Internal-best-friend TreeEnsemble

wschin · wschin · commit c479a062c24b · 2019-01-25T16:10:40.000-08:00
diff --git a/src/Microsoft.ML.FastTree/FastTree.cs b/src/Microsoft.ML.FastTree/FastTree.cs
@@ -54,7 +54,8 @@ public abstract class FastTreeTrainerBase<TArgs, TTransformer, TModel> :
     {
         protected readonly TArgs Args;
         protected readonly bool AllowGC;
-        protected TreeEnsemble TrainedEnsemble;
+        [BestFriend]
+        internal TreeEnsemble TrainedEnsemble;
         protected int FeatureCount;
         private protected RoleMappedData ValidData;
         /// <summary>
@@ -87,7 +88,8 @@ public abstract class FastTreeTrainerBase<TArgs, TTransformer, TModel> :
         protected double[] InitValidScores;
         protected double[][] InitTestScores;
         //protected int Iteration;
-        protected TreeEnsemble Ensemble;
+        [BestFriend]
+        internal TreeEnsemble Ensemble;
 
         protected bool HasValidSet => ValidSet != null;
 
@@ -2853,7 +2855,8 @@ public abstract class TreeEnsembleModelParameters :
         /// </summary>
         public FeatureContributionCalculator FeatureContributionCalculator => new FeatureContributionCalculator(this);
 
-        public TreeEnsembleModelParameters(IHostEnvironment env, string name, TreeEnsemble trainedEnsemble, int numFeatures, string innerArgs)
+        [BestFriend]
+        internal TreeEnsembleModelParameters(IHostEnvironment env, string name, TreeEnsemble trainedEnsemble, int numFeatures, string innerArgs)
             : base(env, name)
         {
             Host.CheckValue(trainedEnsemble, nameof(trainedEnsemble));
diff --git a/src/Microsoft.ML.FastTree/FastTreeClassification.cs b/src/Microsoft.ML.FastTree/FastTreeClassification.cs
@@ -69,7 +69,8 @@ private static VersionInfo GetVersionInfo()
 
         protected override uint VerCategoricalSplitSerialized => 0x00010005;
 
-        public FastTreeBinaryModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
+        [BestFriend]
+        internal FastTreeBinaryModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
             : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)
         {
         }
diff --git a/src/Microsoft.ML.FastTree/FastTreeRanking.cs b/src/Microsoft.ML.FastTree/FastTreeRanking.cs
@@ -1130,7 +1130,8 @@ private static VersionInfo GetVersionInfo()
 
         protected override uint VerCategoricalSplitSerialized => 0x00010005;
 
-        public FastTreeRankingModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
+        [BestFriend]
+        internal FastTreeRankingModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
             : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)
         {
         }
diff --git a/src/Microsoft.ML.FastTree/FastTreeRegression.cs b/src/Microsoft.ML.FastTree/FastTreeRegression.cs
@@ -466,7 +466,8 @@ private static VersionInfo GetVersionInfo()
 
         protected override uint VerCategoricalSplitSerialized => 0x00010005;
 
-        public FastTreeRegressionModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
+        [BestFriend]
+        internal FastTreeRegressionModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
             : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)
         {
         }
diff --git a/src/Microsoft.ML.FastTree/FastTreeTweedie.cs b/src/Microsoft.ML.FastTree/FastTreeTweedie.cs
@@ -469,7 +469,8 @@ private static VersionInfo GetVersionInfo()
 
         protected override uint VerCategoricalSplitSerialized => 0x00010003;
 
-        public FastTreeTweedieModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
+        [BestFriend]
+        internal FastTreeTweedieModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
             : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)
         {
         }
diff --git a/src/Microsoft.ML.FastTree/RandomForestClassification.cs b/src/Microsoft.ML.FastTree/RandomForestClassification.cs
@@ -79,7 +79,8 @@ private static VersionInfo GetVersionInfo()
         /// </summary>
         public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
 
-        public FastForestClassificationModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
+        [BestFriend]
+        internal FastForestClassificationModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
             : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)
         { }
 
diff --git a/src/Microsoft.ML.FastTree/RandomForestRegression.cs b/src/Microsoft.ML.FastTree/RandomForestRegression.cs
@@ -59,7 +59,8 @@ private static VersionInfo GetVersionInfo()
 
         protected override uint VerCategoricalSplitSerialized => 0x00010006;
 
-        public FastForestRegressionModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs, int samplesCount)
+        [BestFriend]
+        internal FastForestRegressionModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs, int samplesCount)
             : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)
         {
             _quantileSampleCount = samplesCount;
diff --git a/src/Microsoft.ML.FastTree/Training/BaggingProvider.cs b/src/Microsoft.ML.FastTree/Training/BaggingProvider.cs
@@ -75,7 +75,8 @@ public int GetBagCount(int numTrees, int bagSize)
         // Divides output values of leaves to bag count.
         // This brings back the final scores generated by model on a same
         // range as when we didn't use bagging
-        public void ScaleEnsembleLeaves(int numTrees, int bagSize, TreeEnsemble ensemble)
+        [BestFriend]
+        internal void ScaleEnsembleLeaves(int numTrees, int bagSize, TreeEnsemble ensemble)
         {
             int bagCount = GetBagCount(numTrees, bagSize);
             for (int t = 0; t < ensemble.NumTrees; t++)
diff --git a/src/Microsoft.ML.FastTree/Training/EnsembleCompression/IEnsembleCompressor.cs b/src/Microsoft.ML.FastTree/Training/EnsembleCompression/IEnsembleCompressor.cs
@@ -4,7 +4,8 @@
 
 namespace Microsoft.ML.Trainers.FastTree.Internal
 {
-    public interface IEnsembleCompressor<TLabel>
+    [BestFriend]
+    internal interface IEnsembleCompressor<TLabel>
     {
         void Initialize(int numTrees, Dataset trainSet, TLabel[] labels, int randomSeed);
 
diff --git a/src/Microsoft.ML.FastTree/Training/EnsembleCompression/LassoBasedEnsembleCompressor.cs b/src/Microsoft.ML.FastTree/Training/EnsembleCompression/LassoBasedEnsembleCompressor.cs
@@ -15,7 +15,8 @@ namespace Microsoft.ML.Trainers.FastTree.Internal
     /// https://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf
     /// </summary>
     /// <remarks>Author was Yasser Ganjisaffar during his internship.</remarks>
-    public class LassoBasedEnsembleCompressor : IEnsembleCompressor<short>
+    [BestFriend]
+    internal class LassoBasedEnsembleCompressor : IEnsembleCompressor<short>
     {
         // This module shouldn't consume more than 4GB of memory
         private const long MaxAvailableMemory = 4L * 1024 * 1024 * 1024;
@@ -533,7 +534,8 @@ private unsafe void LoadTargets(double[] trainScores, int bestIteration)
             }
         }
 
-        public bool Compress(IChannel ch, TreeEnsemble ensemble, double[] trainScores, int bestIteration, int maxTreesAfterCompression)
+        [BestFriend]
+        bool IEnsembleCompressor<short>.Compress(IChannel ch, TreeEnsemble ensemble, double[] trainScores, int bestIteration, int maxTreesAfterCompression)
         {
             LoadTargets(trainScores, bestIteration);
 
@@ -551,7 +553,8 @@ public bool Compress(IChannel ch, TreeEnsemble ensemble, double[] trainScores, i
             return true;
         }
 
-        public TreeEnsemble GetCompressedEnsemble()
+        [BestFriend]
+        TreeEnsemble IEnsembleCompressor<short>.GetCompressedEnsemble()
         {
             return _compressedEnsemble;
         }
diff --git a/src/Microsoft.ML.FastTree/Training/OptimizationAlgorithms/AcceleratedGradientDescent.cs b/src/Microsoft.ML.FastTree/Training/OptimizationAlgorithms/AcceleratedGradientDescent.cs
@@ -7,7 +7,8 @@ namespace Microsoft.ML.Trainers.FastTree.Internal
     //Accelerated gradient descent score tracker
     public class AcceleratedGradientDescent : GradientDescent
     {
-        public AcceleratedGradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)
+        [BestFriend]
+        internal AcceleratedGradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)
             : base(ensemble, trainData, initTrainScores, gradientWrapper)
         {
             UseFastTrainingScoresUpdate = false;
diff --git a/src/Microsoft.ML.FastTree/Training/OptimizationAlgorithms/ConjugateGradientDescent.cs b/src/Microsoft.ML.FastTree/Training/OptimizationAlgorithms/ConjugateGradientDescent.cs
@@ -11,7 +11,8 @@ public class ConjugateGradientDescent : GradientDescent
         private double[] _currentGradient;
         private double[] _currentDk;
 
-        public ConjugateGradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)
+        [BestFriend]
+        internal ConjugateGradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)
             : base(ensemble, trainData, initTrainScores, gradientWrapper)
         {
             _currentDk = new double[trainData.NumDocs];
diff --git a/src/Microsoft.ML.FastTree/Training/OptimizationAlgorithms/GradientDescent.cs b/src/Microsoft.ML.FastTree/Training/OptimizationAlgorithms/GradientDescent.cs
@@ -21,7 +21,8 @@ public class GradientDescent : OptimizationAlgorithm
         private double[] _droppedScores;
         private double[] _scores;
 
-        public GradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)
+        [BestFriend]
+        internal GradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)
             : base(ensemble, trainData, initTrainScores)
         {
             _gradientWrapper = gradientWrapper;
diff --git a/src/Microsoft.ML.FastTree/Training/OptimizationAlgorithms/NoOptimizationAlgorithm.cs b/src/Microsoft.ML.FastTree/Training/OptimizationAlgorithms/NoOptimizationAlgorithm.cs
@@ -12,7 +12,8 @@ public class RandomForestOptimizer : GradientDescent
     {
         private IGradientAdjuster _gradientWrapper;
         // REVIEW: When the FastTree appliation is decoupled with tree learner and boosting logic, this class should be removed.
-        public RandomForestOptimizer(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)
+        [BestFriend]
+        internal RandomForestOptimizer(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)
             : base(ensemble, trainData, initTrainScores, gradientWrapper)
         {
             _gradientWrapper = gradientWrapper;
diff --git a/src/Microsoft.ML.FastTree/Training/OptimizationAlgorithms/OptimizationAlgorithm.cs b/src/Microsoft.ML.FastTree/Training/OptimizationAlgorithms/OptimizationAlgorithm.cs
@@ -25,7 +25,8 @@ public abstract class OptimizationAlgorithm
         public delegate void PreScoreUpdateHandler(IChannel ch);
         public PreScoreUpdateHandler PreScoreUpdateEvent;
 
-        public TreeEnsemble Ensemble;
+        [BestFriend]
+        internal TreeEnsemble Ensemble;
 
         public ScoreTracker TrainingScores;
         public List<ScoreTracker> TrackedScores;
@@ -36,7 +37,8 @@ public abstract class OptimizationAlgorithm
         public Random DropoutRng;
         public bool UseFastTrainingScoresUpdate;
 
-        public OptimizationAlgorithm(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores)
+        [BestFriend]
+        internal OptimizationAlgorithm(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores)
         {
             Ensemble = ensemble;
             TrainingScores = ConstructScoreTracker("train", trainData, initTrainScores);
diff --git a/src/Microsoft.ML.FastTree/TreeEnsemble/TreeEnsemble.cs b/src/Microsoft.ML.FastTree/TreeEnsemble/TreeEnsemble.cs
@@ -45,7 +45,8 @@ internal TreeEnsembleView(TreeEnsemble treeEnsemble)
         }
     }
 
-    public class TreeEnsemble
+    [BestFriend]
+    internal class TreeEnsemble
     {
         /// <summary>
         /// String appended to the text representation of <see cref="TreeEnsemble"/>. This is mainly used in <see cref="ToTreeEnsembleIni"/>.
diff --git a/src/Microsoft.ML.LightGBM/LightGbmBinaryTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmBinaryTrainer.cs
@@ -55,7 +55,8 @@ private static VersionInfo GetVersionInfo()
         protected override uint VerCategoricalSplitSerialized => 0x00010005;
         public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
 
-        public LightGbmBinaryModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
+        [BestFriend]
+        internal LightGbmBinaryModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
             : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)
         {
         }
diff --git a/src/Microsoft.ML.LightGBM/LightGbmRankingTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmRankingTrainer.cs
@@ -50,7 +50,8 @@ private static VersionInfo GetVersionInfo()
         protected override uint VerCategoricalSplitSerialized => 0x00010005;
         public override PredictionKind PredictionKind => PredictionKind.Ranking;
 
-        public LightGbmRankingModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
+        [BestFriend]
+        internal LightGbmRankingModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
             : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)
         {
         }
diff --git a/src/Microsoft.ML.LightGBM/LightGbmRegressionTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmRegressionTrainer.cs
@@ -50,7 +50,8 @@ private static VersionInfo GetVersionInfo()
         protected override uint VerCategoricalSplitSerialized => 0x00010005;
         public override PredictionKind PredictionKind => PredictionKind.Regression;
 
-        public LightGbmRegressionModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
+        [BestFriend]
+        internal LightGbmRegressionModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
             : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)
         {
         }

Original file line number	Diff line number	Diff line change
`@@ -69,7 +69,8 @@ private static VersionInfo GetVersionInfo()`
`69`	`69`
`70`	`70`	`protected override uint VerCategoricalSplitSerialized => 0x00010005;`
`71`	`71`
`72`		`- public FastTreeBinaryModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)`
	`72`	`+ [BestFriend]`
	`73`	`+ internal FastTreeBinaryModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)`
`73`	`74`	`: base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)`
`74`	`75`	`{`
`75`	`76`	`}`
Original file line number	Diff line number	Diff line change
`@@ -1130,7 +1130,8 @@ private static VersionInfo GetVersionInfo()`
`1130`	`1130`
`1131`	`1131`	`protected override uint VerCategoricalSplitSerialized => 0x00010005;`
`1132`	`1132`
`1133`		`- public FastTreeRankingModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)`
	`1133`	`+ [BestFriend]`
	`1134`	`+ internal FastTreeRankingModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)`
`1134`	`1135`	`: base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)`
`1135`	`1136`	`{`
`1136`	`1137`	`}`
Original file line number	Diff line number	Diff line change
`@@ -466,7 +466,8 @@ private static VersionInfo GetVersionInfo()`
`466`	`466`
`467`	`467`	`protected override uint VerCategoricalSplitSerialized => 0x00010005;`
`468`	`468`
`469`		`- public FastTreeRegressionModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)`
	`469`	`+ [BestFriend]`
	`470`	`+ internal FastTreeRegressionModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)`
`470`	`471`	`: base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)`
`471`	`472`	`{`
`472`	`473`	`}`
Original file line number	Diff line number	Diff line change
`@@ -469,7 +469,8 @@ private static VersionInfo GetVersionInfo()`
`469`	`469`
`470`	`470`	`protected override uint VerCategoricalSplitSerialized => 0x00010003;`
`471`	`471`
`472`		`- public FastTreeTweedieModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)`
	`472`	`+ [BestFriend]`
	`473`	`+ internal FastTreeTweedieModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs)`
`473`	`474`	`: base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)`
`474`	`475`	`{`
`475`	`476`	`}`
Original file line number	Diff line number	Diff line change
`@@ -59,7 +59,8 @@ private static VersionInfo GetVersionInfo()`
`59`	`59`
`60`	`60`	`protected override uint VerCategoricalSplitSerialized => 0x00010006;`
`61`	`61`
`62`		`- public FastForestRegressionModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs, int samplesCount)`
	`62`	`+ [BestFriend]`
	`63`	`+ internal FastForestRegressionModelParameters(IHostEnvironment env, TreeEnsemble trainedEnsemble, int featureCount, string innerArgs, int samplesCount)`
`63`	`64`	`: base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)`
`64`	`65`	`{`
`65`	`66`	`_quantileSampleCount = samplesCount;`
Original file line number	Diff line number	Diff line change
`@@ -4,7 +4,8 @@`
`4`	`4`
`5`	`5`	`namespace Microsoft.ML.Trainers.FastTree.Internal`
`6`	`6`	`{`
`7`		`- public interface IEnsembleCompressor<TLabel>`
	`7`	`+ [BestFriend]`
	`8`	`+ internal interface IEnsembleCompressor<TLabel>`
`8`	`9`	`{`
`9`	`10`	`void Initialize(int numTrees, Dataset trainSet, TLabel[] labels, int randomSeed);`
`10`	`11`
Original file line number	Diff line number	Diff line change
`@@ -15,7 +15,8 @@ namespace Microsoft.ML.Trainers.FastTree.Internal`
`15`	`15`	`/// https://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf`
`16`	`16`	`/// </summary>`
`17`	`17`	`/// <remarks>Author was Yasser Ganjisaffar during his internship.</remarks>`
`18`		`- public class LassoBasedEnsembleCompressor : IEnsembleCompressor<short>`
	`18`	`+ [BestFriend]`
	`19`	`+ internal class LassoBasedEnsembleCompressor : IEnsembleCompressor<short>`
`19`	`20`	`{`
`20`	`21`	`// This module shouldn't consume more than 4GB of memory`
`21`	`22`	`private const long MaxAvailableMemory = 4L * 1024 * 1024 * 1024;`
`@@ -533,7 +534,8 @@ private unsafe void LoadTargets(double[] trainScores, int bestIteration)`
`533`	`534`	`}`
`534`	`535`	`}`
`535`	`536`
`536`		`- public bool Compress(IChannel ch, TreeEnsemble ensemble, double[] trainScores, int bestIteration, int maxTreesAfterCompression)`
	`537`	`+ [BestFriend]`
	`538`	`+ bool IEnsembleCompressor<short>.Compress(IChannel ch, TreeEnsemble ensemble, double[] trainScores, int bestIteration, int maxTreesAfterCompression)`
`537`	`539`	`{`
`538`	`540`	`LoadTargets(trainScores, bestIteration);`
`539`	`541`
`@@ -551,7 +553,8 @@ public bool Compress(IChannel ch, TreeEnsemble ensemble, double[] trainScores, i`
`551`	`553`	`return true;`
`552`	`554`	`}`
`553`	`555`
`554`		`- public TreeEnsemble GetCompressedEnsemble()`
	`556`	`+ [BestFriend]`
	`557`	`+ TreeEnsemble IEnsembleCompressor<short>.GetCompressedEnsemble()`
`555`	`558`	`{`
`556`	`559`	`return _compressedEnsemble;`
`557`	`560`	`}`
Original file line number	Diff line number	Diff line change
`@@ -7,7 +7,8 @@ namespace Microsoft.ML.Trainers.FastTree.Internal`
`7`	`7`	`//Accelerated gradient descent score tracker`
`8`	`8`	`public class AcceleratedGradientDescent : GradientDescent`
`9`	`9`	`{`
`10`		`- public AcceleratedGradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)`
	`10`	`+ [BestFriend]`
	`11`	`+ internal AcceleratedGradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)`
`11`	`12`	`: base(ensemble, trainData, initTrainScores, gradientWrapper)`
`12`	`13`	`{`
`13`	`14`	`UseFastTrainingScoresUpdate = false;`
Original file line number	Diff line number	Diff line change
`@@ -11,7 +11,8 @@ public class ConjugateGradientDescent : GradientDescent`
`11`	`11`	`private double[] _currentGradient;`
`12`	`12`	`private double[] _currentDk;`
`13`	`13`
`14`		`- public ConjugateGradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)`
	`14`	`+ [BestFriend]`
	`15`	`+ internal ConjugateGradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)`
`15`	`16`	`: base(ensemble, trainData, initTrainScores, gradientWrapper)`
`16`	`17`	`{`
`17`	`18`	`_currentDk = new double[trainData.NumDocs];`
Original file line number	Diff line number	Diff line change
`@@ -21,7 +21,8 @@ public class GradientDescent : OptimizationAlgorithm`
`21`	`21`	`private double[] _droppedScores;`
`22`	`22`	`private double[] _scores;`
`23`	`23`
`24`		`- public GradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)`
	`24`	`+ [BestFriend]`
	`25`	`+ internal GradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)`
`25`	`26`	`: base(ensemble, trainData, initTrainScores)`
`26`	`27`	`{`
`27`	`28`	`_gradientWrapper = gradientWrapper;`