Introduction to rethnicity package

Fangzhou Xie

July 07, 2024

In this package, I aim to provide a function that could predict ethnicity (race) from names.



I created this package hoping to help applied researchers on their studies regarding ethnic bias and discrimination, and potentially eliminate the racial and ethnic disparities. By using this package, you agree to the following:

  1. You will NOT use this package for purposes other than academic research.
  2. You will NOT disclose the predicted ethnic group to the public, given the names data you might have.
  3. You will NOT discriminate anyone on the basis of race and color, by using the methods provided by this package.
  4. You understand that the method cannot make predictions 100% correct, and you should be cautious about the results.

Again, you should use the package responsibly.

Getting to use the package in 5 minutes

I want to predict ethnicity from last names!

Sure. I have trained a model to predict and classify race based on last names. Simply use it as:

predict_ethnicity(lastnames = "Jackson", method = "lastname")
#>   lastname prob_asian prob_black prob_hispanic prob_white  race
#> 1  Jackson 0.02337378  0.8985271   0.007418934 0.07068021 black

What if I have both first names and last names?

Of course. There is a separate model just to do that. By having both first names and last names, we can achieve higher accuracy than only having last names. The syntax is similar to what we have seen from above.

predict_ethnicity(firstnames = "Samuel", lastnames = "Jackson", method = "fullname")
#>   firstname lastname prob_asian prob_black prob_hispanic prob_white  race
#> 1    Samuel  Jackson 0.01741121  0.8898848   0.006667829 0.08603617 black

What if I have multiple names?

Cool. I got you covered. Just use vectors as input.

firstnames <- c("Samuel", "Will")
lastnames <- c("Jackson", "Smith")
predict_ethnicity(lastnames = lastnames, method = "lastname")
#>   lastname prob_asian prob_black prob_hispanic prob_white  race
#> 1  Jackson 0.02337378  0.8985271   0.007418934 0.07068021 black
#> 2    Smith 0.08850800  0.5421596   0.033163086 0.33616930 black
predict_ethnicity(firstnames = firstnames, lastnames = lastnames, method = "fullname")
#>   firstname lastname prob_asian prob_black prob_hispanic prob_white  race
#> 1    Samuel  Jackson 0.01741121  0.8898848   0.006667829 0.08603617 black
#> 2      Will    Smith 0.04450590  0.5568278   0.007727879 0.39093845 black

Just remember to have the same length for the firstnames and lastnames vectors and the first name and last name for the same person should have same index in each of the vectors.

Wait. I want to predict a million names!

Alright. The package also supports extremely fast execution by multi-threading via the wonderful RcppThread package. To use this, just pass a number to the threads argument and the number need to be greater than 0.

firstnames <- rep("Samuel", 1000)
lastnames <- rep("Jackson", 1000)
# measure the elapsed time
start_time <- Sys.time()
p <- predict_ethnicity(firstnames = firstnames, lastnames = lastnames, threads = parallel::detectCores()-2)
end_time <- Sys.time()
end_time - start_time
#> Time difference of 0.06980777 secs

Processing one thousand names only spent around 0.064 seconds for us. I would call this pretty fast.

For most use cases that I can imagine, the default setting (threads = 0) should be fast enough since we are leveraging C++ routines for the processing. If you have very large dataset, or if you have a powerful machine, or if you just want to run the code faster, you can set the threads argument to be bigger than 0 and you should observe performance boost.

You may need to wisely choose the appropriate number of threads for the job. In general, the more threads you have, the faster it should run. But the relationship is not linear, as there will be more overhead when increasing the number of threads. In the example, I was choosing the number of threads by the maximum number minus 2 (24 - 2 = 22).


How did you train the models?

I first trained the models in Keras with Python, using the Florida Voter Registration dataset. After training a big model for the prediction, I also trained a smaller model than will mimic the prediction of large model (this is called “distillation of knowledge”). By doing so, we could significantly reduce the size of the model while keeping the accuracy. This is very important if we want the package to be lightweight and fast in processing data.

How did you create the package with Keras models?

After the training and testing process, I save the distilled model and export it into C++ by the frugally-deep project. This will allow us to get rid of the dependency on Keras and Python and we can directly making inferences from C++. From here, it is very obvious that we can wrap the inference procedure by Rcpp and call it from R.

Note that one could potentially use the keras package in R to load Keras models trained in Python. But I believe this would have defeated the purpose of having a R package, as the keras package still depends on Python and the installation of Keras. You can argue that we are actually still using Python:)

What is the difference between fullname model vs. lastname model?

I have trained two different model for predicting ethnicity from names, one only leverages last names, while another incorporate both first names and last names. In some applications, researchers may only have access to last names, then they should consider using the lastname method. In other cases, we could also have first names available, then we could incorporate this information and use the fullname method. This will yield better accuracy for the prediction.

What about the performance of the package?

The processing speed is super fast, as the heavy-lifting has been delegated to the underlying C++. What is more, to make it even faster, I used RcppThread to achieve multi-threading. This would be extremely useful if you have a very large dataset at hand. As shown in the example above, we have achieved to process a million names within 10 seconds. In other words, we could predict the race from a name by 10 μs on average.