3

我写了一个函数来匿名化数据框中给定一些键的名称，一旦它匿名化很多名称，它就会爬行，但我不明白为什么。

有问题的数据框是一组通过 Twitter API 收集的 4733 条推文，其中每行是一条包含 32 列数据的推文。无论名称出现在哪一行，这些名称都将被匿名化，因此我不想将函数限制为仅查看这 32 列中的几列。

关键是一个包含 211121 对真实姓名和虚假姓名的数据帧，真实姓名和虚假姓名在数据帧中都是唯一的。在匿名化大约 100k 个名称后，该功能会大大减慢。

该函数如下所示：

pseudonymize <- function(df, key) {
  for(name in key$realNames) {
    df <- as.data.frame(apply(df, 2, function(column) gsub(name, key[key$realNames == name, 2], column)))
  }
}

这里有什么明显的东西会导致速度变慢吗？我完全没有优化代码以提高速度的经验。

编辑1：

以下是要匿名的数据框中的几行。

"https://twitter.com/__jgil/statuses/825559753447313408","__jgil",0.000576911235261567,756,4,13,17,7,16,23,10,0.28166915052161,0.390123456790124,0.00271311644806025,0.474529795261862,0.00641025649383664,"@jadahung20 GIRL I am tooooooo salty tonight lolll","lolll","adjoint","anglais","indefini","anglais","anglais","non","iPhone, Twitter",4057,214,241,"Canada","Nouvelle-Ecosse","Middleton","indefini","Shari"
"https://twitter.com/__paigewhite/statuses/827988259573788673","__paigewhite",0,1917,0,8,8,0,9,9,16,0.143476044852192,0.162056634159209,0.000172947386274259,0,0,"@abbytutty_ i miss emily lololol _Ù÷â_Ù÷É","lololol","adjoint","anglais","indefini","anglais","anglais","non","iPhone, Twitter",8366,392,661,"Canada","Nouvelle-Ecosse","indefini","indefini","Shari"
"https://twitter.com/_brookehynes/statuses/821022926287884288","_brookehynes",0,1917,1,6,7,1,7,8,1,1,1,0.000196850793912616,0.00393656926735126,0.200000002980232,"@tdesj3 @belle lol yea doubt it.","lol","adjoint","indefini","anglais","anglais","anglais","non","iPhone, Twitter",1184,87,70,"Canada","Nouvelle-Ecosse","Halifax","indefini","Shari"

这是关键的几行。

"","realNames","fakeNames"
"1","________","Tajid_Pinkley"
"2","____________aho","Monica_Yujiri"
"3","___________ass","Alexander_Garay-Grajeda"

编辑2：

我已将 DF 简化为仅需要匿名化的两列，这使事情变得更快，但在完成了大约 155k 的名称后它仍然会退出。

根据评论中的要求，这dput()是要匿名的 DF 前三行的输出。

structure(list(
  utilisateur = c("___Yeliab", "__courtlezz", "__courtlezz"),
  texte = c("@EmilyIsPro ik lol", "@NikkiErica21 there was a sighting in sunset ridge too. Keep Winnie and bob safe lol", "@NikkiErica21 lol yes _Ã\231։")
  ),
  row.names = c(NA, 3L),
  class = "data.frame")

这dput()是密钥的前三行。

structure(list(
  realNames = c("________", "____________aho", "___________ass"),
  fakeNames = c("Abhinav_Chang", "Caleb_Dunn-Sparks", "Taryn_Hunzicker")
  ),
  row.names = c(NA, 3L),
  class = "data.frame")
4

1 回答 1

1

将数据作为向量而不是 data.frame 处理会更有效率。我遇到了一些编码问题，因此使用iconv;将文本转换为 UTF-8。如果名称包含非 ASCII 字符，则需要进行一些处理。

key1 <- data.frame(
    realNames = c("________", "____________aho", "___________ass", 
        "___Yeliab", "__courtlezz", "NikkiErica21", "EmilyIsPro", "aho"),
    fakeNames = c("Abhinav_Chang", "Caleb_Dunn-Sparks", "Taryn_Hunzicker", 
        "A_A", "B_B", "C_C", "D_D", "E_E"),
    stringsAsFactors = FALSE
)

pseudonymize1 <- function(df, key) {
    mat <- as.matrix(df)
    dims <- attr(mat, which = "dim")
    cnam <- colnames(df)
    vec <- iconv(unclass(mat), from = "latin1", to = "UTF-8")
    for (name in split(key, f = seq_len(nrow(key)))) {
        vec <- gsub(
            vec, 
            pattern = name$realNames, 
            replacement = name$fakeNames, 
            fixed = TRUE)
    }
    mat <- vec
    attr(mat, which = "dim") <- dims
    df <- as.data.frame(mat, stringsAsFactors = FALSE)
    colnames(df) <- cnam
    df
}
pseudonymize1(df1, key1)
# utilisateur                                                                       texte
# 1         A_A                                                                 @D_D ik lol
# 2         B_B @C_C there was a sighting in sunset ridge too. Keep Winnie and bob safe lol
# 3         B_B                               @C_C lol yes _Ã\u0083\u0099Ã\u0083·Ã\u0083¢

library(microbenchmark)    
microbenchmark(
    pseudonymize(df1, key1),
    pseudonymize1(df1, key1)
)
# Unit: microseconds
#                     expr      min        lq     mean   median        uq      max neval cld
#  pseudonymize(df1, key1) 1842.554 1885.6750 2131.089 1994.755 2294.6850 3007.371   100   b
# pseudonymize1(df1, key1)  287.683  306.1905  333.678  314.950  339.8705  497.301   100  a

我对 155k 个名称的担忧是，当作为正则表达式搜索时，您会发现其他名称中包含的名称。这可以是真名中的真名（例如 EmilyIsPro 中的 Emily），或者是先前替换的假名中的真名。您将需要对此进行测试，并考虑使用随机散列而不是类似名称的假名称。

于 2021-04-22T21:56:47.287 回答