pairedttester.java

Java 编写的多种数据挖掘算法 包括聚类、分类、预处理等

/*
 *    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
 *
 */


package weka.experiment;

import weka.core.Attribute;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Range;
import weka.core.Utils;

import java.io.BufferedReader;
import java.io.FileReader;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.Enumeration;
import java.util.Vector;

/**
 * Calculates T-Test statistics on data stored in a set of instances. <p/>
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -D &lt;index,index2-index4,...&gt;
 *  Specify list of columns that specify a unique
 *  dataset.
 *  First and last are valid indexes. (default none)</pre>
 * 
 * <pre> -R &lt;index&gt;
 *  Set the index of the column containing the run number</pre>
 * 
 * <pre> -F &lt;index&gt;
 *  Set the index of the column containing the fold number</pre>
 * 
 * <pre> -G &lt;index1,index2-index4,...&gt;
 *  Specify list of columns that specify a unique
 *  'result generator' (eg: classifier name and options).
 *  First and last are valid indexes. (default none)</pre>
 * 
 * <pre> -S &lt;significance level&gt;
 *  Set the significance level for comparisons (default 0.05)</pre>
 * 
 * <pre> -V
 *  Show standard deviations</pre>
 * 
 * <pre> -L
 *  Produce table comparisons in Latex table format</pre>
 * 
 * <pre> -csv
 *  Produce table comparisons in CSV table format</pre>
 * 
 * <pre> -html
 *  Produce table comparisons in HTML table format</pre>
 * 
 * <pre> -significance
 *  Produce table comparisons with only the significance values</pre>
 * 
 * <pre> -gnuplot
 *  Produce table comparisons output suitable for GNUPlot</pre>
 * 
 <!-- options-end -->
 *
 * @author Len Trigg ([e-mail])
 * @version $Revision: 1.33 $
 */
public class PairedTTester 
  implements OptionHandler, Tester {
  
  /** for serialization */
  static final long serialVersionUID = 8370014624008728610L;

  /** 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 option setting for the fold number column (-1 means none) */
  protected int m_FoldColumn = -1;

  /** The column to sort on (-1 means default sorting) */
  protected int m_SortColumn = -1;

  /** The sorting of the datasets (according to the sort column) */
  protected int[] m_SortOrder = null;

  /** The sorting of the columns (test base is always first) */
  protected int[] m_ColOrder = null;

  /** 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;

  /** An array containing the indexes of the datasets to display */
  protected int[] m_DisplayedResultsets = null;

  /** 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;
  
  /** the instance of the class to produce the output. */
  protected ResultMatrix m_ResultMatrix = new ResultMatrixPlainText();
  
  /** A list of unique "dataset" specifiers that have been observed */
  protected class DatasetSpecifiers {

    /** the specifiers that have been observed */
    FastVector m_Specifiers = new FastVector();

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

      m_Specifiers.removeAllElements();
    }

    /** 
     * Add an instance to the list of specifiers (if necessary)
     * 
     * @param inst	the instance to add
     */
    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.
     * 
     * @param i		the index
     * @return		the template
     */
    protected Instance specifier(int i) {

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

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

      return m_Specifiers.size();
    }
  }

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

    /** the template */
    Instance m_Template;

    /** the dataset */
    FastVector m_Dataset;

    /**
     * Constructor
     * 
     * @param template	the template
     */
    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
     * @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
     * 
     * @param inst	the instance to add
     */
    protected void add(Instance inst) {
      
      m_Dataset.addElement(inst);
    }

    /**
     * Returns a vector containing the instances in the dataset
     * 
     * @return 		the current contents
     */
    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.stableSort(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 */
  protected class Resultset {

    /** the template */
    Instance m_Template;
    
    /** the dataset */
    FastVector m_Datasets;

    /**
     * Constructir
     * 
     * @param template		the template
     */
    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
     * @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 inst 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'
   */
  protected 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();
  }

  /**
   * Sets the matrix to use to produce the output.
   * @param matrix the instance to use to produce the output
   * @see ResultMatrix
   */
  public void setResultMatrix(ResultMatrix matrix) {
    m_ResultMatrix = matrix;
  }

  /**
   * Gets the instance that produces the output.
   * @return the instance to produce the output
   */
  public ResultMatrix getResultMatrix() {
    return m_ResultMatrix;
  }

  /**
   * 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;
  }

  /**
   * Returns true if standard deviations have been requested.
   * @return true if standard deviations are to be displayed.
   */
  public boolean getShowStdDevs() {
    return m_ShowStdDevs;
  }
  
  /**
   * Separates the instances into resultsets and by dataset/run.
   *
   * @throws Exception if the TTest parameters have not been set.
   */
  protected void prepareData() throws Exception {

    if (m_Instances == null) {
      throw new Exception("No instances have been set");
    }
    if (m_RunColumnSet == -1) {
      m_RunColumn = m_Instances.numAttributes() - 1;
    } else {
      m_RunColumn = m_RunColumnSet;
    }

    if (m_ResultsetKeyColumnsRange == null) {
      throw new Exception("No result specifier columns have been set");
    }
    m_ResultsetKeyColumnsRange.setUpper(m_Instances.numAttributes() - 1);
    m_ResultsetKeyColumns = m_ResultsetKeyColumnsRange.getSelection();

    if (m_DatasetKeyColumnsRange == null) {
      throw new Exception("No dataset specifier columns have been set");
    }
    m_DatasetKeyColumnsRange.setUpper(m_Instances.numAttributes() - 1);
    m_DatasetKeyColumns = m_DatasetKeyColumnsRange.getSelection();
    
    //  Split the data up into result sets
    m_Resultsets.removeAllElements();  
    m_DatasetSpecifiers.removeAllSpecifiers();
    for (int i = 0; i < m_Instances.numInstances(); i++) {
      Instance current = m_Instances.instance(i);
      if (current.isMissing(m_RunColumn)) {
	throw new Exception("Instance has missing value in run "
			    + "column!\n" + current);
      } 
      for (int j = 0; j < m_ResultsetKeyColumns.length; j++) {
	if (current.isMissing(m_ResultsetKeyColumns[j])) {
	  throw new Exception("Instance has missing value in resultset key "