Running ‘testthat.R’ [15s/16s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # Copyright 2015-2017 Philipp Thomann
  > #
  > # This file is part of liquidSVM.
  > #
  > # liquidSVM is free software: you can redistribute it and/or modify
  > # it under the terms of the GNU Affero General Public License as
  > # published by the Free Software Foundation, either version 3 of the
  > # License, or (at your option) any later version.
  > #
  > # liquidSVM is distributed in the hope that it will be useful,
  > # but WITHOUT ANY WARRANTY; without even the implied warranty of
  > # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
  > # GNU Affero General Public License for more details.
  > #
  > # You should have received a copy of the GNU Affero General Public License
  > # along with liquidSVM. If not, see <http://www.gnu.org/licenses/>.
  > 
  > library(testthat)
  > library(liquidSVM)
  > 
  > orig <- options(liquidSVM.warn.suboptimal=FALSE, liquidSVM.default.threads=1)
  > 
  > test_check("liquidSVM")
  ── 1. Error: mlr-regr (@test-mlr.R#37)  ────────────────────────────────────────
  no applicable method for 'trainLearner' applied to an object of class "c('regr.liquidSVM', 'RLearnerRegr', 'RLearner', 'Learner')"
  1: train(lrn, task) at testthat/test-mlr.R:37
  2: measureTime(fun1({
         learner.model = fun2(fun3(do.call(trainLearner, pars)))
     }))
  3: force(expr)
  4: fun1({
         learner.model = fun2(fun3(do.call(trainLearner, pars)))
     })
  5: fun2(fun3(do.call(trainLearner, pars)))
  6: fun3(do.call(trainLearner, pars))
  7: do.call(trainLearner, pars)
  8: (function (.learner, .task, .subset, .weights = NULL, ...) 
     {
         UseMethod("trainLearner")
     })(.learner = structure(list(id = "regr.liquidSVM", type = "regr", package = "liquidSVM", 
         properties = c("numerics", "factors"), par.set = structure(list(pars = list(scale = structure(list(
             id = "scale", type = "logical", len = 1L, lower = NULL, upper = NULL, values = list(
                 `TRUE` = TRUE, `FALSE` = FALSE), cnames = NULL, allow.inf = FALSE, has.default = TRUE, 
             default = TRUE, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), 
             when = "train"), class = c("LearnerParam", "Param")), kernel = structure(list(
             id = "kernel", type = "discrete", len = 1L, lower = NULL, upper = NULL, values = list(
                 gauss_rbf = "gauss_rbf", poisson = "poisson"), cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = "gauss_rbf", trafo = NULL, requires = NULL, 
             tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), partition_choice = structure(list(id = "partition_choice", type = "integer", 
             len = 1L, lower = 0, upper = 6, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), partition_param = structure(list(id = "partition_param", type = "numeric", 
             len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = -1, trafo = NULL, requires = partition_choice >= 
                 1L, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), grid_choice = structure(list(id = "grid_choice", type = "integer", 
             len = 1L, lower = -2, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), folds = structure(list(id = "folds", type = "integer", len = 1L, lower = 1, 
             upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, 
             default = 5, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), 
             when = "train"), class = c("LearnerParam", "Param")), min_gamma = structure(list(
             id = "min_gamma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL, 
             cnames = NULL, allow.inf = FALSE, has.default = FALSE, default = NULL, trafo = NULL, 
             requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), max_gamma = structure(list(id = "max_gamma", type = "numeric", len = 1L, 
             lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = min_gamma <= 
                 max_gamma, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), gamma_steps = structure(list(id = "gamma_steps", type = "integer", 
             len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), min_lambda = structure(list(id = "min_lambda", type = "numeric", len = 1L, 
             lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), max_lambda = structure(list(id = "max_lambda", type = "numeric", len = 1L, 
             lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = min_lambda <= 
                 max_lambda, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), lambda_steps = structure(list(id = "lambda_steps", type = "integer", 
             len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), retrain_method = structure(list(id = "retrain_method", type = "discrete", 
             len = 1L, lower = NULL, upper = NULL, values = list(select_on_entire_train_Set = "select_on_entire_train_Set", 
                 select_on_each_fold = "select_on_each_fold"), cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = "select_on_each_fold", trafo = NULL, requires = NULL, 
             tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), store_solutions_internally = structure(list(id = "store_solutions_internally", 
             type = "logical", len = 1L, lower = NULL, upper = NULL, values = list(`TRUE` = TRUE, 
                 `FALSE` = FALSE), cnames = NULL, allow.inf = FALSE, has.default = TRUE, 
             default = TRUE, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), 
             when = "train"), class = c("LearnerParam", "Param")), display = structure(list(
             id = "display", type = "integer", len = 1L, lower = 0, upper = 7, values = NULL, 
             cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL, 
             requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), threads = structure(list(id = "threads", type = "integer", len = 1L, 
             lower = -1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), clip = structure(list(id = "clip", type = "numeric", len = 1L, lower = -1, 
             upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, 
             default = -1, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), 
             when = "train"), class = c("LearnerParam", "Param"))), forbidden = NULL), class = c("LearnerParamSet", 
         "ParamSet")), par.vals = list(display = 0), predict.type = "response", cache = FALSE, 
         name = "Support Vector Machines", short.name = "liquidSVM", note = "FIXME make integrated cross-validation more accessible.", 
         callees = character(0), help.list = list(), config = list(), fix.factors.prediction = FALSE), class = c("regr.liquidSVM", 
     "RLearnerRegr", "RLearner", "Learner")), .task = structure(list(type = "regr", env = <environment>, 
         weights = NULL, blocking = NULL, coordinates = NULL, task.desc = structure(list(
             id = "trees", type = "regr", target = "Volume", size = 31L, n.feat = c(numerics = 2L, 
             factors = 0L, ordered = 0L, functionals = 0L), has.missings = FALSE, has.weights = FALSE, 
             has.blocking = FALSE, has.coordinates = FALSE), class = c("RegrTaskDesc", 
         "SupervisedTaskDesc", "TaskDesc"))), class = c("RegrTask", "SupervisedTask", 
     "Task")), .subset = NULL, display = 0)
  
  ── 2. Error: mlr-class (@test-mlr.R#52)  ───────────────────────────────────────
  no applicable method for 'trainLearner' applied to an object of class "c('classif.liquidSVM', 'RLearnerClassif', 'RLearner', 'Learner')"
  1: train(lrn, task) at testthat/test-mlr.R:52
  2: measureTime(fun1({
         learner.model = fun2(fun3(do.call(trainLearner, pars)))
     }))
  3: force(expr)
  4: fun1({
         learner.model = fun2(fun3(do.call(trainLearner, pars)))
     })
  5: fun2(fun3(do.call(trainLearner, pars)))
  6: fun3(do.call(trainLearner, pars))
  7: do.call(trainLearner, pars)
  8: (function (.learner, .task, .subset, .weights = NULL, ...) 
     {
         UseMethod("trainLearner")
     })(.learner = structure(list(id = "classif.liquidSVM", type = "classif", package = "liquidSVM", 
         properties = c("twoclass", "multiclass", "numerics", "factors", "prob", "class.weights"
         ), par.set = structure(list(pars = list(scale = structure(list(id = "scale", 
             type = "logical", len = 1L, lower = NULL, upper = NULL, values = list(`TRUE` = TRUE, 
                 `FALSE` = FALSE), cnames = NULL, allow.inf = FALSE, has.default = TRUE, 
             default = TRUE, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), 
             when = "train"), class = c("LearnerParam", "Param")), kernel = structure(list(
             id = "kernel", type = "discrete", len = 1L, lower = NULL, upper = NULL, values = list(
                 gauss_rbf = "gauss_rbf", poisson = "poisson"), cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = "gauss_rbf", trafo = NULL, requires = NULL, 
             tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), partition_choice = structure(list(id = "partition_choice", type = "integer", 
             len = 1L, lower = 0, upper = 6, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), partition_param = structure(list(id = "partition_param", type = "numeric", 
             len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = -1, trafo = NULL, requires = partition_choice >= 
                 1L, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), grid_choice = structure(list(id = "grid_choice", type = "integer", 
             len = 1L, lower = -2, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), folds = structure(list(id = "folds", type = "integer", len = 1L, lower = 1, 
             upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, 
             default = 5, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), 
             when = "train"), class = c("LearnerParam", "Param")), min_gamma = structure(list(
             id = "min_gamma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL, 
             cnames = NULL, allow.inf = FALSE, has.default = FALSE, default = NULL, trafo = NULL, 
             requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), max_gamma = structure(list(id = "max_gamma", type = "numeric", len = 1L, 
             lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = min_gamma <= 
                 max_gamma, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), gamma_steps = structure(list(id = "gamma_steps", type = "integer", 
             len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), min_lambda = structure(list(id = "min_lambda", type = "numeric", len = 1L, 
             lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), max_lambda = structure(list(id = "max_lambda", type = "numeric", len = 1L, 
             lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = min_lambda <= 
                 max_lambda, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), lambda_steps = structure(list(id = "lambda_steps", type = "integer", 
             len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), retrain_method = structure(list(id = "retrain_method", type = "discrete", 
             len = 1L, lower = NULL, upper = NULL, values = list(select_on_entire_train_Set = "select_on_entire_train_Set", 
                 select_on_each_fold = "select_on_each_fold"), cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = "select_on_each_fold", trafo = NULL, requires = NULL, 
             tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), store_solutions_internally = structure(list(id = "store_solutions_internally", 
             type = "logical", len = 1L, lower = NULL, upper = NULL, values = list(`TRUE` = TRUE, 
                 `FALSE` = FALSE), cnames = NULL, allow.inf = FALSE, has.default = TRUE, 
             default = TRUE, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), 
             when = "train"), class = c("LearnerParam", "Param")), display = structure(list(
             id = "display", type = "integer", len = 1L, lower = 0, upper = 7, values = NULL, 
             cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL, 
             requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), threads = structure(list(id = "threads", type = "integer", len = 1L, 
             lower = -1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), mc_type = structure(list(id = "mc_type", type = "discrete", len = 1L, lower = NULL, 
             upper = NULL, values = list(AvA_hinge = "AvA_hinge", OvA_ls = "OvA_ls", OvA_hinge = "OvA_hinge", 
                 AvA_ls = "AvA_ls"), cnames = NULL, allow.inf = FALSE, has.default = TRUE, 
             default = "AvA_hinge", trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), 
             when = "train"), class = c("LearnerParam", "Param")), weights = structure(list(
             id = "weights", type = "numericvector", len = NA_integer_, lower = 0, upper = Inf, 
             values = NULL, cnames = NULL, allow.inf = FALSE, has.default = FALSE, default = NULL, 
             trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param"))), forbidden = NULL), class = c("LearnerParamSet", "ParamSet")), par.vals = list(
             display = 0), predict.type = "response", cache = FALSE, name = "Support Vector Machines", 
         short.name = "liquidSVM", note = "FIXME make integrated cross-validation more accessible.", 
         callees = character(0), help.list = list(), class.weights.param = "weights", 
         config = list(), fix.factors.prediction = FALSE), class = c("classif.liquidSVM", 
     "RLearnerClassif", "RLearner", "Learner")), .task = structure(list(type = "classif", 
         env = <environment>, weights = NULL, blocking = NULL, coordinates = NULL, task.desc = structure(list(
             id = "iris", type = "classif", target = "Species", size = 150L, n.feat = c(numerics = 4L, 
             factors = 0L, ordered = 0L, functionals = 0L), has.missings = FALSE, has.weights = FALSE, 
             has.blocking = FALSE, has.coordinates = FALSE, class.levels = c("setosa", 
             "versicolor", "virginica"), positive = NA_character_, negative = NA_character_, 
             class.distribution = structure(c(setosa = 50L, versicolor = 50L, virginica = 50L
             ), .Dim = 3L, .Dimnames = structure(list(c("setosa", "versicolor", "virginica"
             )), .Names = ""), class = "table")), class = c("ClassifTaskDesc", "SupervisedTaskDesc", 
         "TaskDesc"))), class = c("ClassifTask", "SupervisedTask", "Task")), .subset = NULL, 
         display = 0)
  
  ── 3. Error: mlr-class-prob (@test-mlr.R#67)  ──────────────────────────────────
  no applicable method for 'trainLearner' applied to an object of class "c('classif.liquidSVM', 'RLearnerClassif', 'RLearner', 'Learner')"
  1: train(lrn, task) at testthat/test-mlr.R:67
  2: measureTime(fun1({
         learner.model = fun2(fun3(do.call(trainLearner, pars)))
     }))
  3: force(expr)
  4: fun1({
         learner.model = fun2(fun3(do.call(trainLearner, pars)))
     })
  5: fun2(fun3(do.call(trainLearner, pars)))
  6: fun3(do.call(trainLearner, pars))
  7: do.call(trainLearner, pars)
  8: (function (.learner, .task, .subset, .weights = NULL, ...) 
     {
         UseMethod("trainLearner")
     })(.learner = structure(list(id = "classif.liquidSVM", type = "classif", package = "liquidSVM", 
         properties = c("twoclass", "multiclass", "numerics", "factors", "prob", "class.weights"
         ), par.set = structure(list(pars = list(scale = structure(list(id = "scale", 
             type = "logical", len = 1L, lower = NULL, upper = NULL, values = list(`TRUE` = TRUE, 
                 `FALSE` = FALSE), cnames = NULL, allow.inf = FALSE, has.default = TRUE, 
             default = TRUE, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), 
             when = "train"), class = c("LearnerParam", "Param")), kernel = structure(list(
             id = "kernel", type = "discrete", len = 1L, lower = NULL, upper = NULL, values = list(
                 gauss_rbf = "gauss_rbf", poisson = "poisson"), cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = "gauss_rbf", trafo = NULL, requires = NULL, 
             tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), partition_choice = structure(list(id = "partition_choice", type = "integer", 
             len = 1L, lower = 0, upper = 6, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), partition_param = structure(list(id = "partition_param", type = "numeric", 
             len = 1L, lower = -Inf, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = -1, trafo = NULL, requires = partition_choice >= 
                 1L, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), grid_choice = structure(list(id = "grid_choice", type = "integer", 
             len = 1L, lower = -2, upper = 2, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = 0, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), folds = structure(list(id = "folds", type = "integer", len = 1L, lower = 1, 
             upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, has.default = TRUE, 
             default = 5, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), 
             when = "train"), class = c("LearnerParam", "Param")), min_gamma = structure(list(
             id = "min_gamma", type = "numeric", len = 1L, lower = 0, upper = Inf, values = NULL, 
             cnames = NULL, allow.inf = FALSE, has.default = FALSE, default = NULL, trafo = NULL, 
             requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), max_gamma = structure(list(id = "max_gamma", type = "numeric", len = 1L, 
             lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = min_gamma <= 
                 max_gamma, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), gamma_steps = structure(list(id = "gamma_steps", type = "integer", 
             len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), min_lambda = structure(list(id = "min_lambda", type = "numeric", len = 1L, 
             lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), max_lambda = structure(list(id = "max_lambda", type = "numeric", len = 1L, 
             lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = min_lambda <= 
                 max_lambda, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), lambda_steps = structure(list(id = "lambda_steps", type = "integer", 
             len = 1L, lower = 0, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = FALSE, default = NULL, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), retrain_method = structure(list(id = "retrain_method", type = "discrete", 
             len = 1L, lower = NULL, upper = NULL, values = list(select_on_entire_train_Set = "select_on_entire_train_Set", 
                 select_on_each_fold = "select_on_each_fold"), cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = "select_on_each_fold", trafo = NULL, requires = NULL, 
             tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), store_solutions_internally = structure(list(id = "store_solutions_internally", 
             type = "logical", len = 1L, lower = NULL, upper = NULL, values = list(`TRUE` = TRUE, 
                 `FALSE` = FALSE), cnames = NULL, allow.inf = FALSE, has.default = TRUE, 
             default = TRUE, trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), 
             when = "train"), class = c("LearnerParam", "Param")), display = structure(list(
             id = "display", type = "integer", len = 1L, lower = 0, upper = 7, values = NULL, 
             cnames = NULL, allow.inf = FALSE, has.default = TRUE, default = 0, trafo = NULL, 
             requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param")), threads = structure(list(id = "threads", type = "integer", len = 1L, 
             lower = -1, upper = Inf, values = NULL, cnames = NULL, allow.inf = FALSE, 
             has.default = TRUE, default = 1, trafo = NULL, requires = NULL, tunable = TRUE, 
             special.vals = list(), when = "train"), class = c("LearnerParam", "Param"
         )), mc_type = structure(list(id = "mc_type", type = "discrete", len = 1L, lower = NULL, 
             upper = NULL, values = list(AvA_hinge = "AvA_hinge", OvA_ls = "OvA_ls", OvA_hinge = "OvA_hinge", 
                 AvA_ls = "AvA_ls"), cnames = NULL, allow.inf = FALSE, has.default = TRUE, 
             default = "AvA_hinge", trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), 
             when = "train"), class = c("LearnerParam", "Param")), weights = structure(list(
             id = "weights", type = "numericvector", len = NA_integer_, lower = 0, upper = Inf, 
             values = NULL, cnames = NULL, allow.inf = FALSE, has.default = FALSE, default = NULL, 
             trafo = NULL, requires = NULL, tunable = TRUE, special.vals = list(), when = "train"), class = c("LearnerParam", 
         "Param"))), forbidden = NULL), class = c("LearnerParamSet", "ParamSet")), par.vals = list(
             display = 0), predict.type = "prob", cache = FALSE, name = "Support Vector Machines", 
         short.name = "liquidSVM", note = "FIXME make integrated cross-validation more accessible.", 
         callees = character(0), help.list = list(), class.weights.param = "weights", 
         config = list(), fix.factors.prediction = FALSE), class = c("classif.liquidSVM", 
     "RLearnerClassif", "RLearner", "Learner")), .task = structure(list(type = "classif", 
         env = <environment>, weights = NULL, blocking = NULL, coordinates = NULL, task.desc = structure(list(
             id = "iris", type = "classif", target = "Species", size = 150L, n.feat = c(numerics = 4L, 
             factors = 0L, ordered = 0L, functionals = 0L), has.missings = FALSE, has.weights = FALSE, 
             has.blocking = FALSE, has.coordinates = FALSE, class.levels = c("setosa", 
             "versicolor", "virginica"), positive = NA_character_, negative = NA_character_, 
             class.distribution = structure(c(setosa = 50L, versicolor = 50L, virginica = 50L
             ), .Dim = 3L, .Dimnames = structure(list(c("setosa", "versicolor", "virginica"
             )), .Names = ""), class = "table")), class = c("ClassifTaskDesc", "SupervisedTaskDesc", 
         "TaskDesc"))), class = c("ClassifTask", "SupervisedTask", "Task")), .subset = NULL, 
         display = 0)
  
  ══ testthat results  ═══════════════════════════════════════════════════════════
  [ OK: 130 | SKIPPED: 10 | WARNINGS: 0 | FAILED: 3 ]
  1. Error: mlr-regr (@test-mlr.R#37) 
  2. Error: mlr-class (@test-mlr.R#52) 
  3. Error: mlr-class-prob (@test-mlr.R#67) 
  
  Error: testthat unit tests failed
  Execution halted