Changeset 17311 for branches

Timestamp:

10/04/19 09:34:18 (5 years ago)

Author:

gkronber

Message:

#2994: add names for constraints and derivatives, fix a problem: variableRanges are reset when creating a new solution. fix smaller bugs

Location:

branches/2994-AutoDiffForIntervals/HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Extensions

Files:

• ConstrainedNLS.cs (modified) (3 diffs)
• ConstrainedNLSInternal.cs (modified) (7 diffs)

branches/2994-AutoDiffForIntervals/HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Extensions/ConstrainedNLS.cs

@@ -174 +174 @@
       int idx = 0;
       var formatter = new InfixExpressionFormatter();
+      var constraintDescriptions = state.ConstraintDescriptions.ToArray();
       foreach(var constraintTree in state.constraintTrees) {
         // HACK to remove parameter nodes which occurr multiple times
         var reparsedTree = parser.Parse(formatter.Format(constraintTree));
-        Results.AddOrUpdateResult($"Constraint {idx++}", reparsedTree);
+        Results.AddOrUpdateResult($"{constraintDescriptions[idx++]}", reparsedTree);
       }
 
@@ -191 +192 @@
 
       Results.AddOrUpdateResult("Best solution", CreateSolution((ISymbolicExpressionTree)state.BestTree.Clone(), problem.ProblemData));
-      Results.AddOrUpdateResult("Best solution constraint values", new DoubleArray(state.BestConstraintValues));
+      var bestConstraintValues = new DoubleArray(state.BestConstraintValues);
+      bestConstraintValues.ElementNames = constraintDescriptions;
+      Results.AddOrUpdateResult("Best solution constraint values", bestConstraintValues);
 
 
@@ -215 +218 @@
       var model = new SymbolicRegressionModel(problemData.TargetVariable, tree, new SymbolicDataAnalysisExpressionTreeLinearInterpreter());
       // model.Scale(problemData);
-      return model.CreateRegressionSolution((IRegressionProblemData)problemData.Clone());
+      // var sol = model.CreateRegressionSolution((IRegressionProblemData)problemData.Clone());
+      // model.CreateRegressionSolution produces a new ProblemData and recalculates intervals
+
+      var sol = new SymbolicRegressionSolution(model, (IRegressionProblemData)problemData.Clone());
+
+      // ==> set variable ranges to same range as in original problemData
+      // foreach(var interval in problemData.VariableRanges.GetIntervals()) {
+      //   sol.ProblemData.VariableRanges.SetInterval
+      // }
+      return sol;
     }
   }

branches/2994-AutoDiffForIntervals/HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Extensions/ConstrainedNLSInternal.cs

@@ -41 +41 @@
     public double[] BestConstraintValues => bestConstraintValues;
 
+
+    // for debugging (must be in the same order as processed below)
+    public IEnumerable<string> ConstraintDescriptions {
+      get {
+        foreach (var elem in problemData.IntervalConstraints.Constraints) {
+          if (!elem.Enabled) continue;
+          if (elem.Interval.UpperBound < double.PositiveInfinity) {
+            yield return elem.Expression + " < " + elem.Interval.UpperBound;
+          }
+          if (elem.Interval.LowerBound > double.NegativeInfinity) {
+            yield return "-" + elem.Expression + " < " + (-1) * elem.Interval.LowerBound;
+          }
+        }
+      }
+    }
+
     public bool CheckGradient { get; internal set; }
 
@@ -47 +63 @@
     private SymbolicDataAnalysisExpressionTreeLinearInterpreter interpreter;
     private readonly NLOpt.nlopt_func calculateObjectiveDelegate; // must hold the delegate to prevent GC
-    private readonly NLOpt.nlopt_precond preconditionDelegate;
+    // private readonly NLOpt.nlopt_precond preconditionDelegate;
     private readonly IntPtr[] constraintDataPtr; // must hold the objects to prevent GC
     private readonly NLOpt.nlopt_func[] calculateConstraintDelegates; // must hold the delegates to prevent GC
@@ -217 +233 @@
         // throw new InvalidOperationException($"NLOpt failed {res} {NLOpt.nlopt_get_errmsg(nlopt)}");
         return;
-      } else if(minf <= bestError) {
+      } else if (minf <= bestError) {
         bestSolution = x;
         bestError = minf;
@@ -302 +318 @@
 
       if (double.IsNaN(sse)) {
-        if(grad!=null) Array.Clear(grad, 0, grad.Length); 
+        if (grad != null) Array.Clear(grad, 0, grad.Length);
         return double.MaxValue;
       }
@@ -367 +383 @@
       UpdateThetaValues(curX);
       var intervalEvaluator = new IntervalEvaluator();
+      var refIntervalEvaluator = new IntervalInterpreter();
+
       var constraintData = Marshal.PtrToStructure<ConstraintData>(data);
 
@@ -373 +391 @@
       var interval = intervalEvaluator.Evaluate(constraintData.Tree, dataIntervals, constraintData.ParameterNodes,
         out double[] lowerGradient, out double[] upperGradient);
+
+      var refInterval = refIntervalEvaluator.GetSymbolicExpressionTreeInterval(constraintData.Tree, dataIntervals);
+      if (Math.Abs(interval.LowerBound - refInterval.LowerBound) > Math.Abs(interval.LowerBound) * 1e-4) throw new InvalidProgramException($"Intervals don't match. {interval.LowerBound} <> {refInterval.LowerBound}");
+      if (Math.Abs(interval.UpperBound - refInterval.UpperBound) > Math.Abs(interval.UpperBound) * 1e-4) throw new InvalidProgramException($"Intervals don't match. {interval.UpperBound} <> {refInterval.UpperBound}");
 
       // we transformed this to a constraint c(x) <= 0, so only the upper bound is relevant for us
@@ -405 +427 @@
       UpdateConstraintViolations(constraintData.Idx, interval.UpperBound);
       if (double.IsNaN(interval.UpperBound)) {
-        Array.Clear(grad, 0, grad.Length);
+        if(grad!=null)Array.Clear(grad, 0, grad.Length);
         return double.MaxValue;
       } else return interval.UpperBound;