Adaptive moving average - top performance in R

data.table, moving-average, r, rolling-computation

Solution

December 2018 update

Efficient implementation of adaptive rolling functions has been made in data.table recently - more info in ?froll manual. Additionally an efficient alternative solution using base R has been identified (`fastama` below). Unfortunately Kevin Ushey's answer does not address the question thus it is not included in benchmark. Scale of benchmark has been increased as it pointless to compare microseconds.

set.seed(108)
x = rnorm(1e6)
width = rep(seq(from = 100, to = 500, by = 5), length.out=length(x))
microbenchmark(
  zoo=rollapplyr(x, width = width, FUN=mean, fill=NA),
  mapply=base_mapply(x, width=width, FUN=mean, na.rm=T),
  wmapply=wmapply(x, width=width, FUN=mean, na.rm=T),
  ama=ama(x, width, na.rm=T),
  fastama=fastama(x, width),
  frollmean=frollmean(x, width, na.rm=T, adaptive=TRUE),
  frollmean_exact=frollmean(x, width, na.rm=T, adaptive=TRUE, algo="exact"),
  times=1L
)
#Unit: milliseconds
#            expr          min           lq         mean       median           uq          max neval
#             zoo 32371.938248 32371.938248 32371.938248 32371.938248 32371.938248 32371.938248     1
#          mapply 13351.726032 13351.726032 13351.726032 13351.726032 13351.726032 13351.726032     1
#         wmapply 15114.774972 15114.774972 15114.774972 15114.774972 15114.774972 15114.774972     1
#             ama  9780.239091  9780.239091  9780.239091  9780.239091  9780.239091  9780.239091     1
#         fastama   351.618042   351.618042   351.618042   351.618042   351.618042   351.618042     1
#       frollmean     7.708054     7.708054     7.708054     7.708054     7.708054     7.708054     1
# frollmean_exact   194.115012   194.115012   194.115012   194.115012   194.115012   194.115012     1

ama = function(x, n, na.rm=FALSE, fill=NA, nf.rm=FALSE) {
  # more or less the same as previous forloopply
  stopifnot((nx<-length(x))==length(n))
  if (nf.rm) x[!is.finite(x)] = NA_real_
  ans = rep(NA_real_, nx)
  for (i in seq_along(x)) {
    ans[i] = if (i >= n[i])
      mean(x[(i-n[i]+1):i], na.rm=na.rm)
    else as.double(fill)
  }
  ans
}
fastama = function(x, n, na.rm, fill=NA) {
  if (!missing(na.rm)) stop("fast adaptive moving average implemented in R does not handle NAs, input having NAs will result in incorrect answer so not even try to compare to it")
  # fast implementation of adaptive moving average in R, in case of NAs incorrect answer
  stopifnot((nx<-length(x))==length(n))
  cs = cumsum(x)
  ans = rep(NA_real_, nx)
  for (i in seq_along(cs)) {
    ans[i] = if (i == n[i])
      cs[i]/n[i]
    else if (i > n[i])
      (cs[i]-cs[i-n[i]])/n[i]
    else as.double(fill)
  }
  ans
}

Old answer:

I chose 4 available solutions which doesn't need to do to C++, quite easy to find or google.

# 1. rollapply
library(zoo)
?rollapplyr
# 2. mapply
base_mapply <- function(x, width, FUN, ...){
  FUN <- match.fun(FUN)
  f <- function(i, width, data){
    if(i < width) return(NA_real_)
    return(FUN(data[(i-(width-1)):i], ...))
  }
  mapply(FUN = f, 
         seq_along(x), width,
         MoreArgs = list(data = x))
}
# 3. wmapply - modified version of wapply found: https://rmazing.wordpress.com/2013/04/23/wapply-a-faster-but-less-functional-rollapply-for-vector-setups/
wmapply <- function(x, width, FUN = NULL, ...){
  FUN <- match.fun(FUN)
  SEQ1 <- 1:length(x)
  SEQ1[SEQ1 <  width] <- NA_integer_
  SEQ2 <- lapply(SEQ1, function(i) if(!is.na(i)) (i - (width[i]-1)):i)
  OUT <- lapply(SEQ2, function(i) if(!is.null(i)) FUN(x[i], ...) else NA_real_)
  return(base:::simplify2array(OUT, higher = TRUE))
}
# 4. forloopply - simple loop solution
forloopply <- function(x, width, FUN = NULL, ...){
  FUN <- match.fun(FUN)
  OUT <- numeric()
  for(i in 1:length(x)) {
    if(i < width[i]) next
    OUT[i] <- FUN(x[(i-(width[i]-1)):i], ...)
  }
  return(OUT)
}

Below are the timings for `prod` function. `mean` function might be already optimized inside `rollapplyr`. All results equal.

library(microbenchmark)
# 1a. length(x) = 1000, window = 5-20
x <- runif(1000,0.5,1.5)
width <- rep(seq(from = 5, to = 20, by = 5), length(x)/4)
microbenchmark(
  rollapplyr(data = x, width = width, FUN = prod, fill = NA),
  base_mapply(x = x, width = width, FUN = prod, na.rm=T),
  wmapply(x = x, width = width, FUN = prod, na.rm=T),
  forloopply(x = x, width = width, FUN = prod, na.rm=T),
  times=100L
)
Unit: milliseconds
                                                       expr       min        lq    median       uq       max neval
 rollapplyr(data = x, width = width, FUN = prod, fill = NA) 59.690217 60.694364 61.979876 68.55698 153.60445   100
   base_mapply(x = x, width = width, FUN = prod, na.rm = T) 14.372537 14.694266 14.953234 16.00777  99.82199   100
       wmapply(x = x, width = width, FUN = prod, na.rm = T)  9.384938  9.755893  9.872079 10.09932  84.82886   100
    forloopply(x = x, width = width, FUN = prod, na.rm = T) 14.730428 15.062188 15.305059 15.76560 342.44173   100

# 1b. length(x) = 1000, window = 50-200
x <- runif(1000,0.5,1.5)
width <- rep(seq(from = 50, to = 200, by = 50), length(x)/4)
microbenchmark(
  rollapplyr(data = x, width = width, FUN = prod, fill = NA),
  base_mapply(x = x, width = width, FUN = prod, na.rm=T),
  wmapply(x = x, width = width, FUN = prod, na.rm=T),
  forloopply(x = x, width = width, FUN = prod, na.rm=T),
  times=100L
)
Unit: milliseconds
                                                       expr      min       lq   median       uq      max neval
 rollapplyr(data = x, width = width, FUN = prod, fill = NA) 71.99894 74.19434 75.44112 86.44893 281.6237   100
   base_mapply(x = x, width = width, FUN = prod, na.rm = T) 15.67158 16.10320 16.39249 17.20346 103.6211   100
       wmapply(x = x, width = width, FUN = prod, na.rm = T) 10.88882 11.54721 11.75229 12.19790 106.1170   100
    forloopply(x = x, width = width, FUN = prod, na.rm = T) 15.70704 16.06983 16.40393 17.14210 108.5005   100

# 2a. length(x) = 10000, window = 5-20
x <- runif(10000,0.5,1.5)
width <- rep(seq(from = 5, to = 20, by = 5), length(x)/4)
microbenchmark(
  rollapplyr(data = x, width = width, FUN = prod, fill = NA),
  base_mapply(x = x, width = width, FUN = prod, na.rm=T),
  wmapply(x = x, width = width, FUN = prod, na.rm=T),
  forloopply(x = x, width = width, FUN = prod, na.rm=T),
  times=100L
)
Unit: milliseconds
                                                       expr       min       lq   median       uq       max neval
 rollapplyr(data = x, width = width, FUN = prod, fill = NA) 753.87882 781.8789 809.7680 872.8405 1116.7021   100
   base_mapply(x = x, width = width, FUN = prod, na.rm = T) 148.54919 159.9986 231.5387 239.9183  339.7270   100
       wmapply(x = x, width = width, FUN = prod, na.rm = T)  98.42682 105.2641 117.4923 183.4472  245.4577   100
    forloopply(x = x, width = width, FUN = prod, na.rm = T) 533.95641 602.0652 646.7420 672.7483  922.3317   100

# 2b. length(x) = 10000, window = 50-200
x <- runif(10000,0.5,1.5)
width <- rep(seq(from = 50, to = 200, by = 50), length(x)/4)
microbenchmark(
  rollapplyr(data = x, width = width, FUN = prod, fill = NA),
  base_mapply(x = x, width = width, FUN = prod, na.rm=T),
  wmapply(x = x, width = width, FUN = prod, na.rm=T),
  forloopply(x = x, width = width, FUN = prod, na.rm=T),
  times=100L
)
Unit: milliseconds
                                                       expr      min       lq    median        uq       max neval
 rollapplyr(data = x, width = width, FUN = prod, fill = NA) 912.5829 946.2971 1024.7245 1071.5599 1431.5289   100
   base_mapply(x = x, width = width, FUN = prod, na.rm = T) 171.3189 180.6014  260.8817  269.5672  344.4500   100
       wmapply(x = x, width = width, FUN = prod, na.rm = T) 123.1964 131.1663  204.6064  221.1004  484.3636   100
    forloopply(x = x, width = width, FUN = prod, na.rm = T) 561.2993 696.5583  800.9197  959.6298 1273.5350   100

Problem

I am looking for some performance gains in terms of rolling/sliding window functions in R. It is quite common task which can be used in any ordered observations data set. I would like to share some of my findings, maybe somebody would be able to provide feedback to make it even faster. Important note is that I focus on the case `align="right"` and adaptive rolling window, so `width` is a vector (same length as our observation vector). In case if we have `width` as scalar there are already very well developed functions in `zoo` and `TTR` packages which would be very hard to beat (4 years later: it was easier than I expected) as some of them are even using Fortran (but still user-defined FUNs can be faster using mentioned below `wapply`). `RcppRoll` package is worth to mention due to its great performance, but so far there is no function which answers to that question. Would be great if someone could extend it to answer the question. Consider we have a following data: ``` x = c(120,105,118,140,142,141,135,152,154,138,125,132,131,120) plot(x, type="l") ``` And we want to apply rolling function over `x` vector with variable rolling window `width`. ``` set.seed(1) width = sample(2:4,length(x),TRUE) ``` In this particular case we would have rolling function adaptive to `sample` of `c(2,3,4)`. We will apply `mean` function, expected results: ``` r = f(x, width, FUN = mean) print(r) ## [1] NA NA 114.3333 120.7500 141.0000 135.2500 139.5000 ## [8] 142.6667 147.0000 146.0000 131.5000 128.5000 131.5000 127.6667 plot(x, type="l") lines(r, col="red") ``` Any indicator can be employed to produce `width` argument as different variants of adaptive moving averages, or any other function. Looking for a top performance.

Original source