/// <summary>
/// Predict a target using a linear binary classification model trained with the SDCA trainer, and a custom loss.
/// Note that because we cannot be sure that all loss functions will produce naturally calibrated outputs, setting
/// a custom loss function will not produce a calibrated probability column.
/// </summary>
/// <param name="catalog">The binary classification catalog 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="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>
public static (Scalar <float> score, Scalar <bool> predictedLabel) SdcaNonCalibrated(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label, Vector <float> features, Scalar <float> weights,
ISupportSdcaClassificationLoss lossFunction,
SdcaNonCalibratedBinaryTrainer.Options options,
Action <LinearBinaryModelParameters> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValueOrNull(weights);
Contracts.CheckValueOrNull(options);
Contracts.CheckValueOrNull(onFit);
var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
(env, labelName, featuresName, weightsName) =>
{
options.FeatureColumnName = featuresName;
options.LabelColumnName = labelName;
var trainer = new SdcaNonCalibratedBinaryTrainer(env, options);
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 AveragedPerceptron trainer, and a custom loss.
/// </summary>
/// <param name="catalog">The binary classification catalog 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="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="AveragedPerceptronTrainer"/>.
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static (Scalar <float> score, Scalar <bool> predictedLabel) AveragedPerceptron(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label,
Vector <float> features,
Scalar <float> weights = null,
IClassificationLoss lossFunction = null,
float learningRate = AveragedLinearOptions.AveragedDefault.LearningRate,
bool decreaseLearningRate = AveragedLinearOptions.AveragedDefault.DecreaseLearningRate,
float l2Regularization = AveragedLinearOptions.AveragedDefault.L2Regularization,
int numIterations = AveragedLinearOptions.AveragedDefault.NumberOfIterations,
Action <LinearBinaryModelParameters> onFit = null
)
{
OnlineLinearStaticUtils.CheckUserParams(label, features, weights, learningRate, l2Regularization, numIterations, onFit);
bool hasProbs = lossFunction is LogLoss;
var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new AveragedPerceptronTrainer(env, labelName, featuresName, lossFunction,
learningRate, decreaseLearningRate, l2Regularization, numIterations);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, features, weights);
return(rec.Output);
}
/// <summary>
/// Predict a target using a linear binary classification model trained with the SDCA trainer, and a custom loss.
/// Note that because we cannot be sure that all loss functions will produce naturally calibrated outputs, setting
/// a custom loss function will not produce a calibrated probability column.
/// </summary>
/// <param name="catalog">The binary classification catalog 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="l2Regularization">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="numberOfIterations">The maximum number of passes to perform over 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>
public static (Scalar <float> score, Scalar <bool> predictedLabel) SdcaNonCalibrated(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label, Vector <float> features,
ISupportSdcaClassificationLoss lossFunction,
Scalar <float> weights = null,
float?l2Regularization = null,
float?l1Threshold = null,
int?numberOfIterations = null,
Action <LinearBinaryModelParameters> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValue(lossFunction, nameof(lossFunction));
Contracts.CheckValueOrNull(weights);
Contracts.CheckParam(!(l2Regularization < 0), nameof(l2Regularization), "Must not be negative, if specified.");
Contracts.CheckParam(!(l1Threshold < 0), nameof(l1Threshold), "Must not be negative, if specified.");
Contracts.CheckParam(!(numberOfIterations < 1), nameof(numberOfIterations), "Must be positive if specified");
Contracts.CheckValueOrNull(onFit);
var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new SdcaNonCalibratedBinaryTrainer(env, labelName, featuresName, weightsName, lossFunction, l2Regularization, l1Threshold, numberOfIterations);
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 classification model trained with the <see cref="SgdNonCalibratedTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The binary classification catalog 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 <bool> predictedLabel) StochasticGradientDescentNonCalibratedClassificationTrainer(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label,
Vector <float> features,
Scalar <float> weights,
SgdNonCalibratedTrainer.Options options,
Action <LinearBinaryModelParameters> onFit = null)
{
var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
(env, labelName, featuresName, weightsName) =>
{
options.FeatureColumnName = featuresName;
options.LabelColumnName = labelName;
options.ExampleWeightColumnName = weightsName;
var trainer = new SgdNonCalibratedTrainer(env, options);
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 classification model trained with the <see cref="SgdNonCalibratedTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The binary classification catalog 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="numberOfIterations">The maximum number of iterations; set to 1 to simulate online learning.</param>
/// <param name="learningRate">The initial learning rate used by SGD.</param>
/// <param name="l2Regularization">The L2 weight for <a href='https://en.wikipedia.org/wiki/Regularization_(mathematics)'>regularization</a>.</param>
/// <param name="lossFunction">The loss function to use.</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 <bool> predictedLabel) StochasticGradientDescentNonCalibratedClassificationTrainer(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label,
Vector <float> features,
Scalar <float> weights = null,
int numberOfIterations = SgdNonCalibratedTrainer.Options.Defaults.NumberOfIterations,
double learningRate = SgdNonCalibratedTrainer.Options.Defaults.LearningRate,
float l2Regularization = SgdNonCalibratedTrainer.Options.Defaults.L2Regularization,
IClassificationLoss lossFunction = null,
Action <LinearBinaryModelParameters> onFit = null)
{
var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new SgdNonCalibratedTrainer(env, labelName, featuresName, weightsName,
numberOfIterations, learningRate, l2Regularization, lossFunction);
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 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{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, 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);
}
/// <summary>
/// Predict a target using a linear binary classification model trained with the AveragedPerceptron trainer, and a custom loss.
/// </summary>
/// <param name="catalog">The binary classification catalog 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="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="AveragedPerceptronTrainer"/>.
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static (Scalar <float> score, Scalar <bool> predictedLabel) AveragedPerceptron(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label,
Vector <float> features,
Scalar <float> weights,
IClassificationLoss lossFunction,
AveragedPerceptronTrainer.Options options,
Action <LinearBinaryModelParameters> onFit = null
)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValueOrNull(weights);
Contracts.CheckValueOrNull(options);
Contracts.CheckValueOrNull(onFit);
bool hasProbs = lossFunction is LogLoss;
var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
(env, labelName, featuresName, weightsName) =>
{
options.LabelColumn = labelName;
options.FeatureColumn = featuresName;
options.InitialWeights = weightsName;
var trainer = new AveragedPerceptronTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, features, weights, hasProbs);
return(rec.Output);
}
