Nothing
test_that("classif_nnet", {
# we get mismatches for the check on l.20 & l.22 - why only for these?
skip_on_cran()
requirePackagesOrSkip("nnet", default.method = "load")
set.seed(getOption("mlr.debug.seed"))
capture.output({
m = nnet::nnet(multiclass.formula, size = 3, data = multiclass.train)
p = as.factor(predict(m, newdata = multiclass.test, type = "class"))
p2 = predict(m, newdata = multiclass.test, type = "raw")
set.seed(getOption("mlr.debug.seed"))
m = nnet::nnet(binaryclass.formula, size = 3, data = binaryclass.train)
# for the binaryclass task the mlr positive class is not the same as the ref
# class of nnet
p3 = 1 - predict(m, newdata = binaryclass.test, type = "raw")[, 1]
})
testSimple("classif.nnet", multiclass.df, multiclass.target,
multiclass.train.inds, p, parset = list())
testProb("classif.nnet", multiclass.df, multiclass.target,
multiclass.train.inds, p2, parset = list())
testProb("classif.nnet", binaryclass.df, binaryclass.target,
binaryclass.train.inds, p3, parset = list())
tt = function(formula, data, subset = 1:150, ...) {
nnet::nnet(formula, data = data[subset, ], size = 7, maxit = 50)
}
tp = function(model, newdata) as.factor(predict(model, newdata, type = "class"))
testCV("classif.nnet", multiclass.df, multiclass.target, tune.train = tt,
tune.predict = tp,
parset = list(size = 7, maxit = 50))
# ## make sure that nnet yields the same results independent of predict.type
task = makeClassifTask(data = binaryclass.df, target = binaryclass.target)
lrn = makeLearner("classif.nnet", trace = FALSE, size = 1,
predict.type = "prob")
mod = train(lrn, task = task)
pred1 = predict(mod, task = task)
lrn = makeLearner("classif.nnet", trace = FALSE, size = 1)
mod = train(lrn, task = task)
pred2 = predict(mod, task = task)
expect_equal(pred1$data$response, pred2$data$response)
})
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