butcher
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Published
20 hours ago •
tidymodels
tidymodels
consider warning if specific `axe_*` methods are not available
Please consider adding a warning to axe_*.default() methods that the package, which created the object (and might contain more specific axe_* methods) is not loaded.
I ran into this while trying to butcher() a workflow after loading it back in. (I am not butcher()ing before saving, due to #147)
The xgb_mod.rds is the workflow used in https://github.com/tidymodels/workflows/issues/138
library(butcher)
xgb_mod <- readRDS("xgb_mod.rds")
## No message or warning without `verbose = TRUE` even though nothing is happening
xgb_btchrd <- butcher(xgb_mod)
all.equal(xgb_btchrd, xgb_mod)
#> [1] TRUE
## Cannot butcher if method is not available (but `verbose` message does not say that)
xgb_btchrd <- butcher(xgb_mod, TRUE)
#> x No memory released. Do not butcher.
library(workflows)
## Now we're talking
xgb_btchrd <- butcher(xgb_mod, TRUE)
#> Warning in as.function.default(c(formals(x), body(x)), env = rlang::base_env()):
#> partial argument match of 'env' to 'envir'
#> v Memory released: '943,080 B'
Created on 2022-02-25 by the reprex package (v2.0.1)
Session info
sessioninfo::session_info()
#> - Session info ---------------------------------------------------------------
#> setting value
#> version R version 4.1.2 (2021-11-01)
#> os Windows 10 x64 (build 19043)
#> system x86_64, mingw32
#> ui RTerm
#> language en
#> collate German_Germany.1252
#> ctype German_Germany.1252
#> tz Europe/Berlin
#> date 2022-02-25
#> pandoc 2.17.1.1 @ C:/Program Files/RStudio/bin/quarto/bin/ (via rmarkdown)
#>
#> - Packages -------------------------------------------------------------------
#> package * version date (UTC) lib source
#> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.1.0)
#> backports 1.4.1 2021-12-13 [1] CRAN (R 4.1.2)
#> butcher * 0.1.5.9000 2022-02-25 [1] Github (tidymodels/butcher@d7ed75f)
#> class 7.3-19 2021-05-03 [2] CRAN (R 4.1.2)
#> cli 3.2.0 2022-02-14 [1] CRAN (R 4.1.2)
#> codetools 0.2-18 2020-11-04 [2] CRAN (R 4.1.2)
#> colorspace 2.0-3 2022-02-21 [1] CRAN (R 4.1.2)
#> crayon 1.5.0 2022-02-14 [1] CRAN (R 4.1.2)
#> data.table 1.14.2 2021-09-27 [1] CRAN (R 4.1.1)
#> DBI 1.1.2 2021-12-20 [1] CRAN (R 4.1.2)
#> digest 0.6.29 2021-12-01 [1] CRAN (R 4.1.2)
#> dplyr 1.0.8 2022-02-08 [1] CRAN (R 4.1.2)
#> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.1.0)
#> evaluate 0.15 2022-02-18 [1] CRAN (R 4.1.2)
#> fansi 1.0.2 2022-01-14 [1] CRAN (R 4.1.2)
#> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.1.0)
#> fs 1.5.2.9000 2022-02-03 [1] Github (r-lib/fs@6d1182f)
#> future 1.24.0 2022-02-19 [1] CRAN (R 4.1.2)
#> future.apply 1.8.1 2021-08-10 [1] CRAN (R 4.1.1)
#> generics 0.1.2 2022-01-31 [1] CRAN (R 4.1.2)
#> ggplot2 3.3.5 2021-06-25 [1] CRAN (R 4.1.0)
#> globals 0.14.0 2020-11-22 [1] CRAN (R 4.1.0)
#> glue 1.6.2 2022-02-24 [1] CRAN (R 4.1.2)
#> gower 1.0.0 2022-02-03 [1] CRAN (R 4.1.2)
#> gtable 0.3.0 2019-03-25 [1] CRAN (R 4.1.0)
#> hardhat 0.2.0 2022-01-24 [1] CRAN (R 4.1.2)
#> highr 0.9 2021-04-16 [1] CRAN (R 4.1.0)
#> htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.1.1)
#> ipred 0.9-12 2021-09-15 [1] CRAN (R 4.1.1)
#> jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.1.2)
#> knitr 1.37 2021-12-16 [1] CRAN (R 4.1.2)
#> lattice 0.20-45 2021-09-22 [2] CRAN (R 4.1.2)
#> lava 1.6.10 2021-09-02 [1] CRAN (R 4.1.1)
#> lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.1.1)
#> listenv 0.8.0 2019-12-05 [1] CRAN (R 4.1.0)
#> lobstr 1.1.1 2019-07-02 [1] CRAN (R 4.1.1)
#> lubridate 1.8.0 2021-10-07 [1] CRAN (R 4.1.1)
#> magrittr 2.0.2 2022-01-26 [1] CRAN (R 4.1.2)
#> MASS 7.3-54 2021-05-03 [2] CRAN (R 4.1.2)
#> Matrix 1.3-4 2021-06-01 [2] CRAN (R 4.1.2)
#> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.1.0)
#> nnet 7.3-16 2021-05-03 [2] CRAN (R 4.1.2)
#> parallelly 1.30.0 2021-12-17 [1] CRAN (R 4.1.2)
#> parsnip 0.1.7.9006 2022-02-25 [1] Github (tidymodels/parsnip@3e2447c)
#> pillar 1.7.0 2022-02-01 [1] CRAN (R 4.1.2)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.1.0)
#> prodlim 2019.11.13 2019-11-17 [1] CRAN (R 4.1.1)
#> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.1.0)
#> R.cache 0.15.0 2021-04-30 [1] CRAN (R 4.1.0)
#> R.methodsS3 1.8.1 2020-08-26 [1] CRAN (R 4.1.0)
#> R.oo 1.24.0 2020-08-26 [1] CRAN (R 4.1.0)
#> R.utils 2.11.0 2021-09-26 [1] CRAN (R 4.1.1)
#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.1.1)
#> Rcpp 1.0.8 2022-01-13 [1] CRAN (R 4.1.2)
#> recipes 0.2.0 2022-02-18 [1] CRAN (R 4.1.2)
#> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.1.0)
#> rlang 1.0.1 2022-02-03 [1] CRAN (R 4.1.2)
#> rmarkdown 2.11 2021-09-14 [1] CRAN (R 4.1.1)
#> rpart 4.1-15 2019-04-12 [2] CRAN (R 4.1.2)
#> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.1.0)
#> scales 1.1.1 2020-05-11 [1] CRAN (R 4.1.0)
#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.1.2)
#> stringi 1.7.6 2021-11-29 [1] CRAN (R 4.1.2)
#> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.1.0)
#> styler 1.6.2 2021-09-23 [1] CRAN (R 4.1.1)
#> survival 3.2-13 2021-08-24 [2] CRAN (R 4.1.2)
#> tibble 3.1.6 2021-11-07 [1] CRAN (R 4.1.2)
#> tidyr 1.2.0 2022-02-01 [1] CRAN (R 4.1.2)
#> tidyselect 1.1.2 2022-02-21 [1] CRAN (R 4.1.2)
#> timeDate 3043.102 2018-02-21 [1] CRAN (R 4.1.1)
#> usethis 2.1.5 2021-12-09 [1] CRAN (R 4.1.2)
#> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.1.0)
#> vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.1.0)
#> withr 2.4.3 2021-11-30 [1] CRAN (R 4.1.2)
#> workflows * 0.2.4.9002 2022-02-25 [1] Github (tidymodels/workflows@4e348f8)
#> xfun 0.29 2021-12-14 [1] CRAN (R 4.1.2)
#> xgboost 1.5.2.1 2022-02-21 [1] CRAN (R 4.1.2)
#> yaml 2.3.5 2022-02-21 [1] CRAN (R 4.1.2)
#>
#> [1] C:/Users/Daniel.AK-HAMBURG/Documents/R/win-library/4.1
#> [2] C:/Program Files/R/R-4.1.2/library
#>
#> ------------------------------------------------------------------------------
There are similar reports here as well. https://github.com/tidymodels/parsnip/issues/459
I also faced a similar error. It was when I used butcher to reduce the weight of the xgboost model. Shows the reproducible code and the version of the package.
df <- mtcars
df$am <- as.factor(df$am)
fitted_model <- boost_tree(trees = 15) %>%
set_engine("xgboost") %>%
set_mode("classification")
rec <- recipe(am ~ .,data = df) %>%
step_dummy(all_nominal_predictors())
wfl <- workflow() %>%
add_model(fitted_model) %>%
add_recipe(rec) %>%
fit(df)
temp<- wfl %>% butcher()
predict(temp,new_data=df)
> sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)
Matrix products: default
attached base packages:
[1] stats graphics grDevices utils datasets
[6] methods base
other attached packages:
[1] rlang_1.0.2 lobstr_1.1.1 sparklyr_1.7.5
[4] rpart_4.1.16 butcher_0.1.5 yardstick_0.0.9
[7] workflowsets_0.2.1 workflows_0.2.6 tune_0.2.0
[10] tidyr_1.2.0 tibble_3.1.6 rsample_0.1.1
[13] recipes_0.2.0 purrr_0.3.4 parsnip_0.2.1
[16] modeldata_0.1.1 infer_1.0.0 ggplot2_3.3.5
[19] dplyr_1.0.8 dials_0.1.0 scales_1.1.1
[22] broom_0.7.12 tidymodels_0.2.0
loaded via a namespace (and not attached):
[1] httr_1.4.2 jsonlite_1.8.0 splines_4.1.2
[4] foreach_1.5.2 prodlim_2019.11.13 assertthat_0.2.1
[7] GPfit_1.0-8 yaml_2.3.5 r2d3_0.2.6
[10] globals_0.14.0 ipred_0.9-12 pillar_1.7.0
[13] backports_1.4.1 lattice_0.20-45 glue_1.6.2
[16] pROC_1.18.0 digest_0.6.29 hardhat_0.2.0
[19] colorspace_2.0-3 htmltools_0.5.2 Matrix_1.4-1
[22] plyr_1.8.6 timeDate_3043.102 pkgconfig_2.0.3
[25] lhs_1.1.5 DiceDesign_1.9 listenv_0.8.0
[28] config_0.3.1 gower_1.0.0 lava_1.6.10
[31] generics_0.1.2 usethis_2.1.5 xgboost_1.6.0.1
[34] ellipsis_0.3.2 withr_2.5.0 furrr_0.2.3
[37] nnet_7.3-17 cli_3.2.0 survival_3.3-1
[40] magrittr_2.0.2 crayon_1.5.0 fs_1.5.2
[43] future_1.24.0 fansi_1.0.2 parallelly_1.30.0
[46] MASS_7.3-56 class_7.3-20 tools_4.1.2
[49] data.table_1.14.2 lifecycle_1.0.1 munsell_0.5.0
[52] compiler_4.1.2 signal_0.7-7 forge_0.2.0
[55] grid_4.1.2 iterators_1.0.14 rstudioapi_0.13
[58] rappdirs_0.3.3 htmlwidgets_1.5.4 base64enc_0.1-3
[61] gtable_0.3.0 codetools_0.2-18 DBI_1.1.2
[64] R6_2.5.1 lubridate_1.8.0 fastmap_1.1.0
[67] future.apply_1.8.1 utf8_1.2.2 rprojroot_2.0.3
[70] parallel_4.1.2 Rcpp_1.0.8.3 vctrs_0.3.8
[73] dbplyr_2.1.1 tidyselect_1.1.2
@amazongodman from looking at your example, I am not entirely sure whether it applies to this issue here (or the one reported over at parsnip for that matter), because you must have loaded {workflows}, so workflow's axe_*() methods must be available?
Anyway, my initial example is not a very good one, because one shouldn't use saveRDS()/readRDS() directly on {xgboost} models. That doesn't change the issue I reported here, though.