extras/PipePerformance.md
In WinVector/wrapr: Wrap R Tools for Debugging and Parametric Programming
Some timings for %.>% ("dot arrow").
Keep in mind for any serious application the calculation time on data will far dominate any piping overhead, but it is fun to look.
So we will compare:
magrittr* magrittr::%>% piping.DotArrow* wrapr::%.>% piping.BizarroPipe* ->.; piping.TidyPipe* %>% piping based on rlang/tidyeval (renamed to "%t>%" in this run to avoid name collisions).
library("microbenchmark")
library("wrapr")
suppressPackageStartupMessages(library("ggplot2"))
suppressPackageStartupMessages(library("dplyr"))
library("glmnet")
## Loading required package: Matrix
## Loading required package: foreach
## Loaded glmnet 2.0-10
source('rlangPipe.R')
# load generated examples
prevNames <- ls()
source("pGenFns.R")
genFns <- setdiff(ls(), c(prevNames, 'prevNames', 'genFns'))
# parser translates BizarroPipe to different code!
cat(buildFnsK(5), sep = '\n')
## magrittr_5 <- function() {
## 5 %>%
## sin(.) %>%
## sin(.) %>%
## sin(.) %>%
## sin(.) %>%
## sin(.)
## }
##
##
##
## DotArrow_5 <- function() {
## 5 %.>%
## sin(.) %.>%
## sin(.) %.>%
## sin(.) %.>%
## sin(.) %.>%
## sin(.)
## }
##
##
##
## BizarroPipe_5 <- function() {
## 5 ->.;
## sin(.) ->.;
## sin(.) ->.;
## sin(.) ->.;
## sin(.) ->.;
## sin(.)
## }
##
##
##
## TidyPipe_5 <- function() {
## 5 %t>%
## sin(.) %t>%
## sin(.) %t>%
## sin(.) %t>%
## sin(.) %t>%
## sin(.)
## }
print(BizarroPipe_5)
## function ()
## {
## . <- 5
## . <- sin(.)
## . <- sin(.)
## . <- sin(.)
## . <- sin(.)
## sin(.)
## }
BizarroPipe_10()
## [1] -0.4774053
DotArrow_10()
## [1] -0.4774053
magrittr_10()
## [1] -0.4774053
# get expressions into a nice presentation order
fList <- data.frame(expr= genFns,
stringsAsFactors = FALSE)
fList$size <- as.numeric(gsub("[^0-9]+", "", fList$expr))
fList$fn <- gsub("[_0-9].*$", "", fList$expr)
fList <- fList[order(fList$size, fList$fn), , drop=FALSE]
cmd <- parse(text=paste0(
"microbenchmark(\n ",
paste(paste0(fList$expr,'()'), collapse=',\n '),
",
times=1000L
)\n"
))
print(cmd)
## expression(microbenchmark(BizarroPipe_1(), DotArrow_1(), magrittr_1(),
## TidyPipe_1(), BizarroPipe_2(), DotArrow_2(), magrittr_2(),
## TidyPipe_2(), BizarroPipe_5(), DotArrow_5(), magrittr_5(),
## TidyPipe_5(), BizarroPipe_10(), DotArrow_10(), magrittr_10(),
## TidyPipe_10(), BizarroPipe_15(), DotArrow_15(), magrittr_15(),
## TidyPipe_15(), BizarroPipe_20(), DotArrow_20(), magrittr_20(),
## TidyPipe_20(), BizarroPipe_25(), DotArrow_25(), magrittr_25(),
## TidyPipe_25(), BizarroPipe_50(), DotArrow_50(), magrittr_50(),
## TidyPipe_50(), BizarroPipe_100(), DotArrow_100(), magrittr_100(),
## TidyPipe_100(), BizarroPipe_200(), DotArrow_200(), magrittr_200(),
## TidyPipe_200(), BizarroPipe_500(), DotArrow_500(), magrittr_500(),
## TidyPipe_500(), BizarroPipe_1000(), DotArrow_1000(), magrittr_1000(),
## TidyPipe_1000(), times = 1000L))
gc()
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 1328828 71 2164898 115.7 1770749 94.6
## Vcells 1440666 11 2552219 19.5 2332288 17.8
datFile <- 'timings.RDS'
if(!file.exists(datFile)) {
print("running")
bm <- eval(cmd)
saveRDS(bm, file=datFile)
} else {
print("using cached results")
bm <- readRDS(file=datFile)
}
## [1] "using cached results"
print(bm)
## Unit: nanoseconds
## expr min lq mean median
## BizarroPipe_1() 311 1601.0 3.151041e+03 1852.5
## DotArrow_1() 3830 7593.0 1.192143e+04 9975.0
## magrittr_1() 50716 79380.0 1.080513e+05 103043.5
## TidyPipe_1() 73431 88769.0 1.327619e+05 111100.0
## BizarroPipe_2() 296 1737.0 3.943770e+03 1999.0
## DotArrow_2() 7564 12968.0 1.871724e+04 16515.0
## magrittr_2() 73497 107428.0 1.448151e+05 139355.5
## TidyPipe_2() 147710 173540.5 2.396801e+05 216920.0
## BizarroPipe_5() 398 1965.5 5.850010e+03 2219.0
## DotArrow_5() 18056 26495.5 3.959289e+04 30860.5
## magrittr_5() 141619 184236.0 2.344888e+05 220201.0
## TidyPipe_5() 360258 413047.0 5.291932e+05 467284.0
## BizarroPipe_10() 655 2258.5 8.261371e+03 2551.5
## DotArrow_10() 34342 46855.5 6.760176e+04 52749.5
## magrittr_10() 248196 306015.5 3.839070e+05 348933.5
## TidyPipe_10() 722322 807837.0 1.048367e+06 915988.5
## BizarroPipe_15() 808 2377.0 1.067373e+04 2714.5
## DotArrow_15() 52821 69429.5 1.012108e+05 76585.5
## magrittr_15() 357424 427377.5 5.298084e+05 472379.0
## TidyPipe_15() 1073739 1212790.5 1.509328e+06 1348848.0
## BizarroPipe_20() 933 2793.0 1.365327e+04 3135.5
## DotArrow_20() 71670 88752.5 1.234716e+05 96780.5
## magrittr_20() 456209 548567.0 7.025874e+05 601202.5
## TidyPipe_20() 1449365 1612122.0 1.956197e+06 1746227.5
## BizarroPipe_25() 1108 2593.0 1.602300e+04 2959.0
## DotArrow_25() 90575 109082.5 1.578983e+05 117980.0
## magrittr_25() 567666 670062.5 8.588992e+05 733624.0
## TidyPipe_25() 1810243 2015445.5 2.458534e+06 2181966.0
## BizarroPipe_50() 1977 3648.0 2.836560e+04 4005.5
## DotArrow_50() 178338 211978.0 3.181890e+05 229723.5
## magrittr_50() 1095177 1271808.5 1.609811e+06 1368693.0
## TidyPipe_50() 3695252 4047442.5 4.860431e+06 4335901.5
## BizarroPipe_100() 3747 5619.0 5.799286e+04 6006.5
## DotArrow_100() 363029 428630.0 6.226110e+05 467099.5
## magrittr_100() 2146549 2443022.0 3.167060e+06 2612558.5
## TidyPipe_100() 7578036 8406441.0 1.001283e+07 9016541.5
## BizarroPipe_200() 7296 9405.5 1.212623e+05 9908.0
## DotArrow_200() 748345 908387.0 1.293762e+06 975902.0
## magrittr_200() 4283529 4868351.5 6.075153e+06 5232996.5
## TidyPipe_200() 16371135 17895141.5 2.075790e+07 19047060.0
## BizarroPipe_500() 17764 21088.0 2.729626e+05 21557.0
## DotArrow_500() 2187918 2427491.0 3.050825e+06 2566260.0
## magrittr_500() 10663756 12087194.5 1.459132e+07 13224984.5
## TidyPipe_500() 49190881 54638294.5 6.022440e+07 58984792.0
## BizarroPipe_1000() 35311 40418.5 5.639057e+05 40993.0
## DotArrow_1000() 4664676 5086418.0 6.558716e+06 5341756.5
## magrittr_1000() 22003314 24703752.0 2.955337e+07 27977892.5
## TidyPipe_1000() 132999342 145483873.0 1.526779e+08 151929305.0
## uq max neval
## 2151.0 1268353 1000
## 12878.0 1549847 1000
## 123564.0 2110539 1000
## 171944.0 1479901 1000
## 2346.5 1908292 1000
## 18854.5 2299005 1000
## 161709.0 2150558 1000
## 282054.0 2397078 1000
## 2542.5 3559475 1000
## 35994.0 7195352 1000
## 257735.0 5801282 1000
## 582768.5 4146943 1000
## 2865.0 5645097 1000
## 63530.0 7756307 1000
## 418719.5 7812035 1000
## 1114030.5 16735656 1000
## 3135.5 7945124 1000
## 92641.5 12633368 1000
## 568128.5 10597817 1000
## 1640941.5 18130302 1000
## 3503.0 10499545 1000
## 115903.5 14438773 1000
## 758026.5 14623217 1000
## 2185758.0 16645950 1000
## 3318.5 13025408 1000
## 153371.5 17728721 1000
## 926632.5 22121278 1000
## 2742286.5 23128547 1000
## 4428.0 24268766 1000
## 298828.0 36442295 1000
## 1792959.0 40136380 1000
## 5619984.0 41135537 1000
## 6577.5 51726984 1000
## 645563.0 73742020 1000
## 3524055.5 109609792 1000
## 11363021.0 117788527 1000
## 10529.5 111070907 1000
## 1298696.5 151673919 1000
## 7131093.5 147704377 1000
## 22690128.5 149092740 1000
## 22297.5 250502705 1000
## 3666790.5 11351237 1000
## 16727506.0 123085711 1000
## 63261025.5 164081074 1000
## 42335.0 521124074 1000
## 7757298.0 115883484 1000
## 32264343.0 137927671 1000
## 157734354.5 480277048 1000
autoplot(bm)
d <- as.data.frame(bm)
d$size <- as.numeric(gsub("[^0-9]+", "", d$expr))
d$fn <- gsub("[_0-9].*$", "", d$expr)
d$fn <- reorder(d$fn, d$time)
ggplot(d, aes(x=fn, y=time, color=fn)) +
geom_violin() +
scale_y_log10() +
facet_wrap(~size, labeller="label_both") +
coord_flip() +
xlab("method") +
ylab("time NS") +
theme(legend.position = 'none') +
scale_color_manual(values = colorAssignment) +
ggtitle("distribution of runtime as function of method and problem size",
subtitle = "log-time scale")
# ggplot 2 legend in reverse order, so re-order to get that
d$fn <- reorder(d$fn, -d$time)
ggplot(d, aes(x=size, y=time, color=fn)) +
geom_smooth() +
scale_y_log10() +
scale_x_log10() +
scale_color_manual(values = colorAssignment) +
xlab('size (number of pipe stages)') +
ylab("time NS") +
ggtitle("complexity of runtime as function of method and problem size",
subtitle = "log/log scale")
## `geom_smooth()` using method = 'gam'
# fit a linear function for runtime as a function of size
# per group.
dfits <- d %.>%
split(., .$fn) %.>%
lapply(.,
function(di) {
mi <- lm(time ~ size + I(size*size), data=di)
ctab <- as.data.frame(summary(mi)$coefficients)
ctab$coef <- rownames(ctab)
ctab
}) %.>%
add_name_column(., 'method') %.>%
bind_rows(.) %.>%
arrange(., method, coef) %.>%
select(., method, coef, Estimate, `Std. Error`, `Pr(>|t|)`)
# "Intercept" is roughly start-up cost
# "size" is roughly the slope or growth rate of execution time
# as a function of number of pipe stages.
# "I(size * size)" is where we try to detect super-linear cost,
# check that it is both statistically significant and that
# it has a size that would affect predictions (is it times
# the typical variation in size*size large?).
print(dfits)
## method coef Estimate Std. Error Pr(>|t|)
## 1 BizarroPipe (Intercept) 3.212098e+03 6.296446e+04 9.593148e-01
## 2 BizarroPipe I(size * size) 1.856518e-02 6.832992e-01 9.783247e-01
## 3 BizarroPipe size 5.411272e+02 6.582386e+02 4.110456e-01
## 4 DotArrow (Intercept) 1.754318e+04 2.569601e+04 4.947969e-01
## 5 DotArrow I(size * size) 6.994202e-01 2.788567e-01 1.214890e-02
## 6 DotArrow size 5.830933e+03 2.686294e+02 1.657644e-102
## 7 magrittr (Intercept) 1.273818e+05 4.221528e+04 2.554597e-03
## 8 magrittr I(size * size) 4.011958e-01 4.581263e-01 3.811933e-01
## 9 magrittr size 2.899771e+04 4.413240e+02 0.000000e+00
## 10 TidyPipe (Intercept) 1.815486e+05 7.297796e+04 1.287005e-02
## 11 TidyPipe I(size * size) 6.356617e+01 7.919672e-01 0.000000e+00
## 12 TidyPipe size 8.887253e+04 7.629210e+02 0.000000e+00
# re-run with non-negative least squares
dfitsnn <- d %.>%
split(., .$fn) %.>%
lapply(.,
function(di) {
di$sizesq <- (di$size)^2
# always call glmnet with a non-trivial lambda series
# some notes: https://github.com/WinVector/wrapr/blob/master/extras/glmnetNotes.md
mi <- glmnet(as.matrix(di[, c('size', 'sizesq')]),
di$time,
lower.limits = 0,
alpha=0.0,
lambda=c(0, 1.0e-5, 1.0e-3, 0.1, 1, 10),
intercept = TRUE,
family = 'gaussian')
ctab <- as.data.frame(as.matrix(coef(mi, s=0)))
# lower.limites does not apply to intercept,
# but intercept is always reported even if
# turned off.
if(ctab['(Intercept)',1]<0) {
mi <- glmnet(as.matrix(di[, c('size', 'sizesq')]),
di$time,
lower.limits = 0,
alpha=0.0,
lambda=c(0, 1.0e-5, 1.0e-3, 0.1, 1, 10),
intercept = FALSE,
family = 'gaussian')
ctab <- as.data.frame(as.matrix(coef(mi, s=0)))
}
names(ctab) <- "Estimate"
ctab$coef <- rownames(ctab)
ctab
}) %.>%
add_name_column(., 'method') %.>%
bind_rows(.) %.>%
arrange(., method, coef) %.>%
select(., method, coef, Estimate)
print(dfitsnn)
## method coef Estimate
## 1 BizarroPipe (Intercept) 2.826857e+03
## 2 BizarroPipe size 5.485702e+02
## 3 BizarroPipe sizesq 1.110500e-02
## 4 DotArrow (Intercept) 1.653898e+04
## 5 DotArrow size 5.850334e+03
## 6 DotArrow sizesq 6.799738e-01
## 7 magrittr (Intercept) 1.242702e+05
## 8 magrittr size 2.905782e+04
## 9 magrittr sizesq 3.409400e-01
## 10 TidyPipe (Intercept) 1.667088e+05
## 11 TidyPipe size 8.915924e+04
## 12 TidyPipe sizesq 6.327880e+01
WinVector/wrapr documentation built on Aug. 29, 2023, 4:51 a.m.
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Some timings for %.>% ("dot arrow").
Keep in mind for any serious application the calculation time on data will far dominate any piping overhead, but it is fun to look.
So we will compare:
magrittr*magrittr::%>%piping.DotArrow*wrapr::%.>%piping.BizarroPipe*->.;piping.TidyPipe*%>%piping based onrlang/tidyeval(renamed to "%t>%" in this run to avoid name collisions).
library("microbenchmark")
library("wrapr")
suppressPackageStartupMessages(library("ggplot2"))
suppressPackageStartupMessages(library("dplyr"))
library("glmnet")
## Loading required package: Matrix
## Loading required package: foreach
## Loaded glmnet 2.0-10
source('rlangPipe.R')
# load generated examples
prevNames <- ls()
source("pGenFns.R")
genFns <- setdiff(ls(), c(prevNames, 'prevNames', 'genFns'))
# parser translates BizarroPipe to different code!
cat(buildFnsK(5), sep = '\n')
## magrittr_5 <- function() {
## 5 %>%
## sin(.) %>%
## sin(.) %>%
## sin(.) %>%
## sin(.) %>%
## sin(.)
## }
##
##
##
## DotArrow_5 <- function() {
## 5 %.>%
## sin(.) %.>%
## sin(.) %.>%
## sin(.) %.>%
## sin(.) %.>%
## sin(.)
## }
##
##
##
## BizarroPipe_5 <- function() {
## 5 ->.;
## sin(.) ->.;
## sin(.) ->.;
## sin(.) ->.;
## sin(.) ->.;
## sin(.)
## }
##
##
##
## TidyPipe_5 <- function() {
## 5 %t>%
## sin(.) %t>%
## sin(.) %t>%
## sin(.) %t>%
## sin(.) %t>%
## sin(.)
## }
print(BizarroPipe_5)
## function ()
## {
## . <- 5
## . <- sin(.)
## . <- sin(.)
## . <- sin(.)
## . <- sin(.)
## sin(.)
## }
BizarroPipe_10()
## [1] -0.4774053
DotArrow_10()
## [1] -0.4774053
magrittr_10()
## [1] -0.4774053
# get expressions into a nice presentation order
fList <- data.frame(expr= genFns,
stringsAsFactors = FALSE)
fList$size <- as.numeric(gsub("[^0-9]+", "", fList$expr))
fList$fn <- gsub("[_0-9].*$", "", fList$expr)
fList <- fList[order(fList$size, fList$fn), , drop=FALSE]
cmd <- parse(text=paste0(
"microbenchmark(\n ",
paste(paste0(fList$expr,'()'), collapse=',\n '),
",
times=1000L
)\n"
))
print(cmd)
## expression(microbenchmark(BizarroPipe_1(), DotArrow_1(), magrittr_1(),
## TidyPipe_1(), BizarroPipe_2(), DotArrow_2(), magrittr_2(),
## TidyPipe_2(), BizarroPipe_5(), DotArrow_5(), magrittr_5(),
## TidyPipe_5(), BizarroPipe_10(), DotArrow_10(), magrittr_10(),
## TidyPipe_10(), BizarroPipe_15(), DotArrow_15(), magrittr_15(),
## TidyPipe_15(), BizarroPipe_20(), DotArrow_20(), magrittr_20(),
## TidyPipe_20(), BizarroPipe_25(), DotArrow_25(), magrittr_25(),
## TidyPipe_25(), BizarroPipe_50(), DotArrow_50(), magrittr_50(),
## TidyPipe_50(), BizarroPipe_100(), DotArrow_100(), magrittr_100(),
## TidyPipe_100(), BizarroPipe_200(), DotArrow_200(), magrittr_200(),
## TidyPipe_200(), BizarroPipe_500(), DotArrow_500(), magrittr_500(),
## TidyPipe_500(), BizarroPipe_1000(), DotArrow_1000(), magrittr_1000(),
## TidyPipe_1000(), times = 1000L))
gc()
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 1328828 71 2164898 115.7 1770749 94.6
## Vcells 1440666 11 2552219 19.5 2332288 17.8
datFile <- 'timings.RDS'
if(!file.exists(datFile)) {
print("running")
bm <- eval(cmd)
saveRDS(bm, file=datFile)
} else {
print("using cached results")
bm <- readRDS(file=datFile)
}
## [1] "using cached results"
print(bm)
## Unit: nanoseconds
## expr min lq mean median
## BizarroPipe_1() 311 1601.0 3.151041e+03 1852.5
## DotArrow_1() 3830 7593.0 1.192143e+04 9975.0
## magrittr_1() 50716 79380.0 1.080513e+05 103043.5
## TidyPipe_1() 73431 88769.0 1.327619e+05 111100.0
## BizarroPipe_2() 296 1737.0 3.943770e+03 1999.0
## DotArrow_2() 7564 12968.0 1.871724e+04 16515.0
## magrittr_2() 73497 107428.0 1.448151e+05 139355.5
## TidyPipe_2() 147710 173540.5 2.396801e+05 216920.0
## BizarroPipe_5() 398 1965.5 5.850010e+03 2219.0
## DotArrow_5() 18056 26495.5 3.959289e+04 30860.5
## magrittr_5() 141619 184236.0 2.344888e+05 220201.0
## TidyPipe_5() 360258 413047.0 5.291932e+05 467284.0
## BizarroPipe_10() 655 2258.5 8.261371e+03 2551.5
## DotArrow_10() 34342 46855.5 6.760176e+04 52749.5
## magrittr_10() 248196 306015.5 3.839070e+05 348933.5
## TidyPipe_10() 722322 807837.0 1.048367e+06 915988.5
## BizarroPipe_15() 808 2377.0 1.067373e+04 2714.5
## DotArrow_15() 52821 69429.5 1.012108e+05 76585.5
## magrittr_15() 357424 427377.5 5.298084e+05 472379.0
## TidyPipe_15() 1073739 1212790.5 1.509328e+06 1348848.0
## BizarroPipe_20() 933 2793.0 1.365327e+04 3135.5
## DotArrow_20() 71670 88752.5 1.234716e+05 96780.5
## magrittr_20() 456209 548567.0 7.025874e+05 601202.5
## TidyPipe_20() 1449365 1612122.0 1.956197e+06 1746227.5
## BizarroPipe_25() 1108 2593.0 1.602300e+04 2959.0
## DotArrow_25() 90575 109082.5 1.578983e+05 117980.0
## magrittr_25() 567666 670062.5 8.588992e+05 733624.0
## TidyPipe_25() 1810243 2015445.5 2.458534e+06 2181966.0
## BizarroPipe_50() 1977 3648.0 2.836560e+04 4005.5
## DotArrow_50() 178338 211978.0 3.181890e+05 229723.5
## magrittr_50() 1095177 1271808.5 1.609811e+06 1368693.0
## TidyPipe_50() 3695252 4047442.5 4.860431e+06 4335901.5
## BizarroPipe_100() 3747 5619.0 5.799286e+04 6006.5
## DotArrow_100() 363029 428630.0 6.226110e+05 467099.5
## magrittr_100() 2146549 2443022.0 3.167060e+06 2612558.5
## TidyPipe_100() 7578036 8406441.0 1.001283e+07 9016541.5
## BizarroPipe_200() 7296 9405.5 1.212623e+05 9908.0
## DotArrow_200() 748345 908387.0 1.293762e+06 975902.0
## magrittr_200() 4283529 4868351.5 6.075153e+06 5232996.5
## TidyPipe_200() 16371135 17895141.5 2.075790e+07 19047060.0
## BizarroPipe_500() 17764 21088.0 2.729626e+05 21557.0
## DotArrow_500() 2187918 2427491.0 3.050825e+06 2566260.0
## magrittr_500() 10663756 12087194.5 1.459132e+07 13224984.5
## TidyPipe_500() 49190881 54638294.5 6.022440e+07 58984792.0
## BizarroPipe_1000() 35311 40418.5 5.639057e+05 40993.0
## DotArrow_1000() 4664676 5086418.0 6.558716e+06 5341756.5
## magrittr_1000() 22003314 24703752.0 2.955337e+07 27977892.5
## TidyPipe_1000() 132999342 145483873.0 1.526779e+08 151929305.0
## uq max neval
## 2151.0 1268353 1000
## 12878.0 1549847 1000
## 123564.0 2110539 1000
## 171944.0 1479901 1000
## 2346.5 1908292 1000
## 18854.5 2299005 1000
## 161709.0 2150558 1000
## 282054.0 2397078 1000
## 2542.5 3559475 1000
## 35994.0 7195352 1000
## 257735.0 5801282 1000
## 582768.5 4146943 1000
## 2865.0 5645097 1000
## 63530.0 7756307 1000
## 418719.5 7812035 1000
## 1114030.5 16735656 1000
## 3135.5 7945124 1000
## 92641.5 12633368 1000
## 568128.5 10597817 1000
## 1640941.5 18130302 1000
## 3503.0 10499545 1000
## 115903.5 14438773 1000
## 758026.5 14623217 1000
## 2185758.0 16645950 1000
## 3318.5 13025408 1000
## 153371.5 17728721 1000
## 926632.5 22121278 1000
## 2742286.5 23128547 1000
## 4428.0 24268766 1000
## 298828.0 36442295 1000
## 1792959.0 40136380 1000
## 5619984.0 41135537 1000
## 6577.5 51726984 1000
## 645563.0 73742020 1000
## 3524055.5 109609792 1000
## 11363021.0 117788527 1000
## 10529.5 111070907 1000
## 1298696.5 151673919 1000
## 7131093.5 147704377 1000
## 22690128.5 149092740 1000
## 22297.5 250502705 1000
## 3666790.5 11351237 1000
## 16727506.0 123085711 1000
## 63261025.5 164081074 1000
## 42335.0 521124074 1000
## 7757298.0 115883484 1000
## 32264343.0 137927671 1000
## 157734354.5 480277048 1000
autoplot(bm)
d <- as.data.frame(bm)
d$size <- as.numeric(gsub("[^0-9]+", "", d$expr))
d$fn <- gsub("[_0-9].*$", "", d$expr)
d$fn <- reorder(d$fn, d$time)
ggplot(d, aes(x=fn, y=time, color=fn)) +
geom_violin() +
scale_y_log10() +
facet_wrap(~size, labeller="label_both") +
coord_flip() +
xlab("method") +
ylab("time NS") +
theme(legend.position = 'none') +
scale_color_manual(values = colorAssignment) +
ggtitle("distribution of runtime as function of method and problem size",
subtitle = "log-time scale")
# ggplot 2 legend in reverse order, so re-order to get that
d$fn <- reorder(d$fn, -d$time)
ggplot(d, aes(x=size, y=time, color=fn)) +
geom_smooth() +
scale_y_log10() +
scale_x_log10() +
scale_color_manual(values = colorAssignment) +
xlab('size (number of pipe stages)') +
ylab("time NS") +
ggtitle("complexity of runtime as function of method and problem size",
subtitle = "log/log scale")
## `geom_smooth()` using method = 'gam'
# fit a linear function for runtime as a function of size
# per group.
dfits <- d %.>%
split(., .$fn) %.>%
lapply(.,
function(di) {
mi <- lm(time ~ size + I(size*size), data=di)
ctab <- as.data.frame(summary(mi)$coefficients)
ctab$coef <- rownames(ctab)
ctab
}) %.>%
add_name_column(., 'method') %.>%
bind_rows(.) %.>%
arrange(., method, coef) %.>%
select(., method, coef, Estimate, `Std. Error`, `Pr(>|t|)`)
# "Intercept" is roughly start-up cost
# "size" is roughly the slope or growth rate of execution time
# as a function of number of pipe stages.
# "I(size * size)" is where we try to detect super-linear cost,
# check that it is both statistically significant and that
# it has a size that would affect predictions (is it times
# the typical variation in size*size large?).
print(dfits)
## method coef Estimate Std. Error Pr(>|t|)
## 1 BizarroPipe (Intercept) 3.212098e+03 6.296446e+04 9.593148e-01
## 2 BizarroPipe I(size * size) 1.856518e-02 6.832992e-01 9.783247e-01
## 3 BizarroPipe size 5.411272e+02 6.582386e+02 4.110456e-01
## 4 DotArrow (Intercept) 1.754318e+04 2.569601e+04 4.947969e-01
## 5 DotArrow I(size * size) 6.994202e-01 2.788567e-01 1.214890e-02
## 6 DotArrow size 5.830933e+03 2.686294e+02 1.657644e-102
## 7 magrittr (Intercept) 1.273818e+05 4.221528e+04 2.554597e-03
## 8 magrittr I(size * size) 4.011958e-01 4.581263e-01 3.811933e-01
## 9 magrittr size 2.899771e+04 4.413240e+02 0.000000e+00
## 10 TidyPipe (Intercept) 1.815486e+05 7.297796e+04 1.287005e-02
## 11 TidyPipe I(size * size) 6.356617e+01 7.919672e-01 0.000000e+00
## 12 TidyPipe size 8.887253e+04 7.629210e+02 0.000000e+00
# re-run with non-negative least squares
dfitsnn <- d %.>%
split(., .$fn) %.>%
lapply(.,
function(di) {
di$sizesq <- (di$size)^2
# always call glmnet with a non-trivial lambda series
# some notes: https://github.com/WinVector/wrapr/blob/master/extras/glmnetNotes.md
mi <- glmnet(as.matrix(di[, c('size', 'sizesq')]),
di$time,
lower.limits = 0,
alpha=0.0,
lambda=c(0, 1.0e-5, 1.0e-3, 0.1, 1, 10),
intercept = TRUE,
family = 'gaussian')
ctab <- as.data.frame(as.matrix(coef(mi, s=0)))
# lower.limites does not apply to intercept,
# but intercept is always reported even if
# turned off.
if(ctab['(Intercept)',1]<0) {
mi <- glmnet(as.matrix(di[, c('size', 'sizesq')]),
di$time,
lower.limits = 0,
alpha=0.0,
lambda=c(0, 1.0e-5, 1.0e-3, 0.1, 1, 10),
intercept = FALSE,
family = 'gaussian')
ctab <- as.data.frame(as.matrix(coef(mi, s=0)))
}
names(ctab) <- "Estimate"
ctab$coef <- rownames(ctab)
ctab
}) %.>%
add_name_column(., 'method') %.>%
bind_rows(.) %.>%
arrange(., method, coef) %.>%
select(., method, coef, Estimate)
print(dfitsnn)
## method coef Estimate
## 1 BizarroPipe (Intercept) 2.826857e+03
## 2 BizarroPipe size 5.485702e+02
## 3 BizarroPipe sizesq 1.110500e-02
## 4 DotArrow (Intercept) 1.653898e+04
## 5 DotArrow size 5.850334e+03
## 6 DotArrow sizesq 6.799738e-01
## 7 magrittr (Intercept) 1.242702e+05
## 8 magrittr size 2.905782e+04
## 9 magrittr sizesq 3.409400e-01
## 10 TidyPipe (Intercept) 1.667088e+05
## 11 TidyPipe size 8.915924e+04
## 12 TidyPipe sizesq 6.327880e+01
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