gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment Maven / Gradle / Ivy
/*
* File: LearnerComparisonExperiment.java
* Authors: Justin Basilico
* Company: Sandia National Laboratories
* Project: Cognitive Framework Lite
*
* Copyright October 1, 2007, Sandia Corporation. Under the terms of Contract
* DE-AC04-94AL85000, there is a non-exclusive license for use of this work by
* or on behalf of the U.S. Government. Export of this program may require a
* license from the United States Government. See CopyrightHistory.txt for
* complete details.
*
*
*/
package gov.sandia.cognition.learning.experiment;
import gov.sandia.cognition.learning.algorithm.BatchLearner;
import gov.sandia.cognition.learning.performance.PerformanceEvaluator;
import gov.sandia.cognition.util.Summarizer;
import gov.sandia.cognition.learning.data.PartitionedDataset;
import gov.sandia.cognition.statistics.method.ConfidenceStatistic;
import gov.sandia.cognition.statistics.method.NullHypothesisEvaluator;
import gov.sandia.cognition.util.DefaultPair;
import gov.sandia.cognition.util.Pair;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Collection;
/**
* The {@code LearnerComparisonExperiment} compares the performance of two
* machine learning algorithms to determine (using a statistical test) if the
* two algorithms have significantly different performance.
*
*@param
* The type of the data to perform the experiment with.
* This will be passed to the fold creator to create a number of folds
* on which to validate the performance of the learning algorithms.
* @param
* The type of data created by the fold creator that will go into
* the learning algorithms. Typically, this is the same as the
* InputDataType, but it does not need to be. It just needs to match
* the output of the fold creator and the input of the learning
* algorithms.
* @param The type of the output produced by the learning
* algorithms whose performance will be evaluated on each fold of data.
* @param The type of the statistic generated by the
* performance evaluator on each learned objects for each fold. It is
* created by passing the learned object plus the test data for the
* fold into the performance evaluator.
* @param The type produced by the summarizer at the end of
* the experiment from a collection of the given statistics (one for
* each fold). This represents the performance result for the
* comparison of the performance of the learning algorithms for the
* whole experiment.
* @author Justin Basilico
* @since 2.0
*/
public class LearnerComparisonExperiment
extends AbstractValidationFoldExperiment
implements Serializable
{
/** The evaluator to use to compute the performance of the learned object on
* each fold. */
protected PerformanceEvaluator, ? extends StatisticType> performanceEvaluator;
/** The summarizer for summarizing the result of the performance evaluator
* from all the folds. */
protected Summarizer summarizer;
/** The statistical test to use to determine if the two learners are
* significantly different. */
private NullHypothesisEvaluator> statisticalTest;
/** The learners that the experiment is being performed on. */
protected Pair, ? extends LearnedType>, BatchLearner, ? extends LearnedType>> learners;
/** The performance evaluations made during the experiment. */
protected DefaultPair, ArrayList> statistics;
/** The confidence statistic generated from the underlying performance
* statistics. */
protected ConfidenceStatistic confidence;
/** The summaries of performance. */
protected DefaultPair summaries;
/**
* Creates a new instance of LearnerComparisonExperiment.
*/
public LearnerComparisonExperiment()
{
this(null, null, null, null);
}
/**
* Creates a new instance of LearnerComparisonExperiment.
*
* @param foldCreator The object to use for creating the folds.
* @param performanceEvaluator The evaluator to use to compute the
* performance of the learned object on each fold.
* @param statisticalTest The statistical test to apply to the performance
* results of the two learners to determine if they are
* statistically different.
* @param summarizer The summarizer for summarizing the result of the
* performance evaluator from all the folds.
*/
public LearnerComparisonExperiment(
final ValidationFoldCreator foldCreator,
final PerformanceEvaluator, ? extends StatisticType> performanceEvaluator,
final NullHypothesisEvaluator> statisticalTest,
final Summarizer summarizer)
{
super(foldCreator);
this.setPerformanceEvaluator(performanceEvaluator);
this.setStatisticalTest(statisticalTest);
this.setSummarizer(summarizer);
// The initial number of trials is unknown.
this.setStatistics(null);
this.setConfidence(null);
this.setSummaries(null);
}
/**
* Evaluates the two batch learners using the given data on the same set of
* validation folds and returns the resulting information including the
* confidence statistic that the two are different along with the summary
* of their performance.
*
* @param learners The two learners.
* @param data The data to use.
* @return The experimental results.
*/
public Result evaluate(
final Pair, ? extends LearnedType>, BatchLearner, ? extends LearnedType>> learners,
final Collection data)
{
// The first step in the experiment is to create the folds.
final Collection> folds =
this.getFoldCreator().createFolds(data);
this.setLearners(learners);
// Initialize the collection where we will store the statistics
// generated from the data.
this.setStatistics(
new DefaultPair, ArrayList>(
new ArrayList(folds.size()),
new ArrayList(folds.size())));
// Run the experiment.
this.runExperiment(folds);
// The confidence.
this.setConfidence(this.getStatisticalTest().evaluateNullHypothesis(
this.getStatistics().getFirst(), this.getStatistics().getSecond()));
// Summarize the statistics.
final SummaryType summary1 = this.getSummarizer().summarize(
this.getStatistics().getFirst());
final SummaryType summary2 = this.getSummarizer().summarize(
this.getStatistics().getSecond());
this.setSummaries(
new DefaultPair(summary1, summary2));
return new Result(
this.getConfidence(), this.getSummaries());
}
/**
* {@inheritDoc}
*
* @param fold {@inheritDoc}
*/
protected void runTrial(
final PartitionedDataset fold)
{
// Perform the learning algorithm on this fold for the first learner.
final LearnedType learned1 =
this.getLearners().getFirst().learn(fold.getTrainingSet());
// Compute the statistic and add it to the collection for the first
// learner.
final StatisticType statistic1 =
this.getPerformanceEvaluator().evaluatePerformance(
learned1, fold.getTestingSet());
this.getStatistics().getFirst().add(statistic1);
// Perform the learning algorithm on this fold for the second learner.
final LearnedType learned2 =
this.getLearners().getSecond().learn(fold.getTrainingSet());
// Compute the statistic and add it to the collection for the second
// learner.
final StatisticType statistic2 =
this.getPerformanceEvaluator().evaluatePerformance(
learned2, fold.getTestingSet());
this.getStatistics().getSecond().add(statistic2);
}
/**
* Evaluates the two batch learners using the given data on the same set of
* validation folds and returns the resulting information including the
* confidence statistic that the two are different along with the summary
* of their performance.
*
* @param learner1 The first learner.
* @param learner2 The second learner.
* @param data The data to use.
* @return The experimental results.
*/
public Result evaluate(
final BatchLearner, ? extends LearnedType> learner1,
final BatchLearner, ? extends LearnedType> learner2,
final Collection data)
{
return this.evaluate(
new DefaultPair, ? extends LearnedType>, BatchLearner, ? extends LearnedType>>(
learner1, learner2),
data);
}
/**
* Gets the performance evaluator to apply to each fold.
*
* @return The performance evaluator to apply to each fold.
*/
public PerformanceEvaluator, ? extends StatisticType> getPerformanceEvaluator()
{
return this.performanceEvaluator;
}
/**
* Sets the performance evaluator to apply to each fold.
*
* @param performanceEvaluator
* The performance evaluator to apply to each fold.
*/
public void setPerformanceEvaluator(
final PerformanceEvaluator, ? extends StatisticType> performanceEvaluator)
{
this.performanceEvaluator = performanceEvaluator;
}
/**
* Gets the summarizer of the performance evaluations.
*
* @return The summarizer of the performance evaluations.
*/
public Summarizer getSummarizer()
{
return this.summarizer;
}
/**
* Sets the summarizer of the performance evaluations.
*
* @param summarizer The summarizer of the performance evaluations.
*/
public void setSummarizer(
final Summarizer summarizer)
{
this.summarizer = summarizer;
}
/**
* Gets the statistical test to use to determine if the two learners are
* significantly different.
*
* @return The statistical test.
*/
public NullHypothesisEvaluator> getStatisticalTest()
{
return this.statisticalTest;
}
/**
* Sets the statistical test to use to determine if the two learners are
* significantly different.
*
* @param statisticalTest The statistical test.
*/
public void setStatisticalTest(
final NullHypothesisEvaluator> statisticalTest)
{
this.statisticalTest = statisticalTest;
}
/**
* Gets the learners the experiment is being run on.
*
* @return The learners.
*/
public Pair, ? extends LearnedType>, BatchLearner, ? extends LearnedType>> getLearners()
{
return this.learners;
}
/**
* Sets the learners the experiment is being run on.
*
* @param learners The learners.
*/
protected void setLearners(
final Pair, ? extends LearnedType>, BatchLearner, ? extends LearnedType>> learners)
{
this.learners = learners;
}
/**
* Gets the performance evaluations for the trials of the experiment.
*
* @return The performance evaluations for the trials of the experiment.
*/
public DefaultPair, ArrayList> getStatistics()
{
return this.statistics;
}
/**
* Sets the performance evaluations for the trials of the experiment.
*
* @param statistics
* The performance evaluations for the trials of the experiment.
*/
protected void setStatistics(
final DefaultPair, ArrayList> statistics)
{
this.statistics = statistics;
}
/**
* Gets the confidence statistic that the two learners are different.
*
* @return The confidence statistic of the experiment.
*/
public ConfidenceStatistic getConfidence()
{
return this.confidence;
}
/**
* Sets the confidence statistic that the two learners are different.
*
* @param confidence The confidence statistic of the experiment.
*/
protected void setConfidence(
final ConfidenceStatistic confidence)
{
this.confidence = confidence;
}
/**
* Gets the summaries of the experiment.
*
* @return The summaries of the experiment.
*/
public DefaultPair getSummaries()
{
return this.summaries;
}
/**
* Sets the summaries of the experiment.
*
* @param summaries The summaries of the experiment.
*/
protected void setSummaries(
final DefaultPair summaries)
{
this.summaries = summaries;
}
/**
* Encapsulates the results of the comparison experiment.
*
* @param The type produced by the summarizer at the end of
* the experiment from a collection of the given statistics (one
* for each fold). This represents the performance result for the
* comparison of the performance of the learning algorithms for the
* whole experiment.
*/
public static class Result
{
/** The confidence statistic for the learners. */
private ConfidenceStatistic confidence;
/** The summary of performance for the learners. */
private DefaultPair summaries;
/**
* Creates a new instance of Result.
*
* @param confidence The confidence statistic for the learners.
* @param summaries The summary of performance for the learners.
*/
public Result(
final ConfidenceStatistic confidence,
final DefaultPair summaries)
{
super();
this.setConfidence(confidence);
this.setSummaries(summaries);
}
/**
* Gets the confidence statistic for the learners.
*
* @return The confidence statistic for the learners.
*/
public ConfidenceStatistic getConfidence()
{
return this.confidence;
}
/**
* Sets the confidence statistic for the learners.
*
* @param confidence The confidence statistic for the learners.
*/
public void setConfidence(
final ConfidenceStatistic confidence)
{
this.confidence = confidence;
}
/**
* Gets the summary of performance for the learners.
*
* @return The summary of performance for the learners.
*/
public DefaultPair getSummaries()
{
return this.summaries;
}
/**
* Sets the summary of performance for the learners.
*
* @param summaries The summary of performance for the learners.
*/
public void setSummaries(
final DefaultPair summaries)
{
this.summaries = summaries;
}
}
}
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