Conversion of ITrainer.Train returns predictor, accepts +TrainContext

src/Microsoft.ML.Core/Prediction/ITrainer.cs

@@ -2,9 +2,8 @@
 // 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 System.Collections.Generic;
-using System.IO;
+using Microsoft.ML.Runtime.Data;
 
 namespace Microsoft.ML.Runtime
 {
@@ -27,151 +26,79 @@ namespace Microsoft.ML.Runtime
     public delegate void SignatureSequenceTrainer();
     public delegate void SignatureMatrixRecommendingTrainer();
 
-    /// <summary>
-    /// Interface to provide extra information about a trainer.
-    /// </summary>
-    public interface ITrainerEx : ITrainer
-    {
-        // REVIEW: Ideally trainers should be able to communicate
-        // something about the type of data they are capable of being trained
-        // on, e.g., what ColumnKinds they want, how many of each, of what type,
-        // etc. This interface seems like the most natural conduit for that sort
-        // of extra information.
-
-        // REVIEW: Can we please have consistent naming here?
-        // 'Need' vs. 'Want' looks arbitrary to me, and it's grammatically more correct to
-        // be 'Needs' / 'Wants' anyway.
-
-        /// <summary>
-        /// Whether the trainer needs to see data in normalized form.
-        /// </summary>
-        bool NeedNormalization { get; }
-
-        /// <summary>
-        /// Whether the trainer needs calibration to produce probabilities.
-        /// </summary>
-        bool NeedCalibration { get; }
-
-        /// <summary>
-        /// Whether this trainer could benefit from a cached view of the data.
-        /// </summary>
-        bool WantCaching { get; }
-    }
-
-    public interface ITrainerHost
-    {
-        Random Rand { get; }
-        int Verbosity { get; }
-
-        TextWriter StdOut { get; }
-        TextWriter StdErr { get; }
-    }
-
-    // The Trainer (of Factory) can optionally implement this.
-    public interface IModelCombiner<TModel, TPredictor>
-        where TPredictor : IPredictor
-    {
-        TPredictor CombineModels(IEnumerable<TModel> models);
-    }
-
     public delegate void SignatureModelCombiner(PredictionKind kind);
 
     /// <summary>
-    /// Weakly typed interface for a trainer "session" that produces a predictor.
+    /// The base interface for a trainers. Implementors should not implement this interface directly,
+    /// but rather implement the more specific <see cref="ITrainer{TPredictor}"/>.
     /// </summary>
     public interface ITrainer
     {
         /// <summary>
-        /// Return the type of prediction task for the produced predictor.
+        /// Auxiliary information about the trainer in terms of its capabilities
+        /// and requirements.
         /// </summary>
-        PredictionKind PredictionKind { get; }
+        TrainerInfo Info { get; }
 
         /// <summary>
-        ///  Returns the trained predictor.
-        ///  REVIEW: Consider removing this.
+        /// Return the type of prediction task for the produced predictor.
         /// </summary>
-        IPredictor CreatePredictor();
-    }
-
-    /// <summary>
-    /// Interface implemented by the MetalinearLearners base class.
-    /// Used to distinguish the MetaLinear Learners from the other learners
-    /// </summary>
-    public interface IMetaLinearTrainer
-    {
-
-    }
+        PredictionKind PredictionKind { get; }
 
-    public interface ITrainer<in TDataSet> : ITrainer
-    {
         /// <summary>
-        /// Trains a predictor using the specified dataset.
+        ///  Trains a predictor.
         /// </summary>
-        /// <param name="data"> Training dataset </param>
-        void Train(TDataSet data);
+        /// <param name="context">A context containing at least the training data</param>
+        /// <returns>The trained predictor</returns>
+        /// <seealso cref="ITrainer{TPredictor}.Train(TrainContext)"/>
+        IPredictor Train(TrainContext context);
     }
 
     /// <summary>
-    /// Strongly typed generic interface for a trainer. A trainer object takes
-    /// supervision data and produces a predictor.
+    /// Strongly typed generic interface for a trainer. A trainer object takes training data
+    /// and produces a predictor.
     /// </summary>
-    /// <typeparam name="TDataSet"> Type of the training dataset</typeparam>
     /// <typeparam name="TPredictor"> Type of predictor produced</typeparam>
-    public interface ITrainer<in TDataSet, out TPredictor> : ITrainer<TDataSet>
+    public interface ITrainer<out TPredictor> : ITrainer
         where TPredictor : IPredictor
     {
         /// <summary>
-        ///  Returns the trained predictor.
-        /// </summary>
-        /// <returns>Trained predictor ready to make predictions</returns>
-        new TPredictor CreatePredictor();
-    }
-
-    /// <summary>
-    /// Trainers that want data to do their own validation implement this interface.
-    /// </summary>
-    public interface IValidatingTrainer<in TDataSet> : ITrainer<TDataSet>
-    {
-        /// <summary>
-        /// Trains a predictor using the specified dataset.
+        ///  Trains a predictor.
         /// </summary>
-        /// <param name="data">Training dataset</param>
-        /// <param name="validData">Validation dataset</param>
-        void Train(TDataSet data, TDataSet validData);
+        /// <param name="context">A context containing at least the training data</param>
+        /// <returns>The trained predictor</returns>
+        new TPredictor Train(TrainContext context);
     }
 
-    public interface IIncrementalTrainer<in TDataSet, in TPredictor> : ITrainer<TDataSet>
+    public static class TrainerExtensions
     {
         /// <summary>
-        /// Trains a predictor using the specified dataset and a trained predictor.
+        /// Convenience train extension for the case where one has only a training set with no auxiliary information.
+        /// Equivalent to calling <see cref="ITrainer.Train(TrainContext)"/>
+        /// on a <see cref="TrainContext"/> constructed with <paramref name="trainData"/>.
         /// </summary>
-        /// <param name="data">Training dataset</param>
-        /// <param name="predictor">A trained predictor</param>
-        void Train(TDataSet data, TPredictor predictor);
-    }
+        /// <param name="trainer">The trainer</param>
+        /// <param name="trainData">The training data.</param>
+        /// <returns>The trained predictor</returns>
+        public static IPredictor Train(this ITrainer trainer, RoleMappedData trainData)
+            => trainer.Train(new TrainContext(trainData));
 
-    public interface IIncrementalValidatingTrainer<in TDataSet, in TPredictor> : ITrainer<TDataSet>
-    {
         /// <summary>
-        /// Trains a predictor using the specified dataset and a trained predictor.
+        /// Convenience train extension for the case where one has only a training set with no auxiliary information.
+        /// Equivalent to calling <see cref="ITrainer{TPredictor}.Train(TrainContext)"/>
+        /// on a <see cref="TrainContext"/> constructed with <paramref name="trainData"/>.
         /// </summary>
-        /// <param name="data">Training dataset</param>
-        /// <param name="validData">Validation dataset</param>
-        /// <param name="predictor">A trained predictor</param>
-        void Train(TDataSet data, TDataSet validData, TPredictor predictor);
+        /// <param name="trainer">The trainer</param>
+        /// <param name="trainData">The training data.</param>
+        /// <returns>The trained predictor</returns>
+        public static TPredictor Train<TPredictor>(this ITrainer<TPredictor> trainer, RoleMappedData trainData) where TPredictor : IPredictor
+            => trainer.Train(new TrainContext(trainData));
     }
 
-#if FUTURE
-    public interface IMultiTrainer<in TDataSet, in TFeatures, out TResult> :
-        IMultiTrainer<TDataSet, TDataSet, TFeatures, TResult>
-    {
-    }
-
-    public interface IMultiTrainer<in TDataSet, in TDataBatch, in TFeatures, out TResult> :
-        ITrainer<TDataSet, TFeatures, TResult>
+    // A trainer can optionally implement this to indicate it can combine multiple models into a single predictor.
+    public interface IModelCombiner<TModel, TPredictor>
+        where TPredictor : IPredictor
     {
-        void UpdatePredictor(TDataBatch trainInstance);
-        IPredictor<TFeatures, TResult> GetCurrentPredictor();
+        TPredictor CombineModels(IEnumerable<TModel> models);
     }
-#endif
 }

src/Microsoft.ML.Core/Prediction/TrainContext.cs

@@ -0,0 +1,57 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using Microsoft.ML.Runtime.Data;
+
+namespace Microsoft.ML.Runtime
+{
+    /// <summary>
+    /// Holds information relevant to trainers. Instances of this class are meant to be constructed and passed
+    /// into <see cref="ITrainer{TPredictor}.Train(TrainContext)"/> or <see cref="ITrainer.Train(TrainContext)"/>.
+    /// This holds at least a training set, as well as optioonally a predictor.
+    /// </summary>
+    public sealed class TrainContext
+    {
+        /// <summary>
+        /// The training set. Cannot be <c>null</c>.
+        /// </summary>
+        public RoleMappedData TrainingSet { get; }
+
+        /// <summary>
+        /// The validation set. Can be <c>null</c>. Note that passing a non-<c>null</c> validation set into
+        /// a trainer that does not support validation sets should not be considered an error condition. It
+        /// should simply be ignored in that case.
+        /// </summary>
+        public RoleMappedData ValidationSet { get; }
+
+        /// <summary>
+        /// The initial predictor, for incremental training. Note that if a <see cref="ITrainer"/> implementor
+        /// does not support incremental training, then it can ignore it similarly to how one would ignore
+        /// <see cref="ValidationSet"/>. However, if the trainer does support incremental training and there
+        /// is something wrong with a non-<c>null</c> value of this, then the trainer ought to throw an exception.
+        /// </summary>
+        public IPredictor InitialPredictor { get; }
+
+
+        /// <summary>
+        /// Constructor, given a training set and optional other arguments.
+        /// </summary>
+        /// <param name="trainingSet">Will set <see cref="TrainingSet"/> to this value. This must be specified</param>
+        /// <param name="validationSet">Will set <see cref="ValidationSet"/> to this value if specified</param>
+        /// <param name="initialPredictor">Will set <see cref="InitialPredictor"/> to this value if specified</param>
+        public TrainContext(RoleMappedData trainingSet, RoleMappedData validationSet = null, IPredictor initialPredictor = null)
+        {
+            Contracts.CheckValue(trainingSet, nameof(trainingSet));
+            Contracts.CheckValueOrNull(validationSet);
+            Contracts.CheckValueOrNull(initialPredictor);
+
+            // REVIEW: Should there be code here to ensure that the role mappings between the two are compatible?
+            // That is, all the role mappings are the same and the columns between them have identical types?
+
+            TrainingSet = trainingSet;
+            ValidationSet = validationSet;
+            InitialPredictor = initialPredictor;
+        }
+    }
+}

src/Microsoft.ML.Core/Prediction/TrainerInfo.cs

@@ -0,0 +1,71 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+namespace Microsoft.ML.Runtime
+{
+    /// <summary>
+    /// Instances of this class posses information about trainers, in terms of their requirements and capabilities.
+    /// The intended usage is as the value for <see cref="ITrainer.Info"/>.
+    /// </summary>
+    public sealed class TrainerInfo
+    {
+        // REVIEW: Ideally trainers should be able to communicate
+        // something about the type of data they are capable of being trained
+        // on, e.g., what ColumnKinds they want, how many of each, of what type,
+        // etc. This interface seems like the most natural conduit for that sort
+        // of extra information.
+
+        /// <summary>
+        /// Whether the trainer needs to see data in normalized form. Only non-parametric learners will tend to produce
+        /// normalization here.
+        /// </summary>
+        public bool NeedNormalization { get; }
+
+        /// <summary>
+        /// Whether the trainer needs calibration to produce probabilities. As a general rule only trainers that produce
+        /// binary classifier predictors that also do not have a natural probabilistic interpretation should have a
+        /// <c>true</c> value here.
+        /// </summary>
+        public bool NeedCalibration { get; }
+
+        /// <summary>
+        /// Whether this trainer could benefit from a cached view of the data. Trainers that have few passes over the
+        /// data, or that need to build their own custom data structure over the data, will have a <c>false</c> here.
+        /// </summary>
+        public bool WantCaching { get; }
+
+        /// <summary>
+        /// Whether the trainer supports validation sets via <see cref="TrainContext.ValidationSet"/>. Not implementing
+        /// this interface and returning <c>true</c> from this property is an indication the trainer does not support
+        /// that.
+        /// </summary>
+        public bool SupportsValidation { get; }
+
+        /// <summary>
+        /// Whether the trainer can support incremental trainers via <see cref="TrainContext.InitialPredictor"/>. Not
+        /// implementing this interface and returning <c>true</c> from this property is an indication the trainer does
+        /// not support that.
+        /// </summary>
+        public bool SupportsIncrementalTraining { get; }
+
+        /// <summary>
+        /// Initializes with the given parameters. The parameters have default values for the most typical values
+        /// for most classical trainers.
+        /// </summary>
+        /// <param name="normalization">The value for the property <see cref="NeedNormalization"/></param>
+        /// <param name="calibration">The value for the property <see cref="NeedCalibration"/></param>
+        /// <param name="caching">The value for the property <see cref="WantCaching"/></param>
+        /// <param name="supportValid">The value for the property <see cref="SupportsValidation"/></param>
+        /// <param name="supportIncrementalTrain">The value for the property <see cref="SupportsIncrementalTraining"/></param>
+        public TrainerInfo(bool normalization = true, bool calibration = false, bool caching = true,
+            bool supportValid = false, bool supportIncrementalTrain = false)
+        {
+            NeedNormalization = normalization;
+            NeedCalibration = calibration;
+            WantCaching = caching;
+            SupportsValidation = supportValid;
+            SupportsIncrementalTraining = supportIncrementalTrain;
+        }
+    }
+}

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>();
         }

src/Microsoft.ML.Data/Commands/CrossValidationCommand.cs

@@ -538,7 +538,7 @@ private FoldResult RunFold(int fold)
                     if (_getValidationDataView != null)
                     {
                         ch.Assert(_applyTransformsToValidationData != null);
-                        if (!TrainUtils.CanUseValidationData(trainer))
+                        if (!trainer.Info.SupportsValidation)
                             ch.Warning("Trainer does not accept validation dataset.");
                         else
                         {