com.alpine.model.pack.multiple
This should return a representative class from every jar that is needed to load the object during deserialization.
This should return a representative class from every jar that is needed to load the object during deserialization.
Default implementation returns the class of MLModel, and the class of the model implementation.
Used to identify this model when in a collection of models.
Used to identify this model when in a collection of models. Should be simple characters, so it can be used in a feature name.
identifier for the model.
Returns a streamlined version of this model, throwing away parts which are not required to produce the features with names in requiredOutputFeatureNames.
Returns a streamlined version of this model, throwing away parts which are not required to produce the features with names in requiredOutputFeatureNames.
Behaviour is undefined if requiredOutputFeatureNames is not actually a subset of outputFeatures.map(_columnName).
The resulting model may still have more output features than those required (i.e. for ClassificationRowModel implementations which always have the same output features, or for OneHotEncodingModel whose output features naturally group together).
names of the output features that the new model should produce.
A model which has the same functionality for those output features listed, but potentially fewer input features.
Used to combine several models in parallel. e.g. the input features set is the (distinct) union of the input features of the sub-models. the output features are the concatenation of the output features of the sub-models (with string suffices added to ensure uniqueness of names).