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test_that("classif_neuralnet", {
requirePackagesOrSkip("neuralnet", default.method = "load")
set.seed(getOption("mlr.debug.seed"))
# test with empty paramset
capture.output({
# neuralnet is not dealing with formula with `.` well
nms = names(binaryclass.train)
formula.head = as.character(binaryclass.formula)[2]
varnames = nms[nms != formula.head]
formula.head = paste("as.numeric(", formula.head, ")~")
formula.expand = paste(formula.head, paste(varnames, collapse = "+"))
formula.expand = as.formula(formula.expand)
traindat = binaryclass.train
traindat[[binaryclass.target]] = as.numeric(traindat[[binaryclass.target]]) - 1
m = neuralnet::neuralnet(formula.expand,
data = traindat, err.fct = "ce",
linear.output = FALSE)
p = neuralnet::compute(m,
covariate = binaryclass.test[, -ncol(binaryclass.test)])
p = as.numeric(as.vector(p[[2]]) > 0.5)
p = factor(p, labels = binaryclass.class.levs)
})
set.seed(getOption("mlr.debug.seed"))
testSimple("classif.neuralnet", binaryclass.df, binaryclass.target,
binaryclass.train.inds, p,
parset = list())
# test with params passed
capture.output({
# neuralnet is not dealing with formula with `.` well
nms = names(binaryclass.train)
formula.head = as.character(binaryclass.formula)[2]
varnames = nms[nms != formula.head]
formula.head = paste("as.numeric(", formula.head, ")~")
formula.expand = paste(formula.head, paste(varnames, collapse = "+"))
formula.expand = as.formula(formula.expand)
traindat = binaryclass.train
traindat[[binaryclass.target]] = as.numeric(traindat[[binaryclass.target]]) - 1
m = neuralnet::neuralnet(formula.expand,
hidden = 7, data = traindat,
err.fct = "ce", linear.output = FALSE)
p = neuralnet::compute(m,
covariate = binaryclass.test[, -ncol(binaryclass.test)])
p = as.numeric(as.vector(p[[2]]) > 0.5)
p = factor(p, labels = binaryclass.class.levs)
})
testSimple("classif.neuralnet", binaryclass.df, binaryclass.target,
binaryclass.train.inds, p,
parset = list(hidden = 7, err.fct = "ce"))
# Neuralnet doesn't have the `predict` method
# set.seed(getOption("mlr.debug.seed"))
# lrn = makeLearner("classif.neuralnet",hidden=7)
# task = makeClassifTask(data = binaryclass.df, target = binaryclass.target)
# m2 = try(train(lrn, task, subset = binaryclass.train.inds))
# p2 = predictLearner(.learner=lrn,.model=m2,
# .newdata = binaryclass.test[,-ncol(binaryclass.test)])
# expect_equal(p,p2,tol=1e-4)
})
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