[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

• MLR in R
• Sklearn in Python as well as contributions such as sklearn pandas and lightning
• Sklearn in Julia
• Caret