matrixStats.benchmarks
weightedMedian() benchmarks on subsetted computation
This report benchmark the performance of weightedMedian() on subsetted computation.
Data
> rvector <- function(n, mode = c("logical", "double", "integer"), range = c(-100, +100), na_prob = 0) {
+ mode <- match.arg(mode)
+ if (mode == "logical") {
+ x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+ } else {
+ x <- runif(n, min = range[1], max = range[2])
+ }
+ storage.mode(x) <- mode
+ if (na_prob > 0)
+ x[sample(n, size = na_prob * n)] <- NA
+ x
+ }
> rvectors <- function(scale = 10, seed = 1, ...) {
+ set.seed(seed)
+ data <- list()
+ data[[1]] <- rvector(n = scale * 100, ...)
+ data[[2]] <- rvector(n = scale * 1000, ...)
+ data[[3]] <- rvector(n = scale * 10000, ...)
+ data[[4]] <- rvector(n = scale * 1e+05, ...)
+ data[[5]] <- rvector(n = scale * 1e+06, ...)
+ names(data) <- sprintf("n = %d", sapply(data, FUN = length))
+ data
+ }
> data <- rvectors(mode = "double")
> data <- data[1:3]
Results
n = 1000 vector
> x <- data[["n = 1000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5363024 286.5 8529671 455.6 8529671 455.6
Vcells 12570868 96.0 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMedian_x_w_S = weightedMedian(x_S, w = w_S, ties = "mean", na.rm = FALSE),
+ `weightedMedian(x, w, idxs)` = weightedMedian(x, w = w, idxs = idxs, ties = "mean", na.rm = FALSE),
+ `weightedMedian(x[idxs], w[idxs])` = weightedMedian(x[idxs], w = w[idxs], ties = "mean", na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of weightedMedian_x_w_S(), weightedMedian(x, w, idxs)() and weightedMedian(x[idxs], w[idxs])() on n = 1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMedian_x_w_S | 0.022068 | 0.0262415 | 0.0278228 | 0.0274145 | 0.0288025 | 0.047283 |
| 2 | weightedMedian(x, w, idxs) | 0.038439 | 0.0425555 | 0.0450344 | 0.0443435 | 0.0461070 | 0.086493 |
| 3 | weightedMedian(x[idxs], w[idxs]) | 0.040360 | 0.0436655 | 0.0460851 | 0.0457695 | 0.0479970 | 0.061564 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMedian_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMedian(x, w, idxs) | 1.741843 | 1.621687 | 1.618615 | 1.617520 | 1.600799 | 1.829262 |
| 3 | weightedMedian(x[idxs], w[idxs]) | 1.828893 | 1.663986 | 1.656380 | 1.669536 | 1.666418 | 1.302032 |
Figure: Benchmarking of weightedMedian_x_w_S(), weightedMedian(x, w, idxs)() and weightedMedian(x[idxs], w[idxs])() on n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000 vector
> x <- data[["n = 10000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5350565 285.8 8529671 455.6 8529671 455.6
Vcells 10426269 79.6 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMedian_x_w_S = weightedMedian(x_S, w = w_S, ties = "mean", na.rm = FALSE),
+ `weightedMedian(x, w, idxs)` = weightedMedian(x, w = w, idxs = idxs, ties = "mean", na.rm = FALSE),
+ `weightedMedian(x[idxs], w[idxs])` = weightedMedian(x[idxs], w = w[idxs], ties = "mean", na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of weightedMedian_x_w_S(), weightedMedian(x, w, idxs)() and weightedMedian(x[idxs], w[idxs])() on n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMedian_x_w_S | 0.242221 | 0.2476960 | 0.2750178 | 0.2569040 | 0.291548 | 0.418357 |
| 2 | weightedMedian(x, w, idxs) | 0.407897 | 0.4132000 | 0.4500956 | 0.4246210 | 0.448941 | 0.687807 |
| 3 | weightedMedian(x[idxs], w[idxs]) | 0.413669 | 0.4223365 | 0.4799637 | 0.4393675 | 0.522597 | 0.756214 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMedian_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMedian(x, w, idxs) | 1.683987 | 1.668174 | 1.636605 | 1.652839 | 1.539853 | 1.644067 |
| 3 | weightedMedian(x[idxs], w[idxs]) | 1.707816 | 1.705060 | 1.745209 | 1.710240 | 1.792490 | 1.807581 |
Figure: Benchmarking of weightedMedian_x_w_S(), weightedMedian(x, w, idxs)() and weightedMedian(x[idxs], w[idxs])() on n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 100000 vector
> x <- data[["n = 100000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5350637 285.8 8529671 455.6 8529671 455.6
Vcells 10674329 81.5 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMedian_x_w_S = weightedMedian(x_S, w = w_S, ties = "mean", na.rm = FALSE),
+ `weightedMedian(x, w, idxs)` = weightedMedian(x, w = w, idxs = idxs, ties = "mean", na.rm = FALSE),
+ `weightedMedian(x[idxs], w[idxs])` = weightedMedian(x[idxs], w = w[idxs], ties = "mean", na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of weightedMedian_x_w_S(), weightedMedian(x, w, idxs)() and weightedMedian(x[idxs], w[idxs])() on n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMedian_x_w_S | 2.832098 | 3.054331 | 3.122695 | 3.086237 | 3.116154 | 4.669408 |
| 2 | weightedMedian(x, w, idxs) | 5.086733 | 5.522982 | 5.662233 | 5.658106 | 5.748962 | 7.060010 |
| 3 | weightedMedian(x[idxs], w[idxs]) | 5.088597 | 5.528759 | 5.769173 | 5.755913 | 5.919936 | 8.074829 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMedian_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMedian(x, w, idxs) | 1.796101 | 1.808246 | 1.813252 | 1.833335 | 1.844890 | 1.511971 |
| 3 | weightedMedian(x[idxs], w[idxs]) | 1.796759 | 1.810137 | 1.847498 | 1.865026 | 1.899757 | 1.729305 |
Figure: Benchmarking of weightedMedian_x_w_S(), weightedMedian(x, w, idxs)() and weightedMedian(x[idxs], w[idxs])() on n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
Appendix
Session information
R version 4.1.1 Patched (2021-08-10 r80727)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRlapack.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] microbenchmark_1.4-7 matrixStats_0.60.1 ggplot2_3.3.5
[4] knitr_1.33 R.devices_2.17.0 R.utils_2.10.1
[7] R.oo_1.24.0 R.methodsS3_1.8.1-9001 history_0.0.1-9000
loaded via a namespace (and not attached):
[1] Biobase_2.52.0 httr_1.4.2 splines_4.1.1
[4] bit64_4.0.5 network_1.17.1 assertthat_0.2.1
[7] highr_0.9 stats4_4.1.1 blob_1.2.2
[10] GenomeInfoDbData_1.2.6 robustbase_0.93-8 pillar_1.6.2
[13] RSQLite_2.2.8 lattice_0.20-44 glue_1.4.2
[16] digest_0.6.27 XVector_0.32.0 colorspace_2.0-2
[19] Matrix_1.3-4 XML_3.99-0.7 pkgconfig_2.0.3
[22] zlibbioc_1.38.0 genefilter_1.74.0 purrr_0.3.4
[25] ergm_4.1.2 xtable_1.8-4 scales_1.1.1
[28] tibble_3.1.4 annotate_1.70.0 KEGGREST_1.32.0
[31] farver_2.1.0 generics_0.1.0 IRanges_2.26.0
[34] ellipsis_0.3.2 cachem_1.0.6 withr_2.4.2
[37] BiocGenerics_0.38.0 mime_0.11 survival_3.2-13
[40] magrittr_2.0.1 crayon_1.4.1 statnet.common_4.5.0
[43] memoise_2.0.0 laeken_0.5.1 fansi_0.5.0
[46] R.cache_0.15.0 MASS_7.3-54 R.rsp_0.44.0
[49] progressr_0.8.0 tools_4.1.1 lifecycle_1.0.0
[52] S4Vectors_0.30.0 trust_0.1-8 munsell_0.5.0
[55] tabby_0.0.1-9001 AnnotationDbi_1.54.1 Biostrings_2.60.2
[58] compiler_4.1.1 GenomeInfoDb_1.28.1 rlang_0.4.11
[61] grid_4.1.1 RCurl_1.98-1.4 cwhmisc_6.6
[64] rappdirs_0.3.3 startup_0.15.0 labeling_0.4.2
[67] bitops_1.0-7 base64enc_0.1-3 boot_1.3-28
[70] gtable_0.3.0 DBI_1.1.1 markdown_1.1
[73] R6_2.5.1 lpSolveAPI_5.5.2.0-17.7 rle_0.9.2
[76] dplyr_1.0.7 fastmap_1.1.0 bit_4.0.4
[79] utf8_1.2.2 parallel_4.1.1 Rcpp_1.0.7
[82] vctrs_0.3.8 png_0.1-7 DEoptimR_1.0-9
[85] tidyselect_1.1.1 xfun_0.25 coda_0.19-4
Total processing time was 4.78 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('weightedMedian_subset')
Copyright Dongcan Jiang. Last updated on 2021-08-25 19:32:20 (+0200 UTC). Powered by RSP.