Spark 机器学习 ---Word2Vec

package Spark_MLlib

import org.apache.spark.ml.feature.Word2Vec
import org.apache.spark.sql.SparkSession

object 特征抽取_Word2Vec {
      val spark=SparkSession.builder().master("local").appName("Word2Vec").getOrCreate()
      import spark.implicits._
  def main(args: Array[String]): Unit = {

        val documentDF= spark.createDataFrame(Seq(
          "soyo like spark and hadoop".split(" "),
          "scala is good tool to study".split(" "),
          "but java i want to study and spark".split(" "),
           "soyo like spark and hadoop ".split(" ")
        ).map(Tuple1.apply)).toDF("text")
        val word2Vec=new Word2Vec().setInputCol("text").setOutputCol("result").setVectorSize().setMinCount()  //设置特征向量维数为5
        val word2Vec_model=word2Vec.fit(documentDF)  //训练模型
        val result=word2Vec_model.transform(documentDF) //把文档转换成特征向量
            result.show(false)

  }
}
结果：文档相同或着相似 特征向量就相同或者在特征空间中特征向量越相近
|text                                       |result                                                                                                       |
+-------------------------------------------+-------------------------------------------------------------------------------------------------------------+
|[soyo, like, spark, and, hadoop]           |[0.010919421538710596,-0.013777335733175279,0.02715198565274477,-0.010085364431142808,0.019428260042332113]  |        
|[scala, is, good, tool, to, study]         |[-0.048216115372876324,-0.00931493720660607,0.0237591746263206,0.04614267808695634,0.018560086687405903]     |
|[but, java, i, want, to, study, and, spark]|[0.025922087021172047,-0.027650322022964247,0.029493116540834308,-0.029830976389348507,-0.025802675168961287]|
|[soyo, like, spark, and, hadoop]           |[0.010919421538710596,-0.013777335733175279,0.02715198565274477,-0.010085364431142808,0.019428260042332113]  |
+-------------------------------------------+-------------------------------------------------------------------------------------------------------------+

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