-
Notifications
You must be signed in to change notification settings - Fork 2
/
README.Rmd
85 lines (66 loc) · 3.62 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
---
output:
github_document:
html_preview: false
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# [fairadapt](https://dplecko.github.io/fairadapt/)
<!-- badges: start -->
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://github.com/dplecko/fairadapt/actions?query=workflow%3Abuild)
[](https://github.com/dplecko/fairadapt/actions?query=workflow%3Acheck)
[](https://github.com/dplecko/fairadapt/actions?query=workflow%3Apkgdown)
[](https://github.com/dplecko/fairadapt/actions?query=workflow%3Acoverage)
[](https://app.codecov.io/gh/dplecko/fairadapt)
<!-- badges: end -->
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](https://arxiv.org/abs/1911.06685) (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](https://CRAN.R-project.org) with:
```{r, eval = FALSE}
install.packages("fairadapt")
```
## Example
<!-- example could be expanded to show bias before correction -->
<!-- add plot of adj.mat? visualization of how data changed? -->
An example of how fairadapt can be used is demonstrated below on the UCI Adult dataset.
```{r, example}
# 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)
```