diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
index 163db8e801..7b2864fe36 100644
--- a/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
+++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
@@ -374,9 +374,9 @@ public void TestCrossValidationMacro()
foldGetter(ref fold);
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
if (w == 1)
- Assert.Equal(1.584696, stdev, 6);
+ Assert.Equal(1.585, stdev, 3);
else
- Assert.Equal(1.385165, stdev, 6);
+ Assert.Equal(1.39, stdev, 2);
isWeightedGetter(ref isWeighted);
Assert.True(isWeighted == (w == 1));
}
diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
index 40c8dc1862..30a9ab6625 100644
--- a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
+++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
@@ -3630,10 +3630,10 @@ public void EntryPointLinearPredictorSummary()
DataSaverUtils.SaveDataView(ch, saver, mcOutput.Stats, file);
}
- CheckEquality(@"../Common/EntryPoints", "lr-weights.txt");
- CheckEquality(@"../Common/EntryPoints", "lr-stats.txt");
- CheckEquality(@"../Common/EntryPoints", "mc-lr-weights.txt");
- CheckEquality(@"../Common/EntryPoints", "mc-lr-stats.txt");
+ CheckEquality(@"../Common/EntryPoints", "lr-weights.txt", digitsOfPrecision: 6);
+ CheckEquality(@"../Common/EntryPoints", "lr-stats.txt", digitsOfPrecision: 6);
+ CheckEquality(@"../Common/EntryPoints", "mc-lr-weights.txt", digitsOfPrecision: 3);
+ CheckEquality(@"../Common/EntryPoints", "mc-lr-stats.txt", digitsOfPrecision: 5);
Done();
}
@@ -3675,7 +3675,7 @@ public void EntryPointPcaPredictorSummary()
DataSaverUtils.SaveDataView(ch, saver, output.Summary, file);
}
- CheckEquality(@"../Common/EntryPoints", "pca-weights.txt");
+ CheckEquality(@"../Common/EntryPoints", "pca-weights.txt", digitsOfPrecision: 4);
Done();
}
}
diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
index b2ea240e7f..b0158f60bb 100644
--- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
+++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
@@ -108,7 +108,7 @@ public void BinaryClassifierPerceptronTest()
{
var binaryPredictors = new[] { TestLearners.perceptron };
var binaryClassificationDatasets = GetDatasetsForBinaryClassifierBaseTest();
- RunAllTests(binaryPredictors, binaryClassificationDatasets);
+ RunAllTests(binaryPredictors, binaryClassificationDatasets, digitsOfPrecision: 6);
Done();
}
@@ -253,7 +253,7 @@ public void LinearClassifierTest()
TestLearners.binarySgdHinge
};
var binaryClassificationDatasets = GetDatasetsForBinaryClassifierBaseTest();
- RunAllTests(binaryPredictors, binaryClassificationDatasets);
+ RunAllTests(binaryPredictors, binaryClassificationDatasets, digitsOfPrecision: 5);
Done();
}
@@ -265,7 +265,7 @@ public void LinearClassifierTest()
public void BinaryClassifierLogisticRegressionTest()
{
var binaryPredictors = new[] { TestLearners.logisticRegression };
- RunOneAllTests(TestLearners.logisticRegression, TestDatasets.breastCancer, summary: true);
+ RunOneAllTests(TestLearners.logisticRegression, TestDatasets.breastCancer, summary: true, digitsOfPrecision: 3);
// RunOneAllTests(TestLearners.logisticRegression, TestDatasets.msm);
Done();
}
@@ -278,7 +278,7 @@ public void BinaryClassifierSymSgdTest()
if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
return;
- RunOneAllTests(TestLearners.symSGD, TestDatasets.breastCancer, summary: true);
+ RunOneAllTests(TestLearners.symSGD, TestDatasets.breastCancer, summary: true, digitsOfPrecision: 4);
Done();
}
@@ -288,7 +288,7 @@ public void BinaryClassifierTesterThresholdingTest()
{
var binaryPredictors = new[] { TestLearners.logisticRegression };
var binaryClassificationDatasets = new[] { TestDatasets.breastCancer };
- RunAllTests(binaryPredictors, binaryClassificationDatasets, new[] { "eval=BinaryClassifier{threshold=0.95 useRawScore=-}" }, "withThreshold");
+ RunAllTests(binaryPredictors, binaryClassificationDatasets, new[] { "eval=BinaryClassifier{threshold=0.95 useRawScore=-}" }, "withThreshold", digitsOfPrecision: 3);
Done();
}
@@ -301,7 +301,7 @@ public void BinaryClassifierLogisticRegressionNormTest()
{
var binaryPredictors = new[] { TestLearners.logisticRegressionNorm };
var binaryClassificationDatasets = GetDatasetsForBinaryClassifierBaseTest();
- RunAllTests(binaryPredictors, binaryClassificationDatasets);
+ RunAllTests(binaryPredictors, binaryClassificationDatasets, digitsOfPrecision: 5);
Done();
}
@@ -327,7 +327,7 @@ public void BinaryClassifierLogisticRegressionBinNormTest()
{
var binaryPredictors = new[] { TestLearners.logisticRegressionBinNorm };
var binaryClassificationDatasets = GetDatasetsForBinaryClassifierBaseTest();
- RunAllTests(binaryPredictors, binaryClassificationDatasets);
+ RunAllTests(binaryPredictors, binaryClassificationDatasets, digitsOfPrecision: 6);
Done();
}
@@ -340,7 +340,7 @@ public void BinaryClassifierLogisticRegressionGaussianNormTest()
{
var binaryPredictors = new[] { TestLearners.logisticRegressionGaussianNorm };
var binaryClassificationDatasets = GetDatasetsForBinaryClassifierBaseTest();
- RunAllTests(binaryPredictors, binaryClassificationDatasets);
+ RunAllTests(binaryPredictors, binaryClassificationDatasets, digitsOfPrecision: 4);
Done();
}
@@ -934,7 +934,7 @@ public void RegressorOlsTest()
[TestCategory("Regressor")]
public void RegressorOlsTestOne()
{
- Run_TrainTest(TestLearners.Ols, TestDatasets.generatedRegressionDataset);
+ Run_TrainTest(TestLearners.Ols, TestDatasets.generatedRegressionDataset, digitsOfPrecision: 4);
Done();
}
@@ -1378,7 +1378,7 @@ public void RandomCalibratorPerceptronTest()
public void NoCalibratorLinearSvmTest()
{
var datasets = GetDatasetsForCalibratorTest();
- RunAllTests(new[] { TestLearners.linearSVM }, datasets, new string[] { "cali={}" }, "nocalibration");
+ RunAllTests(new[] { TestLearners.linearSVM }, datasets, new string[] { "cali={}" }, "nocalibration", digitsOfPrecision: 6);
Done();
}
@@ -1390,7 +1390,7 @@ public void NoCalibratorLinearSvmTest()
public void PAVCalibratorLinearSvmTest()
{
var datasets = GetDatasetsForCalibratorTest();
- RunAllTests(new[] { TestLearners.linearSVM }, datasets, new string[] { "cali=PAV" }, "PAVcalibration");
+ RunAllTests(new[] { TestLearners.linearSVM }, datasets, new string[] { "cali=PAV" }, "PAVcalibration", digitsOfPrecision: 5);
Done();
}
@@ -1520,8 +1520,8 @@ public void NnConfigTests()
[TestCategory("Anomaly")]
public void PcaAnomalyTest()
{
- Run_TrainTest(TestLearners.PCAAnomalyDefault, TestDatasets.mnistOneClass);
- Run_TrainTest(TestLearners.PCAAnomalyNoNorm, TestDatasets.mnistOneClass);
+ Run_TrainTest(TestLearners.PCAAnomalyDefault, TestDatasets.mnistOneClass, digitsOfPrecision: 5);
+ Run_TrainTest(TestLearners.PCAAnomalyNoNorm, TestDatasets.mnistOneClass, digitsOfPrecision: 5);
// REVIEW: This next test was misbehaving in a strange way that seems to have gone away
// mysteriously (bad build?).
diff --git a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs
index 4b14152011..55bd13a328 100644
--- a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs
+++ b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs
@@ -506,7 +506,9 @@ protected bool CheckEqualityFromPathsCore(string relPath, string basePath, strin
private void GetNumbersFromFile(ref string firstString, ref string secondString, int digitsOfPrecision)
{
- Regex _matchNumer = new Regex(@"\b[0-9]+\.?[0-9]*(E-[0-9]*)?\b", RegexOptions.IgnoreCase | RegexOptions.Compiled);
+ // The Regex matches both positive and negative decimal point numbers present in a string.
+ // The numbers could be a part of a word. They can also be in Exponential form eg. 3E-9
+ Regex _matchNumer = new Regex(@"-?\b[0-9]+\.?[0-9]*(E-[0-9]*)?\b", RegexOptions.IgnoreCase | RegexOptions.Compiled);
MatchCollection firstCollection = _matchNumer.Matches(firstString);
MatchCollection secondCollection = _matchNumer.Matches(secondString);
diff --git a/test/Microsoft.ML.Tests/OnnxTests.cs b/test/Microsoft.ML.Tests/OnnxTests.cs
index 09808501a6..0d87140652 100644
--- a/test/Microsoft.ML.Tests/OnnxTests.cs
+++ b/test/Microsoft.ML.Tests/OnnxTests.cs
@@ -452,7 +452,7 @@ public void BinaryClassificationLRSaveModelToOnnxTest()
fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\"");
File.WriteAllText(onnxAsJsonPath, fileText);
- CheckEquality(subDir, "BinaryClassificationLRSaveModelToOnnxTest.json");
+ CheckEquality(subDir, "BinaryClassificationLRSaveModelToOnnxTest.json", digitsOfPrecision: 3);
Done();
}
@@ -514,7 +514,7 @@ public void MultiClassificationLRSaveModelToOnnxTest()
fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\"");
File.WriteAllText(onnxAsJsonPath, fileText);
- CheckEquality(subDir, "MultiClassificationLRSaveModelToOnnxTest.json");
+ CheckEquality(subDir, "MultiClassificationLRSaveModelToOnnxTest.json", digitsOfPrecision: 4);
Done();
}
diff --git a/test/Microsoft.ML.Tests/Transformers/PcaTests.cs b/test/Microsoft.ML.Tests/Transformers/PcaTests.cs
index 5561b4df7d..4ca45dddc1 100644
--- a/test/Microsoft.ML.Tests/Transformers/PcaTests.cs
+++ b/test/Microsoft.ML.Tests/Transformers/PcaTests.cs
@@ -53,7 +53,7 @@ public void PcaWorkout()
DataSaverUtils.SaveDataView(ch, saver, savedData, fs, keepHidden: true);
}
- CheckEquality("PCA", "pca.tsv");
+ CheckEquality("PCA", "pca.tsv", digitsOfPrecision: 4);
Done();
}
}