/// <summary>
/// Predict a target using a tree binary classification model trained with the <see cref="LightGbmBinaryTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</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 set of output columns including in order the predicted binary classification score (which will range
/// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) LightGbm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label, Vector <float> features, Scalar <float> weights,
Options options,
Action <CalibratedModelParametersBase <LightGbmBinaryModelParameters, PlattCalibrator> > onFit = null)
{
CheckUserValues(label, features, weights, options, onFit);
var rec = new TrainerEstimatorReconciler.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
options.LabelColumnName = labelName;
options.FeatureColumnName = featuresName;
options.ExampleWeightColumnName = weightsName;
var trainer = new LightGbmBinaryTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, features, weights);
return(rec.Output);
}
/// <summary>
/// Predict a target using a tree binary classification model trained with the <see cref="LightGbmBinaryTrainer"/>.
/// </summary>
/// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</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 set of output columns including in order the predicted binary classification score (which will range
/// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) LightGbm(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
Scalar <bool> 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 <IPredictorWithFeatureWeights <float> > onFit = null)
{
CheckUserValues(label, features, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings, onFit);
var rec = new TrainerEstimatorReconciler.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new LightGbmBinaryTrainer(env, labelName, featuresName, weightsName, numLeaves,
minDataPerLeaf, learningRate, numBoostRound, advancedSettings);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, features, weights);
return(rec.Output);
}
/// <summary>
/// Predict a target using a tree binary classification model trained with the <see cref="LightGbmBinaryTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</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="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 set of output columns including in order the predicted binary classification score (which will range
/// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) LightGbm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label, Vector <float> features, Scalar <float> weights = null,
int?numLeaves = null,
int?minDataPerLeaf = null,
double?learningRate = null,
int numBoostRound = Options.Defaults.NumBoostRound,
Action <CalibratedModelParametersBase <LightGbmBinaryModelParameters, PlattCalibrator> > onFit = null)
{
CheckUserValues(label, features, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound, onFit);
var rec = new TrainerEstimatorReconciler.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new LightGbmBinaryTrainer(env, labelName, featuresName, weightsName, numLeaves,
minDataPerLeaf, learningRate, numBoostRound);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, features, weights);
return(rec.Output);
}
/// <summary>
/// Predict a target using a tree binary classification model trained with the <see cref="LightGbmBinaryTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</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 set of output columns including in order the predicted binary classification score (which will range
/// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) LightGbm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label, Vector <float> features, Scalar <float> weights,
Options options,
Action <IPredictorWithFeatureWeights <float> > onFit = null)
{
CheckUserValues(label, features, weights, options, onFit);
var rec = new TrainerEstimatorReconciler.BinaryClassifier(
(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 LightGbmBinaryTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, features, weights);
return(rec.Output);
}
[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // LightGBM is 64-bit only
public void LightGBMBinaryEstimator()
{
var(pipe, dataView) = GetBinaryClassificationPipeline();
var trainer = new LightGbmBinaryTrainer(Env, "Label", "Features", advancedSettings: s =>
{
s.NumLeaves = 10;
s.NThread = 1;
s.MinDataPerLeaf = 2;
});
var pipeWithTrainer = pipe.Append(trainer);
TestEstimatorCore(pipeWithTrainer, dataView);
var transformedDataView = pipe.Fit(dataView).Transform(dataView);
var model = trainer.Train(transformedDataView, transformedDataView);
Done();
}
