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#' @export
makeRLearner.regr.nnet = function() {
makeRLearnerRegr(
cl = "regr.nnet",
package = "nnet",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "size", default = 3L, lower = 0L),
# FIXME nnet() seems to have no default for size, but if it is 3, par.vals is redundant
makeIntegerLearnerParam(id = "maxit", default = 100L, lower = 1L),
# we hardcode linout = TRUE for regr, and 'entropy', 'softmax' and 'censored' are only for classif
makeLogicalLearnerParam(id = "skip", default = FALSE),
makeNumericLearnerParam(id = "rang", default = 0.7),
makeNumericLearnerParam(id = "decay", default = 0, lower = 0),
makeLogicalLearnerParam(id = "Hess", default = FALSE),
makeLogicalLearnerParam(id = "trace", default = TRUE, tunable = FALSE),
makeIntegerLearnerParam(id = "MaxNWts", default = 1000L, lower = 1L, tunable = FALSE),
makeNumericLearnerParam(id = "abstol", default = 1.0e-4),
makeNumericLearnerParam(id = "reltol", default = 1.0e-8)
),
par.vals = list(size = 3L),
properties = c("numerics", "factors", "weights"),
name = "Neural Network",
short.name = "nnet",
note = "`size` has been set to `3` by default.",
callees = "nnet"
)
}
#' @export
trainLearner.regr.nnet = function(.learner, .task, .subset, .weights = NULL, ...) {
if (is.null(.weights)) {
f = getTaskFormula(.task)
nnet::nnet(f, data = getTaskData(.task, .subset), linout = TRUE, ...)
} else {
f = getTaskFormula(.task)
nnet::nnet(f, data = getTaskData(.task, .subset), linout = TRUE, weights = .weights, ...)
}
}
#' @export
predictLearner.regr.nnet = function(.learner, .model, .newdata, ...) {
predict(.model$learner.model, newdata = .newdata, ...)[, 1L]
}
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