
       " ' SK': 156,\n",
       " 'SKM': 2152,\n",
       " 'KMN': 1056,\n",
       " 'MNT': 1451,\n",
       " 'ST': 2248,\n",
       " 'PRK': 1839,\n",
       " 'RKN': 1962,\n",
       " 'N S': 1535,\n",
       " ' ST': 163,\n",
       " 'STS': 2260,\n",
       " 'KX': 1135,\n",
       " ' LK': 86,\n",
       " 'LKX': 1221,\n",
       " 'RN': 1998,\n",
       " 'PRN': 1842,\n",
       " 'RNF': 2001,\n",
       " 'FLT': 565,\n",
       " 'LT ': 1311,\n",
       " 'T A': 2276,\n",
       " ' AS': 22,\n",
       " 'ASN': 462,\n",
       " 'NK ': 1582,\n",
       " 'K S': 976,\n",
       " ' SF': 153,\n",
       " 'FNK': 581,\n",
       " 'ST ': 2249,\n",
       " 'T S': 2286,\n",
       " ' SL': 157,\n",
       " 'PS': 1848,\n",
       " 'JX': 957,\n",
       " 'PSR': 1857,\n",
       " 'T J': 2279,\n",
       " ' JX': 66,\n",
       " 'RF': 1916,\n",
       " 'KRF': 1095,\n",
       " 'RFL': 1922,\n",
       " 'FL ': 553,\n",
       " 'L J': 1152,\n",
       " ' JN': 61,\n",
       " 'JNF': 896,\n",
       " 'NFR': 1556,\n",
       " 'PLM': 1788,\n",
       " 'LM ': 1237,\n",
       " 'M X': 1351,\n",
       " ' XR': 188,\n",
       " 'PL ': 1781,\n",
       " 'L L': 1154,\n",
       " ' LT': 93,\n",
       " 'ARS': 450,\n",
       " 'RSN': 2049,\n",
       " 'SN ': 2190,\n",
       " 'N F': 1526,\n",
       " 'FRN': 608,\n",
       " 'RNK': 2004,\n",
       " 'HN': 739,\n",
       " 'HN ': 740,\n",
       " 'N K': 1529,\n",
       " 'KSN': 1115,\n",
       " 'NTR': 1694,\n",
       " 'FT': 628,\n",
       " 'TFT': 2306,\n",
       " 'FT ': 629,\n",
       " 'HL': 712,\n",
       " 'R0': 1902,\n",
       " '0 ': 192,\n",
       " 'N0': 1538,\n",
       " 'HLN': 721,\n",
       " 'LNK': 1257,\n",
       " 'NKS': 1592,\n",
       " 'KSR': 1117,\n",
       " 'SR0': 2221,\n",
       " 'R0 ': 1903,\n",
       " '0 K': 198,\n",
       " ' KN': 75,\n",
       " 'KN0': 1064,\n",
       " 'ALT': 391,\n",
       " 'LTR': 1321,\n",
       " ' TR': 175,\n",
       " 'JM': 882,\n",
       " 'KLM': 1040,\n",
       " 'M J': 1342,\n",
       " ' JM': 60,\n",
       " 'JMS': 890,\n",
       " 'NP': 1639,\n",
       " ' N': 109,\n",
       " 'TNP': 2384,\n",
       " 'NPR': 1650,\n",
       " 'RK ': 1954,\n",
       " 'K N': 973,\n",
       " ' NT': 121,\n",
       " 'NTL': 1690,\n",
       " 'PRT': 1846,\n",
       " 'TS ': 2419,\n",
       " 'AL ': 379,\n",
       " 'L K': 1153,\n",
       " ' KL': 73,\n",
       " 'AK': 364,\n",
       " 'KLT': 1045,\n",
       " 'LTS': 1322,\n",
       " ' AK': 16,\n",
       " 'AKN': 372,\n",
       " 'JK': 857,\n",
       " 'JKS': 866,\n",
       " 'X ': 2457,\n",
       " 'RNX': 2012,\n",
       " 'NX ': 1699,\n",
       " 'X J': 2462,\n",
       " 'PKR': 1776,\n",
       " 'KR ': 1093,\n",
       " 'R A': 1889,\n",
       " ' AP': 20,\n",
       " ' H': 39,\n",
       " 'HP': 753,\n",
       " 'K H': 968,\n",
       " ' HP': 48,\n",
       " 'HPR': 761,\n",
       " 'XN': 2533,\n",
       " 'HL ': 713,\n",
       " 'L X': 1161,\n",
       " ' XN': 186,\n",
       " 'XNT': 2546,\n",
       " 'TRL': 2410,\n",
       " 'JF': 833,\n",
       " 'TRF': 2406,\n",
       " ' JF': 55,\n",
       " 'JFR': 838,\n",
       " 'T X': 2288,\n",
       " 'ALN': 387,\n",
       " 'LN ': 1252,\n",
       " ' AT': 23,\n",
       " 'ATM': 476,\n",
       " 'HK': 701,\n",
       " 'HKM': 706,\n",
       " ' JK': 58,\n",
       " 'ART': 451,\n",
       " 'NR': 1654,\n",
       " 'KNR': 1073,\n",
       " 'NR ': 1655,\n",
       " 'R J': 1892,\n",
       " 'R X': 1901,\n",
       " 'HNS': 750,\n",
       " 'XT': 2585,\n",
       " 'RX': 2070,\n",
       " 'HRN': 774,\n",
       " 'NXT': 1708,\n",
       " 'XTN': 2591,\n",
       " 'N R': 1534,\n",
       " ' RX': 150,\n",
       " 'RXL': 2075,\n",
       " 'PRS': 1845,\n",
       " ' ML': 101,\n",
       " 'JRK': 921,\n",
       " 'K J': 969,\n",
       " 'XS': 2574,\n",
       " 'XSN': 2580,\n",
       " 'SP': 2204,\n",
       " 'ARN': 447,\n",
       " 'RNS': 2010,\n",
       " 'NSP': 1678,\n",
       " 'SPK': 2210,\n",
       " 'M K': 1343,\n",
       " 'KRL': 1099,\n",
       " 'TK': 2330,\n",
       " 'TKR': 2341,\n",
       " 'R F': 1890,\n",
       " 'FRL': 606,\n",
       " 'RLT': 1981,\n",
       " 'AF': 331,\n",
       " 'RL ': 1969,\n",
       " 'L A': 1149,\n",
       " ' AF': 13,\n",
       " 'PNT': 1817,\n",
       " 'T T': 2287,\n",
       " ' TN': 173,\n",
       " 'R M': 1895,\n",
       " 'PLN': 1789,\n",
       " ' FL': 31,\n",
       " ' TS': 176,\n",
       " 'TSN': 2426,\n",
       " 'HLR': 723,\n",
       " 'LR ': 1282,\n",
       " 'NTS': 1695,\n",
       " 'STR': 2259,\n",
       " 'TRT': 2416,\n",
       " ' PN': 131,\n",
       " 'JL': 869,\n",
       " 'PTL': 1867,\n",
       " 'TLR': 2356,\n",
       " ' JL': 59,\n",
       " 'MK': 1395,\n",
       " 'X M': 2465,\n",
       " ' MK': 100,\n",
       " 'MKL': 1402,\n",
       " 'KLR': 1043,\n",
       " 'LRK': 1287,\n",
       " ' SN': 159,\n",
       " 'TR0': 2405,\n",
       " 'NJ': 1569,\n",
       " 'J ': 815,\n",
       " 'TRN': 2412,\n",
       " 'RNJ': 2003,\n",
       " 'NJ ': 1570,\n",
       " 'J R': 826,\n",
       " 'RSL': 2047,\n",
       " 'KL ': 1033,\n",
       " 'TSR': 2428,\n",
       " 'HNT': 751,\n",
       " 'SNS': 2201,\n",
       " ' AN': 19,\n",
       " 'ANT': 421,\n",
       " 'TR ': 2404,\n",
       " 'R K': 1893,\n",
       " 'SS': 2234,\n",
       " 'KRP': 1102,\n",
       " 'RPS': 2024,\n",
       " 'PSS': 1858,\n",
       " 'SS ': 2235,\n",
       " 'XF': 2474,\n",
       " 'XFR': 2480,\n",
       " '0L': 234,\n",
       " '0LR': 241,\n",
       " 'LRT': 1294,\n",
       " ' TP': 174,\n",
       " 'SR ': 2220,\n",
       " 'L P': 1157,\n",
       " 'TNS': 2386,\n",
       " 'AH': 345,\n",
       " 'AHR': 351,\n",
       " 'RN ': 1999,\n",
       " 'LSN': 1304,\n",
       " 'SNK': 2195,\n",
       " 'NKM': 1588,\n",
       " 'KMK': 1053,\n",
       " 'MK ': 1396,\n",
       " 'LKN': 1216,\n",
       " 'FS': 614,\n",
       " 'RFS': 1927,\n",
       " 'FS ': 615,\n",
       " 'S P': 2093,\n",
       " 'L F': 1150,\n",
       " 'L S': 1159,\n",
       " 'STF': 2251,\n",
       " 'TFN': 2302,\n",
       " 'NL': 1595,\n",
       " 'R T': 1900,\n",
       " 'TNL': 2381,\n",
       " 'NLT': 1607,\n",
       " 'P M': 1718,\n",
       " 'MR0': 1469,\n",
       " 'LNS': 1263,\n",
       " 'P0': 1725,\n",
       " 'R P': 1897,\n",
       " ' P0': 124,\n",
       " 'KRS': 1104,\n",
       " 'RST': 2053,\n",
       " 'STN': 2257,\n",
       " 'SLN': 2168,\n",
       " 'KNK': 1068,\n",
       " 'K X': 978,\n",
       " 'XLS': 2519,\n",
       " 'XR ': 2561,\n",
       " 'S0': 2098,\n",
       " '0R': 267,\n",
       " 'AS0': 455,\n",
       " 'S0R': 2105,\n",
       " 'TFS': 2305,\n",
       " 'FSN': 622,\n",
       " 'MSP': 1492,\n",
       " 'SPR': 2215,\n",
       " ' PL': 129,\n",
       " 'LMN': 1245,\n",
       " 'JRJ': 920,\n",
       " 'RJ ': 1941,\n",
       " 'J J': 820,\n",
       " 'LRJ': 1286,\n",
       " 'RJN': 1947,\n",
       " 'JNT': 905,\n",
       " 'KXN': 1141,\n",
       " 'XNS': 2545,\n",
       " ' RP': 146,\n",
       " 'HRM': 773,\n",
       " 'RMN': 1992,\n",
       " 'N X': 1537,\n",
       " 'NTN': 1692,\n",
       " ' SS': 162,\n",
       " 'SSL': 2240,\n",
       " 'NSL': 1675,\n",
       " 'SLS': 2171,\n",
       " 'LP': 1266,\n",
       " 'LPK': 1272,\n",
       " 'MPN': 1461,\n",
       " 'N N': 1532,\n",
       " ' NK': 114,\n",
       " 'NKL': 1587,\n",
       " 'MKN': 1404,\n",
       " ' M0': 96,\n",
       " 'STL': 2255,\n",
       " 'L R': 1158,\n",
       " 'RXR': 2079,\n",
       " 'XRT': 2572,\n",
       " 'PNR': 1815,\n",
       " 'AFR': 341,\n",
       " 'F ': 495,\n",
       " 'F P': 505,\n",
       " 'HS': 780,\n",
       " 'HSN': 787,\n",
       " 'MLS': 1422,\n",
       " 'PR ': 1834,\n",
       " 'AKR': 374,\n",
       " 'R R': 1898,\n",
       " 'RPK': 2018,\n",
       " 'KF': 987,\n",
       " 'KFN': 994,\n",
       " 'K T': 977,\n",
       " 'PT ': 1862,\n",
       " ' JR': 63,\n",
       " 'JRM': 923,\n",
       " 'KST': 1119,\n",
       " 'T N': 2283,\n",
       " 'PF': 1736,\n",
       " 'HPF': 755,\n",
       " 'PFR': 1743,\n",
       " 'KRN': 1101,\n",
       " 'LSS': 1307,\n",
       " 'SSN': 2242,\n",
       " 'RNR': 2009,\n",
       " 'MRL': 1474,\n",
       " 'PTN': 1869,\n",
       " 'NPT': 1652,\n",
       " 'ANL': 415,\n",
       " 'PRM': 1841,\n",
       " 'RMK': 1989,\n",
       " 'K L': 971,\n",
       " 'LL': 1222,\n",
       " 'ALL': 385,\n",
       " 'LL ': 1223,\n",
       " 'L M': 1155,\n",
       " 'T R': 2285,\n",
       " ' RN': 145,\n",
       " 'RNT': 2011,\n",
       " 'KLP': 1042,\n",
       " 'LPR': 1277,\n",
       " 'T F': 2277,\n",
       " 'FRJ': 604,\n",
       " 'HX': 805,\n",
       " 'KP': 1077,\n",
       " 'HXF': 807,\n",
       " ' RK': 142,\n",
       " 'RKP': 1963,\n",
       " 'KPR': 1088,\n",
       " 'FM': 567,\n",
       " 'RFM': 1923,\n",
       " 'FMN': 571,\n",
       " 'T H': 2278,\n",
       " ' HL': 45,\n",
       " 'JNS': 904,\n",
       " ' LL': 87,\n",
       " ' 0': 0,\n",
       " 'N 0': 1524,\n",
       " ' 0R': 6,\n",
       " '0RS': 278,\n",
       " 'AJ': 354,\n",
       " 'AJL': 357,\n",
       " 'JL ': 870,\n",
       " 'ANJ': 413,\n",
       " 'NJL': 1574,\n",
       " 'MM': 1425,\n",
       " ' MM': 102,\n",
       " ' SR': 161,\n",
       " 'AM': 393,\n",
       " 'HS ': 781,\n",
       " ' AM': 18,\n",
       " 'AMP': 403,\n",
       " 'MPR': 1463,\n",
       " 'N H': 1527,\n",
       " ' HR': 49,\n",
       " 'HRT': 778,\n",
       " 'XRF': 2563,\n",
       " 'AMN': 402,\n",
       " 'MNL': 1445,\n",
       " 'MRK': 1473,\n",
       " 'ATL': 475,\n",
       " 'TRR': 2414,\n",
       " 'RRR': 2037,\n",
       " 'RRN': 2035,\n",
       " 'J P': 825,\n",
       " 'PNJ': 1809,\n",
       " 'NJM': 1575,\n",
       " 'JMN': 887,\n",
       " 'NP ': 1640,\n",
       " 'P S': 1722,\n",
       " 'HLM': 720,\n",
       " 'XL ': 2510,\n",
       " 'NLS': 1606,\n",
       " 'SJ': 2133,\n",
       " 'JP': 907,\n",
       " 'FTS': 639,\n",
       " 'TSJ': 2422,\n",
       " 'SJP': 2140,\n",
       " 'JPN': 912,\n",
       " 'SSK': 2239,\n",
       " 'SKF': 2147,\n",
       " 'KF ': 988,\n",
       " 'F X': 509,\n",
       " 'XRN': 2568,\n",
       " 'XX': 2597,\n",
       " 'RXX': 2082,\n",
       " 'XXS': 2604,\n",
       " 'SJ ': 2134,\n",
       " 'PLR': 1791,\n",
       " ' TL': 171,\n",
       " 'FNS': 587,\n",
       " 'PLK': 1786,\n",
       " 'LKL': 1214,\n",
       " 'M S': 1349,\n",
       " 'SRN': 2228,\n",
       " 'MNS': 1450,\n",
       " 'NSF': 1672,\n",
       " 'SFL': 2114,\n",
       " 'ALS': 390,\n",
       " 'LST': 1308,\n",
       " 'ALF': 381,\n",
       " 'NSK': 1674,\n",
       " 'PF ': 1737,\n",
       " 'F K': 501,\n",
       " 'FK': 540,\n",
       " 'HXR': 812,\n",
       " 'XRR': 2570,\n",
       " 'RRP': 2036,\n",
       " 'NRT': 1667,\n",
       " ' FK': 30,\n",
       " 'FKT': 550,\n",
       " 'KTR': 1131,\n",
       " 'HJ': 691,\n",
       " 'HJM': 695,\n",
       " 'KRX': 1106,\n",
       " 'RX ': 2071,\n",
       " 'JRS': 927,\n",
       " 'XLT': 2520,\n",
       " 'MSN': 1491,\n",
       " 'RSK': 2046,\n",
       " 'SK ': 2145,\n",
       " 'K0': 979,\n",
       " ' K0': 68,\n",
       " 'K0L': 982,\n",
       " '0LN': 240,\n",
       " 'T L': 2281,\n",
       " 'KMR': 1058,\n",
       " 'MRN': 1476,\n",
       " 'KLK': 1038,\n",
       " 'KLS': 1044,\n",
       " 'LSR': 1306,\n",
       " 'SPN': 2213,\n",
       " 'PNS': 1816,\n",
       " 'NSR': 1679,\n",
       " 'JSK': 934,\n",
       " 'PR0': 1835,\n",
       " '0 J': 197,\n",
       " 'AKX': 377,\n",
       " 'KX ': 1136,\n",
       " 'MJ': 1384,\n",
       " 'MJ ': 1385,\n",
       " 'J T': 828,\n",
       " 'S K': 2089,\n",
       " ' KM': 74,\n",
       " 'PRL': 1840,\n",
       " 'KPN': 1086,\n",
       " 'SM': 2174,\n",
       " 'SNF': 2192,\n",
       " 'ASM': 461,\n",
       " 'RR ': 2028,\n",
       " 'HMS': 736,\n",
       " 'MS ': 1483,\n",
       " 'P F': 1713,\n",
       " 'MXR': 1519,\n",
       " 'S J': 2088,\n",
       " ' FT': 37,\n",
       " 'MKS': 1407,\n",
       " 'KXR': 1143,\n",
       " 'PFK': 1739,\n",
       " 'FKF': 542,\n",
       " 'F A': 497,\n",
       " 'T0': 2289,\n",
       " 'K M': 972,\n",
       " 'MRT': 1480,\n",
       " 'RT0': 2057,\n",
       " 'MRX': 1481,\n",
       " 'MRS': 1479,\n",
       " 'KM ': 1048,\n",
       " 'FR0': 601,\n",
       " '0 A': 194,\n",
       " 'FLP': 562,\n",
       " 'KLF': 1035,\n",
       " 'LF ': 1172,\n",
       " 'TMN': 2368,\n",
       " 'FLR': 563,\n",
       " 'LRS': 1293,\n",
       " 'KNT': 1075,\n",
       " 'R S': 1899,\n",
       " 'TNK': 2380,\n",
       " 'LKS': 1219,\n",
       " 'MH': 1372,\n",
       " 'MT': 1497,\n",
       " 'SL ': 2160,\n",
       " ' MH': 98,\n",
       " 'MHM': 1377,\n",
       " 'HMT': 737,\n",
       " 'APR': 434,\n",
       " 'MTR': 1507,\n",
       " 'KNL': 1069,\n",
       " 'NL ': 1596,\n",
       " 'H0': 669,\n",
       " 'SLT': 2172,\n",
       " ' H0': 40,\n",
       " 'H0R': 672,\n",
       " 'N T': 1536,\n",
       " 'TMR': 2370,\n",
       " 'PSK': 1852,\n",
       " 'SKR': 2155,\n",
       " 'FRR': 610,\n",
       " 'K0R': 985,\n",
       " '0RN': 275,\n",
       " ' SM': 158,\n",
       " 'SML': 2181,\n",
       " 'PNL': 1811,\n",
       " 'KPS': 1089,\n",
       " 'PS ': 1849,\n",
       " 'ATF': 471,\n",
       " 'TF ': 2296,\n",
       " 'LRN': 1290,\n",
       " 'JLS': 879,\n",
       " 'RJL': 1945,\n",
       " 'L N': 1156,\n",
       " 'STK': 2254,\n",
       " 'H ': 654,\n",
       " 'H M': 662,\n",
       " 'MNJ': 1443,\n",
       " 'NJN': 1576,\n",
       " 'JT': 943,\n",
       " 'X P': 2467,\n",
       " 'PRJ': 1838,\n",
       " 'RJT': 1951,\n",
       " 'ATR': 479,\n",
       " 'KTM': 1128,\n",
       " 'TMK': 2365,\n",
       " 'SLM': 2167,\n",
       " 'LMS': 1248,\n",
       " 'KFR': 996,\n",
       " 'HRK': 771,\n",
       " 'KRR': 1103,\n",
       " '0RM': 274,\n",
       " ' LR': 91,\n",
       " 'TKL': 2337,\n",
       " ' FS': 36,\n",
       " 'FSM': 621,\n",
       " 'MTL': 1503,\n",
       " 'TLT': 2358,\n",
       " 'L0': 1162,\n",
       " 'SNR': 2200,\n",
       " 'LL0': 1224,\n",
       " ' NS': 120,\n",
       " 'RNL': 2005,\n",
       " 'ASP': 463,\n",
       " 'FKN': 546,\n",
       " 'TX': 2445,\n",
       " 'NJT': 1580,\n",
       " 'JTX': 956,\n",
       " 'TX ': 2446,\n",
       " 'X A': 2459,\n",
       " 'HLS': 724,\n",
       " 'NN': 1624,\n",
       " 'NM': 1609,\n",
       " 'RKM': 1961,\n",
       " 'MNN': 1447,\n",
       " 'NNM': 1632,\n",
       " 'NMM': 1617,\n",
       " 'MM ': 1426,\n",
       " 'TSK': 2423,\n",
       " 'AMT': 406,\n",
       " 'KMS': 1059,\n",
       " 'RFR': 1926,\n",
       " 'FRK': 605,\n",
       " 'ANF': 411,\n",
       " ' JT': 65,\n",
       " 'HF': 673,\n",
       " 'HFM': 677,\n",
       " 'TRJ': 2408,\n",
       " 'LFS': 1183,\n",
       " 'FST': 626,\n",
       " 'PTS': 1872,\n",
       " ' NX': 122,\n",
       " 'NXL': 1702,\n",
       " 'FLN': 561,\n",
       " 'PRF': 1836,\n",
       " 'RFT': 1928,\n",
       " 'TLP': 2355,\n",
       " 'S N': 2092,\n",
       " 'L H': 1151,\n",
       " 'FX': 642,\n",
       " 'FXR': 650,\n",
       " 'XNK': 2539,\n",
       " 'JLN': 876,\n",
       " 'MLN': 1419,\n",
       " 'HKN': 707,\n",
       " 'LH': 1186,\n",
       " 'ALH': 382,\n",
       " 'LHN': 1191,\n",
       " 'SST': 2246,\n",
       " 'FRS': 611,\n",
       " 'FF': 516,\n",
       " 'FFN': 522,\n",
       " 'TNJ': 2379,\n",
       " 'NST': 1681,\n",
       " 'H0 ': 670,\n",
       " 'ARK': 444,\n",
       " 'NRP': 1664,\n",
       " 'RPL': 2019,\n",
       " 'SLK': 2165,\n",
       " 'K P': 974,\n",
       " 'TT': 2432,\n",
       " 'TLK': 2351,\n",
       " 'LKT': 1220,\n",
       " 'KTT': 1133,\n",
       " 'TTM': 2438,\n",
       " 'TML': 2366,\n",
       " 'ML ': 1411,\n",
       " ' TF': 167,\n",
       " 'KK': 1017,\n",
       " 'ATK': 474,\n",
       " 'TKK': 2336,\n",
       " 'KK ': 1018,\n",
       " ' MS': 106,\n",
       " 'AFL': 337,\n",
       " 'P H': 1714,\n",
       " '0N': 251,\n",
       " 'JN0': 895,\n",
       " 'N0N': 1542,\n",
       " ' PM': 130,\n",
       " 'PML': 1799,\n",
       " 'SH': 2122,\n",
       " ' SH': 154,\n",
       " 'SHM': 2128,\n",
       " 'AR0': 440,\n",
       " '0 M': 200,\n",
       " ' MT': 107,\n",
       " 'MTS': 1508,\n",
       " 'A0': 312,\n",
       " 'A0R': 317,\n",
       " 'NKT': 1593,\n",
       " 'KTN': 1129,\n",
       " 'ARX': 452,\n",
       " 'MNK': 1444,\n",
       " ' N0': 110,\n",
       " 'N0L': 1541,\n",
       " 'K K': 970,\n",
       " 'MSL': 1489,\n",
       " 'SLR': 2170,\n",
       " 'LSP': 1305,\n",
       " 'SP0': 2206,\n",
       " 'PNK': 1810,\n",
       " ' HN': 47,\n",
       " 'KSS': 1118,\n",
       " 'S H': 2087,\n",
       " 'FN ': 577,\n",
       " 'RKR': 1964,\n",
       " 'STT': 2261,\n",
       " 'JSN': 937,\n",
       " 'TLL': 2352,\n",
       " 'SSS': 2245,\n",
       " 'XTS': 2594,\n",
       " 'SNX': 2203,\n",
       " 'NXS': 1707,\n",
       " 'XS ': 2575,\n",
       " 'LNR': 1262,\n",
       " 'SX': 2263,\n",
       " 'SXL': 2267,\n",
       " 'SP ': 2205,\n",
       " 'P 0': 1711,\n",
       " 'LNX': 1265,\n",
       " 'MRF': 1470,\n",
       " 'RFN': 1924,\n",
       " ' KP': 76,\n",
       " 'LMP': 1246,\n",
       " 'AKS': 375,\n",
       " 'XFN': 2479,\n",
       " 'XF ': 2475,\n",
       " 'F R': 506,\n",
       " 'HK ': 702,\n",
       " 'SKT': 2157,\n",
       " 'XP': 2548,\n",
       " 'XPM': 2553,\n",
       " 'RNN': 2007,\n",
       " 'NNT': 1637,\n",
       " ' NN': 117,\n",
       " ' NJ': 113,\n",
       " 'KFL': 992,\n",
       " 'ASK': 459,\n",
       " 'SKN': 2153,\n",
       " 'SPL': 2211,\n",
       " 'MX ': 1512,\n",
       " 'X N': 2466,\n",
       " '0T': 289,\n",
       " 'S 0': 2084,\n",
       " ' 0T': 8,\n",
       " 'AST': 466,\n",
       " 'RNP': 2008,\n",
       " 'NPL': 1646,\n",
       " 'ATS': 480,\n",
       " ' FN': 33,\n",
       " 'SMN': 2183,\n",
       " 'AML': 400,\n",
       " 'RH': 1930,\n",
       " 'PRH': 1837,\n",
       " 'RHN': 1936,\n",
       " 'FKR': 548,\n",
       " 'X R': 2468,\n",
       " ' RL': 143,\n",
       " 'RLF': 1971,\n",
       " 'RKT': 1966,\n",
       " 'KT ': 1122,\n",
       " '0 P': 202,\n",
       " 'MKR': 1406,\n",
       " 'MJR': 1392,\n",
       " 'JR ': 917,\n",
       " 'XM': 2522,\n",
       " 'XMT': 2531,\n",
       " 'MT ': 1498,\n",
       " 'LKF': 1210,\n",
       " 'KFS': 997,\n",
       " 'FSK': 619,\n",
       " 'LKR': 1218,\n",
       " 'SMP': 2184,\n",
       " 'MPS': 1464,\n",
       " 'PSN': 1855,\n",
       " 'JKL': 861,\n",
       " 'P J': 1715,\n",
       " 'KSL': 1113,\n",
       " 'LTM': 1318,\n",
       " 'PLT': 1793,\n",
       " 'APS': 435,\n",
       " 'PST': 1859,\n",
       " 'MR ': 1468,\n",
       " 'P T': 1723,\n",
       " 'TRM': 2411,\n",
       " 'NTK': 1689,\n",
       " 'TKT': 2343,\n",
       " 'MPT': 1465,\n",
       " 'RTP': 2065,\n",
       " 'TPK': 2394,\n",
       " 'MKF': 1398,\n",
       " 'FJ': 532,\n",
       " 'FJL': 535,\n",
       " 'APT': 436,\n",
       " 'PTK': 1866,\n",
       " 'TK ': 2331,\n",
       " 'LSL': 1302,\n",
       " 'PX': 1875,\n",
       " 'PX ': 1876,\n",
       " 'X F': 2460,\n",
       " 'AMR': 404,\n",
       " 'RSS': 2052,\n",
       " 'SSM': 2241,\n",
       " 'SMR': 2185,\n",
       " ' PF': 125,\n",
       " 'NNS': 1636,\n",
       " 'LPN': 1275,\n",
       " 'PXN': 1881,\n",
       " 'XN ': 2534,\n",
       " 'NTP': 1693,\n",
       " 'K F': 967,\n",
       " 'NSS': 1680,\n",
       " 'PRX': 1847,\n",
       " 'PKK': 1771,\n",
       " 'F J': 500,\n",
       " 'FLM': 560,\n",
       " 'FLK': 558,\n",
       " 'SFT': 2120,\n",
       " ...}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import chain\n",
    "\n",
    "def n_grams(seq, n=1):\n",
    "    \"\"\"Returns an itirator over the n-grams given a listTokens\"\"\"\n",
    "    shiftToken = lambda i: (el for j,el in enumerate(seq) if j>=i)\n",
    "    shiftedTokens = (shiftToken(i) for i in range(n))\n",
    "    tupleNGrams = zip(*shiftedTokens)\n",
    "    return tupleNGrams # if join in generator : (\" \".join(i) for i in tupleNGrams)\n",
    "\n",
    "def range_ngrams(listTokens, ngramRange=(1,2)):\n",
    "    \"\"\"Returns an itirator over all n-grams for n in range(ngramRange) given a listTokens.\"\"\"\n",
    "    ngrams = (ngramRange[0], ngramRange[1] + 1)\n",
    "    return chain(*(n_grams(listTokens, i) for i in range(*ngramRange)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max feature len = 19, Avg. feature len = 6\n"
     ]
    }
   ],
   "source": [
    "def find_ngrams(text, ngrams):\n",
    "    a = range_ngrams(text, ngrams)\n",
    "    wi = []\n",
    "    for i in a:\n",
    "        w = ''.join(i)\n",
    "        try:\n",
    "            idx = words_list.index(w)\n",
    "        except:\n",
    "            idx = 0\n",
    "        wi.append(idx)\n",
    "    return wi\n",
    "\n",
    "# build X from index of n-gram sequence\n",
    "X = np.array(sdf.last_name_first_name.apply(lambda c: find_ngrams(c, NGRAMS)))\n",
    "\n",
    "# check max/avg feature\n",
    "X_len = []\n",
    "for x in X:\n",
    "    X_len.append(len(x))\n",
    "\n",
    "max_feature_len = max(X_len)\n",
    "avg_feature_len = int(np.mean(X_len))\n",
    "\n",
    "print(\"Max feature len = %d, Avg. feature len = %d\" % (max_feature_len, avg_feature_len))\n",
    "y = np.array(sdf.race.astype('category').cat.codes)\n",
    "\n",
    "# Split train and test dataset\n",
    "X_train,  X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=21, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a LSTM model\n",
    "\n",
    "ref: http://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "800000 train sequences\n",
      "200000 test sequences\n",
      "Pad sequences (samples x time)\n",
      "X_train shape: (800000, 25)\n",
      "X_test shape: (200000, 25)\n",
      "4 classes\n",
      "Convert class vector to binary class matrix (for use with categorical_crossentropy)\n",
      "y_train shape: (800000, 4)\n",
      "y_test shape: (200000, 4)\n"
     ]
    }
   ],
   "source": [
    "'''The dataset is actually too small for LSTM to be of any advantage\n",
    "compared to simpler, much faster methods such as TF-IDF + LogReg.\n",
    "Notes:\n",
    "\n",
    "- RNNs are tricky. Choice of batch size is important,\n",
    "choice of loss and optimizer is critical, etc.\n",
    "Some configurations won't converge.\n",
    "\n",
    "- LSTM loss decrease patterns during training can be quite different\n",
    "from what you see with CNNs/MLPs/etc.\n",
    "'''\n",
    "import keras\n",
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding, Dropout, Activation\n",
    "from keras.layers import LSTM\n",
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers.convolutional import MaxPooling1D\n",
    "from keras.models import load_model\n",
    "\n",
    "max_features = num_words # 20000\n",
    "feature_len = 25 # avg_feature_len # cut texts after this number of words (among top max_features most common words)\n",
    "batch_size = 32\n",
    "\n",
    "print(len(X_train), 'train sequences')\n",
    "print(len(X_test), 'test sequences')\n",
    "\n",
    "print('Pad sequences (samples x time)')\n",
    "X_train = sequence.pad_sequences(X_train, maxlen=feature_len)\n",
    "X_test = sequence.pad_sequences(X_test, maxlen=feature_len)\n",
    "print('X_train shape:', X_train.shape)\n",
    "print('X_test shape:', X_test.shape)\n",
    "\n",
    "num_classes = np.max(y_train) + 1\n",
    "print(num_classes, 'classes')\n",
    "\n",
    "print('Convert class vector to binary class matrix '\n",
    "      '(for use with categorical_crossentropy)')\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "print('y_train shape:', y_train.shape)\n",
    "print('y_test shape:', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Build model...\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 25, 50)            130300    \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 25, 32)            4832      \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 12, 32)            0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 100)               53200     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4)                 404       \n",
      "=================================================================\n",
      "Total params: 188,736\n",
      "Trainable params: 188,736\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print('Build model...')\n",
    "\n",
    "if False:\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(num_words, 32, input_length=feature_len))\n",
    "    model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "if True:\n",
    "    embedding_vecor_length = 50\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(num_words, embedding_vecor_length, input_length=feature_len))\n",
    "    model.add(Conv1D(activation=\"relu\", padding=\"same\", filters=32, kernel_size=3))\n",
    "    model.add(MaxPooling1D(pool_size=2))\n",
    "    model.add(LSTM(100))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "# try using different optimizers and different optimizer configs\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train...\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:2741: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "Train on 720000 samples, validate on 80000 samples\n",
      "Epoch 1/15\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From /opt/venv37/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "720000/720000 [==============================] - 476s 661us/step - loss: 0.7759 - acc: 0.7063 - val_loss: 0.7541 - val_acc: 0.7138\n",
      "Epoch 2/15\n",
      "720000/720000 [==============================] - 442s 614us/step - loss: 0.7262 - acc: 0.7259 - val_loss: 0.7208 - val_acc: 0.7281\n",
      "Epoch 3/15\n",
      "720000/720000 [==============================] - 454s 631us/step - loss: 0.7085 - acc: 0.7326 - val_loss: 0.7104 - val_acc: 0.7315\n",
      "Epoch 4/15\n",
      "720000/720000 [==============================] - 457s 635us/step - loss: 0.6963 - acc: 0.7370 - val_loss: 0.7040 - val_acc: 0.7334\n",
      "Epoch 5/15\n",
      "720000/720000 [==============================] - 457s 635us/step - loss: 0.6873 - acc: 0.7402 - val_loss: 0.7026 - val_acc: 0.7344\n",
      "Epoch 6/15\n",
      "720000/720000 [==============================] - 423s 587us/step - loss: 0.6809 - acc: 0.7427 - val_loss: 0.6983 - val_acc: 0.7349\n",
      "Epoch 7/15\n",
      "720000/720000 [==============================] - 420s 583us/step - loss: 0.6759 - acc: 0.7442 - val_loss: 0.6993 - val_acc: 0.7351\n",
      "Epoch 8/15\n",
      "720000/720000 [==============================] - 452s 628us/step - loss: 0.6720 - acc: 0.7454 - val_loss: 0.6993 - val_acc: 0.7363\n",
      "Epoch 9/15\n",
      "720000/720000 [==============================] - 444s 617us/step - loss: 0.6690 - acc: 0.7466 - val_loss: 0.6999 - val_acc: 0.7363\n",
      "Epoch 10/15\n",
      "720000/720000 [==============================] - 425s 590us/step - loss: 0.6666 - acc: 0.7477 - val_loss: 0.6970 - val_acc: 0.7358\n",
      "Epoch 11/15\n",
      "720000/720000 [==============================] - 455s 632us/step - loss: 0.6649 - acc: 0.7480 - val_loss: 0.6972 - val_acc: 0.7374\n",
      "Epoch 12/15\n",
      "720000/720000 [==============================] - 435s 605us/step - loss: 0.6629 - acc: 0.7486 - val_loss: 0.6989 - val_acc: 0.7365\n",
      "Epoch 13/15\n",
      "720000/720000 [==============================] - 448s 622us/step - loss: 0.6617 - acc: 0.7490 - val_loss: 0.6987 - val_acc: 0.7354\n",
      "Epoch 14/15\n",
      "720000/720000 [==============================] - 435s 604us/step - loss: 0.6604 - acc: 0.7497 - val_loss: 0.6975 - val_acc: 0.7357\n",
      "Epoch 15/15\n",
      "720000/720000 [==============================] - 438s 609us/step - loss: 0.6593 - acc: 0.7496 - val_loss: 0.7005 - val_acc: 0.7351\n",
      "200000/200000 [==============================] - 27s 134us/step\n",
      "Test score: 0.6971138533592224\n",
      "Test accuracy: 0.73735\n"
     ]
    }
   ],
   "source": [
    "print('Train...')\n",
    "model.fit(X_train, y_train, batch_size=batch_size, epochs=EPOCHS,\n",
    "          validation_split=0.1, verbose=1)\n",
    "score, acc = model.evaluate(X_test, y_test,\n",
    "                            batch_size=batch_size, verbose=1)\n",
    "print('Test score:', score)\n",
    "print('Test accuracy:', acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.6971138533592224\n",
      "Test accuracy: 0.73735\n"
     ]
    }
   ],
   "source": [
    "print('Test score:', score)\n",
    "print('Test accuracy:', acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       asian       0.60      0.24      0.34      5518\n",
      "    hispanic       0.65      0.44      0.53     25195\n",
      "    nh_black       0.62      0.38      0.47     36940\n",
      "    nh_white       0.77      0.91      0.83    132347\n",
      "\n",
      "    accuracy                           0.74    200000\n",
      "   macro avg       0.66      0.49      0.54    200000\n",
      "weighted avg       0.72      0.74      0.71    200000\n",
      "\n",
      "[[  1312    370    728   3108]\n",
      " [   108  11183   1350  12554]\n",
      " [   280   1276  13916  21468]\n",
      " [   504   4439   6345 121059]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict_classes(X_test, verbose=2)\n",
    "p = model.predict_proba(X_test, verbose=2) # to predict probability\n",
    "target_names = list(sdf.race.astype('category').cat.categories)\n",
    "print(classification_report(np.argmax(y_test, axis=1), y_pred, target_names=target_names))\n",
    "print(confusion_matrix(np.argmax(y_test, axis=1), y_pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "              precision    recall  f1-score   support\n",
    "\n",
    "       asian       0.57      0.37      0.45       816\n",
    "    hispanic       0.61      0.34      0.44      1539\n",
    "    nh_black       0.56      0.40      0.47      3509\n",
    "    nh_white       0.79      0.91      0.85     14136\n",
    "\n",
    "    accuracy                           0.75     20000\n",
    "   macro avg       0.63      0.50      0.55     20000\n",
    "weighted avg       0.73      0.75      0.73     20000\n",
    "\n",
    "[[  304    22   101   389]\n",
    " [   26   529    86   898]\n",
    " [   46    48  1389  2026]\n",
    " [  154   274   888 12820]]\n",
    " \n",
    "100k inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.save('../models/fl_voter_soundnames_lstm.h5')\n",
    "model.save('/opt/github/ethnicolr/ethnicolr/models/fl_voter_soundnames/fl_voter_soundnames_lstm.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "words_df = pd.DataFrame(words_list, columns=['vocab'])\n",
    "#words_df.to_csv('../models/fl_voter_soundnames_vocab.csv', index=False, encoding='utf-8')\n",
    "words_df.to_csv('/opt/github/ethnicolr/ethnicolr/models/fl_voter_soundnames/fl_voter_soundnames_vocab.csv', index=False, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  }
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