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File-scoped namespaces in files under Prediction (`Microsoft.ML.Cor…
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…e`) (#6792)

Co-authored-by: Lehonti Ramos <john@doe>
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Lehonti and Lehonti Ramos committed Sep 1, 2023
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99 changes: 49 additions & 50 deletions src/Microsoft.ML.Core/Prediction/IPredictor.cs
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// 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
namespace Microsoft.ML;

/// <summary>
/// Type of prediction task. Note that this is a legacy structure and usage of this should generally be
/// discouraged in future projects. Its presence suggests that there are privileged and supported
/// tasks, and anything outside of this is unsupported. This runs rather contrary to the idea of this
/// being an expandable framework, and it is inappropriately limiting. For legacy pipelines based on
/// <see cref="ITrainer"/> and <see cref="IPredictor"/> it is still useful, but for things based on
/// the <see cref="IEstimator{TTransformer}"/> idiom, it is inappropriate.
/// </summary>
[BestFriend]
internal enum PredictionKind
{
/// <summary>
/// Type of prediction task. Note that this is a legacy structure and usage of this should generally be
/// discouraged in future projects. Its presence suggests that there are privileged and supported
/// tasks, and anything outside of this is unsupported. This runs rather contrary to the idea of this
/// being an expandable framework, and it is inappropriately limiting. For legacy pipelines based on
/// <see cref="ITrainer"/> and <see cref="IPredictor"/> it is still useful, but for things based on
/// the <see cref="IEstimator{TTransformer}"/> idiom, it is inappropriate.
/// </summary>
[BestFriend]
internal enum PredictionKind
{
Unknown = 0,
Custom = 1,
Unknown = 0,
Custom = 1,

BinaryClassification = 2,
MulticlassClassification = 3,
Regression = 4,
MultiOutputRegression = 5,
Ranking = 6,
Recommendation = 7,
AnomalyDetection = 8,
Clustering = 9,
SequenceClassification = 10,
BinaryClassification = 2,
MulticlassClassification = 3,
Regression = 4,
MultiOutputRegression = 5,
Ranking = 6,
Recommendation = 7,
AnomalyDetection = 8,
Clustering = 9,
SequenceClassification = 10,

// More to be added later.
}
// More to be added later.
}

/// <summary>
/// Weakly typed version of IPredictor.
/// </summary>
[BestFriend]
internal interface IPredictor
{
/// <summary>
/// Weakly typed version of IPredictor.
/// Return the type of prediction task.
/// </summary>
[BestFriend]
internal interface IPredictor
{
/// <summary>
/// Return the type of prediction task.
/// </summary>
PredictionKind PredictionKind { get; }
}
PredictionKind PredictionKind { get; }
}

/// <summary>
/// A predictor the produces values of the indicated type.
/// REVIEW: Determine whether this is just a temporary shim or long term solution.
/// </summary>
[BestFriend]
internal interface IPredictorProducing<out TResult> : IPredictor
{
}
/// <summary>
/// A predictor the produces values of the indicated type.
/// REVIEW: Determine whether this is just a temporary shim or long term solution.
/// </summary>
[BestFriend]
internal interface IPredictorProducing<out TResult> : IPredictor
{
}

/// <summary>
/// A predictor that produces values and distributions of the indicated types.
/// Note that from a public API perspective this is bad.
/// </summary>
[BestFriend]
internal interface IDistPredictorProducing<out TResult, out TResultDistribution> : IPredictorProducing<TResult>
{
}
/// <summary>
/// A predictor that produces values and distributions of the indicated types.
/// Note that from a public API perspective this is bad.
/// </summary>
[BestFriend]
internal interface IDistPredictorProducing<out TResult, out TResultDistribution> : IPredictorProducing<TResult>
{
}
175 changes: 87 additions & 88 deletions src/Microsoft.ML.Core/Prediction/ITrainer.cs
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using Microsoft.ML.Data;

namespace Microsoft.ML
{
// REVIEW: Would be nice if the registration under SignatureTrainer were automatic
// given registration for one of the "sub-class" signatures.
namespace Microsoft.ML;

/// <summary>
/// Loadable class signatures for trainers. Typically each trainer should register with
/// both SignatureTrainer and SignatureXxxTrainer where Xxx is the prediction kind.
/// </summary>
[BestFriend]
internal delegate void SignatureTrainer();
// REVIEW: Would be nice if the registration under SignatureTrainer were automatic
// given registration for one of the "sub-class" signatures.

[BestFriend]
internal delegate void SignatureBinaryClassifierTrainer();
[BestFriend]
internal delegate void SignatureMulticlassClassifierTrainer();
[BestFriend]
internal delegate void SignatureRegressorTrainer();
[BestFriend]
internal delegate void SignatureMultiOutputRegressorTrainer();
[BestFriend]
internal delegate void SignatureRankerTrainer();
[BestFriend]
internal delegate void SignatureAnomalyDetectorTrainer();
[BestFriend]
internal delegate void SignatureClusteringTrainer();
[BestFriend]
internal delegate void SignatureSequenceTrainer();
[BestFriend]
internal delegate void SignatureMatrixRecommendingTrainer();
/// <summary>
/// Loadable class signatures for trainers. Typically each trainer should register with
/// both SignatureTrainer and SignatureXxxTrainer where Xxx is the prediction kind.
/// </summary>
[BestFriend]
internal delegate void SignatureTrainer();

[BestFriend]
internal delegate void SignatureBinaryClassifierTrainer();
[BestFriend]
internal delegate void SignatureMulticlassClassifierTrainer();
[BestFriend]
internal delegate void SignatureRegressorTrainer();
[BestFriend]
internal delegate void SignatureMultiOutputRegressorTrainer();
[BestFriend]
internal delegate void SignatureRankerTrainer();
[BestFriend]
internal delegate void SignatureAnomalyDetectorTrainer();
[BestFriend]
internal delegate void SignatureClusteringTrainer();
[BestFriend]
internal delegate void SignatureSequenceTrainer();
[BestFriend]
internal delegate void SignatureMatrixRecommendingTrainer();

/// <summary>
/// The base interface for a trainers. Implementors should not implement this interface directly,
/// but rather implement the more specific <see cref="ITrainer{TPredictor}"/>.
/// </summary>
[BestFriend]
internal interface ITrainer
{
/// <summary>
/// The base interface for a trainers. Implementors should not implement this interface directly,
/// but rather implement the more specific <see cref="ITrainer{TPredictor}"/>.
/// Auxiliary information about the trainer in terms of its capabilities
/// and requirements.
/// </summary>
[BestFriend]
internal interface ITrainer
{
/// <summary>
/// Auxiliary information about the trainer in terms of its capabilities
/// and requirements.
/// </summary>
TrainerInfo Info { get; }
TrainerInfo Info { get; }

/// <summary>
/// Return the type of prediction task for the produced predictor.
/// </summary>
PredictionKind PredictionKind { get; }
/// <summary>
/// Return the type of prediction task for the produced predictor.
/// </summary>
PredictionKind PredictionKind { get; }

/// <summary>
/// Trains a predictor.
/// </summary>
/// <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>
/// Trains a predictor.
/// </summary>
/// <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 training data
/// and produces a predictor.
/// </summary>
/// <typeparam name="TPredictor"> Type of predictor produced</typeparam>
[BestFriend]
internal interface ITrainer<out TPredictor> : ITrainer
where TPredictor : IPredictor
{
/// <summary>
/// Strongly typed generic interface for a trainer. A trainer object takes training data
/// and produces a predictor.
/// Trains a predictor.
/// </summary>
/// <typeparam name="TPredictor"> Type of predictor produced</typeparam>
[BestFriend]
internal interface ITrainer<out TPredictor> : ITrainer
where TPredictor : IPredictor
{
/// <summary>
/// Trains a predictor.
/// </summary>
/// <param name="context">A context containing at least the training data</param>
/// <returns>The trained predictor</returns>
new TPredictor Train(TrainContext context);
}
/// <param name="context">A context containing at least the training data</param>
/// <returns>The trained predictor</returns>
new TPredictor Train(TrainContext context);
}

[BestFriend]
internal static class TrainerExtensions
{
/// <summary>
/// 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="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));
[BestFriend]
internal static class TrainerExtensions
{
/// <summary>
/// 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="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));

/// <summary>
/// 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="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));
}
/// <summary>
/// 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="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));
}

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