R - 缓慢地对有序因子进行排序

Question

基于问题More efficient means of creating a corpus and DTM我已经准备好自己的方法来从大型语料库构建术语文档矩阵，(我希望)不需要术语 x 文档内存。

sparseTDM <- function(vc){
  id = unlist(lapply(vc, function(x){x$meta$id}))
  content = unlist(lapply(vc, function(x){x$content}))
  out = strsplit(content, "\\s", perl = T)
  names(out) = id
  lev.terms = sort(unique(unlist(out)))
  lev.docs = id

  v1 = lapply(
    out,
    function(x, lev) {
      sort(as.integer(factor(x, levels = lev, ordered = TRUE)))
    },
    lev = lev.terms
  )

  v2 = lapply(
    seq_along(v1),
    function(i, x, n){
      rep(i,length(x[[i]]))
    },
    x = v1,
    n = names(v1)
  )

  stm = data.frame(i = unlist(v1), j = unlist(v2)) %>%
    group_by(i, j) %>%
    tally() %>%
    ungroup()

  tmp = simple_triplet_matrix(
    i = stm$i,
    j = stm$j,
    v = stm$n,
    nrow = length(lev.terms),
    ncol = length(lev.docs),
    dimnames = list(Terms = lev.terms, Docs = lev.docs)
  )

  as.TermDocumentMatrix(tmp, weighting = weightTf)
}

它在计算 v1 时变慢。它运行了 30 分钟，我停止了它。

我准备了一个小例子:

b = paste0("string", 1:200000)
a = sample(b,80)
microbenchmark(
  lapply(
    list(a=a),
    function(x, lev) {
      sort(as.integer(factor(x, levels = lev, ordered = TRUE)))
    },
    lev = b
  )
)

结果是:

Unit: milliseconds
expr      min       lq      mean   median       uq      max neval
...  25.80961 28.79981  31.59974 30.79836 33.02461 98.02512   100

Id 和 content 有 126522 个元素，Lev.terms 有 155591 个元素，看来我过早停止了处理。由于最终我将处理 ~6M 文档，我需要问...有什么方法可以加快这段代码的速度吗？

Answer 1

现在我已经加快了替换速度

sort(as.integer(factor(x, levels = lev, ordered = TRUE)))

与

ind = which(lev %in% x)
cnt = as.integer(factor(x, levels = lev[ind], ordered = TRUE))
sort(ind[cnt])

现在时间是:

expr      min       lq     mean   median       uq      max neval
...  5.248479 6.202161 6.892609 6.501382 7.313061 10.17205   100