samoa-api/src/main/java/org/apache/samoa/moa/classifiers/core/attributeclassobservers/NominalAttributeClassObserver.java

/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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 limitations
 * under the License.
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package org.apache.samoa.moa.classifiers.core.attributeclassobservers;

import org.apache.samoa.moa.classifiers.core.AttributeSplitSuggestion;
import org.apache.samoa.moa.classifiers.core.conditionaltests.NominalAttributeBinaryTest;
import org.apache.samoa.moa.classifiers.core.conditionaltests.NominalAttributeMultiwayTest;
import org.apache.samoa.moa.classifiers.core.splitcriteria.SplitCriterion;
import org.apache.samoa.moa.core.AutoExpandVector;
import org.apache.samoa.moa.core.DoubleVector;
import org.apache.samoa.moa.core.ObjectRepository;
import org.apache.samoa.moa.core.Utils;
import org.apache.samoa.moa.options.AbstractOptionHandler;
import org.apache.samoa.moa.tasks.TaskMonitor;

/**
 * Class for observing the class data distribution for a nominal attribute. 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 NominalAttributeClassObserver extends AbstractOptionHandler implements DiscreteAttributeClassObserver {

  private static final long serialVersionUID = 1L;

  protected double totalWeightObserved = 0.0;

  protected double missingWeightObserved = 0.0;

  public AutoExpandVector<DoubleVector> attValDistPerClass = new AutoExpandVector<>();

  @Override
  public void observeAttributeClass(double attVal, int classVal, double weight) {
    if (Utils.isMissingValue(attVal)) {
      this.missingWeightObserved += weight;
    } else {
      int attValInt = (int) attVal;
      DoubleVector valDist = this.attValDistPerClass.get(classVal);
      if (valDist == null) {
        valDist = new DoubleVector();
        this.attValDistPerClass.set(classVal, valDist);
      }
      valDist.addToValue(attValInt, weight);
    }
    this.totalWeightObserved += weight;
  }

  @Override
  public double probabilityOfAttributeValueGivenClass(double attVal,
      int classVal) {
    DoubleVector obs = this.attValDistPerClass.get(classVal);
    return obs != null ? (obs.getValue((int) attVal) + 1.0)
        / (obs.sumOfValues() + obs.numValues()) : 0.0;
  }

  public double totalWeightOfClassObservations() {
    return this.totalWeightObserved;
  }

  public double weightOfObservedMissingValues() {
    return this.missingWeightObserved;
  }

  @Override
  public AttributeSplitSuggestion getBestEvaluatedSplitSuggestion(
      SplitCriterion criterion, double[] preSplitDist, int attIndex,
      boolean binaryOnly) {
    AttributeSplitSuggestion bestSuggestion = null;
    int maxAttValsObserved = getMaxAttValsObserved();
    if (!binaryOnly) {
      double[][] postSplitDists = getClassDistsResultingFromMultiwaySplit(maxAttValsObserved);
      double merit = criterion.getMeritOfSplit(preSplitDist,
          postSplitDists);
      bestSuggestion = new AttributeSplitSuggestion(
          new NominalAttributeMultiwayTest(attIndex), postSplitDists,
          merit);
    }
    for (int valIndex = 0; valIndex < maxAttValsObserved; valIndex++) {
      double[][] postSplitDists = getClassDistsResultingFromBinarySplit(valIndex);
      double merit = criterion.getMeritOfSplit(preSplitDist,
          postSplitDists);
      if ((bestSuggestion == null) || (merit > bestSuggestion.merit)) {
        bestSuggestion = new AttributeSplitSuggestion(
            new NominalAttributeBinaryTest(attIndex, valIndex),
            postSplitDists, merit);
      }
    }
    return bestSuggestion;
  }

  public int getMaxAttValsObserved() {
    int maxAttValsObserved = 0;
    for (DoubleVector attValDist : this.attValDistPerClass) {
      if ((attValDist != null)
          && (attValDist.numValues() > maxAttValsObserved)) {
        maxAttValsObserved = attValDist.numValues();
      }
    }
    return maxAttValsObserved;
  }

  public double[][] getClassDistsResultingFromMultiwaySplit(
      int maxAttValsObserved) {
    DoubleVector[] resultingDists = new DoubleVector[maxAttValsObserved];
    for (int i = 0; i < resultingDists.length; i++) {
      resultingDists[i] = new DoubleVector();
    }
    for (int i = 0; i < this.attValDistPerClass.size(); i++) {
      DoubleVector attValDist = this.attValDistPerClass.get(i);
      if (attValDist != null) {
        for (int j = 0; j < attValDist.numValues(); j++) {
          resultingDists[j].addToValue(i, attValDist.getValue(j));
        }
      }
    }
    double[][] distributions = new double[maxAttValsObserved][];
    for (int i = 0; i < distributions.length; i++) {
      distributions[i] = resultingDists[i].getArrayRef();
    }
    return distributions;
  }

  public double[][] getClassDistsResultingFromBinarySplit(int valIndex) {
    DoubleVector equalsDist = new DoubleVector();
    DoubleVector notEqualDist = new DoubleVector();
    for (int i = 0; i < this.attValDistPerClass.size(); i++) {
      DoubleVector attValDist = this.attValDistPerClass.get(i);
      if (attValDist != null) {
        for (int j = 0; j < attValDist.numValues(); j++) {
          if (j == valIndex) {
            equalsDist.addToValue(i, attValDist.getValue(j));
          } else {
            notEqualDist.addToValue(i, attValDist.getValue(j));
          }
        }
      }
    }
    return new double[][] { equalsDist.getArrayRef(),
        notEqualDist.getArrayRef() };
  }

  @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.");
  }

}