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#' @export
makeRLearner.cluster.dbscan = function() {
makeRLearnerCluster(
cl = "cluster.dbscan",
package = "fpc",
par.set = makeParamSet(
makeNumericLearnerParam(id = "eps", default = 1, lower = 0),
# FIXME eps seems to have no default in dbscan(), if it has 1 par.vals is redundant
makeIntegerLearnerParam(id = "MinPts", default = 5L, lower = 0L),
makeLogicalLearnerParam(id = "scale", default = FALSE),
makeLogicalLearnerParam(id = "showplot", default = FALSE, tunable = FALSE),
makeDiscreteLearnerParam(id = "method", values = c("hybrid", "raw", "dist"), default = "hybrid")
),
par.vals = list(eps = 1),
properties = "numerics",
name = "DBScan Clustering",
note = "A cluster index of NA indicates noise points. Specify `method = 'dist'` if the data should be interpreted as dissimilarity matrix or object. Otherwise Euclidean distances will be used.",
short.name = "dbscan",
callees = "dbscan"
)
}
#' @export
trainLearner.cluster.dbscan = function(.learner, .task, .subset, .weights = NULL, ...) {
data = getTaskData(.task, .subset)
model = fpc::dbscan(data, ...)
# dbscan needs this in the prediction phase
model$data = data
return(model)
}
#' @export
predictLearner.cluster.dbscan = function(.learner, .model, .newdata, ...) {
indices = as.integer(predict(.model$learner.model, .model$learner.model$data, newdata = .newdata, ...))
indices[indices == 0L] = NA_integer_
return(indices)
}
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