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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.
using System.Linq;
using System.Text;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.EntryPoints;
using Microsoft.ML.Model;
using Microsoft.ML.Runtime;
using Microsoft.ML.Trainers.FastTree;
[assembly: LoadableClass(FastTreeRegressionTrainer.Summary, typeof(FastTreeRegressionTrainer), typeof(FastTreeRegressionTrainer.Options),
new[] { typeof(SignatureRegressorTrainer), typeof(SignatureTrainer), typeof(SignatureTreeEnsembleTrainer), typeof(SignatureFeatureScorerTrainer) },
FastTreeRegressionTrainer.UserNameValue,
FastTreeRegressionTrainer.LoadNameValue,
FastTreeRegressionTrainer.ShortName,
// FastRank names
"FastRankRegression",
"FastRankRegressionWrapper",
"frr",
"btr")]
[assembly: LoadableClass(typeof(FastTreeRegressionModelParameters), null, typeof(SignatureLoadModel),
"FastTree Regression Executor",
FastTreeRegressionModelParameters.LoaderSignature)]
namespace Microsoft.ML.Trainers.FastTree
{
/// <summary>
/// The <see cref="IEstimator{TTransformer}"/> for training a decision tree regression model using FastTree.
/// </summary>
/// <remarks>
/// <format type="text/markdown"><)
/// or [FastTree(Options)](xref:Microsoft.ML.TreeExtensions.FastTree(Microsoft.ML.RegressionCatalog.RegressionTrainers,Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer.Options)).
///
/// [!include[io](~/../docs/samples/docs/api-reference/io-columns-regression.md)]
///
/// ### Trainer Characteristics
/// | | |
/// | -- | -- |
/// | Machine learning task | Regression |
/// | Is normalization required? | No |
/// | Is caching required? | No |
/// | Required NuGet in addition to Microsoft.ML | Microsoft.ML.FastTree |
/// | Exportable to ONNX | Yes |
///
/// [!include[algorithm](~/../docs/samples/docs/api-reference/algo-details-fasttree.md)]
/// ]]>
/// </format>
/// </remarks>
/// <seealso cref="TreeExtensions.FastTree(RegressionCatalog.RegressionTrainers, string, string, string, int, int, int, double)"/>
/// <seealso cref="TreeExtensions.FastTree(RegressionCatalog.RegressionTrainers, FastTreeRegressionTrainer.Options)"/>
/// <seealso cref="Options"/>
public sealed partial class FastTreeRegressionTrainer
: BoostingFastTreeTrainerBase<FastTreeRegressionTrainer.Options, RegressionPredictionTransformer<FastTreeRegressionModelParameters>, FastTreeRegressionModelParameters>
{
internal const string LoadNameValue = "FastTreeRegression";
internal const string UserNameValue = "FastTree (Boosted Trees) Regression";
internal const string Summary = "Trains gradient boosted decision trees to fit target values using least-squares.";
internal const string ShortName = "ftr";
private TestHistory _firstTestSetHistory;
private Test _trainRegressionTest;
private Test _testRegressionTest;
/// <summary>
/// The type of prediction for the trainer.
/// </summary>
private protected override PredictionKind PredictionKind => PredictionKind.Regression;
/// <summary>
/// Initializes a new instance of <see cref="FastTreeRegressionTrainer"/>
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="exampleWeightColumnName">The name for the column containing the example weight.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="minimumExampleCountPerLeaf">The minimal number of examples allowed in a leaf of a regression tree, out of the subsampled data.</param>
/// <param name="numberOfLeaves">The max number of leaves in each regression tree.</param>
/// <param name="numberOfTrees">Total number of decision trees to create in the ensemble.</param>
internal FastTreeRegressionTrainer(IHostEnvironment env,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int numberOfLeaves = Defaults.NumberOfLeaves,
int numberOfTrees = Defaults.NumberOfTrees,
int minimumExampleCountPerLeaf = Defaults.MinimumExampleCountPerLeaf,
double learningRate = Defaults.LearningRate)
: base(env, TrainerUtils.MakeR4ScalarColumn(labelColumnName), featureColumnName, exampleWeightColumnName, null, numberOfLeaves, numberOfTrees, minimumExampleCountPerLeaf, learningRate)
{
}
/// <summary>
/// Initializes a new instance of <see cref="FastTreeRegressionTrainer"/> by using the <see cref="Options"/> class.
/// </summary>
/// <param name="env">The instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="options">Algorithm advanced settings.</param>
internal FastTreeRegressionTrainer(IHostEnvironment env, Options options)
: base(env, options, TrainerUtils.MakeR4ScalarColumn(options.LabelColumnName))
{
}
private protected override FastTreeRegressionModelParameters TrainModelCore(TrainContext context)
{
Host.CheckValue(context, nameof(context));
var trainData = context.TrainingSet;
ValidData = context.ValidationSet;
TestData = context.TestSet;
using (var ch = Host.Start("Training"))
{
trainData.CheckRegressionLabel();
trainData.CheckFeatureFloatVector();
trainData.CheckOptFloatWeight();
FeatureCount = trainData.Schema.Feature.Value.Type.GetValueCount();
ConvertData(trainData);
TrainCore(ch);
}
return new FastTreeRegressionModelParameters(Host, TrainedEnsemble, FeatureCount, InnerOptions);
}
private protected override void CheckOptions(IChannel ch)
{
Contracts.AssertValue(ch);
base.CheckOptions(ch);
bool doEarlyStop = FastTreeTrainerOptions.EarlyStoppingRuleFactory != null ||
FastTreeTrainerOptions.EnablePruning;
if (doEarlyStop)
ch.CheckUserArg(FastTreeTrainerOptions.EarlyStoppingMetrics >= 1 && FastTreeTrainerOptions.EarlyStoppingMetrics <= 2,
nameof(FastTreeTrainerOptions.EarlyStoppingMetrics), "earlyStoppingMetrics should be 1 or 2. (1: L1, 2: L2)");
}
private static SchemaShape.Column MakeLabelColumn(string labelColumn)
{
return new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberDataViewType.Single, false);
}
private protected override ObjectiveFunctionBase ConstructObjFunc(IChannel ch)
{
return new ObjectiveImpl(TrainSet, FastTreeTrainerOptions);
}
private protected override OptimizationAlgorithm ConstructOptimizationAlgorithm(IChannel ch)
{
OptimizationAlgorithm optimizationAlgorithm = base.ConstructOptimizationAlgorithm(ch);
if (FastTreeTrainerOptions.UseLineSearch)
{
var lossCalculator = new RegressionTest(optimizationAlgorithm.TrainingScores);
// REVIEW: We should make loss indices an enum in BinaryClassificationTest.
optimizationAlgorithm.AdjustTreeOutputsOverride = new LineSearch(lossCalculator, 1 /*L2 error*/, FastTreeTrainerOptions.MaximumNumberOfLineSearchSteps, FastTreeTrainerOptions.MinimumStepSize);
}
return optimizationAlgorithm;
}
/// <summary>
/// Gets the regression labels that were stored in the dataset skeleton, or
/// constructs them from the ratings if absent. This returns null if the
/// dataset itself is null.
/// </summary>
/// <param name="set">The dataset</param>
/// <returns>The list of regression targets, or null if <paramref name="set"/> was null</returns>
internal static float[] GetDatasetRegressionLabels(Dataset set)
{
if (set == null)
return null;
double[] dlabels = set.Targets;
Contracts.AssertValue(dlabels);
Contracts.Assert(dlabels.Length == set.NumDocs);
// REVIEW: Seems wasteful??
return dlabels.Select(x => (float)x).ToArray(dlabels.Length);
}
private protected override void PrepareLabels(IChannel ch)
{
}
private protected override Test ConstructTestForTrainingData()
{
return new RegressionTest(ConstructScoreTracker(TrainSet));
}
private protected override RegressionPredictionTransformer<FastTreeRegressionModelParameters> MakeTransformer(FastTreeRegressionModelParameters model, DataViewSchema trainSchema)
=> new RegressionPredictionTransformer<FastTreeRegressionModelParameters>(Host, model, trainSchema, FeatureColumn.Name);
/// <summary>
/// Trains a <see cref="FastTreeRegressionTrainer"/> using both training and validation data, returns
/// a <see cref="RegressionPredictionTransformer{FastTreeRegressionModelParameters}"/>.
/// </summary>
public RegressionPredictionTransformer<FastTreeRegressionModelParameters> Fit(IDataView trainData, IDataView validationData)
=> TrainTransformer(trainData, validationData);
private protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema)
{
return new[]
{
new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberDataViewType.Single, false, new SchemaShape(AnnotationUtils.GetTrainerOutputAnnotation()))
};
}
private void AddFullRegressionTests()
{
// Always compute training L1/L2 errors.
Tests.Add(new RegressionTest(ConstructScoreTracker(TrainSet)));
RegressionTest validTest = null;
if (ValidSet != null)
{
validTest = new RegressionTest(ConstructScoreTracker(ValidSet));
Tests.Add(validTest);
}
// If external label is missing use Rating column for L1/L2 error.
// The values may not make much sense if regression value is not an actual label value.
if (TestSets != null)
{
for (int t = 0; t < TestSets.Length; ++t)
Tests.Add(new RegressionTest(ConstructScoreTracker(TestSets[t])));
}
}
#if OLD_TRACING
protected virtual void AddFullNDCGTests()
{
Tests.Add(new NDCGTest(ConstructScoreTracker(TrainSet), TrainSet.Ratings, _args.sortingAlgorithm));
if (ValidSet != null)
{
Test test = new NDCGTest(ConstructScoreTracker(ValidSet), ValidSet.Ratings, _args.sortingAlgorithm);
Tests.Add(test);
}
if (TestSets != null)
{
for (int t = 0; t < TestSets.Length; ++t)
{
Test test = new NDCGTest(ConstructScoreTracker(TestSets[t]), TestSets[t].Ratings, _args.sortingAlgorithm);
if (t == 0)
{
_firstTestSetHistory = new TestHistory(test, 0);
}
Tests.Add(test);
}
}
}
#endif
private protected override void InitializeTests()
{
// Initialize regression tests.
if (FastTreeTrainerOptions.TestFrequency != int.MaxValue)
AddFullRegressionTests();
if (FastTreeTrainerOptions.PrintTestGraph)
{
// If FirstTestHistory is null (which means the tests were not initialized due to /tf==infinity),
// we need initialize first set for graph printing.
// Adding to a tests would result in printing the results after final iteration.
if (_firstTestSetHistory == null)
{
var firstTestSetTest = new RegressionTest(ConstructScoreTracker(TestSets[0]));
_firstTestSetHistory = new TestHistory(firstTestSetTest, 0);
}
}
if (FastTreeTrainerOptions.PrintTrainValidGraph && _trainRegressionTest == null)
{
Test trainRegressionTest = new RegressionTest(ConstructScoreTracker(TrainSet));
_trainRegressionTest = trainRegressionTest;
}
if (FastTreeTrainerOptions.PrintTrainValidGraph && _testRegressionTest == null && TestSets != null && TestSets.Length > 0)
_testRegressionTest = new RegressionTest(ConstructScoreTracker(TestSets[0]));
// Add early stopping if appropriate.
TrainTest = new RegressionTest(ConstructScoreTracker(TrainSet), FastTreeTrainerOptions.EarlyStoppingMetrics);
if (ValidSet != null)
ValidTest = new RegressionTest(ConstructScoreTracker(ValidSet), FastTreeTrainerOptions.EarlyStoppingMetrics);
if (FastTreeTrainerOptions.EnablePruning && ValidTest != null)
{
if (FastTreeTrainerOptions.UseTolerantPruning) // Use simple early stopping condition.
PruningTest = new TestWindowWithTolerance(ValidTest, 0, FastTreeTrainerOptions.PruningWindowSize, FastTreeTrainerOptions.PruningThreshold);
else
PruningTest = new TestHistory(ValidTest, 0);
}
}
private protected override void PrintIterationMessage(IChannel ch, IProgressChannel pch)
{
// REVIEW: Shift this to use progress channels.
#if OLD_TRACING
ch.Info("Finished iteration {0}", Ensemble.NumTrees);
//This needs to be executed every iteration
if (PruningTest != null)
{
if (PruningTest is TestWindowWithTolerance)
{
if (PruningTest.BestIteration != -1)
{
ch.Info("Iteration {0} \t(Best tolerated validation moving average iter {1}:{2}~{3})",
Ensemble.NumTrees,
PruningTest.BestIteration,
(PruningTest as TestWindowWithTolerance).BestAverageValue,
(PruningTest as TestWindowWithTolerance).CurrentAverageValue);
}
else
{
ch.Info("Iteration {0}", Ensemble.NumTrees);
}
}
else
{
ch.Info("Iteration {0} \t(best validation iter {1}:{2}>{3})",
Ensemble.NumTrees,
PruningTest.BestIteration,
PruningTest.BestResult.FinalValue,
PruningTest.ComputeTests().First().FinalValue);
}
}
else
base.PrintIterationMessage(ch, pch);
#else
base.PrintIterationMessage(ch, pch);
#endif
}
private protected override string GetTestGraphHeader()
{
StringBuilder headerBuilder = new StringBuilder("Eval:\tFileName\tNDCG@1\tNDCG@2\tNDCG@3\tNDCG@4\tNDCG@5\tNDCG@6\tNDCG@7\tNDCG@8\tNDCG@9\tNDCG@10");
if (FastTreeTrainerOptions.PrintTrainValidGraph)
{
headerBuilder.Append("\tNDCG@20\tNDCG@40");
headerBuilder.Append("\nNote: Printing train L2 error as NDCG@20 and test L2 error as NDCG@40..\n");
}
return headerBuilder.ToString();
}
private protected override void ComputeTests()
{
if (_firstTestSetHistory != null)
{
_firstTestSetHistory.ComputeTests();
}
if (_trainRegressionTest != null)
{
_trainRegressionTest.ComputeTests();
}
if (_testRegressionTest != null)
{
_testRegressionTest.ComputeTests();
}
if (PruningTest != null)
{
PruningTest.ComputeTests();
}
}
private protected override string GetTestGraphLine()
{
StringBuilder lineBuilder = new StringBuilder();
lineBuilder.AppendFormat("Eval:\tnet.{0:D8}.ini", Ensemble.NumTrees - 1);
foreach (var r in _firstTestSetHistory.ComputeTests())
{
lineBuilder.AppendFormat("\t{0:0.0000}"{0:0.0000}", r.FinalValue);
}
double trainRegression = 0.0;
double validRegression = 0.0;
// We only print non-zero train&valid graph if earlyStoppingTruncation!=0.
// In case /es is not set, we print 0 for train and valid graph NDCG.
// Let's keeping this behavior for backward compatibility with previous FR version.
// Ideally /graphtv should enforce non-zero /es in the commandline validation.
if (_trainRegressionTest != null)
trainRegression = _trainRegressionTest.ComputeTests().Last().FinalValue;
if (_testRegressionTest != null)
validRegression = _testRegressionTest.ComputeTests().Last().FinalValue;
lineBuilder.AppendFormat("\t{0:0.0000}\t{1:0.0000}", trainRegression, validRegression);
return lineBuilder.ToString();
}
private protected override void Train(IChannel ch)
{
base.Train(ch);
// Print final last iteration.
// Note that trainNDCG printed in graph will be from copy of a value from previous iteration
// and will differ slightly from the proper final value computed by FullTest.
// We cannot compute the final NDCG here due to the fact we use FastNDCGTestForTrainSet
// computing NDCG based on label sort saved during gradient computation (and we don't have
// gradients for n+1 iteration).
// Keeping it in sync with original FR code
PrintTestGraph(ch);
}
internal sealed class ObjectiveImpl : ObjectiveFunctionBase, IStepSearch
{
private readonly float[] _labels;
public ObjectiveImpl(Dataset trainData, GamRegressionTrainer.Options options) :
base(
trainData,
options.LearningRate,
0,
options.MaximumTreeOutput,
options.GetDerivativesSampleRate,
false,
options.Seed)
{
_labels = GetDatasetRegressionLabels(trainData);
}
public ObjectiveImpl(Dataset trainData, Options options)
: base(
trainData,
options.LearningRate,
options.Shrinkage,
options.MaximumTreeOutput,
options.GetDerivativesSampleRate,
options.BestStepRankingRegressionTrees,
options.Seed)
{
if (options.DropoutRate > 0 && LearningRate > 0) // Don't do shrinkage if dropouts are used.
Shrinkage = 1.0 / LearningRate;
_labels = GetDatasetRegressionLabels(trainData);
}
public void AdjustTreeOutputs(IChannel ch, InternalRegressionTree tree, DocumentPartitioning partitioning, ScoreTracker trainingScores)
{
double shrinkage = LearningRate * Shrinkage;
for (int l = 0; l < tree.NumLeaves; ++l)
{
double output = tree.GetOutput(l) * shrinkage;
tree.SetOutput(l, output);
}
}
protected override void GetGradientInOneQuery(int query, int threadIndex)
{
int begin = Dataset.Boundaries[query];
int end = Dataset.Boundaries[query + 1];
// Gradient.
unchecked
{
for (int i = begin; i < end; ++i)
Gradient[i] = _labels[i] - Scores[i];
}
}
}
}
/// <summary>
/// Model parameters for <see cref="FastForestRegressionTrainer"/>.
/// </summary>
public sealed class FastTreeRegressionModelParameters : TreeEnsembleModelParametersBasedOnRegressionTree
{
internal const string LoaderSignature = "FastTreeRegressionExec";
internal const string RegistrationName = "FastTreeRegressionPredictor";
private static VersionInfo GetVersionInfo()
{
return new VersionInfo(
modelSignature: "FTREE RE",
// 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,
loaderAssemblyName: typeof(FastTreeRegressionModelParameters).Assembly.FullName);
}
private protected override uint VerNumFeaturesSerialized => 0x00010002;
private protected override uint VerDefaultValueSerialized => 0x00010004;
private protected override uint VerCategoricalSplitSerialized => 0x00010005;
internal FastTreeRegressionModelParameters(IHostEnvironment env, InternalTreeEnsemble trainedEnsemble, int featureCount, string innerArgs)
: base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs)
{
}
private FastTreeRegressionModelParameters(IHostEnvironment env, ModelLoadContext ctx)
: base(env, RegistrationName, ctx, GetVersionInfo())
{
}
private protected override void SaveCore(ModelSaveContext ctx)
{
base.SaveCore(ctx);
ctx.SetVersionInfo(GetVersionInfo());
}
internal static FastTreeRegressionModelParameters Create(IHostEnvironment env, ModelLoadContext ctx)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());
return new FastTreeRegressionModelParameters(env, ctx);
}
private protected override PredictionKind PredictionKind => PredictionKind.Regression;
}
internal static partial class FastTree
{
[TlcModule.EntryPoint(Name = "Trainers.FastTreeRegressor",
Desc = FastTreeRegressionTrainer.Summary,
UserName = FastTreeRegressionTrainer.UserNameValue,
ShortName = FastTreeRegressionTrainer.ShortName)]
public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastTreeRegressionTrainer.Options input)
{
Contracts.CheckValue(env, nameof(env));
var host = env.Register("TrainFastTree");
host.CheckValue(input, nameof(input));
EntryPointUtils.CheckInputArgs(host, input);
return TrainerEntryPointsUtils.Train<FastTreeRegressionTrainer.Options, CommonOutputs.RegressionOutput>(host, input,
() => new FastTreeRegressionTrainer(host, input),
() => TrainerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumnName),
() => TrainerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.ExampleWeightColumnName),
() => TrainerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.RowGroupColumnName));
}
}
}
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