Closed icejean closed 1 year ago
csh01470
commented
1 year ago Hi. thank you for your interest in the catsnip package.
Currently, I am checking the catboost.R in the forked treesnip package.
It may take some time to update code because of test
And can you show me the code where the error message occurred while using the catsnip package?
with output of sessionInfo()
icejean
commented
1 year ago Well, thanks for your reply. :)
library(tidymodels)
library(kableExtra)
library(tidyr)
# This version of treesnip is O.K. for classification, but conflicts with bonsai.
# remotes::install_github("Glemhel/treesnip", INSTALL_opts = c("--no-multiarch"))
# library(treesnip)
# devtools::install_github(repo="csh01470/catsnip", INSTALL_opts = c("--no-multiarch"))
library(catsnip)
tidymodels_prefer()
show_model_info("boost_tree")
countries = c('RUS','USA','SUI')
years = c(1900,1896,1896)
phone_codes = c(7,1,41)
domains = c('ru','us','ch')
label_values = c(0,1,1)
dataset = data.frame(label_values, countries, years, phone_codes, domains)
dataset$countries<-as.factor(dataset$countries)
dataset$domains<-as.factor(dataset$domains)
dataset$label_values<-as.factor(dataset$label_values)
cat_rec <- recipe(label_values ~ countries+years+phone_codes+domains, data = dataset)
cat_model <-
boost_tree(trees = 100, tree_depth= 5, learn_rate= 0.03) %>%
set_engine('catboost', loss_function = 'Logloss', ignored_features = c(4,9),
border_count = 32, l2_leaf_reg = 3.5) %>%
set_mode('classification')
translate(cat_model)
cat_wflow <-
workflow() %>%
add_model(cat_model) %>%
add_recipe(cat_rec)
cat_fit <- fit(cat_wflow, dataset)
res <- predict(cat_fit, new_data = dataset %>% select(-label_values))> cat_fit <- fit(cat_wflow, dataset)
Parameter 'cat_features' is meaningless because column types are taken from data.frame.
Please, convert categorical columns to factors manually.
Error in catboost.from_matrix(as.matrix(float_and_cat_features_data), :
Unsupported label type, expecting double or int, got: character
> sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936 LC_CTYPE=Chinese (Simplified)_China.936
[3] LC_MONETARY=Chinese (Simplified)_China.936 LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_China.936
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] catsnip_0.0.3 kableExtra_1.3.4 yardstick_1.1.0 workflowsets_1.0.0 workflows_1.1.2
[6] tune_1.0.1.9001 tidyr_1.2.1 tibble_3.1.8 rsample_1.1.1 recipes_1.0.3.9000
[11] purrr_1.0.0 parsnip_1.0.3 modeldata_1.0.1 infer_1.0.4 ggplot2_3.4.0
[16] dplyr_1.0.10 dials_1.1.0 scales_1.2.1 broom_1.0.1 tidymodels_1.0.0
loaded via a namespace (and not attached):
[1] colorspace_2.0-2 ellipsis_0.3.2 class_7.3-19 rprojroot_2.0.3 fs_1.5.0
[6] rstudioapi_0.14 listenv_0.9.0 furrr_0.3.1 remotes_2.4.0 prodlim_2019.11.13
[11] fansi_1.0.3 lubridate_1.9.0 xml2_1.3.3 codetools_0.2-18 splines_4.1.0
[16] cachem_1.0.5 knitr_1.41 pkgload_1.2.1 jsonlite_1.8.4 compiler_4.1.0
[21] httr_1.4.4 backports_1.4.1 assertthat_0.2.1 Matrix_1.5-3 fastmap_1.1.0
[26] cli_3.5.0 htmltools_0.5.2 prettyunits_1.1.1 tools_4.1.0 gtable_0.3.1
[31] glue_1.6.2 Rcpp_1.0.9 DiceDesign_1.9 vctrs_0.5.1 svglite_2.1.0
[36] iterators_1.0.14 conflicted_1.1.0 timeDate_4021.107 gower_1.0.1 xfun_0.35
[41] stringr_1.5.0 globals_0.16.2 ps_1.6.0 testthat_3.0.3 rvest_1.0.3
[46] timechange_0.1.1 lifecycle_1.0.3 devtools_2.4.2 future_1.30.0 MASS_7.3-54
[51] ipred_0.9-13 parallel_4.1.0 yaml_2.3.5 memoise_2.0.1 rpart_4.1.19
[56] stringi_1.6.1 desc_1.3.0 foreach_1.5.2 lhs_1.1.5 hardhat_1.2.0
[61] pkgbuild_1.2.0 lava_1.7.0 rlang_1.0.6 pkgconfig_2.0.3 systemfonts_1.0.4
[66] evaluate_0.18 lattice_0.20-44 tidyselect_1.2.0 processx_3.8.0 parallelly_1.33.0
[71] magrittr_2.0.3 bookdown_0.30 R6_2.5.1 generics_0.1.3 DBI_1.1.3
[76] pillar_1.8.1 withr_2.5.0 survival_3.4-0 nnet_7.3-18 future.apply_1.10.0
[81] crayon_1.5.2 catboost_1.1.1 utf8_1.2.2 rmarkdown_2.18 usethis_2.1.6.9000
[86] grid_4.1.0 callr_3.7.3 digest_0.6.31 webshot_0.5.4 GPfit_1.0-8
[91] munsell_0.5.0 viridisLite_0.4.1 sessioninfo_1.2.2
Hi. @icejean
I fixed the this error.
Would you like to reinstall catsnip package and try again?
icejean
commented
1 year ago Hi, I just reinstall catsnip with the following line:
devtools::install_github(repo="csh01470/catsnip", INSTALL_opts="--no-multiarch")Here's the result, it runs with no error but the result is incorrect:
> cat_fit <- fit(cat_wflow, dataset)
> res <- predict(cat_fit, new_data = dataset %>% select(-label_values))
> res
# A tibble: 3 x 1
.pred_class
<fct>
1 0
2 0
3 0 Best regards.
csh01470
commented
1 year ago Hi.
I think it's a problem caused by modifying the rsm parameter.
Do you try to reinstall?
icejean
commented
1 year ago Yes, I reinstall it with the following line:
devtools::install_github(repo="csh01470/catsnip", INSTALL_opts="--no-multiarch")Maybe I should try it again?
icejean
commented
1 year ago O.K. on Linux this time, I'll try it again on Windows then, good job!
> cat_fit <- fit(cat_wflow, dataset)
> res <- predict(cat_fit, new_data = dataset %>% select(-label_values))
> res
# A tibble: 3 × 1
.pred_class
<fct>
1 0
2 1
3 1
> Great, O.K. on windows too!
Error message: Error in catboost.from_matrix(as.matrix(float_and_cat_features_data), : Unsupported label type, expecting double or integer, got character Please read the issue and solution in the following link: https://github.com/catboost/catboost/issues/1874
Mikhail Rudakov has a workaround mentioned in the above link.
But his version would conflict with bonsai, when need to use catboost together with lightgbm, catsnip is the better one, so would you please fix it with catsnip?
Best Regards Jean