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test_that("regr_cvglmnet", {
requirePackagesOrSkip("glmnet", default.method = "load")
parset.list = list(
list(),
list(nlambda = 20L, nfolds = 5L, mxit = 95),
list(family = "poisson", alpha = 0.5)
)
old.predicts.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
x = regr.train
y = x[, regr.class.col]
x[, regr.class.col] = NULL
info = getFixDataInfo(x, factors.to.dummies = TRUE, ordered.to.int = TRUE)
pars = list(x = as.matrix(fixDataForLearner(x, info)), y = y)
pars = c(pars, parset)
glmnet::glmnet.control(factory = TRUE)
ctrl.args = names(formals(glmnet::glmnet.control))
set.seed(getOption("mlr.debug.seed"))
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::cv.glmnet, pars[!names(pars) %in% ctrl.args])
} else {
m = do.call(glmnet::cv.glmnet, pars)
}
newx = regr.test
newx[, regr.class.col] = NULL
p = as.vector(predict(m, as.matrix(fixDataForLearner(newx, info)),
type = "response"))
old.predicts.list[[i]] = p
}
testSimpleParsets("regr.cvglmnet", regr.df, regr.target,
regr.train.inds, old.predicts.list, parset.list)
})
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