CRAN Package Check Results for Package modelDown

Last updated on 2020-09-17 02:08:17 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.1 5.80 166.97 172.77 OK
r-devel-linux-x86_64-debian-gcc 1.1 4.58 135.54 140.12 OK
r-devel-linux-x86_64-fedora-clang 1.1 207.93 NOTE
r-devel-linux-x86_64-fedora-gcc 1.1 196.86 NOTE
r-devel-windows-ix86+x86_64 1.1 16.00 169.00 185.00 OK
r-patched-linux-x86_64 1.1 5.15 160.77 165.92 OK
r-patched-solaris-x86 1.1 2087.80 ERROR
r-release-linux-x86_64 1.1 5.97 163.90 169.87 OK
r-release-macos-x86_64 1.1 NOTE
r-release-windows-ix86+x86_64 1.1 15.00 149.00 164.00 OK
r-oldrel-macos-x86_64 1.1 NOTE
r-oldrel-windows-ix86+x86_64 1.1 11.00 147.00 158.00 OK

Check Details

Version: 1.1
Check: dependencies in R code
Result: NOTE
    Namespaces in Imports field not imported from:
     ‘DALEX’ ‘DT’ ‘auditor’ ‘breakDown’ ‘drifter’ ‘svglite’
     All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-macos-x86_64, r-oldrel-macos-x86_64

Version: 1.1
Check: tests
Result: ERROR
     Running ‘testthat.R’ [33m/10m]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(DALEX)
     Welcome to DALEX (version: 2.0).
     Find examples and detailed introduction at: https://pbiecek.github.io/ema/
     Additional features will be available after installation of: ggpubr.
     Use 'install_dependencies()' to get all suggested dependencies
     > library(devtools)
     Loading required package: usethis
    
     Attaching package: 'devtools'
    
     The following object is masked from 'package:testthat':
    
     test_file
    
     > library(modelDown)
     >
     > test_check("modelDown")
     Preparation of a new explainer is initiated
     -> model label : ranger ( <1b>[33m default <1b>[39m )
     -> data : 3000 rows 10 cols
     -> target variable : 3000 values
     -> predict function : function(model, data) { return(predict(model, data)$prediction[, 2]) }
     -> predicted values : numerical, min = 0.0002222222 , mean = 0.668525 , max = 1
     -> model_info : package ranger , ver. 0.12.1 , task classification ( <1b>[33m default <1b>[39m )
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -0.6606879 , mean = -0.001858343 , max = 0.7136696
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : lm ( <1b>[33m default <1b>[39m )
     -> data : 3000 rows 10 cols
     -> target variable : 3000 values
     -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 0.001706436 , mean = 0.6666667 , max = 1
     -> model_info : package stats , ver. 4.0.2 , task classification ( <1b>[33m default <1b>[39m )
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -0.9989112 , mean = 1.673979e-09 , max = 0.9848484
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : lm ( <1b>[33m default <1b>[39m )
     -> data : 4000 rows 10 cols
     -> target variable : 4000 values
     -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m )
     -> predicted values : numerical, min = 0.001281044 , mean = 0.5 , max = 1
     -> model_info : package stats , ver. 4.0.2 , task classification ( <1b>[33m default <1b>[39m )
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     -> residuals : numerical, min = -0.9970616 , mean = 1.253319e-09 , max = 0.9891096
     <1b>[32m A new explainer has been created! <1b>[39m
     [1] "Generating auditor..."
     [1] "Generating drifter..."
     Preparation of a new explainer is initiated
     -> model label : model_old
     -> data : 4000 rows 10 cols
     -> target variable : not specified! ( <1b>[31m WARNING <1b>[39m )
     -> predict function : predict_function
     -> predicted values : numerical, min = 0.001706436 , mean = 0.5897454 , max = 1
     -> model_info : package stats , ver. 4.0.2 , task classification ( <1b>[33m default <1b>[39m )
     -> model_info : Model info detected classification task but 'y' is a NULL . ( <1b>[31m WARNING <1b>[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.
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     <1b>[32m A new explainer has been created! <1b>[39m
     Preparation of a new explainer is initiated
     -> model label : model_new
     -> data : 4000 rows 10 cols
     -> target variable : not specified! ( <1b>[31m WARNING <1b>[39m )
     -> predict function : predict_function
     -> predicted values : numerical, min = 0.001706436 , mean = 0.5897454 , max = 1
     -> model_info : package stats , ver. 4.0.2 , task classification ( <1b>[33m default <1b>[39m )
     -> model_info : Model info detected classification task but 'y' is a NULL . ( <1b>[31m WARNING <1b>[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.
     -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m )
     <1b>[32m A new explainer has been created! <1b>[39m
     [1] "Generating model_performance..."
     [1] "Generating variable_importance..."
     [1] "Generating variable_response..."
Flavor: r-patched-solaris-x86