AutoML Add Recommendation Task #4246
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…odeGen (dotnet#4043) * add CodeGen Library * rename namespace to ML.CodeGen.* * cancel delay sign in CodeGen * update based on comment * remove useless nuget package * add ComsumeModel class * use consumeModel in CodeGen * use different annotation for different target * target to netstandard2.0 * remove console output * adjust output result * remove useless variable * update CodeGen name to CodeGenerator * rebase to latest branch * fix bug in Normalize function * fix test * rename features to featuresList * move enum out of class * remove useless items * remove NLog.config * update generated CSharp file * change wording and delete useless file * using Uppercase in comment
…arning into dotnet-features/automl
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| @@ -13,6 +13,7 @@ internal enum ColumnPurpose | |||
| TextFeature = 4, | |||
| Weight = 5, | |||
| ImagePath = 6, | |||
| SamplingKey = 7 | |||
| SamplingKey = 7, | |||
| LabelFeature, // CategoricalFeature that requires ValueToKey converter, better naming? | |||
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I think we'd want to use HashToKey (name be off) instead of the mentioned ValueToKey as the ValueToKey will map future unseen values to NA in your test dataset; and as a lesser issue is slow by taking a full pass of the dataset.
src/Microsoft.ML.AutoML/TransformInference/TransformInference.cs
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docs/samples/Microsoft.ML.AutoML.Samples/RecommendationExperiment.cs
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docs/samples/Microsoft.ML.AutoML.Samples/RecommendationExperiment.cs
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docs/samples/Microsoft.ML.AutoML.Samples/RecommendationExperiment.cs
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- remove Label column for recommendation: not required to set up
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docs/samples/Microsoft.ML.AutoML.Samples/RecommendationExperiment.cs
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src/Microsoft.ML.AutoML/TransformInference/TransformInference.cs
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src/Microsoft.ML.AutoML/TransformInference/TransformInference.cs
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This is looking really good, @maryamariyan. Thanks for the great work!
LGTM
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Trains Recommendation models able to predict rating for existing users Conflicts: pkg/Microsoft.ML.AutoML/Microsoft.ML.AutoML.nupkgproj src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs test/Microsoft.ML.AutoML.Tests/ColumnInferenceTests.cs test/Microsoft.ML.AutoML.Tests/ColumnInformationUtilTests.cs test/Microsoft.ML.AutoML.Tests/Microsoft.ML.AutoML.Tests.csproj test/Microsoft.ML.AutoML.Tests/TrainerExtensionsTests.cs test/Microsoft.ML.AutoML.Tests/TransformInferenceTests.cs test/Microsoft.ML.AutoML.Tests/UserInputValidationTests.cs
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Trains Recommendation models able to predict rating for existing users
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What's already be done in this PR
Recommendationtask and experiment inAutoMLMatrixFactorizationasMatrixFactorizationExtensionLabelFeature) and it's corresponding TransformerExtension (LabelCategorical) so thatAutoMLcan construct the pre-process pipeline forMatrixFactorizationExtensioncorrectlyAutoML.Example, and you can play with that!What's need to be done (Feel Free to CRUD)
MatrixFactorizationrequires more time to train a round, and the algorithm for sweeping params requires to train many rounds to find out the best parameter. It's time costy and customers might not like that.CodeGenpartAutoML(it requires some refactor works and shouldn't be done in this PR. But it's important)