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From: yinxusen <git@git.apache.org>
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Subject: [GitHub] spark pull request: [SPARK-13017][Docs] Replace example code
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Message-Id: <20160221030545.09924E0104@git1-us-west.apache.org>
Date: Sun, 21 Feb 2016 03:05:44 +0000 (UTC)

Github user yinxusen commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11142#discussion_r53560990
  
    --- Diff: examples/src/main/scala/org/apache/spark/examples/mllib/TFIDFExample.scala ---
    @@ -0,0 +1,63 @@
    +/*
    + * 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
    +// $example on$
    +import org.apache.spark.SparkContext
    +import org.apache.spark.mllib.feature.{HashingTF, IDF}
    +import org.apache.spark.mllib.linalg.Vector
    +// $example off$
    +import org.apache.spark.rdd.RDD
    +
    +object TFIDFExample {
    +
    +  def main(args: Array[String]) {
    +
    +    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
    --- End diff --
    
    Change the comment to:
    
    ```scala
    // 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.
    ```


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