private ISingleFeaturePredictionTransformer <TScalarPredictor> TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappedData data, int cls)
{
var view = MapLabels(data, cls);
string trainerLabel = data.Schema.Label.Value.Name;
// REVIEW: In principle we could support validation sets and the like via the train context, but
// this is currently unsupported.
var transformer = trainer.Fit(view);
if (_args.UseProbabilities)
{
var calibratedModel = transformer.Model as TDistPredictor;
// REVIEW: restoring the RoleMappedData, as much as we can.
// not having the weight column on the data passed to the TrainCalibrator should be addressed.
var trainedData = new RoleMappedData(view, label: trainerLabel, feature: transformer.FeatureColumn);
if (calibratedModel == null)
{
calibratedModel = CalibratorUtils.GetCalibratedPredictor(Host, ch, Calibrator, transformer.Model, trainedData, Args.MaxCalibrationExamples) as TDistPredictor;
}
Host.Check(calibratedModel != null, "Calibrated predictor does not implement the expected interface");
return(new BinaryPredictionTransformer <TScalarPredictor>(Host, calibratedModel, trainedData.Data.Schema, transformer.FeatureColumn));
}
return(new BinaryPredictionTransformer <TScalarPredictor>(Host, transformer.Model, view.Schema, transformer.FeatureColumn));
}
protected override TVectorPredictor TrainPredictor(IChannel ch, TScalarTrainer trainer, RoleMappedData data, int count)
{
var data0 = data;
string dstName, labName;
var trans = MapLabelsAndInsertTransform(ch, data, out dstName, out labName, count, true, _args);
var newFeatures = trans.Schema.GetTempColumnName() + "NF";
var args3 = new DescribeTransform.Arguments {
columns = new string[] { labName, dstName }, oneRowPerColumn = true
};
var desc = new DescribeTransform(Host, args3, trans);
IDataView viewI;
if (_args.singleColumn && data.Schema.Label.Value.Type.RawKind() == DataKind.Single)
{
viewI = desc;
}
else if (_args.singleColumn)
{
var sch = new TypeReplacementSchema(desc.Schema, new[] { labName }, new[] { NumberDataViewType.Single });
viewI = new TypeReplacementDataView(desc, sch);
#region debug
#if (DEBUG)
DebugChecking0(viewI, labName, false);
#endif
#endregion
}
else if (data.Schema.Label.Value.Type.IsKey())
{
ulong nb = data.Schema.Label.Value.Type.AsKey().GetKeyCount();
var sch = new TypeReplacementSchema(desc.Schema, new[] { labName }, new[] { new VectorDataViewType(NumberDataViewType.Single, (int)nb) });
viewI = new TypeReplacementDataView(desc, sch);
#region debug
#if (DEBUG)
int nb_;
MinMaxLabelOverDataSet(trans, labName, out nb_);
int count3;
data.CheckMulticlassLabel(out count3);
if ((ulong)count3 != nb)
{
throw ch.Except("Count mismatch (KeyCount){0} != {1}", nb, count3);
}
DebugChecking0(viewI, labName, true);
DebugChecking0Vfloat(viewI, labName, nb);
#endif
#endregion
}
else
{
int nb;
if (count <= 0)
{
MinMaxLabelOverDataSet(trans, labName, out nb);
}
else
{
nb = count;
}
var sch = new TypeReplacementSchema(desc.Schema, new[] { labName }, new[] { new VectorDataViewType(NumberDataViewType.Single, nb) });
viewI = new TypeReplacementDataView(desc, sch);
#region debug
#if (DEBUG)
DebugChecking0(viewI, labName, true);
#endif
#endregion
}
ch.Info("Merging column label '{0}' with features '{1}'", labName, data.Schema.Feature.Value.Name);
var args = string.Format("Concat{{col={0}:{1},{2}}}", newFeatures, data.Schema.Feature.Value.Name, labName);
IDataView after_concatenation = ComponentCreation.CreateTransform(Host, args, viewI);
var roles = data.Schema.GetColumnRoleNames()
.Where(kvp => kvp.Key.Value != RoleMappedSchema.ColumnRole.Label.Value)
.Where(kvp => kvp.Key.Value != RoleMappedSchema.ColumnRole.Feature.Value)
.Prepend(RoleMappedSchema.ColumnRole.Feature.Bind(newFeatures))
.Prepend(RoleMappedSchema.ColumnRole.Label.Bind(dstName));
var trainer_input = new RoleMappedData(after_concatenation, roles);
ch.Info("New Features: {0}:{1}", trainer_input.Schema.Feature.Value.Name, trainer_input.Schema.Feature.Value.Type);
ch.Info("New Label: {0}:{1}", trainer_input.Schema.Label.Value.Name, trainer_input.Schema.Label.Value.Type);
// We train the unique binary classifier.
var trainedPredictor = trainer.Train(trainer_input);
var predictors = new TScalarPredictor[] { trainedPredictor };
// We train the reclassification classifier.
if (_args.reclassicationPredictor != null)
{
var pred = CreateFinalPredictor(ch, data, trans, count, _args, predictors, null);
TrainReclassificationPredictor(data0, pred, ScikitSubComponent <ITrainer, SignatureTrainer> .AsSubComponent(_args.reclassicationPredictor));
}
return(CreateFinalPredictor(ch, data, trans, count, _args, predictors, _reclassPredictor));
}
private ISingleFeaturePredictionTransformer <TDistPredictor> TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappedData data, int cls1, int cls2)
{
// this should not be necessary when the legacy constructor doesn't exist, and the label column is not an optional parameter on the
// MetaMulticlassTrainer constructor.
string trainerLabel = data.Schema.Label.Value.Name;
var view = MapLabels(data, cls1, cls2);
var transformer = trainer.Fit(view);
// the validations in the calibrator check for the feature column, in the RoleMappedData
var trainedData = new RoleMappedData(view, label: trainerLabel, feature: transformer.FeatureColumn);
var calibratedModel = transformer.Model as TDistPredictor;
if (calibratedModel == null)
{
calibratedModel = CalibratorUtils.GetCalibratedPredictor(Host, ch, Calibrator, transformer.Model, trainedData, Args.MaxCalibrationExamples) as TDistPredictor;
}
return(new BinaryPredictionTransformer <TDistPredictor>(Host, calibratedModel, trainedData.Data.Schema, transformer.FeatureColumn));
}
/// <summary> /// Train the embedded predictor. /// </summary> protected abstract TVectorPredictor TrainPredictor(IChannel ch, TScalarTrainer trainer, RoleMappedData data, int count);
