weightedMean_subset - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report

weightedMean() benchmarks on subsetted computation

This report benchmark the performance of weightedMean() on subsetted computation.

Data type "integer"

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 = mode)
> data <- data[1:4]

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 <- w[idxs]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3254530 173.9    5709258 305.0  5709258 305.0
Vcells 7938294  60.6   25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x, 
+     w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs], 
+     w = w[idxs], na.rm = FALSE), unit = "ms")

Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+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 weightedMean_x_w_S 0.001531 0.0015845 0.0016795 0.0016275 0.0016715 0.004561
2 weightedMean(x, w, idxs) 0.002270 0.0023210 0.0032812 0.0023775 0.0024460 0.089417
3 weightedMean(x[idxs], w[idxs]) 0.004627 0.0048040 0.0049241 0.0048655 0.0049630 0.008610
expr min lq mean median uq max
1 weightedMean_x_w_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 weightedMean(x, w, idxs) 1.482691 1.464815 1.953624 1.460830 1.463356 19.604692
3 weightedMean(x[idxs], w[idxs]) 3.022208 3.031871 2.931873 2.989555 2.969189 1.887744

Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+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 <- w[idxs]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3250877 173.7    5709258 305.0  5709258 305.0
Vcells 6820531  52.1   25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x, 
+     w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs], 
+     w = w[idxs], na.rm = FALSE), unit = "ms")

Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+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 weightedMean_x_w_S 0.008241 0.0084615 0.0088564 0.008524 0.0086355 0.036244
2 weightedMean(x, w, idxs) 0.016593 0.0168810 0.0173057 0.017060 0.0172010 0.035846
3 weightedMean(x[idxs], w[idxs]) 0.033846 0.0345210 0.0354220 0.034843 0.0353440 0.047289
expr min lq mean median uq max
1 weightedMean_x_w_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 weightedMean(x, w, idxs) 2.013469 1.995036 1.954019 2.001408 1.991894 0.9890189
3 weightedMean(x[idxs], w[idxs]) 4.107026 4.079773 3.999569 4.087635 4.092872 1.3047401

Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+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 <- w[idxs]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3250949 173.7    5709258 305.0  5709258 305.0
Vcells 7037091  53.7   25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x, 
+     w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs], 
+     w = w[idxs], na.rm = FALSE), unit = "ms")

Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+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 weightedMean_x_w_S 0.073563 0.0756315 0.0773616 0.0776105 0.0782045 0.093444
2 weightedMean(x, w, idxs) 0.251108 0.2545820 0.2622681 0.2653170 0.2655925 0.286673
3 weightedMean(x[idxs], w[idxs]) 0.410592 0.4208010 0.4347360 0.4344995 0.4377520 0.728485
expr min lq mean median uq max
1 weightedMean_x_w_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 weightedMean(x, w, idxs) 3.413509 3.366084 3.390159 3.418571 3.396128 3.067859
3 weightedMean(x[idxs], w[idxs]) 5.581502 5.563833 5.619533 5.598463 5.597530 7.795953

Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 1000000 vector

> x <- data[["n = 1000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3251021 173.7    5709258 305.0  5709258 305.0
Vcells 9197140  70.2   25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x, 
+     w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs], 
+     w = w[idxs], na.rm = FALSE), unit = "ms")

Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 weightedMean_x_w_S 1.043374 1.214168 1.295131 1.264495 1.360686 1.683094
2 weightedMean(x, w, idxs) 8.374396 9.454982 9.922480 9.753282 10.199404 13.196994
3 weightedMean(x[idxs], w[idxs]) 9.828954 14.754568 15.383674 15.084395 15.431275 26.039318
expr min lq mean median uq max
1 weightedMean_x_w_S 1.000000 1.00000 1.00000 1.000000 1.00000 1.000000
2 weightedMean(x, w, idxs) 8.026265 7.78721 7.66137 7.713187 7.49578 7.840913
3 weightedMean(x[idxs], w[idxs]) 9.420355 12.15200 11.87808 11.929189 11.34081 15.471101

Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.

Data type "double"

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 = mode)
> data <- data[1:4]

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 <- w[idxs]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3251099 173.7    5709258 305.0  5709258 305.0
Vcells 7356020  56.2   25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x, 
+     w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs], 
+     w = w[idxs], na.rm = FALSE), unit = "ms")

Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+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 weightedMean_x_w_S 0.00153 0.0016075 0.0017469 0.0016625 0.0017975 0.004865
2 weightedMean(x, w, idxs) 0.00224 0.0022870 0.0026696 0.0023330 0.0024010 0.032507
3 weightedMean(x[idxs], w[idxs]) 0.00457 0.0049725 0.0053566 0.0051435 0.0055415 0.011743
expr min lq mean median uq max
1 weightedMean_x_w_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 weightedMean(x, w, idxs) 1.464052 1.422706 1.528164 1.403308 1.335744 6.681809
3 weightedMean(x[idxs], w[idxs]) 2.986928 3.093313 3.066363 3.093835 3.082893 2.413772

Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+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 <- w[idxs]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3251165 173.7    5709258 305.0  5709258 305.0
Vcells 7381126  56.4   25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x, 
+     w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs], 
+     w = w[idxs], na.rm = FALSE), unit = "ms")

Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+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 weightedMean_x_w_S 0.008132 0.0084440 0.0090100 0.0086185 0.0088180 0.023303
2 weightedMean(x, w, idxs) 0.016094 0.0166165 0.0174300 0.0169310 0.0171770 0.040581
3 weightedMean(x[idxs], w[idxs]) 0.034302 0.0361780 0.0380877 0.0370390 0.0384335 0.060759
expr min lq mean median uq max
1 weightedMean_x_w_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 weightedMean(x, w, idxs) 1.979095 1.967847 1.934503 1.964495 1.947947 1.741450
3 weightedMean(x[idxs], w[idxs]) 4.218151 4.284462 4.227254 4.297616 4.358528 2.607347

Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+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 <- w[idxs]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3251237 173.7    5709258 305.0  5709258 305.0
Vcells 7628674  58.3   25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x, 
+     w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs], 
+     w = w[idxs], na.rm = FALSE), unit = "ms")

Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+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 weightedMean_x_w_S 0.073528 0.0770400 0.0802106 0.0786250 0.0803380 0.102687
2 weightedMean(x, w, idxs) 0.234819 0.2360200 0.2477158 0.2485910 0.2556555 0.272487
3 weightedMean(x[idxs], w[idxs]) 0.428035 0.4349115 0.4582200 0.4528735 0.4658515 0.843115
expr min lq mean median uq max
1 weightedMean_x_w_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 weightedMean(x, w, idxs) 3.193600 3.063603 3.088319 3.161730 3.182249 2.653569
3 weightedMean(x[idxs], w[idxs]) 5.821388 5.645269 5.712714 5.759917 5.798644 8.210533

Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 1000000 vector

> x <- data[["n = 1000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3251309 173.7    5709258 305.0  5709258 305.0
Vcells 10104133  77.1   25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x, 
+     w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs], 
+     w = w[idxs], na.rm = FALSE), unit = "ms")

Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 weightedMean_x_w_S 1.222968 1.523889 1.611831 1.609341 1.700403 1.983512
2 weightedMean(x, w, idxs) 10.783339 13.336184 13.620122 13.594388 14.025677 14.598417
3 weightedMean(x[idxs], w[idxs]) 12.677596 15.040961 16.662921 15.482764 16.042821 29.830299
expr min lq mean median uq max
1 weightedMean_x_w_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 weightedMean(x, w, idxs) 8.817352 8.751414 8.450094 8.447177 8.248443 7.359883
3 weightedMean(x[idxs], w[idxs]) 10.366253 9.870116 10.337884 9.620561 9.434717 15.039132

Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R version 3.6.1 Patched (2019-08-27 r77078)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-3-6-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_1.4-6    matrixStats_0.55.0-9000 ggplot2_3.2.1          
[4] knitr_1.24              R.devices_2.16.0        R.utils_2.9.0          
[7] R.oo_1.22.0             R.methodsS3_1.7.1       history_0.0.0-9002     

loaded via a namespace (and not attached):
 [1] Biobase_2.45.0       bit64_0.9-7          splines_3.6.1       
 [4] network_1.15         assertthat_0.2.1     highr_0.8           
 [7] stats4_3.6.1         blob_1.2.0           robustbase_0.93-5   
[10] pillar_1.4.2         RSQLite_2.1.2        backports_1.1.4     
[13] lattice_0.20-38      glue_1.3.1           digest_0.6.20       
[16] colorspace_1.4-1     sandwich_2.5-1       Matrix_1.2-17       
[19] XML_3.98-1.20        lpSolve_5.6.13.3     pkgconfig_2.0.2     
[22] genefilter_1.66.0    purrr_0.3.2          ergm_3.10.4         
[25] xtable_1.8-4         mvtnorm_1.0-11       scales_1.0.0        
[28] tibble_2.1.3         annotate_1.62.0      IRanges_2.18.2      
[31] TH.data_1.0-10       withr_2.1.2          BiocGenerics_0.30.0 
[34] lazyeval_0.2.2       mime_0.7             survival_2.44-1.1   
[37] magrittr_1.5         crayon_1.3.4         statnet.common_4.3.0
[40] memoise_1.1.0        laeken_0.5.0         R.cache_0.13.0      
[43] MASS_7.3-51.4        R.rsp_0.43.1         tools_3.6.1         
[46] multcomp_1.4-10      S4Vectors_0.22.1     trust_0.1-7         
[49] munsell_0.5.0        AnnotationDbi_1.46.1 compiler_3.6.1      
[52] rlang_0.4.0          grid_3.6.1           RCurl_1.95-4.12     
[55] cwhmisc_6.6          rappdirs_0.3.1       labeling_0.3        
[58] bitops_1.0-6         base64enc_0.1-3      boot_1.3-23         
[61] gtable_0.3.0         codetools_0.2-16     DBI_1.0.0           
[64] markdown_1.1         R6_2.4.0             zoo_1.8-6           
[67] dplyr_0.8.3          bit_1.1-14           zeallot_0.1.0       
[70] parallel_3.6.1       Rcpp_1.0.2           vctrs_0.2.0         
[73] DEoptimR_1.0-8       tidyselect_0.2.5     xfun_0.9            
[76] coda_0.19-3

Total processing time was 14.03 secs.

Reproducibility

To reproduce this report, do:

html <- matrixStats:::benchmark('weightedMean_subset')

Copyright Dongcan Jiang. Last updated on 2019-09-10 21:14:28 (-0700 UTC). Powered by RSP.

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