gov.sandia.cognition.learning.algorithm.tree

Class CategorizationTreeLearner<InputType,OutputType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeLearner<InputType,OutputType>

gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner<InputType,OutputType>

Type Parameters:

InputType - The input type for the tree.

OutputType - The output type for the tree.

All Implemented Interfaces:

IterativeAlgorithm, BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,CategorizationTree<InputType,OutputType>>, SupervisedBatchLearner<InputType,OutputType,CategorizationTree<InputType,OutputType>>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

public class CategorizationTreeLearner<InputType,OutputType>
extends AbstractDecisionTreeLearner<InputType,OutputType>
implements SupervisedBatchLearner<InputType,OutputType,CategorizationTree<InputType,OutputType>>

The CategorizationTreeLearner class implements a supervised learning algorithm for learning a categorization tree.

Since:

2.0

Author:

Justin Basilico

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_LEAF_COUNT_THRESHOLD The default threshold for making a leaf node based on count.
static int	DEFAULT_MAX_DEPTH The default maximum depth to grow the tree to.
protected int	leafCountThreshold The threshold for making a node a leaf, determined by how many instances fall in the threshold.
protected int	maxDepth The maximum depth for the tree.
protected java.util.Map<OutputType,java.lang.Double>	priors Prior probabilities for the different categories.
protected java.util.Map<OutputType,java.lang.Integer>	trainCounts How often each category appears in training data.

Fields inherited from class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeLearner

deciderLearner

Fields inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

DEFAULT_ITERATION, iteration

Constructor Summary

Constructors

Constructor and Description
CategorizationTreeLearner() Creates a new instance of CategorizationTreeLearner.
CategorizationTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner) Creates a new instance of CategorizationTreeLearner.
CategorizationTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner, int leafCountThreshold, int maxDepth) Creates a new instance of CategorizationTreeLearner.
CategorizationTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner, int leafCountThreshold, int maxDepth, java.util.Map<OutputType,java.lang.Double> priors) Creates a new instance of CategorizationTreeLearner.

Method Summary

Modifier and Type	Method and Description
CategorizationTreeLearner<InputType,OutputType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
int	getLeafCountThreshold() Gets the leaf count threshold, which determines the number of elements at which to make an element into a leaf.
int	getMaxDepth() Gets the maximum depth to grow the tree.
static <OutputType> DefaultDataDistribution<OutputType>	getOutputCounts(java.util.Collection<? extends InputOutputPair<?,OutputType>> data) Creates a histogram of values based on the output values in the given collection of pairs.
CategorizationTree<InputType,OutputType>	learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
protected CategorizationTreeNode<InputType,OutputType,?>	learnNode(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data, AbstractDecisionTreeNode<InputType,OutputType,?> parent) Recursively learns the categorization tree using the given collection of data, returning the created node.
void	setCategoryPriors(java.util.Map<OutputType,java.lang.Double> priors) Set prior category probabilities.
void	setLeafCountThreshold(int leafCountThreshold) Sets the leaf count threshold, which determines the number of elements at which to make an element into a leaf.
void	setMaxDepth(int maxDepth) Sets the maximum depth to grow the tree.

Methods inherited from class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeLearner

areAllOutputsEqual, getDeciderLearner, learnChildNodes, setDeciderLearner, splitData

Methods inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

addIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getIteration, getListeners, removeIterativeAlgorithmListener, setIteration, setListeners

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

DEFAULT_LEAF_COUNT_THRESHOLD

public static final int DEFAULT_LEAF_COUNT_THRESHOLD

The default threshold for making a leaf node based on count.

See Also:

Constant Field Values

DEFAULT_MAX_DEPTH

public static final int DEFAULT_MAX_DEPTH

The default maximum depth to grow the tree to.

See Also:

Constant Field Values

leafCountThreshold

protected int leafCountThreshold

The threshold for making a node a leaf, determined by how many instances fall in the threshold.

maxDepth

protected int maxDepth

The maximum depth for the tree. Ignored if less than 1.

priors

protected java.util.Map<OutputType,java.lang.Double> priors

Prior probabilities for the different categories. If null, the priors default to the category frequencies of the training data.

trainCounts

protected transient java.util.Map<OutputType,java.lang.Integer> trainCounts

How often each category appears in training data.

Constructor Detail

CategorizationTreeLearner

public CategorizationTreeLearner()

Creates a new instance of CategorizationTreeLearner.

CategorizationTreeLearner

public CategorizationTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner)

Creates a new instance of CategorizationTreeLearner.

Parameters:

deciderLearner - The learner for the decision function

CategorizationTreeLearner

public CategorizationTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner,
                                 int leafCountThreshold,
                                 int maxDepth)

Creates a new instance of CategorizationTreeLearner.

Parameters:

deciderLearner - The learner for the decision function.

leafCountThreshold - The leaf count threshold. Must be non-negative.

maxDepth - The maximum depth for the tree.

CategorizationTreeLearner

public CategorizationTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner,
                                 int leafCountThreshold,
                                 int maxDepth,
                                 java.util.Map<OutputType,java.lang.Double> priors)

Creates a new instance of CategorizationTreeLearner.

Parameters:

deciderLearner - The learner for the decision function.

leafCountThreshold - The leaf count threshold. Must be non-negative.

maxDepth - The maximum depth for the tree.

priors - Prior probabilities for categories. (See setCategoryPriors().)

Method Detail

clone

public CategorizationTreeLearner<InputType,OutputType> clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractDecisionTreeLearner<InputType,OutputType>

Returns:

A clone of this object.

learn

public CategorizationTree<InputType,OutputType> learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data)

Description copied from interface: BatchLearner

The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.

Specified by:

learn in interface BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,CategorizationTree<InputType,OutputType>>

Parameters:

data - The data that the learning algorithm will use to create an object of ResultType.

Returns:

The object that is created based on the given data using the learning algorithm.

learnNode

protected CategorizationTreeNode<InputType,OutputType,?> learnNode(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data,
                                                                   AbstractDecisionTreeNode<InputType,OutputType,?> parent)

Recursively learns the categorization tree using the given collection of data, returning the created node.

Specified by:

learnNode in class AbstractDecisionTreeLearner<InputType,OutputType>

Parameters:

data - The set of data to learn a node from.

parent - The parent node.

Returns:

The categorization tree node learned from the given data.

getOutputCounts

public static <OutputType> DefaultDataDistribution<OutputType> getOutputCounts(java.util.Collection<? extends InputOutputPair<?,OutputType>> data)

Creates a histogram of values based on the output values in the given collection of pairs.

Type Parameters:

OutputType - The type of the outputs to count over.

Parameters:

data - The data to create the output count histogram for.

Returns:

The output count histogram.

getLeafCountThreshold

public int getLeafCountThreshold()

Gets the leaf count threshold, which determines the number of elements at which to make an element into a leaf.

Returns:

The leaf count threshold.

setLeafCountThreshold

public void setLeafCountThreshold(int leafCountThreshold)

Sets the leaf count threshold, which determines the number of elements at which to make an element into a leaf.

Parameters:

leafCountThreshold - The leaf count threshold. Must be non-negative.

getMaxDepth

public int getMaxDepth()

Gets the maximum depth to grow the tree.

Returns:

The maximum depth to grow the tree. Zero or less means no maximum depth.

setMaxDepth

public void setMaxDepth(int maxDepth)

Sets the maximum depth to grow the tree.

Parameters:

maxDepth - The maximum depth to grow the tree. Zero or less means no maximum depth.

setCategoryPriors

public void setCategoryPriors(java.util.Map<OutputType,java.lang.Double> priors)

Set prior category probabilities. A higher prior probability for a category will cause the tree learner to weight examples from that category more highly.

If the priors are not manually specified (through this method or passing priors into the constructor), prior probabilities default to the frequencies of the different categories in the training data.

Parameters:

priors - If null, use default prior probabilities. Otherwise, priors becomes the new prior weights. In the latter case, priors.keySet() contain the same values as the possible categories in data passed to the learn() method.