Lockdown Microsoft.ML.FastTree public surface

src/Microsoft.ML.Data/Dirty/PredictorInterfaces.cs

@@ -68,22 +68,6 @@ internal interface IQuantileValueMapper
         ValueMapper<VBuffer<float>, VBuffer<float>> GetMapper(float[] quantiles);
     }
 
-    /// <summary>
-    /// Interface for quantile distribution
-    /// </summary>
-    /// <typeparam name="TResult">Type of statistics result</typeparam>
-    [BestFriend]
-    internal interface IQuantileDistribution<TResult> : IDistribution<TResult>, ISampleableDistribution<TResult>
-    {
-        TResult Median { get; }
-
-        /// <summary>
-        /// Returns an estimate of the p-th quantile, the data value where proportionately p of the data has value
-        /// less than or equal to the returned value.
-        /// </summary>
-        TResult GetQuantile(float p);
-    }
-
     [BestFriend]
     internal interface ISampleableDistribution<TResult> : IDistribution<TResult>
     {

src/Microsoft.ML.FastTree/BoostingFastTree.cs

@@ -14,11 +14,11 @@ public abstract class BoostingFastTreeTrainerBase<TArgs, TTransformer, TModel> :
         where TArgs : BoostedTreeArgs, new()
         where TModel : class
     {
-        protected 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)
         {
         }
 
-        protected BoostingFastTreeTrainerBase(IHostEnvironment env,
+        private protected BoostingFastTreeTrainerBase(IHostEnvironment env,
             SchemaShape.Column label,
             string featureColumn,
             string weightColumn,
@@ -32,7 +32,7 @@ protected BoostingFastTreeTrainerBase(IHostEnvironment env,
             FastTreeTrainerOptions.LearningRates = learningRate;
         }
 
-        protected override void CheckArgs(IChannel ch)
+        private protected override void CheckArgs(IChannel ch)
         {
             if (FastTreeTrainerOptions.OptimizationAlgorithm == BoostedTreeArgs.OptimizationAlgorithmType.AcceleratedGradientDescent)
                 FastTreeTrainerOptions.UseLineSearch = true;
@@ -102,7 +102,7 @@ private protected override OptimizationAlgorithm ConstructOptimizationAlgorithm(
             return optimizationAlgorithm;
         }
 
-        protected override IGradientAdjuster MakeGradientWrapper(IChannel ch)
+        private protected override IGradientAdjuster MakeGradientWrapper(IChannel ch)
         {
             if (!FastTreeTrainerOptions.BestStepRankingRegressionTrees)
                 return base.MakeGradientWrapper(ch);
@@ -115,7 +115,7 @@ protected override IGradientAdjuster MakeGradientWrapper(IChannel ch)
                 return new BestStepRegressionGradientWrapper();
         }
 
-        protected override bool ShouldStop(IChannel ch, ref IEarlyStoppingCriterion earlyStoppingRule, ref int bestIteration)
+        private protected override bool ShouldStop(IChannel ch, ref IEarlyStoppingCriterion earlyStoppingRule, ref int bestIteration)
         {
             if (FastTreeTrainerOptions.EarlyStoppingRule == null)
                 return false;
@@ -147,7 +147,7 @@ protected override bool ShouldStop(IChannel ch, ref IEarlyStoppingCriterion earl
             return shouldStop;
         }
 
-        protected override int GetBestIteration(IChannel ch)
+        private protected override int GetBestIteration(IChannel ch)
         {
             int bestIteration = Ensemble.NumTrees;
             if (!FastTreeTrainerOptions.WriteLastEnsemble && PruningTest != null)
@@ -161,15 +161,15 @@ protected override int GetBestIteration(IChannel ch)
         /// <summary>
         /// Retrieves max tree output if best regression step option is active or returns negative value otherwise.
         /// </summary>
-        protected double BsrMaxTreeOutput()
+        private protected double BsrMaxTreeOutput()
         {
             if (FastTreeTrainerOptions.BestStepRankingRegressionTrees)
                 return FastTreeTrainerOptions.MaxTreeOutput;
             else
                 return -1;
         }
 
-        protected override bool ShouldRandomStartOptimizer()
+        private protected override bool ShouldRandomStartOptimizer()
         {
             return FastTreeTrainerOptions.RandomStart;
         }

src/Microsoft.ML.FastTree/Dataset/Dataset.cs

@@ -13,7 +13,7 @@ namespace Microsoft.ML.Trainers.FastTree
     /// <summary>
     /// A dataset of features.
     /// </summary>
-    public sealed class Dataset
+    internal sealed class Dataset
     {
         private readonly DatasetSkeleton _datasetSkeleton;
         private readonly FeatureFlockBase[] _flocks;

src/Microsoft.ML.FastTree/Dataset/DatasetUtils.cs

@@ -10,7 +10,7 @@ namespace Microsoft.ML.Trainers.FastTree
     /// <summary>
     /// Loads training/validation/test sets from file
     /// </summary>
-    public static class DatasetUtils
+    internal static class DatasetUtils
     {
         private const string DefaultTransformFormat = "Name={0}\nTransform=Linear\nSlope=1\nIntercept=0";
 

src/Microsoft.ML.FastTree/Dataset/Feature.cs

@@ -10,7 +10,7 @@ namespace Microsoft.ML.Trainers.FastTree
     /// <summary>
     /// Represents a binned feature
     /// </summary>
-    public abstract class Feature
+    internal abstract class Feature
     {
         private IntArray _bins;
 
@@ -190,7 +190,7 @@ public IntArray Bins
         }
     }
 
-    public sealed class TsvFeature : Feature
+    internal sealed class TsvFeature : Feature
     {
         private readonly uint[] _valueMap;
         private string _name;

src/Microsoft.ML.FastTree/Dataset/FeatureFlock.cs

@@ -23,7 +23,7 @@ namespace Microsoft.ML.Trainers.FastTree
     /// are then used in <see cref="LeastSquaresRegressionTreeLearner"/> to find splitting on which bin will yield the
     /// best least squares solution
     /// </summary>
-    public readonly struct PerBinStats
+    internal readonly struct PerBinStats
     {
         /// <summary>Sum of all target values in a partition for the bin.</summary>
         public readonly Double SumTargets;
@@ -967,7 +967,7 @@ protected sealed override void SubtractCore(SufficientStatsBase other)
     /// <see cref="Dataset.MapFeatureToFlockAndSubFeature"/> to see some details of this
     /// dataset-wide versus flock-wide feature index.
     /// </summary>
-    public abstract class FeatureFlockBase
+    internal abstract class FeatureFlockBase
     {
         /// <summary>
         /// The number of features contained within this flock.

src/Microsoft.ML.FastTree/Dataset/FeatureHistogram.cs

@@ -16,7 +16,7 @@ namespace Microsoft.ML.Trainers.FastTree
     /// <summary>
     /// Class to represent statistics of the feature used by LeastSquaresRegressionTreeLearner
     /// </summary>
-    public sealed class FeatureHistogram
+    internal sealed class FeatureHistogram
     {
         public readonly FloatType[] SumTargetsByBin;
         public readonly double[] SumWeightsByBin;
@@ -138,7 +138,7 @@ public void SumupWeighted(int numDocsInLeaf, double sumTargets, double sumWeight
         }
     }
 
-    public sealed class SumupInputData
+    internal sealed class SumupInputData
     {
         public int TotalCount;
         public double SumTargets;

src/Microsoft.ML.FastTree/Dataset/IntArray.cs

@@ -13,13 +13,13 @@ namespace Microsoft.ML.Trainers.FastTree
 #else
 #endif
 
-    public enum IntArrayType { Dense, Sparse, Repeat, Segmented, Current };
-    public enum IntArrayBits { Bits32 = 32, Bits16 = 16, Bits10 = 10, Bits8 = 8, Bits4 = 4, Bits1 = 1, Bits0 = 0 };
+    internal enum IntArrayType { Dense, Sparse, Repeat, Segmented, Current };
+    internal enum IntArrayBits { Bits32 = 32, Bits16 = 16, Bits10 = 10, Bits8 = 8, Bits4 = 4, Bits1 = 1, Bits0 = 0 };
 
     /// <summary>
     /// An object representing an array of integers
     /// </summary>
-    public abstract class IntArray : IEnumerable<int>
+    internal abstract class IntArray : IEnumerable<int>
     {
         // The level of compression to use with features.
         // 0x1 - Use 10 bit.
@@ -360,7 +360,7 @@ public IntArray Compress(uint[] workarray = null)
     /// <summary>
     /// Interface for objects that can index into an <see cref="IntArray"/>, but only with a non-decreasing sequence of indices.
     /// </summary>
-    public interface IIntArrayForwardIndexer
+    internal interface IIntArrayForwardIndexer
     {
         /// <summary>
         /// Gets the element at the given index.