The previous post introduced the topic of how to compile Swift code for use in R using a useless, toy example. This one goes a bit further and makes a case for why one might want to do this by showing how to use one of Apple’s machine learning libraries, specifically the Natural Language one, focusing on extracting parts of speech from text.
I made a parts-of-speech directory to keep the code self-contained. In it are two files. The first is partsofspeech.swift (swiftc seems to dislike dashes in names of library code and I dislike underscores):
import NaturalLanguage
import CoreML
extension Array where Element == String {
var SEXP: SEXP? {
let charVec = Rf_protect(Rf_allocVector(SEXPTYPE(STRSXP), count))
defer { Rf_unprotect(1) }
for (idx, elem) in enumerated() { SET_STRING_ELT(charVec, idx, Rf_mkChar(elem)) }
return(charVec)
}
}
@_cdecl ("part_of_speech")
public func part_of_speech(_ x: SEXP) -> SEXP {
let text = String(cString: R_CHAR(STRING_ELT(x, 0)))
let tagger = NLTagger(tagSchemes: [.lexicalClass])
tagger.string = text
let options: NLTagger.Options = [.omitPunctuation, .omitWhitespace]
var txts = [String]()
var tags = [String]()
tagger.enumerateTags(in: text.startIndex..<text.endIndex, unit: .word, scheme: .lexicalClass, options: options) { tag, tokenRange in
if let tag = tag {
txts.append("\(text[tokenRange])")
tags.append("\(tag.rawValue)")
}
return true
}
let out = Rf_protect(Rf_allocVector(SEXPTYPE(VECSXP), 2))
SET_VECTOR_ELT(out, 0, txts.SEXP)
SET_VECTOR_ELT(out, 1, tags.SEXP)
Rf_unprotect(1)
return(out!)
}
The other is bridge code that seems to be the same for every one of these (or could be) so I’ve just named it swift-r-glue.h (it’s the same as the bridge code in the previous post):
#define USE_RINTERNALS
#include <R.h>
#include <Rinternals.h>
const char* R_CHAR(SEXP x);
Let’s walk through the Swift code.
We need to two imports:
import NaturalLanguage
import CoreML
to make use of the NLP functionality provided by Apple.
The following extension to the String Array class:
extension Array where Element == String {
var SEXP: SEXP? {
let charVec = Rf_protect(Rf_allocVector(SEXPTYPE(STRSXP), count))
defer { Rf_unprotect(1) }
for (idx, elem) in enumerated() { SET_STRING_ELT(charVec, idx, Rf_mkChar(elem)) }
return(charVec)
}
}
will reduce the amount of code we need to type later on to turn Swift String Arrays to R character vectors.
The start of the function:
@_cdecl ("part_of_speech")
public func part_of_speech(_ x: SEXP) -> SEXP {
tells swiftc to make this a C-compatible call and notes that the function takes one parameter (in this case, it’s expecting a length 1 character vector) and returns an R-compatible value (which will be a list that we’ll turn into a data.frame in R just for brevity).
The following sets up our inputs and outputs:
let text = String(cString: R_CHAR(STRING_ELT(x, 0)))
let tagger = NLTagger(tagSchemes: [.lexicalClass])
tagger.string = text
let options: NLTagger.Options = [.omitPunctuation, .omitWhitespace]
var txts = [String]()
var tags = [String]()
We convert the passed-in parameter to a Swift String, initialize the NLP tagger, and setup two arrays to hold the results (sentence component in txts and the part of speech that component is in tags).
The following code is mostly straight from Apple and (inefficiently) populates the previous two arrays:
tagger.enumerateTags(in: text.startIndex..<text.endIndex, unit: .word, scheme: .lexicalClass, options: options) { tag, tokenRange in
if let tag = tag {
txts.append("\(text[tokenRange])")
tags.append("\(tag.rawValue)")
}
return true
}
Finally, we use the Swift-R bridge to make a list much like one would in C:
let out = Rf_protect(Rf_allocVector(SEXPTYPE(VECSXP), 2))
SET_VECTOR_ELT(out, 0, txts.SEXP)
SET_VECTOR_ELT(out, 1, tags.SEXP)
Rf_unprotect(1)
return(out!)
To get a shared library we can use from R, we just need to compile this like last time:
swiftc \
-I /Library/Frameworks/R.framework/Headers \
-F/Library/Frameworks \
-framework R \
-import-objc-header swift-r-glue.h \
-emit-library \
partsofspeech.swift
Let’s run that on some text! First, we’ll load the new shared library into R:
dyn.load("libpartsofspeech.dylib")
Next, we’ll make a wrapper function to avoid messy .Call(…)s and to make a data.frame:
parts_of_speech <- function(x) {
res <- .Call("part_of_speech", x)
as.data.frame(stats::setNames(res, c("name", "tag")))
}
Finally, let’s try this on some text!
tibble::as_tibble(
parts_of_speech(paste0(c(
"The comm wasn't working. Feeling increasingly ridiculous, he pushed",
"the button for the 1MC channel several more times. Nothing. He opened",
"his eyes and saw that all the lights on the panel were out. Then he",
"turned around and saw that the lights on the refrigerator and the",
"ovens were out. It wasn’t just the coffeemaker; the entire galley was",
"in open revolt. Holden looked at the ship name, Rocinante, newly",
"stenciled onto the galley wall, and said, Baby, why do you hurt me",
"when I love you so much?"
), collapse = " "))
)
## # A tibble: 92 x 2
## name tag
## <chr> <chr>
## 1 The Determiner
## 2 comm Noun
## 3 was Verb
## 4 n't Adverb
## 5 working Verb
## 6 Feeling Verb
## 7 increasingly Adverb
## 8 ridiculous Adjective
## 9 he Pronoun
## 10 pushed Verb
## # … with 82 more rows
FIN
If you’re playing along at home, try adding a function to this Swift file that uses Apple’s entity tagger.
The next installment of this topic will be how to wrap all this into a package (then all these examples get tweaked and go into the tome.
