weka.attributeSelection.HoldOutSubsetEvaluator Maven / Gradle / Ivy

Go to download

Show more of this group Show more artifacts with this name
Show all versions of weka-dev Show documentation

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.

There is a newer version: 3.9.6

Show newest version

/*
 *   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 .
 */

/*
 *    HoldOutSubsetEvaluator.java
 *    Copyright (C) 2000-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.attributeSelection;

import java.util.BitSet;

import weka.core.Instance;
import weka.core.Instances;

/** 
 * Abstract attribute subset evaluator capable of evaluating subsets with
 * respect to a data set that is distinct from that used to initialize/
 * train the subset evaluator.
 *
 * @author Mark Hall ([email protected])
 * @version $Revision: 8034 $
 */
public abstract class HoldOutSubsetEvaluator 
  extends ASEvaluation
  implements SubsetEvaluator {

  /** for serialization */
  private static final long serialVersionUID = 8280529785412054174L;
  
  /**
   * Evaluates a subset of attributes with respect to a set of instances.
   * @param subset a bitset representing the attribute subset to be
   * evaluated
   * @param holdOut a set of instances (possibly seperate and distinct
   * from those use to build/train the evaluator) with which to
   * evaluate the merit of the subset
   * @return the "merit" of the subset on the holdOut data
   * @exception Exception if the subset cannot be evaluated
   */
  public abstract double evaluateSubset(BitSet subset, Instances holdOut)
    throws Exception;

  /**
   * Evaluates a subset of attributes with respect to a single instance.
   * @param subset a bitset representing the attribute subset to be
   * evaluated
   * @param holdOut a single instance (possibly not one of those used to
   * build/train the evaluator) with which to evaluate the merit of the subset
   * @param retrain true if the classifier should be retrained with respect
   * to the new subset before testing on the holdOut instance.
   * @return the "merit" of the subset on the holdOut instance
   * @exception Exception if the subset cannot be evaluated
   */
  public abstract double evaluateSubset(BitSet subset, 
					Instance holdOut,
					boolean retrain)
    throws Exception;
}