Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/5596#discussion_r28745764
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
---
@@ -0,0 +1,238 @@
+/*
+ * 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.{Estimator, Model}
+import org.apache.spark.ml.param.{HasInputCol, ParamMap, Params, _}
+import org.apache.spark.mllib.feature
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+import org.apache.spark.util.Utils
+
+/**
+ * Params for [[Word2Vec]] and [[Word2VecModel]].
+ */
+private[feature] trait Word2VecParams extends Params
+ with HasInputCol with HasMaxIter with HasLearningRate {
+
+ /**
+ * The dimension of the code that you want to transform from words.
+ */
+ val vectorSize = new IntParam(
+ this, "vectorSize", "the dimension of codes after transforming from
words", Some(100))
+
+ /** @group getParam */
+ def getVectorSize: Int = get(vectorSize)
+
+ /**
+ * Number of partitions for sentences of words.
+ */
+ val numPartitions = new IntParam(
+ this, "numPartitions", "number of partitions for sentences of words",
Some(1))
+
+ /** @group getParam */
+ def getNumPartitions: Int = get(numPartitions)
+
+ /**
+ * A random seed to random an initial vector.
+ */
+ val seed = new LongParam(
+ this, "seed", "a random seed to random an initial vector",
Some(Utils.random.nextLong()))
+
+ /** @group getParam */
+ def getSeed: Long = get(seed)
+
+ /**
+ * The minimum count of words that can be kept in training set.
+ */
+ val minCount = new IntParam(
+ this, "minCount", "the minimum count of words to filter words",
Some(5))
+
+ /** @group getParam */
+ def getMinCount: Int = get(minCount)
+
+ /**
+ * The column name of the output column - synonyms.
+ */
+ val synonymsCol = new Param[String](this, "synonymsCol", "Synonyms
column name")
+
+ /** @group getParam */
+ def getSynonymsCol: String = get(synonymsCol)
+
+ /**
+ * The column name of the output column - code.
+ */
+ val codeCol = new Param[String](this, "codeCol", "Code column name")
+
+ /** @group getParam */
+ def getCodeCol: String = get(codeCol)
+
+ /**
+ * The number of synonyms that you want to have.
+ */
+ val numSynonyms = new IntParam(this, "numSynonyms", "number of synonyms
to find", Some(0))
+
+ /** @group getParam */
+ def getNumSynonyms: Int = get(numSynonyms)
+}
+
+/**
+ * :: 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
+class Word2Vec extends Estimator[Word2VecModel] with Word2VecParams {
+
+ /** @group setParam */
+ def setInputCol(value: String): this.type = set(inputCol, value)
+
+ /** @group setParam */
+ def setVectorSize(value: Int) = set(vectorSize, value)
+
+ /** @group setParam */
+ def setLearningRate(value: Double) = set(learningRate, value)
+
+ /** @group setParam */
+ def setNumPartitions(value: Int) = set(numPartitions, value)
+
+ /** @group setParam */
+ def setMaxIter(value: Int) = set(maxIter, value)
+
+ /** @group setParam */
+ def setSeed(value: Long) = set(seed, value)
+
+ /** @group setParam */
+ def setMinCount(value: Int) = set(minCount, value)
+
+ override def fit(dataset: DataFrame, paramMap: ParamMap): Word2VecModel
= {
+ transformSchema(dataset.schema, paramMap, logging = true)
+ val map = this.paramMap ++ paramMap
+ val input = dataset.select(map(inputCol)).map { case Row(v:
Seq[String]) => v }
+ val wordVectors = new feature.Word2Vec()
+ .setLearningRate(map(learningRate))
+ .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 = {
+ val map = this.paramMap ++ paramMap
+ val inputType = schema(map(inputCol)).dataType
+ require(inputType.isInstanceOf[ArrayType],
+ s"Input column ${map(inputCol)} must be a Iterable[String] column")
--- End diff --
Also check the elementType of inputType?
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