The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

fairadapt

Lifecycle R build status R check status pkgdown build status covr status Codecov test coverage

The R package fairadapt is intended for removing bias from machine learning algorithms. In particular, it implements the pre-processing procedure described in Plecko & Meinshausen, 2019 (all the code used for producing the figures in the paper can be found in the jmlr-paper folder). The main idea is to adapt the training and testing data in a way which prevents any further training procedure from learning an undesired bias. The package currently offers the pre-processing step, after which the user can use the adapted data to train any classifier. However, some caution on the training step is still advised, so for more involved applications with resolving variables, the user should refer to the original paper.

Installation

You can install the released version of fairadapt from CRAN with:

install.packages("fairadapt")

Example

An example of how fairadapt can be used is demonstrated below on the UCI Adult dataset.

# loading the package
library(fairadapt)

vars <- c("sex", "age", "native_country", "marital_status", "education_num",
          "workclass", "hours_per_week", "occupation", "income")

# initialising the adjacency matrix
adj.mat <- c(
  0, 0, 0, 1, 1, 1, 1, 1, 1, # sex
  0, 0, 0, 1, 1, 1, 1, 1, 1, # age
  0, 0, 0, 1, 1, 1, 1, 1, 1, # native_country
  0, 0, 0, 0, 1, 1, 1, 1, 1, # marital_status
  0, 0, 0, 0, 0, 1, 1, 1, 1, # education_num
  0, 0, 0, 0, 0, 0, 0, 0, 1, # workclass
  0, 0, 0, 0, 0, 0, 0, 0, 1, # hours_per_week
  0, 0, 0, 0, 0, 0, 0, 0, 1, # occupation
  0, 0, 0, 0, 0, 0, 0, 0, 0  # income
)

adj.mat <- matrix(adj.mat, nrow = length(vars), ncol = length(vars),
                  dimnames = list(vars, vars), byrow = TRUE)

# reading in the UCI Adult data
adult <- readRDS(
  system.file("extdata", "uci_adult.rds", package = "fairadapt")
)
n <- nrow(adult) / 2

mod <- fairadapt(income ~ ., 
                 train.data = head(adult[, vars], n = n),
                 test.data = tail(adult[, vars], n = n), 
                 prot.attr = "sex", adj.mat = adj.mat, 
                 res.vars = "hours_per_week")

adapt.train <- adaptedData(mod)
adapt.test  <- adaptedData(mod, train = FALSE)

summary(mod)
#> 
#> Call:
#> fairadapt(formula = income ~ ., prot.attr = "sex", adj.mat = adj.mat, 
#>     train.data = head(adult[, vars], n = n), test.data = tail(adult[, 
#>         vars], n = n), res.vars = "hours_per_week")
#> 
#> Protected attribute:                 sex
#> Protected attribute levels:          Female, Male
#> Adapted variables:                   marital_status, education_num, workclass, occupation, income
#> Resolving variables:                 hours_per_week, age, native_country
#> 
#> Number of training samples:          1000
#> Number of test samples:              1000
#> Quantile method:                     rangerQuants
#> 
#> Total variation (before adaptation): -0.2014
#> Total variation (after adaptation):  -0.01676

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.