No more superclass referencing subclass

Author: TomFinley

src/Microsoft.ML.FastTree/FastTree.cs

@@ -85,6 +85,8 @@ public abstract class FastTreeTrainerBase<TArgs, TPredictor> :
 
         public bool HasCategoricalFeatures => Utils.Size(CategoricalFeatures) > 0;
 
+        private protected virtual bool NeedCalibration => false;
+
         private protected FastTreeTrainerBase(IHostEnvironment env, TArgs args)
             : base(env, RegisterName)
         {
@@ -93,7 +95,7 @@ private protected FastTreeTrainerBase(IHostEnvironment env, TArgs args)
             // The discretization step renders this trainer non-parametric, and therefore it does not need normalization.
             // Also since it builds its own internal discretized columnar structures, it cannot benefit from caching.
             // Finally, even the binary classifiers, being logitboost, tend to not benefit from external calibration.
-            Info = new TrainerInfo(normalization: false, caching: false, calibration: this is FastForestClassification);
+            Info = new TrainerInfo(normalization: false, caching: false, calibration: NeedCalibration);
             int numThreads = Args.NumThreads ?? Environment.ProcessorCount;
             if (Host.ConcurrencyFactor > 0 && numThreads > Host.ConcurrencyFactor)
             {

src/Microsoft.ML.FastTree/GamTrainer.cs

@@ -107,6 +107,7 @@ public sealed class Arguments : ArgumentsBase
         internal const string ShortName = "gam";
 
         public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
+        private protected override bool NeedCalibration => true;
 
         public BinaryClassificationGamTrainer(IHostEnvironment env, Arguments args)
             : base(env, args) { }
@@ -225,6 +226,7 @@ public abstract class ArgumentsBase : LearnerInputBaseWithWeight
         protected int[] FeatureMap;
 
         public override TrainerInfo Info { get; }
+        private protected virtual bool NeedCalibration => false;
 
         private protected GamTrainerBase(IHostEnvironment env, TArgs args)
             : base(env, RegisterName)
@@ -240,7 +242,7 @@ private protected GamTrainerBase(IHostEnvironment env, TArgs args)
             Host.CheckParam(0 < args.NumIterations, nameof(args.NumIterations), "Must be positive.");
 
             Args = args;
-            Info = new TrainerInfo(normalization: false, calibration: this is BinaryClassificationGamTrainer, caching: false);
+            Info = new TrainerInfo(normalization: false, calibration: NeedCalibration, caching: false);
             _gainConfidenceInSquaredStandardDeviations = Math.Pow(ProbabilityFunctions.Probit(1 - (1 - Args.GainConfidenceLevel) * 0.5), 2);
             _entropyCoefficient = Args.EntropyCoefficient * 1e-6;
             int numThreads = args.NumThreads ?? Environment.ProcessorCount;

src/Microsoft.ML.FastTree/RandomForestClassification.cs

@@ -130,11 +130,11 @@ public sealed class Arguments : FastForestArgumentsBase
         private bool[] _trainSetLabels;
 
         public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
+        private protected override bool NeedCalibration => true;
 
         public FastForestClassification(IHostEnvironment env, Arguments args)
             : base(env, args)
         {
-            
         }
 
         public override IPredictorWithFeatureWeights<Float> Train(TrainContext context)