general cleanup

Author: artidoro

src/Microsoft.ML.Data/DataLoadSave/Text/TextLoader.cs

@@ -905,7 +905,7 @@ private Schema ComputeOutputSchema()
 
         internal const string Summary = "Loads text data file.";
 
-        public const string LoaderSignature = "TextLoader";
+        internal const string LoaderSignature = "TextLoader";
 
         private const uint VerForceVectorSupported = 0x0001000A;
         private const uint VersionNoMinCount = 0x0001000C;

src/Microsoft.ML.Data/DataLoadSave/TransformWrapper.cs

@@ -20,7 +20,7 @@ namespace Microsoft.ML.Data
     // It needs to become internal.
     public sealed class TransformWrapper : ITransformer
     {
-        public const string LoaderSignature = "TransformWrapper";
+        internal const string LoaderSignature = "TransformWrapper";
         private const string TransformDirTemplate = "Step_{0:000}";
 
         private readonly IHost _host;

src/Microsoft.ML.Data/Prediction/CalibratorCatalog.cs

@@ -224,7 +224,7 @@ protected VersionInfo GetVersionInfo()
                 loaderAssemblyName: typeof(CalibratorTransformer<>).Assembly.FullName);
         }
 
-    private sealed class Mapper<TCalibrator> : MapperBase
+        private sealed class Mapper<TCalibrator> : MapperBase
         where TCalibrator : class, ICalibrator
         {
             private TCalibrator _calibrator;

src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs

@@ -257,8 +257,8 @@ private protected GenericScorer GetGenericScorer()
     public sealed class AnomalyPredictionTransformer<TModel> : SingleFeaturePredictionTransformerBase<TModel>
         where TModel : class
     {
-        public readonly string ThresholdColumn;
-        public readonly float Threshold;
+        internal readonly string ThresholdColumn;
+        internal readonly float Threshold;
 
         [BestFriend]
         internal AnomalyPredictionTransformer(IHostEnvironment env, TModel model, Schema inputSchema, string featureColumn,
@@ -326,8 +326,8 @@ private static VersionInfo GetVersionInfo()
     public sealed class BinaryPredictionTransformer<TModel> : SingleFeaturePredictionTransformerBase<TModel>
         where TModel : class
     {
-        public readonly string ThresholdColumn;
-        public readonly float Threshold;
+        internal readonly string ThresholdColumn;
+        internal readonly float Threshold;
 
         [BestFriend]
         internal BinaryPredictionTransformer(IHostEnvironment env, TModel model, Schema inputSchema, string featureColumn,

src/Microsoft.ML.FastTree/FastTreeRegression.cs

@@ -36,7 +36,7 @@ namespace Microsoft.ML.Trainers.FastTree
     public sealed partial class FastTreeRegressionTrainer
         : BoostingFastTreeTrainerBase<FastTreeRegressionTrainer.Options, RegressionPredictionTransformer<FastTreeRegressionModelParameters>, FastTreeRegressionModelParameters>
     {
-        public const string LoadNameValue = "FastTreeRegression";
+        internal const string LoadNameValue = "FastTreeRegression";
         internal const string UserNameValue = "FastTree (Boosted Trees) Regression";
         internal const string Summary = "Trains gradient boosted decision trees to fit target values using least-squares.";
         internal const string ShortName = "ftr";
@@ -142,7 +142,7 @@ private protected override OptimizationAlgorithm ConstructOptimizationAlgorithm(
         /// </summary>
         /// <param name="set">The dataset</param>
         /// <returns>The list of regression targets, or null if <paramref name="set"/> was null</returns>
-        public static float[] GetDatasetRegressionLabels(Dataset set)
+        internal static float[] GetDatasetRegressionLabels(Dataset set)
         {
             if (set == null)
                 return null;

src/Microsoft.ML.FastTree/FastTreeTweedie.cs

@@ -149,7 +149,7 @@ private protected override OptimizationAlgorithm ConstructOptimizationAlgorithm(
         /// </summary>
         /// <param name="set">The dataset</param>
         /// <returns>The list of regression targets, or null if <paramref name="set"/> was null</returns>
-        public static float[] GetDatasetRegressionLabels(Dataset set)
+        internal static float[] GetDatasetRegressionLabels(Dataset set)
         {
             if (set == null)
                 return null;

src/Microsoft.ML.LightGBM/LightGbmRankingTrainer.cs

@@ -75,9 +75,9 @@ private static LightGbmRankingModelParameters Create(IHostEnvironment env, Model
     /// <include file='doc.xml' path='doc/members/member[@name="LightGBM"]/*' />
     public sealed class LightGbmRankingTrainer : LightGbmTrainerBase<float, RankingPredictionTransformer<LightGbmRankingModelParameters>, LightGbmRankingModelParameters>
     {
-        public const string UserName = "LightGBM Ranking";
-        public const string LoadNameValue = "LightGBMRanking";
-        public const string ShortName = "LightGBMRank";
+        internal const string UserName = "LightGBM Ranking";
+        internal const string LoadNameValue = "LightGBMRanking";
+        internal const string ShortName = "LightGBMRank";
 
         public override PredictionKind PredictionKind => PredictionKind.Ranking;
 

src/Microsoft.ML.OnnxTransform/DnnImageFeaturizerTransform.cs

@@ -61,7 +61,7 @@ public sealed class DnnImageFeaturizerEstimator : IEstimator<TransformerChain<Co
         /// For an example, see Microsoft.ML.DnnImageFeaturizer.ResNet18 </param>
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
         /// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
-        public DnnImageFeaturizerEstimator(IHostEnvironment env, string outputColumnName, Func<DnnImageFeaturizerInput, EstimatorChain<ColumnCopyingTransformer>> modelFactory, string inputColumnName = null)
+        internal DnnImageFeaturizerEstimator(IHostEnvironment env, string outputColumnName, Func<DnnImageFeaturizerInput, EstimatorChain<ColumnCopyingTransformer>> modelFactory, string inputColumnName = null)
         {
             _modelChain = modelFactory(new DnnImageFeaturizerInput(outputColumnName, inputColumnName ?? outputColumnName, env, new DnnImageModelSelector()));
         }

src/Microsoft.ML.OnnxTransform/OnnxCatalog.cs

@@ -2,6 +2,7 @@
 // The .NET Foundation licenses this file to you under the MIT license.
 // See the LICENSE file in the project root for more information.
 
+using System;
 using Microsoft.ML.Data;
 using Microsoft.ML.Transforms;
 
@@ -61,5 +62,19 @@ public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog
             bool fallbackToCpu = false)
         => new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnNames, inputColumnNames, modelFile, gpuDeviceId, fallbackToCpu);
 
+        /// <summary>
+        /// Applies a pre-trained DNN model to featurize an image.
+        /// </summary>
+        /// <param name="catalog">The transform's catalog.</param>
+        /// <param name="outputColumnName">The name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
+        /// <param name="modelFactory">An extension method on the <see cref="DnnImageModelSelector"/> that creates a chain of two
+        /// <see cref="OnnxScoringEstimator"/> (one for preprocessing and one with a pretrained image DNN) with specific models
+        /// included in a package together with that extension method.</param>
+        /// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
+        public static DnnImageFeaturizerEstimator DnnFeaturizeImage(this TransformsCatalog catalog,
+            string outputColumnName,
+            Func<DnnImageFeaturizerInput, EstimatorChain<ColumnCopyingTransformer>> modelFactory,
+            string inputColumnName = null)
+        => new DnnImageFeaturizerEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, modelFactory, inputColumnName);
     }
 }

src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachineModelParameters.cs

@@ -285,17 +285,17 @@ public float[] GetLatentWeights()
 
     public sealed class FieldAwareFactorizationMachinePredictionTransformer : PredictionTransformerBase<FieldAwareFactorizationMachineModelParameters>
     {
-        public const string LoaderSignature = "FAFMPredXfer";
+        internal const string LoaderSignature = "FAFMPredXfer";
 
         /// <summary>
         /// The name of the feature column used by the prediction transformer.
         /// </summary>
-        public IReadOnlyList<string> FeatureColumns { get; }
+        internal IReadOnlyList<string> FeatureColumns { get; }
 
         /// <summary>
         /// The type of the feature columns.
         /// </summary>
-        public IReadOnlyList<ColumnType> FeatureColumnTypes { get; }
+        internal IReadOnlyList<ColumnType> FeatureColumnTypes { get; }
 
         private readonly string _thresholdColumn;
         private readonly float _threshold;