问题
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
回答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.
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 inquanteda::tokenize(x, what = "fastest word"). It's not the best because stringi can do much smarter implementations of whitespace delimiters. (Even the character class\\sis smarter, but slightly slower -- this is implemented aswhat = "fasterword"). Your question was not about tokenization though, so this point is just context.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 thequanteda::dfm()function since your method is more elegant and faster. Really, really glad I saw this!ngram creation. Here I think I can actually help in terms of performance. We implement this in quanteda through an argument to
quanteda::tokenize(), calledgrams = c(1)where the value can be any integer set. Our match for unigrams and bigrams would bengrams = 1:2, for instance. You can examine the code at https://github.com/kbenoit/quanteda/blob/master/R/tokenize.R, see the internal functionngram(). I've reproduced this below and made a wrapper so that we can directly compare it to yourfind_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.)
回答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.
来源：https://stackoverflow.com/questions/31570437/really-fast-word-ngram-vectorization-in-r