Splitting OLS to a separate package called AdditionalLearners #611
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| <Project Sdk="Microsoft.NET.Sdk" DefaultTargets="Pack"> | ||
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| <PropertyGroup> | ||
| <TargetFramework>netstandard2.0</TargetFramework> | ||
| <PackageDescription>ML.NET additional learners. They depend on other packages, like MlNetMklDeps. </PackageDescription> | ||
| </PropertyGroup> | ||
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| <ItemGroup> | ||
| <ProjectReference Include="../Microsoft.ML/Microsoft.ML.nupkgproj" /> | ||
| <PackageReference Include="MlNetMklDeps" Version="$(MlNetMklDepsPackageVersion)" /> | ||
| </ItemGroup> | ||
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| </Project> | ||
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| @@ -0,0 +1,5 @@ | ||
| <Project DefaultTargets="Pack"> | ||
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| <Import Project="Microsoft.ML.AdditionalLearners.nupkgproj" /> | ||
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Contributor
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MklDependentLearners maybe? it just separation of standard/additional learner is unclear from the names #Closed
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Author
There was a problem hiding this comment. was thinking that we could use it for other learners, that we don;t want on the core package because of other dependencies. We could bundle them all in one package.. In reply to: 206356744 [](ancestors = 206356744)
Contributor
There was a problem hiding this comment. I suggest we continue the discussion in the other comment (Learners.nupkgproj) and close this one In reply to: 206357569 [](ancestors = 206357569,206356744) |
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| </Project> | ||
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| @@ -0,0 +1,17 @@ | ||
| <Project Sdk="Microsoft.NET.Sdk"> | ||
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| <PropertyGroup> | ||
| <TargetFramework>netstandard2.0</TargetFramework> | ||
| <IncludeInPackage>Microsoft.ML.AdditionalLearners</IncludeInPackage> | ||
| <AllowUnsafeBlocks>true</AllowUnsafeBlocks> | ||
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Member
There was a problem hiding this comment. I don't think |
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| </PropertyGroup> | ||
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| <ItemGroup> | ||
| <ProjectReference Include="..\Microsoft.ML.Core\Microsoft.ML.Core.csproj" /> | ||
| <ProjectReference Include="..\Microsoft.ML.Data\Microsoft.ML.Data.csproj" /> | ||
| <ProjectReference Include="..\Microsoft.ML.StandardLearners\Microsoft.ML.StandardLearners.csproj" /> | ||
| <ProjectReference Include="..\Microsoft.ML\Microsoft.ML.csproj" /> | ||
| <PackageReference Include="MlNetMklDeps" Version="$(MlNetMklDepsPackageVersion)" /> | ||
| </ItemGroup> | ||
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| </Project> | ||
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@@ -7,14 +7,15 @@ | |
| using System; | ||
| using System.Collections.Generic; | ||
| using System.IO; | ||
| using Microsoft.ML.Runtime; | ||
| using Microsoft.ML.Runtime.AdditionalLearners; | ||
| using Microsoft.ML.Runtime.Internal.Internallearn; | ||
| using Microsoft.ML.Runtime.Internal.Utilities; | ||
| using Microsoft.ML.Runtime.CommandLine; | ||
| using Microsoft.ML.Runtime.Data; | ||
| using Microsoft.ML.Runtime.EntryPoints; | ||
| using Microsoft.ML.Runtime.Learners; | ||
| using Microsoft.ML.Runtime.Model; | ||
| using Microsoft.ML.Runtime.Training; | ||
| using System.Runtime.InteropServices; | ||
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@@ -30,7 +31,7 @@ | |
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| [assembly: LoadableClass(typeof(void), typeof(OlsLinearRegressionTrainer), null, typeof(SignatureEntryPointModule), OlsLinearRegressionTrainer.LoadNameValue)] | ||
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| namespace Microsoft.ML.Runtime.AdditionalLearners | ||
| { | ||
| /// <include file='doc.xml' path='doc/members/member[@name="OLS"]/*' /> | ||
| public sealed class OlsLinearRegressionTrainer : TrainerBase<OlsLinearRegressionPredictor> | ||
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@@ -220,7 +221,7 @@ private OlsLinearRegressionPredictor TrainCore(IChannel ch, FloatLabelCursor.Fac | |
| catch (DllNotFoundException) | ||
| { | ||
| // REVIEW: Is there no better way? | ||
| throw ch.ExceptNotSupp("The MKL library (Microsoft.ML.MklImports) or one of its dependencies is missing."); | ||
| } | ||
| // Solve for beta in (LL')beta = X'y: | ||
| Mkl.Pptrs(Mkl.Layout.RowMajor, Mkl.UpLo.Lo, m, 1, xtx, xty, 1); | ||
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@@ -466,7 +467,7 @@ public static void Pptri(Layout layout, UpLo uplo, int n, Double[] ap) | |
| Desc = "Train an OLS regression model.", | ||
| UserName = UserNameValue, | ||
| ShortName = ShortName, | ||
| XmlInclude = new[] { @"<include file='../Microsoft.ML.AdditionalLearners/Standard/doc.xml' path='doc/members/member[@name=""OLS""]/*' />" })] | ||
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Member
There was a problem hiding this comment. Is this the right path?
Member
Author
There was a problem hiding this comment. The reference is relative to the CSharpApi.cs file, in Microsoft.ML. This line of xml comment will appear atop the OrdinaryLeastSqaresregressor in the C#api, and from there make its way in the XML. In reply to: 206638688 [](ancestors = 206638688) |
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| public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, Arguments input) | ||
| { | ||
| Contracts.CheckValue(env, nameof(env)); | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,27 @@ | ||
| <?xml version="1.0" encoding="utf-8"?> | ||
| <doc> | ||
| <members> | ||
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| <member name="OLS"> | ||
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Member
There was a problem hiding this comment. Should we also remove the doc from |
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| <summary> | ||
| Train an OLS regression model. | ||
| </summary> | ||
| <remarks> | ||
| <a href='https://en.wikipedia.org/wiki/Ordinary_least_squares'>Ordinary least squares (OLS)</a> is a parameterized regression method. | ||
| It assumes that the conditional mean of the dependent variable follows a linear function of the dependent variables. | ||
| The parameters of the regressor can be estimated by minimizing the squares of the difference between observed values and the predictions. | ||
| </remarks> | ||
| <example> | ||
| <code language="csharp"> | ||
| new OrdinaryLeastSquaresRegressor | ||
| { | ||
| L2Weight = 0.1, | ||
| PerParameterSignificance = false, | ||
| NormalizeFeatures = Microsoft.ML.Models.NormalizeOption.Yes | ||
| } | ||
| </code> | ||
| </example> | ||
| </member> | ||
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| </members> | ||
| </doc> | ||
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| Original file line number | Diff line number | Diff line change |
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@@ -28,7 +28,6 @@ | |
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| namespace Microsoft.ML.Runtime.Learners | ||
| { | ||
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| /// <include file='doc.xml' path='doc/members/member[@name="LBFGS"]/*' /> | ||
| /// <include file='doc.xml' path='docs/members/example[@name="LogisticRegressionBinaryClassifier"]/*' /> | ||
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@@ -282,64 +281,7 @@ protected override void ComputeTrainingStatistics(IChannel ch, FloatLabelCursor. | |
| } | ||
| } | ||
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Contributor
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Hmm, it's too bad we have to completely drop good code.
Contributor
There was a problem hiding this comment. Maybe have a virtual 'UpdateStdErrors' method that does nothing here, and have a better version of LR in MKL-0dependent assembly that overrides this? In reply to: 206635936 [](ancestors = 206635936)
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| _stats = new LinearModelStatistics(Host, NumGoodRows, numParams, deviance, nullDeviance); | ||
| } | ||
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| protected override void ProcessPriorDistribution(Float label, Float weight) | ||
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@@ -382,7 +324,7 @@ protected override ParameterMixingCalibratedPredictor CreatePredictor() | |
| CurrentWeights.GetItemOrDefault(0, ref bias); | ||
| CurrentWeights.CopyTo(ref weights, 1, CurrentWeights.Length - 1); | ||
| return new ParameterMixingCalibratedPredictor(Host, | ||
| new LinearBinaryPredictor(Host, ref weights, bias), | ||
| new PlattCalibrator(Host, -1, 0)); | ||
| } | ||
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The name "additional learners" is something we might want to reconsider, since in future as we publish more wrappers we will surely want descriptive names.
I think naming it
OrdinaryLeastSquaresis probably a fine choice since that's literally all it is? #PendingThere was a problem hiding this comment.
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I was thinking that maybe we can have SymSGD here too, and other learners that need to drag dependencies?
@Ivanidzo4ka didn't like it either and suggested MklDepsLearners.
In reply to: 206582236 [](ancestors = 206582236)
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MklDeps also doesn't feel good, but it's better. Can we have a name that conveys 'these learners use MKL's optimized linear algebra', without actually saying 'MKL' in the name?
I don't think this should be 'all learners with big dependencies', because it doesn't actually improve the experience too much (if you need one of these learners, you have to grab all). I'd much rather have 'learners depending on MKL' and 'learners depending on OtherHeaveDependency' separate
In reply to: 206584367 [](ancestors = 206584367,206582236)
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renaming it to HalLearners. If that doesn's sound good, please suggest a name :)
In reply to: 206634151 [](ancestors = 206634151,206584367,206582236)
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I like HAL learners.
In reply to: 206661889 [](ancestors = 206661889,206634151,206584367,206582236)