source: branches/ParameterBinding/HeuristicLab.ExtLibs/HeuristicLab.LibSVM/1.6.3/LibSVM-1.6.3/PerformanceEvaluator.cs @ 12858

/*
 * SVM.NET Library
 * Copyright (C) 2008 Matthew Johnson
 * 
 * This program 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.
 * 
 * This program 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 this program.  If not, see <http://www.gnu.org/licenses/>.
 */


using System;
using System.Collections.Generic;
using System.Globalization;
using System.IO;

namespace SVM {
  /// <summary>
  /// Class encoding a member of a ranked set of labels.
  /// </summary>
  public class RankPair : IComparable<RankPair> {
    private double _score, _label;

    /// <summary>
    /// Constructor.
    /// </summary>
    /// <param name="score">Score for this pair</param>
    /// <param name="label">Label associated with the given score</param>
    public RankPair(double score, double label) {
      _score = score;
      _label = label;
    }

    /// <summary>
    /// The score for this pair.
    /// </summary>
    public double Score {
      get {
        return _score;
      }
    }

    /// <summary>
    /// The Label for this pair.
    /// </summary>
    public double Label {
      get {
        return _label;
      }
    }

    #region IComparable<RankPair> Members

    /// <summary>
    /// Compares this pair to another.  It will end up in a sorted list in decending score order.
    /// </summary>
    /// <param name="other">The pair to compare to</param>
    /// <returns>Whether this should come before or after the argument</returns>
    public int CompareTo(RankPair other) {
      return other.Score.CompareTo(Score);
    }

    #endregion

    /// <summary>
    /// Returns a string representation of this pair.
    /// </summary>
    /// <returns>A string in the for Score:Label</returns>
    public override string ToString() {
      return string.Format("{0}:{1}", Score, Label);
    }
  }

  /// <summary>
  /// Class encoding the point on a 2D curve.
  /// </summary>
  public class CurvePoint {
    private float _x, _y;

    /// <summary>
    /// Constructor.
    /// </summary>
    /// <param name="x">X coordinate</param>
    /// <param name="y">Y coordinate</param>
    public CurvePoint(float x, float y) {
      _x = x;
      _y = y;
    }

    /// <summary>
    /// X coordinate
    /// </summary>
    public float X {
      get {
        return _x;
      }
    }

    /// <summary>
    /// Y coordinate
    /// </summary>
    public float Y {
      get {
        return _y;
      }
    }

    /// <summary>
    /// Creates a string representation of this point.
    /// </summary>
    /// <returns>string in the form (x, y)</returns>
    public override string ToString() {
      return string.Format("({0}, {1})", _x, _y);
    }
  }

  /// <summary>
  /// Class which evaluates an SVM model using several standard techniques.
  /// </summary>
  public class PerformanceEvaluator {
    private class ChangePoint {
      public ChangePoint(int tp, int fp, int tn, int fn) {
        TP = tp;
        FP = fp;
        TN = tn;
        FN = fn;
      }

      public int TP, FP, TN, FN;

      public override string ToString() {
        return string.Format("{0}:{1}:{2}:{3}", TP, FP, TN, FN);
      }
    }

    private List<CurvePoint> _prCurve;
    private double _ap;

    private List<CurvePoint> _rocCurve;
    private double _auc;

    private List<RankPair> _data;
    private List<ChangePoint> _changes;

    /// <summary>
    /// Constructor.
    /// </summary>
    /// <param name="set">A pre-computed ranked pair set</param>
    public PerformanceEvaluator(List<RankPair> set) {
      _data = set;
      computeStatistics();
    }

    /// <summary>
    /// Constructor.
    /// </summary>
    /// <param name="model">Model to evaluate</param>
    /// <param name="problem">Problem to evaluate</param>
    /// <param name="category">Label to be evaluate for</param>
    public PerformanceEvaluator(Model model, Problem problem, double category) : this(model, problem, category, "tmp.results") { }
    /// <summary>
    /// Constructor.
    /// </summary>
    /// <param name="model">Model to evaluate</param>
    /// <param name="problem">Problem to evaluate</param>
    /// <param name="resultsFile">Results file for output</param>
    /// <param name="category">Category to evaluate for</param>
    public PerformanceEvaluator(Model model, Problem problem, double category, string resultsFile) {
      Prediction.Predict(problem, resultsFile, model, true);
      parseResultsFile(resultsFile, problem.Y, category);

      computeStatistics();
    }

    /// <summary>
    /// Constructor.
    /// </summary>
    /// <param name="resultsFile">Results file</param>
    /// <param name="correctLabels">The correct labels of each data item</param>
    /// <param name="category">The category to evaluate for</param>
    public PerformanceEvaluator(string resultsFile, double[] correctLabels, double category) {
      parseResultsFile(resultsFile, correctLabels, category);
      computeStatistics();
    }

    private void parseResultsFile(string resultsFile, double[] labels, double category) {
      StreamReader input = new StreamReader(resultsFile);
      string[] parts = input.ReadLine().Split(new char[] { ' ' }, StringSplitOptions.RemoveEmptyEntries);
      int confidenceIndex = -1;
      for (int i = 1; i < parts.Length; i++)
        if (double.Parse(parts[i], CultureInfo.InvariantCulture) == category) {
          confidenceIndex = i;
          break;
        }
      _data = new List<RankPair>();
      for (int i = 0; i < labels.Length; i++) {
        parts = input.ReadLine().Split(new char[] { ' ' }, StringSplitOptions.RemoveEmptyEntries);
        double confidence = double.Parse(parts[confidenceIndex], CultureInfo.InvariantCulture);
        _data.Add(new RankPair(confidence, labels[i] == category ? 1 : 0));
      }
      input.Close();
    }

    private void computeStatistics() {
      _data.Sort();

      findChanges();
      computePR();
      computeRoC();
    }

    private void findChanges() {
      int tp, fp, tn, fn;
      tp = fp = tn = fn = 0;
      for (int i = 0; i < _data.Count; i++) {
        if (_data[i].Label == 1)
          fn++;
        else tn++;
      }
      _changes = new List<ChangePoint>();
      for (int i = 0; i < _data.Count; i++) {
        if (_data[i].Label == 1) {
          tp++;
          fn--;
        } else {
          fp++;
          tn--;
        }
        _changes.Add(new ChangePoint(tp, fp, tn, fn));
      }
    }

    private float computePrecision(ChangePoint p) {
      return (float)p.TP / (p.TP + p.FP);
    }

    private float computeRecall(ChangePoint p) {
      return (float)p.TP / (p.TP + p.FN);
    }

    private void computePR() {
      _prCurve = new List<CurvePoint>();
      _prCurve.Add(new CurvePoint(0, 1));
      float precision = computePrecision(_changes[0]);
      float recall = computeRecall(_changes[0]);
      float precisionSum = 0;
      if (_changes[0].TP > 0) {
        precisionSum += precision;
        _prCurve.Add(new CurvePoint(recall, precision));
      }
      for (int i = 1; i < _changes.Count; i++) {
        precision = computePrecision(_changes[i]);
        recall = computeRecall(_changes[i]);
        if (_changes[i].TP > _changes[i - 1].TP) {
          precisionSum += precision;
          _prCurve.Add(new CurvePoint(recall, precision));
        }
      }
      _prCurve.Add(new CurvePoint(1, (float)(_changes[0].TP + _changes[0].FN) / (_changes[0].FP + _changes[0].TN)));
      _ap = precisionSum / (_changes[0].FN + _changes[0].TP);
    }

    /// <summary>
    /// Writes the Precision-Recall curve to a tab-delimited file.
    /// </summary>
    /// <param name="filename">Filename for output</param>
    public void WritePRCurve(string filename) {
      StreamWriter output = new StreamWriter(filename);
      output.WriteLine(_ap);
      for (int i = 0; i < _prCurve.Count; i++)
        output.WriteLine("{0}\t{1}", _prCurve[i].X, _prCurve[i].Y);
      output.Close();
    }

    /// <summary>
    /// Writes the Receiver Operating Characteristic curve to a tab-delimited file.
    /// </summary>
    /// <param name="filename">Filename for output</param>
    public void WriteROCCurve(string filename) {
      StreamWriter output = new StreamWriter(filename);
      output.WriteLine(_auc);
      for (int i = 0; i < _rocCurve.Count; i++)
        output.WriteLine("{0}\t{1}", _rocCurve[i].X, _rocCurve[i].Y);
      output.Close();
    }

    /// <summary>
    /// Receiver Operating Characteristic curve
    /// </summary>
    public List<CurvePoint> ROCCurve {
      get {
        return _rocCurve;
      }
    }

    /// <summary>
    /// Returns the area under the ROC Curve
    /// </summary>
    public double AuC {
      get {
        return _auc;
      }
    }

    /// <summary>
    /// Precision-Recall curve
    /// </summary>
    public List<CurvePoint> PRCurve {
      get {
        return _prCurve;
      }
    }

    /// <summary>
    /// The average precision
    /// </summary>
    public double AP {
      get {
        return _ap;
      }
    }

    private float computeTPR(ChangePoint cp) {
      return computeRecall(cp);
    }

    private float computeFPR(ChangePoint cp) {
      return (float)cp.FP / (cp.FP + cp.TN);
    }

    private void computeRoC() {
      _rocCurve = new List<CurvePoint>();
      _rocCurve.Add(new CurvePoint(0, 0));
      float tpr = computeTPR(_changes[0]);
      float fpr = computeFPR(_changes[0]);
      _rocCurve.Add(new CurvePoint(fpr, tpr));
      _auc = 0;
      for (int i = 1; i < _changes.Count; i++) {
        float newTPR = computeTPR(_changes[i]);
        float newFPR = computeFPR(_changes[i]);
        if (_changes[i].TP > _changes[i - 1].TP) {
          _auc += tpr * (newFPR - fpr) + .5 * (newTPR - tpr) * (newFPR - fpr);
          tpr = newTPR;
          fpr = newFPR;
          _rocCurve.Add(new CurvePoint(fpr, tpr));
        }
      }
      _rocCurve.Add(new CurvePoint(1, 1));
      _auc += tpr * (1 - fpr) + .5 * (1 - tpr) * (1 - fpr);
    }

  }
}