weka.classifiers.evaluation
Class EvaluationUtils

java.lang.Object
  weka.classifiers.evaluation.EvaluationUtils

public class EvaluationUtils

extends java.lang.Object

Contains utility functions for generating lists of predictions in various manners.

Version:

$Revision: 1.9 $

Author:

Len Trigg (len@reeltwo.com)

Field Summary

private int

m_Seed Seed used to randomize data in cross-validation

Constructor Summary

EvaluationUtils()

Method Summary

FastVector	getCVPredictions(Classifier classifier, Instances data, int numFolds) Generate a bunch of predictions ready for processing, by performing a cross-validation on the supplied dataset.
Prediction	getPrediction(Classifier classifier, Instance test) Generate a single prediction for a test instance given the pre-trained classifier.
int	getSeed() Gets the seed for randomization during cross-validation
FastVector	getTestPredictions(Classifier classifier, Instances test) Generate a bunch of predictions ready for processing, by performing a evaluation on a test set assuming the classifier is already trained.
FastVector	getTrainTestPredictions(Classifier classifier, Instances train, Instances test) Generate a bunch of predictions ready for processing, by performing a evaluation on a test set after training on the given training set.
void	setSeed(int seed) Sets the seed for randomization during cross-validation

Methods inherited from class java.lang.Object

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

Field Detail

m_Seed

private int m_Seed

Seed used to randomize data in cross-validation

Constructor Detail

EvaluationUtils

public EvaluationUtils()

Method Detail

setSeed

public void setSeed(int seed)

Sets the seed for randomization during cross-validation

getSeed

public int getSeed()

Gets the seed for randomization during cross-validation

getCVPredictions

public FastVector getCVPredictions(Classifier classifier,
                                   Instances data,
                                   int numFolds)
                            throws java.lang.Exception

Generate a bunch of predictions ready for processing, by performing a cross-validation on the supplied dataset.

Parameters:

classifier - the Classifier to evaluate

data - the dataset

numFolds - the number of folds in the cross-validation.

Throws:

java.lang.Exception - if an error occurs

getTrainTestPredictions

public FastVector getTrainTestPredictions(Classifier classifier,
                                          Instances train,
                                          Instances test)
                                   throws java.lang.Exception

Generate a bunch of predictions ready for processing, by performing a evaluation on a test set after training on the given training set.

Parameters:

classifier - the Classifier to evaluate

train - the training dataset

test - the test dataset

Throws:

java.lang.Exception - if an error occurs

getTestPredictions

public FastVector getTestPredictions(Classifier classifier,
                                     Instances test)
                              throws java.lang.Exception

Generate a bunch of predictions ready for processing, by performing a evaluation on a test set assuming the classifier is already trained.

Parameters:

classifier - the pre-trained Classifier to evaluate

test - the test dataset

Throws:

java.lang.Exception - if an error occurs

getPrediction

public Prediction getPrediction(Classifier classifier,
                                Instance test)
                         throws java.lang.Exception

Generate a single prediction for a test instance given the pre-trained classifier.

Parameters:

classifier - the pre-trained Classifier to evaluate

test - the test instance

Throws:

java.lang.Exception - if an error occurs