alan-turing-institute/MLJ.jl: v0.17.0

Diff since v0.16.11

Bumps the versions of the following dependencies:

• MLJBase 0.19
• MLJEnsembles 0.2
• MLJModels 0.15
• OpenML 0.2
• ScientificTypes 3

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)

Closed issues:

• Add facility to quickly define a model stack with meta-learner (#76)
• Bug in MultinomialNBClassifier (#97)
• Add docs for 'pipe' syntax (#231)
• Use alphabetical ordering for ambiguous provider package (#257)
• FAQ for Julia Meetup 22.10.2019 (#286)
• More arrows (#307)
• Support for class weights (and interpretation) (#328)
• Visualizing hyperparameter tuning results for arbitrary numbers of parameters (#416)
• Check number of levels of y_train before calling fit (#542)
• @load_MNIST (#584)
• Programmatic creation of pipelines (#594)
• Unable to retrieve machine in Mac which is saved from Windows (#840)
• Broken Link (#858)
• Problems with compilation failure due to "ArrayLikeVariate not defined" (#863)
• @pipeline throws LoadError/UndefVarError in Pluto notebook (#865)
• transformations like in R with formulas y ~ a + a * b + b^3. (#867)
• Loading a Flux model into a MLJ machine (#870)
• Stratified CV not working - LoadError: MethodError: no method matching iterate(::CategoricalValue{String, UInt32}) (#871)
• Add new MLJText models to list of models (#872)
• Add doc-string for PerformanceEvaluation to manual (#873)
• Add entry to manual explaining new interface point for exported learning networks. (#875)

Merged pull requests:

• Some typo fixes on Linear Pipeline doc page (#866) (@mjsmith037)
• Update dependency compats, in preparation for 0.17 release (#878) (@ablaom)
• For a 0.17 release (#879) (@ablaom)