Resolve merge conflicts and some more PR related comments.

Author: codemzs

src/Microsoft.ML.FastTree/BoostingFastTree.cs

@@ -14,11 +14,11 @@ public abstract class BoostingFastTreeTrainerBase<TArgs, TTransformer, TModel> :
         where TArgs : BoostedTreeArgs, new()
         where TModel : class
     {
-        internal BoostingFastTreeTrainerBase(IHostEnvironment env, TArgs args, SchemaShape.Column label) : base(env, args, label)
+        private protected BoostingFastTreeTrainerBase(IHostEnvironment env, TArgs args, SchemaShape.Column label) : base(env, args, label)
         {
         }
 
-        internal BoostingFastTreeTrainerBase(IHostEnvironment env,
+        private protected BoostingFastTreeTrainerBase(IHostEnvironment env,
             SchemaShape.Column label,
             string featureColumn,
             string weightColumn,
@@ -161,7 +161,7 @@ private protected override int GetBestIteration(IChannel ch)
         /// <summary>
         /// Retrieves max tree output if best regression step option is active or returns negative value otherwise.
         /// </summary>
-        internal double BsrMaxTreeOutput()
+        private protected double BsrMaxTreeOutput()
         {
             if (FastTreeTrainerOptions.BestStepRankingRegressionTrees)
                 return FastTreeTrainerOptions.MaxTreeOutput;

src/Microsoft.ML.FastTree/GamClassification.cs

@@ -112,7 +112,7 @@ private protected override CalibratedModelParametersBase<BinaryClassificationGam
             return new ValueMapperCalibratedModelParameters<BinaryClassificationGamModelParameters, PlattCalibrator>(Host, predictor, calibrator);
         }
 
-        internal override ObjectiveFunctionBase CreateObjectiveFunction()
+        private protected override ObjectiveFunctionBase CreateObjectiveFunction()
         {
             return new FastTreeBinaryClassificationTrainer.ObjectiveImpl(
                 TrainSet,
@@ -129,7 +129,7 @@ internal override ObjectiveFunctionBase CreateObjectiveFunction()
             );
         }
 
-        internal override void DefinePruningTest()
+        private protected override void DefinePruningTest()
         {
             var validTest = new BinaryClassificationTest(ValidSetScore,
                 ConvertTargetsToBool(ValidSet.Targets), _sigmoidParameter);

src/Microsoft.ML.FastTree/GamModelParameters.cs

@@ -115,7 +115,7 @@ private protected GamModelParametersBase(IHostEnvironment env, string name,
             _outputType = NumberDataViewType.Single;
         }
 
-        internal GamModelParametersBase(IHostEnvironment env, string name, ModelLoadContext ctx)
+        private protected GamModelParametersBase(IHostEnvironment env, string name, ModelLoadContext ctx)
             : base(env, name)
         {
             Host.CheckValue(ctx, nameof(ctx));

src/Microsoft.ML.FastTree/GamRegression.cs

@@ -74,12 +74,12 @@ private protected override RegressionGamModelParameters TrainModelCore(TrainCont
             return new RegressionGamModelParameters(Host, BinUpperBounds, BinEffects, MeanEffect, InputLength, FeatureMap);
         }
 
-        internal override ObjectiveFunctionBase CreateObjectiveFunction()
+        private protected override ObjectiveFunctionBase CreateObjectiveFunction()
         {
             return new FastTreeRegressionTrainer.ObjectiveImpl(TrainSet, Args);
         }
 
-        internal override void DefinePruningTest()
+        private protected override void DefinePruningTest()
         {
             var validTest = new RegressionTest(ValidSetScore, Args.PruningMetrics);
             // Because we specify pruning metrics as L2 by default, the results array will have 1 value

src/Microsoft.ML.FastTree/GamTrainer.cs

@@ -110,22 +110,22 @@ public abstract class OptionsBase : LearnerInputBaseWithWeight
         private const string RegisterName = "GamTraining";
 
         //Parameters of training
-        internal readonly TArgs Args;
+        private protected readonly TArgs Args;
         private readonly double _gainConfidenceInSquaredStandardDeviations;
         private readonly double _entropyCoefficient;
 
         //Dataset information
-        internal Dataset TrainSet;
-        internal Dataset ValidSet;
+        private protected Dataset TrainSet;
+        private protected Dataset ValidSet;
         /// <summary>
         /// Whether a validation set was passed in
         /// </summary>
-        internal bool HasValidSet => ValidSet != null;
-        internal ScoreTracker TrainSetScore;
-        internal ScoreTracker ValidSetScore;
-        internal TestHistory PruningTest;
-        internal int PruningLossIndex;
-        internal int InputLength;
+        private protected bool HasValidSet => ValidSet != null;
+        private protected ScoreTracker TrainSetScore;
+        private protected ScoreTracker ValidSetScore;
+        private protected TestHistory PruningTest;
+        private protected int PruningLossIndex;
+        private protected int InputLength;
         private LeastSquaresRegressionTreeLearner.LeafSplitCandidates _leafSplitCandidates;
         private SufficientStatsBase[] _histogram;
         private ILeafSplitStatisticsCalculator _leafSplitHelper;
@@ -136,10 +136,10 @@ public abstract class OptionsBase : LearnerInputBaseWithWeight
         private SubGraph _subGraph;
 
         //Results of training
-        internal double MeanEffect;
-        internal double[][] BinEffects;
-        internal double[][] BinUpperBounds;
-        internal int[] FeatureMap;
+        private protected double MeanEffect;
+        private protected double[][] BinEffects;
+        private protected double[][] BinUpperBounds;
+        private protected int[] FeatureMap;
 
         public override TrainerInfo Info { get; }
         private protected virtual bool NeedCalibration => false;
@@ -224,7 +224,7 @@ private void DefineScoreTrackers()
                 ValidSetScore = new ScoreTracker("valid", ValidSet, null);
         }
 
-        internal abstract void DefinePruningTest();
+        private protected abstract void DefinePruningTest();
 
         private protected abstract void CheckLabel(RoleMappedData data);
 
@@ -520,7 +520,7 @@ private void CenterGraph()
         /// Process bins such that only bin upper bounds and bin effects remain where
         /// the effect changes.
         /// </summary>
-        internal void CreateEfficientBinning()
+        private protected void CreateEfficientBinning()
         {
             BinUpperBounds = new double[TrainSet.NumFeatures][];
             var newBinEffects = new List<double>();
@@ -595,7 +595,7 @@ private void InitializeThreads()
             ThreadTaskManager.Initialize(numThreads);
         }
 
-        internal abstract ObjectiveFunctionBase CreateObjectiveFunction();
+        private protected abstract ObjectiveFunctionBase CreateObjectiveFunction();
 
         private class LeafSplitHelper : ILeafSplitStatisticsCalculator
         {

src/Microsoft.ML.FastTree/RandomForest.cs

@@ -14,7 +14,7 @@ public abstract class RandomForestTrainerBase<TArgs, TTransformer, TModel> : Fas
         /// <summary>
         /// Constructor invoked by the maml code-path.
         /// </summary>
-        internal RandomForestTrainerBase(IHostEnvironment env, TArgs args, SchemaShape.Column label, bool quantileEnabled = false)
+        private protected RandomForestTrainerBase(IHostEnvironment env, TArgs args, SchemaShape.Column label, bool quantileEnabled = false)
             : base(env, args, label)
         {
             _quantileEnabled = quantileEnabled;
@@ -23,7 +23,7 @@ internal RandomForestTrainerBase(IHostEnvironment env, TArgs args, SchemaShape.C
         /// <summary>
         /// Constructor invoked by the API code-path.
         /// </summary>
-        internal RandomForestTrainerBase(IHostEnvironment env,
+        private protected RandomForestTrainerBase(IHostEnvironment env,
             SchemaShape.Column label,
             string featureColumn,
             string weightColumn,