/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="OnlineGradientDescentTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="label">The label, or dependent variable.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="lossFunction">The custom loss. Defaults to <see cref="SquaredLoss"/> if not provided.</param>
/// <param name="learningRate">The learning Rate.</param>
/// <param name="decreaseLearningRate">Decrease learning rate as iterations progress.</param>
/// <param name="l2Regularization">L2 regularization weight.</param>
/// <param name="numIterations">Number of training iterations through the data.</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, as well as the calibrator on top of that model. 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 set of output columns including in order the predicted binary classification score (which will range
/// from negative to positive infinity), and the predicted label.</returns>
/// <seealso cref="OnlineGradientDescentTrainer"/>.
/// <returns>The predicted output.</returns>
public static Scalar <float> OnlineGradientDescent(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label,
Vector <float> features,
Scalar <float> weights = null,
IRegressionLoss lossFunction = null,
float learningRate = OnlineGradientDescentTrainer.Options.OgdDefaultArgs.LearningRate,
bool decreaseLearningRate = OnlineGradientDescentTrainer.Options.OgdDefaultArgs.DecreaseLearningRate,
float l2Regularization = OnlineGradientDescentTrainer.Options.OgdDefaultArgs.L2Regularization,
int numIterations = OnlineLinearOptions.OnlineDefault.NumberOfIterations,
Action <LinearRegressionModelParameters> onFit = null)
{
OnlineLinearStaticUtils.CheckUserParams(label, features, weights, learningRate, l2Regularization, numIterations, onFit);
Contracts.CheckValueOrNull(lossFunction);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new OnlineGradientDescentTrainer(env, labelName, featuresName, learningRate,
decreaseLearningRate, l2Regularization, numIterations, lossFunction);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
/// <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);
}
/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="OnlineGradientDescentTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="label">The label, or dependent variable.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="options">Advanced arguments to the algorithm.</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, as well as the calibrator on top of that model. 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 set of output columns including in order the predicted binary classification score (which will range
/// from negative to positive infinity), and the predicted label.</returns>
/// <seealso cref="OnlineGradientDescentTrainer"/>.
/// <returns>The predicted output.</returns>
public static Scalar <float> OnlineGradientDescent(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label,
Vector <float> features,
Scalar <float> weights,
OnlineGradientDescentTrainer.Options options,
Action <LinearRegressionModelParameters> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValueOrNull(weights);
Contracts.CheckValue(options, nameof(options));
Contracts.CheckValueOrNull(onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
options.LabelColumnName = labelName;
options.FeatureColumnName = featuresName;
var trainer = new OnlineGradientDescentTrainer(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 linear regression model trained with the SDCA trainer.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="label">The label, or dependent variable.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="l2Regularization">The L2 regularization hyperparameter.</param>
/// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
/// <param name="numberOfIterations">The maximum number of passes to perform over the data.</param>
/// <param name="lossFunction">The custom loss, if unspecified will be <see cref="SquaredLoss"/>.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TShape, 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 predicted output.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static Scalar <float> Sdca(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label, Vector <float> features, Scalar <float> weights = null,
float?l2Regularization = null,
float?l1Threshold = null,
int?numberOfIterations = null,
ISupportSdcaRegressionLoss lossFunction = null,
Action <LinearRegressionModelParameters> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValueOrNull(weights);
Contracts.CheckParam(!(l2Regularization < 0), nameof(l2Regularization), "Must not be negative, if specified.");
Contracts.CheckParam(!(l1Threshold < 0), nameof(l1Threshold), "Must not be negative, if specified.");
Contracts.CheckParam(!(numberOfIterations < 1), nameof(numberOfIterations), "Must be positive if specified");
Contracts.CheckValueOrNull(lossFunction);
Contracts.CheckValueOrNull(onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new SdcaRegressionTrainer(env, labelName, featuresName, weightsName, lossFunction, l2Regularization, l1Threshold, numberOfIterations);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
/// <summary>
/// Predict matrix entry using matrix factorization
/// </summary>
/// <typeparam name="T">The type of physical value of matrix's row and column index. It must be an integer type such as uint.</typeparam>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="label">The label variable.</param>
/// <param name="matrixColumnIndex">The column index of the considered matrix.</param>
/// <param name="matrixRowIndex">The row index of the considered matrix.</param>
/// <param name="options">Advanced algorithm 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 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 predicted output.</returns>
public static Scalar <float> MatrixFactorization <T>(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label, Key <T> matrixColumnIndex, Key <T> matrixRowIndex,
MatrixFactorizationTrainer.Options options,
Action <MatrixFactorizationPredictor> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(matrixColumnIndex, nameof(matrixColumnIndex));
Contracts.CheckValue(matrixRowIndex, nameof(matrixRowIndex));
Contracts.CheckValue(options, nameof(options));
Contracts.CheckValueOrNull(onFit);
var rec = new MatrixFactorizationReconciler <T>((env, labelColName, matrixColumnIndexColName, matrixRowIndexColName) =>
{
options.MatrixColumnIndexColumnName = matrixColumnIndexColName;
options.MatrixRowIndexColumnName = matrixRowIndexColName;
options.LabelColumnName = labelColName;
var trainer = new MatrixFactorizationTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, matrixColumnIndex, matrixRowIndex);
return(rec.Output);
}
/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Trainers.LogisticRegression"/> trainer.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="label">The label, or dependent variable.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="enforceNonNegativity">Enforce non-negative weights.</param>
/// <param name="l1Regularization">Weight of L1 regularization term.</param>
/// <param name="l2Regularization">Weight of L2 regularization term.</param>
/// <param name="historySize">Memory size for <see cref="Microsoft.ML.Trainers.LogisticRegression"/>. Low=faster, less accurate.</param>
/// <param name="optimizationTolerance">Threshold for optimizer convergence.</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 predicted output.</returns>
public static Scalar <float> PoissonRegression(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label,
Vector <float> features,
Scalar <float> weights = null,
float l1Regularization = Options.Defaults.L1Regularization,
float l2Regularization = Options.Defaults.L2Regularization,
float optimizationTolerance = Options.Defaults.OptimizationTolerance,
int historySize = Options.Defaults.HistorySize,
bool enforceNonNegativity = Options.Defaults.EnforceNonNegativity,
Action <PoissonRegressionModelParameters> onFit = null)
{
LbfgsStaticUtils.ValidateParams(label, features, weights, l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new PoissonRegression(env, labelName, featuresName, weightsName,
l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity);
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="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 RegressionCatalog.RegressionTrainers catalog,
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);
}
/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Learners.LogisticRegression"/> trainer.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="label">The label, or dependent variable.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="options">Advanced arguments to the algorithm.</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 predicted output.</returns>
public static Scalar <float> PoissonRegression(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label,
Vector <float> features,
Scalar <float> weights,
PoissonRegression.Options options,
Action <PoissonRegressionModelParameters> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValue(options, nameof(options));
Contracts.CheckValueOrNull(onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
options.LabelColumn = labelName;
options.FeatureColumn = featuresName;
options.WeightColumn = weightsName != null ? Optional <string> .Explicit(weightsName) : Optional <string> .Implicit(DefaultColumnNames.Weight);
var trainer = new PoissonRegression(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
/// <summary>
/// Predict matrix entry using matrix factorization
/// </summary>
/// <typeparam name="T">The type of physical value of matrix's row and column index. It must be an integer type such as uint.</typeparam>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="label">The label variable.</param>
/// <param name="matrixColumnIndex">The column index of the considered matrix.</param>
/// <param name="matrixRowIndex">The row index of the considered matrix.</param>
/// <param name="regularizationCoefficient">The frobenius norms of factor matrices.</param>
/// <param name="approximationRank">Rank of the two factor matrices whose product is used to approximate the consdered matrix</param>
/// <param name="learningRate">Initial learning rate.</param>
/// <param name="numIterations">Number of training iterations.</param>
/// <param name="advancedSettings">A delegate to set more 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 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 predicted output.</returns>
public static Scalar <float> MatrixFactorization <T>(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label, Key <T> matrixColumnIndex, Key <T> matrixRowIndex,
float regularizationCoefficient = 0.1f,
int approximationRank = 8,
float learningRate = 0.1f,
int numIterations = 20,
Action <MatrixFactorizationTrainer.Arguments> advancedSettings = null,
Action <MatrixFactorizationPredictor> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(matrixColumnIndex, nameof(matrixColumnIndex));
Contracts.CheckValue(matrixRowIndex, nameof(matrixRowIndex));
Contracts.CheckParam(regularizationCoefficient >= 0, nameof(regularizationCoefficient), "Must be non-negative");
Contracts.CheckParam(approximationRank > 0, nameof(approximationRank), "Must be positive");
Contracts.CheckParam(learningRate > 0, nameof(learningRate), "Must be positive");
Contracts.CheckParam(numIterations > 0, nameof(numIterations), "Must be positive");
Contracts.CheckValueOrNull(advancedSettings);
Contracts.CheckValueOrNull(onFit);
var rec = new MatrixFactorizationReconciler <T>((env, labelColName, matrixColumnIndexColName, matrixRowIndexColName) =>
{
var trainer = new MatrixFactorizationTrainer(env, matrixColumnIndexColName, matrixRowIndexColName, labelColName, advancedSettings:
args =>
{
args.Lambda = regularizationCoefficient;
args.K = approximationRank;
args.Eta = learningRate;
args.NumIterations = numIterations;
// The previous settings may be overwritten by the line below.
advancedSettings?.Invoke(args);
});
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, matrixColumnIndex, matrixRowIndex);
return(rec.Output);
}
/// <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);
}
/// <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.LabelColumn = labelName;
options.FeatureColumn = featuresName;
options.WeightColumn = weightsName != null ? Optional <string> .Explicit(weightsName) : Optional <string> .Implicit(DefaultColumnNames.Weight);
var trainer = new LightGbmRegressorTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
