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modeldown's Issues

Error 'factor_columns[[i]]': index is out of range

example code

library(DALEX)
library(live)
library(modelDown)
proba <- e1071::svm(quality~.,data=wine)
modelDown(DALEX::explain(proba, data = wine, y = wine$quality))

output

[1] "Generating model_performance..."
[1] "Generating variable_importance..."
[1] "Generating variable_response..."
[1] "Generating prediction_breakdown..."
Błąd w poleceniu 'factor_columns[[i]]':indeks jest poza granicami

packages can't install

To be honest the result through the web visualization, this idea is very good, but your package loading will take a long time, there are a lot of related packages at the same time, I found that after carefully explore the package you just use the DALEX function through the R - markdown displayed in package, I can understand for plagiarism? Also, your package is hard to download on AZURE or on your local computer, it takes a long time, and it's basically unusable.

Session info

Use archivist to save session state

In case of problem with archivist intergration, add link to R's sessionInfo (as text file)

Downloading generated data

Each plot should contain link that allows downloading data frames (DALEX results) used to generate the plot.

No need to save the plot itself.

Do we want to save the data only as R object or as text as well?

DALEX on custom stacked models

Pseudo-reprex below to illustrate workflow.

There are 2 stages of stacking, and below is abbreviated to final stage.

# input data for prediction; this data are themselves a result of stacked model
df <- tibble::tribble(
  ~x1, ~x2, ~true,           ~pred1,           ~pred2,           ~pred3,           ~pred4,           ~pred5,
  "0016",   1, 11255, 9782.06546666667, 8226.73783726366, 8423.53411898339, 7663.85714285714, 7778.32234611454,
  "0016",   2, 10155, 9917.16225000001,  7390.2726470072, 7548.50621212894, 6011.57142857143,  7020.0197927677,
  "0016",   3,  9905, 8365.66048333333, 4748.35733132711, 4897.40398331136, 5625.14285714286, 5197.59820269678,
  "0026",   1,  9569, 10542.7790333333, 12448.8281473898, 12982.2853847065, 9529.42857142857, 9913.60100542533,
  "0026",   2, 15004,      12332.88455, 13118.3179554928, 13490.4519001908, 9449.14285714286, 9782.48187764126,
  "0027",   1,  4623, 6228.92556666668, 7901.02224985066,  8072.3059097473, 7663.85714285714,  7564.7019858157,
  "0027",   2,  3666, 3902.33416666666, 5351.58779239503, 5501.55032427708, 5757.85714285714, 5791.90612060224,
  "0027",   3,  2046, 3730.91108333333, 5405.90164588071, 5431.22100425988,             5700, 5574.85787520228,
  "0345",   1,  7848, 7911.66811666667, 7332.14726332333, 7535.03388134704, 8428.85714285714, 7504.20919309283,
  "0345",   2,  5594,        6249.8431, 5302.09068924222, 5602.24650648537,             6253, 5936.17306199591,
  "0348",   1,  6118,        5888.9112,  6782.1549012783, 6983.85792156352, 7145.28571428571, 6996.64665890851,
  "0348",   2,  4115,        4655.3621, 4061.92478416692,  4339.3944039624, 5379.71428571429, 5201.36079952954,
  "0348",   3,  3792, 4703.56786666666, 4862.77758785772, 4886.36623749198, 5413.85714285714,  5316.2047603152,
  "1000",   1,  9982,        8894.2428, 8950.05680053561, 8724.27457157357, 7643.14285714286, 8427.52273508174,
  "1000",   2,  4218, 5103.73553333333, 6755.30317981863, 6492.15505744351,             7836, 6900.52725335413,
  "1022",   1,  9021, 8966.84941666667, 8921.14926298024, 8514.45660876879, 8590.57142857143, 8566.07119574923,
  "1022",   2, 11692, 10205.8180333333, 8895.88440879051, 8417.59814231434, 8185.85714285714, 8225.60579235643,
  "1022",   3,  9420, 9664.82173333334, 9422.99681882565, 8835.71873031759, 7853.57142857143, 8126.76078652109,
  "1022",   4,  6850, 7419.07043333333, 8995.48869657391, 8194.63910112673, 7604.14285714286, 7815.14405713875,
  "1022",   5,  6850, 7419.07043333333,  8817.8438463534, 7883.22080414475, 6846.14285714286, 7515.84608489043
)

# model list for stacking
md <- list(rf,
           pca,
           svm,
           enet)

model_pred_stack <- function(df, md) {
  
  # iterate over list of models in md, and average prediction
  temp <- 0
  for (i in 1:length(md)) {
    temp <- temp + predict(md[[i]], df)
  }
  temp <- temp / length(md)
  return(temp)
}

model_pred <- model_pred_stack(df = df, md)

# with DALEX, have to loop over list of models one by one, which doesn't reflect intention of stacking; otherwise, modelDown will complain
explain_stacked <- explain(
  md[[1]],
  data = df,
  y = df$true,
  label = "stacked"
)

modelDown(explain_stacked,
          device = "svg",
          output_folder = "output_data/modelDown_stacked")

# passing list of models; modelDown fails to generate diagnostics, aside from data description
explain_stacked <- explain(
  md,
  data = df,
  y = df$true,
  label = "stacked"
)

modelDown(explain_stacked,
          device = "svg",
          output_folder = "output_data/modelDown_stacked")

First tab design

First tab should present information on model correctness. Looks should be improved. Plots from model performance and from auditor should be ordered so that it's easy to analyze model.

Box plot from model performance should be placed before plot with distribution of residuals

Fill up DESCRIPTION

Lots of missing information in DESCRIPTION
please update
Do you really need R in 3.5?
You can start with R 3.0 in the requirements

Variable response order

Variables in 'Variable Response' tab should be order from most to least important - as in output of variable importance.

What if we have many explainers? Which order should be used then?

Error in file.copy

I was executing your examples: https://htmlpreview.github.io/?https://raw.githubusercontent.com/kromash/modelDown/master/docs/reference/modelDown.html

and got following error:

> modelDown::modelDown(explainer_ranger, explainer_glm) #all defaults
Error in file.copy(css_files_paths, to, recursive = TRUE, overwrite = TRUE) : 
  invalid 'from' argument

Here is the traceback:

> traceback()
3: file.copy(css_files_paths, to, recursive = TRUE, overwrite = TRUE)
2: copyAssets(system.file("extdata", "template", package = "modelDown"), 
       output_folder)
1: modelDown::modelDown(explainer_ranger, explainer_glm)

and here is my session info

> devtools::session_info()
Session info ---------------------------------------------------------------------------------
 setting  value                       
 version  R version 3.4.4 (2018-03-15)
 system   x86_64, darwin15.6.0        
 ui       RStudio (1.1.442)           
 language (EN)                        
 collate  en_US.UTF-8                 
 tz       Europe/Warsaw               
 date     2018-05-31                  

Packages -------------------------------------------------------------------------------------
 package       * version    date       source                                 
 agricolae       1.2-8      2017-09-12 cran (@1.2-8)                          
 ALEPlot         1.0        2017-11-13 CRAN (R 3.4.2)                         
 AlgDesign       1.1-7.3    2014-10-15 CRAN (R 3.2.0)                         
 assertthat      0.2.0      2017-04-11 CRAN (R 3.4.0)                         
 backports       1.1.2      2017-12-13 cran (@1.1.2)                          
 base          * 3.4.4      2018-03-15 local                                  
 base64enc       0.1-3      2015-07-28 CRAN (R 3.2.0)                         
 bindr           0.1.1      2018-03-13 CRAN (R 3.4.4)                         
 bindrcpp        0.2.2      2018-03-29 CRAN (R 3.4.4)                         
 BiocInstaller   1.16.5     2015-05-20 Bioconductor                           
 boot            1.3-20     2017-08-06 CRAN (R 3.4.4)                         
 breakDown     * 0.1.6      2018-05-17 local (pbiecek/breakDown@NA)           
 callr           2.0.3      2018-04-11 CRAN (R 3.4.4)                         
 cluster         2.0.7-1    2018-04-09 CRAN (R 3.4.4)                         
 coda            0.19-1     2016-12-08 cran (@0.19-1)                         
 colorspace      1.3-2      2016-12-14 CRAN (R 3.4.0)                         
 combinat        0.0-8      2012-10-29 CRAN (R 3.1.0)                         
 commonmark      1.4        2017-09-01 cran (@1.4)                            
 compiler        3.4.4      2018-03-15 local                                  
 crayon          1.3.4      2017-09-16 CRAN (R 3.4.1)                         
 curl            3.2        2018-03-28 CRAN (R 3.4.4)                         
 DALEX         * 0.2.2      2018-05-22 CRAN (R 3.4.4)                         
 datasets      * 3.4.4      2018-03-15 local                                  
 debugme         1.1.0      2017-10-22 CRAN (R 3.4.2)                         
 deldir          0.1-15     2018-04-01 CRAN (R 3.4.4)                         
 desc            1.1.1      2017-08-03 cran (@1.1.1)                          
 devtools        1.13.5     2018-02-18 CRAN (R 3.4.3)                         
 digest          0.6.15     2018-01-28 cran (@0.6.15)                         
 dplyr           0.7.4      2017-09-28 CRAN (R 3.4.2)                         
 evaluate        0.10.1     2017-06-24 CRAN (R 3.4.1)                         
 expm            0.999-2    2017-03-29 cran (@0.999-2)                        
 factorMerger    0.3.6      2018-04-04 CRAN (R 3.4.4)                         
 gdata           2.18.0     2017-06-06 CRAN (R 3.4.0)                         
 gdtools       * 0.1.7      2018-02-27 CRAN (R 3.4.3)                         
 ggiraph       * 0.4.2      2017-12-19 CRAN (R 3.4.3)                         
 ggplot2       * 2.2.1.9000 2018-05-31 Github (thomasp85/ggplot2@dfa0bc3)     
 ggpubr          0.1.6      2017-11-14 cran (@0.1.6)                          
 git2r           0.21.0     2018-01-04 CRAN (R 3.4.3)                         
 glue            1.2.0      2017-10-29 cran (@1.2.0)                          
 gmodels         2.16.2     2015-07-22 CRAN (R 3.4.0)                         
 graphics      * 3.4.4      2018-03-15 local                                  
 grDevices     * 3.4.4      2018-03-15 local                                  
 grid            3.4.4      2018-03-15 local                                  
 gridExtra       2.3        2017-09-09 CRAN (R 3.4.1)                         
 gtable          0.2.0      2016-02-26 CRAN (R 3.2.3)                         
 gtools          3.5.0      2015-05-29 CRAN (R 3.2.0)                         
 highlight       0.4.7.2    2017-10-04 cran (@0.4.7.2)                        
 highr           0.6        2016-05-09 CRAN (R 3.4.0)                         
 hms             0.4.2      2018-03-10 CRAN (R 3.4.4)                         
 htmltools       0.3.6      2017-04-28 CRAN (R 3.4.0)                         
 htmlwidgets     1.0        2018-01-20 cran (@1.0)                            
 httpuv          1.3.6.2    2018-03-02 CRAN (R 3.4.3)                         
 httr            1.3.1      2017-08-20 CRAN (R 3.4.1)                         
 jsonlite        1.5        2017-06-01 CRAN (R 3.4.0)                         
 kableExtra      0.9.0      2018-05-21 CRAN (R 3.4.4)                         
 klaR            0.6-14     2018-03-19 CRAN (R 3.4.4)                         
 knitr           1.20       2018-02-20 cran (@1.20)                           
 labeling        0.3        2014-08-23 CRAN (R 3.2.2)                         
 lattice         0.20-35    2017-03-25 CRAN (R 3.4.4)                         
 lazyeval        0.2.1      2017-10-29 CRAN (R 3.4.2)                         
 LearnBayes      2.15.1     2018-03-18 CRAN (R 3.4.4)                         
 magrittr        1.5        2014-11-22 CRAN (R 3.2.2)                         
 MASS            7.3-49     2018-02-23 CRAN (R 3.4.4)                         
 Matrix          1.2-14     2018-04-09 CRAN (R 3.4.4)                         
 memoise         1.1.0      2017-04-21 CRAN (R 3.4.0)                         
 methods       * 3.4.4      2018-03-15 local                                  
 MI2template     0.1.0.0000 2017-12-02 Github (mi2-warsaw/MI2template@a2e7f45)
 mime            0.5        2016-07-07 CRAN (R 3.4.0)                         
 miniUI          0.1.1      2016-01-15 CRAN (R 3.2.3)                         
 modelDown       0.0.0.9000 2018-05-31 Github (kromash/modelDown@8ff72a4)     
 munsell         0.4.3      2016-02-13 CRAN (R 3.2.3)                         
 mvtnorm         1.0-7      2018-01-25 cran (@1.0-7)                          
 nlme            3.1-137    2018-04-07 CRAN (R 3.4.4)                         
 officer         0.2.2      2018-03-14 CRAN (R 3.4.4)                         
 pdp             0.6.0      2017-07-20 CRAN (R 3.4.1)                         
 pillar          1.2.1      2018-02-27 CRAN (R 3.4.3)                         
 pkgbuild        0.0.0.9000 2017-10-25 Github (r-lib/pkgbuild@a70858f)        
 pkgconfig       2.0.1      2017-03-21 CRAN (R 3.4.0)                         
 pkgdown         0.1.0.9000 2017-11-21 Github (hadley/pkgdown@33673a9)        
 pkgload         0.0.0.9000 2017-11-21 Github (r-lib/pkgload@70eaef8)         
 plyr            1.8.4      2016-06-08 CRAN (R 3.4.0)                         
 proxy           0.4-22     2018-04-08 CRAN (R 3.4.4)                         
 purrr           0.2.4      2017-10-18 cran (@0.2.4)                          
 questionr       0.6.2      2017-11-01 CRAN (R 3.4.2)                         
 R.methodsS3     1.7.1      2016-02-16 CRAN (R 3.2.3)                         
 R.oo            1.21.0     2016-11-01 CRAN (R 3.4.0)                         
 R.utils         2.6.0      2017-11-05 CRAN (R 3.4.2)                         
 R6              2.2.2      2017-06-17 CRAN (R 3.4.0)                         
 randomForest  * 4.6-14     2018-03-25 CRAN (R 3.4.4)                         
 ranger        * 0.9.0      2018-01-09 cran (@0.9.0)                          
 Rcpp            0.12.16    2018-03-13 cran (@0.12.16)                        
 readr           1.1.1      2017-05-16 CRAN (R 3.4.0)                         
 reshape2        1.4.3      2017-12-11 cran (@1.4.3)                          
 rlang           0.2.0.9001 2018-05-31 Github (r-lib/rlang@4e7e8f7)           
 rmarkdown       1.9        2018-03-01 CRAN (R 3.4.3)                         
 roxygen2        6.0.1.9000 2017-10-25 Github (klutometis/roxygen@bbf259d)    
 rprojroot       1.3-2      2018-01-03 CRAN (R 3.4.3)                         
 rstudioapi      0.7        2017-09-07 CRAN (R 3.4.1)                         
 rvest           0.3.2      2016-06-17 CRAN (R 3.4.0)                         
 rvg             0.1.8      2018-02-13 CRAN (R 3.4.3)                         
 scales          0.5.0.9000 2018-05-31 Github (hadley/scales@d767915)         
 shiny           1.0.5      2017-08-23 CRAN (R 3.4.1)                         
 sp              1.2-7      2018-01-19 cran (@1.2-7)                          
 spData          0.2.8.3    2018-03-25 CRAN (R 3.4.4)                         
 spdep           0.7-7      2018-04-03 CRAN (R 3.4.4)                         
 splines         3.4.4      2018-03-15 local                                  
 stats         * 3.4.4      2018-03-15 local                                  
 stringi         1.1.7      2018-03-12 cran (@1.1.7)                          
 stringr         1.3.0      2018-02-19 cran (@1.3.0)                          
 survival        2.41-3     2017-04-04 CRAN (R 3.4.4)                         
 testthat        2.0.0      2017-12-13 CRAN (R 3.4.3)                         
 tibble          1.4.2      2018-01-22 cran (@1.4.2)                          
 tools           3.4.4      2018-03-15 local                                  
 usethis         1.3.0      2018-02-24 CRAN (R 3.4.3)                         
 utils         * 3.4.4      2018-03-15 local                                  
 uuid            0.1-2      2015-07-28 CRAN (R 3.4.0)                         
 viridisLite     0.3.0      2018-02-01 cran (@0.3.0)                          
 WhatIfPlots   * 0.1        <NA>       local                                  
 whisker         0.3-2      2013-04-28 CRAN (R 3.1.0)                         
 withr           2.1.2      2018-05-31 Github (jimhester/withr@70d6321)       
 xml2            1.2.0      2018-01-24 cran (@1.2.0)                          
 xtable          1.8-2      2016-02-05 CRAN (R 3.2.3)                         
 yaImpute        1.0-29     2017-12-10 CRAN (R 3.4.3)                         
 yaml            2.1.18     2018-03-08 cran (@2.1.18)                         
 zip             1.0.0      2017-04-25 CRAN (R 3.4.0) 

"Generating variable_response..." fails with Error in Error in seq.default(from = min(y, na.rm = TRUE), to = max(y, na.rm = TRUE), : 'from' must be a finite number

Hello,
Running the modelDown_example script raise the following error:

[1] "Generating variable_response..."
Variable district  is of the class factor. Type of explainer changed to 'factor'.
Variable district  is of the class factor. Type of explain
er changed to 'factor'.
Variable district  is of the class factor. Type of explainer changed to 'factor'.
Error in pairs[[whichMax]] : 
  attempt to select less than one element in get1index
Called from: mergePairLRT(fm, successive, fmList$factor, fmList$model)

The issue is maybe related to DALEX::variable_response() but is easily reproducible here.

Best regards,
Christophe
Stack-trace

Error in pairs[[whichMax]] : 
  attempt to select less than one element in get1index 
21.
mergePairLRT(fm, successive, fmList$factor, fmList$model) 
20.
mergeLRT(fm, successive) 
19.
mergeFactors.default(preds_combined$scores, preds_combined$level, 
    abbreviate = FALSE) 
18.
mergeFactors(preds_combined$scores, preds_combined$level, abbreviate = FALSE) 
17.
variable_response(explainer, variable_name, type = type) at generator.R#17
16.
FUN(X[[i]], ...) 
15.
lapply(explainers, function(explainer) {
    variable_response(explainer, variable_name, type = type)
}) at generator.R#17
14.
FUN(X[[i]], ...) 
13.
lapply(types, function(type) {
    lapply(explainers, function(explainer) {
        variable_response(explainer, variable_name, type = type)
    }) ... at generator.R#16
12.
make_variable_plot(variable_name, types, explainers, img_folder, 
    options) at generator.R#35
11.
FUN(X[[i]], ...) 
10.
lapply(variables, make_variable_plot_model, explainers, img_folder, 
    options) at generator.R#51
9.
generator_env$generator(explainers, options, file.path(output_folder, 
    "img")) 
8.
FUN(X[[i]], ...) 
7.
lapply(modules_names, function(module_name) {
    print(paste("Generating ", module_name, "...", sep = ""))
    generator_path <- system.file("extdata", "modules", module_name, 
        "generator.R", package = "modelDown") ... 
6.
generateModules(modules, output_folder, explainers, options) 
5.
modelDown(explainer_lm, explainer_rf, explainer_gbm, explainer_svm, 
    modules = c("model_performance", "variable_importance", "variable_response", 
        "prediction_breakdown"), output_folder = "modelDown_example", 
    pb.observations = c(161, 731, 2741, 4454), vr.type = "ale",  ... at modelDown_example.R#39
4.
eval(ei, envir) 
3.
eval(ei, envir) 
2.
withVisible(eval(ei, envir)) 
1.
source("~/R/modelDown_example.R", echo = TRUE) 

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.1 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C               LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8     LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
 [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] bindrcpp_0.2.2      kableExtra_0.9.0    ggplot2_3.1.0       modelDown_0.1.1     e1071_1.7-0         gbm_2.1.4           DALEX_0.2.4         breakDown_0.1.6    
[9] randomForest_4.6-14

loaded via a namespace (and not attached):
 [1] httr_1.3.1         viridisLite_0.3.0  splines_3.5.1      gtools_3.8.1       shiny_1.2.0        assertthat_0.2.0   expm_0.999-3       sp_1.3-1          
 [9] highr_0.7          pdp_0.7.0          LearnBayes_2.15.1  pillar_1.3.0       lattice_0.20-38    glue_1.3.0         digest_0.6.18      RColorBrewer_1.1-2
[17] promises_1.0.1     rvest_0.3.2        colorspace_1.3-2   htmltools_0.3.6    httpuv_1.4.5       Matrix_1.2-15      plyr_1.8.4         klaR_0.6-14       
[25] pkgconfig_2.0.2    questionr_0.7.0    gmodels_2.18.1     purrr_0.2.5        xtable_1.8-3       mvtnorm_1.0-8      scales_1.0.0       gdata_2.18.0      
[33] whisker_0.3-2      later_0.7.5        tibble_1.4.2       proxy_0.4-22       combinat_0.0-8     ggpubr_0.2         withr_2.1.2        ALEPlot_1.1       
[41] agricolae_1.2-8    lazyeval_0.2.1     survival_2.43-3    magrittr_1.5       crayon_1.3.4       mime_0.6           deldir_0.1-15      evaluate_0.12     
[49] nlme_3.1-137       MASS_7.3-51.1      xml2_1.2.0         class_7.3-14       tools_3.5.1        hms_0.4.2          stringr_1.3.1      munsell_0.5.0     
[57] cluster_2.0.7-1    compiler_3.5.1     rlang_0.3.0.1      grid_3.5.1         rstudioapi_0.8     miniUI_0.1.1.1     labeling_0.3       rmarkdown_1.11    
[65] boot_1.3-20        gtable_0.2.0       reshape2_1.4.3     AlgDesign_1.1-7.3  R6_2.3.0           yaImpute_1.0-30    gridExtra_2.3      knitr_1.20        
[73] dplyr_0.7.8        bindr_0.1.1        factorMerger_0.3.6 spdep_0.8-1        readr_1.2.1        stringi_1.2.4      Rcpp_1.0.0         spData_0.2.9.6    
[81] tidyselect_0.2.5   coda_0.19-2 ```

Save plots as svg images

Possibly with parameter to enable plots generation as png images (what's the default value?)

Check efficiency - initial implementation slow on Chrome

Archivist link placement

Archivist link is presented differently across modules:
In auditor - before the plot, with label: 'Load:'
In Model Performance and other - under the plot, with label: 'Get this object:'

@pbiecek which one is better?

Add plot description

Each plot (or plot type?) should also contain links to provide information on how to read the plot, what it presents, how it was generated:

  • one link with business description
  • one link with technical description (from DALEX package)

Add auditor features

Add results from auditor package to first tab

Model Ranking Plot as first plot.

Autocorrelation, LIFT chart, model correlation, ROC, RROC.

plotScaleLocation?

Update DESCRIPTION file

Edit package description

Edit versions:
Version: 0.1.1 -> 1.0.0
DALEX (>= 0.2.2) -> DALEX (>= 0.2.8)

Custom parameters for explainer functions

It would be great to have an option to pass arguments to explainers.

For example, I would like to generate modelDown with my own loss function for a variable_importance() function.
An analogy for doing something like this, but with modelDown:

library("breakDown")
library("randomForest")
HR_rf_model <- randomForest(factor(status == "fired")~., data = HR, ntree = 100)
explainer_rf  <- explain(HR_rf_model, data = HR, y = HR$status == "fired")

custom_loss_auc <- function(y, yhat) {
  1 - mltools::auc_roc(yhat, y)
}

vd_rf <- variable_importance(explainer_rf, loss = custom_loss_auc)
vd_rf

How about adding parameter params that takes a list with names corresponding to explainer's parameters? For the example above it would be something like this:

modelDown(explainer_rf, params = list("variable_importance" = list(loss = custom_loss))

devtools::check() problems

A.
Consider adding importFrom("utils", "capture.output", "tail") - roxygen
B.
Undefined global functions or variables: DEFAULT_DEVICE DEFAULT_FONT_SIZE
(from config.R file - should it be removed and variables moved to R script?)

Storing results

Are the results of computations stored in any way? For example, PDP takes some time to compute and it would be nice to have access to the results

Archivist link generation

output_folder should be ignored, for example:
for parameters
remote_repository_path = "MI2DataLab/modelDown_example/docs",
output_folder = "../modelDown_Titanic_example"

correct link: archivist::aread("MI2DataLab/modelDown_example/docs/repository/e617d10e1606257c56ba7d192c5b8fe0")

current link:
archivist::aread("MI2DataLab/modelDown_example/docs/../modelDown_Titanic_example/repository/e617d10e1606257c56ba7d192c5b8fe0")

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