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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.classification;
import java.io.Serializable;
import java.util.List;
import static scala.collection.JavaConversions.seqAsJavaList;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import static org.apache.spark.mllib.classification.LogisticRegressionSuite.generateMultinomialLogisticInput;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.SQLContext;
public class JavaOneVsRestSuite implements Serializable {
private transient JavaSparkContext jsc;
private transient SQLContext jsql;
private transient DataFrame dataset;
private transient JavaRDD<LabeledPoint> datasetRDD;
@Before
public void setUp() {
jsc = new JavaSparkContext("local", "JavaLOneVsRestSuite");
jsql = new SQLContext(jsc);
int nPoints = 3;
// The following weights and xMean/xVariance are computed from iris dataset with lambda=0.2.
// As a result, we are drawing samples from probability distribution of an actual model.
double[] weights = {
-0.57997, 0.912083, -0.371077, -0.819866, 2.688191,
-0.16624, -0.84355, -0.048509, -0.301789, 4.170682 };
double[] xMean = {5.843, 3.057, 3.758, 1.199};
double[] xVariance = {0.6856, 0.1899, 3.116, 0.581};
List<LabeledPoint> points = seqAsJavaList(generateMultinomialLogisticInput(
weights, xMean, xVariance, true, nPoints, 42));
datasetRDD = jsc.parallelize(points, 2);
dataset = jsql.createDataFrame(datasetRDD, LabeledPoint.class);
}
@After
public void tearDown() {
jsc.stop();
jsc = null;
}
@Test
public void oneVsRestDefaultParams() {
OneVsRest ova = new OneVsRest();
ova.setClassifier(new LogisticRegression());
Assert.assertEquals(ova.getLabelCol() , "label");
Assert.assertEquals(ova.getPredictionCol() , "prediction");
OneVsRestModel ovaModel = ova.fit(dataset);
DataFrame predictions = ovaModel.transform(dataset).select("label", "prediction");
predictions.collectAsList();
Assert.assertEquals(ovaModel.getLabelCol(), "label");
Assert.assertEquals(ovaModel.getPredictionCol() , "prediction");
}
}
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