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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. For example, a feature selection tool (wrapping some supervised model) might also include a target y as input. 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, fitresult, Xout), which:

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

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