Package weka.experiment

Class Experiment

java.lang.Object
weka.experiment.Experiment
All Implemented Interfaces:
Serializable, OptionHandler, RevisionHandler
Direct Known Subclasses:
RemoteExperiment
public class Experiment extends Object implements Serializable, OptionHandler, RevisionHandler
Holds all the necessary configuration information for a standard type experiment. This object is able to be serialized for storage on disk. Valid options are: 

 -L <num>
  The lower run number to start the experiment from.
  (default 1)
 
 -U <num>
  The upper run number to end the experiment at (inclusive).
  (default 10)
 
 -T <arff file>
  The dataset to run the experiment on.
  (required, may be specified multiple times)
 
 -P <class name>
  The full class name of a ResultProducer (required).
  eg: weka.experiment.RandomSplitResultProducer
 
 -D <class name>
  The full class name of a ResultListener (required).
  eg: weka.experiment.CSVResultListener
 
 -N <string>
  A string containing any notes about the experiment.
  (default none)
 
 Options specific to result producer weka.experiment.RandomSplitResultProducer:
 
 -P <percent>
  The percentage of instances to use for training.
  (default 66)
 
 -D
 Save raw split evaluator output.
 
 -O <file/directory name/path>
  The filename where raw output will be stored.
  If a directory name is specified then then individual
  outputs will be gzipped, otherwise all output will be
  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
 
 -W <class name>
  The full class name of a SplitEvaluator.
  eg: weka.experiment.ClassifierSplitEvaluator
 
 -R
  Set when data is not to be randomized and the data sets' size.
  Is not to be determined via probabilistic rounding.
 
 Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
 
 -W <class name>
  The full class name of the classifier.
  eg: weka.classifiers.bayes.NaiveBayes
 
 -C <index>
  The index of the class for which IR statistics
  are to be output. (default 1)
 
 -I <index>
  The index of an attribute to output in the
  results. This attribute should identify an
  instance in order to know which instances are
  in the test set of a cross validation. if 0
  no output (default 0).
 
 -P
  Add target and prediction columns to the result
  for each fold.
 
 Options specific to classifier weka.classifiers.rules.ZeroR:
 
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
All options after -- will be passed to the result producer.

Version:
$Revision: 13476 $
Author:
Len Trigg (trigg@cs.waikato.ac.nz)
See Also:

  • Field Details

    • FILE_EXTENSION

      public static String FILE_EXTENSION
      The filename extension that should be used for experiment files

  • Constructor Details

    • Experiment

      public Experiment()

  • Method Details

    • classFirst

      public void classFirst(boolean flag)
      Sets whether the first attribute is treated as the class for all datasets involved in the experiment. This information is not output with the result of the experiments!
      Parameters:
      flag - whether the class attribute is the first and not the last

    • getAdvanceDataSetFirst

      public boolean getAdvanceDataSetFirst()
      Get the value of m_DataSetFirstFirst.
      Returns:
      Value of m_DataSetFirstFirst.

    • setAdvanceDataSetFirst

      public void setAdvanceDataSetFirst(boolean newAdvanceDataSetFirst)
      Set the value of m_AdvanceDataSetFirst.
      Parameters:
      newAdvanceDataSetFirst - Value to assign to m_AdvanceRunFirst.

    • getUsePropertyIterator

      public boolean getUsePropertyIterator()
      Gets whether the custom property iterator should be used.
      Returns:
      true if so

    • setUsePropertyIterator

      public void setUsePropertyIterator(boolean newUsePropertyIterator)
      Sets whether the custom property iterator should be used.
      Parameters:
      newUsePropertyIterator - true if so

    • getPropertyPath

      public PropertyNode[] getPropertyPath()
      Gets the path of properties taken to get to the custom property to iterate over.
      Returns:
      an array of PropertyNodes

    • setPropertyPath

      public void setPropertyPath(PropertyNode[] newPropertyPath)
      Sets the path of properties taken to get to the custom property to iterate over.
      Parameters:
      newPropertyPath - an array of PropertyNodes

    • setPropertyArray

      public void setPropertyArray(Object newPropArray)
      Sets the array of values to set the custom property to.
      Parameters:
      newPropArray - a value of type Object which should be an array of the appropriate values.

    • getPropertyArray

      public Object getPropertyArray()
      Gets the array of values to set the custom property to.
      Returns:
      a value of type Object which should be an array of the appropriate values.

    • getPropertyArrayLength

      public int getPropertyArrayLength()
      Gets the number of custom iterator values that have been defined for the experiment.
      Returns:
      the number of custom property iterator values.

    • getPropertyArrayValue

      public Object getPropertyArrayValue(int index)
      Gets a specified value from the custom property iterator array.
      Parameters:
      index - the index of the value wanted
      Returns:
      the property array value

    • getCurrentRunNumber

      public int getCurrentRunNumber()
      When an experiment is running, this returns the current run number.
      Returns:
      the current run number.

    • getCurrentDatasetNumber

      public int getCurrentDatasetNumber()
      When an experiment is running, this returns the current dataset number.
      Returns:
      the current dataset number.

    • getCurrentPropertyNumber

      public int getCurrentPropertyNumber()
      When an experiment is running, this returns the index of the current custom property value.
      Returns:
      the index of the current custom property value.

    • initialize

      public void initialize() throws Exception
      Prepares an experiment for running, initializing current iterator settings.
      Throws:
      Exception - if an error occurs

    • hasMoreIterations

      public boolean hasMoreIterations()
      Returns true if there are more iterations to carry out in the experiment.
      Returns:
      true if so

    • nextIteration

      public void nextIteration() throws Exception
      Carries out the next iteration of the experiment.
      Throws:
      Exception - if an error occurs

    • advanceCounters

      public void advanceCounters()
      Increments iteration counters appropriately.

    • runExperiment

      public void runExperiment(boolean verbose)

    • runExperiment

      public void runExperiment()
      Runs all iterations of the experiment, continuing past errors.

    • postProcess

      public void postProcess() throws Exception
      Signals that the experiment is finished running, so that cleanup can be done.
      Throws:
      Exception - if an error occurs

    • getDatasets

      public DefaultListModel getDatasets()
      Gets the datasets in the experiment.
      Returns:
      the datasets in the experiment.

    • setDatasets

      public void setDatasets(DefaultListModel ds)
      Set the datasets to use in the experiment
      Parameters:
      ds - the list of datasets to use

    • getResultListener

      public ResultListener getResultListener()
      Gets the result listener where results will be sent.
      Returns:
      the result listener where results will be sent.

    • setResultListener

      public void setResultListener(ResultListener newResultListener)
      Sets the result listener where results will be sent.
      Parameters:
      newResultListener - the result listener where results will be sent.

    • getResultProducer

      public ResultProducer getResultProducer()
      Get the result producer used for the current experiment.
      Returns:
      the result producer used for the current experiment.

    • setResultProducer

      public void setResultProducer(ResultProducer newResultProducer)
      Set the result producer used for the current experiment.
      Parameters:
      newResultProducer - result producer to use for the current experiment.

    • getRunUpper

      public int getRunUpper()
      Get the upper run number for the experiment.
      Returns:
      the upper run number for the experiment.

    • setRunUpper

      public void setRunUpper(int newRunUpper)
      Set the upper run number for the experiment.
      Parameters:
      newRunUpper - the upper run number for the experiment.

    • getRunLower

      public int getRunLower()
      Get the lower run number for the experiment.
      Returns:
      the lower run number for the experiment.

    • setRunLower

      public void setRunLower(int newRunLower)
      Set the lower run number for the experiment.
      Parameters:
      newRunLower - the lower run number for the experiment.

    • getNotes

      public String getNotes()
      Get the user notes.
      Returns:
      User notes associated with the experiment.

    • setNotes

      public void setNotes(String newNotes)
      Set the user notes.
      Parameters:
      newNotes - New user notes.

    • listOptions

      public Enumeration<Option> listOptions()
      Returns an enumeration describing the available options..
      Specified by:
      listOptions in interface OptionHandler
      Returns:
      an enumeration of all the available options.

    • setOptions

      public void setOptions(String[] options) throws Exception
      Parses a given list of options.

      Valid options are: 

       -L <num>
  The lower run number to start the experiment from.
  (default 1)
 
       -U <num>
  The upper run number to end the experiment at (inclusive).
  (default 10)
 
       -T <arff file>
  The dataset to run the experiment on.
  (required, may be specified multiple times)
 
       -P <class name>
  The full class name of a ResultProducer (required).
  eg: weka.experiment.RandomSplitResultProducer
 
       -D <class name>
  The full class name of a ResultListener (required).
  eg: weka.experiment.CSVResultListener
 
       -N <string>
  A string containing any notes about the experiment.
  (default none)
 
       Options specific to result producer weka.experiment.RandomSplitResultProducer:
 
       -P <percent>
  The percentage of instances to use for training.
  (default 66)
 
       -D
 Save raw split evaluator output.
 
       -O <file/directory name/path>
  The filename where raw output will be stored.
  If a directory name is specified then then individual
  outputs will be gzipped, otherwise all output will be
  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
 
       -W <class name>
  The full class name of a SplitEvaluator.
  eg: weka.experiment.ClassifierSplitEvaluator
 
       -R
  Set when data is not to be randomized and the data sets' size.
  Is not to be determined via probabilistic rounding.
 
       Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
 
       -W <class name>
  The full class name of the classifier.
  eg: weka.classifiers.bayes.NaiveBayes
 
       -C <index>
  The index of the class for which IR statistics
  are to be output. (default 1)
 
       -I <index>
  The index of an attribute to output in the
  results. This attribute should identify an
  instance in order to know which instances are
  in the test set of a cross validation. if 0
  no output (default 0).
 
       -P
  Add target and prediction columns to the result
  for each fold.
 
       Options specific to classifier weka.classifiers.rules.ZeroR:
 
       -D
  If set, classifier is run in debug mode and
  may output additional info to the console
      All options after -- will be passed to the result producer.

      Specified by:
      setOptions in interface OptionHandler
      Parameters:
      options - the list of options as an array of strings
      Throws:
      Exception - if an option is not supported

    • getOptions

      public String[] getOptions()
      Gets the current settings of the experiment iterator.
      Specified by:
      getOptions in interface OptionHandler
      Returns:
      an array of strings suitable for passing to setOptions

    • toString

      public String toString()
      Gets a string representation of the experiment configuration.
      Overrides:
      toString in class Object
      Returns:
      a value of type 'String'

    • read

      public static Experiment read(String filename) throws Exception
      Loads an experiment from a file.
      Parameters:
      filename - the file to load the experiment from
      Returns:
      the experiment
      Throws:
      Exception - if loading fails

    • write

      public static void write(String filename, Experiment exp) throws Exception
      Writes the experiment to disk.
      Parameters:
      filename - the file to write to
      exp - the experiment to save
      Throws:
      Exception - if writing fails

    • main

      public static void main(String[] args)
      Configures/Runs the Experiment from the command line.
      Parameters:
      args - command line arguments to the Experiment.

    • getRevision

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