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