/// <summary>
/// Predict a target using a linear regression model trained with the SDCA trainer.
/// </summary>
/// <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="l2Const">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="maxIterations">The maximum number of passes to perform over the data.</param>
/// <param name="loss">The custom loss, if unspecified will be <see cref="SquaredLossSDCARegressionLossFunction"/>.</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> PredictSdcaRegression(this Scalar <float> label, Vector <float> features, Scalar <float> weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
ISupportSdcaRegressionLoss loss = null,
Action <LinearRegressionPredictor> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValueOrNull(weights);
Contracts.CheckParam(!(l2Const < 0), nameof(l2Const), "Must not be negative");
Contracts.CheckParam(!(l1Threshold < 0), nameof(l1Threshold), "Must not be negative");
Contracts.CheckParam(!(maxIterations < 1), nameof(maxIterations), "Must be positive if specified");
Contracts.CheckValueOrNull(loss);
Contracts.CheckValueOrNull(onFit);
var args = new SdcaRegressionTrainer.Arguments()
{
L2Const = l2Const,
L1Threshold = l1Threshold,
MaxIterations = maxIterations
};
if (loss != null)
{
args.LossFunction = new TrivialRegressionLossFactory(loss);
}
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new SdcaRegressionTrainer(env, args, featuresName, labelName, weightsName);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, SdcaRegressionTrainer.Arguments input)
{
Contracts.CheckValue(env, nameof(env));
var host = env.Register("TrainSDCA");
host.CheckValue(input, nameof(input));
EntryPointUtils.CheckInputArgs(host, input);
return(LearnerEntryPointsUtils.Train <SdcaRegressionTrainer.Arguments, CommonOutputs.RegressionOutput>(host, input,
() => new SdcaRegressionTrainer(host, input),
() => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn)));
}