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Merged
merged 12 commits into from
Jun 12, 2019
Merged

Bugfix/hardwired sigmoid #3850

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6de2abf
fixed Hardcoded sigmoid value. Let Microsoft.ML.Tests see internals o…
michaelgsharp Jun 5, 2019
d21eecf
Removed extra whitespace between comment and code
michaelgsharp Jun 10, 2019
8c682ac
Merge branch 'master' into bugfix/hardwired-sigmoid
michaelgsharp Jun 10, 2019
7537b38
changed which parameter was internal
michaelgsharp Jun 10, 2019
bdba6ea
changed unneeded internal parameter back to private
michaelgsharp Jun 10, 2019
bbb076b
Put comments in correct format, sorted the using list
michaelgsharp Jun 10, 2019
7c48cf5
Added direct tests between ML.NET and LightGBM with non-default sigmoid.
michaelgsharp Jun 10, 2019
56d674b
Update test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs
michaelgsharp Jun 11, 2019
e989921
Update test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs
michaelgsharp Jun 11, 2019
8d96665
Update test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs
michaelgsharp Jun 11, 2019
cb43df3
renamed sigmoid to sigmoidScale
michaelgsharp Jun 11, 2019
7461e4b
changed sigmoid from a default parameter so it can come before the ou…
michaelgsharp Jun 11, 2019
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renamed sigmoid to sigmoidScale
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@michaelgsharp
michaelgsharp committed Jun 11, 2019
commit cb43df3473ad29e93779c99b76feaf86a9c92721
16 changes: 8 additions & 8 deletions test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -464,9 +464,9 @@ public void LightGbmMulticlassEstimatorCompareOva()
[LightGBMFact]
public void LightGbmMulticlassEstimatorCompareOvaUsingSigmoids()
{
var sigmoid = .790;
var sigmoidScale = .790;
// Train ML.NET LightGBM and native LightGBM and apply the trained models to the training set.
LightGbmHelper(useSoftmax: false, out string modelString, out List<GbmExample> mlnetPredictions, out double[] nativeResult1, out double[] nativeResult0, sigmoid: sigmoid);
LightGbmHelper(useSoftmax: false, out string modelString, out List<GbmExample> mlnetPredictions, out double[] nativeResult1, out double[] nativeResult0, sigmoid: sigmoidScale);

// The i-th predictor returned by LightGBM produces the raw score, denoted by z_i, of the i-th class.
// Assume that we have n classes in total. The i-th class probability can be computed via
Expand All @@ -482,11 +482,11 @@ public void LightGbmMulticlassEstimatorCompareOvaUsingSigmoids()
Assert.Equal(nativeResult0[j + i * _classNumber], mlnetPredictions[i].Score[j], 6);
if (float.IsNaN((float)nativeResult1[j + i * _classNumber]))
continue;
sum += MathUtils.SigmoidSlow((float)sigmoid * (float)nativeResult1[j + i * _classNumber]);
sum += MathUtils.SigmoidSlow((float)sigmoidScale * (float)nativeResult1[j + i * _classNumber]);
}
for (int j = 0; j < _classNumber; ++j)
{
double prob = MathUtils.SigmoidSlow((float)sigmoid * (float)nativeResult1[j + i * _classNumber]);
double prob = MathUtils.SigmoidSlow((float)sigmoidScale * (float)nativeResult1[j + i * _classNumber]);
Assert.Equal(prob / sum, mlnetPredictions[i].Score[j], 6);
}
}
Expand All @@ -501,12 +501,12 @@ public void LightGbmMulticlassEstimatorCompareOvaUsingSigmoids()
public void LightGbmMulticlassEstimatorCompareOvaUsingDifferentSigmoids()
{
// Run native implemenation twice, see that results are different with different sigmoid values.
var firstSigmoid = .790;
var secondSigmoid = .2;
var firstSigmoidScale = .790;
var secondSigmoidScale = .2;

// Train native LightGBM with both sigmoid values and apply the trained models to the training set.
LightGbmHelper(useSoftmax: false, out string firstModelString, out List<GbmExample> firstMlnetPredictions, out double[] firstNativeResult1, out double[] firstNativeResult0, sigmoid: firstSigmoid);
LightGbmHelper(useSoftmax: false, out string secondModelString, out List<GbmExample> secondMlnetPredictions, out double[] secondNativeResult1, out double[] secondNativeResult0, sigmoid: secondSigmoid);
LightGbmHelper(useSoftmax: false, out string firstModelString, out List<GbmExample> firstMlnetPredictions, out double[] firstNativeResult1, out double[] firstNativeResult0, sigmoid: firstSigmoidScale);
LightGbmHelper(useSoftmax: false, out string secondModelString, out List<GbmExample> secondMlnetPredictions, out double[] secondNativeResult1, out double[] secondNativeResult0, sigmoid: secondSigmoidScale);

// Compare native LightGBM's results when 2 different sigmoid values are used.
for (int i = 0; i < _rowNumber; ++i)
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