/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="OnlineGradientDescentTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The regression catalog 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="l2Regularization">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="OnlineGradientDescentTrainer"/>.
/// <returns>The predicted output.</returns>
public static Scalar <float> OnlineGradientDescent(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label,
Vector <float> features,
Scalar <float> weights = null,
IRegressionLoss lossFunction = null,
float learningRate = OnlineGradientDescentTrainer.Options.OgdDefaultArgs.LearningRate,
bool decreaseLearningRate = OnlineGradientDescentTrainer.Options.OgdDefaultArgs.DecreaseLearningRate,
float l2Regularization = OnlineGradientDescentTrainer.Options.OgdDefaultArgs.L2Regularization,
int numIterations = OnlineLinearOptions.OnlineDefault.NumberOfIterations,
Action <LinearRegressionModelParameters> onFit = null)
{
OnlineLinearStaticUtils.CheckUserParams(label, features, weights, learningRate, l2Regularization, numIterations, onFit);
Contracts.CheckValueOrNull(lossFunction);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new OnlineGradientDescentTrainer(env, labelName, featuresName, learningRate,
decreaseLearningRate, l2Regularization, numIterations, lossFunction);
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 regression model trained with the <see cref="OnlineGradientDescentTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The regression catalog 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="options">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 RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label,
Vector <float> features,
Scalar <float> weights,
OnlineGradientDescentTrainer.Options options,
Action <LinearRegressionModelParameters> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValueOrNull(weights);
Contracts.CheckValue(options, nameof(options));
Contracts.CheckValueOrNull(onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
options.LabelColumnName = labelName;
options.FeatureColumnName = featuresName;
var trainer = new OnlineGradientDescentTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
public void OnlineLinearWorkout()
{
var dataPath = GetDataPath("breast-cancer.txt");
var data = TextLoader.CreateReader(Env, ctx => (Label: ctx.LoadFloat(0), Features: ctx.LoadFloat(1, 10)))
.Read(dataPath);
var pipe = data.MakeNewEstimator()
.Append(r => (r.Label, Features: r.Features.Normalize()));
var trainData = pipe.Fit(data).Transform(data).AsDynamic;
var ogdTrainer = new OnlineGradientDescentTrainer(Env, "Label", "Features");
TestEstimatorCore(ogdTrainer, trainData);
var ogdModel = ogdTrainer.Fit(trainData);
ogdTrainer.Train(trainData, ogdModel.Model);
var apTrainer = new AveragedPerceptronTrainer(Env, "Label", "Features", lossFunction: new HingeLoss(), advancedSettings: s =>
{
s.LearningRate = 0.5f;
});
TestEstimatorCore(apTrainer, trainData);
var apModel = apTrainer.Fit(trainData);
apTrainer.Train(trainData, apModel.Model);
Done();
}
/// <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);
}
public Arguments()
{
BasePredictors = new[]
{
ComponentFactoryUtils.CreateFromFunction(
env => {
var trainerEstimator = new OnlineGradientDescentTrainer(env);
return(TrainerUtils.MapTrainerEstimatorToTrainer <OnlineGradientDescentTrainer,
LinearRegressionModelParameters, LinearRegressionModelParameters>(env, trainerEstimator));
})
};
}
public void OnlineLinearWorkout()
{
var dataPath = GetDataPath("breast-cancer.txt");
var regressionData = TextLoaderStatic.CreateReader(ML, ctx => (Label: ctx.LoadFloat(0), Features: ctx.LoadFloat(1, 10)))
.Read(dataPath);
var regressionPipe = regressionData.MakeNewEstimator()
.Append(r => (r.Label, Features: r.Features.Normalize()));
var regressionTrainData = regressionPipe.Fit(regressionData).Transform(regressionData).AsDynamic;
var ogdTrainer = new OnlineGradientDescentTrainer(ML, "Label", "Features");
TestEstimatorCore(ogdTrainer, regressionTrainData);
var ogdModel = ogdTrainer.Fit(regressionTrainData);
ogdTrainer.Train(regressionTrainData, ogdModel.Model);
var binaryData = TextLoaderStatic.CreateReader(ML, ctx => (Label: ctx.LoadBool(0), Features: ctx.LoadFloat(1, 10)))
.Read(dataPath);
var binaryPipe = binaryData.MakeNewEstimator()
.Append(r => (r.Label, Features: r.Features.Normalize()));
var binaryTrainData = binaryPipe.Fit(binaryData).Transform(binaryData).AsDynamic;
var apTrainer = new AveragedPerceptronTrainer(ML, "Label", "Features", lossFunction: new HingeLoss(), advancedSettings: s =>
{
s.LearningRate = 0.5f;
});
TestEstimatorCore(apTrainer, binaryTrainData);
var apModel = apTrainer.Fit(binaryTrainData);
apTrainer.Train(binaryTrainData, apModel.Model);
var svmTrainer = new LinearSvmTrainer(ML, "Label", "Features");
TestEstimatorCore(svmTrainer, binaryTrainData);
var svmModel = svmTrainer.Fit(binaryTrainData);
svmTrainer.Train(binaryTrainData, apModel.Model);
Done();
}
public void OnlineLinearWorkout()
{
var dataPath = GetDataPath("breast-cancer.txt");
var data = TextLoader.CreateReader(Env, ctx => (Label: ctx.LoadFloat(0), Features: ctx.LoadFloat(1, 10)))
.Read(new MultiFileSource(dataPath));
var pipe = data.MakeNewEstimator()
.Append(r => (r.Label, Features: r.Features.Normalize()));
var trainData = pipe.Fit(data).Transform(data).AsDynamic;
IEstimator <ITransformer> est = new OnlineGradientDescentTrainer(Env, new OnlineGradientDescentTrainer.Arguments());
TestEstimatorCore(est, trainData);
est = new AveragedPerceptronTrainer(Env, new AveragedPerceptronTrainer.Arguments());
TestEstimatorCore(est, trainData);
Done();
}
