com.imsl.datamining.decisionTree

Class DecisionTreeInfoGain

java.lang.Object

com.imsl.datamining.PredictiveModel

com.imsl.datamining.decisionTree.DecisionTree

com.imsl.datamining.decisionTree.DecisionTreeInfoGain

All Implemented Interfaces:

Serializable, Cloneable

Direct Known Subclasses:

ALACART, C45

public abstract class DecisionTreeInfoGain
extends DecisionTree
implements Serializable, Cloneable

Abstract class that extends DecisionTree for classes that use an information gain criteria.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	DecisionTreeInfoGain.GainCriteria Specifies which information gain criteria to use in determining the best split at each node.

Nested classes/interfaces inherited from class com.imsl.datamining.decisionTree.DecisionTree

DecisionTree.MaxTreeSizeExceededException, DecisionTree.PruningFailedToConvergeException, DecisionTree.PureNodeException

Nested classes/interfaces inherited from class com.imsl.datamining.PredictiveModel

PredictiveModel.CloneNotSupportedException, PredictiveModel.PredictiveModelException, PredictiveModel.StateChangeException, PredictiveModel.SumOfProbabilitiesNotOneException, PredictiveModel.VariableType

Constructor Summary

Constructors

Constructor and Description
DecisionTreeInfoGain(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType) Constructs a DecisionTree object for a single response variable and multiple predictor variables.

Method Summary

Modifier and Type	Method and Description
protected double[][]	getCountXY(double[][] xy, int nRows, int xIdx, int yIdx, int maxNumberOfCategories, int[] uniqueX, int[] uniqueY, double[] frequencies) Calculates a two-way frequency table with input frequencies.
protected double	getCriteriaValueCategorical(double[][] tableXY, double[] classCounts, int nRows, int maxNumCats) Calculates and returns the value of the criterion on the node represented by the data set S = xy.
protected abstract int	selectSplitVariable(double[][] xy, double[] classCounts, double[] parentFreq, double[] splitValue, double[] splitCriterionValue, int[] splitPartition) Abstract method for selecting the next split variable and split definition for the node.
void	setGainCriteria(DecisionTreeInfoGain.GainCriteria gainCriteria) Specifies which criteria to use in gain calculations in order to determine the best split at each node.
void	setUseRatio(boolean ratio) Sets the flag to use or not use the gain ratio instead of the gain to determine the best split.
boolean	useGainRatio() Returns whether or not the gain ratio is to be used instead of the gain to determine the best split.

Methods inherited from class com.imsl.datamining.decisionTree.DecisionTree

fitModel, getCostComplexityValues, getDecisionTree, getFittedMeanSquaredError, getMaxDepth, getMaxNodes, getMeanSquaredPredictionError, getMinCostComplexityValue, getMinObsPerChildNode, getMinObsPerNode, getNodeAssigments, getNumberOfComplexityValues, getNumberOfRandomFeatures, isAutoPruningFlag, isRandomFeatureSelection, predict, predict, predict, printDecisionTree, printDecisionTree, pruneTree, setAutoPruningFlag, setConfiguration, setCostComplexityValues, setMaxDepth, setMaxNodes, setMinCostComplexityValue, setMinObsPerChildNode, setMinObsPerNode, setNumberOfRandomFeatures, setRandomFeatureSelection

Methods inherited from class com.imsl.datamining.PredictiveModel

clone, getClassCounts, getClassErrors, getClassLabels, getClassProbabilities, getCostMatrix, getMaxNumberOfCategories, getMaxNumberOfIterations, getNumberOfClasses, getNumberOfColumns, getNumberOfMissing, getNumberOfPredictors, getNumberOfRows, getNumberOfUniquePredictorValues, getPredictorIndexes, getPredictorTypes, getPrintLevel, getPriorProbabilities, getRandomObject, getResponseColumnIndex, getResponseVariableAverage, getResponseVariableMostFrequentClass, getResponseVariableType, getTotalWeight, getVariableType, getWeights, getXY, isConstantSeries, isMustFitModel, isUserFixedNClasses, setClassCounts, setClassLabels, setClassProbabilities, setCostMatrix, setMaxNumberOfCategories, setMaxNumberOfIterations, setMustFitModel, setNumberOfClasses, setPredictorIndex, setPredictorTypes, setPrintLevel, setPriorProbabilities, setRandomObject, setResponseColumnIndex, setTrainingData, setVariableType, setWeights

Methods inherited from class java.lang.Object

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

Constructor Detail

DecisionTreeInfoGain

public DecisionTreeInfoGain(double[][] xy,
                            int responseColumnIndex,
                            PredictiveModel.VariableType[] varType)

Constructs a DecisionTree object for a single response variable and multiple predictor variables.

Parameters:

xy - a double matrix with rows containing the observations on the predictor variables and one response variable

responseColumnIndex - an int specifying the column index of the response variable

varType - a PredictiveModel.VariableType array containing the type of each variable

Method Detail

selectSplitVariable

protected abstract int selectSplitVariable(double[][] xy,
                                           double[] classCounts,
                                           double[] parentFreq,
                                           double[] splitValue,
                                           double[] splitCriterionValue,
                                           int[] splitPartition)

Abstract method for selecting the next split variable and split definition for the node.

Specified by:

selectSplitVariable in class DecisionTree

Parameters:

xy - a double matrix containing the data

classCounts - a double array containing the counts for each class of the response variable, when it is categorical

parentFreq - a double array used to indicate which subset of the observations belong in the current node

splitValue - a double array representing the resulting split point if the selected variable is quantitative

splitCriterionValue - a double, the value of the criterion used to determine the splitting variable

splitPartition - an int array indicating the resulting split partition if the selected variable is categorical

Returns:

an int specifying the column index of the split variable in this.getPredictorIndexes

setGainCriteria

public void setGainCriteria(DecisionTreeInfoGain.GainCriteria gainCriteria)

Specifies which criteria to use in gain calculations in order to determine the best split at each node.

Parameters:

gainCriteria - a DecisionTreeInfoGain.GainCriteria specifying which criteria to use in gain calculations in order to determine the best split at each node

Default: gainCriteria = DecisionTreeInfoGain.GainCriteria.SHANNON_ENTROPY

useGainRatio

public boolean useGainRatio()

Returns whether or not the gain ratio is to be used instead of the gain to determine the best split.

Returns:

a boolean indicating if the gain ratio is to be used

true, uses the gain ratio; false uses the gain.

setUseRatio

public void setUseRatio(boolean ratio)

Sets the flag to use or not use the gain ratio instead of the gain to determine the best split.

Parameters:

ratio - a boolean indicating if the gain ratio is to be used

true uses the gain ratio; false uses the gain.

Default: useRatio=false

getCriteriaValueCategorical

protected double getCriteriaValueCategorical(double[][] tableXY,
                                             double[] classCounts,
                                             int nRows,
                                             int maxNumCats)

Calculates and returns the value of the criterion on the node represented by the data set S = xy.

Parameters:

tableXY -

classCounts - an int array containing the total counts of response variable by category

nRows - an int, the number of rows in xy

maxNumCats - an int, the maximum number of categorical values allowed in the problem

Returns:

a double, the value of the splitting criteria

getCountXY

protected double[][] getCountXY(double[][] xy,
                                int nRows,
                                int xIdx,
                                int yIdx,
                                int maxNumberOfCategories,
                                int[] uniqueX,
                                int[] uniqueY,
                                double[] frequencies)

Calculates a two-way frequency table with input frequencies. Note: The function assumes that the data is encoded as indices 0,1,...,K < maxNumberOfCategories. @param xy a double matrix containing the data to be tabulated

Parameters:

nRows - an int the number of rows in xy

xIdx - an int the column index of x

yIdx - an int the column index of y

maxNumberOfCategories - an int the maximum number of categories in either x or y

uniqueX - an int array containing indicators for the categories of x

uniqueY - an int array containing indicators for the categories of y

frequencies - an double array of length nRows containing the row frequencies for the data

Returns:

a maxNumberOfCategories by maxNumberOfCategories double matrix containing the cross-tabulated frequencies for x and y. The categories of x vary along the rows and the categories of y vary along the columns.