/// <summary>
/// Predict a target using a linear classification model trained with the <see cref="SgdNonCalibratedTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The binary classification catalog trainer object.</param>
/// <param name="label">The name of the label column.</param>
/// <param name="features">The name of the feature column.</param>
/// <param name="weights">The name for the example weight column.</param>
/// <param name="options">Advanced arguments to the algorithm.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}.Fit(DataView{TTupleInShape})"/> method is called on the
/// <see cref="Estimator{TTupleInShape, TTupleOutShape, 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> score, Scalar <bool> predictedLabel) StochasticGradientDescentNonCalibratedClassificationTrainer(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label,
Vector <float> features,
Scalar <float> weights,
SgdNonCalibratedTrainer.Options options,
Action <LinearBinaryModelParameters> onFit = null)
{
var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
(env, labelName, featuresName, weightsName) =>
{
options.FeatureColumnName = featuresName;
options.LabelColumnName = labelName;
options.ExampleWeightColumnName = weightsName;
var trainer = new SgdNonCalibratedTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Output);
}
/// <summary>
/// Predict a target using a linear classification model trained with the <see cref="SgdNonCalibratedTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The binary classification catalog trainer object.</param>
/// <param name="label">The name of the label column.</param>
/// <param name="features">The name of the feature column.</param>
/// <param name="weights">The name for the example weight column.</param>
/// <param name="numberOfIterations">The maximum number of iterations; set to 1 to simulate online learning.</param>
/// <param name="learningRate">The initial learning rate used by SGD.</param>
/// <param name="l2Regularization">The L2 weight for <a href='https://en.wikipedia.org/wiki/Regularization_(mathematics)'>regularization</a>.</param>
/// <param name="lossFunction">The loss function to use.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}.Fit(DataView{TTupleInShape})"/> method is called on the
/// <see cref="Estimator{TTupleInShape, TTupleOutShape, 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> score, Scalar <bool> predictedLabel) StochasticGradientDescentNonCalibratedClassificationTrainer(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label,
Vector <float> features,
Scalar <float> weights = null,
int numberOfIterations = SgdNonCalibratedTrainer.Options.Defaults.NumberOfIterations,
double learningRate = SgdNonCalibratedTrainer.Options.Defaults.LearningRate,
float l2Regularization = SgdNonCalibratedTrainer.Options.Defaults.L2Regularization,
IClassificationLoss lossFunction = null,
Action <LinearBinaryModelParameters> onFit = null)
{
var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new SgdNonCalibratedTrainer(env, labelName, featuresName, weightsName,
numberOfIterations, learningRate, l2Regularization, lossFunction);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Output);
}