Last updated on 2019-04-26 17:51:32 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.2.2.1 | 53.68 | 66.13 | 119.81 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.2.2.1 | 45.90 | 60.01 | 105.91 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.2.2.1 | 169.77 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.2.2.1 | 156.81 | ERROR | |||
| r-patched-linux-x86_64 | 1.2.2.1 | 51.78 | 68.83 | 120.61 | OK | |
| r-patched-solaris-x86 | 1.2.2.1 | 259.60 | NOTE | |||
| r-release-linux-x86_64 | 1.2.2.1 | 50.33 | 61.73 | 112.06 | ERROR | |
| r-release-windows-ix86+x86_64 | 1.2.2.1 | 129.00 | 137.00 | 266.00 | NOTE | --no-vignettes |
| r-release-osx-x86_64 | 1.2.2.1 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 1.2.2.1 | 123.00 | 128.00 | 251.00 | NOTE | --no-vignettes |
| r-oldrel-osx-x86_64 | 1.2.2.1 | NOTE |
Version: 1.2.2.1
Check: tests
Result: ERROR
Running 'testthat.R' [13s/14s]
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#35) ----------------------------------------
regr.liquidSVM: Setting parameter display without available description object!
Did you mean one of these hyperparameters instead: d scale folds
You can switch off this check by using configureMlr!
1: makeLearner("regr.liquidSVM", display = 0) at testthat/test-mlr.R:35
2: setHyperPars(learner = wl, ..., par.vals = par.vals)
3: setHyperPars2(learner, insert(par.vals, args))
4: setHyperPars2.Learner(learner, insert(par.vals, args))
5: stop(msg)
-- 2. Error: mlr-class (@test-mlr.R#51) ---------------------------------------
classif.liquidSVM: Setting parameter display without available description object!
Did you mean one of these hyperparameters instead: d scale folds
You can switch off this check by using configureMlr!
1: makeLearner("classif.liquidSVM", display = 0) at testthat/test-mlr.R:51
2: setHyperPars(learner = wl, ..., par.vals = par.vals)
3: setHyperPars2(learner, insert(par.vals, args))
4: setHyperPars2.Learner(learner, insert(par.vals, args))
5: stop(msg)
-- 3. Error: mlr-class-prob (@test-mlr.R#66) ----------------------------------
classif.liquidSVM: Setting parameter display without available description object!
Did you mean one of these hyperparameters instead: d scale folds
You can switch off this check by using configureMlr!
1: makeLearner("classif.liquidSVM", display = 0, predict.type = "prob") at testthat/test-mlr.R:66
2: setHyperPars(learner = wl, ..., par.vals = par.vals)
3: setHyperPars2(learner, insert(par.vals, args))
4: setHyperPars2.Learner(learner, insert(par.vals, args))
5: stop(msg)
== testthat results ===========================================================
OK: 130 SKIPPED: 10 WARNINGS: 0 FAILED: 3
1. Error: mlr-regr (@test-mlr.R#35)
2. Error: mlr-class (@test-mlr.R#51)
3. Error: mlr-class-prob (@test-mlr.R#66)
Error: testthat unit tests failed
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.2.2.1
Check: tests
Result: ERROR
Running ‘testthat.R’ [11s/15s]
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#35) ────────────────────────────────────────
regr.liquidSVM: Setting parameter display without available description object!
Did you mean one of these hyperparameters instead: d scale folds
You can switch off this check by using configureMlr!
1: makeLearner("regr.liquidSVM", display = 0) at testthat/test-mlr.R:35
2: setHyperPars(learner = wl, ..., par.vals = par.vals)
3: setHyperPars2(learner, insert(par.vals, args))
4: setHyperPars2.Learner(learner, insert(par.vals, args))
5: stop(msg)
── 2. Error: mlr-class (@test-mlr.R#51) ───────────────────────────────────────
classif.liquidSVM: Setting parameter display without available description object!
Did you mean one of these hyperparameters instead: d scale folds
You can switch off this check by using configureMlr!
1: makeLearner("classif.liquidSVM", display = 0) at testthat/test-mlr.R:51
2: setHyperPars(learner = wl, ..., par.vals = par.vals)
3: setHyperPars2(learner, insert(par.vals, args))
4: setHyperPars2.Learner(learner, insert(par.vals, args))
5: stop(msg)
── 3. Error: mlr-class-prob (@test-mlr.R#66) ──────────────────────────────────
classif.liquidSVM: Setting parameter display without available description object!
Did you mean one of these hyperparameters instead: d scale folds
You can switch off this check by using configureMlr!
1: makeLearner("classif.liquidSVM", display = 0, predict.type = "prob") at testthat/test-mlr.R:66
2: setHyperPars(learner = wl, ..., par.vals = par.vals)
3: setHyperPars2(learner, insert(par.vals, args))
4: setHyperPars2.Learner(learner, insert(par.vals, args))
5: stop(msg)
══ testthat results ═══════════════════════════════════════════════════════════
OK: 130 SKIPPED: 10 WARNINGS: 0 FAILED: 3
1. Error: mlr-regr (@test-mlr.R#35)
2. Error: mlr-class (@test-mlr.R#51)
3. Error: mlr-class-prob (@test-mlr.R#66)
Error: testthat unit tests failed
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.2.2.1
Check: installed package size
Result: NOTE
installed size is 11.5Mb
sub-directories of 1Mb or more:
doc 2.2Mb
libs 8.7Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64
Version: 1.2.2.1
Check: tests
Result: ERROR
Running ‘testthat.R’ [17s/61s]
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#35) ────────────────────────────────────────
regr.liquidSVM: Setting parameter display without available description object!
Did you mean one of these hyperparameters instead: d scale folds
You can switch off this check by using configureMlr!
1: makeLearner("regr.liquidSVM", display = 0) at testthat/test-mlr.R:35
2: setHyperPars(learner = wl, ..., par.vals = par.vals)
3: setHyperPars2(learner, insert(par.vals, args))
4: setHyperPars2.Learner(learner, insert(par.vals, args))
5: stop(msg)
── 2. Error: mlr-class (@test-mlr.R#51) ───────────────────────────────────────
classif.liquidSVM: Setting parameter display without available description object!
Did you mean one of these hyperparameters instead: d scale folds
You can switch off this check by using configureMlr!
1: makeLearner("classif.liquidSVM", display = 0) at testthat/test-mlr.R:51
2: setHyperPars(learner = wl, ..., par.vals = par.vals)
3: setHyperPars2(learner, insert(par.vals, args))
4: setHyperPars2.Learner(learner, insert(par.vals, args))
5: stop(msg)
── 3. Error: mlr-class-prob (@test-mlr.R#66) ──────────────────────────────────
classif.liquidSVM: Setting parameter display without available description object!
Did you mean one of these hyperparameters instead: d scale folds
You can switch off this check by using configureMlr!
1: makeLearner("classif.liquidSVM", display = 0, predict.type = "prob") at testthat/test-mlr.R:66
2: setHyperPars(learner = wl, ..., par.vals = par.vals)
3: setHyperPars2(learner, insert(par.vals, args))
4: setHyperPars2.Learner(learner, insert(par.vals, args))
5: stop(msg)
══ testthat results ═══════════════════════════════════════════════════════════
OK: 130 SKIPPED: 10 WARNINGS: 0 FAILED: 3
1. Error: mlr-regr (@test-mlr.R#35)
2. Error: mlr-class (@test-mlr.R#51)
3. Error: mlr-class-prob (@test-mlr.R#66)
Error: testthat unit tests failed
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.2.2.1
Check: tests
Result: ERROR
Running ‘testthat.R’ [19s/91s]
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#35) ────────────────────────────────────────
regr.liquidSVM: Setting parameter display without available description object!
Did you mean one of these hyperparameters instead: d scale folds
You can switch off this check by using configureMlr!
1: makeLearner("regr.liquidSVM", display = 0) at testthat/test-mlr.R:35
2: setHyperPars(learner = wl, ..., par.vals = par.vals)
3: setHyperPars2(learner, insert(par.vals, args))
4: setHyperPars2.Learner(learner, insert(par.vals, args))
5: stop(msg)
── 2. Error: mlr-class (@test-mlr.R#51) ───────────────────────────────────────
classif.liquidSVM: Setting parameter display without available description object!
Did you mean one of these hyperparameters instead: d scale folds
You can switch off this check by using configureMlr!
1: makeLearner("classif.liquidSVM", display = 0) at testthat/test-mlr.R:51
2: setHyperPars(learner = wl, ..., par.vals = par.vals)
3: setHyperPars2(learner, insert(par.vals, args))
4: setHyperPars2.Learner(learner, insert(par.vals, args))
5: stop(msg)
── 3. Error: mlr-class-prob (@test-mlr.R#66) ──────────────────────────────────
classif.liquidSVM: Setting parameter display without available description object!
Did you mean one of these hyperparameters instead: d scale folds
You can switch off this check by using configureMlr!
1: makeLearner("classif.liquidSVM", display = 0, predict.type = "prob") at testthat/test-mlr.R:66
2: setHyperPars(learner = wl, ..., par.vals = par.vals)
3: setHyperPars2(learner, insert(par.vals, args))
4: setHyperPars2.Learner(learner, insert(par.vals, args))
5: stop(msg)
══ testthat results ═══════════════════════════════════════════════════════════
OK: 130 SKIPPED: 10 WARNINGS: 0 FAILED: 3
1. Error: mlr-regr (@test-mlr.R#35)
2. Error: mlr-class (@test-mlr.R#51)
3. Error: mlr-class-prob (@test-mlr.R#66)
Error: testthat unit tests failed
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 1.2.2.1
Check: tests
Result: ERROR
Running ‘testthat.R’ [13s/13s]
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", 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", 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", 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
Flavor: r-release-linux-x86_64
Version: 1.2.2.1
Flags: --no-vignettes
Check: whether package can be installed
Result: NOTE
Found the following notes/warnings:
Non-staged installation was used
Flavor: r-release-windows-ix86+x86_64
Version: 1.2.2.1
Flags: --no-vignettes
Check: installed package size
Result: NOTE
installed size is 5.8Mb
sub-directories of 1Mb or more:
doc 2.2Mb
libs 3.0Mb
Flavors: r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64