Changeset 11308
 Timestamp:
 08/26/14 16:33:53 (5 years ago)
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trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/SupportVectorMachine/SupportVectorMachineUtil.cs
r11171 r11308 20 20 #endregion 21 21 22 using System; 22 23 using System.Collections.Generic; 23 24 using System.Linq; 25 using System.Linq.Expressions; 26 using System.Threading.Tasks; 27 using HeuristicLab.Common; 28 using HeuristicLab.Data; 24 29 using HeuristicLab.Problems.DataAnalysis; 25 30 using LibSVM; … … 60 65 return new svm_problem() { l = targetVector.Length, y = targetVector, x = nodes }; 61 66 } 67 68 /// <summary> 69 /// Instantiate and return a svm_parameter object with default values. 70 /// </summary> 71 /// <returns>A svm_parameter object with default values</returns> 72 public static svm_parameter DefaultParameters() { 73 svm_parameter parameter = new svm_parameter(); 74 parameter.svm_type = svm_parameter.NU_SVR; 75 parameter.kernel_type = svm_parameter.RBF; 76 parameter.C = 1; 77 parameter.nu = 0.5; 78 parameter.gamma = 1; 79 parameter.p = 1; 80 parameter.cache_size = 500; 81 parameter.probability = 0; 82 parameter.eps = 0.001; 83 parameter.degree = 3; 84 parameter.shrinking = 1; 85 parameter.coef0 = 0; 86 87 return parameter; 88 } 89 90 /// <summary> 91 /// Generate a collection of training indices corresponding to folds in the data (used for crossvalidation) 92 /// </summary> 93 /// <remarks>This method is aimed to be lightweight and as such does not clone the dataset.</remarks> 94 /// <param name="problemData">The problem data</param> 95 /// <param name="nFolds">The number of folds to generate</param> 96 /// <returns>A sequence of folds representing each a sequence of row numbers</returns> 97 public static IEnumerable<IEnumerable<int>> GenerateFolds(IRegressionProblemData problemData, int nFolds) { 98 int size = problemData.TrainingPartition.Size; 99 100 int foldSize = size / nFolds; // rounding to integer 101 var trainingIndices = problemData.TrainingIndices; 102 103 for (int i = 0; i < nFolds; ++i) { 104 int n = i * foldSize; 105 int s = n + 2 * foldSize > size ? foldSize + size % foldSize : foldSize; 106 yield return trainingIndices.Skip(n).Take(s); 107 } 108 } 109 110 /// <summary> 111 /// Performs crossvalidation 112 /// </summary> 113 /// <param name="problemData">The problem data</param> 114 /// <param name="parameters">The svm parameters</param> 115 /// <param name="folds">The svm_problem instances for each fold</param> 116 /// <param name="avgTestMSE">The average test mean squared error (not used atm)</param> 117 public static void CrossValidate(IRegressionProblemData problemData, svm_parameter parameters, IEnumerable<IEnumerable<int>> folds, out double avgTestMSE) { 118 avgTestMSE = 0; 119 120 var calc = new OnlineMeanSquaredErrorCalculator(); 121 var ds = problemData.Dataset; 122 var targetVariable = problemData.TargetVariable; 123 var inputVariables = problemData.AllowedInputVariables; 124 125 var svmProblem = CreateSvmProblem(ds, targetVariable, inputVariables, problemData.TrainingIndices); 126 var partitions = folds.ToList(); 127 128 for (int i = 0; i < partitions.Count; ++i) { 129 var test = partitions[i]; 130 var training = new List<int>(); 131 for (int j = 0; j < i; ++j) 132 training.AddRange(partitions[j]); 133 134 for (int j = i + 1; j < partitions.Count; ++j) 135 training.AddRange(partitions[j]); 136 137 var p = CreateSvmProblem(ds, targetVariable, inputVariables, training); 138 var model = svm.svm_train(p, parameters); 139 calc.Reset(); 140 foreach (var row in test) { 141 calc.Add(svmProblem.y[row], svm.svm_predict(model, svmProblem.x[row])); 142 } 143 double error = calc.MeanSquaredError; 144 avgTestMSE += error; 145 } 146 147 avgTestMSE /= partitions.Count; 148 } 149 150 /// <summary> 151 /// Dynamically generate a setter for svm_parameter fields 152 /// </summary> 153 /// <param name="parameters"></param> 154 /// <param name="fieldName"></param> 155 /// <returns></returns> 156 private static Action<svm_parameter, double> GenerateSetter(string fieldName) { 157 var targetExp = Expression.Parameter(typeof(svm_parameter)); 158 var valueExp = Expression.Parameter(typeof(double)); 159 160 // Expression.Property can be used here as well 161 var fieldExp = Expression.Field(targetExp, fieldName); 162 var assignExp = Expression.Assign(fieldExp, Expression.Convert(valueExp, fieldExp.Type)); 163 var setter = Expression.Lambda<Action<svm_parameter, double>>(assignExp, targetExp, valueExp).Compile(); 164 return setter; 165 } 166 167 public static svm_parameter GridSearch(IRegressionProblemData problemData, IEnumerable<IEnumerable<int>> folds, Dictionary<string, IEnumerable<double>> parameterRanges, int maxDegreeOfParallelism = 1) { 168 DoubleValue mse = new DoubleValue(Double.MaxValue); 169 var bestParam = DefaultParameters(); 170 171 // search for C, gamma and epsilon parameter combinations 172 173 var pNames = parameterRanges.Keys.ToList(); 174 var pRanges = pNames.Select(x => parameterRanges[x]); 175 176 var crossProduct = pRanges.CartesianProduct(); 177 var setters = pNames.Select(GenerateSetter).ToList(); 178 Parallel.ForEach(crossProduct, new ParallelOptions { MaxDegreeOfParallelism = maxDegreeOfParallelism }, nuple => { 179 // foreach (var nuple in crossProduct) { 180 var list = nuple.ToList(); 181 var parameters = DefaultParameters(); 182 for (int i = 0; i < pNames.Count; ++i) { 183 var s = setters[i]; 184 s(parameters, list[i]); 185 } 186 double testMSE; 187 CrossValidate(problemData, parameters, folds, out testMSE); 188 if (testMSE < mse.Value) { 189 lock (mse) { mse.Value = testMSE; } 190 lock (bestParam) { // set best parameter values to the best found so far 191 bestParam = (svm_parameter)parameters.Clone(); 192 } 193 } 194 }); 195 return bestParam; 196 } 62 197 } 63 198 }
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