Last updated: 2021-08-27
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Knit directory: BreedingSchemeOpt/
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| File | Version | Author | Date | Message |
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| html | 92bd15b | wolfemd | 2021-04-22 | Build site. |
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| Rmd | 9df9d9b | wolfemd | 2021-04-22 | Publish the initial files for the Breeding Scheme Optimization Group project |
AlphaSimHlpR first steps: Installation and quickly running the AlphaSimHlpR tutorial example.
Generate a baseline population. As this is an example, I won’t use empirical inputs. Ignore costs for now.
BaselineControlFile.txt and BaselinePopulationFile.txt in this repository.I want to set-up “burn-in” generations of phenotypic selection. See if I can work that in AlphaSimHlpR.
SPOILER ALERT: Not quite working yet. More work needed.
The first step is to read in the control files defining the baseline breeding program and other breeding scheme parameters in a list bsp.
The tutorial example repeatedly runs the runBreedingScheme() function, which in turn depends on specified functions: initializeFunc=initFuncADChk, productPipeline=prodPipeFncChk, populationImprovement=popImprov1Cyc.
selCritPipeAdv and selCritPopImprov are set to selCritIID.
Now, to continue, we can’t use the runBreedingScheme() function because it will re-run the initializeFunc() and subsequently runMacs2(), which overwrites our founder haplotypes.
Make my own function: runBreedingScheme_wBurnIn().
Specify number of burn-in and post-burn in cycles.
Also let’s uses two separate bsp objects, one for before, another for after. Use with caution.
rm(list=ls()); gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 720741 38.5 1347232 72 NA 1347232 72.0
Vcells 1339776 10.3 8388608 64 65536 1971303 15.1library(tidyverse); library(magrittr); ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──✓ ggplot2 3.3.5 ✓ purrr 0.3.4
✓ tibble 3.1.3 ✓ dplyr 1.0.7
✓ tidyr 1.1.3 ✓ stringr 1.4.0
✓ readr 2.0.1 ✓ forcats 0.5.1── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
Attaching package: 'magrittr'The following object is masked from 'package:purrr':
set_namesThe following object is masked from 'package:tidyr':
extractsuppressMessages(library(AlphaSimHlpR))
bsp <- specifyPopulation(ctrlFileName="data/BaselinePopulationFile.txt")
bsp <- specifyPipeline(bsp, ctrlFileName="data/BaselineControlFile.txt")runBreedingScheme_wBurnIn <- function(replication=NULL,
nBurnInCycles=10,nPostBurnInCycles=10,
bspBurnIn,bspPostBurnIn,
initializeFunc,
productPipeline,
populationImprovement){
cat("******", replication, "\n")
# This initiates the founding population
initList <- initializeFunc(bspBurnIn)
SP <- initList$SP
bspBurnIn <- initList$bsp
records <- initList$records
# Burn-in cycles
for (cycle in 1:nBurnInCycles){
cat(cycle, " ")
records <- productPipeline(records, bspBurnIn, SP)
records <- populationImprovement(records, bspBurnIn, SP)
}
# records <- AlphaSimHlpR:::lastCycStgOut(records, bspPostBurnIn, SP)
cat("\n")
cat("Burn-in Cycles")
cat("\n")
# Post burn-in cycles
for (cycle in (nBurnInCycles+1):(nBurnInCycles+nPostBurnInCycles)){
cat(cycle, " ")
records <- productPipeline(records, bspPostBurnIn, SP)
records <- populationImprovement(records, bspPostBurnIn, SP)
}
cat("\n")
# Finalize the stageOutputs
records <- AlphaSimHlpR:::lastCycStgOut(records, bspPostBurnIn, SP)
return(list(records=records,
bspBurnIn=bspBurnIn,
bspPostBurnIn=bspPostBurnIn,
SP=SP))
}For burn-in, using phenotypic selection, so the bsp already created.
For post burn-in, change selCritPopImprov to selCritGRM, but ignore selCritPipeAdv for now.
bspBurnIn<-bsp
bspPostBurnIn<-bspBurnIn
bspPostBurnIn[["selCritPopImprov"]] <- selCritGRM
test_sim<-runBreedingScheme_wBurnIn(replication = 1,
nBurnInCycles=2,nPostBurnInCycles=2,
bspBurnIn=bspBurnIn,
bspPostBurnIn=bspPostBurnIn,
initializeFunc=initializeScheme,
productPipeline=productPipeline,
populationImprovement=popImprov1Cyc)Unfortunately, haven’t gotten this to work yet. It seems likely more tweaking to the package is necessary.
I traced the problem at least as far as the populationImprovement() function in the post-burn-in phase and the makeGRM().
Set-up an experiment. No burn-in simulations.
# rm(list=ls()); gc()
# library(tidyverse); library(magrittr);
# suppressMessages(library(AlphaSimHlpR))
# bsp <- specifyPopulation(ctrlFileName="BaselinePopulationFile.txt")
# bsp <- specifyPipeline(bsp, ctrlFileName="BaselineControlFile.txt")# test<-runBreedingScheme(replication = 1, nCycles = 5, # just 5 for now
# initializeFunc=initializeScheme,
# productPipeline=prodPipeFncChk,
# populationImprovement=popImprov1Cyc, bsp)
#
# test_burnin$records$stageOutputs %>% ggplot(.,aes(x=year,y=genValMean, color=stage)) + geom_line()
# test_burnin$records
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] AlphaSimHlpR_0.2.1 AlphaSimR_1.0.3 R6_2.5.0 magrittr_2.0.1
[5] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[9] readr_2.0.1 tidyr_1.1.3 tibble_3.1.3 ggplot2_3.3.5
[13] tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 lubridate_1.7.10 assertthat_0.2.1 rprojroot_2.0.2
[5] digest_0.6.27 utf8_1.2.2 cellranger_1.1.0 backports_1.2.1
[9] reprex_2.0.1 evaluate_0.14 httr_1.4.2 pillar_1.6.2
[13] rlang_0.4.11 readxl_1.3.1 rstudioapi_0.13 whisker_0.4
[17] jquerylib_0.1.4 rmarkdown_2.10 munsell_0.5.0 broom_0.7.9
[21] compiler_4.1.0 httpuv_1.6.1 modelr_0.1.8 xfun_0.25
[25] pkgconfig_2.0.3 htmltools_0.5.1.1 tidyselect_1.1.1 fansi_0.5.0
[29] crayon_1.4.1 tzdb_0.1.2 dbplyr_2.1.1 withr_2.4.2
[33] later_1.2.0 grid_4.1.0 jsonlite_1.7.2 gtable_0.3.0
[37] lifecycle_1.0.0 DBI_1.1.1 git2r_0.28.0 scales_1.1.1
[41] cli_3.0.1 stringi_1.7.3 fs_1.5.0 promises_1.2.0.1
[45] xml2_1.3.2 bslib_0.2.5.1 ellipsis_0.3.2 generics_0.1.0
[49] vctrs_0.3.8 tools_4.1.0 glue_1.4.2 hms_1.1.0
[53] yaml_2.2.1 colorspace_2.0-2 rvest_1.0.1 knitr_1.33
[57] haven_2.4.3 sass_0.4.0