context("classif_quaDA")
test_that("classif_quaDA", {
requirePackagesOrSkip("DiscriMiner", default.method = "load")
set.seed(getOption("mlr.debug.seed"))
m = DiscriMiner::quaDA(multiclass.train[, -multiclass.class.col], group = multiclass.train[, multiclass.class.col])
#m2 = DiscriMiner::quaDA(binaryclass.train[,1:10], group = binaryclass.train[,binaryclass.class.col], prob = TRUE)
p = DiscriMiner::classify(m, newdata = multiclass.test[, -multiclass.class.col])
#p2 = DiscriMiner::classify(m2, newdata = binaryclass.test[,1:10])
testSimple("classif.quaDA", multiclass.df, multiclass.target, multiclass.train.inds, p$pred_class)
tt = function(formula, data, subset, ...) {
j = which(colnames(data) == as.character(formula)[2])
m = DiscriMiner::quaDA(variables = data[subset, -j], group = data[subset, j])
list(model = m, target = j)
}
tp = function(model, newdata) {
DiscriMiner::classify(model$model, newdata = newdata[, -model$target])$pred_class
}
testCV("classif.quaDA", multiclass.df, multiclass.target, tune.train = tt, tune.predict = tp)
})
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