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   * Licensed to the Apache Software Foundation (ASF) under one or more
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   * 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
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   *
   *    http://www.apache.org/licenses/LICENSE-2.0
   *
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  package org.apache.spark.ml.feature;
  
  import java.util.Arrays;
  import java.util.List;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.ml.linalg.Vector;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.RowFactory;
  import org.apache.spark.sql.types.DataTypes;
  import org.apache.spark.sql.types.Metadata;
  import org.apache.spark.sql.types.StructField;
  import org.apache.spark.sql.types.StructType;
  
  
  public class JavaHashingTFSuite extends SharedSparkSession {
  
    @Test
    public void hashingTF() {
      List<Row> data = Arrays.asList(
        RowFactory.create(0.0, "Hi I heard about Spark"),
        RowFactory.create(0.0, "I wish Java could use case classes"),
        RowFactory.create(1.0, "Logistic regression models are neat")
      );
      StructType schema = new StructType(new StructField[]{
        new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
        new StructField("sentence", DataTypes.StringType, false, Metadata.empty())
      });
  
      Dataset<Row> sentenceData = spark.createDataFrame(data, schema);
      Tokenizer tokenizer = new Tokenizer()
        .setInputCol("sentence")
        .setOutputCol("words");
      Dataset<Row> wordsData = tokenizer.transform(sentenceData);
      int numFeatures = 20;
      HashingTF hashingTF = new HashingTF()
        .setInputCol("words")
        .setOutputCol("rawFeatures")
        .setNumFeatures(numFeatures);
      Dataset<Row> featurizedData = hashingTF.transform(wordsData);
      IDF idf = new IDF().setInputCol("rawFeatures").setOutputCol("features");
      IDFModel idfModel = idf.fit(featurizedData);
      Dataset<Row> rescaledData = idfModel.transform(featurizedData);
      for (Row r : rescaledData.select("features", "label").takeAsList(3)) {
        Vector features = r.getAs(0);
        Assert.assertEquals(features.size(), numFeatures);
      }
    }
  }
  