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/*
* File: KernelBasedIterativeRegression.java
* Authors: Justin Basilico
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright September 17, 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.algorithm.regression;
import gov.sandia.cognition.algorithm.MeasurablePerformanceAlgorithm;
import gov.sandia.cognition.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.learning.algorithm.AbstractAnytimeSupervisedBatchLearner;
import gov.sandia.cognition.learning.function.kernel.Kernel;
import gov.sandia.cognition.learning.data.InputOutputPair;
import gov.sandia.cognition.learning.function.scalar.KernelScalarFunction;
import gov.sandia.cognition.util.DefaultNamedValue;
import gov.sandia.cognition.util.DefaultWeightedValue;
import gov.sandia.cognition.util.NamedValue;
import gov.sandia.cognition.util.ObjectUtil;
import gov.sandia.cognition.util.WeightedValue;
import java.util.ArrayList;
import java.util.LinkedHashMap;
/**
* The {@code KernelBasedIterativeRegression} class implements an online version of
* the Support Vector Regression algorithm. It learns a scalar kernel function
* that uses the given kernel to map inputs onto real numbers. The code is based
* on the pseudocode in the book "Kernel Methods for Pattern Analysis" by
* J. Shawe-Taylor and N. Cristianini. However, this the pseudo-code in the
* book is incorrect and seems to be missing an extra division by the kernel
* of the example with itself. It also does not check to make sure that the
* error is outside of the minimum sensitivity range. This implementation also
* includes a bias term, which is also omitted from the pseudo-code in the book.
*
* The update to the weight that is implemented is:
*
* alpha_i = alpha_i +
* (y_i + epsilon * sign(alpha_i) + sum alpha_j k(x_j, x_i) + b)
* / k(x_i, x_i)
*
* The loss function underlying the implementation is the epsilon-insensitive
* loss. This parameter is named minSensitivity in this implementation. It
* means that errors less than or equal to minSensitivity are ignored.
*
* @param Input parameter to the Kernels
* @author Justin Basilico
* @since 2.0
*/
@PublicationReference(
author={
"John Shawe-Taylor",
"Nello Cristianini"
},
title="Kernel Methods for Pattern Analysis",
type=PublicationType.Book,
year=2004,
url="http://www.kernel-methods.net/"
)
public class KernelBasedIterativeRegression
extends AbstractAnytimeSupervisedBatchLearner>
implements MeasurablePerformanceAlgorithm
{
/** The default maximum number of iterations, {@value}. */
public static final int DEFAULT_MAX_ITERATIONS = 100;
/** The default minimum sensitivity, {@value}. */
public static final double DEFAULT_MIN_SENSITIVITY = 10.0;
/** The kernel to use. */
private Kernel kernel;
/** The bound on sensitivity. */
private double minSensitivity;
/** The result categorizer. */
private KernelScalarFunction result;
/** The number of errors on the most recent iteration. */
private int errorCount;
/** The mapping of weight objects to non-zero weighted examples
* (support vectors). */
private transient LinkedHashMap, DefaultWeightedValue> supportsMap;
/**
* Creates a new instance of KernelBasedIterativeRegression.
*/
public KernelBasedIterativeRegression()
{
this( null );
}
/**
* Creates a new KernelBasedIterativeRegression with the given kernel.
*
* @param kernel The kernel to use.
*/
public KernelBasedIterativeRegression(
final Kernel kernel )
{
this( kernel, DEFAULT_MIN_SENSITIVITY );
}
/**
* Creates a new KernelBasedIterativeRegression with the given kernel.
*
* @param kernel The kernel to use.
* @param minSensitivity The minimum sensitivity to errors.
*/
public KernelBasedIterativeRegression(
final Kernel kernel,
final double minSensitivity )
{
this( kernel, minSensitivity, DEFAULT_MAX_ITERATIONS );
}
/**
* Creates a new KernelBasedIterativeRegression with the given kernel and
* maximum number of iterations.
*
* @param kernel The kernel to use.
* @param minSensitivity The minimum sensitivity to errors.
* @param maxIterations The maximum number of iterations.
*/
public KernelBasedIterativeRegression(
final Kernel kernel,
final double minSensitivity,
final int maxIterations )
{
super( maxIterations );
this.setKernel( kernel );
this.setMinSensitivity( minSensitivity );
this.setResult( null );
this.setErrorCount( 0 );
this.setSupportsMap( null );
}
@Override
public KernelBasedIterativeRegression clone()
{
KernelBasedIterativeRegression clone =
(KernelBasedIterativeRegression) super.clone();
clone.setKernel( ObjectUtil.cloneSmart( this.getKernel() ) );
clone.setResult( ObjectUtil.cloneSafe( this.getResult() ) );
clone.setSupportsMap( ObjectUtil.cloneSmart( this.getSupportsMap() ) );
return clone;
}
protected boolean initializeAlgorithm()
{
if (this.getData() == null)
{
// Error: No data to learn on.
return false;
}
// Count the number of valid examples.
int validCount = 0;
for (InputOutputPair example : this.getData())
{
if (example != null)
{
validCount++;
}
}
if (validCount <= 0)
{
// Nothing to perform learning on.
return false;
}
// Set up the learning variables.
this.setErrorCount( validCount );
this.setSupportsMap( new LinkedHashMap, DefaultWeightedValue>() );
this.setResult( new KernelScalarFunction(
this.getKernel(), this.getSupportsMap().values(), 0.0 ) );
return true;
}
protected boolean step()
{
// Reset the number of errors for the new iteration.
this.setErrorCount( 0 );
if (this.getData().size() == 1)
{
// If there is only one data point, there is nothing to fit.
InputOutputPair first =
this.getData().iterator().next();
this.getResult().getExamples().clear();
this.getResult().setBias( first.getOutput() );
return false;
}
// Loop over all the training instances.
for (InputOutputPair example : this.getData())
{
if (example == null)
{
continue;
}
// Compute the predicted classification and get the actual
// classification.
final InputType input = example.getInput();
final double actual = example.getOutput();
final double prediction = this.result.evaluate( input );
final double error = actual - prediction;
// This is the update psuedo-code as listed in the book:
// alphahat_i = alpha_i
// alpha_i = alpha_i + y_i - epsilon * sign(alpha_i)
// - sum alpha_j k(x_j, x_i)
// if ( alphahat_i * alpha_i < 0 ) then alpha_i = 0
// where when alpha_i is zero the value in [+1, -1] is used for
// sign(alpha_i) that minimizes the size of the update.
//
// However, this code doesn't work as it is listed in the book.
// Instead it adds an extra division by the value k(x_i, x_i)
// to the update, making it:
// alpha_i = alpha_i +
// (y_i - epsilon * sign(alpha_i) - sum alpha_j k(x_j, x_i) )
// / k(x_i, x_i)
//
// Also a check is made such that the weight value (alpha_i) is
// not updated when the prediction error is less than the minimum
// sensitivity.
DefaultWeightedValue support = this.supportsMap.get( example );
final double oldWeight = support == null ? 0.0 : support.getWeight();
double newWeight = oldWeight;
// TODO: Determine if this check to see if the error is outside the minimum
// sensitivity still preserves the support-vector nature of the algorithm or
// if it makes it a more greedy algorithm such as the Perceptron algorithm.
if (Math.abs( error ) >= this.minSensitivity)
{
double weightUpdate = error;
// This part computes the epsilon * sign(alpha_i) to deal with
// the case where alpha_i is zero, in which case the sign must
// be either interpreted as -1 or +1 based on which provides
// a smaller update.
if (oldWeight == 0.0)
{
double positiveUpdate = weightUpdate - this.minSensitivity;
double negativeUpdate = weightUpdate + this.minSensitivity;
if (Math.abs( positiveUpdate ) <= Math.abs( negativeUpdate ))
{
weightUpdate -= this.minSensitivity;
}
else
{
weightUpdate += this.minSensitivity;
}
}
else if (oldWeight > 0.0)
{
// This functions as -epsilon * sign(alpha_i) where
// sign(alpha_i) = +1.
weightUpdate -= this.minSensitivity;
}
else
{
// This functions as -epsilon * sign(alpha_i) where
// sign(alpha_i) = -1.
weightUpdate += this.minSensitivity;
}
// Divide the update by the kernel applied to itself, while
// avoiding a divide-by-zero error.
final double selfKernel = this.kernel.evaluate( input, input );
if (selfKernel != 0.0)
{
weightUpdate /= selfKernel;
}
// Compute the new weight by adding the old weight and the
// weight update.
newWeight = oldWeight + weightUpdate;
// This removes unneeded weights.
if (oldWeight * newWeight < 0.0)
{
newWeight = 0.0;
}
}
// Compute the weight to see if this was considered an "error".
final double difference = newWeight - oldWeight;
if (difference != 0.0)
{
// We need to change the kernel scalar function..
this.setErrorCount( this.getErrorCount() + 1 );
// We are going to update the weight for this example and the
// global bias.
final double oldBias = this.result.getBias();
final double newBias = oldBias + difference;
if (support == null)
{
// Add a support for this example.
support = new DefaultWeightedValue( input, newWeight );
this.supportsMap.put( example, support );
}
else if (newWeight == 0.0)
{
// This example is no longer a support.
this.supportsMap.remove( example );
}
else
{
// Update the weight for the support.
support.setWeight( newWeight );
}
// Update the bias.
this.result.setBias( newBias );
}
// else - The classification was correct, no need to update.
}
// Keep going while the error count is positive.
return this.getErrorCount() > 0;
}
protected void cleanupAlgorithm()
{
if (this.getSupportsMap() != null)
{
// Make the result object have a more efficient backing collection
// at the end.
this.getResult().setExamples(
new ArrayList>(
this.getSupportsMap().values() ) );
this.setSupportsMap( null );
}
}
/**
* Gets the kernel to use.
*
* @return The kernel to use.
*/
public Kernel getKernel()
{
return this.kernel;
}
/**
* Sets the kernel to use.
*
* @param kernel The kernel to use.
*/
public void setKernel(
final Kernel kernel )
{
this.kernel = kernel;
}
public KernelScalarFunction getResult()
{
return this.result;
}
/**
* Sets the object currently being result.
*
* @param result The object currently being result.
*/
protected void setResult(
final KernelScalarFunction result )
{
this.result = result;
}
/**
* Gets the error count of the most recent iteration.
*
* @return The current error count.
*/
public int getErrorCount()
{
return this.errorCount;
}
/**
* Sets the error count of the most recent iteration.
*
* @param errorCount The current error count.
*/
protected void setErrorCount(
final int errorCount )
{
this.errorCount = errorCount;
}
/**
* Gets the mapping of examples to weight objects (support vectors).
*
* @return The mapping of examples to weight objects.
*/
protected LinkedHashMap, DefaultWeightedValue> getSupportsMap()
{
return supportsMap;
}
/**
* Gets the mapping of examples to weight objects (support vectors).
*
* @param supportsMap The mapping of examples to weight objects.
*/
protected void setSupportsMap(
final LinkedHashMap, DefaultWeightedValue> supportsMap )
{
this.supportsMap = supportsMap;
}
/**
* Gets the minimum sensitivity that an example can have on the result
* function.
*
* @return The minimum sensitivity.
*/
public double getMinSensitivity()
{
return this.minSensitivity;
}
/**
* Sets the minimum sensitivity that an example can have on the result
* function.
*
* @param minSensitivity The minimum sensitivity.
*/
public void setMinSensitivity(
final double minSensitivity )
{
if (minSensitivity < 0.0)
{
throw new IllegalArgumentException(
"minSensitivity must be non-negative." );
}
this.minSensitivity = minSensitivity;
}
/**
* Gets the performance, which is the error count on the last iteration.
*
* @return The performance of the algorithm.
*/
public NamedValue getPerformance()
{
return new DefaultNamedValue("error count", this.getErrorCount());
}
}
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