diff --git a/DESCRIPTION b/DESCRIPTION index fd870eb..4babaa4 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -10,3 +10,5 @@ License: What license is it under? Encoding: UTF-8 LazyData: true RoxygenNote: 7.1.1 +Depends: + R (>= 2.10) diff --git a/NAMESPACE b/NAMESPACE index bdaa312..2a0e709 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -8,6 +8,7 @@ export(compute_p_r_cond_s_g) export(get_census_race_counts) export(swap_census_geography) import(dplyr) +import(tidytext) importFrom(dplyr,across) importFrom(dplyr,all_of) importFrom(dplyr,bind_cols) diff --git a/R/lgm.R b/R/lgm.R new file mode 100644 index 0000000..49c457a --- /dev/null +++ b/R/lgm.R @@ -0,0 +1,74 @@ +#' Get race probabilities for a vector of names based on a language model +#' +#' @param names a character vector of names for which to retrieve probabilities +#' @param model the type of character-level language model to use. Currently +#' only trigram +#' +#' @examples +#' names <- c("alexander", "wesley", "smith", "johnson") +#' race_from_letters(names) + +race_from_letters <- function(names, model = "trigram") { + if (model == "trigram") { + lgm <- trigram_language_models + names <- get_ngrams(names, 3) + } + probabilities <- suppressMessages(names %>% + dplyr::inner_join(lgm)) %>% + dplyr::group_by(name) %>% + dplyr::summarize( + whi_prob = prod(whi_cond_prob), + bla_prob = prod(bla_cond_prob), + his_prob = prod(his_cond_prob), + asi_prob = prod(asi_cond_prob), + oth_prob = prod(oth_cond_prob) + ) + probabilities <- probabilities %>% + dplyr::mutate( + total = whi_prob + bla_prob + his_prob + asi_prob + oth_prob, + ) %>% + dplyr::mutate( + whi_prob = whi_prob / total, + bla_prob = bla_prob / total, + his_prob = his_prob / total, + asi_prob = asi_prob / total, + oth_prob = oth_prob / total + ) %>% + select(name, contains("prob")) + return(probabilities) +} + +#' Converts a character vector into a dataframe with n+1 columns. The first +#' n columns contain elements of trigrams, and the last contains the original +#' character vector +#' +#' @param names a character vector of names for conversion to letter-level +#' trigrams +#' @param n the n-gram size to use. Defaults to 3 (trigram) +#' +#' @import tidytext +names <- c("alexander", "wesley", "smith", "johnson") +n <- 3 +get_ngrams <- function(names, n = 3) { + names <- paste0("<<", names, ">>") + ngrams <- names %>% + tidyr::as_tibble() %>% + tidytext::unnest_character_shingles( + output = "ngram", + input = value, + drop = FALSE, + n = n + ) %>% + dplyr::mutate( + ngram = toupper(ngram), + value = substr(value, 3, (str_length(value)-2)) + ) %>% + tidyr::separate( + ngram, + into = c("discard", c(paste0("L", 1:n))), + sep = "" + ) %>% + select(-discard) %>% + rename("name" = "value") + return(ngrams) +} diff --git a/data/surnames2010.rda b/data/surnames2010.rda new file mode 100644 index 0000000..7f1bca3 Binary files /dev/null and b/data/surnames2010.rda differ diff --git a/data/trigram_language_models.rda b/data/trigram_language_models.rda new file mode 100644 index 0000000..dd3bafa Binary files /dev/null and b/data/trigram_language_models.rda differ diff --git a/inst/Names_2010Census.csv b/inst/Names_2010Census.csv new file mode 100644 index 0000000..c515cea --- /dev/null +++ b/inst/Names_2010Census.csv @@ -0,0 +1,162255 @@ +name,rank,count,prop100k,cum_prop100k,pctwhite,pctblack,pctapi,pctaian,pct2prace,pcthispanic +SMITH,1,2442977,828.19,828.19,70.9,23.11,0.5,0.89,2.19,2.4 +JOHNSON,2,1932812,655.24,1483.42,58.97,34.63,0.54,0.94,2.56,2.36 +WILLIAMS,3,1625252,550.97,2034.39,45.75,47.68,0.46,0.82,2.81,2.49 +BROWN,4,1437026,487.16,2521.56,57.95,35.6,0.51,0.87,2.55,2.52 +JONES,5,1425470,483.24,3004.8,55.19,38.48,0.44,1,2.61,2.29 +GARCIA,6,1166120,395.32,3400.12,5.38,0.45,1.41,0.47,0.26,92.03 +MILLER,7,1161437,393.74,3793.86,84.11,10.76,0.54,0.66,1.77,2.17 +DAVIS,8,1116357,378.45,4172.31,62.2,31.6,0.49,0.82,2.45,2.44 +RODRIGUEZ,9,1094924,371.19,4543.5,4.75,0.54,0.57,0.18,0.18,93.77 +MARTINEZ,10,1060159,359.4,4902.9,5.28,0.49,0.6,0.51,0.22,92.91 +HERNANDEZ,11,1043281,353.68,5256.58,3.79,0.36,0.6,0.19,0.16,94.89 +LOPEZ,12,874523,296.47,5553.05,4.86,0.57,1.02,0.38,0.25,92.92 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+TRIPLETT,1965,18408,6.24,49096.88,65.43,29.29,0.51,0.59,2.33,1.86 +HATHAWAY,1966,18401,6.24,49103.12,90.3,4.76,0.6,0.46,1.67,2.22 +SALCEDO,1967,18387,6.23,49109.35,4.6,0.21,5.11,0.27,0.45,89.37 +PRESLEY,1968,18384,6.23,49115.58,75.28,19.26,0.49,0.95,2.18,1.84 +ERNST,1969,18361,6.22,49121.81,94.97,0.58,0.69,0.29,1.35,2.12 +HAYWOOD,1970,18360,6.22,49128.03,44.5,49.08,0.38,0.45,3.03,2.55 +PUTNAM,1971,18340,6.22,49134.25,93.78,1.59,0.68,0.46,1.46,2.03 +NAJERA,1971,18340,6.22,49140.47,3.66,0.27,0.61,0.28,0.24,94.95 +STAPLETON,1973,18332,6.21,49146.68,84.93,9.56,0.4,0.56,1.88,2.67 +BATISTA,1974,18323,6.21,49152.89,17.4,1.93,0.73,0.13,0.31,79.5 +WILKES,1975,18314,6.21,49159.1,69.32,25.46,0.36,0.58,2.04,2.23 +LOPES,1976,18310,6.21,49165.31,55.63,13.6,2.95,0.58,4.21,23.02 +RICHEY,1977,18296,6.2,49171.51,86.7,7.89,0.43,0.63,1.9,2.45 +OH,1978,18285,6.2,49177.71,1.99,0.19,95.48,0.05,1.78,0.51 +KEYES,1979,18252,6.19,49183.9,60.5,22.06,0.9,0.78,2.21,13.54 +DILL,1980,18215,6.18,49190.07,87.66,6.62,0.7,1,1.81,2.21 +HUMPHRIES,1981,18201,6.17,49196.24,79.89,15.12,0.43,0.43,1.9,2.24 +YOUNGBLOOD,1982,18197,6.17,49202.41,69.8,23.1,0.34,1.69,2.99,2.08 +CAPPS,1983,18194,6.17,49208.58,93.83,1.22,0.45,0.89,1.62,2 +CATES,1984,18190,6.17,49214.75,86.89,6.94,0.64,1,1.84,2.7 +COVARRUBIAS,1985,18189,6.17,49220.91,3.3,0.07,0.58,0.16,0.13,95.77 +ADDISON,1986,18169,6.16,49227.07,50.75,42.95,0.5,1.14,2.36,2.3 +HA,1987,18156,6.16,49233.23,3.67,0.69,92.93,0.04,2.03,0.63 +MCRAE,1987,18156,6.16,49239.38,52.7,41.41,0.41,0.44,2.46,2.58 +CAO,1989,18138,6.15,49245.53,1.38,(S),95.22,(S),1.05,2.12 +DICKENS,1990,18123,6.14,49251.68,63.46,31.12,0.39,0.86,1.93,2.23 +JUDD,1991,18110,6.14,49257.82,88.56,5.51,1.02,0.64,1.86,2.42 +BARBOSA,1992,18106,6.14,49263.95,25.77,7.06,2.09,0.2,0.99,63.88 +HAWLEY,1993,18100,6.14,49270.09,89.27,4.38,0.71,1.47,1.77,2.4 +RAHMAN,1994,18097,6.14,49276.22,8.56,6.21,79.43,0.44,3.95,1.41 +CRENSHAW,1995,18096,6.13,49282.36,52.05,42.13,0.37,0.46,2.51,2.47 +LACKEY,1996,18082,6.13,49288.49,84.07,10.37,0.49,0.97,1.94,2.16 +VARNER,1997,18081,6.13,49294.62,81.12,14.2,0.57,0.64,1.8,1.67 +STALEY,1998,18080,6.13,49300.75,82.57,12.55,0.62,0.71,1.66,1.89 +KOWALSKI,1999,18077,6.13,49306.88,96.05,0.28,0.45,0.22,1.15,1.85 +KINCAID,2000,18075,6.13,49313,86.07,8.68,0.66,0.46,1.86,2.27 +MOYA,2001,18071,6.13,49319.13,7.23,0.73,1.95,0.51,0.28,89.3 +VOGT,2002,18061,6.12,49325.25,94.18,0.93,0.77,0.23,1.23,2.66 +KIMBLE,2003,18040,6.12,49331.37,59.02,35.37,0.49,0.65,2.27,2.2 +BARRAZA,2004,18030,6.11,49337.48,3.98,0.18,0.28,0.28,0.2,95.07 +HAY,2005,18029,6.11,49343.59,85.74,7.14,2.83,0.37,1.63,2.3 +PLATT,2006,18022,6.11,49349.7,86.57,7.75,0.49,0.55,2.09,2.55 +FERNANDES,2007,18018,6.11,49355.81,60.69,10.13,13.78,0.43,3.56,11.41 +ABREU,2008,17998,6.1,49361.91,13.97,1.29,0.87,0.08,0.82,82.98 +DOTY,2009,17965,6.09,49368,91.99,2.89,0.56,0.52,1.54,2.5 +CRUM,2010,17944,6.08,49374.09,86.41,9.07,0.42,0.49,1.44,2.17 +YI,2011,17934,6.08,49380.17,1.41,0.18,94.45,0.03,2.13,1.8 +CORLEY,2012,17931,6.08,49386.24,74.48,20.1,0.44,0.64,1.86,2.49 +FLAHERTY,2013,17926,6.08,49392.32,95.71,0.45,0.64,0.27,1.13,1.8 +DRUMMOND,2014,17922,6.08,49398.4,65.5,28.82,0.4,0.42,2.35,2.5 +BERNSTEIN,2015,17916,6.07,49404.47,95.21,0.69,0.75,0.08,1.03,2.25 +DARNELL,2016,17915,6.07,49410.54,87.59,7.27,0.45,0.56,1.9,2.22 +HWANG,2017,17900,6.07,49416.61,1.44,(S),96.04,(S),1.85,0.47 +FONG,2018,17889,6.06,49422.68,4.99,1.15,81.11,0.04,5.87,6.83 +MCCORD,2019,17875,6.06,49428.74,79.73,14.79,0.39,1.06,1.86,2.18 +AKINS,2020,17868,6.06,49434.79,62.81,31.45,0.45,0.65,2.45,2.19 +BERGMAN,2021,17846,6.05,49440.84,94.37,0.89,0.61,0.53,1.47,2.14 +DELONG,2022,17844,6.05,49446.89,93.44,0.82,0.8,0.96,1.73,2.24 +MORELAND,2023,17832,6.05,49452.94,73.99,20.18,0.51,0.72,2.37,2.23 +LORENZO,2024,17825,6.04,49458.98,19.68,1.43,9.4,0.96,1.78,66.75 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100644 index 0000000..79b3ab3 --- /dev/null +++ b/inst/get_trigram_language_model.ipynb @@ -0,0 +1,819 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "editorial-hungarian", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from nltk import ngrams\n", + "\n", + "from collections import Counter, defaultdict\n", + "from tqdm import tqdm\n", + "\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "behavioral-gasoline", + "metadata": {}, + "source": [ + "### Import surnames and get weights" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "million-generic", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(167613, 6)\n" + ] + }, + { + "data": { + "text/html": [ + "
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surnamep_whip_blap_hisp_asip_oth
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1JOHNSON0.006552
2WILLIAMS0.005510
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" + ], + "text/plain": [ + " surname weight\n", + "0 SMITH 0.008282\n", + "1 JOHNSON 0.006552\n", + "2 WILLIAMS 0.005510\n", + "3 BROWN 0.004872\n", + "4 JONES 0.004832" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "surnames_counts = pd.read_csv(\"Names_2010Census.csv\")\n", + "surnames_counts['weight'] = surnames_counts['prop100k'] / 100000\n", + "surnames_counts = surnames_counts[['name', 'weight']]\n", + "surnames_counts = surnames_counts.rename(columns={\"name\": \"surname\", \"weight\": \"weight\"})\n", + "print(surnames_counts.shape)\n", + "surnames_counts.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "brutal-bacteria", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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surnamep_whip_blap_hisp_asip_othweight
0SMITH0.70900.23110.02400.00500.03080.008282
1JOHNSON0.58970.34630.02360.00540.03500.006552
2WILLIAMS0.45750.47680.02490.00460.03630.005510
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4JONES0.55190.38480.02290.00440.03610.004832
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" + ], + "text/plain": [ + " surname p_whi p_bla p_his p_asi p_oth weight\n", + "0 SMITH 0.7090 0.2311 0.0240 0.0050 0.0308 0.008282\n", + "1 JOHNSON 0.5897 0.3463 0.0236 0.0054 0.0350 0.006552\n", + "2 WILLIAMS 0.4575 0.4768 0.0249 0.0046 0.0363 0.005510\n", + "3 BROWN 0.5795 0.3560 0.0252 0.0051 0.0342 0.004872\n", + "4 JONES 0.5519 0.3848 0.0229 0.0044 0.0361 0.004832" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "surnames = pd.merge(surnames, surnames_counts, on=['surname'])\n", + "surnames.head()" + ] + }, + { + "cell_type": "markdown", + "id": "warming-nicaragua", + "metadata": {}, + "source": [ + "### Function to count trygram occurrences" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "african-savings", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "def get_counts(surnames):\n", + " whi_model = defaultdict(lambda: defaultdict(lambda: 0))\n", + " bla_model = defaultdict(lambda: defaultdict(lambda: 0))\n", + " his_model = defaultdict(lambda: defaultdict(lambda: 0))\n", + " asi_model = defaultdict(lambda: defaultdict(lambda: 0))\n", + " oth_model = defaultdict(lambda: defaultdict(lambda: 0))\n", + "\n", + " for ii in tqdm(range(0, surnames.shape[0])):\n", + " try:\n", + " surname = list(surnames['surname'][ii].upper())\n", + " surname_ngrams = ngrams(surnames['surname'][ii], n = 3, \n", + " pad_right = True, pad_left = True, \n", + " left_pad_symbol='<', right_pad_symbol='>')\n", + " for l1, l2, l3 in surname_ngrams:\n", + " whi_model[(l1, l2)][l3] += surnames['p_whi'][ii]*surnames['weight'][ii]\n", + " bla_model[(l1, l2)][l3] += surnames['p_bla'][ii]*surnames['weight'][ii]\n", + " his_model[(l1, l2)][l3] += surnames['p_his'][ii]*surnames['weight'][ii]\n", + " asi_model[(l1, l2)][l3] += surnames['p_asi'][ii]*surnames['weight'][ii]\n", + " oth_model[(l1, l2)][l3] += surnames['p_oth'][ii]*surnames['weight'][ii]\n", + " except:\n", + " print(\"Error on case {}\".format(ii))\n", + " \n", + " return whi_model, bla_model, his_model, asi_model, oth_model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "focal-manual", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 3%|▎ | 5062/162253 [00:04<02:23, 1098.36it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error on case 4909\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 162253/162253 [02:55<00:00, 922.91it/s] \n" + ] + } + ], + "source": [ + "whi_model, bla_model, his_model, asi_model, oth_model = get_counts(surnames)" + ] + }, + { + "cell_type": "markdown", + "id": "flying-heading", + "metadata": {}, + "source": [ + "### Convert counts to conditional probabilities " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "powerful-glossary", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":10: RuntimeWarning: invalid value encountered in double_scalars\n", + " bla_model[l1_l2][l3] /= bla_total_count\n", + ":12: RuntimeWarning: invalid value encountered in double_scalars\n", + " asi_model[l1_l2][l3] /= asi_total_count\n", + ":13: RuntimeWarning: invalid value encountered in double_scalars\n", + " oth_model[l1_l2][l3] /= oth_total_count\n", + ":9: RuntimeWarning: invalid value encountered in double_scalars\n", + " whi_model[l1_l2][l3] /= whi_total_count\n" + ] + } + ], + "source": [ + "for l1_l2 in whi_model:\n", + " whi_total_count = float(sum(whi_model[l1_l2].values()))\n", + " bla_total_count = float(sum(bla_model[l1_l2].values()))\n", + " his_total_count = float(sum(his_model[l1_l2].values()))\n", + " asi_total_count = float(sum(asi_model[l1_l2].values()))\n", + " oth_total_count = float(sum(oth_model[l1_l2].values()))\n", + " \n", + " for l3 in whi_model[l1_l2]:\n", + " whi_model[l1_l2][l3] /= whi_total_count\n", + " bla_model[l1_l2][l3] /= bla_total_count\n", + " his_model[l1_l2][l3] /= his_total_count\n", + " asi_model[l1_l2][l3] /= asi_total_count\n", + " oth_model[l1_l2][l3] /= oth_total_count" + ] + }, + { + "cell_type": "markdown", + "id": "separated-example", + "metadata": {}, + "source": [ + "### Function to get P(R|S) for a single name and race" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "magnetic-quantum", + "metadata": {}, + "outputs": [], + "source": [ + "def get_name_prob(surname, model, return_probs = False):\n", + " probs = defaultdict()\n", + " surname = surname.upper()\n", + " p_surname = 1\n", + " surname_ngrams = ngrams(surname, n = 3, \n", + " pad_right = True, pad_left = True, \n", + " left_pad_symbol='<', right_pad_symbol='>')\n", + " for l1, l2, l3 in surname_ngrams:\n", + " cond_prob = model[(l1, l2)][l3]\n", + " probs[(l1, l2, l3)] = cond_prob\n", + " p_surname *= cond_prob\n", + " \n", + " if return_probs:\n", + " return p_surname, probs\n", + " else:\n", + " return p_surname" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "subsequent-demand", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0006297469929376308,\n", + " defaultdict(None,\n", + " {('<', '<', 'S'): 0.10301164973888023,\n", + " ('<', 'S', 'M'): 0.11203091254685146,\n", + " ('S', 'M', 'I'): 0.7781645677040535,\n", + " ('M', 'I', 'T'): 0.3960144375590823,\n", + " ('I', 'T', 'H'): 0.3663670976402352,\n", + " ('T', 'H', '>'): 0.4833291080316953,\n", + " ('H', '>', '>'): 1.0}))" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_name_prob(\"Smith\", whi_model, return_probs = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "legal-beijing", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0020614759943320266,\n", + " defaultdict(None,\n", + " {('<', '<', 'S'): 0.08376543645490296,\n", + " ('<', 'S', 'M'): 0.22566689407628474,\n", + " ('S', 'M', 'I'): 0.8963251034236016,\n", + " ('M', 'I', 'T'): 0.5706655660271983,\n", + " ('I', 'T', 'H'): 0.4802278038253288,\n", + " ('T', 'H', '>'): 0.4439673634165719,\n", + " ('H', '>', '>'): 1.0}))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_name_prob(\"Smith\", bla_model, return_probs = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "attended-steam", + "metadata": {}, + "outputs": [], + "source": [ + "models = {\n", + " \"whi\": whi_model,\n", + " \"bla\": bla_model,\n", + " \"his\": his_model,\n", + " \"asi\": asi_model,\n", + " \"oth\": oth_model\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "weird-round", + "metadata": {}, + "source": [ + "### Get full distribution over race groups" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "interpreted-munich", + "metadata": {}, + "outputs": [], + "source": [ + "def get_race_probs(surname, models):\n", + " results = defaultdict()\n", + " for race, model in models.items():\n", + " results[race] = get_name_prob(surname, model)\n", + " total = sum(results.values())\n", + " for race in results:\n", + " results[race] /= total\n", + " return results" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "specific-liberty", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " surname p_whi p_bla p_his p_asi p_oth weight\n", + "0 SMITH 0.7090 0.2311 0.0240 0.0050 0.0308 0.008282\n", + "1 JOHNSON 0.5897 0.3463 0.0236 0.0054 0.0350 0.006552\n", + "2 WILLIAMS 0.4575 0.4768 0.0249 0.0046 0.0363 0.005510\n", + "3 BROWN 0.5795 0.3560 0.0252 0.0051 0.0342 0.004872\n", + "4 JONES 0.5519 0.3848 0.0229 0.0044 0.0361 0.004832\n" + ] + }, + { + "data": { + "text/plain": [ + "defaultdict(None,\n", + " {'whi': 0.17779520314835587,\n", + " 'bla': 0.5820123753000933,\n", + " 'his': 0.0014056554570174005,\n", + " 'asi': 0.0008225314773946631,\n", + " 'oth': 0.2379642346171387})" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(surnames.head())\n", + "get_race_probs(\"smith\", models)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "floral-string", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(None,\n", + " {'whi': 0.07758016566850848,\n", + " 'bla': 0.7179600975892731,\n", + " 'his': 0.0010928060188440149,\n", + " 'asi': 0.0005604610304707167,\n", + " 'oth': 0.20280646969290372})" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_race_probs(\"johnson\", models)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "danish-providence", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0067298366923897535,\n", + " defaultdict(None,\n", + " {('<', '<', 'J'): 0.06834777322076013,\n", + " ('<', 'J', 'O'): 0.625683436532375,\n", + " ('J', 'O', 'H'): 0.48490907287117685,\n", + " ('O', 'H', 'N'): 0.9326873905538675,\n", + " ('H', 'N', 'S'): 0.9372152664113177,\n", + " ('N', 'S', 'O'): 0.4883239499946842,\n", + " ('S', 'O', 'N'): 0.9699644308808777,\n", + " ('O', 'N', '>'): 0.7838372426315613,\n", + " ('N', '>', '>'): 1.0}))" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_name_prob(\"johnson\", bla_model, return_probs = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "genetic-effectiveness", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0007272017585248665,\n", + " defaultdict(None,\n", + " {('<', '<', 'J'): 0.02624867441121226,\n", + " ('<', 'J', 'O'): 0.5710038122841791,\n", + " ('J', 'O', 'H'): 0.5187862857295419,\n", + " ('O', 'H', 'N'): 0.7095044680298122,\n", + " ('H', 'N', 'S'): 0.6607486559777127,\n", + " ('N', 'S', 'O'): 0.31101019827161863,\n", + " ('S', 'O', 'N'): 0.9027292881134743,\n", + " ('O', 'N', '>'): 0.7105529775243876,\n", + " ('N', '>', '>'): 1.0}))" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_name_prob(\"johnson\", whi_model, return_probs = True)" + ] + }, + { + "cell_type": "markdown", + "id": "successful-firmware", + "metadata": {}, + "source": [ + "### Save out the models" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "french-stations", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Save out white model\n", + "df = pd.DataFrame.from_dict(whi_model).transpose()\n", + "df.to_csv(\"whi_cond_probs.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "configured-registration", + "metadata": {}, + "outputs": [], + "source": [ + "# Save out black model\n", + "df = pd.DataFrame.from_dict(bla_model).transpose()\n", + "df.to_csv(\"bla_cond_probs.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "bulgarian-transport", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame.from_dict(his_model).transpose()\n", + "df.to_csv(\"his_cond_probs.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "heavy-notice", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame.from_dict(asi_model).transpose()\n", + "df.to_csv(\"asi_cond_probs.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "critical-twelve", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame.from_dict(oth_model).transpose()\n", + "df.to_csv(\"oth_cond_probs.csv\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/inst/his_cond_probs.csv b/inst/his_cond_probs.csv new file mode 100644 index 0000000..3904aca --- /dev/null +++ b/inst/his_cond_probs.csv @@ -0,0 +1,694 @@ +,,S,J,W,B,G,M,D,R,H,L,A,T,P,C,Y,K,N,F,E,O,V,Z,Q,I,U,X,> 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+++ b/inst/process_language_model.R @@ -0,0 +1,59 @@ +library(tidyverse) + +# Process white cond probs +whi_model <- readr::read_csv("inst/whi_cond_probs.csv") %>% + dplyr::rename("L1" = "X1", + "L2" = "X2") %>% + tidyr::pivot_longer( + -one_of("L1", "L2"), + names_to = "L3", + values_to = "whi_cond_prob" + ) + +# Process black cond probs +bla_pmodel <- read_csv("inst/bla_cond_probs.csv") %>% + dplyr::rename("L1" = "X1", + "L2" = "X2") %>% + tidyr::pivot_longer( + -one_of("L1", "L2"), + names_to = "L3", + values_to = "bla_cond_prob" + ) + +# Process hispanic cond probs +his_model <- read_csv("inst/his_cond_probs.csv") %>% + dplyr::rename("L1" = "X1", + "L2" = "X2") %>% + tidyr::pivot_longer( + -one_of("L1", "L2"), + names_to = "L3", + values_to = "his_cond_prob" + ) + +# Process asian cond probs +asi_model <- read_csv("inst/asi_cond_probs.csv") %>% + dplyr::rename("L1" = "X1", + "L2" = "X2") %>% + tidyr::pivot_longer( + -one_of("L1", "L2"), + names_to = "L3", + values_to = "asi_cond_prob" + ) + +# Process other cond probs +oth_model <- read_csv("inst/oth_cond_probs.csv") %>% + dplyr::rename("L1" = "X1", + "L2" = "X2") %>% + tidyr::pivot_longer( + -one_of("L1", "L2"), + names_to = "L3", + values_to = "oth_cond_prob" + ) + +trigram_language_models <- whi_model %>% + inner_join(bla_pmodel) %>% + inner_join(his_model) %>% + inner_join(asi_model) %>% + inner_join(oth_model) + +usethis::use_data(trigram_language_models, overwrite = TRUE) diff --git a/inst/surnames2010.csv b/inst/surnames2010.csv new file mode 100644 index 0000000..dd17a65 --- /dev/null +++ b/inst/surnames2010.csv @@ -0,0 +1,167614 @@ +surname,p_whi,p_bla,p_his,p_asi,p_oth +SMITH,0.709,0.2311,0.024,0.005,0.0308 +JOHNSON,0.5897,0.3463,0.0236,0.0054,0.035 +WILLIAMS,0.4575,0.4768,0.0249,0.0046,0.0363 +BROWN,0.5795,0.356,0.0252,0.0051,0.034199999999999994 +JONES,0.5519,0.3848,0.0229,0.0044,0.0361 +GARCIA,0.0538,0.0045,0.9203,0.0141,0.0073 +MILLER,0.8411,0.1076,0.0217,0.0054,0.024300000000000002 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The first +n columns contain elements of trigrams, and the last contains the original +character vector} +\format{ +An object of class \code{character} of length 4. +} +\usage{ +names +} +\arguments{ +\item{names}{a character vector of names for conversion to letter-level +trigrams} + +\item{n}{the n-gram size to use. Defaults to 3 (trigram)} +} +\description{ +Converts a character vector into a dataframe with n+1 columns. The first +n columns contain elements of trigrams, and the last contains the original +character vector +} +\keyword{datasets} diff --git a/man/race_from_letters.Rd b/man/race_from_letters.Rd new file mode 100644 index 0000000..e1d3d4f --- /dev/null +++ b/man/race_from_letters.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgm.R +\name{race_from_letters} +\alias{race_from_letters} +\title{Get race probabilities for a vector of names based on a language model} +\usage{ +race_from_letters(names, model = "trigram") +} +\arguments{ +\item{names}{a character vector of names for which to retrieve probabilities} + +\item{model}{the type of character-level language model to use. Currently +only trigram} +} +\description{ +Get race probabilities for a vector of names based on a language model +} +\examples{ +names <- c("alexander", "wesley", "smith", "johnson") +race_from_letters(names) +}