Efficient recursive random sampling

Question

Imagine a df in the following format:

   ID1 ID2
1    A   1
2    A   2
3    A   3
4    A   4
5    A   5
6    B   1
7    B   2
8    B   3
9    B   4
10   B   5
11   C   1
12   C   2
13   C   3
14   C   4
15   C   5

The problem is to randomly select one row (ideally adjustable to n rows) for the first unique value in ID1, remove the corresponding ID2 value from the dataset, randomly select a value from the remaining pool of ID2 values for the second ID1 value (i.e. recursively), and so on.

So, for example, for the first ID1 value, it would do sample(1:5, 1), with the result 2. For the second ID1 value, it would do sample(c(1, 3:5), 1), with the result 3. For the third ID1 value, it would do sample(c(1, 4:5), 1), with the result 5. It cannot happen that there isn't at least one unique ID2 value left to assign to a particular ID1. However, with multiple ID2 values to select (e.g. three), it may happen that there isn't a sufficient number of them; in that case, select as much as possible. In the end, the results should have a similar format:

  ID1 ID2
1   A   2
2   B   3
3   C   5

It should be efficient enough to handle reasonably large datasets (tens of thousands unique values in ID1 and hundreds of thousands unique values per ID2).

I tried multiple ways to solve this problem, but honestly none of them are meaningful and would likely only contribute to confusion, so I'm not sharing them here.

Sample data:

df <- data.frame(ID1 = rep(LETTERS[1:3], each = 5),
                 ID2 = rep(1:5, 3))

Answer 1

Possible Solutions

Below are some approaches:

• base R recursion using Reduce + subset
• max bipartite matching using igraph
• base R dynamic programming using for loops

---

1. Recursion

You can try the code below (Reduce is applied to recursively adding unvisited ID2 values)

lst <- split(df, ~ID1)
lst[[1]] <- lst[[1]][sample(1:nrow(lst[[1]]), 1), ]
Reduce(
  function(x, y) {
    y <- subset(y, !ID2 %in% x$ID2)
    rbind(x, y[sample(nrow(y), 1), ])
  },
  lst
)

which gives

   ID1 ID2
4    A   4
7    B   2
11   C   1

---

2. Bipartite Matching

As we can see, this problem can be interpreted as a matching problem in graph theory

library(igraph)
library(dplyr)

g <- df %>%
  arrange(sample(n())) %>%
  graph_from_data_frame() %>%
  set_vertex_attr(
    name = "type",
    value = names(V(.)) %in% df$ID1
  )

type.convert(
  setNames(
    rev(
      stack(
        max_bipartite_match(g)$matching[unique(df$ID1)]
      )
    ), names(df)
  ),
  as.is = TRUE
)

and we can get

  ID1 ID2
1   A   2
2   B   5
3   C   1

---

3. for loop Dynamic Programming

  lst <- with(df, split(ID2, ID1))
  v <- c()
  for (k in seq_along(lst)) {
    u <- lst[[k]][!lst[[k]] %in% v]
    v <- c(v, u[sample(length(u), 1)])
  }
  type.convert(
    data.frame(ID1 = names(lst), ID2 = v),
    as.is = TRUE
  )

which gives

  ID1 ID2
1   A   4
2   B   5
3   C   3

Answer 2

I think this algorithm does what you want, but it's not very efficient. It may provide others with a starting point for faster solutions.

all_ID1 <- unique(df$ID1)
available <- unique(df$ID2)
new_ID2 <-  numeric(length(all_ID1))

for(i in seq_along(all_ID1))
{
  ID2_group <- df$ID2[df$ID1 == all_ID1[i]]
  sample_space <- ID2_group[ID2_group %in% available]
  new_ID2[i]<- sample(sample_space, 1)
  available <- available[available != new_ID2[i]]
}

data.frame(ID1 = all_ID1, ID2 = new_ID2)
#>   ID1 ID2
#> 1   A   5
#> 2   B   1
#> 3   C   2

Note that this will not work if you run out of unique ID2 values. For example, if you had letters A:F in the ID1 column, each with ID2 values of 1:5, then by the time you get to selecting an ID2 value for the ID1 value "F", there are no unique ID2 values left, since numbers 1 to 5 have all been assigned to letters A:E. You don't state in your question what should happen when there are no unique ID2 values left to assign to a particular ID1 - should they be NA, or are repeats allowed at that point?

---

Edit

The following modification allows arbitrary n to be chosen. If all the available numbers run out, the sample space gets replenished:

AC_function <- function(ID1, ID2, n = 1)
{
  all_ID1   <- rep(unique(ID1), each = n)
  available <- unique(ID2)
  new_ID2   <- numeric(length(all_ID1))

   for(i in seq_along(all_ID1))
   {
     ID2_group    <- ID2[ID1 == all_ID1[i]]
     sample_space <- ID2_group[ID2_group %in% available]
     
     if(length(sample_space) < 1) {
        available    <- unique(ID2)
        sample_space <- ID2_group[ID2_group %in% available]
     }
     if(length(sample_space) == 1) {
        new_ID2[i] <- sample_space
        available <- available[available != new_ID2[i]]
     }
     else {
        new_ID2[i]   <- sample(sample_space, 1)
        available    <- available[available != new_ID2[i]]
     }
   }

  data.frame(ID1 = all_ID1, ID2 = new_ID2)
}

For example:

AC_function(df$ID1, df$ID2)
#>   ID1 ID2
#> 1   A   2
#> 2   B   4
#> 3   C   5

AC_function(df$ID1, df$ID2, n = 2)
#>   ID1 ID2
#> 1   A   1
#> 2   A   2
#> 3   B   5
#> 4   B   4
#> 5   C   3
#> 6   C   2

^{Created on 2021-11-03 by the reprex package (v2.0.0)}

Answer 3

selected <- c()

for(i in unique(df[,1])) {

    x <- df[df[,"ID1"]==i,"ID2"]

    y <- setdiff(x,selected)
    selected <- unique(c(sample(y,1),selected))
    

}

data.frame(ID1 = unique(df[,1]), ID2 =selected)

gives,

  ID1 ID2
1   A   4
2   B   2
3   C   3

Answer 4

Welcome to update the benchmark!

df <- data.frame(
  ID1 = rep(LETTERS, each = 10000),
  ID2 = sample(1000, length(LETTERS) * 10000, replace = TRUE)
)

f_TIC1 <- function() {
  lst <- split(df, ~ID1)
  lst[[1]] <- lst[[1]][sample(1:nrow(lst[[1]]), 1), ]
  Reduce(
    function(x, y) {
      y <- subset(y, !ID2 %in% x$ID2)
      rbind(x, y[sample(nrow(y), 1), ])
    },
    lst
  )
}

library(igraph)
library(dplyr)
f_TIC2 <- function() {
  g <- df %>%
    arrange(sample(n())) %>%
    graph_from_data_frame() %>%
    set_vertex_attr(
      name = "type",
      value = names(V(.)) %in% df$ID1
    )

  type.convert(
    setNames(
      rev(
        stack(
          max_bipartite_match(g)$matching[unique(df$ID1)]
        )
      ), names(df)
    ),
    as.is = TRUE
  )
}

f_TIC3 <- function() {
  lst <- with(df, split(ID2, ID1))
  v <- c()
  for (k in seq_along(lst)) {
    u <- lst[[k]][!lst[[k]] %in% v]
    v <- c(v, u[sample(length(u), 1)])
  }
  type.convert(
    data.frame(ID1 = names(lst), ID2 = v),
    as.is = TRUE
  )
}

f_GKi1 <- function() {
  . <- split(df$ID2, df$ID1)
  data.frame(ID1 = type.convert(names(.), as.is=TRUE),
    ID2 = Reduce(function(x, y) {c(x, sample(y[!y %in% x], 1))}, c(list(NULL), .)))
}

f_GKi2 <- function() {
  . <- split(df$ID2, df$ID1)
  x <- df$ID2[0]
  for(y in .) {
    y <- y[!y %in% x]
    x <- c(x, y[sample.int(length(y),1)])
  }
  data.frame(ID1 = type.convert(names(.), as.is=TRUE), ID2 = x)
}

library(fastmatch)
library(dqrng)
f_GKi3 <- function() {
  . <- split(df$ID2, df$ID1)
  x <- df$ID2[0]
  for(y in .) {
    y <- y[!y %fin% x]
    x <- c(x, y[dqsample.int(length(y),1)])
  }
  data.frame(ID1 = type.convert(names(.), as.is=TRUE), ID2 = x)
}

f_GKi4 <- function() {
  . <- split(df$ID2, df$ID1)
  x <- vector(typeof(df$ID2), length(.))
  for(i in seq_along(.)) {
    y <- .[[i]]
    y <- y[!y %fin% x[seq_len(i-1)]]
    x[i] <- y[dqsample.int(length(y),1)]
  }
  data.frame(ID1 = type.convert(names(.), as.is=TRUE), ID2 = x)
}

f_Onyambu <- function() {
  data <- df[order(df$ID1, df$ID2),] #Just in case it is not sorted
  n <- 1
  st <- table(data[[1]])
  s <- min(st)
  m <- length(st) 
  size <- min(m*n, s) 
  samples <- sample(s, size)
  index <- rep(seq(s), each = n, length = size) * s - s + samples
  data[index, ]
}

bm <- microbenchmark::microbenchmark(
  f_TIC1(),
  f_TIC2(),
  f_TIC3(),
  f_GKi1(),
  f_GKi2(),
  f_GKi3(),
  f_GKi4(),
  f_Onyambu()
)
ggplot2::autoplot(bm)
bm
#Unit: milliseconds
#        expr       min        lq      mean    median        uq       max neval
#    f_TIC1()  43.85147  46.00637  48.77332  46.53265  48.06150  86.60333   100
#    f_TIC2() 138.12085 143.15468 154.59155 146.49701 169.47343 191.70579   100
#    f_TIC3()  13.30333  13.89822  15.16400  14.49575  15.57266  52.16352   100
#    f_GKi1()  13.42718  13.88382  16.22395  14.31689  15.69188  52.70818   100
#    f_GKi2()  13.34032  13.80074  14.70703  14.52709  15.46372  17.80398   100
#    f_GKi3()  11.86203  12.09923  14.73456  12.26890  13.84257  50.41542   100
#    f_GKi4()  11.86614  12.08120  13.19142  12.20973  13.74152  50.82025   100
# f_Onyambu() 201.06478 203.11184 206.04584 204.10129 205.60191 242.28008   100

Currently GKi3 and GKi4 are the fastest followed by TIC3, GKi1 and GKi2 which are more or less equal as they use the same logic from TIC1, which was optimizes in GKi1 and reused in TIC3 and GKi2.

Answer 5

You can use sample in Reduce on the split df.

df <- data.frame(ID1 = rep(LETTERS[1:3], each = 5),
                 ID2 = rep(1:5, 3))
set.seed(42)

. <- split(df$ID2, df$ID1)
data.frame(ID1 = `storage.mode<-`(names(.), typeof(df$ID1)),
           ID2 = Reduce(function(x, y) {
             y <- y[!y %in% x]
             c(x, y[sample.int(length(y),1)])}, c(list(NULL), .)))
#  ID1 ID2
#1   A   1
#2   B   2
#3   C   3

Or using a for loop:

. <- split(df$ID2, df$ID1)
x <- df$ID2[0]
for(y in .) {
  y <- y[!y %in% x]
  x <- c(x, y[sample.int(length(y),1)])
}
data.frame(ID1 = `storage.mode<-`(names(.), typeof(df$ID1)), ID2 = x)
#  ID1 ID2
#1   A   1
#2   B   2
#3   C   3

Or using fastmatch and dqrng instead of base:

library(fastmatch)
library(dqrng)
. <- split(df$ID2, df$ID1)
x <- df$ID2[0]
for(y in .) {
  y <- y[!y %fin% x]
  x <- c(x, y[dqsample.int(length(y),1)])
}
data.frame(ID1 = `storage.mode<-`(names(.), typeof(df$ID1)), ID2 = x)
#  ID1 ID2
#1   A   2
#2   B   1
#3   C   5

and creating the result vector with final size:

library(fastmatch)
library(dqrng)
. <- split(df$ID2, df$ID1)
x <- vector(typeof(df$ID2), length(.))
for(i in seq_along(.)) {
  y <- .[[i]]
  y <- y[!y %fin% x[seq_len(i-1)]]
  x[i] <- y[dqsample.int(length(y),1)]
}
data.frame(ID1 = `storage.mode<-`(names(.), typeof(df$ID1)), ID2 = x)
#  ID1 ID2
#1   A   3
#2   B   1
#3   C   2

Answer 6

DISCLAIMER: This solution assumes that the data is arranged/ordered. If the data is not ordered. Please order it according to the ID1 column first then use the function:

There is another way of doing this without using for-loop/ Recursion or even higher level functions. We need to note that sample function in R is vectorized. Therefore, if all the groups in your dataframe are of the same size, or rather increasing in size, then you could make use of the vectorized sample.

n <- 1 # to be sampled from each group
s <- 5 # size of each group - Note that you have to give the minimum size. 
m <- length(unique(df[[1]])) # number of groups.
size <- min(m*n, s) #Total number of sampled data from the dataframe
samples <- sample(s, size)
index <- rep(seq(s), each = n, length = size) * s - s + samples
df[index, ]

This can be written in a function:

sub_sample <- function(data, n){
  st <- table(data[[1]])
  s <- min(st)
  m <- length(st) 
  size <- min(m*n, s) 
  samples <- sample(s, size)
  st1 <- rep(c(0, cumsum(head(st,-1))), each = n, length = size)
  index <- st1 + samples
  data[index, ]
}

sub_sample(df, 1)
   ID1 ID2
1    A   1
7    B   2
13   C   3

sub_sample(df, 2)
   ID1 ID2
1    A   1
5    A   5
8    B   3
7    B   2
14   C   4

Note that when subsetting n=2 we only have 1 group C row. Why? That is because group C has 5 rows. But we have already used 4 samples for groups A and B. We only remain with 1 sample for group C.

Speed Test:

when n = 1:

Unit: milliseconds
              expr        min         lq      mean     median        uq       max neval
          f_TIC1()  35.682390  41.610310  53.68912  45.618234  49.88343 227.73160   100
          f_TIC2() 151.643959 166.402584 196.51770 179.098992 192.16335 401.36526   100
          f_TIC3()  11.059033  12.268831  14.53906  13.278606  15.38623  23.32695   100
          f_GKi1()  10.725358  11.879908  14.70369  13.108852  17.86946  26.71074   100
          f_GKi2()  10.816891  11.941173  16.55455  12.989787  17.92708 198.44482   100
          f_GKi3()   8.942479   9.950978  14.94726  10.857187  13.35428 171.08643   100
          f_GKi4()   9.085794   9.834465  13.98820  10.666282  13.20658 191.47267   100
 sub_sample(df, 1)   7.878367   8.737534  11.22173   9.508084  14.22219  19.82063   100

When n>1, This code easily tackles that. The others need to be tweaked alittle but their speed drops drastically. This method works like a charm even when n = group size. Most of others take too long or even fail