Running 'testthat.R' [10s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > library(localModel)
  > library(DALEX)
  Welcome to DALEX (version: 2.4.3).
  Find examples and detailed introduction at: http://ema.drwhy.ai/
  Additional features will be available after installation of: ggpubr.
  Use 'install_dependencies()' to get all suggested dependencies
  > library(randomForest)
  randomForest 4.7-1.1
  Type rfNews() to see new features/changes/bug fixes.
  > 
  > test_check("localModel")
  Preparation of a new explainer is initiated
    -> model label       :  randomForest  ( [33m default [39m )
    -> data              :  500  rows  6  cols 
    -> target variable   :  not specified! ( [31m WARNING [39m )
    -> predict function  :  yhat.randomForest  will be used ( [33m default [39m )
    -> predicted values  :  No value for predict function target column. ( [33m default [39m )
    -> model_info        :  package randomForest , ver. 4.7.1.1 , task multiclass ( [33m default [39m ) 
    -> model_info        :  Model info detected multiclass task but 'y' is a NULL .  ( [31m WARNING [39m )
    -> model_info        :  By deafult multiclass tasks supports only factor 'y' parameter. 
    -> model_info        :  Consider changing to a factor vector with true class names.
    -> model_info        :  Otherwise I will not be able to calculate residuals or loss function.
    -> predicted values  :  predict function returns multiple columns:  3  ( [33m default [39m ) 
    -> residual function :  difference between 1 and probability of true class ( [33m default [39m )
   [32m A new explainer has been created! [39m 
  Preparation of a new explainer is initiated
    -> model label       :  randomForest  ( [33m default [39m )
    -> data              :  500  rows  6  cols 
    -> target variable   :  not specified! ( [31m WARNING [39m )
    -> predict function  :  yhat.randomForest  will be used ( [33m default [39m )
    -> predicted values  :  No value for predict function target column. ( [33m default [39m )
    -> model_info        :  package randomForest , ver. 4.7.1.1 , task regression ( [33m default [39m ) 
    -> model_info        :  Model info detected regression task but 'y' is a NULL .  ( [31m WARNING [39m )
    -> model_info        :  By deafult regressions tasks supports only numercical 'y' parameter. 
    -> model_info        :  Consider changing to numerical vector.
    -> model_info        :  Otherwise I will not be able to calculate residuals or loss function.
    -> predicted values  :  numerical, min =  1942.187 , mean =  3498.114 , max =  5854.127  
    -> residual function :  difference between y and yhat ( [33m default [39m )
   [32m A new explainer has been created! [39m 
  Preparation of a new explainer is initiated
    -> model label       :  randomForest  ( [33m default [39m )
    -> data              :  500  rows  6  cols 
    -> target variable   :  not specified! ( [31m WARNING [39m )
    -> predict function  :  yhat.randomForest  will be used ( [33m default [39m )
    -> predicted values  :  No value for predict function target column. ( [33m default [39m )
    -> model_info        :  package randomForest , ver. 4.7.1.1 , task classification ( [33m default [39m ) 
    -> model_info        :  Model info detected classification task but 'y' is a NULL .  ( [31m WARNING [39m )
    -> model_info        :  By deafult classification tasks supports only numercical 'y' parameter. 
    -> model_info        :  Consider changing to numerical vector with 0 and 1 values.
    -> model_info        :  Otherwise I will not be able to calculate residuals or loss function.
    -> predicted values  :  numerical, min =  0 , mean =  0.4576 , max =  1  
    -> residual function :  difference between y and yhat ( [33m default [39m )
   [32m A new explainer has been created! [39m 
  Preparation of a new explainer is initiated
    -> model label       :  lm  ( [33m default [39m )
    -> data              :  500  rows  6  cols 
    -> target variable   :  not specified! ( [31m WARNING [39m )
    -> predict function  :  yhat.glm  will be used ( [33m default [39m )
    -> predicted values  :  No value for predict function target column. ( [33m default [39m )
    -> model_info        :  package stats , ver. 4.2.2 , task classification ( [33m default [39m ) 
    -> model_info        :  Model info detected classification task but 'y' is a NULL .  ( [31m WARNING [39m )
    -> model_info        :  By deafult classification tasks supports only numercical 'y' parameter. 
    -> model_info        :  Consider changing to numerical vector with 0 and 1 values.
    -> model_info        :  Otherwise I will not be able to calculate residuals or loss function.
    -> predicted values  :  numerical, min =  0.009202999 , mean =  0.464 , max =  0.9979422  
    -> residual function :  difference between y and yhat ( [33m default [39m )
   [32m A new explainer has been created! [39m 
  [ FAIL 2 | WARN 1 | SKIP 0 | PASS 18 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error (???): Function works with single- and multi-column output ────────────
  Error in `approx(lambda, seq(lambda), sfrac)`: need at least two non-NA values to interpolate
  Backtrace:
       ▆
    1. ├─testthat::expect_output(...)
    2. │ └─testthat:::quasi_capture(...)
    3. │   ├─testthat (local) .capture(...)
    4. │   │ └─testthat::capture_output_lines(code, print, width = width)
    5. │   │   └─testthat:::eval_with_output(code, print = print, width = width)
    6. │   │     ├─withr::with_output_sink(path, withVisible(code))
    7. │   │     │ └─base::force(code)
    8. │   │     └─base::withVisible(code)
    9. │   └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
   10. └─localModel::individual_surrogate_model(...)
   11.   └─localModel:::combine_explanations(...)
   12.     └─base::lapply(...)
   13.       └─localModel (local) FUN(X[[i]], ...)
   14.         └─localModel:::single_column_surrogate(...)
   15.           └─glmnet::cv.glmnet(...)
   16.             └─glmnet:::cv.glmnet.raw(...)
   17.               ├─glmnet::buildPredmat(...)
   18.               └─glmnet:::buildPredmat.default(...)
   19.                 ├─stats::predict(...)
   20.                 ├─glmnet:::predict.elnet(...)
   21.                 ├─base::NextMethod("predict")
   22.                 └─glmnet::predict.glmnet(...)
   23.                   └─glmnet:::lambda.interp(lambda, s)
   24.                     └─stats::approx(lambda, seq(lambda), sfrac)
  ── Error ('test_main.R:16'): Reproducibility ───────────────────────────────────
  Error in `approx(lambda, seq(lambda), sfrac)`: need at least two non-NA values to interpolate
  Backtrace:
       ▆
    1. ├─testthat::expect_false(...) at test_main.R:16:2
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─localModel::individual_surrogate_model(...)
    5.   └─localModel:::combine_explanations(...)
    6.     └─base::lapply(...)
    7.       └─localModel (local) FUN(X[[i]], ...)
    8.         └─localModel:::single_column_surrogate(...)
    9.           └─glmnet::cv.glmnet(...)
   10.             └─glmnet:::cv.glmnet.raw(...)
   11.               ├─glmnet::buildPredmat(...)
   12.               └─glmnet:::buildPredmat.default(...)
   13.                 ├─stats::predict(...)
   14.                 ├─glmnet:::predict.elnet(...)
   15.                 ├─base::NextMethod("predict")
   16.                 └─glmnet::predict.glmnet(...)
   17.                   └─glmnet:::lambda.interp(lambda, s)
   18.                     └─stats::approx(lambda, seq(lambda), sfrac)
  
  [ FAIL 2 | WARN 1 | SKIP 0 | PASS 18 ]
  Error: Test failures
  Execution halted