spark TFIDFExample 源码
spark TFIDFExample 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/mllib/TFIDFExample.scala
/*
* 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.
*/
// scalastyle:off println
package org.apache.spark.examples.mllib
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
// $example on$
import org.apache.spark.mllib.feature.{HashingTF, IDF}
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.rdd.RDD
// $example off$
object TFIDFExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("TFIDFExample")
val sc = new SparkContext(conf)
// $example on$
// Load documents (one per line).
val documents: RDD[Seq[String]] = sc.textFile("data/mllib/kmeans_data.txt")
.map(_.split(" ").toSeq)
val hashingTF = new HashingTF()
val tf: RDD[Vector] = hashingTF.transform(documents)
// While applying HashingTF only needs a single pass to the data, applying IDF needs two passes:
// First to compute the IDF vector and second to scale the term frequencies by IDF.
tf.cache()
val idf = new IDF().fit(tf)
val tfidf: RDD[Vector] = idf.transform(tf)
// spark.mllib IDF implementation provides an option for ignoring terms which occur in less than
// a minimum number of documents. In such cases, the IDF for these terms is set to 0.
// This feature can be used by passing the minDocFreq value to the IDF constructor.
val idfIgnore = new IDF(minDocFreq = 2).fit(tf)
val tfidfIgnore: RDD[Vector] = idfIgnore.transform(tf)
// $example off$
println("tfidf: ")
tfidf.collect.foreach(x => println(x))
println("tfidfIgnore: ")
tfidfIgnore.collect.foreach(x => println(x))
sc.stop()
}
}
// scalastyle:on println
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