Changeset 12325 for branches

Timestamp:

04/17/15 19:08:21 (10 years ago)

Author:

gkronber

Message:

#2205: added solution cache

File:

• branches/OptimizationNetworks/HeuristicLab.Networks/3.3/FeatureSelection Network/FeatureSelectionConnector.cs (modified) (3 diffs)

branches/OptimizationNetworks/HeuristicLab.Networks/3.3/FeatureSelection Network/FeatureSelectionConnector.cs

@@ -1 +1 @@
 using System;
+using System.Collections;
+using System.Collections.Generic;
 using System.Linq;
 using System.Threading;
@@ -49 +51 @@
     }
 
+    private Dictionary<BinaryVector, double> solutionCache = new Dictionary<BinaryVector, double>(new BinaryVectorComparer());
+
     private void Selection_MessageReceived(object sender, EventArgs<IMessage, CancellationToken> e) {
       // get parameters
@@ -55 +59 @@
       parametersPort.SendMessage(parameters, e.Value2);
       var problemData = (IRegressionProblemData)parameters["ProblemData"];
-      problemData = (IRegressionProblemData)problemData.Clone();
 
-      // build coordinates
+      // filter allowed variables
       var selectionMsg = e.Value;
       var selection = (BinaryVector)selectionMsg["Selection"];
-      var allowedVariables = problemData.InputVariables;
-      foreach (var t in selection.Zip(allowedVariables, Tuple.Create)) {
-        problemData.InputVariables.SetItemCheckedState(t.Item2, t.Item1);
+
+      // if possible return the cached answer 
+      double solutionQuality;
+      if (solutionCache.TryGetValue(selection, out solutionQuality)) {
+        selectionMsg["Quality"] = new DoubleValue(solutionQuality);
+      } else {
+        var allowedVariables = problemData.AllowedInputVariables;
+
+        var selectedInputVariables = from t in selection.Zip(allowedVariables, Tuple.Create)
+                                     where t.Item1
+                                     select t.Item2;
+
+        var clonedProblemData = new RegressionProblemData(problemData.Dataset, selectedInputVariables, problemData.TargetVariable);
+
+        // solve Regression
+        var regressionConPort = (IMessagePort)Ports["Regression Connector"];
+        var regressionMsg = regressionConPort.PrepareMessage();
+        regressionMsg["ProblemData"] = clonedProblemData;
+        regressionConPort.SendMessage(regressionMsg, e.Value2);
+        var solution = (IRegressionSolution)regressionMsg["Linear regression solution"];
+
+        selectionMsg["Quality"] = new DoubleValue(solution.TestNormalizedMeanSquaredError);
+        // cache solution quality
+        solutionCache.Add(selection, solution.TestNormalizedMeanSquaredError);
       }
+    }
+  }
 
-      // solve Regression
-      var regressionConPort = (IMessagePort)Ports["Regression Connector"];
-      var regressionMsg = regressionConPort.PrepareMessage();
-      regressionMsg["ProblemData"] = problemData;
-      regressionConPort.SendMessage(regressionMsg, e.Value2);
-      var solution = (IRegressionSolution)regressionMsg["Linear regression solution"];
+  internal class BinaryVectorComparer : IEqualityComparer<BinaryVector> {
+    public bool Equals(BinaryVector x, BinaryVector y) {
+      return x.Length == y.Length &&
+        x.Zip(y, Tuple.Create).All(t => t.Item1 == t.Item2);
+    }
 
-      selectionMsg["Quality"] = new DoubleValue(solution.TestNormalizedMeanSquaredError);
+    public int GetHashCode(BinaryVector obj) {
+      // return number of set bits
+      return obj.Count(t => t);
     }
   }