For 0.17 release

Open ablaom opened this issue 4 years ago • 0 comments

[x] #861
[x] update compat for MLJBase 0.19 when released
[x] update compat for OpenML to 0.2
[x] update compat for MLJEnsembles to 0.2
[x] Manual

Release notes:

Bumps the versions of the following dependencies:

Following are the changes relevant to most users. Developers and advanced users can refer to the release notes linked above for a complete list.

(breaking) schema(X) no longer includes the nrows property. Use nrows(X) instead (https://github.com/JuliaAI/MLJBase.jl/issues/698)
(mildly breaking) unpack(table, p1, p2, ...) now includes an extra component in its return value, namely a table with all columns not selected by any of the predicates p1, p2, ... Frequently, users' existing code will safely ignore the extra component (https://github.com/JuliaAI/MLJBase.jl/issues/691)
(breaking) Change syntax EnsembleModel(atom=...) to EnsembleModel(model=...) for consistency with other MLJ model wrappers (eg, TunedModel) but additionally allow passing model as non-keyword argument, as in EnsembleModel(my_tree, ...).
(breaking) The default scale for unbounded NumericRanges is changed from :log to :log10 (https://github.com/JuliaAI/MLJBase.jl/issues/677).
(breaking) Remove deprecated code for exporting learning networks by hand (https://github.com/JuliaAI/MLJBase.jl/issues/643), which should instead be achieved using return! method (docs).
(mildly breaking) The range(model, :hyperparameter, ...) constructor now tries to infer type information for the range from the corresponding model struct field type for :hyperparameter, rather than from the type of the current value (https://github.com/JuliaAI/MLJBase.jl/issues/666)
(breaking) Dissallow previously deprecated use of wrapped_model=... in BinaryThresholdPredictor. Correct syntax is BinaryThresholdPredictor(model=...) or BinaryThresholdPredictor(model, ...) (https://github.com/JuliaAI/MLJModels.jl/421)
(enhancement) Add a new Pipeline type for constructing pipelines without macros. Pipelines are to be constructed using the syntax model1 |> model2 |> ... or with the constructor Pipeline which exposes more options. The @pipeline macro is deprecated (https://github.com/JuliaAI/MLJBase.jl/issues/664)
(enhancement) Add the metamodel TransformedTargetModel for wrapping supervised models in transformations of the target variable, which can be learned transformations (eg, standardisation). Previously this functionality was available as part of @pipeline (https://github.com/JuliaAI/MLJBase.jl/issues/678)
(enhancement) The partition function can now be called with a tuple of data arguments, for "synchronised" partitioning, but this requires specifying multi=true (because some tables are tuples) as in (Xtrain, ytrain), (Xtest, ytest) = partition((X, y), rng=123, multi=true) (https://github.com/JuliaAI/MLJBase.jl/issues/696)
(enhancement) Create a way to include the state, after training, of arbitrary nodes of a learning network, in the report of a model created by exporting the learning network (https://github.com/JuliaAI/MLJBase.jl/issues/644)
(new models) Add the following new models to the registry from MLJText.jl: BM25Transformer, BagOfWordsTransformer (https://github.com/JuliaAI/MLJModels.jl/419)
(enhancement) Implement the Tables.jl interface for objects returned by schema (https://github.com/JuliaAI/ScientificTypes.jl/pull/174)

Post-release:

[x] update examples/lightning_tour/ https://github.com/alan-turing-institute/MLJ.jl/pull/886
[x] Binder example @ablaom (removed)
[x] Update DataScienceTutorials.jl @OkonSamuel
[x] Update MLJTutorial.jl
[x] Update ablaom/HelloJulia.jl
[ ] Update readme for ShapML.jl https://github.com/nredell/ShapML.jl/pull/16
[x] Update readme for TableTransformers.jl (see bottom of the readme page) https://github.com/JuliaML/TableTransforms.jl/pull/20
[x] Update https://expandingman.gitlab.io/Shapley.jl/ (unnecessary)

Nov 05 '21 02:11 ablaom