Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5596#discussion_r29059360
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
---
@@ -0,0 +1,190 @@
+/*
+ * 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.annotation.AlphaComponent
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.mllib.feature
+import org.apache.spark.mllib.linalg.{VectorUDT, Vectors}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.{DataFrame, Row}
+
+/**
+ * Params for [[Word2Vec]] and [[Word2VecModel]].
+ */
+private[feature] trait Word2VecBase extends Params
+ with HasInputCol with HasOutputCol with HasMaxIter with HasStepSize {
+
+ /**
+ * The dimension of the code that you want to transform from words.
+ */
+ final val vectorSize = new IntParam(
+ this, "vectorSize", "the dimension of codes after transforming from
words")
+
+ setDefault(vectorSize -> 100)
+
+ /** @group getParam */
+ def getVectorSize: Int = getOrDefault(vectorSize)
+
+ /**
+ * Number of partitions for sentences of words.
+ */
+ final val numPartitions = new IntParam(
+ this, "numPartitions", "number of partitions for sentences of words")
+
+ setDefault(numPartitions -> 1)
+
+ /** @group getParam */
+ def getNumPartitions: Int = getOrDefault(numPartitions)
+
+ /**
+ * A random seed to random an initial vector.
+ */
+ final val seed = new LongParam(this, "seed", "a random seed to random an
initial vector")
+
+ setDefault(seed -> 42L)
+
+ /** @group getParam */
+ def getSeed: Long = getOrDefault(seed)
+
+ /**
+ * The minimum number of times a token must appear to be included in the
word2vec model's
+ * vocabulary.
+ */
+ 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 = getOrDefault(minCount)
+
+ setDefault(stepSize -> 0.025)
+ setDefault(maxIter -> 1)
+
+ /**
+ * Validate and transform the input schema.
+ */
+ protected def validateAndTransformSchema(schema: StructType, paramMap:
ParamMap): StructType = {
+ val map = extractParamMap(paramMap)
+ SchemaUtils.checkColumnType(schema, map(inputCol), new
ArrayType(StringType, true))
+ SchemaUtils.appendColumn(schema, map(outputCol), new VectorUDT)
+ }
+}
+
+/**
+ * :: AlphaComponent ::
+ * 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.
+ */
+@AlphaComponent
+final class Word2Vec extends Estimator[Word2VecModel] with Word2VecBase {
+
+ /** @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, paramMap: ParamMap): Word2VecModel
= {
+ transformSchema(dataset.schema, paramMap, logging = true)
+ val map = extractParamMap(paramMap)
+ val input = dataset.select(map(inputCol)).map { case Row(v:
Seq[String]) => v }
+ val wordVectors = new feature.Word2Vec()
+ .setLearningRate(map(stepSize))
+ .setMinCount(map(minCount))
+ .setNumIterations(map(maxIter))
+ .setNumPartitions(map(numPartitions))
+ .setSeed(map(seed))
+ .setVectorSize(map(vectorSize))
+ .fit(input)
+ val model = new Word2VecModel(this, map, wordVectors)
+ Params.inheritValues(map, this, model)
+ model
+ }
+
+ override def transformSchema(schema: StructType, paramMap: ParamMap):
StructType = {
+ validateAndTransformSchema(schema, paramMap)
+ }
+}
+
+/**
+ * :: AlphaComponent ::
+ * Model fitted by [[Word2Vec]].
+ */
+@AlphaComponent
+class Word2VecModel private[ml] (
+ override val parent: Word2Vec,
+ override val fittingParamMap: ParamMap,
+ wordVectors: feature.Word2VecModel)
+ extends Model[Word2VecModel] with Word2VecBase {
+
+ /** @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, paramMap: ParamMap):
DataFrame = {
+ transformSchema(dataset.schema, paramMap, logging = true)
+ val map = extractParamMap(paramMap)
+ val bWordVectors =
dataset.sqlContext.sparkContext.broadcast(wordVectors)
+ val word2Vec = udf { v: Seq[String] =>
+ if (v.size == 0) {
+ Vectors.zeros(map(vectorSize))
--- End diff --
Output sparse vector.
---
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