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

import java.io.FileReader;

import com.prudsys.pdm.Core.CategoricalAttribute;
import com.prudsys.pdm.Core.Category;
import com.prudsys.pdm.Core.MiningModel;
import com.prudsys.pdm.Models.AssociationRules.AssociationRulesMiningModel;
import com.prudsys.pdm.Models.AssociationRules.AssociationRulesSettings;
import com.prudsys.pdm.Models.AssociationRules.ItemSet;
import com.prudsys.pdm.Models.AssociationRules.RuleSet;

/**
 * Applies an association rules model from the PMML file 'AssociationRulesModel.xml'
 * to a data set.
 */
public class AssociationRulesApply extends BasisExample {

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

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

    // Read association rules from PMML file:
    MiningModel model = new AssociationRulesMiningModel();
    FileReader reader = new FileReader("data/pmml/AssociationRulesModel.xml");
    model.readPmml(reader);
    CategoricalAttribute categoryItemId = (CategoricalAttribute)
          ((AssociationRulesSettings) model.getMiningSettings()).getItemId();
    System.out.println("-------------> PMML model read successfully");

    // Build recommendations:
    ((AssociationRulesMiningModel) model).buildRecommendations();

    // Apply rules to new data vector (2, 5):
    ItemSet is = new ItemSet();
    is.addItem( 2 );   // direct key, not recommended
    is.addItem( (int) categoryItemId.getKey( new Category("5") ));  // correct input
    RuleSet rs = (RuleSet) ((AssociationRulesMiningModel) model).applyModel(is);
    if (rs != null) {
      System.out.println("nvec = " + rs.getSize());
      System.out.println("prem: " + rs.getPremise().toString());
      System.out.println("conc: " + rs.getConclusion().toString());
      System.out.println("Recommendation as string: ");
      for (int i = 0; i < rs.getPremise().getSize(); i++) {
        Category recItem = categoryItemId.getCategory(rs.getPremise().getItemAt(i));
        System.out.print(recItem.getDisplayValue());
        if (i < rs.getPremise().getSize()-1) System.out.print(", ");
      };
      System.out.print(" => ");
      for (int i = 0; i < rs.getConclusion().getSize(); i++) {
        Category recItem = categoryItemId.getCategory(rs.getConclusion().getItemAt(i));
        System.out.println(recItem.getDisplayValue());
        if (i < rs.getConclusion().getSize()-1) System.out.print(", ");
        else System.out.println();
      };
    }
    else
      System.out.println("No recommendations found.");
  }

  /**
   * Example of applying association rules to new data.
   *
   * @param args arguments (ignored)
   */
  public static void main(String[] args) {

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