diff --git a/.editorconfig b/.editorconfig index 6b302074c4..da28cd876b 100644 --- a/.editorconfig +++ b/.editorconfig @@ -24,9 +24,6 @@ dotnet_diagnostic.MSML_NoBestFriendInternal.severity = none # MSML_NoInstanceInitializers: No initializers on instance fields or properties dotnet_diagnostic.MSML_NoInstanceInitializers.severity = none -# MSML_ParameterLocalVarName: Parameter or local variable name not standard -dotnet_diagnostic.MSML_ParameterLocalVarName.severity = none - [test/Microsoft.ML.CodeAnalyzer.Tests/**.cs] # BaseTestClass does not apply for analyzer testing. # MSML_ExtendBaseTestClass: Test classes should be derived from BaseTestClass diff --git a/test/Microsoft.Extensions.ML.Tests/UriLoaderTests.cs b/test/Microsoft.Extensions.ML.Tests/UriLoaderTests.cs index 99c9bcf68f..47ff67d496 100644 --- a/test/Microsoft.Extensions.ML.Tests/UriLoaderTests.cs +++ b/test/Microsoft.Extensions.ML.Tests/UriLoaderTests.cs @@ -74,7 +74,7 @@ public void no_reload_no_change() class UriLoaderMock : UriModelLoader { - public Func ETagMatches { get; set; } = (_, __) => false; + public Func ETagMatches { get; set; } = delegate { return false; }; public UriLoaderMock(IOptions contextOptions, ILogger logger) : base(contextOptions, logger) diff --git a/test/Microsoft.ML.AutoML.Tests/UserInputValidationTests.cs b/test/Microsoft.ML.AutoML.Tests/UserInputValidationTests.cs index 2f125efc2d..341733e727 100644 --- a/test/Microsoft.ML.AutoML.Tests/UserInputValidationTests.cs +++ b/test/Microsoft.ML.AutoML.Tests/UserInputValidationTests.cs @@ -188,16 +188,16 @@ public void ValidateFeaturesColInvalidType() [Fact] public void ValidateTextColumnNotText() { - const string TextPurposeColName = "TextColumn"; + const string textPurposeColName = "TextColumn"; var schemaBuilder = new DataViewSchema.Builder(); schemaBuilder.AddColumn(DefaultColumnNames.Features, NumberDataViewType.Single); schemaBuilder.AddColumn(DefaultColumnNames.Label, NumberDataViewType.Single); - schemaBuilder.AddColumn(TextPurposeColName, NumberDataViewType.Single); + schemaBuilder.AddColumn(textPurposeColName, NumberDataViewType.Single); var schema = schemaBuilder.ToSchema(); var dataView = DataViewTestFixture.BuildDummyDataView(schema); var columnInfo = new ColumnInformation(); - columnInfo.TextColumnNames.Add(TextPurposeColName); + columnInfo.TextColumnNames.Add(textPurposeColName); foreach (var task in new[] { TaskKind.Recommendation, TaskKind.Regression }) { diff --git a/test/Microsoft.ML.Benchmarks/ImageClassificationBench.cs b/test/Microsoft.ML.Benchmarks/ImageClassificationBench.cs index 0e8512dde4..ab16f8db49 100644 --- a/test/Microsoft.ML.Benchmarks/ImageClassificationBench.cs +++ b/test/Microsoft.ML.Benchmarks/ImageClassificationBench.cs @@ -205,10 +205,10 @@ public static void UnZip(String gzArchiveName, String destFolder) public static string GetAbsolutePath(string relativePath) { - FileInfo _dataRoot = new FileInfo(typeof( + FileInfo dataRoot = new FileInfo(typeof( ImageClassificationBench).Assembly.Location); - string assemblyFolderPath = _dataRoot.Directory.FullName; + string assemblyFolderPath = dataRoot.Directory.FullName; string fullPath = Path.Combine(assemblyFolderPath, relativePath); diff --git a/test/Microsoft.ML.Benchmarks/PredictionEngineBench.cs b/test/Microsoft.ML.Benchmarks/PredictionEngineBench.cs index d27e5b8c55..743bd15f6a 100644 --- a/test/Microsoft.ML.Benchmarks/PredictionEngineBench.cs +++ b/test/Microsoft.ML.Benchmarks/PredictionEngineBench.cs @@ -34,7 +34,7 @@ public void SetupIrisPipeline() PetalWidth = 5.1f, }; - string _irisDataPath = BaseTestClass.GetDataPath("iris.txt"); + string irisDataPath = BaseTestClass.GetDataPath("iris.txt"); var env = new MLContext(seed: 1); @@ -53,7 +53,7 @@ public void SetupIrisPipeline() }; var loader = new TextLoader(env, options: options); - IDataView data = loader.Load(_irisDataPath); + IDataView data = loader.Load(irisDataPath); var pipeline = new ColumnConcatenatingEstimator(env, "Features", new[] { "SepalLength", "SepalWidth", "PetalLength", "PetalWidth" }) .Append(env.Transforms.Conversion.MapValueToKey("Label")) @@ -73,7 +73,7 @@ public void SetupSentimentPipeline() SentimentText = "Not a big fan of this." }; - string _sentimentDataPath = BaseTestClass.GetDataPath("wikipedia-detox-250-line-data.tsv"); + string sentimentDataPath = BaseTestClass.GetDataPath("wikipedia-detox-250-line-data.tsv"); var mlContext = new MLContext(seed: 1); @@ -89,7 +89,7 @@ public void SetupSentimentPipeline() }; var loader = new TextLoader(mlContext, options: options); - IDataView data = loader.Load(_sentimentDataPath); + IDataView data = loader.Load(sentimentDataPath); var pipeline = mlContext.Transforms.Text.FeaturizeText("Features", "SentimentText") .Append(mlContext.BinaryClassification.Trainers.SdcaNonCalibrated( @@ -108,7 +108,7 @@ public void SetupBreastCancerPipeline() Features = new[] { 5f, 1f, 1f, 1f, 2f, 1f, 3f, 1f, 1f } }; - string _breastCancerDataPath = BaseTestClass.GetDataPath("breast-cancer.txt"); + string breastCancerDataPath = BaseTestClass.GetDataPath("breast-cancer.txt"); var env = new MLContext(seed: 1); @@ -124,7 +124,7 @@ public void SetupBreastCancerPipeline() }; var loader = new TextLoader(env, options: options); - IDataView data = loader.Load(_breastCancerDataPath); + IDataView data = loader.Load(breastCancerDataPath); var pipeline = env.BinaryClassification.Trainers.SdcaNonCalibrated( new SdcaNonCalibratedBinaryTrainer.Options { NumberOfThreads = 1, ConvergenceTolerance = 1e-2f, }); diff --git a/test/Microsoft.ML.CpuMath.PerformanceTests/PerformanceTests.cs b/test/Microsoft.ML.CpuMath.PerformanceTests/PerformanceTests.cs index 0f3a4c0c1b..37cbb9142c 100644 --- a/test/Microsoft.ML.CpuMath.PerformanceTests/PerformanceTests.cs +++ b/test/Microsoft.ML.CpuMath.PerformanceTests/PerformanceTests.cs @@ -48,13 +48,13 @@ private float NextFloat(Random rand, int expRange) private int GetSeed() { int seed = DefaultSeed; - string CPUMATH_SEED = Environment.GetEnvironmentVariable("CPUMATH_SEED"); + string cpumathSeed = Environment.GetEnvironmentVariable("CPUMATH_SEED"); - if (CPUMATH_SEED != null) + if (cpumathSeed != null) { - if (!int.TryParse(CPUMATH_SEED, out seed)) + if (!int.TryParse(cpumathSeed, out seed)) { - if (string.Equals(CPUMATH_SEED, "random", StringComparison.OrdinalIgnoreCase)) + if (string.Equals(cpumathSeed, "random", StringComparison.OrdinalIgnoreCase)) { seed = new Random().Next(); } diff --git a/test/Microsoft.ML.NugetPackageVersionUpdater/Program.cs b/test/Microsoft.ML.NugetPackageVersionUpdater/Program.cs index 2e70a68bad..d5b467c634 100644 --- a/test/Microsoft.ML.NugetPackageVersionUpdater/Program.cs +++ b/test/Microsoft.ML.NugetPackageVersionUpdater/Program.cs @@ -56,10 +56,10 @@ private static void UpdatePackageVersion(string projectFiles, IDictionary(onnx_out.Keys); - for(var i =0; i < onnx_out.Count; ++i) + var onnxOut = output.Output.FirstOrDefault(); + Assert.True(onnxOut.Count == 3, "Output missing data."); + var keys = new List(onnxOut.Keys); + for(var i =0; i < onnxOut.Count; ++i) { - Assert.Equal(onnx_out[keys[i]], input.Input[i]); + Assert.Equal(onnxOut[keys[i]], input.Input[i]); } } diff --git a/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs b/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs index 5b9022e1be..857e642be9 100644 --- a/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs +++ b/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs @@ -108,7 +108,7 @@ public void TensorFlowTransforCifarEndToEndTest2() { var imageHeight = 32; var imageWidth = 32; - var model_location = "cifar_model/frozen_model.pb"; + var modelLocation = "cifar_model/frozen_model.pb"; var dataFile = GetDataPath("images/images.tsv"); var imageFolder = Path.GetDirectoryName(dataFile); @@ -125,7 +125,7 @@ public void TensorFlowTransforCifarEndToEndTest2() var pipeEstimator = new ImageLoadingEstimator(mlContext, imageFolder, ("ImageReal", "ImagePath")) .Append(new ImageResizingEstimator(mlContext, "ImageCropped", imageHeight, imageWidth, "ImageReal")) .Append(new ImagePixelExtractingEstimator(mlContext, "Input", "ImageCropped", interleavePixelColors: true)) - .Append(mlContext.Model.LoadTensorFlowModel(model_location).ScoreTensorFlowModel("Output", "Input")) + .Append(mlContext.Model.LoadTensorFlowModel(modelLocation).ScoreTensorFlowModel("Output", "Input")) .Append(new ColumnConcatenatingEstimator(mlContext, "Features", "Output")) .Append(new ValueToKeyMappingEstimator(mlContext, "Label")) .AppendCacheCheckpoint(mlContext) @@ -345,7 +345,7 @@ private class TypesData public void TensorFlowTransformInputOutputTypesTest() { // This an identity model which returns the same output as input. - var model_location = "model_types_test"; + var modelLocation = "model_types_test"; //Data var data = new List( @@ -382,7 +382,7 @@ public void TensorFlowTransformInputOutputTypesTest() var inputs = new string[] { "f64", "f32", "i64", "i32", "i16", "i8", "u64", "u32", "u16", "u8", "b" }; var outputs = new string[] { "o_f64", "o_f32", "o_i64", "o_i32", "o_i16", "o_i8", "o_u64", "o_u32", "o_u16", "o_u8", "o_b" }; - var trans = mlContext.Model.LoadTensorFlowModel(model_location).ScoreTensorFlowModel(outputs, inputs).Fit(loader).Transform(loader); ; + var trans = mlContext.Model.LoadTensorFlowModel(modelLocation).ScoreTensorFlowModel(outputs, inputs).Fit(loader).Transform(loader); ; using (var cursor = trans.GetRowCursorForAllColumns()) { @@ -546,8 +546,8 @@ public void TensorFlowTransformInceptionTest() public void TensorFlowInputsOutputsSchemaTest() { var mlContext = new MLContext(seed: 1); - var model_location = "mnist_model/frozen_saved_model.pb"; - var schema = TensorFlowUtils.GetModelSchema(mlContext, model_location); + var modelLocation = "mnist_model/frozen_saved_model.pb"; + var schema = TensorFlowUtils.GetModelSchema(mlContext, modelLocation); Assert.Equal(86, schema.Count); Assert.True(schema.TryGetColumnIndex("Placeholder", out int col)); var type = (VectorDataViewType)schema[col].Type; @@ -607,8 +607,8 @@ public void TensorFlowInputsOutputsSchemaTest() Assert.Equal(1, inputOps.Length); Assert.Equal("sequential/dense_1/BiasAdd", inputOps.GetValues()[0].ToString()); - model_location = "model_matmul/frozen_saved_model.pb"; - schema = TensorFlowUtils.GetModelSchema(mlContext, model_location); + modelLocation = "model_matmul/frozen_saved_model.pb"; + schema = TensorFlowUtils.GetModelSchema(mlContext, modelLocation); char name = 'a'; for (int i = 0; i < schema.Count; i++) { @@ -663,7 +663,7 @@ public void TensorFlowTransformMNISTLRTrainingTest() { const double expectedMicroAccuracy = 0.72173913043478266; const double expectedMacroAccruacy = 0.67482993197278918; - var model_location = "mnist_lr_model"; + var modelLocation = "mnist_lr_model"; try { var mlContext = new MLContext(seed: 1); @@ -686,7 +686,7 @@ public void TensorFlowTransformMNISTLRTrainingTest() labelColumnName: "OneHotLabel", dnnLabel: "Label", optimizationOperation: "SGDOptimizer", - modelPath: model_location, + modelPath: modelLocation, lossOperation: "Loss", epoch: 10, learningRateOperation: "SGDOptimizer/learning_rate", @@ -724,16 +724,16 @@ public void TensorFlowTransformMNISTLRTrainingTest() { // This test changes the state of the model. // Cleanup folder so that other test can also use the same model. - CleanUp(model_location); + CleanUp(modelLocation); } } - private void CleanUp(string model_location) + private void CleanUp(string modelLocation) { - var directories = Directory.GetDirectories(model_location, "variables-*"); + var directories = Directory.GetDirectories(modelLocation, "variables-*"); if (directories != null && directories.Length > 0) { - var varDir = Path.Combine(model_location, "variables"); + var varDir = Path.Combine(modelLocation, "variables"); if (Directory.Exists(varDir)) Directory.Delete(varDir, true); Directory.Move(directories[0], varDir); diff --git a/test/Microsoft.ML.Tests/TextLoaderTests.cs b/test/Microsoft.ML.Tests/TextLoaderTests.cs index ec6feec37c..14b3f8475b 100644 --- a/test/Microsoft.ML.Tests/TextLoaderTests.cs +++ b/test/Microsoft.ML.Tests/TextLoaderTests.cs @@ -287,38 +287,38 @@ public void CanSuccessfullyRetrieveQuotedData() using (var cursor = data.GetRowCursorForAllColumns()) { - var IDGetter = cursor.GetGetter(cursor.Schema[0]); - var TextGetter = cursor.GetGetter>(cursor.Schema[1]); + var idGetter = cursor.GetGetter(cursor.Schema[0]); + var textGetter = cursor.GetGetter>(cursor.Schema[1]); Assert.True(cursor.MoveNext()); - float ID = 0; - IDGetter(ref ID); - Assert.Equal(1, ID); + float id = 0; + idGetter(ref id); + Assert.Equal(1, id); - ReadOnlyMemory Text = new ReadOnlyMemory(); - TextGetter(ref Text); - Assert.Equal("This text contains comma, within quotes.", Text.ToString()); + ReadOnlyMemory text = new ReadOnlyMemory(); + textGetter(ref text); + Assert.Equal("This text contains comma, within quotes.", text.ToString()); Assert.True(cursor.MoveNext()); - ID = 0; - IDGetter(ref ID); - Assert.Equal(2, ID); + id = 0; + idGetter(ref id); + Assert.Equal(2, id); - Text = new ReadOnlyMemory(); - TextGetter(ref Text); - Assert.Equal("This text contains extra punctuations and special characters.;*<>?!@#$%^&*()_+=-{}|[]:;'", Text.ToString()); + text = new ReadOnlyMemory(); + textGetter(ref text); + Assert.Equal("This text contains extra punctuations and special characters.;*<>?!@#$%^&*()_+=-{}|[]:;'", text.ToString()); Assert.True(cursor.MoveNext()); - ID = 0; - IDGetter(ref ID); - Assert.Equal(3, ID); + id = 0; + idGetter(ref id); + Assert.Equal(3, id); - Text = new ReadOnlyMemory(); - TextGetter(ref Text); - Assert.Equal("This text has no quotes", Text.ToString()); + text = new ReadOnlyMemory(); + textGetter(ref text); + Assert.Equal("This text has no quotes", text.ToString()); Assert.False(cursor.MoveNext()); } @@ -548,28 +548,28 @@ public void CanSuccessfullyTrimSpaces() using (var cursor = data.GetRowCursorForAllColumns()) { - var IDGetter = cursor.GetGetter(cursor.Schema[0]); - var TextGetter = cursor.GetGetter>(cursor.Schema[1]); + var idGetter = cursor.GetGetter(cursor.Schema[0]); + var textGetter = cursor.GetGetter>(cursor.Schema[1]); Assert.True(cursor.MoveNext()); - float ID = 0; - IDGetter(ref ID); - Assert.Equal(1, ID); + float id = 0; + idGetter(ref id); + Assert.Equal(1, id); - ReadOnlyMemory Text = new ReadOnlyMemory(); - TextGetter(ref Text); - Assert.Equal("There is a space at the end", Text.ToString()); + ReadOnlyMemory text = new ReadOnlyMemory(); + textGetter(ref text); + Assert.Equal("There is a space at the end", text.ToString()); Assert.True(cursor.MoveNext()); - ID = 0; - IDGetter(ref ID); - Assert.Equal(2, ID); + id = 0; + idGetter(ref id); + Assert.Equal(2, id); - Text = new ReadOnlyMemory(); - TextGetter(ref Text); - Assert.Equal("There is no space at the end", Text.ToString()); + text = new ReadOnlyMemory(); + textGetter(ref text); + Assert.Equal("There is no space at the end", text.ToString()); Assert.False(cursor.MoveNext()); } diff --git a/test/Microsoft.ML.Tests/Transformers/CategoricalImputerTests.cs b/test/Microsoft.ML.Tests/Transformers/CategoricalImputerTests.cs index a9dd5e75a6..e7f84fbfb0 100644 --- a/test/Microsoft.ML.Tests/Transformers/CategoricalImputerTests.cs +++ b/test/Microsoft.ML.Tests/Transformers/CategoricalImputerTests.cs @@ -34,18 +34,18 @@ private class SchemaAllTypes public double double_t; public string str; - internal SchemaAllTypes(byte num_i, float num_f, string s) + internal SchemaAllTypes(byte numI, float numF, string s) { - uint8_t = num_i; - int8_t = (sbyte)num_i; - int16_t = num_i; - uint16_t = num_i; - int32_t = num_i; - uint32_t = num_i; - int64_t = num_i; - uint64_t = num_i; - float_t = num_f; - double_t = num_f; + uint8_t = numI; + int8_t = (sbyte)numI; + int16_t = numI; + uint16_t = numI; + int32_t = numI; + uint32_t = numI; + int64_t = numI; + uint64_t = numI; + float_t = numF; + double_t = numF; str = s; } } diff --git a/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs b/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs index c4dad74a6d..d7571176b7 100644 --- a/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs +++ b/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs @@ -131,10 +131,10 @@ public void ChangeDetection() public void ChangePointDetectionWithSeasonality() { var env = new MLContext(1); - const int ChangeHistorySize = 10; - const int SeasonalitySize = 10; - const int NumberOfSeasonsInTraining = 5; - const int MaxTrainingSize = NumberOfSeasonsInTraining * SeasonalitySize; + const int changeHistorySize = 10; + const int seasonalitySize = 10; + const int numberOfSeasonsInTraining = 5; + const int maxTrainingSize = numberOfSeasonsInTraining * seasonalitySize; List data = new List(); var dataView = env.Data.LoadFromEnumerable(data); @@ -144,16 +144,16 @@ public void ChangePointDetectionWithSeasonality() Confidence = 95, Source = "Value", Name = "Change", - ChangeHistoryLength = ChangeHistorySize, - TrainingWindowSize = MaxTrainingSize, - SeasonalWindowSize = SeasonalitySize + ChangeHistoryLength = changeHistorySize, + TrainingWindowSize = maxTrainingSize, + SeasonalWindowSize = seasonalitySize }; - for (int j = 0; j < NumberOfSeasonsInTraining; j++) - for (int i = 0; i < SeasonalitySize; i++) + for (int j = 0; j < numberOfSeasonsInTraining; j++) + for (int i = 0; i < seasonalitySize; i++) data.Add(new Data(i)); - for (int i = 0; i < ChangeHistorySize; i++) + for (int i = 0; i < changeHistorySize; i++) data.Add(new Data(i * 100)); // Train @@ -180,21 +180,21 @@ public void ChangePointDetectionWithSeasonality() [LessThanNetCore30OrNotNetCoreFact("netcoreapp3.0 output differs from Baseline")] public void ChangePointDetectionWithSeasonalityPredictionEngineNoColumn() { - const int ChangeHistorySize = 10; - const int SeasonalitySize = 10; - const int NumberOfSeasonsInTraining = 5; - const int MaxTrainingSize = NumberOfSeasonsInTraining * SeasonalitySize; + const int changeHistorySize = 10; + const int seasonalitySize = 10; + const int numberOfSeasonsInTraining = 5; + const int maxTrainingSize = numberOfSeasonsInTraining * seasonalitySize; List data = new List(); var ml = new MLContext(seed: 1); var dataView = ml.Data.LoadFromEnumerable(data); - for (int j = 0; j < NumberOfSeasonsInTraining; j++) - for (int i = 0; i < SeasonalitySize; i++) + for (int j = 0; j < numberOfSeasonsInTraining; j++) + for (int i = 0; i < seasonalitySize; i++) data.Add(new Data(i)); - for (int i = 0; i < ChangeHistorySize; i++) + for (int i = 0; i < changeHistorySize; i++) data.Add(new Data(i * 100)); @@ -205,9 +205,9 @@ public void ChangePointDetectionWithSeasonalityPredictionEngineNoColumn() Confidence = 95, Source = "Value", Name = "Change", - ChangeHistoryLength = ChangeHistorySize, - TrainingWindowSize = MaxTrainingSize, - SeasonalWindowSize = SeasonalitySize + ChangeHistoryLength = changeHistorySize, + TrainingWindowSize = maxTrainingSize, + SeasonalWindowSize = seasonalitySize })); // Train. @@ -256,21 +256,21 @@ public void ChangePointDetectionWithSeasonalityPredictionEngineNoColumn() [LessThanNetCore30OrNotNetCoreFact("netcoreapp3.0 output differs from Baseline")] public void ChangePointDetectionWithSeasonalityPredictionEngine() { - const int ChangeHistorySize = 10; - const int SeasonalitySize = 10; - const int NumberOfSeasonsInTraining = 5; - const int MaxTrainingSize = NumberOfSeasonsInTraining * SeasonalitySize; + const int changeHistorySize = 10; + const int seasonalitySize = 10; + const int numberOfSeasonsInTraining = 5; + const int maxTrainingSize = numberOfSeasonsInTraining * seasonalitySize; List data = new List(); var ml = new MLContext(seed: 1); var dataView = ml.Data.LoadFromEnumerable(data); - for (int j = 0; j < NumberOfSeasonsInTraining; j++) - for (int i = 0; i < SeasonalitySize; i++) + for (int j = 0; j < numberOfSeasonsInTraining; j++) + for (int i = 0; i < seasonalitySize; i++) data.Add(new Data(i)); - for (int i = 0; i < ChangeHistorySize; i++) + for (int i = 0; i < changeHistorySize; i++) data.Add(new Data(i * 100)); @@ -282,9 +282,9 @@ public void ChangePointDetectionWithSeasonalityPredictionEngine() Confidence = 95, Source = "Value", Name = "Change", - ChangeHistoryLength = ChangeHistorySize, - TrainingWindowSize = MaxTrainingSize, - SeasonalWindowSize = SeasonalitySize + ChangeHistoryLength = changeHistorySize, + TrainingWindowSize = maxTrainingSize, + SeasonalWindowSize = seasonalitySize })); // Train. @@ -329,9 +329,9 @@ public void ChangePointDetectionWithSeasonalityPredictionEngine() public void SsaForecast() { var env = new MLContext(1); - const int ChangeHistorySize = 10; - const int SeasonalitySize = 10; - const int NumberOfSeasonsInTraining = 5; + const int changeHistorySize = 10; + const int seasonalitySize = 10; + const int numberOfSeasonsInTraining = 5; List data = new List(); var dataView = env.Data.LoadFromEnumerable(data); @@ -350,11 +350,11 @@ public void SsaForecast() IsAdaptive = true }; - for (int j = 0; j < NumberOfSeasonsInTraining; j++) - for (int i = 0; i < SeasonalitySize; i++) + for (int j = 0; j < numberOfSeasonsInTraining; j++) + for (int i = 0; i < seasonalitySize; i++) data.Add(new Data(i)); - for (int i = 0; i < ChangeHistorySize; i++) + for (int i = 0; i < changeHistorySize; i++) data.Add(new Data(i * 100)); // Train @@ -384,9 +384,9 @@ public void SsaForecast() [Trait("Category", "SkipInCI")] public void SsaForecastPredictionEngine() { - const int ChangeHistorySize = 10; - const int SeasonalitySize = 10; - const int NumberOfSeasonsInTraining = 5; + const int changeHistorySize = 10; + const int seasonalitySize = 10; + const int numberOfSeasonsInTraining = 5; List data = new List(); @@ -407,11 +407,11 @@ public void SsaForecastPredictionEngine() VariableHorizon = true }; - for (int j = 0; j < NumberOfSeasonsInTraining; j++) - for (int i = 0; i < SeasonalitySize; i++) + for (int j = 0; j < numberOfSeasonsInTraining; j++) + for (int i = 0; i < seasonalitySize; i++) data.Add(new Data(i)); - for (int i = 0; i < ChangeHistorySize; i++) + for (int i = 0; i < changeHistorySize; i++) data.Add(new Data(i * 100)); // Train diff --git a/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesEstimatorTests.cs b/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesEstimatorTests.cs index 358cdb661f..9344e9e746 100644 --- a/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesEstimatorTests.cs +++ b/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesEstimatorTests.cs @@ -43,24 +43,24 @@ public TimeSeriesEstimatorTests(ITestOutputHelper output) : base(output) [Fact] public void TestSsaChangePointEstimator() { - int Confidence = 95; - int ChangeHistorySize = 10; - int SeasonalitySize = 10; - int NumberOfSeasonsInTraining = 5; - int MaxTrainingSize = NumberOfSeasonsInTraining * SeasonalitySize; + int confidence = 95; + int changeHistorySize = 10; + int seasonalitySize = 10; + int numberOfSeasonsInTraining = 5; + int maxTrainingSize = numberOfSeasonsInTraining * seasonalitySize; List data = new List(); var dataView = ML.Data.LoadFromEnumerable(data); - for (int j = 0; j < NumberOfSeasonsInTraining; j++) - for (int i = 0; i < SeasonalitySize; i++) + for (int j = 0; j < numberOfSeasonsInTraining; j++) + for (int i = 0; i < seasonalitySize; i++) data.Add(new Data(i)); - for (int i = 0; i < ChangeHistorySize; i++) + for (int i = 0; i < changeHistorySize; i++) data.Add(new Data(i * 100)); var pipe = new SsaChangePointEstimator(Env, "Change", - Confidence, ChangeHistorySize, MaxTrainingSize, SeasonalitySize, "Value"); + confidence, changeHistorySize, maxTrainingSize, seasonalitySize, "Value"); var xyData = new List { new TestDataXY() { A = new float[inputSize] } }; var stringData = new List { new TestDataDifferntType() { data_0 = new string[inputSize] } }; @@ -77,20 +77,20 @@ public void TestSsaChangePointEstimator() [Fact] public void TestSsaForecastingEstimator() { - const int ChangeHistorySize = 10; - const int SeasonalitySize = 10; - const int NumberOfSeasonsInTraining = 5; + const int changeHistorySize = 10; + const int seasonalitySize = 10; + const int numberOfSeasonsInTraining = 5; List data = new List(); var ml = new MLContext(seed: 1); var dataView = ml.Data.LoadFromEnumerable(data); - for (int j = 0; j < NumberOfSeasonsInTraining; j++) - for (int i = 0; i < SeasonalitySize; i++) + for (int j = 0; j < numberOfSeasonsInTraining; j++) + for (int i = 0; i < seasonalitySize; i++) data.Add(new Data(i)); - for (int i = 0; i < ChangeHistorySize; i++) + for (int i = 0; i < changeHistorySize; i++) data.Add(new Data(i * 100)); // Train @@ -113,24 +113,24 @@ public void TestSsaForecastingEstimator() [Fact] public void TestSsaSpikeEstimator() { - int Confidence = 95; - int PValueHistorySize = 10; - int SeasonalitySize = 10; - int NumberOfSeasonsInTraining = 5; - int MaxTrainingSize = NumberOfSeasonsInTraining * SeasonalitySize; + int confidence = 95; + int pValueHistorySize = 10; + int seasonalitySize = 10; + int numberOfSeasonsInTraining = 5; + int maxTrainingSize = numberOfSeasonsInTraining * seasonalitySize; List data = new List(); var dataView = ML.Data.LoadFromEnumerable(data); - for (int j = 0; j < NumberOfSeasonsInTraining; j++) - for (int i = 0; i < SeasonalitySize; i++) + for (int j = 0; j < numberOfSeasonsInTraining; j++) + for (int i = 0; i < seasonalitySize; i++) data.Add(new Data(i)); - for (int i = 0; i < PValueHistorySize; i++) + for (int i = 0; i < pValueHistorySize; i++) data.Add(new Data(i * 100)); var pipe = new SsaSpikeEstimator(Env, "Change", - Confidence, PValueHistorySize, MaxTrainingSize, SeasonalitySize, "Value"); + confidence, pValueHistorySize, maxTrainingSize, seasonalitySize, "Value"); var xyData = new List { new TestDataXY() { A = new float[inputSize] } }; var stringData = new List { new TestDataDifferntType() { data_0 = new string[inputSize] } }; @@ -147,17 +147,17 @@ public void TestSsaSpikeEstimator() [Fact] public void TestIidChangePointEstimator() { - int Confidence = 95; - int ChangeHistorySize = 10; + int confidence = 95; + int changeHistorySize = 10; List data = new List(); var dataView = ML.Data.LoadFromEnumerable(data); - for (int i = 0; i < ChangeHistorySize; i++) + for (int i = 0; i < changeHistorySize; i++) data.Add(new Data(i * 100)); var pipe = new IidChangePointEstimator(Env, - "Change", Confidence, ChangeHistorySize, "Value"); + "Change", confidence, changeHistorySize, "Value"); var xyData = new List { new TestDataXY() { A = new float[inputSize] } }; var stringData = new List { new TestDataDifferntType() { data_0 = new string[inputSize] } }; @@ -174,17 +174,17 @@ public void TestIidChangePointEstimator() [Fact] public void TestIidSpikeEstimator() { - int Confidence = 95; - int PValueHistorySize = 10; + int confidence = 95; + int pValueHistorySize = 10; List data = new List(); var dataView = ML.Data.LoadFromEnumerable(data); - for (int i = 0; i < PValueHistorySize; i++) + for (int i = 0; i < pValueHistorySize; i++) data.Add(new Data(i * 100)); var pipe = new IidSpikeEstimator(Env, - "Change", Confidence, PValueHistorySize, "Value"); + "Change", confidence, pValueHistorySize, "Value"); var xyData = new List { new TestDataXY() { A = new float[inputSize] } }; var stringData = new List { new TestDataDifferntType() { data_0 = new string[inputSize] } }; diff --git a/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesSimpleApiTests.cs b/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesSimpleApiTests.cs index 293975dc86..aad10ccef2 100644 --- a/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesSimpleApiTests.cs +++ b/test/Microsoft.ML.TimeSeries.Tests/TimeSeriesSimpleApiTests.cs @@ -38,17 +38,17 @@ private sealed class Data public void ChangeDetection() { var env = new MLContext(1); - const int Size = 10; - var data = new List(Size); + const int size = 10; + var data = new List(size); var dataView = env.Data.LoadFromEnumerable(data); - for (int i = 0; i < Size / 2; i++) + for (int i = 0; i < size / 2; i++) data.Add(new Data(5)); - for (int i = 0; i < Size / 2; i++) + for (int i = 0; i < size / 2; i++) data.Add(new Data((float)(5 + i * 1.1))); // Build the pipeline - var learningPipeline = ML.Transforms.DetectIidChangePoint("Data", "Value", 80, Size); + var learningPipeline = ML.Transforms.DetectIidChangePoint("Data", "Value", 80, size); // Train var detector = learningPipeline.Fit(dataView); @@ -76,23 +76,23 @@ public void ChangeDetection() public void ChangePointDetectionWithSeasonality() { var env = new MLContext(1); - const int ChangeHistorySize = 10; - const int SeasonalitySize = 10; - const int NumberOfSeasonsInTraining = 5; - const int MaxTrainingSize = NumberOfSeasonsInTraining * SeasonalitySize; + const int changeHistorySize = 10; + const int seasonalitySize = 10; + const int numberOfSeasonsInTraining = 5; + const int maxTrainingSize = numberOfSeasonsInTraining * seasonalitySize; var data = new List(); var dataView = env.Data.LoadFromEnumerable(data); - for (int j = 0; j < NumberOfSeasonsInTraining; j++) - for (int i = 0; i < SeasonalitySize; i++) + for (int j = 0; j < numberOfSeasonsInTraining; j++) + for (int i = 0; i < seasonalitySize; i++) data.Add(new Data(i)); - for (int i = 0; i < ChangeHistorySize; i++) + for (int i = 0; i < changeHistorySize; i++) data.Add(new Data(i * 100)); // Build the pipeline - var learningPipeline = ML.Transforms.DetectChangePointBySsa("Data", "Value", 95, ChangeHistorySize, MaxTrainingSize, SeasonalitySize); + var learningPipeline = ML.Transforms.DetectChangePointBySsa("Data", "Value", 95, changeHistorySize, maxTrainingSize, seasonalitySize); // Train var detector = learningPipeline.Fit(dataView); // Transform @@ -120,20 +120,20 @@ public void ChangePointDetectionWithSeasonality() public void SpikeDetection() { var env = new MLContext(1); - const int Size = 10; - const int PvalHistoryLength = Size / 4; + const int size = 10; + const int pvalHistoryLength = size / 4; // Generate sample series data with a spike - List data = new List(Size); + List data = new List(size); var dataView = env.Data.LoadFromEnumerable(data); - for (int i = 0; i < Size / 2; i++) + for (int i = 0; i < size / 2; i++) data.Add(new Data(5)); data.Add(new Data(10)); // This is the spike - for (int i = 0; i < Size / 2 - 1; i++) + for (int i = 0; i < size / 2 - 1; i++) data.Add(new Data(5)); // Build the pipeline - var learningPipeline = ML.Transforms.DetectIidSpike("Data", "Value", 80, PvalHistoryLength); + var learningPipeline = ML.Transforms.DetectIidSpike("Data", "Value", 80, pvalHistoryLength); // Train var detector = learningPipeline.Fit(dataView); // Transform @@ -170,22 +170,22 @@ public void SpikeDetection() public void SsaSpikeDetection() { var env = new MLContext(1); - const int Size = 16; - const int ChangeHistoryLength = Size / 4; - const int TrainingWindowSize = Size / 2; - const int SeasonalityWindowSize = Size / 8; + const int size = 16; + const int changeHistoryLength = size / 4; + const int trainingWindowSize = size / 2; + const int seasonalityWindowSize = size / 8; // Generate sample series data with a spike - List data = new List(Size); + List data = new List(size); var dataView = env.Data.LoadFromEnumerable(data); - for (int i = 0; i < Size / 2; i++) + for (int i = 0; i < size / 2; i++) data.Add(new Data(5)); data.Add(new Data(10)); // This is the spike - for (int i = 0; i < Size / 2 - 1; i++) + for (int i = 0; i < size / 2 - 1; i++) data.Add(new Data(5)); // Build the pipeline - var learningPipeline = ML.Transforms.DetectSpikeBySsa("Data", "Value", 80, ChangeHistoryLength, TrainingWindowSize, SeasonalityWindowSize); + var learningPipeline = ML.Transforms.DetectSpikeBySsa("Data", "Value", 80, changeHistoryLength, trainingWindowSize, seasonalityWindowSize); // Train var detector = learningPipeline.Fit(dataView); // Transform