Running ‘testthat.R’ [212s/249s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library("checkmate")
  > library("testthat")
  > library("mlr3")
  > library("paradox")
  > library("mlr3pipelines")
  > 
  > test_check("mlr3pipelines")
  Training debug.multi with input list(input_1 = 1, input_2 = 1)
  ── 1. Error: LearnerClassifAvg (@test_learner_weightedaverage.R#23)  ───────────
  cannot coerce class 'c("PredictionDataClassif", "PredictionData")' to a data.frame
  Backtrace:
    1. (pipeline_greplicate(PipeOpLearnerCV$new(lrn("classif.rpart", ...
    2. mlr3pipelines:::graph_reduce(self, input, "train", single_input)
    3. op[[fun]](input)
    4. private$.train(input)
    5. private$.train_task(intask)
    7. base::lapply(map(res$data$prediction, "test"), as.data.table)
    9. data.table:::as.data.table.default(X[[i]], ...)
   12. base::as.data.frame.default(x, ...)
  
  ── 2. Error: Target Trafo Pipeline (@test_mlr_graphs_targettrafo.R#25)  ────────
  Assertion on 'pdata$response' failed: Must have length 506, but has length 0.
  Backtrace:
    1. tt$predict(task)
    2. mlr3pipelines:::graph_reduce(self, input, "predict", single_input)
    3. op[[fun]](input)
    4. private$.predict(input)
    5. inputs[[1]](inputs[-1])
    6. private$.invert(inputs[[1L]], predict_phase_state)
   11. get(mlr_reflections$task_types[type]$prediction)$new(...)
   12. .subset2(public_bind_env, "initialize")(...)
   13. mlr3:::.__PredictionRegr__initialize(...)
   15. mlr3:::check_prediction_data.PredictionDataRegr(pdata)
   16. checkmate::assert_numeric(...)
   17. checkmate::makeAssertion(x, res, .var.name, add)
   18. checkmate:::mstop(...)
  
  ── 3. Error: More Complex Target Trafo Pipelines (@test_mlr_graphs_targettrafo.R
  non-numeric argument to mathematical function
  Backtrace:
   1. tt$predict(task)
   2. mlr3pipelines:::graph_reduce(self, input, "predict", single_input)
   3. op[[fun]](input)
   4. private$.predict(input)
   5. inputs[[1]](inputs[-1])
   6. private$.invert(inputs[[1L]], predict_phase_state)
   8. self$param_set$values$inverter(pred)
  
  ── 4. Failure: PipeOpWeightedClassifAvg - prob - train and predict (@test_pipeop
  out[[1]] not equivalent to prds[[4]].
  Component "data": Component "truth": 59 string mismatches
  Component "data": Component 3: target is matrix, current is factor
  Component "data": Component 4: 'current' is not a factor
  
  ── 5. Failure: PipeOpWeightedClassifAvg - prob - train and predict (@test_pipeop
  out[[1]] not equivalent to prds[[4]].
  Component "data": Component "truth": 59 string mismatches
  Component "data": Component 3: target is matrix, current is factor
  Component "data": Component 4: 'current' is not a factor
  
  ── 6. Error: PipeOpLearnerCV - basic properties (@test_pipeop_learnercv.R#11)  ─
  cannot coerce class 'c("PredictionDataClassif", "PredictionData")' to a data.frame
  Backtrace:
    1. mlr3pipelines:::train_pipeop(po, list(task = task))
    2. po$train(inputs) /data/gannet/ripley/R/packages/tests-devel/mlr3pipelines.Rcheck/tests/helper_functions.R:394:2
    3. private$.train(input)
    4. private$.train_task(intask)
    6. base::lapply(map(res$data$prediction, "test"), as.data.table)
    8. data.table:::as.data.table.default(X[[i]], ...)
   11. base::as.data.frame.default(x, ...)
  
  ── 7. Error: PipeOpLearnerCV - within resampling (@test_pipeop_learnercv.R#55)  
  cannot coerce class 'c("PredictionDataClassif", "PredictionData")' to a data.frame
  Backtrace:
    1. mlr3::resample(tsk("iris"), gr, rsmp("holdout"))
    2. mlr3:::workhorse(...)
    3. mlr3:::learner_train(learner$clone(), task, sets[["train"]])
    4. mlr3misc::encapsulate(...)
    9. mlr3:::.f(learner = <environment>, task = <environment>)
   10. get_private(learner)$.train(task)
   11. self$graph$train(task)
   12. mlr3pipelines:::graph_reduce(self, input, "train", single_input)
   13. op[[fun]](input)
   14. private$.train(input)
   15. private$.train_task(intask)
   17. base::lapply(map(res$data$prediction, "test"), as.data.table)
   19. data.table:::as.data.table.default(X[[i]], ...)
   22. base::as.data.frame.default(x, ...)
  
  ── 8. Error: PipeOpLearnerCV - model active binding to state (@test_pipeop_learn
  cannot coerce class 'c("PredictionDataClassif", "PredictionData")' to a data.frame
  Backtrace:
    1. po$train(list(task))
    2. private$.train(input)
    3. private$.train_task(intask)
    5. base::lapply(map(res$data$prediction, "test"), as.data.table)
    7. data.table:::as.data.table.default(X[[i]], ...)
   10. base::as.data.frame.default(x, ...)
  
  ── 9. Failure: PipeOpOVRUnite- train and predict (@test_pipeop_ovr.R#91)  ──────
  `po$predict(list(as.Multiplicity(tin)))` did not throw an error.
  
  ── 10. Error: train and predict (@test_pipeop_randomresponse.R#43)  ────────────
  object 'response' not found
  Backtrace:
   1. g2$predict(task2)
   2. mlr3pipelines:::graph_reduce(self, input, "predict", single_input)
   3. op[[fun]](input)
   4. private$.predict(input)
   6. base::get("response")
  
  ── 11. Error: PipeOpTargetMutate - log base 2 trafo (@test_pipeop_targetmutate.R
  Assertion on 'pdata$response' failed: Must have length 506, but has length 0.
  Backtrace:
    1. g$predict(task)
    2. mlr3pipelines:::graph_reduce(self, input, "predict", single_input)
    3. op[[fun]](input)
    4. private$.predict(input)
    5. inputs[[1]](inputs[-1])
    6. private$.invert(inputs[[1L]], predict_phase_state)
   11. get(mlr_reflections$task_types[type]$prediction)$new(...)
   12. .subset2(public_bind_env, "initialize")(...)
   13. mlr3:::.__PredictionRegr__initialize(...)
   15. mlr3:::check_prediction_data.PredictionDataRegr(pdata)
   16. checkmate::assert_numeric(...)
   17. checkmate::makeAssertion(x, res, .var.name, add)
   18. checkmate:::mstop(...)
  
  ── 12. Error: stacking (@test_usecases.R#142)  ─────────────────────────────────
  cannot coerce class 'c("PredictionDataClassif", "PredictionData")' to a data.frame
  Backtrace:
    1. pipe$train(task)
    2. mlr3pipelines:::graph_reduce(self, input, "train", single_input)
    3. op[[fun]](input)
    4. private$.train(input)
    5. private$.train_task(intask)
    7. base::lapply(map(res$data$prediction, "test"), as.data.table)
    9. data.table:::as.data.table.default(X[[i]], ...)
   12. base::as.data.frame.default(x, ...)
  
  ══ testthat results  ═══════════════════════════════════════════════════════════
  [ OK: 9708 | SKIPPED: 64 | WARNINGS: 6 | FAILED: 12 ]
  1. Error: LearnerClassifAvg (@test_learner_weightedaverage.R#23) 
  2. Error: Target Trafo Pipeline (@test_mlr_graphs_targettrafo.R#25) 
  3. Error: More Complex Target Trafo Pipelines (@test_mlr_graphs_targettrafo.R#58) 
  4. Failure: PipeOpWeightedClassifAvg - prob - train and predict (@test_pipeop_ensemble.R#124) 
  5. Failure: PipeOpWeightedClassifAvg - prob - train and predict (@test_pipeop_ensemble.R#137) 
  6. Error: PipeOpLearnerCV - basic properties (@test_pipeop_learnercv.R#11) 
  7. Error: PipeOpLearnerCV - within resampling (@test_pipeop_learnercv.R#55) 
  8. Error: PipeOpLearnerCV - model active binding to state (@test_pipeop_learnercv.R#90) 
  9. Failure: PipeOpOVRUnite- train and predict (@test_pipeop_ovr.R#91) 
  1. ...
  
  Error: testthat unit tests failed
  Execution halted