Really fast word ngram vectorization in R

Question

edit: The new package text2vec is excellent, and solves this problem (and many others) really well.

text2vec on CRAN text2vec on github vignette that illustrates ngram tokenization

I have a pretty large text dataset in R, which I've imported as a character vector:

#Takes about 15 seconds
system.time({
  set.seed(1)
  samplefun <- function(n, x, collapse){
    paste(sample(x, n, replace=TRUE), collapse=collapse)
  }
  words <- sapply(rpois(10000, 3) + 1, samplefun, letters, '')
  sents1 <- sapply(rpois(1000000, 5) + 1, samplefun, words, ' ')
})

I can convert this character data to a bag-of-words representation as follows:

library(stringi)
library(Matrix)
tokens <- stri_split_fixed(sents1, ' ')
token_vector <- unlist(tokens)
bagofwords <- unique(token_vector)
n.ids <- sapply(tokens, length)
i <- rep(seq_along(n.ids), n.ids)
j <- match(token_vector, bagofwords)
M <- sparseMatrix(i=i, j=j, x=1L)
colnames(M) <- bagofwords

So R can vectorize 1,000,000 million short sentences into a bag-of-words representation in about 3 seconds (not bad!):

> M[1:3, 1:7]
10 x 7 sparse Matrix of class "dgCMatrix"
      fqt hqhkl sls lzo xrnh zkuqc mqh
 [1,]   1     1   1   1    .     .   .
 [2,]   .     .   .   .    1     1   1
 [3,]   .     .   .   .    .     .   .

I can throw this sparse matrix into glmnet or irlba and do some pretty awesome quantitative analysis of textual data. Hooray!

Now I'd like to extend this analysis to a bag-of-ngrams matrix, rather than a bag-of-words matrix. So far, the fastest way I've found to do this is as follows (all of the ngram functions I could find on CRAN choked on this dataset, so I got a little help from SO):

find_ngrams <- function(dat, n, verbose=FALSE){
  library(pbapply)
  stopifnot(is.list(dat))
  stopifnot(is.numeric(n))
  stopifnot(n>0)
  if(n == 1) return(dat)
  pblapply(dat, function(y) {
    if(length(y)<=1) return(y)
    c(y, unlist(lapply(2:n, function(n_i) {
      if(n_i > length(y)) return(NULL)
      do.call(paste, unname(as.data.frame(embed(rev(y), n_i), stringsAsFactors=FALSE)), quote=FALSE)
    })))
  })
}

text_to_ngrams <- function(sents, n=2){
  library(stringi)
  library(Matrix)
  tokens <- stri_split_fixed(sents, ' ')
  tokens <- find_ngrams(tokens, n=n, verbose=TRUE)
  token_vector <- unlist(tokens)
  bagofwords <- unique(token_vector)
  n.ids <- sapply(tokens, length)
  i <- rep(seq_along(n.ids), n.ids)
  j <- match(token_vector, bagofwords)
  M <- sparseMatrix(i=i, j=j, x=1L)
  colnames(M) <- bagofwords
  return(M)
}

test1 <- text_to_ngrams(sents1)

This takes about 150 seconds (not bad for a pure r function), but I'd like to go faster and extend to bigger datasets.

Are there any really fast functions in R for n-gram vectorization of text? Ideally I'm looking for an Rcpp function that takes a character vector as input, and returns a sparse matrix of documents x ngrams as output, but would also be happy to have some guidance writing the Rcpp function myself.

Even a faster version of the find_ngrams function would be helpful, as that's the main bottleneck. R is surprisingly fast at tokenization.

Edit 1 Here's another example dataset:

sents2 <- sapply(rpois(100000, 500) + 1, samplefun, words, ' ')

In this case, my functions for creating a bag-of-words matrix take about 30 seconds and my functions for creating a bag-of-ngrams matrix take about 500 seconds. Again, existing n-gram vectorizers in R seem to choke on this dataset (though I'd love to be proven wrong!)

Edit 2 Timings vs tau:

zach_t1 <- system.time(zach_ng1 <- text_to_ngrams(sents1))
tau_t1 <- system.time(tau_ng1 <- tau::textcnt(as.list(sents1), n = 2L, method = "string", recursive = TRUE))
tau_t1 / zach_t1 #1.598655

zach_t2 <- system.time(zach_ng2 <- text_to_ngrams(sents2))
tau_t2 <- system.time(tau_ng2 <- tau::textcnt(as.list(sents2), n = 2L, method = "string", recursive = TRUE))
tau_t2 / zach_t2 #1.9295619

Answer 1

This is a really interesting problem, and one that I have spent a lot of time grappling with in the quanteda package. It involves three aspects that I will comment on, although it's only the third that really addresses your question. But the first two points explain why I have only focused on the ngram creation function, since -- as you point out -- that is where the speed improvement can be made.

1. Tokenization. Here you are using string::str_split_fixed() on the space character, which is the fastest, but not the best method for tokenizing. We implemented this almost exactly the same was in quanteda::tokenize(x, what = "fastest word"). It's not the best because stringi can do much smarter implementations of whitespace delimiters. (Even the character class \\s is smarter, but slightly slower -- this is implemented as what = "fasterword"). Your question was not about tokenization though, so this point is just context.

2. Tabulating the document-feature matrix. Here we also use the Matrix package, and index the documents and features (I call them features, not terms), and create a sparse matrix directly as you do in the code above. But your use of match() is a lot faster than the match/merge methods we were using through data.table. I am going to recode the quanteda::dfm() function since your method is more elegant and faster. Really, really glad I saw this!

3. ngram creation. Here I think I can actually help in terms of performance. We implement this in quanteda through an argument to quanteda::tokenize(), called grams = c(1) where the value can be any integer set. Our match for unigrams and bigrams would be ngrams = 1:2, for instance. You can examine the code at https://github.com/kbenoit/quanteda/blob/master/R/tokenize.R, see the internal function ngram(). I've reproduced this below and made a wrapper so that we can directly compare it to your find_ngrams() function.

Code:

# wrapper
find_ngrams2 <- function(x, ngrams = 1, concatenator = " ") { 
    if (sum(1:length(ngrams)) == sum(ngrams)) {
        result <- lapply(x, ngram, n = length(ngrams), concatenator = concatenator, include.all = TRUE)
    } else {
        result <- lapply(x, function(x) {
            xnew <- c()
            for (n in ngrams) 
                xnew <- c(xnew, ngram(x, n, concatenator = concatenator, include.all = FALSE))
            xnew
        })
    }
    result
}

# does the work
ngram <- function(tokens, n = 2, concatenator = "_", include.all = FALSE) {

    if (length(tokens) < n) 
        return(NULL)

    # start with lower ngrams, or just the specified size if include.all = FALSE
    start <- ifelse(include.all, 
                    1, 
                    ifelse(length(tokens) < n, 1, n))

    # set max size of ngram at max length of tokens
    end <- ifelse(length(tokens) < n, length(tokens), n)

    all_ngrams <- c()
    # outer loop for all ngrams down to 1
    for (width in start:end) {
        new_ngrams <- tokens[1:(length(tokens) - width + 1)]
        # inner loop for ngrams of width > 1
        if (width > 1) {
            for (i in 1:(width - 1)) 
                new_ngrams <- paste(new_ngrams, 
                                    tokens[(i + 1):(length(tokens) - width + 1 + i)], 
                                    sep = concatenator)
        }
        # paste onto previous results and continue
        all_ngrams <- c(all_ngrams, new_ngrams)
    }

    all_ngrams
}

Here is the comparison for a simple text:

txt <- c("The quick brown fox named Seamus jumps over the lazy dog.", 
         "The dog brings a newspaper from a boy named Seamus.")
tokens <- tokenize(toLower(txt), removePunct = TRUE)
tokens
# [[1]]
# [1] "the"    "quick"  "brown"  "fox"    "named"  "seamus" "jumps"  "over"   "the"    "lazy"   "dog"   
# 
# [[2]]
# [1] "the"       "dog"       "brings"    "a"         "newspaper" "from"      "a"         "boy"       "named"     "seamus"   
# 
# attr(,"class")
# [1] "tokenizedTexts" "list"     

microbenchmark::microbenchmark(zach_ng <- find_ngrams(tokens, 2),
                               ken_ng <- find_ngrams2(tokens, 1:2))
# Unit: microseconds
#                                expr     min       lq     mean   median       uq     max neval
#   zach_ng <- find_ngrams(tokens, 2) 288.823 326.0925 433.5831 360.1815 542.9585 897.469   100
# ken_ng <- find_ngrams2(tokens, 1:2)  74.216  87.5150 130.0471 100.4610 146.3005 464.794   100

str(zach_ng)
# List of 2
# $ : chr [1:21] "the" "quick" "brown" "fox" ...
# $ : chr [1:19] "the" "dog" "brings" "a" ...
str(ken_ng)
# List of 2
# $ : chr [1:21] "the" "quick" "brown" "fox" ...
# $ : chr [1:19] "the" "dog" "brings" "a" ...

For your really large, simulated text, here is the comparison:

tokens <- stri_split_fixed(sents1, ' ')
zach_ng1_t1 <- system.time(zach_ng1 <- find_ngrams(tokens, 2))
ken_ng1_t1 <- system.time(ken_ng1 <- find_ngrams2(tokens, 1:2))
zach_ng1_t1
#    user  system elapsed 
# 230.176   5.243 246.389 
ken_ng1_t1
#   user  system elapsed 
# 58.264   1.405  62.889 

Already an improvement, I'd be delighted if this could be improved further. I also should be able to implement the faster dfm() method into quanteda so that you can get what you want simply through:

dfm(sents1, ngrams = 1:2, what = "fastestword",
    toLower = FALSE, removePunct = FALSE, removeNumbers = FALSE, removeTwitter = TRUE)) 

(That already works but is slower than your overall result, because the way you create the final sparse matrix object is faster - but I will change this soon.)

Answer 2

Here is a test using the dev version of tokenizers, which you can get using devtools::install_github("ropensci/tokenizers").

Using the definitions of sents1, sents2, and find_ngrams() above:

library(stringi)
library(magrittr)
library(tokenizers)
library(microbenchmark)
library(pbapply)


set.seed(198)
sents1_sample <- sample(sents1, 1000)
sents2_sample <- sample(sents2, 1000)

test_sents1 <- microbenchmark(
  find_ngrams(stri_split_fixed(sents1_sample, ' '), n = 2), 
  tokenize_ngrams(sents1_sample, n = 2),
  times = 25)
test_sents1

Results:

Unit: milliseconds
                                                     expr       min        lq       mean
 find_ngrams(stri_split_fixed(sents1_sample, " "), n = 2) 79.855282 83.292816 102.564965
                    tokenize_ngrams(sents1_sample, n = 2)  4.048635  5.147252   5.472604
    median         uq        max neval cld
 93.622532 109.398341 226.568870    25   b
  5.479414   5.805586   6.595556    25  a 

Testing on sents2

test_sents2 <- microbenchmark(
  find_ngrams(stri_split_fixed(sents2_sample, ' '), n = 2), 
  tokenize_ngrams(sents2_sample, n = 2),
  times = 25)
test_sents2

Results:

Unit: milliseconds
                                                     expr      min       lq     mean
 find_ngrams(stri_split_fixed(sents2_sample, " "), n = 2) 509.4257 521.7575 562.9227
                    tokenize_ngrams(sents2_sample, n = 2) 288.6050 295.3262 306.6635
   median       uq      max neval cld
 529.4479 554.6749 844.6353    25   b
 306.4858 310.6952 332.5479    25  a 

Checking just straight up timing

timing <- system.time({find_ngrams(stri_split_fixed(sents1, ' '), n = 2)})
timing

   user  system elapsed 
 90.499   0.506  91.309 

timing_tokenizers <- system.time({tokenize_ngrams(sents1, n = 2)})
timing_tokenizers

   user  system elapsed 
  6.940   0.022   6.964 

timing <- system.time({find_ngrams(stri_split_fixed(sents2, ' '), n = 2)})
timing

   user  system elapsed 
138.957   3.131 142.581 

timing_tokenizers <- system.time({tokenize_ngrams(sents2, n = 2)})
timing_tokenizers

   user  system elapsed 
  65.22    1.57   66.91

A lot will depend on the texts being tokenized, but that seems to indicate a speedup of 2x to 20x.