Changeset 11426 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis

Timestamp:

10/08/14 11:53:07 (10 years ago)

Author:

bburlacu

Message:

#2237: Fixed thread synchronisation bug. Removed unused variables in GridSearch methods.

File:

• trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestUtil.cs (modified) (5 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestUtil.cs

@@ -43 +43 @@
 
   public static class RandomForestUtil {
+    private static readonly object locker = new object();
+
     private static void CrossValidate(IRegressionProblemData problemData, Tuple<IEnumerable<int>, IEnumerable<int>>[] partitions, int nTrees, double r, double m, int seed, out double avgTestMse) {
       avgTestMse = 0;
@@ -91 +93 @@
       Parallel.ForEach(crossProduct, new ParallelOptions { MaxDegreeOfParallelism = maxDegreeOfParallelism }, parameterCombination => {
         var parameterValues = parameterCombination.ToList();
-        double testMSE;
         var parameters = new RFParameter();
         for (int i = 0; i < setters.Count; ++i) { setters[i](parameters, parameterValues[i]); }
         double rmsError, outOfBagRmsError, avgRelError, outOfBagAvgRelError;
-        var model = RandomForestModel.CreateRegressionModel(problemData, problemData.TrainingIndices, (int)parameters.n, parameters.r, parameters.m, seed,
-                                                            out rmsError, out outOfBagRmsError, out avgRelError, out outOfBagAvgRelError);
-        if (bestOutOfBagRmsError > outOfBagRmsError) {
-          lock (bestParameters) {
+        RandomForestModel.CreateRegressionModel(problemData, problemData.TrainingIndices, (int)parameters.n, parameters.r, parameters.m, seed, out rmsError, out outOfBagRmsError, out avgRelError, out outOfBagAvgRelError);
+
+        lock (locker) {
+          if (bestOutOfBagRmsError > outOfBagRmsError) {
             bestOutOfBagRmsError = outOfBagRmsError;
             bestParameters = (RFParameter)parameters.Clone();
@@ -119 +120 @@
         for (int i = 0; i < setters.Count; ++i) { setters[i](parameters, parameterValues[i]); }
         double rmsError, outOfBagRmsError, avgRelError, outOfBagAvgRelError;
-        var model = RandomForestModel.CreateClassificationModel(problemData, problemData.TrainingIndices, (int)parameters.n, parameters.r, parameters.m, seed,
+        RandomForestModel.CreateClassificationModel(problemData, problemData.TrainingIndices, (int)parameters.n, parameters.r, parameters.m, seed,
                                                                 out rmsError, out outOfBagRmsError, out avgRelError, out outOfBagAvgRelError);
-        if (bestOutOfBagRmsError > outOfBagRmsError) {
-          lock (bestParameters) {
+
+        lock (locker) {
+          if (bestOutOfBagRmsError > outOfBagRmsError) {
             bestOutOfBagRmsError = outOfBagRmsError;
             bestParameters = (RFParameter)parameters.Clone();
@@ -147 +149 @@
         }
         CrossValidate(problemData, partitions, (int)parameters.n, parameters.r, parameters.m, seed, out testMSE);
-        if (testMSE < mse.Value) {
-          lock (mse) {
+
+        lock (locker) {
+          if (testMSE < mse.Value) {
             mse.Value = testMSE;
             bestParameter = (RFParameter)parameters.Clone();
@@ -173 +176 @@
         }
         CrossValidate(problemData, partitions, (int)parameters.n, parameters.r, parameters.m, seed, out testAccuracy);
-        if (testAccuracy > accuracy.Value) {
-          lock (accuracy) {
+
+        lock (locker) {
+          if (testAccuracy > accuracy.Value) {
             accuracy.Value = testAccuracy;
             bestParameter = (RFParameter)parameters.Clone();