/// <summary>
/// FastTree <see cref="RegressionCatalog"/> extension method.
/// Predicts a target using a decision tree regression model trained with the <see cref="FastTreeRegressionTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features column.</param>
/// <param name="weights">The optional weights column.</param>
/// <param name="options">Algorithm advanced settings.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way;
/// it is only a way for the caller to be informed about what was learnt.</param>
/// <returns>The Score output column indicating the predicted value.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static Scalar <float> FastTree(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label, Vector <float> features, Scalar <float> weights,
FastTreeRegressionTrainer.Options options,
Action <FastTreeRegressionModelParameters> onFit = null)
{
Contracts.CheckValueOrNull(options);
CheckUserValues(label, features, weights, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
options.LabelColumnName = labelName;
options.FeatureColumnName = featuresName;
options.ExampleWeightColumnName = weightsName;
var trainer = new FastTreeRegressionTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
public void FastTreeRegressorEstimator()
{
using (var env = new LocalEnvironment(seed: 1, conc: 1))
{
// "loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+}"
var reader = new TextLoader(env,
new TextLoader.Arguments()
{
Separator = ";",
HasHeader = true,
Column = new[]
{
new TextLoader.Column("Label", DataKind.R4, 11),
new TextLoader.Column("Features", DataKind.R4, new [] { new TextLoader.Range(0, 10) })
}
});
var data = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.generatedRegressionDatasetmacro.trainFilename)));
// Pipeline.
var pipeline = new FastTreeRegressionTrainer(env, "Label", "Features", advancedSettings: s => {
s.NumTrees = 10;
s.NumThreads = 1;
s.NumLeaves = 5;
});
TestEstimatorCore(pipeline, data);
}
}
/// <summary>
/// FastTree <see cref="RegressionContext"/> extension method.
/// Predicts a target using a decision tree regression model trained with the <see cref="FastTreeRegressionTrainer"/>.
/// </summary>
/// <param name="ctx">The <see cref="RegressionContext"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features column.</param>
/// <param name="weights">The optional weights column.</param>
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
/// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of a regression tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="advancedSettings">Algorithm advanced settings.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way;
/// it is only a way for the caller to be informed about what was learnt.</param>
/// <returns>The Score output column indicating the predicted value.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static Scalar <float> FastTree(this RegressionContext.RegressionTrainers ctx,
Scalar <float> label, Vector <float> features, Scalar <float> weights = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
double learningRate = Defaults.LearningRates,
Action <FastTreeRegressionTrainer.Arguments> advancedSettings = null,
Action <FastTreeRegressionModelParameters> onFit = null)
{
CheckUserValues(label, features, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate, advancedSettings, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new FastTreeRegressionTrainer(env, labelName, featuresName, weightsName, numLeaves,
numTrees, minDatapointsInLeaves, learningRate, advancedSettings);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
///<param name="scoreTracker"></param>
/// <param name="resultType">1: L1, 2: L2. Otherwise, return all.</param>
public RegressionTest(ScoreTracker scoreTracker, int?resultType = null)
: base(scoreTracker)
{
_labels = FastTreeRegressionTrainer.GetDatasetRegressionLabels(scoreTracker.Dataset);
Contracts.Check(scoreTracker.Dataset.NumDocs == _labels.Length, "Mismatch between dataset and labels");
_resultType = resultType;
}
public void FastTreeRegressorEstimator()
{
// Pipeline.
var pipeline = new FastTreeRegressionTrainer(Env, "Label", "Features", advancedSettings: s => {
s.NumTrees = 10;
s.NumThreads = 1;
s.NumLeaves = 5;
});
TestEstimatorCore(pipeline, GetRegressionPipeline());
Done();
}
public void FastTreeRegressorEstimator()
{
var dataView = GetRegressionPipeline();
var trainer = new FastTreeRegressionTrainer(Env, "Label", "Features", advancedSettings: s =>
{
s.NumTrees = 10;
s.NumThreads = 1;
s.NumLeaves = 5;
});
TestEstimatorCore(trainer, dataView);
var model = trainer.Train(dataView, dataView);
Done();
}
/// <summary>
/// FastTree <see cref="RegressionCatalog"/> extension method.
/// Predicts a target using a decision tree regression model trained with the <see cref="FastTreeRegressionTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features column.</param>
/// <param name="weights">The optional weights column.</param>
/// <param name="numberOfTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="numberOfLeaves">The maximum number of leaves per decision tree.</param>
/// <param name="minimumExampleCountPerLeaf">The minimal number of data points allowed in a leaf of a regression tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way;
/// it is only a way for the caller to be informed about what was learnt.</param>
/// <returns>The Score output column indicating the predicted value.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static Scalar <float> FastTree(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label, Vector <float> features, Scalar <float> weights = null,
int numberOfLeaves = Defaults.NumberOfLeaves,
int numberOfTrees = Defaults.NumberOfTrees,
int minimumExampleCountPerLeaf = Defaults.MinimumExampleCountPerLeaf,
double learningRate = Defaults.LearningRate,
Action <FastTreeRegressionModelParameters> onFit = null)
{
CheckUserValues(label, features, weights, numberOfLeaves, numberOfTrees, minimumExampleCountPerLeaf, learningRate, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new FastTreeRegressionTrainer(env, labelName, featuresName, weightsName, numberOfLeaves,
numberOfTrees, minimumExampleCountPerLeaf, learningRate);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
