source: branches/Classification-Extensions/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/QualityCalculator.cs @ 11622

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using HeuristicLab.Common;

namespace HeuristicLab.Problems.DataAnalysis {
  public class QualityCalculator //: IOnlineCalculator
  {

    public QualityCalculator(double positiveClassValue) {
      this.positiveClassValue = positiveClassValue;
      Reset();
    }

    private double positiveClassValue;
    private int truePositive, falsePositive, trueNegative, falseNegative;

    public double TruePositiveRate {
      get {
        double divisor = truePositive + falseNegative;
        return divisor.IsAlmost(0) ? double.NaN : truePositive / divisor;
      }
    }
    public double TrueNegativeRate {
      get {
        double divisor = falsePositive + trueNegative;
        return divisor.IsAlmost(0) ? double.NaN : trueNegative / divisor;
      }
    }
    public double PositivePredictiveValue {
      get {
        double divisor = truePositive + falsePositive;
        return divisor.IsAlmost(0) ? double.NaN : truePositive / divisor;
      }
    }
    public double NegativePredictiveValue {
      get {
        double divisor = trueNegative + falseNegative;
        return divisor.IsAlmost(0) ? double.NaN : trueNegative / divisor;
      }
    }
    public double FalsePositiveRate {
      get {
        double divisor = falsePositive + trueNegative;
        return divisor.IsAlmost(0) ? double.NaN : falsePositive / divisor;
      }
    }
    public double FalseDiscoveryRate {
      get {
        double divisor = falsePositive + truePositive;
        return divisor.IsAlmost(0) ? double.NaN : falsePositive / divisor;
      }
    }
    //#region IOnlineCalculator Members
    private OnlineCalculatorError errorState;
    public OnlineCalculatorError ErrorState {
      get { return errorState; }
    }

    /*
    public double Value
    {
        get { return FalsePositiveRate; }
    }*/

    public void Reset() {
      truePositive = 0;
      falseNegative = 0;
      trueNegative = 0;
      falseNegative = 0;
      errorState = OnlineCalculatorError.InsufficientElementsAdded;
    }
    public void Add(double original, double estimated) {
      // ignore cases where original is NaN completely 
      if (!double.IsNaN(original)) {
        if (original.IsAlmost(positiveClassValue) || estimated.IsAlmost(positiveClassValue)) //positive class/positive estimation
                {
          if (estimated.IsAlmost(original)) {
            truePositive++;
          } else {
            if (estimated.IsAlmost(positiveClassValue))
              falsePositive++;
            else
              falseNegative++;
          }
        } else { //negative class/negative estimation
          trueNegative++;
        }
        errorState = OnlineCalculatorError.None; // number of (non-NaN) samples >= 1
      }
    }
    //#endregion

    public void Calculate(IEnumerable<double> originalValues, IEnumerable<double> estimatedValues, out OnlineCalculatorError errorState) {
      IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
      IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();

      // always move forward both enumerators (do not use short-circuit evaluation!)
      while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
        double original = originalEnumerator.Current;
        double estimated = estimatedEnumerator.Current;
        Add(original, estimated);
        if (ErrorState != OnlineCalculatorError.None) break;
      }

      // check if both enumerators are at the end to make sure both enumerations have the same length
      if (ErrorState == OnlineCalculatorError.None &&
          (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext())) {
        throw new ArgumentException("Number of elements in originalValues and estimatedValues enumerations doesn't match.");
      } else {
        errorState = ErrorState;
      }
    }
  }
}