supportvectormachinebuild.java

一个数据挖掘软件ALPHAMINERR的整个过程的JAVA版源代码

/*
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 *    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 1.0
  */
package com.prudsys.pdm.Examples;

import java.io.FileWriter;

import com.prudsys.pdm.Automat.MiningAutomationAssignment;
import com.prudsys.pdm.Core.MiningAlgorithm;
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.RegressionDeviationAssessment;
import com.prudsys.pdm.Models.Regression.SVM.SupportVectorCallback;
import com.prudsys.pdm.Models.Regression.SVM.SupportVectorSettings;
import com.prudsys.pdm.Transform.Special.NumTargetStream;
import com.prudsys.pdm.Transform.Special.ZetNormalStream;
import com.prudsys.pdm.Utils.GeneralUtils;
import com.prudsys.pdm.Utils.PmmlUtils;

/**
 * Builds an SVM regression model using LIBSVM and writes it to
 * PMML file 'SupportVectorMachineModel.xml'.
 */
public class SupportVectorMachineBuild extends BasisExample {

  /**
   * Empty constructor.
   */
  public SupportVectorMachineBuild() {
  }

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

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

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

    // Numerization of all categorical attributes (with respect to target):
    NumTargetStream nums = new NumTargetStream( inputData0 );
    nums.setTargetAttributeName( targetAttribute.getName() );
    nums.setExcludedAttributeName( targetAttribute.getName() );

    // Z Normalization of all (now numeric) attributes:
    ZetNormalStream lns = new ZetNormalStream( nums.createTransformedStream() );
    lns.setExcludedAttributeName( targetAttribute.getName() );

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

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

    // Assign settings:
    miningSettings.setTarget(targetAttribute);
    miningSettings.setSvmType( SupportVectorSettings.SVM_EPSILON_SVR );
    miningSettings.setKernelType( SupportVectorSettings.KERNEL_RBF);
    miningSettings.setC(1.0);
    miningSettings.setDegree(2.0);
    miningSettings.setGamma(1.0);
    miningSettings.setLossEpsilon(0.1);
    miningSettings.verifySettings();

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

    // 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("cacheSize", "60");
    GeneralUtils.displayMiningAlgSpecParameters(miningAlgorithmSpecification);

    // Create automation parameter, if automation is required:
    MiningAutomationAssignment maa = new MiningAutomationAssignment();
    RegressionDeviationAssessment rda = new RegressionDeviationAssessment();
    rda.setAssessmentData(inputData);
    maa.setMiningModelAssessment( rda );
    maa.setMiningAutomationCallback( new SupportVectorCallback() );
    maa.setMinAssessment(0);
    maa.setMaxAssessment(1.0);
    maa.setMaxIterationNumber(40);

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

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

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

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

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

  /**
   * Example of building an SVM model.
   *
   * @param args arguments (ignored)
   */
  public static void main(String[] args) {

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