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Re-enabling disabled tests due to wrong nrBins value #4869

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Merged
mstfbl merged 1 commit into from
Feb 21, 2020
Merged

Re-enabling disabled tests due to wrong nrBins value #4869

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2 changes: 1 addition & 1 deletion src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -513,7 +513,7 @@ private MatrixFactorizationModelParameters TrainCore(IChannel ch, RoleMappedData

private SafeTrainingAndModelBuffer PrepareBuffer()
{
return new SafeTrainingAndModelBuffer(_host, _fun, _k, _threads, Math.Max(20, 2 * _threads),
return new SafeTrainingAndModelBuffer(_host, _fun, _k, _threads, _threads == 1 ? 1 : Math.Max(20, 2 * _threads),
_iter, _lambda, _eta, _alpha, _c, _doNmf, _quiet, copyData: false);
}

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2 changes: 0 additions & 2 deletions test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
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Expand Up @@ -2124,7 +2124,6 @@ public void EntryPointMulticlassPipelineEnsemble()

[LessThanNetCore30OrNotNetCoreFact("netcoreapp3.0 output differs from Baseline")]
//Skipping test temporarily. This test will be re-enabled once the cause of failures has been determined
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[Trait("Category", "SkipInCI")]
public void EntryPointPipelineEnsembleGetSummary()
{
var dataPath = GetDataPath("breast-cancer-withheader.txt");
Expand Down Expand Up @@ -6194,7 +6193,6 @@ public void TestOvaMacro()

[Fact]
//Skipping test temporarily. This test will be re-enabled once the cause of failures has been determined
[Trait("Category", "SkipInCI")]
public void TestOvaMacroWithUncalibratedLearner()
{
var dataPath = GetDataPath(@"iris.txt");
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1 change: 0 additions & 1 deletion test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
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Expand Up @@ -189,7 +189,6 @@ public void MulticlassSdcaTest()
[TestCategory("Logistic Regression")]
[TestCategory("FastTree")]
//Skipping test temporarily. This test will be re-enabled once the cause of failures has been determined
[Trait("Category", "SkipInCI")]
public void MulticlassTreeFeaturizedLRTest()
{
RunMTAThread(() =>
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23 changes: 12 additions & 11 deletions test/Microsoft.ML.Tests/TrainerEstimators/MatrixFactorizationTests.cs
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Expand Up @@ -55,7 +55,6 @@ public void MatrixFactorization_Estimator()

[MatrixFactorizationFact]
//Skipping test temporarily. This test will be re-enabled once the cause of failures has been determined
[Trait("Category", "SkipInCI")]
public void MatrixFactorizationSimpleTrainAndPredict()
{
var mlContext = new MLContext(seed: 1);
Expand Down Expand Up @@ -94,13 +93,13 @@ public void MatrixFactorizationSimpleTrainAndPredict()
var rightMatrix = model.Model.RightFactorMatrix;
Assert.Equal(leftMatrix.Count, model.Model.NumberOfRows * model.Model.ApproximationRank);
Assert.Equal(rightMatrix.Count, model.Model.NumberOfColumns * model.Model.ApproximationRank);
// MF produce different matrices on different platforms, so at least test their content on windows.
// MF produce different matrices on different platforms, so check their content on Windows.
if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
Assert.Equal(0.33491, leftMatrix[0], 5);
Assert.Equal(0.571346991, leftMatrix[leftMatrix.Count - 1], 5);
Assert.Equal(0.2433036714792256, rightMatrix[0], 5);
Assert.Equal(0.381277978420258, rightMatrix[rightMatrix.Count - 1], 5);
Assert.Equal(0.301269173622131, leftMatrix[0], 5);
Assert.Equal(0.558746933937073, leftMatrix[leftMatrix.Count - 1], 5);
Assert.Equal(0.27028301358223, rightMatrix[0], 5);
Assert.Equal(0.390790820121765, rightMatrix[rightMatrix.Count - 1], 5);
}
// Read the test data set as an IDataView
var testData = reader.Load(new MultiFileSource(GetDataPath(TestDatasets.trivialMatrixFactorization.testFilename)));
Expand All @@ -124,12 +123,14 @@ public void MatrixFactorizationSimpleTrainAndPredict()
var metrices = mlContext.Recommendation().Evaluate(prediction, labelColumnName: labelColumnName, scoreColumnName: scoreColumnName);

// Determine if the selected metric is reasonable for different platforms
double tolerance = Math.Pow(10, -7);
// Windows tolerance is set at 1e-7, and Linux tolerance is set at 1e-5
double windowsTolerance = Math.Pow(10, -7);
double linuxTolerance = Math.Pow(10, -5);
if (RuntimeInformation.IsOSPlatform(OSPlatform.Linux))
{
// Linux case
var expectedUnixL2Error = 0.614457914950479; // Linux baseline
Assert.InRange(metrices.MeanSquaredError, expectedUnixL2Error - tolerance, expectedUnixL2Error + tolerance);
var expectedUnixL2Error = 0.612974867782832; // Linux baseline
Assert.InRange(metrices.MeanSquaredError, expectedUnixL2Error - linuxTolerance, expectedUnixL2Error + linuxTolerance);
}
else if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
{
Expand All @@ -141,8 +142,8 @@ public void MatrixFactorizationSimpleTrainAndPredict()
else if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
// Windows case
var expectedWindowsL2Error = 0.6098110249191965; // Windows baseline
Assert.InRange(metrices.MeanSquaredError, expectedWindowsL2Error - tolerance, expectedWindowsL2Error + tolerance);
var expectedWindowsL2Error = 0.622283290742721; // Windows baseline
Assert.InRange(metrices.MeanSquaredError, expectedWindowsL2Error - windowsTolerance, expectedWindowsL2Error + windowsTolerance);
}

var modelWithValidation = pipeline.Fit(data, testData);
Expand Down