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test_that("classif_sparseLDA", {
suppressMessages(requirePackagesOrSkip(c("sparseLDA", "MASS", "elasticnet")))
parset.list = list(
list(),
list(lambda = 1, maxIte = 50),
list(lambda = 2, maxIte = 60),
list(tol = 1e-4, maxIte = 100),
list(tol = 1e-5, lambda = 0.5),
list(tol = 3e-5, lambda = 0.8, maxIte = 150)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
target.col = which(colnames(multiclass.train) == multiclass.target)
X = multiclass.train[, -target.col]
y = multiclass.train[, multiclass.target]
lvls = unique(as.character(y))
y = sapply(lvls, function(lvl) as.integer(as.character(y) == lvl))
pars = c(list(x = X, y = y), parset)
m = do.call(sparseLDA::sda, pars)
old.predicts.list[[i]] = sparseLDA:::predict.sda(m,
newdata = subset(multiclass.test, select = m$varNames))$class
old.probs.list[[i]] = sparseLDA:::predict.sda(m,
newdata = subset(multiclass.test, select = m$varNames))$posterior
}
testSimpleParsets("classif.sparseLDA", multiclass.df, multiclass.target,
multiclass.train.inds, old.predicts.list, parset.list)
testProbParsets("classif.sparseLDA", multiclass.df, multiclass.target,
multiclass.train.inds, old.probs.list, parset.list)
})
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