Class Evaluation

java.lang.Object
weka.classifiers.Evaluation
All Implemented Interfaces:
RevisionHandler, Summarizable

public class Evaluation extends Object implements Summarizable, RevisionHandler
Class for evaluating machine learning models.

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General options when evaluating a learning scheme from the command-line:

-t filename
Name of the file with the training data. (required)

-T filename
Name of the file with the test data. If missing a cross-validation is performed.

-c index
Index of the class attribute (1, 2, ...; default: last).

-x number
The number of folds for the cross-validation (default: 10).

-no-cv
No cross validation. If no test file is provided, no evaluation is done.

-split-percentage percentage
Sets the percentage for the train/test set split, e.g., 66.

-preserve-order
Preserves the order in the percentage split instead of randomizing the data first with the seed value ('-s').

-s seed
Random number seed for the cross-validation and percentage split (default: 1).

-m filename
The name of a file containing a cost matrix.

-l filename
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.

-d filename
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.

-v
Outputs no statistics for the training data.

-o
Outputs statistics only, not the classifier.

-i
Outputs information-retrieval statistics per class.

-k
Outputs information-theoretic statistics.

-p range
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.

-distribution
Outputs the distribution instead of only the prediction in conjunction with the '-p' option (only nominal classes).

-r
Outputs cumulative margin distribution (and nothing else).

-g
Only for classifiers that implement "Graphable." Outputs the graph representation of the classifier (and nothing else).

-xml filename | xml-string
Retrieves the options from the XML-data instead of the command line.

-threshold-file file
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.

-threshold-label label
The class label to determine the threshold data for (default is the first label)

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Example usage as the main of a classifier (called FunkyClassifier):

 public static void main(String [] args) {
   runClassifier(new FunkyClassifier(), args);
 }
 

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Example usage from within an application:

 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());
 
Version:
$Revision: 10974 $
Author:
Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)
  • Constructor Summary

    Constructors
    Constructor
    Description
    Initializes all the counters for the evaluation.
    Evaluation(Instances data, CostMatrix costMatrix)
    Initializes all the counters for the evaluation and also takes a cost matrix as parameter.
  • Method Summary

    Modifier and Type
    Method
    Description
    double
    areaUnderROC(int classIndex)
    Returns the area under ROC for those predictions that have been collected in the evaluateClassifier(Classifier, Instances) method.
    final double
    Gets the average cost, that is, total cost of misclassifications (incorrect plus unclassified) over the total number of instances.
    double[][]
    Returns a copy of the confusion matrix.
    final double
    Gets the number of instances correctly classified (that is, for which a correct prediction was made).
    final double
    Returns the correlation coefficient if the class is numeric.
    void
    crossValidateModel(String classifierString, Instances data, int numFolds, String[] options, Random random)
    Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
    void
    crossValidateModel(Classifier classifier, Instances data, int numFolds, Random random, Object... forPredictionsPrinting)
    Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
    boolean
    Tests whether the current evaluation object is equal to another evaluation object
    final double
    Returns the estimated error rate or the root mean squared error (if the class is numeric).
    static String
    evaluateModel(String classifierString, String[] options)
    Evaluates a classifier with the options given in an array of strings.
    static String
    evaluateModel(Classifier classifier, String[] options)
    Evaluates a classifier with the options given in an array of strings.
    double[]
    evaluateModel(Classifier classifier, Instances data, Object... forPredictionsPrinting)
    Evaluates the classifier on a given set of instances.
    double
    evaluateModelOnce(double[] dist, Instance instance)
    Evaluates the supplied distribution on a single instance.
    void
    evaluateModelOnce(double prediction, Instance instance)
    Evaluates the supplied prediction on a single instance.
    double
    evaluateModelOnce(Classifier classifier, Instance instance)
    Evaluates the classifier on a single instance.
    double
    Evaluates the supplied distribution on a single instance.
    double
    Evaluates the classifier on a single instance and records the prediction (if the class is nominal).
    double
    falseNegativeRate(int classIndex)
    Calculate the false negative rate with respect to a particular class.
    double
    falsePositiveRate(int classIndex)
    Calculate the false positive rate with respect to a particular class.
    double
    fMeasure(int classIndex)
    Calculate the F-Measure with respect to a particular class.
    double[]
    Get the current weighted class counts
    Returns the revision string.
    final double
    Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).
    final double
    Returns value of kappa statistic if class is nominal.
    final double
    Return the total Kononenko & Bratko Information score in bits
    final double
    Return the Kononenko & Bratko Information score in bits per instance.
    final double
    Return the Kononenko & Bratko Relative Information score
    static void
    main(String[] args)
    A test method for this class.
    final double
    Returns the mean absolute error.
    final double
    Returns the mean absolute error of the prior.
    double
    numFalseNegatives(int classIndex)
    Calculate number of false negatives with respect to a particular class.
    double
    numFalsePositives(int classIndex)
    Calculate number of false positives with respect to a particular class.
    final double
    Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
    double
    numTrueNegatives(int classIndex)
    Calculate the number of true negatives with respect to a particular class.
    double
    numTruePositives(int classIndex)
    Calculate the number of true positives with respect to a particular class.
    final double
    Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
    final double
    Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
    final double
    Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
    double
    precision(int classIndex)
    Calculate the precision with respect to a particular class.
    Returns the predictions that have been collected.
    static void
    printClassifications(Classifier classifier, Instances train, ConverterUtils.DataSource testSource, int classIndex, Range attributesToOutput, boolean printDistribution, StringBuffer text)
    Prints the predictions for the given dataset into a supplied StringBuffer
    static void
    printClassifications(Classifier classifier, Instances train, ConverterUtils.DataSource testSource, int classIndex, Range attributesToOutput, StringBuffer predsText)
    Prints the predictions for the given dataset into a String variable.
    final double
    Calculate the entropy of the prior distribution
    double
    recall(int classIndex)
    Calculate the recall with respect to a particular class.
    final double
    Returns the relative absolute error.
    final double
    Returns the root mean prior squared error.
    final double
    Returns the root mean squared error.
    final double
    Returns the root relative squared error if the class is numeric.
    void
    Sets the class prior probabilities
    final double
    Returns the total SF, which is the null model entropy minus the scheme entropy.
    final double
    Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
    final double
    Returns the entropy per instance for the null model
    final double
    Returns the entropy per instance for the scheme
    final double
    Returns the total entropy for the null model
    final double
    Returns the total entropy for the scheme
    Generates a breakdown of the accuracy for each class (with default title), incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.
    Generates a breakdown of the accuracy for each class, incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.
    Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
    Calls toMatrixString() with a default title.
    Outputs the performance statistics as a classification confusion matrix.
    Calls toSummaryString() with no title and no complexity stats
    toSummaryString(boolean printComplexityStatistics)
    Calls toSummaryString() with a default title.
    toSummaryString(String title, boolean printComplexityStatistics)
    Outputs the performance statistics in summary form.
    final double
    Gets the total cost, that is, the cost of each prediction times the weight of the instance, summed over all instances.
    double
    trueNegativeRate(int classIndex)
    Calculate the true negative rate with respect to a particular class.
    double
    truePositiveRate(int classIndex)
    Calculate the true positive rate with respect to a particular class.
    final double
    Gets the number of instances not classified (that is, for which no prediction was made by the classifier).
    void
    Updates the class prior probabilities (when incrementally training)
    void
    disables the use of priors, e.g., in case of de-serialized schemes that have no access to the original training set, but are evaluated on a set set.
    double
    Calculates the weighted (by class size) AUC.
    double
    Calculates the weighted (by class size) false negative rate.
    double
    Calculates the weighted (by class size) false positive rate.
    double
    Calculates the weighted (by class size) F-Measure.
    double
    Calculates the weighted (by class size) false precision.
    double
    Calculates the weighted (by class size) recall.
    double
    Calculates the weighted (by class size) true negative rate.
    double
    Calculates the weighted (by class size) true positive rate.
    static String
    wekaStaticWrapper(Sourcable classifier, String className)
    Wraps a static classifier in enough source to test using the weka class libraries.

    Methods inherited from class java.lang.Object

    getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
  • Constructor Details

    • Evaluation

      public Evaluation(Instances data) throws Exception
      Initializes all the counters for the evaluation. Use useNoPriors() if the dataset is the test set and you can't initialize with the priors from the training set via setPriors(Instances).
      Parameters:
      data - set of training instances, to get some header information and prior class distribution information
      Throws:
      Exception - if the class is not defined
      See Also:
    • Evaluation

      public Evaluation(Instances data, CostMatrix costMatrix) throws Exception
      Initializes all the counters for the evaluation and also takes a cost matrix as parameter. Use useNoPriors() if the dataset is the test set and you can't initialize with the priors from the training set via setPriors(Instances).
      Parameters:
      data - set of training instances, to get some header information and prior class distribution information
      costMatrix - the cost matrix---if null, default costs will be used
      Throws:
      Exception - if cost matrix is not compatible with data, the class is not defined or the class is numeric
      See Also:
  • Method Details

    • areaUnderROC

      public double areaUnderROC(int classIndex)
      Returns the area under ROC for those predictions that have been collected in the evaluateClassifier(Classifier, Instances) method. Returns Instance.missingValue() if the area is not available.
      Parameters:
      classIndex - the index of the class to consider as "positive"
      Returns:
      the area under the ROC curve or not a number
    • weightedAreaUnderROC

      public double weightedAreaUnderROC()
      Calculates the weighted (by class size) AUC.
      Returns:
      the weighted AUC.
    • confusionMatrix

      public double[][] confusionMatrix()
      Returns a copy of the confusion matrix.
      Returns:
      a copy of the confusion matrix as a two-dimensional array
    • crossValidateModel

      public void crossValidateModel(Classifier classifier, Instances data, int numFolds, Random random, Object... forPredictionsPrinting) throws Exception
      Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances. Now performs a deep copy of the classifier before each call to buildClassifier() (just in case the classifier is not initialized properly).
      Parameters:
      classifier - the classifier with any options set.
      data - the data on which the cross-validation is to be performed
      numFolds - the number of folds for the cross-validation
      random - random number generator for randomization
      forPredictionsString - varargs parameter that, if supplied, is expected to hold a StringBuffer to print predictions to, a Range of attributes to output and a Boolean (true if the distribution is to be printed)
      Throws:
      Exception - if a classifier could not be generated successfully or the class is not defined
    • crossValidateModel

      public void crossValidateModel(String classifierString, Instances data, int numFolds, String[] options, Random random) throws Exception
      Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
      Parameters:
      classifierString - a string naming the class of the classifier
      data - the data on which the cross-validation is to be performed
      numFolds - the number of folds for the cross-validation
      options - the options to the classifier. Any options
      random - the random number generator for randomizing the data accepted by the classifier will be removed from this array.
      Throws:
      Exception - if a classifier could not be generated successfully or the class is not defined
    • evaluateModel

      public static String evaluateModel(String classifierString, String[] options) throws Exception
      Evaluates a classifier with the options given in an array of strings.

      Valid options are:

      -t filename
      Name of the file with the training data. (required)

      -T filename
      Name of the file with the test data. If missing a cross-validation is performed.

      -c index
      Index of the class attribute (1, 2, ...; default: last).

      -x number
      The number of folds for the cross-validation (default: 10).

      -no-cv
      No cross validation. If no test file is provided, no evaluation is done.

      -split-percentage percentage
      Sets the percentage for the train/test set split, e.g., 66.

      -preserve-order
      Preserves the order in the percentage split instead of randomizing the data first with the seed value ('-s').

      -s seed
      Random number seed for the cross-validation and percentage split (default: 1).

      -m filename
      The name of a file containing a cost matrix.

      -l filename
      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.

      -d filename
      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.

      -v
      Outputs no statistics for the training data.

      -o
      Outputs statistics only, not the classifier.

      -i
      Outputs detailed information-retrieval statistics per class.

      -k
      Outputs information-theoretic statistics.

      -p range
      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.

      -distribution
      Outputs the distribution instead of only the prediction in conjunction with the '-p' option (only nominal classes).

      -r
      Outputs cumulative margin distribution (and nothing else).

      -g
      Only for classifiers that implement "Graphable." Outputs the graph representation of the classifier (and nothing else).

      -xml filename | xml-string
      Retrieves the options from the XML-data instead of the command line.

      -threshold-file file
      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.

      -threshold-label label
      The class label to determine the threshold data for (default is the first label)

      Parameters:
      classifierString - class of machine learning classifier as a string
      options - the array of string containing the options
      Returns:
      a string describing the results
      Throws:
      Exception - if model could not be evaluated successfully
    • main

      public static void main(String[] args)
      A test method for this class. Just extracts the first command line argument as a classifier class name and calls evaluateModel.
      Parameters:
      args - an array of command line arguments, the first of which must be the class name of a classifier.
    • evaluateModel

      public static String evaluateModel(Classifier classifier, String[] options) throws Exception
      Evaluates a classifier with the options given in an array of strings.

      Valid options are:

      -t name of training file
      Name of the file with the training data. (required)

      -T name of test file
      Name of the file with the test data. If missing a cross-validation is performed.

      -c class index
      Index of the class attribute (1, 2, ...; default: last).

      -x number of folds
      The number of folds for the cross-validation (default: 10).

      -no-cv
      No cross validation. If no test file is provided, no evaluation is done.

      -split-percentage percentage
      Sets the percentage for the train/test set split, e.g., 66.

      -preserve-order
      Preserves the order in the percentage split instead of randomizing the data first with the seed value ('-s').

      -s seed
      Random number seed for the cross-validation and percentage split (default: 1).

      -m file with cost matrix
      The name of a file containing a cost matrix.

      -l filename
      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.

      -d filename
      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.

      -v
      Outputs no statistics for the training data.

      -o
      Outputs statistics only, not the classifier.

      -i
      Outputs detailed information-retrieval statistics per class.

      -k
      Outputs information-theoretic statistics.

      -p range
      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.

      -distribution
      Outputs the distribution instead of only the prediction in conjunction with the '-p' option (only nominal classes).

      -r
      Outputs cumulative margin distribution (and nothing else).

      -g
      Only for classifiers that implement "Graphable." Outputs the graph representation of the classifier (and nothing else).

      -xml filename | xml-string
      Retrieves the options from the XML-data instead of the command line.

      Parameters:
      classifier - machine learning classifier
      options - the array of string containing the options
      Returns:
      a string describing the results
      Throws:
      Exception - if model could not be evaluated successfully
    • evaluateModel

      public double[] evaluateModel(Classifier classifier, Instances data, Object... forPredictionsPrinting) throws Exception
      Evaluates the classifier on a given set of instances. Note that the data must have exactly the same format (e.g. order of attributes) as the data used to train the classifier! Otherwise the results will generally be meaningless.
      Parameters:
      classifier - machine learning classifier
      data - set of test instances for evaluation
      forPredictionsString - varargs parameter that, if supplied, is expected to hold a StringBuffer to print predictions to, a Range of attributes to output and a Boolean (true if the distribution is to be printed)
      Returns:
      the predictions
      Throws:
      Exception - if model could not be evaluated successfully
    • evaluateModelOnceAndRecordPrediction

      public double evaluateModelOnceAndRecordPrediction(Classifier classifier, Instance instance) throws Exception
      Evaluates the classifier on a single instance and records the prediction (if the class is nominal).
      Parameters:
      classifier - machine learning classifier
      instance - the test instance to be classified
      Returns:
      the prediction made by the clasifier
      Throws:
      Exception - if model could not be evaluated successfully or the data contains string attributes
    • evaluateModelOnce

      public double evaluateModelOnce(Classifier classifier, Instance instance) throws Exception
      Evaluates the classifier on a single instance.
      Parameters:
      classifier - machine learning classifier
      instance - the test instance to be classified
      Returns:
      the prediction made by the clasifier
      Throws:
      Exception - if model could not be evaluated successfully or the data contains string attributes
    • evaluateModelOnce

      public double evaluateModelOnce(double[] dist, Instance instance) throws Exception
      Evaluates the supplied distribution on a single instance.
      Parameters:
      dist - the supplied distribution
      instance - the test instance to be classified
      Returns:
      the prediction
      Throws:
      Exception - if model could not be evaluated successfully
    • evaluateModelOnceAndRecordPrediction

      public double evaluateModelOnceAndRecordPrediction(double[] dist, Instance instance) throws Exception
      Evaluates the supplied distribution on a single instance.
      Parameters:
      dist - the supplied distribution
      instance - the test instance to be classified
      Returns:
      the prediction
      Throws:
      Exception - if model could not be evaluated successfully
    • evaluateModelOnce

      public void evaluateModelOnce(double prediction, Instance instance) throws Exception
      Evaluates the supplied prediction on a single instance.
      Parameters:
      prediction - the supplied prediction
      instance - the test instance to be classified
      Throws:
      Exception - if model could not be evaluated successfully
    • predictions

      public FastVector predictions()
      Returns the predictions that have been collected.
      Returns:
      a reference to the FastVector containing the predictions that have been collected. This should be null if no predictions have been collected (e.g. if the class is numeric).
    • wekaStaticWrapper

      public static String wekaStaticWrapper(Sourcable classifier, String className) throws Exception
      Wraps a static classifier in enough source to test using the weka class libraries.
      Parameters:
      classifier - a Sourcable Classifier
      className - the name to give to the source code class
      Returns:
      the source for a static classifier that can be tested with weka libraries.
      Throws:
      Exception - if code-generation fails
    • numInstances

      public final double numInstances()
      Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
      Returns:
      the number of test instances with known class
    • incorrect

      public final double incorrect()
      Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made). (Actually the sum of the weights of these instances)
      Returns:
      the number of incorrectly classified instances
    • pctIncorrect

      public final double pctIncorrect()
      Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
      Returns:
      the percent of incorrectly classified instances (between 0 and 100)
    • totalCost

      public final double totalCost()
      Gets the total cost, that is, the cost of each prediction times the weight of the instance, summed over all instances.
      Returns:
      the total cost
    • avgCost

      public final double avgCost()
      Gets the average cost, that is, total cost of misclassifications (incorrect plus unclassified) over the total number of instances.
      Returns:
      the average cost.
    • correct

      public final double correct()
      Gets the number of instances correctly classified (that is, for which a correct prediction was made). (Actually the sum of the weights of these instances)
      Returns:
      the number of correctly classified instances
    • pctCorrect

      public final double pctCorrect()
      Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
      Returns:
      the percent of correctly classified instances (between 0 and 100)
    • unclassified

      public final double unclassified()
      Gets the number of instances not classified (that is, for which no prediction was made by the classifier). (Actually the sum of the weights of these instances)
      Returns:
      the number of unclassified instances
    • pctUnclassified

      public final double pctUnclassified()
      Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
      Returns:
      the percent of unclassified instances (between 0 and 100)
    • errorRate

      public final double errorRate()
      Returns the estimated error rate or the root mean squared error (if the class is numeric). If a cost matrix was given this error rate gives the average cost.
      Returns:
      the estimated error rate (between 0 and 1, or between 0 and maximum cost)
    • kappa

      public final double kappa()
      Returns value of kappa statistic if class is nominal.
      Returns:
      the value of the kappa statistic
    • correlationCoefficient

      public final double correlationCoefficient() throws Exception
      Returns the correlation coefficient if the class is numeric.
      Returns:
      the correlation coefficient
      Throws:
      Exception - if class is not numeric
    • meanAbsoluteError

      public final double meanAbsoluteError()
      Returns the mean absolute error. Refers to the error of the predicted values for numeric classes, and the error of the predicted probability distribution for nominal classes.
      Returns:
      the mean absolute error
    • meanPriorAbsoluteError

      public final double meanPriorAbsoluteError()
      Returns the mean absolute error of the prior.
      Returns:
      the mean absolute error
    • relativeAbsoluteError

      public final double relativeAbsoluteError() throws Exception
      Returns the relative absolute error.
      Returns:
      the relative absolute error
      Throws:
      Exception - if it can't be computed
    • rootMeanSquaredError

      public final double rootMeanSquaredError()
      Returns the root mean squared error.
      Returns:
      the root mean squared error
    • rootMeanPriorSquaredError

      public final double rootMeanPriorSquaredError()
      Returns the root mean prior squared error.
      Returns:
      the root mean prior squared error
    • rootRelativeSquaredError

      public final double rootRelativeSquaredError()
      Returns the root relative squared error if the class is numeric.
      Returns:
      the root relative squared error
    • priorEntropy

      public final double priorEntropy() throws Exception
      Calculate the entropy of the prior distribution
      Returns:
      the entropy of the prior distribution
      Throws:
      Exception - if the class is not nominal
    • KBInformation

      public final double KBInformation() throws Exception
      Return the total Kononenko & Bratko Information score in bits
      Returns:
      the K&B information score
      Throws:
      Exception - if the class is not nominal
    • KBMeanInformation

      public final double KBMeanInformation() throws Exception
      Return the Kononenko & Bratko Information score in bits per instance.
      Returns:
      the K&B information score
      Throws:
      Exception - if the class is not nominal
    • KBRelativeInformation

      public final double KBRelativeInformation() throws Exception
      Return the Kononenko & Bratko Relative Information score
      Returns:
      the K&B relative information score
      Throws:
      Exception - if the class is not nominal
    • SFPriorEntropy

      public final double SFPriorEntropy()
      Returns the total entropy for the null model
      Returns:
      the total null model entropy
    • SFMeanPriorEntropy

      public final double SFMeanPriorEntropy()
      Returns the entropy per instance for the null model
      Returns:
      the null model entropy per instance
    • SFSchemeEntropy

      public final double SFSchemeEntropy()
      Returns the total entropy for the scheme
      Returns:
      the total scheme entropy
    • SFMeanSchemeEntropy

      public final double SFMeanSchemeEntropy()
      Returns the entropy per instance for the scheme
      Returns:
      the scheme entropy per instance
    • SFEntropyGain

      public final double SFEntropyGain()
      Returns the total SF, which is the null model entropy minus the scheme entropy.
      Returns:
      the total SF
    • SFMeanEntropyGain

      public final double SFMeanEntropyGain()
      Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
      Returns:
      the SF per instance
    • toCumulativeMarginDistributionString

      public String toCumulativeMarginDistributionString() throws Exception
      Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
      Returns:
      the cumulative margin distribution
      Throws:
      Exception - if the class attribute is nominal
    • toSummaryString

      public String toSummaryString()
      Calls toSummaryString() with no title and no complexity stats
      Specified by:
      toSummaryString in interface Summarizable
      Returns:
      a summary description of the classifier evaluation
    • toSummaryString

      public String toSummaryString(boolean printComplexityStatistics)
      Calls toSummaryString() with a default title.
      Parameters:
      printComplexityStatistics - if true, complexity statistics are returned as well
      Returns:
      the summary string
    • toSummaryString

      public String toSummaryString(String title, boolean printComplexityStatistics)
      Outputs the performance statistics in summary form. Lists number (and percentage) of instances classified correctly, incorrectly and unclassified. Outputs the total number of instances classified, and the number of instances (if any) that had no class value provided.
      Parameters:
      title - the title for the statistics
      printComplexityStatistics - if true, complexity statistics are returned as well
      Returns:
      the summary as a String
    • toMatrixString

      public String toMatrixString() throws Exception
      Calls toMatrixString() with a default title.
      Returns:
      the confusion matrix as a string
      Throws:
      Exception - if the class is numeric
    • toMatrixString

      public String toMatrixString(String title) throws Exception
      Outputs the performance statistics as a classification confusion matrix. For each class value, shows the distribution of predicted class values.
      Parameters:
      title - the title for the confusion matrix
      Returns:
      the confusion matrix as a String
      Throws:
      Exception - if the class is numeric
    • toClassDetailsString

      public String toClassDetailsString() throws Exception
      Generates a breakdown of the accuracy for each class (with default title), incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure. Should be useful for ROC curves, recall/precision curves.
      Returns:
      the statistics presented as a string
      Throws:
      Exception - if class is not nominal
    • toClassDetailsString

      public String toClassDetailsString(String title) throws Exception
      Generates a breakdown of the accuracy for each class, incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure. Should be useful for ROC curves, recall/precision curves.
      Parameters:
      title - the title to prepend the stats string with
      Returns:
      the statistics presented as a string
      Throws:
      Exception - if class is not nominal
    • numTruePositives

      public double numTruePositives(int classIndex)
      Calculate the number of true positives with respect to a particular class. This is defined as

       correctly classified positives
       
      Parameters:
      classIndex - the index of the class to consider as "positive"
      Returns:
      the true positive rate
    • truePositiveRate

      public double truePositiveRate(int classIndex)
      Calculate the true positive rate with respect to a particular class. This is defined as

       correctly classified positives
       ------------------------------
             total positives
       
      Parameters:
      classIndex - the index of the class to consider as "positive"
      Returns:
      the true positive rate
    • weightedTruePositiveRate

      public double weightedTruePositiveRate()
      Calculates the weighted (by class size) true positive rate.
      Returns:
      the weighted true positive rate.
    • numTrueNegatives

      public double numTrueNegatives(int classIndex)
      Calculate the number of true negatives with respect to a particular class. This is defined as

       correctly classified negatives
       
      Parameters:
      classIndex - the index of the class to consider as "positive"
      Returns:
      the true positive rate
    • trueNegativeRate

      public double trueNegativeRate(int classIndex)
      Calculate the true negative rate with respect to a particular class. This is defined as

       correctly classified negatives
       ------------------------------
             total negatives
       
      Parameters:
      classIndex - the index of the class to consider as "positive"
      Returns:
      the true positive rate
    • weightedTrueNegativeRate

      public double weightedTrueNegativeRate()
      Calculates the weighted (by class size) true negative rate.
      Returns:
      the weighted true negative rate.
    • numFalsePositives

      public double numFalsePositives(int classIndex)
      Calculate number of false positives with respect to a particular class. This is defined as

       incorrectly classified negatives
       
      Parameters:
      classIndex - the index of the class to consider as "positive"
      Returns:
      the false positive rate
    • falsePositiveRate

      public double falsePositiveRate(int classIndex)
      Calculate the false positive rate with respect to a particular class. This is defined as

       incorrectly classified negatives
       --------------------------------
              total negatives
       
      Parameters:
      classIndex - the index of the class to consider as "positive"
      Returns:
      the false positive rate
    • weightedFalsePositiveRate

      public double weightedFalsePositiveRate()
      Calculates the weighted (by class size) false positive rate.
      Returns:
      the weighted false positive rate.
    • numFalseNegatives

      public double numFalseNegatives(int classIndex)
      Calculate number of false negatives with respect to a particular class. This is defined as

       incorrectly classified positives
       
      Parameters:
      classIndex - the index of the class to consider as "positive"
      Returns:
      the false positive rate
    • falseNegativeRate

      public double falseNegativeRate(int classIndex)
      Calculate the false negative rate with respect to a particular class. This is defined as

       incorrectly classified positives
       --------------------------------
              total positives
       
      Parameters:
      classIndex - the index of the class to consider as "positive"
      Returns:
      the false positive rate
    • weightedFalseNegativeRate

      public double weightedFalseNegativeRate()
      Calculates the weighted (by class size) false negative rate.
      Returns:
      the weighted false negative rate.
    • recall

      public double recall(int classIndex)
      Calculate the recall with respect to a particular class. This is defined as

       correctly classified positives
       ------------------------------
             total positives
       

      (Which is also the same as the truePositiveRate.)

      Parameters:
      classIndex - the index of the class to consider as "positive"
      Returns:
      the recall
    • weightedRecall

      public double weightedRecall()
      Calculates the weighted (by class size) recall.
      Returns:
      the weighted recall.
    • precision

      public double precision(int classIndex)
      Calculate the precision with respect to a particular class. This is defined as

       correctly classified positives
       ------------------------------
        total predicted as positive
       
      Parameters:
      classIndex - the index of the class to consider as "positive"
      Returns:
      the precision
    • weightedPrecision

      public double weightedPrecision()
      Calculates the weighted (by class size) false precision.
      Returns:
      the weighted precision.
    • fMeasure

      public double fMeasure(int classIndex)
      Calculate the F-Measure with respect to a particular class. This is defined as

       2 * recall * precision
       ----------------------
         recall + precision
       
      Parameters:
      classIndex - the index of the class to consider as "positive"
      Returns:
      the F-Measure
    • weightedFMeasure

      public double weightedFMeasure()
      Calculates the weighted (by class size) F-Measure.
      Returns:
      the weighted F-Measure.
    • setPriors

      public void setPriors(Instances train) throws Exception
      Sets the class prior probabilities
      Parameters:
      train - the training instances used to determine the prior probabilities
      Throws:
      Exception - if the class attribute of the instances is not set
    • getClassPriors

      public double[] getClassPriors()
      Get the current weighted class counts
      Returns:
      the weighted class counts
    • updatePriors

      public void updatePriors(Instance instance) throws Exception
      Updates the class prior probabilities (when incrementally training)
      Parameters:
      instance - the new training instance seen
      Throws:
      Exception - if the class of the instance is not set
    • useNoPriors

      public void useNoPriors()
      disables the use of priors, e.g., in case of de-serialized schemes that have no access to the original training set, but are evaluated on a set set.
    • equals

      public boolean equals(Object obj)
      Tests whether the current evaluation object is equal to another evaluation object
      Overrides:
      equals in class Object
      Parameters:
      obj - the object to compare against
      Returns:
      true if the two objects are equal
    • printClassifications

      public static void printClassifications(Classifier classifier, Instances train, ConverterUtils.DataSource testSource, int classIndex, Range attributesToOutput, StringBuffer predsText) throws Exception
      Prints the predictions for the given dataset into a String variable.
      Parameters:
      classifier - the classifier to use
      train - the training data
      testSource - the test set
      classIndex - the class index (1-based), if -1 ot does not override the class index is stored in the data file (by using the last attribute)
      attributesToOutput - the indices of the attributes to output
      Throws:
      Exception - if test file cannot be opened
    • printClassifications

      public static void printClassifications(Classifier classifier, Instances train, ConverterUtils.DataSource testSource, int classIndex, Range attributesToOutput, boolean printDistribution, StringBuffer text) throws Exception
      Prints the predictions for the given dataset into a supplied StringBuffer
      Parameters:
      classifier - the classifier to use
      train - the training data
      testSource - the test set
      classIndex - the class index (1-based), if -1 ot does not override the class index is stored in the data file (by using the last attribute)
      attributesToOutput - the indices of the attributes to output
      printDistribution - prints the complete distribution for nominal classes, not just the predicted value
      text - StringBuffer to hold the printed predictions
      Throws:
      Exception - if test file cannot be opened
    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Returns:
      the revision