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Home Page: https://ModelOriented.github.io/modelDown/
modelDown generates a website with HTML summaries for predictive models
Home Page: https://ModelOriented.github.io/modelDown/
In the index.html page on the right side, there is an Explainers section, but it's empty
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..."
BÅÄ
d w poleceniu 'factor_columns[[i]]':indeks jest poza granicami
To maintain the code please add travis support
You can find some hints here: https://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2017_12_19_travis
For R packages you can automatically add pkgdown website with pkgdown package
Find some examples here: https://github.com/mi2-warsaw/MI2DataLab_Seminarium/tree/master/2017_11_28_pkgdown
Let's move the MI2DataLab/modelDown package to ModelOriented/modelDown. All DrWhy projects will be in one organization.
Suggested date for the movement: August 21st
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.
Currently it links to https://gitlab.com/Romaszko/modeldown
Use archivist to save session state
In case of problem with archivist intergration, add link to R's sessionInfo (as text file)
in pkgdown and DESCRIPTION
In your package folder you shall be able to run devtools::check() without any error message
now, there is a problem with (for example) #2
Add examples to modelDown
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?
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")
New plots in https://mi2datalab.github.io/modelDown_example/index.html
Add info about grant
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
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
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?
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)
Write description of how to use package with new features as blog post.
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 ```
Possibly with parameter to enable plots generation as png images (what's the default value?)
Check efficiency - initial implementation slow on Chrome
sales and salary are factor variables, instead of averages it would be better to show counts
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?
Display results of check_drift function (https://modeloriented.github.io/drifter/reference/check_drift.html)
Show them in new tab that replaces 'Prediction breakdown' tab. In case user doesn't provide additional model, tab should be hidden.
Enable passing additional model (models?) (as DALEX explainers or just models with data and y?) to modelDown function so that check_drift function can be used
The link to the getting started guide 404's. Is there another location for this or has this repository been sunsetted?
ModelOriented instead of pbiecek
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:
We need to update modelDown donttest examples.
Full logs from command check --as-cran --run-donttest
are here: https://www.stats.ox.ac.uk/pub/bdr/donttest/modelDown.out
Add results from auditor package to first tab
Model Ranking Plot as first plot.
Autocorrelation, LIFT chart, model correlation, ROC, RROC.
plotScaleLocation?
Edit package description
Edit versions:
Version: 0.1.1 -> 1.0.0
DALEX (>= 0.2.2) -> DALEX (>= 0.2.8)
Please add the table produced by 'print()' function for variable importance
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))
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?)
Increase relative font size
there is only one function, but it should be described along with all its parameters
Screenshots for each view with short (1 sentence) comments
Please not that the footer is still grey.
(it's in the class="footer")
New theme for DALEX plots is on GitHub here: https://github.com/pbiecek/DALEX/
So you can also use color codes from
https://github.com/pbiecek/DALEX/blob/master/R/theme_drwhy.R
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
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")
Generate modules with try(, silent=TRUE)
if one module throws exception, other modules might still be generated and website will be generated
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Bring data to life with SVG, Canvas and HTML. ððð
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
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