R data.table - 不同抽样比例的分组抽样

R data.table - sample by group with different sampling proportion

我想从 data.table 中高效地按组随机抽样,但应该可以为每个组抽取不同比例的样本。

如果我想从每个组中抽取分数 sampling_fraction,我可以从 this question and related 的答案中得到启发,做如下事情:

DT = data.table(a = sample(1:2), b = sample(1:1000,20))

group_sampler <- function(data, group_col, sample_fraction){
  # this function samples sample_fraction <0,1> from each group in the data.table
  # inputs:
  #   data - data.table
  #   group_col - column(s) used to group by
  #   sample_fraction - a value between 0 and 1 indicating what % of each group should be sampled
  data[,.SD[sample(.N, ceiling(.N*sample_fraction))],by = eval(group_col)]
}

# what % of data should be sampled
sampling_fraction = 0.5

# perform the sampling
sampled_dt <- group_sampler(DT, 'a', sampling_fraction)

但是如果我想从第 1 组抽样 10%,从第 2 组抽样 50% 怎么办?

您可以使用 .GRP,但要确保匹配正确的组。您可能需要将 group_col 定义为因子变量。

group_sampler <- function(data, group_col, sample_fractions) {
  # this function samples sample_fraction <0,1> from each group in the data.table
  # inputs:
  #   data - data.table
  #   group_col - column(s) used to group by
  #   sample_fraction - a value between 0 and 1 indicating what % of each group should be sampled
  stopifnot(length(sample_fractions) == uniqueN(data[[group_col]]))
  data[, .SD[sample(.N, ceiling(.N*sample_fractions[.GRP]))], keyby = group_col]
}

编辑以回应 chinsoon12 的评论:

让函数的最后一行更安全(而不是依赖正确的顺序):

data[, .SD[sample(.N, ceiling(.N*sample_fractions[[unlist(.BY)]]))], keyby = group_col]

然后将 sample_fractions 作为命名向量传递:

group_sampler(DT, 'a', sample_fractions= c(x = 0.1, y = 0.9))

这是一个使用查找 table 的选项(因此不依赖于向量或组的排序)。

library(data.table)
DT = data.table(group = sample(1:2), val = sample(1:1000,20))

sample_props <- data.table(group = 1:2, prop = c(.1,.5))

group_sampler <- function(data, group_col, sample_props){
  # this function samples sample_fraction <0,1> from each group in the data.table
  # inputs:
  #   data - data.table with data
  #   group_col - column(s) used to group by (must be in both data.tables)
  #   sample_props - data.table with sample proportions
  ret <- merge(DT, sample_props, by = group_col)
  ret <- ret[,.SD[sample(.N, ceiling(.N*prop))], eval(group_col)]
  return(ret[,prop := NULL][])
}

# perform the sampling
group_sampler(DT, 'group', sample_props)
#>    group val
#> 1:     1 721
#> 2:     2 542
#> 3:     2 680
#> 4:     2 613
#> 5:     2 170
#> 6:     2 175

reprex package (v0.3.0)

于 2019-10-15 创建