R. Quanteda 包。如何过滤 dfm_tfidf 中存在的值？

Question

所以我有一个 dfm_tfidf 并且我想过滤掉低于某个阈值的值。
代码:

dfmat2 <-
  matrix(c(1,1,2,1,0,0, 1,1,0,0,2,3),
         byrow = TRUE, nrow = 2,
         dimnames = list(docs = c("document1", "document2"),
                         features = c("this", "is", "a", "sample",
                                      "another", "example"))) %>%
  as.dfm()


#it works
dfmat2 %>% dfm_trim(min_termfreq = 3)

#it does not work
dfm_tfidf(dfmat2) %>% dfm_trim( min_termfreq = 1)
# "Warning message: In dfm_trim.dfm(., min_termfreq = 1) : dfm has been previously weighted"

问题 :如何过滤掉 dfm_tfidf 中存在的值？
谢谢

Answer 1

这是一个基于绝对最小值在稀疏矩阵空间中执行此操作的函数。但要注意，因为 tf-idf 绝对值在不同的 dfm 对象中意义不大。

library("quanteda")
## Package version: 2.1.1

dfmat2 <-
  matrix(c(1, 1, 2, 1, 0, 0, 1, 1, 0, 0, 2, 3),
    byrow = TRUE, nrow = 2,
    dimnames = list(
      docs = c("document1", "document2"),
      features = c(
        "this", "is", "a", "sample",
        "another", "example"
      )
    )
  ) %>%
  as.dfm()

# function to trim features based on absolute minimum threshold
# operating directly on sparse matrix
dfm_trimabs <- function(x, min) {
  maxvals <- sapply(
    split(dfmat3@x, featnames(dfmat3)[as(x, "dgTMatrix")@j + 1]),
    max
  )
  dfm_keep(x, names(maxvals)[maxvals >= min])
}

现在将其应用到上面的示例之前和之后:

# before trimming
dfm_tfidf(dfmat2)
## Document-feature matrix of: 2 documents, 6 features (33.3% sparse).
##            features
## docs        this is       a  sample another example
##   document1    0  0 0.60206 0.30103 0       0      
##   document2    0  0 0       0       0.60206 0.90309

# after trimming
dfm_tfidf(dfmat2) %>%
  dfm_trimabs(min = 0.5)
## Document-feature matrix of: 2 documents, 3 features (50.0% sparse).
##            features
## docs              a another example
##   document1 0.60206 0       0      
##   document2 0       0.60206 0.90309