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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.examples.mllib;
// $example on$
import java.util.HashMap;
import java.util.Map;
import scala.Tuple2;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.DecisionTree;
import org.apache.spark.mllib.tree.model.DecisionTreeModel;
import org.apache.spark.mllib.util.MLUtils;
// $example off$
class JavaDecisionTreeRegressionExample {
public static void main(String[] args) {
// $example on$
SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTreeRegressionExample");
JavaSparkContext jsc = new JavaSparkContext(sparkConf);
// Load and parse the data file.
String datapath = "data/mllib/sample_libsvm_data.txt";
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD();
// Split the data into training and test sets (30% held out for testing)
JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
JavaRDD<LabeledPoint> trainingData = splits[0];
JavaRDD<LabeledPoint> testData = splits[1];
// Set parameters.
// Empty categoricalFeaturesInfo indicates all features are continuous.
Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
String impurity = "variance";
int maxDepth = 5;
int maxBins = 32;
// Train a DecisionTree model.
DecisionTreeModel model = DecisionTree.trainRegressor(trainingData,
categoricalFeaturesInfo, impurity, maxDepth, maxBins);
// Evaluate model on test instances and compute test error
JavaPairRDD<Double, Double> predictionAndLabel =
testData.mapToPair(p -> new Tuple2<>(model.predict(p.features()), p.label()));
double testMSE = predictionAndLabel.mapToDouble(pl -> {
double diff = pl._1() - pl._2();
return diff * diff;
}).mean();
System.out.println("Test Mean Squared Error: " + testMSE);
System.out.println("Learned regression tree model:\n" + model.toDebugString());
// Save and load model
model.save(jsc.sc(), "target/tmp/myDecisionTreeRegressionModel");
DecisionTreeModel sameModel = DecisionTreeModel
.load(jsc.sc(), "target/tmp/myDecisionTreeRegressionModel");
// $example off$
}
}
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