ethnicolr/models/ethnicolr_keras_lstm_nc_12_cat_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ethnicolr: NC Voter Registration Name Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We build new LSTM models based on NC data. We start by assuming y = concatenation of ethnic code and race code. We remove U and also UN --- assuming they are 'missing at random.' This gives us 12 categories.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-21 15:33:59.210271: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2021-12-21 15:33:59.210323: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5.0\n",
      "2.5.2\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "import tensorflow as tf\n",
    "print(keras.__version__)\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NGRAMS = (2, 3)\n",
    "SAMPLE = 1000000\n",
    "EPOCHS = 15\n",
    "\n",
    "# Florida voter\n",
    "df = pd.read_csv('/opt/data/nc_voter/nc_voter_name_gender_race_ethnic_age.csv.gz', usecols=['last_name', 'first_name', 'race_code', 'ethnic_code'], nrows=SAMPLE * 2)\n",
    "df.dropna(subset=['first_name', 'last_name'], inplace=True)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "https://s3.amazonaws.com/dl.ncsbe.gov/data/layout_ncvoter.txt\n",
    "\n",
    "/* ****************************************************************************\n",
    "Race codes\n",
    "race               description\n",
    "*******************************************************************************\n",
    "A                  ASIAN\n",
    "B                  BLACK or AFRICAN AMERICAN\n",
    "I                  AMERICAN INDIAN or ALASKA NATIVE\n",
    "M                  TWO or MORE RACES \n",
    "O                  OTHER\n",
    "P                  NATIVE HAWAIIAN or PACIFIC ISLANDER\n",
    "U                  UNDESIGNATED\n",
    "W                  WHITE\n",
    "**************************************************************************** */\n",
    "\n",
    "\n",
    "/* ****************************************************************************\n",
    "Ethnic codes\n",
    "ethnicity          description\n",
    "*******************************************************************************\n",
    "HL                 HISPANIC or LATINO\n",
    "NL                 NOT HISPANIC or NOT LATINO\n",
    "UN                 UNDESIGNATED\n",
    "**************************************************************************** */\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop U and UN from race and ethnic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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_code</th>\n",
       "      <th>ethnic_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AABEL</td>\n",
       "      <td>EVELYN</td>\n",
       "      <td>W</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AARON</td>\n",
       "      <td>CLAUDIA</td>\n",
       "      <td>W</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AARON</td>\n",
       "      <td>SANDRA</td>\n",
       "      <td>W</td>\n",
       "      <td>HL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AARON</td>\n",
       "      <td>WILLIE</td>\n",
       "      <td>W</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AARONSON</td>\n",
       "      <td>GENA</td>\n",
       "      <td>W</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999993</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>YVETTE</td>\n",
       "      <td>W</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999995</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>ANTHONY</td>\n",
       "      <td>B</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999996</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>ANTOINETTE</td>\n",
       "      <td>B</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999997</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>BETTY</td>\n",
       "      <td>B</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999998</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>BILLY</td>\n",
       "      <td>B</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1547925 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        last_name  first_name race_code ethnic_code\n",
       "0           AABEL      EVELYN         W          NL\n",
       "2           AARON     CLAUDIA         W          NL\n",
       "6           AARON      SANDRA         W          HL\n",
       "7           AARON      WILLIE         W          NL\n",
       "8        AARONSON        GENA         W          NL\n",
       "...           ...         ...       ...         ...\n",
       "1999993     LEAHY      YVETTE         W          NL\n",
       "1999995      LEAK     ANTHONY         B          NL\n",
       "1999996      LEAK  ANTOINETTE         B          NL\n",
       "1999997      LEAK       BETTY         B          NL\n",
       "1999998      LEAK       BILLY         B          NL\n",
       "\n",
       "[1547925 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df[(df.race_code=='U')|(df.ethnic_code=='UN')].index, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race_code  ethnic_code\n",
       "A          HL                 149\n",
       "           NL               10142\n",
       "B          HL                1497\n",
       "           NL              266127\n",
       "I          HL                 157\n",
       "           NL                5284\n",
       "M          HL                2634\n",
       "           NL                5054\n",
       "O          HL               18580\n",
       "           NL               17627\n",
       "W          HL               10598\n",
       "           NL             1210076\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['race_code', 'ethnic_code']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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_code</th>\n",
       "      <th>ethnic_code</th>\n",
       "      <th>race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AABEL</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AARON</td>\n",
       "      <td>SANDRA</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AARONSON</td>\n",
       "      <td>GENA</td>\n",
       "      <td>W</td>\n",
       "      <td>NL</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999993</th>\n",
       "      <td>LEAHY</td>\n",
       "      <td>YVETTE</td>\n",
       "      <td>W</td>\n",
       "      <td>NL</td>\n",
       "      <td>NL+W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999995</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>ANTHONY</td>\n",
       "      <td>B</td>\n",
       "      <td>NL</td>\n",
       "      <td>NL+B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999996</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>ANTOINETTE</td>\n",
       "      <td>B</td>\n",
       "      <td>NL</td>\n",
       "      <td>NL+B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999997</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>BETTY</td>\n",
       "      <td>B</td>\n",
       "      <td>NL</td>\n",
       "      <td>NL+B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999998</th>\n",
       "      <td>LEAK</td>\n",
       "      <td>BILLY</td>\n",
       "      <td>B</td>\n",
       "      <td>NL</td>\n",
       "      <td>NL+B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1547925 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        last_name  first_name race_code ethnic_code  race\n",
       "0           AABEL      EVELYN         W          NL  NL+W\n",
       "2           AARON     CLAUDIA         W          NL  NL+W\n",
       "6           AARON      SANDRA         W          HL  HL+W\n",
       "7           AARON      WILLIE         W          NL  NL+W\n",
       "8        AARONSON        GENA         W          NL  NL+W\n",
       "...           ...         ...       ...         ...   ...\n",
       "1999993     LEAHY      YVETTE         W          NL  NL+W\n",
       "1999995      LEAK     ANTHONY         B          NL  NL+B\n",
       "1999996      LEAK  ANTOINETTE         B          NL  NL+B\n",
       "1999997      LEAK       BETTY         B          NL  NL+B\n",
       "1999998      LEAK       BILLY         B          NL  NL+B\n",
       "\n",
       "[1547925 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['race'] = df['ethnic_code'] + '+' + df['race_code']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race\n",
       "HL+A       149\n",
       "HL+B      1491\n",
       "HL+I       156\n",
       "HL+M      2606\n",
       "HL+O     17325\n",
       "HL+W     10281\n",
       "NL+A      9682\n",
       "NL+B    215904\n",
       "NL+I      5048\n",
       "NL+M      5016\n",
       "NL+O     17326\n",
       "NL+W    829509\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['race']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>last_name</th>\n",
       "      <th>first_name</th>\n",
       "      <th>race_code</th>\n",
       "      <th>ethnic_code</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>GENA</td>\n",
       "      <td>W</td>\n",
       "      <td>NL</td>\n",
       "      <td>NL+W</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1287013</th>\n",
       "      <td>QUICK</td>\n",
       "      <td>MARIA</td>\n",
       "      <td>O</td>\n",
       "      <td>NL</td>\n",
       "      <td>NL+O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1038780</th>\n",
       "      <td>NAZ</td>\n",
       "      <td>UZMA</td>\n",
       "      <td>O</td>\n",
       "      <td>NL</td>\n",
       "      <td>NL+O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1791269</th>\n",
       "      <td>VOUNN</td>\n",
       "      <td>RET</td>\n",
       "      <td>O</td>\n",
       "      <td>NL</td>\n",
       "      <td>NL+O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1581968</th>\n",
       "      <td>SIGUENZA</td>\n",
       "      <td>CAROLYN MARIE</td>\n",
       "      <td>O</td>\n",
       "      <td>NL</td>\n",
       "      <td>NL+O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117583</th>\n",
       "      <td>MULLINS</td>\n",
       "      <td>CONNIE</td>\n",
       "      <td>O</td>\n",
       "      <td>NL</td>\n",
       "      <td>NL+O</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9954108 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        last_name     first_name race_code ethnic_code  race\n",
       "0           AABEL         EVELYN         W          NL  NL+W\n",
       "2           AARON        CLAUDIA         W          NL  NL+W\n",
       "6           AARON         SANDRA         W          HL  HL+W\n",
       "7           AARON         WILLIE         W          NL  NL+W\n",
       "8        AARONSON           GENA         W          NL  NL+W\n",
       "...           ...            ...       ...         ...   ...\n",
       "1287013     QUICK          MARIA         O          NL  NL+O\n",
       "1038780       NAZ           UZMA         O          NL  NL+O\n",
       "1791269     VOUNN            RET         O          NL  NL+O\n",
       "1581968  SIGUENZA  CAROLYN MARIE         O          NL  NL+O\n",
       "1117583   MULLINS         CONNIE         O          NL  NL+O\n",
       "\n",
       "[9954108 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_size = df['race'].value_counts().max()\n",
    "lst = [df]\n",
    "for class_index, group in df.groupby('race'):\n",
    "    lst.append(group.sample(max_size-len(group), replace=True))\n",
    "odf = pd.concat(lst)\n",
    "odf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>HL+A</th>\n",
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       "      <th>NL+W</th>\n",
       "      <td>829509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      first_name\n",
       "race            \n",
       "HL+A      829509\n",
       "HL+B      829509\n",
       "HL+I      829509\n",
       "HL+M      829509\n",
       "HL+O      829509\n",
       "HL+W      829509\n",
       "NL+A      829509\n",
       "NL+B      829509\n",
       "NL+I      829509\n",
       "NL+M      829509\n",
       "NL+O      829509\n",
       "NL+W      829509"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odf.groupby('race').agg({'first_name': 'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1202736</th>\n",
       "      <td>DIAZ</td>\n",
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       "    <tr>\n",
       "      <th>1444502</th>\n",
       "      <td>EVANS</td>\n",
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       "      <td>MALDONADO</td>\n",
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       "    <tr>\n",
       "      <th>1464755</th>\n",
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       "    <tr>\n",
       "      <th>1359959</th>\n",
       "      <td>RODRIGUEZ</td>\n",
       "      <td>TRISTAN</td>\n",
       "      <td>M</td>\n",
       "      <td>HL</td>\n",
       "      <td>HL+M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1593736</th>\n",
       "      <td>STOLARSKI</td>\n",
       "      <td>WANDA</td>\n",
       "      <td>W</td>\n",
       "      <td>HL</td>\n",
       "      <td>HL+W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1381683</th>\n",
       "      <td>ALLEN</td>\n",
       "      <td>CHANTEL</td>\n",
       "      <td>B</td>\n",
       "      <td>HL</td>\n",
       "      <td>HL+B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126373</th>\n",
       "      <td>LACKEY</td>\n",
       "      <td>GRIER</td>\n",
       "      <td>W</td>\n",
       "      <td>NL</td>\n",
       "      <td>NL+W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1805751</th>\n",
       "      <td>CASTELLON</td>\n",
       "      <td>FRANCISCO</td>\n",
       "      <td>A</td>\n",
       "      <td>HL</td>\n",
       "      <td>HL+A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         last_name first_name race_code ethnic_code  race\n",
       "1202736       DIAZ     ALBINO         O          HL  HL+O\n",
       "1659672  FERNANDEZ      JESSE         W          HL  HL+W\n",
       "1444502      EVANS     DWAYNE         I          NL  NL+I\n",
       "510742   MALDONADO     MEDINA         I          HL  HL+I\n",
       "1464755      GROSS      SCOTT         O          NL  NL+O\n",
       "...            ...        ...       ...         ...   ...\n",
       "1359959  RODRIGUEZ    TRISTAN         M          HL  HL+M\n",
       "1593736  STOLARSKI      WANDA         W          HL  HL+W\n",
       "1381683      ALLEN    CHANTEL         B          HL  HL+B\n",
       "126373      LACKEY      GRIER         W          NL  NL+W\n",
       "1805751  CASTELLON  FRANCISCO         A          HL  HL+A\n",
       "\n",
       "[1000000 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf = odf.sample(SAMPLE, random_state=21)\n",
    "sdf"
   ]
  },
  {
   "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>first_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HL+A</th>\n",
       "      <td>83345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HL+B</th>\n",
       "      <td>83134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HL+I</th>\n",
       "      <td>83668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HL+M</th>\n",
       "      <td>83366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HL+O</th>\n",
       "      <td>83054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HL+W</th>\n",
       "      <td>82852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+A</th>\n",
       "      <td>83706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+B</th>\n",
       "      <td>83335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+I</th>\n",
       "      <td>83141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+M</th>\n",
       "      <td>83691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+O</th>\n",
       "      <td>83159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL+W</th>\n",
       "      <td>83549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      first_name\n",
       "race            \n",
       "HL+A       83345\n",
       "HL+B       83134\n",
       "HL+I       83668\n",
       "HL+M       83366\n",
       "HL+O       83054\n",
       "HL+W       82852\n",
       "NL+A       83706\n",
       "NL+B       83335\n",
       "NL+I       83141\n",
       "NL+M       83691\n",
       "NL+O       83159\n",
       "NL+W       83549"
      ]
     },
     "execution_count": 12,
     "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",
    "rdf = sdf.groupby('race').agg({'first_name': 'count'})\n",
    "rdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "rdf.to_csv('./nc_voter_reg/lstm/nc_name_race.csv', columns=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing the input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_words = 11953\n",
      "CPU times: user 24min 39s, sys: 316 ms, total: 24min 39s\n",
      "Wall time: 24min 42s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 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 = ['UNK']\n",
    "words_list.extend([w[1] for w in words])\n",
    "num_words = len(words_list)\n",
    "print(\"num_words = %d\" % num_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       " 'chl': 5058,\n",
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       " 'r J': 9523,\n",
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       " 'ey': 6031,\n",
       " 'y ': 11450,\n",
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       " 'ass': 4615,\n",
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       " 'Br': 1022,\n",
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       " 'ann': 4507,\n",
       " 'nna': 8601,\n",
       " 'Hi': 1865,\n",
       " 'it': 7224,\n",
       " 'tc': 10378,\n",
       " 'hc': 6592,\n",
       " 'oc': 8851,\n",
       " 'ck': 5092,\n",
       " 'k ': 7438,\n",
       " ' O': 205,\n",
       " 'Ol': 2915,\n",
       " 'iv': 7259,\n",
       " 've': 11099,\n",
       " 'Hit': 1879,\n",
       " 'itc': 7228,\n",
       " 'tch': 10380,\n",
       " 'chc': 5051,\n",
       " 'hco': 6595,\n",
       " 'coc': 5135,\n",
       " 'ock': 8859,\n",
       " 'ck ': 5093,\n",
       " 'k O': 7453,\n",
       " ' Ol': 214,\n",
       " 'Oli': 2920,\n",
       " 'liv': 7856,\n",
       " 'ive': 7262,\n",
       " 'ver': 11113,\n",
       " 'La': 2400,\n",
       " 'br': 4908,\n",
       " 'Mi': 2628,\n",
       " 'dy': 5501,\n",
       " 'Lab': 2405,\n",
       " 'abr': 4242,\n",
       " 'bra': 4909,\n",
       " 'rad': 9572,\n",
       " 'dor': 5430,\n",
       " 'or ': 9112,\n",
       " 'r M': 9526,\n",
       " ' Mi': 187,\n",
       " 'Min': 2640,\n",
       " 'ind': 7109,\n",
       " 'ndy': 8449,\n",
       " 'Er': 1506,\n",
       " 'na ': 8377,\n",
       " 'a E': 4154,\n",
       " ' Er': 81,\n",
       " 'Eri': 1512,\n",
       " 'ric': 9706,\n",
       " 'Ox': 3009,\n",
       " 'xe': 11390,\n",
       " 'Ca': 1061,\n",
       " 'Oxe': 3010,\n",
       " 'xen': 11395,\n",
       " 'end': 5829,\n",
       " 'ndi': 8435,\n",
       " 'ne ': 8452,\n",
       " 'e C': 5521,\n",
       " ' Ca': 42,\n",
       " 'Car': 1077,\n",
       " 'arr': 4586,\n",
       " 'rry': 9850,\n",
       " 'Re': 3194,\n",
       " 'ol': 8994,\n",
       " 'ds': 5449,\n",
       " ' I': 122,\n",
       " 'Ig': 1987,\n",
       " 'gn': 6410,\n",
       " 'Rey': 3218,\n",
       " 'eyn': 6046,\n",
       " 'yno': 11633,\n",
       " 'nol': 8629,\n",
       " 'old': 9000,\n",
       " 'lds': 7772,\n",
       " 'ds ': 5450,\n",
       " 's I': 9971,\n",
       " ' Ig': 129,\n",
       " 'Ign': 1990,\n",
       " 'gna': 6411,\n",
       " 'nac': 8381,\n",
       " 'aci': 4257,\n",
       " 'Qu': 3159,\n",
       " 'ui': 10789,\n",
       " 'il': 7056,\n",
       " 'Qui': 3163,\n",
       " 'uin': 10804,\n",
       " 'int': 7125,\n",
       " 'nta': 8686,\n",
       " 'ani': 4502,\n",
       " 'nil': 8541,\n",
       " 'ill': 7070,\n",
       " 'lla': 7879,\n",
       " 'la ': 7713,\n",
       " 'Ela': 1450,\n",
       " 'lad': 7718,\n",
       " 'adi': 4282,\n",
       " 'dio': 5373,\n",
       " 'oo': 9069,\n",
       " 'ok': 8978,\n",
       " 'Da': 1200,\n",
       " 'Cro': 1162,\n",
       " 'roo': 9814,\n",
       " 'ook': 9079,\n",
       " 'ok ': 8979,\n",
       " 'k D': 7442,\n",
       " ' Da': 54,\n",
       " 'Dan': 1215,\n",
       " 'tr': 10523,\n",
       " ' W': 307,\n",
       " 'Wi': 3886,\n",
       " 'lf': 7806,\n",
       " 'fr': 6197,\n",
       " 'Con': 1146,\n",
       " 'ont': 9063,\n",
       " 'ntr': 8698,\n",
       " 'tre': 10525,\n",
       " 'rer': 9659,\n",
       " 'ras': 9587,\n",
       " 'as ': 4596,\n",
       " 's W': 9985,\n",
       " ' Wi': 311,\n",
       " 'Wil': 3894,\n",
       " 'ilf': 7064,\n",
       " 'lfr': 7814,\n",
       " 'fre': 6199,\n",
       " 'red': 9645,\n",
       " 'edo': 5647,\n",
       " 'Ke': 2257,\n",
       " 'rb': 9595,\n",
       " 'ba': 4787,\n",
       " 'Kea': 2260,\n",
       " 'ear': 5592,\n",
       " 'arn': 4582,\n",
       " 'rns': 9791,\n",
       " ' Ba': 31,\n",
       " 'Bar': 932,\n",
       " 'arb': 4570,\n",
       " 'rba': 9597,\n",
       " 'bar': 4800,\n",
       " 'ara': 4569,\n",
       " 'n ': 8323,\n",
       " 'yl': 11602,\n",
       " 'All': 745,\n",
       " 'len': 7793,\n",
       " 'en ': 5824,\n",
       " 'n T': 8343,\n",
       " 'Tay': 3549,\n",
       " 'ayl': 4728,\n",
       " 'ylo': 11608,\n",
       " 'lor': 7932,\n",
       " 'Le': 2430,\n",
       " 'em': 5806,\n",
       " 'mu': 8287,\n",
       " 'us': 10970,\n",
       " 'Ro': 3246,\n",
       " 'od': 8866,\n",
       " 'fo': 6183,\n",
       " 'Lem': 2444,\n",
       " 'emu': 5821,\n",
       " 'mus': 8299,\n",
       " 'us ': 10971,\n",
       " ' Ro': 247,\n",
       " 'Rod': 3251,\n",
       " 'odo': 8883,\n",
       " 'dol': 5425,\n",
       " 'olf': 9002,\n",
       " 'lfo': 7813,\n",
       " 'lc': 7749,\n",
       " 'Ia': 1960,\n",
       " 'Alc': 736,\n",
       " 'lca': 7750,\n",
       " 'can': 4988,\n",
       " 'ia ': 6879,\n",
       " 'a I': 4158,\n",
       " ' Ia': 123,\n",
       " 'Ian': 1963,\n",
       " 'Mc': 2584,\n",
       " 'cc': 5000,\n",
       " 'cu': 5182,\n",
       " 'ou': 9183,\n",
       " 'ug': 10765,\n",
       " 'gh': 6353,\n",
       " ' L': 172,\n",
       " 'ap': 4540,\n",
       " 'po': 9410,\n",
       " 'Mcc': 2587,\n",
       " 'ccu': 5007,\n",
       " 'cul': 5188,\n",
       " 'ull': 10850,\n",
       " 'llo': 7889,\n",
       " 'lou': 7935,\n",
       " 'oug': 9192,\n",
       " 'ugh': 10771,\n",
       " 'gh ': 6354,\n",
       " 'h L': 6521,\n",
       " ' La': 173,\n",
       " 'Lap': 2419,\n",
       " 'apo': 4552,\n",
       " 'por': 9422,\n",
       " 'ors': 9132,\n",
       " 'rsc': 9856,\n",
       " 'Mo': 2652,\n",
       " 'ny': 8737,\n",
       " 'ya': 11498,\n",
       " 'Mon': 2665,\n",
       " 'on ': 9043,\n",
       " 'n B': 8325,\n",
       " 'Ban': 928,\n",
       " 'any': 4517,\n",
       " 'nya': 8740,\n",
       " 'yar': 11514,\n",
       " 'sa': 10015,\n",
       " 'Des': 1250,\n",
       " 'esa': 5920,\n",
       " 'sai': 10026,\n",
       " 'ai ': 4377,\n",
       " 'i K': 6841,\n",
       " 'Kar': 2249,\n",
       " 'z-': 11756,\n",
       " '-P': 538,\n",
       " 'gi': 6370,\n",
       " 'ez-': 6055,\n",
       " 'z-P': 11772,\n",
       " '-Pe': 540,\n",
       " 'Pen': 3054,\n",
       " 'ena': 5826,\n",
       " 'ngi': 8500,\n",
       " 'gie': 6376,\n",
       " 'iem': 6973,\n",
       " 'emi': 5812,\n",
       " 'mil': 8180,\n",
       " 'Gl': 1748,\n",
       " 'Ez': 1575,\n",
       " 'zr': 11909,\n",
       " 'Glo': 1752,\n",
       " 'lov': 7936,\n",
       " 'ove': 9213,\n",
       " ' Ez': 89,\n",
       " 'Ezr': 1579,\n",
       " 'zra': 11910,\n",
       " 'Ri': 3226,\n",
       " 'Rio': 3238,\n",
       " 'ios': 7147,\n",
       " 'os ': 9140,\n",
       " 's M': 9975,\n",
       " ' Ma': 182,\n",
       " 'May': 2581,\n",
       " 'aya': 4718,\n",
       " 't ': 10303,\n",
       " 'ah': 4354,\n",
       " 'Bry': 1029,\n",
       " 'rya': 9936,\n",
       " 'yan': 11511,\n",
       " 'nt ': 8684,\n",
       " 't J': 10313,\n",
       " 'Jer': 2154,\n",
       " 'rem': 9654,\n",
       " 'mia': 8172,\n",
       " 'iah': 6888,\n",
       " 'St': 3465,\n",
       " 'kl': 7566,\n",
       " 'd ': 5213,\n",
       " 'Str': 3471,\n",
       " 'tri': 10526,\n",
       " 'ick': 6930,\n",
       " 'ckl': 5102,\n",
       " 'kla': 7567,\n",
       " 'lan': 7728,\n",
       " 'nd ': 8427,\n",
       " 'd W': 5236,\n",
       " 'lli': 7886,\n",
       " 'lia': 7835,\n",
       " 'iam': 6893,\n",
       " 'Ha': 1815,\n",
       " 'mm': 8205,\n",
       " 'm ': 8055,\n",
       " 'da': 5255,\n",
       " 'Ham': 1829,\n",
       " 'amm': 4480,\n",
       " 'mm ': 8206,\n",
       " 'm B': 8057,\n",
       " 'ren': 9655,\n",
       " 'nda': 8429,\n",
       " 'Ve': 3782,\n",
       " 'dw': 5495,\n",
       " 'wi': 11270,\n",
       " 'Vel': 3790,\n",
       " 'ele': 5785,\n",
       " 'lez': 7805,\n",
       " 'z M': 11742,\n",
       " 'art': 4588,\n",
       " 'rti': 9880,\n",
       " 'nez': 8480,\n",
       " 'z E': 11734,\n",
       " 'Edw': 1411,\n",
       " 'dwi': 5498,\n",
       " 'win': 11281,\n",
       " 'cl': 5113,\n",
       " ' H': 110,\n",
       " 'Mcl': 2595,\n",
       " 'cle': 5115,\n",
       " 'lea': 7780,\n",
       " 'an ': 4492,\n",
       " 'n H': 8331,\n",
       " ' Ha': 111,\n",
       " 'Har': 1834,\n",
       " 'aro': 4583,\n",
       " 'rol': 9811,\n",
       " 'Th': 3578,\n",
       " 'ho': 6712,\n",
       " 'om': 9019,\n",
       " 's T': 9982,\n",
       " ' Th': 272,\n",
       " 'Tho': 3582,\n",
       " 'hom': 6725,\n",
       " 'oma': 9022,\n",
       " 'mas': 8108,\n",
       " 'Is': 2068,\n",
       " 'sr': 10211,\n",
       " 'ae': 4296,\n",
       " 'Pol': 3109,\n",
       " 'oll': 9007,\n",
       " 'lar': 7731,\n",
       " 'ard': 4572,\n",
       " 'rd ': 9621,\n",
       " 'd I': 5222,\n",
       " ' Is': 138,\n",
       " 'Isr': 2078,\n",
       " 'sra': 10212,\n",
       " 'rae': 9573,\n",
       " 'ael': 4307,\n",
       " 'Hu': 1924,\n",
       " 'ys': 11670,\n",
       " 'Hug': 1932,\n",
       " 'ghe': 6357,\n",
       " 'hes': 6621,\n",
       " 'es ': 5918,\n",
       " 's C': 9965,\n",
       " 'Cry': 1164,\n",
       " 'rys': 9949,\n",
       " 'yst': 11683,\n",
       " 'tal': 10358,\n",
       " 'ig': 6997,\n",
       " 'ht': 6766,\n",
       " 'Bri': 1026,\n",
       " 'rig': 9710,\n",
       " 'igh': 7006,\n",
       " 'ght': 6366,\n",
       " 'ht ': 6767,\n",
       " 't Z': 10328,\n",
       " 'ias': 6899,\n",
       " 'ye': 11549,\n",
       " ' F': 90,\n",
       " 'Fr': 1660,\n",
       " 'nk': 8565,\n",
       " 'ki': 7538,\n",
       " 'Hay': 1840,\n",
       " 'aye': 4722,\n",
       " 'yes': 11564,\n",
       " 's F': 9968,\n",
       " ' Fr': 96,\n",
       " 'Fra': 1661,\n",
       " 'ran': 9582,\n",
       " 'ank': 4504,\n",
       " 'nki': 8573,\n",
       " 'kie': 7544,\n",
       " 'Lu': 2514,\n",
       " 'uc': 10698,\n",
       " 'Li': 2457,\n",
       " 'Luc': 2518,\n",
       " 'uca': 10700,\n",
       " 'cas': 4991,\n",
       " 's L': 9974,\n",
       " ' Li': 175,\n",
       " 'Lis': 2475,\n",
       " 'isa': 7202,\n",
       " 'Cas': 1078,\n",
       " 'ast': 4616,\n",
       " 'lon': 7928,\n",
       " 'n F': 8329,\n",
       " 'cis': 5088,\n",
       " 'isc': 7204,\n",
       " 'sco': 10055,\n",
       " 'oy': 9248,\n",
       " 'yb': 11521,\n",
       " ' P': 227,\n",
       " 'Ph': 3066,\n",
       " 'ip': 7151,\n",
       " 'Roy': 3272,\n",
       " 'oyb': 9252,\n",
       " 'yba': 11522,\n",
       " 'bal': 4797,\n",
       " 'al ': 4440,\n",
       " 'l P': 7680,\n",
       " ' Ph': 230,\n",
       " 'Phi': 3069,\n",
       " 'hil': 6656,\n",
       " 'lip': 7850,\n",
       " 'rl': 9752,\n",
       " 'hu': 6780,\n",
       " 'Par': 3036,\n",
       " 'arl': 4580,\n",
       " 'rli': 9759,\n",
       " 'lie': 7839,\n",
       " 'r C': 9516,\n",
       " 'Chu': 1109,\n",
       " 'huc': 6785,\n",
       " 'uck': 10705,\n",
       " 'cki': 5101,\n",
       " 'Sp': 3449,\n",
       " 'pe': 9338,\n",
       " 'Et': 1540,\n",
       " 'Spe': 3451,\n",
       " 'pei': 9347,\n",
       " 'eig': 5736,\n",
       " 'hts': 6778,\n",
       " 'ts ': 10531,\n",
       " 's E': 9967,\n",
       " ' Et': 83,\n",
       " 'Eth': 1543,\n",
       " 'hel': 6614,\n",
       " 'ew': 5999,\n",
       " 'ws': 11328,\n",
       " 'Am': 758,\n",
       " 'Mat': 2576,\n",
       " 'att': 4639,\n",
       " 'tth': 10553,\n",
       " 'hew': 6625,\n",
       " 'ews': 6015,\n",
       " 'ws ': 11329,\n",
       " 's A': 9963,\n",
       " ' Am': 16,\n",
       " 'Ama': 759,\n",
       " 'man': 8104,\n",
       " 'Cl': 1126,\n",
       " 'ty': 10591,\n",
       " 'ex': 6018,\n",
       " 'Cla': 1127,\n",
       " 'lat': 7733,\n",
       " 'tty': 10563,\n",
       " 'ty ': 10592,\n",
       " 'y A': 11452,\n",
       " 'Ale': 738,\n",
       " 'lex': 7803,\n",
       " 'Pr': 3121,\n",
       " 'to': 10496,\n",
       " 'op': 9091,\n",
       " 'ph': 9364,\n",
       " 'Pra': 3122,\n",
       " 'sto': 10248,\n",
       " 'top': 10512,\n",
       " 'oph': 9098,\n",
       " 'phe': 9367,\n",
       " 'Of': 2886,\n",
       " 'fe': 6119,\n",
       " 'Hil': 1873,\n",
       " 'ila': 7059,\n",
       " 'rio': 9718,\n",
       " 'o O': 8779,\n",
       " ' Of': 210,\n",
       " 'Ofe': 2887,\n",
       " 'fel': 6125,\n",
       " 'tw': 10585,\n",
       " 'ru': 9894,\n",
       " 'u ': 10621,\n",
       " ' G': 98,\n",
       " 'Gi': 1728,\n",
       " 'Etw': 1546,\n",
       " 'twa': 10586,\n",
       " 'war': 11232,\n",
       " 'aru': 4589,\n",
       " 'ru ': 9895,\n",
       " 'u G': 10628,\n",
       " ' Gi': 102,\n",
       " 'Git': 1744,\n",
       " 'ita': 7226,\n",
       " 'The': 3580,\n",
       " 'rri': 9845,\n",
       " 'ien': 6974,\n",
       " ' Fe': 92,\n",
       " 'Fel': 1614,\n",
       " 'Ge': 1704,\n",
       " 'Ger': 1716,\n",
       " 'erm': 5905,\n",
       " 'rma': 9768,\n",
       " 'n C': 8326,\n",
       " 'Cam': 1073,\n",
       " 'ami': 4477,\n",
       " 'ilo': 7073,\n",
       " 'yo': 11637,\n",
       " 'Ni': 2778,\n",
       " 'Pry': 3127,\n",
       " 'ryo': 9948,\n",
       " 'yor': 11646,\n",
       " 'r N': 9527,\n",
       " ' Ni': 199,\n",
       " 'Nin': 2791,\n",
       " 'Go': 1757,\n",
       " 'Gor': 1772,\n",
       " 'orm': 9126,\n",
       " 'n J': 8333,\n",
       " 'Jed': 2142,\n",
       " 'ec': 5616,\n",
       " 'Bl': 989,\n",
       " 'lap': 7730,\n",
       " 'ape': 4544,\n",
       " 'pec': 9342,\n",
       " 'eck': 5623,\n",
       " 'k B': 7440,\n",
       " ' Bl': 36,\n",
       " 'Bla': 990,\n",
       " 'lak': 7725,\n",
       " 'Wo': 3902,\n",
       " ...}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max feature len = 37, Avg. feature len = 12\n",
      "CPU times: user 1min 33s, sys: 396 ms, total: 1min 33s\n",
      "Wall time: 1min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "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": 18,
   "metadata": {},
   "outputs": [
    {
     "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",
      "12 classes\n",
      "Convert class vector to binary class matrix (for use with categorical_crossentropy)\n",
      "y_train shape: (800000, 12)\n",
      "y_test shape: (200000, 12)\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",
    "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 = tf.keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = tf.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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Build model...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-21 16:00:40.470862: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2021-12-21 16:00:40.470906: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2021-12-21 16:00:40.470926: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (jupyter-server-2): /proc/driver/nvidia/version does not exist\n",
      "2021-12-21 16:00:40.471412: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 25, 50)            597650    \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, 25, 32)            4832      \n",
      "_________________________________________________________________\n",
      "max_pooling1d (MaxPooling1D) (None, 12, 32)            0         \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 100)               53200     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 12)                1212      \n",
      "=================================================================\n",
      "Total params: 656,894\n",
      "Trainable params: 656,894\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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-21 16:00:41.263387: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2021-12-21 16:00:41.264190: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2250000000 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "22500/22500 [==============================] - 379s 16ms/step - loss: 1.6927 - accuracy: 0.3797 - val_loss: 1.2747 - val_accuracy: 0.5254\n",
      "Epoch 2/15\n",
      "22500/22500 [==============================] - 352s 16ms/step - loss: 1.1901 - accuracy: 0.5637 - val_loss: 1.0297 - val_accuracy: 0.6198\n",
      "Epoch 3/15\n",
      "22500/22500 [==============================] - 353s 16ms/step - loss: 0.9671 - accuracy: 0.6447 - val_loss: 0.8992 - val_accuracy: 0.6678\n",
      "Epoch 4/15\n",
      "22500/22500 [==============================] - 345s 15ms/step - loss: 0.8556 - accuracy: 0.6838 - val_loss: 0.8434 - val_accuracy: 0.6901\n",
      "Epoch 5/15\n",
      "22500/22500 [==============================] - 320s 14ms/step - loss: 0.7969 - accuracy: 0.7063 - val_loss: 0.8189 - val_accuracy: 0.6994\n",
      "Epoch 6/15\n",
      "22500/22500 [==============================] - 360s 16ms/step - loss: 0.7642 - accuracy: 0.7177 - val_loss: 0.7994 - val_accuracy: 0.7060\n",
      "Epoch 7/15\n",
      "22500/22500 [==============================] - 391s 17ms/step - loss: 0.7423 - accuracy: 0.7253 - val_loss: 0.7839 - val_accuracy: 0.7121\n",
      "Epoch 8/15\n",
      "22500/22500 [==============================] - 376s 17ms/step - loss: 0.7272 - accuracy: 0.7309 - val_loss: 0.7801 - val_accuracy: 0.7140\n",
      "Epoch 9/15\n",
      "22500/22500 [==============================] - 364s 16ms/step - loss: 0.7199 - accuracy: 0.7335 - val_loss: 0.7727 - val_accuracy: 0.7171\n",
      "Epoch 10/15\n",
      "22500/22500 [==============================] - 377s 17ms/step - loss: 0.7081 - accuracy: 0.7391 - val_loss: 0.7637 - val_accuracy: 0.7199\n",
      "Epoch 11/15\n",
      "22500/22500 [==============================] - 445s 20ms/step - loss: 0.7034 - accuracy: 0.7400 - val_loss: 0.7564 - val_accuracy: 0.7224\n",
      "Epoch 12/15\n",
      "22500/22500 [==============================] - 455s 20ms/step - loss: 0.6993 - accuracy: 0.7413 - val_loss: 0.7605 - val_accuracy: 0.7225\n",
      "Epoch 13/15\n",
      "22500/22500 [==============================] - 457s 20ms/step - loss: 0.6980 - accuracy: 0.7421 - val_loss: 0.7502 - val_accuracy: 0.7265\n",
      "Epoch 14/15\n",
      "22500/22500 [==============================] - 454s 20ms/step - loss: 0.6906 - accuracy: 0.7456 - val_loss: 0.7491 - val_accuracy: 0.7256\n",
      "Epoch 15/15\n",
      "22500/22500 [==============================] - 455s 20ms/step - loss: 0.6903 - accuracy: 0.7453 - val_loss: 0.7457 - val_accuracy: 0.7279\n",
      "6250/6250 [==============================] - 41s 7ms/step - loss: 0.7482 - accuracy: 0.7271\n",
      "Test score: 0.7481632828712463\n",
      "Test accuracy: 0.7271400094032288\n",
      "CPU times: user 2h 35min 50s, sys: 18min 22s, total: 2h 54min 12s\n",
      "Wall time: 1h 38min 41s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.7481632828712463\n",
      "Test accuracy: 0.7271400094032288\n"
     ]
    }
   ],
   "source": [
    "print('Test score:', score)\n",
    "print('Test accuracy:', acc)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# 100k sample\n",
    "Test score: 0.5205668917775154\n",
    "Test accuracy: 0.84155"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6250/6250 - 32s\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        HL+A       0.99      1.00      1.00     16669\n",
      "        HL+B       0.92      0.98      0.95     16627\n",
      "        HL+I       0.99      1.00      0.99     16734\n",
      "        HL+M       0.89      0.92      0.90     16673\n",
      "        HL+O       0.54      0.59      0.56     16611\n",
      "        HL+W       0.55      0.53      0.54     16570\n",
      "        NL+A       0.76      0.80      0.78     16741\n",
      "        NL+B       0.49      0.41      0.45     16667\n",
      "        NL+I       0.76      0.91      0.83     16628\n",
      "        NL+M       0.79      0.88      0.83     16738\n",
      "        NL+O       0.50      0.29      0.37     16632\n",
      "        NL+W       0.40      0.43      0.41     16710\n",
      "\n",
      "    accuracy                           0.73    200000\n",
      "   macro avg       0.72      0.73      0.72    200000\n",
      "weighted avg       0.72      0.73      0.72    200000\n",
      "\n",
      "[[16669     0     0     0     0     0     0     0     0     0     0     0]\n",
      " [    0 16270     0   102    45    26     0    99    27    29     0    29]\n",
      " [    0     0 16734     0     0     0     0     0     0     0     0     0]\n",
      " [    7    65    30 15304   490   273    56   103   100    72   104    69]\n",
      " [   33   208    42   514  9729  3624   279   501   167   127   773   614]\n",
      " [   10   135    45   344  4235  8846   135   513   260   230   510  1307]\n",
      " [   21    46     5    61   313   259 13321   424   220   125   987   959]\n",
      " [   10   437    11   253   283   488   517  6856  1619  1439   898  3856]\n",
      " [    3    37     5    48    91    63   109   424 15090   130   133   495]\n",
      " [    0    63     7    55    90   124    64   562   251 14660   205   657]\n",
      " [   11   152    25   248  2381  1311  2283  1703   518   487  4804  2709]\n",
      " [    9   189    13   305   422   993   750  2891  1537  1317  1139  7145]]\n"
     ]
    }
   ],
   "source": [
    "p = model.predict(X_test, verbose=2) # to predict probability\n",
    "y_pred = np.argmax(p, axis=-1)\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",
    "\n",
    "conf_mat = confusion_matrix(np.argmax(y_test, axis=1), y_pred)\n",
    "print(conf_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def plot_confusion_matrix(cm,\n",
    "                          target_names,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=None,\n",
    "                          normalize=True):\n",
    "    \"\"\"\n",
    "    given a sklearn confusion matrix (cm), make a nice plot\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    cm:           confusion matrix from sklearn.metrics.confusion_matrix\n",
    "\n",
    "    target_names: given classification classes such as [0, 1, 2]\n",
    "                  the class names, for example: ['high', 'medium', 'low']\n",
    "\n",
    "    title:        the text to display at the top of the matrix\n",
    "\n",
    "    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm\n",
    "                  see http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "                  plt.get_cmap('jet') or plt.cm.Blues\n",
    "\n",
    "    normalize:    If False, plot the raw numbers\n",
    "                  If True, plot the proportions\n",
    "\n",
    "    Usage\n",
    "    -----\n",
    "    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by\n",
    "                                                              # sklearn.metrics.confusion_matrix\n",
    "                          normalize    = True,                # show proportions\n",
    "                          target_names = y_labels_vals,       # list of names of the classes\n",
    "                          title        = best_estimator_name) # title of graph\n",
    "\n",
    "    Citiation\n",
    "    ---------\n",
    "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "\n",
    "    \"\"\"\n",
    "    import matplotlib.pyplot as plt\n",
    "    import numpy as np\n",
    "    import itertools\n",
    "\n",
    "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
    "    misclass = 1 - accuracy\n",
    "\n",
    "    if cmap is None:\n",
    "        cmap = plt.get_cmap('Blues')\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "\n",
    "    if target_names is not None:\n",
    "        tick_marks = np.arange(len(target_names))\n",
    "        plt.xticks(tick_marks, target_names, rotation=45)\n",
    "        plt.yticks(tick_marks, target_names)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "\n",
    "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        if normalize:\n",
    "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        else:\n",
    "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(conf_mat, target_names, normalize=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('./nc_voter_reg/lstm/nc_voter_name_lstm_oversample.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "words_df = pd.DataFrame(words_list, columns=['vocab'])\n",
    "words_df.to_csv('./nc_voter_reg/lstm/nc_voter_name_vocab_oversample.csv', index=False, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}