Benchmarks
Last updated: 2022-07-18
Checks: 7 0
Knit directory:
ampel-leipzig-meld/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | fb43d01 | Sebastian Gibb | 2022-06-19 | chore: rebuild site |
| html | ebe29cf | Sebastian Gibb | 2022-06-16 | chore: rebuild site |
| html | 8035219 | Sebastian Gibb | 2022-06-15 | chore: rebuild site |
| html | d3e9462 | Sebastian Gibb | 2022-06-06 | chore: rebuild site |
| html | b20484a | Sebastian Gibb | 2022-06-06 | chore: rebuild site |
| Rmd | baac1e4 | Sebastian Gibb | 2022-06-06 | fix: bootstraping elastic net |
| html | 983ec69 | Sebastian Gibb | 2022-03-17 | chore: rebuild site |
| Rmd | 057f935 | Sebastian Gibb | 2022-03-17 | feat: add elastic net bootstrap and timeROC evaluations |
| Rmd | d586eff | Sebastian Gibb | 2022-02-10 | feat: add benchmark table and plots |
| html | 373e7d8 | Sebastian Gibb | 2021-10-20 | chore: rebuild site |
| html | df8964f | Sebastian Gibb | 2021-10-15 | chore: rebuild site |
| Rmd | 0ac3045 | Sebastian Gibb | 2021-10-15 | refactor: reset figure width |
| Rmd | 173a1ca | Sebastian Gibb | 2021-10-14 | refactor: increase figure width |
| Rmd | 1e08cb2 | Sebastian Gibb | 2021-09-21 | fix: load bmrk_aggr and set p.value |
| Rmd | 939fcae | Sebastian Gibb | 2021-09-20 | feat: add benchmark comparison across datasets |
| Rmd | c66640c | Sebastian Gibb | 2021-09-14 | feat: first nnet tests |
| html | c66640c | Sebastian Gibb | 2021-09-14 | feat: first nnet tests |
| html | afa48d9 | Sebastian Gibb | 2021-08-07 | chore: rebuild site |
| Rmd | 4478f6a | Sebastian Gibb | 2021-08-02 | Revert "fix: working directory for targets" |
| Rmd | cac14f6 | Sebastian Gibb | 2021-08-02 | fix: working directory for targets |
| Rmd | 3957af7 | Sebastian Gibb | 2021-08-02 | refactor: move common yaml headers into _site.yml |
| html | 3aab3e1 | Sebastian Gibb | 2021-08-01 | chore: rebuild site |
| html | 3810a79 | Sebastian Gibb | 2021-07-15 | chore: rebuild site |
| Rmd | 1dc8b12 | Sebastian Gibb | 2021-07-14 | feat: add mlr benchmarks |
library("targets")
library("data.table")
library("mlr3")
library("mlr3misc")
library("mlr3proba")
library("mlr3viz")
library("viridis")
library("ggplot2")
tar_load(bmrk_results)
tar_load(bmrk_aggr)
agg <- bmrk_results$aggregate(msr("surv.cindex", id = "harrell"))
agg[, `:=`
(nr = NULL, resample_result = NULL, resampling_id = NULL, iters = NULL)
]
setorder(agg, task_id, -harrell)
p.value <- 0.051 Overview
1.1 Table
lapply(split(agg, agg$task_id), knitr::kable, digits = 4)$ln_eldd
| task_id | learner_id | harrell |
|---|---|---|
| ln_eldd | scale.svmregression.tuned | 0.9210 |
| ln_eldd | scale.ridge.tuned | 0.9208 |
| ln_eldd | scale.elasticnet.tuned | 0.9188 |
| ln_eldd | scale.penridge.tuned | 0.9178 |
| ln_eldd | scale.penlasso.tuned | 0.9159 |
| ln_eldd | penridge.tuned | 0.9152 |
| ln_eldd | ridge.tuned | 0.9145 |
| ln_eldd | scale.ranger.tuned | 0.9110 |
| ln_eldd | ranger.tuned | 0.9104 |
| ln_eldd | scale.surv.rfsrc.tuned | 0.9077 |
| ln_eldd | elasticnet.tuned | 0.9052 |
| ln_eldd | surv.rfsrc.tuned | 0.9046 |
| ln_eldd | penlasso.tuned | 0.8991 |
| ln_eldd | scale.lasso.tuned | 0.8677 |
| ln_eldd | lasso.tuned | 0.8616 |
| ln_eldd | scale.cox | 0.8580 |
| ln_eldd | cox | 0.8578 |
| ln_eldd | scale.surv.xgboost.tuned | 0.8413 |
| ln_eldd | scale.deepsurv.tuned | 0.8399 |
| ln_eldd | surv.xgboost.tuned | 0.8357 |
| ln_eldd | scale.coxtime.tuned | 0.8346 |
| ln_eldd | scale.svmvanbelle1.tuned | 0.8231 |
| ln_eldd | svmvanbelle1.tuned | 0.7927 |
| ln_eldd | coxtime.tuned | 0.7720 |
| ln_eldd | deepsurv.tuned | 0.7438 |
| ln_eldd | svmregression.tuned | 0.5695 |
$zlog_eldd
| task_id | learner_id | harrell |
|---|---|---|
| zlog_eldd | scale.ridge.tuned | 0.9213 |
| zlog_eldd | scale.svmregression.tuned | 0.9191 |
| zlog_eldd | scale.penridge.tuned | 0.9172 |
| zlog_eldd | scale.elasticnet.tuned | 0.9168 |
| zlog_eldd | ridge.tuned | 0.9148 |
| zlog_eldd | scale.ranger.tuned | 0.9141 |
| zlog_eldd | scale.penlasso.tuned | 0.9134 |
| zlog_eldd | elasticnet.tuned | 0.9134 |
| zlog_eldd | ranger.tuned | 0.9126 |
| zlog_eldd | penlasso.tuned | 0.9112 |
| zlog_eldd | penridge.tuned | 0.9091 |
| zlog_eldd | lasso.tuned | 0.9087 |
| zlog_eldd | scale.surv.rfsrc.tuned | 0.9079 |
| zlog_eldd | surv.rfsrc.tuned | 0.9060 |
| zlog_eldd | scale.cox | 0.8754 |
| zlog_eldd | cox | 0.8715 |
| zlog_eldd | scale.lasso.tuned | 0.8412 |
| zlog_eldd | deepsurv.tuned | 0.8333 |
| zlog_eldd | scale.surv.xgboost.tuned | 0.8317 |
| zlog_eldd | scale.coxtime.tuned | 0.8308 |
| zlog_eldd | surv.xgboost.tuned | 0.8279 |
| zlog_eldd | scale.svmvanbelle1.tuned | 0.8254 |
| zlog_eldd | svmvanbelle1.tuned | 0.8233 |
| zlog_eldd | coxtime.tuned | 0.8185 |
| zlog_eldd | scale.deepsurv.tuned | 0.8169 |
| zlog_eldd | svmregression.tuned | 0.8091 |
1.2 Boxplots
m <- agg[, max(harrell), by = task_id]
autoplot(bmrk_results) +
geom_boxplot(aes(fill = learner_id)) +
geom_jitter(position = position_jitter(0.2)) +
scale_fill_viridis(discrete = TRUE) +
geom_hline(
aes(yintercept = V1), linetype = "dashed", color = "red", data = m
)
| Version | Author | Date |
|---|---|---|
| ebe29cf | Sebastian Gibb | 2022-06-16 |
| 8035219 | Sebastian Gibb | 2022-06-15 |
| b20484a | Sebastian Gibb | 2022-06-06 |
| 983ec69 | Sebastian Gibb | 2022-03-17 |
| 373e7d8 | Sebastian Gibb | 2021-10-20 |
| df8964f | Sebastian Gibb | 2021-10-15 |
| c66640c | Sebastian Gibb | 2021-09-14 |
| afa48d9 | Sebastian Gibb | 2021-08-07 |
| 3810a79 | Sebastian Gibb | 2021-07-15 |
autoplot(
bmrk_results,
measure = msr("surv.cindex", id = "uno", weight_meth = "G2")) +
geom_boxplot(aes(fill = learner_id)) +
geom_jitter(position = position_jitter(0.2)) +
scale_fill_viridis(discrete = TRUE)Warning: Removed 135 rows containing non-finite values (stat_boxplot).
Removed 135 rows containing non-finite values (stat_boxplot).Warning: Removed 135 rows containing missing values (geom_point).
2 Aggregated performance
Aggregated performance across all 2 datasets.
autoplot(bmrk_aggr, type = "box", meas = "harrell") +
geom_boxplot(aes(fill = learner_id)) +
geom_jitter(position = position_jitter(0.2)) +
scale_fill_viridis(discrete = TRUE)
autoplot(bmrk_aggr, type = "box", meas = "uno") +
geom_boxplot(aes(fill = learner_id)) +
geom_jitter(position = position_jitter(0.2)) +
scale_fill_viridis(discrete = TRUE)Warning: Removed 49 rows containing non-finite values (stat_boxplot).
Removed 49 rows containing non-finite values (stat_boxplot).Warning: Removed 49 rows containing missing values (geom_point).
3 Pairwise comparison
p-value is set to 0.05 (otherwise an error is thrown because of insignificant results).
autoplot(
bmrk_aggr, type = "fn", meas = "harrell", p.value = p.value,
col = palette.colors(2L)[2L]
)
4 Critical difference
p-value is set to 0.05 (otherwise an error is thrown because of insignificant results).
autoplot(
bmrk_aggr, type = "cd", meas = "harrell", style = 2, p.value = p.value,
minimize = FALSE
) + scale_color_viridis(discrete = TRUE)
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-unknown-linux-gnu (64-bit)
Matrix products: default
BLAS/LAPACK: /gnu/store/ras6dprsw3wm3swk23jjp8ww5dwxj333-openblas-0.3.18/lib/libopenblasp-r0.3.18.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.3.6 viridis_0.6.2 viridisLite_0.4.0 mlr3viz_0.5.9
[5] mlr3proba_0.4.11 mlr3misc_0.10.0 mlr3_0.13.3 data.table_1.14.2
[9] targets_0.12.1
loaded via a namespace (and not attached):
[1] fs_1.5.2 bbotk_0.5.3 rprojroot_2.0.3
[4] mlr3pipelines_0.4.1 tools_4.2.0 backports_1.4.1
[7] bslib_0.3.1 utf8_1.2.2 R6_2.5.1
[10] colorspace_2.0-3 withr_2.5.0 tidyselect_1.1.2
[13] gridExtra_2.3 processx_3.5.3 compiler_4.2.0
[16] git2r_0.30.1 cli_3.3.0 ooplah_0.2.0
[19] lgr_0.4.3 labeling_0.4.2 bookdown_0.26
[22] sass_0.4.1 BWStest_0.2.2 scales_1.2.0
[25] checkmate_2.1.0 mvtnorm_1.1-3 callr_3.7.0
[28] multcompView_0.1-8 palmerpenguins_0.1.0 mlr3tuning_0.13.1
[31] stringr_1.4.0 digest_0.6.29 mlr3extralearners_0.5.37
[34] rmarkdown_2.14 param6_0.2.4 paradox_0.9.0
[37] set6_0.2.4 pkgconfig_2.0.3 htmltools_0.5.2
[40] parallelly_1.31.1 mlr3benchmark_0.1.3 fastmap_1.1.0
[43] highr_0.9 rlang_1.0.2 SuppDists_1.1-9.7
[46] jquerylib_0.1.4 generics_0.1.2 farver_2.1.0
[49] jsonlite_1.8.0 dplyr_1.0.9 magrittr_2.0.3
[52] Matrix_1.4-1 Rcpp_1.0.8.3 munsell_0.5.0
[55] fansi_1.0.3 lifecycle_1.0.1 stringi_1.7.6
[58] whisker_0.4 yaml_2.3.5 MASS_7.3-57
[61] grid_4.2.0 parallel_4.2.0 dictionar6_0.1.3
[64] listenv_0.8.0 promises_1.2.0.1 crayon_1.5.1
[67] lattice_0.20-45 PMCMRplus_1.9.4 splines_4.2.0
[70] knitr_1.39 ps_1.7.0 pillar_1.7.0
[73] igraph_1.3.1 uuid_1.1-0 base64url_1.4
[76] kSamples_1.2-9 codetools_0.2-18 glue_1.6.2
[79] evaluate_0.15 vctrs_0.4.1 httpuv_1.6.5
[82] distr6_1.6.9 gtable_0.3.0 purrr_0.3.4
[85] future_1.26.1 cachem_1.0.6 xfun_0.31
[88] Rmpfr_0.8-7 later_1.3.0 survival_3.3-1
[91] tibble_3.1.7 memoise_2.0.1 workflowr_1.7.0
[94] gmp_0.6-5 globals_0.15.0 ellipsis_0.3.2