I recieve the follwing error
Error in 0:s_max : result would be too long a vector
when I try to tune a hyperpartemr in surv.cv_glmnet or classif.cv_glmnet using similar code as in Hyperband algorithm from section 5.3 Multi-Fidelity Tuning via Hyperband
library(mlr3)
library(mlr3proba)
library(mlr3hyperband)
library(mlr3extralearners)
library(mlr3learners)
instance = ti(
# task = tsk("rats"),
# learner = lrn("surv.cv_glmnet",
# id = "class_semipar_cvglmnet",
# alpha = to_tune(p_dbl(0, 1, tags = "budget"))),
task = tsk("sonar"),
learner = lrn("classif.cv_glmnet",
id = "class_semipar_cvglmnet",
alpha = to_tune(p_dbl(0, 1, tags = "budget"))),
resampling = rsmp("holdout"),
#measure = msr("surv.cindex"),
measures = msr("classif.ce"),
terminator = trm("none")
)
tuner = tnr("hyperband", eta = 2, repetitions = 1) #same error with eta=5,10
#hyperband_schedule(r_min = 0.01, r_max = 1, eta = 2)
tuner$optimize(instance)
> sessionInfo()
R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: Asia/Dubai
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] mlr3proba_0.5.2 mlr3learners_0.7.0 mlr3extralearners_0.7.0-9000
[4] mlr3hyperband_0.6.0 mlr3tuning_1.0.0 paradox_1.0.1
[7] mlr3_0.20.2
loaded via a namespace (and not attached):
[1] utf8_1.2.3 future_1.33.0 generics_0.1.3 distr6_1.8.0 lattice_0.21-8
[6] listenv_0.9.0 digest_0.6.33 magrittr_2.0.3 grid_4.3.1 ooplah_0.2.0
[11] xgboost_1.7.8.1 jsonlite_1.8.7 Matrix_1.6-1.1 backports_1.4.1 survival_3.5-7
[16] param6_0.2.4 fansi_1.0.4 scales_1.3.0 codetools_0.2-19 mlr3measures_0.6.0
[21] palmerpenguins_0.1.1 cli_3.6.1 rlang_1.1.1 crayon_1.5.2 parallelly_1.36.0
[26] mlr3viz_0.9.0 future.apply_1.11.0 splines_4.3.1 munsell_0.5.0 withr_2.5.1
[31] mlr3pipelines_0.6.0 tools_4.3.1 parallel_4.3.1 uuid_1.1-1 set6_0.2.6
[36] checkmate_2.2.0 dplyr_1.1.3 colorspace_2.1-0 ggplot2_3.5.0 globals_0.16.2
[41] bbotk_1.0.1 vctrs_0.6.3 R6_2.5.1 lifecycle_1.0.3 dictionar6_0.1.3
[46] mlr3misc_0.15.1 pkgconfig_2.0.3 pillar_1.9.0 gtable_0.3.4 data.table_1.15.4
[51] glue_1.6.2 Rcpp_1.0.11 lgr_0.4.4 tibble_3.2.1 tidyselect_1.2.0
[56] rstudioapi_0.15.0 compiler_4.3.1
Thanks in advance for your kind help
The budget parameter needs a lower bound different from 0.
alpha = to_tune(p_dbl(0.01, 1, tags = "budget")