all the parameters, but lacking leaves and monotonicity

interpretml · Dec 26, 2024 · 0a889c2 · 0a889c2
1 parent 19f41ef
commit 0a889c2
Showing 1 changed file with 23 additions and 23 deletions.
diff --git a/shared/libebm/tests/boosting_unusual_inputs.cpp b/shared/libebm/tests/boosting_unusual_inputs.cpp
@@ -2127,21 +2127,21 @@ static double RandomizedTesting(const AccelerationFlags acceleration) {
          double validationMetricIteration = 0.0;
          for(size_t iRound = 0; iRound < cRounds; ++iRound) {
             for(IntEbm iTerm = 0; iTerm < static_cast<IntEbm>(terms.size()); ++iTerm) {
-               //const IntEbm cRealBins = features[terms[iTerm][0]].CountRealBins();
+               const IntEbm cRealBins = features[terms[iTerm][0]].CountRealBins();
                //const IntEbm cDimensions = terms[iTerm].size();
 
-               //const TermBoostFlags boostFlags =
-               //      static_cast<TermBoostFlags>(ChooseAny(rng, boostFlagsAny) | ChooseFrom(rng, boostFlagsChoose));
+               const TermBoostFlags boostFlags =
+                     static_cast<TermBoostFlags>(ChooseAny(rng, boostFlagsAny) | ChooseFrom(rng, boostFlagsChoose));
 
-               //const double learningRate = 0.015625;
-               //const IntEbm minSamplesLeaf = TestRand(rng, 5) + 1;
-               //const double minHessian = 0 == TestRand(rng, 5) ? 0.015625 : 0.0;
-               //const double regAlpha = 0 == TestRand(rng, 5) ? 0.015625 : 0.0;
-               //const double regLambda = 0 == TestRand(rng, 5) ? 0.015625 : 0.0;
-               //const double maxDeltaStep = 0 == TestRand(rng, 5) ? 1.0 : 0.0;
-               //const double categoricalSmoothing = 10.0;
-               //const IntEbm maxCategoricalThreshold = 1 + TestRand(rng, cRealBins + 1);
-               //const double categoricalInclusionPercent = 0 == TestRand(rng, 2) ? 0.75 : 1.0;
+               const double learningRate = 0.015625;
+               const IntEbm minSamplesLeaf = TestRand(rng, 5) + 1;
+               const double minHessian = 0 == TestRand(rng, 5) ? 0.015625 : 0.0;
+               const double regAlpha = 0 == TestRand(rng, 5) ? 0.015625 : 0.0;
+               const double regLambda = 0 == TestRand(rng, 5) ? 0.015625 : 0.0;
+               const double maxDeltaStep = 0 == TestRand(rng, 5) ? 1.0 : 0.0;
+               const double categoricalSmoothing = 10.0;
+               const IntEbm maxCategoricalThreshold = 1 + TestRand(rng, cRealBins + 1);
+               const double categoricalInclusionPercent = 0 == TestRand(rng, 2) ? 0.75 : 1.0;
 
                //// we allow 1 cut more than the number of bins to test excessive leaves.
                //const IntEbm cLeaves = 1 + TestRand(rng, cRealBins + 1);
@@ -2151,17 +2151,17 @@ static double RandomizedTesting(const AccelerationFlags acceleration) {
                //const std::vector<MonotoneDirection> monotonicity(cDimensions, direction);
 
                validationMetricIteration = test.Boost(iTerm
-                                                     // ,
-                                                     //boostFlags,
-                                                     //learningRate,
-                                                     //minSamplesLeaf,
-                                                     //minHessian,
-                                                     //regAlpha,
-                                                     //regLambda,
-                                                     //maxDeltaStep,
-                                                     //categoricalSmoothing,
-                                                     //maxCategoricalThreshold,
-                                                     //categoricalInclusionPercent,
+                                                      ,
+                                                     boostFlags,
+                                                     learningRate,
+                                                     minSamplesLeaf,
+                                                     minHessian,
+                                                     regAlpha,
+                                                     regLambda,
+                                                     maxDeltaStep,
+                                                     categoricalSmoothing,
+                                                     maxCategoricalThreshold,
+                                                     categoricalInclusionPercent
                                                      //leaves,
                                                      //monotonicity
                                                      )