From 0a889c27e1229ad38ca0874bd3a0a0b6c253650b Mon Sep 17 00:00:00 2001 From: Paul Koch Date: Wed, 25 Dec 2024 20:36:08 -0800 Subject: [PATCH] all the parameters, but lacking leaves and monotonicity --- .../libebm/tests/boosting_unusual_inputs.cpp | 46 +++++++++---------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/shared/libebm/tests/boosting_unusual_inputs.cpp b/shared/libebm/tests/boosting_unusual_inputs.cpp index d6d0a5d87..d343cb1b1 100644 --- 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(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(ChooseAny(rng, boostFlagsAny) | ChooseFrom(rng, boostFlagsChoose)); + const TermBoostFlags boostFlags = + static_cast(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 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 )