/// <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 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);
}
PredictSdcaClassification <TVal>(this Key <uint, TVal> label, Vector <float> features,
ISupportSdcaClassificationLoss loss = null,
Scalar <float> weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <MulticlassLogisticRegressionPredictor> onFit = null)
{
public SdcaMultiClassTrainer(IHostEnvironment env, Arguments args)
: base(args, env, LoadNameValue)
{
_loss = args.LossFunction.CreateComponent(env);
base.Loss = _loss;
NeedShuffle = args.Shuffle;
_args = args;
}
public SdcaMultiClassTrainer(IHostEnvironment env, Arguments args,
string featureColumn, string labelColumn, string weightColumn = null)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), args, MakeFeatureColumn(featureColumn), MakeLabelColumn(labelColumn), MakeWeightColumn(weightColumn))
{
_loss = args.LossFunction.CreateComponent(env);
Loss = _loss;
_args = args;
}
Sdca <TVal>(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
Key <uint, TVal> label,
Vector <float> features,
ISupportSdcaClassificationLoss loss = null,
Scalar <float> weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <MulticlassLogisticRegressionModelParameters> onFit = null)
{
this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
Key <uint, TVal> label,
Vector <float> features,
ISupportSdcaClassificationLoss loss = null,
Scalar <float> weights = null,
float?l2Regularization = null,
float?l1Threshold = null,
int?numberOfIterations = null,
Action <MulticlassLogisticRegressionModelParameters> onFit = null)
{
/// <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="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="loss">The custom loss.</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{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="Sdca(BinaryClassificationContext.BinaryClassificationTrainers, Scalar{bool}, Vector{float}, Scalar{float}, float?, float?, int?, Action{LinearBinaryPredictor, ParameterMixingCalibratedPredictor})"/>
public static (Scalar <float> score, Scalar <bool> predictedLabel) Sdca(
this BinaryClassificationContext.BinaryClassificationTrainers ctx,
Scalar <bool> label, Vector <float> features,
ISupportSdcaClassificationLoss loss,
Scalar <float> weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <LinearBinaryPredictor> onFit = null
)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValue(loss, nameof(loss));
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);
bool hasProbs = loss is LogLoss;
var args = new LinearClassificationTrainer.Arguments()
{
L2Const = l2Const,
L1Threshold = l1Threshold,
MaxIterations = maxIterations,
LossFunction = new TrivialSdcaClassificationLossFactory(loss)
};
var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new LinearClassificationTrainer(env, args, featuresName, labelName, weightsName);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans =>
{
var model = trans.Model;
if (model is ParameterMixingCalibratedPredictor cali)
{
onFit((LinearBinaryPredictor)cali.SubPredictor);
}
else
{
onFit((LinearBinaryPredictor)model);
}
}));
}
return(trainer);
}, label, features, weights, hasProbs);
return(rec.Output);
}
Sdca <TVal>(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
Key <uint, TVal> label,
Vector <float> features,
ISupportSdcaClassificationLoss loss = null,
Scalar <float> weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <SdcaMultiClassTrainer.Arguments> advancedSettings = null,
Action <MulticlassLogisticRegressionPredictor> onFit = null)
{
public SdcaMultiClassTrainer(IHostEnvironment env, Arguments args,
string featureColumn, string labelColumn, string weightColumn = null)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), args, TrainerUtils.MakeR4VecFeature(featureColumn),
TrainerUtils.MakeU4ScalarLabel(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
{
Host.CheckValue(labelColumn, nameof(labelColumn));
Host.CheckValue(featureColumn, nameof(featureColumn));
_loss = args.LossFunction.CreateComponent(env);
Loss = _loss;
_args = args;
}
internal SdcaNonCalibratedMulticlassTrainer(IHostEnvironment env,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
ISupportSdcaClassificationLoss loss = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null)
: base(env, labelColumn: labelColumn, featureColumn: featureColumn, weights: weights, loss: loss,
l2Const: l2Const, l1Threshold: l1Threshold, maxIterations: maxIterations)
{
}
public SdcaMultiClassTrainer(IHostEnvironment env, Arguments args,
string featureColumn, string labelColumn, string weightColumn = null)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), args, MakeFeatureColumn(featureColumn), MakeLabelColumn(labelColumn), MakeWeightColumn(weightColumn))
{
_loss = args.LossFunction.CreateComponent(env);
Loss = _loss;
_args = args;
OutputColumns = new[]
{
new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Vector, DataKind.R4, false),
new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, DataKind.U4, true)
};
}
/// <summary>
/// Predict a target using a linear multiclass classification model trained with the SDCA trainer.
/// </summary>
/// <param name="catalog">The multiclass classification catalog trainer object.</param>
/// <param name="labelColumn">The labelColumn, or dependent variable.</param>
/// <param name="featureColumn">The features, or independent variables.</param>
/// <param name="loss">The optional custom loss.</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>
public static SdcaMultiClassTrainer StochasticDualCoordinateAscent(this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
ISupportSdcaClassificationLoss loss = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new SdcaMultiClassTrainer(env, labelColumn, featureColumn, weights, loss, l2Const, l1Threshold, maxIterations));
}
/// <summary>
/// Predict a target using a linear multiclass classification model trained with the SDCA trainer.
/// </summary>
/// <param name="ctx">The multiclass 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="loss">The optional custom loss.</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="advancedSettings">A delegate to set more settings.</param>
public static SdcaMultiClassTrainer StochasticDualCoordinateAscent(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
string label = DefaultColumnNames.Label,
string features = DefaultColumnNames.Features,
string weights = null,
ISupportSdcaClassificationLoss loss = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <SdcaMultiClassTrainer.Arguments> advancedSettings = null)
{
Contracts.CheckValue(ctx, nameof(ctx));
var env = CatalogUtils.GetEnvironment(ctx);
return(new SdcaMultiClassTrainer(env, features, label, weights, loss, l2Const, l1Threshold, maxIterations, advancedSettings));
}
/// <summary>
/// Initializes a new instance of <see cref="SdcaMultiClassTrainer"/>
/// </summary>
/// <param name="env">The environment to use.</param>
/// <param name="featureColumn">The features, or independent variables.</param>
/// <param name="labelColumn">The label, or dependent variable.</param>
/// <param name="loss">The custom loss.</param>
/// <param name="weightColumn">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="advancedSettings">A delegate to set more settings.
/// The settings here will override the ones provided in the direct method signature,
/// if both are present and have different values.
/// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
public SdcaMultiClassTrainer(IHostEnvironment env,
string featureColumn,
string labelColumn,
string weightColumn = null,
ISupportSdcaClassificationLoss loss = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <Arguments> advancedSettings = null)
: base(env, featureColumn, TrainerUtils.MakeU4ScalarColumn(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn), advancedSettings,
l2Const, l1Threshold, maxIterations)
{
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
_loss = loss ?? Args.LossFunction.CreateComponent(env);
Loss = _loss;
}
/// <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="loss">The custom loss.</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), and the predicted label.</returns>
public static (Scalar <float> score, Scalar <bool> predictedLabel) Sdca(
this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label, Vector <float> features,
ISupportSdcaClassificationLoss loss,
Scalar <float> weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <LinearBinaryModelParameters> onFit = null
)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValue(loss, nameof(loss));
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);
bool hasProbs = loss is LogLoss;
var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new SdcaBinaryTrainer(env, labelName, featuresName, weightsName, loss, l2Const, l1Threshold, maxIterations);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans =>
{
var model = trans.Model;
if (model is ParameterMixingCalibratedModelParameters <LinearBinaryModelParameters, PlattCalibrator> cali)
{
onFit(cali.SubModel);
}
else
{
onFit((LinearBinaryModelParameters)model);
}
}));
}
return(trainer);
}, label, features, weights, hasProbs);
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="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="loss">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) Sdca(
this BinaryClassificationContext.BinaryClassificationTrainers ctx,
Scalar <bool> label, Vector <float> features,
Scalar <float> weights,
ISupportSdcaClassificationLoss loss,
SdcaBinaryTrainer.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 = loss is LogLoss;
var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
(env, labelName, featuresName, weightsName) =>
{
options.FeatureColumn = featuresName;
options.LabelColumn = labelName;
var trainer = new SdcaBinaryTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans =>
{
var model = trans.Model;
if (model is ParameterMixingCalibratedPredictor cali)
{
onFit((LinearBinaryModelParameters)cali.SubPredictor);
}
else
{
onFit((LinearBinaryModelParameters)model);
}
}));
}
return(trainer);
}, label, features, weights, hasProbs);
return(rec.Output);
}
public static PredictionEngine <TIn, TOut> SdcaNonCalibrated <TIn, TOut>(
IEnumerable <TIn> trainDataset,
string outputColumnName = "PredictedLabel",
string exampleWeightColumnName = null,
ISupportSdcaClassificationLoss lossFunction = null,
float?l1Regularization = null,
float?l2Regularization = null,
int?maximumNumberOfIterations = null,
Action <ITransformer> additionModelAction = null
)
where TIn : class, new()
where TOut : class, new()
{
var context = new MLContext();
var type = typeof(TIn);
var labelColumnName = Preprocessing.LabelColumn(type.GetProperties()).Name;
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessor = context.OneHotEncoding(properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var pipeline = context.Transforms.Conversion.MapValueToKey(labelColumnName)
.Append(preprocessor.OneHotEncodingEstimator)
.Append(context.Transforms.Concatenate("Features", preprocessor.CombinedFeatures.ToArray()))
.Append(context.Transforms.ProjectToPrincipalComponents(outputColumnName: "PCAFeatures", inputColumnName: "Features", rank: 2))
.AppendCacheCheckpoint(context)
.Append(context.MulticlassClassification.Trainers.SdcaNonCalibrated(
labelColumnName,
featureColumnName: "Features",
exampleWeightColumnName,
lossFunction,
l2Regularization,
l1Regularization,
maximumNumberOfIterations
))
.Append(context.Transforms.Conversion.MapKeyToValue(outputColumnName));
var model = pipeline.Fit(trainDataframe);
var predictEngine = context.Model.CreatePredictionEngine <TIn, TOut>(model);
additionModelAction?.Invoke(model);
return(predictEngine);
}
public TrivialClassificationLossFactory(ISupportSdcaClassificationLoss loss)
{
_loss = loss;
}
