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test_that("classif_glmnet", {
requirePackagesOrSkip("glmnet", default.method = "load")
parset.list = list(
list(),
list(alpha = 0.5),
list(devmax = 0.8),
list(s = 0.1)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
s = parset[["s"]]
if (is.null(s)) {
s = 0.01
}
parset[["s"]] = NULL
x = binaryclass.train
y = x[, binaryclass.class.col]
x[, binaryclass.class.col] = NULL
pars = list(x = as.matrix(x), y = y, family = "binomial")
pars = c(pars, parset)
glmnet::glmnet.control(factory = TRUE)
ctrl.args = names(formals(glmnet::glmnet.control))
if (any(names(pars) %in% ctrl.args)) {
on.exit(glmnet::glmnet.control(factory = TRUE))
do.call(glmnet::glmnet.control, pars[names(pars) %in% ctrl.args])
m = do.call(glmnet::glmnet, pars[!names(pars) %in% ctrl.args])
} else {
m = do.call(glmnet::glmnet, pars)
}
newx = binaryclass.test
newx[, binaryclass.class.col] = NULL
p = factor(predict(m, as.matrix(newx), type = "class", s = s)[, 1])
p2 = predict(m, as.matrix(newx), type = "response", s = s)[, 1]
old.predicts.list[[i]] = p
old.probs.list[[i]] = 1 - p2
}
testSimpleParsets("classif.glmnet", binaryclass.df, binaryclass.target,
binaryclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.glmnet", binaryclass.df, binaryclass.target,
binaryclass.train.inds, old.probs.list, parset.list)
})
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