Benchmarks
Last updated: 2022-06-06
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Knit directory:
ampel-leipzig-meld/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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.ridge.tuned | 0.9212 |
| ln_eldd | scale.penridge.tuned | 0.9211 |
| ln_eldd | scale.svmregression.tuned | 0.9197 |
| ln_eldd | scale.elasticnet.tuned | 0.9195 |
| ln_eldd | scale.penlasso.tuned | 0.9135 |
| ln_eldd | ridge.tuned | 0.9132 |
| ln_eldd | penridge.tuned | 0.9129 |
| ln_eldd | ranger.tuned | 0.9119 |
| ln_eldd | scale.ranger.tuned | 0.9105 |
| ln_eldd | scale.surv.rfsrc.tuned | 0.9081 |
| ln_eldd | elasticnet.tuned | 0.9060 |
| ln_eldd | surv.rfsrc.tuned | 0.9054 |
| ln_eldd | penlasso.tuned | 0.9044 |
| ln_eldd | cox | 0.8632 |
| ln_eldd | scale.cox | 0.8552 |
| ln_eldd | scale.coxtime.tuned | 0.8472 |
| ln_eldd | scale.deepsurv.tuned | 0.8438 |
| ln_eldd | scale.surv.xgboost.tuned | 0.8368 |
| ln_eldd | surv.xgboost.tuned | 0.8365 |
| ln_eldd | lasso.tuned | 0.8335 |
| ln_eldd | scale.svmvanbelle1.tuned | 0.8331 |
| ln_eldd | scale.lasso.tuned | 0.8275 |
| ln_eldd | svmvanbelle1.tuned | 0.7794 |
| ln_eldd | deepsurv.tuned | 0.7494 |
| ln_eldd | coxtime.tuned | 0.7336 |
| ln_eldd | svmregression.tuned | 0.5711 |
$zlog_eldd
| task_id | learner_id | harrell |
|---|---|---|
| zlog_eldd | scale.ridge.tuned | 0.9211 |
| zlog_eldd | scale.svmregression.tuned | 0.9197 |
| zlog_eldd | scale.elasticnet.tuned | 0.9178 |
| zlog_eldd | scale.penridge.tuned | 0.9173 |
| zlog_eldd | ridge.tuned | 0.9154 |
| zlog_eldd | scale.penlasso.tuned | 0.9136 |
| zlog_eldd | elasticnet.tuned | 0.9133 |
| zlog_eldd | penridge.tuned | 0.9129 |
| zlog_eldd | penlasso.tuned | 0.9129 |
| zlog_eldd | ranger.tuned | 0.9120 |
| zlog_eldd | scale.ranger.tuned | 0.9113 |
| zlog_eldd | surv.rfsrc.tuned | 0.9074 |
| zlog_eldd | lasso.tuned | 0.9054 |
| zlog_eldd | scale.surv.rfsrc.tuned | 0.9042 |
| zlog_eldd | cox | 0.8619 |
| zlog_eldd | scale.cox | 0.8551 |
| zlog_eldd | scale.deepsurv.tuned | 0.8475 |
| zlog_eldd | scale.coxtime.tuned | 0.8462 |
| zlog_eldd | surv.xgboost.tuned | 0.8365 |
| zlog_eldd | scale.surv.xgboost.tuned | 0.8299 |
| zlog_eldd | scale.svmvanbelle1.tuned | 0.8267 |
| zlog_eldd | svmvanbelle1.tuned | 0.8190 |
| zlog_eldd | svmregression.tuned | 0.8093 |
| zlog_eldd | scale.lasso.tuned | 0.8007 |
| zlog_eldd | coxtime.tuned | 0.7994 |
| zlog_eldd | deepsurv.tuned | 0.7901 |
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
)
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 156 rows containing non-finite values (stat_boxplot).
Removed 156 rows containing non-finite values (stat_boxplot).Warning: Removed 156 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 50 rows containing non-finite values (stat_boxplot).
Removed 50 rows containing non-finite values (stat_boxplot).Warning: Removed 50 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 4.1.3 (2022-03-10)
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] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.3.5 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_0.12.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 bbotk_0.5.2 rprojroot_2.0.3
[4] mlr3pipelines_0.4.0 mlr3learners_0.5.2 tools_4.1.3
[7] backports_1.4.1 bslib_0.3.1 utf8_1.2.2
[10] R6_2.5.1 colorspace_2.0-3 withr_2.5.0
[13] tidyselect_1.1.2 gridExtra_2.3 processx_3.5.3
[16] compiler_4.1.3 git2r_0.30.1 cli_3.2.0
[19] ooplah_0.2.0 lgr_0.4.3 labeling_0.4.2
[22] bookdown_0.25 sass_0.4.1 BWStest_0.2.2
[25] scales_1.1.1 checkmate_2.0.0 mvtnorm_1.1-3
[28] callr_3.7.0 multcompView_0.1-8 palmerpenguins_0.1.0
[31] mlr3tuning_0.13.0 stringr_1.4.0 digest_0.6.29
[34] mlr3extralearners_0.5.9 rmarkdown_2.13 param6_0.2.4
[37] paradox_0.8.0 set6_0.2.4 pkgconfig_2.0.3
[40] htmltools_0.5.2 parallelly_1.31.0 mlr3benchmark_0.1.3
[43] fastmap_1.1.0 highr_0.9 rlang_1.0.2
[46] SuppDists_1.1-9.7 jquerylib_0.1.4 generics_0.1.2
[49] farver_2.1.0 jsonlite_1.8.0 dplyr_1.0.8
[52] magrittr_2.0.3 Matrix_1.4-1 Rcpp_1.0.8.3
[55] munsell_0.5.0 fansi_1.0.3 lifecycle_1.0.1
[58] stringi_1.7.6 whisker_0.4 yaml_2.3.5
[61] MASS_7.3-56 grid_4.1.3 parallel_4.1.3
[64] dictionar6_0.1.3 listenv_0.8.0 promises_1.2.0.1
[67] crayon_1.5.1 lattice_0.20-45 PMCMRplus_1.9.4
[70] splines_4.1.3 knitr_1.38 ps_1.6.0
[73] pillar_1.7.0 igraph_1.3.0 uuid_1.0-4
[76] base64url_1.4 kSamples_1.2-9 codetools_0.2-18
[79] glue_1.6.2 evaluate_0.15 vctrs_0.4.0
[82] httpuv_1.6.5 distr6_1.6.9 gtable_0.3.0
[85] purrr_0.3.4 future_1.24.0 cachem_1.0.6
[88] xfun_0.30 Rmpfr_0.8-7 later_1.3.0
[91] survival_3.3-1 tibble_3.1.6 memoise_2.0.1
[94] workflowr_1.7.0 gmp_0.6-5 globals_0.14.0
[97] ellipsis_0.3.2