abclass

The package abclass provides implementations of the multi-category angle-based classifiers (Zhang & Liu, 2014) with the large-margin unified machines (Liu, et al., 2011) for high-dimensional data.

Note This package is still very experimental and under active development. The function interface is subject to change without guarantee of backward compatibility.

Installation

One can install the released version from CRAN.

install.packages("abclass")

Alternatively, the version under development can be installed as follows:

if (! require(remotes)) install.packages("remotes")
remotes::install_github("wenjie2wang/abclass", upgrade = "never")

Getting Started

A toy example is as follows:

library(abclass)
set.seed(123)

## toy examples for demonstration purpose
## reference: example 1 in Zhang and Liu (2014)
ntrain <- 200  # size of training set
ntest <- 10000 # size of testing set
p0 <- 10       # number of actual predictors
p1 <- 100      # number of random predictors
k <- 5         # number of categories

n <- ntrain + ntest; p <- p0 + p1
train_idx <- seq_len(ntrain)
y <- sample(k, size = n, replace = TRUE)         # response
mu <- matrix(rnorm(p0 * k), nrow = k, ncol = p0) # mean vector
## normalize the mean vector so that they are distributed on the unit circle
mu <- mu / apply(mu, 1, function(a) sqrt(sum(a ^ 2)))
x0 <- t(sapply(y, function(i) rnorm(p0, mean = mu[i, ], sd = 0.25)))
x1 <- matrix(rnorm(p1 * n, sd = 0.3), nrow = n, ncol = p1)
x <- cbind(x0, x1)
train_x <- x[train_idx, ]
test_x <- x[- train_idx, ]
y <- factor(paste0("label_", y))
train_y <- y[train_idx]
test_y <- y[- train_idx]

### logistic deviance loss with elastic-net penalty
model1 <- cv.abclass(train_x, train_y, nlambda = 100, nfolds = 3,
                     grouped = FALSE, loss = "logistic")
pred1 <- predict(model1, test_x, s = "cv_min")
table(test_y, pred1)

##          pred1
## test_y    label_1 label_2 label_3 label_4 label_5
##   label_1    1879      20       0     143      17
##   label_2       8    1638       0       0     409
##   label_3     308      23    1652       0      14
##   label_4     111      10       5    1617     152
##   label_5      33      29       3       5    1924

mean(test_y == pred1) # accuracy

## [1] 0.871

### hinge-boost loss with groupwise lasso
model2 <- cv.abclass(train_x, train_y, nlambda = 100, nfolds = 3,
                     grouped = TRUE, loss = "hinge-boost")
pred2 <- predict(model2, test_x, s = "cv_1se")
table(test_y, pred2)

##          pred2
## test_y    label_1 label_2 label_3 label_4 label_5
##   label_1    2046       4       0       4       5
##   label_2      43    1826       0       1     185
##   label_3      83       0    1887      13      14
##   label_4     476       6       1    1381      31
##   label_5      19      12       3       0    1960

mean(test_y == pred2) # accuracy

## [1] 0.91

## tuning by ET-Lasso instead of cross-validation
model3 <- et.abclass(train_x, train_y, nlambda = 100,
                     loss = "lum", alpha = 0.5)
pred3 <- predict(model3, test_x)
table(test_y, pred3)

##          pred3
## test_y    label_1 label_2 label_3 label_4 label_5
##   label_1    2033       8       4       6       8
##   label_2       7    1993       1       1      53
##   label_3       4       2    1989       0       2
##   label_4     194      22      12    1622      45
##   label_5       6      15       0       2    1971

mean(test_y == pred3) # accuracy

## [1] 0.9608

References

• Zhang, C., & Liu, Y. (2014). Multicategory Angle-Based Large-Margin Classification. Biometrika, 101(3), 625–640.
• Liu, Y., Zhang, H. H., & Wu, Y. (2011). Hard or soft classification? large-margin unified machines. Journal of the American Statistical Association, 106(493), 166–177.

License

GNU General Public License (≥ 3)

Copyright holder: Eli Lilly and Company