Meta issue: lssues for possible collaboration with UCL

[ablaom]

• Disintegration of MLJModels (medium) Disintegration of MLJModels MLJModels.jl#244 : priority would be for GLM, with a blank repo at https://github.com/alan-turing-institute/MLJGLMInterface.jl ; you could use https://github.com/alan-turing-institute/MLJNaiveBayesInterface.jl as a template. done

• Universal transformer for wrapping univariate transformers (medium) https://github.com/alan-turing-institute/MLJModels.jl/issues/288 : more detailed design proposal needed. Familiarity with the logic of existing Standardizer helpful. This may already be a good template for what we want to do here (just replace UnivariateStandardizer by a user-specified one). Need to worry about inverse_transform when implemented.

• Disintegration of MLJBase (medium) parts of Disintegration of MLJBase (discussion and tracking issue) MLJBase.jl#416 , in particular Serialization and OpenML, which seem to be hefty. Worth exploring which dependencies are causing most latency. Also, StatisticalMeasures (medium-long). Added note: Measures currently depend on UnivariateFinite, which in turn depends on Distributions, but only the base API. See this issue: Migrate UnivariateFinite (for categorical distributions) out to new package MLJBase.jl#504

• pdfnorm for Distributions.jl (??) Feature request: provide L2 norm of each distribution's pdf JuliaStats/Distributions.jl#806: this is one I believe Mose discussed with @fkiraly but was not completed, in an earlier engagement. Would be good to know what the status of that work is.

• investigate source of package compiler issues (medium) (MLJBase + PackageCompiler + Distributed => error MLJBase.jl#427). Suggest commenting out src/composition/ for a start.

• [ ]

• Review/Redesign of model registry (long) Review of model registry MLJModels.jl#321

• Test new API proposal to improve data resampling performance (medium) taking performance issue more seriously MLJBase.jl#309 (comment) done

• Add visualisation to model tuning results (medium) Add plot recipe for visualising multiple hyperparameter tuning outcomes MLJTuning.jl#41

• Populate model metadata with good default hyperparameter ranges (short-medium) Populate default hyperparameter ranges in the model metadata. MLJModels.jl#322

• Allow use of sample and class weights in sk-learn models (medium) Add sample weights support for sk-learn models MLJScikitLearnInterface.jl#17 (and the related https://github.com/alan-turing-institute/MLJModels.jl/issues/127)

• Add control over logging level (short) [FR] allow disabling of logging #255

added mid November

• cleanup of measures (short) name, aliases cleanup for measures MLJBase.jl#450 done

added early January 2020

• roll out data front-ends for models (medium) Implement the optional data front-end that models will be able to implement after Realize performance improvements for models implementing new data front-end MLJBase.jl#501 .

• TLC for DataScienceTutorials (short - medium) The tutorials need updating to latest version of MLJ, and some contributors have made PR's that are languishing.

[ablaom]

cc @giordano @vollmersj

[fkiraly]

this is one I believe Mose discussed with @fkiraly but was not completed, in an earlier engagement. Would be good to know what the status of that work is.

None of that happened ... well, in Julia.
It exists in R now, with a number of other useful distribution methods:
https://github.com/alan-turing-institute/distr6
xoopR/distr6#196
@RaphaelS1 and @aintoha know all about it.

Ultimately, and perhaps not too surprisingly, we also ended up at a point where double dispatch would be great (function space cross-products of distribution defining functions), but R6 doesn't have an easy way for double dispatch. R7 perhaps...

On a side note, @aintoha also calculated a larger batch of integrals that might be useful to re-use instead of re-deriving them.

[ablaom]

It exists in R now, with a number of other useful distribution methods:
https://github.com/alan-turing-institute/distr6
xoopR/distr6#196
@RaphaelS1 and @aintoha know all about it.

Really good to know, thanks!!

cc @giordano

R7 perhaps...

Ha ha.

[ablaom]

@giordano I have reviewed the checklist today, 11 March 2021. The items with a checkbox still look good, more-or-less in the order given. In particular, to start with a review of latency in MLJBase, and to move measures out. Let's talk details in a call.

[ablaom]

Some miscellaneous "smaller" issues:

JuliaAI/MLJBase.jl#573

cc @giordano

[ablaom]

Closing as stale