Running 'testthat.R' [159s]
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")
  
  ## 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-0.8.1-py36_0
    astor-0.8.0-py36_0
    blas-1.0-mkl
    ca-certificates-2019.11.27-0
    certifi-2019.6.16-py36_1
    gast-0.3.2-py_0
    grpcio-1.16.1-py36h351948d_1
    h5py-2.9.0-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-3.11.2-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-py36h343c172_0
    numpy-1.16.4-py36h19fb1c0_0
    numpy-base-1.16.4-py36hc3f5095_0
    openssl-1.1.1d-he774522_3
    pandas-0.24.2-py36ha925a31_0
    pip-19.1.1-py36_0
    protobuf-3.11.2-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-5.2-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-0.16.0-py_0
    wheel-0.33.4-py36_0
    wincertstore-0.2-py36h7fe50ca_0
    wrapt-1.11.2-py36he774522_0
    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:
  
  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
    -> 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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  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 
  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: 4.8.1
  
  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: 4 | 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