cfssubseteval.java

这是关于数据挖掘的一些算法

/*
 *    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.
 */

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

package  weka.attributeSelection;

import weka.core.Capabilities;
import weka.core.ContingencyTables;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.filters.Filter;
import weka.filters.supervised.attribute.Discretize;

import java.util.BitSet;
import java.util.Enumeration;
import java.util.Vector;

/** 
 <!-- globalinfo-start -->
 * CfsSubsetEval :<br/>
 * <br/>
 * Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them.<br/>
 * <br/>
 * Subsets of features that are highly correlated with the class while having low intercorrelation are preferred.<br/>
 * <br/>
 * For more information see:<br/>
 * <br/>
 * M. A. Hall (1998). Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;phdthesis{Hall1998,
 *    address = {Hamilton, New Zealand},
 *    author = {M. A. Hall},
 *    school = {University of Waikato},
 *    title = {Correlation-based Feature Subset Selection for Machine Learning},
 *    year = {1998}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -M
 *  Treat missing values as a seperate value.</pre>
 * 
 * <pre> -L
 *  Don't include locally predictive attributes.</pre>
 * 
 <!-- options-end -->
 *
 * @author Mark Hall ([e-mail])
 * @version $Revision: 1.26 $
 * @see Discretize
 */
public class CfsSubsetEval
  extends SubsetEvaluator
  implements OptionHandler, TechnicalInformationHandler {
  
  /** for serialization */
  static final long serialVersionUID = 747878400813276317L;

  /** The training instances */
  private Instances m_trainInstances;
  /** Discretise attributes when class in nominal */
  private Discretize m_disTransform;
  /** The class index */
  private int m_classIndex;
  /** Is the class numeric */
  private boolean m_isNumeric;
  /** Number of attributes in the training data */
  private int m_numAttribs;
  /** Number of instances in the training data */
  private int m_numInstances;
  /** Treat missing values as seperate values */
  private boolean m_missingSeperate;
  /** Include locally predicitive attributes */
  private boolean m_locallyPredictive;
  /** Holds the matrix of attribute correlations */
  //  private Matrix m_corr_matrix;
  private float [][] m_corr_matrix;
  /** Standard deviations of attributes (when using pearsons correlation) */
  private double[] m_std_devs;
  /** Threshold for admitting locally predictive features */
  private double m_c_Threshold;

  /**
   * Returns a string describing this attribute evaluator
   * @return a description of the evaluator suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return "CfsSubsetEval :\n\nEvaluates the worth of a subset of attributes "
      +"by considering the individual predictive ability of each feature "
      +"along with the degree of redundancy between them.\n\n"
      +"Subsets of features that are highly correlated with the class "
      +"while having low intercorrelation are preferred.\n\n"
      + "For more information see:\n\n"
      + getTechnicalInformation().toString();
  }

  /**
   * Returns an instance of a TechnicalInformation object, containing 
   * detailed information about the technical background of this class,
   * e.g., paper reference or book this class is based on.
   * 
   * @return the technical information about this class
   */
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation        result;
    
    result = new TechnicalInformation(Type.PHDTHESIS);
    result.setValue(Field.AUTHOR, "M. A. Hall");
    result.setValue(Field.YEAR, "1998");
    result.setValue(Field.TITLE, "Correlation-based Feature Subset Selection for Machine Learning");
    result.setValue(Field.SCHOOL, "University of Waikato");
    result.setValue(Field.ADDRESS, "Hamilton, New Zealand");
    
    return result;
  }

  /**
   * Constructor
   */
  public CfsSubsetEval () {
    resetOptions();
  }


  /**
   * Returns an enumeration describing the available options.
   * @return an enumeration of all the available options.
   *
   **/
  public Enumeration listOptions () {
    Vector newVector = new Vector(3);
    newVector.addElement(new Option("\tTreat missing values as a seperate " 
                                    + "value.", "M", 0, "-M"));
    newVector.addElement(new Option("\tDon't include locally predictive attributes" 
                                    + ".", "L", 0, "-L"));
    return  newVector.elements();
  }


  /**
   * Parses and sets a given list of options. <p/>
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -M
   *  Treat missing values as a seperate value.</pre>
   * 
   * <pre> -L
   *  Don't include locally predictive attributes.</pre>
   * 
   <!-- options-end -->
   *
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   *
   **/
  public void setOptions (String[] options)
    throws Exception {

    resetOptions();
    setMissingSeperate(Utils.getFlag('M', options));
    setLocallyPredictive(!Utils.getFlag('L', options));
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String locallyPredictiveTipText() {
    return "Identify locally predictive attributes. Iteratively adds "
      +"attributes with the highest correlation with the class as long "
      +"as there is not already an attribute in the subset that has a "
      +"higher correlation with the attribute in question";
  }

  /**
   * Include locally predictive attributes
   *
   * @param b true or false
   */
  public void setLocallyPredictive (boolean b) {
    m_locallyPredictive = b;
  }


  /**
   * Return true if including locally predictive attributes
   *
   * @return true if locally predictive attributes are to be used
   */
  public boolean getLocallyPredictive () {
    return  m_locallyPredictive;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String missingSeperateTipText() {
    return "Treat missing as a separate value. Otherwise, counts for missing "
      +"values are distributed across other values in proportion to their "
      +"frequency.";
  }

  /**
   * Treat missing as a seperate value
   *
   * @param b true or false
   */
  public void setMissingSeperate (boolean b) {
    m_missingSeperate = b;
  }


  /**
   * Return true is missing is treated as a seperate value
   *
   * @return true if missing is to be treated as a seperate value
   */
  public boolean getMissingSeperate () {
    return  m_missingSeperate;
  }


  /**
   * Gets the current settings of CfsSubsetEval
   *
   * @return an array of strings suitable for passing to setOptions()
   */
  public String[] getOptions () {
    String[] options = new String[2];
    int current = 0;

    if (getMissingSeperate()) {
      options[current++] = "-M";
    }

    if (!getLocallyPredictive()) {
      options[current++] = "-L";
    }

    while (current < options.length) {
      options[current++] = "";
    }

    return  options;
  }

  /**
   * Returns the capabilities of this evaluator.
   *
   * @return            the capabilities of this evaluator
   * @see               Capabilities
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();
    
    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.DATE_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);
    
    // class
    result.enable(Capability.NOMINAL_CLASS);
    result.enable(Capability.NUMERIC_CLASS);
    result.enable(Capability.DATE_CLASS);
    result.enable(Capability.MISSING_CLASS_VALUES);
    
    return result;
  }

  /**
   * Generates a attribute evaluator. Has to initialize all fields of the 
   * evaluator that are not being set via options.
   *
   * CFS also discretises attributes (if necessary) and initializes
   * the correlation matrix.
   *
   * @param data set of instances serving as training data 
   * @throws Exception if the evaluator has not been 
   * generated successfully
   */
  public void buildEvaluator (Instances data)
    throws Exception {

    // can evaluator handle data?
    getCapabilities().testWithFail(data);

    m_trainInstances = new Instances(data);
    m_trainInstances.deleteWithMissingClass();
    m_classIndex = m_trainInstances.classIndex();
    m_numAttribs = m_trainInstances.numAttributes();
    m_numInstances = m_trainInstances.numInstances();
    m_isNumeric = m_trainInstances.attribute(m_classIndex).isNumeric();

    if (!m_isNumeric) {
      m_disTransform = new Discretize();
      m_disTransform.setUseBetterEncoding(true);
      m_disTransform.setInputFormat(m_trainInstances);
      m_trainInstances = Filter.useFilter(m_trainInstances, m_disTransform);
    }

    m_std_devs = new double[m_numAttribs];
    m_corr_matrix = new float [m_numAttribs][];
    for (int i = 0; i < m_numAttribs; i++) {
      m_corr_matrix[i] = new float [i+1];
    }

    for (int i = 0; i < m_corr_matrix.length; i++) {
      m_corr_matrix[i][i] = 1.0f;
      m_std_devs[i] = 1.0;
    }

    for (int i = 0; i < m_numAttribs; i++) {
      for (int j = 0; j < m_corr_matrix[i].length - 1; j++) {
        m_corr_matrix[i][j] = -999;
      }
    }
  }


  /**
   * evaluates a subset of attributes
   *
   * @param subset a bitset representing the attribute subset to be 
   * evaluated 
   * @return the merit
   * @throws Exception if the subset could not be evaluated
   */
  public double evaluateSubset (BitSet subset)
    throws Exception {
    double num = 0.0;
    double denom = 0.0;
    float corr;
    int larger, smaller;
    // do numerator
    for (int i = 0; i < m_numAttribs; i++) {
      if (i != m_classIndex) {
        if (subset.get(i)) {
          if (i > m_classIndex) {
            larger = i; smaller = m_classIndex;
          } else {
            smaller = i; larger = m_classIndex;
          }
          /*      int larger = (i > m_classIndex ? i : m_classIndex);
                  int smaller = (i > m_classIndex ? m_classIndex : i); */
          if (m_corr_matrix[larger][smaller] == -999) {
            corr = correlate(i, m_classIndex);
            m_corr_matrix[larger][smaller] = corr;
            num += (m_std_devs[i] * corr);
          }
          else {
            num += (m_std_devs[i] * m_corr_matrix[larger][smaller]);
          }
        }
      }
    }

    // do denominator
    for (int i = 0; i < m_numAttribs; i++) {
      if (i != m_classIndex) {
        if (subset.get(i)) {
          denom += (1.0 * m_std_devs[i] * m_std_devs[i]);

          for (int j = 0; j < m_corr_matrix[i].length - 1; j++) {
            if (subset.get(j)) {
              if (m_corr_matrix[i][j] == -999) {
                corr = correlate(i, j);
                m_corr_matrix[i][j] = corr;
                denom += (2.0 * m_std_devs[i] * m_std_devs[j] * corr);
              }
              else {
                denom += (2.0 * m_std_devs[i] * m_std_devs[j] * m_corr_matrix[i][j]);
              }
            }
          }
        }
      }
    }

    if (denom < 0.0) {
      denom *= -1.0;
    }

    if (denom == 0.0) {
      return  (0.0);
    }

    double merit = (num/Math.sqrt(denom));

    if (merit < 0.0) {
      merit *= -1.0;
    }

    return  merit;
  }


  private float correlate (int att1, int att2) {
    if (!m_isNumeric) {
      return  (float) symmUncertCorr(att1, att2);
    }

    boolean att1_is_num = (m_trainInstances.attribute(att1).isNumeric());
    boolean att2_is_num = (m_trainInstances.attribute(att2).isNumeric());

    if (att1_is_num && att2_is_num) {
      return  (float) num_num(att1, att2);
    }
    else {if (att2_is_num) {
      return  (float) num_nom2(att1, att2);
    }
    else {if (att1_is_num) {
      return  (float) num_nom2(att2, att1);
    }
    }
    }

    return (float) nom_nom(att1, att2);
  }


  private double symmUncertCorr (int att1, int att2) {
    int i, j, k, ii, jj;
    int ni, nj;
    double sum = 0.0;
    double sumi[], sumj[];
    double counts[][];
    Instance inst;
    double corr_measure;
    boolean flag = false;
    double temp = 0.0;

    if (att1 == m_classIndex || att2 == m_classIndex) {
      flag = true;
    }

    ni = m_trainInstances.attribute(att1).numValues() + 1;
    nj = m_trainInstances.attribute(att2).numValues() + 1;
    counts = new double[ni][nj];
    sumi = new double[ni];
    sumj = new double[nj];

    for (i = 0; i < ni; i++) {
      sumi[i] = 0.0;

      for (j = 0; j < nj; j++) {
        sumj[j] = 0.0;
        counts[i][j] = 0.0;
      }
    }

    // Fill the contingency table
    for (i = 0; i < m_numInstances; i++) {
      inst = m_trainInstances.instance(i);

      if (inst.isMissing(att1)) {
        ii = ni - 1;
      }
      else {
        ii = (int)inst.value(att1);
      }

      if (inst.isMissing(att2)) {
        jj = nj - 1;
      }
      else {
        jj = (int)inst.value(att2);
      }

      counts[ii][jj]++;
    }

    // get the row totals
    for (i = 0; i < ni; i++) {
      sumi[i] = 0.0;

      for (j = 0; j < nj; j++) {
        sumi[i] += counts[i][j];
        sum += counts[i][j];
      }
    }

    // get the column totals
    for (j = 0; j < nj; j++) {
      sumj[j] = 0.0;

      for (i = 0; i < ni; i++) {
        sumj[j] += counts[i][j];
      }
    }

    // distribute missing counts
    if (!m_missingSeperate && 
        (sumi[ni-1] < m_numInstances) && 
        (sumj[nj-1] < m_numInstances)) {
      double[] i_copy = new double[sumi.length];
      double[] j_copy = new double[sumj.length];
      double[][] counts_copy = new double[sumi.length][sumj.length];

      for (i = 0; i < ni; i++) {
        System.arraycopy(counts[i], 0, counts_copy[i], 0, sumj.length);
      }

      System.arraycopy(sumi, 0, i_copy, 0, sumi.length);
      System.arraycopy(sumj, 0, j_copy, 0, sumj.length);
      double total_missing = 
        (sumi[ni - 1] + sumj[nj - 1] - counts[ni - 1][nj - 1]);

      // do the missing i's
      if (sumi[ni - 1] > 0.0) {
        for (j = 0; j < nj - 1; j++) {
          if (counts[ni - 1][j] > 0.0) {
            for (i = 0; i < ni - 1; i++) {
              temp = ((i_copy[i]/(sum - i_copy[ni - 1]))*counts[ni - 1][j]);
              counts[i][j] += temp;
              sumi[i] += temp;
            }

            counts[ni - 1][j] = 0.0;
          }
        }
      }

      sumi[ni - 1] = 0.0;