Predict Race/Ethnicity Based on Sequence of Characters in the Name
Project description
ethnicolr: Predict Race and Ethnicity From Name
We exploit the US census data, the Florida voting registration data, and the Wikipedia data collected by Skiena and colleagues, to predict race and ethnicity based on first and last name or just the last name. The granularity at which we predict the race depends on the dataset. For instance, Skiena et al.’ Wikipedia data is at the ethnic group level, while the census data we use in the model (the raw data has additional categories of Native Americans and Bi-racial) merely categorizes between Non-Hispanic Whites, Non-Hispanic Blacks, Asians, and Hispanics.
New Package With New Models in Pytorch
Streamlit App
Caveats and Notes
If you picked a person at random with the last name ‘Smith’ in the US in 2010 and asked us to guess this person’s race (as measured by the census), the best guess would be based on what is available from the aggregated Census file. It is the Bayes Optimal Solution. So what good are last-name-only predictive models for? A few things—if you want to impute race and ethnicity for last names that are not in the census file, infer the race and ethnicity in different years than when the census was conducted (if some assumptions hold), infer the race of people in different countries (if some assumptions hold), etc. The biggest benefit comes in cases where both the first name and last name are known.
Install
We strongly recommend installing ethnicolor inside a Python virtual environment (see venv documentation)
pip install ethnicolr
Or
conda install -c soodoku ethnicolr
Notes:
The models are run and verified on TensorFlow 2.x using Python 3.7 and 3.8.
If you install on Windows, Theano installation typically needs admin. privileges on the shell.
General API
To see the available command line options for any function, please type in <function-name> --help
# census_ln --help
usage: census_ln [-h] [-y {2000,2010}] [-o OUTPUT] -l LAST input
Appends Census columns by last name
positional arguments:
input Input file
optional arguments:
-h, --help show this help message and exit
-y {2000,2010}, --year {2000,2010}
Year of Census data (default=2000)
-o OUTPUT, --output OUTPUT
Output file with Census data columns
-l LAST, --last LAST Name of the column containing the last name
Examples
To append census data from 2010 to a file with column header in the first row, specify the column name carrying last names using the -l option, keeping the rest the same:
census_ln -y 2010 -o output-census2010.csv -l last_name input-with-header.csv
To predict race/ethnicity using Wikipedia full name model, specify the column name of last name and first name by using -l and -f flags respectively.
pred_wiki_name -o output-wiki-pred-race.csv -l last_name -f first_name input-with-header.csv
Functions
We expose 6 functions, each of which either takes a pandas DataFrame or a CSV.
census_ln(df, lname_col, year=2000)
What it does:
Removes extra space
For names in the census file, it appends relevant data of what probability the name provided is of a certain race/ethnicity
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
lname_col : {string} name of the column containing the last name
Year : {2000, 2010}, default=2000 year of census to use
Output: Appends the following columns to the pandas DataFrame or CSV: pctwhite, pctblack, pctapi, pctaian, pct2prace, pcthispanic. See here for what the column names mean.
>>> import pandas as pd >>> from ethnicolr import census_ln, pred_census_ln >>> names = [{'name': 'smith'}, ... {'name': 'zhang'}, ... {'name': 'jackson'}] >>> df = pd.DataFrame(names) >>> df name 0 smith 1 zhang 2 jackson >>> census_ln(df, 'name') name pctwhite pctblack pctapi pctaian pct2prace pcthispanic 0 smith 73.35 22.22 0.40 0.85 1.63 1.56 1 zhang 0.61 0.09 98.16 0.02 0.96 0.16 2 jackson 41.93 53.02 0.31 1.04 2.18 1.53pred_census_ln(df, lname_col, year=2000, num_iter=100, conf_int=1.0)
What it does:
Removes extra space.
Uses the last name census 2000 model or last name census 2010 model to predict race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {string} name of the column containing the last name
year : {2000, 2010}, default=2000 year of census to use
num_iter : int, default=100 number of iterations to calculate uncertainty in model
conf_int : float, default=1.0 confidence interval in predicted class
Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, or hispanic), api (percentage chance asian), black, hispanic, white. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
(Using the same dataframe from example above)
>>> census_ln(df, 'name') name pctwhite pctblack pctapi pctaian pct2prace pcthispanic 0 smith 73.35 22.22 0.40 0.85 1.63 1.56 1 zhang 0.61 0.09 98.16 0.02 0.96 0.16 2 jackson 41.93 53.02 0.31 1.04 2.18 1.53 >>> census_ln(df, 'name', 2010) name race pctwhite pctblack pctapi pctaian pct2prace pcthispanic 0 smith white 70.9 23.11 0.5 0.89 2.19 2.4 1 zhang api 0.99 0.16 98.06 0.02 0.62 0.15 2 jackson black 39.89 53.04 0.39 1.06 3.12 2.5 >>> pred_census_ln(df, 'name') name race api black hispanic white 0 smith white 0.002019 0.247235 0.014485 0.736260 1 zhang api 0.997807 0.000149 0.000470 0.001574 2 jackson black 0.002797 0.528193 0.014605 0.454405pred_wiki_ln( df, lname_col, num_iter=100, conf_int=1.0)
What it does:
Removes extra space.
Uses the last name wiki model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
lname_col : {string} name of the column containing the last name
num_iter : int, default=100 number of iterations to calculate uncertainty in model
conf_int : float, default=1.0 confidence interval in predicted class
Output: Appends the following columns to the pandas DataFrame or CSV: race (categorical variable — category with the highest probability). For each race it will provide the mean, standard error, lower & upper bound of confidence interval
"Asian,GreaterEastAsian,EastAsian", "Asian,GreaterEastAsian,Japanese", "Asian,IndianSubContinent", "GreaterAfrican,Africans", "GreaterAfrican,Muslim", "GreaterEuropean,British","GreaterEuropean,EastEuropean", "GreaterEuropean,Jewish","GreaterEuropean,WestEuropean,French", "GreaterEuropean,WestEuropean,Germanic","GreaterEuropean,WestEuropean,Hispanic", "GreaterEuropean,WestEuropean,Italian","GreaterEuropean,WestEuropean,Nordic".
>>> import pandas as pd >>> names = [ ... {"last": "smith", "first": "john", "true_race": "GreaterEuropean,British"}, ... { ... "last": "zhang", ... "first": "simon", ... "true_race": "Asian,GreaterEastAsian,EastAsian", ... }, ... ] >>> df = pd.DataFrame(names) >>> from ethnicolr import pred_wiki_ln, pred_wiki_name >>> odf = pred_wiki_ln(df,'last', conf_int=0.9) ['Asian,GreaterEastAsian,EastAsian', 'Asian,GreaterEastAsian,Japanese', 'Asian,IndianSubContinent', 'GreaterAfrican,Africans', 'GreaterAfrican,Muslim', 'GreaterEuropean,British', 'GreaterEuropean,EastEuropean', 'GreaterEuropean,Jewish', 'GreaterEuropean,WestEuropean,French', 'GreaterEuropean,WestEuropean,Germanic', 'GreaterEuropean,WestEuropean,Hispanic', 'GreaterEuropean,WestEuropean,Italian', 'GreaterEuropean,WestEuropean,Nordic'] >>> odf last first true_race ... GreaterEuropean,WestEuropean,Nordic_lb GreaterEuropean,WestEuropean,Nordic_ub race 0 Smith john GreaterEuropean,British 0.016103 ... 0.014135 0.007382 0.048828 GreaterEuropean,British 1 Zhang simon Asian,GreaterEastAsian,EastAsian 0.863391 ... 0.017452 0.001844 0.027252 Asian,GreaterEastAsian,EastAsian [2 rows x 56 columns] >>> odf.iloc[0, :8] last Smith first john true_race GreaterEuropean,British Asian,GreaterEastAsian,EastAsian_mean 0.016103 Asian,GreaterEastAsian,EastAsian_std 0.009735 Asian,GreaterEastAsian,EastAsian_lb 0.005873 Asian,GreaterEastAsian,EastAsian_ub 0.034637 Asian,GreaterEastAsian,Japanese_mean 0.003814 Name: 0, dtype: objectpred_wiki_name(df, namecol, num_iter=100, conf_int=1.0)
What it does:
Removes extra space.
Uses the full name wiki model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {string} name of the column containing the name.
num_iter : int, default=100 number of iterations to calculate uncertainty of predictions
conf_int : float, default=1.0 confidence interval
Output: Appends the following columns to the pandas DataFrame or CSV: race (categorical variable—category with the highest probability), “Asian,GreaterEastAsian,EastAsian”, “Asian,GreaterEastAsian,Japanese”, “Asian,IndianSubContinent”, “GreaterAfrican,Africans”, “GreaterAfrican,Muslim”, “GreaterEuropean,British”,”GreaterEuropean,EastEuropean”, “GreaterEuropean,Jewish”,”GreaterEuropean,WestEuropean,French”, “GreaterEuropean,WestEuropean,Germanic”,”GreaterEuropean,WestEuropean,Hispanic”, “GreaterEuropean,WestEuropean,Italian”,”GreaterEuropean,WestEuropean,Nordic”. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
(Using the same dataframe from example above)
>>> odf = pred_wiki_name(df,'last', 'first', conf_int=0.9) ['Asian,GreaterEastAsian,EastAsian', 'Asian,GreaterEastAsian,Japanese', 'Asian,IndianSubContinent', 'GreaterAfrican,Africans', 'GreaterAfrican,Muslim', 'GreaterEuropean,British', 'GreaterEuropean,EastEuropean', 'GreaterEuropean,Jewish', 'GreaterEuropean,WestEuropean,French', 'GreaterEuropean,WestEuropean,Germanic', 'GreaterEuropean,WestEuropean,Hispanic', 'GreaterEuropean,WestEuropean,Italian', 'GreaterEuropean,WestEuropean,Nordic'] >>> odf last first true_race __name Asian,GreaterEastAsian,EastAsian_mean ... GreaterEuropean,WestEuropean,Nordic_mean GreaterEuropean,WestEuropean,Nordic_std GreaterEuropean,WestEuropean,Nordic_lb GreaterEuropean,WestEuropean,Nordic_ub race 0 Smith john GreaterEuropean,British Smith John 0.004111 ... 0.006246 0.004760 0.001048 0.016288 GreaterEuropean,British 1 Zhang simon Asian,GreaterEastAsian,EastAsian Zhang Simon 0.944203 ... 0.000793 0.002557 0.000019 0.002470 Asian,GreaterEastAsian,EastAsian [2 rows x 57 columns] >>> odf.iloc[0,:8] last Smith first john true_race GreaterEuropean,British __name Smith John Asian,GreaterEastAsian,EastAsian_mean 0.004111 Asian,GreaterEastAsian,EastAsian_std 0.002929 Asian,GreaterEastAsian,EastAsian_lb 0.001356 Asian,GreaterEastAsian,EastAsian_ub 0.010571 Name: 0, dtype: object
pred_fl_reg_ln(df, lname_col, num_iter=100, conf_int=1.0)
What it does?:
Removes extra space, if there.
Uses the last name FL registration model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
lname_col : {string} name of the column containing the last name
num_iter : int, default=100 number of iterations to calculate the uncertainty
conf_int : float, default=1.0 confidence interval
Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, or hispanic), asian (percentage chance Asian), hispanic, nh_black, nh_white. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
>>> import pandas as pd >>> names = [ ... {"last": "sawyer", "first": "john", "true_race": "nh_white"}, ... {"last": "torres", "first": "raul", "true_race": "hispanic"}, ... ] >>> df = pd.DataFrame(names) >>> from ethnicolr import pred_fl_reg_ln, pred_fl_reg_name, pred_fl_reg_ln_five_cat, pred_fl_reg_name_five_cat >>> odf = pred_fl_reg_ln(df, 'last', conf_int=0.9) ['asian', 'hispanic', 'nh_black', 'nh_white'] >>> odf last first true_race asian_mean asian_std asian_lb asian_ub hispanic_mean hispanic_std hispanic_lb hispanic_ub nh_black_mean nh_black_std nh_black_lb nh_black_ub nh_white_mean nh_white_std nh_white_lb nh_white_ub race 0 Sawyer john nh_white 0.009859 0.006819 0.005338 0.019673 0.021488 0.004602 0.014802 0.030148 0.180929 0.052784 0.105756 0.270238 0.787724 0.051082 0.705290 0.860286 nh_white 1 Torres raul hispanic 0.006463 0.001985 0.003915 0.010146 0.878119 0.021998 0.839274 0.909151 0.013118 0.005002 0.007364 0.021633 0.102300 0.017828 0.075911 0.130929 hispanic [2 rows x 20 columns] >>> odf.iloc[0] last Sawyer first john true_race nh_white asian_mean 0.009859 asian_std 0.006819 asian_lb 0.005338 asian_ub 0.019673 hispanic_mean 0.021488 hispanic_std 0.004602 hispanic_lb 0.014802 hispanic_ub 0.030148 nh_black_mean 0.180929 nh_black_std 0.052784 nh_black_lb 0.105756 nh_black_ub 0.270238 nh_white_mean 0.787724 nh_white_std 0.051082 nh_white_lb 0.70529 nh_white_ub 0.860286 race nh_white Name: 0, dtype: objectpred_fl_reg_name(df, lname_col, num_iter=100, conf_int=1.0)
What it does:
Removes extra space.
Uses the full name FL model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {list} name of the column containing the name.
num_iter : int, default=100 number of iterations to calculate the uncertainty
conf_int : float, default=1.0 confidence interval in predicted class
Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, or hispanic), asian (percentage chance Asian), hispanic, nh_black, nh_white. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
(Using the same dataframe from example above)
>>> odf = pred_fl_reg_name(df, 'last', 'first', conf_int=0.9) ['asian', 'hispanic', 'nh_black', 'nh_white'] >>> odf last first true_race asian_mean asian_std asian_lb asian_ub hispanic_mean hispanic_std hispanic_lb hispanic_ub nh_black_mean nh_black_std nh_black_lb nh_black_ub nh_white_mean nh_white_std nh_white_lb nh_white_ub race 0 Sawyer john nh_white 0.001534 0.000850 0.000636 0.002691 0.006818 0.002557 0.003684 0.011660 0.028068 0.015095 0.011488 0.055149 0.963581 0.015738 0.935445 0.983224 nh_white 1 Torres raul hispanic 0.005791 0.002906 0.002446 0.011748 0.890561 0.029581 0.841328 0.937706 0.011397 0.004682 0.005829 0.020796 0.092251 0.026675 0.049868 0.139210 hispanic >>> odf.iloc[1] last Torres first raul true_race hispanic asian_mean 0.005791 asian_std 0.002906 asian_lb 0.002446 asian_ub 0.011748 hispanic_mean 0.890561 hispanic_std 0.029581 hispanic_lb 0.841328 hispanic_ub 0.937706 nh_black_mean 0.011397 nh_black_std 0.004682 nh_black_lb 0.005829 nh_black_ub 0.020796 nh_white_mean 0.092251 nh_white_std 0.026675 nh_white_lb 0.049868 nh_white_ub 0.13921 race hispanic Name: 1, dtype: object
pred_fl_reg_ln_five_cat(df, namecol, num_iter=100, conf_int=1.0)
What it does?:
Removes extra space, if there.
Uses the last name FL registration model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
lname_col : {string, list, int} name of location of the column containing the last name
num_iter : int, default=100 number of iterations to calculate uncertainty
conf_int : float, default=1.0 confidence interval
Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, hispanic or other), asian (percentage chance Asian), hispanic, nh_black, nh_white, other. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
(Using the same dataframe from example above)
>>> odf = pred_fl_reg_ln_five_cat(df,'last') ['asian', 'hispanic', 'nh_black', 'nh_white', 'other'] >>> odf last first true_race asian_mean asian_std asian_lb asian_ub hispanic_mean hispanic_std ... nh_white_mean nh_white_std nh_white_lb nh_white_ub other_mean other_std other_lb other_ub race 0 Sawyer john nh_white 0.100038 0.020539 0.073266 0.143334 0.044263 0.013077 ... 0.376639 0.048289 0.296989 0.452834 0.248466 0.021040 0.219721 0.283785 nh_white 1 Torres raul hispanic 0.062390 0.021863 0.033837 0.103737 0.774414 0.043238 ... 0.030393 0.009591 0.019713 0.046483 0.117761 0.019524 0.089418 0.150615 hispanic [2 rows x 24 columns] >>> odf.iloc[0] last Sawyer first john true_race nh_white asian_mean 0.100038 asian_std 0.020539 asian_lb 0.073266 asian_ub 0.143334 hispanic_mean 0.044263 hispanic_std 0.013077 hispanic_lb 0.02476 hispanic_ub 0.067965 nh_black_mean 0.230593 nh_black_std 0.063948 nh_black_lb 0.130577 nh_black_ub 0.343513 nh_white_mean 0.376639 nh_white_std 0.048289 nh_white_lb 0.296989 nh_white_ub 0.452834 other_mean 0.248466 other_std 0.02104 other_lb 0.219721 other_ub 0.283785 race nh_white Name: 0, dtype: object
pred_fl_reg_name_five_cat(df, namecol, num_iter=100, conf_int=1.0)
What it does:
Removes extra space.
Uses the full name FL model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {string, list} string or list of the name or location of the column containing the first name, last name.
num_iter : int, default=100 number of iterations to calculate uncertainty
conf_int : float, default=1.0 confidence interval
Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, hispanic, or other), asian (percentage chance Asian), hispanic, nh_black, nh_white, other. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
(Using the same dataframe from example above)
>>> odf = pred_fl_reg_name_five_cat(df, 'last','first') ['asian', 'hispanic', 'nh_black', 'nh_white', 'other'] >>> odf last first true_race asian_mean asian_std asian_lb asian_ub hispanic_mean hispanic_std ... nh_white_mean nh_white_std nh_white_lb nh_white_ub other_mean other_std other_lb other_ub race 0 Sawyer john nh_white 0.039310 0.011657 0.025982 0.059719 0.019737 0.005813 ... 0.650306 0.059327 0.553913 0.733201 0.192242 0.021004 0.160185 0.226063 nh_white 1 Torres raul hispanic 0.020086 0.011765 0.008240 0.041741 0.899110 0.042237 ... 0.019073 0.009901 0.010166 0.040081 0.055774 0.017897 0.036245 0.088741 hispanic [2 rows x 24 columns] >>> odf.iloc[1] last Torres first raul true_race hispanic asian_mean 0.020086 asian_std 0.011765 asian_lb 0.00824 asian_ub 0.041741 hispanic_mean 0.89911 hispanic_std 0.042237 hispanic_lb 0.823799 hispanic_ub 0.937612 nh_black_mean 0.005956 nh_black_std 0.006528 nh_black_lb 0.002686 nh_black_ub 0.010134 nh_white_mean 0.019073 nh_white_std 0.009901 nh_white_lb 0.010166 nh_white_ub 0.040081 other_mean 0.055774 other_std 0.017897 other_lb 0.036245 other_ub 0.088741 race hispanic Name: 1, dtype: object
pred_nc_reg_name(df, namecol, num_iter=100, conf_int=1.0)
What it does:
Removes extra space.
Uses the full name NC model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {string, list} string or list of the name or location of the column containing the first name, last name.
num_iter : int, default=100 number of iterations to calculate uncertainty
conf_int : float, default=1.0 confidence interval
Output: Appends the following columns to the pandas DataFrame or CSV: race + ethnicity. The codebook is here. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
>>> import pandas as pd >>> names = [ ... {"last": "hernandez", "first": "hector", "true_race": "HL+O"}, ... {"last": "zhang", "first": "simon", "true_race": "NL+A"}, ... ] >>> df = pd.DataFrame(names) >>> from ethnicolr import pred_nc_reg_name >>> odf = pred_nc_reg_name(df, 'last','first', conf_int=0.9) ['HL+A', 'HL+B', 'HL+I', 'HL+M', 'HL+O', 'HL+W', 'NL+A', 'NL+B', 'NL+I', 'NL+M', 'NL+O', 'NL+W'] >>> odf last first true_race __name HL+A_mean HL+A_std HL+A_lb HL+A_ub HL+B_mean HL+B_std HL+B_lb HL+B_ub HL+I_mean ... NL+M_mean NL+M_std NL+M_lb NL+M_ub NL+O_mean NL+O_std NL+O_lb NL+O_ub NL+W_mean NL+W_std NL+W_lb NL+W_ub race 0 hernandez hector HL+O Hernandez Hector 2.727371e-13 0.0 2.727372e-13 2.727372e-13 6.542178e-04 0.0 6.542183e-04 6.542183e-04 0.000032 ... 7.863581e-06 0.0 7.863589e-06 7.863589e-06 0.184513 0.0 0.184514 0.184514 0.001256 0.0 0.001256 0.001256 HL+O 1 zhang simon NL+A Zhang Simon 1.985421e-06 0.0 1.985423e-06 1.985423e-06 8.708256e-09 0.0 8.708265e-09 8.708265e-09 0.000049 ... 1.446786e-07 0.0 1.446784e-07 1.446784e-07 0.003238 0.0 0.003238 0.003238 0.000154 0.0 0.000154 0.000154 NL+A [2 rows x 53 columns] >>> odf.iloc[0] last hernandez first hector true_race HL+O __name Hernandez Hector HL+A_mean 0.0 HL+A_std 0.0 HL+A_lb 0.0 HL+A_ub 0.0 HL+B_mean 0.000654 HL+B_std 0.0 HL+B_lb 0.000654 HL+B_ub 0.000654 HL+I_mean 0.000032 HL+I_std 0.0 HL+I_lb 0.000032 HL+I_ub 0.000032 HL+M_mean 0.000541 HL+M_std 0.0 HL+M_lb 0.000541 HL+M_ub 0.000541 HL+O_mean 0.58944 HL+O_std 0.0 HL+O_lb 0.58944 HL+O_ub 0.58944 HL+W_mean 0.221309 HL+W_std 0.0 HL+W_lb 0.221309 HL+W_ub 0.221309 NL+A_mean 0.000044 NL+A_std 0.0 NL+A_lb 0.000044 NL+A_ub 0.000044 NL+B_mean 0.002199 NL+B_std 0.0 NL+B_lb 0.002199 NL+B_ub 0.002199 NL+I_mean 0.000004 NL+I_std 0.0 NL+I_lb 0.000004 NL+I_ub 0.000004 NL+M_mean 0.000008 NL+M_std 0.0 NL+M_lb 0.000008 NL+M_ub 0.000008 NL+O_mean 0.184513 NL+O_std 0.0 NL+O_lb 0.184514 NL+O_ub 0.184514 NL+W_mean 0.001256 NL+W_std 0.0 NL+W_lb 0.001256 NL+W_ub 0.001256 race HL+O Name: 0, dtype: object
Application
To illustrate how the package can be used, we impute the race of the campaign contributors recorded by FEC for the years 2000 and 2010 and tally campaign contributions by race.
Data on race of all the people in the DIME data is posted here The underlying python scripts are posted here
Data
In particular, we utilize the last-name–race data from the 2000 census and 2010 census, the Wikipedia data collected by Skiena and colleagues, and the Florida voter registration data from early 2017.
Evaluation
SCAN Health Plan, a Medicare Advantage plan that serves over 200,000 members throughout California used the software to better assess racial disparities of health among the people they serve. They only had racial data on about 47% of their members so used it to learn the race of the remaining 53%. On the data they had labels for, they found .9 AUC and 83% accuracy for the last name model.
Evaluation on NC Data: https://github.com/appeler/nc_race_ethnicity
Contributor Code of Conduct
The project welcomes contributions from everyone! In fact, it depends on it. To maintain this welcoming atmosphere, and to collaborate in a fun and productive way, we expect contributors to the project to abide by the Contributor Code of Conduct.
License
The package is released under the MIT License.
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