/**
* 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 edu.uci.jforestsx.learning.bagging;
import java.util.Random;
import edu.uci.jforestsx.config.TrainingConfig;
import edu.uci.jforestsx.eval.EvaluationMetric;
import edu.uci.jforestsx.learning.LearningModule;
import edu.uci.jforestsx.learning.trees.Ensemble;
import edu.uci.jforestsx.learning.trees.Tree;
import edu.uci.jforestsx.learning.trees.TreeLeafInstances;
import edu.uci.jforestsx.sample.Predictions;
import edu.uci.jforestsx.sample.Sample;
import edu.uci.jforestsx.util.ConfigHolder;
/**
* @author Yasser Ganjisaffar <ganjisaffar at gmail dot com>
*/
public abstract class Bagging extends LearningModule {
protected int bagCount;
protected double baggingTrainFraction;
protected boolean backfit;
protected Sample curValidSet;
protected double lastValidMeasurement;
protected Predictions validPredictions;
protected boolean printIntermediateValidMeasurements;
protected Random rnd;
protected EvaluationMetric evaluationMetric;
public Bagging() {
super("Bagging");
}
public void init(ConfigHolder configHolder, int maxNumTrainInstances, int maxNumValidInstances, EvaluationMetric evaluationMetric)
throws Exception {
TrainingConfig trainingConfig = configHolder.getConfig(TrainingConfig.class);
BaggingConfig baggingConfig = configHolder.getConfig(BaggingConfig.class);
this.bagCount = baggingConfig.bagCount;
this.baggingTrainFraction = baggingConfig.trainFraction;
this.backfit = baggingConfig.backfitting;
validPredictions = getNewPredictions();
validPredictions.allocate(maxNumValidInstances);
printIntermediateValidMeasurements = configHolder.getConfig(TrainingConfig.class).printIntermediateValidMeasurements;
this.evaluationMetric = evaluationMetric;
rnd = new Random(trainingConfig.randomSeed);
}
protected abstract Predictions getNewPredictions();
@Override
public Ensemble learn(Sample trainSet, Sample validSet) throws Exception {
curValidSet = validSet;
validPredictions.setSample(curValidSet);
validPredictions.reset();
Ensemble ensemble = new Ensemble();
subLearner.setTreeWeight(treeWeight / bagCount);
for (int iteration = 1; iteration <= bagCount; iteration++) {
System.out.println("Iteration: " + iteration);
Sample subLearnerTrainSet = trainSet.getRandomSubSample(baggingTrainFraction, rnd);
//((TreeLearner) subLearner).setRnd();
Sample subLearnerOutOfTrainSet = trainSet.getOutOfSample(subLearnerTrainSet);
Sample subLearnerValidSet = (validSet == null || validSet.isEmpty() ? subLearnerOutOfTrainSet : validSet);
Ensemble subEnsemble = subLearner.learn(subLearnerTrainSet, subLearnerValidSet);
for (int t = 0; t < subEnsemble.getNumTrees(); t++) {
Tree tree = subEnsemble.getTreeAt(t);
double curTreeWeight = subEnsemble.getWeightAt(t);
if (backfit) {
tree.backfit(subLearnerOutOfTrainSet);
}
ensemble.addTree(tree, curTreeWeight);
System.out.println(tree.numLeaves);
}
if (validSet != null && !validSet.isEmpty()) {
for (int t = 0; t < subEnsemble.getNumTrees(); t++) {
validPredictions.update(subEnsemble.getTreeAt(t), 1.0 / bagCount);
}
lastValidMeasurement = validPredictions.evaluate(evaluationMetric);
if (printIntermediateValidMeasurements) {
printValidMeasurement(iteration, lastValidMeasurement, evaluationMetric);
}
}
onIterationEnd();
}
onLearningEnd();
return ensemble;
}
@Override
public void postProcess(Tree tree, TreeLeafInstances treeLeafInstances) {
if (parentLearner != null) {
parentLearner.postProcess(tree, treeLeafInstances);
}
}
@Override
public double getValidationMeasurement() throws Exception {
return lastValidMeasurement;
}
}