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weka.experiment.CostSensitiveClassifierSplitEvaluator 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 .
 */

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

package weka.experiment;

import java.io.BufferedReader;
import java.io.ByteArrayOutputStream;
import java.io.File;
import java.io.FileReader;
import java.io.ObjectOutputStream;
import java.lang.management.ManagementFactory;
import java.lang.management.ThreadMXBean;
import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import weka.classifiers.AbstractClassifier;
import weka.classifiers.CostMatrix;
import weka.classifiers.Evaluation;
import weka.core.AdditionalMeasureProducer;
import weka.core.Attribute;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Summarizable;
import weka.core.Utils;

/**
 *  SplitEvaluator that produces results for a
 * classification scheme on a nominal class attribute, including weighted
 * misclassification costs.
 * 

 * 
 *
 *  Valid options are:
 * 

 *
 * 
 * -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
 * 
 *
 * 
 * -D <directory>
 *  Name of a directory to search for cost files when loading
 *  costs on demand (default current directory).
 * 
 *
 * 
 *
 * All options after -- will be passed to the classifier.
 *
 * @author Len Trigg ([email protected])
 * @version $Revision: 11323 $
 */
public class CostSensitiveClassifierSplitEvaluator extends
  ClassifierSplitEvaluator {

  /** for serialization */
  static final long serialVersionUID = -8069566663019501276L;

  /**
   * The directory used when loading cost files on demand, null indicates
   * current directory
   */
  protected File m_OnDemandDirectory = new File(System.getProperty("user.dir"));

  /** The length of a result */
  private static final int RESULT_SIZE = 33;

  /**
   * Returns a string describing this split evaluator
   *
   * @return a description of the split evaluator suitable for displaying in the
   *         explorer/experimenter gui
   */
  @Override
  public String globalInfo() {
    return " SplitEvaluator that produces results for a classification scheme "
      + "on a nominal class attribute, including weighted misclassification "
      + "costs.";
  }

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

    Vector newVector = new Vector(1);

    newVector.addAll(Collections.list(super.listOptions()));

    newVector.addElement(new Option(
      "\tName of a directory to search for cost files when loading\n"
        + "\tcosts on demand (default current directory).", "D", 1,
      "-D "));

    return newVector.elements();
  }

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

   *
   *  Valid options are:
   * 

   *
   * 
   * -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
   * 
   *
   * 
   * -D <directory>
   *  Name of a directory to search for cost files when loading
   *  costs on demand (default current directory).
   * 
   *
   * 
   *
   * All options after -- will be passed to the classifier.
   *
   * @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 demandDir = Utils.getOption('D', options);
    if (demandDir.length() != 0) {
      setOnDemandDirectory(new File(demandDir));
    }

    super.setOptions(options);

    Utils.checkForRemainingOptions(options);
  }

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

    Vector options = new Vector();

    options.add("-D");
    options.add("" + getOnDemandDirectory());

    Collections.addAll(options, super.getOptions());

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

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String onDemandDirectoryTipText() {
    return "The directory to look in for cost files. This directory will be "
      + "searched for cost files when loading on demand.";
  }

  /**
   * Returns the directory that will be searched for cost files when loading on
   * demand.
   *
   * @return The cost file search directory.
   */
  public File getOnDemandDirectory() {

    return m_OnDemandDirectory;
  }

  /**
   * Sets the directory that will be searched for cost files when loading on
   * demand.
   *
   * @param newDir The cost file search directory.
   */
  public void setOnDemandDirectory(File newDir) {

    if (newDir.isDirectory()) {
      m_OnDemandDirectory = newDir;
    } else {
      m_OnDemandDirectory = new File(newDir.getParent());
    }
  }

  /**
   * Gets the data types of each of the result columns produced for a single
   * run. The number of result fields must be constant for a given
   * SplitEvaluator.
   *
   * @return an array containing objects of the type of each result column. The
   *         objects should be Strings, or Doubles.
   */
  @Override
  public Object[] getResultTypes() {
    int addm = (m_AdditionalMeasures != null) ? m_AdditionalMeasures.length : 0;
    Object[] resultTypes = new Object[RESULT_SIZE + addm];
    Double doub = new Double(0);
    int current = 0;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;

    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;

    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;

    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;

    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;

    // Timing stats
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;

    // sizes
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;
    resultTypes[current++] = doub;

    resultTypes[current++] = "";

    // add any additional measures
    for (int i = 0; i < addm; i++) {
      resultTypes[current++] = doub;
    }
    if (current != RESULT_SIZE + addm) {
      throw new Error("ResultTypes didn't fit RESULT_SIZE");
    }
    return resultTypes;
  }

  /**
   * Gets the names of each of the result columns produced for a single run. The
   * number of result fields must be constant for a given SplitEvaluator.
   *
   * @return an array containing the name of each result column
   */
  @Override
  public String[] getResultNames() {
    int addm = (m_AdditionalMeasures != null) ? m_AdditionalMeasures.length : 0;
    String[] resultNames = new String[RESULT_SIZE + addm];
    int current = 0;
    resultNames[current++] = "Number_of_training_instances";
    resultNames[current++] = "Number_of_testing_instances";

    // Basic performance stats - right vs wrong
    resultNames[current++] = "Number_correct";
    resultNames[current++] = "Number_incorrect";
    resultNames[current++] = "Number_unclassified";
    resultNames[current++] = "Percent_correct";
    resultNames[current++] = "Percent_incorrect";
    resultNames[current++] = "Percent_unclassified";
    resultNames[current++] = "Total_cost";
    resultNames[current++] = "Average_cost";

    // Sensitive stats - certainty of predictions
    resultNames[current++] = "Mean_absolute_error";
    resultNames[current++] = "Root_mean_squared_error";
    resultNames[current++] = "Relative_absolute_error";
    resultNames[current++] = "Root_relative_squared_error";

    // SF stats
    resultNames[current++] = "SF_prior_entropy";
    resultNames[current++] = "SF_scheme_entropy";
    resultNames[current++] = "SF_entropy_gain";
    resultNames[current++] = "SF_mean_prior_entropy";
    resultNames[current++] = "SF_mean_scheme_entropy";
    resultNames[current++] = "SF_mean_entropy_gain";

    // K&B stats
    resultNames[current++] = "KB_information";
    resultNames[current++] = "KB_mean_information";
    resultNames[current++] = "KB_relative_information";

    // Timing stats
    resultNames[current++] = "Elapsed_Time_training";
    resultNames[current++] = "Elapsed_Time_testing";
    resultNames[current++] = "UserCPU_Time_training";
    resultNames[current++] = "UserCPU_Time_testing";
    resultNames[current++] = "UserCPU_Time_millis_training";
    resultNames[current++] = "UserCPU_Time_millis_testing";

    // sizes
    resultNames[current++] = "Serialized_Model_Size";
    resultNames[current++] = "Serialized_Train_Set_Size";
    resultNames[current++] = "Serialized_Test_Set_Size";

    // Classifier defined extras
    resultNames[current++] = "Summary";
    // add any additional measures
    for (int i = 0; i < addm; i++) {
      resultNames[current++] = m_AdditionalMeasures[i];
    }
    if (current != RESULT_SIZE + addm) {
      throw new Error("ResultNames didn't fit RESULT_SIZE");
    }
    return resultNames;
  }

  /**
   * Gets the results for the supplied train and test datasets. Now performs a
   * deep copy of the classifier before it is built and evaluated (just in case
   * the classifier is not initialized properly in buildClassifier()).
   *
   * @param train the training Instances.
   * @param test the testing Instances.
   * @return 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 a problem occurs while getting the results
   */
  @Override
  public Object[] getResult(Instances train, Instances test) throws Exception {

    if (train.classAttribute().type() != Attribute.NOMINAL) {
      throw new Exception("Class attribute is not nominal!");
    }
    if (m_Template == null) {
      throw new Exception("No classifier has been specified");
    }
    ThreadMXBean thMonitor = ManagementFactory.getThreadMXBean();
    boolean canMeasureCPUTime = thMonitor.isThreadCpuTimeSupported();
    if (canMeasureCPUTime && !thMonitor.isThreadCpuTimeEnabled()) {
      thMonitor.setThreadCpuTimeEnabled(true);
    }

    int addm = (m_AdditionalMeasures != null) ? m_AdditionalMeasures.length : 0;
    Object[] result = new Object[RESULT_SIZE + addm];
    long thID = Thread.currentThread().getId();
    long CPUStartTime = -1, trainCPUTimeElapsed = -1, testCPUTimeElapsed = -1, trainTimeStart, trainTimeElapsed, testTimeStart, testTimeElapsed;

    String costName = train.relationName() + CostMatrix.FILE_EXTENSION;
    File costFile = new File(getOnDemandDirectory(), costName);
    if (!costFile.exists()) {
      throw new Exception("On-demand cost file doesn't exist: " + costFile);
    }
    CostMatrix costMatrix = new CostMatrix(new BufferedReader(new FileReader(
      costFile)));

    Evaluation eval = new Evaluation(train, costMatrix);
    m_Classifier = AbstractClassifier.makeCopy(m_Template);

    trainTimeStart = System.currentTimeMillis();
    if (canMeasureCPUTime) {
      CPUStartTime = thMonitor.getThreadUserTime(thID);
    }
    m_Classifier.buildClassifier(train);
    if (canMeasureCPUTime) {
      trainCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
    }
    trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
    testTimeStart = System.currentTimeMillis();
    if (canMeasureCPUTime) {
      CPUStartTime = thMonitor.getThreadUserTime(thID);
    }
    eval.evaluateModel(m_Classifier, test);
    if (canMeasureCPUTime) {
      testCPUTimeElapsed = thMonitor.getThreadUserTime(thID) - CPUStartTime;
    }
    testTimeElapsed = System.currentTimeMillis() - testTimeStart;
    thMonitor = null;

    m_result = eval.toSummaryString();
    // The results stored are all per instance -- can be multiplied by the
    // number of instances to get absolute numbers
    int current = 0;
    result[current++] = new Double(train.numInstances());
    result[current++] = new Double(eval.numInstances());

    result[current++] = new Double(eval.correct());
    result[current++] = new Double(eval.incorrect());
    result[current++] = new Double(eval.unclassified());
    result[current++] = new Double(eval.pctCorrect());
    result[current++] = new Double(eval.pctIncorrect());
    result[current++] = new Double(eval.pctUnclassified());
    result[current++] = new Double(eval.totalCost());
    result[current++] = new Double(eval.avgCost());

    result[current++] = new Double(eval.meanAbsoluteError());
    result[current++] = new Double(eval.rootMeanSquaredError());
    result[current++] = new Double(eval.relativeAbsoluteError());
    result[current++] = new Double(eval.rootRelativeSquaredError());

    result[current++] = new Double(eval.SFPriorEntropy());
    result[current++] = new Double(eval.SFSchemeEntropy());
    result[current++] = new Double(eval.SFEntropyGain());
    result[current++] = new Double(eval.SFMeanPriorEntropy());
    result[current++] = new Double(eval.SFMeanSchemeEntropy());
    result[current++] = new Double(eval.SFMeanEntropyGain());

    // K&B stats
    result[current++] = new Double(eval.KBInformation());
    result[current++] = new Double(eval.KBMeanInformation());
    result[current++] = new Double(eval.KBRelativeInformation());

    // Timing stats
    result[current++] = new Double(trainTimeElapsed / 1000.0);
    result[current++] = new Double(testTimeElapsed / 1000.0);
    if (canMeasureCPUTime) {
      result[current++] =
        new Double((trainCPUTimeElapsed / 1000000.0) / 1000.0);
      result[current++] = new Double((testCPUTimeElapsed / 1000000.0) / 1000.0);

      result[current++] =
        new Double(trainCPUTimeElapsed / 1000000.0);
      result[current++] = new Double(testCPUTimeElapsed / 1000000.0);
    } else {
      result[current++] = new Double(Utils.missingValue());
      result[current++] = new Double(Utils.missingValue());
      result[current++] = new Double(Utils.missingValue());
      result[current++] = new Double(Utils.missingValue());
    }

    // sizes
    ByteArrayOutputStream bastream = new ByteArrayOutputStream();
    ObjectOutputStream oostream = new ObjectOutputStream(bastream);
    oostream.writeObject(m_Classifier);
    result[current++] = new Double(bastream.size());
    bastream = new ByteArrayOutputStream();
    oostream = new ObjectOutputStream(bastream);
    oostream.writeObject(train);
    result[current++] = new Double(bastream.size());
    bastream = new ByteArrayOutputStream();
    oostream = new ObjectOutputStream(bastream);
    oostream.writeObject(test);
    result[current++] = new Double(bastream.size());

    if (m_Classifier instanceof Summarizable) {
      result[current++] = ((Summarizable) m_Classifier).toSummaryString();
    } else {
      result[current++] = null;
    }

    for (int i = 0; i < addm; i++) {
      if (m_doesProduce[i]) {
        try {
          double dv = ((AdditionalMeasureProducer) m_Classifier)
            .getMeasure(m_AdditionalMeasures[i]);
          if (!Utils.isMissingValue(dv)) {
            Double value = new Double(dv);
            result[current++] = value;
          } else {
            result[current++] = null;
          }
        } catch (Exception ex) {
          System.err.println(ex);
        }
      } else {
        result[current++] = null;
      }
    }

    if (current != RESULT_SIZE + addm) {
      throw new Error("Results didn't fit RESULT_SIZE");
    }

    m_Evaluation = eval;

    return result;
  }

  /**
   * Returns a text description of the split evaluator.
   *
   * @return a text description of the split evaluator.
   */
  @Override
  public String toString() {

    String result = "CostSensitiveClassifierSplitEvaluator: ";
    if (m_Template == null) {
      return result + " classifier";
    }
    return result + m_Template.getClass().getName() + " " + m_ClassifierOptions
      + "(version " + m_ClassifierVersion + ")";
  }

  /**
   * Returns the revision string.
   *
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 11323 $");
  }
} // CostSensitiveClassifierSplitEvaluator