Running ‘run-base.R’ [530s/502s]
  Running ‘run-basenocran.R’
  Running ‘run-classif1.R’
  Running ‘run-classif2.R’
  Running ‘run-cluster.R’
  Running ‘run-featsel.R’
  Running ‘run-learners-classif.R’
  Running ‘run-learners-classiflabelswitch.R’
  Running ‘run-learners-cluster.R’
  Running ‘run-learners-general.R’
  Running ‘run-learners-multilabel.R’
  Running ‘run-learners-regr.R’
  Running ‘run-learners-surv.R’
  Running ‘run-lint.R’
  Running ‘run-multilabel.R’
  Running ‘run-parallel.R’
  Running ‘run-regr.R’
  Running ‘run-stack.R’
  Running ‘run-surv.R’
  Running ‘run-tune.R’
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
  
  OMP: Warning #96: Cannot form a team with 6 threads, using 2 instead.
  OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set).
  ── 1. Error: MultilabelBinaryRelevanceWrapper with glmnet (#958) (@test_base_mul
  lazy-load database '/data/gannet/ripley/R/test-clang/glmnet/help/glmnet.rdb' is corrupt
  1: makeLearner("classif.glmnet", predict.type = "response") at testthat/test_base_multilabel.R:57
  2: do.call(constructor, list())
  3: (function () 
     {
         makeRLearnerClassif(cl = "classif.glmnet", package = "glmnet", par.set = makeParamSet(makeNumericLearnerParam(id = "alpha", 
             default = 1, lower = 0, upper = 1), makeNumericLearnerParam(id = "s", lower = 0, 
             when = "predict"), makeLogicalLearnerParam(id = "exact", default = FALSE, 
             when = "predict"), makeIntegerLearnerParam(id = "nlambda", default = 100L, 
             lower = 1L), makeNumericLearnerParam(id = "lambda.min.ratio", lower = 0, 
             upper = 1), makeNumericVectorLearnerParam(id = "lambda", lower = 0), makeLogicalLearnerParam(id = "standardize", 
             default = TRUE), makeLogicalLearnerParam(id = "intercept", default = TRUE), 
             makeNumericLearnerParam(id = "thresh", default = 1e-07, lower = 0), makeIntegerLearnerParam(id = "dfmax", 
                 lower = 0L), makeIntegerLearnerParam(id = "pmax", lower = 0L), makeIntegerVectorLearnerParam(id = "exclude", 
                 lower = 1L), makeNumericVectorLearnerParam(id = "penalty.factor", lower = 0, 
                 upper = 1), makeNumericVectorLearnerParam(id = "lower.limits", upper = 0), 
             makeNumericVectorLearnerParam(id = "upper.limits", lower = 0), makeIntegerLearnerParam(id = "maxit", 
                 default = 100000L, lower = 1L), makeDiscreteLearnerParam(id = "type.logistic", 
                 values = c("Newton", "modified.Newton")), makeDiscreteLearnerParam(id = "type.multinomial", 
                 values = c("ungrouped", "grouped")), makeNumericLearnerParam(id = "fdev", 
                 default = 1e-05, lower = 0, upper = 1), makeNumericLearnerParam(id = "devmax", 
                 default = 0.999, lower = 0, upper = 1), makeNumericLearnerParam(id = "eps", 
                 default = 1e-06, lower = 0, upper = 1), makeNumericLearnerParam(id = "big", 
                 default = 9.9e+35), makeIntegerLearnerParam(id = "mnlam", default = 5, 
                 lower = 1), makeNumericLearnerParam(id = "pmin", default = 1e-09, lower = 0, 
                 upper = 1), makeNumericLearnerParam(id = "exmx", default = 250), makeNumericLearnerParam(id = "prec", 
                 default = 1e-10), makeIntegerLearnerParam(id = "mxit", default = 100L, 
                 lower = 1L)), properties = c("numerics", "factors", "prob", "twoclass", 
             "multiclass", "weights"), par.vals = list(s = 0.01), name = "GLM with Lasso or Elasticnet Regularization", 
             short.name = "glmnet", note = "The family parameter is set to `binomial` for two-class problems and to `multinomial` otherwise.\n      Factors automatically get converted to dummy columns, ordered factors to integer.\n      Parameter `s` (value of the regularization parameter used for predictions) is set to `0.1` by default,\n      but needs to be tuned by the user.\n      glmnet uses a global control object for its parameters. mlr resets all control parameters to their defaults\n      before setting the specified parameters and after training.\n      If you are setting glmnet.control parameters through glmnet.control,\n      you need to save and re-set them after running the glmnet learner.", 
             callees = c("glmnet", "glmnet.control", "predict.glmnet"))
     })()
  4: makeRLearnerClassif(cl = "classif.glmnet", package = "glmnet", par.set = makeParamSet(makeNumericLearnerParam(id = "alpha", 
         default = 1, lower = 0, upper = 1), makeNumericLearnerParam(id = "s", lower = 0, 
         when = "predict"), makeLogicalLearnerParam(id = "exact", default = FALSE, when = "predict"), 
         makeIntegerLearnerParam(id = "nlambda", default = 100L, lower = 1L), makeNumericLearnerParam(id = "lambda.min.ratio", 
             lower = 0, upper = 1), makeNumericVectorLearnerParam(id = "lambda", lower = 0), 
         makeLogicalLearnerParam(id = "standardize", default = TRUE), makeLogicalLearnerParam(id = "intercept", 
             default = TRUE), makeNumericLearnerParam(id = "thresh", default = 1e-07, 
             lower = 0), makeIntegerLearnerParam(id = "dfmax", lower = 0L), makeIntegerLearnerParam(id = "pmax", 
             lower = 0L), makeIntegerVectorLearnerParam(id = "exclude", lower = 1L), makeNumericVectorLearnerParam(id = "penalty.factor", 
             lower = 0, upper = 1), makeNumericVectorLearnerParam(id = "lower.limits", 
             upper = 0), makeNumericVectorLearnerParam(id = "upper.limits", lower = 0), 
         makeIntegerLearnerParam(id = "maxit", default = 100000L, lower = 1L), makeDiscreteLearnerParam(id = "type.logistic", 
             values = c("Newton", "modified.Newton")), makeDiscreteLearnerParam(id = "type.multinomial", 
             values = c("ungrouped", "grouped")), makeNumericLearnerParam(id = "fdev", 
             default = 1e-05, lower = 0, upper = 1), makeNumericLearnerParam(id = "devmax", 
             default = 0.999, lower = 0, upper = 1), makeNumericLearnerParam(id = "eps", 
             default = 1e-06, lower = 0, upper = 1), makeNumericLearnerParam(id = "big", 
             default = 9.9e+35), makeIntegerLearnerParam(id = "mnlam", default = 5, lower = 1), 
         makeNumericLearnerParam(id = "pmin", default = 1e-09, lower = 0, upper = 1), 
         makeNumericLearnerParam(id = "exmx", default = 250), makeNumericLearnerParam(id = "prec", 
             default = 1e-10), makeIntegerLearnerParam(id = "mxit", default = 100L, lower = 1L)), 
         properties = c("numerics", "factors", "prob", "twoclass", "multiclass", "weights"), 
         par.vals = list(s = 0.01), name = "GLM with Lasso or Elasticnet Regularization", 
         short.name = "glmnet", note = "The family parameter is set to `binomial` for two-class problems and to `multinomial` otherwise.\n      Factors automatically get converted to dummy columns, ordered factors to integer.\n      Parameter `s` (value of the regularization parameter used for predictions) is set to `0.1` by default,\n      but needs to be tuned by the user.\n      glmnet uses a global control object for its parameters. mlr resets all control parameters to their defaults\n      before setting the specified parameters and after training.\n      If you are setting glmnet.control parameters through glmnet.control,\n      you need to save and re-set them after running the glmnet learner.", 
         callees = c("glmnet", "glmnet.control", "predict.glmnet"))
  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: makeParamHelpList(callees, package, par.set)
  8: capture.output(tools::Rd2HTML(ghf(h)))
  9: evalVis(expr)
  10: withVisible(eval(expr, pf))
  11: eval(expr, pf)
  12: eval(expr, pf)
  13: tools::Rd2HTML(ghf(h))
  14: prepare_Rd(Rd, defines = defines, stages = stages, fragment = fragment, ...)
  15: ghf(h)
  16: tools:::fetchRdDB(RdDB, basename(file))
  17: lazyLoadDBexec(filebase, fun)
  18: fun(environment())
  19: fetch(key)
  
  ── 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: 3584 SKIPPED: 0 FAILED: 2
  1. Error: MultilabelBinaryRelevanceWrapper with glmnet (#958) (@test_base_multilabel.R#57) 
  2. Error: tuning allows usage of budget (@test_base_tuning.R#120) 
  
  Error: testthat unit tests failed
  Execution halted