R data.table 滚动最大值

Question

我想在 data.table 中使用(自适应)非 equi 连接来执行滚动最大值。以 this answer 为基础，我想我已经接近了，但还不是很接近。

对于 ex 的所有行，我需要 max(value[row:end_window])

> ex
                    time    value end_window row
  1: 2022-03-14 08:20:02 13344.77        540   1
  2: 2022-03-14 08:20:02 13343.52        541   2
  3: 2022-03-14 08:20:03 13342.27        547   3
  4: 2022-03-14 08:20:04 13343.27        541   4
  5: 2022-03-14 08:20:04 13343.02        541   5

我尝试像这样计算 rollmax:

res <-ex[ex,.(value,row,end_window),on=.(row>=row,row<=end_window)][
  ,.(rollmax = max(value), end_window),by=.(row)]

但这并不是我所期望的。对于 row 1 是正确的，因为 max(ex$value[1:540]) 确实是 13361.77。

res
   row  rollmax end_window
1:   1 13361.77        540
2:   1 13361.77        541
3:   1 13361.77        547
4:   1 13361.77        541

但是，对于第 596 行，情况并非如此:

> res <- res[order(row)]
> res[596]
   row end_window  rollmax end_window row
1: 596        622 13327.42        622 596
> max(ex$value[596:622])
[1] 13328.67

有什么想法吗？

数据:

> dput(ex)
structure(list(time = structure(c(1647246002.24923, 1647246002.79761, 
1647246003.33618, 1647246004.39149, 1647246004.44229, 1647246005.04222, 
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1647246007.82673, 1647246007.86367, 1647246008.44411, 1647246008.98759, 
1647246009.02739, 1647246010.60053, 1647246011.15027, 1647246011.17698, 
1647246011.72366, 1647246012.79303, 1647246013.33196, 1647246013.87512, 
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13346.52, 13344.02, 13343.27, 13342.27, 13342.02, 13342.52, 13342.27, 
13342.52, 13343.27, 13343.77, 13344.02, 13344.77, 13344.02, 13344.52, 
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13345.52, 13345.02, 13345.52, 13344.52, 13345.77, 13345.02, 13345.52, 
13345.27, 13345.02, 13345.52, 13345.27, 13345.02, 13344.52, 13344.02, 
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13344.42, 13344.52, 13344.92, 13345.27, 13344.92, 13344.17, 13343.92, 
13343.92, 13344.17, 13344.52, 13346.02, 13345.92, 13345.67, 13345.52, 
13345.42, 13345.67, 13346.17, 13345.67, 13346.17, 13346.67, 13348.05, 
13348.67, 13347.42, 13347.77, 13347.67, 13348.52, 13348.42, 13348.17, 
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13348.27, 13347.77, 13347.92, 13348.77, 13350.17, 13351.02, 13350.67, 
13350.77, 13350.42, 13350.27, 13349.92, 13350.02, 13347.67, 13348.17, 
13346.02, 13345.42, 13345.52, 13344.92, 13345.17, 13346.02, 13346.17, 
13346.02, 13344.42, 13344.67, 13344.52, 13343.27, 13344.02, 13343.27, 
13343.02, 13341.77, 13340.02, 13341.02, 13341.27, 13339.77, 13339.27, 
13337.77, 13337.42, 13339.17, 13338.52, 13338.02, 13337.27, 13335.52, 
13334.27, 13334.62, 13334.27, 13334.37, 13333.77, 13333.12, 13332.92, 
13333.42, 13331.42, 13329.92, 13329.92, 13330.27, 13329.67, 13330.27, 
13329.77, 13331.02, 13331.27, 13332.02, 13330.77, 13332.02, 13331.52, 
13331.02, 13332.02, 13332.52, 13331.77, 13332.27, 13329.52, 13329.77, 
13329.52, 13329.27, 13329.02, 13329.52, 13329.77, 13330.02, 13329.77, 
13329.02, 13329.27, 13329.52, 13328.77, 13329.02, 13329.67, 13330.17, 
13329.02, 13329.52, 13329.02, 13328.17, 13329.02, 13328.67, 13327.42, 
13327.77, 13328.67, 13327.42, 13327.17, 13327.42, 13326.77, 13327.92, 
13327.52, 13326.92, 13326.92, 13327.42, 13325.92, 13326.42, 13325.42, 
13323.17, 13323.67, 13324.42, 13324.27, 13323.55, 13324.92, 13324.77, 
13324.02, 13323.17, 13323.02, 13322.52, 13322.27), end_window = c(540L, 
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---

编辑:下面的方法执行正确的操作，但当应用于 1M 行时，它会在几秒钟内轻松填满 64gb 的 RAM。

idxlist_ <- list()
for(i in 1:nrow(ex)) {
  if(!is.na(ex$end_window[i])) {
    idxlist_[[i]] <- 0:(ex$end_window[i]-i)
  } else(
    idxlist_[[i]] <- 0
  )
}
res <- rollapplyr(ex$value, width=idxlist_, max, align = 'left', fill=NA)
> res[596]
[1] 13328.67
> max(ex$value[596:622])
[1] 13328.67

Answer 1

只取 end_window 最后一个实例的第一个元素似乎可以解决眼前的问题，但使用 .EACHI 的建议是正确的。对于大型数据集，join 会比简单的 group 操作更快。

ex[, end_window := pmin(end_window, .N)] # don't search beyond the extent of ex

# join solution
f1 <- function(ex) ex[ex, .(rollmax = value, row, end_window), on = .(row >= row, row <= end_window)][, .(rollmax = max(rollmax), end_window = end_window[1]), row]
# grouping operation
f2 <- function(ex) ex[, .(rollmax = max(ex$value[row:end_window]), end_window), row]
# join by .EACHI from jangorecki's suggestion
f3 <- function(ex) setnames(setcolorder(ex[ex, .(max(value)), on = .(row >= row, row <= end_window), .EACHI], c(1,3,2)), c("row", "rollmax", "end_window"))

res1 <- f1(ex)
res2 <- f2(ex)
res3 <- f3(ex)
f1(ex)[596]
#>    row  rollmax end_window
#> 1: 596 13328.67        622
f2(ex)[596]
#>    row  rollmax end_window
#> 1: 596 13328.67        622
f3(ex)[596]
#>    row  rollmax end_window
#> 1: 596 13328.67        622
max(ex$value[596:622])
#> [1] 13328.67

microbenchmark::microbenchmark(f1 = f1(ex),
                               f2 = f2(ex),
                               f3 = f3(ex),
                               check = "identical")
#> Unit: milliseconds
#>  expr    min      lq     mean  median      uq     max neval
#>    f1 7.5080 8.66995 9.886949 9.18510 9.73970 21.3667   100
#>    f2 3.2476 3.70190 4.641003 4.19860 5.07995 14.8099   100
#>    f3 3.9210 4.73330 5.323673 5.14675 5.73335 13.9769   100

在更大的数据集上尝试 f2 和 f3。令人惊讶的是，现在连接速度更快了。

ex <- data.table(value = cumsum(rnorm(1e6, 0.1)), end_window = 1:1e6 + sample(50:500, 1e6, TRUE), row = 1:1e6)[, end_window := pmin(end_window, .N)]
microbenchmark::microbenchmark(f2 = f2(ex),
                               f3 = f3(ex),
                               times = 10,
                               check = "identical")
#> Unit: seconds
#>  expr      min       lq     mean   median       uq      max neval
#>    f2 5.730260 5.802243 7.053432 7.080656 8.146056 8.395844    10
#>    f3 2.607298 2.998779 3.302622 3.396466 3.695056 3.745173    10