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Merge pull request #108 from mlr-org/add_more_kernlab
regr.rvm and classif.lssvm
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‎DESCRIPTION

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Package: mlr3extralearners
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Title: Extra Learners For mlr3
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Version: 0.5.7
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Version: 0.5.8
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Authors@R:
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c(person(given = "Raphael",
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family = "Sonabend",

‎NAMESPACE

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export(LearnerClassifJRip)
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export(LearnerClassifKSVM)
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export(LearnerClassifLMT)
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export(LearnerClassifLSSVM)
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export(LearnerClassifLiblineaR)
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export(LearnerClassifLightGBM)
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export(LearnerClassifMob)
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export(LearnerRegrM5Rules)
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export(LearnerRegrMars)
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export(LearnerRegrMob)
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export(LearnerRegrRVM)
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export(LearnerRegrRandomForest)
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export(LearnerRegrRandomForestSRC)
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export(LearnerSurvAkritas)

‎NEWS.md

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# mlr3extralearners 0.5.8
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* Add `regr.rvm` and `classif.lssvm`
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# mlr3extralearners 0.5.7
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* Introduced new custom hyperparameters for `randomForestSRC::rfsrc()`,
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* Fix learners requiring distr6. distr6 1.6.0 now forced and param6 added to suggests
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# mlr3extralearners 0.5.5
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* Bugfix `regr.gausspr`

‎R/learner_kernlab_classif_gausspr.R

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},
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.predict = function(task) {
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out = setNames(vector("list", 1L), self$predict_type)
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out[[1]] = mlr3misc::invoke(
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getMethod("predict", "gausspr"),

‎R/learner_kernlab_classif_lssvm.R

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#' @title Classification Least Squares Support Vector Machine Learner
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#' @author RaphaelS1
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#' @name mlr_learners_classif.lssvm
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#'
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#' @template class_learner
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#' @templateVar id classif.lssvm
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#' @templateVar caller lssvm
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#'
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#' @details Parameters `sigma`, `degree`, `scale`, `offset`, `order`, `length`,
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#' `lambda`, and `normalized` are added to make tuning `kpar` easier.
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#' If `kpar` is provided then these new parameters are ignored. If none are
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#' provided then the default "automatic" is used for `kpar`.
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#'
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#' @template seealso_learner
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#' @template example
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#' @export
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LearnerClassifLSSVM = R6Class("LearnerClassifLSSVM",
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inherit = LearnerClassif,
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public = list(
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#' @description
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#' Creates a new instance of this [R6][R6::R6Class] class.
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initialize = function() {
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ps = ps(
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scaled = p_uty(default = TRUE, tag = "train"),
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kernel = p_fct(default = "rbfdot",
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levels = c(
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"rbfdot", "polydot", "vanilladot", "tanhdot",
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"laplacedot", "besseldot", "anovadot", "splinedot", "stringdot"),
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tags = "train"),
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sigma = p_dbl(tag = c("train", "kpar")),
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degree = p_dbl(tag = c("train", "kpar")),
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scale = p_dbl(tag = c("train", "kpar")),
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offset = p_dbl(tag = c("train", "kpar")),
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order = p_dbl(tag = c("train", "kpar")),
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length = p_int(lower = 0, tag = c("train", "kpar")),
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lambda = p_dbl(tag = c("train", "kpar")),
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normalized = p_lgl(tag = c("train", "kpar")),
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kpar = p_uty(default = "automatic", tag = "train"),
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tau = p_dbl(default = 0.01, tag = "train"),
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reduced = p_lgl(default = TRUE, tag = "train"),
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rank = p_int(tag = "train"),
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delta = p_int(default = 40, tag = "train"),
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tol = p_dbl(default = 0.0001, tag = "train"),
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fit = p_lgl(default = TRUE, tag = "train"),
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cross = p_int(lower = 0, default = 0, tag = "train"),
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na.action = p_uty(default = na.omit, tag = "train")
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)
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super$initialize(
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id = "classif.lssvm",
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packages = "kernlab",
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feature_types = c("numeric", "integer"),
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predict_types = "response",
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properties = c("twoclass", "multiclass"),
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param_set = ps,
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man = "mlr3extralearners::mlr_learners_classif.lssvm"
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)
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}
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),
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private = list(
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.train = function(task) {
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# get parameters for training
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pars = self$param_set$get_values(tags = "train")
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kpars = self$param_set$get_values(tags = "kpar")
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kpar = self$param_set$values$kpar
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if (is.null(kpar)) {
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if (length(kpars)) {
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kpar = kpars
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} else {
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kpar = "automatic"
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}
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}
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pars = pars[setdiff(names(pars), c("kpar", names(kpars)))]
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# set column names to ensure consistency in fit and predict
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self$state$feature_names = task$feature_names
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mlr3misc::invoke(kernlab::lssvm,
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x = task$formula(),
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data = task$data(),
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kpar = kpar,
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.args = pars)
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},
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.predict = function(task) {
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out = setNames(vector("list", 1L), self$predict_type)
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out[[1]] = mlr3misc::invoke(
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getMethod("predict", "lssvm"),
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self$model,
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task$data(cols = self$state$feature_names),
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type = self$predict_type
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)
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out
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}
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)
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)
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.extralrns_dict$add("classif.lssvm", LearnerClassifLSSVM)

‎R/learner_kernlab_regr_rvm.R

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#' @title Regression Relevance Vector Machine Learner
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#' @author RaphaelS1
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#' @name mlr_learners_regr.rvm
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#'
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#' @template class_learner
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#' @templateVar id regr.rvm
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#' @templateVar caller rvm
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#'
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#' @details Parameters `sigma`, `degree`, `scale`, `offset`, `order`, `length`,
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#' `lambda`, and `normalized` are added to make tuning `kpar` easier.
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#' If `kpar` is provided then these new parameters are ignored. If none are
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#' provided then the default "automatic" is used for `kpar`.
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#'
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#' @template seealso_learner
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#' @template example
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#' @export
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LearnerRegrRVM = R6Class("LearnerRegrRVM",
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inherit = LearnerRegr,
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public = list(
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#' @description
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#' Creates a new instance of this [R6][R6::R6Class] class.
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initialize = function() {
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ps = ps(
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kernel = p_fct(default = "rbfdot",
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levels = c(
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"rbfdot", "polydot", "vanilladot", "tanhdot",
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"laplacedot", "besseldot", "anovadot", "splinedot", "stringdot"),
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tags = "train"),
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sigma = p_dbl(tag = c("train", "kpar")),
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degree = p_dbl(tag = c("train", "kpar")),
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scale = p_dbl(tag = c("train", "kpar")),
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offset = p_dbl(tag = c("train", "kpar")),
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order = p_dbl(tag = c("train", "kpar")),
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length = p_int(lower = 0, tag = c("train", "kpar")),
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lambda = p_dbl(tag = c("train", "kpar")),
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normalized = p_lgl(tag = c("train", "kpar")),
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kpar = p_uty(default = "automatic", tag = "train"),
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alpha = p_uty(default = 5, tag = "train"),
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var = p_dbl(lower = 10^-3, default = 0.1, tag = "train"),
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var.fix = p_lgl(default = FALSE, tag = "train"),
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iterations = p_int(default = 100, lower = 0, tags = "train"),
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tol = p_dbl(lower = 0, default = .Machine$double.eps, tag = "train"),
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minmaxdiff = p_dbl(lower = 0, default = 1e-3, tag = "train"),
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verbosity = p_lgl(default = FALSE, tag = "train"),
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fit = p_lgl(default = TRUE, tag = "train"),
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cross = p_int(lower = 0, default = 0, tag = "train"),
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na.action = p_uty(default = na.omit, tag = "train")
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)
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super$initialize(
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id = "regr.rvm",
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packages = "kernlab",
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feature_types = c("numeric", "integer", "logical", "character",
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"factor", "ordered"),
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predict_types = "response",
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param_set = ps,
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man = "mlr3extralearners::mlr_learners_regr.rvm"
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)
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}
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),
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private = list(
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.train = function(task) {
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# get parameters for training
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pars = self$param_set$get_values(tags = "train")
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kpars = self$param_set$get_values(tags = "kpar")
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kpar = self$param_set$values$kpar
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if (is.null(kpar)) {
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if (length(kpars)) {
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kpar = kpars
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} else {
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kpar = "automatic"
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}
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}
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pars = pars[setdiff(names(pars), c("kpar", names(kpars)))]
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# set column names to ensure consistency in fit and predict
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self$state$feature_names = task$feature_names
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mlr3misc::invoke(kernlab::rvm,
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x = task$formula(),
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data = task$data(),
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kpar = kpar,
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.args = pars)
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},
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.predict = function(task) {
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list(response = as.numeric(mlr3misc::invoke(
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getMethod("predict", "rvm"),
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self$model,
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task$data(cols = self$state$feature_names)
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)))
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}
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)
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)
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.extralrns_dict$add("regr.rvm", LearnerRegrRVM)

‎man/mlr_learners_classif.lssvm.Rd

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