Changeset 18011

Timestamp:

07/15/21 23:04:44 (3 years ago)

Author:

bburlacu

Message:

#3087: Implement ceres-based parameter optimizer in new evaluator. Revert constant optimization evaluator to old behavior.

Location:

branches/3087_Ceres_Integration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4

Files:

• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj (modified) (1 diff)
• SingleObjective/Evaluators/ParameterOptimizationEvaluator.cs (added)
• SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs (modified) (2 diffs)

branches/3087_Ceres_Integration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj

@@ -136 +136 @@
     <Compile Include="SingleObjective\ConstantOptimizationAnalyzer.cs" />
     <Compile Include="SingleObjective\Evaluators\NMSESingleObjectiveConstraintsEvaluator.cs" />
+    <Compile Include="SingleObjective\Evaluators\ParameterOptimizationEvaluator.cs" />
     <Compile Include="SingleObjective\Evaluators\SymbolicRegressionMeanRelativeErrorEvaluator.cs" />
     <Compile Include="SingleObjective\ShapeConstrainedRegressionSingleObjectiveProblem.cs" />

branches/3087_Ceres_Integration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs

@@ -30 +30 @@
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.NativeInterpreter;
 using HeuristicLab.Optimization;
 using HeuristicLab.Parameters;
@@ -212 +211 @@
       bool updateConstantsInTree = true, Action<double[], double, object> iterationCallback = null, EvaluationsCounter counter = null) {
 
+      // Numeric constants in the tree become variables for parameter optimization.
+      // Variables in the tree become parameters (fixed values) for parameter optimization.
+      // For each parameter (variable in the original tree) we store the 
+      // variable name, variable value (for factor vars) and lag as a DataForVariable object.
+      // A dictionary is used to find parameters
+      double[] initialConstants;
+      var parameters = new List<TreeToAutoDiffTermConverter.DataForVariable>();
+
+      TreeToAutoDiffTermConverter.ParametricFunction func;
+      TreeToAutoDiffTermConverter.ParametricFunctionGradient func_grad;
+      if (!TreeToAutoDiffTermConverter.TryConvertToAutoDiff(tree, updateVariableWeights, applyLinearScaling, out parameters, out initialConstants, out func, out func_grad))
+        throw new NotSupportedException("Could not optimize constants of symbolic expression tree due to not supported symbols used in the tree.");
+      if (parameters.Count == 0) return 0.0; // constant expressions always have a R² of 0.0 
+      var parameterEntries = parameters.ToArray(); // order of entries must be the same for x
+
+      // extract inital constants
+      double[] c;
+      if (applyLinearScaling) {
+        c = new double[initialConstants.Length + 2];
+        c[0] = 0.0;
+        c[1] = 1.0;
+        Array.Copy(initialConstants, 0, c, 2, initialConstants.Length);
+      } else {
+        c = (double[])initialConstants.Clone();
+      }
+
       double originalQuality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
 
-      var nodesToOptimize = new HashSet<ISymbolicExpressionTreeNode>();
-      var originalNodeValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
-
-      foreach (var node in tree.IterateNodesPrefix().OfType<SymbolicExpressionTreeTerminalNode>()) {
-        if (node is VariableTreeNode && !updateVariableWeights) {
-          continue;
+      if (counter == null) counter = new EvaluationsCounter();
+      var rowEvaluationsCounter = new EvaluationsCounter();
+
+      alglib.lsfitstate state;
+      alglib.lsfitreport rep;
+      int retVal;
+
+      IDataset ds = problemData.Dataset;
+      double[,] x = new double[rows.Count(), parameters.Count];
+      int row = 0;
+      foreach (var r in rows) {
+        int col = 0;
+        foreach (var info in parameterEntries) {
+          if (ds.VariableHasType<double>(info.variableName)) {
+            x[row, col] = ds.GetDoubleValue(info.variableName, r + info.lag);
+          } else if (ds.VariableHasType<string>(info.variableName)) {
+            x[row, col] = ds.GetStringValue(info.variableName, r) == info.variableValue ? 1 : 0;
+          } else throw new InvalidProgramException("found a variable of unknown type");
+          col++;
         }
-        if (node is ConstantTreeNode && node.Parent.Symbol is Power && node.Parent.GetSubtree(1) == node) {
-          // do not optimize exponents
-          continue;
+        row++;
+      }
+      double[] y = ds.GetDoubleValues(problemData.TargetVariable, rows).ToArray();
+      int n = x.GetLength(0);
+      int m = x.GetLength(1);
+      int k = c.Length;
+
+      alglib.ndimensional_pfunc function_cx_1_func = CreatePFunc(func);
+      alglib.ndimensional_pgrad function_cx_1_grad = CreatePGrad(func_grad);
+      alglib.ndimensional_rep xrep = (p, f, obj) => iterationCallback(p, f, obj);
+
+      try {
+        alglib.lsfitcreatefg(x, y, c, n, m, k, false, out state);
+        alglib.lsfitsetcond(state, 0.0, maxIterations);
+        alglib.lsfitsetxrep(state, iterationCallback != null);
+        alglib.lsfitfit(state, function_cx_1_func, function_cx_1_grad, xrep, rowEvaluationsCounter);
+        alglib.lsfitresults(state, out retVal, out c, out rep);
+      } catch (ArithmeticException) {
+        return originalQuality;
+      } catch (alglib.alglibexception) {
+        return originalQuality;
+      }
+
+      counter.FunctionEvaluations += rowEvaluationsCounter.FunctionEvaluations / n;
+      counter.GradientEvaluations += rowEvaluationsCounter.GradientEvaluations / n;
+
+      //retVal == -7  => constant optimization failed due to wrong gradient
+      //          -8  => optimizer detected  NAN / INF  in  the target
+      //                 function and/ or gradient
+      if (retVal != -7 && retVal != -8) {
+        if (applyLinearScaling) {
+          var tmp = new double[c.Length - 2];
+          Array.Copy(c, 2, tmp, 0, tmp.Length);
+          UpdateConstants(tree, tmp, updateVariableWeights);
+        } else UpdateConstants(tree, c, updateVariableWeights);
+      }
+      var quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
+
+      if (!updateConstantsInTree) UpdateConstants(tree, initialConstants, updateVariableWeights);
+
+      if (originalQuality - quality > 0.001 || double.IsNaN(quality)) {
+        UpdateConstants(tree, initialConstants, updateVariableWeights);
+        return originalQuality;
+      }
+      return quality;
+    }
+
+    private static void UpdateConstants(ISymbolicExpressionTree tree, double[] constants, bool updateVariableWeights) {
+      int i = 0;
+      foreach (var node in tree.Root.IterateNodesPrefix().OfType<SymbolicExpressionTreeTerminalNode>()) {
+        ConstantTreeNode constantTreeNode = node as ConstantTreeNode;
+        VariableTreeNodeBase variableTreeNodeBase = node as VariableTreeNodeBase;
+        FactorVariableTreeNode factorVarTreeNode = node as FactorVariableTreeNode;
+        if (constantTreeNode != null) {
+          if (constantTreeNode.Parent.Symbol is Power
+              && constantTreeNode.Parent.GetSubtree(1) == constantTreeNode) continue; // exponents in powers are not optimizated (see TreeToAutoDiffTermConverter)
+          constantTreeNode.Value = constants[i++];
+        } else if (updateVariableWeights && variableTreeNodeBase != null)
+          variableTreeNodeBase.Weight = constants[i++];
+        else if (factorVarTreeNode != null) {
+          for (int j = 0; j < factorVarTreeNode.Weights.Length; j++)
+            factorVarTreeNode.Weights[j] = constants[i++];
         }
-        nodesToOptimize.Add(node);
-        if (node is ConstantTreeNode constant) {
-          originalNodeValues[node] = constant.Value;
-        } else if (node is VariableTreeNode variable) {
-          originalNodeValues[node] = variable.Weight;
-        }
-      }
-
-      var options = new SolverOptions {
-        Iterations = maxIterations
+      }
+    }
+
+    private static alglib.ndimensional_pfunc CreatePFunc(TreeToAutoDiffTermConverter.ParametricFunction func) {
+      return (double[] c, double[] x, ref double fx, object o) => {
+        fx = func(c, x);
+        var counter = (EvaluationsCounter)o;
+        counter.FunctionEvaluations++;
       };
-      var summary = new OptimizationSummary();
-      var optimizedNodeValues = ParameterOptimizer.OptimizeTree(tree, problemData.Dataset, problemData.TrainingIndices, problemData.TargetVariable, nodesToOptimize, options, ref summary);
-
-      counter.FunctionEvaluations += summary.ResidualEvaluations;
-      counter.GradientEvaluations += summary.JacobianEvaluations;
-
-      // check if the fitting of the parameters was successful
-      UpdateNodeValues(optimizedNodeValues);
-      
-      var quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
-      if (quality < originalQuality || !updateConstantsInTree) {
-        UpdateNodeValues(originalNodeValues);
-      }
-      return Math.Max(quality, originalQuality);
-    }
-
-    private static void UpdateNodeValues(IDictionary<ISymbolicExpressionTreeNode, double> values) {
-      foreach (var item in values) {
-        var node = item.Key;
-        if (node is ConstantTreeNode constant) {
-          constant.Value = item.Value;
-        } else if (node is VariableTreeNode variable) {
-          variable.Weight = item.Value;
-        }
-      }
-    }
-
+    }
+
+    private static alglib.ndimensional_pgrad CreatePGrad(TreeToAutoDiffTermConverter.ParametricFunctionGradient func_grad) {
+      return (double[] c, double[] x, ref double fx, double[] grad, object o) => {
+        var tuple = func_grad(c, x);
+        fx = tuple.Item2;
+        Array.Copy(tuple.Item1, grad, grad.Length);
+        var counter = (EvaluationsCounter)o;
+        counter.GradientEvaluations++;
+      };
+    }
     public static bool CanOptimizeConstants(ISymbolicExpressionTree tree) {
       return TreeToAutoDiffTermConverter.IsCompatible(tree);