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Merged
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Aug 17, 2018
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Replace SubComponent with IComponentFactory in ML.Ensemble #681

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77fff03
Convert Ensemble stacking SubComponent to IComponentFactory.
eerhardt Aug 15, 2018
84119d3
Replace SubComponent with IComponentFactory in ML.Ensemble
eerhardt Aug 15, 2018
7c06f93
Convert SimpleComponentFactory classes to ComponentFactoryUtils stati…
eerhardt Aug 16, 2018
d41dbe6
Move Stacking arguments abstract properties to internal get methods.
eerhardt Aug 16, 2018
6f852cb
Merge remote-tracking branch 'upstream/master' into EnsembleSubComponent
eerhardt Aug 17, 2018
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eerhardt Aug 17, 2018
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75 changes: 59 additions & 16 deletions src/Microsoft.ML.Core/EntryPoints/ComponentFactory.cs
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Expand Up @@ -38,30 +38,73 @@ public interface IComponentFactory<in TArg1, out TComponent> : IComponentFactory
}

/// <summary>
/// A class for creating a component when we take one extra parameter
/// (and an <see cref="IHostEnvironment"/>) that simply wraps a delegate which
/// creates the component.
/// An interface for creating a component when we take two extra parameters (and an <see cref="IHostEnvironment"/>).
/// </summary>
public class SimpleComponentFactory<TArg1, TComponent> : IComponentFactory<TArg1, TComponent>
public interface IComponentFactory<in TArg1, in TArg2, out TComponent> : IComponentFactory
{
private Func<IHostEnvironment, TArg1, TComponent> _factory;
TComponent CreateComponent(IHostEnvironment env, TArg1 argument1, TArg2 argument2);
}

public SimpleComponentFactory(Func<IHostEnvironment, TArg1, TComponent> factory)
/// <summary>
/// A utility class for creating IComponentFactory instances.
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@TomFinley TomFinley Aug 16, 2018 •
edited by eerhardt
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IComponentFactory [](start = 37, length = 17)

Might prefer this to be in <see tag or something. #Resolved

/// </summary>
public static class ComponentFactoryUtils
{
/// <summary>
/// Creates a component factory with no extra parameters (other than an <see cref="IHostEnvironment"/>)
/// that simply wraps a delegate which creates the component.
/// </summary>
public static IComponentFactory<TComponent> CreateFromFunction<TComponent>(Func<IHostEnvironment, TComponent> factory)
{
_factory = factory;
return new SimpleComponentFactory<TComponent>(factory);
}

public TComponent CreateComponent(IHostEnvironment env, TArg1 argument1)
/// <summary>
/// Creates a component factory when we take one extra parameter (and an
/// <see cref="IHostEnvironment"/>) that simply wraps a delegate which creates the component.
/// </summary>
public static IComponentFactory<TArg1, TComponent> CreateFromFunction<TArg1, TComponent>(Func<IHostEnvironment, TArg1, TComponent> factory)
{
return _factory(env, argument1);
return new SimpleComponentFactory<TArg1, TComponent>(factory);
}
}

/// <summary>
/// An interface for creating a component when we take two extra parameters (and an <see cref="IHostEnvironment"/>).
/// </summary>
public interface IComponentFactory<in TArg1, in TArg2, out TComponent> : IComponentFactory
{
TComponent CreateComponent(IHostEnvironment env, TArg1 argument1, TArg2 argument2);
/// <summary>
/// A class for creating a component with no extra parameters (other than an <see cref="IHostEnvironment"/>)
/// that simply wraps a delegate which creates the component.
/// </summary>
private sealed class SimpleComponentFactory<TComponent> : IComponentFactory<TComponent>
{
private readonly Func<IHostEnvironment, TComponent> _factory;

public SimpleComponentFactory(Func<IHostEnvironment, TComponent> factory)
{
_factory = factory;
}

public TComponent CreateComponent(IHostEnvironment env)
{
return _factory(env);
}
}

/// <summary>
/// A class for creating a component when we take one extra parameter
/// (and an <see cref="IHostEnvironment"/>) that simply wraps a delegate which
/// creates the component.
/// </summary>
private sealed class SimpleComponentFactory<TArg1, TComponent> : IComponentFactory<TArg1, TComponent>
{
private readonly Func<IHostEnvironment, TArg1, TComponent> _factory;

public SimpleComponentFactory(Func<IHostEnvironment, TArg1, TComponent> factory)
{
_factory = factory;
}

public TComponent CreateComponent(IHostEnvironment env, TArg1 argument1)
{
return _factory(env, argument1);
}
}
}
}
2 changes: 1 addition & 1 deletion src/Microsoft.ML.Data/Commands/CrossValidationCommand.cs
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Expand Up @@ -164,7 +164,7 @@ private void RunCore(IChannel ch, string cmd)
new[]
{
new KeyValuePair<string, IComponentFactory<IDataView, IDataTransform>>(
"", new SimpleComponentFactory<IDataView, IDataTransform>(
"", ComponentFactoryUtils.CreateFromFunction<IDataView, IDataTransform>(
(env, input) =>
{
var args = new GenerateNumberTransform.Arguments();
Expand Down
1 change: 1 addition & 0 deletions src/Microsoft.ML.Ensemble/Microsoft.ML.Ensemble.csproj
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Expand Up @@ -10,6 +10,7 @@
<ProjectReference Include="..\Microsoft.ML.Data\Microsoft.ML.Data.csproj" />
<ProjectReference Include="..\Microsoft.ML.StandardLearners\Microsoft.ML.StandardLearners.csproj" />
<ProjectReference Include="..\Microsoft.ML.Transforms\Microsoft.ML.Transforms.csproj" />
<ProjectReference Include="..\Microsoft.ML.FastTree\Microsoft.ML.FastTree.csproj" />
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@TomFinley TomFinley Aug 15, 2018 •
edited by eerhardt
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[](start = 4, length = 84)

We need this due to the usage of FastTree as the default traniner for some ensemble classes. This could be fine, but I wonder if using something from StandardLearners might make a bit more sense, if we envision that someday FastTree will be factored into a separate nuget package. (Maybe we don't want that though, in which case this dependency is probably fine.) #Resolved

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@eerhardt eerhardt Aug 15, 2018 •
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One advantage to this change is that it brings to light these dependencies. Before we had a weakly-typed dependency using strings and DI. So if we ever did refactor FastTree to be separate, we could have easily broken this dependency.

I've logged #682 for this issue. #Resolved

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@TomFinley TomFinley Aug 16, 2018 •
edited by eerhardt
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Thanks @eerhardt ... yes I expect we'll see some more of this "hidden dependency" becoming exposed as we move away from direct usage of subcomponents and dependency injection. I recall a similar issue in #446 with the normalizers being defined in Microsoft.ML.Transforms and used in Microsoft.ML.Data, despite the former project depending on the latter. 😄 #Resolved

</ItemGroup>

</Project>
2 changes: 1 addition & 1 deletion src/Microsoft.ML.Ensemble/OutputCombiners/BaseScalarStacking.cs
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Expand Up @@ -9,7 +9,7 @@

namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners
{
public abstract class BaseScalarStacking<TSigBase> : BaseStacking<Single, TSigBase>
public abstract class BaseScalarStacking : BaseStacking<Single>
{
internal BaseScalarStacking(IHostEnvironment env, string name, ArgumentsBase args)
: base(env, name, args)
Expand Down
16 changes: 7 additions & 9 deletions src/Microsoft.ML.Ensemble/OutputCombiners/BaseStacking.cs
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Expand Up @@ -7,6 +7,7 @@
using System.Threading.Tasks;
using Microsoft.ML.Runtime.CommandLine;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.Internal.Internallearn;
using Microsoft.ML.Runtime.Internal.Utilities;
using Microsoft.ML.Runtime.Model;
Expand All @@ -15,7 +16,7 @@
namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners
{
using ColumnRole = RoleMappedSchema.ColumnRole;
public abstract class BaseStacking<TOutput, TSigBase> : IStackingTrainer<TOutput>
public abstract class BaseStacking<TOutput> : IStackingTrainer<TOutput>
{
public abstract class ArgumentsBase
{
Expand All @@ -24,13 +25,10 @@ public abstract class ArgumentsBase
[TGUI(Label = "Validation Dataset Proportion")]
public Single ValidationDatasetProportion = 0.3f;

[Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50,
Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)]
[TGUI(Label = "Base predictor")]
public SubComponent<ITrainer<IPredictorProducing<TOutput>>, TSigBase> BasePredictorType;
internal abstract IComponentFactory<ITrainer<IPredictorProducing<TOutput>>> GetPredictorFactory();
}

protected readonly SubComponent<ITrainer<IPredictorProducing<TOutput>>, TSigBase> BasePredictorType;
protected readonly IComponentFactory<ITrainer<IPredictorProducing<TOutput>>> BasePredictorType;
protected readonly IHost Host;
protected IPredictorProducing<TOutput> Meta;

Expand All @@ -45,10 +43,10 @@ internal BaseStacking(IHostEnvironment env, string name, ArgumentsBase args)
Host.CheckUserArg(0 <= args.ValidationDatasetProportion && args.ValidationDatasetProportion < 1,
nameof(args.ValidationDatasetProportion),
"The validation proportion for stacking should be greater than or equal to 0 and less than 1");
Host.CheckUserArg(args.BasePredictorType.IsGood(), nameof(args.BasePredictorType));

ValidationDatasetProportion = args.ValidationDatasetProportion;
BasePredictorType = args.BasePredictorType;
BasePredictorType = args.GetPredictorFactory();
Host.CheckValue(BasePredictorType, nameof(BasePredictorType));
}

internal BaseStacking(IHostEnvironment env, string name, ModelLoadContext ctx)
Expand Down Expand Up @@ -187,7 +185,7 @@ public void Train(List<FeatureSubsetModel<IPredictorProducing<TOutput>>> models,
var view = bldr.GetDataView();
var rmd = new RoleMappedData(view, DefaultColumnNames.Label, DefaultColumnNames.Features);

var trainer = BasePredictorType.CreateInstance(host);
var trainer = BasePredictorType.CreateComponent(host);
if (trainer.Info.NeedNormalization)
ch.Warning("The trainer specified for stacking wants normalization, but we do not currently allow this.");
Meta = trainer.Train(rmd);
Expand Down
20 changes: 17 additions & 3 deletions src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs
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Expand Up @@ -8,7 +8,10 @@
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.Ensemble.OutputCombiners;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.FastTree;
using Microsoft.ML.Runtime.Internal.Internallearn;
using Microsoft.ML.Runtime.Internal.Utilities;
using Microsoft.ML.Runtime.Learners;
using Microsoft.ML.Runtime.Model;

[assembly: LoadableClass(typeof(MultiStacking), typeof(MultiStacking.Arguments), typeof(SignatureCombiner),
Expand All @@ -20,7 +23,7 @@
namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners
{
using TVectorPredictor = IPredictorProducing<VBuffer<Single>>;
public sealed class MultiStacking : BaseStacking<VBuffer<Single>, SignatureMultiClassClassifierTrainer>, ICanSaveModel, IMultiClassOutputCombiner
public sealed class MultiStacking : BaseStacking<VBuffer<Single>>, ICanSaveModel, IMultiClassOutputCombiner
{
public const string LoadName = "MultiStacking";
public const string LoaderSignature = "MultiStackingCombiner";
Expand All @@ -38,13 +41,24 @@ private static VersionInfo GetVersionInfo()
[TlcModule.Component(Name = LoadName, FriendlyName = Stacking.UserName)]
public sealed class Arguments : ArgumentsBase, ISupportMulticlassOutputCombinerFactory
{
[Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50,
Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureMultiClassClassifierTrainer))]
[TGUI(Label = "Base predictor")]
public IComponentFactory<ITrainer<TVectorPredictor>> BasePredictorType;

internal override IComponentFactory<ITrainer<TVectorPredictor>> GetPredictorFactory() => BasePredictorType;

public IMultiClassOutputCombiner CreateComponent(IHostEnvironment env) => new MultiStacking(env, this);

public Arguments()
{
// REVIEW: Perhaps we can have a better non-parametetric learner.
BasePredictorType = new SubComponent<ITrainer<TVectorPredictor>, SignatureMultiClassClassifierTrainer>(
"OVA", "p=FastTreeBinaryClassification");
BasePredictorType = ComponentFactoryUtils.CreateFromFunction(
env => new Ova(env, new Ova.Arguments()
{
PredictorType = ComponentFactoryUtils.CreateFromFunction(
e => new FastTreeBinaryClassificationTrainer(e, new FastTreeBinaryClassificationTrainer.Arguments()))
}));
}
}

Expand Down
14 changes: 12 additions & 2 deletions src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs
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Expand Up @@ -7,6 +7,8 @@
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.Ensemble.OutputCombiners;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.FastTree;
using Microsoft.ML.Runtime.Internal.Internallearn;
using Microsoft.ML.Runtime.Model;

[assembly: LoadableClass(typeof(RegressionStacking), typeof(RegressionStacking.Arguments), typeof(SignatureCombiner),
Expand All @@ -19,7 +21,7 @@ namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners
{
using TScalarPredictor = IPredictorProducing<Single>;

public sealed class RegressionStacking : BaseScalarStacking<SignatureRegressorTrainer>, IRegressionOutputCombiner, ICanSaveModel
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@TomFinley TomFinley Aug 15, 2018 •
edited by eerhardt
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SignatureRegressorTrainer [](start = 64, length = 25)

What are we going to do about this sort of restriction? I had suggested previously using something like ITrainer<IRegressionPredictor> or something like this, but I'm not sure what you thought of that idea. #Resolved

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@eerhardt eerhardt Aug 16, 2018 •
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Which restriction are you referring to? The Signature Type restriction, where we can't use typeof(TSig) in an attribute?

My hope is that some day we remove signature types all together. I haven't thought of/discovered a good replacement for them yet. Maybe an approach similar to ComponentKind which is just a string "Kind".

However, that still wouldn't help in this case, because we need a different signature/kind/etc for each concrete class, but we want to define the argument on the base class. #Resolved

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I meant just restricting the DI and API usage to only consider actual, in this case, regression trainers, which is one of the functions of the signatures. Currently this is done in a fairly half-hearted fashion using this IPredictorProducing<float> and IPredictorProducing<VBuffer<float>>, but if we got rid of this and replaced it with, say, IRegressionPredictor and IBinaryClassificationPredictor and IMulticlassPredictor (or something like that), then we could just have this be c omponent factory producing ITrainer<IRegressionPredictor> in this case. I think MEF should work well with that restriction as well, from a DI perspective.

In reply to: 210463209 [](ancestors = 210463209)

public sealed class RegressionStacking : BaseScalarStacking, IRegressionOutputCombiner, ICanSaveModel
{
public const string LoadName = "RegressionStacking";
public const string LoaderSignature = "RegressionStacking";
Expand All @@ -37,9 +39,17 @@ private static VersionInfo GetVersionInfo()
[TlcModule.Component(Name = LoadName, FriendlyName = Stacking.UserName)]
public sealed class Arguments : ArgumentsBase, ISupportRegressionOutputCombinerFactory
{
[Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50,
Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureRegressorTrainer))]
[TGUI(Label = "Base predictor")]
public IComponentFactory<ITrainer<TScalarPredictor>> BasePredictorType;

internal override IComponentFactory<ITrainer<TScalarPredictor>> GetPredictorFactory() => BasePredictorType;

public Arguments()
{
BasePredictorType = new SubComponent<ITrainer<TScalarPredictor>, SignatureRegressorTrainer>("FastTreeRegression");
BasePredictorType = ComponentFactoryUtils.CreateFromFunction(
env => new FastTreeRegressionTrainer(env, new FastTreeRegressionTrainer.Arguments()));
}

public IRegressionOutputCombiner CreateComponent(IHostEnvironment env) => new RegressionStacking(env, this);
Expand Down
15 changes: 12 additions & 3 deletions src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs
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Expand Up @@ -5,9 +5,10 @@
using System;
using Microsoft.ML.Runtime;
using Microsoft.ML.Runtime.CommandLine;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.Ensemble.OutputCombiners;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.FastTree;
using Microsoft.ML.Runtime.Internal.Internallearn;
using Microsoft.ML.Runtime.Model;

[assembly: LoadableClass(typeof(Stacking), typeof(Stacking.Arguments), typeof(SignatureCombiner), Stacking.UserName, Stacking.LoadName)]
Expand All @@ -16,7 +17,7 @@
namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners
{
using TScalarPredictor = IPredictorProducing<Single>;
public sealed class Stacking : BaseScalarStacking<SignatureBinaryClassifierTrainer>, IBinaryOutputCombiner, ICanSaveModel
public sealed class Stacking : BaseScalarStacking, IBinaryOutputCombiner, ICanSaveModel
{
public const string UserName = "Stacking";
public const string LoadName = "Stacking";
Expand All @@ -35,9 +36,17 @@ private static VersionInfo GetVersionInfo()
[TlcModule.Component(Name = LoadName, FriendlyName = UserName)]
public sealed class Arguments : ArgumentsBase, ISupportBinaryOutputCombinerFactory
{
[Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50,
Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureBinaryClassifierTrainer))]
[TGUI(Label = "Base predictor")]
public IComponentFactory<ITrainer<TScalarPredictor>> BasePredictorType;

internal override IComponentFactory<ITrainer<TScalarPredictor>> GetPredictorFactory() => BasePredictorType;

public Arguments()
{
BasePredictorType = new SubComponent<ITrainer<TScalarPredictor>, SignatureBinaryClassifierTrainer>("FastTreeBinaryClassification");
BasePredictorType = ComponentFactoryUtils.CreateFromFunction(
env => new FastTreeBinaryClassificationTrainer(env, new FastTreeBinaryClassificationTrainer.Arguments()));
}

public IBinaryOutputCombiner CreateComponent(IHostEnvironment env) => new Stacking(env, this);
Expand Down
11 changes: 5 additions & 6 deletions src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseSubModelSelector.cs
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Expand Up @@ -54,16 +54,16 @@ public virtual IList<FeatureSubsetModel<IPredictorProducing<TOutput>>> Prune(ILi
return models;
}

private SubComponent<IEvaluator, SignatureEvaluator> GetEvaluatorSubComponent()
private IEvaluator GetEvaluator(IHostEnvironment env)
{
switch (PredictionKind)
{
case PredictionKind.BinaryClassification:
return new SubComponent<IEvaluator, SignatureEvaluator>(BinaryClassifierEvaluator.LoadName);
return new BinaryClassifierEvaluator(env, new BinaryClassifierEvaluator.Arguments());
case PredictionKind.Regression:
return new SubComponent<IEvaluator, SignatureEvaluator>(RegressionEvaluator.LoadName);
return new RegressionEvaluator(env, new RegressionEvaluator.Arguments());
case PredictionKind.MultiClassClassification:
return new SubComponent<IEvaluator, SignatureEvaluator>(MultiClassClassifierEvaluator.LoadName);
return new MultiClassClassifierEvaluator(env, new MultiClassClassifierEvaluator.Arguments());
default:
throw Host.Except("Unrecognized prediction kind '{0}'", PredictionKind);
}
Expand All @@ -83,10 +83,9 @@ public virtual void CalculateMetrics(FeatureSubsetModel<IPredictorProducing<TOut
IDataScorerTransform scorePipe = ScoreUtils.GetScorer(model.Predictor, testData, Host, testData.Schema);
// REVIEW: Should we somehow allow the user to customize the evaluator?
// By what mechanism should we allow that?
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I think this comment need to be moved to few line below

var evalComp = GetEvaluatorSubComponent();
RoleMappedData scoredTestData = new RoleMappedData(scorePipe,
GetColumnRoles(testData.Schema, scorePipe.Schema));
IEvaluator evaluator = evalComp.CreateInstance(Host);
IEvaluator evaluator = GetEvaluator(Host);
// REVIEW: with the new evaluators, metrics of individual models are no longer
// printed to the Console. Consider adding an option on the combiner to print them.
// REVIEW: Consider adding an option to the combiner to save a data view
Expand Down
15 changes: 13 additions & 2 deletions src/Microsoft.ML.Ensemble/Trainer/Binary/EnsembleTrainer.cs
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Expand Up @@ -13,6 +13,8 @@
using Microsoft.ML.Runtime.Ensemble.Selector;
using Microsoft.ML.Ensemble.EntryPoints;
using Microsoft.ML.Runtime.Internal.Internallearn;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.Learners;
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need some sorting


[assembly: LoadableClass(EnsembleTrainer.Summary, typeof(EnsembleTrainer), typeof(EnsembleTrainer.Arguments),
new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) },
Expand All @@ -26,7 +28,7 @@ namespace Microsoft.ML.Runtime.Ensemble
/// A generic ensemble trainer for binary classification.
/// </summary>
public sealed class EnsembleTrainer : EnsembleTrainerBase<Single, TScalarPredictor,
IBinarySubModelSelector, IBinaryOutputCombiner, SignatureBinaryClassifierTrainer>,
IBinarySubModelSelector, IBinaryOutputCombiner>,
IModelCombiner<TScalarPredictor, TScalarPredictor>
{
public const string LoadNameValue = "WeightedEnsemble";
Expand All @@ -44,9 +46,18 @@ public sealed class Arguments : ArgumentsBase
[TGUI(Label = "Output combiner", Description = "Output combiner type")]
public ISupportBinaryOutputCombinerFactory OutputCombiner = new MedianFactory();

[Argument(ArgumentType.Multiple, HelpText = "Base predictor type", ShortName = "bp,basePredictorTypes", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureBinaryClassifierTrainer))]
public IComponentFactory<ITrainer<TScalarPredictor>>[] BasePredictors;

internal override IComponentFactory<ITrainer<TScalarPredictor>>[] GetPredictorFactories() => BasePredictors;

public Arguments()
{
BasePredictors = new[] { new SubComponent<ITrainer<TScalarPredictor>, SignatureBinaryClassifierTrainer>("LinearSVM") };
BasePredictors = new[]
{
ComponentFactoryUtils.CreateFromFunction(
env => new LinearSvm(env, new LinearSvm.Arguments()))
};
}
}

Expand Down
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