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* LightGBM and test. * add test baselines and nuget source for lightGBM binaries. * Add entrypoint for lightGBM. * add unsafe flag for release build. * update nuget version. * make lightgbm test single threaded. * install gcc on OS machines to resolve dependencies on openmp thatis needed by lightgbm native code. * PR comments. Leave BREW and GCC in bash script to verify macOS tests work. * remove brew and gcc from build script. * PR feedback. * disable test on macOS. * disable test on macOS. * PR feedback.
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| <Project Sdk="Microsoft.NET.Sdk" DefaultTargets="Pack"> | ||
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| <PropertyGroup> | ||
| <TargetFramework>netstandard2.0</TargetFramework> | ||
| <PackageDescription>ML.NET component for LightGBM</PackageDescription> | ||
| </PropertyGroup> | ||
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| <ItemGroup> | ||
| <ProjectReference Include="../Microsoft.ML/Microsoft.ML.nupkgproj" /> | ||
| <PackageReference Include="LightGBM" Version="$(LightGBMPackageVersion)" /> | ||
| </ItemGroup> | ||
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| </Project> |
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pkg/Microsoft.ML.LightGBM/Microsoft.ML.LightGBM.symbols.nupkgproj
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| <Project DefaultTargets="Pack"> | ||
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| <Import Project="Microsoft.ML.LightGBM.nupkgproj" /> | ||
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| </Project> |
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| // 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. | ||
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| using Microsoft.ML.Runtime; | ||
| using Microsoft.ML.Runtime.Data; | ||
| using Microsoft.ML.Runtime.EntryPoints; | ||
| using Microsoft.ML.Runtime.FastTree; | ||
| using Microsoft.ML.Runtime.Internal.Calibration; | ||
| using Microsoft.ML.Runtime.Internal.Internallearn; | ||
| using Microsoft.ML.Runtime.LightGBM; | ||
| using Microsoft.ML.Runtime.Model; | ||
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| [assembly: LoadableClass(LightGbmBinaryTrainer.Summary, typeof(LightGbmBinaryTrainer), typeof(LightGbmArguments), | ||
| new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer), typeof(SignatureTreeEnsembleTrainer) }, | ||
| "LightGBM Binary Classification", LightGbmBinaryTrainer.LoadNameValue, LightGbmBinaryTrainer.ShortName, DocName = "trainer/LightGBM.md")] | ||
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| [assembly: LoadableClass(typeof(IPredictorProducing<float>), typeof(LightGbmBinaryPredictor), null, typeof(SignatureLoadModel), | ||
| "LightGBM Binary Executor", | ||
| LightGbmBinaryPredictor.LoaderSignature)] | ||
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| [assembly: LoadableClass(typeof(void), typeof(LightGbm), null, typeof(SignatureEntryPointModule), "LightGBM")] | ||
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| namespace Microsoft.ML.Runtime.LightGBM | ||
| { | ||
| public sealed class LightGbmBinaryPredictor : FastTreePredictionWrapper | ||
| { | ||
| public const string LoaderSignature = "LightGBMBinaryExec"; | ||
| public const string RegistrationName = "LightGBMBinaryPredictor"; | ||
| private static VersionInfo GetVersionInfo() | ||
| { | ||
| // REVIEW: can we decouple the version from FastTree predictor version ? | ||
| return new VersionInfo( | ||
| modelSignature: "LGBBINCL", | ||
| // verWrittenCur: 0x00010001, // Initial | ||
| // verWrittenCur: 0x00010002, // _numFeatures serialized | ||
| // verWrittenCur: 0x00010003, // Ini content out of predictor | ||
| //verWrittenCur: 0x00010004, // Add _defaultValueForMissing | ||
| verWrittenCur: 0x00010005, // Categorical splits. | ||
| verReadableCur: 0x00010004, | ||
| verWeCanReadBack: 0x00010001, | ||
| loaderSignature: LoaderSignature); | ||
| } | ||
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| protected override uint VerNumFeaturesSerialized { get { return 0x00010002; } } | ||
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| protected override uint VerDefaultValueSerialized { get { return 0x00010004; } } | ||
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| protected override uint VerCategoricalSplitSerialized { get { return 0x00010005; } } | ||
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| internal LightGbmBinaryPredictor(IHostEnvironment env, FastTree.Internal.Ensemble trainedEnsemble, int featureCount, string innerArgs) | ||
| : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs) | ||
| { | ||
| } | ||
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| private LightGbmBinaryPredictor(IHostEnvironment env, ModelLoadContext ctx) | ||
| : base(env, RegistrationName, ctx, GetVersionInfo()) | ||
| { | ||
| } | ||
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| protected override void SaveCore(ModelSaveContext ctx) | ||
| { | ||
| base.SaveCore(ctx); | ||
| ctx.SetVersionInfo(GetVersionInfo()); | ||
| } | ||
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| public static IPredictorProducing<float> Create(IHostEnvironment env, ModelLoadContext ctx) | ||
| { | ||
| Contracts.CheckValue(env, nameof(env)); | ||
| env.CheckValue(ctx, nameof(ctx)); | ||
| ctx.CheckAtModel(GetVersionInfo()); | ||
| var predictor = new LightGbmBinaryPredictor(env, ctx); | ||
| ICalibrator calibrator; | ||
| ctx.LoadModelOrNull<ICalibrator, SignatureLoadModel>(env, out calibrator, @"Calibrator"); | ||
| if (calibrator == null) | ||
| return predictor; | ||
| return new CalibratedPredictor(env, predictor, calibrator); | ||
| } | ||
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| public override PredictionKind PredictionKind { get { return PredictionKind.BinaryClassification; } } | ||
| } | ||
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| public sealed class LightGbmBinaryTrainer : LightGbmTrainerBase<float, IPredictorWithFeatureWeights<float>> | ||
| { | ||
| public const string Summary = "LightGBM Binary Classifier"; | ||
| public const string LoadNameValue = "LightGBMBinary"; | ||
| public const string ShortName = "LightGBM"; | ||
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| public LightGbmBinaryTrainer(IHostEnvironment env, LightGbmArguments args) | ||
| : base(env, args, PredictionKind.BinaryClassification, "LGBBINCL") | ||
| { | ||
| } | ||
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| public override IPredictorWithFeatureWeights<float> CreatePredictor() | ||
| { | ||
| Host.Check(TrainedEnsemble != null, "The predictor cannot be created before training is complete"); | ||
| var innerArgs = LightGbmInterfaceUtils.JoinParameters(Options); | ||
| var pred = new LightGbmBinaryPredictor(Host, TrainedEnsemble, FeatureCount, innerArgs); | ||
| var cali = new PlattCalibrator(Host, -0.5, 0); | ||
| return new FeatureWeightsCalibratedPredictor(Host, pred, cali); | ||
| } | ||
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| protected override void CheckDataValid(IChannel ch, RoleMappedData data) | ||
| { | ||
| Host.AssertValue(ch); | ||
| base.CheckDataValid(ch, data); | ||
| var labelType = data.Schema.Label.Type; | ||
| if (!(labelType.IsBool || labelType.IsKey || labelType == NumberType.R4)) | ||
| { | ||
| throw ch.ExceptParam(nameof(data), | ||
| $"Label column '{data.Schema.Label.Name}' is of type '{labelType}', but must be key, boolean or R4."); | ||
| } | ||
| } | ||
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| protected override void CheckAndUpdateParametersBeforeTraining(IChannel ch, RoleMappedData data, float[] labels, int[] groups) | ||
| { | ||
| Options["objective"] = "binary"; | ||
| // Add default metric. | ||
| if (!Options.ContainsKey("metric")) | ||
| Options["metric"] = "binary_logloss"; | ||
| } | ||
| } | ||
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| /// <summary> | ||
| /// A component to train an LightGBM model. | ||
| /// </summary> | ||
| public static partial class LightGbm | ||
| { | ||
| [TlcModule.EntryPoint( | ||
| Name = "Trainers.LightGbmBinaryClassifier", | ||
| Desc = "Train an LightGBM binary class model", | ||
| UserName = LightGbmBinaryTrainer.Summary, | ||
| ShortName = LightGbmBinaryTrainer.ShortName)] | ||
| public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, LightGbmArguments input) | ||
| { | ||
| Contracts.CheckValue(env, nameof(env)); | ||
| var host = env.Register("TrainLightGBM"); | ||
| host.CheckValue(input, nameof(input)); | ||
| EntryPointUtils.CheckInputArgs(host, input); | ||
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| return LearnerEntryPointsUtils.Train<LightGbmArguments, CommonOutputs.BinaryClassificationOutput>(host, input, | ||
| () => new LightGbmBinaryTrainer(host, input), | ||
| getLabel: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn), | ||
| getWeight: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.WeightColumn)); | ||
| } | ||
| } | ||
| } |
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