pairedttester.java

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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 2 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, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    PairedTTester.java
 *    Copyright (C) 1999 Len Trigg
 *    Modified by Prem Melville
 *
 */


package weka.experiment;

import weka.core.*;
import weka.core.Instances;
import weka.core.Instance;
import weka.core.Range;
import weka.core.Attribute;
import weka.core.Utils;
import weka.core.FastVector;
import weka.core.Statistics;
import weka.core.OptionHandler;
import java.io.BufferedReader;
import java.io.FileReader;
import java.util.Date;
import java.text.SimpleDateFormat;
import java.util.Enumeration;
import java.util.Vector;
import java.util.*;
import weka.core.Option;

/**
 * Calculates T-Test statistics on data stored in a set of instances.<p>
 *
 * Valid options from the command-line are:<p>
 *
 * -D num,num2... <br>
 * The column numbers that uniquely specify a dataset.
 * (default last) <p>
 *
 * -R num <br>
 * The column number containing the run number.
 * (default last) <p>
 *
 * -S num <br>
 * The significance level for T-Tests.
 * (default 0.05) <p>
 *
 * -R num,num2... <br>
 * The column numbers that uniquely specify one result generator (eg:
 * scheme name plus options).
 * (default last) <p>
 *
 * @author Len Trigg ([e-mail])
 * @version $Revision: 1.7 $
 */
public class PairedTTester implements OptionHandler {

  /** The set of instances we will analyse */
  protected Instances m_Instances;

  /** The index of the column containing the run number */
  protected int m_RunColumn = 0;

  /** The option setting for the run number column (-1 means last) */
  protected int m_RunColumnSet = -1;

  /** The significance level for comparisons */
  protected double m_SignificanceLevel = 0.05;

  /**
   * The range of columns that specify a unique "dataset"
   * (eg: scheme plus configuration)
   */
  protected Range m_DatasetKeyColumnsRange = new Range();

  /** An array containing the indexes of just the selected columns */ 
  protected int [] m_DatasetKeyColumns;

  /** The list of dataset specifiers */
  protected DatasetSpecifiers m_DatasetSpecifiers = 
    new DatasetSpecifiers();

  /**
   * The range of columns that specify a unique result set
   * (eg: scheme plus configuration)
   */
  protected Range m_ResultsetKeyColumnsRange = new Range();

  /** An array containing the indexes of just the selected columns */ 
  protected int [] m_ResultsetKeyColumns;

  /** Stores a vector for each resultset holding all instances in each set */
  protected FastVector m_Resultsets = new FastVector();

  /** Indicates whether the instances have been partitioned */
  protected boolean m_ResultsetsValid;

  /** Indicates whether standard deviations should be displayed */
  protected boolean m_ShowStdDevs = false;

  /** Produce tables in latex format */
  protected boolean m_latexOutput = false;
      
    //=============== BEGIN EDIT melville ===============
    /** Precision desired - number of decimal places */
    protected int m_Precision = 2;
    
    /** Flag to indicate whether learning curves are to be produces */
    protected boolean m_LearningCurve = false;
    
    /** Flag to indicate whether learning curves are specified by fraction */
    protected boolean m_Fraction = false;
    
    /** Points on the learning curve */
    protected double [] m_Points;
    //=============== END EDIT melville ===============
    
  /* A list of unique "dataset" specifiers that have been observed */
  private class DatasetSpecifiers {

    FastVector m_Specifiers = new FastVector();

    /**
     * Removes all specifiers.
     */
    protected void removeAllSpecifiers() {

      m_Specifiers.removeAllElements();
    }

    /** 
     * Add an instance to the list of specifiers (if necessary)
     */
    protected void add(Instance inst) {
      
      for (int i = 0; i < m_Specifiers.size(); i++) {
	Instance specifier = (Instance)m_Specifiers.elementAt(i);
	boolean found = true;
	for (int j = 0; j < m_DatasetKeyColumns.length; j++) {
	  if (inst.value(m_DatasetKeyColumns[j]) !=
	      specifier.value(m_DatasetKeyColumns[j])) {
	    found = false;
	  }
	}
	if (found) {
	  return;
	}
      }
      m_Specifiers.addElement(inst);
    }

    /**
     * Get the template at the given position.
     */
    protected Instance specifier(int i) {

      return (Instance)m_Specifiers.elementAt(i);
    }

    /**
     * Gets the number of specifiers.
     */
    protected int numSpecifiers() {

      return m_Specifiers.size();
    }
  }

  /* Utility class to store the instances pertaining to a dataset */
  private class Dataset {

    Instance m_Template;
    FastVector m_Dataset;

    public Dataset(Instance template) {

      m_Template = template;
      m_Dataset = new FastVector();
      add(template);
    }
    
    /**
     * Returns true if the two instances match on those attributes that have
     * been designated key columns (eg: scheme name and scheme options)
     *
     * @param first the first instance
     * @param second the second instance
     * @return true if first and second match on the currently set key columns
     */
    protected boolean matchesTemplate(Instance first) {
      
      for (int i = 0; i < m_DatasetKeyColumns.length; i++) {
	if (first.value(m_DatasetKeyColumns[i]) !=
	    m_Template.value(m_DatasetKeyColumns[i])) {
	  return false;
	}
      }
      return true;
    }

    /**
     * Adds the given instance to the dataset
     */
    protected void add(Instance inst) {
      
      m_Dataset.addElement(inst);
    }

    /**
     * Returns a vector containing the instances in the dataset
     */
    protected FastVector contents() {

      return m_Dataset;
    }

    /**
     * Sorts the instances in the dataset by the run number.
     *
     * @param runColumn a value of type 'int'
     */
    public void sort(int runColumn) {

      double [] runNums = new double [m_Dataset.size()];
      for (int j = 0; j < runNums.length; j++) {
	runNums[j] = ((Instance) m_Dataset.elementAt(j)).value(runColumn);
      }
      int [] index = Utils.sort(runNums);
      FastVector newDataset = new FastVector(runNums.length);
      for (int j = 0; j < index.length; j++) {
	newDataset.addElement(m_Dataset.elementAt(index[j]));
      }
      m_Dataset = newDataset;
    }
  }
 
  /* Utility class to store the instances in a resultset */
  private class Resultset {

    Instance m_Template;
    FastVector m_Datasets;

    public Resultset(Instance template) {

      m_Template = template;
      m_Datasets = new FastVector();
      add(template);
    }
    
    /**
     * Returns true if the two instances match on those attributes that have
     * been designated key columns (eg: scheme name and scheme options)
     *
     * @param first the first instance
     * @param second the second instance
     * @return true if first and second match on the currently set key columns
     */
    protected boolean matchesTemplate(Instance first) {
      
      for (int i = 0; i < m_ResultsetKeyColumns.length; i++) {
	if (first.value(m_ResultsetKeyColumns[i]) !=
	    m_Template.value(m_ResultsetKeyColumns[i])) {
	  return false;
	}
      }
      return true;
    }

    /**
     * Returns a string descriptive of the resultset key column values
     * for this resultset
     *
     * @return a value of type 'String'
     */
    protected String templateString() {

      String result = "";
      String tempResult = "";
      for (int i = 0; i < m_ResultsetKeyColumns.length; i++) {
	tempResult = m_Template.toString(m_ResultsetKeyColumns[i]) + ' ';

	// compact the string
        tempResult = Utils.removeSubstring(tempResult, "weka.classifiers.");
        tempResult = Utils.removeSubstring(tempResult, "weka.filters.");
        tempResult = Utils.removeSubstring(tempResult, "weka.attributeSelection.");
	result += tempResult;
      }
      return result.trim();
    }
    
    /**
     * Returns a vector containing all instances belonging to one dataset.
     *
     * @param index a template instance
     * @return a value of type 'FastVector'
     */
    public FastVector dataset(Instance inst) {

      for (int i = 0; i < m_Datasets.size(); i++) {
	if (((Dataset)m_Datasets.elementAt(i)).matchesTemplate(inst)) {
	  return ((Dataset)m_Datasets.elementAt(i)).contents();
	} 
      }
      return null;
    }
    
    /**
     * Adds an instance to this resultset
     *
     * @param newInst a value of type 'Instance'
     */
    public void add(Instance newInst) {
      
      for (int i = 0; i < m_Datasets.size(); i++) {
	if (((Dataset)m_Datasets.elementAt(i)).matchesTemplate(newInst)) {
	  ((Dataset)m_Datasets.elementAt(i)).add(newInst);
	  return;
	}
      }
      Dataset newDataset = new Dataset(newInst);
      m_Datasets.addElement(newDataset);
    }

    /**
     * Sorts the instances in each dataset by the run number.
     *
     * @param runColumn a value of type 'int'
     */
    public void sort(int runColumn) {

      for (int i = 0; i < m_Datasets.size(); i++) {
	((Dataset)m_Datasets.elementAt(i)).sort(runColumn);
      }
    }
  } // Resultset


  /**
   * Returns a string descriptive of the key column values for
   * the "datasets
   *
   * @param template the template
   * @return a value of type 'String'
   */
  private String templateString(Instance template) {
    
    String result = "";
    for (int i = 0; i < m_DatasetKeyColumns.length; i++) {
      result += template.toString(m_DatasetKeyColumns[i]) + ' ';
    }
    if (result.startsWith("weka.classifiers.")) {
      result = result.substring("weka.classifiers.".length());
    }
    return result.trim();
  }

  /**
   * Set whether latex is output
   * @param l true if tables are to be produced in Latex format
   */
  public void setProduceLatex(boolean l) {
    m_latexOutput = l;
  }

  /**
   * Get whether latex is output
   * @return true if Latex is to be output
   */
  public boolean getProduceLatex() {
    return m_latexOutput;
  }

  /**
   * Set whether standard deviations are displayed or not.
   * @param s true if standard deviations are to be displayed
   */
  public void setShowStdDevs(boolean s) {
    m_ShowStdDevs = s;
  }