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weka.experiment.LearningRateResultProducer 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 .
 */

/*
 *    LearningRateResultProducer.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.experiment;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

import weka.core.AdditionalMeasureProducer;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;

/**
 *  Tells a sub-ResultProducer to reproduce the current
 * run for varying sized subsamples of the dataset. Normally used with an
 * AveragingResultProducer and CrossValidationResultProducer combo to generate
 * learning curve results. For non-numeric result fields, the first value is
 * used.
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -X <num steps>
 *  The number of steps in the learning rate curve.
 *  (default 10)
 * 
 * 
 * 
 * -W <class name>
 *  The full class name of a ResultProducer.
 *  eg: weka.experiment.CrossValidationResultProducer
 * 
 * 
 * 
 * Options specific to result producer weka.experiment.AveragingResultProducer:
 * 
 * 
 * 
 * -F <field name>
 *  The name of the field to average over.
 *  (default "Fold")
 * 
 * 
 * 
 * -X <num results>
 *  The number of results expected per average.
 *  (default 10)
 * 
 * 
 * 
 * -S
 *  Calculate standard deviations.
 *  (default only averages)
 * 
 * 
 * 
 * -W <class name>
 *  The full class name of a ResultProducer.
 *  eg: weka.experiment.CrossValidationResultProducer
 * 
 * 
 * 
 * Options specific to result producer weka.experiment.CrossValidationResultProducer:
 * 
 * 
 * 
 * -X <number of folds>
 *  The number of folds to use for the cross-validation.
 *  (default 10)
 * 
 * 
 * 
 * -D
 * Save raw split evaluator output.
 * 
 * 
 * 
 * -O <file/directory name/path>
 *  The filename where raw output will be stored.
 *  If a directory name is specified then then individual
 *  outputs will be gzipped, otherwise all output will be
 *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
 * 
 * 
 * 
 * -W <class name>
 *  The full class name of a SplitEvaluator.
 *  eg: weka.experiment.ClassifierSplitEvaluator
 * 
 * 
 * 
 * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
 * 
 * 
 * 
 * -W <class name>
 *  The full class name of the classifier.
 *  eg: weka.classifiers.bayes.NaiveBayes
 * 
 * 
 * 
 * -C <index>
 *  The index of the class for which IR statistics
 *  are to be output. (default 1)
 * 
 * 
 * 
 * -I <index>
 *  The index of an attribute to output in the
 *  results. This attribute should identify an
 *  instance in order to know which instances are
 *  in the test set of a cross validation. if 0
 *  no output (default 0).
 * 
 * 
 * 
 * -P
 *  Add target and prediction columns to the result
 *  for each fold.
 * 
 * 
 * 
 * Options specific to classifier weka.classifiers.rules.ZeroR:
 * 
 * 
 * 
 * -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * 
 * 
 * 
 * 
 * All options after -- will be passed to the result producer.
 * 
 * @author Len Trigg ([email protected])
 * @version $Revision: 10203 $
 */
public class LearningRateResultProducer implements ResultListener,
  ResultProducer, OptionHandler, AdditionalMeasureProducer, RevisionHandler {

  /** for serialization */
  static final long serialVersionUID = -3841159673490861331L;

  /** The dataset of interest */
  protected Instances m_Instances;

  /** The ResultListener to send results to */
  protected ResultListener m_ResultListener = new CSVResultListener();

  /** The ResultProducer used to generate results */
  protected ResultProducer m_ResultProducer = new AveragingResultProducer();

  /** The names of any additional measures to look for in SplitEvaluators */
  protected String[] m_AdditionalMeasures = null;

  /**
   * The minimum number of instances to use. If this is zero, the first step
   * will contain m_StepSize instances
   */
  protected int m_LowerSize = 0;

  /**
   * The maximum number of instances to use. -1 indicates no maximum (other than
   * the total number of instances)
   */
  protected int m_UpperSize = -1;

  /** The number of instances to add at each step */
  protected int m_StepSize = 10;

  /** The current dataset size during stepping */
  protected int m_CurrentSize = 0;

  /** The name of the key field containing the learning rate step number */
  public static String STEP_FIELD_NAME = "Total_instances";

  /**
   * Returns a string describing this result producer
   * 
   * @return a description of the result producer suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String globalInfo() {
    return "Tells a sub-ResultProducer to reproduce the current run for "
      + "varying sized subsamples of the dataset. Normally used with "
      + "an AveragingResultProducer and CrossValidationResultProducer "
      + "combo to generate learning curve results. For non-numeric "
      + "result fields, the first value is used.";
  }

  /**
   * Determines if there are any constraints (imposed by the destination) on the
   * result columns to be produced by resultProducers. Null should be returned
   * if there are NO constraints, otherwise a list of column names should be
   * returned as an array of Strings.
   * 
   * @param rp the ResultProducer to which the constraints will apply
   * @return an array of column names to which resutltProducer's results will be
   *         restricted.
   * @throws Exception if constraints can't be determined
   */
  @Override
  public String[] determineColumnConstraints(ResultProducer rp)
    throws Exception {
    return null;
  }

  /**
   * Gets the keys for a specified run number. Different run numbers correspond
   * to different randomizations of the data. Keys produced should be sent to
   * the current ResultListener
   * 
   * @param run the run number to get keys for.
   * @throws Exception if a problem occurs while getting the keys
   */
  @Override
  public void doRunKeys(int run) throws Exception {

    if (m_ResultProducer == null) {
      throw new Exception("No ResultProducer set");
    }
    if (m_ResultListener == null) {
      throw new Exception("No ResultListener set");
    }
    if (m_Instances == null) {
      throw new Exception("No Instances set");
    }

    // Tell the resultproducer to send results to us
    m_ResultProducer.setResultListener(this);
    m_ResultProducer.setInstances(m_Instances);

    // For each subsample size
    if (m_LowerSize == 0) {
      m_CurrentSize = m_StepSize;
    } else {
      m_CurrentSize = m_LowerSize;
    }
    while (m_CurrentSize <= m_Instances.numInstances()
      && ((m_UpperSize == -1) || (m_CurrentSize <= m_UpperSize))) {
      m_ResultProducer.doRunKeys(run);
      m_CurrentSize += m_StepSize;
    }
  }

  /**
   * Gets the results for a specified run number. Different run numbers
   * correspond to different randomizations of the data. Results produced should
   * be sent to the current ResultListener
   * 
   * @param run the run number to get results for.
   * @throws Exception if a problem occurs while getting the results
   */
  @Override
  public void doRun(int run) throws Exception {

    if (m_ResultProducer == null) {
      throw new Exception("No ResultProducer set");
    }
    if (m_ResultListener == null) {
      throw new Exception("No ResultListener set");
    }
    if (m_Instances == null) {
      throw new Exception("No Instances set");
    }

    // Randomize on a copy of the original dataset
    Instances runInstances = new Instances(m_Instances);
    runInstances.randomize(new Random(run));

    /*
     * if (runInstances.classAttribute().isNominal() &&
     * (m_Instances.numInstances() / m_StepSize >= 1)) { //
     * runInstances.stratify(m_Instances.numInstances() / m_StepSize); }
     */

    // Tell the resultproducer to send results to us
    m_ResultProducer.setResultListener(this);

    // For each subsample size
    if (m_LowerSize == 0) {
      m_CurrentSize = m_StepSize;
    } else {
      m_CurrentSize = m_LowerSize;
    }
    while (m_CurrentSize <= m_Instances.numInstances()
      && ((m_UpperSize == -1) || (m_CurrentSize <= m_UpperSize))) {
      m_ResultProducer.setInstances(new Instances(runInstances, 0,
        m_CurrentSize));
      m_ResultProducer.doRun(run);
      m_CurrentSize += m_StepSize;
    }
  }

  /**
   * Prepare for the results to be received.
   * 
   * @param rp the ResultProducer that will generate the results
   * @throws Exception if an error occurs during preprocessing.
   */
  @Override
  public void preProcess(ResultProducer rp) throws Exception {

    if (m_ResultListener == null) {
      throw new Exception("No ResultListener set");
    }
    m_ResultListener.preProcess(this);
  }

  /**
   * Prepare to generate results. The ResultProducer should call
   * preProcess(this) on the ResultListener it is to send results to.
   * 
   * @throws Exception if an error occurs during preprocessing.
   */
  @Override
  public void preProcess() throws Exception {

    if (m_ResultProducer == null) {
      throw new Exception("No ResultProducer set");
    }
    // Tell the resultproducer to send results to us
    m_ResultProducer.setResultListener(this);
    m_ResultProducer.preProcess();
  }

  /**
   * When this method is called, it indicates that no more results will be sent
   * that need to be grouped together in any way.
   * 
   * @param rp the ResultProducer that generated the results
   * @throws Exception if an error occurs
   */
  @Override
  public void postProcess(ResultProducer rp) throws Exception {

    m_ResultListener.postProcess(this);
  }

  /**
   * When this method is called, it indicates that no more requests to generate
   * results for the current experiment will be sent. The ResultProducer should
   * call preProcess(this) on the ResultListener it is to send results to.
   * 
   * @throws Exception if an error occurs
   */
  @Override
  public void postProcess() throws Exception {

    m_ResultProducer.postProcess();
  }

  /**
   * Accepts results from a ResultProducer.
   * 
   * @param rp the ResultProducer that generated the results
   * @param key an array of Objects (Strings or Doubles) that uniquely identify
   *          a result for a given ResultProducer with given compatibilityState
   * @param result the results stored in an array. The objects stored in the
   *          array may be Strings, Doubles, or null (for the missing value).
   * @throws Exception if the result could not be accepted.
   */
  @Override
  public void acceptResult(ResultProducer rp, Object[] key, Object[] result)
    throws Exception {

    if (m_ResultProducer != rp) {
      throw new Error("Unrecognized ResultProducer sending results!!");
    }
    // Add in current step as key field
    Object[] newKey = new Object[key.length + 1];
    System.arraycopy(key, 0, newKey, 0, key.length);
    newKey[key.length] = new String("" + m_CurrentSize);
    // Pass on to result listener
    m_ResultListener.acceptResult(this, newKey, result);
  }

  /**
   * Determines whether the results for a specified key must be generated.
   * 
   * @param rp the ResultProducer wanting to generate the results
   * @param key an array of Objects (Strings or Doubles) that uniquely identify
   *          a result for a given ResultProducer with given compatibilityState
   * @return true if the result should be generated
   * @throws Exception if it could not be determined if the result is needed.
   */
  @Override
  public boolean isResultRequired(ResultProducer rp, Object[] key)
    throws Exception {

    if (m_ResultProducer != rp) {
      throw new Error("Unrecognized ResultProducer sending results!!");
    }
    // Add in current step as key field
    Object[] newKey = new Object[key.length + 1];
    System.arraycopy(key, 0, newKey, 0, key.length);
    newKey[key.length] = new String("" + m_CurrentSize);
    // Pass on request to result listener
    return m_ResultListener.isResultRequired(this, newKey);
  }

  /**
   * Gets the names of each of the columns produced for a single run.
   * 
   * @return an array containing the name of each column
   * @throws Exception if key names cannot be generated
   */
  @Override
  public String[] getKeyNames() throws Exception {

    String[] keyNames = m_ResultProducer.getKeyNames();
    String[] newKeyNames = new String[keyNames.length + 1];
    System.arraycopy(keyNames, 0, newKeyNames, 0, keyNames.length);
    // Think of a better name for this key field
    newKeyNames[keyNames.length] = STEP_FIELD_NAME;
    return newKeyNames;
  }

  /**
   * Gets the data types of each of the columns produced for a single run. This
   * method should really be static.
   * 
   * @return an array containing objects of the type of each column. The objects
   *         should be Strings, or Doubles.
   * @throws Exception if the key types could not be determined (perhaps because
   *           of a problem from a nested sub-resultproducer)
   */
  @Override
  public Object[] getKeyTypes() throws Exception {

    Object[] keyTypes = m_ResultProducer.getKeyTypes();
    Object[] newKeyTypes = new Object[keyTypes.length + 1];
    System.arraycopy(keyTypes, 0, newKeyTypes, 0, keyTypes.length);
    newKeyTypes[keyTypes.length] = "";
    return newKeyTypes;
  }

  /**
   * Gets the names of each of the columns produced for a single run. A new
   * result field is added for the number of results used to produce each
   * average. If only averages are being produced the names are not altered, if
   * standard deviations are produced then "Dev_" and "Avg_" are prepended to
   * each result deviation and average field respectively.
   * 
   * @return an array containing the name of each column
   * @throws Exception if the result names could not be determined (perhaps
   *           because of a problem from a nested sub-resultproducer)
   */
  @Override
  public String[] getResultNames() throws Exception {

    return m_ResultProducer.getResultNames();
  }

  /**
   * Gets the data types of each of the columns produced for a single run.
   * 
   * @return an array containing objects of the type of each column. The objects
   *         should be Strings, or Doubles.
   * @throws Exception if the result types could not be determined (perhaps
   *           because of a problem from a nested sub-resultproducer)
   */
  @Override
  public Object[] getResultTypes() throws Exception {

    return m_ResultProducer.getResultTypes();
  }

  /**
   * Gets a description of the internal settings of the result producer,
   * sufficient for distinguishing a ResultProducer instance from another with
   * different settings (ignoring those settings set through this interface).
   * For example, a cross-validation ResultProducer may have a setting for the
   * number of folds. For a given state, the results produced should be
   * compatible. Typically if a ResultProducer is an OptionHandler, this string
   * will represent the command line arguments required to set the
   * ResultProducer to that state.
   * 
   * @return the description of the ResultProducer state, or null if no state is
   *         defined
   */
  @Override
  public String getCompatibilityState() {

    String result = " ";
    // + "-F " + Utils.quote(getKeyFieldName())
    // + " -X " + getStepSize() + " ";
    if (m_ResultProducer == null) {
      result += "";
    } else {
      result += "-W " + m_ResultProducer.getClass().getName();
      result += " -- " + m_ResultProducer.getCompatibilityState();
    }

    return result.trim();
  }

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

    Vector newVector = new Vector(2);

    newVector.addElement(new Option(
      "\tThe number of steps in the learning rate curve.\n" + "\t(default 10)",
      "X", 1, "-X "));
    newVector.addElement(new Option(
      "\tThe full class name of a ResultProducer.\n"
        + "\teg: weka.experiment.CrossValidationResultProducer", "W", 1,
      "-W "));

    if ((m_ResultProducer != null)
      && (m_ResultProducer instanceof OptionHandler)) {
      newVector.addElement(new Option("", "", 0,
        "\nOptions specific to result producer "
          + m_ResultProducer.getClass().getName() + ":"));
      newVector.addAll(Collections.list(((OptionHandler) m_ResultProducer)
        .listOptions()));
    }
    return newVector.elements();
  }

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

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -X <num steps>
   *  The number of steps in the learning rate curve.
   *  (default 10)
   * 
   * 
   * 
   * -W <class name>
   *  The full class name of a ResultProducer.
   *  eg: weka.experiment.CrossValidationResultProducer
   * 
   * 
   * 
   * Options specific to result producer weka.experiment.AveragingResultProducer:
   * 
   * 
   * 
   * -F <field name>
   *  The name of the field to average over.
   *  (default "Fold")
   * 
   * 
   * 
   * -X <num results>
   *  The number of results expected per average.
   *  (default 10)
   * 
   * 
   * 
   * -S
   *  Calculate standard deviations.
   *  (default only averages)
   * 
   * 
   * 
   * -W <class name>
   *  The full class name of a ResultProducer.
   *  eg: weka.experiment.CrossValidationResultProducer
   * 
   * 
   * 
   * Options specific to result producer weka.experiment.CrossValidationResultProducer:
   * 
   * 
   * 
   * -X <number of folds>
   *  The number of folds to use for the cross-validation.
   *  (default 10)
   * 
   * 
   * 
   * -D
   * Save raw split evaluator output.
   * 
   * 
   * 
   * -O <file/directory name/path>
   *  The filename where raw output will be stored.
   *  If a directory name is specified then then individual
   *  outputs will be gzipped, otherwise all output will be
   *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
   * 
   * 
   * 
   * -W <class name>
   *  The full class name of a SplitEvaluator.
   *  eg: weka.experiment.ClassifierSplitEvaluator
   * 
   * 
   * 
   * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
   * 
   * 
   * 
   * -W <class name>
   *  The full class name of the classifier.
   *  eg: weka.classifiers.bayes.NaiveBayes
   * 
   * 
   * 
   * -C <index>
   *  The index of the class for which IR statistics
   *  are to be output. (default 1)
   * 
   * 
   * 
   * -I <index>
   *  The index of an attribute to output in the
   *  results. This attribute should identify an
   *  instance in order to know which instances are
   *  in the test set of a cross validation. if 0
   *  no output (default 0).
   * 
   * 
   * 
   * -P
   *  Add target and prediction columns to the result
   *  for each fold.
   * 
   * 
   * 
   * Options specific to classifier weka.classifiers.rules.ZeroR:
   * 
   * 
   * 
   * -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console
   * 
   * 
   * 
   * 
   * All options after -- will be passed to the result producer.
   * 
   * @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 stepSize = Utils.getOption('S', options);
    if (stepSize.length() != 0) {
      setStepSize(Integer.parseInt(stepSize));
    } else {
      setStepSize(10);
    }

    String lowerSize = Utils.getOption('L', options);
    if (lowerSize.length() != 0) {
      setLowerSize(Integer.parseInt(lowerSize));
    } else {
      setLowerSize(0);
    }

    String upperSize = Utils.getOption('U', options);
    if (upperSize.length() != 0) {
      setUpperSize(Integer.parseInt(upperSize));
    } else {
      setUpperSize(-1);
    }

    String rpName = Utils.getOption('W', options);
    if (rpName.length() == 0) {
      throw new Exception("A ResultProducer must be specified with"
        + " the -W option.");
    }
    // Do it first without options, so if an exception is thrown during
    // the option setting, listOptions will contain options for the actual
    // RP.
    setResultProducer((ResultProducer) Utils.forName(ResultProducer.class,
      rpName, null));
    if (getResultProducer() instanceof OptionHandler) {
      ((OptionHandler) getResultProducer()).setOptions(Utils
        .partitionOptions(options));
    }
  }

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

    String[] seOptions = new String[0];
    if ((m_ResultProducer != null)
      && (m_ResultProducer instanceof OptionHandler)) {
      seOptions = ((OptionHandler) m_ResultProducer).getOptions();
    }

    String[] options = new String[seOptions.length + 9];
    int current = 0;

    options[current++] = "-S";
    options[current++] = "" + getStepSize();
    options[current++] = "-L";
    options[current++] = "" + getLowerSize();
    options[current++] = "-U";
    options[current++] = "" + getUpperSize();
    if (getResultProducer() != null) {
      options[current++] = "-W";
      options[current++] = getResultProducer().getClass().getName();
    }
    options[current++] = "--";

    System.arraycopy(seOptions, 0, options, current, seOptions.length);
    current += seOptions.length;
    while (current < options.length) {
      options[current++] = "";
    }
    return options;
  }

  /**
   * Set a list of method names for additional measures to look for in
   * SplitEvaluators. This could contain many measures (of which only a subset
   * may be produceable by the current resultProducer) if an experiment is the
   * type that iterates over a set of properties.
   * 
   * @param additionalMeasures an array of measure names, null if none
   */
  @Override
  public void setAdditionalMeasures(String[] additionalMeasures) {
    m_AdditionalMeasures = additionalMeasures;

    if (m_ResultProducer != null) {
      System.err.println("LearningRateResultProducer: setting additional "
        + "measures for " + "ResultProducer");
      m_ResultProducer.setAdditionalMeasures(m_AdditionalMeasures);
    }
  }

  /**
   * Returns an enumeration of any additional measure names that might be in the
   * result producer
   * 
   * @return an enumeration of the measure names
   */
  @Override
  public Enumeration enumerateMeasures() {
    Vector newVector = new Vector();
    if (m_ResultProducer instanceof AdditionalMeasureProducer) {
      Enumeration en = ((AdditionalMeasureProducer) m_ResultProducer)
        .enumerateMeasures();
      while (en.hasMoreElements()) {
        String mname = en.nextElement();
        newVector.add(mname);
      }
    }
    return newVector.elements();
  }

  /**
   * Returns the value of the named measure
   * 
   * @param additionalMeasureName the name of the measure to query for its value
   * @return the value of the named measure
   * @throws IllegalArgumentException if the named measure is not supported
   */
  @Override
  public double getMeasure(String additionalMeasureName) {
    if (m_ResultProducer instanceof AdditionalMeasureProducer) {
      return ((AdditionalMeasureProducer) m_ResultProducer)
        .getMeasure(additionalMeasureName);
    } else {
      throw new IllegalArgumentException("LearningRateResultProducer: "
        + "Can't return value for : " + additionalMeasureName + ". "
        + m_ResultProducer.getClass().getName() + " "
        + "is not an AdditionalMeasureProducer");
    }
  }

  /**
   * Sets the dataset that results will be obtained for.
   * 
   * @param instances a value of type 'Instances'.
   */
  @Override
  public void setInstances(Instances instances) {

    m_Instances = instances;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String lowerSizeTipText() {
    return "Set the minmum number of instances in a dataset. Setting zero "
      + "here will actually use  number of instances at the first "
      + "step (since it makes no sense to use zero instances :-))";
  }

  /**
   * Get the value of LowerSize.
   * 
   * @return Value of LowerSize.
   */
  public int getLowerSize() {

    return m_LowerSize;
  }

  /**
   * Set the value of LowerSize.
   * 
   * @param newLowerSize Value to assign to LowerSize.
   */
  public void setLowerSize(int newLowerSize) {

    m_LowerSize = newLowerSize;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String upperSizeTipText() {
    return "Set the maximum number of instances in a dataset. Setting -1 "
      + "sets no upper limit (other than the total number of instances "
      + "in the full dataset)";
  }

  /**
   * Get the value of UpperSize.
   * 
   * @return Value of UpperSize.
   */
  public int getUpperSize() {

    return m_UpperSize;
  }

  /**
   * Set the value of UpperSize.
   * 
   * @param newUpperSize Value to assign to UpperSize.
   */
  public void setUpperSize(int newUpperSize) {

    m_UpperSize = newUpperSize;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String stepSizeTipText() {
    return "Set the number of instances to add at each step.";
  }

  /**
   * Get the value of StepSize.
   * 
   * @return Value of StepSize.
   */
  public int getStepSize() {

    return m_StepSize;
  }

  /**
   * Set the value of StepSize.
   * 
   * @param newStepSize Value to assign to StepSize.
   */
  public void setStepSize(int newStepSize) {

    m_StepSize = newStepSize;
  }

  /**
   * Sets the object to send results of each run to.
   * 
   * @param listener a value of type 'ResultListener'
   */
  @Override
  public void setResultListener(ResultListener listener) {

    m_ResultListener = listener;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String resultProducerTipText() {
    return "Set the resultProducer for which learning rate results should be "
      + "generated.";
  }

  /**
   * Get the ResultProducer.
   * 
   * @return the ResultProducer.
   */
  public ResultProducer getResultProducer() {

    return m_ResultProducer;
  }

  /**
   * Set the ResultProducer.
   * 
   * @param newResultProducer new ResultProducer to use.
   */
  public void setResultProducer(ResultProducer newResultProducer) {

    m_ResultProducer = newResultProducer;
    m_ResultProducer.setResultListener(this);
  }

  /**
   * Gets a text descrption of the result producer.
   * 
   * @return a text description of the result producer.
   */
  @Override
  public String toString() {

    String result = "LearningRateResultProducer: ";
    result += getCompatibilityState();
    if (m_Instances == null) {
      result += ": ";
    } else {
      result += ": " + Utils.backQuoteChars(m_Instances.relationName());
    }
    return result;
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10203 $");
  }
} // LearningRateResultProducer