From f93ab2572e402e00f9f7224634aae5762b89a113 Mon Sep 17 00:00:00 2001 From: Michael Sharp <51342856+michaelgsharp@users.noreply.github.com> Date: Mon, 15 May 2023 18:45:57 -0600 Subject: [PATCH] Adds the ability to load a pre-trained LightGBM file and import it into ML.Net. (#6569) * need to finish multiclass * multiclass * reverting test for now * reverting test for now * added test and fixed objective parsing * minor testing changes --- .../LightGbmBinaryTrainer.cs | 35 +- src/Microsoft.ML.LightGbm/LightGbmCatalog.cs | 65 + .../LightGbmMulticlassTrainer.cs | 59 +- .../LightGbmRankingTrainer.cs | 58 +- .../LightGbmRegressionTrainer.cs | 34 +- .../LightGbmTrainerBase.cs | 106 +- .../WrappedLightGbmBooster.cs | 42 +- .../TrainerEstimators/TreeEstimators.cs | 21 +- test/data/iris-lgbm.txt | 7337 +++++++++++++++++ 9 files changed, 7707 insertions(+), 50 deletions(-) create mode 100644 test/data/iris-lgbm.txt diff --git a/src/Microsoft.ML.LightGbm/LightGbmBinaryTrainer.cs b/src/Microsoft.ML.LightGbm/LightGbmBinaryTrainer.cs index afbd41cb15..c6d56aae2f 100644 --- a/src/Microsoft.ML.LightGbm/LightGbmBinaryTrainer.cs +++ b/src/Microsoft.ML.LightGbm/LightGbmBinaryTrainer.cs @@ -2,7 +2,9 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using System; using System.Collections.Generic; +using System.IO; using Microsoft.ML; using Microsoft.ML.Calibrators; using Microsoft.ML.CommandLine; @@ -228,6 +230,26 @@ internal LightGbmBinaryTrainer(IHostEnvironment env, { } + /// + /// Initializes a new instance of + /// + /// The private instance of . + /// A pre-trained of a LightGBM model file inferencing + /// The name of the feature column. + internal LightGbmBinaryTrainer(IHostEnvironment env, + Stream lightGbmModel, + string featureColumnName = DefaultColumnNames.Features) + : base(env, + LoadNameValue, + new Options() + { + FeatureColumnName = featureColumnName, + LightGbmModel = lightGbmModel + }, + new SchemaShape.Column()) + { + } + private protected override CalibratedModelParametersBase CreatePredictor() { Host.Check(TrainedEnsemble != null, "The predictor cannot be created before training is complete"); @@ -241,11 +263,16 @@ private protected override void CheckDataValid(IChannel ch, RoleMappedData data) { Host.AssertValue(ch); base.CheckDataValid(ch, data); - var labelType = data.Schema.Label.Value.Type; - if (!(labelType is BooleanDataViewType || labelType is KeyDataViewType || labelType == NumberDataViewType.Single)) + + // If using a pre-trained model file we don't need a label column + if (LightGbmTrainerOptions.LightGbmModel == null) { - throw ch.ExceptParam(nameof(data), - $"Label column '{data.Schema.Label.Value.Name}' is of type '{labelType.RawType}', but must be unsigned int, boolean or float."); + var labelType = data.Schema.Label.Value.Type; + if (!(labelType is BooleanDataViewType || labelType is KeyDataViewType || labelType == NumberDataViewType.Single)) + { + throw ch.ExceptParam(nameof(data), + $"Label column '{data.Schema.Label.Value.Name}' is of type '{labelType.RawType}', but must be unsigned int, boolean or float."); + } } } diff --git a/src/Microsoft.ML.LightGbm/LightGbmCatalog.cs b/src/Microsoft.ML.LightGbm/LightGbmCatalog.cs index 49356996f3..efa11bdc7f 100644 --- a/src/Microsoft.ML.LightGbm/LightGbmCatalog.cs +++ b/src/Microsoft.ML.LightGbm/LightGbmCatalog.cs @@ -2,6 +2,7 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using System.IO; using Microsoft.ML.Data; using Microsoft.ML.Runtime; using Microsoft.ML.Trainers.LightGbm; @@ -67,6 +68,22 @@ public static LightGbmRegressionTrainer LightGbm(this RegressionCatalog.Regressi return new LightGbmRegressionTrainer(env, options); } + /// + /// Create from a pre-trained LightGBM model, which predicts a target using a gradient boosting decision tree regression. + /// + /// The . + /// A pre-trained of a LightGBM model file inferencing + /// The name of the feature column. The column data must be a known-sized vector of . + public static LightGbmRegressionTrainer LightGbm(this RegressionCatalog.RegressionTrainers catalog, + Stream lightGbmModel, + string featureColumnName = DefaultColumnNames.Features + ) + { + Contracts.CheckValue(catalog, nameof(catalog)); + var env = CatalogUtils.GetEnvironment(catalog); + return new LightGbmRegressionTrainer(env, lightGbmModel, featureColumnName); + } + /// /// Create , which predicts a target using a gradient boosting decision tree binary classification. /// @@ -119,6 +136,22 @@ public static LightGbmBinaryTrainer LightGbm(this BinaryClassificationCatalog.Bi return new LightGbmBinaryTrainer(env, options); } + /// + /// Create from a pre-trained LightGBM model, which predicts a target using a gradient boosting decision tree binary classification. + /// + /// The . + /// A pre-trained of a LightGBM model file inferencing + /// The name of the feature column. The column data must be a known-sized vector of . + public static LightGbmBinaryTrainer LightGbm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, + Stream lightGbmModel, + string featureColumnName = DefaultColumnNames.Features + ) + { + Contracts.CheckValue(catalog, nameof(catalog)); + var env = CatalogUtils.GetEnvironment(catalog); + return new LightGbmBinaryTrainer(env, lightGbmModel, featureColumnName); + } + /// /// Create , which predicts a target using a gradient boosting decision tree ranking model. /// @@ -174,6 +207,22 @@ public static LightGbmRankingTrainer LightGbm(this RankingCatalog.RankingTrainer return new LightGbmRankingTrainer(env, options); } + /// + /// Create from a pre-trained LightGBM model, which predicts a target using a gradient boosting decision tree ranking model. + /// + /// The . + /// A pre-trained of a LightGBM model file inferencing + /// The name of the feature column. The column data must be a known-sized vector of . + public static LightGbmRankingTrainer LightGbm(this RankingCatalog.RankingTrainers catalog, + Stream lightGbmModel, + string featureColumnName = DefaultColumnNames.Features + ) + { + Contracts.CheckValue(catalog, nameof(catalog)); + var env = CatalogUtils.GetEnvironment(catalog); + return new LightGbmRankingTrainer(env, lightGbmModel, featureColumnName); + } + /// /// Create , which predicts a target using a gradient boosting decision tree multiclass classification model. /// @@ -225,5 +274,21 @@ public static LightGbmMulticlassTrainer LightGbm(this MulticlassClassificationCa var env = CatalogUtils.GetEnvironment(catalog); return new LightGbmMulticlassTrainer(env, options); } + + /// + /// Create from a pre-trained LightGBM model, which predicts a target using a gradient boosting decision tree multiclass classification model. + /// + /// The . + /// A pre-trained of a LightGBM model file inferencing + /// The name of the feature column. The column data must be a known-sized vector of . + public static LightGbmMulticlassTrainer LightGbm(this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, + Stream lightGbmModel, + string featureColumnName = DefaultColumnNames.Features + ) + { + Contracts.CheckValue(catalog, nameof(catalog)); + var env = CatalogUtils.GetEnvironment(catalog); + return new LightGbmMulticlassTrainer(env, lightGbmModel, featureColumnName); + } } } diff --git a/src/Microsoft.ML.LightGbm/LightGbmMulticlassTrainer.cs b/src/Microsoft.ML.LightGbm/LightGbmMulticlassTrainer.cs index 3b196e8ae0..ae0f3d85e0 100644 --- a/src/Microsoft.ML.LightGbm/LightGbmMulticlassTrainer.cs +++ b/src/Microsoft.ML.LightGbm/LightGbmMulticlassTrainer.cs @@ -4,6 +4,7 @@ using System; using System.Collections.Generic; +using System.IO; using System.Linq; using Microsoft.ML; using Microsoft.ML.Calibrators; @@ -170,6 +171,26 @@ internal LightGbmMulticlassTrainer(IHostEnvironment env, { } + /// + /// Initializes a new instance of + /// + /// The private instance of . + /// A pre-trained of a LightGBM model file inferencing + /// The name of the feature column. + internal LightGbmMulticlassTrainer(IHostEnvironment env, + Stream lightGbmModel, + string featureColumnName = DefaultColumnNames.Features) + : base(env, + LoadNameValue, + new Options() + { + FeatureColumnName = featureColumnName, + LightGbmModel = lightGbmModel + }, + new SchemaShape.Column()) + { + } + private InternalTreeEnsemble GetBinaryEnsemble(int classID) { var res = new InternalTreeEnsemble(); @@ -213,11 +234,15 @@ private protected override void CheckDataValid(IChannel ch, RoleMappedData data) { Host.AssertValue(ch); base.CheckDataValid(ch, data); - var labelType = data.Schema.Label.Value.Type; - if (!(labelType is BooleanDataViewType || labelType is KeyDataViewType || labelType == NumberDataViewType.Single)) + // If using a pre-trained model file we don't need a label or group column + if (LightGbmTrainerOptions.LightGbmModel == null) { - throw ch.ExceptParam(nameof(data), - $"Label column '{data.Schema.Label.Value.Name}' is of type '{labelType.RawType}', but must be of unsigned int, boolean or float."); + var labelType = data.Schema.Label.Value.Type; + if (!(labelType is BooleanDataViewType || labelType is KeyDataViewType || labelType == NumberDataViewType.Single)) + { + throw ch.ExceptParam(nameof(data), + $"Label column '{data.Schema.Label.Value.Name}' is of type '{labelType.RawType}', but must be of unsigned int, boolean or float."); + } } } @@ -227,6 +252,21 @@ private protected override void InitializeBeforeTraining() _numberOfClasses = 0; } + private protected override void AdditionalLoadPreTrainedModel(string modelText) + { + string[] lines = modelText.Split(new char[] { '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries); + // Jump to the "objective" value in the file. It's at the beginning. + int i = 0; + while (!lines[i].StartsWith("objective")) + i++; + + // Format in the file is objective=multiclass num_class:4 + var split = lines[i].Split(' '); + _numberOfClassesIncludingNan = int.Parse(split[1].Split(':')[1]); + _numberOfClasses = _numberOfClassesIncludingNan; + } + + private protected override void ConvertNaNLabels(IChannel ch, RoleMappedData data, float[] labels) { // Only initialize one time. @@ -317,11 +357,14 @@ private protected override void CheckAndUpdateParametersBeforeTraining(IChannel private protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema) { - bool success = inputSchema.TryFindColumn(LabelColumn.Name, out var labelCol); - Contracts.Assert(success); + SchemaShape.Column labelCol = default; + if (LightGbmTrainerOptions.LightGbmModel == null) + { + bool success = inputSchema.TryFindColumn(LabelColumn.Name, out labelCol); + Contracts.Assert(success); + } - var metadata = new SchemaShape(labelCol.Annotations.Where(x => x.Name == AnnotationUtils.Kinds.KeyValues) - .Concat(AnnotationUtils.GetTrainerOutputAnnotation())); + var metadata = LightGbmTrainerOptions.LightGbmModel == null ? new SchemaShape(labelCol.Annotations.Where(x => x.Name == AnnotationUtils.Kinds.KeyValues).Concat(AnnotationUtils.GetTrainerOutputAnnotation())) : new SchemaShape(AnnotationUtils.GetTrainerOutputAnnotation()); return new[] { new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Vector, NumberDataViewType.Single, false, new SchemaShape(AnnotationUtils.AnnotationsForMulticlassScoreColumn(labelCol))), diff --git a/src/Microsoft.ML.LightGbm/LightGbmRankingTrainer.cs b/src/Microsoft.ML.LightGbm/LightGbmRankingTrainer.cs index 84e1637515..15c8950e81 100644 --- a/src/Microsoft.ML.LightGbm/LightGbmRankingTrainer.cs +++ b/src/Microsoft.ML.LightGbm/LightGbmRankingTrainer.cs @@ -4,6 +4,7 @@ using System; using System.Collections.Generic; +using System.IO; using Microsoft.ML; using Microsoft.ML.CommandLine; using Microsoft.ML.Data; @@ -215,27 +216,52 @@ internal LightGbmRankingTrainer(IHostEnvironment env, Host.CheckNonEmpty(rowGroupIdColumnName, nameof(rowGroupIdColumnName)); } + /// + /// Initializes a new instance of + /// + /// The private instance of . + /// A pre-trained of a LightGBM model file inferencing + /// The name of the feature column. + internal LightGbmRankingTrainer(IHostEnvironment env, + Stream lightGbmModel, + string featureColumnName = DefaultColumnNames.Features) + : base(env, + LoadNameValue, + new Options() + { + FeatureColumnName = featureColumnName, + LightGbmModel = lightGbmModel + }, + new SchemaShape.Column()) + { + } + private protected override void CheckDataValid(IChannel ch, RoleMappedData data) { Host.AssertValue(ch); base.CheckDataValid(ch, data); - // Check label types. - var labelCol = data.Schema.Label.Value; - var labelType = labelCol.Type; - if (!(labelType is KeyDataViewType || labelType == NumberDataViewType.Single)) - { - throw ch.ExceptParam(nameof(data), - $"Label column '{labelCol.Name}' is of type '{labelType.RawType}', but must be Key or Single."); - } - // Check group types. - if (!data.Schema.Group.HasValue) - throw ch.ExceptValue(nameof(data.Schema.Group), "Group column is missing."); - var groupCol = data.Schema.Group.Value; - var groupType = groupCol.Type; - if (!(groupType == NumberDataViewType.UInt32 || groupType is KeyDataViewType)) + + // If using a pre-trained model file we don't need a label or group column + if (LightGbmTrainerOptions.LightGbmModel == null) { - throw ch.ExceptParam(nameof(data), - $"Group column '{groupCol.Name}' is of type '{groupType.RawType}', but must be UInt32 or Key."); + // Check label types. + var labelCol = data.Schema.Label.Value; + var labelType = labelCol.Type; + if (!(labelType is KeyDataViewType || labelType == NumberDataViewType.Single)) + { + throw ch.ExceptParam(nameof(data), + $"Label column '{labelCol.Name}' is of type '{labelType.RawType}', but must be Key or Single."); + } + // Check group types. + if (!data.Schema.Group.HasValue) + throw ch.ExceptValue(nameof(data.Schema.Group), "Group column is missing."); + var groupCol = data.Schema.Group.Value; + var groupType = groupCol.Type; + if (!(groupType == NumberDataViewType.UInt32 || groupType is KeyDataViewType)) + { + throw ch.ExceptParam(nameof(data), + $"Group column '{groupCol.Name}' is of type '{groupType.RawType}', but must be UInt32 or Key."); + } } } diff --git a/src/Microsoft.ML.LightGbm/LightGbmRegressionTrainer.cs b/src/Microsoft.ML.LightGbm/LightGbmRegressionTrainer.cs index 6a1e71f773..d16ced2de7 100644 --- a/src/Microsoft.ML.LightGbm/LightGbmRegressionTrainer.cs +++ b/src/Microsoft.ML.LightGbm/LightGbmRegressionTrainer.cs @@ -3,6 +3,7 @@ // See the LICENSE file in the project root for more information. using System.Collections.Generic; +using System.IO; using Microsoft.ML; using Microsoft.ML.CommandLine; using Microsoft.ML.Data; @@ -192,6 +193,26 @@ internal LightGbmRegressionTrainer(IHostEnvironment env, Options options) { } + /// + /// Initializes a new instance of + /// + /// The private instance of . + /// A pre-trained of a LightGBM model file inferencing + /// The name of the feature column. + internal LightGbmRegressionTrainer(IHostEnvironment env, + Stream lightGbmModel, + string featureColumnName = DefaultColumnNames.Features) + : base(env, + LoadNameValue, + new Options() + { + FeatureColumnName = featureColumnName, + LightGbmModel = lightGbmModel + }, + new SchemaShape.Column()) + { + } + private protected override LightGbmRegressionModelParameters CreatePredictor() { Host.Check(TrainedEnsemble != null, @@ -204,11 +225,16 @@ private protected override void CheckDataValid(IChannel ch, RoleMappedData data) { Host.AssertValue(ch); base.CheckDataValid(ch, data); - var labelType = data.Schema.Label.Value.Type; - if (!(labelType is BooleanDataViewType || labelType is KeyDataViewType || labelType == NumberDataViewType.Single)) + + // If using a pre-trained model file we don't need a label column + if (LightGbmTrainerOptions.LightGbmModel == null) { - throw ch.ExceptParam(nameof(data), - $"Label column '{data.Schema.Label.Value.Name}' is of type '{labelType.RawType}', but must be an unsigned int, boolean or float."); + var labelType = data.Schema.Label.Value.Type; + if (!(labelType is BooleanDataViewType || labelType is KeyDataViewType || labelType == NumberDataViewType.Single)) + { + throw ch.ExceptParam(nameof(data), + $"Label column '{data.Schema.Label.Value.Name}' is of type '{labelType.RawType}', but must be an unsigned int, boolean or float."); + } } } diff --git a/src/Microsoft.ML.LightGbm/LightGbmTrainerBase.cs b/src/Microsoft.ML.LightGbm/LightGbmTrainerBase.cs index 4f17e19128..96bc24b5b7 100644 --- a/src/Microsoft.ML.LightGbm/LightGbmTrainerBase.cs +++ b/src/Microsoft.ML.LightGbm/LightGbmTrainerBase.cs @@ -4,7 +4,11 @@ using System; using System.Collections.Generic; +using System.Globalization; +using System.IO; using System.Linq; +using System.Reflection.Emit; +using System.Runtime.InteropServices.ComTypes; using System.Text; using Microsoft.ML.CommandLine; using Microsoft.ML.Data; @@ -60,7 +64,7 @@ public class OptionsBase : TrainerInputBaseWithGroupId {nameof(UseZeroAsMissingValue), "zero_as_missing" } }; - private protected string GetOptionName(string name) + internal string GetOptionName(string name) { if (NameMapping.ContainsKey(name)) return NameMapping[name]; @@ -237,6 +241,8 @@ private protected OptionsBase() { } private BoosterParameterBase.OptionsBase _boosterParameter; + internal Stream LightGbmModel = null; + /// /// Booster parameter to use /// @@ -356,25 +362,93 @@ private protected override TModel TrainModelCore(TrainContext context) InitializeBeforeTraining(); Host.CheckValue(context, nameof(context)); - Dataset dtrain = null; Dataset dvalid = null; - CategoricalMetaData catMetaData; + try { - using (var ch = Host.Start("Loading data for LightGBM")) + if (LightGbmTrainerOptions.LightGbmModel != null) { - using (var pch = Host.StartProgressChannel("Loading data for LightGBM")) + LightGbmTrainerOptions.LightGbmModel.Position = 0; + using (var ch = Host.Start("Loading LightGBM model file")) { - dtrain = LoadTrainingData(ch, context.TrainingSet, out catMetaData); - if (context.ValidationSet != null) - dvalid = LoadValidationData(ch, dtrain, context.ValidationSet, catMetaData); + StreamReader reader = new StreamReader(LightGbmTrainerOptions.LightGbmModel); + string modelText = reader.ReadToEnd(); + + AdditionalLoadPreTrainedModel(modelText); + + // Load objective into options + string[] lines = modelText.Split(new char[] { '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries); + // Jump to the "objective" value in the file. It's at the beginning. + int i = 0; + while (!lines[i].StartsWith("objective")) + i++; + + // Format in the file is objective=multiclass num_class:4 + var split = lines[i].Split(' '); + GbmOptions["objective"] = split[0].Split('=')[1]; + + var modelParameters = Booster.GetParameters(modelText); + // Going to set the parameters via reflection so that we don't have manually set them on the options object + Type optionsType = LightGbmTrainerOptions.GetType(); + var optionsFields = optionsType.GetFields(System.Reflection.BindingFlags.Public | System.Reflection.BindingFlags.NonPublic | System.Reflection.BindingFlags.FlattenHierarchy | System.Reflection.BindingFlags.Instance); + + foreach (var field in optionsFields) + { + var lightGbmName = LightGbmTrainerOptions.GetOptionName(field.Name); + if (modelParameters.ContainsKey(lightGbmName)) + { + if (field.FieldType.Name.StartsWith("Nullable")) + { + if (field.FieldType.GenericTypeArguments[0] == typeof(double)) + { + field.SetValue(LightGbmTrainerOptions, Double.Parse(modelParameters[lightGbmName])); + } + else if (field.FieldType.GenericTypeArguments[0] == typeof(int)) + { + field.SetValue(LightGbmTrainerOptions, int.Parse(modelParameters[lightGbmName])); + } + else if (field.FieldType.GenericTypeArguments[0] == typeof(float)) + { + field.SetValue(LightGbmTrainerOptions, float.Parse(modelParameters[lightGbmName])); + } + // TODO: throw for unknown type + } + else if (field.FieldType.Name.StartsWith("Boolean")) + { + if (modelParameters[lightGbmName] == "1") + field.SetValue(LightGbmTrainerOptions, true); + else + field.SetValue(LightGbmTrainerOptions, false); + } + else + field.SetValue(LightGbmTrainerOptions, Convert.ChangeType(modelParameters[lightGbmName], field.FieldType)); + } + } + + var catBoundaries = !String.IsNullOrEmpty(modelParameters["categorical_feature"]) ? modelParameters["categorical_feature"].Split(',').Select(x => int.Parse(x, CultureInfo.InvariantCulture)).ToArray() : null; + TrainedEnsemble = Booster.GetModel(catBoundaries, modelText); + FeatureCount = Booster.GetNumFeatures(modelText); } } - using (var ch = Host.Start("Training with LightGBM")) + else { - using (var pch = Host.StartProgressChannel("Training with LightGBM")) - TrainCore(ch, pch, dtrain, catMetaData, dvalid); + CategoricalMetaData catMetaData; + + using (var ch = Host.Start("Loading data for LightGBM")) + { + using (var pch = Host.StartProgressChannel("Loading data for LightGBM")) + { + dtrain = LoadTrainingData(ch, context.TrainingSet, out catMetaData); + if (context.ValidationSet != null) + dvalid = LoadValidationData(ch, dtrain, context.ValidationSet, catMetaData); + } + } + using (var ch = Host.Start("Training with LightGBM")) + { + using (var pch = Host.StartProgressChannel("Training with LightGBM")) + TrainCore(ch, pch, dtrain, catMetaData, dvalid); + } } } finally @@ -388,6 +462,9 @@ private protected override TModel TrainModelCore(TrainContext context) private protected virtual void InitializeBeforeTraining() { } + // For loading addtional info when we are loading a pre-trained model. + private protected virtual void AdditionalLoadPreTrainedModel(string modelText) { } + private void InitParallelTraining() { if (ParallelTraining.ParallelType() != "serial" && ParallelTraining.NumMachines() > 1) @@ -420,7 +497,9 @@ private void DisposeParallelTraining() private protected virtual void CheckDataValid(IChannel ch, RoleMappedData data) { data.CheckFeatureFloatVector(); - ch.CheckParam(data.Schema.Label.HasValue, nameof(data), "Need a label column"); + // If we are loading a pre-trained model we don't need a label column + if (LightGbmTrainerOptions.LightGbmModel == null) + ch.CheckParam(data.Schema.Label.HasValue, nameof(data), "Need a label column"); } private protected virtual void GetDefaultParameters(IChannel ch, int numRow, bool hasCategorical, int totalCats, bool hiddenMsg = false) @@ -620,6 +699,7 @@ private void TrainCore(IChannel ch, IProgressChannel pch, Dataset dtrain, Catego Host.AssertValue(dtrain); Host.AssertValueOrNull(dvalid); Host.CheckAlive(); + // For multi class, the number of labels is required. ch.Assert(((ITrainer)this).PredictionKind != PredictionKind.MulticlassClassification || GbmOptions.ContainsKey("num_class"), "LightGBM requires the number of classes to be specified in the parameters."); @@ -632,7 +712,7 @@ private void TrainCore(IChannel ch, IProgressChannel pch, Dataset dtrain, Catego dvalid: dvalid, numIteration: LightGbmTrainerOptions.NumberOfIterations, verboseEval: LightGbmTrainerOptions.Verbose, earlyStoppingRound: LightGbmTrainerOptions.EarlyStoppingRound)) { - TrainedEnsemble = bst.GetModel(catMetaData.CategoricalBoudaries); + TrainedEnsemble = Booster.GetModel(catMetaData.CategoricalBoudaries, bst.GetModelString()); } } } diff --git a/src/Microsoft.ML.LightGbm/WrappedLightGbmBooster.cs b/src/Microsoft.ML.LightGbm/WrappedLightGbmBooster.cs index a02d21de6b..99569c17cb 100644 --- a/src/Microsoft.ML.LightGbm/WrappedLightGbmBooster.cs +++ b/src/Microsoft.ML.LightGbm/WrappedLightGbmBooster.cs @@ -186,10 +186,44 @@ private static int[] GetCatThresholds(UInt32[] catThreshold, int lowerBound, int return cats.ToArray(); } - public InternalTreeEnsemble GetModel(int[] categoricalFeatureBoudaries) + public static int GetNumFeatures(string modelString) + { + string[] lines = modelString.Split(new char[] { '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries); + // Jump to the "max_feature_idx" value in the file. It's at the beginning. + int i = 0; + while (!lines[i].StartsWith("max_feature_idx")) + i++; + + // Stored 0 based in the file, need the actual count so adding 1. + return int.Parse(lines[i].Split('=')[1]) + 1; + } + + public static Dictionary GetParameters(string modelString) + { + Dictionary parameters = new Dictionary(); + string[] lines = modelString.Split(new char[] { '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries); + + // Jump to the parameters section in the file. It's at the very end. + int i = 0; + while (!lines[i].StartsWith("parameters")) + i++; + + // Increment once more to get to the first parameter + i++; + + while (i <= lines.Length && !lines[i].StartsWith("end of parameters")) + { + var param = lines[i].Substring(1, lines[i].Length - 2).Split(':'); + parameters[param[0]] = param[1].Trim(); + i++; + } + + return parameters; + } + + public static InternalTreeEnsemble GetModel(int[] categoricalFeatureBoundaries, string modelString) { InternalTreeEnsemble res = new InternalTreeEnsemble(); - string modelString = GetModelString(); string[] lines = modelString.Split('\n'); int i = 0; for (; i < lines.Length;) @@ -219,11 +253,11 @@ public InternalTreeEnsemble GetModel(int[] categoricalFeatureBoudaries) var defaultValue = GetDefalutValue(threshold, decisionType); var categoricalSplitFeatures = new int[numberOfLeaves - 1][]; var categoricalSplit = new bool[numberOfLeaves - 1]; - if (categoricalFeatureBoudaries != null) + if (categoricalFeatureBoundaries != null) { // Add offsets to split features. for (int node = 0; node < numberOfLeaves - 1; ++node) - splitFeature[node] = categoricalFeatureBoudaries[splitFeature[node]]; + splitFeature[node] = categoricalFeatureBoundaries[splitFeature[node]]; } if (numCat > 0) diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs b/test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs index 2384545fa0..5f073c58e5 100644 --- a/test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs +++ b/test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs @@ -20,6 +20,7 @@ using Microsoft.ML.Transforms; using Xunit; using FluentAssertions; +using System.IO; namespace Microsoft.ML.Tests.TrainerEstimators { @@ -303,11 +304,29 @@ public void LightGbmMulticlassEstimator() var (pipeline, dataView) = GetMulticlassPipeline(); var trainer = ML.MulticlassClassification.Trainers.LightGbm(learningRate: 0.4); var pipe = pipeline.Append(trainer) - .Append(new KeyToValueMappingEstimator(Env, "PredictedLabel")); + .Append(new KeyToValueMappingEstimator(Env, "PredictedLabel")); TestEstimatorCore(pipe, dataView); Done(); } + /// + /// LightGbmMulticlass TrainerEstimator test loading a pre-trained model. + /// + [LightGBMFact] + public void LightGbmMulticlassEstimatorFile() + { + var modelPath = GetDataPath("iris-lgbm.txt"); + var (pipeline, dataView) = GetMulticlassPipeline(); + using (var fStream = new FileStream(modelPath, FileMode.Open, FileAccess.Read, FileShare.ReadWrite)) + { + var trainer = ML.MulticlassClassification.Trainers.LightGbm(fStream); + var pipe = pipeline.Append(trainer); + var data = pipe.Fit(dataView).Transform(dataView).Preview(); + TestEstimatorCore(pipe, dataView); + } + Done(); + } + /// /// LightGbmMulticlass TrainerEstimator test with options /// diff --git a/test/data/iris-lgbm.txt b/test/data/iris-lgbm.txt new file mode 100644 index 0000000000..cd8796f5d9 --- /dev/null +++ b/test/data/iris-lgbm.txt @@ -0,0 +1,7337 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0] +[first_metric_only: 0] +[max_delta_step: 0] +[lambda_l1: 0] +[lambda_l2: 0] +[min_gain_to_split: 0] +[drop_rate: 0.1] +[max_drop: 50] +[skip_drop: 0.5] +[xgboost_dart_mode: 0] +[uniform_drop: 0] +[drop_seed: 4] +[top_rate: 0.2] +[other_rate: 0.1] +[min_data_per_group: 100] +[max_cat_threshold: 32] +[cat_l2: 10] +[cat_smooth: 10] +[max_cat_to_onehot: 4] +[top_k: 20] +[monotone_constraints: ] +[feature_contri: ] +[forcedsplits_filename: ] +[forcedbins_filename: ] +[refit_decay_rate: 0.9] +[cegb_tradeoff: 1] +[cegb_penalty_split: 0] +[cegb_penalty_feature_lazy: ] +[cegb_penalty_feature_coupled: ] +[verbosity: 1] +[max_bin: 255] +[max_bin_by_feature: ] +[min_data_in_bin: 3] +[bin_construct_sample_cnt: 200000] +[histogram_pool_size: -1] +[data_random_seed: 1] +[output_model: LightGBM_model.txt] +[snapshot_freq: -1] +[input_model: ] +[output_result: LightGBM_predict_result.txt] +[initscore_filename: ] +[valid_data_initscores: ] +[pre_partition: 0] +[enable_bundle: 1] +[max_conflict_rate: 0] +[is_enable_sparse: 1] +[sparse_threshold: 0.8] +[use_missing: 1] +[zero_as_missing: 0] +[two_round: 0] +[save_binary: 0] +[header: 0] +[label_column: ] +[weight_column: ] +[group_column: ] +[ignore_column: ] +[categorical_feature: ] +[predict_raw_score: 0] +[predict_leaf_index: 0] +[predict_contrib: 0] +[num_iteration_predict: -1] +[pred_early_stop: 0] +[pred_early_stop_freq: 10] +[pred_early_stop_margin: 10] +[convert_model_language: ] +[convert_model: gbdt_prediction.cpp] +[num_class: 4] +[is_unbalance: 0] +[scale_pos_weight: 1] +[sigmoid: 1] +[boost_from_average: 1] +[reg_sqrt: 0] +[alpha: 0.9] +[fair_c: 1] +[poisson_max_delta_step: 0.7] +[tweedie_variance_power: 1.5] +[max_position: 20] +[lambdamart_norm: 1] +[label_gain: ] +[metric_freq: 1] +[is_provide_training_metric: 0] +[eval_at: ] +[multi_error_top_k: 1] +[num_machines: 1] +[local_listen_port: 12400] +[time_out: 120] +[machine_list_filename: ] +[machines: ] +[gpu_platform_id: -1] +[gpu_device_id: -1] +[gpu_use_dp: 0] + +end of parameters + +pandas_categorical:null