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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This is the stable version. Apart from bugfixes, this version does not receive any other updates.
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/*
 *   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 .
 */

/*
 *    ParallelIteratedSingleClassifierEnhancer.java
 *    Copyright (C) 2009-2014 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.atomic.AtomicInteger;

import weka.core.Instances;
import weka.core.Option;
import weka.core.Utils;

/**
 * Abstract utility class for handling settings common to meta classifiers that
 * build an ensemble in parallel from a single base learner.
 *
 * @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
 * @author Bernhard Pfahringer ([email protected])
 * @version $Revision: 11909 $
 */
public abstract class ParallelIteratedSingleClassifierEnhancer extends
  IteratedSingleClassifierEnhancer {

  /** For serialization */
  private static final long serialVersionUID = -5026378741833046436L;

  /** The number of threads to have executing at any one time */
  protected int m_numExecutionSlots = 1;

  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  @Override
  public Enumeration
   *
   * @param options the list of options as an array of strings
   * @exception Exception if an option is not supported
   */
  @Override
  public void setOptions(String[] options) throws Exception {

    String iterations = Utils.getOption("num-slots", options);
    if (iterations.length() != 0) {
      setNumExecutionSlots(Integer.parseInt(iterations));
    } else {
      setNumExecutionSlots(1);
    }

    super.setOptions(options);
  }

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

    String[] superOptions = super.getOptions();
    String[] options = new String[superOptions.length + 2];

    int current = 0;
    options[current++] = "-num-slots";
    options[current++] = "" + getNumExecutionSlots();

    System.arraycopy(superOptions, 0, options, current, superOptions.length);

    return options;
  }

  /**
   * Set the number of execution slots (threads) to use for building the members
   * of the ensemble.
   *
   * @param numSlots the number of slots to use.
   */
  public void setNumExecutionSlots(int numSlots) {
    m_numExecutionSlots = numSlots;
  }

  /**
   * Get the number of execution slots (threads) to use for building the members
   * of the ensemble.
   *
   * @return the number of slots to use
   */
  public int getNumExecutionSlots() {
    return m_numExecutionSlots;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String numExecutionSlotsTipText() {
    return "The number of execution slots (threads) to use for "
      + "constructing the ensemble.";
  }

  /**
   * Stump method for building the classifiers
   *
   * @param data the training data to be used for generating the ensemble
   * @exception Exception if the classifier could not be built successfully
   */
  @Override
  public void buildClassifier(Instances data) throws Exception {
    super.buildClassifier(data);

    if (m_numExecutionSlots < 0) {
      throw new Exception("Number of execution slots needs to be >= 0!");
    }
  }

  /**
   * Start the pool of execution threads
   */

  /**
   * Does the actual construction of the ensemble
   *
   * @throws Exception if something goes wrong during the training process
   */
  protected void buildClassifiers() throws Exception {

    if (m_numExecutionSlots != 1) {

      int numCores =
        (m_numExecutionSlots == 0) ? Runtime.getRuntime().availableProcessors()
          : m_numExecutionSlots;
      ExecutorService executorPool = Executors.newFixedThreadPool(numCores);

      final CountDownLatch doneSignal =
        new CountDownLatch(m_Classifiers.length);
      final AtomicInteger numFailed = new AtomicInteger();

      for (int i = 0; i < m_Classifiers.length; i++) {

        final Classifier currentClassifier = m_Classifiers[i];
        // MultiClassClassifier may produce occasional NULL classifiers ...
        if (currentClassifier == null)
          continue;
        final int iteration = i;

        if (m_Debug) {
          System.out.print("Training classifier (" + (i + 1) + ")");
        }
        Runnable newTask = new Runnable() {
          @Override
          public void run() {
            try {
              currentClassifier.buildClassifier(getTrainingSet(iteration));
            } catch (Throwable ex) {
              ex.printStackTrace();
              numFailed.incrementAndGet();
              if (m_Debug) {
                System.err.println("Iteration " + iteration + " failed!");
              }
            } finally {
              doneSignal.countDown();
            }
          }
        };
        // launch this task
        executorPool.submit(newTask);
      }
      // wait for all tasks to finish, then shutdown pool
      doneSignal.await();
      executorPool.shutdownNow();
      if (m_Debug && numFailed.intValue() > 0) {
        System.err
          .println("Problem building classifiers - some iterations failed.");
      }

    } else {
      // simple single-threaded execution
      for (int i = 0; i < m_Classifiers.length; i++) {
        m_Classifiers[i].buildClassifier(getTrainingSet(i));
      }
    }
  }

  /**
   * Gets a training set for a particular iteration. Implementations need to be
   * careful with thread safety and should probably be synchronized to be on the
   * safe side.
   *
   * @param iteration the number of the iteration for the requested training set
   * @return the training set for the supplied iteration number
   * @throws Exception if something goes wrong.
   */
  protected abstract Instances getTrainingSet(int iteration) throws Exception;
}