principalcomponents.java

wekaUT是 university texas austin 开发的基于weka的半指导学习(semi supervised learning)的分类器

/*
 *    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.
 */

/*
 *    PrincipalComponents.java
 *    Copyright (C) 2000 Mark Hall
 *
 */

package weka.attributeSelection;

import  java.io.*;
import  java.util.*;
import  weka.core.*;
import  weka.filters.unsupervised.attribute.ReplaceMissingValues;
import  weka.filters.unsupervised.attribute.Normalize;
import  weka.filters.unsupervised.attribute.NominalToBinary;
import  weka.filters.unsupervised.attribute.Remove;
import  weka.filters.Filter;

/**
 * Class for performing principal components analysis/transformation. <p>
 *
 * Valid options are:<p>
 * -N <br>
 * Don't normalize the input data. <p>
 *
 * -R <variance> <br>
 * Retain enough pcs to account for this proportion of the variance. <p>
 *
 * -T <br>
 * Transform through the PC space and back to the original space. <p>
 *
 * @author Mark Hall ([e-mail])
 * @author Gabi Schmidberger ([e-mail])
 * @version $Revision: 1.1.1.1 $
 */
public class PrincipalComponents extends UnsupervisedAttributeEvaluator 
  implements AttributeTransformer, OptionHandler {
  
  /** The data to transform analyse/transform */
  private Instances m_trainInstances;

  /** Keep a copy for the class attribute (if set) */
  private Instances m_trainCopy;

  /** The header for the transformed data format */
  private Instances m_transformedFormat;

  /** The header for data transformed back to the original space */
  private Instances m_originalSpaceFormat;

  /** Data has a class set */
  private boolean m_hasClass;

  /** Class index */
  private int m_classIndex;

  /** Number of attributes */
  private int m_numAttribs;

  /** Number of instances */
  private int m_numInstances;

  /** Correlation matrix for the original data */
  private double [][] m_correlation;

  /** Will hold the unordered linear transformations of the (normalized)
      original data */
  private double [][] m_eigenvectors;
  
  /** Eigenvalues for the corresponding eigenvectors */
  private double [] m_eigenvalues = null;

  /** Sorted eigenvalues */
  private int [] m_sortedEigens;

  /** sum of the eigenvalues */
  private double m_sumOfEigenValues = 0.0;
  
  /** Filters for original data */
  private ReplaceMissingValues m_replaceMissingFilter;
  private Normalize m_normalizeFilter;
  private NominalToBinary m_nominalToBinFilter;
  private Remove m_attributeFilter;
  
  /** used to remove the class column if a class column is set */
  private Remove m_attribFilter;

  /** The number of attributes in the pc transformed data */
  private int m_outputNumAtts = -1;
  
  /** normalize the input data? */
  private boolean m_normalize = true;

  /** the amount of varaince to cover in the original data when
      retaining the best n PC's */
  private double m_coverVariance = 0.95;

  /** transform the data through the pc space and back to the original
      space ? */
  private boolean m_transBackToOriginal = false;

  /** holds the transposed eigenvectors for converting back to the
      original space */
  private double [][] m_eTranspose;

  /**
   * Returns a string describing this attribute transformer
   * @return a description of the evaluator suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return "Performs a principal components analysis and transformation of "
      +"the data. Use in conjunction with a Ranker search. Dimensionality "
      +"reduction is accomplished by choosing enough eigenvectors to "
      +"account for some percentage of the variance in the original data---"
      +"default 0.95 (95%). Attribute noise can be filtered by transforming "
      +"to the PC space, eliminating some of the worst eigenvectors, and "
      +"then transforming back to the original space.";
  }

  /**
   * Returns an enumeration describing the available options. <p>
   *
   * @return an enumeration of all the available options.
   **/
  public Enumeration listOptions () {
    Vector newVector = new Vector(3);
    newVector.addElement(new Option("\tDon't normalize input data." 
				    , "D", 0, "-D"));

    newVector.addElement(new Option("\tRetain enough PC attributes to account "
				    +"\n\tfor this proportion of variance in "
				    +"the original data. (default = 0.95)",
				    "R",1,"-R"));
    
    newVector.addElement(new Option("\tTransform through the PC space and "
				    +"\n\tback to the original space."
				    , "O", 0, "-O"));
    return  newVector.elements();
  }

  /**
   * Parses a given list of options.
   *
   * Valid options are:<p>
   * -N <br>
   * Don't normalize the input data. <p>
   *
   * -R <variance> <br>
   * Retain enough pcs to account for this proportion of the variance. <p>
   *
   * -T <br>
   * Transform through the PC space and back to the original space. <p>
   *
   * @param options the list of options as an array of strings
   * @exception Exception if an option is not supported
   */
  public void setOptions (String[] options)
    throws Exception {
    resetOptions();
    String optionString;

    optionString = Utils.getOption('R', options);
    if (optionString.length() != 0) {
      Double temp;
      temp = Double.valueOf(optionString);
      setVarianceCovered(temp.doubleValue());
    }
    setNormalize(!Utils.getFlag('D', options));

    setTransformBackToOriginal(Utils.getFlag('O', options));
  }

  /**
   * Reset to defaults
   */
  private void resetOptions() {
    m_coverVariance = 0.95;
    m_normalize = true;
    m_sumOfEigenValues = 0.0;
    m_transBackToOriginal = false;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String normalizeTipText() {
    return "Normalize input data.";
  }

  /**
   * Set whether input data will be normalized.
   * @param n true if input data is to be normalized
   */
  public void setNormalize(boolean n) {
    m_normalize = n;
  }

  /**
   * Gets whether or not input data is to be normalized
   * @return true if input data is to be normalized
   */
  public boolean getNormalize() {
    return m_normalize;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String varianceCoveredTipText() {
    return "Retain enough PC attributes to account for this proportion of "
      +"variance.";
  }

  /**
   * Sets the amount of variance to account for when retaining
   * principal components
   * @param vc the proportion of total variance to account for
   */
  public void setVarianceCovered(double vc) {
    m_coverVariance = vc;
  }

  /**
   * Gets the proportion of total variance to account for when
   * retaining principal components
   * @return the proportion of variance to account for
   */
  public double getVarianceCovered() {
    return m_coverVariance;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String transformBackToOriginalTipText() {
    return "Transform through the PC space and back to the original space. "
      +"If only the best n PCs are retained (by setting varianceCovered < 1) "
      +"then this option will give a dataset in the original space but with "
      +"less attribute noise.";
  }

  /**
   * Sets whether the data should be transformed back to the original
   * space
   * @param b true if the data should be transformed back to the
   * original space
   */
  public void setTransformBackToOriginal(boolean b) {
    m_transBackToOriginal = b;
  }
  
  /**
   * Gets whether the data is to be transformed back to the original
   * space.
   * @return true if the data is to be transformed back to the original space
   */
  public boolean getTransformBackToOriginal() {
    return m_transBackToOriginal;
  }

  /**
   * Gets the current settings of PrincipalComponents
   *
   * @return an array of strings suitable for passing to setOptions()
   */
  public String[] getOptions () {

    String[] options = new String[4];
    int current = 0;

    if (!getNormalize()) {
      options[current++] = "-D";
    }

    options[current++] = "-R"; options[current++] = ""+getVarianceCovered();

    if (getTransformBackToOriginal()) {
      options[current++] = "-O";
    }
    
    while (current < options.length) {
      options[current++] = "";
    }
    
    return  options;
  }

  /**
   * Initializes principal components and performs the analysis
   * @param data the instances to analyse/transform
   * @exception Exception if analysis fails
   */
  public void buildEvaluator(Instances data) throws Exception {
    buildAttributeConstructor(data);
  }

  private void buildAttributeConstructor (Instances data) throws Exception {
    m_eigenvalues = null;
    m_outputNumAtts = -1;
    m_attributeFilter = null;
    m_nominalToBinFilter = null;
    m_sumOfEigenValues = 0.0;

    if (data.checkForStringAttributes()) {
      throw  new UnsupportedAttributeTypeException("Can't handle string attributes!");
    }
    m_trainInstances = data;

    // make a copy of the training data so that we can get the class
    // column to append to the transformed data (if necessary)
    m_trainCopy = new Instances(m_trainInstances);
    
    m_replaceMissingFilter = new ReplaceMissingValues();
    m_replaceMissingFilter.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, 
					m_replaceMissingFilter);

    if (m_normalize) {
      m_normalizeFilter = new Normalize();
      m_normalizeFilter.setInputFormat(m_trainInstances);
      m_trainInstances = Filter.useFilter(m_trainInstances, m_normalizeFilter);
    }

    m_nominalToBinFilter = new NominalToBinary();
    m_nominalToBinFilter.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, 
					m_nominalToBinFilter);
    
    // delete any attributes with only one distinct value or are all missing
    Vector deleteCols = new Vector();
    for (int i=0;i<m_trainInstances.numAttributes();i++) {
      if (m_trainInstances.numDistinctValues(i) <=1) {
	deleteCols.addElement(new Integer(i));
      }
    }

    if (m_trainInstances.classIndex() >=0) {
      // get rid of the class column
      m_hasClass = true;
      m_classIndex = m_trainInstances.classIndex();
      deleteCols.addElement(new Integer(m_classIndex));
    }

    // remove columns from the data if necessary
    if (deleteCols.size() > 0) {
      m_attributeFilter = new Remove();
      int [] todelete = new int [deleteCols.size()];
      for (int i=0;i<deleteCols.size();i++) {
	todelete[i] = ((Integer)(deleteCols.elementAt(i))).intValue();
      }
      m_attributeFilter.setAttributeIndicesArray(todelete);
      m_attributeFilter.setInvertSelection(false);
      m_attributeFilter.setInputFormat(m_trainInstances);
      m_trainInstances = Filter.useFilter(m_trainInstances, m_attributeFilter);
    }

    m_numInstances = m_trainInstances.numInstances();
    m_numAttribs = m_trainInstances.numAttributes();

    fillCorrelation();

    double [] d = new double[m_numAttribs]; 
    double [][] v = new double[m_numAttribs][m_numAttribs];


    Matrix corr = new Matrix(m_correlation);
    corr.eigenvalueDecomposition(v, d);
    //if (debug) {
    //  Matrix V = new Matrix(v);
    //  boolean b = corr.testEigen(V, d, true);
    //  if (!b)
    //	System.out.println("Problem with eigenvektors!!!");
    //  else
    //	System.out.println("***** everything's fine !!!");
    //  }
    
    m_eigenvectors = (double [][])v.clone();
    m_eigenvalues = (double [])d.clone();

    // any eigenvalues less than 0 are not worth anything --- change to 0
    for (int i = 0; i < m_eigenvalues.length; i++) {
      if (m_eigenvalues[i] < 0) {
	m_eigenvalues[i] = 0.0;
      }
    }
    m_sortedEigens = Utils.sort(m_eigenvalues);
    m_sumOfEigenValues = Utils.sum(m_eigenvalues);

    m_transformedFormat = setOutputFormat();
    if (m_transBackToOriginal) {
      m_originalSpaceFormat = setOutputFormatOriginal();
      
      // new ordered eigenvector matrix
      int numVectors = (m_transformedFormat.classIndex() < 0) 
	? m_transformedFormat.numAttributes()
	: m_transformedFormat.numAttributes() - 1;