Running ‘run-base.R’ [529s/492s]
Running the tests in ‘tests/run-base.R’ failed.
Complete output:
  > library(testthat)
  > test_check("mlr", filter = "base_")
  Loading required package: mlr
  Loading required package: ParamHelpers
  
  Attaching package: 'rex'
  
  The following object is masked from 'package:testthat':
  
      matches
  
  
  Attaching package: 'BBmisc'
  
  The following object is masked from 'package:base':
  
      isFALSE
  
  ── 1. Error: MulticlassWrapper (@test_base_MulticlassWrapper.R#23)  ────────────
  For learner classif.lqa please install the following packages: lqa
  1: makeLearner("classif.lqa") at testthat/test_base_MulticlassWrapper.R:23
  2: do.call(constructor, list())
  3: (function () 
     {
         makeRLearnerClassif(cl = "classif.lqa", package = "lqa", par.set = makeParamSet(makeDiscreteLearnerParam(id = "penalty", 
             values = c("adaptive.lasso", "ao", "bridge", "enet", "fused.lasso", "genet", 
                 "icb", "lasso", "licb", "oscar", "penalreg", "ridge", "scad", "weighted.fusion")), 
             makeNumericLearnerParam(id = "lambda", lower = 0, requires = quote(penalty %in% 
                 c("adaptive.lasso", "ao", "bridge", "genet", "lasso", "oscar", "penalreg", 
                     "ridge", "scad"))), makeNumericLearnerParam(id = "gamma", lower = 1 + 
                 .Machine$double.eps, requires = quote(penalty %in% c("ao", "bridge", 
                 "genet", "weighted.fusion"))), makeNumericLearnerParam(id = "alpha", 
                 lower = 0, upper = 1, requires = quote(penalty == "genet")), makeNumericLearnerParam(id = "oscar.c", 
                 lower = 0, requires = quote(penalty == "oscar")), makeNumericLearnerParam(id = "a", 
                 lower = 2 + .Machine$double.eps, requires = quote(penalty == "scad")), 
             makeNumericLearnerParam(id = "lambda1", lower = 0, requires = quote(penalty %in% 
                 c("enet", "fused.lasso", "icb", "licb", "weighted.fusion"))), makeNumericLearnerParam(id = "lambda2", 
                 lower = 0, requires = quote(penalty %in% c("enet", "fused.lasso", "icb", 
                     "licb", "weighted.fusion"))), makeDiscreteLearnerParam(id = "method", 
                 default = "lqa.update2", values = c("lqa.update2", "ForwardBoost", "GBlockBoost")), 
             makeNumericLearnerParam(id = "var.eps", default = .Machine$double.eps, lower = 0), 
             makeIntegerLearnerParam(id = "max.steps", lower = 1L, default = 5000L), makeNumericLearnerParam(id = "conv.eps", 
                 default = 0.001, lower = 0), makeLogicalLearnerParam(id = "conv.stop", 
                 default = TRUE), makeNumericLearnerParam(id = "c1", default = 1e-08, 
                 lower = 0), makeIntegerLearnerParam(id = "digits", default = 5L, lower = 1L)), 
             properties = c("numerics", "prob", "twoclass"), par.vals = list(penalty = "lasso", 
                 lambda = 0.1), name = "Fitting penalized Generalized Linear Models with the LQA algorithm", 
             short.name = "lqa", note = "`penalty` has been set to `\"lasso\"` and `lambda` to `0.1` by default. The parameters `lambda`, `gamma`, `alpha`, `oscar.c`, `a`, `lambda1` and `lambda2` are the tuning parameters of the `penalty` function being used, and correspond to the parameters as named in the respective help files. Parameter `c` for penalty method `oscar` has been named `oscar.c`. Parameters `lambda1` and `lambda2` correspond to the parameters named 'lambda_1' and 'lambda_2' of the penalty functions `enet`, `fused.lasso`, `icb`, `licb`, as well as `weighted.fusion`.", 
             callees = c("lqa", "lqa.control", "adaptive.lasso", "ao", "bridge", "enet", 
                 "fused.lasso", "genet", "icb", "lasso", "licb", "oscar", "penalreg", 
                 "ridge", "scad", "weighted.fusion"))
     })()
  4: makeRLearnerClassif(cl = "classif.lqa", package = "lqa", par.set = makeParamSet(makeDiscreteLearnerParam(id = "penalty", 
         values = c("adaptive.lasso", "ao", "bridge", "enet", "fused.lasso", "genet", 
             "icb", "lasso", "licb", "oscar", "penalreg", "ridge", "scad", "weighted.fusion")), 
         makeNumericLearnerParam(id = "lambda", lower = 0, requires = quote(penalty %in% 
             c("adaptive.lasso", "ao", "bridge", "genet", "lasso", "oscar", "penalreg", 
                 "ridge", "scad"))), makeNumericLearnerParam(id = "gamma", lower = 1 + 
             .Machine$double.eps, requires = quote(penalty %in% c("ao", "bridge", "genet", 
             "weighted.fusion"))), makeNumericLearnerParam(id = "alpha", lower = 0, upper = 1, 
             requires = quote(penalty == "genet")), makeNumericLearnerParam(id = "oscar.c", 
             lower = 0, requires = quote(penalty == "oscar")), makeNumericLearnerParam(id = "a", 
             lower = 2 + .Machine$double.eps, requires = quote(penalty == "scad")), makeNumericLearnerParam(id = "lambda1", 
             lower = 0, requires = quote(penalty %in% c("enet", "fused.lasso", "icb", 
                 "licb", "weighted.fusion"))), makeNumericLearnerParam(id = "lambda2", 
             lower = 0, requires = quote(penalty %in% c("enet", "fused.lasso", "icb", 
                 "licb", "weighted.fusion"))), makeDiscreteLearnerParam(id = "method", 
             default = "lqa.update2", values = c("lqa.update2", "ForwardBoost", "GBlockBoost")), 
         makeNumericLearnerParam(id = "var.eps", default = .Machine$double.eps, lower = 0), 
         makeIntegerLearnerParam(id = "max.steps", lower = 1L, default = 5000L), makeNumericLearnerParam(id = "conv.eps", 
             default = 0.001, lower = 0), makeLogicalLearnerParam(id = "conv.stop", default = TRUE), 
         makeNumericLearnerParam(id = "c1", default = 1e-08, lower = 0), makeIntegerLearnerParam(id = "digits", 
             default = 5L, lower = 1L)), properties = c("numerics", "prob", "twoclass"), 
         par.vals = list(penalty = "lasso", lambda = 0.1), name = "Fitting penalized Generalized Linear Models with the LQA algorithm", 
         short.name = "lqa", note = "`penalty` has been set to `\"lasso\"` and `lambda` to `0.1` by default. The parameters `lambda`, `gamma`, `alpha`, `oscar.c`, `a`, `lambda1` and `lambda2` are the tuning parameters of the `penalty` function being used, and correspond to the parameters as named in the respective help files. Parameter `c` for penalty method `oscar` has been named `oscar.c`. Parameters `lambda1` and `lambda2` correspond to the parameters named 'lambda_1' and 'lambda_2' of the penalty functions `enet`, `fused.lasso`, `icb`, `licb`, as well as `weighted.fusion`.", 
         callees = c("lqa", "lqa.control", "adaptive.lasso", "ao", "bridge", "enet", "fused.lasso", 
             "genet", "icb", "lasso", "licb", "oscar", "penalreg", "ridge", "scad", "weighted.fusion"))
  5: addClasses(makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, 
         name, short.name, note, callees), c(cl, "RLearnerClassif"))
  6: makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, 
         short.name, note, callees)
  7: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load")
  8: stopf("For %s please install the following packages: %s", why, ps)
  
  ── 2. Error: tuning allows usage of budget (@test_base_tuning.R#120)  ──────────
  Assertion on 'discrete.names' failed: Must be of type 'logical flag', not 'NULL'.
  1: tuneParams(lrn, binaryclass.task, resampling = rdesc, par.set = ps, control = ctrl) at testthat/test_base_tuning.R:120
  2: sel.func(learner, task, resampling, measures, par.set, control, opt.path, show.info, 
         resample.fun)
  3: sampleValue(par.set, start, trafo = FALSE)
  4: sampleValue.ParamSet(par.set, start, trafo = FALSE)
  5: lapply(par$pars, sampleValue, discrete.names = discrete.names, trafo = trafo)
  6: FUN(X[[i]], ...)
  7: sampleValue.Param(X[[i]], ...)
  8: assertFlag(discrete.names)
  9: makeAssertion(x, res, .var.name, add)
  10: mstop("Assertion on '%s' failed: %s.", var.name, res)
  
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 3578 SKIPPED: 0 FAILED: 2
  1. Error: MulticlassWrapper (@test_base_MulticlassWrapper.R#23) 
  2. Error: tuning allows usage of budget (@test_base_tuning.R#120) 
  
  Error: testthat unit tests failed
  Execution halted