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/*
* 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 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.{BLAS, Vector, VectorUDT, Vectors}
import org.apache.spark.sql.{DataFrame, SQLContext}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
/**
* Params for [[Word2Vec]] and [[Word2VecModel]].
*/
private[feature] trait Word2VecBase extends Params
with HasInputCol with HasOutputCol with HasMaxIter with HasStepSize with HasSeed {
/**
* The dimension of the code that you want to transform from words.
* Default: 100
* @group param
*/
final val vectorSize = new IntParam(
this, "vectorSize", "the dimension of codes after transforming from words")
setDefault(vectorSize -> 100)
/** @group getParam */
def getVectorSize: Int = $(vectorSize)
/**
* Number of partitions for sentences of words.
* Default: 1
* @group param
*/
final val numPartitions = new IntParam(
this, "numPartitions", "number of partitions for sentences of words")
setDefault(numPartitions -> 1)
/** @group getParam */
def getNumPartitions: Int = $(numPartitions)
/**
* The minimum number of times a token must appear to be included in the word2vec model's
* vocabulary.
* Default: 5
* @group param
*/
final val minCount = new IntParam(this, "minCount", "the minimum number of times a token must " +
"appear to be included in the word2vec model's vocabulary")
setDefault(minCount -> 5)
/** @group getParam */
def getMinCount: Int = $(minCount)
setDefault(stepSize -> 0.025)
setDefault(maxIter -> 1)
/**
* Validate and transform the input schema.
*/
protected def validateAndTransformSchema(schema: StructType): StructType = {
SchemaUtils.checkColumnType(schema, $(inputCol), new ArrayType(StringType, true))
SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
}
}
/**
* :: Experimental ::
* Word2Vec trains a model of `Map(String, Vector)`, i.e. transforms a word into a code for further
* natural language processing or machine learning process.
*/
@Experimental
final class Word2Vec(override val uid: String) extends Estimator[Word2VecModel] with Word2VecBase {
def this() = this(Identifiable.randomUID("w2v"))
/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
/** @group setParam */
def setVectorSize(value: Int): this.type = set(vectorSize, value)
/** @group setParam */
def setStepSize(value: Double): this.type = set(stepSize, value)
/** @group setParam */
def setNumPartitions(value: Int): this.type = set(numPartitions, value)
/** @group setParam */
def setMaxIter(value: Int): this.type = set(maxIter, value)
/** @group setParam */
def setSeed(value: Long): this.type = set(seed, value)
/** @group setParam */
def setMinCount(value: Int): this.type = set(minCount, value)
override def fit(dataset: DataFrame): Word2VecModel = {
transformSchema(dataset.schema, logging = true)
val input = dataset.select($(inputCol)).map(_.getAs[Seq[String]](0))
val wordVectors = new feature.Word2Vec()
.setLearningRate($(stepSize))
.setMinCount($(minCount))
.setNumIterations($(maxIter))
.setNumPartitions($(numPartitions))
.setSeed($(seed))
.setVectorSize($(vectorSize))
.fit(input)
copyValues(new Word2VecModel(uid, wordVectors).setParent(this))
}
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
override def copy(extra: ParamMap): Word2Vec = defaultCopy(extra)
}
/**
* :: Experimental ::
* Model fitted by [[Word2Vec]].
*/
@Experimental
class Word2VecModel private[ml] (
override val uid: String,
@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.
*/
@transient lazy val getVectors: DataFrame = {
val sc = SparkContext.getOrCreate()
val sqlContext = SQLContext.getOrCreate(sc)
import sqlContext.implicits._
val wordVec = wordVectors.getVectors.mapValues(vec => Vectors.dense(vec.map(_.toDouble)))
sc.parallelize(wordVec.toSeq).toDF("word", "vector")
}
/**
* Find "num" number of words closest in similarity to the given word.
* Returns a dataframe with the words and the cosine similarities between the
* synonyms and the given word.
*/
def findSynonyms(word: String, num: Int): DataFrame = {
findSynonyms(wordVectors.transform(word), num)
}
/**
* Find "num" number of words closest to similarity to the given vector representation
* of the word. Returns a dataframe with the words and the cosine similarities between the
* synonyms and the given word vector.
*/
def findSynonyms(word: Vector, num: Int): DataFrame = {
val sc = SparkContext.getOrCreate()
val sqlContext = SQLContext.getOrCreate(sc)
import sqlContext.implicits._
sc.parallelize(wordVectors.findSynonyms(word, num)).toDF("word", "similarity")
}
/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
/**
* Transform a sentence column to a vector column to represent the whole sentence. The transform
* is performed by averaging all word vectors it contains.
*/
override def transform(dataset: DataFrame): DataFrame = {
transformSchema(dataset.schema, logging = true)
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 = $(vectorSize)
val word2Vec = udf { sentence: Seq[String] =>
if (sentence.size == 0) {
Vectors.sparse(d, Array.empty[Int], Array.empty[Double])
} else {
val sum = Vectors.zeros(d)
sentence.foreach { word =>
bVectors.value.get(word).foreach { v =>
BLAS.axpy(1.0, v, sum)
}
}
BLAS.scal(1.0 / sentence.size, sum)
sum
}
}
dataset.withColumn($(outputCol), word2Vec(col($(inputCol))))
}
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
override def copy(extra: ParamMap): Word2VecModel = {
val copied = new Word2VecModel(uid, wordVectors)
copyValues(copied, extra).setParent(parent)
}
}
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