The test pipeline for consuming an ONNX model would fail due to the

Score column being named "Score0". The ONNX model will rename the output
columns by design, therefore a different class with the ColumnName of
"Score0" is needed. This fixes the test pipeline to address this issue.

Fixes dotnet#2981

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

@@ -3,6 +3,7 @@
 // See the LICENSE file in the project root for more information.
 
 using System.IO;
+using Microsoft.ML.Data;
 using Microsoft.ML.Functional.Tests.Datasets;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;
@@ -14,6 +15,12 @@
 
 namespace Microsoft.ML.Functional.Tests
 {
+    internal sealed class OnnxScoreColumn
+    {
+        [ColumnName("Score0")]
+        public float[] Score { get; set; }
+    }
+
     public class ONNX : BaseTestClass
     {
         public ONNX(ITestOutputHelper output) : base(output)
@@ -51,15 +58,9 @@ public void SaveOnnxModelLoadAndScoreFastTree()
             var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(modelPath);
             var onnxModel = onnxEstimator.Fit(data);
 
-            // TODO #2980: ONNX outputs don't match the outputs of the model, so we must hand-correct this for now.
-            // TODO #2981: ONNX models cannot be fit as part of a pipeline, so we must use a workaround like this.
-            var onnxWorkaroundPipeline = onnxModel.Append(
-                mlContext.Transforms.CopyColumns("Score", "Score0").Fit(onnxModel.Transform(data)));
-
             // Create prediction engine and test predictions.
             var originalPredictionEngine = mlContext.Model.CreatePredictionEngine<HousingRegression, ScoreColumn>(model);
-            // TODO #2982: ONNX produces vector types and not the original output type.
-            var onnxPredictionEngine = mlContext.Model.CreatePredictionEngine<HousingRegression, VectorScoreColumn>(onnxWorkaroundPipeline);
+            var onnxPredictionEngine = mlContext.Model.CreatePredictionEngine<HousingRegression, OnnxScoreColumn>(onnxModel);
 
             // Take a handful of examples out of the dataset and compute predictions.
             var dataEnumerator = mlContext.Data.CreateEnumerable<HousingRegression>(mlContext.Data.TakeRows(data, 5), false);