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test_that("regr_penalized", {
requirePackages("!penalized", default.method = "load")
parset.list = list(
list(maxiter = 100),
list(lambda1 = 2),
list(lambda1 = 2, lambda2 = 1),
list(lambda2 = 2),
list(fusedl = TRUE, lambda1 = 2, maxiter = 20L),
list(fusedl = TRUE, lambda1 = 2, lambda2 = 1, maxiter = 5L),
list(fusedl = TRUE, lambda2 = 2, maxiter = 20L)
)
# to make test of empty list feasable (in terms of time), number of obs need
# to be reduced
regr.train.inds = sample(seq(1, 506), size = 150)
regr.test.inds = setdiff(seq_len(nrow(regr.df)), regr.train.inds)
regr.train = regr.df[regr.train.inds, ]
regr.test = regr.df[regr.test.inds, ]
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
pars = list(regr.formula, data = regr.train)
pars = c(pars, parset)
capture.output({
m = do.call(penalized::penalized, pars)
})
# FIXME: should be removed, reported in issue 840
m@formula$unpenalized[[2L]] = as.symbol(regr.target)
p = penalized::predict(m, data = regr.test)
old.predicts.list[[i]] = p[, "mu"]
}
testSimpleParsets("regr.penalized", regr.df, regr.target,
regr.train.inds, old.predicts.list, parset.list)
parset.list = list(
list(maxiter = 100),
list(lambda1 = 2, lambda2 = 1),
list(lambda1 = 1, lambda2 = 2),
list(fusedl = TRUE, lambda1 = 2, lambda2 = 1, maxiter = 2L),
list(fusedl = TRUE, lambda1 = 1, lambda2 = 2, maxiter = 4L)
)
tt = function(formula, data, subset = seq_len(nrow(data)), ...) {
penalized::penalized(formula, data = data[subset, ], ...)
}
tp = function(model, newdata, ...) {
penalized::predict(model, data = newdata, ...)[, "mu"]
}
testCVParsets("regr.penalized", regr.df, regr.target,
tune.train = tt, tune.predict = tp, parset.list = parset.list)
})
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