Spark 机器学习 ---TF-IDF
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package Spark_MLlib import org.apache.spark.ml.feature.{HashingTF, IDF, Tokenizer} import org.apache.spark.sql.SparkSession /** * TF-IDF */ object 特征抽取 { val spark=SparkSession.builder().master("local").appName("TF-IDF").getOrCreate() import spark.implicits._ def main(args: Array[String]): Unit = { val soureceData= spark.createDataFrame(Seq( (0,"soyo spark like spark hadoop spark and spark like spark"), (1,"i wish i can like java i"), (2,"but i dont know how to soyo"), (3,"spark is good spark tool") )).toDF("label","sentence") //进行分词 val tokenizer=new Tokenizer().setInputCol("sentence").setOutputCol("words") val wordsData=tokenizer.transform(soureceData) wordsData.show(false) //表示不省略,打印字符串的所有单词 val hashTF=new HashingTF().setInputCol("words").setOutputCol("rawsFeatures").setNumFeatures(1000) //生成特征向量 val featuredData=hashTF.transform(wordsData) featuredData.show(false) val idf=new IDF().setInputCol("rawsFeatures").setOutputCol("features") val idfModel=idf.fit(featuredData) val result=idfModel.transform(featuredData) result.show(false) result.select("label","features").show(false) } }
结果:
+-----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|label|features |
+-----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|0 |(1000,[105,107,181,330,333],[2.5541281188299534,0.5108256237659907,0.9162907318741551,1.0216512475319814,0.9162907318741551]) |
|1 |(1000,[329,330,495,833,967],[1.5324768712979722,0.5108256237659907,0.9162907318741551,0.9162907318741551,0.9162907318741551]) |
|2 |(1000,[83,107,237,329,388,779,977],[0.9162907318741551,0.5108256237659907,0.9162907318741551,0.5108256237659907,0.9162907318741551,0.9162907318741551,0.9162907318741551])|
|3 |(1000,[105,111,168,281],[1.0216512475319814,0.9162907318741551,0.9162907318741551,0.9162907318741551]) |
+-----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
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