Changeset 5657 for branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis/3.4/DiscriminantFunctionClassificationSolution.cs
 Timestamp:
 03/10/11 12:37:11 (12 years ago)
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 1 edited
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branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis/3.4/DiscriminantFunctionClassificationSolution.cs
r5649 r5657 74 74 75 75 public IEnumerable<double> Thresholds { 76 get { return Model.Thresholds; } 76 get { 77 return Model.Thresholds; 78 } 79 protected set { Model.Thresholds = value; } 77 80 } 78 81 … … 88 91 } 89 92 #endregion 93 94 public override IEnumerable<double> GetEstimatedClassValues(IEnumerable<int> rows) { 95 if (Model.Thresholds == null  Model.Thresholds.Count() == 0) RecalculateClassIntermediates(); 96 return base.GetEstimatedClassValues(rows); 97 } 98 99 private void RecalculateClassIntermediates() { 100 int slices = 100; 101 List<double> estimatedValues = EstimatedValues.ToList(); 102 List<int> classInstances = (from classValue in ProblemData.Dataset.GetVariableValues(ProblemData.TargetVariable) 103 group classValue by classValue into grouping 104 select grouping.Count()).ToList(); 105 double maxEstimatedValue = estimatedValues.Max(); 106 double minEstimatedValue = estimatedValues.Min(); 107 List<KeyValuePair<double, double>> estimatedTargetValues = 108 (from row in ProblemData.TrainingIndizes 109 select new KeyValuePair<double, double>( 110 estimatedValues[row], 111 ProblemData.Dataset[ProblemData.TargetVariable, row])).ToList(); 112 113 List<double> originalClasses = ProblemData.ClassValues.OrderBy(x => x).ToList(); 114 int nClasses = originalClasses.Distinct().Count(); 115 double[] thresholds = new double[nClasses + 1]; 116 thresholds[0] = double.NegativeInfinity; 117 thresholds[thresholds.Length  1] = double.PositiveInfinity; 118 119 for (int i = 1; i < thresholds.Length  1; i++) { 120 double lowerThreshold = thresholds[i  1]; 121 double actualThreshold = minEstimatedValue; 122 double thresholdIncrement = (maxEstimatedValue  minEstimatedValue) / slices; 123 124 double lowestBestThreshold = double.NaN; 125 double highestBestThreshold = double.NaN; 126 double bestClassificationScore = double.PositiveInfinity; 127 bool seriesOfEqualClassificationScores = false; 128 129 while (actualThreshold < maxEstimatedValue) { 130 double classificationScore = 0.0; 131 132 foreach (KeyValuePair<double, double> estimatedTarget in estimatedTargetValues) { 133 //all positives 134 if (estimatedTarget.Value.IsAlmost(originalClasses[i  1])) { 135 if (estimatedTarget.Key > lowerThreshold && estimatedTarget.Key < actualThreshold) 136 //true positive 137 classificationScore += ProblemData.GetClassificationPenalty(originalClasses[i  1], originalClasses[i  1]); 138 else 139 //false negative 140 classificationScore += ProblemData.GetClassificationPenalty(originalClasses[i], originalClasses[i  1]); 141 } 142 //all negatives 143 else { 144 if (estimatedTarget.Key > lowerThreshold && estimatedTarget.Key < actualThreshold) 145 //false positive 146 classificationScore += ProblemData.GetClassificationPenalty(originalClasses[i  1], originalClasses[i]); 147 else 148 //true negative, consider only upper class 149 classificationScore += ProblemData.GetClassificationPenalty(originalClasses[i], originalClasses[i]); 150 } 151 } 152 153 //new best classification score found 154 if (classificationScore < bestClassificationScore) { 155 bestClassificationScore = classificationScore; 156 lowestBestThreshold = actualThreshold; 157 highestBestThreshold = actualThreshold; 158 seriesOfEqualClassificationScores = true; 159 } 160 //equal classification scores => if seriesOfEqualClassifcationScores == true update highest threshold 161 else if (Math.Abs(classificationScore  bestClassificationScore) < double.Epsilon && seriesOfEqualClassificationScores) 162 highestBestThreshold = actualThreshold; 163 //worse classificatoin score found reset seriesOfEqualClassifcationScores 164 else seriesOfEqualClassificationScores = false; 165 166 actualThreshold += thresholdIncrement; 167 } 168 //scale lowest thresholds and highest found optimal threshold according to the misclassification matrix 169 double falseNegativePenalty = ProblemData.GetClassificationPenalty(originalClasses[i], originalClasses[i  1]); 170 double falsePositivePenalty = ProblemData.GetClassificationPenalty(originalClasses[i  1], originalClasses[i]); 171 thresholds[i] = (lowestBestThreshold * falsePositivePenalty + highestBestThreshold * falseNegativePenalty) / (falseNegativePenalty + falsePositivePenalty); 172 } 173 Thresholds = new List<double>(thresholds); 174 } 90 175 } 91 176 }
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