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#' @export
makeRLearner.regr.plsr = function() {
makeRLearnerRegr(cl = "regr.plsr",
package = "pls",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "ncomp", lower = 1),
makeDiscreteLearnerParam(id = "method", default = "kernelpls",
values = c("kernelpls", "widekernelpls", "simpls", "oscorespls")),
makeLogicalLearnerParam(id = "scale", default = TRUE),
makeLogicalLearnerParam(id = "model", default = TRUE, tunable = FALSE),
makeLogicalLearnerParam(id = "x", default = FALSE, tunable = FALSE),
makeLogicalLearnerParam(id = "y", default = FALSE, tunable = FALSE)
),
properties = c("numerics", "factors"),
name = "Partial Least Squares Regression",
short.name = "plsr",
callees = "plsr"
)
}
#' @export
trainLearner.regr.plsr = function(.learner, .task, .subset, .weights, ...) {
f = getTaskFormula(.task)
d = getTaskData(.task, .subset)
pls::plsr(f, data = d, ...)
}
#' @export
predictLearner.regr.plsr = function(.learner, .model, .newdata, ...) {
predict(.model$learner.model, newdata = .newdata, comps = seq_len(.model$learner.model$ncomp), ...)[, 1L]
}
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