Last updated: 2018-09-18
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Modified: analysis/nonnegative.Rmd
In flashr Issue #83, I identify some possible ways to speed up fitting when var_type = "constant" or when var_type = "zero" and S (the standard errors) is stored as a scalar. I also identify ways to reduce memory usage when loadings or factors are not fixed (and they will almost never both have fixed elements).
Here I implement the suggested changes and profile the code on data from the GTEx project. I compare elapsed time and total memory used in five scenarios: 1. var_type = "by_column"; 2. var_type = "constant"; 3. var_type = "zero" (with standard errors fixed); 4. var_type = "constant" with missing data; and 5. var_type = "by_column" with fixed factors. (The fifth scenario was added to help ensure that no bugs were introduced into the code; little improvement is expected.)
First I load the data and generate missing entries.
devtools::load_all("~/GitHub/ebnm")Loading ebnmlibrary("profmem")
gtex <- readRDS(gzcon(url("https://github.com/stephenslab/gtexresults/blob/master/data/MatrixEQTLSumStats.Portable.Z.rds?raw=TRUE")))
set.seed(666)
strong <- gtex$strong.z
strong_missing <- strong
strong_missing[sample(1:length(strong),
floor(0.1*length(strong)),
replace=FALSE)] <- NA
K <- 3 # number of factors to greedily add in scenarios 1-4
# fixed factors for scenario 5:
FF <- cbind(rep(1, 44),
c(rep(0, 6), rep(NA, 10), rep(0, 28)),
c(rep(0, 43), 1))Next I load the branch with the proposed changes. For each scenario (except the fifth), I add three factors greedily and then backfit.
system("cd ~/GitHub/flashr; git checkout efficient-tau2")
devtools::load_all("~/GitHub/flashr")Loading flashrdata <- flash_set_data(strong)
data_fixS <- flash_set_data(strong, S = 1)
data_missing <- flash_set_data(strong_missing)
t_bycol_aft <- system.time({
mem_bycol_aft <- profmem::profmem({
fl_bycol_after <- flash(data, Kmax=K,
var_type="by_column",
backfit=TRUE, verbose=FALSE)
})
})
t_const_aft <- system.time({
mem_const_aft <- profmem::profmem({
fl_const_after <- flash(data, Kmax=K,
var_type="constant",
backfit=TRUE, verbose=FALSE)
})
})
t_zero_aft <- system.time({
mem_zero_aft <- profmem::profmem({
fl_zero_after <- flash(data_fixS, Kmax=K,
var_type="zero",
backfit=TRUE, verbose=FALSE)
})
})
t_miss_aft <- system.time({
mem_miss_aft <- profmem::profmem({
fl_miss_after <- flash(data_missing, Kmax=K,
var_type="constant",
backfit=TRUE, verbose=FALSE)
})
})
t_fixed_aft <- system.time({
mem_fixed_aft <- profmem::profmem({
fl_fixed_after <- flash_add_fixed_factors(data_missing, FF,
backfit=TRUE,
verbose=FALSE)
})
})Finally, I run the code from the master branch. (I run this code second so that none of the performance gains can be attributed to caching.)
system("cd ~/GitHub/flashr; git checkout master")
devtools::load_all("~/GitHub/flashr")Loading flashrdata <- flash_set_data(strong)
data_fixS <- flash_set_data(strong, S = 1)
data_missing <- flash_set_data(strong_missing)
t_bycol_bef <- system.time({
mem_bycol_bef <- profmem::profmem({
fl_bycol_before <- flash(data, Kmax=K,
var_type="by_column",
backfit=TRUE, verbose=FALSE)
})
})
t_const_bef <- system.time({
mem_const_bef <- profmem::profmem({
fl_const_before <- flash(data, Kmax=K,
var_type="constant",
backfit=TRUE, verbose=FALSE)
})
})
t_zero_bef <- system.time({
mem_zero_bef <- profmem::profmem({
fl_zero_before <- flash(data_fixS, Kmax=K,
var_type="zero",
backfit=TRUE, verbose=FALSE)
})
})
t_miss_bef <- system.time({
mem_miss_bef <- profmem::profmem({
fl_miss_before <- flash(data_missing, Kmax=K,
var_type="constant",
backfit=TRUE, verbose=FALSE)
})
})
t_fixed_bef <- system.time({
mem_fixed_bef <- profmem::profmem({
fl_fixed_before <- flash_add_fixed_factors(data_missing, FF,
backfit=TRUE,
verbose=FALSE)
})
})The elapsed time per trial (in seconds) is as follows.
rnames <- c("before", "after")
cnames <- c("by_column", "constant", "zero", "missing", "fixed")
t_table <- t(matrix(c(t_bycol_bef[3],
t_const_bef[3],
t_zero_bef[3],
t_miss_bef[3],
t_fixed_bef[3],
t_bycol_aft[3],
t_const_aft[3],
t_zero_aft[3],
t_miss_aft[3],
t_fixed_aft[3]),
nrow=5, ncol=2))
rownames(t_table) <- rnames
colnames(t_table) <- cnames
round(t_table, digits = 1) by_column constant zero missing fixed
before 204.5 307.6 504.2 179.4 24.6
after 148.6 176.6 270.2 193.1 28.1The memory used per trial (in GB) is as follows.
mem_table <- t(matrix(c(sum(mem_bycol_bef$bytes, na.rm=TRUE),
sum(mem_const_bef$bytes, na.rm=TRUE),
sum(mem_zero_bef$bytes, na.rm=TRUE),
sum(mem_miss_bef$bytes, na.rm=TRUE),
sum(mem_fixed_bef$bytes, na.rm=TRUE),
sum(mem_bycol_aft$bytes, na.rm=TRUE),
sum(mem_const_aft$bytes, na.rm=TRUE),
sum(mem_zero_aft$bytes, na.rm=TRUE),
sum(mem_miss_aft$bytes, na.rm=TRUE),
sum(mem_fixed_aft$bytes, na.rm=TRUE)),
nrow=5, ncol=2)) / 1024^3
rownames(mem_table) <- rnames
colnames(mem_table) <- cnames
round(mem_table, digits = 1) by_column constant zero missing fixed
before 35.6 51.7 67.0 36.4 6.7
after 22.7 29.6 35.4 29.7 6.3Finally, I run a quick check to verify that I am getting the same flash object in each case.
identical(fl_bycol_before$objective, fl_bycol_after$objective)[1] TRUEidentical(fl_const_before$objective, fl_const_after$objective)[1] FALSEidentical(fl_zero_before$objective, fl_zero_after$objective)[1] TRUEidentical(fl_miss_before$objective, fl_miss_after$objective)[1] TRUEidentical(fl_fixed_before$objective, fl_fixed_after$objective)[1] TRUEsessionInfo()R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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] flashr_0.6-1 profmem_0.5.0 ebnm_0.1-14
loaded via a namespace (and not attached):
[1] Rcpp_0.12.18 pillar_1.2.1 plyr_1.8.4
[4] compiler_3.4.3 git2r_0.21.0 workflowr_1.0.1
[7] R.methodsS3_1.7.1 R.utils_2.6.0 iterators_1.0.9
[10] tools_3.4.3 testthat_2.0.0 digest_0.6.15
[13] tibble_1.4.2 gtable_0.2.0 evaluate_0.10.1
[16] memoise_1.1.0 lattice_0.20-35 rlang_0.2.0
[19] Matrix_1.2-12 foreach_1.4.4 commonmark_1.4
[22] yaml_2.1.17 parallel_3.4.3 withr_2.1.1.9000
[25] stringr_1.3.0 roxygen2_6.0.1.9000 xml2_1.2.0
[28] knitr_1.20 devtools_1.13.4 rprojroot_1.3-2
[31] grid_3.4.3 R6_2.2.2 rmarkdown_1.8
[34] reshape2_1.4.3 ggplot2_2.2.1 ashr_2.2-13
[37] magrittr_1.5 whisker_0.3-2 scales_0.5.0
[40] backports_1.1.2 codetools_0.2-15 htmltools_0.3.6
[43] MASS_7.3-48 softImpute_1.4 colorspace_1.3-2
[46] stringi_1.1.6 lazyeval_0.2.1 munsell_0.4.3
[49] doParallel_1.0.11 pscl_1.5.2 truncnorm_1.0-8
[52] SQUAREM_2017.10-1 R.oo_1.21.0 This reproducible R Markdown analysis was created with workflowr 1.0.1