weka.classifiers.trees.lmt
Class LMTNode

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.trees.lmt.LogisticBase
              |
              +--weka.classifiers.trees.lmt.LMTNode

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable, WeightedInstancesHandler

public class LMTNode

extends LogisticBase

Class for logistic model tree structure.

Version:

$Revision: 1.1 $

Author:

Niels Landwehr

See Also:

Serialized Form

Field Summary

double	m_alpha Alpha-value (for pruning) at the node
double	m_numIncorrectModel Weighted number of training examples currently misclassified by the logistic model at the node
double	m_numIncorrectTree Weighted number of training examples currently misclassified by the subtree rooted at the node

Constructor Summary

LMTNode(ModelSelection modelSelection, int numBoostingIterations, boolean fastRegression, boolean errorOnProbabilities, int minNumInstances)
Constructor for logistic model tree node.

Method Summary

int	assignIDs(int lastID) Assigns unique IDs to all nodes in the tree
int	assignLeafModelNumbers(int leafCounter) Assigns numbers to the logistic regression models at the leaves of the tree
void	buildClassifier(Instances data) Method for building a logistic model tree (only called for the root node).
void	buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight) Method for building the tree structure.
void	calculateAlphas() Updates the alpha field for all nodes.
void	cleanup() Cleanup in order to save memory.
double[]	distributionForInstance(Instance instance) Returns the class probabilities for an instance given by the logistic model tree.
java.lang.String	getModelParameters() Returns a string describing the number of LogitBoost iterations performed at this node, the total number of LogitBoost iterations performed (including iterations at higher levels in the tree), and the number of training examples at this node.
java.util.Vector	getNodes() Return a list of all inner nodes in the tree
void	getNodes(java.util.Vector nodeList) Fills a list with all inner nodes in the tree
int	getNumInnerNodes() Method to count the number of inner nodes in the tree
int	getNumLeaves() Returns the number of leaves in the tree.
java.lang.String	graph() Returns graph describing the tree.
boolean	hasModels() Returns true if the logistic regression model at this node has changed compared to the one at the parent node.
double[]	modelDistributionForInstance(Instance instance) Returns the class probabilities for an instance according to the logistic model at the node.
void	modelErrors() Updates the numIncorrectModel field for all nodes.
java.lang.String	modelsToString() Returns a string describing the logistic regression function at the node.
int	numLeaves() Returns the number of leaves (normal count).
int	numNodes() Returns the number of nodes.
void	prune(double alpha) Prunes a logistic model tree using the CART pruning scheme, given a cost-complexity parameter alpha.
int	prune(double[] alphas, double[] errors, Instances test) Method for performing one fold in the cross-validation of the cost-complexity parameter.
java.lang.String	toString() Returns a description of the logistic model tree (tree structure and logistic models)
void	treeErrors() Updates the numIncorrectTree field for all nodes.

Methods inherited from class weka.classifiers.trees.lmt.LogisticBase

getMaxIterations, getNumRegressions, getUsedAttributes, percentAttributesUsed, setHeuristicStop, setMaxIterations

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, getOptions, listOptions, makeCopies, setDebug, setOptions

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_alpha

public double m_alpha

Alpha-value (for pruning) at the node

m_numIncorrectModel

public double m_numIncorrectModel

Weighted number of training examples currently misclassified by the logistic model at the node

m_numIncorrectTree

public double m_numIncorrectTree

Weighted number of training examples currently misclassified by the subtree rooted at the node

Constructor Detail

LMTNode

public LMTNode(ModelSelection modelSelection,
               int numBoostingIterations,
               boolean fastRegression,
               boolean errorOnProbabilities,
               int minNumInstances)

Constructor for logistic model tree node.

Parameters:

modelSelection - selection method for local splitting model

numBoostingIterations - sets the numBoostingIterations parameter

fastRegression - sets the fastRegression parameter

Method Detail

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception

Method for building a logistic model tree (only called for the root node). Grows an initial logistic model tree and prunes it back using the CART pruning scheme.

Overrides:

buildClassifier in class LogisticBase

Parameters:

data - the training data

Throws:

java.lang.Exception - if something goes wrong

buildTree

public void buildTree(Instances data,
                      SimpleLinearRegression[][] higherRegressions,
                      double totalInstanceWeight)
               throws java.lang.Exception

Method for building the tree structure. Builds a logistic model, splits the node and recursively builds tree for child nodes.

Parameters:

data - the training data passed on to this node

higherRegressions - An array of regression functions produced by LogitBoost at higher levels in the tree. They represent a logistic regression model that is refined locally at this node.

totalInstanceWeight - the total number of training examples

Throws:

java.lang.Exception - if something goes wrong

prune

public void prune(double alpha)
           throws java.lang.Exception

Prunes a logistic model tree using the CART pruning scheme, given a cost-complexity parameter alpha.

Parameters:

alpha - the cost-complexity measure

java.lang.Exception

prune

public int prune(double[] alphas,
                 double[] errors,
                 Instances test)
          throws java.lang.Exception

Method for performing one fold in the cross-validation of the cost-complexity parameter. Generates a sequence of alpha-values with error estimates for the corresponding (partially pruned) trees, given the test set of that fold.

Parameters:

alphas - array to hold the generated alpha-values

errors - array to hold the corresponding error estimates

test - test set of that fold (to obtain error estimates)

Throws:

if - something goes wrong

java.lang.Exception

getNumInnerNodes

public int getNumInnerNodes()

Method to count the number of inner nodes in the tree

Returns:

the number of inner nodes

getNumLeaves

public int getNumLeaves()

Returns the number of leaves in the tree. Leaves are only counted if their logistic model has changed compared to the one of the parent node.

Returns:

the number of leaves

modelErrors

public void modelErrors()
                 throws java.lang.Exception

Updates the numIncorrectModel field for all nodes. This is needed for calculating the alpha-values.

java.lang.Exception

treeErrors

public void treeErrors()

Updates the numIncorrectTree field for all nodes. This is needed for calculating the alpha-values.

calculateAlphas

public void calculateAlphas()
                     throws java.lang.Exception

Updates the alpha field for all nodes.

java.lang.Exception

getNodes

public java.util.Vector getNodes()

Return a list of all inner nodes in the tree

Returns:

the list of nodes

getNodes

public void getNodes(java.util.Vector nodeList)

Fills a list with all inner nodes in the tree

Parameters:

nodeList - the list to be filled

hasModels

public boolean hasModels()

Returns true if the logistic regression model at this node has changed compared to the one at the parent node.

Returns:

whether it has changed

modelDistributionForInstance

public double[] modelDistributionForInstance(Instance instance)
                                      throws java.lang.Exception

Returns the class probabilities for an instance according to the logistic model at the node.

Parameters:

instance - the instance

Returns:

the array of probabilities

java.lang.Exception

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Returns the class probabilities for an instance given by the logistic model tree.

Overrides:

distributionForInstance in class LogisticBase

Parameters:

instance - the instance

Returns:

the array of probabilities

Throws:

java.lang.Exception - if distribution can't be computed successfully

numLeaves

public int numLeaves()

Returns the number of leaves (normal count).

Returns:

the number of leaves

numNodes

public int numNodes()

Returns the number of nodes.

Returns:

the number of nodes

toString

public java.lang.String toString()

Returns a description of the logistic model tree (tree structure and logistic models)

Overrides:

toString in class LogisticBase

Returns:

describing string

getModelParameters

public java.lang.String getModelParameters()

Returns a string describing the number of LogitBoost iterations performed at this node, the total number of LogitBoost iterations performed (including iterations at higher levels in the tree), and the number of training examples at this node.

Returns:

the describing string

assignIDs

public int assignIDs(int lastID)

Assigns unique IDs to all nodes in the tree

assignLeafModelNumbers

public int assignLeafModelNumbers(int leafCounter)

Assigns numbers to the logistic regression models at the leaves of the tree

modelsToString

public java.lang.String modelsToString()

Returns a string describing the logistic regression function at the node.

graph

public java.lang.String graph()
                       throws java.lang.Exception

Returns graph describing the tree.

Throws:

java.lang.Exception - if something goes wrong

cleanup

public void cleanup()

Cleanup in order to save memory.

Overrides:

cleanup in class LogisticBase