Remove AwesomeAssertions and standardise on xUnit assertions

.github/copilot-instructions.md

@@ -12,7 +12,7 @@ ML.NET is a cross-platform, open-source machine learning framework for .NET. It
 
 - .NET SDK version selected by `global.json`
 - Build system: Microsoft Arcade SDK (`eng/common/`)
-- Test framework: xUnit (with `AwesomeAssertions`, `Xunit.Combinatorial`)
+- Test framework: xUnit (with `Xunit.Combinatorial`)
 - Native dependencies: MKL, OpenMP, libmf, oneDNN
 - Major dependencies: TorchSharp, ONNX Runtime, TensorFlow, LightGBM
 - Central package management: `Directory.Packages.props`
@@ -136,7 +136,7 @@ public class MyFeatureTests : TestDataPipeBase
 
 Name test classes as `{Feature}Tests`, test methods as PascalCase descriptive names (e.g., `RandomizedPcaTrainerBaselineTest`). Do not use `Test_` prefixes or `_Should_` patterns.
 
-Use `Assert.*` (xUnit) or `AwesomeAssertions` for fluent assertions. Do not use `Assert.That` (NUnit style).
+Use the built-in `Assert.*` (xUnit) assertions. Do not use `Assert.That` (NUnit style) or additional assertion libraries.
 
 Test data: use `Microsoft.ML.TestDatabases` package or files in `test/data/`, referenced via `GetDataPath("filename")` from the base class. Baseline output comparison uses files in `test/BaselineOutput/`. Update baselines carefully since they are the source of truth for output format stability.
 

Directory.Packages.props

@@ -69,7 +69,6 @@
 
     <!-- Test-only dependencies -->
     <PackageVersion Include="ApprovalTests" Version="$(ApprovalTestsVersion)" />
-    <PackageVersion Include="AwesomeAssertions" Version="$(AwesomeAssertionsVersion)" />
     <PackageVersion Include="BenchmarkDotNet" Version="$(BenchmarkDotNetVersion)" />
     <PackageVersion Include="BenchmarkDotNet.Diagnostics.Windows" Version="$(BenchmarkDotNetVersion)" />
     <PackageVersion Include="coverlet.collector" Version="$(CoverletCollectorVersion)" />

docs/specs/ML.Net Sweepable API.md

@@ -7,6 +7,8 @@ Search space defines a range of hyper-parameter for tuner to search from. Sweepa
 
 via attribute
 ```csharp
+using Xunit;
+
 public class Option
 {
     [Range(2, 32768, init: 2, logBase: true)]
@@ -31,17 +33,19 @@ var ss = new SearchSpace<Option>();
 var parameter = ss.SampleFromFeatureSpace(new []{0,0,0,0,0,0});
 
 // auto-binding
-parameter.WindowSize.Should().Be(2);
-parameter.SeriesLength.Should().Be(2);
-parameter.UseSoftmax.Should().BeTrue();
-parameter.AnotherOption.WindowSize.Should().Be(2);
+Assert.Equal(2, parameter.WindowSize);
+Assert.Equal(2, parameter.SeriesLength);
+Assert.True(parameter.UseSoftmax);
+Assert.Equal(2, parameter.AnotherOption.WindowSize);
 
 // search space can also map parameter back to feature space
-ss.MappingToFeatureSpace(parameter).Should().BeEquivalantTo(0.0, 0.0, 0.0, 0.0, 0.0, 0.0);
+Assert.Equivalent(new[] { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 }, ss.MappingToFeatureSpace(parameter), strict: true);
 ```
 
 or correspondingly, via scratch
 ``` csharp
+using Xunit;
+
 var ss = new SearchSpace();
 ss.Add("WindowSize", new UniformIntOption(2, 32768, true, 2));
 ss.Add("SeriesLength", new ChoiceOption(2,3,4));
@@ -51,13 +55,13 @@ ss.Add("AnotherOption", ss.Clone());
 var parameter = ss.SampleFromFeatureSpace(new []{0,0,0,0,0,0});
 
 // auto-binding doesn't exist for scratch api
-parameter["WindowSize"].AsType<int>.Should().Be(2);
-parameter["SeriesLength"].AsType<int>.Should().Be(2);
-parameter["UseSoftmax"].AsType<bool>.Should().BeTrue();
-parameter["AnotherOption"]["WindowSize"].AsType<int>.Should().Be(2);
+Assert.Equal(2, parameter["WindowSize"].AsType<int>());
+Assert.Equal(2, parameter["SeriesLength"].AsType<int>());
+Assert.True(parameter["UseSoftmax"].AsType<bool>());
+Assert.Equal(2, parameter["AnotherOption"]["WindowSize"].AsType<int>());
 
 // search space can also map parameter back to feature space
-ss.MappingToFeatureSpace(parameter).Should().BeEquivalantTo(0.0, 0.0, 0.0, 0.0, 0.0, 0.0);
+Assert.Equivalent(new[] { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 }, ss.MappingToFeatureSpace(parameter), strict: true);
 ```
 
 Currently, in order to make auto-binding work, there's a limitation on the parameter type that can be added to search space, which has to be either a Json primitive type or a nested search space.

eng/Versions.props

@@ -86,7 +86,6 @@
     <BenchmarkDotNetVersion>0.13.12</BenchmarkDotNetVersion>
     <DotNetRuntime60Version>6.0.36</DotNetRuntime60Version>
     <DotNetRuntime80Version>8.0.16</DotNetRuntime80Version>
-    <AwesomeAssertionsVersion>8.1.0</AwesomeAssertionsVersion>
     <MicrosoftCodeAnalysisTestingVersion>1.1.2</MicrosoftCodeAnalysisTestingVersion>
     <MicrosoftDotNetBuildTasksTemplatingVersion>9.0.0-beta.24212.4</MicrosoftDotNetBuildTasksTemplatingVersion>
     <MicrosoftDotNetPlatformAbstractionsVersion>5.0.0-preview.5.20278.1</MicrosoftDotNetPlatformAbstractionsVersion>

src/Microsoft.ML.SearchSpace/Readme.md

@@ -25,6 +25,8 @@ Provides some basic tuning algoritions (GridSearch, etc..)
 ## Examples
 
 ``` csharp
+using Xunit;
+
 private class BasicSearchSpace
 {
     [Range(-1000, 1000, init: 0)]
@@ -55,17 +57,17 @@ var tuner = new DefaultValueTuner(ss);
 
 // Get one sampling result from tuner
 BasicSearchSpace param = tuner.Propose();
-// param.UniformInt.Should().Be(0);
-// param.ChoiceStr.Should().Be("a"); // no default value designate for ChoiceStr, so the first value will be used.
-// param.NestSS.UniformInt.Should().Be(0);
+// Assert.Equal(0, param.UniformInt);
+// Assert.Equal("a", param.ChoiceStr); // no default value designate for ChoiceStr, so the first value will be used.
+// Assert.Equal(0, param.NestSS.UniformInt);
 
 // Or you can also sample from scratch
 // The feature of ss is a 4-d [0, 1) vector.
 BasicSearchSpace param2 = ss.SampleFromFeatureSpace(new[] {0.5, 0, 0.5, 0});
-// (param == param2).Should().BeTrue();
+// Assert.True(param == param2);
 
 // You can also map param back to feature space
-ss.MappingToFeatureSpace(param).Should().BeEquivalentTo(0.5, 0, 0.5, 0);
+Assert.Equivalent(new[] { 0.5, 0, 0.5, 0 }, ss.MappingToFeatureSpace(param), strict: true);
 ```
 
 

test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs

@@ -7,7 +7,6 @@
 using System.Globalization;
 using System.Linq;
 using System.Threading;
-using FluentAssertions;
 using Microsoft.ML.Data;
 using Microsoft.ML.Runtime;
 using Microsoft.ML.TestFramework;
@@ -85,7 +84,7 @@ public void AutoFit_UCI_Adult_AutoZero_Test()
             var result = context.Auto()
                 .CreateBinaryClassificationExperiment(settings)
                 .Execute(trainData, new ColumnInformation() { LabelColumnName = DatasetUtil.UciAdultLabel });
-            result.BestRun.ValidationMetrics.Accuracy.Should().BeGreaterThanOrEqualTo(0.7);
+            Assert.True(result.BestRun.ValidationMetrics.Accuracy >= 0.7);
             Assert.NotNull(result.BestRun.Estimator);
             Assert.NotNull(result.BestRun.Model);
             Assert.NotNull(result.BestRun.TrainerName);
@@ -285,7 +284,7 @@ public void AutoFitMultiTest(bool useNumberOfCVFolds)
                     .CreateMulticlassClassificationExperiment(settings)
                     .Execute(trainData, numberOfCVFolds, DatasetUtil.TrivialMulticlassDatasetLabel);
 
-                result.BestRun.Results.First().ValidationMetrics.MicroAccuracy.Should().BeGreaterThan(0.7);
+                Assert.True(result.BestRun.Results.First().ValidationMetrics.MicroAccuracy > 0.7);
                 var scoredData = result.BestRun.Results.First().Model.Transform(trainData);
                 Assert.Equal(NumberDataViewType.Single, scoredData.Schema[DefaultColumnNames.PredictedLabel].Type);
 
@@ -347,7 +346,7 @@ public void AutoFitMultiClassification_Image_TrainTest()
                             .CreateMulticlassClassificationExperiment(settings)
                             .Execute(trainDataset, testDataset, columnInference.ColumnInformation);
 
-            result.BestRun.ValidationMetrics.MicroAccuracy.Should().BeGreaterThan(0.1);
+            Assert.True(result.BestRun.ValidationMetrics.MicroAccuracy > 0.1);
 
             var scoredData = result.BestRun.Model.Transform(trainData);
             Assert.Equal(TextDataViewType.Instance, scoredData.Schema[DefaultColumnNames.PredictedLabel].Type);
@@ -370,7 +369,7 @@ public void AutoFitMultiClassification_Image_CV()
                             .CreateMulticlassClassificationExperiment(settings)
                             .Execute(trainData, 5, columnInference.ColumnInformation);
 
-            result.BestRun.Results.Select(x => x.ValidationMetrics.MicroAccuracy).Max().Should().BeGreaterThan(0.1);
+            Assert.True(result.BestRun.Results.Select(x => x.ValidationMetrics.MicroAccuracy).Max() > 0.1);
 
             var scoredData = result.BestRun.Results.First().Model.Transform(trainData);
             Assert.Equal(TextDataViewType.Instance, scoredData.Schema[DefaultColumnNames.PredictedLabel].Type);

test/Microsoft.ML.AutoML.Tests/AutoMLExperimentTests.cs

@@ -10,7 +10,6 @@
 using System.Text;
 using System.Threading;
 using System.Threading.Tasks;
-using FluentAssertions;
 using Microsoft.Data.Analysis;
 using Microsoft.Extensions.DependencyInjection;
 using Microsoft.ML.AutoML.CodeGen;
@@ -54,9 +53,7 @@ public async Task AutoMLExperiment_throw_timeout_exception_when_ct_is_canceled_a
                 }
             };
 
-            var runExperimentAction = async () => await experiment.RunAsync(cts.Token);
-
-            await runExperimentAction.Should().ThrowExactlyAsync<TimeoutException>();
+            await Assert.ThrowsAsync<TimeoutException>(() => experiment.RunAsync(cts.Token));
         }
 
         [Fact]
@@ -84,8 +81,7 @@ public async Task AutoMLExperiment_cancel_trial_when_exceeds_memory_limit_Async(
                       .SetTuner<RandomSearchTuner>()
                       .SetMaximumMemoryUsageInMegaByte(0.01);
 
-            var runExperimentAction = async () => await experiment.RunAsync();
-            await runExperimentAction.Should().ThrowExactlyAsync<TimeoutException>();
+            await Assert.ThrowsAsync<TimeoutException>(() => experiment.RunAsync());
         }
 
         [LightGBMFact]
@@ -111,8 +107,7 @@ public async Task AutoMLExperiment_lgbm_cancel_trial_when_exceeds_memory_limit_A
                     .SetTrainingTimeInSeconds(10)
                     .SetMaximumMemoryUsageInMegaByte(10);
 
-            var runExperimentAction = async () => await experiment.RunAsync();
-            await runExperimentAction.Should().ThrowExactlyAsync<TimeoutException>();
+            await Assert.ThrowsAsync<TimeoutException>(() => experiment.RunAsync());
         }
 
         [Fact]
@@ -143,9 +138,9 @@ public async Task AutoMLExperiment_return_current_best_trial_when_ct_is_canceled
             var res = await experiment.RunAsync(cts.Token);
 
             stopWatch.Stop();
-            stopWatch.ElapsedMilliseconds.Should().BeLessThanOrEqualTo(5000);
-            cts.IsCancellationRequested.Should().BeTrue();
-            res.Metric.Should().BeGreaterThan(0);
+            Assert.True(stopWatch.ElapsedMilliseconds <= 5000);
+            Assert.True(cts.IsCancellationRequested);
+            Assert.True(res.Metric > 0);
         }
 
         [Fact]
@@ -174,8 +169,8 @@ public async Task AutoMLExperiment_finish_training_when_time_is_up_Async()
             cts.CancelAfter(100 * 1000);
 
             var res = await experiment.RunAsync(cts.Token);
-            res.Metric.Should().BeGreaterThan(0);
-            cts.IsCancellationRequested.Should().BeFalse();
+            Assert.True(res.Metric > 0);
+            Assert.False(cts.IsCancellationRequested);
             cts.Dispose();
         }
 
@@ -202,7 +197,7 @@ public async Task AutoMLExperiment_finish_training_when_reach_to_max_model_async
                 }
             };
             await experiment.RunAsync();
-            runModelCounts.Should().Be(5);
+            Assert.Equal(5, runModelCounts);
         }
 
 
@@ -228,7 +223,7 @@ public async Task AutoMLExperiment_UCI_Adult_Train_Test_Split_Test()
                     .SetMaxModelToExplore(1);
 
             var result = await experiment.RunAsync();
-            result.Metric.Should().BeGreaterThan(0.8);
+            Assert.True(result.Metric > 0.8);
         }
 
         [Fact(Skip = "skip in CI build")]
@@ -253,7 +248,7 @@ public async Task AutoMLExperiment_UCI_Adult_Fairlearn_Test()
                     .SetMaxModelToExplore(100);
 
             var result = await experiment.RunAsync();
-            result.Metric.Should().BeGreaterThan(0.8);
+            Assert.True(result.Metric > 0.8);
         }
 
         [Fact]
@@ -278,7 +273,7 @@ public async Task AutoMLExperiment_UCI_Adult_CV_5_Test()
                     .SetMaxModelToExplore(1);
 
             var result = await experiment.RunAsync();
-            result.Metric.Should().BeGreaterThan(0.8);
+            Assert.True(result.Metric > 0.8);
         }
 
         [Fact]
@@ -305,7 +300,7 @@ public async Task AutoMLExperiment_Iris_CV_5_Test()
                     .SetMaxModelToExplore(1);
 
             var result = await experiment.RunAsync();
-            result.Metric.Should().BeGreaterThan(0.8);
+            Assert.True(result.Metric > 0.8);
         }
 
         [Fact]
@@ -332,7 +327,7 @@ public async Task AutoMLExperiment_Iris_Train_Test_Split_Test()
                     .SetMaxModelToExplore(1);
 
             var result = await experiment.RunAsync();
-            result.Metric.Should().BeGreaterThan(0.8);
+            Assert.True(result.Metric > 0.8);
         }
 
         [Fact]
@@ -359,7 +354,7 @@ public async Task AutoMLExperiment_Taxi_Fare_Train_Test_Split_Test()
                     .SetMaxModelToExplore(1);
 
             var result = await experiment.RunAsync();
-            result.Metric.Should().BeGreaterThan(0.5);
+            Assert.True(result.Metric > 0.5);
 
             // test subsamping
             experiment = context.Auto().CreateExperiment();
@@ -368,9 +363,9 @@ public async Task AutoMLExperiment_Taxi_Fare_Train_Test_Split_Test()
                     .SetPipeline(pipeline)
                     .SetMaxModelToExplore(1);
             result = await experiment.RunAsync();
-            result.Metric.Should().BeGreaterThan(0.5);
-            result.TrialSettings.Parameter[nameof(TrainValidateDatasetManager)]["TrainValidateDatasetSubsamplingKey"]
-                .AsType<float>().Should().Be(0.1f);
+            Assert.True(result.Metric > 0.5);
+            Assert.Equal(0.1f, result.TrialSettings.Parameter[nameof(TrainValidateDatasetManager)]["TrainValidateDatasetSubsamplingKey"]
+                .AsType<float>());
         }
 
         [Fact]
@@ -389,7 +384,7 @@ public async Task AutoMLExperiment_Taxi_Fare_CV_5_Test()
                     .SetMaxModelToExplore(1);
 
             var result = await experiment.RunAsync();
-            result.Metric.Should().BeGreaterThan(0.5);
+            Assert.True(result.Metric > 0.5);
         }
 
         [Fact]
@@ -408,8 +403,8 @@ public async Task AutoMLExperiment_Taxi_Fare_CV_5_SamplingKey_Test()
                     .SetMaxModelToExplore(1);
 
             var result = await experiment.RunAsync();
-            result.Metric.Should().BeGreaterThan(0.2);
-            result.Metric.Should().BeLessThan(0.5);
+            Assert.True(result.Metric > 0.2);
+            Assert.True(result.Metric < 0.5);
         }
 
         [Fact]
@@ -418,12 +413,12 @@ public void AutoMLExperiment_should_use_seed_from_context_if_provided()
             var context = new MLContext();
             var experiment = context.Auto().CreateExperiment();
             var settings = experiment.ServiceCollection.BuildServiceProvider().GetRequiredService<AutoMLExperiment.AutoMLExperimentSettings>();
-            settings.Seed.Should().BeNull();
+            Assert.Null(settings.Seed);
 
             context = new MLContext(1);
             experiment = context.Auto().CreateExperiment();
             settings = experiment.ServiceCollection.BuildServiceProvider().GetRequiredService<AutoMLExperiment.AutoMLExperimentSettings>();
-            settings.Seed.Should().Be(1);
+            Assert.Equal(1, settings.Seed);
         }
     }
 

test/Microsoft.ML.AutoML.Tests/ColumnInferenceTests.cs

@@ -9,7 +9,6 @@
 using ApprovalTests;
 using ApprovalTests.Namers;
 using ApprovalTests.Reporters;
-using FluentAssertions;
 using Microsoft.ML.Data;
 using Microsoft.ML.TestFramework;
 using Newtonsoft.Json;
@@ -205,9 +204,9 @@ public void TrySplitColumns_should_split_on_dataset_with_newline_between_double_
             var sample = TextFileSample.CreateFromFullFile(dataset);
             var result = TextFileContents.TrySplitColumns(context, sample, TextFileContents.DefaultSeparators);
 
-            result.ColumnCount.Should().Be(4);
-            result.Separator.Should().Be(',');
-            result.IsSuccess.Should().BeTrue();
+            Assert.Equal(4, result.ColumnCount);
+            Assert.Equal(',', result.Separator);
+            Assert.True(result.IsSuccess);
         }
 
         [Fact]