BinaryTreeNumericAttributeClassObserver.java

package org.apache.samoa.moa.classifiers.core.attributeclassobservers;

/*
 * #%L
 * SAMOA
 * %%
 * Copyright (C) 2014 - 2015 Apache Software Foundation
 * %%
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 * 
 *      http://www.apache.org/licenses/LICENSE-2.0
 * 
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
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import java.io.Serializable;

import org.apache.samoa.moa.classifiers.core.AttributeSplitSuggestion;
import org.apache.samoa.moa.classifiers.core.conditionaltests.NumericAttributeBinaryTest;
import org.apache.samoa.moa.classifiers.core.splitcriteria.SplitCriterion;
import org.apache.samoa.moa.core.DoubleVector;
import org.apache.samoa.moa.core.ObjectRepository;
import org.apache.samoa.moa.options.AbstractOptionHandler;
import org.apache.samoa.moa.tasks.TaskMonitor;

/**
 * Class for observing the class data distribution for a numeric attribute using a binary tree. This observer monitors
 * the class distribution of a given attribute. Used in naive Bayes and decision trees to monitor data statistics on
 * leaves.
 * 
 * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
 * @version $Revision: 7 $
 */
public class BinaryTreeNumericAttributeClassObserver extends AbstractOptionHandler
    implements NumericAttributeClassObserver {

  private static final long serialVersionUID = 1L;

  public class Node implements Serializable {

    private static final long serialVersionUID = 1L;

    public double cut_point;

    public DoubleVector classCountsLeft = new DoubleVector();

    public DoubleVector classCountsRight = new DoubleVector();

    public Node left;

    public Node right;

    public Node(double val, int label, double weight) {
      this.cut_point = val;
      this.classCountsLeft.addToValue(label, weight);
    }

    public void insertValue(double val, int label, double weight) {
      if (val == this.cut_point) {
        this.classCountsLeft.addToValue(label, weight);
      } else if (val <= this.cut_point) {
        this.classCountsLeft.addToValue(label, weight);
        if (this.left == null) {
          this.left = new Node(val, label, weight);
        } else {
          this.left.insertValue(val, label, weight);
        }
      } else { // val > cut_point
        this.classCountsRight.addToValue(label, weight);
        if (this.right == null) {
          this.right = new Node(val, label, weight);
        } else {
          this.right.insertValue(val, label, weight);
        }
      }
    }
  }

  public Node root = null;

  @Override
  public void observeAttributeClass(double attVal, int classVal, double weight) {
    if (Double.isNaN(attVal)) { // Instance.isMissingValue(attVal)
    } else {
      if (this.root == null) {
        this.root = new Node(attVal, classVal, weight);
      } else {
        this.root.insertValue(attVal, classVal, weight);
      }
    }
  }

  @Override
  public double probabilityOfAttributeValueGivenClass(double attVal,
      int classVal) {
    // TODO: NaiveBayes broken until implemented
    return 0.0;
  }

  @Override
  public AttributeSplitSuggestion getBestEvaluatedSplitSuggestion(
      SplitCriterion criterion, double[] preSplitDist, int attIndex,
      boolean binaryOnly) {
    return searchForBestSplitOption(this.root, null, null, null, null, false,
        criterion, preSplitDist, attIndex);
  }

  protected AttributeSplitSuggestion searchForBestSplitOption(
      Node currentNode, AttributeSplitSuggestion currentBestOption,
      double[] actualParentLeft,
      double[] parentLeft, double[] parentRight, boolean leftChild,
      SplitCriterion criterion, double[] preSplitDist, int attIndex) {
    if (currentNode == null) {
      return currentBestOption;
    }
    DoubleVector leftDist = new DoubleVector();
    DoubleVector rightDist = new DoubleVector();
    if (parentLeft == null) {
      leftDist.addValues(currentNode.classCountsLeft);
      rightDist.addValues(currentNode.classCountsRight);
    } else {
      leftDist.addValues(parentLeft);
      rightDist.addValues(parentRight);
      if (leftChild) {
        // get the exact statistics of the parent value
        DoubleVector exactParentDist = new DoubleVector();
        exactParentDist.addValues(actualParentLeft);
        exactParentDist.subtractValues(currentNode.classCountsLeft);
        exactParentDist.subtractValues(currentNode.classCountsRight);

        // move the subtrees
        leftDist.subtractValues(currentNode.classCountsRight);
        rightDist.addValues(currentNode.classCountsRight);

        // move the exact value from the parent
        rightDist.addValues(exactParentDist);
        leftDist.subtractValues(exactParentDist);

      } else {
        leftDist.addValues(currentNode.classCountsLeft);
        rightDist.subtractValues(currentNode.classCountsLeft);
      }
    }
    double[][] postSplitDists = new double[][] { leftDist.getArrayRef(),
        rightDist.getArrayRef() };
    double merit = criterion.getMeritOfSplit(preSplitDist, postSplitDists);
    if ((currentBestOption == null) || (merit > currentBestOption.merit)) {
      currentBestOption = new AttributeSplitSuggestion(
          new NumericAttributeBinaryTest(attIndex,
              currentNode.cut_point, true), postSplitDists, merit);

    }
    currentBestOption = searchForBestSplitOption(currentNode.left,
        currentBestOption, currentNode.classCountsLeft.getArrayRef(), postSplitDists[0], postSplitDists[1], true,
        criterion, preSplitDist, attIndex);
    currentBestOption = searchForBestSplitOption(currentNode.right,
        currentBestOption, currentNode.classCountsLeft.getArrayRef(), postSplitDists[0], postSplitDists[1], false,
        criterion, preSplitDist, attIndex);
    return currentBestOption;
  }

  @Override
  public void getDescription(StringBuilder sb, int indent) {
    // TODO Auto-generated method stub
  }

  @Override
  protected void prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) {
    // TODO Auto-generated method stub
  }

  @Override
  public void observeAttributeTarget(double attVal, double target) {
    throw new UnsupportedOperationException("Not supported yet.");
  }

}