Rebasing and fixing to reflect changes in master.

Author: Rogan Carr

test/Microsoft.ML.Functional.Tests/Training.cs

@@ -32,9 +32,9 @@ public void CompareTrainerEvaluations()
  // Get the dataset.
  var data = mlContext.Data.LoadFromTextFile<TweetSentiment>(GetDataPath(TestDatasets.Sentiment.trainFilename),
  separatorChar: TestDatasets.Sentiment.fileSeparator,
- hasHeader: TestDatasets.Sentiment.fileHasHeader, 
+ hasHeader: TestDatasets.Sentiment.fileHasHeader,
  allowQuoting: TestDatasets.Sentiment.allowQuoting);
- var trainTestSplit = mlContext.BinaryClassification.TrainTestSplit(data);
+ var trainTestSplit = mlContext.Data.TrainTestSplit(data);
  var trainData = trainTestSplit.TrainSet;
  var testData = trainTestSplit.TestSet;
 
@@ -266,6 +266,7 @@ public void ContinueTrainingLogisticRegressionMulticlass()
 
  // Create a training pipeline.
  var featurizationPipeline = mlContext.Transforms.Concatenate("Features", Iris.Features)
+ .Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
  .AppendCacheCheckpoint(mlContext);
 
  var trainer = mlContext.MulticlassClassification.Trainers.LogisticRegression(
@@ -467,8 +468,7 @@ public void MetacomponentsFunctionAsExpectedOva()
  var binaryClassificationPipeline = mlContext.Transforms.Concatenate("Features", Iris.Features)
  .AppendCacheCheckpoint(mlContext)
  .Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
- .Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(binaryclassificationTrainer))
- .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
+ .Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(binaryclassificationTrainer));
 
  // Fit the binary classification pipeline.
  var binaryClassificationModel = binaryClassificationPipeline.Fit(data);
@@ -503,40 +503,28 @@ public void MetacomponentsFunctionAsExpectedOva()
  // Create a model training an OVA trainer with a ranking trainer.
  var rankingTrainer = mlContext.Ranking.Trainers.FastTree(
  new FastTreeRankingTrainer.Options { NumberOfTrees = 2, NumberOfThreads = 1, });
+ // Todo #2920: Make this fail somehow.
  var rankingPipeline = mlContext.Transforms.Concatenate("Features", Iris.Features)
  .AppendCacheCheckpoint(mlContext)
  .Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
  .Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(rankingTrainer))
  .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
 
  // Fit the invalid pipeline.
- // Todo #2920: Make this fail somehow.
- var rankingModel = rankingPipeline.Fit(data);
-
- // Transform the data
- var rankingPredictions = rankingModel.Transform(data);
-
- // Evaluate the model.
- var rankingMetrics = mlContext.MulticlassClassification.Evaluate(rankingPredictions);
+ Assert.Throws<ArgumentOutOfRangeException>(() => rankingPipeline.Fit(data));
 
  // Create a model training an OVA trainer with a regressor.
  var regressionTrainer = mlContext.Regression.Trainers.PoissonRegression(
  new PoissonRegression.Options { NumberOfIterations = 10, NumberOfThreads = 1, });
+ // Todo #2920: Make this fail somehow.
  var regressionPipeline = mlContext.Transforms.Concatenate("Features", Iris.Features)
  .AppendCacheCheckpoint(mlContext)
  .Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
  .Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(regressionTrainer))
  .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
 
  // Fit the invalid pipeline.
- // Todo #2920: Make this fail somehow.
- var regressionModel = regressionPipeline.Fit(data);
-
- // Transform the data
- var regressionPredictions = regressionModel.Transform(data);
-
- // Evaluate the model.
- var regressionMetrics = mlContext.MulticlassClassification.Evaluate(regressionPredictions);
+ Assert.Throws<ArgumentOutOfRangeException>(() => regressionPipeline.Fit(data));
  }
  }
 }