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[SPARK-11626][ML]ml.feature.Word2Vec.transform() function very slow #9592

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wangyum wants to merge 4 commits into from
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[SPARK-11626][ML]ml.feature.Word2Vec.transform() function very slow #9592

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a9f894d
ml.feature.Word2Vec.transform() function very slow, we shouldn't read…
Nov 10, 2015
a2b2835
modify wordVectors to transient.
Nov 11, 2015
fec6460
improve implementation to avoid mapping per record
mengxr Nov 11, 2015
8b0df79
Merge pull request #1 from mengxr/SPARK-11626
Nov 11, 2015
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34 changes: 16 additions & 18 deletions mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -17,18 +17,16 @@

package org.apache.spark.ml.feature

import org.apache.spark.annotation.Experimental
import org.apache.spark.SparkContext
import org.apache.spark.annotation.Experimental
import org.apache.spark.ml.{Estimator, Model}
import org.apache.spark.ml.param._
import org.apache.spark.ml.param.shared._
import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
import org.apache.spark.mllib.feature
import org.apache.spark.mllib.linalg.{VectorUDT, Vector, Vectors}
import org.apache.spark.mllib.linalg.BLAS._
import org.apache.spark.sql.DataFrame
import org.apache.spark.mllib.linalg.{BLAS, Vector, VectorUDT, Vectors}
import org.apache.spark.sql.{DataFrame, SQLContext}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.types._

/**
Expand Down Expand Up @@ -148,10 +146,9 @@ final class Word2Vec(override val uid: String) extends Estimator[Word2VecModel]
@Experimental
class Word2VecModel private[ml] (
override val uid: String,
wordVectors: feature.Word2VecModel)
@transient wordVectors: feature.Word2VecModel)
extends Model[Word2VecModel] with Word2VecBase {


/**
* Returns a dataframe with two fields, "word" and "vector", with "word" being a String and
* and the vector the DenseVector that it is mapped to.
Expand Down Expand Up @@ -197,22 +194,23 @@ class Word2VecModel private[ml] (
*/
override def transform(dataset: DataFrame): DataFrame = {
transformSchema(dataset.schema, logging = true)
val bWordVectors = dataset.sqlContext.sparkContext.broadcast(wordVectors)
val vectors = wordVectors.getVectors
.mapValues(vv => Vectors.dense(vv.map(_.toDouble)))
.map(identity) // mapValues doesn't return a serializable map (SI-7005)
val bVectors = dataset.sqlContext.sparkContext.broadcast(vectors)
val d = $(vectorSize)
val word2Vec = udf { sentence: Seq[String] =>
if (sentence.size == 0) {
Vectors.sparse($(vectorSize), Array.empty[Int], Array.empty[Double])
Vectors.sparse(d, Array.empty[Int], Array.empty[Double])
} else {
val cum = Vectors.zeros($(vectorSize))
val model = bWordVectors.value.getVectors
for (word <- sentence) {
if (model.contains(word)) {
axpy(1.0, bWordVectors.value.transform(word), cum)
} else {
// pass words which not belong to model
val sum = Vectors.zeros(d)
sentence.foreach { word =>
bVectors.value.get(word).foreach { v =>
BLAS.axpy(1.0, v, sum)
}
}
scal(1.0 / sentence.size, cum)
cum
BLAS.scal(1.0 / sentence.size, sum)
sum
}
}
dataset.withColumn($(outputCol), word2Vec(col($(inputCol))))
Expand Down