Running 'testthat.R' [204s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > library(DALEXtra)
  Loading required package: DALEX
  Welcome to DALEX (version: 1.0.1).
  Find examples and detailed introduction at: https://pbiecek.github.io/ema/
  Additional features will be available after installation of: iBreakDown, ggpubr.
  Use 'install_dependencies()' to get all suggested dependencies
  > 
  > test_check("DALEXtra")
  
  ## Package Plan ##
  
    environment location: C:\Users\CRAN\AppData\Local\R-MINI~1\envs\myenv
  
  
  The following packages will be REMOVED:
  
    _tflow_select-2.3.0-mkl
    absl-py-0.9.0-py36_0
    astor-0.8.0-py36_0
    blas-1.0-mkl
    ca-certificates-2020.1.1-0
    certifi-2019.6.16-py36_1
    gast-0.3.3-py_0
    grpcio-1.27.2-py36h351948d_0
    h5py-2.10.0-py36h5e291fa_0
    hdf5-1.10.4-h7ebc959_0
    icc_rt-2019.0.0-h0cc432a_1
    intel-openmp-2019.4-245
    joblib-0.13.2-py36_0
    keras-2.2.4-0
    keras-applications-1.0.8-py_0
    keras-base-2.2.4-py36_0
    keras-preprocessing-1.1.0-py_1
    libmklml-2019.0.5-0
    libprotobuf-3.11.4-h7bd577a_0
    markdown-3.1.1-py36_0
    mkl-2019.4-245
    mkl-service-2.0.2-py36he774522_0
    mkl_fft-1.0.12-py36h14836fe_0
    mkl_random-1.0.2-py36hb452f36_0
    numpy-1.16.4-py36h19fb1c0_0
    numpy-base-1.16.4-py36hc3f5095_0
    openssl-1.1.1e-he774522_0
    pandas-0.24.2-py36ha925a31_0
    pip-19.1.1-py36_0
    protobuf-3.11.4-py36h33f27b4_0
    pyreadline-2.1-py36_1
    python-3.6.8-h9f7ef89_7
    python-dateutil-2.8.1-py_0
    pytz-2019.3-py_0
    pyyaml-5.3-py36he774522_0
    scikit-learn-0.21.2-py36h6288b17_0
    scipy-1.2.1-py36h29ff71c_0
    setuptools-41.0.1-py36_0
    six-1.12.0-py36_0
    sqlite-3.28.0-he774522_0
    tensorboard-1.14.0-py36he3c9ec2_0
    tensorflow-1.14.0-mkl_py36hb88db5b_0
    tensorflow-base-1.14.0-mkl_py36ha978198_0
    tensorflow-estimator-1.14.0-py_0
    termcolor-1.1.0-py36_1
    vc-14.1-h0510ff6_4
    vs2015_runtime-14.16.27012-hf0eaf9b_1
    werkzeug-1.0.0-py_0
    wheel-0.33.4-py36_0
    wincertstore-0.2-py36h7fe50ca_0
    wrapt-1.12.1-py36he774522_1
    yaml-0.1.7-hc54c509_2
    zlib-1.2.11-h62dcd97_3
  
  
  Preparing transaction: ...working... done
  Verifying transaction: ...working... done
  Executing transaction: ...working... done
  
  Remove all packages in environment C:\Users\CRAN\AppData\Local\R-MINI~1\envs\myenv:
  
  WARNING conda.gateways.disk.delete:unlink_or_rename_to_trash(140): Could not remove or rename C:\Users\CRAN\AppData\Local\R-MINI~1\envs\myenv\Library\bin\libssl-1_1-x64.dll.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.  Please remove this file manually (you may need to reboot to free file handles)
  WARNING conda.gateways.disk.delete:unlink_or_rename_to_trash(140): Could not remove or rename C:\Users\CRAN\AppData\Local\R-MINI~1\envs\myenv\Library\bin\libssl-1_1-x64.dll.c~.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.conda_trash.  Please remove this file manually (you may need to reboot to free file handles)
  Der Befehl "conda" ist entweder falsch geschrieben oder
  konnte nicht gefunden werden.
  Der Befehl "conda" ist entweder falsch geschrieben oder
  konnte nicht gefunden werden.
  -- 1. Error: creating env (@test_create_env.R#10)  -----------------------------
  Unrecognized error occured when creating anaconda virtual env. Try to configure you environment manually using Anaconda prompt. For usefull commands see ?explain_scikitlearn
  Backtrace:
   1. DALEXtra::create_env(...)
   2. base::tryCatch(...)
   3. base:::tryCatchList(expr, classes, parentenv, handlers)
   4. base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])
   5. value[[3L]](cond)
  
  -- 2. Error: if check (@test_create_env.R#27)  ---------------------------------
  subscript out of bounds
  Backtrace:
   1. reticulate::use_condaenv("myenv")
   2. reticulate::use_python(conda_env_python[[1]], required = required)
   3. base::unique(c(.globals$use_python_versions, python))
  
  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
    -> model_info        :  package mlr , ver. 2.17.0 , task regression ( [33m default [39m ) 
    -> 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  
   [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
    -> model_info        :  package mlr , ver. 2.17.0 , task regression ( [33m default [39m ) 
    -> 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  
   [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
    -> model_info        :  package mlr , ver. 2.17.0 , task regression ( [33m default [39m ) 
    -> 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  
   [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
    -> model_info        :  package mlr3 , ver. 0.1.8 , task classification ( [33m default [39m ) 
    -> 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  
   [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
    -> model_info        :  package mlr3 , ver. 0.1.8 , task regression ( [33m default [39m ) 
    -> 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  
   [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 
    -> model_info        :  package mlr , ver. 2.17.0 , task classification ( [33m default [39m ) 
    -> 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  
   [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
    -> model_info        :  package mlr , ver. 2.17.0 , task regression ( [33m default [39m ) 
    -> 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  
   [32m A new explainer has been created! [39m 
  Der Befehl "conda" ist entweder falsch geschrieben oder
  konnte nicht gefunden werden.
  Der Befehl "conda" ist entweder falsch geschrieben oder
  konnte nicht gefunden werden.
  -- 3. Error: creating explainer (@test_scikitlearn_explain.R#8)  ---------------
  Unrecognized error occured when creating anaconda virtual env. Try to configure you environment manually using Anaconda prompt. For usefull commands see ?explain_scikitlearn
  Backtrace:
   1. DALEXtra::create_env(...)
   2. base::tryCatch(...)
   3. base:::tryCatchList(expr, classes, parentenv, handlers)
   4. base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])
   5. value[[3L]](cond)
  
  additional arguments ignored in warning()
  
  
  ==> WARNING: A newer version of conda exists. <==
    current version: 4.7.12
    latest version: 4.8.3
  
  Please update conda by running
  
      $ conda update -n base -c defaults conda
  
  
  -- 4. 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: 65 | SKIPPED: 5 | WARNINGS: 6 | FAILED: 4 ]
  1. Error: creating env (@test_create_env.R#10) 
  2. Error: if check (@test_create_env.R#27) 
  3. Error: creating explainer (@test_scikitlearn_explain.R#8) 
  4. Error: prints (@tests_prints.R#9) 
  
  Error: testthat unit tests failed
  In addition: Warning message:
  glm.fit: algorithm did not converge 
  Execution halted