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MLJ

A Machine Learning Toolbox for Julia.

Build Status #mlj Documentation Cite MLJ

MLJ is a machine learning framework for Julia aiming to provide a convenient way to use and combine a multitude of tools and models available in the Julia ML/Stats ecosystem. MLJ is released under the MIT licensed and sponsored by the Alan Turing Institute.


Using MLJ • Models Available • MLJ Universe • Contributing • MLJ Cheatsheet • Citing MLJ

Key goals

  • Offer a consistent way to use, compose and tune machine learning models in Julia,
  • Promote the improvement of the Julia ML/Stats ecosystem by making it easier to use models from a wide range of packages,
  • Unlock performance gains by exploiting Julia's support for parallelism, automatic differentiation, GPU, optimisation etc.

Key features

  • Data agnostic, train models on any data supported by the Tables.jl interface,
  • Extensive support for model composition (pipelines and learning networks),
  • Convenient syntax to tune and evaluate (composite) models,
  • Consistent interface to handle probabilistic predictions.

Using MLJ

Initially it is recommended that MLJ and associated packages be installed in a new environment to avoid package conflicts. You can do this with

julia> using Pkg; Pkg.activate("My_MLJ_env", shared=true)

Installing MLJ is also done with the package manager:

julia> Pkg.add(["MLJ", "MLJModels"])

It is important to note that MLJ is essentially a big wrapper providing a unified access to model providing packages and so you will also need to make sure these packages are available in your environment. For instance, if you want to use a Decision Tree Classifier, you need to have DecisionTree.jl installed:

julia> Pkg.add("DecisionTree");
julia> using MLJ;
julia> @load DecisionTreeClassifier

For a list of models and their packages see the table below, or run

using MLJ
models()

We recommend you start with models marked as coming from mature packages such as DecisionTree, ScikitLearn or XGBoost.

Tutorials

The best place to get started with MLJ is to go the MLJ Tutorials website. Each of the tutorial can be downloaded as a notebook or Julia script to facilitate experimentation with the packages. For more comprehensive documentation, see the user manual.

You're also welcome to join the #mlj Julia slack channel to ask questions and make suggestions.

Available Models

MLJ provides access to to a wide variety of machine learning models. We are always looking for help adding new models or test existing ones. Currently available models are listed below; for the most up-to-date list, run using MLJ; models().

  • experimental: indicates the package is fairly new and/or is under active development; you can help by testing these packages and making them more robust,
  • medium: indicates the package is fairly mature but may benefit from optimisations and/or extra features; you can help by suggesting either,
  • high: indicates the package is very mature and functionalities are expected to have been fairly optimised and tested.
Package Models Maturity Note
Clustering.jl KMeans, KMedoids high †
DecisionTree.jl DecisionTreeClassifier, DecisionTreeRegressor, AdaBoostStumpClassifier high †
EvoTrees.jl EvoTreeRegressor, EvoTreeClassifier, EvoTreeCount, EvoTreeGaussian low gradient boosting models
GLM.jl LinearRegressor, LinearBinaryClassifier, LinearCountRegressor medium †
LIBSVM.jl LinearSVC, SVC, NuSVC, NuSVR, EpsilonSVR, OneClassSVM high also via ScikitLearn.jl
MLJModels.jl (builtins) StaticTransformer, FeatureSelector, FillImputer, UnivariateStandardizer, Standardizer, UnivariateBoxCoxTransformer, OneHotEncoder, ConstantRegressor, ConstantClassifier medium
MLJLinearModels.jl LinearRegressor, RidgeRegressor, LassoRegressor, ElasticNetRegressor, QuantileRegressor, HuberRegressor, RobustRegressor, LADRegressor, LogisticClassifier, MultinomialClassifier experimental
MultivariateStats.jl RidgeRegressor, PCA, KernelPCA, ICA, LDA, BayesianLDA, SubspaceLDA, BayesianSubspaceLDA high †
NaiveBayes.jl GaussianNBClassifier, MultinomialNBClassifier, HybridNBClassifier low
NearestNeighbors.jl KNNClassifier, KNNRegressor high
ScikitLearn.jl ARDRegressor, AdaBoostClassifier, AdaBoostRegressor, AffinityPropagation, AgglomerativeClustering, BaggingClassifier, BaggingRegressor, BayesianLDA, BayesianQDA, BayesianRidgeRegressor, BernoulliNBClassifier, Birch, ComplementNBClassifier, DBSCAN, DummyClassifier, DummyRegressor, ElasticNetCVRegressor, ElasticNetRegressor, ExtraTreesClassifier, ExtraTreesRegressor, FeatureAgglomeration, GaussianNBClassifier, GaussianProcessClassifier, GaussianProcessRegressor, GradientBoostingClassifier, GradientBoostingRegressor, HuberRegressor, KMeans, KNeighborsClassifier, KNeighborsRegressor, LarsCVRegressor, LarsRegressor, LassoCVRegressor, LassoLarsCVRegressor, LassoLarsICRegressor, LassoLarsRegressor, LassoRegressor, LinearRegressor, LogisticCVClassifier, LogisticClassifier, MeanShift, MiniBatchKMeans, MultiTaskElasticNetCVRegressor, MultiTaskElasticNetRegressor, MultiTaskLassoCVRegressor, MultiTaskLassoRegressor, MultinomialNBClassifier, OPTICS, OrthogonalMatchingPursuitCVRegressor, OrthogonalMatchingPursuitRegressor, PassiveAggressiveClassifier, PassiveAggressiveRegressor, PerceptronClassifier, ProbabilisticSGDClassifier, RANSACRegressor, RandomForestClassifier, RandomForestRegressor, RidgeCVClassifier, RidgeCVRegressor, RidgeClassifier, RidgeRegressor, SGDClassifier, SGDRegressor, SVMClassifier, SVMLClassifier, SVMLRegressor, SVMNuClassifier, SVMNuRegressor, SVMRegressor, SpectralClustering, TheilSenRegressor high †
XGBoost.jl XGBoostRegressor, XGBoostClassifier, XGBoostCount high

Note (†): some models are missing, your help is welcome to complete the interface. Get in touch with Thibaut Lienart on Slack if you would like to help, thanks!

The MLJ Universe

The functionality of MLJ is distributed over a number of repositories illustrated in the dependency chart below. Click on the appropriate link for further information:


Code Organization  •  Road Map  •  Contributing

MLJ  •  MLJBase  •  MLJModelInterface  •  MLJModels  •  MLJTuning  •  MLJLinearModels  •  MLJFlux
MLJTutorials  •  MLJScientificTypes  •  ScientificTypes


Dependency Chart

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

Citing MLJ

Cite MLJ 
@software{anthony_blaom_2019_3541506,
  author       = {Anthony Blaom and
                  Franz Kiraly and
                  Thibaut Lienart and
                  Sebastian Vollmer},
  title        = {alan-turing-institute/MLJ.jl: v0.5.3},
  month        = nov,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {v0.5.3},
  doi          = {10.5281/zenodo.3541506},
  url          = {https://doi.org/10.5281/zenodo.3541506}
}

Contributors

Core design: A. Blaom, F. Kiraly, S. Vollmer

Active maintainers: A. Blaom, T. Lienart

Active collaborators: D. Arenas, D. Buchaca, J. Hoffimann, S. Okon, J. Samaroo, S. Vollmer

Past collaborators: D. Aluthge, E. Barp, G. Bohner, M. K. Borregaard, V. Churavy, H. Devereux, M. Giordano, M. Innes, F. Kiraly, M. Nook, Z. Nugent, P. Oleśkiewicz, A. Shridar, Y. Simillides, A. Sengupta, A. Stechemesser.

License

MLJ is supported by the Alan Turing Institute and released under the MIT "Expat" License.