nrBinsFix and re-enabling related tests (#4869)

Author: mstfbl

src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs

@@ -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);
         }
 

test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

@@ -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
-        [Trait("Category", "SkipInCI")]
         public void EntryPointPipelineEnsembleGetSummary()
         {
             var dataPath = GetDataPath("breast-cancer-withheader.txt");
@@ -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");

test/Microsoft.ML.Predictor.Tests/TestPredictors.cs

@@ -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(() =>

test/Microsoft.ML.Tests/TrainerEstimators/MatrixFactorizationTests.cs

@@ -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);
@@ -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)));
@@ -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))
             {
@@ -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);