GaussianNumericAttributeClassObserver.java

package com.yahoo.labs.samoa.moa.classifiers.core.attributeclassobservers;

/*
 * #%L
 * SAMOA
 * %%
 *    Copyright (C) 2007 University of Waikato, Hamilton, New Zealand
 * %%
 * 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
 * limitations under the License.
 * #L%
 */

import com.yahoo.labs.samoa.moa.core.ObjectRepository;
import com.yahoo.labs.samoa.moa.tasks.TaskMonitor;
import com.yahoo.labs.samoa.moa.core.Utils;

import java.util.Set;
import java.util.TreeSet;
import com.yahoo.labs.samoa.moa.classifiers.core.AttributeSplitSuggestion;
import com.yahoo.labs.samoa.moa.classifiers.core.conditionaltests.NumericAttributeBinaryTest;
import com.yahoo.labs.samoa.moa.classifiers.core.splitcriteria.SplitCriterion;

import com.yahoo.labs.samoa.moa.core.AutoExpandVector;
import com.yahoo.labs.samoa.moa.core.DoubleVector;
import com.yahoo.labs.samoa.moa.core.GaussianEstimator;
import com.yahoo.labs.samoa.moa.options.AbstractOptionHandler;
import com.github.javacliparser.IntOption;

/**
 * Class for observing the class data distribution for a numeric attribute using gaussian estimators.
 * 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 GaussianNumericAttributeClassObserver extends AbstractOptionHandler
        implements NumericAttributeClassObserver {

    private static final long serialVersionUID = 1L;

    protected DoubleVector minValueObservedPerClass = new DoubleVector();

    protected DoubleVector maxValueObservedPerClass = new DoubleVector();

    protected AutoExpandVector<GaussianEstimator> attValDistPerClass = new AutoExpandVector<>();

    /**
     * @param classVal
     * @return The requested Estimator if it exists, or null if not present.
     */
    public GaussianEstimator getEstimator(int classVal) {
    	return this.attValDistPerClass.get(classVal);
    }
    
    public IntOption numBinsOption = new IntOption("numBins", 'n',
            "The number of bins.", 10, 1, Integer.MAX_VALUE);

    @Override
    public void observeAttributeClass(double attVal, int classVal, double weight) {
        if (!Utils.isMissingValue(attVal)) {
            GaussianEstimator valDist = this.attValDistPerClass.get(classVal);
            if (valDist == null) {
                valDist = new GaussianEstimator();
                this.attValDistPerClass.set(classVal, valDist);
                this.minValueObservedPerClass.setValue(classVal, attVal);
                this.maxValueObservedPerClass.setValue(classVal, attVal);
            } else {
                if (attVal < this.minValueObservedPerClass.getValue(classVal)) {
                    this.minValueObservedPerClass.setValue(classVal, attVal);
                }
                if (attVal > this.maxValueObservedPerClass.getValue(classVal)) {
                    this.maxValueObservedPerClass.setValue(classVal, attVal);
                }
            }
            valDist.addObservation(attVal, weight);
        }
    }

    @Override
    public double probabilityOfAttributeValueGivenClass(double attVal,
            int classVal) {
        GaussianEstimator obs = this.attValDistPerClass.get(classVal);
        return obs != null ? obs.probabilityDensity(attVal) : 0.0;
    }

    @Override
    public AttributeSplitSuggestion getBestEvaluatedSplitSuggestion(
            SplitCriterion criterion, double[] preSplitDist, int attIndex,
            boolean binaryOnly) {
        AttributeSplitSuggestion bestSuggestion = null;
        double[] suggestedSplitValues = getSplitPointSuggestions();
        for (double splitValue : suggestedSplitValues) {
            double[][] postSplitDists = getClassDistsResultingFromBinarySplit(splitValue);
            double merit = criterion.getMeritOfSplit(preSplitDist,
                    postSplitDists);
            if ((bestSuggestion == null) || (merit > bestSuggestion.merit)) {
                bestSuggestion = new AttributeSplitSuggestion(
                        new NumericAttributeBinaryTest(attIndex, splitValue,
                        true), postSplitDists, merit);
            }
        }
        return bestSuggestion;
    }

    public double[] getSplitPointSuggestions() {
        Set<Double> suggestedSplitValues = new TreeSet<>();
        double minValue = Double.POSITIVE_INFINITY;
        double maxValue = Double.NEGATIVE_INFINITY;
        for (int i = 0; i < this.attValDistPerClass.size(); i++) {
            GaussianEstimator estimator = this.attValDistPerClass.get(i);
            if (estimator != null) {
                if (this.minValueObservedPerClass.getValue(i) < minValue) {
                    minValue = this.minValueObservedPerClass.getValue(i);
                }
                if (this.maxValueObservedPerClass.getValue(i) > maxValue) {
                    maxValue = this.maxValueObservedPerClass.getValue(i);
                }
            }
        }
        if (minValue < Double.POSITIVE_INFINITY) {
            double range = maxValue - minValue;
            for (int i = 0; i < this.numBinsOption.getValue(); i++) {
                double splitValue = range / (this.numBinsOption.getValue() + 1.0) * (i + 1)
                        + minValue;
                if ((splitValue > minValue) && (splitValue < maxValue)) {
                    suggestedSplitValues.add(splitValue);
                }
            }
        }
        double[] suggestions = new double[suggestedSplitValues.size()];
        int i = 0;
        for (double suggestion : suggestedSplitValues) {
            suggestions[i++] = suggestion;
        }
        return suggestions;
    }

    // assume all values equal to splitValue go to lhs
    public double[][] getClassDistsResultingFromBinarySplit(double splitValue) {
        DoubleVector lhsDist = new DoubleVector();
        DoubleVector rhsDist = new DoubleVector();
        for (int i = 0; i < this.attValDistPerClass.size(); i++) {
            GaussianEstimator estimator = this.attValDistPerClass.get(i);
            if (estimator != null) {
                if (splitValue < this.minValueObservedPerClass.getValue(i)) {
                    rhsDist.addToValue(i, estimator.getTotalWeightObserved());
                } else if (splitValue >= this.maxValueObservedPerClass.getValue(i)) {
                    lhsDist.addToValue(i, estimator.getTotalWeightObserved());
                } else {
                    double[] weightDist = estimator.estimatedWeight_LessThan_EqualTo_GreaterThan_Value(splitValue);
                    lhsDist.addToValue(i, weightDist[0] + weightDist[1]);
                    rhsDist.addToValue(i, weightDist[2]);
                }
            }
        }
        return new double[][]{lhsDist.getArrayRef(), rhsDist.getArrayRef()};
    }

    @Override
    public void getDescription(StringBuilder sb, int indent) {
    }

    @Override
    protected void prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) {
    }

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