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Rogan Carr
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Addressing PR comments.
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‎test/Microsoft.ML.Functional.Tests/Training.cs

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@@ -31,7 +31,8 @@ public void CompareTrainerEvaluations()
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// Get the dataset.
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var data = mlContext.Data.LoadFromTextFile<TweetSentiment>(GetDataPath(TestDatasets.Sentiment.trainFilename),
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separatorChar: TestDatasets.Sentiment.fileSeparator, hasHeader: TestDatasets.Sentiment.fileHasHeader,
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separatorChar: TestDatasets.Sentiment.fileSeparator,
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hasHeader: TestDatasets.Sentiment.fileHasHeader,
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allowQuoting: TestDatasets.Sentiment.allowQuoting);
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var trainTestSplit = mlContext.BinaryClassification.TrainTestSplit(data);
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var trainData = trainTestSplit.TrainSet;
@@ -85,7 +86,8 @@ public void ContinueTrainingAveragePerceptron()
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// Get the dataset.
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var data = mlContext.Data.LoadFromTextFile<TweetSentiment>(GetDataPath(TestDatasets.Sentiment.trainFilename),
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separatorChar: TestDatasets.Sentiment.fileSeparator, hasHeader: TestDatasets.Sentiment.fileHasHeader,
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separatorChar: TestDatasets.Sentiment.fileSeparator,
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hasHeader: TestDatasets.Sentiment.fileHasHeader,
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allowQuoting: TestDatasets.Sentiment.allowQuoting);
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// Create a transformation pipeline.
@@ -128,7 +130,8 @@ public void ContinueTrainingFieldAwareFactorizationMachines()
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// Get the dataset.
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var data = mlContext.Data.LoadFromTextFile<TweetSentiment>(GetDataPath(TestDatasets.Sentiment.trainFilename),
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separatorChar: TestDatasets.Sentiment.fileSeparator, hasHeader: TestDatasets.Sentiment.fileHasHeader,
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separatorChar: TestDatasets.Sentiment.fileSeparator,
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hasHeader: TestDatasets.Sentiment.fileHasHeader,
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allowQuoting: TestDatasets.Sentiment.allowQuoting);
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// Create a transformation pipeline.
@@ -171,7 +174,8 @@ public void ContinueTrainingLinearSupportVectorMachine()
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// Get the dataset.
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var data = mlContext.Data.LoadFromTextFile<TweetSentiment>(GetDataPath(TestDatasets.Sentiment.trainFilename),
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separatorChar: TestDatasets.Sentiment.fileSeparator, hasHeader: TestDatasets.Sentiment.fileHasHeader,
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separatorChar: TestDatasets.Sentiment.fileSeparator,
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hasHeader: TestDatasets.Sentiment.fileHasHeader,
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allowQuoting: TestDatasets.Sentiment.allowQuoting);
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// Create a transformation pipeline.
@@ -214,7 +218,8 @@ public void ContinueTrainingLogisticRegression()
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// Get the dataset.
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var data = mlContext.Data.LoadFromTextFile<TweetSentiment>(GetDataPath(TestDatasets.Sentiment.trainFilename),
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separatorChar: TestDatasets.Sentiment.fileSeparator, hasHeader: TestDatasets.Sentiment.fileHasHeader,
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separatorChar: TestDatasets.Sentiment.fileSeparator,
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hasHeader: TestDatasets.Sentiment.fileHasHeader,
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allowQuoting: TestDatasets.Sentiment.allowQuoting);
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// Create a transformation pipeline.
@@ -256,8 +261,8 @@ public void ContinueTrainingLogisticRegressionMulticlass()
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var mlContext = new MLContext(seed: 1);
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var data = mlContext.Data.LoadFromTextFile<Iris>(GetDataPath(TestDatasets.iris.trainFilename),
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hasHeader: TestDatasets.iris.fileHasHeader,
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separatorChar: TestDatasets.iris.fileSeparator);
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hasHeader: TestDatasets.iris.fileHasHeader,
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separatorChar: TestDatasets.iris.fileSeparator);
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// Create a training pipeline.
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var featurizationPipeline = mlContext.Transforms.Concatenate("Features", Iris.Features)
@@ -306,7 +311,8 @@ public void ContinueTrainingOnlineGradientDescent()
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// Get the dataset.
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var data = mlContext.Data.LoadFromTextFile<HousingRegression>(GetDataPath(TestDatasets.housing.trainFilename),
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separatorChar: TestDatasets.housing.fileSeparator, hasHeader: TestDatasets.housing.fileHasHeader);
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separatorChar: TestDatasets.housing.fileSeparator,
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hasHeader: TestDatasets.housing.fileHasHeader);
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// Create a transformation pipeline.
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var featurizationPipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
@@ -349,7 +355,8 @@ public void ContinueTrainingLinearSymbolicStochasticGradientDescent()
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// Get the dataset.
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var data = mlContext.Data.LoadFromTextFile<TweetSentiment>(GetDataPath(TestDatasets.Sentiment.trainFilename),
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separatorChar: TestDatasets.Sentiment.fileSeparator, hasHeader: TestDatasets.Sentiment.fileHasHeader,
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separatorChar: TestDatasets.Sentiment.fileSeparator,
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hasHeader: TestDatasets.Sentiment.fileHasHeader,
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allowQuoting: TestDatasets.Sentiment.allowQuoting);
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// Create a transformation pipeline.
@@ -396,7 +403,8 @@ public void ContinueTrainingPoissonRegression()
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// Get the dataset.
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var data = mlContext.Data.LoadFromTextFile<HousingRegression>(GetDataPath(TestDatasets.housing.trainFilename),
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separatorChar: TestDatasets.housing.fileSeparator, hasHeader: TestDatasets.housing.fileHasHeader);
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separatorChar: TestDatasets.housing.fileSeparator,
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hasHeader: TestDatasets.housing.fileHasHeader);
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// Create a transformation pipeline.
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var featurizationPipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
@@ -439,8 +447,8 @@ public void MetacomponentsFunctionAsExpectedOva()
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var mlContext = new MLContext(seed: 1);
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var data = mlContext.Data.LoadFromTextFile<Iris>(GetDataPath(TestDatasets.iris.trainFilename),
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hasHeader: TestDatasets.iris.fileHasHeader,
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separatorChar: TestDatasets.iris.fileSeparator);
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hasHeader: TestDatasets.iris.fileHasHeader,
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separatorChar: TestDatasets.iris.fileSeparator);
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// Create a model training an OVA trainer with a binary classifier.
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var anomalyDetectionTrainer = mlContext.AnomalyDetection.Trainers.AnalyzeRandomizedPrincipalComponents();

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