/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Runtime.Learners.OnlineGradientDescentTrainer"/> trainer.
/// </summary>
/// <param name="ctx">The regression context 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="l2RegularizerWeight">L2 regularization weight.</param>
/// <param name="numIterations">Number of training iterations through the data.</param>
/// <param name="advancedSettings">A delegate to supply more 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 RegressionContext.RegressionTrainers ctx,
Scalar <float> label,
Vector <float> features,
Scalar <float> weights = null,
IRegressionLoss lossFunction = null,
float learningRate = OnlineGradientDescentTrainer.Arguments.OgdDefaultArgs.LearningRate,
bool decreaseLearningRate = OnlineGradientDescentTrainer.Arguments.OgdDefaultArgs.DecreaseLearningRate,
float l2RegularizerWeight = OnlineGradientDescentTrainer.Arguments.OgdDefaultArgs.L2RegularizerWeight,
int numIterations = OnlineLinearArguments.OnlineDefaultArgs.NumIterations,
Action <AveragedLinearArguments> advancedSettings = null,
Action <LinearRegressionPredictor> onFit = null)
{
OnlineLinearStaticUtils.CheckUserParams(label, features, weights, learningRate, l2RegularizerWeight, numIterations, onFit, advancedSettings);
Contracts.CheckValueOrNull(lossFunction);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new OnlineGradientDescentTrainer(env, labelName, featuresName, learningRate,
decreaseLearningRate, l2RegularizerWeight, numIterations, weightsName, lossFunction, 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 binary classification model trained with the AveragedPerceptron trainer, and a custom loss.
/// </summary>
/// <param name="ctx">The binary classification context trainer object.</param>
/// <param name="label">The label, or dependent variable.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="lossFunction">The custom loss.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="learningRate">The learning Rate.</param>
/// <param name="decreaseLearningRate">Decrease learning rate as iterations progress.</param>
/// <param name="l2RegularizerWeight">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="AveragedPerceptronTrainer"/>.
public static (Scalar <float> score, Scalar <bool> predictedLabel) AveragedPerceptron(
this BinaryClassificationContext.BinaryClassificationTrainers ctx,
IClassificationLoss lossFunction,
Scalar <bool> label, Vector <float> features, Scalar <float> weights = null,
float learningRate = AveragedLinearArguments.AveragedDefaultArgs.LearningRate,
bool decreaseLearningRate = AveragedLinearArguments.AveragedDefaultArgs.DecreaseLearningRate,
float l2RegularizerWeight = AveragedLinearArguments.AveragedDefaultArgs.L2RegularizerWeight,
int numIterations = AveragedLinearArguments.AveragedDefaultArgs.NumIterations,
Action <LinearBinaryPredictor> onFit = null
)
{
OnlineLinearStaticUtils.CheckUserParams(label, features, weights, learningRate, l2RegularizerWeight, numIterations, onFit);
bool hasProbs = lossFunction is HingeLoss;
var args = new AveragedPerceptronTrainer.Arguments()
{
LearningRate = learningRate,
DecreaseLearningRate = decreaseLearningRate,
L2RegularizerWeight = l2RegularizerWeight,
NumIterations = numIterations
};
if (lossFunction != null)
{
args.LossFunction = new TrivialClassificationLossFactory(lossFunction);
}
var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
(env, labelName, featuresName, weightsName) =>
{
args.FeatureColumn = featuresName;
args.LabelColumn = labelName;
args.InitialWeights = weightsName;
var trainer = new AveragedPerceptronTrainer(env, args);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, features, weights, hasProbs);
return(rec.Output);
}