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#' @export
makeRLearner.classif.geoDA = function() {
makeRLearnerClassif(
cl = "classif.geoDA",
package = "DiscriMiner",
par.set = makeParamSet(
makeDiscreteLearnerParam(id = "validation", values = list(crossval = "crossval", learntest = "learntest", NULL = NULL), default = NULL, tunable = FALSE)
),
par.vals = list(validation = NULL),
# FIXME default of geoDa for validation is NULL, par.vals is redundant here.
properties = c("twoclass", "multiclass", "numerics"),
name = "Geometric Predictive Discriminant Analysis",
short.name = "geoda",
callees = c("geoDA", "classify")
)
}
#' @export
trainLearner.classif.geoDA = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE, recode.target = "drop.levels")
DiscriMiner::geoDA(variables = d$data, group = d$target, ...)
}
#' @export
predictLearner.classif.geoDA = function(.learner, .model, .newdata, ...) {
m = .model$learner.model
p = DiscriMiner::classify(m, newdata = .newdata)
# p$scores #we loose this information
p$pred_class
}
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