weightedMean - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report

weightedMean() benchmarks

This report benchmark the performance of weightedMean() against alternative methods.

Alternative methods

  • stats::weighted.mean()
  • stats:::weighted.mean.default()

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"]]
> w <- runif(length(x))
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3253690 173.8    5709258 305.0  5709258 305.0
Vcells 10683390  81.6   32912165 251.1 87357391 666.5
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x, 
+     w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() 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 0.001799 0.0019990 0.0022183 0.0021315 0.0022670 0.010164
3 stats:::weighted.mean.default 0.007978 0.0086310 0.0091380 0.0089075 0.0092150 0.017101
2 stats::weighted.mean 0.010164 0.0109585 0.0117989 0.0112575 0.0115765 0.050782
expr min lq mean median uq max
1 weightedMean 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 stats:::weighted.mean.default 4.434686 4.317659 4.119407 4.178982 4.064843 1.682507
2 stats::weighted.mean 5.649805 5.481991 5.318930 5.281492 5.106528 4.996261

Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on integer+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000 vector

> x <- data[["n = 10000"]]
> w <- runif(length(x))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3251465 173.7    5709258 305.0  5709258 305.0
Vcells 6807038  52.0   26329732 200.9 87357391 666.5
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x, 
+     w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() 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 0.011350 0.0115630 0.0119662 0.0118160 0.0119335 0.022352
3 stats:::weighted.mean.default 0.059287 0.0609965 0.0631667 0.0617805 0.0634735 0.105014
2 stats::weighted.mean 0.062161 0.0633895 0.0657534 0.0642060 0.0657900 0.095198
expr min lq mean median uq max
1 weightedMean 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 stats:::weighted.mean.default 5.223524 5.275145 5.278744 5.228546 5.318934 4.698193
2 stats::weighted.mean 5.476740 5.482098 5.494913 5.433819 5.513052 4.259037

Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on integer+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 100000 vector

> x <- data[["n = 100000"]]
> w <- runif(length(x))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3251537 173.7    5709258 305.0  5709258 305.0
Vcells 6897598  52.7   26329732 200.9 87357391 666.5
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x, 
+     w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() 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 0.104369 0.108219 0.1186231 0.1164295 0.1246330 0.147534
3 stats:::weighted.mean.default 0.598650 0.627936 0.7646579 0.6632800 0.7220650 6.491542
2 stats::weighted.mean 0.612813 0.641460 0.8342262 0.6877985 0.7376575 6.687976
expr min lq mean median uq max
1 weightedMean 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
3 stats:::weighted.mean.default 5.735899 5.802456 6.446112 5.696838 5.793530 44.00031
2 stats::weighted.mean 5.871600 5.927425 7.032577 5.907425 5.918637 45.33176

Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on integer+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 1000000 vector

> x <- data[["n = 1000000"]]
> w <- runif(length(x))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3251609 173.7    5709258 305.0  5709258 305.0
Vcells 7797647  59.5   26329732 200.9 87357391 666.5
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x, 
+     w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() 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 1.071714 1.250920 1.331716 1.336521 1.394437 1.857999
3 stats:::weighted.mean.default 6.385673 7.282114 12.330859 7.765860 13.345553 269.733846
2 stats::weighted.mean 6.654515 7.435343 12.565293 7.879948 13.545939 266.984058
expr min lq mean median uq max
1 weightedMean 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000
3 stats:::weighted.mean.default 5.958374 5.821407 9.259373 5.810503 9.570567 145.1744
2 stats::weighted.mean 6.209226 5.943900 9.435413 5.895865 9.714271 143.6944

Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() 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"]]
> w <- runif(length(x))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3251681 173.7    5709258 305.0  5709258 305.0
Vcells 7354677  56.2   26329732 200.9 87357391 666.5
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x, 
+     w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() 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 0.001873 0.002131 0.0023838 0.0023080 0.0024775 0.009741
3 stats:::weighted.mean.default 0.007760 0.008604 0.0090266 0.0088345 0.0091680 0.013523
2 stats::weighted.mean 0.010181 0.010803 0.0115920 0.0111110 0.0114995 0.047691
expr min lq mean median uq max
1 weightedMean 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 stats:::weighted.mean.default 4.143086 4.037541 3.786587 3.827773 3.700505 1.388256
2 stats::weighted.mean 5.435665 5.069451 4.862771 4.814125 4.641574 4.895904

Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on double+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000 vector

> x <- data[["n = 10000"]]
> w <- runif(length(x))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3251753 173.7    5709258 305.0  5709258 305.0
Vcells 7363724  56.2   26329732 200.9 87357391 666.5
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x, 
+     w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() 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 0.011454 0.0116345 0.0125948 0.0119475 0.0137065 0.020861
3 stats:::weighted.mean.default 0.054687 0.0560620 0.0588038 0.0570060 0.0587000 0.094116
2 stats::weighted.mean 0.057985 0.0591660 0.0613805 0.0597465 0.0610195 0.091445
expr min lq mean median uq max
1 weightedMean 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 stats:::weighted.mean.default 4.774489 4.818600 4.668873 4.771375 4.282640 4.511577
2 stats::weighted.mean 5.062424 5.085393 4.873457 5.000753 4.451866 4.383539

Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on double+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 100000 vector

> x <- data[["n = 100000"]]
> w <- runif(length(x))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3251825 173.7    5709258 305.0  5709258 305.0
Vcells 7454142  56.9   26329732 200.9 87357391 666.5
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x, 
+     w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() 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 0.104343 0.1205490 0.1331922 0.1317125 0.1415450 0.176506
3 stats:::weighted.mean.default 0.573308 0.6238395 0.8089427 0.6603815 0.7104230 6.749770
2 stats::weighted.mean 0.577805 0.6358815 0.7690570 0.6812455 0.7217285 7.441084
expr min lq mean median uq max
1 weightedMean 1.000000 1.000000 1.00000 1.000000 1.000000 1.00000
3 stats:::weighted.mean.default 5.494456 5.174987 6.07350 5.013810 5.019061 38.24102
2 stats::weighted.mean 5.537554 5.274880 5.77404 5.172216 5.098933 42.15768

Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on double+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 1000000 vector

> x <- data[["n = 1000000"]]
> w <- runif(length(x))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3251897 173.7    5709258 305.0  5709258 305.0
Vcells 8354578  63.8   26329732 200.9 87357391 666.5
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x, 
+     w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE), 
+     unit = "ms")

Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() 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 1.186222 1.385917 1.475081 1.441063 1.503171 2.267329
2 stats::weighted.mean 6.686669 7.629785 9.613018 7.811571 9.362951 21.811615
3 stats:::weighted.mean.default 6.488990 7.586513 9.028397 7.840875 8.884047 17.437721
expr min lq mean median uq max
1 weightedMean 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 stats::weighted.mean 5.636946 5.505223 6.516942 5.420701 6.228800 9.619960
3 stats:::weighted.mean.default 5.470300 5.474000 6.120610 5.441035 5.910203 7.690865

Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() 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 15.42 secs.

Reproducibility

To reproduce this report, do:

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

Copyright Henrik Bengtsson. Last updated on 2019-09-10 21:14:44 (-0700 UTC). Powered by RSP.

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