Make double assertions compare with tolerance instead of precision#6923

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
ericstj merged 1 commit into from
Dec 22, 2023
Merged

Make double assertions compare with tolerance instead of precision #6923

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
20 changes: 10 additions & 10 deletions test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -94,7 +94,7 @@ public void EntryPointTrainTestSplit()
int testRows = CountRows(splitOutput.TestData);

Assert.Equal(totalRows, trainRows + testRows);
Assert.Equal(0.9, (double)trainRows / totalRows, 1);
Assert.Equal(0.9, (double)trainRows / totalRows, 0.1);
}

private static int CountRows(IDataView dataView)
Expand DownExpand Up@@ -5005,7 +5005,7 @@ public void TestSimpleTrainExperiment()
Assert.True(b);
double auc = 0;
getter(ref auc);
Assert.Equal(0.93, auc, 2);
Assert.Equal(0.93, auc, 0.01);
b = cursor.MoveNext();
Assert.False(b);
}
Expand DownExpand Up@@ -5210,7 +5210,7 @@ public void TestCrossValidationMacro()
if (w == 1)
Assert.Equal(1.585, stdev, .001);
else
Assert.Equal(1.39, stdev, 2);
Assert.Equal(1.39, stdev, 0.01);
isWeightedGetter(ref isWeighted);
Assert.True(isWeighted == (w == 1));
}
Expand DownExpand Up@@ -5379,7 +5379,7 @@ public void TestCrossValidationMacroWithMulticlass()
getter(ref stdev);
foldGetter(ref fold);
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
Assert.Equal(0.024809923969586353, stdev, 3);
Assert.Equal(0.024809923969586353, stdev, 0.001);

double sum = 0;
double val = 0;
Expand DownExpand Up@@ -5788,7 +5788,7 @@ public void TestCrossValidationMacroWithStratification()
getter(ref stdev);
foldGetter(ref fold);
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
Assert.Equal(0.02582, stdev, 5);
Assert.Equal(0.02582, stdev, 0.00001);

double sum = 0;
double val = 0;
Expand DownExpand Up@@ -6089,9 +6089,9 @@ public void TestCrossValidationMacroWithNonDefaultNames()
foldGetter(ref fold);
Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
var stdevValues = stdev.GetValues();
Assert.Equal(0.02462, stdevValues[0], 5);
Assert.Equal(0.02763, stdevValues[1], 5);
Assert.Equal(0.03273, stdevValues[2], 5);
Assert.Equal(0.02462, stdevValues[0], 0.00001);
Assert.Equal(0.02763, stdevValues[1], 0.00001);
Assert.Equal(0.03273, stdevValues[2], 0.00001);

var sumBldr = new BufferBuilder<double>(R8Adder.Instance);
sumBldr.Reset(avg.Length, true);
Expand DownExpand Up@@ -6291,7 +6291,7 @@ public void TestOvaMacro()
Assert.True(b);
double acc = 0;
getter(ref acc);
Assert.Equal(0.96, acc, 2);
Assert.Equal(0.96, acc, 0.01);
b = cursor.MoveNext();
Assert.False(b);
}
Expand DownExpand Up@@ -6463,7 +6463,7 @@ public void TestOvaMacroWithUncalibratedLearner()
Assert.True(b);
double acc = 0;
getter(ref acc);
Assert.Equal(0.71, acc, 2);
Assert.Equal(0.71, acc, 0.01);
b = cursor.MoveNext();
Assert.False(b);
}
Expand Down
6 changes: 3 additions & 3 deletions test/Microsoft.ML.Core.Tests/UnitTests/TestLoss.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -39,16 +39,16 @@ private void TestHelper(IScalarLoss lossFunc, double label, double output, doubl
{
Double loss = lossFunc.Loss((float)output, (float)label);
float derivative = lossFunc.Derivative((float)output, (float)label);
Assert.Equal(expectedLoss, loss, 5);
Assert.Equal(expectedUpdate, -derivative, 5);
Assert.Equal(expectedLoss, loss, 0.00001);
Assert.Equal(expectedUpdate, -derivative, 0.00001);

if (differentiable)
{
// In principle, the update should be the negative of the first derivative of the loss.
// Use a simple finite difference method to see if it's in the right ballpark.
float almostOutput = Math.Max((float)output * (1 + _epsilon), (float)output + _epsilon);
Double almostLoss = lossFunc.Loss(almostOutput, (float)label);
Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 1);
Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 0.1);
}
}

Expand Down
2 changes: 1 addition & 1 deletion test/Microsoft.ML.Core.Tests/UnitTests/TestVBuffer.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -293,7 +293,7 @@ public void TestSparsifyNormalize(int startRange, bool normalize, float[] expect
var actualValues = a.GetValues().ToArray();
Assert.Equal(expectedValues.Length, actualValues.Length);
for (int i = 0; i < expectedValues.Length; i++)
Assert.Equal(expectedValues[i], actualValues[i], precision: 6);
Assert.Equal(expectedValues[i], actualValues[i], 0.000001);
}

/// <summary>
Expand Down
18 changes: 9 additions & 9 deletions test/Microsoft.ML.CpuMath.UnitTests/UnitTests.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,7 @@ public void SumTest(string mode, string test, Dictionary<string, string> environ
}

var actual = CpuMathUtils.Sum(src);
Assert.Equal((double)expected, (double)actual, 2);
Assert.Equal((double)expected, (double)actual, 0.01);
return RemoteExecutor.SuccessExitCode;
}, mode, test, options);
}
Expand All@@ -668,7 +668,7 @@ public void SumSqUTest(string mode, string test, Dictionary<string, string> envi
}

var actual = CpuMathUtils.SumSq(src);
Assert.Equal((double)expected, (double)actual, 2);
Assert.Equal((double)expected, (double)actual, 0.01);
return RemoteExecutor.SuccessExitCode;
}, mode, test, options);
}
Expand All@@ -693,7 +693,7 @@ public void SumSqDiffUTest(string mode, string test, string scale, Dictionary<st
expected += (src[i] - defaultScale) * (src[i] - defaultScale);
}

Assert.Equal((double)expected, (double)actual, 1);
Assert.Equal((double)expected, (double)actual, 0.1);
return RemoteExecutor.SuccessExitCode;
}, mode, test, scale, options);
}
Expand All@@ -716,7 +716,7 @@ public void SumAbsUTest(string mode, string test, Dictionary<string, string> env
}

var actual = CpuMathUtils.SumAbs(src);
Assert.Equal((double)expected, (double)actual, 2);
Assert.Equal((double)expected, (double)actual, 0.01);
return RemoteExecutor.SuccessExitCode;
}, mode, test, options);
}
Expand All@@ -741,7 +741,7 @@ public void SumAbsDiffUTest(string mode, string test, string scale, Dictionary<s
expected += Math.Abs(src[i] - defaultScale);
}

Assert.Equal((double)expected, (double)actual, 2);
Assert.Equal((double)expected, (double)actual, 0.01);
return RemoteExecutor.SuccessExitCode;
}, mode, test, scale, options);
}
Expand DownExpand Up@@ -769,7 +769,7 @@ public void MaxAbsUTest(string mode, string test, Dictionary<string, string> env
}
}

Assert.Equal((double)expected, (double)actual, 2);
Assert.Equal((double)expected, (double)actual, 0.01);
return RemoteExecutor.SuccessExitCode;
}, mode, test, options);
}
Expand DownExpand Up@@ -797,7 +797,7 @@ public void MaxAbsDiffUTest(string mode, string test, string scale, Dictionary<s
expected = abs;
}
}
Assert.Equal((double)expected, (double)actual, 2);
Assert.Equal((double)expected, (double)actual, 0.01);
return RemoteExecutor.SuccessExitCode;
}, mode, test, scale, options);
}
Expand DownExpand Up@@ -827,7 +827,7 @@ public void DotUTest(string mode, string test, Dictionary<string, string> enviro
}

var actual = CpuMathUtils.DotProductDense(src, dst, dst.Length);
Assert.Equal((double)expected, (double)actual, 1);
Assert.Equal((double)expected, (double)actual, 0.1);
return RemoteExecutor.SuccessExitCode;
}, mode, test, options);
}
Expand DownExpand Up@@ -861,7 +861,7 @@ public void DotSUTest(string mode, string test, Dictionary<string, string> envir
}

var actual = CpuMathUtils.DotProductSparse(src, dst, idx, limit);
Assert.Equal((double)expected, (double)actual, 2);
Assert.Equal((double)expected, (double)actual, 0.01);
return RemoteExecutor.SuccessExitCode;
}, mode, test, options);
}
Expand Down
22 changes: 11 additions & 11 deletions test/Microsoft.ML.Fairlearn.Tests/MetricTest.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ public void RegressionMetricTest()
{
RegressionGroupMetric regressionMetric = mlContext.Fairlearn().Metric.Regression(eval: data, labelColumn: "Price", scoreColumn: "Score", sensitiveFeatureColumn: "Gender");
var metricByGroup = regressionMetric.ByGroup();
Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 3);
Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 3);
Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 3);
Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 3);
Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 0.001);
Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 0.001);
Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 0.001);
Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 0.001);
metricByGroup.Description();
Dictionary<string, double> metricOverall = regressionMetric.Overall();
Assert.Equal(125.5, metricOverall["MSE"], 1);
Assert.Equal(11.202678, metricOverall["RMS"], 4);
Assert.Equal(125.5, metricOverall["MSE"], 0.1);
Assert.Equal(11.202678, metricOverall["RMS"], 0.0001);
Dictionary<string, double> diff = regressionMetric.DifferenceBetweenGroups();
Assert.Equal(14.81138, diff["RMS"], 4);
Assert.Equal(2037.5, diff["RSquared"], 1);
Assert.Equal(14.81138, diff["RMS"], 0.0001);
Assert.Equal(2037.5, diff["RSquared"], 0.1);

}

Expand All@@ -70,10 +70,10 @@ public void BinaryClassificationMetricTest()

BinaryGroupMetric metrics = mlContext.Fairlearn().Metric.BinaryClassification(eval: df, labelColumn: "label", predictedColumn: "PredictedLabel", sensitiveFeatureColumn: "group_id");
var metricByGroup = metrics.ByGroup();
Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 1);
Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 1);
Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 0.1);
Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 0.1);
var metricOverall = metrics.Overall();
Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 1);
Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 0.1);
}
}
}
8 changes: 4 additions & 4 deletions test/Microsoft.ML.Fairlearn.Tests/UtilityTest.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -31,10 +31,10 @@ public void DemographyParityTest()
PrimitiveDataFrameColumn<float> ypred = new PrimitiveDataFrameColumn<float>("pred", fl);
var gSinged = dp.Gamma(ypred);

Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 1);
Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 1);
Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 1);
Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 1);
Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 0.1);
Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 0.1);
Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 0.1);
Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 0.1);
}
}
}
4 changes: 2 additions & 2 deletions test/Microsoft.ML.IntegrationTests/IntrospectiveTraining.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -119,8 +119,8 @@ public void InspectFastTreeModelParameters()
var expectedThresholds = new float[] { 0.0911167f, 0.06509889f, 0.019873254f, 0.0361835f };
for (int i = 0; i < finalTree.NumberOfNodes; ++i)
{
Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 6);
Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 6);
Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 0.000001);
Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 0.000001);
}
}

Expand Down
4 changes: 2 additions & 2 deletions test/Microsoft.ML.IntegrationTests/ONNX.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -71,7 +71,7 @@ public void SaveOnnxModelLoadAndScoreFastTree()
var originalPrediction = originalPredictionEngine.Predict(row);
var onnxPrediction = onnxPredictionEngine.Predict(row);
// Check that the predictions are identical.
Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
}
}

Expand DownExpand Up@@ -170,7 +170,7 @@ public void SaveOnnxModelLoadAndScoreSDCA()
var originalPrediction = originalPredictionEngine.Predict(row);
var onnxPrediction = onnxPredictionEngine.Predict(row);
// Check that the predictions are identical.
Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
}
}
}
Expand Down
2 changes: 1 addition & 1 deletion test/Microsoft.ML.IntegrationTests/Training.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -498,7 +498,7 @@ public void MetacomponentsFunctionWithKeyHandling()
// Evaluate the model.
var binaryClassificationMetrics = mlContext.MulticlassClassification.Evaluate(binaryClassificationPredictions);

Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 4);
Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 0.0001);
}
}
}
6 changes: 3 additions & 3 deletions test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -476,9 +476,9 @@ public void TestOnnxNoneDimValue()
var transformedValues = onnxTransformer.Transform(idv);
var predictions = mlContext.Data.CreateEnumerable<PredictionNoneDimension>(transformedValues, reuseRowObject: false).ToArray();

Assert.Equal(-0.080, Math.Round(predictions[0].variable[0], 3));
Assert.Equal(1.204, Math.Round(predictions[1].variable[0], 3));
Assert.Equal(2.27, Math.Round(predictions[2].variable[0], 3));
Assert.Equal(-0.080, predictions[0].variable[0], 0.001);
Assert.Equal(1.204, predictions[1].variable[0], 0.001);
Assert.Equal(2.27, predictions[2].variable[0], 0.001);
}

/// <summary>
Expand Down
2 changes: 1 addition & 1 deletion test/Microsoft.ML.Predictor.Tests/TestGamPublicInterfaces.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -43,7 +43,7 @@ public void TestGamDirectInstantiation()
Assert.Equal(binUpperBounds.Length, gam.NumberOfShapeFunctions);

// Check the intercept
Assert.Equal(intercept, gam.Bias, 6);
Assert.Equal(intercept, gam.Bias, 0.000001);

// Check that the binUpperBounds were made correctly
CheckArrayOfArrayEquality(binUpperBounds, gam.GetBinUpperBounds());
Expand Down
6 changes: 3 additions & 3 deletions test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -737,8 +737,8 @@ private void CombineAndTestTreeEnsembles(IDataView idv, PredictorModel[] fastTre
probGetters[i](ref probs[i]);
predGetters[i](ref preds[i]);
}
Assert.Equal(score, 0.4 * scores.Sum() / predCount, 5);
Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 6);
Assert.Equal(score, 0.4 * scores.Sum() / predCount, 0.00001);
Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 0.000001);
Assert.True(pred == score > 0);
}
}
Expand DownExpand Up@@ -953,7 +953,7 @@ private void CombineAndTestEnsembles(IDataView idv, string name, string options,
for (int j = 0; j < predCount; j++)
sum += vectorScores[j].GetItemOrDefault(i);
if (float.IsNaN(sum))
Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 3);
Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 0.001);
}
Assert.Equal(probs.Count(p => p >= prob), probs.Count(p => p <= prob));
}
Expand Down
4 changes: 2 additions & 2 deletions test/Microsoft.ML.Tests/AnomalyDetectionTests.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -33,8 +33,8 @@ public void RandomizedPcaTrainerBaselineTest()
// Evaluate
var metrics = ML.AnomalyDetection.Evaluate(transformedData, falsePositiveCount: 5);

Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 5);
Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 5);
Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 0.00001);
Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 0.00001);
}

/// <summary>
Expand Down
2 changes: 1 addition & 1 deletion test/Microsoft.ML.Tests/EvaluateTests.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -65,7 +65,7 @@ public void MulticlassEvaluatorTopKArray()
var metrics2 = mlContext.MulticlassClassification.Evaluate(inputDV2, topKPredictionCount: 4);
var output2 = metrics2.TopKAccuracyForAllK.ToArray();
for (int i = 0; i < expectedTopKArray2.Length; i++)
Assert.Equal(expectedTopKArray2[i], output2[i], precision: 7);
Assert.Equal(expectedTopKArray2[i], output2[i], 0.0000001);
}
}
}
2 changes: 1 addition & 1 deletion test/Microsoft.ML.Tests/LearningRateSchedulerTest.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -48,7 +48,7 @@ internal void TestPolynomialDecay(float[] expectedValues, bool cycle)
trainState.CurrentBatchIndex = i % trainState.BatchesPerEpoch;
trainState.CurrentEpoch = i / trainState.BatchesPerEpoch;
float decayedLR = learningRateScheduler.GetLearningRate(trainState);
Assert.Equal((double)expectedValues[i], (double)decayedLR, 4);
Assert.Equal((double)expectedValues[i], (double)decayedLR, 0.0001);
}
}
}
Expand Down
2 changes: 1 addition & 1 deletion test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -87,7 +87,7 @@ public void PredictClusters()
Assert.Equal(double.NaN, metrics.NormalizedMutualInformation);
//Calculate dbi is false by default so Dbi would be 0
Assert.Equal(0d, metrics.DaviesBouldinIndex);
Assert.Equal(0d, metrics.AverageDistance, 5);
Assert.Equal(0d, metrics.AverageDistance, 0.00001);
}
}
}
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
Make double assertions compare with tolerance instead of precision#6923

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

ericstj
merged 1 commit into

from

Dec 22, 2023
 Merged


Make double assertions compare with tolerance instead of precision
#6923
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


20 changes: 10 additions & 10 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -94,7 +94,7 @@ public void EntryPointTrainTestSplit()
 int testRows = CountRows(splitOutput.TestData);

 Assert.Equal(totalRows, trainRows + testRows);
 Assert.Equal(0.9, (double)trainRows / totalRows, 1);
 Assert.Equal(0.9, (double)trainRows / totalRows, 0.1);
 }

 private static int CountRows(IDataView dataView)
Expand DownExpand Up@@ -5005,7 +5005,7 @@ public void TestSimpleTrainExperiment()
 Assert.True(b);
 double auc = 0;
 getter(ref auc);
 Assert.Equal(0.93, auc, 2);
 Assert.Equal(0.93, auc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -5210,7 +5210,7 @@ public void TestCrossValidationMacro()
 if (w == 1)
 Assert.Equal(1.585, stdev, .001);
 else
 Assert.Equal(1.39, stdev, 2);
 Assert.Equal(1.39, stdev, 0.01);
 isWeightedGetter(ref isWeighted);
 Assert.True(isWeighted == (w == 1));
 }
Expand DownExpand Up@@ -5379,7 +5379,7 @@ public void TestCrossValidationMacroWithMulticlass()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.024809923969586353, stdev, 3);
 Assert.Equal(0.024809923969586353, stdev, 0.001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -5788,7 +5788,7 @@ public void TestCrossValidationMacroWithStratification()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.02582, stdev, 5);
 Assert.Equal(0.02582, stdev, 0.00001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -6089,9 +6089,9 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 var stdevValues = stdev.GetValues();
 Assert.Equal(0.02462, stdevValues[0], 5);
 Assert.Equal(0.02763, stdevValues[1], 5);
 Assert.Equal(0.03273, stdevValues[2], 5);
 Assert.Equal(0.02462, stdevValues[0], 0.00001);
 Assert.Equal(0.02763, stdevValues[1], 0.00001);
 Assert.Equal(0.03273, stdevValues[2], 0.00001);

 var sumBldr = new BufferBuilder<double>(R8Adder.Instance);
 sumBldr.Reset(avg.Length, true);
Expand DownExpand Up@@ -6291,7 +6291,7 @@ public void TestOvaMacro()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 Assert.Equal(0.96, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -6463,7 +6463,7 @@ public void TestOvaMacroWithUncalibratedLearner()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.71, acc, 2);
 Assert.Equal(0.71, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestLoss.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,16 +39,16 @@ private void TestHelper(IScalarLoss lossFunc, double label, double output, doubl
 {
 Double loss = lossFunc.Loss((float)output, (float)label);
 float derivative = lossFunc.Derivative((float)output, (float)label);
 Assert.Equal(expectedLoss, loss, 5);
 Assert.Equal(expectedUpdate, -derivative, 5);
 Assert.Equal(expectedLoss, loss, 0.00001);
 Assert.Equal(expectedUpdate, -derivative, 0.00001);

 if (differentiable)
 {
 // In principle, the update should be the negative of the first derivative of the loss.
 // Use a simple finite difference method to see if it's in the right ballpark.
 float almostOutput = Math.Max((float)output * (1 + _epsilon), (float)output + _epsilon);
 Double almostLoss = lossFunc.Loss(almostOutput, (float)label);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 1);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 0.1);
 }
 }

Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestVBuffer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -293,7 +293,7 @@ public void TestSparsifyNormalize(int startRange, bool normalize, float[] expect
 var actualValues = a.GetValues().ToArray();
 Assert.Equal(expectedValues.Length, actualValues.Length);
 for (int i = 0; i < expectedValues.Length; i++)
 Assert.Equal(expectedValues[i], actualValues[i], precision: 6);
 Assert.Equal(expectedValues[i], actualValues[i], 0.000001);
 }

 /// <summary>
Expand Down

18 changes: 9 additions & 9 deletions
test/Microsoft.ML.CpuMath.UnitTests/UnitTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,7 @@ public void SumTest(string mode, string test, Dictionary<string, string> environ
 }

 var actual = CpuMathUtils.Sum(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -668,7 +668,7 @@ public void SumSqUTest(string mode, string test, Dictionary<string, string> envi
 }

 var actual = CpuMathUtils.SumSq(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -693,7 +693,7 @@ public void SumSqDiffUTest(string mode, string test, string scale, Dictionary<st
 expected += (src[i] - defaultScale) * (src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand All@@ -716,7 +716,7 @@ public void SumAbsUTest(string mode, string test, Dictionary<string, string> env
 }

 var actual = CpuMathUtils.SumAbs(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -741,7 +741,7 @@ public void SumAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected += Math.Abs(src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -769,7 +769,7 @@ public void MaxAbsUTest(string mode, string test, Dictionary<string, string> env
 }
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -797,7 +797,7 @@ public void MaxAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected = abs;
 }
 }
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -827,7 +827,7 @@ public void DotUTest(string mode, string test, Dictionary<string, string> enviro
 }

 var actual = CpuMathUtils.DotProductDense(src, dst, dst.Length);
 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -861,7 +861,7 @@ public void DotSUTest(string mode, string test, Dictionary<string, string> envir
 }

 var actual = CpuMathUtils.DotProductSparse(src, dst, idx, limit);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand Down

22 changes: 11 additions & 11 deletions
test/Microsoft.ML.Fairlearn.Tests/MetricTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ public void RegressionMetricTest()
 {
 RegressionGroupMetric regressionMetric = mlContext.Fairlearn().Metric.Regression(eval: data, labelColumn: "Price", scoreColumn: "Score", sensitiveFeatureColumn: "Gender");
 var metricByGroup = regressionMetric.ByGroup();
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 3);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 3);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 3);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 3);
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 0.001);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 0.001);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 0.001);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 0.001);
 metricByGroup.Description();
 Dictionary<string, double> metricOverall = regressionMetric.Overall();
 Assert.Equal(125.5, metricOverall["MSE"], 1);
 Assert.Equal(11.202678, metricOverall["RMS"], 4);
 Assert.Equal(125.5, metricOverall["MSE"], 0.1);
 Assert.Equal(11.202678, metricOverall["RMS"], 0.0001);
 Dictionary<string, double> diff = regressionMetric.DifferenceBetweenGroups();
 Assert.Equal(14.81138, diff["RMS"], 4);
 Assert.Equal(2037.5, diff["RSquared"], 1);
 Assert.Equal(14.81138, diff["RMS"], 0.0001);
 Assert.Equal(2037.5, diff["RSquared"], 0.1);

 }

Expand All@@ -70,10 +70,10 @@ public void BinaryClassificationMetricTest()

 BinaryGroupMetric metrics = mlContext.Fairlearn().Metric.BinaryClassification(eval: df, labelColumn: "label", predictedColumn: "PredictedLabel", sensitiveFeatureColumn: "group_id");
 var metricByGroup = metrics.ByGroup();
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 1);
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 0.1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 0.1);
 var metricOverall = metrics.Overall();
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 1);
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 0.1);
 }
 }
}

8 changes: 4 additions & 4 deletions
test/Microsoft.ML.Fairlearn.Tests/UtilityTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -31,10 +31,10 @@ public void DemographyParityTest()
 PrimitiveDataFrameColumn<float> ypred = new PrimitiveDataFrameColumn<float>("pred", fl);
 var gSinged = dp.Gamma(ypred);

 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 0.1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 0.1);
 }
 }
}

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/IntrospectiveTraining.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -119,8 +119,8 @@ public void InspectFastTreeModelParameters()
 var expectedThresholds = new float[] { 0.0911167f, 0.06509889f, 0.019873254f, 0.0361835f };
 for (int i = 0; i < finalTree.NumberOfNodes; ++i)
 {
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 6);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 6);
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 0.000001);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 0.000001);
 }
 }

Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/ONNX.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -71,7 +71,7 @@ public void SaveOnnxModelLoadAndScoreFastTree()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }

Expand DownExpand Up@@ -170,7 +170,7 @@ public void SaveOnnxModelLoadAndScoreSDCA()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.IntegrationTests/Training.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -498,7 +498,7 @@ public void MetacomponentsFunctionWithKeyHandling()
 // Evaluate the model.
 var binaryClassificationMetrics = mlContext.MulticlassClassification.Evaluate(binaryClassificationPredictions);

 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 4);
 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 0.0001);
 }
 }
}

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -476,9 +476,9 @@ public void TestOnnxNoneDimValue()
 var transformedValues = onnxTransformer.Transform(idv);
 var predictions = mlContext.Data.CreateEnumerable<PredictionNoneDimension>(transformedValues, reuseRowObject: false).ToArray();

 Assert.Equal(-0.080, Math.Round(predictions[0].variable[0], 3));
 Assert.Equal(1.204, Math.Round(predictions[1].variable[0], 3));
 Assert.Equal(2.27, Math.Round(predictions[2].variable[0], 3));
 Assert.Equal(-0.080, predictions[0].variable[0], 0.001);
 Assert.Equal(1.204, predictions[1].variable[0], 0.001);
 Assert.Equal(2.27, predictions[2].variable[0], 0.001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Predictor.Tests/TestGamPublicInterfaces.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -43,7 +43,7 @@ public void TestGamDirectInstantiation()
 Assert.Equal(binUpperBounds.Length, gam.NumberOfShapeFunctions);

 // Check the intercept
 Assert.Equal(intercept, gam.Bias, 6);
 Assert.Equal(intercept, gam.Bias, 0.000001);

 // Check that the binUpperBounds were made correctly
 CheckArrayOfArrayEquality(binUpperBounds, gam.GetBinUpperBounds());
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Predictor.Tests/TestPredictors.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -737,8 +737,8 @@ private void CombineAndTestTreeEnsembles(IDataView idv, PredictorModel[] fastTre
 probGetters[i](ref probs[i]);
 predGetters[i](ref preds[i]);
 }
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 5);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 6);
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 0.00001);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 0.000001);
 Assert.True(pred == score > 0);
 }
 }
Expand DownExpand Up@@ -953,7 +953,7 @@ private void CombineAndTestEnsembles(IDataView idv, string name, string options,
 for (int j = 0; j < predCount; j++)
 sum += vectorScores[j].GetItemOrDefault(i);
 if (float.IsNaN(sum))
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 3);
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 0.001);
 }
 Assert.Equal(probs.Count(p => p >= prob), probs.Count(p => p <= prob));
 }
Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.Tests/AnomalyDetectionTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -33,8 +33,8 @@ public void RandomizedPcaTrainerBaselineTest()
 // Evaluate
 var metrics = ML.AnomalyDetection.Evaluate(transformedData, falsePositiveCount: 5);

 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 5);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 5);
 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 0.00001);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 0.00001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/EvaluateTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -65,7 +65,7 @@ public void MulticlassEvaluatorTopKArray()
 var metrics2 = mlContext.MulticlassClassification.Evaluate(inputDV2, topKPredictionCount: 4);
 var output2 = metrics2.TopKAccuracyForAllK.ToArray();
 for (int i = 0; i < expectedTopKArray2.Length; i++)
 Assert.Equal(expectedTopKArray2[i], output2[i], precision: 7);
 Assert.Equal(expectedTopKArray2[i], output2[i], 0.0000001);
 }
 }
}

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/LearningRateSchedulerTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -48,7 +48,7 @@ internal void TestPolynomialDecay(float[] expectedValues, bool cycle)
 trainState.CurrentBatchIndex = i % trainState.BatchesPerEpoch;
 trainState.CurrentEpoch = i / trainState.BatchesPerEpoch;
 float decayedLR = learningRateScheduler.GetLearningRate(trainState);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 4);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -87,7 +87,7 @@ public void PredictClusters()
 Assert.Equal(double.NaN, metrics.NormalizedMutualInformation);
 //Calculate dbi is false by default so Dbi would be 0
 Assert.Equal(0d, metrics.DaviesBouldinIndex);
 Assert.Equal(0d, metrics.AverageDistance, 5);
 Assert.Equal(0d, metrics.AverageDistance, 0.00001);
 }
 }
}
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Make double assertions compare with tolerance instead of precision#6923

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

ericstj
merged 1 commit into

from

Dec 22, 2023
 Merged


Make double assertions compare with tolerance instead of precision
#6923
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


20 changes: 10 additions & 10 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -94,7 +94,7 @@ public void EntryPointTrainTestSplit()
 int testRows = CountRows(splitOutput.TestData);

 Assert.Equal(totalRows, trainRows + testRows);
 Assert.Equal(0.9, (double)trainRows / totalRows, 1);
 Assert.Equal(0.9, (double)trainRows / totalRows, 0.1);
 }

 private static int CountRows(IDataView dataView)
Expand DownExpand Up@@ -5005,7 +5005,7 @@ public void TestSimpleTrainExperiment()
 Assert.True(b);
 double auc = 0;
 getter(ref auc);
 Assert.Equal(0.93, auc, 2);
 Assert.Equal(0.93, auc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -5210,7 +5210,7 @@ public void TestCrossValidationMacro()
 if (w == 1)
 Assert.Equal(1.585, stdev, .001);
 else
 Assert.Equal(1.39, stdev, 2);
 Assert.Equal(1.39, stdev, 0.01);
 isWeightedGetter(ref isWeighted);
 Assert.True(isWeighted == (w == 1));
 }
Expand DownExpand Up@@ -5379,7 +5379,7 @@ public void TestCrossValidationMacroWithMulticlass()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.024809923969586353, stdev, 3);
 Assert.Equal(0.024809923969586353, stdev, 0.001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -5788,7 +5788,7 @@ public void TestCrossValidationMacroWithStratification()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.02582, stdev, 5);
 Assert.Equal(0.02582, stdev, 0.00001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -6089,9 +6089,9 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 var stdevValues = stdev.GetValues();
 Assert.Equal(0.02462, stdevValues[0], 5);
 Assert.Equal(0.02763, stdevValues[1], 5);
 Assert.Equal(0.03273, stdevValues[2], 5);
 Assert.Equal(0.02462, stdevValues[0], 0.00001);
 Assert.Equal(0.02763, stdevValues[1], 0.00001);
 Assert.Equal(0.03273, stdevValues[2], 0.00001);

 var sumBldr = new BufferBuilder<double>(R8Adder.Instance);
 sumBldr.Reset(avg.Length, true);
Expand DownExpand Up@@ -6291,7 +6291,7 @@ public void TestOvaMacro()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 Assert.Equal(0.96, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -6463,7 +6463,7 @@ public void TestOvaMacroWithUncalibratedLearner()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.71, acc, 2);
 Assert.Equal(0.71, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestLoss.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,16 +39,16 @@ private void TestHelper(IScalarLoss lossFunc, double label, double output, doubl
 {
 Double loss = lossFunc.Loss((float)output, (float)label);
 float derivative = lossFunc.Derivative((float)output, (float)label);
 Assert.Equal(expectedLoss, loss, 5);
 Assert.Equal(expectedUpdate, -derivative, 5);
 Assert.Equal(expectedLoss, loss, 0.00001);
 Assert.Equal(expectedUpdate, -derivative, 0.00001);

 if (differentiable)
 {
 // In principle, the update should be the negative of the first derivative of the loss.
 // Use a simple finite difference method to see if it's in the right ballpark.
 float almostOutput = Math.Max((float)output * (1 + _epsilon), (float)output + _epsilon);
 Double almostLoss = lossFunc.Loss(almostOutput, (float)label);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 1);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 0.1);
 }
 }

Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestVBuffer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -293,7 +293,7 @@ public void TestSparsifyNormalize(int startRange, bool normalize, float[] expect
 var actualValues = a.GetValues().ToArray();
 Assert.Equal(expectedValues.Length, actualValues.Length);
 for (int i = 0; i < expectedValues.Length; i++)
 Assert.Equal(expectedValues[i], actualValues[i], precision: 6);
 Assert.Equal(expectedValues[i], actualValues[i], 0.000001);
 }

 /// <summary>
Expand Down

18 changes: 9 additions & 9 deletions
test/Microsoft.ML.CpuMath.UnitTests/UnitTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,7 @@ public void SumTest(string mode, string test, Dictionary<string, string> environ
 }

 var actual = CpuMathUtils.Sum(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -668,7 +668,7 @@ public void SumSqUTest(string mode, string test, Dictionary<string, string> envi
 }

 var actual = CpuMathUtils.SumSq(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -693,7 +693,7 @@ public void SumSqDiffUTest(string mode, string test, string scale, Dictionary<st
 expected += (src[i] - defaultScale) * (src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand All@@ -716,7 +716,7 @@ public void SumAbsUTest(string mode, string test, Dictionary<string, string> env
 }

 var actual = CpuMathUtils.SumAbs(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -741,7 +741,7 @@ public void SumAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected += Math.Abs(src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -769,7 +769,7 @@ public void MaxAbsUTest(string mode, string test, Dictionary<string, string> env
 }
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -797,7 +797,7 @@ public void MaxAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected = abs;
 }
 }
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -827,7 +827,7 @@ public void DotUTest(string mode, string test, Dictionary<string, string> enviro
 }

 var actual = CpuMathUtils.DotProductDense(src, dst, dst.Length);
 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -861,7 +861,7 @@ public void DotSUTest(string mode, string test, Dictionary<string, string> envir
 }

 var actual = CpuMathUtils.DotProductSparse(src, dst, idx, limit);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand Down

22 changes: 11 additions & 11 deletions
test/Microsoft.ML.Fairlearn.Tests/MetricTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ public void RegressionMetricTest()
 {
 RegressionGroupMetric regressionMetric = mlContext.Fairlearn().Metric.Regression(eval: data, labelColumn: "Price", scoreColumn: "Score", sensitiveFeatureColumn: "Gender");
 var metricByGroup = regressionMetric.ByGroup();
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 3);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 3);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 3);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 3);
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 0.001);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 0.001);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 0.001);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 0.001);
 metricByGroup.Description();
 Dictionary<string, double> metricOverall = regressionMetric.Overall();
 Assert.Equal(125.5, metricOverall["MSE"], 1);
 Assert.Equal(11.202678, metricOverall["RMS"], 4);
 Assert.Equal(125.5, metricOverall["MSE"], 0.1);
 Assert.Equal(11.202678, metricOverall["RMS"], 0.0001);
 Dictionary<string, double> diff = regressionMetric.DifferenceBetweenGroups();
 Assert.Equal(14.81138, diff["RMS"], 4);
 Assert.Equal(2037.5, diff["RSquared"], 1);
 Assert.Equal(14.81138, diff["RMS"], 0.0001);
 Assert.Equal(2037.5, diff["RSquared"], 0.1);

 }

Expand All@@ -70,10 +70,10 @@ public void BinaryClassificationMetricTest()

 BinaryGroupMetric metrics = mlContext.Fairlearn().Metric.BinaryClassification(eval: df, labelColumn: "label", predictedColumn: "PredictedLabel", sensitiveFeatureColumn: "group_id");
 var metricByGroup = metrics.ByGroup();
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 1);
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 0.1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 0.1);
 var metricOverall = metrics.Overall();
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 1);
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 0.1);
 }
 }
}

8 changes: 4 additions & 4 deletions
test/Microsoft.ML.Fairlearn.Tests/UtilityTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -31,10 +31,10 @@ public void DemographyParityTest()
 PrimitiveDataFrameColumn<float> ypred = new PrimitiveDataFrameColumn<float>("pred", fl);
 var gSinged = dp.Gamma(ypred);

 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 0.1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 0.1);
 }
 }
}

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/IntrospectiveTraining.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -119,8 +119,8 @@ public void InspectFastTreeModelParameters()
 var expectedThresholds = new float[] { 0.0911167f, 0.06509889f, 0.019873254f, 0.0361835f };
 for (int i = 0; i < finalTree.NumberOfNodes; ++i)
 {
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 6);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 6);
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 0.000001);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 0.000001);
 }
 }

Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/ONNX.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -71,7 +71,7 @@ public void SaveOnnxModelLoadAndScoreFastTree()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }

Expand DownExpand Up@@ -170,7 +170,7 @@ public void SaveOnnxModelLoadAndScoreSDCA()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.IntegrationTests/Training.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -498,7 +498,7 @@ public void MetacomponentsFunctionWithKeyHandling()
 // Evaluate the model.
 var binaryClassificationMetrics = mlContext.MulticlassClassification.Evaluate(binaryClassificationPredictions);

 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 4);
 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 0.0001);
 }
 }
}

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -476,9 +476,9 @@ public void TestOnnxNoneDimValue()
 var transformedValues = onnxTransformer.Transform(idv);
 var predictions = mlContext.Data.CreateEnumerable<PredictionNoneDimension>(transformedValues, reuseRowObject: false).ToArray();

 Assert.Equal(-0.080, Math.Round(predictions[0].variable[0], 3));
 Assert.Equal(1.204, Math.Round(predictions[1].variable[0], 3));
 Assert.Equal(2.27, Math.Round(predictions[2].variable[0], 3));
 Assert.Equal(-0.080, predictions[0].variable[0], 0.001);
 Assert.Equal(1.204, predictions[1].variable[0], 0.001);
 Assert.Equal(2.27, predictions[2].variable[0], 0.001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Predictor.Tests/TestGamPublicInterfaces.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -43,7 +43,7 @@ public void TestGamDirectInstantiation()
 Assert.Equal(binUpperBounds.Length, gam.NumberOfShapeFunctions);

 // Check the intercept
 Assert.Equal(intercept, gam.Bias, 6);
 Assert.Equal(intercept, gam.Bias, 0.000001);

 // Check that the binUpperBounds were made correctly
 CheckArrayOfArrayEquality(binUpperBounds, gam.GetBinUpperBounds());
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Predictor.Tests/TestPredictors.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -737,8 +737,8 @@ private void CombineAndTestTreeEnsembles(IDataView idv, PredictorModel[] fastTre
 probGetters[i](ref probs[i]);
 predGetters[i](ref preds[i]);
 }
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 5);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 6);
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 0.00001);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 0.000001);
 Assert.True(pred == score > 0);
 }
 }
Expand DownExpand Up@@ -953,7 +953,7 @@ private void CombineAndTestEnsembles(IDataView idv, string name, string options,
 for (int j = 0; j < predCount; j++)
 sum += vectorScores[j].GetItemOrDefault(i);
 if (float.IsNaN(sum))
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 3);
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 0.001);
 }
 Assert.Equal(probs.Count(p => p >= prob), probs.Count(p => p <= prob));
 }
Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.Tests/AnomalyDetectionTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -33,8 +33,8 @@ public void RandomizedPcaTrainerBaselineTest()
 // Evaluate
 var metrics = ML.AnomalyDetection.Evaluate(transformedData, falsePositiveCount: 5);

 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 5);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 5);
 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 0.00001);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 0.00001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/EvaluateTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -65,7 +65,7 @@ public void MulticlassEvaluatorTopKArray()
 var metrics2 = mlContext.MulticlassClassification.Evaluate(inputDV2, topKPredictionCount: 4);
 var output2 = metrics2.TopKAccuracyForAllK.ToArray();
 for (int i = 0; i < expectedTopKArray2.Length; i++)
 Assert.Equal(expectedTopKArray2[i], output2[i], precision: 7);
 Assert.Equal(expectedTopKArray2[i], output2[i], 0.0000001);
 }
 }
}

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/LearningRateSchedulerTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -48,7 +48,7 @@ internal void TestPolynomialDecay(float[] expectedValues, bool cycle)
 trainState.CurrentBatchIndex = i % trainState.BatchesPerEpoch;
 trainState.CurrentEpoch = i / trainState.BatchesPerEpoch;
 float decayedLR = learningRateScheduler.GetLearningRate(trainState);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 4);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -87,7 +87,7 @@ public void PredictClusters()
 Assert.Equal(double.NaN, metrics.NormalizedMutualInformation);
 //Calculate dbi is false by default so Dbi would be 0
 Assert.Equal(0d, metrics.DaviesBouldinIndex);
 Assert.Equal(0d, metrics.AverageDistance, 5);
 Assert.Equal(0d, metrics.AverageDistance, 0.00001);
 }
 }
}
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Make double assertions compare with tolerance instead of precision#6923

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

ericstj
merged 1 commit into

from

Dec 22, 2023
 Merged


Make double assertions compare with tolerance instead of precision
#6923
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


20 changes: 10 additions & 10 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -94,7 +94,7 @@ public void EntryPointTrainTestSplit()
 int testRows = CountRows(splitOutput.TestData);

 Assert.Equal(totalRows, trainRows + testRows);
 Assert.Equal(0.9, (double)trainRows / totalRows, 1);
 Assert.Equal(0.9, (double)trainRows / totalRows, 0.1);
 }

 private static int CountRows(IDataView dataView)
Expand DownExpand Up@@ -5005,7 +5005,7 @@ public void TestSimpleTrainExperiment()
 Assert.True(b);
 double auc = 0;
 getter(ref auc);
 Assert.Equal(0.93, auc, 2);
 Assert.Equal(0.93, auc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -5210,7 +5210,7 @@ public void TestCrossValidationMacro()
 if (w == 1)
 Assert.Equal(1.585, stdev, .001);
 else
 Assert.Equal(1.39, stdev, 2);
 Assert.Equal(1.39, stdev, 0.01);
 isWeightedGetter(ref isWeighted);
 Assert.True(isWeighted == (w == 1));
 }
Expand DownExpand Up@@ -5379,7 +5379,7 @@ public void TestCrossValidationMacroWithMulticlass()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.024809923969586353, stdev, 3);
 Assert.Equal(0.024809923969586353, stdev, 0.001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -5788,7 +5788,7 @@ public void TestCrossValidationMacroWithStratification()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.02582, stdev, 5);
 Assert.Equal(0.02582, stdev, 0.00001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -6089,9 +6089,9 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 var stdevValues = stdev.GetValues();
 Assert.Equal(0.02462, stdevValues[0], 5);
 Assert.Equal(0.02763, stdevValues[1], 5);
 Assert.Equal(0.03273, stdevValues[2], 5);
 Assert.Equal(0.02462, stdevValues[0], 0.00001);
 Assert.Equal(0.02763, stdevValues[1], 0.00001);
 Assert.Equal(0.03273, stdevValues[2], 0.00001);

 var sumBldr = new BufferBuilder<double>(R8Adder.Instance);
 sumBldr.Reset(avg.Length, true);
Expand DownExpand Up@@ -6291,7 +6291,7 @@ public void TestOvaMacro()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 Assert.Equal(0.96, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -6463,7 +6463,7 @@ public void TestOvaMacroWithUncalibratedLearner()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.71, acc, 2);
 Assert.Equal(0.71, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestLoss.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,16 +39,16 @@ private void TestHelper(IScalarLoss lossFunc, double label, double output, doubl
 {
 Double loss = lossFunc.Loss((float)output, (float)label);
 float derivative = lossFunc.Derivative((float)output, (float)label);
 Assert.Equal(expectedLoss, loss, 5);
 Assert.Equal(expectedUpdate, -derivative, 5);
 Assert.Equal(expectedLoss, loss, 0.00001);
 Assert.Equal(expectedUpdate, -derivative, 0.00001);

 if (differentiable)
 {
 // In principle, the update should be the negative of the first derivative of the loss.
 // Use a simple finite difference method to see if it's in the right ballpark.
 float almostOutput = Math.Max((float)output * (1 + _epsilon), (float)output + _epsilon);
 Double almostLoss = lossFunc.Loss(almostOutput, (float)label);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 1);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 0.1);
 }
 }

Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestVBuffer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -293,7 +293,7 @@ public void TestSparsifyNormalize(int startRange, bool normalize, float[] expect
 var actualValues = a.GetValues().ToArray();
 Assert.Equal(expectedValues.Length, actualValues.Length);
 for (int i = 0; i < expectedValues.Length; i++)
 Assert.Equal(expectedValues[i], actualValues[i], precision: 6);
 Assert.Equal(expectedValues[i], actualValues[i], 0.000001);
 }

 /// <summary>
Expand Down

18 changes: 9 additions & 9 deletions
test/Microsoft.ML.CpuMath.UnitTests/UnitTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,7 @@ public void SumTest(string mode, string test, Dictionary<string, string> environ
 }

 var actual = CpuMathUtils.Sum(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -668,7 +668,7 @@ public void SumSqUTest(string mode, string test, Dictionary<string, string> envi
 }

 var actual = CpuMathUtils.SumSq(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -693,7 +693,7 @@ public void SumSqDiffUTest(string mode, string test, string scale, Dictionary<st
 expected += (src[i] - defaultScale) * (src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand All@@ -716,7 +716,7 @@ public void SumAbsUTest(string mode, string test, Dictionary<string, string> env
 }

 var actual = CpuMathUtils.SumAbs(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -741,7 +741,7 @@ public void SumAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected += Math.Abs(src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -769,7 +769,7 @@ public void MaxAbsUTest(string mode, string test, Dictionary<string, string> env
 }
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -797,7 +797,7 @@ public void MaxAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected = abs;
 }
 }
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -827,7 +827,7 @@ public void DotUTest(string mode, string test, Dictionary<string, string> enviro
 }

 var actual = CpuMathUtils.DotProductDense(src, dst, dst.Length);
 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -861,7 +861,7 @@ public void DotSUTest(string mode, string test, Dictionary<string, string> envir
 }

 var actual = CpuMathUtils.DotProductSparse(src, dst, idx, limit);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand Down

22 changes: 11 additions & 11 deletions
test/Microsoft.ML.Fairlearn.Tests/MetricTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ public void RegressionMetricTest()
 {
 RegressionGroupMetric regressionMetric = mlContext.Fairlearn().Metric.Regression(eval: data, labelColumn: "Price", scoreColumn: "Score", sensitiveFeatureColumn: "Gender");
 var metricByGroup = regressionMetric.ByGroup();
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 3);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 3);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 3);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 3);
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 0.001);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 0.001);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 0.001);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 0.001);
 metricByGroup.Description();
 Dictionary<string, double> metricOverall = regressionMetric.Overall();
 Assert.Equal(125.5, metricOverall["MSE"], 1);
 Assert.Equal(11.202678, metricOverall["RMS"], 4);
 Assert.Equal(125.5, metricOverall["MSE"], 0.1);
 Assert.Equal(11.202678, metricOverall["RMS"], 0.0001);
 Dictionary<string, double> diff = regressionMetric.DifferenceBetweenGroups();
 Assert.Equal(14.81138, diff["RMS"], 4);
 Assert.Equal(2037.5, diff["RSquared"], 1);
 Assert.Equal(14.81138, diff["RMS"], 0.0001);
 Assert.Equal(2037.5, diff["RSquared"], 0.1);

 }

Expand All@@ -70,10 +70,10 @@ public void BinaryClassificationMetricTest()

 BinaryGroupMetric metrics = mlContext.Fairlearn().Metric.BinaryClassification(eval: df, labelColumn: "label", predictedColumn: "PredictedLabel", sensitiveFeatureColumn: "group_id");
 var metricByGroup = metrics.ByGroup();
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 1);
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 0.1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 0.1);
 var metricOverall = metrics.Overall();
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 1);
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 0.1);
 }
 }
}

8 changes: 4 additions & 4 deletions
test/Microsoft.ML.Fairlearn.Tests/UtilityTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -31,10 +31,10 @@ public void DemographyParityTest()
 PrimitiveDataFrameColumn<float> ypred = new PrimitiveDataFrameColumn<float>("pred", fl);
 var gSinged = dp.Gamma(ypred);

 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 0.1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 0.1);
 }
 }
}

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/IntrospectiveTraining.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -119,8 +119,8 @@ public void InspectFastTreeModelParameters()
 var expectedThresholds = new float[] { 0.0911167f, 0.06509889f, 0.019873254f, 0.0361835f };
 for (int i = 0; i < finalTree.NumberOfNodes; ++i)
 {
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 6);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 6);
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 0.000001);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 0.000001);
 }
 }

Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/ONNX.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -71,7 +71,7 @@ public void SaveOnnxModelLoadAndScoreFastTree()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }

Expand DownExpand Up@@ -170,7 +170,7 @@ public void SaveOnnxModelLoadAndScoreSDCA()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.IntegrationTests/Training.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -498,7 +498,7 @@ public void MetacomponentsFunctionWithKeyHandling()
 // Evaluate the model.
 var binaryClassificationMetrics = mlContext.MulticlassClassification.Evaluate(binaryClassificationPredictions);

 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 4);
 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 0.0001);
 }
 }
}

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -476,9 +476,9 @@ public void TestOnnxNoneDimValue()
 var transformedValues = onnxTransformer.Transform(idv);
 var predictions = mlContext.Data.CreateEnumerable<PredictionNoneDimension>(transformedValues, reuseRowObject: false).ToArray();

 Assert.Equal(-0.080, Math.Round(predictions[0].variable[0], 3));
 Assert.Equal(1.204, Math.Round(predictions[1].variable[0], 3));
 Assert.Equal(2.27, Math.Round(predictions[2].variable[0], 3));
 Assert.Equal(-0.080, predictions[0].variable[0], 0.001);
 Assert.Equal(1.204, predictions[1].variable[0], 0.001);
 Assert.Equal(2.27, predictions[2].variable[0], 0.001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Predictor.Tests/TestGamPublicInterfaces.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -43,7 +43,7 @@ public void TestGamDirectInstantiation()
 Assert.Equal(binUpperBounds.Length, gam.NumberOfShapeFunctions);

 // Check the intercept
 Assert.Equal(intercept, gam.Bias, 6);
 Assert.Equal(intercept, gam.Bias, 0.000001);

 // Check that the binUpperBounds were made correctly
 CheckArrayOfArrayEquality(binUpperBounds, gam.GetBinUpperBounds());
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Predictor.Tests/TestPredictors.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -737,8 +737,8 @@ private void CombineAndTestTreeEnsembles(IDataView idv, PredictorModel[] fastTre
 probGetters[i](ref probs[i]);
 predGetters[i](ref preds[i]);
 }
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 5);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 6);
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 0.00001);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 0.000001);
 Assert.True(pred == score > 0);
 }
 }
Expand DownExpand Up@@ -953,7 +953,7 @@ private void CombineAndTestEnsembles(IDataView idv, string name, string options,
 for (int j = 0; j < predCount; j++)
 sum += vectorScores[j].GetItemOrDefault(i);
 if (float.IsNaN(sum))
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 3);
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 0.001);
 }
 Assert.Equal(probs.Count(p => p >= prob), probs.Count(p => p <= prob));
 }
Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.Tests/AnomalyDetectionTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -33,8 +33,8 @@ public void RandomizedPcaTrainerBaselineTest()
 // Evaluate
 var metrics = ML.AnomalyDetection.Evaluate(transformedData, falsePositiveCount: 5);

 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 5);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 5);
 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 0.00001);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 0.00001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/EvaluateTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -65,7 +65,7 @@ public void MulticlassEvaluatorTopKArray()
 var metrics2 = mlContext.MulticlassClassification.Evaluate(inputDV2, topKPredictionCount: 4);
 var output2 = metrics2.TopKAccuracyForAllK.ToArray();
 for (int i = 0; i < expectedTopKArray2.Length; i++)
 Assert.Equal(expectedTopKArray2[i], output2[i], precision: 7);
 Assert.Equal(expectedTopKArray2[i], output2[i], 0.0000001);
 }
 }
}

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/LearningRateSchedulerTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -48,7 +48,7 @@ internal void TestPolynomialDecay(float[] expectedValues, bool cycle)
 trainState.CurrentBatchIndex = i % trainState.BatchesPerEpoch;
 trainState.CurrentEpoch = i / trainState.BatchesPerEpoch;
 float decayedLR = learningRateScheduler.GetLearningRate(trainState);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 4);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -87,7 +87,7 @@ public void PredictClusters()
 Assert.Equal(double.NaN, metrics.NormalizedMutualInformation);
 //Calculate dbi is false by default so Dbi would be 0
 Assert.Equal(0d, metrics.DaviesBouldinIndex);
 Assert.Equal(0d, metrics.AverageDistance, 5);
 Assert.Equal(0d, metrics.AverageDistance, 0.00001);
 }
 }
}
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
Make double assertions compare with tolerance instead of precision#6923

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

ericstj
merged 1 commit into

from

Dec 22, 2023
 Merged


Make double assertions compare with tolerance instead of precision
#6923
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


20 changes: 10 additions & 10 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -94,7 +94,7 @@ public void EntryPointTrainTestSplit()
 int testRows = CountRows(splitOutput.TestData);

 Assert.Equal(totalRows, trainRows + testRows);
 Assert.Equal(0.9, (double)trainRows / totalRows, 1);
 Assert.Equal(0.9, (double)trainRows / totalRows, 0.1);
 }

 private static int CountRows(IDataView dataView)
Expand DownExpand Up@@ -5005,7 +5005,7 @@ public void TestSimpleTrainExperiment()
 Assert.True(b);
 double auc = 0;
 getter(ref auc);
 Assert.Equal(0.93, auc, 2);
 Assert.Equal(0.93, auc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -5210,7 +5210,7 @@ public void TestCrossValidationMacro()
 if (w == 1)
 Assert.Equal(1.585, stdev, .001);
 else
 Assert.Equal(1.39, stdev, 2);
 Assert.Equal(1.39, stdev, 0.01);
 isWeightedGetter(ref isWeighted);
 Assert.True(isWeighted == (w == 1));
 }
Expand DownExpand Up@@ -5379,7 +5379,7 @@ public void TestCrossValidationMacroWithMulticlass()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.024809923969586353, stdev, 3);
 Assert.Equal(0.024809923969586353, stdev, 0.001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -5788,7 +5788,7 @@ public void TestCrossValidationMacroWithStratification()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.02582, stdev, 5);
 Assert.Equal(0.02582, stdev, 0.00001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -6089,9 +6089,9 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 var stdevValues = stdev.GetValues();
 Assert.Equal(0.02462, stdevValues[0], 5);
 Assert.Equal(0.02763, stdevValues[1], 5);
 Assert.Equal(0.03273, stdevValues[2], 5);
 Assert.Equal(0.02462, stdevValues[0], 0.00001);
 Assert.Equal(0.02763, stdevValues[1], 0.00001);
 Assert.Equal(0.03273, stdevValues[2], 0.00001);

 var sumBldr = new BufferBuilder<double>(R8Adder.Instance);
 sumBldr.Reset(avg.Length, true);
Expand DownExpand Up@@ -6291,7 +6291,7 @@ public void TestOvaMacro()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 Assert.Equal(0.96, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -6463,7 +6463,7 @@ public void TestOvaMacroWithUncalibratedLearner()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.71, acc, 2);
 Assert.Equal(0.71, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestLoss.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,16 +39,16 @@ private void TestHelper(IScalarLoss lossFunc, double label, double output, doubl
 {
 Double loss = lossFunc.Loss((float)output, (float)label);
 float derivative = lossFunc.Derivative((float)output, (float)label);
 Assert.Equal(expectedLoss, loss, 5);
 Assert.Equal(expectedUpdate, -derivative, 5);
 Assert.Equal(expectedLoss, loss, 0.00001);
 Assert.Equal(expectedUpdate, -derivative, 0.00001);

 if (differentiable)
 {
 // In principle, the update should be the negative of the first derivative of the loss.
 // Use a simple finite difference method to see if it's in the right ballpark.
 float almostOutput = Math.Max((float)output * (1 + _epsilon), (float)output + _epsilon);
 Double almostLoss = lossFunc.Loss(almostOutput, (float)label);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 1);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 0.1);
 }
 }

Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestVBuffer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -293,7 +293,7 @@ public void TestSparsifyNormalize(int startRange, bool normalize, float[] expect
 var actualValues = a.GetValues().ToArray();
 Assert.Equal(expectedValues.Length, actualValues.Length);
 for (int i = 0; i < expectedValues.Length; i++)
 Assert.Equal(expectedValues[i], actualValues[i], precision: 6);
 Assert.Equal(expectedValues[i], actualValues[i], 0.000001);
 }

 /// <summary>
Expand Down

18 changes: 9 additions & 9 deletions
test/Microsoft.ML.CpuMath.UnitTests/UnitTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,7 @@ public void SumTest(string mode, string test, Dictionary<string, string> environ
 }

 var actual = CpuMathUtils.Sum(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -668,7 +668,7 @@ public void SumSqUTest(string mode, string test, Dictionary<string, string> envi
 }

 var actual = CpuMathUtils.SumSq(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -693,7 +693,7 @@ public void SumSqDiffUTest(string mode, string test, string scale, Dictionary<st
 expected += (src[i] - defaultScale) * (src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand All@@ -716,7 +716,7 @@ public void SumAbsUTest(string mode, string test, Dictionary<string, string> env
 }

 var actual = CpuMathUtils.SumAbs(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -741,7 +741,7 @@ public void SumAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected += Math.Abs(src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -769,7 +769,7 @@ public void MaxAbsUTest(string mode, string test, Dictionary<string, string> env
 }
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -797,7 +797,7 @@ public void MaxAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected = abs;
 }
 }
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -827,7 +827,7 @@ public void DotUTest(string mode, string test, Dictionary<string, string> enviro
 }

 var actual = CpuMathUtils.DotProductDense(src, dst, dst.Length);
 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -861,7 +861,7 @@ public void DotSUTest(string mode, string test, Dictionary<string, string> envir
 }

 var actual = CpuMathUtils.DotProductSparse(src, dst, idx, limit);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand Down

22 changes: 11 additions & 11 deletions
test/Microsoft.ML.Fairlearn.Tests/MetricTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ public void RegressionMetricTest()
 {
 RegressionGroupMetric regressionMetric = mlContext.Fairlearn().Metric.Regression(eval: data, labelColumn: "Price", scoreColumn: "Score", sensitiveFeatureColumn: "Gender");
 var metricByGroup = regressionMetric.ByGroup();
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 3);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 3);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 3);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 3);
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 0.001);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 0.001);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 0.001);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 0.001);
 metricByGroup.Description();
 Dictionary<string, double> metricOverall = regressionMetric.Overall();
 Assert.Equal(125.5, metricOverall["MSE"], 1);
 Assert.Equal(11.202678, metricOverall["RMS"], 4);
 Assert.Equal(125.5, metricOverall["MSE"], 0.1);
 Assert.Equal(11.202678, metricOverall["RMS"], 0.0001);
 Dictionary<string, double> diff = regressionMetric.DifferenceBetweenGroups();
 Assert.Equal(14.81138, diff["RMS"], 4);
 Assert.Equal(2037.5, diff["RSquared"], 1);
 Assert.Equal(14.81138, diff["RMS"], 0.0001);
 Assert.Equal(2037.5, diff["RSquared"], 0.1);

 }

Expand All@@ -70,10 +70,10 @@ public void BinaryClassificationMetricTest()

 BinaryGroupMetric metrics = mlContext.Fairlearn().Metric.BinaryClassification(eval: df, labelColumn: "label", predictedColumn: "PredictedLabel", sensitiveFeatureColumn: "group_id");
 var metricByGroup = metrics.ByGroup();
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 1);
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 0.1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 0.1);
 var metricOverall = metrics.Overall();
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 1);
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 0.1);
 }
 }
}

8 changes: 4 additions & 4 deletions
test/Microsoft.ML.Fairlearn.Tests/UtilityTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -31,10 +31,10 @@ public void DemographyParityTest()
 PrimitiveDataFrameColumn<float> ypred = new PrimitiveDataFrameColumn<float>("pred", fl);
 var gSinged = dp.Gamma(ypred);

 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 0.1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 0.1);
 }
 }
}

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/IntrospectiveTraining.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -119,8 +119,8 @@ public void InspectFastTreeModelParameters()
 var expectedThresholds = new float[] { 0.0911167f, 0.06509889f, 0.019873254f, 0.0361835f };
 for (int i = 0; i < finalTree.NumberOfNodes; ++i)
 {
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 6);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 6);
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 0.000001);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 0.000001);
 }
 }

Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/ONNX.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -71,7 +71,7 @@ public void SaveOnnxModelLoadAndScoreFastTree()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }

Expand DownExpand Up@@ -170,7 +170,7 @@ public void SaveOnnxModelLoadAndScoreSDCA()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.IntegrationTests/Training.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -498,7 +498,7 @@ public void MetacomponentsFunctionWithKeyHandling()
 // Evaluate the model.
 var binaryClassificationMetrics = mlContext.MulticlassClassification.Evaluate(binaryClassificationPredictions);

 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 4);
 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 0.0001);
 }
 }
}

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -476,9 +476,9 @@ public void TestOnnxNoneDimValue()
 var transformedValues = onnxTransformer.Transform(idv);
 var predictions = mlContext.Data.CreateEnumerable<PredictionNoneDimension>(transformedValues, reuseRowObject: false).ToArray();

 Assert.Equal(-0.080, Math.Round(predictions[0].variable[0], 3));
 Assert.Equal(1.204, Math.Round(predictions[1].variable[0], 3));
 Assert.Equal(2.27, Math.Round(predictions[2].variable[0], 3));
 Assert.Equal(-0.080, predictions[0].variable[0], 0.001);
 Assert.Equal(1.204, predictions[1].variable[0], 0.001);
 Assert.Equal(2.27, predictions[2].variable[0], 0.001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Predictor.Tests/TestGamPublicInterfaces.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -43,7 +43,7 @@ public void TestGamDirectInstantiation()
 Assert.Equal(binUpperBounds.Length, gam.NumberOfShapeFunctions);

 // Check the intercept
 Assert.Equal(intercept, gam.Bias, 6);
 Assert.Equal(intercept, gam.Bias, 0.000001);

 // Check that the binUpperBounds were made correctly
 CheckArrayOfArrayEquality(binUpperBounds, gam.GetBinUpperBounds());
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Predictor.Tests/TestPredictors.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -737,8 +737,8 @@ private void CombineAndTestTreeEnsembles(IDataView idv, PredictorModel[] fastTre
 probGetters[i](ref probs[i]);
 predGetters[i](ref preds[i]);
 }
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 5);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 6);
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 0.00001);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 0.000001);
 Assert.True(pred == score > 0);
 }
 }
Expand DownExpand Up@@ -953,7 +953,7 @@ private void CombineAndTestEnsembles(IDataView idv, string name, string options,
 for (int j = 0; j < predCount; j++)
 sum += vectorScores[j].GetItemOrDefault(i);
 if (float.IsNaN(sum))
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 3);
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 0.001);
 }
 Assert.Equal(probs.Count(p => p >= prob), probs.Count(p => p <= prob));
 }
Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.Tests/AnomalyDetectionTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -33,8 +33,8 @@ public void RandomizedPcaTrainerBaselineTest()
 // Evaluate
 var metrics = ML.AnomalyDetection.Evaluate(transformedData, falsePositiveCount: 5);

 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 5);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 5);
 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 0.00001);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 0.00001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/EvaluateTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -65,7 +65,7 @@ public void MulticlassEvaluatorTopKArray()
 var metrics2 = mlContext.MulticlassClassification.Evaluate(inputDV2, topKPredictionCount: 4);
 var output2 = metrics2.TopKAccuracyForAllK.ToArray();
 for (int i = 0; i < expectedTopKArray2.Length; i++)
 Assert.Equal(expectedTopKArray2[i], output2[i], precision: 7);
 Assert.Equal(expectedTopKArray2[i], output2[i], 0.0000001);
 }
 }
}

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/LearningRateSchedulerTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -48,7 +48,7 @@ internal void TestPolynomialDecay(float[] expectedValues, bool cycle)
 trainState.CurrentBatchIndex = i % trainState.BatchesPerEpoch;
 trainState.CurrentEpoch = i / trainState.BatchesPerEpoch;
 float decayedLR = learningRateScheduler.GetLearningRate(trainState);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 4);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -87,7 +87,7 @@ public void PredictClusters()
 Assert.Equal(double.NaN, metrics.NormalizedMutualInformation);
 //Calculate dbi is false by default so Dbi would be 0
 Assert.Equal(0d, metrics.DaviesBouldinIndex);
 Assert.Equal(0d, metrics.AverageDistance, 5);
 Assert.Equal(0d, metrics.AverageDistance, 0.00001);
 }
 }
}
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Make double assertions compare with tolerance instead of precision#6923

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

ericstj
merged 1 commit into

from

Dec 22, 2023
 Merged


Make double assertions compare with tolerance instead of precision
#6923
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


20 changes: 10 additions & 10 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -94,7 +94,7 @@ public void EntryPointTrainTestSplit()
 int testRows = CountRows(splitOutput.TestData);

 Assert.Equal(totalRows, trainRows + testRows);
 Assert.Equal(0.9, (double)trainRows / totalRows, 1);
 Assert.Equal(0.9, (double)trainRows / totalRows, 0.1);
 }

 private static int CountRows(IDataView dataView)
Expand DownExpand Up@@ -5005,7 +5005,7 @@ public void TestSimpleTrainExperiment()
 Assert.True(b);
 double auc = 0;
 getter(ref auc);
 Assert.Equal(0.93, auc, 2);
 Assert.Equal(0.93, auc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -5210,7 +5210,7 @@ public void TestCrossValidationMacro()
 if (w == 1)
 Assert.Equal(1.585, stdev, .001);
 else
 Assert.Equal(1.39, stdev, 2);
 Assert.Equal(1.39, stdev, 0.01);
 isWeightedGetter(ref isWeighted);
 Assert.True(isWeighted == (w == 1));
 }
Expand DownExpand Up@@ -5379,7 +5379,7 @@ public void TestCrossValidationMacroWithMulticlass()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.024809923969586353, stdev, 3);
 Assert.Equal(0.024809923969586353, stdev, 0.001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -5788,7 +5788,7 @@ public void TestCrossValidationMacroWithStratification()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.02582, stdev, 5);
 Assert.Equal(0.02582, stdev, 0.00001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -6089,9 +6089,9 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 var stdevValues = stdev.GetValues();
 Assert.Equal(0.02462, stdevValues[0], 5);
 Assert.Equal(0.02763, stdevValues[1], 5);
 Assert.Equal(0.03273, stdevValues[2], 5);
 Assert.Equal(0.02462, stdevValues[0], 0.00001);
 Assert.Equal(0.02763, stdevValues[1], 0.00001);
 Assert.Equal(0.03273, stdevValues[2], 0.00001);

 var sumBldr = new BufferBuilder<double>(R8Adder.Instance);
 sumBldr.Reset(avg.Length, true);
Expand DownExpand Up@@ -6291,7 +6291,7 @@ public void TestOvaMacro()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 Assert.Equal(0.96, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -6463,7 +6463,7 @@ public void TestOvaMacroWithUncalibratedLearner()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.71, acc, 2);
 Assert.Equal(0.71, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestLoss.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,16 +39,16 @@ private void TestHelper(IScalarLoss lossFunc, double label, double output, doubl
 {
 Double loss = lossFunc.Loss((float)output, (float)label);
 float derivative = lossFunc.Derivative((float)output, (float)label);
 Assert.Equal(expectedLoss, loss, 5);
 Assert.Equal(expectedUpdate, -derivative, 5);
 Assert.Equal(expectedLoss, loss, 0.00001);
 Assert.Equal(expectedUpdate, -derivative, 0.00001);

 if (differentiable)
 {
 // In principle, the update should be the negative of the first derivative of the loss.
 // Use a simple finite difference method to see if it's in the right ballpark.
 float almostOutput = Math.Max((float)output * (1 + _epsilon), (float)output + _epsilon);
 Double almostLoss = lossFunc.Loss(almostOutput, (float)label);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 1);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 0.1);
 }
 }

Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestVBuffer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -293,7 +293,7 @@ public void TestSparsifyNormalize(int startRange, bool normalize, float[] expect
 var actualValues = a.GetValues().ToArray();
 Assert.Equal(expectedValues.Length, actualValues.Length);
 for (int i = 0; i < expectedValues.Length; i++)
 Assert.Equal(expectedValues[i], actualValues[i], precision: 6);
 Assert.Equal(expectedValues[i], actualValues[i], 0.000001);
 }

 /// <summary>
Expand Down

18 changes: 9 additions & 9 deletions
test/Microsoft.ML.CpuMath.UnitTests/UnitTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,7 @@ public void SumTest(string mode, string test, Dictionary<string, string> environ
 }

 var actual = CpuMathUtils.Sum(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -668,7 +668,7 @@ public void SumSqUTest(string mode, string test, Dictionary<string, string> envi
 }

 var actual = CpuMathUtils.SumSq(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -693,7 +693,7 @@ public void SumSqDiffUTest(string mode, string test, string scale, Dictionary<st
 expected += (src[i] - defaultScale) * (src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand All@@ -716,7 +716,7 @@ public void SumAbsUTest(string mode, string test, Dictionary<string, string> env
 }

 var actual = CpuMathUtils.SumAbs(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -741,7 +741,7 @@ public void SumAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected += Math.Abs(src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -769,7 +769,7 @@ public void MaxAbsUTest(string mode, string test, Dictionary<string, string> env
 }
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -797,7 +797,7 @@ public void MaxAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected = abs;
 }
 }
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -827,7 +827,7 @@ public void DotUTest(string mode, string test, Dictionary<string, string> enviro
 }

 var actual = CpuMathUtils.DotProductDense(src, dst, dst.Length);
 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -861,7 +861,7 @@ public void DotSUTest(string mode, string test, Dictionary<string, string> envir
 }

 var actual = CpuMathUtils.DotProductSparse(src, dst, idx, limit);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand Down

22 changes: 11 additions & 11 deletions
test/Microsoft.ML.Fairlearn.Tests/MetricTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ public void RegressionMetricTest()
 {
 RegressionGroupMetric regressionMetric = mlContext.Fairlearn().Metric.Regression(eval: data, labelColumn: "Price", scoreColumn: "Score", sensitiveFeatureColumn: "Gender");
 var metricByGroup = regressionMetric.ByGroup();
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 3);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 3);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 3);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 3);
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 0.001);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 0.001);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 0.001);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 0.001);
 metricByGroup.Description();
 Dictionary<string, double> metricOverall = regressionMetric.Overall();
 Assert.Equal(125.5, metricOverall["MSE"], 1);
 Assert.Equal(11.202678, metricOverall["RMS"], 4);
 Assert.Equal(125.5, metricOverall["MSE"], 0.1);
 Assert.Equal(11.202678, metricOverall["RMS"], 0.0001);
 Dictionary<string, double> diff = regressionMetric.DifferenceBetweenGroups();
 Assert.Equal(14.81138, diff["RMS"], 4);
 Assert.Equal(2037.5, diff["RSquared"], 1);
 Assert.Equal(14.81138, diff["RMS"], 0.0001);
 Assert.Equal(2037.5, diff["RSquared"], 0.1);

 }

Expand All@@ -70,10 +70,10 @@ public void BinaryClassificationMetricTest()

 BinaryGroupMetric metrics = mlContext.Fairlearn().Metric.BinaryClassification(eval: df, labelColumn: "label", predictedColumn: "PredictedLabel", sensitiveFeatureColumn: "group_id");
 var metricByGroup = metrics.ByGroup();
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 1);
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 0.1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 0.1);
 var metricOverall = metrics.Overall();
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 1);
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 0.1);
 }
 }
}

8 changes: 4 additions & 4 deletions
test/Microsoft.ML.Fairlearn.Tests/UtilityTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -31,10 +31,10 @@ public void DemographyParityTest()
 PrimitiveDataFrameColumn<float> ypred = new PrimitiveDataFrameColumn<float>("pred", fl);
 var gSinged = dp.Gamma(ypred);

 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 0.1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 0.1);
 }
 }
}

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/IntrospectiveTraining.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -119,8 +119,8 @@ public void InspectFastTreeModelParameters()
 var expectedThresholds = new float[] { 0.0911167f, 0.06509889f, 0.019873254f, 0.0361835f };
 for (int i = 0; i < finalTree.NumberOfNodes; ++i)
 {
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 6);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 6);
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 0.000001);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 0.000001);
 }
 }

Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/ONNX.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -71,7 +71,7 @@ public void SaveOnnxModelLoadAndScoreFastTree()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }

Expand DownExpand Up@@ -170,7 +170,7 @@ public void SaveOnnxModelLoadAndScoreSDCA()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.IntegrationTests/Training.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -498,7 +498,7 @@ public void MetacomponentsFunctionWithKeyHandling()
 // Evaluate the model.
 var binaryClassificationMetrics = mlContext.MulticlassClassification.Evaluate(binaryClassificationPredictions);

 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 4);
 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 0.0001);
 }
 }
}

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -476,9 +476,9 @@ public void TestOnnxNoneDimValue()
 var transformedValues = onnxTransformer.Transform(idv);
 var predictions = mlContext.Data.CreateEnumerable<PredictionNoneDimension>(transformedValues, reuseRowObject: false).ToArray();

 Assert.Equal(-0.080, Math.Round(predictions[0].variable[0], 3));
 Assert.Equal(1.204, Math.Round(predictions[1].variable[0], 3));
 Assert.Equal(2.27, Math.Round(predictions[2].variable[0], 3));
 Assert.Equal(-0.080, predictions[0].variable[0], 0.001);
 Assert.Equal(1.204, predictions[1].variable[0], 0.001);
 Assert.Equal(2.27, predictions[2].variable[0], 0.001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Predictor.Tests/TestGamPublicInterfaces.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -43,7 +43,7 @@ public void TestGamDirectInstantiation()
 Assert.Equal(binUpperBounds.Length, gam.NumberOfShapeFunctions);

 // Check the intercept
 Assert.Equal(intercept, gam.Bias, 6);
 Assert.Equal(intercept, gam.Bias, 0.000001);

 // Check that the binUpperBounds were made correctly
 CheckArrayOfArrayEquality(binUpperBounds, gam.GetBinUpperBounds());
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Predictor.Tests/TestPredictors.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -737,8 +737,8 @@ private void CombineAndTestTreeEnsembles(IDataView idv, PredictorModel[] fastTre
 probGetters[i](ref probs[i]);
 predGetters[i](ref preds[i]);
 }
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 5);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 6);
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 0.00001);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 0.000001);
 Assert.True(pred == score > 0);
 }
 }
Expand DownExpand Up@@ -953,7 +953,7 @@ private void CombineAndTestEnsembles(IDataView idv, string name, string options,
 for (int j = 0; j < predCount; j++)
 sum += vectorScores[j].GetItemOrDefault(i);
 if (float.IsNaN(sum))
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 3);
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 0.001);
 }
 Assert.Equal(probs.Count(p => p >= prob), probs.Count(p => p <= prob));
 }
Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.Tests/AnomalyDetectionTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -33,8 +33,8 @@ public void RandomizedPcaTrainerBaselineTest()
 // Evaluate
 var metrics = ML.AnomalyDetection.Evaluate(transformedData, falsePositiveCount: 5);

 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 5);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 5);
 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 0.00001);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 0.00001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/EvaluateTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -65,7 +65,7 @@ public void MulticlassEvaluatorTopKArray()
 var metrics2 = mlContext.MulticlassClassification.Evaluate(inputDV2, topKPredictionCount: 4);
 var output2 = metrics2.TopKAccuracyForAllK.ToArray();
 for (int i = 0; i < expectedTopKArray2.Length; i++)
 Assert.Equal(expectedTopKArray2[i], output2[i], precision: 7);
 Assert.Equal(expectedTopKArray2[i], output2[i], 0.0000001);
 }
 }
}

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/LearningRateSchedulerTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -48,7 +48,7 @@ internal void TestPolynomialDecay(float[] expectedValues, bool cycle)
 trainState.CurrentBatchIndex = i % trainState.BatchesPerEpoch;
 trainState.CurrentEpoch = i / trainState.BatchesPerEpoch;
 float decayedLR = learningRateScheduler.GetLearningRate(trainState);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 4);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -87,7 +87,7 @@ public void PredictClusters()
 Assert.Equal(double.NaN, metrics.NormalizedMutualInformation);
 //Calculate dbi is false by default so Dbi would be 0
 Assert.Equal(0d, metrics.DaviesBouldinIndex);
 Assert.Equal(0d, metrics.AverageDistance, 5);
 Assert.Equal(0d, metrics.AverageDistance, 0.00001);
 }
 }
}
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Make double assertions compare with tolerance instead of precision#6923

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

ericstj
merged 1 commit into

from

Dec 22, 2023
 Merged


Make double assertions compare with tolerance instead of precision
#6923
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


20 changes: 10 additions & 10 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -94,7 +94,7 @@ public void EntryPointTrainTestSplit()
 int testRows = CountRows(splitOutput.TestData);

 Assert.Equal(totalRows, trainRows + testRows);
 Assert.Equal(0.9, (double)trainRows / totalRows, 1);
 Assert.Equal(0.9, (double)trainRows / totalRows, 0.1);
 }

 private static int CountRows(IDataView dataView)
Expand DownExpand Up@@ -5005,7 +5005,7 @@ public void TestSimpleTrainExperiment()
 Assert.True(b);
 double auc = 0;
 getter(ref auc);
 Assert.Equal(0.93, auc, 2);
 Assert.Equal(0.93, auc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -5210,7 +5210,7 @@ public void TestCrossValidationMacro()
 if (w == 1)
 Assert.Equal(1.585, stdev, .001);
 else
 Assert.Equal(1.39, stdev, 2);
 Assert.Equal(1.39, stdev, 0.01);
 isWeightedGetter(ref isWeighted);
 Assert.True(isWeighted == (w == 1));
 }
Expand DownExpand Up@@ -5379,7 +5379,7 @@ public void TestCrossValidationMacroWithMulticlass()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.024809923969586353, stdev, 3);
 Assert.Equal(0.024809923969586353, stdev, 0.001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -5788,7 +5788,7 @@ public void TestCrossValidationMacroWithStratification()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.02582, stdev, 5);
 Assert.Equal(0.02582, stdev, 0.00001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -6089,9 +6089,9 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 var stdevValues = stdev.GetValues();
 Assert.Equal(0.02462, stdevValues[0], 5);
 Assert.Equal(0.02763, stdevValues[1], 5);
 Assert.Equal(0.03273, stdevValues[2], 5);
 Assert.Equal(0.02462, stdevValues[0], 0.00001);
 Assert.Equal(0.02763, stdevValues[1], 0.00001);
 Assert.Equal(0.03273, stdevValues[2], 0.00001);

 var sumBldr = new BufferBuilder<double>(R8Adder.Instance);
 sumBldr.Reset(avg.Length, true);
Expand DownExpand Up@@ -6291,7 +6291,7 @@ public void TestOvaMacro()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 Assert.Equal(0.96, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -6463,7 +6463,7 @@ public void TestOvaMacroWithUncalibratedLearner()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.71, acc, 2);
 Assert.Equal(0.71, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestLoss.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,16 +39,16 @@ private void TestHelper(IScalarLoss lossFunc, double label, double output, doubl
 {
 Double loss = lossFunc.Loss((float)output, (float)label);
 float derivative = lossFunc.Derivative((float)output, (float)label);
 Assert.Equal(expectedLoss, loss, 5);
 Assert.Equal(expectedUpdate, -derivative, 5);
 Assert.Equal(expectedLoss, loss, 0.00001);
 Assert.Equal(expectedUpdate, -derivative, 0.00001);

 if (differentiable)
 {
 // In principle, the update should be the negative of the first derivative of the loss.
 // Use a simple finite difference method to see if it's in the right ballpark.
 float almostOutput = Math.Max((float)output * (1 + _epsilon), (float)output + _epsilon);
 Double almostLoss = lossFunc.Loss(almostOutput, (float)label);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 1);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 0.1);
 }
 }

Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestVBuffer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -293,7 +293,7 @@ public void TestSparsifyNormalize(int startRange, bool normalize, float[] expect
 var actualValues = a.GetValues().ToArray();
 Assert.Equal(expectedValues.Length, actualValues.Length);
 for (int i = 0; i < expectedValues.Length; i++)
 Assert.Equal(expectedValues[i], actualValues[i], precision: 6);
 Assert.Equal(expectedValues[i], actualValues[i], 0.000001);
 }

 /// <summary>
Expand Down

18 changes: 9 additions & 9 deletions
test/Microsoft.ML.CpuMath.UnitTests/UnitTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,7 @@ public void SumTest(string mode, string test, Dictionary<string, string> environ
 }

 var actual = CpuMathUtils.Sum(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -668,7 +668,7 @@ public void SumSqUTest(string mode, string test, Dictionary<string, string> envi
 }

 var actual = CpuMathUtils.SumSq(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -693,7 +693,7 @@ public void SumSqDiffUTest(string mode, string test, string scale, Dictionary<st
 expected += (src[i] - defaultScale) * (src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand All@@ -716,7 +716,7 @@ public void SumAbsUTest(string mode, string test, Dictionary<string, string> env
 }

 var actual = CpuMathUtils.SumAbs(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -741,7 +741,7 @@ public void SumAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected += Math.Abs(src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -769,7 +769,7 @@ public void MaxAbsUTest(string mode, string test, Dictionary<string, string> env
 }
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -797,7 +797,7 @@ public void MaxAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected = abs;
 }
 }
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -827,7 +827,7 @@ public void DotUTest(string mode, string test, Dictionary<string, string> enviro
 }

 var actual = CpuMathUtils.DotProductDense(src, dst, dst.Length);
 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -861,7 +861,7 @@ public void DotSUTest(string mode, string test, Dictionary<string, string> envir
 }

 var actual = CpuMathUtils.DotProductSparse(src, dst, idx, limit);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand Down

22 changes: 11 additions & 11 deletions
test/Microsoft.ML.Fairlearn.Tests/MetricTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ public void RegressionMetricTest()
 {
 RegressionGroupMetric regressionMetric = mlContext.Fairlearn().Metric.Regression(eval: data, labelColumn: "Price", scoreColumn: "Score", sensitiveFeatureColumn: "Gender");
 var metricByGroup = regressionMetric.ByGroup();
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 3);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 3);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 3);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 3);
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 0.001);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 0.001);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 0.001);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 0.001);
 metricByGroup.Description();
 Dictionary<string, double> metricOverall = regressionMetric.Overall();
 Assert.Equal(125.5, metricOverall["MSE"], 1);
 Assert.Equal(11.202678, metricOverall["RMS"], 4);
 Assert.Equal(125.5, metricOverall["MSE"], 0.1);
 Assert.Equal(11.202678, metricOverall["RMS"], 0.0001);
 Dictionary<string, double> diff = regressionMetric.DifferenceBetweenGroups();
 Assert.Equal(14.81138, diff["RMS"], 4);
 Assert.Equal(2037.5, diff["RSquared"], 1);
 Assert.Equal(14.81138, diff["RMS"], 0.0001);
 Assert.Equal(2037.5, diff["RSquared"], 0.1);

 }

Expand All@@ -70,10 +70,10 @@ public void BinaryClassificationMetricTest()

 BinaryGroupMetric metrics = mlContext.Fairlearn().Metric.BinaryClassification(eval: df, labelColumn: "label", predictedColumn: "PredictedLabel", sensitiveFeatureColumn: "group_id");
 var metricByGroup = metrics.ByGroup();
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 1);
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 0.1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 0.1);
 var metricOverall = metrics.Overall();
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 1);
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 0.1);
 }
 }
}

8 changes: 4 additions & 4 deletions
test/Microsoft.ML.Fairlearn.Tests/UtilityTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -31,10 +31,10 @@ public void DemographyParityTest()
 PrimitiveDataFrameColumn<float> ypred = new PrimitiveDataFrameColumn<float>("pred", fl);
 var gSinged = dp.Gamma(ypred);

 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 0.1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 0.1);
 }
 }
}

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/IntrospectiveTraining.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -119,8 +119,8 @@ public void InspectFastTreeModelParameters()
 var expectedThresholds = new float[] { 0.0911167f, 0.06509889f, 0.019873254f, 0.0361835f };
 for (int i = 0; i < finalTree.NumberOfNodes; ++i)
 {
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 6);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 6);
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 0.000001);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 0.000001);
 }
 }

Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/ONNX.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -71,7 +71,7 @@ public void SaveOnnxModelLoadAndScoreFastTree()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }

Expand DownExpand Up@@ -170,7 +170,7 @@ public void SaveOnnxModelLoadAndScoreSDCA()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.IntegrationTests/Training.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -498,7 +498,7 @@ public void MetacomponentsFunctionWithKeyHandling()
 // Evaluate the model.
 var binaryClassificationMetrics = mlContext.MulticlassClassification.Evaluate(binaryClassificationPredictions);

 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 4);
 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 0.0001);
 }
 }
}

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -476,9 +476,9 @@ public void TestOnnxNoneDimValue()
 var transformedValues = onnxTransformer.Transform(idv);
 var predictions = mlContext.Data.CreateEnumerable<PredictionNoneDimension>(transformedValues, reuseRowObject: false).ToArray();

 Assert.Equal(-0.080, Math.Round(predictions[0].variable[0], 3));
 Assert.Equal(1.204, Math.Round(predictions[1].variable[0], 3));
 Assert.Equal(2.27, Math.Round(predictions[2].variable[0], 3));
 Assert.Equal(-0.080, predictions[0].variable[0], 0.001);
 Assert.Equal(1.204, predictions[1].variable[0], 0.001);
 Assert.Equal(2.27, predictions[2].variable[0], 0.001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Predictor.Tests/TestGamPublicInterfaces.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -43,7 +43,7 @@ public void TestGamDirectInstantiation()
 Assert.Equal(binUpperBounds.Length, gam.NumberOfShapeFunctions);

 // Check the intercept
 Assert.Equal(intercept, gam.Bias, 6);
 Assert.Equal(intercept, gam.Bias, 0.000001);

 // Check that the binUpperBounds were made correctly
 CheckArrayOfArrayEquality(binUpperBounds, gam.GetBinUpperBounds());
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Predictor.Tests/TestPredictors.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -737,8 +737,8 @@ private void CombineAndTestTreeEnsembles(IDataView idv, PredictorModel[] fastTre
 probGetters[i](ref probs[i]);
 predGetters[i](ref preds[i]);
 }
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 5);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 6);
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 0.00001);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 0.000001);
 Assert.True(pred == score > 0);
 }
 }
Expand DownExpand Up@@ -953,7 +953,7 @@ private void CombineAndTestEnsembles(IDataView idv, string name, string options,
 for (int j = 0; j < predCount; j++)
 sum += vectorScores[j].GetItemOrDefault(i);
 if (float.IsNaN(sum))
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 3);
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 0.001);
 }
 Assert.Equal(probs.Count(p => p >= prob), probs.Count(p => p <= prob));
 }
Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.Tests/AnomalyDetectionTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -33,8 +33,8 @@ public void RandomizedPcaTrainerBaselineTest()
 // Evaluate
 var metrics = ML.AnomalyDetection.Evaluate(transformedData, falsePositiveCount: 5);

 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 5);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 5);
 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 0.00001);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 0.00001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/EvaluateTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -65,7 +65,7 @@ public void MulticlassEvaluatorTopKArray()
 var metrics2 = mlContext.MulticlassClassification.Evaluate(inputDV2, topKPredictionCount: 4);
 var output2 = metrics2.TopKAccuracyForAllK.ToArray();
 for (int i = 0; i < expectedTopKArray2.Length; i++)
 Assert.Equal(expectedTopKArray2[i], output2[i], precision: 7);
 Assert.Equal(expectedTopKArray2[i], output2[i], 0.0000001);
 }
 }
}

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/LearningRateSchedulerTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -48,7 +48,7 @@ internal void TestPolynomialDecay(float[] expectedValues, bool cycle)
 trainState.CurrentBatchIndex = i % trainState.BatchesPerEpoch;
 trainState.CurrentEpoch = i / trainState.BatchesPerEpoch;
 float decayedLR = learningRateScheduler.GetLearningRate(trainState);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 4);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -87,7 +87,7 @@ public void PredictClusters()
 Assert.Equal(double.NaN, metrics.NormalizedMutualInformation);
 //Calculate dbi is false by default so Dbi would be 0
 Assert.Equal(0d, metrics.DaviesBouldinIndex);
 Assert.Equal(0d, metrics.AverageDistance, 5);
 Assert.Equal(0d, metrics.AverageDistance, 0.00001);
 }
 }
}
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
Make double assertions compare with tolerance instead of precision#6923

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

ericstj
merged 1 commit into

from

Dec 22, 2023
 Merged


Make double assertions compare with tolerance instead of precision
#6923
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


1 commit


Select commit


File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


20 changes: 10 additions & 10 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -94,7 +94,7 @@ public void EntryPointTrainTestSplit()
 int testRows = CountRows(splitOutput.TestData);

 Assert.Equal(totalRows, trainRows + testRows);
 Assert.Equal(0.9, (double)trainRows / totalRows, 1);
 Assert.Equal(0.9, (double)trainRows / totalRows, 0.1);
 }

 private static int CountRows(IDataView dataView)
Expand DownExpand Up@@ -5005,7 +5005,7 @@ public void TestSimpleTrainExperiment()
 Assert.True(b);
 double auc = 0;
 getter(ref auc);
 Assert.Equal(0.93, auc, 2);
 Assert.Equal(0.93, auc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -5210,7 +5210,7 @@ public void TestCrossValidationMacro()
 if (w == 1)
 Assert.Equal(1.585, stdev, .001);
 else
 Assert.Equal(1.39, stdev, 2);
 Assert.Equal(1.39, stdev, 0.01);
 isWeightedGetter(ref isWeighted);
 Assert.True(isWeighted == (w == 1));
 }
Expand DownExpand Up@@ -5379,7 +5379,7 @@ public void TestCrossValidationMacroWithMulticlass()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.024809923969586353, stdev, 3);
 Assert.Equal(0.024809923969586353, stdev, 0.001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -5788,7 +5788,7 @@ public void TestCrossValidationMacroWithStratification()
 getter(ref stdev);
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 Assert.Equal(0.02582, stdev, 5);
 Assert.Equal(0.02582, stdev, 0.00001);

 double sum = 0;
 double val = 0;
Expand DownExpand Up@@ -6089,9 +6089,9 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 foldGetter(ref fold);
 Assert.True(ReadOnlyMemoryUtils.EqualsStr("Standard Deviation", fold));
 var stdevValues = stdev.GetValues();
 Assert.Equal(0.02462, stdevValues[0], 5);
 Assert.Equal(0.02763, stdevValues[1], 5);
 Assert.Equal(0.03273, stdevValues[2], 5);
 Assert.Equal(0.02462, stdevValues[0], 0.00001);
 Assert.Equal(0.02763, stdevValues[1], 0.00001);
 Assert.Equal(0.03273, stdevValues[2], 0.00001);

 var sumBldr = new BufferBuilder<double>(R8Adder.Instance);
 sumBldr.Reset(avg.Length, true);
Expand DownExpand Up@@ -6291,7 +6291,7 @@ public void TestOvaMacro()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 Assert.Equal(0.96, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand DownExpand Up@@ -6463,7 +6463,7 @@ public void TestOvaMacroWithUncalibratedLearner()
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.71, acc, 2);
 Assert.Equal(0.71, acc, 0.01);
 b = cursor.MoveNext();
 Assert.False(b);
 }
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestLoss.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -39,16 +39,16 @@ private void TestHelper(IScalarLoss lossFunc, double label, double output, doubl
 {
 Double loss = lossFunc.Loss((float)output, (float)label);
 float derivative = lossFunc.Derivative((float)output, (float)label);
 Assert.Equal(expectedLoss, loss, 5);
 Assert.Equal(expectedUpdate, -derivative, 5);
 Assert.Equal(expectedLoss, loss, 0.00001);
 Assert.Equal(expectedUpdate, -derivative, 0.00001);

 if (differentiable)
 {
 // In principle, the update should be the negative of the first derivative of the loss.
 // Use a simple finite difference method to see if it's in the right ballpark.
 float almostOutput = Math.Max((float)output * (1 + _epsilon), (float)output + _epsilon);
 Double almostLoss = lossFunc.Loss(almostOutput, (float)label);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 1);
 Assert.Equal((almostLoss - loss) / (almostOutput - output), derivative, 0.1);
 }
 }

Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestVBuffer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -293,7 +293,7 @@ public void TestSparsifyNormalize(int startRange, bool normalize, float[] expect
 var actualValues = a.GetValues().ToArray();
 Assert.Equal(expectedValues.Length, actualValues.Length);
 for (int i = 0; i < expectedValues.Length; i++)
 Assert.Equal(expectedValues[i], actualValues[i], precision: 6);
 Assert.Equal(expectedValues[i], actualValues[i], 0.000001);
 }

 /// <summary>
Expand Down

18 changes: 9 additions & 9 deletions
test/Microsoft.ML.CpuMath.UnitTests/UnitTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,7 @@ public void SumTest(string mode, string test, Dictionary<string, string> environ
 }

 var actual = CpuMathUtils.Sum(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -668,7 +668,7 @@ public void SumSqUTest(string mode, string test, Dictionary<string, string> envi
 }

 var actual = CpuMathUtils.SumSq(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -693,7 +693,7 @@ public void SumSqDiffUTest(string mode, string test, string scale, Dictionary<st
 expected += (src[i] - defaultScale) * (src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand All@@ -716,7 +716,7 @@ public void SumAbsUTest(string mode, string test, Dictionary<string, string> env
 }

 var actual = CpuMathUtils.SumAbs(src);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand All@@ -741,7 +741,7 @@ public void SumAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected += Math.Abs(src[i] - defaultScale);
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -769,7 +769,7 @@ public void MaxAbsUTest(string mode, string test, Dictionary<string, string> env
 }
 }

 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -797,7 +797,7 @@ public void MaxAbsDiffUTest(string mode, string test, string scale, Dictionary<s
 expected = abs;
 }
 }
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, scale, options);
 }
Expand DownExpand Up@@ -827,7 +827,7 @@ public void DotUTest(string mode, string test, Dictionary<string, string> enviro
 }

 var actual = CpuMathUtils.DotProductDense(src, dst, dst.Length);
 Assert.Equal((double)expected, (double)actual, 1);
 Assert.Equal((double)expected, (double)actual, 0.1);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand DownExpand Up@@ -861,7 +861,7 @@ public void DotSUTest(string mode, string test, Dictionary<string, string> envir
 }

 var actual = CpuMathUtils.DotProductSparse(src, dst, idx, limit);
 Assert.Equal((double)expected, (double)actual, 2);
 Assert.Equal((double)expected, (double)actual, 0.01);
 return RemoteExecutor.SuccessExitCode;
 }, mode, test, options);
 }
Expand Down

22 changes: 11 additions & 11 deletions
test/Microsoft.ML.Fairlearn.Tests/MetricTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -38,17 +38,17 @@ public void RegressionMetricTest()
 {
 RegressionGroupMetric regressionMetric = mlContext.Fairlearn().Metric.Regression(eval: data, labelColumn: "Price", scoreColumn: "Score", sensitiveFeatureColumn: "Gender");
 var metricByGroup = regressionMetric.ByGroup();
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 3);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 3);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 3);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 3);
 Assert.Equal(-2.30578, Convert.ToSingle(metricByGroup["RSquared"][0]), 0.001);
 Assert.Equal(-2039.81453, Convert.ToSingle(metricByGroup["RSquared"][1]), 0.001);
 Assert.Equal(1.00000, Convert.ToSingle(metricByGroup["RMS"][0]), 0.001);
 Assert.Equal(15.811388, Convert.ToSingle(metricByGroup["RMS"][1]), 0.001);
 metricByGroup.Description();
 Dictionary<string, double> metricOverall = regressionMetric.Overall();
 Assert.Equal(125.5, metricOverall["MSE"], 1);
 Assert.Equal(11.202678, metricOverall["RMS"], 4);
 Assert.Equal(125.5, metricOverall["MSE"], 0.1);
 Assert.Equal(11.202678, metricOverall["RMS"], 0.0001);
 Dictionary<string, double> diff = regressionMetric.DifferenceBetweenGroups();
 Assert.Equal(14.81138, diff["RMS"], 4);
 Assert.Equal(2037.5, diff["RSquared"], 1);
 Assert.Equal(14.81138, diff["RMS"], 0.0001);
 Assert.Equal(2037.5, diff["RSquared"], 0.1);

 }

Expand All@@ -70,10 +70,10 @@ public void BinaryClassificationMetricTest()

 BinaryGroupMetric metrics = mlContext.Fairlearn().Metric.BinaryClassification(eval: df, labelColumn: "label", predictedColumn: "PredictedLabel", sensitiveFeatureColumn: "group_id");
 var metricByGroup = metrics.ByGroup();
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 1);
 Assert.Equal(0.8, Convert.ToSingle(metricByGroup["Accuracy"][0]), 0.1);
 Assert.Equal(0.6, Convert.ToSingle(metricByGroup["Accuracy"][1]), 0.1);
 var metricOverall = metrics.Overall();
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 1);
 Assert.Equal(0.7, Convert.ToSingle(metricOverall["Accuracy"]), 0.1);
 }
 }
}

8 changes: 4 additions & 4 deletions
test/Microsoft.ML.Fairlearn.Tests/UtilityTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -31,10 +31,10 @@ public void DemographyParityTest()
 PrimitiveDataFrameColumn<float> ypred = new PrimitiveDataFrameColumn<float>("pred", fl);
 var gSinged = dp.Gamma(ypred);

 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][0]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][1]), 0.1);
 Assert.Equal(-0.1, Convert.ToSingle(gSinged["value"][2]), 0.1);
 Assert.Equal(0.1, Convert.ToSingle(gSinged["value"][3]), 0.1);
 }
 }
}

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/IntrospectiveTraining.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -119,8 +119,8 @@ public void InspectFastTreeModelParameters()
 var expectedThresholds = new float[] { 0.0911167f, 0.06509889f, 0.019873254f, 0.0361835f };
 for (int i = 0; i < finalTree.NumberOfNodes; ++i)
 {
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 6);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 6);
 Assert.Equal(expectedSplitGains[i], finalTree.SplitGains[i], 0.000001);
 Assert.Equal((double)expectedThresholds[i], (double)finalTree.NumericalSplitThresholds[i], 0.000001);
 }
 }

Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.IntegrationTests/ONNX.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -71,7 +71,7 @@ public void SaveOnnxModelLoadAndScoreFastTree()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }

Expand DownExpand Up@@ -170,7 +170,7 @@ public void SaveOnnxModelLoadAndScoreSDCA()
 var originalPrediction = originalPredictionEngine.Predict(row);
 var onnxPrediction = onnxPredictionEngine.Predict(row);
 // Check that the predictions are identical.
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], precision: 4);
 Assert.Equal(originalPrediction.Score, onnxPrediction.Score[0], 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.IntegrationTests/Training.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -498,7 +498,7 @@ public void MetacomponentsFunctionWithKeyHandling()
 // Evaluate the model.
 var binaryClassificationMetrics = mlContext.MulticlassClassification.Evaluate(binaryClassificationPredictions);

 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 4);
 Assert.Equal(0.4367, binaryClassificationMetrics.LogLoss, 0.0001);
 }
 }
}

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -476,9 +476,9 @@ public void TestOnnxNoneDimValue()
 var transformedValues = onnxTransformer.Transform(idv);
 var predictions = mlContext.Data.CreateEnumerable<PredictionNoneDimension>(transformedValues, reuseRowObject: false).ToArray();

 Assert.Equal(-0.080, Math.Round(predictions[0].variable[0], 3));
 Assert.Equal(1.204, Math.Round(predictions[1].variable[0], 3));
 Assert.Equal(2.27, Math.Round(predictions[2].variable[0], 3));
 Assert.Equal(-0.080, predictions[0].variable[0], 0.001);
 Assert.Equal(1.204, predictions[1].variable[0], 0.001);
 Assert.Equal(2.27, predictions[2].variable[0], 0.001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Predictor.Tests/TestGamPublicInterfaces.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -43,7 +43,7 @@ public void TestGamDirectInstantiation()
 Assert.Equal(binUpperBounds.Length, gam.NumberOfShapeFunctions);

 // Check the intercept
 Assert.Equal(intercept, gam.Bias, 6);
 Assert.Equal(intercept, gam.Bias, 0.000001);

 // Check that the binUpperBounds were made correctly
 CheckArrayOfArrayEquality(binUpperBounds, gam.GetBinUpperBounds());
Expand Down

6 changes: 3 additions & 3 deletions
test/Microsoft.ML.Predictor.Tests/TestPredictors.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -737,8 +737,8 @@ private void CombineAndTestTreeEnsembles(IDataView idv, PredictorModel[] fastTre
 probGetters[i](ref probs[i]);
 predGetters[i](ref preds[i]);
 }
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 5);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 6);
 Assert.Equal(score, 0.4 * scores.Sum() / predCount, 0.00001);
 Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 0.000001);
 Assert.True(pred == score > 0);
 }
 }
Expand DownExpand Up@@ -953,7 +953,7 @@ private void CombineAndTestEnsembles(IDataView idv, string name, string options,
 for (int j = 0; j < predCount; j++)
 sum += vectorScores[j].GetItemOrDefault(i);
 if (float.IsNaN(sum))
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 3);
 Assert.Equal((double)vectorScore.GetItemOrDefault(i), (double)sum / predCount, 0.001);
 }
 Assert.Equal(probs.Count(p => p >= prob), probs.Count(p => p <= prob));
 }
Expand Down

4 changes: 2 additions & 2 deletions
test/Microsoft.ML.Tests/AnomalyDetectionTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -33,8 +33,8 @@ public void RandomizedPcaTrainerBaselineTest()
 // Evaluate
 var metrics = ML.AnomalyDetection.Evaluate(transformedData, falsePositiveCount: 5);

 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 5);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 5);
 Assert.Equal(0.98667, metrics.AreaUnderRocCurve, 0.00001);
 Assert.Equal(0.90000, metrics.DetectionRateAtFalsePositiveCount, 0.00001);
 }

 /// <summary>
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/EvaluateTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -65,7 +65,7 @@ public void MulticlassEvaluatorTopKArray()
 var metrics2 = mlContext.MulticlassClassification.Evaluate(inputDV2, topKPredictionCount: 4);
 var output2 = metrics2.TopKAccuracyForAllK.ToArray();
 for (int i = 0; i < expectedTopKArray2.Length; i++)
 Assert.Equal(expectedTopKArray2[i], output2[i], precision: 7);
 Assert.Equal(expectedTopKArray2[i], output2[i], 0.0000001);
 }
 }
}

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/LearningRateSchedulerTest.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -48,7 +48,7 @@ internal void TestPolynomialDecay(float[] expectedValues, bool cycle)
 trainState.CurrentBatchIndex = i % trainState.BatchesPerEpoch;
 trainState.CurrentEpoch = i / trainState.BatchesPerEpoch;
 float decayedLR = learningRateScheduler.GetLearningRate(trainState);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 4);
 Assert.Equal((double)expectedValues[i], (double)decayedLR, 0.0001);
 }
 }
 }
Expand Down

2 changes: 1 addition & 1 deletion
test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -87,7 +87,7 @@ public void PredictClusters()
 Assert.Equal(double.NaN, metrics.NormalizedMutualInformation);
 //Calculate dbi is false by default so Dbi would be 0
 Assert.Equal(0d, metrics.DaviesBouldinIndex);
 Assert.Equal(0d, metrics.AverageDistance, 5);
 Assert.Equal(0d, metrics.AverageDistance, 0.00001);
 }
 }
}
Loading