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Code Organization

Dependency chart for MLJ repositories. Repositories with dashed connections do not currently exist but are planned/proposed.

Repositories of some possible interest outside of MLJ, or beyond its conventional use, are marked with a ⟂ symbol:

  • MLJ.jl is the general user's point-of-entry for choosing, loading, composing, evaluating and tuning machine learning models. It pulls in most code from other repositories described below. MLJ also hosts the MLJ manual which documents functionality across the repositories, although some pages point to documentation hosted locally by a particular package.

  • MLJModelInterface.jl is a lightweight package imported by packages implementing MLJ's interface for their machine learning models. It's only dependencies are ScientificTypesBase.jl (which depends only on the standard library module Random) and StatisticalTraits.jl (which depends only on ScientificTypesBase.jl).

  • (⟂) MLJBase.jl is a large repository with two main purposes: (i) to give "dummy" methods defined in MLJModelInterface their intended functionality (which depends on third party packages, such as Tables.jl, Distributions.jl and CategoricalArrays.jl); and (ii) provide functionality essential to the MLJ user that has not been relegated to its own "satellite" repository for some reason. See the MLJBase.jl readme for a detailed description of MLJBase's contents.

  • StatisticalMeasures.jl provides performance measures (metrics) such as losses and scores.

  • MLJModels.jl hosts the MLJ model registry, which contains metadata on all the models the MLJ user can search and load from MLJ. Moreover, it provides the functionality for loading model code from MLJ on demand. Finally, it furnishes some commonly used transformers for data pre-processing, such as ContinuousEncoder and Standardizer.

  • MLJTuning.jl provides MLJ's TunedModel wrapper for hyper-parameter optimization, including the extendable API for tuning strategies, and selected in-house implementations, such as Grid and RandomSearch.

  • MLJEnsembles.jl provides MLJ's EnsembleModel wrapper, for creating homogeneous model ensembles.

  • MLJIteration.jl provides the IteratedModel wrapper for controlling iterative models (snapshots, early stopping criteria, etc)

  • MLJFlow.jl provides integration with the platform-agnostic machine learning tracking tool MLflow.

  • (⟂) OpenML.jl provides integration with the OpenML data science exchange platform

  • (⟂) MLJLinearModels.jl provides a wide range of julia-native penalized linear models such as Lasso, Elastic-Net, Robust regression, LAD regression, etc.

  • MLJFlux.jl an experimental package for gradient-descent models, such as traditional neural-networks, built with Flux.jl, in MLJ.

  • (⟂) ScientificTypesBase.jl is an ultra lightweight package providing "scientific" types, such as Continuous, OrderedFactor, Image and Table. It's purpose is to formalize conventions around the scientific interpretation of ordinary machine types, such as Float32 and DataFrame.

  • (⟂) ScientificTypes.jl articulates the particular convention for the scientific interpretation of data that MLJ adopts

  • (⟂) StatisticalTraits.jl An ultra lightweight package defining fall-back implementations for a collection of traits possessed by statistical objects, principally models and measures (metrics).

  • (⟂) DataScienceTutorials collects tutorials on how to use MLJ, which are deployed here

  • MLJTestInterface provides tests for implementations of the MLJ model interface

  • MLJTestIntegration provides tests for the entire MLJ ecosystem. (Called when you run ENV["MLJ_TEST_INTEGRATION"]="true"; Pkg.test("MLJ").