Resolve PR comments

Author: kere-nel

src/Microsoft.ML.FastTree/FastTree.cs

@@ -3049,6 +3049,7 @@ private enum AggregateFunction
         bool ISingleCanSaveOnnx.SaveAsOnnx(OnnxContext ctx, string[] outputNames, string featureColumn)
         {
             Host.CheckValue(ctx, nameof(ctx));
+            Host.Assert(Utils.Size(outputNames) >= 1);
 
             //Nodes.
             var nodesTreeids = new List<long>();

src/Microsoft.ML.StandardTrainers/Standard/LinearModelParameters.cs

@@ -240,6 +240,8 @@ JToken ISingleCanSavePfa.SaveAsPfa(BoundPfaContext ctx, JToken input)
         bool ISingleCanSaveOnnx.SaveAsOnnx(OnnxContext ctx, string[] outputs, string featureColumn)
         {
             Host.CheckValue(ctx, nameof(ctx));
+            Host.Assert(Utils.Size(outputs) >= 1);
+
             string opType = "LinearRegressor";
             string scoreVarName = (Utils.Size(outputs) == 2) ? outputs[1] : outputs[0]; // Get Score from PredictedLabel and/or Score columns
 

test/Microsoft.ML.Tests/OnnxConversionTest.cs

@@ -203,21 +203,24 @@ public void BinaryClassificationTrainersOnnxConversionTest()
             string dataPath = GetDataPath("breast-cancer.txt");
             // Now read the file (remember though, readers are lazy, so the actual reading will happen when the data is accessed).
             var dataView = mlContext.Data.LoadFromTextFile<BreastCancerBinaryClassification>(dataPath, separatorChar: '\t', hasHeader: true);
-            IEstimator<ITransformer>[] estimators = {
-                mlContext.BinaryClassification.Trainers.SymbolicSgdLogisticRegression(),
-                mlContext.BinaryClassification.Trainers.SgdCalibrated(),
+            List<IEstimator<ITransformer>> estimators = new List<IEstimator<ITransformer>>()
+            {
                 mlContext.BinaryClassification.Trainers.AveragedPerceptron(),
                 mlContext.BinaryClassification.Trainers.FastForest(),
-                mlContext.BinaryClassification.Trainers.LinearSvm(),
-                mlContext.BinaryClassification.Trainers.SdcaNonCalibrated(),
-                mlContext.BinaryClassification.Trainers.SgdNonCalibrated(),
                 mlContext.BinaryClassification.Trainers.FastTree(),
                 mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(),
-                mlContext.BinaryClassification.Trainers.LightGbm(),
+                mlContext.BinaryClassification.Trainers.LinearSvm(),
                 mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(),
+                mlContext.BinaryClassification.Trainers.SdcaNonCalibrated(),
                 mlContext.BinaryClassification.Trainers.SgdCalibrated(),
+                mlContext.BinaryClassification.Trainers.SgdNonCalibrated(),
                 mlContext.BinaryClassification.Trainers.SymbolicSgdLogisticRegression(),
             };
+            if (Environment.Is64BitProcess)
+            {
+                estimators.Add(mlContext.BinaryClassification.Trainers.LightGbm());
+            }
+
             var initialPipeline = mlContext.Transforms.ReplaceMissingValues("Features").
                 Append(mlContext.Transforms.NormalizeMinMax("Features"));
             foreach (var estimator in estimators)