guhjy / sufrep

Sufficient Representation for Categorical Variables https://arxiv.org/abs/1908.09874v1

sufrep: Sufficient Representations of Categorical Variables

This package implements the methods for providing sufficient representations of categorical variables mentioned in Johannemann et al. (2019).

To install this package, run the following commands in R:

library(devtools)
install_github("grf-labs/sufrep")

Example usage:

library(sufrep)

set.seed(12345)
n <- 100
p <- 3

X <- matrix(rnorm(n * p), n, p)
G <- as.factor(sample(5, size = n, replace = TRUE))

# One-hot encoding
onehot_encoder <- make_encoder(X = X, G = G, method = "one_hot")

train.df <- onehot_encoder(X = X, G = G)
print(head(train.df))

#         [,1]    [,2]    [,3] [,4] [,5] [,6] [,7]
# [1,]  0.5855  0.2239 -1.4361    0    0    0    1
# [2,]  0.7095 -1.1562 -0.6293    1    0    0    0
# [3,] -0.1093  0.4224  0.2435    0    0    1    0
# [4,] -0.4535 -1.3248  1.0584    0    0    0    1
# [5,]  0.6059  0.1411  0.8313    0    1    0    0
# [6,] -1.8180 -0.5360  0.1052    0    0    0    0

# "Means" encoding
means_encoder <- make_encoder(X = X, G = G, method = "means")

train.df <- means_encoder(X = X, G = G)
print(head(train.df))

#         [,1]    [,2]    [,3]    [,4]      [,5]     [,6]
# [1,]  0.5855  0.2239 -1.4361  0.1464  0.014645 -0.21528
# [2,]  0.7095 -1.1562 -0.6293  0.1892  0.177011  0.03227
# [3,] -0.1093  0.4224  0.2435 -0.3276 -0.009544  0.06307
# [4,] -0.4535 -1.3248  1.0584  0.1464  0.014645 -0.21528
# [5,]  0.6059  0.1411  0.8313  0.4915  0.159208  0.17173
# [6,] -1.8180 -0.5360  0.1052  0.5474 -0.056997 -0.16276

References

Jonathan Johannemann, Vitor Hadad, Susan Athey, and Stefan Wager. Sufficient Representations of Categorical Variables. 2019.