Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/8551#discussion_r46874564
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala ---
@@ -192,115 +192,45 @@ object LDAExample {
vocabSize: Int,
stopwordFile: String): (RDD[(Long, Vector)], Array[String], Long) = {
+ val sqlContext = SQLContext.getOrCreate(sc)
+ import sqlContext.implicits._
+
// Get dataset of document texts
// One document per line in each text file. If the input consists of
many small files,
// this can result in a large number of small partitions, which can
degrade performance.
// In this case, consider using coalesce() to create fewer, larger
partitions.
- val textRDD: RDD[String] = sc.textFile(paths.mkString(","))
-
- // Split text into words
- val tokenizer = new SimpleTokenizer(sc, stopwordFile)
- val tokenized: RDD[(Long, IndexedSeq[String])] =
textRDD.zipWithIndex().map { case (text, id) =>
- id -> tokenizer.getWords(text)
- }
- tokenized.cache()
-
- // Counts words: RDD[(word, wordCount)]
- val wordCounts: RDD[(String, Long)] = tokenized
- .flatMap { case (_, tokens) => tokens.map(_ -> 1L) }
- .reduceByKey(_ + _)
- wordCounts.cache()
- val fullVocabSize = wordCounts.count()
- // Select vocab
- // (vocab: Map[word -> id], total tokens after selecting vocab)
- val (vocab: Map[String, Int], selectedTokenCount: Long) = {
- val tmpSortedWC: Array[(String, Long)] = if (vocabSize == -1 ||
fullVocabSize <= vocabSize) {
- // Use all terms
- wordCounts.collect().sortBy(-_._2)
- } else {
- // Sort terms to select vocab
- wordCounts.sortBy(_._2, ascending = false).take(vocabSize)
- }
- (tmpSortedWC.map(_._1).zipWithIndex.toMap, tmpSortedWC.map(_._2).sum)
+ val df = sc.textFile(paths.mkString(",")).toDF("docs")
+ val customizedStopWords: Array[String] = if (stopwordFile.isEmpty) {
+ Array.empty[String]
+ } else {
+ val stopWordText = sc.textFile(stopwordFile).collect()
+ stopWordText.flatMap(_.stripMargin.split("\\s+"))
}
-
- val documents = tokenized.map { case (id, tokens) =>
- // Filter tokens by vocabulary, and create word count vector
representation of document.
- val wc = new mutable.HashMap[Int, Int]()
- tokens.foreach { term =>
- if (vocab.contains(term)) {
- val termIndex = vocab(term)
- wc(termIndex) = wc.getOrElse(termIndex, 0) + 1
- }
- }
- val indices = wc.keys.toArray.sorted
- val values = indices.map(i => wc(i).toDouble)
-
- val sb = Vectors.sparse(vocab.size, indices, values)
- (id, sb)
- }
-
- val vocabArray = new Array[String](vocab.size)
- vocab.foreach { case (term, i) => vocabArray(i) = term }
-
- (documents, vocabArray, selectedTokenCount)
+ val tokenizer = new RegexTokenizer()
+ .setInputCol("docs")
+ .setOutputCol("rawTokens")
+ val stopWordsRemover = new StopWordsRemover()
+ .setInputCol("rawTokens")
+ .setOutputCol("tokens")
+ stopWordsRemover.setStopWords(stopWordsRemover.getStopWords ++
customizedStopWords)
+ val countVectorizer = new CountVectorizer()
+ .setVocabSize(vocabSize)
+ .setInputCol("tokens")
+ .setOutputCol("vectors")
--- End diff --
How about calling these "features" instead of "vectors"?
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