CRAN Package Check Results for Package DALEXtra

Last updated on 2020-02-28 02:51:17 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.2.0 10.94 130.16 141.10 NOTE
r-devel-linux-x86_64-debian-gcc 0.2.0 8.89 98.39 107.28 NOTE
r-devel-linux-x86_64-fedora-clang 0.2.0 169.27 NOTE
r-devel-linux-x86_64-fedora-gcc 0.2.0 166.78 NOTE
r-devel-windows-ix86+x86_64 0.2.0 25.00 136.00 161.00 ERROR
r-devel-windows-ix86+x86_64-gcc8 0.2.0 30.00 267.00 297.00 ERROR
r-patched-linux-x86_64 0.2.0 9.54 112.59 122.13 NOTE
r-patched-solaris-x86 0.2.0 221.40 NOTE
r-release-linux-x86_64 0.2.0 9.63 111.19 120.82 NOTE
r-release-windows-ix86+x86_64 0.2.0 22.00 255.00 277.00 ERROR
r-release-osx-x86_64 0.2.0 OK
r-oldrel-windows-ix86+x86_64 0.2.0 11.00 241.00 252.00 ERROR
r-oldrel-osx-x86_64 0.2.0 OK

Check Details

Version: 0.2.0
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: 'mljar'
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64

Version: 0.2.0
Check: examples
Result: ERROR
    Running examples in 'DALEXtra-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: create_env
    > ### Title: Create your conda virtual env with DALEX
    > ### Aliases: create_env
    >
    > ### ** Examples
    >
    > if(DALEXtra:::is_conda()) {
    + create_env(system.file("extdata", "testing_environment.yml", package = "DALEXtra"))
    + } else {
    + "conda is required"
    + }
    Error: lexical error: invalid char in json text.
     NA
     (right here) ------^
    Execution halted
Flavor: r-devel-windows-ix86+x86_64

Version: 0.2.0
Check: tests
Result: ERROR
     Running 'testthat.R' [35s]
    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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 1792.597 , mean = 3506.836 , max = 6241.447
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -257.2555 , mean = 4.687686 , max = 472.356
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     <1b>[32m A new explainer has been created! <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 1971.308 , mean = 3507.76 , max = 5770.462
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -708.3335 , mean = 3.763243 , max = 1285.827
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     <1b>[32m A new explainer has been created! <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 2115.758 , mean = 3502.258 , max = 6055.129
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -512.7577 , mean = 9.265594 , max = 778.5281
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     <1b>[32m A new explainer has been created! <1b>[39m
    
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     Preparation of a new explainer is initiated
     -> model label : R6 ( <1b>[33m default <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> predict function : yhat.LearnerClassif will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 0.07326007 , mean = 0.6778432 , max = 0.9444444
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -0.9444444 , mean = -0.3556865 , max = 0.9267399
     -> model_info : package mlr3 , ver. 0.1.6 , task classification ( <1b>[33m default <1b>[39m )
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : R6 ( <1b>[33m default <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> predict function : yhat.LearnerRegr will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 2289.664 , mean = 3487.019 , max = 5737.175
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -1044.133 , mean = 4.321552e-14 , max = 1080.867
     -> model_info : package mlr3 , ver. 0.1.6 , task regression ( <1b>[33m default <1b>[39m )
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : WrappedModel ( <1b>[33m default <1b>[39m )
     -> data : 524 rows 17 cols
     -> target variable : 524 values
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 0.2700385 , mean = 0.3322221 , max = 0.5650134
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -0.5650134 , mean = -0.02115338 , max = 0.7299615
     -> model_info : package mlr , ver. 2.17.0 , task classification ( <1b>[33m default <1b>[39m )
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : WrappedModel ( <1b>[33m default <1b>[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`. ( <1b>[31m WARNING <1b>[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 ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -26.84399 , mean = 2.481275 , max = 458.6342
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     <1b>[32m A new explainer has been created! <1b>[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
Flavor: r-devel-windows-ix86+x86_64

Version: 0.2.0
Check: examples
Result: ERROR
    Running examples in 'DALEXtra-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: explain_scikitlearn
    > ### Title: Wrapper for Python Scikit-Learn Models
    > ### Aliases: explain_scikitlearn
    >
    > ### ** Examples
    >
    > library("DALEXtra")
    > if(DALEXtra:::is_conda()) {
    + # Explainer build (Keep in mind that 18th column is target)
    + titanic_test <- read.csv(system.file("extdata", "titanic_test.csv", package = "DALEXtra"))
    + # Keep in mind that when pickle is being built and loaded,
    + # not only Python version but libraries versions has to match aswell
    + explainer <- explain_scikitlearn(system.file("extdata", "scikitlearn.pkl", package = "DALEXtra"),
    + yml = system.file("extdata", "testing_environment.yml", package = "DALEXtra"),
    + data = titanic_test[,1:17], y = titanic_test$survived)
    + plot(model_performance(explainer))
    +
    + # Predictions with newdata
    + predict(explainer, titanic_test[1:10,1:17])
    +
    + } else {
    + print('Conda is required.')
    + }
    There already exists environment named the same as it is specified in .yml file - myenv. It will be used
    Warning in system2(command = python, args = paste0("\"", config_script, :
     running command '"C:/Users/CRAN/AppData/Local/r-miniconda/envs/myenv/python.exe" "D:/RCompile/CRANpkg/lib/4.0gcc8/reticulate/config/config.py"' had status 2
    Warning: Error in python_config(python_version, required_module, python_version, : Error 2 occurred running C:/Users/CRAN/AppData/Local/r-miniconda/envs/myenv/python.exe
    
    Error: Yours environment has to match environment where pickle file was created. It also includes encoding, python version, and libraries version. Specifying .yml file or path to virtual environment may help. For more information look warnings() and then ?explain_scikitlearn
    Execution halted
Flavor: r-devel-windows-ix86+x86_64-gcc8

Version: 0.2.0
Check: tests
Result: ERROR
     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: 1.0).
     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-1.1.1d-he774522_4
     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-1.11.2-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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 1792.597 , mean = 3506.836 , max = 6241.447
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -257.2555 , mean = 4.687686 , max = 472.356
     <1b>[32m A new explainer has been created! <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 1971.308 , mean = 3507.76 , max = 5770.462
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -708.3335 , mean = 3.763243 , max = 1285.827
     <1b>[32m A new explainer has been created! <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 2115.758 , mean = 3502.258 , max = 6055.129
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -512.7577 , mean = 9.265594 , max = 778.5281
     <1b>[32m A new explainer has been created! <1b>[39m
    
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     Preparation of a new explainer is initiated
     -> model label : R6 ( <1b>[33m default <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr3 , ver. 0.1.6 , task classification ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.LearnerClassif will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 0.07326007 , mean = 0.6778432 , max = 0.9444444
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -0.9444444 , mean = -0.3556865 , max = 0.9267399
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : R6 ( <1b>[33m default <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr3 , ver. 0.1.6 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.LearnerRegr will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 2289.664 , mean = 3487.019 , max = 5737.175
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -1044.133 , mean = 4.321552e-14 , max = 1080.867
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : WrappedModel ( <1b>[33m default <1b>[39m )
     -> data : 524 rows 17 cols
     -> target variable : 524 values
     -> model_info : package mlr , ver. 2.17.0 , task classification ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 0.2700385 , mean = 0.3322221 , max = 0.5650134
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -0.5650134 , mean = -0.02115338 , max = 0.7299615
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : WrappedModel ( <1b>[33m default <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : predict_function
     -> predicted values : numerical, min = 12.77413 , mean = 18.00703 , max = 66.75339
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -26.84399 , mean = 2.481275 , max = 458.6342
     <1b>[32m A new explainer has been created! <1b>[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: 4.8.2
    
     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
Flavor: r-devel-windows-ix86+x86_64-gcc8

Version: 0.2.0
Check: examples
Result: ERROR
    Running examples in 'DALEXtra-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: explain_scikitlearn
    > ### Title: Wrapper for Python Scikit-Learn Models
    > ### Aliases: explain_scikitlearn
    >
    > ### ** Examples
    >
    > library("DALEXtra")
    > if(DALEXtra:::is_conda()) {
    + # Explainer build (Keep in mind that 18th column is target)
    + titanic_test <- read.csv(system.file("extdata", "titanic_test.csv", package = "DALEXtra"))
    + # Keep in mind that when pickle is being built and loaded,
    + # not only Python version but libraries versions has to match aswell
    + explainer <- explain_scikitlearn(system.file("extdata", "scikitlearn.pkl", package = "DALEXtra"),
    + yml = system.file("extdata", "testing_environment.yml", package = "DALEXtra"),
    + data = titanic_test[,1:17], y = titanic_test$survived)
    + plot(model_performance(explainer))
    +
    + # Predictions with newdata
    + predict(explainer, titanic_test[1:10,1:17])
    +
    + } else {
    + print('Conda is required.')
    + }
    There already exists environment named the same as it is specified in .yml file - myenv. It will be used
    Warning in system2(command = python, args = paste0("\"", config_script, :
     running command '"C:/Users/CRAN/AppData/Local/r-miniconda/envs/myenv/python.exe" "D:/temp/Rtmp8Q1SJt/RLIBS_335ac5db976c1/reticulate/config/config.py"' had status 2
    Warning: Error in python_config(python_version, required_module, python_version, : Error 2 occurred running C:/Users/CRAN/AppData/Local/r-miniconda/envs/myenv/python.exe
    
    Error: Yours environment has to match environment where pickle file was created. It also includes encoding, python version, and libraries version. Specifying .yml file or path to virtual environment may help. For more information look warnings() and then ?explain_scikitlearn
    Execution halted
Flavor: r-release-windows-ix86+x86_64

Version: 0.2.0
Check: tests
Result: ERROR
     Running 'testthat.R' [156s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > library(testthat)
     > library(DALEXtra)
     Loading required package: DALEX
     Welcome to DALEX (version: 1.0).
     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-1.1.1d-he774522_4
     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-1.11.2-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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 1792.597 , mean = 3506.836 , max = 6241.447
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -257.2555 , mean = 4.687686 , max = 472.356
     <1b>[32m A new explainer has been created! <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 1971.308 , mean = 3507.76 , max = 5770.462
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -708.3335 , mean = 3.763243 , max = 1285.827
     <1b>[32m A new explainer has been created! <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 2115.758 , mean = 3502.258 , max = 6055.129
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -512.7577 , mean = 9.265594 , max = 778.5281
     <1b>[32m A new explainer has been created! <1b>[39m
    
     |
     | | 0%
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     |======================================================================| 100%additional arguments ignored in warning()
     Preparation of a new explainer is initiated
     -> model label : R6 ( <1b>[33m default <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr3 , ver. 0.1.6 , task classification ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.LearnerClassif will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 0.07326007 , mean = 0.6778432 , max = 0.9444444
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -0.9444444 , mean = -0.3556865 , max = 0.9267399
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : R6 ( <1b>[33m default <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr3 , ver. 0.1.6 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.LearnerRegr will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 2289.664 , mean = 3487.019 , max = 5737.175
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -1044.133 , mean = 4.321552e-14 , max = 1080.867
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : WrappedModel ( <1b>[33m default <1b>[39m )
     -> data : 524 rows 17 cols
     -> target variable : 524 values
     -> model_info : package mlr , ver. 2.17.0 , task classification ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 0.2700385 , mean = 0.3322221 , max = 0.5650134
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -0.5650134 , mean = -0.02115338 , max = 0.7299615
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : WrappedModel ( <1b>[33m default <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : predict_function
     -> predicted values : numerical, min = 12.77413 , mean = 18.00703 , max = 66.75339
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -26.84399 , mean = 2.481275 , max = 458.6342
     <1b>[32m A new explainer has been created! <1b>[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: 4.8.2
    
     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
Flavor: r-release-windows-ix86+x86_64

Version: 0.2.0
Check: examples
Result: ERROR
    Running examples in 'DALEXtra-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: explain_scikitlearn
    > ### Title: Wrapper for Python Scikit-Learn Models
    > ### Aliases: explain_scikitlearn
    >
    > ### ** Examples
    >
    > library("DALEXtra")
    > if(DALEXtra:::is_conda()) {
    + # Explainer build (Keep in mind that 18th column is target)
    + titanic_test <- read.csv(system.file("extdata", "titanic_test.csv", package = "DALEXtra"))
    + # Keep in mind that when pickle is being built and loaded,
    + # not only Python version but libraries versions has to match aswell
    + explainer <- explain_scikitlearn(system.file("extdata", "scikitlearn.pkl", package = "DALEXtra"),
    + yml = system.file("extdata", "testing_environment.yml", package = "DALEXtra"),
    + data = titanic_test[,1:17], y = titanic_test$survived)
    + plot(model_performance(explainer))
    +
    + # Predictions with newdata
    + predict(explainer, titanic_test[1:10,1:17])
    +
    + } else {
    + print('Conda is required.')
    + }
    There already exists environment named the same as it is specified in .yml file - myenv. It will be used
    Warning in system2(command = python, args = paste0("\"", config_script, :
     running command '"C:/Users/CRAN/AppData/Local/r-miniconda/envs/myenv/python.exe" "D:/temp/RtmpqWzhxR/RLIBS_2d26c6d292085/reticulate/config/config.py"' had status 2
    Warning: Error in python_config(python_version, required_module, python_version, : Error 2 occurred running C:/Users/CRAN/AppData/Local/r-miniconda/envs/myenv/python.exe
    
    Error: Yours environment has to match environment where pickle file was created. It also includes encoding, python version, and libraries version. Specifying .yml file or path to virtual environment may help. For more information look warnings() and then ?explain_scikitlearn
    Execution halted
Flavor: r-oldrel-windows-ix86+x86_64

Version: 0.2.0
Check: tests
Result: ERROR
     Running 'testthat.R' [148s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > library(testthat)
     > library(DALEXtra)
     Loading required package: DALEX
     Welcome to DALEX (version: 1.0).
     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-1.1.1d-he774522_4
     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-1.11.2-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:
    
     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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 1792.597 , mean = 3506.836 , max = 6241.447
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -257.2555 , mean = 4.687686 , max = 472.356
     <1b>[32m A new explainer has been created! <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 1971.308 , mean = 3507.76 , max = 5770.462
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -708.3335 , mean = 3.763243 , max = 1285.827
     <1b>[32m A new explainer has been created! <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 2115.758 , mean = 3502.258 , max = 6055.129
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -512.7577 , mean = 9.265594 , max = 778.5281
     <1b>[32m A new explainer has been created! <1b>[39m
    
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     |======================================================================| 100%additional arguments ignored in warning()
     Preparation of a new explainer is initiated
     -> model label : R6 ( <1b>[33m default <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr3 , ver. 0.1.7 , task classification ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.LearnerClassif will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 0.07326007 , mean = 0.6778432 , max = 0.9444444
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -0.9444444 , mean = -0.3556865 , max = 0.9267399
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : R6 ( <1b>[33m default <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr3 , ver. 0.1.7 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.LearnerRegr will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 2289.664 , mean = 3487.019 , max = 5737.175
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -1044.133 , mean = 4.321552e-14 , max = 1080.867
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : WrappedModel ( <1b>[33m default <1b>[39m )
     -> data : 524 rows 17 cols
     -> target variable : 524 values
     -> model_info : package mlr , ver. 2.17.0 , task classification ( <1b>[33m default <1b>[39m )
     -> predict function : yhat.WrappedModel will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 0.2700385 , mean = 0.3322221 , max = 0.5650134
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -0.5650134 , mean = -0.02115338 , max = 0.7299615
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : WrappedModel ( <1b>[33m default <1b>[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`. ( <1b>[31m WARNING <1b>[39m )
     -> data : It is highly recommended to pass `data` without the target variable column
     -> model_info : package mlr , ver. 2.17.0 , task regression ( <1b>[33m default <1b>[39m )
     -> predict function : predict_function
     -> predicted values : numerical, min = 12.77413 , mean = 18.00703 , max = 66.75339
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -26.84399 , mean = 2.481275 , max = 458.6342
     <1b>[32m A new explainer has been created! <1b>[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: 4.8.2
    
     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
Flavor: r-oldrel-windows-ix86+x86_64