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Re-enabling MatrixFactorizationSimpleTrainAndPredict() #4846

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Re-enabling MatrixFactorizationSimpleTrainAndPredict() #4846

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cfb7fa8
Updating expected unix error
mstfbl Feb 14, 2020
5fc1459
Update MatrixFactorizationTests.cs
mstfbl Feb 14, 2020
e75751f
Testing 1000 iterations of MatrixFactorizationSimpleTrainAndPredict
mstfbl Feb 14, 2020
08acf6f
Merge branch 'debuggingMatrixFactorizationSimpleTrainAndPredict' of h…
mstfbl Feb 14, 2020
6365e31
Update job-template.yml
mstfbl Feb 14, 2020
9e3d8ec
Updating Linux tolerance to 1e-4
mstfbl Feb 14, 2020
0e45307
Reverting debugging code
mstfbl Feb 17, 2020
127d373
Update MatrixFactorizationTests.cs
mstfbl Feb 18, 2020
09b00bc
Merge remote-tracking branch 'upstream/master' into debuggingMatrixFa…
mstfbl Feb 19, 2020
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12 changes: 6 additions & 6 deletions test/Microsoft.ML.Tests/TrainerEstimators/MatrixFactorizationTests.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -54,8 +54,6 @@ 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 @@ -94,7 +92,7 @@ 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 matrixes on different platforms, so at least test thier content on windows.
// MF produce different matrixes on different platforms, so at least test their content on windows.
if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
Assert.Equal(0.33491, leftMatrix[0], 5);
Expand Down Expand Up @@ -124,12 +122,14 @@ 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);
double windowsTolerance = Math.Pow(10, -7);
// 1e-7 is too small for Linux, so we try 1e-4
double linuxTolerance = Math.Pow(10, -4);
if (RuntimeInformation.IsOSPlatform(OSPlatform.Linux))
{
// Linux case
var expectedUnixL2Error = 0.614457914950479; // Linux baseline
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614457914950479 [](start = 44, length = 15)

I tried several run, below is my test result:

CenterOS:
Expected: 0.614457914950479 +- 1e-7
Passed: 0.6144579149504698
Failed: 0.6144409592507726

Ubuntu:
Expected: 0.614457914950479 +- 1e-7
Passed: 0.61445791495047
Failed: 0.614440959250773

builds:
https://dev.azure.com/dnceng/public/_build/results?buildId=521052&view=logs&j=887d69fe-3a72-535d-d1ef-c8cd8825499f&t=22ac88eb-9536-588d-9093-3c6614becdd0
https://dev.azure.com/dnceng/public/_build/results?buildId=521051&view=logs&j=5aa5c7df-492a-5eaf-973a-62b7b0f0ee3b&t=ffdbd604-f3e2-5332-cf61-c8dd00799b47
https://dev.azure.com/dnceng/public/_build/results?buildId=521050&view=logs&j=28859320-f5de-51e0-1fd2-7bea8c11cf7a
https://dev.azure.com/dnceng/public/_build/results?buildId=521049&view=logs&j=ac97a0ee-ee90-5a55-af17-a5a589fa545f

I'm not sure how the tolerance was set as before but seems we do have a certain failed value that like not caused by numbrical randomness, can you investigate further the root cause?

Assert.InRange(metrices.MeanSquaredError, expectedUnixL2Error - tolerance, expectedUnixL2Error + tolerance);
Assert.InRange(metrices.MeanSquaredError, expectedUnixL2Error - linuxTolerance, expectedUnixL2Error + linuxTolerance);
}
else if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
{
Expand All @@ -142,7 +142,7 @@ public void MatrixFactorizationSimpleTrainAndPredict()
{
// Windows case
var expectedWindowsL2Error = 0.6098110249191965; // Windows baseline
Assert.InRange(metrices.MeanSquaredError, expectedWindowsL2Error - tolerance, expectedWindowsL2Error + tolerance);
Assert.InRange(metrices.MeanSquaredError, expectedWindowsL2Error - windowsTolerance, expectedWindowsL2Error + windowsTolerance);
}

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