/// <summary>
/// Predict a target using a tree regression model trained with the <see cref="LightGbmRegressorTrainer"/>.
/// </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 weights column.</param>
/// <param name="numLeaves">The number of leaves to use.</param>
/// <param name="numBoostRound">Number of iterations.</param>
/// <param name="minDataPerLeaf">The minimal number of documents allowed in a leaf of the 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> LightGbm(this RegressionContext.RegressionTrainers ctx,
Scalar <float> label, Vector <float> features, Scalar <float> weights = null,
int?numLeaves = null,
int?minDataPerLeaf = null,
double?learningRate = null,
int numBoostRound = LightGbmArguments.Defaults.NumBoostRound,
Action <LightGbmArguments> advancedSettings = null,
Action <LightGbmRegressionModelParameters> onFit = null)
{
CheckUserValues(label, features, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new LightGbmRegressorTrainer(env, labelName, featuresName, weightsName, numLeaves,
minDataPerLeaf, learningRate, numBoostRound, advancedSettings);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
public void LightGBMRegressorEstimator()
{
// Pipeline.
var pipeline = new LightGbmRegressorTrainer(Env, "Label", "Features", advancedSettings: s => {
s.NThread = 1;
s.NormalizeFeatures = NormalizeOption.Warn;
s.CatL2 = 5;
});
TestEstimatorCore(pipeline, GetRegressionPipeline());
Done();
}
[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // LightGBM is 64-bit only
public void LightGBMRegressorEstimator()
{
var dataView = GetRegressionPipeline();
var trainer = new LightGbmRegressorTrainer(Env, "Label", "Features", advancedSettings: s =>
{
s.NThread = 1;
s.NormalizeFeatures = NormalizeOption.Warn;
s.CatL2 = 5;
});
TestEstimatorCore(trainer, dataView);
var model = trainer.Train(dataView, dataView);
Done();
}
/// <summary>
/// Predict a target using a tree regression model trained with the <see cref="LightGbmRegressorTrainer"/>.
/// </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 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>
public static Scalar <float> LightGbm(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label, Vector <float> features, Scalar <float> weights,
Options options,
Action <LightGbmRegressionModelParameters> onFit = null)
{
CheckUserValues(label, features, weights, options, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
options.LabelColumnName = labelName;
options.FeatureColumnName = featuresName;
options.ExampleWeightColumnName = weightsName;
var trainer = new LightGbmRegressorTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
/// <summary>
/// Predict a target using a tree regression model trained with the <see cref="LightGbmRegressorTrainer"/>.
/// </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 weights column.</param>
/// <param name="numberOfLeaves">The number of leaves to use.</param>
/// <param name="minimumExampleCountPerLeaf">The minimal number of data points allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="numberOfIterations">Number of iterations.</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> LightGbm(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label, Vector <float> features, Scalar <float> weights = null,
int?numberOfLeaves = null,
int?minimumExampleCountPerLeaf = null,
double?learningRate = null,
int numberOfIterations = Options.Defaults.NumberOfIterations,
Action <LightGbmRegressionModelParameters> onFit = null)
{
CheckUserValues(label, features, weights, numberOfLeaves, minimumExampleCountPerLeaf, learningRate, numberOfIterations, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new LightGbmRegressorTrainer(env, labelName, featuresName, weightsName, numberOfLeaves,
minimumExampleCountPerLeaf, learningRate, numberOfIterations);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}