org.tribuo.regression.rtree.impl

Class RegressorTrainingNode

java.lang.Object

org.tribuo.common.tree.AbstractTrainingNode<Regressor>

org.tribuo.regression.rtree.impl.RegressorTrainingNode

All Implemented Interfaces:

Serializable, Node<Regressor>

public class RegressorTrainingNode
extends AbstractTrainingNode<Regressor>

A decision tree node used at training time. Contains a list of the example indices currently found in this node, the current impurity and a bunch of other statistics.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	RegressorTrainingNode.InvertedData Tuple containing an inverted dataset (i.e., feature-wise not exmaple-wise).

Field Summary

Fields inherited from class org.tribuo.common.tree.AbstractTrainingNode

DEFAULT_SIZE, depth, greaterThan, lessThanOrEqual, numExamples, split, splitID, splitValue

Constructor Summary

Constructors

Constructor and Description
RegressorTrainingNode(RegressorImpurity impurity, RegressorTrainingNode.InvertedData tuple, int dimIndex, String dimName, int numExamples, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Regressor> outputInfo)

Method Summary

Modifier and Type	Method and Description
List<AbstractTrainingNode<Regressor>>	buildTree(int[] featureIDs) Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
Node<Regressor>	convertTree() Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.
double	getImpurity() The impurity score of this node.
static RegressorTrainingNode.InvertedData	invertData(Dataset<Regressor> examples) Inverts a training dataset from row major to column major.

Methods inherited from class org.tribuo.common.tree.AbstractTrainingNode

copy, getDepth, getNextNode, getNumExamples, isLeaf

Methods inherited from class java.lang.Object

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

Constructor Detail

RegressorTrainingNode

public RegressorTrainingNode(RegressorImpurity impurity,
                             RegressorTrainingNode.InvertedData tuple,
                             int dimIndex,
                             String dimName,
                             int numExamples,
                             ImmutableFeatureMap featureIDMap,
                             ImmutableOutputInfo<Regressor> outputInfo)

Method Detail

getImpurity

public double getImpurity()

Description copied from interface: Node

The impurity score of this node.

Returns:

The node impurity.

buildTree

public List<AbstractTrainingNode<Regressor>> buildTree(int[] featureIDs)

Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).

Specified by:

buildTree in class AbstractTrainingNode<Regressor>

Parameters:

featureIDs - Indices of the features available in this split.

Returns:

A possibly empty list of TrainingNodes.

convertTree

public Node<Regressor> convertTree()

Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.

Specified by:

convertTree in class AbstractTrainingNode<Regressor>

Returns:

A subtree using the SplitNode and LeafNode classes.

invertData

public static RegressorTrainingNode.InvertedData invertData(Dataset<Regressor> examples)

Inverts a training dataset from row major to column major. This partially de-sparsifies the dataset so it's very expensive in terms of memory.

Parameters:

examples - An input dataset.

Returns:

A list of TreeFeatures which contain InvertedFeatures.