Ticket #2057: SVM-parameter-tuning.patch

HeuristicLab.Algorithms.DataAnalysis-3.4.csproj

@@ -262 +262 @@
     <Compile Include="RandomForest\RandomForestRegression.cs" />
     <Compile Include="RandomForest\RandomForestRegressionSolution.cs" />
     <Compile Include="RandomForest\RandomForestUtil.cs" />
+    <Compile Include="SupportVectorMachine\SupportVectorParameterTuningEvaluator.cs" />
+    <Compile Include="SupportVectorMachine\SupportVectorParameterTuningProblem.cs" />
     <Compile Include="SupportVectorMachine\SupportVectorClassification.cs" />
     <Compile Include="SupportVectorMachine\SupportVectorClassificationSolution.cs" />
     <Compile Include="SupportVectorMachine\SupportVectorMachineModel.cs" />

SupportVectorMachine/SupportVectorMachineUtil.cs

@@ -143 +143 @@
         var trainingRows = folds.SelectMany((par, j) => j != p ? par : Enumerable.Empty<int>());
         var testRows = folds[i];
         var trainingSvmProblem = CreateSvmProblem(problemData.Dataset, targetVariable, problemData.AllowedInputVariables, trainingRows);
-        var testSvmProblem = CreateSvmProblem(problemData.Dataset, targetVariable, problemData.AllowedInputVariables, testRows);
-        partitions[i] = new Tuple<svm_problem, svm_problem>(trainingSvmProblem, testSvmProblem);
+        var rangeTransform = RangeTransform.Compute(trainingSvmProblem);
+        var testSvmProblem = rangeTransform.Scale(CreateSvmProblem(problemData.Dataset, targetVariable, problemData.AllowedInputVariables, testRows));
+        partitions[i] = new Tuple<svm_problem, svm_problem>(rangeTransform.Scale(trainingSvmProblem), testSvmProblem);
       }
       return partitions;
     }

SupportVectorMachine/SupportVectorParameterTuningEvaluator.cs

@@ +1 @@
+#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 HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Encodings.RealVectorEncoding;
+using HeuristicLab.Operators;
+using HeuristicLab.Optimization;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Problems.DataAnalysis;
+using LibSVM;
+
+namespace HeuristicLab.Algorithms.DataAnalysis {
+  [Item("SupportVectorParameterTuningEvaluator", "")]
+  [StorableClass]
+  public class SupportVectorParameterTuningEvaluator : InstrumentedOperator, ISingleObjectiveEvaluator {
+    public ILookupParameter<DoubleValue> QualityParameter {
+      get { return (ILookupParameter<DoubleValue>)Parameters["Quality"]; }
+    }
+
+    [StorableConstructor]
+    protected SupportVectorParameterTuningEvaluator(bool deserializing) : base(deserializing) { }
+    protected SupportVectorParameterTuningEvaluator(SupportVectorParameterTuningEvaluator original, Cloner cloner) : base(original, cloner) { }
+
+    public SupportVectorParameterTuningEvaluator()
+      : base() {
+      Parameters.Add(new LookupParameter<RealVector>("Point"));
+      Parameters.Add(new LookupParameter<IRegressionProblemData>("ProblemData"));
+      Parameters.Add(new LookupParameter<DoubleValue>("Quality"));
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SupportVectorParameterTuningEvaluator(this, cloner);
+    }
+
+    public override IOperation InstrumentedApply() {
+      var problemData = (IRegressionProblemData)Parameters["ProblemData"].ActualValue;
+      var p = (RealVector)Parameters["Point"].ActualValue;
+      var parameters = new svm_parameter {
+        C = Math.Exp(p[0]),
+        gamma = Math.Exp(p[1]),
+        p = Math.Exp(p[2]),
+        eps = 0.001,
+        svm_type = svm_parameter.EPSILON_SVR,
+        kernel_type = svm_parameter.RBF
+      };
+
+      var cvLoss = SupportVectorMachineUtil.CrossValidate(problemData, parameters, 5, false);
+      QualityParameter.ActualValue = new DoubleValue(cvLoss);
+      return base.InstrumentedApply();
+    }
+
+  }
+}

SupportVectorMachine/SupportVectorParameterTuningProblem.cs

@@ +1 @@
+#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 HeuristicLab.Analysis;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Encodings.RealVectorEncoding;
+using HeuristicLab.Optimization;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.PluginInfrastructure;
+using HeuristicLab.Problems.DataAnalysis;
+using HeuristicLab.Problems.Instances;
+
+namespace HeuristicLab.Algorithms.DataAnalysis {
+  [Item("SVM Parameter Tuning Problem", "")]
+  [StorableClass]
+  [Creatable("Problems")]
+  public sealed class SupportVectorParameterTuningProblem : SingleObjectiveHeuristicOptimizationProblem<ISingleObjectiveEvaluator, IRealVectorCreator>, IStorableContent {
+    public string Filename { get; set; }
+
+
+    [StorableConstructor]
+    private SupportVectorParameterTuningProblem(bool deserializing) : base(deserializing) { }
+    private SupportVectorParameterTuningProblem(SupportVectorParameterTuningProblem original, Cloner cloner)
+      : base(original, cloner) {
+      RegisterEventHandlers();
+    }
+    public SupportVectorParameterTuningProblem()
+      : base(new SupportVectorParameterTuningEvaluator(), new UniformRandomRealVectorCreator()) {
+      Parameters.Add(new ValueParameter<RegressionProblemData>("ProblemData", ""));
+      Parameters.Add(new ValueParameter<IntValue>("Length", "", new IntValue(3)));
+      var bounds = new DoubleMatrix(new double[,]
+      {
+        {-7,7},
+        {-7,7},
+        {-7,7}
+      });
+      Parameters.Add(new ValueParameter<DoubleMatrix>("Bounds", "", bounds));
+      SolutionCreator.RealVectorParameter.ActualName = "Point";
+
+      Parameters["Length"].Hidden = true;
+      Parameters["Bounds"].Hidden = true;
+
+      InitializeOperators();
+      RegisterEventHandlers();
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SupportVectorParameterTuningProblem(this, cloner);
+    }
+
+    #region Events
+    #endregion
+
+    #region Helpers
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      RegisterEventHandlers();
+    }
+
+    private void RegisterEventHandlers() {
+    }
+
+    private void InitializeOperators() {
+      Operators.AddRange(ApplicationManager.Manager.GetInstances<IRealVectorOperator>().Cast<IOperator>());
+    }
+
+    #endregion
+
+  }
+}