weka.classifiers.evaluation

Class AggregateableEvaluation

java.lang.Object

weka.classifiers.evaluation.Evaluation

weka.classifiers.evaluation.AggregateableEvaluation

All Implemented Interfaces:

java.io.Serializable, Aggregateable<Evaluation>, RevisionHandler, Summarizable

public class AggregateableEvaluation
extends Evaluation
implements Aggregateable<Evaluation>

Subclass of Evaluation that provides a method for aggregating the results stored in another Evaluation object.

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.evaluation.Evaluation

BUILT_IN_EVAL_METRICS, k_MarginResolution, m_ClassIsNominal, m_ClassNames, m_ClassPriors, m_ClassPriorsSum, m_ComplexityStatisticsAvailable, m_ConfLevel, m_ConfusionMatrix, m_Correct, m_CostMatrix, m_CoverageStatisticsAvailable, m_DiscardPredictions, m_Header, m_Incorrect, m_MarginCounts, m_MaxTarget, m_metricsToDisplay, m_MinTarget, m_MissingClass, m_NoPriors, m_NumClasses, m_NumFolds, m_NumTrainClassVals, m_pluginMetrics, m_Predictions, m_PriorEstimator, m_SumAbsErr, m_SumClass, m_SumClassPredicted, m_SumErr, m_SumKBInfo, m_SumPredicted, m_SumPriorAbsErr, m_SumPriorEntropy, m_SumPriorSqrErr, m_SumSchemeEntropy, m_SumSqrClass, m_SumSqrErr, m_SumSqrPredicted, m_TotalCost, m_TotalCoverage, m_TotalSizeOfRegions, m_TrainClassVals, m_TrainClassWeights, m_Unclassified, m_WithClass, MIN_SF_PROB

Constructor Summary

Constructors

Constructor and Description
AggregateableEvaluation(Evaluation eval) Constructs a new AggregateableEvaluation object based on an Evaluation object
AggregateableEvaluation(Instances data) Constructs a new AggregateableEvaluation object
AggregateableEvaluation(Instances data, CostMatrix costMatrix) Constructs a new AggregateableEvaluation object

Method Summary

Modifier and Type	Method and Description
AggregateableEvaluation	aggregate(Evaluation evaluation) Adds the statistics encapsulated in the supplied Evaluation object into this one.
void	finalizeAggregation() Call to complete the aggregation process.

Methods inherited from class weka.classifiers.evaluation.Evaluation

addNumericTrainClass, areaUnderPRC, areaUnderROC, avgCost, confusionMatrix, correct, correlationCoefficient, coverageOfTestCasesByPredictedRegions, crossValidateModel, crossValidateModel, crossValidateModel, equals, errorRate, evaluateModel, evaluateModel, evaluateModel, evaluateModelOnce, evaluateModelOnce, evaluateModelOnce, evaluateModelOnceAndRecordPrediction, evaluateModelOnceAndRecordPrediction, evaluationForSingleInstance, evaluationForSingleInstance, falseNegativeRate, falsePositiveRate, fMeasure, getAllEvaluationMetricNames, getClassPriors, getDiscardPredictions, getGlobalInfo, getHeader, getMetricsToDisplay, getModelFromFile, getPluginMetric, getPluginMetrics, getRevision, handleCostOption, incorrect, kappa, KBInformation, KBMeanInformation, KBRelativeInformation, main, makeDistribution, makeOptionString, matthewsCorrelationCoefficient, meanAbsoluteError, meanPriorAbsoluteError, missingClass, num2ShortID, numFalseNegatives, numFalsePositives, numInstances, numTrueNegatives, numTruePositives, pctCorrect, pctIncorrect, pctUnclassified, precision, predictions, priorEntropy, recall, relativeAbsoluteError, rootMeanPriorSquaredError, rootMeanSquaredError, rootRelativeSquaredError, saveClassifier, setDiscardPredictions, setMetricsToDisplay, setNumericPriorsFromBuffer, setPriors, SFEntropyGain, SFMeanEntropyGain, SFMeanPriorEntropy, SFMeanSchemeEntropy, SFPriorEntropy, SFSchemeEntropy, sizeOfPredictedRegions, toClassDetailsString, toClassDetailsString, toCumulativeMarginDistributionString, toggleEvalMetrics, toMatrixString, toMatrixString, toSummaryString, toSummaryString, toSummaryString, totalCost, trueNegativeRate, truePositiveRate, unclassified, unweightedMacroFmeasure, unweightedMicroFmeasure, updateMargins, updateNumericScores, updatePriors, updateStatsForClassifier, updateStatsForConditionalDensityEstimator, updateStatsForIntervalEstimator, updateStatsForPredictor, useNoPriors, weightedAreaUnderPRC, weightedAreaUnderROC, weightedFalseNegativeRate, weightedFalsePositiveRate, weightedFMeasure, weightedMatthewsCorrelation, weightedPrecision, weightedRecall, weightedTrueNegativeRate, weightedTruePositiveRate, wekaStaticWrapper, withClass

Methods inherited from class java.lang.Object

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

Constructor Detail

AggregateableEvaluation

public AggregateableEvaluation(Instances data)
                        throws java.lang.Exception

Constructs a new AggregateableEvaluation object

Parameters:

data - the Instances to use

Throws:

java.lang.Exception - if a problem occurs

AggregateableEvaluation

public AggregateableEvaluation(Instances data,
                               CostMatrix costMatrix)
                        throws java.lang.Exception

Constructs a new AggregateableEvaluation object

Parameters:

data - the Instances to use

costMatrix - the cost matrix to use

Throws:

java.lang.Exception - if a problem occurs

AggregateableEvaluation

public AggregateableEvaluation(Evaluation eval)
                        throws java.lang.Exception

Constructs a new AggregateableEvaluation object based on an Evaluation object

Parameters:

eval - the Evaluation object to use

Throws:

java.lang.Exception

Method Detail

aggregate

public AggregateableEvaluation aggregate(Evaluation evaluation)

Adds the statistics encapsulated in the supplied Evaluation object into this one. Does not perform any checks for compatibility between the supplied Evaluation object and this one.

Specified by:

aggregate in interface Aggregateable<Evaluation>

Parameters:

evaluation - the evaluation object to aggregate

Returns:

the result of aggregation

finalizeAggregation

public void finalizeAggregation()

Description copied from interface: Aggregateable

Call to complete the aggregation process. Allows implementers to do any final processing based on how many objects were aggregated.

Specified by:

finalizeAggregation in interface Aggregateable<Evaluation>