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/*
* File: CrossFoldCreator.java
* Authors: Justin Basilico
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright September 26, 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.collection.RangeExcludedArrayList;
import gov.sandia.cognition.learning.data.DefaultPartitionedDataset;
import gov.sandia.cognition.learning.data.PartitionedDataset;
import gov.sandia.cognition.math.Permutation;
import gov.sandia.cognition.util.AbstractRandomized;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
import java.util.Random;
/**
* The {@code CrossFoldCreator} implements a validation fold creator that
* creates folds for a typical k-fold cross-validation experiment. That is, it
* splits the data into k folds where each item appears in the testing set in
* exactly 1 fold and in the training set in the remaining k - 1 folds. At the
* limit where k is equal to the size of the data, this becomes leave-one-out
* cross-validation, but is typically used in the case where leave-one-out
* cross-validation is too costly to run and k is set to a much smaller value.
*
* @param The type of data to create the folds for.
* @author Justin Basilico
* @since 2.0
*/
public class CrossFoldCreator
extends AbstractRandomized
implements ValidationFoldCreator
{
/** The default number of folds is 10. */
public static final int DEFAULT_NUM_FOLDS = 10;
/** The number of folds to create. */
protected int numFolds;
/**
* Creates a new instance of CrossFoldCreator with a default number of folds
* (10) and a default Random number generator.
*/
public CrossFoldCreator()
{
this(DEFAULT_NUM_FOLDS);
}
/**
* Creates a new CrossFoldCreator.
*
* @param numFolds The number of folds to create.
*/
public CrossFoldCreator(
final int numFolds)
{
this(numFolds, new Random());
}
/**
* Creates a new CrossFoldCreator.
*
* @param numFolds The number of folds to create.
* @param random The random number generator to use.
*/
public CrossFoldCreator(
final int numFolds,
final Random random)
{
super(random);
this.setNumFolds(numFolds);
}
/**
* Creates the requested number of cross-validation folds from the given
* data. The number of folds returned will be the minimum of the number
* of requested folds and the size of the data because it cannot create
* more folds than elements of the data.
*
* @param data The data to create the folds for.
* @return The created cross-validation folds.
*/
public List> createFolds(
final Collection data)
{
return CrossFoldCreator.createFolds(data, this.getNumFolds(),
this.getRandom());
}
/**
* Creates the requested number of cross-validation folds from the given
* data. The number of folds returned will be the minimum of the number
* of requested folds and the size of the data because it cannot create
* more folds than elements of the data.
*
* @param The type of data to create folds over.
* @param data The data to create the folds for.
* @param numFolds The number of folds to create.
* @param random The random number generator to use.
* @return The created cross-validation folds.
*/
public static List> createFolds(
final Collection data,
final int numFolds,
final Random random)
{
final int total = data.size();
if (total < 2)
{
throw new IllegalArgumentException(
"data must have at least 2 items");
}
CrossFoldCreator.checkNumFolds(numFolds);
// Randomize the data before splitting it.
final ArrayList reordering =
Permutation.createReordering(data, random);
// If there is less data than folds, we need a smaller number of
// actual folds. This means that the algorithm defaults to a
// leave-one-out type of validation.
final int numActualFolds = Math.min(total, numFolds);
final ArrayList> datasets =
new ArrayList>(numActualFolds);
// We will create partitions with the same backing list of data.
int fromIndex = 0;
int toIndex = 0;
for (int i = 0; i < numActualFolds; i++)
{
// Figure out the chunk that will be witheld as training data.
fromIndex = toIndex;
final int foldSize = (total - fromIndex) / (numActualFolds - i);
toIndex = fromIndex + foldSize;
// Create the training set by excluding the testing set indices
// from the larger set of data.
final List training =
new RangeExcludedArrayList(reordering,
fromIndex, toIndex - 1);
// Create the testing set by taking the sub list of the set of
// all the data.
final List testing =
reordering.subList(fromIndex, toIndex);
datasets.add(new DefaultPartitionedDataset(training, testing));
}
return datasets;
}
/**
* Gets the number of folds to create.
*
* @return The number of folds to create.
*/
public int getNumFolds()
{
return this.numFolds;
}
/**
* Sets the number of folds to create. The number of folds must be greater
* than one.
*
* @param numFolds The number of folds to create.
*/
public void setNumFolds(
final int numFolds)
{
CrossFoldCreator.checkNumFolds(numFolds);
this.numFolds = numFolds;
}
/**
* Checks the given number of folds to make sure that it is greater than
* 1.
*
* @param numFolds The number of folds.
*/
protected static void checkNumFolds(
final int numFolds)
{
if (numFolds <= 1)
{
throw new IllegalArgumentException(
"numFolds must be greater than 1.");
}
}
}