Changeset 16500 for branches/2974_Constants_Optimization/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4

Timestamp:

01/04/19 17:37:13 (6 years ago)

Author:

mkommend

Message:

#2974: Added intermediate version of new constants optimization for profiling.

Location:

branches/2974_Constants_Optimization

Files:

• . (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Constants Optimization/IConstantsOptimizer.cs (added)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Constants Optimization/LMConstantsOptimizer.cs (added)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Constants Optimization/Util.cs (modified) (5 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Converters/TreeToAutoDiffTermConverter.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic-3.4.csproj (modified) (1 diff)

branches/2974_Constants_Optimization

@@ -1 +1 @@
 packages
+TestResults

@@ -1 +1 @@
 packages
+TestResults

branches/2974_Constants_Optimization/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Constants Optimization/Util.cs

@@ -23 +23 @@
 using System.Collections.Generic;
 using System.Linq;
+using HeuristicLab.Common;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using static HeuristicLab.Problems.DataAnalysis.Symbolic.TreeToAutoDiffTermConverter;
@@ -48 +49 @@
     }
 
-    public static Dictionary<DataForVariable, AutoDiff.Variable> ExtractParameters(IDataset dataset) {
-      var parameters = new Dictionary<DataForVariable, AutoDiff.Variable>();
+    public static List<DataForVariable> GenerateVariables(IDataset dataset) {
+      var variables = new List<DataForVariable>();
       foreach (var doubleVariable in dataset.DoubleVariables) {
         var data = new DataForVariable(doubleVariable, string.Empty, 0);
-        var param = new AutoDiff.Variable();
-        parameters.Add(data, param);
+        variables.Add(data);
       }
 
@@ -59 +59 @@
         foreach (var stringValue in dataset.GetStringValues(stringVariable).Distinct()) {
           var data = new DataForVariable(stringVariable, stringValue, 0);
-          var param = new AutoDiff.Variable();
-          parameters.Add(data, param);
+          variables.Add(data);
         }
       }
-      return parameters;
+      return variables;
+    }
+
+    public static List<DataForVariable> ExtractLaggedVariables(ISymbolicExpressionTree tree) {
+      var variables = new HashSet<DataForVariable>();
+      foreach (var laggedNode in tree.IterateNodesPrefix().OfType<ILaggedTreeNode>()) {
+        var laggedVariableTreeNode = laggedNode as LaggedVariableTreeNode;
+        if (laggedVariableTreeNode != null) {
+          var data = new DataForVariable(laggedVariableTreeNode.VariableName, string.Empty, laggedVariableTreeNode.Lag);
+          if (!variables.Contains(data)) variables.Add(data);
+        }
+      }
+      return variables.ToList();
     }
 
@@ -79 +90 @@
           for (int j = 0; j < factorVarTreeNode.Weights.Length; j++)
             constants.Add(factorVarTreeNode.Weights[j]);
-        }        
+        } else throw new NotSupportedException(string.Format("Terminal nodes of type {0} are not supported.", node.GetType().GetPrettyName()));
       }
       return constants.ToArray();
@@ -97 +108 @@
           for (int j = 0; j < factorVarTreeNode.Weights.Length; j++)
             factorVarTreeNode.Weights[j] = constants[i++];
-        }
+        } else throw new NotSupportedException(string.Format("Terminal nodes of type {0} are not supported.", node.GetType().GetPrettyName()));
       }
     }

branches/2974_Constants_Optimization/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Converters/TreeToAutoDiffTermConverter.cs

@@ -131 +131 @@
     }
 
-    public static bool TryConvertToAutoDiff(ISymbolicExpressionTree tree, bool makeVariableWeightsVariable, bool addLinearScalingTerms, Dictionary<DataForVariable, AutoDiff.Variable> parameters,
-      out ParametricFunction func,
-      out ParametricFunctionGradient func_grad,
-      out double[] initialConstants
-  ) {
+    public static bool TryConvertToAutoDiff(ISymbolicExpressionTree tree, bool addLinearScalingTerms, IEnumerable<DataForVariable> variables,
+      out IParametricCompiledTerm autoDiffTerm, out double[] initialConstants) {
       // use a transformator object which holds the state (variable list, parameter list, ...) for recursive transformation of the tree
-      var transformator = new TreeToAutoDiffTermConverter(makeVariableWeightsVariable, parameters);
+      //TODO change ctor
+      var transformator = new TreeToAutoDiffTermConverter(true);
+      var parameters = new AutoDiff.Variable[variables.Count()];
+
+      int i = 0;
+      foreach(var variable in variables) { 
+        var autoDiffVar = new AutoDiff.Variable();
+        transformator.parameters.Add(variable, autoDiffVar);
+        parameters[i] = autoDiffVar;
+        i++;
+      }
+
       AutoDiff.Term term;
       try {
-
-
+        term = transformator.ConvertToAutoDiff(tree.Root.GetSubtree(0));
         if (addLinearScalingTerms) {
-          // scaling variables α, β are given at the beginning of the parameter vector
+          // scaling variables α, β are given at the end of the parameter vector
           var alpha = new AutoDiff.Variable();
           var beta = new AutoDiff.Variable();
+
+          term = term * alpha + beta;
+
+          transformator.variables.Add(alpha);
           transformator.variables.Add(beta);
-          transformator.variables.Add(alpha);
-          term = transformator.ConvertToAutoDiff(tree.Root.GetSubtree(0));
-          term = term * alpha + beta;
-        } else {
-          term = transformator.ConvertToAutoDiff(tree.Root.GetSubtree(0));
-        }
-
-        var compiledTerm = term.Compile(transformator.variables.ToArray(), parameters.Values.ToArray());
-        func = (vars, @params) => compiledTerm.Evaluate(vars, @params);
-        func_grad = (vars, @params) => compiledTerm.Differentiate(vars, @params);
+
+          transformator.initialConstants.Add(1.0);
+          transformator.initialConstants.Add(0.0);
+        }
+
+        var compiledTerm = term.Compile(transformator.variables.ToArray(), parameters);
+        autoDiffTerm = compiledTerm;
         initialConstants = transformator.initialConstants.ToArray();
 
         return true;
       } catch (ConversionException) {
-        func = null;
-        func_grad = null;
+        autoDiffTerm = null;
         initialConstants = null;
       }
@@ -174 +181 @@
     private readonly bool makeVariableWeightsVariable;
 
-    private TreeToAutoDiffTermConverter(bool makeVariableWeightsVariable, Dictionary<DataForVariable, AutoDiff.Variable> parameters = null) {
+    private TreeToAutoDiffTermConverter(bool makeVariableWeightsVariable) {
       this.makeVariableWeightsVariable = makeVariableWeightsVariable;
       this.initialConstants = new List<double>();
-      if (parameters == null)
-        this.parameters = new Dictionary<DataForVariable, AutoDiff.Variable>();
-      else
-        this.parameters = parameters;
+      this.parameters = new Dictionary<DataForVariable, AutoDiff.Variable>();
       this.variables = new List<AutoDiff.Variable>();
     }

branches/2974_Constants_Optimization/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic-3.4.csproj

@@ -213 +213 @@
       <SubType>Code</SubType>
     </Compile>
+    <Compile Include="Constants Optimization\IConstantsOptimizer.cs" />
+    <Compile Include="Constants Optimization\LMConstantsOptimizer.cs" />
     <Compile Include="Constants Optimization\Util.cs" />
     <Compile Include="Converters\LinearModelToTreeConverter.cs" />

@@ -1 +1 @@
 packages
+TestResults