[645] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2008 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using HeuristicLab.Core;
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| 24 | using HeuristicLab.Data;
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[695] | 25 | using HeuristicLab.DataAnalysis;
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[645] | 26 |
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[2349] | 27 | namespace HeuristicLab.Modeling {
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| 28 | public class SimpleTheilInequalityCoefficientEvaluator : SimpleEvaluatorBase {
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[645] | 29 | public override string Description {
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| 30 | get {
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[2349] | 31 | return @"Calculates the Theil inequality coefficient (Theil's U2 not U1!) of estimated values vs. real values of 'TargetVariable'.
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[695] | 32 |
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| 33 | U2 = Sqrt(1/N * Sum(P_t - A_t)^2 ) / Sqrt(1/N * Sum(A_t)^2 )
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| 34 |
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| 35 | where P_t is the predicted change of the target variable and A_t is the measured (original) change.
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| 36 | (P_t = y'_t - y_(t-1), A_t = y_t - y_(t-1)).
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| 37 |
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| 38 | U2 is 0 for a perfect prediction and 1 for the naive model y'_t = y_(t-1). An U2 > 1 means the
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| 39 | model is worse than the naive model (=> model is useless).";
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[645] | 40 | }
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| 41 | }
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| 42 |
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[2349] | 43 | public override string OutputVariableName {
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| 44 | get {
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| 45 | return "TheilInequalityCoefficient";
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| 46 | }
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[645] | 47 | }
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| 48 |
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[2349] | 49 | public override double Evaluate(double[,] values) {
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| 50 | return Calculate(values);
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| 51 | }
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[645] | 52 |
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[2349] | 53 | public static double Calculate(double[,] values) {
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| 54 | int n = values.GetLength(0);
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[645] | 55 | double errorsSquaredSum = 0.0;
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| 56 | double originalSquaredSum = 0.0;
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[695] | 57 | int nSamples = 0;
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[2349] | 58 | for (int sample = 1; sample < n; sample++) {
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[2357] | 59 | double prevValue = values[sample - 1, ORIGINAL_INDEX];
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| 60 | double estimatedChange = values[sample, ESTIMATION_INDEX] - prevValue;
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| 61 | double originalChange = values[sample, ORIGINAL_INDEX] - prevValue;
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[712] | 62 | if (!double.IsNaN(originalChange) && !double.IsInfinity(originalChange)) {
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[645] | 63 | double error = estimatedChange - originalChange;
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| 64 | errorsSquaredSum += error * error;
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| 65 | originalSquaredSum += originalChange * originalChange;
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[695] | 66 | nSamples++;
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[645] | 67 | }
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| 68 | }
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[692] | 69 | double quality = Math.Sqrt(errorsSquaredSum / nSamples) / Math.Sqrt(originalSquaredSum / nSamples);
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[712] | 70 | if (double.IsNaN(quality) || double.IsInfinity(quality))
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[645] | 71 | quality = double.MaxValue;
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[2349] | 72 | return quality;
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[645] | 73 | }
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| 74 | }
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| 75 | }
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