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/*
* 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.
*/
package org.apache.spark.ml.feature;
import java.util.Arrays;
import java.util.List;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.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.SQLContext;
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 {
private transient JavaSparkContext jsc;
private transient SQLContext jsql;
@Before
public void setUp() {
jsc = new JavaSparkContext("local", "JavaHashingTFSuite");
jsql = new SQLContext(jsc);
}
@After
public void tearDown() {
jsc.stop();
jsc = null;
}
@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 = jsql.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);
}
}
}
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