enabling scanning the constructors with non-public visibility, and re…

src/Microsoft.ML.Core/ComponentModel/ComponentCatalog.cs

@@ -454,9 +454,9 @@ private static bool TryGetIniters(Type instType, Type loaderType, Type[] parmTyp
             var parmTypesWithEnv = Utils.Concat(new Type[1] { typeof(IHostEnvironment) }, parmTypes);
             if (Utils.Size(parmTypes) == 0 && (getter = FindInstanceGetter(instType, loaderType)) != null)
                 return true;
-            if (instType.IsAssignableFrom(loaderType) && (ctor = loaderType.GetConstructor(parmTypes ?? Type.EmptyTypes)) != null)
+            if (instType.IsAssignableFrom(loaderType) && (ctor = loaderType.GetConstructor(BindingFlags.Instance | BindingFlags.Public | BindingFlags.NonPublic, null, parmTypes ?? Type.EmptyTypes, null)) != null)
                 return true;
-            if (instType.IsAssignableFrom(loaderType) && (ctor = loaderType.GetConstructor(parmTypesWithEnv ?? Type.EmptyTypes)) != null)
+            if (instType.IsAssignableFrom(loaderType) && (ctor = loaderType.GetConstructor(BindingFlags.Instance | BindingFlags.Public | BindingFlags.NonPublic, null, parmTypesWithEnv ?? Type.EmptyTypes, null)) != null)
             {
                 requireEnvironment = true;
                 return true;

src/Microsoft.ML.FastTree/FastTreeClassification.cs

@@ -142,7 +142,7 @@ public FastTreeBinaryClassificationTrainer(IHostEnvironment env, string labelCol
         /// <summary>
         /// Initializes a new instance of <see cref="FastTreeBinaryClassificationTrainer"/> by using the legacy <see cref="Arguments"/> class.
         /// </summary>
-        public FastTreeBinaryClassificationTrainer(IHostEnvironment env, Arguments args)
+        internal FastTreeBinaryClassificationTrainer(IHostEnvironment env, Arguments args)
             : base(env, args, MakeLabelColumn(args.LabelColumn))
         {
             _outputColumns = new[]

src/Microsoft.ML.FastTree/FastTreeRanking.cs

@@ -82,7 +82,7 @@ public FastTreeRankingTrainer(IHostEnvironment env, string labelColumn, string f
         /// <summary>
         /// Initializes a new instance of <see cref="FastTreeRankingTrainer"/> by using the legacy <see cref="Arguments"/> class.
         /// </summary>
-        public FastTreeRankingTrainer(IHostEnvironment env, Arguments args)
+        internal FastTreeRankingTrainer(IHostEnvironment env, Arguments args)
                 : base(env, args, MakeLabelColumn(args.LabelColumn))
         {
             _outputColumns = new[]

src/Microsoft.ML.FastTree/FastTreeRegression.cs

@@ -75,7 +75,7 @@ public FastTreeRegressionTrainer(IHostEnvironment env, string labelColumn, strin
         /// <summary>
         /// Initializes a new instance of <see cref="FastTreeRegressionTrainer"/> by using the legacy <see cref="Arguments"/> class.
         /// </summary>
-        public FastTreeRegressionTrainer(IHostEnvironment env, Arguments args)
+        internal FastTreeRegressionTrainer(IHostEnvironment env, Arguments args)
             : base(env, args, MakeLabelColumn(args.LabelColumn))
         {
             _outputColumns = new[]

src/Microsoft.ML.FastTree/FastTreeTweedie.cs

@@ -67,7 +67,7 @@ public FastTreeTweedieTrainer(IHostEnvironment env, string labelColumn, string f
         /// <summary>
         /// Initializes a new instance of <see cref="FastTreeTweedieTrainer"/> by using the legacy <see cref="Arguments"/> class.
         /// </summary>
-        public FastTreeTweedieTrainer(IHostEnvironment env, Arguments args)
+        internal FastTreeTweedieTrainer(IHostEnvironment env, Arguments args)
             : base(env, args, MakeLabelColumn(args.LabelColumn))
         {
             Initialize();

src/Microsoft.ML.FastTree/GamClassification.cs

@@ -46,7 +46,7 @@ public sealed class Arguments : ArgumentsBase
         public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
         private protected override bool NeedCalibration => true;
 
-        public BinaryClassificationGamTrainer(IHostEnvironment env, Arguments args)
+        internal BinaryClassificationGamTrainer(IHostEnvironment env, Arguments args)
             : base(env, args)
         {
             _sigmoidParameter = 1;

src/Microsoft.ML.FastTree/GamRegression.cs

@@ -40,7 +40,7 @@ public partial class Arguments : ArgumentsBase
 
         public override PredictionKind PredictionKind => PredictionKind.Regression;
 
-        public RegressionGamTrainer(IHostEnvironment env, Arguments args)
+        internal RegressionGamTrainer(IHostEnvironment env, Arguments args)
             : base(env, args) { }
 
         internal override void CheckLabel(RoleMappedData data)

src/Microsoft.ML.FastTree/RandomForestClassification.cs

@@ -158,7 +158,7 @@ public FastForestClassification(IHostEnvironment env, string labelColumn, string
         /// <summary>
         /// Initializes a new instance of <see cref="FastForestClassification"/> by using the legacy <see cref="Arguments"/> class.
         /// </summary>
-        public FastForestClassification(IHostEnvironment env, Arguments args)
+        internal FastForestClassification(IHostEnvironment env, Arguments args)
             : base(env, args, MakeLabelColumn(args.LabelColumn))
         {
             _outputColumns = new[]

src/Microsoft.ML.FastTree/RandomForestRegression.cs

@@ -178,7 +178,7 @@ public FastForestRegression(IHostEnvironment env, string labelColumn, string fea
         /// <summary>
         /// Initializes a new instance of <see cref="FastForestRegression"/> by using the legacy <see cref="Arguments"/> class.
         /// </summary>
-        public FastForestRegression(IHostEnvironment env, Arguments args)
+        internal FastForestRegression(IHostEnvironment env, Arguments args)
             : base(env, args, MakeLabelColumn(args.LabelColumn), true)
         {
             _outputColumns = new[]

src/Microsoft.ML.StandardLearners/Standard/MultiClass/Pkpd.cs

@@ -73,7 +73,7 @@ public sealed class Arguments : ArgumentsBase
         /// Developers should instantiate <see cref="Pkpd"/> by supplying the trainer argument directly to the <see cref="Pkpd"/> constructor
         /// using the other public constructor.
         /// </summary>
-        public Pkpd(IHostEnvironment env, Arguments args)
+        internal Pkpd(IHostEnvironment env, Arguments args)
             : base(env, args, LoadNameValue)
         {
         }

src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs

@@ -70,7 +70,7 @@ protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSc
             };
         }
 
-        public SdcaMultiClassTrainer(IHostEnvironment env, Arguments args)
+        internal SdcaMultiClassTrainer(IHostEnvironment env, Arguments args)
             : this(env, args, args.FeatureColumn, args.LabelColumn)
         {
         }

src/Microsoft.ML.StandardLearners/Standard/SdcaRegression.cs

@@ -67,7 +67,7 @@ public SdcaRegressionTrainer(IHostEnvironment env, Arguments args, string featur
             };
         }
 
-        public SdcaRegressionTrainer(IHostEnvironment env, Arguments args)
+        internal SdcaRegressionTrainer(IHostEnvironment env, Arguments args)
             : this(env, args, args.FeatureColumn, args.LabelColumn)
         {
         }

test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs

@@ -122,7 +122,7 @@ public void TrainSentiment()
                     }, text);
 
                 // Train
-                var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments() { MaxIterations = 20 });
+                var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments() { MaxIterations = 20 }, "Features", "Label");
                 var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
 
                 var predicted = trainer.Train(trainRoles);

test/Microsoft.ML.Tests/Scenarios/Api/DecomposableTrainAndPredict.cs

@@ -30,7 +30,7 @@ void DecomposableTrainAndPredict()
                 var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.irisData.trainFilename)));
                 var term = TermTransform.Create(env, loader, "Label");
                 var concat = new ConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth").Transform(term);
-                var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 });
+                var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 }, "Features", "Label");
 
                 IDataView trainData = trainer.Info.WantCaching ? (IDataView)new CacheDataView(env, concat, prefetch: null) : concat;
                 var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");

test/Microsoft.ML.Tests/Scenarios/Api/Estimators/DecomposableTrainAndPredict.cs

@@ -30,7 +30,7 @@ void New_DecomposableTrainAndPredict()
                 var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
                 var term = TermTransform.Create(env, loader, "Label");
                 var concat = new ConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth").Transform(term);
-                var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 });
+                var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 }, "Features", "Label");
 
                 IDataView trainData = trainer.Info.WantCaching ? (IDataView)new CacheDataView(env, concat, prefetch: null) : concat;
                 var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");

test/Microsoft.ML.Tests/Scenarios/Api/Extensibility.cs

@@ -35,7 +35,7 @@ void Extensibility()
                 var concat = new ConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
                     .Transform(term);
 
-                var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 });
+                var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 }, "Features", "Label");
 
                 IDataView trainData = trainer.Info.WantCaching ? (IDataView)new CacheDataView(env, concat, prefetch: null) : concat;
                 var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");

test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/IrisPlantClassificationTests.cs

@@ -46,7 +46,7 @@ public void TrainAndPredictIrisModelUsingDirectInstantiationTest()
                 pipeline = NormalizeTransform.CreateMinMaxNormalizer(env, pipeline, "Features");
 
                 // Train
-                var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments() { NumThreads = 1 } );
+                var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments() { NumThreads = 1 }, "Features", "Label");
 
                 // Explicity adding CacheDataView since caching is not working though trainer has 'Caching' On/Auto
                 var cached = new CacheDataView(env, pipeline, prefetch: null);