/// <summary>
/// Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Trainers.StochasticGradientDescentClassificationTrainer"/> trainer.
/// </summary>
/// <param name="ctx">The binary classificaiton context 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="maxIterations">The maximum number of iterations; set to 1 to simulate online learning.</param>
/// <param name="initLearningRate">The initial learning rate used by SGD.</param>
/// <param name="l2Weight">The L2 regularization constant.</param>
/// <param name="loss">The loss function to use.</param>
/// <param name="advancedSettings">A delegate to apply all the 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 <float> probability, Scalar <bool> predictedLabel) StochasticGradientDescentClassificationTrainer(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
Scalar <bool> label,
Vector <float> features,
Scalar <float> weights = null,
int maxIterations = Arguments.Defaults.MaxIterations,
double initLearningRate = Arguments.Defaults.InitLearningRate,
float l2Weight = Arguments.Defaults.L2Weight,
ISupportClassificationLossFactory loss = null,
Action <Arguments> advancedSettings = null,
Action <IPredictorWithFeatureWeights <float> > onFit = null)
{
var rec = new TrainerEstimatorReconciler.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new StochasticGradientDescentClassificationTrainer(env, labelName, featuresName, weightsName, maxIterations, initLearningRate, l2Weight, loss, advancedSettings);
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 binary classification model trained with the <see cref="Microsoft.ML.Trainers.StochasticGradientDescentClassificationTrainer"/> trainer.
/// </summary>
/// <param name="ctx">The binary classificaiton context 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 <float> probability, Scalar <bool> predictedLabel) StochasticGradientDescentClassificationTrainer(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
Scalar <bool> label,
Vector <float> features,
Scalar <float> weights,
Options options,
Action <IPredictorWithFeatureWeights <float> > onFit = null)
{
var rec = new TrainerEstimatorReconciler.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
options.FeatureColumn = featuresName;
options.LabelColumn = labelName;
options.WeightColumn = weightsName != null ? Optional <string> .Explicit(weightsName) : Optional <string> .Implicit(DefaultColumnNames.Weight);
var trainer = new StochasticGradientDescentClassificationTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Output);
}
public void TestEstimatorHogwildSGD()
{
(IEstimator <ITransformer> pipe, IDataView dataView) = GetBinaryClassificationPipeline();
var trainer = new StochasticGradientDescentClassificationTrainer(Env, "Label", "Features");
var pipeWithTrainer = pipe.Append(trainer);
TestEstimatorCore(pipeWithTrainer, dataView);
var transformedDataView = pipe.Fit(dataView).Transform(dataView);
var model = trainer.Fit(transformedDataView);
trainer.Train(transformedDataView, model.Model);
TestEstimatorCore(pipe, dataView);
Done();
}
