Unsupervised models

Unsupervised models implement the MLJ model interface in a very similar fashion. The main differences are:

• The fit method, which still returns (fitresult, cache, report) will typically have only one training argument X, as in MLJModelInterface.fit(model, verbosity, X), although this is not a hard requirement; see Transformers requiring a target variable in training below. Furthermore, in the case of models that subtype Static <: Unsupervised (see Static models) fit has no training arguments at all, but does not need to be implemented as a fallback returns (nothing, nothing, nothing).

• A transform and/or predict method is implemented, and has the same signature as predict does in the supervised case, as in MLJModelInterface.transform(model, fitresult, Xnew). However, it may only have one data argument Xnew, unless model <: Static, in which case there is no restriction. A use-case for predict is K-means clustering that predicts labels and transforms input features into a space of lower dimension. See the Transformers that also predict section of the MLJ manual for an example.

• The target_scitype refers to the output of predict, if implemented. A new trait, output_scitype, is for the output of transform. Unless the model is Static (see Static models) the trait input_scitype is for the single data argument of transform (and predict, if implemented). If fit has more than one data argument, you must overload the trait fit_data_scitype, which bounds the allowed data passed to fit(model, verbosity, data...) and will always be a Tuple type.

• An inverse_transform can be optionally implemented. The signature is the same as transform, as in MLJModelInterface.inverse_transform(model::MyUnsupervisedModel, fitresult, Xout), which:

  • must make sense for any Xout for which scitype(Xout) <: output_scitype(MyUnsupervisedModel); and
  • must return an object Xin satisfying scitype(Xin) <: input_scitype(MyUnsupervisedModel).

For sample implementations, see MLJ's built-in transformers and the clustering models at MLJClusteringInterface.jl.

Transformers requiring a target variable in training

An Unsupervised model that is not Static may include a second argument y in it's fit signature, as in fit(::MyTransformer, verbosity, X, y). For example, some feature selection tools require a target variable y in training. (Unlike Supervised models, an Unsupervised model is not required to implement predict, and in pipelines it is the output of transform, and not predict, that is always propagated to the next model.) Such a model should overload the trait target_in_fit, as in this example:

MLJModelInterface.target_in_fit(::Type{<:MyTransformer}) = true

This ensures that such models can appear in pipelines, and that a target provided to the pipeline model is passed on to the model in training.

If the model implements more than one fit signature (e.g., one with a target y and one without) then fit_data_scitype must also be overloaded, as in this example:

MLJModelInterface.fit_data_scitype(::Type{<:MyTransformer}) = Union{
    Tuple{Table(Continuous)},
	Tuple{Table(Continous), AbstractVector{<:Finite}},
}