Github user sethah commented on a diff in the pull request: https://github.com/apache/spark/pull/17673#discussion_r143020756 --- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Word2VecCBOWSolver.scala --- @@ -0,0 +1,344 @@ +/* + * 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 + * (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.ml.feature + +import com.github.fommil.netlib.BLAS.{getInstance => blas} + +import org.apache.spark.internal.Logging +import org.apache.spark.mllib.feature +import org.apache.spark.rdd.RDD +import org.apache.spark.util.random.XORShiftRandom + +object Word2VecCBOWSolver extends Logging { + // learning rate is updated for every batch of size batchSize + private val batchSize = 10000 + + // power to raise the unigram distribution with + private val power = 0.75 + + private val MAX_EXP = 6 + + case class Vocabulary( + totalWordCount: Long, + vocabMap: Map[String, Int], + unigramTable: Array[Int], + samplingTable: Array[Float]) + + /** + * This method implements Word2Vec Continuous Bag Of Words based implementation using + * negative sampling optimization, using BLAS for vectorizing operations where applicable. + * The algorithm is parallelized in the same way as the skip-gram based estimation. + * We divide input data into N equally sized random partitions. + * We then generate initial weights and broadcast them to the N partitions. This way + * all the partitions start with the same initial weights. We then run N independent + * estimations that each estimate a model on a partition. The weights learned + * from each of the N models are averaged and rebroadcast the weights. + * This process is repeated `maxIter` number of times. + * + * @param input A RDD of strings. Each string would be considered a sentence. + * @return Estimated word2vec model + */ + def fitCBOW[S <: Iterable[String]]( + word2Vec: Word2Vec, + input: RDD[S]): feature.Word2VecModel = { + + val negativeSamples = word2Vec.getNegativeSamples + val sample = word2Vec.getSample --- End diff -- `samplingThreshold`
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