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)

v0.16.5

MLJ v0.16.5

Diff since v0.16.4

Closed issues:

• Multiple Motivations for using same Mathematical Composite Functional notation f(g(h(x))) for language syntax to encode Custom objective/loss/cost functions (XGBoost), as to encode Distributed Parallel Workflow Pipeline sequence. (#488)
• Add link to TreeParzen from "Tuning Models" section of the manual, and the doc string for LatinHypercube (#690)
• Coercing exotic table types (#774)
• Remove cap on StatsBase in docs/Project.toml (#785)
• Improve docs around weight specification (#787)
• Bug: evaluate! crashes being called several times in a row when acceleration is used (#788)
• Make it possible to use deterministic metrics for models providing probabilistic prediction types (#789)
• Add deterministic metric to Getting Started evaluate! example (#790)
• Pipeline with XGBoost doesn't seem to serialize properly (#794)
• MLJ universe graphic in README.md transparency issue (#796)
• Re-export logpdf from Distributions (#797)
• Measures for Multi-Target models (#800)

Merged pull requests:

• Improve docs re weights (#791) (@ablaom)
• Doc improvements. No new release (#792) (@ablaom)
• Typo (#798) (@flyingdutchman23)
• Use new MLJEnsembles.jl package (#801) (@giordano)
• Export logpdf (#802) (@ablaom)

v0.16.4

MLJ v0.16.4

Diff since v0.16.3

• Re-export BinaryThresholdClassifier from MLJModels (for wrapping binary probabilistic classifiers as deterministic classifiers using a user-specified threshold)

• Extend Distributions compatibility to version 0.25^

Merged pull requests:

• CompatHelper: bump compat for "Distributions" to "0.25" (#784) (@github-actions[bot])
• For a 0.16.4 release (#786) (@ablaom)

v0.16.3

MLJ v0.16.3

Diff since v0.16.2

Closed issues:

• LoadError: UndefVarError: refcode not defined (#782)

Merged pull requests:

• CompatHelper: bump compat for "CategoricalArrays" to "0.10" (#779) (@github-actions[bot])
• For a 0.16.3 release (#783) (@ablaom)

v0.16.2

MLJ v0.16.2

Diff since v0.16.1

Closed issues:

• Expose feature importances of EvoTreeClassifier (#745)
• New BetaML models (to be checked) (#749)
• Error for the iris dataset example in Getting Started page (#768)
• Issue to track MLJModelInterface 1.0 rollout (#776)

Merged pull requests:

• Fix some new issues with the manual arising from recent code re-organization (#770) (@ablaom)
• Doc fixes. No new release. (#771) (@ablaom)
• Bump compat for MLJIteration and update manual for iteration and tuning (#777) (@ablaom)
• For a 0.16.2 release (#778) (@ablaom)

v0.16.1

MLJ v0.16.1

Diff since v0.16.0

• (enhancment) Explicitly include MLJIteration, re-exporting its methods and types, so that using MLJIteration is no longer required to make use of the newly released package (#139). For documentation, see here.
• Update MLJBase and import the new packages MLJOpenML and MLJSerialization, which provide functionality contained in the older MLJBase versions. Should have no effect on the MLJ user (JuliaAI/MLJBase.jl#416)

Closed issues:

• Model wrapper for controlling iterative models. (#139)
• Restore broken ensemble testing (#683)
• No more symbols in CategoricalArrays (#691)
• Can't load KMeans from ParallelKMeans (#740)
• DecisionTreeClassifier does not appear to be a Supervised model. (#741)
• Unable to retrieve saved machines (#743)
• Need help in creating a MLJModelInterface.Model interface of a complex model (#744)
• Meaning of the various methods for unsupervised models ? (#748)
• Load issue (#752)
• MultinomialNBClassifier not available. (#753)
• Evaluate with acceleration is only working on a single worker (#754)
• Add to docs for new implementations: fit should not mutate model hyper-parameters (#755)

Merged pull requests:

• Typo (#750) (@PallHaraldsson)
• Doc updates. No new release (#751) (@ablaom)
• Add iteration docs (#759) (@ablaom)
• Add MLJIteration documentation to the MLJ manual. No new release (#760) (@ablaom)
• Improvements to landing page of manual (#761) (@ablaom)
• Documentation updates. No new release (#762) (@ablaom)
• Add MLJIteration and re-export it's constructors/types (#764) (@ablaom)
• Adaptations to further disintegration of MLJBase (#766) (@ablaom)
• For a 0.16.1 release (#767) (@ablaom)

v0.16.0

MLJ v0.16.0

Diff since v0.15.2

Release notes:

Update MLJModels and MLJBase compatibility requirements. Includes some breaking changes. Most significantly note that @load now returns a model type instead of an instance (see https://github.com/alan-turing-institute/MLJ.jl/blob/dev/docs/src/loading_model_code.md). For full list of changes, see:

MLJBase 0.17.0 release notes
MLJModels 0.14.0 release notes

Closed issues:

• Can't use @load within a module (#321)
• Add option to cache data at nodes of learning networks to avoid repeating operations (transform, predict, etc) (#702)

Merged pull requests:

• Documentation updates for adding data front-end to model implementations (#727) (@ablaom)
• use add instead of develop when recommending old OpenSpecFun_jll (#737) (@KristofferC)
• For a 0.16 release (#738) (@ablaom)

v0.15.2

MLJ v0.15.2

Diff since v0.15.1

Closed issues:

• @load should only do import or using, not define a const (#721)
• When using the Standardizer method, why can't the fit! function take an argument of type AbstractMatrix{Continuous}? (#730)

Merged pull requests:

• update roadmap (#732) (@ablaom)
• Update Roadmap; no new release (#733) (@ablaom)
• For 0.15.2 release (#734) (@ablaom)

v0.15.1

MLJ v0.15.1

Diff since v0.15.0

Closed issues:

• Unsupported const declaration (#715)
• TunedModel is not fitted with measure=misclassification_rate (#725)

Merged pull requests:

• Move from Travis CI to GitHub Actions CI (#717) (@DilumAluthge)
• Delete the docs/src/_old folder (#718) (@DilumAluthge)
• switch ci to github actions (#719) (@ablaom)
• Update citations to JOSS paper (#720) (@ablaom)
• removed type piracy of show for MersenneTwister (#722) (@ExpandingMan)
• No new release. Update docs about pkg needed for DecisionTreeClassifier (#723) (@ablaom)
• Add entry to tuning section of manual for Latin hyper cube (#724) (@ablaom)
• Add examples to tuning (#728) (@ablaom)
• For a 0.15.1 release (bump [compat] for MLJTuning) (#729) (@ablaom)

v0.15.0

MLJ v0.15.0

Diff since v0.14.1

• Extend compat for MLJBase, MLJModels, MLJScientificTypes, CategoricalArrays. Includes a minor breaking change in behaviour of the coerce method; see https://github.com/alan-turing-institute/MLJBase.jl/releases/tag/v0.16.0.

Closed issues:

• root: nothing does not appear to be a Supervised model. (#686)
• Review TagBot configurations for all MLJ repos (#692)
• less fidgity alternative to @load (#693)
• ERROR: MethodError: no method matching PCA() (#699)
• MLJDecisionTreeInterface.jl (#700)
• Just a doc typo (#704)
• Saving snapshots of a TunedModel as it trains (#708)
• All CV scores in a TunedModel (#709)
• Undefvarerror when tuning a model (#711)

Merged pull requests:

• Shorten MLJ design paper following JOSS review (#676) (@ablaom)
• Design paper update. No release (#677) (@ablaom)
• Update Slack URL (#679) (@logankilpatrick)
• Doc update. No new release. (#680) (@ablaom)
• Paper references (#681) (@darenasc)
• Fix bibliography in paper. No new release (#682) (@ablaom)
• Suspend EnsembleModel testing (#684) (@ablaom)
• Update affiliations in paper. No new release (#688) (@ablaom)
• Add verbose affiliations to paper.md. No release (#694) (@ablaom)
• fife -> St Andrews . No release (#695) (@ablaom)
• Added latin_hypercube docs (#697) (@ludoro)
• Update model_search.md (#705) (@dsweber2)
• For 0.15 Release (#710) (@ablaom)
• Revert "Added latin_hypercube docs" (#712) (@ablaom)
• Extend compat for MLJBase, MLJModels, MLJScientificTypes, CategoricalArrays (#713) (@ablaom)