Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1719#discussion_r15721930
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala ---
@@ -0,0 +1,375 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements. See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* Add a comment to this line
+* (the "License"); you may not use this file except in compliance with
+* the License. You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.mllib.feature
+
+import scala.util.{Random => Random}
+import scala.collection.mutable.ArrayBuffer
+import scala.collection.mutable
+
+import com.github.fommil.netlib.BLAS.{getInstance => blas}
+
+import org.apache.spark._
+import org.apache.spark.rdd._
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.HashPartitioner
+
+/**
+ * Entry in vocabulary
+ */
+private case class VocabWord(
+ var word: String,
+ var cn: Int,
+ var point: Array[Int],
+ var code: Array[Int],
+ var codeLen:Int
+)
+
+/**
+ * Vector representation of word
+ */
+class Word2Vec(
+ val size: Int,
+ val startingAlpha: Double,
+ val window: Int,
+ val minCount: Int)
+ extends Serializable with Logging {
+
+ private val EXP_TABLE_SIZE = 1000
+ private val MAX_EXP = 6
+ private val MAX_CODE_LENGTH = 40
+ private val MAX_SENTENCE_LENGTH = 1000
+ private val layer1Size = size
+ private val modelPartitionNum = 100
+
+ private var trainWordsCount = 0
+ private var vocabSize = 0
+ private var vocab: Array[VocabWord] = null
+ private var vocabHash = mutable.HashMap.empty[String, Int]
+ private var alpha = startingAlpha
+
+ private def learnVocab(dataset: RDD[String]) {
+ vocab = dataset.flatMap(line => line.split(" "))
+ .map(w => (w, 1))
+ .reduceByKey(_ + _)
+ .map(x => VocabWord(x._1, x._2, new Array[Int](MAX_CODE_LENGTH), new
Array[Int](MAX_CODE_LENGTH), 0))
+ .filter(_.cn >= minCount)
+ .collect()
+ .sortWith((a, b)=> a.cn > b.cn)
+
+ vocabSize = vocab.length
+ var a = 0
+ while (a < vocabSize) {
+ vocabHash += vocab(a).word -> a
+ trainWordsCount += vocab(a).cn
+ a += 1
+ }
+ logInfo("trainWordsCount = " + trainWordsCount)
+ }
+
+ private def createExpTable(): Array[Double] = {
+ val expTable = new Array[Double](EXP_TABLE_SIZE)
+ var i = 0
+ while (i < EXP_TABLE_SIZE) {
+ val tmp = math.exp((2.0 * i / EXP_TABLE_SIZE - 1.0) * MAX_EXP)
+ expTable(i) = tmp / (tmp + 1)
+ i += 1
+ }
+ expTable
+ }
+
+ private def createBinaryTree() {
+ val count = new Array[Long](vocabSize * 2 + 1)
+ val binary = new Array[Int](vocabSize * 2 + 1)
+ val parentNode = new Array[Int](vocabSize * 2 + 1)
+ val code = new Array[Int](MAX_CODE_LENGTH)
+ val point = new Array[Int](MAX_CODE_LENGTH)
+ var a = 0
+ while (a < vocabSize) {
+ count(a) = vocab(a).cn
+ a += 1
+ }
+ while (a < 2 * vocabSize) {
+ count(a) = 1e9.toInt
+ a += 1
+ }
+ var pos1 = vocabSize - 1
+ var pos2 = vocabSize
+
+ var min1i = 0
+ var min2i = 0
+
+ a = 0
+ while (a < vocabSize - 1) {
+ if (pos1 >= 0) {
+ if (count(pos1) < count(pos2)) {
+ min1i = pos1
+ pos1 -= 1
+ } else {
+ min1i = pos2
+ pos2 += 1
+ }
+ } else {
+ min1i = pos2
+ pos2 += 1
+ }
+ if (pos1 >= 0) {
+ if (count(pos1) < count(pos2)) {
+ min2i = pos1
+ pos1 -= 1
+ } else {
+ min2i = pos2
+ pos2 += 1
+ }
+ } else {
+ min2i = pos2
+ pos2 += 1
+ }
+ count(vocabSize + a) = count(min1i) + count(min2i)
+ parentNode(min1i) = vocabSize + a
+ parentNode(min2i) = vocabSize + a
+ binary(min2i) = 1
+ a += 1
+ }
+ // Now assign binary code to each vocabulary word
+ var i = 0
+ a = 0
+ while (a < vocabSize) {
+ var b = a
+ i = 0
+ while (b != vocabSize * 2 - 2) {
+ code(i) = binary(b)
+ point(i) = b
+ i += 1
+ b = parentNode(b)
+ }
+ vocab(a).codeLen = i
+ vocab(a).point(0) = vocabSize - 2
+ b = 0
+ while (b < i) {
+ vocab(a).code(i - b - 1) = code(b)
+ vocab(a).point(i - b) = point(b) - vocabSize
+ b += 1
+ }
+ a += 1
+ }
+ }
+
+ /**
+ * Computes the vector representation of each word in
+ * vocabulary
+ * @param dataset an RDD of strings
+ * @return a Word2VecModel
+ */
+
+ def fit(dataset:RDD[String]): Word2VecModel = {
+
+ learnVocab(dataset)
+
+ createBinaryTree()
+
+ val sc = dataset.context
+
+ val expTable = sc.broadcast(createExpTable())
+ val V = sc.broadcast(vocab)
+ val VHash = sc.broadcast(vocabHash)
+
+ val sentences = dataset.flatMap(line => line.split(" ")).mapPartitions
{
+ iter => { new Iterator[Array[Int]] {
+ def hasNext = iter.hasNext
+ def next = {
+ var sentence = new ArrayBuffer[Int]
+ var sentenceLength = 0
+ while (iter.hasNext && sentenceLength < MAX_SENTENCE_LENGTH) {
+ val word = VHash.value.get(iter.next)
+ word match {
+ case Some(w) => {
+ sentence += w
+ sentenceLength += 1
+ }
+ case None =>
+ }
+ }
+ sentence.toArray
+ }
+ }
+ }
+ }
+
+ val newSentences = sentences.repartition(1).cache()
+ val temp = Array.fill[Double](vocabSize *
layer1Size)((Random.nextDouble - 0.5) / layer1Size)
--- End diff --
Try to fix the seed to make the computation reproducible.
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