v0.17.3

MLJ v0.17.3

Diff since v0.17.2

Merged pull requests:

• Add documentation for the new model docstring standard (#906) (@ablaom)
• For a 0.17.2 release (#912) (@ablaom)

v0.17.2

MLJ v0.17.2

Diff since v0.17.1

Closed issues:

• Discussion: Outlier Detection API in MLJ (#780)
• Is MLJ ready for Julia 1.7? (#869)
• Documentation of 'Common MLJ Workflows' shows wrong partition syntax (#894)
• [Discussion] Review model documentation strings (#898)
• Update list of models in docs for MLJText model change (#900)
• Proposal for new Model docstrings standard (#901)

Merged pull requests:

• Documentation fix. No new release (#895) (@ablaom)
• Explain outlier detection models (#896) (@davnn)
• Update list_of_supported_models.md (#897) (@zsz00)
• Remove binder notebook from readme and documentation (#902) (@ablaom)
• BagOfWordsTransformer -> CountTransformer in List of Models (#903) (@ablaom)
• A number of documentation updates. No new release. (#904) (@ablaom)
• Tweak docs for nodes (#905) (@ablaom)
• Export UnivariateFillImputer (#909) (@ablaom)
• For a 0.17.2 release (#910) (@ablaom)

v0.17.1

MLJ v0.17.1

Diff since v0.17.0

• Programatically export all measure types, aliases and instances, which catches a few previously missed cases (#892)
• Programatically export almost all model and measure traits (#892)
• Bump MLJBase requirement to ensure built-in measure instances (eg log_loss) have doc-strings.

Closed issues:

• Extend model serialization to composite models with a components needing specialised serialization (#678)
• New measures RSquared and aliases rsq, rsquared need exporting (#880)
• Re-export all traits. (#888)
• What does MLJ.save really save? (#889)
• Programmatically re-export measures and their aliases from MLJBase (#890)

Merged pull requests:

• Fix typos in Getting Started (#881) (@takuti)
• Typos in docs. No new release. (#882) (@ablaom)
• Fix MLJFlux.jl project URL (#883) (@i-aki-y)
• Broken link fixes in docs. No new release. (#884) (@ablaom)
• Update examples/lightning_tour/ for MLJ 0.17 (#886) (@ablaom)
• Added recent measures to src/MLJ.jl (#887) (@sayantan1410)
• Clean up export of measure-related methods (#892) (@ablaom)
• For a 0.17.1 release (#893) (@ablaom)

v0.17.0

MLJ 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 (JuliaAI/MLJBase.jl#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 (JuliaAI/MLJBase.jl#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 (JuliaAI/MLJBase.jl#677).

• (breaking) Remove deprecated code for exporting learning networks by hand (JuliaAI/MLJBase.jl#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 (JuliaAI/MLJBase.jl#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 (JuliaAI/MLJBase.jl#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 (JuliaAI/MLJBase.jl#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), 0.6, rng=123, multi=true) (JuliaAI/MLJBase.jl#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 (JuliaAI/MLJBase.jl#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 (JuliaAI/ScientificTypes.jl#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)

v0.16.11

MLJ v0.16.11

Diff since v0.16.10

Merged pull requests:

• Simplify the exports of composition.jl (#859) (@davnn)
• For a 0.16.11 release (#860) (@ablaom)

v0.16.10

MLJ v0.16.10

Diff since v0.16.9

Closed issues:

• Link to video to learn design patterns in julia is broken (#850)
• re-export MLJIteration (#853)

Merged pull requests:

• Add new iteration controls to docs and re-export MLJIteration (#854) (@ablaom)
• Further doc updates for new iteration controls (#855) (@ablaom)
• For a 0.16.10 release (#856) (@ablaom)
• fix broken link (#857) (@ablaom)

v0.16.9

MLJ v0.16.9

Diff since v0.16.8

Closed issues:

• Document the Explicit tuning strategy (#822)
• Add TSVD transformer to list of models (#824)
• Remove @234 business from display of MLJ objects? (#842)
• Inconsistent output type for different clustering models (#846)

Merged pull requests:

• Documentation fix. No new release (#837) (@ablaom)
• More documentation fixes. No new release (#838) (@ablaom)
• Use isordered instead of private CategoricalArray fields (#839) (@nalimilan)
• Doc updates. No release (#843) (@ablaom)
• Add detection models to list of models in the documentation (#844) (@ablaom)
• Re export TimeSeriesCV and add to docs (#847) (@ablaom)
• Typo (#848) (@ablaom)
• For a 0.16.9 release (#849) (@ablaom)

v0.16.8

MLJ v0.16.8

Diff since v0.16.7

• (code re-organization) Suspend use of MLJOpenML.jl in favour of OpenML.jl, a non-breaking change (JuliaAI/MLJOpenML.jl#19 (comment))

Closed issues:

• Issue to tag new releases (#571)
• Migrate packages to JuliaAI (#765)
• export training_losses (#772)
• Add link to support channel if an official channel exists (#773)
• Saving the out of sample Loss in iterated models (#793)
• Have evaluate! automatically run the right kinds of predictions for each metric (#795)
• Update "Traits" section of "Adding models for general use" in the manual (#799)
• export Stack (#804)
• "applicable method may be too new" error instantiating models loaded in function scope with @load (#809)
• Following end-to-end tutorial on AMES but got error (#815)
• Test dependency related fail in CI (#817)
• MLJ: machine / evaluate! are random in unclear ways (#823)
• Document hyper-parameter access requirements to ensure compatibility with MLJTuning API (#827)
• r2 metrics (#830)
• Error MLJ in linux (#833)

Merged pull requests:

• To generate updated docs. No new release. (#819) (@ablaom)
• Add a code of conduct (#820) (@ablaom)
• Bump MLJOpenML compat and add new methods to manual (#821) (@ablaom)
• Document hyper-parameter access required for models (#828) (@ablaom)
• Update lightning tour (#831) (@ablaom)
• For a 0.16.8 release (#832) (@ablaom)
• Fix typo in documentation (#835) (@rikhuijzer)
• Document the Explicit tuning strategy (#836) (@rikhuijzer)

v0.16.7

MLJ v0.16.7

Diff since v0.16.6

Closed issues:

• Tuned Model interface doesnt have class_weights (#808)
• MLJBase breaking ParallelKMeans CI (#811)

Merged pull requests:

• Update linguist to better reflect the source language of the package (#812) (@paniash)
• Adapt to renaming of ScientificTypes (#813) (@ablaom)
• For a 0.16.7 release (#814) (@ablaom)

v0.16.6

MLJ v0.16.6

Diff since v0.16.5

• (enhancement) Re-export Stack from MLJBase to make model stacking available.

Merged pull requests:

• Doc improvements and sundry small items (#805) (@ablaom)
• For a 0.16.6 release (#806) (@ablaom)
• Bump MLJBase compat to make Stack available (#807) (@ablaom)