[DEPRECATED] AnalyticalEngine
/!\ This package is deprecated in favour of MLJ.
Who's behind this
- Thibaut Lienart (Imperial College London)
- Miguel Morin (Alan Turing Institute)
- Sebastian Vollmer (University of Warwick, Alan Turing Institute)
- Franz Kiraly (University College London)
- Mike Innes (Julia Computing)
- Avik Sengupta (Julia Computing)
- Valentin Churavy (Massachusetts Institute of Technology)
Aims and Milestones
Milestones
- March 2018
- [working prototype]
have a basic GeneralizedLinearRegression
that works well and showcases the ideas + works with Flux
- [WIP] have a basic pipeline
JuliaDB -> AnalyticalEngine
- have an interface with
DecisionTree.jl
- [WIP] have a way to deal with hyperparameters that works well with meta-learning
- August 2018
- have a full pipeline
JuliaDB -> FeatEng -> AnalyticalEngine
- have a working framework for metalearning
- have working tools for hyperparameter tuning (BayesianOpt, K-Folds, ...)
- Longer term
- In the spirit of MLR we'd like to interface with as many dedicated packages ("solvers") as possible and promote the creation and maintenance of those.
- In a first phase we won't care too much about this, focusing on the general pipeline, hyperparameter management etc but eventually this will become the key focus once we have a strong central API.
- There are a ton of packages implementing / re-implementing specific capabilities, hopefully the API will lead to the merging / concentration of packages solving generic tasks efficiently
Aims
- Major aims: offer a modern SkLearn-style package that can:
- work efficiently with large databases (via JuliaDB)
- work efficiently with different compute infrastructure (parallel, GPU, ...)
- work with generic optimisation algorithms (via
Optim.jl
)
- work with auto-diff algorithms (via
Flux.jl
)
- offer extensible meta-learning
- offer modern and extensible hyperparameter tuning (such as Bayesian opt)
- be extended easily by researchers/users in such a way that the maths matches well with the code
Inspiration