matrixStats.benchmarks
colWeightedMedians() and rowWeightedMedians() benchmarks on subsetted computation
This report benchmark the performance of colWeightedMedians() and rowWeightedMedians on subsetted computation.
Data
> rmatrix <- function(nrow, ncol, mode = c("logical", "double", "integer", "index"), range = c(-100,
+ +100), na_prob = 0) {
+ mode <- match.arg(mode)
+ n <- nrow * ncol
+ if (mode == "logical") {
+ x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+ } else if (mode == "index") {
+ x <- seq_len(n)
+ mode <- "integer"
+ } 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
+ dim(x) <- c(nrow, ncol)
+ x
+ }
> rmatrices <- function(scale = 10, seed = 1, ...) {
+ set.seed(seed)
+ data <- list()
+ data[[1]] <- rmatrix(nrow = scale * 1, ncol = scale * 1, ...)
+ data[[2]] <- rmatrix(nrow = scale * 10, ncol = scale * 10, ...)
+ data[[3]] <- rmatrix(nrow = scale * 100, ncol = scale * 1, ...)
+ data[[4]] <- t(data[[3]])
+ data[[5]] <- rmatrix(nrow = scale * 10, ncol = scale * 100, ...)
+ data[[6]] <- t(data[[5]])
+ names(data) <- sapply(data, FUN = function(x) paste(dim(x), collapse = "x"))
+ data
+ }
> data <- rmatrices(mode = "double")
Results
10x10 matrix
> X <- data[["10x10"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> w <- runif(nrow(X))
> w_S <- w[rows]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5332124 284.8 8529671 455.6 8529671 455.6
Vcells 10812817 82.5 31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colWeightedMedians_X_w_S = colWeightedMedians(X_S, w = w_S, na.rm = FALSE),
+ `colWeightedMedians(X, w, rows, cols)` = colWeightedMedians(X, w = w, rows = rows, cols = cols,
+ na.rm = FALSE), `colWeightedMedians(X[rows, cols], w[rows])` = colWeightedMedians(X[rows,
+ cols], w = w[rows], na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5322502 284.3 8529671 455.6 8529671 455.6
Vcells 10780820 82.3 31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowWeightedMedians_X_w_S = rowWeightedMedians(X_S, w = w_S, na.rm = FALSE),
+ `rowWeightedMedians(X, w, cols, rows)` = rowWeightedMedians(X, w = w, rows = cols, cols = rows,
+ na.rm = FALSE), `rowWeightedMedians(X[cols, rows], w[rows])` = rowWeightedMedians(X[cols,
+ rows], w = w[rows], na.rm = FALSE), unit = "ms")
Table: Benchmarking of colWeightedMedians_X_w_S(), colWeightedMedians(X, w, rows, cols)() and colWeightedMedians(X[rows, cols], w[rows])() on 10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 0.052045 | 0.0536070 | 0.0575650 | 0.0569805 | 0.0602065 | 0.089569 |
| 3 | colWeightedMedians(X[rows, cols], w[rows]) | 0.053224 | 0.0560555 | 0.0594848 | 0.0583805 | 0.0623330 | 0.078030 |
| 2 | colWeightedMedians(X, w, rows, cols) | 0.053628 | 0.0553440 | 0.0642821 | 0.0584975 | 0.0625230 | 0.524478 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 3 | colWeightedMedians(X[rows, cols], w[rows]) | 1.022654 | 1.045675 | 1.033350 | 1.024570 | 1.035320 | 0.8711719 |
| 2 | colWeightedMedians(X, w, rows, cols) | 1.030416 | 1.032402 | 1.116686 | 1.026623 | 1.038476 | 5.8555750 |
Table: Benchmarking of rowWeightedMedians_X_w_S(), rowWeightedMedians(X, w, cols, rows)() and rowWeightedMedians(X[cols, rows], w[rows])() on 10x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMedians_X_w_S | 0.052127 | 0.0544615 | 0.0596529 | 0.0583315 | 0.063122 | 0.080281 |
| 3 | rowWeightedMedians(X[cols, rows], w[rows]) | 0.053777 | 0.0559745 | 0.0597013 | 0.0594850 | 0.061987 | 0.073712 |
| 2 | rowWeightedMedians(X, w, cols, rows) | 0.053930 | 0.0567925 | 0.0644175 | 0.0602810 | 0.064068 | 0.412862 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 | 1.0000000 |
| 3 | rowWeightedMedians(X[cols, rows], w[rows]) | 1.031654 | 1.027781 | 1.000812 | 1.019775 | 0.9820189 | 0.9181749 |
| 2 | rowWeightedMedians(X, w, cols, rows) | 1.034589 | 1.042801 | 1.079873 | 1.033421 | 1.0149869 | 5.1427112 |
Figure: Benchmarking of colWeightedMedians_X_w_S(), colWeightedMedians(X, w, rows, cols)() and colWeightedMedians(X[rows, cols], w[rows])() on 10x10 data as well as rowWeightedMedians_X_w_S(), rowWeightedMedians(X, w, cols, rows)() and rowWeightedMedians(X[cols, rows], w[rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colWeightedMedians_X_w_S() and rowWeightedMedians_X_w_S() on 10x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 52.045 | 53.6070 | 57.56501 | 56.9805 | 60.2065 | 89.569 |
| 2 | rowWeightedMedians_X_w_S | 52.127 | 54.4615 | 59.65287 | 58.3315 | 63.1220 | 80.281 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 1.000000 | 1.00000 | 1.00000 | 1.00000 | 1.000000 | 1.0000000 |
| 2 | rowWeightedMedians_X_w_S | 1.001576 | 1.01594 | 1.03627 | 1.02371 | 1.048425 | 0.8963034 |
Figure: Benchmarking of colWeightedMedians_X_w_S() and rowWeightedMedians_X_w_S() on 10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
100x100 matrix
> X <- data[["100x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> w <- runif(nrow(X))
> w_S <- w[rows]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5321501 284.2 8529671 455.6 8529671 455.6
Vcells 10452190 79.8 31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colWeightedMedians_X_w_S = colWeightedMedians(X_S, w = w_S, na.rm = FALSE),
+ `colWeightedMedians(X, w, rows, cols)` = colWeightedMedians(X, w = w, rows = rows, cols = cols,
+ na.rm = FALSE), `colWeightedMedians(X[rows, cols], w[rows])` = colWeightedMedians(X[rows,
+ cols], w = w[rows], na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5321477 284.2 8529671 455.6 8529671 455.6
Vcells 10462243 79.9 31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowWeightedMedians_X_w_S = rowWeightedMedians(X_S, w = w_S, na.rm = FALSE),
+ `rowWeightedMedians(X, w, cols, rows)` = rowWeightedMedians(X, w = w, rows = cols, cols = rows,
+ na.rm = FALSE), `rowWeightedMedians(X[cols, rows], w[rows])` = rowWeightedMedians(X[cols,
+ rows], w = w[rows], na.rm = FALSE), unit = "ms")
Table: Benchmarking of colWeightedMedians_X_w_S(), colWeightedMedians(X, w, rows, cols)() and colWeightedMedians(X[rows, cols], w[rows])() on 100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 0.444418 | 0.4576690 | 0.5007292 | 0.4747745 | 0.4999890 | 0.755358 |
| 3 | colWeightedMedians(X[rows, cols], w[rows]) | 0.454004 | 0.4635380 | 0.5023220 | 0.4798105 | 0.5053505 | 0.800361 |
| 2 | colWeightedMedians(X, w, rows, cols) | 0.456333 | 0.4705215 | 0.5184292 | 0.4861820 | 0.5227245 | 0.913797 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | colWeightedMedians(X[rows, cols], w[rows]) | 1.02157 | 1.012824 | 1.003181 | 1.010607 | 1.010723 | 1.059578 |
| 2 | colWeightedMedians(X, w, rows, cols) | 1.02681 | 1.028083 | 1.035348 | 1.024027 | 1.045472 | 1.209753 |
Table: Benchmarking of rowWeightedMedians_X_w_S(), rowWeightedMedians(X, w, cols, rows)() and rowWeightedMedians(X[cols, rows], w[rows])() on 100x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMedians_X_w_S | 0.441649 | 0.4660425 | 0.5114300 | 0.4851390 | 0.5296885 | 0.778382 |
| 3 | rowWeightedMedians(X[cols, rows], w[rows]) | 0.456554 | 0.4748245 | 0.5169599 | 0.4937935 | 0.5134185 | 0.872021 |
| 2 | rowWeightedMedians(X, w, cols, rows) | 0.457541 | 0.4791175 | 0.5078214 | 0.4954720 | 0.5111390 | 0.777244 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.0000000 | 1.000000 | 1.0000000 | 1.000000 |
| 3 | rowWeightedMedians(X[cols, rows], w[rows]) | 1.033748 | 1.018844 | 1.0108127 | 1.017839 | 0.9692838 | 1.120300 |
| 2 | rowWeightedMedians(X, w, cols, rows) | 1.035983 | 1.028055 | 0.9929442 | 1.021299 | 0.9649804 | 0.998538 |
Figure: Benchmarking of colWeightedMedians_X_w_S(), colWeightedMedians(X, w, rows, cols)() and colWeightedMedians(X[rows, cols], w[rows])() on 100x100 data as well as rowWeightedMedians_X_w_S(), rowWeightedMedians(X, w, cols, rows)() and rowWeightedMedians(X[cols, rows], w[rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colWeightedMedians_X_w_S() and rowWeightedMedians_X_w_S() on 100x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 444.418 | 457.6690 | 500.7292 | 474.7745 | 499.9890 | 755.358 |
| 2 | rowWeightedMedians_X_w_S | 441.649 | 466.0425 | 511.4300 | 485.1390 | 529.6885 | 778.382 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 1.0000000 | 1.000000 | 1.00000 | 1.00000 | 1.0000 | 1.000000 |
| 2 | rowWeightedMedians_X_w_S | 0.9937694 | 1.018296 | 1.02137 | 1.02183 | 1.0594 | 1.030481 |
Figure: Benchmarking of colWeightedMedians_X_w_S() and rowWeightedMedians_X_w_S() on 100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
1000x10 matrix
> X <- data[["1000x10"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> w <- runif(nrow(X))
> w_S <- w[rows]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5322239 284.3 8529671 455.6 8529671 455.6
Vcells 10458130 79.8 31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colWeightedMedians_X_w_S = colWeightedMedians(X_S, w = w_S, na.rm = FALSE),
+ `colWeightedMedians(X, w, rows, cols)` = colWeightedMedians(X, w = w, rows = rows, cols = cols,
+ na.rm = FALSE), `colWeightedMedians(X[rows, cols], w[rows])` = colWeightedMedians(X[rows,
+ cols], w = w[rows], na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5322227 284.3 8529671 455.6 8529671 455.6
Vcells 10468203 79.9 31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowWeightedMedians_X_w_S = rowWeightedMedians(X_S, w = w_S, na.rm = FALSE),
+ `rowWeightedMedians(X, w, cols, rows)` = rowWeightedMedians(X, w = w, rows = cols, cols = rows,
+ na.rm = FALSE), `rowWeightedMedians(X[cols, rows], w[rows])` = rowWeightedMedians(X[cols,
+ rows], w = w[rows], na.rm = FALSE), unit = "ms")
Table: Benchmarking of colWeightedMedians_X_w_S(), colWeightedMedians(X, w, rows, cols)() and colWeightedMedians(X[rows, cols], w[rows])() on 1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 0.292534 | 0.2982060 | 0.3339655 | 0.3050530 | 0.3642510 | 0.505192 |
| 2 | colWeightedMedians(X, w, rows, cols) | 0.308501 | 0.3136840 | 0.3580004 | 0.3216080 | 0.3897435 | 0.628624 |
| 3 | colWeightedMedians(X[rows, cols], w[rows]) | 0.307387 | 0.3121495 | 0.3499378 | 0.3253825 | 0.3805265 | 0.560474 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | colWeightedMedians(X, w, rows, cols) | 1.054582 | 1.051904 | 1.071968 | 1.054269 | 1.069986 | 1.244327 |
| 3 | colWeightedMedians(X[rows, cols], w[rows]) | 1.050774 | 1.046758 | 1.047826 | 1.066642 | 1.044682 | 1.109428 |
Table: Benchmarking of rowWeightedMedians_X_w_S(), rowWeightedMedians(X, w, cols, rows)() and rowWeightedMedians(X[cols, rows], w[rows])() on 1000x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMedians_X_w_S | 0.291770 | 0.2968595 | 0.3270638 | 0.3010230 | 0.350930 | 0.490349 |
| 2 | rowWeightedMedians(X, w, cols, rows) | 0.309691 | 0.3155940 | 0.3553845 | 0.3248840 | 0.379625 | 0.644361 |
| 3 | rowWeightedMedians(X[cols, rows], w[rows]) | 0.308279 | 0.3137730 | 0.3548879 | 0.3299175 | 0.385463 | 0.500003 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | rowWeightedMedians(X, w, cols, rows) | 1.061422 | 1.063109 | 1.086591 | 1.079266 | 1.081768 | 1.314086 |
| 3 | rowWeightedMedians(X[cols, rows], w[rows]) | 1.056582 | 1.056975 | 1.085072 | 1.095988 | 1.098404 | 1.019688 |
Figure: Benchmarking of colWeightedMedians_X_w_S(), colWeightedMedians(X, w, rows, cols)() and colWeightedMedians(X[rows, cols], w[rows])() on 1000x10 data as well as rowWeightedMedians_X_w_S(), rowWeightedMedians(X, w, cols, rows)() and rowWeightedMedians(X[cols, rows], w[rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colWeightedMedians_X_w_S() and rowWeightedMedians_X_w_S() on 1000x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | rowWeightedMedians_X_w_S | 291.770 | 296.8595 | 327.0638 | 301.023 | 350.930 | 490.349 |
| 1 | colWeightedMedians_X_w_S | 292.534 | 298.2060 | 333.9655 | 305.053 | 364.251 | 505.192 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | rowWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 |
| 1 | colWeightedMedians_X_w_S | 1.002619 | 1.004536 | 1.021102 | 1.013388 | 1.037959 | 1.03027 |
Figure: Benchmarking of colWeightedMedians_X_w_S() and rowWeightedMedians_X_w_S() on 1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
10x1000 matrix
> X <- data[["10x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> w <- runif(nrow(X))
> w_S <- w[rows]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5322463 284.3 8529671 455.6 8529671 455.6
Vcells 10457457 79.8 31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colWeightedMedians_X_w_S = colWeightedMedians(X_S, w = w_S, na.rm = FALSE),
+ `colWeightedMedians(X, w, rows, cols)` = colWeightedMedians(X, w = w, rows = rows, cols = cols,
+ na.rm = FALSE), `colWeightedMedians(X[rows, cols], w[rows])` = colWeightedMedians(X[rows,
+ cols], w = w[rows], na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5322439 284.3 8529671 455.6 8529671 455.6
Vcells 10467510 79.9 31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowWeightedMedians_X_w_S = rowWeightedMedians(X_S, w = w_S, na.rm = FALSE),
+ `rowWeightedMedians(X, w, cols, rows)` = rowWeightedMedians(X, w = w, rows = cols, cols = rows,
+ na.rm = FALSE), `rowWeightedMedians(X[cols, rows], w[rows])` = rowWeightedMedians(X[cols,
+ rows], w = w[rows], na.rm = FALSE), unit = "ms")
Table: Benchmarking of colWeightedMedians_X_w_S(), colWeightedMedians(X, w, rows, cols)() and colWeightedMedians(X[rows, cols], w[rows])() on 10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 2.255763 | 2.447366 | 2.622357 | 2.516212 | 2.587087 | 9.047498 |
| 2 | colWeightedMedians(X, w, rows, cols) | 2.265093 | 2.481343 | 2.602557 | 2.541040 | 2.600634 | 8.935241 |
| 3 | colWeightedMedians(X[rows, cols], w[rows]) | 2.252489 | 2.491293 | 2.612389 | 2.555455 | 2.616852 | 4.107836 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 1.0000000 | 1.000000 | 1.0000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | colWeightedMedians(X, w, rows, cols) | 1.0041361 | 1.013883 | 0.9924496 | 1.009867 | 1.005236 | 0.9875925 |
| 3 | colWeightedMedians(X[rows, cols], w[rows]) | 0.9985486 | 1.017949 | 0.9961989 | 1.015596 | 1.011505 | 0.4540301 |
Table: Benchmarking of rowWeightedMedians_X_w_S(), rowWeightedMedians(X, w, cols, rows)() and rowWeightedMedians(X[cols, rows], w[rows])() on 10x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMedians_X_w_S | 2.222791 | 2.423980 | 2.513887 | 2.510527 | 2.560751 | 3.277597 |
| 3 | rowWeightedMedians(X[cols, rows], w[rows]) | 2.264434 | 2.491439 | 2.604456 | 2.533383 | 2.576276 | 8.796850 |
| 2 | rowWeightedMedians(X, w, cols, rows) | 2.285834 | 2.500108 | 2.635925 | 2.545445 | 2.598762 | 8.930997 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | rowWeightedMedians(X[cols, rows], w[rows]) | 1.018735 | 1.027830 | 1.036028 | 1.009104 | 1.006063 | 2.683933 |
| 2 | rowWeightedMedians(X, w, cols, rows) | 1.028362 | 1.031406 | 1.048545 | 1.013908 | 1.014844 | 2.724861 |
Figure: Benchmarking of colWeightedMedians_X_w_S(), colWeightedMedians(X, w, rows, cols)() and colWeightedMedians(X[rows, cols], w[rows])() on 10x1000 data as well as rowWeightedMedians_X_w_S(), rowWeightedMedians(X, w, cols, rows)() and rowWeightedMedians(X[cols, rows], w[rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colWeightedMedians_X_w_S() and rowWeightedMedians_X_w_S() on 10x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | rowWeightedMedians_X_w_S | 2.222791 | 2.423980 | 2.513887 | 2.510527 | 2.560751 | 3.277597 |
| 1 | colWeightedMedians_X_w_S | 2.255763 | 2.447366 | 2.622357 | 2.516212 | 2.587087 | 9.047498 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | rowWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | colWeightedMedians_X_w_S | 1.014834 | 1.009648 | 1.043148 | 1.002264 | 1.010285 | 2.760406 |
Figure: Benchmarking of colWeightedMedians_X_w_S() and rowWeightedMedians_X_w_S() on 10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
100x1000 matrix
> X <- data[["100x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> w <- runif(nrow(X))
> w_S <- w[rows]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5322665 284.3 8529671 455.6 8529671 455.6
Vcells 10502411 80.2 31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colWeightedMedians_X_w_S = colWeightedMedians(X_S, w = w_S, na.rm = FALSE),
+ `colWeightedMedians(X, w, rows, cols)` = colWeightedMedians(X, w = w, rows = rows, cols = cols,
+ na.rm = FALSE), `colWeightedMedians(X[rows, cols], w[rows])` = colWeightedMedians(X[rows,
+ cols], w = w[rows], na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5322647 284.3 8529671 455.6 8529671 455.6
Vcells 10602474 80.9 31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowWeightedMedians_X_w_S = rowWeightedMedians(X_S, w = w_S, na.rm = FALSE),
+ `rowWeightedMedians(X, w, cols, rows)` = rowWeightedMedians(X, w = w, rows = cols, cols = rows,
+ na.rm = FALSE), `rowWeightedMedians(X[cols, rows], w[rows])` = rowWeightedMedians(X[cols,
+ rows], w = w[rows], na.rm = FALSE), unit = "ms")
Table: Benchmarking of colWeightedMedians_X_w_S(), colWeightedMedians(X, w, rows, cols)() and colWeightedMedians(X[rows, cols], w[rows])() on 100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 4.311679 | 4.741583 | 4.956463 | 4.791073 | 4.830357 | 21.410768 |
| 3 | colWeightedMedians(X[rows, cols], w[rows]) | 4.393377 | 4.883563 | 5.138474 | 4.936115 | 5.002079 | 21.825685 |
| 2 | colWeightedMedians(X, w, rows, cols) | 4.404769 | 4.925762 | 5.076675 | 4.989513 | 5.077012 | 7.161016 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 3 | colWeightedMedians(X[rows, cols], w[rows]) | 1.018948 | 1.029944 | 1.036722 | 1.030274 | 1.035551 | 1.0193789 |
| 2 | colWeightedMedians(X, w, rows, cols) | 1.021590 | 1.038843 | 1.024254 | 1.041419 | 1.051063 | 0.3344586 |
Table: Benchmarking of rowWeightedMedians_X_w_S(), rowWeightedMedians(X, w, cols, rows)() and rowWeightedMedians(X[cols, rows], w[rows])() on 100x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMedians_X_w_S | 4.374137 | 4.838030 | 5.129654 | 4.905475 | 4.956457 | 21.734457 |
| 2 | rowWeightedMedians(X, w, cols, rows) | 4.486593 | 4.932139 | 4.989111 | 4.996608 | 5.054518 | 5.998196 |
| 3 | rowWeightedMedians(X[cols, rows], w[rows]) | 4.505412 | 4.920116 | 5.160906 | 5.007936 | 5.052717 | 21.755484 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.0000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | rowWeightedMedians(X, w, cols, rows) | 1.025709 | 1.019452 | 0.9726017 | 1.018578 | 1.019784 | 0.2759763 |
| 3 | rowWeightedMedians(X[cols, rows], w[rows]) | 1.030012 | 1.016967 | 1.0060923 | 1.020887 | 1.019421 | 1.0009674 |
Figure: Benchmarking of colWeightedMedians_X_w_S(), colWeightedMedians(X, w, rows, cols)() and colWeightedMedians(X[rows, cols], w[rows])() on 100x1000 data as well as rowWeightedMedians_X_w_S(), rowWeightedMedians(X, w, cols, rows)() and rowWeightedMedians(X[cols, rows], w[rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colWeightedMedians_X_w_S() and rowWeightedMedians_X_w_S() on 100x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 4.311679 | 4.741583 | 4.956463 | 4.791073 | 4.830357 | 21.41077 |
| 2 | rowWeightedMedians_X_w_S | 4.374137 | 4.838030 | 5.129654 | 4.905475 | 4.956457 | 21.73446 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | rowWeightedMedians_X_w_S | 1.014486 | 1.020341 | 1.034943 | 1.023878 | 1.026106 | 1.015118 |
Figure: Benchmarking of colWeightedMedians_X_w_S() and rowWeightedMedians_X_w_S() on 100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
1000x100 matrix
> X <- data[["1000x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> w <- runif(nrow(X))
> w_S <- w[rows]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5322871 284.3 8529671 455.6 8529671 455.6
Vcells 10504742 80.2 31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colWeightedMedians_X_w_S = colWeightedMedians(X_S, w = w_S, na.rm = FALSE),
+ `colWeightedMedians(X, w, rows, cols)` = colWeightedMedians(X, w = w, rows = rows, cols = cols,
+ na.rm = FALSE), `colWeightedMedians(X[rows, cols], w[rows])` = colWeightedMedians(X[rows,
+ cols], w = w[rows], na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5322853 284.3 8529671 455.6 8529671 455.6
Vcells 10604805 81.0 31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowWeightedMedians_X_w_S = rowWeightedMedians(X_S, w = w_S, na.rm = FALSE),
+ `rowWeightedMedians(X, w, cols, rows)` = rowWeightedMedians(X, w = w, rows = cols, cols = rows,
+ na.rm = FALSE), `rowWeightedMedians(X[cols, rows], w[rows])` = rowWeightedMedians(X[cols,
+ rows], w = w[rows], na.rm = FALSE), unit = "ms")
Table: Benchmarking of colWeightedMedians_X_w_S(), colWeightedMedians(X, w, rows, cols)() and colWeightedMedians(X[rows, cols], w[rows])() on 1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 2.730721 | 2.975335 | 3.051785 | 3.033352 | 3.054703 | 4.592462 |
| 3 | colWeightedMedians(X[rows, cols], w[rows]) | 2.876968 | 3.092077 | 3.269317 | 3.157447 | 3.183505 | 11.976339 |
| 2 | colWeightedMedians(X, w, rows, cols) | 2.819557 | 3.096354 | 3.269797 | 3.160439 | 3.191239 | 12.535302 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | colWeightedMedians(X[rows, cols], w[rows]) | 1.053556 | 1.039236 | 1.071280 | 1.040910 | 1.042165 | 2.607825 |
| 2 | colWeightedMedians(X, w, rows, cols) | 1.032532 | 1.040674 | 1.071438 | 1.041896 | 1.044697 | 2.729538 |
Table: Benchmarking of rowWeightedMedians_X_w_S(), rowWeightedMedians(X, w, cols, rows)() and rowWeightedMedians(X[cols, rows], w[rows])() on 1000x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMedians_X_w_S | 2.776292 | 3.010362 | 3.084377 | 3.043135 | 3.087946 | 4.491698 |
| 3 | rowWeightedMedians(X[cols, rows], w[rows]) | 2.857283 | 3.130842 | 3.216857 | 3.186015 | 3.230631 | 4.242627 |
| 2 | rowWeightedMedians(X, w, cols, rows) | 2.839546 | 3.110234 | 3.399287 | 3.186598 | 3.242221 | 12.830877 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 3 | rowWeightedMedians(X[cols, rows], w[rows]) | 1.029172 | 1.040022 | 1.042952 | 1.046951 | 1.046207 | 0.9445486 |
| 2 | rowWeightedMedians(X, w, cols, rows) | 1.022784 | 1.033176 | 1.102099 | 1.047143 | 1.049960 | 2.8565761 |
Figure: Benchmarking of colWeightedMedians_X_w_S(), colWeightedMedians(X, w, rows, cols)() and colWeightedMedians(X[rows, cols], w[rows])() on 1000x100 data as well as rowWeightedMedians_X_w_S(), rowWeightedMedians(X, w, cols, rows)() and rowWeightedMedians(X[cols, rows], w[rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colWeightedMedians_X_w_S() and rowWeightedMedians_X_w_S() on 1000x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 2.730721 | 2.975335 | 3.051785 | 3.033352 | 3.054703 | 4.592462 |
| 2 | rowWeightedMedians_X_w_S | 2.776292 | 3.010362 | 3.084377 | 3.043135 | 3.087946 | 4.491698 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | rowWeightedMedians_X_w_S | 1.016688 | 1.011772 | 1.010679 | 1.003225 | 1.010883 | 0.9780588 |
Figure: Benchmarking of colWeightedMedians_X_w_S() and rowWeightedMedians_X_w_S() on 1000x100 data (original and transposed). 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 18.68 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('colRowWeightedMedians_subset')
Copyright Dongcan Jiang. Last updated on 2021-08-25 19:11:59 (+0200 UTC). Powered by RSP.