Package weka.estimators

Class CheckEstimator

java.lang.Object
weka.estimators.CheckEstimator
All Implemented Interfaces:
OptionHandler, RevisionHandler
public class CheckEstimator extends Object implements OptionHandler, RevisionHandler
Class for examining the capabilities and finding problems with estimators. If you implement a estimator using the WEKA.libraries, you should run the checks on it to ensure robustness and correct operation. Passing all the tests of this object does not mean bugs in the estimator don't exist, but this will help find some common ones.

Typical usage:

java weka.estimators.CheckEstimator -W estimator_name estimator_options

This class uses code from the CheckEstimatorClass ATTENTION! Current estimators can only 1. split on a nominal class attribute 2. build estimators for nominal and numeric attributes 3. build estimators independendly of the class type The functionality to test on other class and attribute types is left in big parts in the code. CheckEstimator reports on the following:

  • Estimator abilities
    • Possible command line options to the estimator
    • Whether the estimator can predict nominal, numeric, string, date or relational class attributes. Warnings will be displayed if performance is worse than ZeroR
    • Whether the estimator can be trained incrementally
    • Whether the estimator can build estimates for numeric attributes
    • Whether the estimator can handle nominal attributes
    • Whether the estimator can handle string attributes
    • Whether the estimator can handle date attributes
    • Whether the estimator can handle relational attributes
    • Whether the estimator build estimates for multi-instance data
    • Whether the estimator can handle missing attribute values
    • Whether the estimator can handle missing class values
    • Whether a nominal estimator only handles 2 class problems
    • Whether the estimator can handle instance weights
  • Correct functioning
    • Correct initialisation during addvalues (i.e. no result changes when addValues called repeatedly)
    • Whether incremental training produces the same results as during non-incremental training (which may or may not be OK)
    • Whether the estimator alters the data pased to it (number of instances, instance order, instance weights, etc)
  • Degenerate cases
    • building estimator with zero training instances
    • all but one attribute attribute values missing
    • all attribute attribute values missing
    • all but one class values missing
    • all class values missing
Running CheckEstimator with the debug option set will output the training and test datasets for any failed tests.

The weka.estimators.AbstractEstimatorTest uses this class to test all the estimators. Any changes here, have to be checked in that abstract test class, too.

Valid options are: 

 -D
  Turn on debugging output.
 
 -S
  Silent mode - prints nothing to stdout.
 
 -N <num>
  The number of instances in the datasets (default 100).
 
 -W
  Full name of the estimator analysed.
  eg: weka.estimators.NormalEstimator
 
 Options specific to estimator weka.estimators.NormalEstimator:
 
 -D
  If set, estimator is run in debug mode and
  may output additional info to the console
Options after -- are passed to the designated estimator.

Version:
$Revision: 15521 $
Author:
Len Trigg (trigg@cs.waikato.ac.nz), FracPete (fracpete at waikato dot ac dot nz)
See Also:

  • Constructor Details

    • CheckEstimator

      public CheckEstimator()

  • Method Details

    • 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: 

       -D
  Turn on debugging output.
 
       -S
  Silent mode - prints nothing to stdout.
 
       -N <num>
  The number of instances in the datasets (default 100).
 
       -W
  Full name of the estimator analysed.
  eg: weka.estimators.NormalEstimator
 
       Options specific to estimator weka.estimators.NormalEstimator:
 
       -D
  If set, estimator is run in debug mode and
  may output additional info to the console
      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 CheckEstimator.
      Specified by:
      getOptions in interface OptionHandler
      Returns:
      an array of strings suitable for passing to setOptions

    • setPostProcessor

      public void setPostProcessor(CheckEstimator.PostProcessor value)
      sets the PostProcessor to use
      Parameters:
      value - the new PostProcessor
      See Also:
      • m_PostProcessor

    • getPostProcessor

      public CheckEstimator.PostProcessor getPostProcessor()
      returns the current PostProcessor, can be null
      Returns:
      the current PostProcessor

    • hasClasspathProblems

      public boolean hasClasspathProblems()
      returns TRUE if the estimator returned a "not in classpath" Exception
      Returns:
      true if CLASSPATH problems occurred

    • doTests

      public void doTests()
      Begin the tests, reporting results to System.out

    • setDebug

      public void setDebug(boolean debug)
      Set debugging mode
      Parameters:
      debug - true if debug output should be printed

    • getDebug

      public boolean getDebug()
      Get whether debugging is turned on
      Returns:
      true if debugging output is on

    • setSilent

      public void setSilent(boolean value)
      Set slient mode, i.e., no output at all to stdout
      Parameters:
      value - whether silent mode is active or not

    • getSilent

      public boolean getSilent()
      Get whether silent mode is turned on
      Returns:
      true if silent mode is on

    • setNumInstances

      public void setNumInstances(int value)
      Sets the number of instances to use in the datasets (some estimators might require more instances).
      Parameters:
      value - the number of instances to use

    • getNumInstances

      public int getNumInstances()
      Gets the current number of instances to use for the datasets.
      Returns:
      the number of instances

    • setEstimator

      public void setEstimator(Estimator newEstimator)
      Set the estimator for boosting.
      Parameters:
      newEstimator - the Estimator to use.

    • getEstimator

      public Estimator getEstimator()
      Get the estimator used as the estimator
      Returns:
      the estimator used as the estimator

    • getMinMax

      public static int getMinMax(Instances inst, int attrIndex, double[] minMax) throws Exception
      Find the minimum and the maximum of the attribute and return it in the last parameter..
      Parameters:
      inst - instances used to build the estimator
      attrIndex - index of the attribute
      minMax - the array to return minimum and maximum in
      Returns:
      number of not missing values
      Throws:
      Exception - if parameter minMax wasn't initialized properly

    • getRevision

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

    • main

      public static void main(String[] args)
      Test method for this class
      Parameters:
      args - the commandline parameters