/// <summary>
/// Trains a linear binary classifier using the averaged perceptron.
/// <a href='https://en.wikipedia.org/wiki/Perceptron'>Wikipedia entry for Perceptron</a>
/// </summary>
/// <param name="env">The local instance of the <see cref="IHostEnvironment"/></param>
/// <param name="lossFunction">The classification loss function. </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 optional name of the weights column.</param>
/// <param name="learningRate">The learning rate. </param>
/// <param name="decreaseLearningRate">Wheather to decrease learning rate as iterations progress.</param>
/// <param name="l2RegularizerWeight">L2 Regularization Weight.</param>
/// <param name="numIterations">The number of training iteraitons.</param>
/// <param name="advancedSettings">A delegate to supply more advanced arguments to the algorithm.</param>
public AveragedPerceptronTrainer(IHostEnvironment env,
string label,
string features,
string weights = null,
ISupportClassificationLossFactory lossFunction = null,
float learningRate = Arguments.AveragedDefaultArgs.LearningRate,
bool decreaseLearningRate = Arguments.AveragedDefaultArgs.DecreaseLearningRate,
float l2RegularizerWeight = Arguments.AveragedDefaultArgs.L2RegularizerWeight,
int numIterations = Arguments.AveragedDefaultArgs.NumIterations,
Action <Arguments> advancedSettings = null)
: this(env, new Arguments
{
LabelColumn = label,
FeatureColumn = features,
InitialWeights = weights,
LearningRate = learningRate,
DecreaseLearningRate = decreaseLearningRate,
L2RegularizerWeight = l2RegularizerWeight,
NumIterations = numIterations
})
{
if (lossFunction == null)
{
lossFunction = new HingeLoss.Arguments();
}
LossFunction = lossFunction.CreateComponent(env);
if (advancedSettings != null)
{
advancedSettings.Invoke(_args);
}
}
/// <summary>
/// Predict a target using a linear binary classification model trained with the <see cref="StochasticGradientDescentClassificationTrainer"/> trainer.
/// </summary>
/// <param name="ctx">The binary classificaiton context trainer object.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">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>
public static StochasticGradientDescentClassificationTrainer StochasticGradientDescent(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
int maxIterations = SgdArguments.Defaults.MaxIterations,
double initLearningRate = SgdArguments.Defaults.InitLearningRate,
float l2Weight = SgdArguments.Defaults.L2Weight,
ISupportClassificationLossFactory loss = null,
Action <SgdArguments> advancedSettings = null)
{
Contracts.CheckValue(ctx, nameof(ctx));
var env = CatalogUtils.GetEnvironment(ctx);
return(new StochasticGradientDescentClassificationTrainer(env, labelColumn, featureColumn, weights, maxIterations, initLearningRate, l2Weight, loss, advancedSettings));
}
/// <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);
}
