package com.yahoo.labs.samoa.evaluation;

/*
 * #%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.AbstractMOAObject;
import com.yahoo.labs.samoa.moa.core.Measurement;
import com.yahoo.labs.samoa.instances.Instance;
import com.yahoo.labs.samoa.instances.Utils;

/**
 * Classification evaluator that performs basic incremental evaluation.
 * 
 * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
 * @author Albert Bifet (abifet at cs dot waikato dot ac dot nz)
 * @version $Revision: 7 $
 */
public class BasicClassificationPerformanceEvaluator extends AbstractMOAObject implements
    ClassificationPerformanceEvaluator {

  private static final long serialVersionUID = 1L;

  protected double weightObserved;

  protected double weightCorrect;

  protected double[] columnKappa;

  protected double[] rowKappa;

  protected int numClasses;

  private double weightCorrectNoChangeClassifier;

  private int lastSeenClass;

  @Override
  public void reset() {
    reset(this.numClasses);
  }

  public void reset(int numClasses) {
    this.numClasses = numClasses;
    this.rowKappa = new double[numClasses];
    this.columnKappa = new double[numClasses];
    for (int i = 0; i < this.numClasses; i++) {
      this.rowKappa[i] = 0.0;
      this.columnKappa[i] = 0.0;
    }
    this.weightObserved = 0.0;
    this.weightCorrect = 0.0;
    this.weightCorrectNoChangeClassifier = 0.0;
    this.lastSeenClass = 0;
  }

  @Override
  public void addResult(Instance inst, double[] classVotes) {
    double weight = inst.weight();
    int trueClass = (int) inst.classValue();
    if (weight > 0.0) {
      if (this.weightObserved == 0) {
        reset(inst.numClasses());
      }
      this.weightObserved += weight;
      int predictedClass = Utils.maxIndex(classVotes);
      if (predictedClass == trueClass) {
        this.weightCorrect += weight;
      }
      if (rowKappa.length > 0) {
        this.rowKappa[predictedClass] += weight;
      }
      if (columnKappa.length > 0) {
        this.columnKappa[trueClass] += weight;
      }
    }
    if (this.lastSeenClass == trueClass) {
      this.weightCorrectNoChangeClassifier += weight;
    }
    this.lastSeenClass = trueClass;
  }

  @Override
  public Measurement[] getPerformanceMeasurements() {
    return new Measurement[] {
        new Measurement("classified instances",
            getTotalWeightObserved()),
        new Measurement("classifications correct (percent)",
            getFractionCorrectlyClassified() * 100.0),
        new Measurement("Kappa Statistic (percent)",
            getKappaStatistic() * 100.0),
        new Measurement("Kappa Temporal Statistic (percent)",
            getKappaTemporalStatistic() * 100.0)
    };

  }

  public double getTotalWeightObserved() {
    return this.weightObserved;
  }

  public double getFractionCorrectlyClassified() {
    return this.weightObserved > 0.0 ? this.weightCorrect
        / this.weightObserved : 0.0;
  }

  public double getFractionIncorrectlyClassified() {
    return 1.0 - getFractionCorrectlyClassified();
  }

  public double getKappaStatistic() {
    if (this.weightObserved > 0.0) {
      double p0 = getFractionCorrectlyClassified();
      double pc = 0.0;
      for (int i = 0; i < this.numClasses; i++) {
        pc += (this.rowKappa[i] / this.weightObserved)
            * (this.columnKappa[i] / this.weightObserved);
      }
      return (p0 - pc) / (1.0 - pc);
    } else {
      return 0;
    }
  }

  public double getKappaTemporalStatistic() {
    if (this.weightObserved > 0.0) {
      double p0 = this.weightCorrect / this.weightObserved;
      double pc = this.weightCorrectNoChangeClassifier / this.weightObserved;

      return (p0 - pc) / (1.0 - pc);
    } else {
      return 0;
    }
  }

  @Override
  public void getDescription(StringBuilder sb, int indent) {
    Measurement.getMeasurementsDescription(getPerformanceMeasurements(),
        sb, indent);
  }
}