context("classif_lqa")
test_that("classif_lqa", {
requirePackagesOrSkip("lqa", default.method = "load")
parset.list = list(
list(),
list(penalty = "lasso", lambda = 0.01),
list(penalty = "fused.lasso", lambda1 = 0.001, lambda2 = 0.01)
)
parset.list.lqa = list(
list(family = binomial(), penalty = lqa::lasso(0.1)),
list(family = binomial(), penalty = lqa::lasso(0.01)),
list(family = binomial(), penalty = lqa::fused.lasso(c(0.001, 0.01)))
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list.lqa)) {
parset = parset.list.lqa[[i]]
x = binaryclass.train
y = as.numeric(x[, binaryclass.class.col] == binaryclass.class.levs[1L])
x[, binaryclass.class.col] = NULL
pars = list(x = x, y = y)
pars = c(pars, parset)
m = do.call(lqa::lqa.default, pars)
newx = binaryclass.test
newx[, binaryclass.class.col] = NULL
newx = cbind(1, newx)
p = lqa::predict.lqa(m, newx)$mu.new
p2 = ifelse(p > 0.5, binaryclass.class.levs[1L], binaryclass.class.levs[2L])
old.predicts.list[[i]] = p2
old.probs.list[[i]] = p
}
testSimpleParsets("classif.lqa", binaryclass.df, binaryclass.target, binaryclass.train.inds,
old.predicts.list, parset.list)
testProbParsets("classif.lqa", binaryclass.df, binaryclass.target, binaryclass.train.inds,
old.probs.list, parset.list)
})
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