Running 'testthat.R' [39s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > library(DALEXtra)
  Loading required package: DALEX
  Welcome to DALEX (version: 0.4.9).
  Find examples and detailed introduction at: https://pbiecek.github.io/PM_VEE/
  Additional features will be available after installation of: iBreakDown, ALEPlot, breakDown, pdp, factorMerger, ggpubr.
  Use 'install_dependencies()' to get all suggested dependencies
  > 
  > test_check("DALEXtra")
  -- 1. Error: creating env (@test_create_env.R#7)  ------------------------------
  lexical error: invalid char in json text.
                                         NA
                       (right here) ------^
  Backtrace:
   1. reticulate::use_condaenv("myenv")
   2. reticulate::conda_list(conda)
   3. jsonlite::fromJSON(conda_envs)
   4. jsonlite:::parse_and_simplify(...)
   5. jsonlite:::parseJSON(txt, bigint_as_char)
   6. jsonlite:::parse_string(txt, bigint_as_char)
  
  -- 2. Error: if check (@test_create_env.R#27)  ---------------------------------
  lexical error: invalid char in json text.
                                         NA
                       (right here) ------^
  Backtrace:
   1. reticulate::use_condaenv("myenv")
   2. reticulate::conda_list(conda)
   3. jsonlite::fromJSON(conda_envs)
   4. jsonlite:::parse_and_simplify(...)
   5. jsonlite:::parseJSON(txt, bigint_as_char)
   6. jsonlite:::parse_string(txt, bigint_as_char)
  
  Preparation of a new explainer is initiated
    -> model label       :  LM 
    -> data              :  9000  rows  6  cols 
    -> target variable   :  9000  values 
    -> data              :  A column identical to the target variable `y` has been found in the `data`.  ( [31m WARNING [39m )
    -> data              :  It is highly recommended to pass `data` without the target variable column
    -> predict function  :  yhat.WrappedModel  will be used ( [33m default [39m )
    -> predicted values  :  numerical, min =  1792.597 , mean =  3506.836 , max =  6241.447  
    -> residual function :  difference between y and yhat ( [33m default [39m )
    -> residuals         :  numerical, min =  -257.2555 , mean =  4.687686 , max =  472.356  
    -> model_info        :  package mlr , ver. 2.17.0 , task regression ( [33m default [39m ) 
   [32m A new explainer has been created! [39m 
  Preparation of a new explainer is initiated
    -> model label       :  RF 
    -> data              :  9000  rows  6  cols 
    -> target variable   :  9000  values 
    -> data              :  A column identical to the target variable `y` has been found in the `data`.  ( [31m WARNING [39m )
    -> data              :  It is highly recommended to pass `data` without the target variable column
    -> predict function  :  yhat.WrappedModel  will be used ( [33m default [39m )
    -> predicted values  :  numerical, min =  1971.308 , mean =  3507.76 , max =  5770.462  
    -> residual function :  difference between y and yhat ( [33m default [39m )
    -> residuals         :  numerical, min =  -708.3335 , mean =  3.763243 , max =  1285.827  
    -> model_info        :  package mlr , ver. 2.17.0 , task regression ( [33m default [39m ) 
   [32m A new explainer has been created! [39m 
  Preparation of a new explainer is initiated
    -> model label       :  GBM 
    -> data              :  9000  rows  6  cols 
    -> target variable   :  9000  values 
    -> data              :  A column identical to the target variable `y` has been found in the `data`.  ( [31m WARNING [39m )
    -> data              :  It is highly recommended to pass `data` without the target variable column
    -> predict function  :  yhat.WrappedModel  will be used ( [33m default [39m )
    -> predicted values  :  numerical, min =  2115.758 , mean =  3502.258 , max =  6055.129  
    -> residual function :  difference between y and yhat ( [33m default [39m )
    -> residuals         :  numerical, min =  -512.7577 , mean =  9.265594 , max =  778.5281  
    -> model_info        :  package mlr , ver. 2.17.0 , task regression ( [33m default [39m ) 
   [32m A new explainer has been created! [39m 
  
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    |======================================================================| 100%additional arguments ignored in warning()
  Preparation of a new explainer is initiated
    -> model label       :  R6  ( [33m default [39m )
    -> data              :  2207  rows  8  cols 
    -> target variable   :  2207  values 
    -> data              :  A column identical to the target variable `y` has been found in the `data`.  ( [31m WARNING [39m )
    -> data              :  It is highly recommended to pass `data` without the target variable column
    -> predict function  :  yhat.LearnerClassif  will be used ( [33m default [39m )
    -> predicted values  :  numerical, min =  0.07326007 , mean =  0.6778432 , max =  0.9444444  
    -> residual function :  difference between y and yhat ( [33m default [39m )
    -> residuals         :  numerical, min =  -0.9444444 , mean =  -0.3556865 , max =  0.9267399  
    -> model_info        :  package mlr3 , ver. 0.1.6 , task classification ( [33m default [39m ) 
   [32m A new explainer has been created! [39m 
  Preparation of a new explainer is initiated
    -> model label       :  R6  ( [33m default [39m )
    -> data              :  1000  rows  6  cols 
    -> target variable   :  1000  values 
    -> data              :  A column identical to the target variable `y` has been found in the `data`.  ( [31m WARNING [39m )
    -> data              :  It is highly recommended to pass `data` without the target variable column
    -> predict function  :  yhat.LearnerRegr  will be used ( [33m default [39m )
    -> predicted values  :  numerical, min =  2289.664 , mean =  3487.019 , max =  5737.175  
    -> residual function :  difference between y and yhat ( [33m default [39m )
    -> residuals         :  numerical, min =  -1044.133 , mean =  4.321552e-14 , max =  1080.867  
    -> model_info        :  package mlr3 , ver. 0.1.6 , task regression ( [33m default [39m ) 
   [32m A new explainer has been created! [39m 
  Preparation of a new explainer is initiated
    -> model label       :  WrappedModel  ( [33m default [39m )
    -> data              :  524  rows  17  cols 
    -> target variable   :  524  values 
    -> predict function  :  yhat.WrappedModel  will be used ( [33m default [39m )
    -> predicted values  :  numerical, min =  0.2700385 , mean =  0.3322221 , max =  0.5650134  
    -> residual function :  difference between y and yhat ( [33m default [39m )
    -> residuals         :  numerical, min =  -0.5650134 , mean =  -0.02115338 , max =  0.7299615  
    -> model_info        :  package mlr , ver. 2.17.0 , task classification ( [33m default [39m ) 
   [32m A new explainer has been created! [39m 
  Preparation of a new explainer is initiated
    -> model label       :  WrappedModel  ( [33m default [39m )
    -> data              :  524  rows  18  cols 
    -> target variable   :  524  values 
    -> data              :  A column identical to the target variable `y` has been found in the `data`.  ( [31m WARNING [39m )
    -> data              :  It is highly recommended to pass `data` without the target variable column
    -> predict function  :  predict_function 
    -> predicted values  :  numerical, min =  12.77413 , mean =  18.00703 , max =  66.75339  
    -> residual function :  difference between y and yhat ( [33m default [39m )
    -> residuals         :  numerical, min =  -26.84399 , mean =  2.481275 , max =  458.6342  
    -> model_info        :  package mlr , ver. 2.17.0 , task regression ( [33m default [39m ) 
   [32m A new explainer has been created! [39m 
  -- 3. Error: creating explainer (@test_scikitlearn_explain.R#7)  ---------------
  lexical error: invalid char in json text.
                                         NA
                       (right here) ------^
  Backtrace:
   1. "myenv" %in% reticulate::conda_list()$name
   2. reticulate::conda_list()
   3. jsonlite::fromJSON(conda_envs)
   4. jsonlite:::parse_and_simplify(...)
   5. jsonlite:::parseJSON(txt, bigint_as_char)
   6. jsonlite:::parse_string(txt, bigint_as_char)
  
  additional arguments ignored in warning()
  -- 4. Error: env change error (@test_scikitlearn_explain.R#46)  ----------------
  lexical error: invalid char in json text.
                                         NA
                       (right here) ------^
  Backtrace:
   1. "myenv" %in% reticulate::conda_list()$name
   2. reticulate::conda_list()
   3. jsonlite::fromJSON(conda_envs)
   4. jsonlite:::parse_and_simplify(...)
   5. jsonlite:::parseJSON(txt, bigint_as_char)
   6. jsonlite:::parse_string(txt, bigint_as_char)
  
  -- 5. Error: prints (@tests_prints.R#9)  ---------------------------------------
  lexical error: invalid char in json text.
                                         NA
                       (right here) ------^
  Backtrace:
   1. DALEXtra::explain_scikitlearn(...)
   2. DALEXtra:::prepeare_env(yml, condaenv, env)
   3. DALEXtra::create_env(yml, condaenv)
   5. reticulate::conda_list()
   6. jsonlite::fromJSON(conda_envs)
   7. jsonlite:::parse_and_simplify(...)
   8. jsonlite:::parseJSON(txt, bigint_as_char)
   9. jsonlite:::parse_string(txt, bigint_as_char)
  
  == testthat results  ===========================================================
  [ OK: 64 | SKIPPED: 5 | WARNINGS: 0 | FAILED: 5 ]
  1. Error: creating env (@test_create_env.R#7) 
  2. Error: if check (@test_create_env.R#27) 
  3. Error: creating explainer (@test_scikitlearn_explain.R#7) 
  4. Error: env change error (@test_scikitlearn_explain.R#46) 
  5. Error: prints (@tests_prints.R#9) 
  
  Error: testthat unit tests failed
  In addition: Warning message:
  glm.fit: algorithm did not converge 
  Execution halted