weka-src.rar_ weka source code_Filtered Associator_filteredassoc

/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Evaluation.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.classifiers; import weka.classifiers.evaluation.NominalPrediction; import weka.classifiers.evaluation.ThresholdCurve; import weka.classifiers.pmml.consumer.PMMLClassifier; import weka.classifiers.xml.XMLClassifier; import weka.core.Drawable; import weka.core.FastVector; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.Range; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.Summarizable; import weka.core.Utils; import weka.core.Version; import weka.core.converters.ConverterUtils.DataSink; import weka.core.converters.ConverterUtils.DataSource; import weka.core.pmml.PMMLFactory; import weka.core.pmml.PMMLModel; import weka.core.xml.KOML; import weka.core.xml.XMLOptions; import weka.core.xml.XMLSerialization; import weka.estimators.Estimator; import weka.estimators.KernelEstimator; import java.beans.BeanInfo; import java.beans.Introspector; import java.beans.MethodDescriptor; import java.io.BufferedInputStream; import java.io.BufferedOutputStream; import java.io.BufferedReader; import java.io.FileInputStream; import java.io.FileOutputStream; import java.io.FileReader; import java.io.InputStream; import java.io.ObjectInputStream; import java.io.ObjectOutputStream; import java.io.OutputStream; import java.io.Reader; import java.lang.reflect.Method; import java.util.Date; import java.util.Enumeration; import java.util.Random; import java.util.zip.GZIPInputStream; import java.util.zip.GZIPOutputStream; /** * Class for evaluating machine learning models. <p/> * * ------------------------------------------------------------------- <p/> * * General options when evaluating a learning scheme from the command-line: <p/> * * -t filename <br/> * Name of the file with the training data. (required) <p/> * * -T filename <br/> * Name of the file with the test data. If missing a cross-validation * is performed. <p/> * * -c index <br/> * Index of the class attribute (1, 2, ...; default: last). <p/> * * -x number <br/> * The number of folds for the cross-validation (default: 10). <p/> * * -no-cv <br/> * No cross validation. If no test file is provided, no evaluation * is done. <p/> * * -split-percentage percentage <br/> * Sets the percentage for the train/test set split, e.g., 66. <p/> * * -preserve-order <br/> * Preserves the order in the percentage split instead of randomizing * the data first with the seed value ('-s'). <p/> * * -s seed <br/> * Random number seed for the cross-validation and percentage split * (default: 1). <p/> * * -m filename <br/> * The name of a file containing a cost matrix. <p/> * * -l filename <br/> * Loads classifier from the given file. In case the filename ends with ".xml", * a PMML file is loaded or, if that fails, options are loaded from XML. <p/> * * -d filename <br/> * Saves classifier built from the training data into the given file. In case * the filename ends with ".xml" the options are saved XML, not the model. <p/> * * -v <br/> * Outputs no statistics for the training data. <p/> * * -o <br/> * Outputs statistics only, not the classifier. <p/> * * -i <br/> * Outputs information-retrieval statistics per class. <p/> * * -k <br/> * Outputs information-theoretic statistics. <p/> * * -p range <br/> * Outputs predictions for test instances (or the train instances if no test * instances provided and -no-cv is used), along with the attributes in the specified range * (and nothing else). Use '-p 0' if no attributes are desired. <p/> * * -distribution <br/> * Outputs the distribution instead of only the prediction * in conjunction with the '-p' option (only nominal classes). <p/> * * -r <br/> * Outputs cumulative margin distribution (and nothing else). <p/> * * -g <br/> * Only for classifiers that implement "Graphable." Outputs * the graph representation of the classifier (and nothing * else). <p/> * * -xml filename | xml-string <br/> * Retrieves the options from the XML-data instead of the command line. <p/> * * -threshold-file file <br/> * The file to save the threshold data to. * The format is determined by the extensions, e.g., '.arff' for ARFF * format or '.csv' for CSV. <p/> * * -threshold-label label <br/> * The class label to determine the threshold data for * (default is the first label) <p/> * * ------------------------------------------------------------------- <p/> * * Example usage as the main of a classifier (called FunkyClassifier): * <code> <pre> * public static void main(String [] args) { * runClassifier(new FunkyClassifier(), args); * } * </pre> </code> * <p/> * * ------------------------------------------------------------------ <p/> * * Example usage from within an application: * <code> <pre> * Instances trainInstances = ... instances got from somewhere * Instances testInstances = ... instances got from somewhere * Classifier scheme = ... scheme got from somewhere * * Evaluation evaluation = new Evaluation(trainInstances); * evaluation.evaluateModel(scheme, testInstances); * System.out.println(evaluation.toSummaryString()); * </pre> </code> * * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @author Len Trigg (trigg@cs.waikato.ac.nz) * @version $Revision: 6346 $ */ public class Evaluation implements Summarizable, RevisionHandler { /** The number of classes. */ protected int m_NumClasses; /** The number of folds for a cross-validation. */ protected int m_NumFolds; /** The weight of all incorrectly classified instances. */ protected double m_Incorrect; /** The weight of all correctly classified instances. */ protected double m_Correct; /** The weight of all unclassified instances. */ protected double m_Unclassified; /*** The weight of all instances that had no class assigned to them. */ protected double m_MissingClass; /** The weight of all instances that had a class assigned to them. */ protected double m_WithClass; /** Array for storing the confusion matrix. */ protected double [][] m_ConfusionMatrix; /** The names of the classes. */ protected String [] m_ClassNames; /** Is the class nominal or numeric? */ protected boolean m_ClassIsNominal; /** The prior probabilities of the classes */ protected double [] m_ClassPriors; /** The sum of counts for priors */ protected double m_ClassPriorsSum; /** The cost matrix (if given). */ protected CostMatrix m_CostMatrix; /** The total cost of predictions (includes instance weights) */ protected double m_TotalCost; /** Sum of errors. */ protected double m_SumErr; /** Sum of absolute errors. */ protected double m_SumAbsErr; /** Sum of squared errors. */ protected double m_SumSqrErr; /** Sum of class values. */ protected double m_SumClass; /** Sum of squared class values. */ protected double m_SumSqrClass; /*** Sum of predicted values. */ protected double m_SumPredicted; /** Sum of squared predicted values. */ protected double m_SumSqrPredicted; /** Sum of predicted * class values