Running ‘testthat.R’ [50s/67s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > if (requireNamespace("testthat", quietly = TRUE)) {
  +   library(checkmate)
  +   library(testthat)
  +   library(mlr3)
  +   library(mcboost)
  + 
  +   test_check("mcboost")
  + }
  Loading required namespace: mlr3pipelines
  INFO  [12:50:35.394] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 3/3) 
  INFO  [12:50:35.606] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/3) 
  INFO  [12:50:35.675] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/3) 
  INFO  [12:50:36.729] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/3) 
  INFO  [12:50:36.834] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/3) 
  INFO  [12:50:37.002] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 3/3) 
  INFO  [12:50:37.524] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 3/3) 
  INFO  [12:50:37.638] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/3) 
  INFO  [12:50:37.841] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/3) 
  INFO  [12:50:38.527] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 3/3) 
  INFO  [12:50:38.655] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/3) 
  INFO  [12:50:38.733] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/3) 
  INFO  [12:50:39.374] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 3/3) 
  INFO  [12:50:39.535] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/3) 
  INFO  [12:50:39.616] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/3) 
  INFO  [12:50:40.637] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/3) 
  INFO  [12:50:40.737] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 3/3) 
  INFO  [12:50:40.814] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/3) 
  INFO  [12:50:41.265] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 3/3) 
  INFO  [12:50:41.390] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/3) 
  INFO  [12:50:41.466] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/3) 
  INFO  [12:50:42.166] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/3) 
  INFO  [12:50:42.278] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/3) 
  INFO  [12:50:42.386] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 3/3) 
  INFO  [12:50:42.765] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 3/3) 
  INFO  [12:50:42.919] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/3) 
  INFO  [12:50:42.998] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/3) 
  INFO  [12:50:43.489] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/3) 
  INFO  [12:50:43.585] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/3) 
  INFO  [12:50:43.673] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 3/3) 
  INFO  [12:50:44.010] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 3/3) 
  INFO  [12:50:44.087] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/3) 
  INFO  [12:50:44.163] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/3) 
  INFO  [12:50:45.888] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/2) 
  INFO  [12:50:45.991] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/2) 
  INFO  [12:50:46.391] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/2) 
  INFO  [12:50:46.470] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/2) 
  INFO  [12:50:47.078] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/2) 
  INFO  [12:50:47.179] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/2) 
  INFO  [12:50:47.568] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/2) 
  INFO  [12:50:47.693] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/2) 
  INFO  [12:50:48.259] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/2) 
  INFO  [12:50:48.334] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/2) 
  INFO  [12:50:48.726] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/2) 
  INFO  [12:50:48.974] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/2) 
  INFO  [12:50:49.569] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/2) 
  INFO  [12:50:49.733] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/2) 
  INFO  [12:50:50.169] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/2) 
  INFO  [12:50:50.271] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/2) 
  INFO  [12:50:50.860] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/2) 
  INFO  [12:50:50.946] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/2) 
  INFO  [12:50:51.321] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 1/2) 
  INFO  [12:50:51.397] [mlr3]  Applying learner 'regr.rpart' on task 'tmptsk' (iter 2/2) 
  INFO  [12:51:26.678] [mlr3]  Applying learner 'classif.rpart' on task 'sonar' (iter 3/3) 
  INFO  [12:51:26.768] [mlr3]  Applying learner 'classif.rpart' on task 'sonar' (iter 1/3) 
  INFO  [12:51:26.849] [mlr3]  Applying learner 'classif.rpart' on task 'sonar' (iter 2/3) 
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error (test_auditor_fitters.R:32:3): RidgeAuditorFitters work ───────────────
  Error: need at least two non-NA values to interpolate
  Backtrace:
       █
    1. └─rf$fit(iris[, 1:4], runif(150)) test_auditor_fitters.R:32:2
    2.   └─mcboost:::.__LearnerAuditorFitter__fit(...)
    3.     └─l$predict(data)
    4.       └─mcboost:::.__LearnerPredictor__predict(...)
    5.         └─self$learner$predict_newdata(data)
    6.           └─mlr3:::.__Learner__predict_newdata(...)
    7.             └─self$predict(task)
    8.               └─mlr3:::.__Learner__predict(...)
    9.                 └─mlr3:::learner_predict(self, task, row_ids)
   10.                   └─mlr3misc::encapsulate(...)
   11.                     ├─mlr3misc::invoke(...)
   12.                     │ └─base::eval.parent(expr, n = 1L)
   13.                     │   └─base::eval(expr, p)
   14.                     │     └─base::eval(expr, p)
   15.                     └─mlr3:::.f(task = <environment>, learner = <environment>)
   16.                       └─get_private(learner)$.predict(task)
   17.                         └─mlr3learners:::.__LearnerRegrGlmnet__.predict(...)
   18.                           ├─mlr3misc::invoke(...)
   19.                           │ └─base::eval.parent(expr, n = 1L)
   20.                           │   └─base::eval(expr, p)
   21.                           │     └─base::eval(expr, p)
   22.                           ├─stats::predict(...)
   23.                           ├─glmnet:::predict.elnet(...)
   24.                           ├─base::NextMethod("predict")
   25.                           └─glmnet::predict.glmnet(...)
   26.                             └─glmnet:::lambda.interp(lambda, s)
   27.                               └─stats::approx(lambda, seq(lambda), sfrac)
  ── Error (test_mcboost.R:171:5): MCBoost various settings ──────────────────────
  Error: need at least two non-NA values to interpolate
  Backtrace:
       █
    1. └─mc$multicalibrate(data, labels) test_mcboost.R:171:4
    2.   └─mcboost:::.__MCBoost__multicalibrate(...)
    3.     └─self$auditor_fitter$fit_to_resid(data_m, resid_m, idx[mask])
    4.       └─mcboost:::.__AuditorFitter__fit_to_resid(...)
    5.         └─self$fit(data, resid, mask)
    6.           └─mcboost:::.__LearnerAuditorFitter__fit(...)
    7.             └─l$predict(data)
    8.               └─mcboost:::.__LearnerPredictor__predict(...)
    9.                 └─self$learner$predict_newdata(data)
   10.                   └─mlr3:::.__Learner__predict_newdata(...)
   11.                     └─self$predict(task)
   12.                       └─mlr3:::.__Learner__predict(...)
   13.                         └─mlr3:::learner_predict(self, task, row_ids)
   14.                           └─mlr3misc::encapsulate(...)
   15.                             ├─mlr3misc::invoke(...)
   16.                             │ └─base::eval.parent(expr, n = 1L)
   17.                             │   └─base::eval(expr, p)
   18.                             │     └─base::eval(expr, p)
   19.                             └─mlr3:::.f(task = <environment>, learner = <environment>)
   20.                               └─get_private(learner)$.predict(task)
   21.                                 └─mlr3learners:::.__LearnerRegrGlmnet__.predict(...)
   22.                                   ├─mlr3misc::invoke(...)
   23.                                   │ └─base::eval.parent(expr, n = 1L)
   24.                                   │   └─base::eval(expr, p)
   25.                                   │     └─base::eval(expr, p)
   26.                                   ├─stats::predict(...)
   27.                                   ├─glmnet:::predict.elnet(...)
   28.                                   ├─base::NextMethod("predict")
   29.                                   └─glmnet::predict.glmnet(...)
   30.                                     └─glmnet:::lambda.interp(lambda, s)
   31.                                       └─stats::approx(lambda, seq(lambda), sfrac)
  ── Error (test_mcboost.R:231:3): MCBoost args for self-defined init predictor ──
  Error: need at least two non-NA values to interpolate
  Backtrace:
       █
    1. └─mc$multicalibrate(data, labels, predictor_args = runif(208)) test_mcboost.R:231:2
    2.   └─mcboost:::.__MCBoost__multicalibrate(...)
    3.     └─self$auditor_fitter$fit_to_resid(data_m, resid_m, idx[mask])
    4.       └─mcboost:::.__AuditorFitter__fit_to_resid(...)
    5.         └─self$fit(data, resid, mask)
    6.           └─mcboost:::.__LearnerAuditorFitter__fit(...)
    7.             └─l$predict(data)
    8.               └─mcboost:::.__LearnerPredictor__predict(...)
    9.                 └─self$learner$predict_newdata(data)
   10.                   └─mlr3:::.__Learner__predict_newdata(...)
   11.                     └─self$predict(task)
   12.                       └─mlr3:::.__Learner__predict(...)
   13.                         └─mlr3:::learner_predict(self, task, row_ids)
   14.                           └─mlr3misc::encapsulate(...)
   15.                             ├─mlr3misc::invoke(...)
   16.                             │ └─base::eval.parent(expr, n = 1L)
   17.                             │   └─base::eval(expr, p)
   18.                             │     └─base::eval(expr, p)
   19.                             └─mlr3:::.f(task = <environment>, learner = <environment>)
   20.                               └─get_private(learner)$.predict(task)
   21.                                 └─mlr3learners:::.__LearnerRegrGlmnet__.predict(...)
   22.                                   ├─mlr3misc::invoke(...)
   23.                                   │ └─base::eval.parent(expr, n = 1L)
   24.                                   │   └─base::eval(expr, p)
   25.                                   │     └─base::eval(expr, p)
   26.                                   ├─stats::predict(...)
   27.                                   ├─glmnet:::predict.elnet(...)
   28.                                   ├─base::NextMethod("predict")
   29.                                   └─glmnet::predict.glmnet(...)
   30.                                     └─glmnet:::lambda.interp(lambda, s)
   31.                                       └─stats::approx(lambda, seq(lambda), sfrac)
  ── Error (test_pipeop_mcboost.R:14:3): MCBoost class instantiation ─────────────
  Error: need at least two non-NA values to interpolate
  Backtrace:
       █
    1. └─gr$train(tsk$clone()$filter(tid)) test_pipeop_mcboost.R:14:2
    2.   └─mlr3pipelines:::.__Graph__train(...)
    3.     └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
    4.       └─op[[fun]](input)
    5.         └─mlr3pipelines:::.__PipeOp__train(...)
    6.           └─private$.train(input)
    7.             └─mcboost:::.__PipeOpMCBoost__.train(...)
    8.               └─mc$multicalibrate(d, l, predictor_args = inputs$prediction)
    9.                 └─mcboost:::.__MCBoost__multicalibrate(...)
   10.                   └─self$auditor_fitter$fit_to_resid(data_m, resid_m, idx[mask])
   11.                     └─mcboost:::.__AuditorFitter__fit_to_resid(...)
   12.                       └─self$fit(data, resid, mask)
   13.                         └─mcboost:::.__LearnerAuditorFitter__fit(...)
   14.                           └─l$predict(data)
   15.                             └─mcboost:::.__LearnerPredictor__predict(...)
   16.                               └─self$learner$predict_newdata(data)
   17.                                 └─mlr3:::.__Learner__predict_newdata(...)
   18.                                   └─self$predict(task)
   19.                                     └─mlr3:::.__Learner__predict(...)
   20.                                       └─mlr3:::learner_predict(self, task, row_ids)
   21.                                         └─mlr3misc::encapsulate(...)
   22.                                           ├─mlr3misc::invoke(...)
   23.                                           │ └─base::eval.parent(expr, n = 1L)
   24.                                           │   └─base::eval(expr, p)
   25.                                           │     └─base::eval(expr, p)
   26.                                           └─mlr3:::.f(task = <environment>, learner = <environment>)
   27.                                             └─get_private(learner)$.predict(task)
   28.                                               └─mlr3learners:::.__LearnerRegrGlmnet__.predict(...)
   29.                                                 ├─mlr3misc::invoke(...)
   30.                                                 │ └─base::eval.parent(expr, n = 1L)
   31.                                                 │   └─base::eval(expr, p)
   32.                                                 │     └─base::eval(expr, p)
   33.                                                 ├─stats::predict(...)
   34.                                                 ├─glmnet:::predict.elnet(...)
   35.                                                 ├─base::NextMethod("predict")
   36.                                                 └─glmnet::predict.glmnet(...)
   37.                                                   └─glmnet:::lambda.interp(lambda, s)
   38.                                                     └─stats::approx(lambda, seq(lambda), sfrac)
  ── Error (test_pipeop_mcboost.R:30:3): MCBoost ppl ─────────────────────────────
  Error: need at least two non-NA values to interpolate
  Backtrace:
       █
    1. └─pp$train(tsk("sonar")) test_pipeop_mcboost.R:30:2
    2.   └─mlr3pipelines:::.__Graph__train(...)
    3.     └─mlr3pipelines:::graph_reduce(self, input, "train", single_input)
    4.       └─op[[fun]](input)
    5.         └─mlr3pipelines:::.__PipeOp__train(...)
    6.           └─private$.train(input)
    7.             └─mcboost:::.__PipeOpMCBoost__.train(...)
    8.               └─mc$multicalibrate(d, l, predictor_args = inputs$prediction)
    9.                 └─mcboost:::.__MCBoost__multicalibrate(...)
   10.                   └─self$auditor_fitter$fit_to_resid(data_m, resid_m, idx[mask])
   11.                     └─mcboost:::.__AuditorFitter__fit_to_resid(...)
   12.                       └─self$fit(data, resid, mask)
   13.                         └─mcboost:::.__LearnerAuditorFitter__fit(...)
   14.                           └─l$predict(data)
   15.                             └─mcboost:::.__LearnerPredictor__predict(...)
   16.                               └─self$learner$predict_newdata(data)
   17.                                 └─mlr3:::.__Learner__predict_newdata(...)
   18.                                   └─self$predict(task)
   19.                                     └─mlr3:::.__Learner__predict(...)
   20.                                       └─mlr3:::learner_predict(self, task, row_ids)
   21.                                         └─mlr3misc::encapsulate(...)
   22.                                           ├─mlr3misc::invoke(...)
   23.                                           │ └─base::eval.parent(expr, n = 1L)
   24.                                           │   └─base::eval(expr, p)
   25.                                           │     └─base::eval(expr, p)
   26.                                           └─mlr3:::.f(task = <environment>, learner = <environment>)
   27.                                             └─get_private(learner)$.predict(task)
   28.                                               └─mlr3learners:::.__LearnerRegrGlmnet__.predict(...)
   29.                                                 ├─mlr3misc::invoke(...)
   30.                                                 │ └─base::eval.parent(expr, n = 1L)
   31.                                                 │   └─base::eval(expr, p)
   32.                                                 │     └─base::eval(expr, p)
   33.                                                 ├─stats::predict(...)
   34.                                                 ├─glmnet:::predict.elnet(...)
   35.                                                 ├─base::NextMethod("predict")
   36.                                                 └─glmnet::predict.glmnet(...)
   37.                                                   └─glmnet:::lambda.interp(lambda, s)
   38.                                                     └─stats::approx(lambda, seq(lambda), sfrac)
  
  [ FAIL 5 | WARN 0 | SKIP 0 | PASS 110 ]
  Error: Test failures
  Execution halted