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/*
* File: AbstractBaggingLearner.java
* Authors: Justin Basilico
* Project: Cognitive Foundry Learning Core
*
* Copyright 2011 Cognitive Foundry. All rights reserved.
*/
package gov.sandia.cognition.learning.algorithm.ensemble;
import gov.sandia.cognition.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.collection.CollectionUtil;
import gov.sandia.cognition.evaluator.Evaluator;
import gov.sandia.cognition.learning.algorithm.AbstractAnytimeSupervisedBatchLearner;
import gov.sandia.cognition.learning.algorithm.BatchLearner;
import gov.sandia.cognition.learning.algorithm.BatchLearnerContainer;
import gov.sandia.cognition.learning.data.InputOutputPair;
import gov.sandia.cognition.util.ArgumentChecker;
import gov.sandia.cognition.util.Randomized;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Random;
/**
* Learns an ensemble by randomly sampling with replacement
* (duplicates allowed) some percentage of the size of the data (defaults to
* 100%) on each iteration to train a new ensemble member. The random sample is
* referred to as a bag. Each learned ensemble member is given equal weight.
* The idea here is that randomly sampling from the data and learning an
* ensemble member that has high variance (such as a decision tree) with
* respect to the input data, one can improve the performance of that algorithm.
*
* By default, the algorithm runs the maxIterations number of steps to create
* that number of ensemble members. However, one can also use out-of-bag (OOB)
* error on each iteration to determine a stopping criteria. The OOB error is
* determined by looking at the performance of the categorizer on the examples
* that it has not seen.
*
* @param
* The input type for supervised learning. Passed on to the internal
* learning algorithm. Also the input type for the learned ensemble.
* @param
* The output type for supervised learning. Passed on to the internal
* learning algorithm. Also the output type of the learned ensemble.
* @param
* The type of ensemble member created by the inner learning algorithm.
* Usually an evaluator.
* @param
* The type of ensemble that the algorithm fills with ensemble members.
* @author Justin Basilico
* @since 3.3.3
*/
@PublicationReference(
title="Bagging Predictors",
author="Leo Breiman",
year=1996,
type=PublicationType.Journal,
publication="Machine Learning",
pages={123, 140},
url="http://www.springerlink.com/index/L4780124W2874025.pdf")
public abstract class AbstractBaggingLearner>
extends AbstractAnytimeSupervisedBatchLearner
implements Randomized,
BatchLearnerContainer>, ? extends MemberType>>
{
/** The default maximum number of iterations is {@value}. */
public static final int DEFAULT_MAX_ITERATIONS = 100;
/** The default percent to sample is 1.0 (which represents 100%). */
public static final double DEFAULT_PERCENT_TO_SAMPLE = 1.0;
/** The learner to use to create the categorizer for each iteration. */
protected BatchLearner>,
? extends MemberType> learner;
/** The percentage of the data to sample with replacement on each iteration.
* Must be positive. Represented as a floating point number with 1.0
* meaning 100%.
*/
protected double percentToSample;
/** The random number generator to use. */
protected Random random;
/** The ensemble being created by the learner. */
protected transient EnsembleType ensemble;
/** The data stored for efficient random access. */
protected transient ArrayList> dataList;
/** An indicator of whether or not the data is in the current bag. */
protected transient int[] dataInBag;
/** The current bag of data. */
protected transient ArrayList> bag;
/**
* Creates a new instance of AbstractBaggingLearner.
*/
public AbstractBaggingLearner()
{
this(null);
}
/**
* Creates a new instance of AbstractBaggingLearner.
*
* @param learner
* The learner to use to create the ensemble member on each iteration.
*/
public AbstractBaggingLearner(
final BatchLearner>, ? extends MemberType> learner)
{
this(learner, DEFAULT_MAX_ITERATIONS, DEFAULT_PERCENT_TO_SAMPLE, new Random());
}
/**
* Creates a new instance of AbstractBaggingLearner.
*
* @param learner
* The learner to use to create the ensemble member on each iteration.
* @param maxIterations
* The maximum number of iterations to run for, which is also the
* number of learners to create.
* @param percentToSample
* The percentage of the total size of the data to sample on each
* iteration. Must be positive.
* @param random
* The random number generator to use.
*/
public AbstractBaggingLearner(
final BatchLearner>, ? extends MemberType> learner,
final int maxIterations,
final double percentToSample,
final Random random)
{
super(maxIterations);
this.setLearner(learner);
this.setPercentToSample(percentToSample);
this.setRandom(random);
this.setEnsemble(null);
this.setDataList(null);
this.setDataInBag(null);
this.setBag(null);
}
@Override
protected boolean initializeAlgorithm()
{
final int dataSize = CollectionUtil.size(this.getData());
if (dataSize <= 0)
{
// This is an invalid dataset.
return false;
}
if (this.getRandom() == null)
{
this.setRandom(new Random());
}
// Create a random-access version of the data.
this.setDataList(CollectionUtil.asArrayList(this.getData()));
this.setDataInBag(new int[dataSize]);
this.setBag(new ArrayList>());
// Create the ensemble where we will be storing the output.
this.setEnsemble(this.createInitialEnsemble());
return true;
}
@Override
protected boolean step()
{
// Figure out how many to sample.
final int dataSize = this.dataList.size();
final int sampleCount = Math.max(1, (int) (this.percentToSample * dataSize));
// Clear out the bag from the previous iteration.
this.bag.clear();
for (int i = 0; i < dataSize; i++)
{
this.dataInBag[i] = 0;
}
// Fill the bag.
this.fillBag(sampleCount);
// Learn the categorizer on the new bag of data.
final MemberType learned = this.learner.learn(this.bag);
// Add the categorizer to the ensemble and give it equal weight.
this.addEnsembleMember(learned);
// We keep going until we've created the requested number of members,
// which is checked by the super-class.
return true;
}
/**
* Create the initial, empty ensemble for the algorithm to use.
*
* @return
* A new ensemble for the algorithm to use.
*/
protected abstract EnsembleType createInitialEnsemble();
/**
* Adds a new member to the ensemble.
*
* @param member
* The new member to add to the ensemble.
*/
protected abstract void addEnsembleMember(
final MemberType member);
/**
* Fills the internal bag field by sampling the given number of samples.
*
* @param sampleCount
* The number to sample.
*/
protected void fillBag(
final int sampleCount)
{
final int dataSize = this.dataList.size();
// Create the bag by sampling with replacement.
for (int i = 0; i < sampleCount; i++)
{
final int index = this.getRandom().nextInt(dataSize);
final InputOutputPair example =
this.dataList.get(index);
this.bag.add(example);
this.dataInBag[index] += 1;
}
}
@Override
protected void cleanupAlgorithm()
{
// To clean up we remove the reference to the copy of the data
// collection that we made.
this.setDataList(null);
this.setDataInBag(null);
this.setBag(null);
}
/**
* Gets the ensemble created by this learner.
*
* @return The ensemble created by this learner.
*/
@Override
public EnsembleType getResult()
{
// The result is the ensemble.
return this.ensemble;
}
/**
* Gets the learner used to learn each ensemble member.
*
* @return
* The learner used for each ensemble member.
*/
@Override
public BatchLearner>, ? extends MemberType> getLearner()
{
return this.learner;
}
/**
* Sets the learner used to learn each ensemble member. Must be a supervised
* learning algorithm that takes in a collection of input-output pairs of
* the given data types and produces an evaluator for those data types.
*
* @param learner
* The learner used for each ensemble member.
*/
public void setLearner(
final BatchLearner>, ? extends MemberType> learner)
{
this.learner = learner;
}
/**
* Gets the percentage of the total data to sample on each iteration.
*
* @return
* The percentage of the total data to sample on each iteration.
*/
public double getPercentToSample()
{
return percentToSample;
}
/**
* Sets the percentage of the data to sample (with replacement) on each
* iteration. Must be greater than zero. The percent is represented as a
* floating point number with 1.0 representing 100%.
*
* @param percentToSample
* The percent of the data to sample on each iteration. Must be greater
* than zero. Defaults to 100%.
*/
public void setPercentToSample(
final double percentToSample)
{
ArgumentChecker.assertIsPositive("percentToSample", percentToSample);
this.percentToSample = percentToSample;
}
@Override
public Random getRandom()
{
return this.random;
}
@Override
public void setRandom(
final Random random)
{
this.random = random;
}
/**
* Gets the ensemble created by this learner.
*
* @return
* The ensemble created by this learner.
*/
public EnsembleType getEnsemble()
{
return this.ensemble;
}
/**
* Sets the ensemble created by this learner.
*
* @param ensemble
* The ensemble created by this learner.
*/
protected void setEnsemble(
final EnsembleType ensemble)
{
this.ensemble = ensemble;
}
/**
* Gets the data the learner is using as an array list.
*
* @return The data as an array list.
*/
public ArrayList>
getDataList()
{
return this.dataList;
}
/**
* Sets the data the learner is using as an array list.
*
* @param dataList The data as an array list.
*/
protected void setDataList(
final ArrayList> dataList)
{
this.dataList = dataList;
}
/**
* Gets the array of counts of the number of samples of each example in
* the current bag.
*
* @return
* The bag counts.
*/
public int[] getDataInBag()
{
return dataInBag;
}
/**
* Sets the array of counts of the number of samples of each example in
* the current bag.
*
* @param dataInBag
* The bag counts.
*/
protected void setDataInBag(
final int[] dataInBag)
{
this.dataInBag = dataInBag;
}
/**
* Gets the most recently created bag.
*
* @return
* The most recently created bag.
*/
public ArrayList> getBag()
{
return this.bag;
}
/**
* Sets the most recently created bag.
*
* @param bag
* The most recently created bag.
*/
protected void setBag(
final ArrayList> bag)
{
this.bag = bag;
}
}
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