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A single jar with all the Cognitive Foundry components.
/*
* File: RegressionTreeLearner.java
* Authors: Justin Basilico
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright November 29, 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.tree;
import gov.sandia.cognition.evaluator.Evaluator;
import gov.sandia.cognition.learning.algorithm.BatchLearner;
import gov.sandia.cognition.learning.algorithm.SupervisedBatchLearner;
import gov.sandia.cognition.learning.data.DatasetUtil;
import gov.sandia.cognition.learning.data.InputOutputPair;
import gov.sandia.cognition.learning.function.categorization.Categorizer;
import gov.sandia.cognition.util.ArgumentChecker;
import gov.sandia.cognition.util.ObjectUtil;
import java.util.Collection;
/**
* The {@code RegressionTreeLearner} class implements a learning algorithm for
* a regression tree that makes use of a decider learner and a regression
* learner. The tree grows as a decision tree until it gets to a leaf node
* (determined by a minimum number of nodes), and then learns a regression
* function at the leaf node.
*
* @param The type of the input to the tree.
* @author Justin Basilico
* @since 2.0
*/
public class RegressionTreeLearner
extends AbstractDecisionTreeLearner
implements SupervisedBatchLearner>
{
/** The default threshold for making a leaf node based on count. */
public static final int DEFAULT_LEAF_COUNT_THRESHOLD = 4;
/** The default maximum depth to grow the tree to. */
public static final int DEFAULT_MAX_DEPTH = -1;
/** The learning algorithm for the regression function. */
protected BatchLearner
>,
? extends Evaluator>
regressionLearner;
/** The threshold for making a node a leaf, determined by how many
* instances fall in the threshold. */
protected int leafCountThreshold;
/** The maximum depth for the tree. Ignored if less than 1. */
protected int maxDepth;
/**
* Creates a new instance of RegressionTreeLearner
*/
public RegressionTreeLearner()
{
this(null);
}
/**
* Creates a new instance of CategorizationTreeLearner with a mean node
* learner
*
* @param deciderLearner The learner for the decision function.
*/
public RegressionTreeLearner(
final DeciderLearner deciderLearner)
{
this(deciderLearner, null);
}
/**
* Creates a new instance of CategorizationTreeLearner.
*
* @param deciderLearner The learner for the decision function.
* @param regressionLearner The learner for the regression function.
*/
public RegressionTreeLearner(
final DeciderLearner deciderLearner,
final BatchLearner
>,
? extends Evaluator>
regressionLearner)
{
this(deciderLearner, regressionLearner,
DEFAULT_LEAF_COUNT_THRESHOLD, DEFAULT_MAX_DEPTH);
}
/**
* Creates a new instance of CategorizationTreeLearner.
*
* @param deciderLearner The learner for the decision function.
* @param regressionLearner The learner for the regression function.
* @param leafCountThreshold
* The leaf count threshold, which determines the number of
* elements at which to learn a regression function.
* @param maxDepth
* The maximum depth to learn the tree. Must be positive.
*/
public RegressionTreeLearner(
final DeciderLearner deciderLearner,
final BatchLearner
>,
? extends Evaluator>
regressionLearner,
final int leafCountThreshold,
final int maxDepth)
{
super(deciderLearner);
this.setRegressionLearner(regressionLearner);
this.setLeafCountThreshold(leafCountThreshold);
this.setMaxDepth(maxDepth);
}
@Override
public RegressionTreeLearner clone()
{
final RegressionTreeLearner result = (RegressionTreeLearner) super.clone();
result.regressionLearner = ObjectUtil.cloneSafe(this.regressionLearner);
return result;
}
@Override
public RegressionTree learn(
Collection> data)
{
if (data == null)
{
// Bad data.
return null;
}
else
{
// Recursively learn the node.
return new RegressionTree(
this.learnNode(data, null));
}
}
/**
* Recursively learns the regression tree using the given collection
* of data, returning the created node.
*
* @param data The set of data to learn a node from.
* @param parent The parent node.
* @return The regression tree node learned from the given data.
*/
@Override
protected RegressionTreeNode learnNode(
final Collection> data,
final AbstractDecisionTreeNode parent)
{
if (data == null || data.size() <= 0)
{
// Invalid data, nothing to learn.
return null;
}
// Figure out the depth of the node.
int depth = parent == null ? 1 : 1 + parent.getDepth();
// Determine if this is a leaf node by checking the cound threshold and
// determining if all the outputs are equal.
final boolean isLeaf =
data.size() <= this.leafCountThreshold
|| (this.maxDepth > 0 && depth >= maxDepth)
|| this.areAllOutputsEqual(data);
// We use the mean value as part of the node.
final double mean = DatasetUtil.computeOutputMean(data);
// Learn the decision function for this node.
Categorizer decider = null;
if (!isLeaf)
{
// Only learn for a leaf node.
decider = this.getDeciderLearner().learn(data);
}
// If we couldn't learn a decider, then this is also aleaf node.
if (isLeaf || decider == null)
{
// This is a leaf node.
// Build a regression function for the node.
Evaluator scalarFunction = null;
if (this.regressionLearner != null)
{
scalarFunction = this.regressionLearner.learn(data);
}
// else - Without a regression learner the output value for the
// tree will be the mean.
// Create the leaf node.
return new RegressionTreeNode(
parent, scalarFunction, mean);
}
// We give the node we are creating the most common output value.
final RegressionTreeNode node =
new RegressionTreeNode(
parent, decider, mean);
// Learn the child nodes.
this.learnChildNodes(node, data, decider);
// Return the new node we've created.
return node;
}
/**
* Gets the regression learner that is to be used to fit a function
* approximator to the values in the tree.
*
* @return The regression learner.
*/
public BatchLearner
>,
? extends Evaluator>
getRegressionLearner()
{
return this.regressionLearner;
}
/**
* Sets the regression learner that is to be used to fit a function
* approximator to the values in the tree.
*
* @param regressionLearner The regression learner.
*/
public void setRegressionLearner(
final BatchLearner
>,
? extends Evaluator>
regressionLearner)
{
this.regressionLearner = regressionLearner;
}
/**
* Gets the leaf count threshold, which determines the number of elements
* at which to learn a regression function.
*
* @return The leaf count threshold.
*/
public int getLeafCountThreshold()
{
return this.leafCountThreshold;
}
/**
* Sets the leaf count threshold, which determines the number of elements
* at which to learn a regression function.
*
* @param leafCountThreshold
* The leaf count threshold. Must be non-negative.
*/
public void setLeafCountThreshold(
final int leafCountThreshold)
{
ArgumentChecker.assertIsNonNegative("leafCountThreshold", leafCountThreshold);
this.leafCountThreshold = leafCountThreshold;
}
/**
* Gets the maximum depth to grow the tree.
*
* @return
* The maximum depth to grow the tree. Zero or less means no
* maximum depth.
*/
public int getMaxDepth()
{
return this.maxDepth;
}
/**
* Sets the maximum depth to grow the tree.
*
* @param maxDepth
* The maximum depth to grow the tree. Zero or less means no
* maximum depth.
*/
public void setMaxDepth(
final int maxDepth)
{
this.maxDepth = maxDepth;
}
}