package com.yahoo.labs.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
 * limitations under the License.
 * #L%
 */

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.Utils;

import com.yahoo.labs.samoa.moa.core.AutoExpandVector;
import com.yahoo.labs.samoa.moa.core.DoubleVector;
import com.yahoo.labs.samoa.moa.core.GreenwaldKhannaQuantileSummary;
import com.yahoo.labs.samoa.moa.core.ObjectRepository;
import com.yahoo.labs.samoa.moa.options.AbstractOptionHandler;
import com.github.javacliparser.IntOption;
import com.yahoo.labs.samoa.moa.tasks.TaskMonitor;

/**
 * Class for observing the class data distribution for a numeric attribute using Greenwald and Khanna methodology. 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 GreenwaldKhannaNumericAttributeClassObserver extends AbstractOptionHandler implements
    NumericAttributeClassObserver {

  private static final long serialVersionUID = 1L;

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

  public IntOption numTuplesOption = new IntOption("numTuples", 'n',
      "The number of tuples.", 10, 1, Integer.MAX_VALUE);

  @Override
  public void observeAttributeClass(double attVal, int classVal, double weight) {
    if (!Utils.isMissingValue(attVal)) {
      GreenwaldKhannaQuantileSummary valDist = this.attValDistPerClass.get(classVal);
      if (valDist == null) {
        valDist = new GreenwaldKhannaQuantileSummary(this.numTuplesOption.getValue());
        this.attValDistPerClass.set(classVal, valDist);
      }
      // TODO: not taking weight into account
      valDist.insert(attVal);
    }
  }

  @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) {
    AttributeSplitSuggestion bestSuggestion = null;
    for (GreenwaldKhannaQuantileSummary qs : this.attValDistPerClass) {
      if (qs != null) {
        double[] cutpoints = qs.getSuggestedCutpoints();
        for (double cutpoint : cutpoints) {
          double[][] postSplitDists = getClassDistsResultingFromBinarySplit(cutpoint);
          double merit = criterion.getMeritOfSplit(preSplitDist,
              postSplitDists);
          if ((bestSuggestion == null)
              || (merit > bestSuggestion.merit)) {
            bestSuggestion = new AttributeSplitSuggestion(
                new NumericAttributeBinaryTest(attIndex,
                    cutpoint, true), postSplitDists, merit);
          }
        }
      }
    }
    return bestSuggestion;
  }

  // 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++) {
      GreenwaldKhannaQuantileSummary estimator = this.attValDistPerClass.get(i);
      if (estimator != null) {
        long countBelow = estimator.getCountBelow(splitValue);
        lhsDist.addToValue(i, countBelow);
        rhsDist.addToValue(i, estimator.getTotalCount() - countBelow);
      }
    }
    return new double[][] { lhsDist.getArrayRef(), rhsDist.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.");
  }
}