Changeset 17633 for branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4

Timestamp:

06/25/20 16:26:46 (4 years ago)

Author:

pfleck

Message:

#3040 Reenabled the old optimize button in the simplifier and added a new button for const opt with vectors.

File:

• branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4/InteractiveSymbolicRegressionSolutionSimplifierView.cs (modified) (2 diffs)

branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4/InteractiveSymbolicRegressionSolutionSimplifierView.cs

@@ -43 +43 @@
 
       var tree = Content?.Model?.SymbolicExpressionTree;
-      //btnOptimizeConstants.Enabled = tree != null && NonlinearLeastSquaresConstantOptimizationEvaluator.CanOptimizeConstants(tree);
-      btnOptimizeConstants.Enabled = tree != null && TensorFlowConstantOptimizationEvaluator.CanOptimizeConstants(tree);
+      btnOptimizeConstants.Enabled = tree != null && NonlinearLeastSquaresConstantOptimizationEvaluator.CanOptimizeConstants(tree);
+      btnVectorOptimizeConstants.Enabled = tree != null && TensorFlowConstantOptimizationEvaluator.CanOptimizeConstants(tree);
+      nudLearningRate.Enabled = tree != null && TensorFlowConstantOptimizationEvaluator.CanOptimizeConstants(tree);
     }
 
@@ -64 +65 @@
       int reps = 0;
 
-      //do {
-      //  prevResult = result;
-      //  tree = NonlinearLeastSquaresConstantOptimizationEvaluator.OptimizeTree(tree, regressionProblemData, regressionProblemData.TrainingIndices,
-      //    applyLinearScaling: true, maxIterations: constOptIterations, updateVariableWeights: true,
-      //    cancellationToken: cancellationToken, iterationCallback: (args, func, obj) => {
-      //      double newProgressValue = progress.ProgressValue + (1.0 / (constOptIterations + 2) / maxRepetitions); // (constOptIterations + 2) iterations are reported
-      //      progress.ProgressValue = Math.Min(newProgressValue, 1.0);
-      //    });
-      //  result = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(model.Interpreter, tree,
-      //    model.LowerEstimationLimit, model.UpperEstimationLimit, regressionProblemData, regressionProblemData.TrainingIndices, applyLinearScaling: true);
-      //  reps++;
-      //  improvement = result - prevResult;
-      //} while (improvement > minimumImprovement && reps < maxRepetitions &&
-      //         progress.ProgressState != ProgressState.StopRequested &&
-      //         progress.ProgressState != ProgressState.CancelRequested);
-      //return tree;
+      do {
+        prevResult = result;
+        tree = NonlinearLeastSquaresConstantOptimizationEvaluator.OptimizeTree(tree, regressionProblemData, regressionProblemData.TrainingIndices,
+          applyLinearScaling: true, maxIterations: constOptIterations, updateVariableWeights: true,
+          cancellationToken: cancellationToken, iterationCallback: (args, func, obj) => {
+            double newProgressValue = progress.ProgressValue + (1.0 / (constOptIterations + 2) / maxRepetitions); // (constOptIterations + 2) iterations are reported
+            progress.ProgressValue = Math.Min(newProgressValue, 1.0);
+          });
+        result = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(model.Interpreter, tree,
+          model.LowerEstimationLimit, model.UpperEstimationLimit, regressionProblemData, regressionProblemData.TrainingIndices, applyLinearScaling: true);
+        reps++;
+        improvement = result - prevResult;
+      } while (improvement > minimumImprovement && reps < maxRepetitions &&
+               progress.ProgressState != ProgressState.StopRequested &&
+               progress.ProgressState != ProgressState.CancelRequested);
+      return tree;
+    }
+
+    protected override ISymbolicExpressionTree VectorOptimizeConstants(ISymbolicExpressionTree tree, CancellationToken cancellationToken, IProgress progress) {
       const int maxIterations = 1000;
+      var regressionProblemData = Content.ProblemData;
+      progress.CanBeStopped = true;
+
+      var learningRate = Math.Pow(10, (double)nudLearningRate.Value);
+
       return TensorFlowConstantOptimizationEvaluator.OptimizeTree(tree, regressionProblemData,
         regressionProblemData.TrainingIndices,
-        applyLinearScaling: false, updateVariableWeights: true, maxIterations: maxIterations, learningRate: 0.0001,
+        applyLinearScaling: false, updateVariableWeights: true, maxIterations: maxIterations, learningRate: learningRate,
         cancellationToken: cancellationToken,
         progress: new SynchronousProgress<double>(cost => {