neuralnetworkbuild.java

一个数据挖掘软件ALPHAMINERR的整个过程的JAVA版源代码

/*
 *    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.
 */

 /**
  * Title: XELOPES Data Mining Library
  * Description: The XELOPES library is an open platform-independent and data-source-independent library for Embedded Data Mining.
  * Copyright: Copyright (c) 2002 Prudential Systems Software GmbH
  * Company: ZSoft (www.zsoft.ru), Prudsys (www.prudsys.com)
  * @author Carsten Weisse
  * @author Michael Thess
  * @version 1.2
  */
package com.prudsys.pdm.Examples;

import java.io.FileWriter;

import com.prudsys.pdm.Core.MiningAlgorithmSpecification;
import com.prudsys.pdm.Core.MiningAttribute;
import com.prudsys.pdm.Core.MiningDataSpecification;
import com.prudsys.pdm.Core.MiningException;
import com.prudsys.pdm.Core.MiningModel;
import com.prudsys.pdm.Input.MiningInputStream;
import com.prudsys.pdm.Input.Records.Arff.MiningArffStream;
import com.prudsys.pdm.Models.Regression.NeuralNetwork.NeuralNetworkAlgorithm;
import com.prudsys.pdm.Models.Regression.NeuralNetwork.NeuralNetworkSettings;
import com.prudsys.pdm.Transform.Special.BinningStream;
import com.prudsys.pdm.Transform.Special.LinearNormalStream;
import com.prudsys.pdm.Utils.GeneralUtils;
import com.prudsys.pdm.Utils.PmmlUtils;

/**
 * Builds a neural network using the Backpropagation algorithm and writes it to
 * PMML file 'NeuralNetworkModel.xml'.
 */
public class NeuralNetworkBuild extends BasisExample {

  /**
   * Empty constructor.
   */
  public NeuralNetworkBuild() {
    debug = 0;
  }

  /**
   * Run the example of this class.
   *
   * @throws Exception error while example is running
   */
  public void runExample() throws Exception {

    // Open data source 'vowel' and get metadata:
    MiningInputStream inputData0 = new MiningArffStream("data/arff/vowel.arff");
    MiningDataSpecification metaData = inputData0.getMetaData();

    // Get target attribute:
    MiningAttribute targetAttribute = (MiningAttribute) metaData.getMiningAttribute("class");

    // (-1,+1) Normalization of all (now numeric) attributes:
    LinearNormalStream lns = new LinearNormalStream( inputData0 );
    lns.setLowerBound(-1);
    lns.setUpperBound(+1);
    lns.setExcludedAttributeName( targetAttribute.getName() );

    // Binning of all categorical attributes:
    BinningStream bns = new BinningStream( lns.createTransformedStream() );
    bns.setExcludedAttributeName( targetAttribute.getName() );

    // Create transformed stream:
    MiningInputStream inputData = bns.createTransformedStream();
    metaData = inputData.getMetaData();

    // Create MiningSettings object and assign metadata:
    NeuralNetworkSettings miningSettings = new NeuralNetworkSettings();
    miningSettings.setDataSpecification(metaData);

    // Assign settings:
    miningSettings.setAutoBuildNetwork(true);
    miningSettings.setLearningType( NeuralNetworkSettings.BACK_PROPAGATION_WITH_MOMENTUM );
    miningSettings.setLearningRate(0.3);
    miningSettings.setMomentum(0.2);
    miningSettings.setMaxNumberOfIterations(20);
    miningSettings.setTarget(targetAttribute);
    miningSettings.verifySettings();

    // Get default mining algorithm specification from 'algorithms.xml':
    MiningAlgorithmSpecification miningAlgorithmSpecification =
      MiningAlgorithmSpecification.getMiningAlgorithmSpecification("Backpropagation", null);
    if (miningAlgorithmSpecification == null)
      throw new MiningException("Can't find application Backpropagation.");

    // Get class name from algorithms specification:
    String className = miningAlgorithmSpecification.getClassname();
    if (className == null)
      throw new MiningException("classname attribute expected.");

    // Set and display mining parameters:
    miningAlgorithmSpecification.setMAPValue("decreasingRate", "1");
    GeneralUtils.displayMiningAlgSpecParameters(miningAlgorithmSpecification);

    // Create algorithm object with default values:
    NeuralNetworkAlgorithm algorithm = (NeuralNetworkAlgorithm)
        GeneralUtils.createMiningAlgorithmInstance(className);

    // Put it all together:
    algorithm.setMiningInputStream(inputData);
    algorithm.setMiningSettings(miningSettings);
    algorithm.setMiningAlgorithmSpecification(miningAlgorithmSpecification);
    algorithm.verify();

    // Build the mining model:
    MiningModel model = algorithm.buildModel();
    System.out.println("calculation time [s]: " + algorithm.getTimeSpentToBuildModel());

    // Write to PMML:
    FileWriter writer = new FileWriter("data/pmml/NeuralNetworkModel.xml");
    model.writePmml(writer);

    // Show in browser:
    if (debug == 2)
      PmmlUtils.openPmmlBrowser("NeuralNetworkModel.xml");
  }

  /**
   * Example of building a neural network.
   *
   * @param args arguments (ignored)
   */
  public static void main(String[] args) {

    try {
      new NeuralNetworkBuild().runExample();
    }
    catch (Exception ex) {
      ex.printStackTrace();
    }
  }
}