internal EnsemblePredictor(IHostEnvironment env, PredictionKind kind,
FeatureSubsetModel <TScalarPredictor>[] models, IOutputCombiner <Single> combiner, Single[] weights = null)
: base(env, LoaderSignature, models, combiner, weights)
{
PredictionKind = kind;
InputType = InitializeMappers(out _mappers);
}
private protected EnsembleModelParametersBase(IHostEnvironment env, string name, FeatureSubsetModel <TOutput>[] models,
IOutputCombiner <TOutput> combiner, Single[] weights)
: base(env, name)
{
Host.Check(Utils.Size(models) > 0, "Ensemble was created with no models.");
Host.Check(weights == null || weights.Length == models.Length);
Models = models;
Combiner = combiner;
Weights = weights;
}
public static SchemaBindablePipelineEnsembleBase Create(IHostEnvironment env, IPredictorModel[] predictors, IOutputCombiner combiner, string scoreColumnKind)
{
switch (scoreColumnKind)
{
case MetadataUtils.Const.ScoreColumnKind.BinaryClassification:
var binaryCombiner = combiner as IBinaryOutputCombiner;
if (binaryCombiner == null)
{
throw env.Except("Combiner type incompatible with score column kind");
}
return(new ImplOneWithCalibrator(env, predictors, binaryCombiner));
case MetadataUtils.Const.ScoreColumnKind.Regression:
case MetadataUtils.Const.ScoreColumnKind.AnomalyDetection:
var regressionCombiner = combiner as IRegressionOutputCombiner;
if (regressionCombiner == null)
{
throw env.Except("Combiner type incompatible with score column kind");
}
return(new ImplOne(env, predictors, regressionCombiner, scoreColumnKind));
case MetadataUtils.Const.ScoreColumnKind.MultiClassClassification:
var vectorCombiner = combiner as IMultiClassOutputCombiner;
if (vectorCombiner == null)
{
throw env.Except("Combiner type incompatible with score column kind");
}
return(new ImplVec(env, predictors, vectorCombiner));
default:
throw env.Except("Unknown score kind");
}
}
protected SchemaBindablePipelineEnsemble(IHostEnvironment env, IPredictorModel[] predictors,
IOutputCombiner <T> combiner, string registrationName, string scoreColumnKind)
: base(env, predictors, registrationName, scoreColumnKind)
{
Combiner = combiner;
}
public Bound(SchemaBindablePipelineEnsemble <T> parent, RoleMappedSchema schema)
: base(parent, schema)
{
_combiner = parent.Combiner;
}
internal EnsembleDistributionPredictor(IHostEnvironment env, PredictionKind kind,
FeatureSubsetModel <TDistPredictor>[] models, IOutputCombiner <Single> combiner, Single[] weights = null)
: base(env, RegistrationName, models, combiner, weights)
{
PredictionKind = kind;
_probabilityCombiner = new Median(env);
InputType = InitializeMappers(out _mappers);
ComputeAveragedWeights(out _averagedWeights);
}
