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weka.filters.unsupervised.instance.RemoveMisclassified Maven / Gradle / Ivy

/*
 *   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 .
 */

/*
 *    RemoveMisclassified.java
 *    Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.filters.unsupervised.instance;

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

import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.core.*;
import weka.filters.Filter;
import weka.filters.UnsupervisedFilter;

/**
 *  A filter that removes instances which are
 * incorrectly classified. Useful for removing outliers.
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -W <classifier specification>
 *  Full class name of classifier to use, followed
 *  by scheme options. eg:
 *   "weka.classifiers.bayes.NaiveBayes -D"
 *  (default: weka.classifiers.rules.ZeroR)
 * 
 * 
 * 
 * -C <class index>
 *  Attribute on which misclassifications are based.
 *  If < 0 will use any current set class or default to the last attribute.
 * 
 * 
 * 
 * -F <number of folds>
 *  The number of folds to use for cross-validation cleansing.
 *  (<2 = no cross-validation - default).
 * 
 * 
 * 
 * -T <threshold>
 *  Threshold for the max error when predicting numeric class.
 *  (Value should be >= 0, default = 0.1).
 * 
 * 
 * 
 * -I
 *  The maximum number of cleansing iterations to perform.
 *  (<1 = until fully cleansed - default)
 * 
 * 
 * 
 * -V
 *  Invert the match so that correctly classified instances are discarded.
 * 
 * 
 * 
 * 
 * @author Richard Kirkby ([email protected])
 * @author Malcolm Ware ([email protected])
 * @version $Revision: 14508 $
 */
public class RemoveMisclassified extends Filter implements UnsupervisedFilter,
  OptionHandler, WeightedAttributesHandler, WeightedInstancesHandler {

  /** for serialization */
  static final long serialVersionUID = 5469157004717663171L;

  /** The classifier used to do the cleansing */
  protected Classifier m_cleansingClassifier = new weka.classifiers.rules.ZeroR();

  /** The attribute to treat as the class for purposes of cleansing. */
  protected int m_classIndex = -1;

  /**
   * The number of cross validation folds to perform (<2 = no cross
   * validation)
   */
  protected int m_numOfCrossValidationFolds = 0;

  /**
   * The maximum number of cleansing iterations to perform (<1 = until fully
   * cleansed)
   */
  protected int m_numOfCleansingIterations = 0;

  /** The threshold for deciding when a numeric value is correctly classified */
  protected double m_numericClassifyThreshold = 0.1;

  /**
   * Whether to invert the match so the correctly classified instances are
   * discarded
   */
  protected boolean m_invertMatching = false;

  /** Have we processed the first batch (i.e. training data)? */
  protected boolean m_firstBatchFinished = false;

  /**
   * Returns the Capabilities of this filter.
   * 
   * @return the capabilities of this object
   * @see Capabilities
   */
  @Override
  public Capabilities getCapabilities() {
    Capabilities result;

    if (getClassifier() == null) {
      result = super.getCapabilities();
      result.disableAll();
    } else {
      result = getClassifier().getCapabilities();
    }

    result.setMinimumNumberInstances(0);

    return result;
  }

  /**
   * Sets the format of the input instances.
   * 
   * @param instanceInfo an Instances object containing the input instance
   *          structure (any instances contained in the object are ignored -
   *          only the structure is required).
   * @return true if the outputFormat may be collected immediately
   * @throws Exception if the inputFormat can't be set successfully
   */
  @Override
  public boolean setInputFormat(Instances instanceInfo) throws Exception {

    super.setInputFormat(instanceInfo);
    setOutputFormat(instanceInfo);
    m_firstBatchFinished = false;
    return true;
  }

  /**
   * Cleanses the data based on misclassifications when used training data.
   * 
   * @param data the data to train with and cleanse
   * @return the cleansed data
   * @throws Exception if something goes wrong
   */
  private Instances cleanseTrain(Instances data) throws Exception {

    Instance inst;
    Instances buildSet = new Instances(data);
    Instances temp;
    Instances inverseSet = new Instances(data, data.numInstances());
    int count = 0;
    double ans;
    int iterations = 0;
    int classIndex = m_classIndex;
    if (classIndex < 0) {
      classIndex = data.classIndex();
    }
    if (classIndex < 0) {
      classIndex = data.numAttributes() - 1;
    }

    // loop until perfect
    while (count != buildSet.numInstances()) {

      // check if hit maximum number of iterations
      iterations++;
      if (m_numOfCleansingIterations > 0
        && iterations > m_numOfCleansingIterations) {
        break;
      }

      // build classifier
      count = buildSet.numInstances();
      buildSet.setClassIndex(classIndex);
      m_cleansingClassifier.buildClassifier(buildSet);

      temp = new Instances(buildSet, buildSet.numInstances());

      // test on training data
      for (int i = 0; i < buildSet.numInstances(); i++) {
        inst = buildSet.instance(i);
        ans = m_cleansingClassifier.classifyInstance(inst);
        if (buildSet.classAttribute().isNumeric()) {
          if (ans >= inst.classValue() - m_numericClassifyThreshold
            && ans <= inst.classValue() + m_numericClassifyThreshold) {
            temp.add(inst);
          } else if (m_invertMatching) {
            inverseSet.add(inst);
          }
        } else { // class is nominal
          if (ans == inst.classValue()) {
            temp.add(inst);
          } else if (m_invertMatching) {
            inverseSet.add(inst);
          }
        }
      }
      buildSet = temp;
    }

    if (m_invertMatching) {
      inverseSet.setClassIndex(data.classIndex());
      return inverseSet;
    } else {
      buildSet.setClassIndex(data.classIndex());
      return buildSet;
    }
  }

  /**
   * Cleanses the data based on misclassifications when performing
   * cross-validation.
   * 
   * @param data the data to train with and cleanse
   * @return the cleansed data
   * @throws Exception if something goes wrong
   */
  private Instances cleanseCross(Instances data) throws Exception {

    Instance inst;
    Instances crossSet = new Instances(data);
    Instances temp = new Instances(data, data.numInstances());
    Instances inverseSet = new Instances(data, data.numInstances());
    int count = 0;
    double ans;
    int iterations = 0;
    int classIndex = m_classIndex;
    if (classIndex < 0) {
      classIndex = data.classIndex();
    }
    if (classIndex < 0) {
      classIndex = data.numAttributes() - 1;
    }

    // loop until perfect
    while (count != crossSet.numInstances()
      && crossSet.numInstances() >= m_numOfCrossValidationFolds) {

      count = crossSet.numInstances();

      // check if hit maximum number of iterations
      iterations++;
      if (m_numOfCleansingIterations > 0
        && iterations > m_numOfCleansingIterations) {
        break;
      }

      crossSet.setClassIndex(classIndex);

      if (crossSet.classAttribute().isNominal()) {
        crossSet.stratify(m_numOfCrossValidationFolds);
      }
      // do the folds
      temp = new Instances(crossSet, crossSet.numInstances());

      for (int fold = 0; fold < m_numOfCrossValidationFolds; fold++) {
        Instances train = crossSet.trainCV(m_numOfCrossValidationFolds, fold);
        m_cleansingClassifier.buildClassifier(train);
        Instances test = crossSet.testCV(m_numOfCrossValidationFolds, fold);
        // now test
        for (int i = 0; i < test.numInstances(); i++) {
          inst = test.instance(i);
          ans = m_cleansingClassifier.classifyInstance(inst);
          if (crossSet.classAttribute().isNumeric()) {
            if (ans >= inst.classValue() - m_numericClassifyThreshold
              && ans <= inst.classValue() + m_numericClassifyThreshold) {
              temp.add(inst);
            } else if (m_invertMatching) {
              inverseSet.add(inst);
            }
          } else { // class is nominal
            if (ans == inst.classValue()) {
              temp.add(inst);
            } else if (m_invertMatching) {
              inverseSet.add(inst);
            }
          }
        }
      }
      crossSet = temp;
    }

    if (m_invertMatching) {
      inverseSet.setClassIndex(data.classIndex());
      return inverseSet;
    } else {
      crossSet.setClassIndex(data.classIndex());
      return crossSet;
    }

  }

  /**
   * Input an instance for filtering.
   * 
   * @param instance the input instance
   * @return true if the filtered instance may now be collected with output().
   * @throws NullPointerException if the input format has not been defined.
   * @throws Exception if the input instance was not of the correct format or if
   *           there was a problem with the filtering.
   */
  @Override
  public boolean input(Instance instance) throws Exception {

    if (inputFormatPeek() == null) {
      throw new NullPointerException("No input instance format defined");
    }

    if (m_NewBatch) {
      resetQueue();
      m_NewBatch = false;
    }
    if (m_firstBatchFinished) {
      push(instance);
      return true;
    } else {
      bufferInput(instance);
      return false;
    }
  }

  /**
   * Signify that this batch of input to the filter is finished.
   * 
   * @return true if there are instances pending output
   * @throws IllegalStateException if no input structure has been defined
   */
  @Override
  public boolean batchFinished() throws Exception {

    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }

    if (!m_firstBatchFinished) {

      Instances filtered;
      if (m_numOfCrossValidationFolds < 2) {
        filtered = cleanseTrain(getInputFormat());
      } else {
        filtered = cleanseCross(getInputFormat());
      }

      for (int i = 0; i < filtered.numInstances(); i++) {
        push(filtered.instance(i), false); // No need to copy
      }

      m_firstBatchFinished = true;
      flushInput();
    }
    m_NewBatch = true;
    return (numPendingOutput() != 0);
  }

  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options.
   */
  @Override
  public Enumeration listOptions() {

    Vector newVector = new Vector(6);

    newVector.addElement(new Option(
      "\tFull class name of classifier to use, followed\n"
        + "\tby scheme options. eg:\n"
        + "\t\t\"weka.classifiers.bayes.NaiveBayes -D\"\n"
        + "\t(default: weka.classifiers.rules.ZeroR)", "W", 1,
      "-W "));
    newVector
      .addElement(new Option(
        "\tAttribute on which misclassifications are based.\n"
          + "\tIf < 0 will use any current set class or default to the last attribute.",
        "C", 1, "-C "));
    newVector.addElement(new Option(
      "\tThe number of folds to use for cross-validation cleansing.\n"
        + "\t(<2 = no cross-validation - default).", "F", 1,
      "-F "));
    newVector
      .addElement(new Option(
        "\tThreshold for the max error when predicting numeric class.\n"
          + "\t(Value should be >= 0, default = 0.1).", "T", 1,
        "-T "));
    newVector.addElement(new Option(
      "\tThe maximum number of cleansing iterations to perform.\n"
        + "\t(<1 = until fully cleansed - default)", "I", 1, "-I"));
    newVector
      .addElement(new Option(
        "\tInvert the match so that correctly classified instances are discarded.\n",
        "V", 0, "-V"));

    return newVector.elements();
  }

  /**
   * Parses a given list of options.
   * 

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -W <classifier specification>
   *  Full class name of classifier to use, followed
   *  by scheme options. eg:
   *   "weka.classifiers.bayes.NaiveBayes -D"
   *  (default: weka.classifiers.rules.ZeroR)
   * 
   * 
   * 
   * -C <class index>
   *  Attribute on which misclassifications are based.
   *  If < 0 will use any current set class or default to the last attribute.
   * 
   * 
   * 
   * -F <number of folds>
   *  The number of folds to use for cross-validation cleansing.
   *  (<2 = no cross-validation - default).
   * 
   * 
   * 
   * -T <threshold>
   *  Threshold for the max error when predicting numeric class.
   *  (Value should be >= 0, default = 0.1).
   * 
   * 
   * 
   * -I
   *  The maximum number of cleansing iterations to perform.
   *  (<1 = until fully cleansed - default)
   * 
   * 
   * 
   * -V
   *  Invert the match so that correctly classified instances are discarded.
   * 
   * 
   * 
   * 
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  @Override
  public void setOptions(String[] options) throws Exception {

    String classifierString = Utils.getOption('W', options);
    if (classifierString.length() == 0) {
      classifierString = weka.classifiers.rules.ZeroR.class.getName();
    }
    String[] classifierSpec = Utils.splitOptions(classifierString);
    if (classifierSpec.length == 0) {
      throw new Exception("Invalid classifier specification string");
    }
    String classifierName = classifierSpec[0];
    classifierSpec[0] = "";
    setClassifier(AbstractClassifier.forName(classifierName, classifierSpec));

    String cString = Utils.getOption('C', options);
    if (cString.length() != 0) {
      setClassIndex((new Double(cString)).intValue());
    } else {
      setClassIndex(-1);
    }

    String fString = Utils.getOption('F', options);
    if (fString.length() != 0) {
      setNumFolds((new Double(fString)).intValue());
    } else {
      setNumFolds(0);
    }

    String tString = Utils.getOption('T', options);
    if (tString.length() != 0) {
      setThreshold((new Double(tString)).doubleValue());
    } else {
      setThreshold(0.1);
    }

    String iString = Utils.getOption('I', options);
    if (iString.length() != 0) {
      setMaxIterations((new Double(iString)).intValue());
    } else {
      setMaxIterations(0);
    }

    if (Utils.getFlag('V', options)) {
      setInvert(true);
    } else {
      setInvert(false);
    }

    Utils.checkForRemainingOptions(options);

  }

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

    Vector options = new Vector();

    options.add("-W");
    options.add("" + getClassifierSpec());
    options.add("-C");
    options.add("" + getClassIndex());
    options.add("-F");
    options.add("" + getNumFolds());
    options.add("-T");
    options.add("" + getThreshold());
    options.add("-I");
    options.add("" + getMaxIterations());
    if (getInvert()) {
      options.add("-V");
    }

    return options.toArray(new String[0]);
  }

  /**
   * Returns a string describing this filter
   * 
   * @return a description of the filter suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String globalInfo() {
    return "A filter that removes instances which are incorrectly classified. "
      + "Useful for removing outliers.";
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String classifierTipText() {

    return "The classifier upon which to base the misclassifications.";
  }

  /**
   * Sets the classifier to classify instances with.
   * 
   * @param classifier The classifier to be used (with its options set).
   */
  public void setClassifier(Classifier classifier) {

    m_cleansingClassifier = classifier;
  }

  /**
   * Gets the classifier used by the filter.
   * 
   * @return The classifier to be used.
   */
  public Classifier getClassifier() {

    return m_cleansingClassifier;
  }

  /**
   * Gets the classifier specification string, which contains the class name of
   * the classifier and any options to the classifier.
   * 
   * @return the classifier string.
   */
  protected String getClassifierSpec() {

    Classifier c = getClassifier();
    if (c instanceof OptionHandler) {
      return c.getClass().getName() + " "
        + Utils.joinOptions(((OptionHandler) c).getOptions());
    }
    return c.getClass().getName();
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String classIndexTipText() {

    return "Index of the class upon which to base the misclassifications. "
      + "If < 0 will use any current set class or default to the last attribute.";
  }

  /**
   * Sets the attribute on which misclassifications are based. If < 0 will
   * use any current set class or default to the last attribute.
   * 
   * @param classIndex the class index.
   */
  public void setClassIndex(int classIndex) {

    m_classIndex = classIndex;
  }

  /**
   * Gets the attribute on which misclassifications are based.
   * 
   * @return the class index.
   */
  public int getClassIndex() {

    return m_classIndex;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String numFoldsTipText() {

    return "The number of cross-validation folds to use. If < 2 then no cross-validation will be performed.";
  }

  /**
   * Sets the number of cross-validation folds to use - < 2 means no
   * cross-validation.
   * 
   * @param numOfFolds the number of folds.
   */
  public void setNumFolds(int numOfFolds) {

    m_numOfCrossValidationFolds = numOfFolds;
  }

  /**
   * Gets the number of cross-validation folds used by the filter.
   * 
   * @return the number of folds.
   */
  public int getNumFolds() {

    return m_numOfCrossValidationFolds;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String thresholdTipText() {

    return "Threshold for the max allowable error when predicting a numeric class. Should be >= 0.";
  }

  /**
   * Sets the threshold for the max error when predicting a numeric class. The
   * value should be >= 0.
   * 
   * @param threshold the numeric theshold.
   */
  public void setThreshold(double threshold) {

    m_numericClassifyThreshold = threshold;
  }

  /**
   * Gets the threshold for the max error when predicting a numeric class.
   * 
   * @return the numeric threshold.
   */
  public double getThreshold() {

    return m_numericClassifyThreshold;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String maxIterationsTipText() {

    return "The maximum number of iterations to perform. < 1 means filter will go until fully cleansed.";
  }

  /**
   * Sets the maximum number of cleansing iterations to perform - < 1 means
   * go until fully cleansed
   * 
   * @param iterations the maximum number of iterations.
   */
  public void setMaxIterations(int iterations) {

    m_numOfCleansingIterations = iterations;
  }

  /**
   * Gets the maximum number of cleansing iterations performed
   * 
   * @return the maximum number of iterations.
   */
  public int getMaxIterations() {

    return m_numOfCleansingIterations;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String invertTipText() {

    return "Whether or not to invert the selection. If true, correctly classified instances will be discarded.";
  }

  /**
   * Set whether selection is inverted.
   * 
   * @param invert whether or not to invert selection.
   */
  public void setInvert(boolean invert) {

    m_invertMatching = invert;
  }

  /**
   * Get whether selection is inverted.
   * 
   * @return whether or not selection is inverted.
   */
  public boolean getInvert() {

    return m_invertMatching;
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 14508 $");
  }

  /**
   * Main method for testing this class.
   * 
   * @param argv should contain arguments to the filter: use -h for help
   */
  public static void main(String[] argv) {
    runFilter(new RemoveMisclassified(), argv);
  }
}