private protected stuff, other comments

TomFinley · TomFinley · commit acc45b9d5b78 · 2018-07-12T16:01:18.000-07:00
diff --git a/src/Microsoft.ML.Core/Utilities/ObjectPool.cs b/src/Microsoft.ML.Core/Utilities/ObjectPool.cs
@@ -39,7 +39,7 @@ public abstract class ObjectPoolBase<T>
         public int Count => _pool.Count;
         public int NumCreated { get { return _numCreated; } }
 
-        protected internal ObjectPoolBase()
+        private protected ObjectPoolBase()
         {
             _pool = new ConcurrentBag<T>();
         }
diff --git a/src/Microsoft.ML.Data/Commands/TrainCommand.cs b/src/Microsoft.ML.Data/Commands/TrainCommand.cs
@@ -235,10 +235,10 @@ public static IPredictor Train(IHostEnvironment env, IChannel ch, RoleMappedData
         }
 
         public static IPredictor Train(IHostEnvironment env, IChannel ch, RoleMappedData data, ITrainer trainer, string name, RoleMappedData validData,
-            SubComponent<ICalibratorTrainer, SignatureCalibrator> calibrator, int maxCalibrationExamples, bool? cacheData, IPredictor inpPredictor = null)
+            SubComponent<ICalibratorTrainer, SignatureCalibrator> calibrator, int maxCalibrationExamples, bool? cacheData, IPredictor inputPredictor = null)
         {
             ICalibratorTrainer caliTrainer = !calibrator.IsGood() ? null : calibrator.CreateInstance(env);
-            return TrainCore(env, ch, data, trainer, name, validData, caliTrainer, maxCalibrationExamples, cacheData, inpPredictor);
+            return TrainCore(env, ch, data, trainer, name, validData, caliTrainer, maxCalibrationExamples, cacheData, inputPredictor);
         }
 
         private static IPredictor TrainCore(IHostEnvironment env, IChannel ch, RoleMappedData data, ITrainer trainer, string name, RoleMappedData validData,
diff --git a/src/Microsoft.ML.Data/Training/EarlyStoppingCriteria.cs b/src/Microsoft.ML.Data/Training/EarlyStoppingCriteria.cs
@@ -139,9 +139,9 @@ public class Arguments : ArgumentsBase
             public int WindowSize = 5;
         }
 
-        protected internal Queue<Float> PastScores;
+        protected Queue<Float> PastScores;
 
-        internal MovingWindowEarlyStoppingCriterion(Arguments args, bool lowerIsBetter)
+        private protected MovingWindowEarlyStoppingCriterion(Arguments args, bool lowerIsBetter)
             : base(args, lowerIsBetter)
         {
             Contracts.CheckUserArg(0 <= Args.Threshold && args.Threshold <= 1, nameof(args.Threshold), "Must be in range [0,1].");
diff --git a/src/Microsoft.ML.Data/Training/TrainerBase.cs b/src/Microsoft.ML.Data/Training/TrainerBase.cs
@@ -7,7 +7,11 @@ namespace Microsoft.ML.Runtime.Training
     public abstract class TrainerBase<TPredictor> : ITrainer<TPredictor>, ITrainerEx
         where TPredictor : IPredictor
     {
-        public const string NoTrainingInstancesMessage = "No valid training instances found, all instances have missing features.";
+        /// <summary>
+        /// A standard string to use in errors or warnings by subclasses, to communicate the idea that no valid
+        /// instances were able to be found.
+        /// </summary>
+        protected const string NoTrainingInstancesMessage = "No valid training instances found, all instances have missing features.";
 
         protected IHost Host { get; }
 
diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseDiverseSelector.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseDiverseSelector.cs
@@ -23,7 +23,7 @@ public abstract class DiverseSelectorArguments : ArgumentsBase
         private readonly IComponentFactory<IDiversityMeasure<TOutput>> _diversityMetricType;
         private ConcurrentDictionary<FeatureSubsetModel<IPredictorProducing<TOutput>>, TOutput[]> _predictions;
 
-        protected internal BaseDiverseSelector(IHostEnvironment env, DiverseSelectorArguments args, string name,
+        private protected BaseDiverseSelector(IHostEnvironment env, DiverseSelectorArguments args, string name,
             IComponentFactory<IDiversityMeasure<TOutput>> diversityMetricType)
             : base(args, env, name)
         {
diff --git a/src/Microsoft.ML.Ensemble/Trainer/Binary/EnsembleTrainer.cs b/src/Microsoft.ML.Ensemble/Trainer/Binary/EnsembleTrainer.cs
@@ -62,7 +62,7 @@ public EnsembleTrainer(IHostEnvironment env, Arguments args)
 
         public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
 
-        protected internal override TScalarPredictor CreatePredictor(List<FeatureSubsetModel<TScalarPredictor>> models)
+        private protected override TScalarPredictor CreatePredictor(List<FeatureSubsetModel<TScalarPredictor>> models)
         {
             if (models.All(m => m.Predictor is TDistPredictor))
                 return new EnsembleDistributionPredictor(Host, PredictionKind, CreateModels<TDistPredictor>(models), Combiner);
diff --git a/src/Microsoft.ML.Ensemble/Trainer/EnsembleTrainerBase.cs b/src/Microsoft.ML.Ensemble/Trainer/EnsembleTrainerBase.cs
@@ -59,17 +59,17 @@ public abstract class ArgumentsBase : LearnerInputBaseWithLabel
 
         private const int DefaultNumModels = 50;
         /// <summary> Command-line arguments </summary>
-        protected internal readonly ArgumentsBase Args;
-        protected internal readonly int NumModels;
+        private protected readonly ArgumentsBase Args;
+        private protected readonly int NumModels;
 
         /// <summary> Ensemble members </summary>
-        protected internal readonly ITrainer<IPredictorProducing<TOutput>>[] Trainers;
+        private protected readonly ITrainer<IPredictorProducing<TOutput>>[] Trainers;
 
         private readonly ISubsetSelector _subsetSelector;
-        protected internal ISubModelSelector<TOutput> SubModelSelector;
-        protected internal IOutputCombiner<TOutput> Combiner;
+        private protected ISubModelSelector<TOutput> SubModelSelector;
+        private protected IOutputCombiner<TOutput> Combiner;
 
-        protected internal EnsembleTrainerBase(ArgumentsBase args, IHostEnvironment env, string name)
+        private protected EnsembleTrainerBase(ArgumentsBase args, IHostEnvironment env, string name)
             : base(env, name)
         {
             Args = args;
@@ -133,7 +133,7 @@ private TPredictor TrainCore(IChannel ch, RoleMappedData data)
                 validationDataSetProportion = Math.Max(validationDataSetProportion, stackingTrainer.ValidationDatasetProportion);
 
             var needMetrics = Args.ShowMetrics || Combiner is IWeightedAverager;
-            var Models = new List<FeatureSubsetModel<IPredictorProducing<TOutput>>>();
+            var models = new List<FeatureSubsetModel<IPredictorProducing<TOutput>>>();
 
             _subsetSelector.Initialize(data, NumModels, Args.BatchSize, validationDataSetProportion);
             int batchNumber = 1;
@@ -179,16 +179,16 @@ private TPredictor TrainCore(IChannel ch, RoleMappedData data)
                 if (stackingTrainer != null)
                     stackingTrainer.Train(modelsList, _subsetSelector.GetTestData(null, batch), Host);
 
-                Models.AddRange(modelsList);
-                int modelSize = Utils.Size(Models);
+                models.AddRange(modelsList);
+                int modelSize = Utils.Size(models);
                 if (modelSize < Utils.Size(Trainers))
                     ch.Warning("{0} of {1} trainings failed.", Utils.Size(Trainers) - modelSize, Utils.Size(Trainers));
                 ch.Check(modelSize > 0, "Ensemble training resulted in no valid models.");
             }
-            return CreatePredictor(Models);
+            return CreatePredictor(models);
         }
 
-        protected internal abstract TPredictor CreatePredictor(List<FeatureSubsetModel<IPredictorProducing<TOutput>>> models);
+        private protected abstract TPredictor CreatePredictor(List<FeatureSubsetModel<IPredictorProducing<TOutput>>> models);
 
         private bool EnsureMinimumFeaturesSelected(Subset subset)
         {
@@ -203,7 +203,7 @@ private bool EnsureMinimumFeaturesSelected(Subset subset)
             return false;
         }
 
-        protected internal virtual void PrintMetrics(IChannel ch, List<FeatureSubsetModel<IPredictorProducing<TOutput>>> models)
+        private protected virtual void PrintMetrics(IChannel ch, List<FeatureSubsetModel<IPredictorProducing<TOutput>>> models)
         {
             // REVIEW: The formatting of this method is bizarre and seemingly not even self-consistent
             // w.r.t. its usage of |. Is this intentional?
@@ -216,7 +216,7 @@ protected internal virtual void PrintMetrics(IChannel ch, List<FeatureSubsetMode
                 ch.Info("{0}{1}", string.Join("", model.Metrics.Select(m => string.Format("| {0} |", m.Value))), model.Predictor.GetType().Name);
         }
 
-        protected internal static FeatureSubsetModel<T>[] CreateModels<T>(List<FeatureSubsetModel<IPredictorProducing<TOutput>>> models) where T : IPredictor
+        private protected static FeatureSubsetModel<T>[] CreateModels<T>(List<FeatureSubsetModel<IPredictorProducing<TOutput>>> models) where T : IPredictor
         {
             var subsetModels = new FeatureSubsetModel<T>[models.Count];
             for (int i = 0; i < models.Count; i++)
diff --git a/src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs b/src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs
@@ -63,7 +63,7 @@ public MulticlassDataPartitionEnsembleTrainer(IHostEnvironment env, Arguments ar
 
         public override PredictionKind PredictionKind => PredictionKind.MultiClassClassification;
 
-        protected internal override EnsembleMultiClassPredictor CreatePredictor(List<FeatureSubsetModel<TVectorPredictor>> models)
+        private protected override EnsembleMultiClassPredictor CreatePredictor(List<FeatureSubsetModel<TVectorPredictor>> models)
         {
             return new EnsembleMultiClassPredictor(Host, CreateModels<TVectorPredictor>(models), Combiner as IMultiClassOutputCombiner);
         }
diff --git a/src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs b/src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs
@@ -57,7 +57,7 @@ public RegressionEnsembleTrainer(IHostEnvironment env, Arguments args)
 
         public override PredictionKind PredictionKind => PredictionKind.Regression;
 
-        protected internal override TScalarPredictor CreatePredictor(List<FeatureSubsetModel<TScalarPredictor>> models)
+        private protected override TScalarPredictor CreatePredictor(List<FeatureSubsetModel<TScalarPredictor>> models)
         {
             return new EnsemblePredictor(Host, PredictionKind, CreateModels<TScalarPredictor>(models), Combiner);
         }
diff --git a/src/Microsoft.ML.FastTree/FastTree.cs b/src/Microsoft.ML.FastTree/FastTree.cs
@@ -89,7 +89,7 @@ public abstract class FastTreeTrainerBase<TArgs, TPredictor> :
 
         public override bool SupportsValidation => true;
 
-        protected internal FastTreeTrainerBase(IHostEnvironment env, TArgs args)
+        private protected FastTreeTrainerBase(IHostEnvironment env, TArgs args)
             : base(env, RegisterName)
         {
             Host.CheckValue(args, nameof(args));
@@ -1880,7 +1880,7 @@ private void MakeBoundariesAndCheckLabels(out long missingInstances, out long to
                         missingInstances = cursor.BadFeaturesRowCount;
                     }
 
-                    ch.Check(totalInstances > 0, TrainerBase<IPredictor>.NoTrainingInstancesMessage);
+                    ch.Check(totalInstances > 0, "All instances skipped due to missing features.");
 
                     if (missingInstances > 0)
                         ch.Warning("Skipped {0} instances with missing features during training", missingInstances);
@@ -2806,7 +2806,7 @@ public abstract class FastTreePredictionWrapper :
         public bool CanSavePfa => true;
         public bool CanSaveOnnx => true;
 
-        protected internal FastTreePredictionWrapper(IHostEnvironment env, string name, Ensemble trainedEnsemble, int numFeatures, string innerArgs)
+        protected FastTreePredictionWrapper(IHostEnvironment env, string name, Ensemble trainedEnsemble, int numFeatures, string innerArgs)
             : base(env, name)
         {
             Host.CheckValue(trainedEnsemble, nameof(trainedEnsemble));
diff --git a/src/Microsoft.ML.FastTree/GamTrainer.cs b/src/Microsoft.ML.FastTree/GamTrainer.cs
@@ -75,7 +75,7 @@ internal override void CheckLabel(RoleMappedData data)
             data.CheckRegressionLabel();
         }
 
-        protected internal override RegressionGamPredictor CreatePredictor()
+        private protected override RegressionGamPredictor CreatePredictor()
         {
             return new RegressionGamPredictor(Host, InputLength, TrainSet, BinEffects, FeatureMap);
         }
@@ -137,7 +137,7 @@ private bool[] ConvertTargetsToBool(double[] targets)
             return boolArray;
         }
 
-        protected internal override BinaryClassGamPredictor CreatePredictor()
+        private protected override BinaryClassGamPredictor CreatePredictor()
         {
             return new BinaryClassGamPredictor(Host, InputLength, TrainSet, BinEffects, FeatureMap);
         }
@@ -231,7 +231,7 @@ public abstract class ArgumentsBase : LearnerInputBaseWithWeight
 
         public override bool WantCaching => false;
 
-        protected internal GamTrainerBase(IHostEnvironment env, TArgs args)
+        private protected GamTrainerBase(IHostEnvironment env, TArgs args)
             : base(env, RegisterName)
         {
             Contracts.CheckValue(env, nameof(env));
@@ -276,7 +276,7 @@ public sealed override TPredictor Train(TrainContext context)
             }
         }
 
-        protected internal abstract TPredictor CreatePredictor();
+        private protected abstract TPredictor CreatePredictor();
 
         internal abstract void CheckLabel(RoleMappedData data);
 
@@ -571,7 +571,7 @@ public abstract class GamPredictorBase : PredictorBase<Float>,
 
         public ColumnType OutputType => NumberType.Float;
 
-        protected internal GamPredictorBase(IHostEnvironment env, string name, int inputLength, Dataset trainSet, double[][] binEffects, int[] featureMap)
+        private protected GamPredictorBase(IHostEnvironment env, string name, int inputLength, Dataset trainSet, double[][] binEffects, int[] featureMap)
             : base(env, name)
         {
             Host.CheckValue(trainSet, nameof(trainSet));
diff --git a/src/Microsoft.ML.FastTree/Training/Test.cs b/src/Microsoft.ML.FastTree/Training/Test.cs
@@ -207,8 +207,8 @@ public class TestHistory : Test
         protected IList<TestResult[]> History;
         protected int Iteration { get; private set; }
 
-        public TestResult BestResult { get; protected internal set; }
-        public int BestIteration { get; protected internal set; }
+        public TestResult BestResult { get; private protected set; }
+        public int BestIteration { get; private protected set; }
 
         // scenarioWithoutHistory - simple test scenario we want to track the history and look for best iteration
         // lossIndex - index of lossFunction in case Test returns more than one loss (default should be 0)
diff --git a/src/Microsoft.ML.LightGBM/LightGbmBinaryTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmBinaryTrainer.cs
@@ -93,7 +93,7 @@ public LightGbmBinaryTrainer(IHostEnvironment env, LightGbmArguments args)
         {
         }
 
-        protected internal override IPredictorWithFeatureWeights<float> CreatePredictor()
+        private protected override IPredictorWithFeatureWeights<float> CreatePredictor()
         {
             Host.Check(TrainedEnsemble != null, "The predictor cannot be created before training is complete");
             var innerArgs = LightGbmInterfaceUtils.JoinParameters(Options);
diff --git a/src/Microsoft.ML.LightGBM/LightGbmMulticlassTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmMulticlassTrainer.cs
@@ -54,7 +54,7 @@ private LightGbmBinaryPredictor CreateBinaryPredictor(int classID, string innerA
             return new LightGbmBinaryPredictor(Host, GetBinaryEnsemble(classID), FeatureCount, innerArgs);
         }
 
-        protected internal override OvaPredictor CreatePredictor()
+        private protected override OvaPredictor CreatePredictor()
         {
             Host.Check(TrainedEnsemble != null, "The predictor cannot be created before training is complete.");
 
diff --git a/src/Microsoft.ML.LightGBM/LightGbmRankingTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmRankingTrainer.cs
@@ -102,7 +102,7 @@ protected override void CheckDataValid(IChannel ch, RoleMappedData data)
             }
         }
 
-        protected internal override LightGbmRankingPredictor CreatePredictor()
+        private protected override LightGbmRankingPredictor CreatePredictor()
         {
             Host.Check(TrainedEnsemble != null, "The predictor cannot be created before training is complete");
             var innerArgs = LightGbmInterfaceUtils.JoinParameters(Options);
diff --git a/src/Microsoft.ML.LightGBM/LightGbmRegressionTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmRegressionTrainer.cs
@@ -85,7 +85,7 @@ public LightGbmRegressorTrainer(IHostEnvironment env, LightGbmArguments args)
         {
         }
 
-        protected internal override LightGbmRegressionPredictor CreatePredictor()
+        private protected override LightGbmRegressionPredictor CreatePredictor()
         {
             Host.Check(TrainedEnsemble != null,
                 "The predictor cannot be created before training is complete");
diff --git a/src/Microsoft.ML.LightGBM/LightGbmTrainerBase.cs b/src/Microsoft.ML.LightGBM/LightGbmTrainerBase.cs
@@ -37,26 +37,26 @@ private sealed class CategoricalMetaData
             public bool[] IsCategoricalFeature;
         }
 
-        protected internal readonly LightGbmArguments Args;
+        private protected readonly LightGbmArguments Args;
 
         /// <summary>
         /// Stores argumments as objects to convert them to invariant string type in the end so that 
         /// the code is culture agnostic. When retrieving key value from this dictionary as string 
         /// please convert to string invariant by string.Format(CultureInfo.InvariantCulture, "{0}", Option[key]). 
         /// </summary>
-        protected internal readonly Dictionary<string, object> Options;
-        protected internal readonly IParallel ParallelTraining;
+        private protected readonly Dictionary<string, object> Options;
+        private protected readonly IParallel ParallelTraining;
 
         // Store _featureCount and _trainedEnsemble to construct predictor.
-        protected internal int FeatureCount;
-        protected internal FastTree.Internal.Ensemble TrainedEnsemble;
+        private protected int FeatureCount;
+        private protected FastTree.Internal.Ensemble TrainedEnsemble;
 
         public override bool NeedNormalization => false;
         public override bool NeedCalibration => false;
         public override bool WantCaching => false;
         public override bool SupportsValidation => true;
 
-        protected internal LightGbmTrainerBase(IHostEnvironment env, LightGbmArguments args, string name)
+        private protected LightGbmTrainerBase(IHostEnvironment env, LightGbmArguments args, string name)
             : base(env, name)
         {
             Host.CheckValue(args, nameof(args));
@@ -851,7 +851,7 @@ private static int GetNumSampleRow(int numRow, int numCol)
             return ret;
         }
 
-        protected internal abstract TPredictor CreatePredictor();
+        private protected abstract TPredictor CreatePredictor();
 
         /// <summary>
         /// This function will be called before training. It will check the label/group and add parameters for specific applications.
diff --git a/src/Microsoft.ML.StandardLearners/Standard/LinearClassificationTrainer.cs b/src/Microsoft.ML.StandardLearners/Standard/LinearClassificationTrainer.cs
@@ -985,9 +985,10 @@ public StandardArrayDualsTable(int length)
                 _duals = new Float[length];
             }
 
-            public override Float this[long index] {
-                get { return _duals[(int)index]; }
-                set { _duals[(int)index] = value; }
+            public override Float this[long index]
+            {
+                get => _duals[(int)index];
+                set => _duals[(int)index] = value;
             }
 
             public override void ApplyAt(long index, Visitor manip)
@@ -1003,21 +1004,18 @@ private sealed class BigArrayDualsTable : DualsTableBase
         {
             private BigArray<Float> _duals;
 
-            public override long Length { get { return _duals.Length; } }
+            public override long Length => _duals.Length;
 
             public BigArrayDualsTable(long length)
             {
                 Contracts.Assert(length <= 1L << 50);
                 _duals = new BigArray<Float>(length);
             }
 
-            public override Float this[long index] {
-                get {
-                    return _duals[index];
-                }
-                set {
-                    _duals[index] = value;
-                }
+            public override Float this[long index]
+            {
+                get => _duals[index];
+                set => _duals[index] = value;
             }
 
             public override void ApplyAt(long index, Visitor manip)
@@ -1104,7 +1102,7 @@ protected Func<UInt128, long, long> GetIndexFromIdAndRowGetter(IdToIdxLookup idT
         /// the table growing operation initializes a new larger bucket and rehash the existing entries to
         /// the new bucket. Such operation has an expected complexity proportional to the size.
         /// </summary>
-        protected internal sealed class IdToIdxLookup
+        protected sealed class IdToIdxLookup
         {
             // Utilizing this struct gives better cache behavior than using parallel arrays.
             private struct Entry
@@ -1131,7 +1129,7 @@ public Entry(long itNext, UInt128 value)
             /// <summary>
             /// Gets the count of id entries.
             /// </summary>
-            public long Count { get { return _count; } }
+            public long Count => _count;
 
             /// <summary>
             /// Initializes an instance of the <see cref="IdToIdxLookup"/> class with the specified size.

Original file line number	Diff line number	Diff line change
`@@ -39,7 +39,7 @@ public abstract class ObjectPoolBase<T>`
`39`	`39`	`public int Count => _pool.Count;`
`40`	`40`	`public int NumCreated { get { return _numCreated; } }`
`41`	`41`
`42`		`- protected internal ObjectPoolBase()`
	`42`	`+ private protected ObjectPoolBase()`
`43`	`43`	`{`
`44`	`44`	`_pool = new ConcurrentBag<T>();`
`45`	`45`	`}`
Original file line number	Diff line number	Diff line change
`@@ -235,10 +235,10 @@ public static IPredictor Train(IHostEnvironment env, IChannel ch, RoleMappedData`
`235`	`235`	`}`
`236`	`236`
`237`	`237`	`public static IPredictor Train(IHostEnvironment env, IChannel ch, RoleMappedData data, ITrainer trainer, string name, RoleMappedData validData,`
`238`		`- SubComponent<ICalibratorTrainer, SignatureCalibrator> calibrator, int maxCalibrationExamples, bool? cacheData, IPredictor inpPredictor = null)`
	`238`	`+ SubComponent<ICalibratorTrainer, SignatureCalibrator> calibrator, int maxCalibrationExamples, bool? cacheData, IPredictor inputPredictor = null)`
`239`	`239`	`{`
`240`	`240`	`ICalibratorTrainer caliTrainer = !calibrator.IsGood() ? null : calibrator.CreateInstance(env);`
`241`		`- return TrainCore(env, ch, data, trainer, name, validData, caliTrainer, maxCalibrationExamples, cacheData, inpPredictor);`
	`241`	`+ return TrainCore(env, ch, data, trainer, name, validData, caliTrainer, maxCalibrationExamples, cacheData, inputPredictor);`
`242`	`242`	`}`
`243`	`243`
`244`	`244`	`private static IPredictor TrainCore(IHostEnvironment env, IChannel ch, RoleMappedData data, ITrainer trainer, string name, RoleMappedData validData,`
Original file line number	Diff line number	Diff line change
`@@ -139,9 +139,9 @@ public class Arguments : ArgumentsBase`
`139`	`139`	`public int WindowSize = 5;`
`140`	`140`	`}`
`141`	`141`
`142`		`- protected internal Queue<Float> PastScores;`
	`142`	`+ protected Queue<Float> PastScores;`
`143`	`143`
`144`		`- internal MovingWindowEarlyStoppingCriterion(Arguments args, bool lowerIsBetter)`
	`144`	`+ private protected MovingWindowEarlyStoppingCriterion(Arguments args, bool lowerIsBetter)`
`145`	`145`	`: base(args, lowerIsBetter)`
`146`	`146`	`{`
`147`	`147`	`Contracts.CheckUserArg(0 <= Args.Threshold && args.Threshold <= 1, nameof(args.Threshold), "Must be in range [0,1].");`
Original file line number	Diff line number	Diff line change
`@@ -23,7 +23,7 @@ public abstract class DiverseSelectorArguments : ArgumentsBase`
`23`	`23`	`private readonly IComponentFactory<IDiversityMeasure<TOutput>> _diversityMetricType;`
`24`	`24`	`private ConcurrentDictionary<FeatureSubsetModel<IPredictorProducing<TOutput>>, TOutput[]> _predictions;`
`25`	`25`
`26`		`- protected internal BaseDiverseSelector(IHostEnvironment env, DiverseSelectorArguments args, string name,`
	`26`	`+ private protected BaseDiverseSelector(IHostEnvironment env, DiverseSelectorArguments args, string name,`
`27`	`27`	`IComponentFactory<IDiversityMeasure<TOutput>> diversityMetricType)`
`28`	`28`	`: base(args, env, name)`
`29`	`29`	`{`
Original file line number	Diff line number	Diff line change
`@@ -62,7 +62,7 @@ public EnsembleTrainer(IHostEnvironment env, Arguments args)`
`62`	`62`
`63`	`63`	`public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;`
`64`	`64`
`65`		`- protected internal override TScalarPredictor CreatePredictor(List<FeatureSubsetModel<TScalarPredictor>> models)`
	`65`	`+ private protected override TScalarPredictor CreatePredictor(List<FeatureSubsetModel<TScalarPredictor>> models)`
`66`	`66`	`{`
`67`	`67`	`if (models.All(m => m.Predictor is TDistPredictor))`
`68`	`68`	`return new EnsembleDistributionPredictor(Host, PredictionKind, CreateModels<TDistPredictor>(models), Combiner);`
Original file line number	Diff line number	Diff line change
`@@ -63,7 +63,7 @@ public MulticlassDataPartitionEnsembleTrainer(IHostEnvironment env, Arguments ar`
`63`	`63`
`64`	`64`	`public override PredictionKind PredictionKind => PredictionKind.MultiClassClassification;`
`65`	`65`
`66`		`- protected internal override EnsembleMultiClassPredictor CreatePredictor(List<FeatureSubsetModel<TVectorPredictor>> models)`
	`66`	`+ private protected override EnsembleMultiClassPredictor CreatePredictor(List<FeatureSubsetModel<TVectorPredictor>> models)`
`67`	`67`	`{`
`68`	`68`	`return new EnsembleMultiClassPredictor(Host, CreateModels<TVectorPredictor>(models), Combiner as IMultiClassOutputCombiner);`
`69`	`69`	`}`
Original file line number	Diff line number	Diff line change
`@@ -57,7 +57,7 @@ public RegressionEnsembleTrainer(IHostEnvironment env, Arguments args)`
`57`	`57`
`58`	`58`	`public override PredictionKind PredictionKind => PredictionKind.Regression;`
`59`	`59`
`60`		`- protected internal override TScalarPredictor CreatePredictor(List<FeatureSubsetModel<TScalarPredictor>> models)`
	`60`	`+ private protected override TScalarPredictor CreatePredictor(List<FeatureSubsetModel<TScalarPredictor>> models)`
`61`	`61`	`{`
`62`	`62`	`return new EnsemblePredictor(Host, PredictionKind, CreateModels<TScalarPredictor>(models), Combiner);`
`63`	`63`	`}`