/*
* 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;
import java.util.HashMap;
import scala.Tuple2;
import org.apache.spark.api.java.function.Function2;
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.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
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;
import org.apache.spark.SparkConf;
/**
* Classification and regression using decision trees.
*/
public final class JavaDecisionTree {
public static void main(String[] args) {
String datapath = "data/mllib/sample_libsvm_data.txt";
if (args.length == 1) {
datapath = args[0];
} else if (args.length > 1) {
System.err.println("Usage: JavaDecisionTree <libsvm format data file>");
System.exit(1);
}
SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTree");
JavaSparkContext sc = new JavaSparkContext(sparkConf);
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD().cache();
// Compute the number of classes from the data.
Integer numClasses = data.map(new Function<LabeledPoint, Double>() {
@Override public Double call(LabeledPoint p) {
return p.label();
}
}).countByValue().size();
// Set parameters.
// Empty categoricalFeaturesInfo indicates all features are continuous.
HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
String impurity = "gini";
Integer maxDepth = 5;
Integer maxBins = 32;
// Train a DecisionTree model for classification.
final DecisionTreeModel model = DecisionTree.trainClassifier(data, numClasses,
categoricalFeaturesInfo, impurity, maxDepth, maxBins);
// Evaluate model on training instances and compute training error
JavaPairRDD<Double, Double> predictionAndLabel =
data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
@Override public Tuple2<Double, Double> call(LabeledPoint p) {
return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
}
});
Double trainErr =
1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
@Override public Boolean call(Tuple2<Double, Double> pl) {
return !pl._1().equals(pl._2());
}
}).count() / data.count();
System.out.println("Training error: " + trainErr);
System.out.println("Learned classification tree model:\n" + model);
// Train a DecisionTree model for regression.
impurity = "variance";
final DecisionTreeModel regressionModel = DecisionTree.trainRegressor(data,
categoricalFeaturesInfo, impurity, maxDepth, maxBins);
// Evaluate model on training instances and compute training error
JavaPairRDD<Double, Double> regressorPredictionAndLabel =
data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
@Override public Tuple2<Double, Double> call(LabeledPoint p) {
return new Tuple2<Double, Double>(regressionModel.predict(p.features()), p.label());
}
});
Double trainMSE =
regressorPredictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
@Override public Double call(Tuple2<Double, Double> pl) {
Double diff = pl._1() - pl._2();
return diff * diff;
}
}).reduce(new Function2<Double, Double, Double>() {
@Override public Double call(Double a, Double b) {
return a + b;
}
}) / data.count();
System.out.println("Training Mean Squared Error: " + trainMSE);
System.out.println("Learned regression tree model:\n" + regressionModel);
sc.stop();
}
}