text2vec (version 0.6.4)

itoken: Iterators (and parallel iterators) over input objects

Description

This family of function creates iterators over input objects in order to create vocabularies, or DTM and TCM matrices. iterators usually used in following functions : create_vocabulary, create_dtm, vectorizers, create_tcm. See them for details.

Usage

itoken(iterable, ...)
# S3 method for character
itoken(iterable, preprocessor = identity,
  tokenizer = space_tokenizer, n_chunks = 10,
  progressbar = interactive(), ids = NULL, ...)
# S3 method for list
itoken(iterable, n_chunks = 10,
  progressbar = interactive(), ids = names(iterable), ...)
# S3 method for iterator
itoken(iterable, preprocessor = identity,
  tokenizer = space_tokenizer, progressbar = interactive(), ...)
itoken_parallel(iterable, ...)
# S3 method for character
itoken_parallel(iterable, preprocessor = identity,
  tokenizer = space_tokenizer, n_chunks = 10, ids = NULL, ...)
# S3 method for iterator
itoken_parallel(iterable, preprocessor = identity,
  tokenizer = space_tokenizer, n_chunks = 1L, ...)
# S3 method for list
itoken_parallel(iterable, n_chunks = 10, ids = NULL,
  ...)

Arguments

iterable

an object from which to generate an iterator

...

arguments passed to other methods

preprocessor

function which takes chunk of character vectors and does all pre-processing. Usually preprocessor should return a character vector of preprocessed/cleaned documents. See "Details" section.

tokenizer

function which takes a character vector from preprocessor, split it into tokens and returns a list of character vectors. If you need to perform stemming - call stemmer inside tokenizer. See examples section.

n_chunks

integer, the number of pieces that object should be divided into. Then each chunk is processed independently (and in case itoken_parallel in parallel if some parallel backend is registered). Usually there is tradeoff: larger number of chunks means lower memory footprint, but slower (if preprocessor, tokenizer functions are efficiently vectorized). And small number of chunks means larger memory footprint but faster execution (again if user supplied preprocessor, tokenizer functions are efficiently vectorized).

progressbar

logical indicates whether to show progress bar.

ids

vector of document ids. If ids is not provided, names(iterable) will be used. If names(iterable) == NULL, incremental ids will be assigned.

Details

S3 methods for creating an itoken iterator from list of tokens

• list: all elements of the input list should be character vectors containing tokens

• character: raw text source: the user must provide a tokenizer function

• ifiles: from files, a user must provide a function to read in the file (to ifiles) and a function to tokenize it (to itoken)

• idir: from a directory, the user must provide a function to read in the files (to idir) and a function to tokenize it (to itoken)

• ifiles_parallel: from files in parallel

See Also

ifiles, idir, create_vocabulary, create_dtm, vectorizers, create_tcm

Examples

data("movie_review")
txt = movie_review$review[1:100]
ids = movie_review$id[1:100]
it = itoken(txt, tolower, word_tokenizer, n_chunks = 10)
it = itoken(txt, tolower, word_tokenizer, n_chunks = 10, ids = ids)
# Example of stemming tokenizer
# stem_tokenizer =function(x) {
#   lapply(word_tokenizer(x), SnowballC::wordStem, language="en")
# }
it = itoken_parallel(movie_review$review[1:100], n_chunks = 4)
system.time(dtm <- create_dtm(it, hash_vectorizer(2**16), type = 'TsparseMatrix'))