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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.
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/*
 *   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 3 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, see .
 */

/*
 *    ResultProducer.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */


package weka.experiment;

import java.io.Serializable;

import weka.core.Instances;

/**
 * This interface defines the methods required for an object 
 * that produces results for different randomizations of a dataset. 
 *
 * Possible implementations of ResultProducer: 

 * 

 *   Random test/train splits
 *   
CrossValidation splits
 *   
LearningCurve splits (multiple results per run?)
 *   
Averaging results of other result producers
* 
 *
 * @author Len Trigg ([email protected])
 * @version $Revision: 8034 $
 */

public interface ResultProducer extends Serializable {
  
  /**
   * Sets the dataset that results will be obtained for.
   *
   * @param instances a value of type 'Instances'.
   */
  void setInstances(Instances instances);

  /**
   * Sets the object to send results of each run to.
   *
   * @param listener a value of type 'ResultListener'
   */
  void setResultListener(ResultListener listener);

  /**
   * Sets a list of method names for additional measures to look for
   * in SplitEvaluators.
   * @param additionalMeasures a list of method names
   */
  void setAdditionalMeasures(String [] additionalMeasures);

  /**
   * Prepare to generate results. The ResultProducer should call
   * preProcess(this) on the ResultListener it is to send results to.
   *
   * @exception Exception if an error occurs during preprocessing.
   */
  void preProcess() throws 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. The ResultProducer should call
   * preProcess(this) on the ResultListener it is to send results to.
   *
   * @exception Exception if an error occurs
   */
  void postProcess() throws 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, but only
   * if the ResultListener says the result is required (it may already
   * have that result). A single run may produce multiple results.
   *
   * @param run the run number to generate results for.
   * @exception Exception if a problem occurs while getting the results
   */
  void doRun(int run) throws 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
   *
   * @param run the run number to get keys for.
   * @exception Exception if a problem occurs while getting the keys
   */
  void doRunKeys(int run) throws Exception;

  /**
   * Gets the names of each of the key columns produced for a single run.
   * The names should not contain spaces (use '_' instead for easy 
   * translation.)
   *
   * @return an array containing the name of each key column
   * @exception Exception if the key names could not be determined (perhaps
   * because of a problem from a nested sub-resultproducer)
   */
  String [] getKeyNames() throws Exception;

  /**
   * Gets the data types of each of the key columns produced for a single run.
   *
   * @return an array containing objects of the type of each key column. The 
   * objects should be Strings, or Doubles.
   * @exception Exception if the key types could not be determined (perhaps
   * because of a problem from a nested sub-resultproducer)
   */
  Object [] getKeyTypes() throws Exception;

  /**
   * Gets the names of each of the result columns produced for a single run.
   * The names should not contain spaces (use '_' instead for easy 
   * translation.)
   *
   * @return an array containing the name of each result column
   * @exception Exception if the result names could not be determined (perhaps
   * because of a problem from a nested sub-resultproducer)
   */
  String [] getResultNames() throws Exception;

  /**
   * Gets the data types of each of the result columns produced for a 
   * single run.
   *
   * @return an array containing objects of the type of each result column. 
   * The objects should be Strings, or Doubles.
   * @exception Exception if the result types could not be determined (perhaps
   * because of a problem from a nested sub-resultproducer)
   */
  Object [] getResultTypes() throws Exception;

  /**
   * 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 those command line arguments required
   * to set the ResultProducer to that state.
   *
   * @return the description of the ResultProducer state, or null
   * if no state is defined
   */
  String getCompatibilityState();

} // ResultProducer