Changeset 16690 for branches/2974_Constants_Optimization

Timestamp:

03/16/19 12:51:33 (6 years ago)

Author:

gkronber

Message:

#2974: added code in the unit tests to make behaviour of new and old ConstOpt consistent to allow direct comparison of results (+fixed a small bug in the unit tests)

File:

• branches/2974_Constants_Optimization/UnitTests/ConstantsOptimizationTests.cs (modified) (4 diffs)

branches/2974_Constants_Optimization/UnitTests/ConstantsOptimizationTests.cs

@@ -47 +47 @@
       var new_result = LMConstantsOptimizer.OptimizeConstants(new_optimizedTree, problemData.Dataset, problemData.TargetVariable, problemData.TrainingIndices, applyLinearScaling, maxIterations: 10);
 
-      //check R² values
-      //Assert.AreEqual(old_result, new_result);
-
-      //check numeric values of constants
+      // extract constants
       var old_constants = Util.ExtractConstants(old_optimizedTree, applyLinearScaling);
       var new_constants = Util.ExtractConstants(new_optimizedTree, applyLinearScaling);
-      //Assert.IsTrue(old_constants.SequenceEqual(new_constants));
-
+
+      {
+        // consistency with old ConstOpt style
+        var initalR2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(
+          new SymbolicDataAnalysisExpressionTreeLinearInterpreter(),
+          tree, double.MinValue, double.MaxValue, problemData, problemData.TrainingIndices, applyLinearScaling);
+
+        // LMConstantsOptimizer returns 0 if optimization was not successful
+        if (initalR2 - new_result > 0.001) {
+          new_result = initalR2;
+          new_constants = old_constants;
+        }
+      }
+
+      //check R² values
+      Assert.AreEqual(old_result, new_result, 1E-6);
+
+      //check numeric values of constants
       for (int i = 0; i < old_constants.Length; i++) {
-        Assert.AreEqual(old_constants[i], new_constants[i], 0.00000001);
+        if (old_constants[i] == 0) {
+          Assert.AreEqual(old_constants[i], new_constants[i], 1E-8); // check absolute error
+        } else {
+          Assert.AreEqual(0.0, 1.0 - new_constants[i] / old_constants[i], 1E-6); // check percentual error
+        }
       }
     }
@@ -147 +164 @@
     [TestProperty("Time", "short")]
     public void ConstantsOptimizationTest_Nguyen07() {
-      var problemData = new NguyenFunctionFive(seed).GenerateRegressionData();
+      var problemData = new NguyenFunctionSeven(seed).GenerateRegressionData();
       var modelTemplate = "({0}) * LOG(({1})*X + ({2})) + ({3}) * LOG(({4})*X*X + ({5})) + ({6})";
 
@@ -162 +179 @@
       modelString = string.Format(modelTemplate, constants);
       modelTree = new InfixExpressionParser().Parse(modelString);
+      // This test fails not because of a problem in ConstOpt but because of the code in 
+      // OnlinePearsonsRCalculator
+      //         double xVar = sxCalculator.PopulationVariance;
+      //         double yVar = syCalculator.PopulationVariance;
+      //         if (xVar.IsAlmost(0.0) || yVar.IsAlmost(0.0)) {
+      //           return 0.0;
+      //         } else { ...
+      // PopulationVariance might become close to zero, but the result of cov / (sqrt(xvar) * sqrt(yvar) might still be correct.
+      // Currently, we just return 0. Which means that we reset optimized constants to initial values
       CompareConstantsOptimizationResults(problemData, modelTree);
 
@@ -223 +249 @@
       CompareConstantsOptimizationResults(problemData, modelTree);
     }
+
+    [TestInitialize]
+    public void InitEnvironment() {
+      System.Threading.Thread.CurrentThread.CurrentCulture = System.Globalization.CultureInfo.InvariantCulture;
+    }
   }
 }