weka.attributeSelection.RankedOutputSearch Maven / Gradle / Ivy

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 .
 */

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


package weka.attributeSelection;


/** 
 * Interface for search methods capable of producing a
 * ranked list of attributes.
 *
 * @author Mark Hall ([email protected])
 * @version $Revision: 8034 $
 */
public interface RankedOutputSearch {


  // ===============
  // Public methods.
  // ===============
  
  /**
   * Returns a X by 2 list of attribute indexes and corresponding
   * evaluations from best (highest) to worst.
   * @return the ranked list of attribute indexes in an array of ints
   * @exception Exception if the ranking can't be produced
   */
  double[][] rankedAttributes() throws Exception;

  /**
   * Sets a threshold by which attributes can be discarded from the
   * ranking. This threshold is used by the AttributeSelection module
   * which does the actual discarding of attributes---the implementer
   * of this method needs only to provide a variable in which to store the
   * supplied threshold. -Double.MAX_VALUE is reserved to mean no threshold,
   * ie, retain all attributes.
   * @param threshold the threshold.
   */
  void setThreshold(double threshold);

  /**
   * Gets the threshold by which attributes can be discarded. Discarding
   * of attributes is done by the AttributeSelection module using the
   * threshold returned by this method.
   * @return a threshold by which to discard attributes
   */
  double getThreshold();

  /**
   * Specify the number of attributes to select from the ranked list. < 0
   * indicates that all attributes are to be retained. NumToSelect has
   * precedence over threshold, ie. if there is a non -1 value for NumToSelect
   * then this will take precedence over any threshold value.
   * @param numToSelect the number of attributes to retain
   */
  void setNumToSelect(int numToSelect);

  /**
   * Gets the user specified number of attributes to be retained.
   * @return the number of attributes to retain
   */
  int getNumToSelect();

  /**
   * Gets the calculated number of attributes to retain. This is the
   * actual number of attributes to retain. This is the same as
   * getNumToSelect if the user specifies a number which is not less
   * than zero. Otherwise it should be the number of attributes in the
   * (potentially transformed) data.
   */
  int getCalculatedNumToSelect();
  
  /**
   * Sets whether or not ranking is to be performed.
   * When a search method is capable of producing a ranked list
   * of attributes, the user has the choice of seeing the results of a
   * normal search or seeing a ranked list.
   * @param doRanking true if ranked list is to be produced
   */
  void setGenerateRanking(boolean doRanking);

  /**
   * Gets whether the user has opted to see a ranked list of
   * attributes rather than the normal result of the search
   * @return true if a ranked list has been requested.
   */
  boolean getGenerateRanking();

}