/// <summary>
/// Predict a target using a linear binary classification model trained with the SDCA trainer, and log-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="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), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) Sdca(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label, Vector <float> features, Scalar <float> weights,
SdcaBinaryTrainer.Options options,
Action <CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator> > 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.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
options.LabelColumnName = labelName;
options.FeatureColumnName = featuresName;
var trainer = new SdcaBinaryTrainer(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 logistic regression trained with the <see cref="SgdBinaryTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The binary classificaiton 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 <float> probability, Scalar <bool> predictedLabel) StochasticGradientDescentClassificationTrainer(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label,
Vector <float> features,
Scalar <float> weights,
SgdBinaryTrainer.Options options,
Action <CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator> > onFit = null)
{
var rec = new TrainerEstimatorReconciler.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
options.FeatureColumn = featuresName;
options.LabelColumn = labelName;
options.WeightColumn = weightsName;
var trainer = new SgdBinaryTrainer(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 SDCA trainer, and log-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="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), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) Sdca(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label, Vector <float> features, Scalar <float> weights = null,
float?l2Regularization = null,
float?l1Threshold = null,
int?numberOfIterations = null,
Action <CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator> > onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
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.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new SdcaBinaryTrainer(env, labelName, featuresName, weightsName, 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 binary classification model trained with the <see cref="Microsoft.ML.Trainers.LogisticRegressionBinaryClassificationTrainer"/> trainer.
/// </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="weights">The optional example weights.</param>
/// <param name="enforceNonNegativity">Enforce non-negative weights.</param>
/// <param name="l1Regularization">Weight of L1 regularization term.</param>
/// <param name="l2Regularization">Weight of L2 regularization term.</param>
/// <param name="historySize">Memory size for <see cref="Microsoft.ML.Trainers.LogisticRegressionBinaryClassificationTrainer"/>. Low=faster, less accurate.</param>
/// <param name="optimizationTolerance">Threshold for optimizer convergence.</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. 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) LogisticRegressionBinaryClassifier(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label,
Vector <float> features,
Scalar <float> weights = null,
float l1Regularization = Options.Defaults.L1Regularization,
float l2Regularization = Options.Defaults.L2Regularization,
float optimizationTolerance = Options.Defaults.OptimizationTolerance,
int historySize = Options.Defaults.HistorySize,
bool enforceNonNegativity = Options.Defaults.EnforceNonNegativity,
Action <CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator> > onFit = null)
{
LbfgsStaticUtils.ValidateParams(label, features, weights, l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity, onFit);
var rec = new TrainerEstimatorReconciler.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new LogisticRegressionBinaryClassificationTrainer(env, labelName, featuresName, weightsName,
l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity);
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="catalog">The binary classificaiton 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 <float> probability, Scalar <bool> predictedLabel) StochasticGradientDescentClassificationTrainer(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
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);
}
/// <summary>
/// Predict a target using a linear binary classification model trained with the SDCA trainer, and log-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="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), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) Sdca(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label, Vector <float> features, Scalar <float> weights,
SdcaBinaryTrainer.Options options,
Action <LinearBinaryModelParameters, ParameterMixingCalibratedPredictor> 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.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
options.LabelColumn = labelName;
options.FeatureColumn = featuresName;
var trainer = new SdcaBinaryTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans =>
{
// Under the default log-loss we assume a calibrated predictor.
var model = trans.Model;
var cali = (ParameterMixingCalibratedPredictor)model;
var pred = (LinearBinaryModelParameters)cali.SubPredictor;
onFit(pred, cali);
}));
}
return(trainer);
}, label, features, weights);
return(rec.Output);
}
/// <summary>
/// Predict a target using a linear binary classification model trained with the SDCA trainer, and log-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="weights">The optional example weights.</param>
/// <param name="l2Const">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="maxIterations">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), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) Sdca(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label, Vector <float> features, Scalar <float> weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator> > onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValueOrNull(weights);
Contracts.CheckParam(!(l2Const < 0), nameof(l2Const), "Must not be negative, if specified.");
Contracts.CheckParam(!(l1Threshold < 0), nameof(l1Threshold), "Must not be negative, if specified.");
Contracts.CheckParam(!(maxIterations < 1), nameof(maxIterations), "Must be positive if specified");
Contracts.CheckValueOrNull(onFit);
var rec = new TrainerEstimatorReconciler.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new SdcaBinaryTrainer(env, labelName, featuresName, weightsName, loss: new LogLoss(), l2Const, l1Threshold, maxIterations);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans =>
{
// Under the default log-loss we assume a calibrated predictor.
var pred = trans.Model;
onFit((CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator>)pred);
}));
}
return(trainer);
}, label, features, weights);
return(rec.Output);
}
/// <summary>
/// FastTree <see cref="BinaryClassificationContext"/> extension method.
/// Predict a target using a decision tree binary classificaiton model trained with the <see cref="FastTreeBinaryClassificationTrainer"/>.
/// </summary>
/// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features column.</param>
/// <param name="weights">The optional weights column.</param>
/// <param name="options">Algorithm advanced settings.</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. 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), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) FastTree(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
Scalar <bool> label, Vector <float> features, Scalar <float> weights,
FastTreeBinaryClassificationTrainer.Options options,
Action <IPredictorWithFeatureWeights <float> > onFit = null)
{
Contracts.CheckValueOrNull(options);
CheckUserValues(label, features, weights, onFit);
var rec = new TrainerEstimatorReconciler.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
options.LabelColumn = labelName;
options.FeatureColumn = featuresName;
options.WeightColumn = weightsName != null ? Optional <string> .Explicit(weightsName) : Optional <string> .Implicit(DefaultColumnNames.Weight);
var trainer = new FastTreeBinaryClassificationTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, label, features, weights);
return(rec.Output);
}
