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## weka.experiment
Class RandomSplitResultProducer

    java.lang.Object
      weka.experiment.RandomSplitResultProducer

All Implemented Interfaces:
  java.io.Serializable, AdditionalMeasureProducer, OptionHandler, RevisionHandler, ResultProducer

    public class RandomSplitResultProducer

extends java.lang.Object
implements ResultProducer, OptionHandler, AdditionalMeasureProducer, RevisionHandler

Generates a single train/test split and calls the appropriate SplitEvaluator to generate some results.

Valid options are:

     -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 split evaluator.

Version:
  $Revision: 6255 $
Author:
  Len Trigg (trigg@cs.waikato.ac.nz)
See Also:
  Serialized Form

Field Summary
`static java.lang.String` | `DATASET_FIELD_NAME` The name of the key field containing the dataset name
`static java.lang.String` | `RUN_FIELD_NAME` The name of the key field containing the run number
`static java.lang.String` | `TIMESTAMP_FIELD_NAME` The name of the result field containing the timestamp

Constructor Summary
`RandomSplitResultProducer()`

Method Summary
`void` | `doRun(int run)` Gets the results for a specified run number.
`void` | `doRunKeys(int run)` Gets the keys for a specified run number.
`java.util.Enumeration` | `enumerateMeasures()` Returns an enumeration of any additional measure names that might be in the SplitEvaluator
`java.lang.String` | `getCompatibilityState()` Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
`java.lang.String[]` | `getKeyNames()` Gets the names of each of the columns produced for a single run.
`java.lang.Object[]` | `getKeyTypes()` Gets the data types of each of the columns produced for a single run.
`double` | `getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`java.lang.String[]` | `getOptions()` Gets the current settings of the result producer.
`java.io.File` | `getOutputFile()` Get the value of OutputFile.
`boolean` | `getRandomizeData()` Get if dataset is to be randomized
`boolean` | `getRawOutput()` Get if raw split evaluator output is to be saved
`java.lang.String[]` | `getResultNames()` Gets the names of each of the columns produced for a single run.
`java.lang.Object[]` | `getResultTypes()` Gets the data types of each of the columns produced for a single run.
`java.lang.String` | `getRevision()` Returns the revision string.
`SplitEvaluator` | `getSplitEvaluator()` Get the SplitEvaluator.
`static java.lang.Double` | `getTimestamp()` Gets a Double representing the current date and time.
`double` | `getTrainPercent()` Get the value of TrainPercent.
`java.lang.String` | `globalInfo()` Returns a string describing this result producer
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options..
`java.lang.String` | `outputFileTipText()` Returns the tip text for this property
`void` | `postProcess()` Perform any postprocessing.
`void` | `preProcess()` Prepare to generate results.
`java.lang.String` | `randomizeDataTipText()` Returns the tip text for this property
`java.lang.String` | `rawOutputTipText()` Returns the tip text for this property
`void` | `setAdditionalMeasures(java.lang.String[] additionalMeasures)` Set a list of method names for additional measures to look for in SplitEvaluators.
`void` | `setInstances(Instances instances)` Sets the dataset that results will be obtained for.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setOutputFile(java.io.File newOutputFile)` Set the value of OutputFile.
`void` | `setRandomizeData(boolean d)` Set to true if dataset is to be randomized
`void` | `setRawOutput(boolean d)` Set to true if raw split evaluator output is to be saved
`void` | `setResultListener(ResultListener listener)` Sets the object to send results of each run to.
`void` | `setSplitEvaluator(SplitEvaluator newSplitEvaluator)` Set the SplitEvaluator.
`void` | `setTrainPercent(double newTrainPercent)` Set the value of TrainPercent.
`java.lang.String` | `splitEvaluatorTipText()` Returns the tip text for this property
`java.lang.String` | `toString()` Gets a text descrption of the result producer.
`java.lang.String` | `trainPercentTipText()` Returns the tip text for this property

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

Field Detail

### DATASET_FIELD_NAME

    public static java.lang.String DATASET_FIELD_NAME

  The name of the key field containing the dataset name

### RUN_FIELD_NAME

    public static java.lang.String RUN_FIELD_NAME

  The name of the key field containing the run number

### TIMESTAMP_FIELD_NAME

    public static java.lang.String TIMESTAMP_FIELD_NAME

  The name of the result field containing the timestamp

Constructor Detail

### RandomSplitResultProducer

    public RandomSplitResultProducer()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing this result producer

  Returns:
    a description of the result producer suitable for displaying in the explorer/experimenter gui

### setInstances

    public void setInstances(Instances instances)

  Sets the dataset that results will be obtained for.

  Specified by:
    `setInstances` in interface `ResultProducer`
  Parameters:
    `instances` - a value of type 'Instances'.

### setAdditionalMeasures

    public void setAdditionalMeasures(java.lang.String[] additionalMeasures)

  Set a list of method names for additional measures to look for in SplitEvaluators. This could contain many measures (of which only a subset may be produceable by the current SplitEvaluator) if an experiment is the type that iterates over a set of properties.

  Specified by:
    `setAdditionalMeasures` in interface `ResultProducer`
  Parameters:
    `additionalMeasures` - an array of measure names, null if none

### enumerateMeasures

    public java.util.Enumeration enumerateMeasures()

  Returns an enumeration of any additional measure names that might be in the SplitEvaluator

  Specified by:
    `enumerateMeasures` in interface `AdditionalMeasureProducer`
  Returns:
    an enumeration of the measure names

### getMeasure

    public double getMeasure(java.lang.String additionalMeasureName)

  Returns the value of the named measure

  Specified by:
    `getMeasure` in interface `AdditionalMeasureProducer`
  Parameters:
    `additionalMeasureName` - the name of the measure to query for its value
  Returns:
    the value of the named measure
  Throws:
    `java.lang.IllegalArgumentException` - if the named measure is not supported

### setResultListener

    public void setResultListener(ResultListener listener)

  Sets the object to send results of each run to.

  Specified by:
    `setResultListener` in interface `ResultProducer`
  Parameters:
    `listener` - a value of type 'ResultListener'

### getTimestamp

    public static java.lang.Double getTimestamp()

  Gets a Double representing the current date and time. eg: 1:46pm on 20/5/1999 -> 19990520.1346

  Returns:
    a value of type Double

### preProcess

    public void preProcess()
                    throws java.lang.Exception

  Prepare to generate results.

  Specified by:
    `preProcess` in interface `ResultProducer`
  Throws:
    `java.lang.Exception` - if an error occurs during preprocessing.

### postProcess

    public void postProcess()
                     throws java.lang.Exception

  Perform any postprocessing. When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.

  Specified by:
    `postProcess` in interface `ResultProducer`
  Throws:
    `java.lang.Exception` - if an error occurs

### doRunKeys

    public void doRunKeys(int run)
                   throws java.lang.Exception

  Gets the keys for a specified run number. Different run numbers correspond to different randomizations of the data. Keys produced should be sent to the current ResultListener

  Specified by:
    `doRunKeys` in interface `ResultProducer`
  Parameters:
    `run` - the run number to get keys for.
  Throws:
    `java.lang.Exception` - if a problem occurs while getting the keys

### doRun

    public void doRun(int run)
               throws java.lang.Exception

  Gets the results for a specified run number. Different run numbers correspond to different randomizations of the data. Results produced should be sent to the current ResultListener

  Specified by:
    `doRun` in interface `ResultProducer`
  Parameters:
    `run` - the run number to get results for.
  Throws:
    `java.lang.Exception` - if a problem occurs while getting the results

### getKeyNames

    public java.lang.String[] getKeyNames()

  Gets the names of each of the columns produced for a single run. This method should really be static.

  Specified by:
    `getKeyNames` in interface `ResultProducer`
  Returns:
    an array containing the name of each column

### getKeyTypes

    public java.lang.Object[] getKeyTypes()

  Gets the data types of each of the columns produced for a single run. This method should really be static.

  Specified by:
    `getKeyTypes` in interface `ResultProducer`
  Returns:
    an array containing objects of the type of each column. The objects should be Strings, or Doubles.

### getResultNames

    public java.lang.String[] getResultNames()

  Gets the names of each of the columns produced for a single run. This method should really be static.

  Specified by:
    `getResultNames` in interface `ResultProducer`
  Returns:
    an array containing the name of each column

### getResultTypes

    public java.lang.Object[] getResultTypes()

  Gets the data types of each of the columns produced for a single run. This method should really be static.

  Specified by:
    `getResultTypes` in interface `ResultProducer`
  Returns:
    an array containing objects of the type of each column. The objects should be Strings, or Doubles.

### getCompatibilityState

    public java.lang.String getCompatibilityState()

  Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface). For example, a cross-validation ResultProducer may have a setting for the number of folds. For a given state, the results produced should be compatible. Typically if a ResultProducer is an OptionHandler, this string will represent the command line arguments required to set the ResultProducer to that state.

  Specified by:
    `getCompatibilityState` in interface `ResultProducer`
  Returns:
    the description of the ResultProducer state, or null if no state is defined

### outputFileTipText

    public java.lang.String outputFileTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getOutputFile

    public java.io.File getOutputFile()

  Get the value of OutputFile.

  Returns:
    Value of OutputFile.

### setOutputFile

    public void setOutputFile(java.io.File newOutputFile)

  Set the value of OutputFile.

  Parameters:
    `newOutputFile` - Value to assign to OutputFile.

### randomizeDataTipText

    public java.lang.String randomizeDataTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getRandomizeData

    public boolean getRandomizeData()

  Get if dataset is to be randomized

  Returns:
    true if dataset is to be randomized

### setRandomizeData

    public void setRandomizeData(boolean d)

  Set to true if dataset is to be randomized

  Parameters:
    `d` - true if dataset is to be randomized

### rawOutputTipText

    public java.lang.String rawOutputTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getRawOutput

    public boolean getRawOutput()

  Get if raw split evaluator output is to be saved

  Returns:
    true if raw split evalutor output is to be saved

### setRawOutput

    public void setRawOutput(boolean d)

  Set to true if raw split evaluator output is to be saved

  Parameters:
    `d` - true if output is to be saved

### trainPercentTipText

    public java.lang.String trainPercentTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getTrainPercent

    public double getTrainPercent()

  Get the value of TrainPercent.

  Returns:
    Value of TrainPercent.

### setTrainPercent

    public void setTrainPercent(double newTrainPercent)

  Set the value of TrainPercent.

  Parameters:
    `newTrainPercent` - Value to assign to TrainPercent.

### splitEvaluatorTipText

    public java.lang.String splitEvaluatorTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getSplitEvaluator

    public SplitEvaluator getSplitEvaluator()

  Get the SplitEvaluator.

  Returns:
    the SplitEvaluator.

### setSplitEvaluator

    public void setSplitEvaluator(SplitEvaluator newSplitEvaluator)

  Set the SplitEvaluator.

  Parameters:
    `newSplitEvaluator` - new SplitEvaluator to use.

### listOptions

    public java.util.Enumeration 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(java.lang.String[] options)
                    throws java.lang.Exception

  Parses a given list of options.

  Valid options are:

       -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 split evaluator.

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

### getOptions

    public java.lang.String[] getOptions()

  Gets the current settings of the result producer.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Returns:
    an array of strings suitable for passing to setOptions

### toString

    public java.lang.String toString()

  Gets a text descrption of the result producer.

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    a text description of the result producer.

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Returns:
    the revision

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