MLJ.jl Versions Save

MLJ v0.19.2

Diff since v0.19.1

Closed issues:

• @from_network does more strange eval stuff (#703)
• Create new package for MLJ-universe-wide integration tests (#885)
• Stack of TunedModels (#980)
• Please add CatBoost or any alternate package (pure Julia) which can beat it (#992)
• Update list of models for BetaML (#993)
• Update List of Supported Models Clustering.jl Section (#1000)
• predict should work on DataFrameRow (#1004)
• Documentation generation fails silently (#1007)
• Clarify and fix documentation around reformat. (#1010)
• Reporting a vulnerability (#1015)
• What causes the Distributed.ProcessExitedException(3) error in Julia and how can I resolve it in my Pluto notebook? (#1018)
• Add link to Mt Everest blog (#1021)
• Remove "experimental" label for acceleration API docs (#1026)

Merged pull requests:

• Fix TransformedTarget example in manual (no new release) (#999) (@ablaom)
• updating Clustering.jl model list to address #1000 (#1001) (@john-waczak)
• Add CatBoost to list of models and 3rd party packages (#1002) (@ablaom)
• Some small documentations improvements. Not to trigger a new release. (#1003) (@ablaom)
• Add auto-generated Model Browser section to the manual (#1005) (@ablaom)
• Add new auto-generated Model Browser section to the manual. Not to trigger new release. (#1006) (@ablaom)
• Add Model Browser entry for SelfOrganizingMap (#1008) (@ablaom)
• Update documentation (#1009) (@ablaom)
• Clarify data front-end in docs (#1011) (@ablaom)
• Doc fixes. No new release. (#1012) (@ablaom)
• Update model browser and list of models to reflect addition of CatBoost.jl and some OutlierDetectionPython.jl models (#1013) (@ablaom)
• Update to the manual. No new release. (#1014) (@ablaom)
• Make docs fail on error (#1017) (@rikhuijzer)
• Cleaned up Adding Models for General Use documentation (#1019) (@antoninkriz)
• CompatHelper: bump compat for StatsBase to 0.34, (keep existing compat) (#1020) (@github-actions[bot])
• Remove CatBoost.jl from third party packages (#1024) (@tylerjthomas9)

MLJ v0.19.1

Diff since v0.19.0

Closed issues:

• Support for ProbabilisticSet type in MLJModelInterface.jl (#978)
• question about Isotonic Regression (#986)
• predict_mode of pipeline model return a UnivariateFinite after upgrade to 0.19.0 (#987)
• MLJ Tuning optimizers are no working with julia 1.8.3 and julia 1.9.0 (#990)
• WARNING: both MLJBase and DataFrames export "transform"; uses of it in module Main must be qualified (#991)
• CURANDError: kernel launch failure (code 201, CURAND_STATUS_LAUNCH_FAILURE) (#997)

Merged pull requests:

• Document changes and sundries. No new release. (#985) (@ablaom)
• (re) updated model names of BetaML (#994) (@sylvaticus)
• Exclude bib, md, and drawio from repo stats (#995) (@rikhuijzer)
• For a 0.19.1 release (#998) (@ablaom)

MLJ v0.19.0

Diff since v0.18.6

MLJBase compatibility is bumped to 0.21 and MLJModels compatibility is bumped to 0.16. This makes a new simplified method for exporting learning networks available but also introduces some breaking changes:

• (mildy breaking) The value method is no longer exported by MLJ as essentially private (#891)
• MLJBase 0.21 release notes
• MLJModels 0.16 release notes

Closed issues:

• Do not re-export value (#891)
• Large models name change in BetaML (#963)
• Add ConformalPrediction.jl to list of 3rd party packages (#967)
• Documentation for BinaryThresholdPredictor (#973)

Merged pull requests:

• Update docs for new learning network export method (#972) (@ablaom)
• added to docs (#979) (@pat-alt)
• For a 0.19 release (#984) (@ablaom)

MLJ v0.18.6

Diff since v0.18.5

Closed issues:

• DBSCAN from Clustering.jl not registered (#845)
• Update manual re new reporting_operations trait (#956)
• Improvement in the Preparing Data part (#964)
• serializable and restore! should be "safe" to use any time (#965)
• Adds EvoLinearRegressor to list of models (#966)
• export InteractionTransformer from MLJModels (#969)
• Encoders for feature engineering (#970)
• Clarify meaning of "table" in documentation (#971)
• re-export serializable and restore! (#975)

Merged pull requests:

• Updated names of BetaML models (#968) (@sylvaticus)
• add the interface package to add (#974) (@xgdgsc)
• Minor doc improvements and new exports (#976) (@ablaom)
• For a 0.18.6 release (#977) (@ablaom)

MLJ v0.18.5

Diff since v0.18.4

Merged pull requests:

• Fix mini typos in docs (#958) (@svilupp)
• Documentation updates (#959) (@ablaom)
• Bump version (#961) (@jbrea)
• For a 0.18.5 release (#962) (@ablaom)

MLJ v0.18.4

Diff since v0.18.3

Closed issues:

• Tutorial not working (#951)
• Bump MLJBase compat and re-export feature_importances (#954)

Merged pull requests:

• Update link for telco example to Data Science Tutorials version (#952) (@ablaom)
• Update API spec re training losses and feature importances (#953) (@ablaom)
• For a 0.18.4 release (#955) (@ablaom)

MLJ v0.18.3

Diff since v0.18.2

Closed issues:

• Feature request: ability to convert scitype warnings into errors (#908)
• Confusing true_negative(x, y) error (#919)
• Show is too long for MulticlassPrecision and MulticlassTruePositiveRate (#923)
• DOC: Link giving 404 not found (#929)
• Re-export scitype_check_level (#936)
• models(matching(X, y)) returns empty but shouldn't (#937)
• LoadError on Getting Started Fit and Predict exercise (#940)
• Change in Julia version generating the Manifest.toml 's ? (#941)
• export PerformanceEvaluation (#944)
• Make docs regarding Random Forest and Ensebles more clear (#945)
• Compile time for DataFrames, typename hack not working (#946)
• Factor out performance evaluation tools (#947)

Merged pull requests:

• Fix level of confidence interval in telco example to make it 95% (#938) (@ablaom)
• Update the list of models in the manual (#942) (@ablaom)
• Re-export default_scitype_check_level (#943) (@ablaom)
• Clarify homogeneous ensembles comments (#948) (@ablaom)
• For a 0.18.3 release (#949) (@ablaom)

MLJ v0.18.2

Diff since v0.18.1

Closed issues:

• Update Save method documentation (#899)
• DOC: Link giving 404 not found (#929)
• Question about using acceleration to implement parallelism (#934)

Merged pull requests:

• Fix a table in telco tutorial (#927) (@ablaom)
• add MLCourse (#928) (@jbrea)
• Add link to MLCourse in the documentation (#930) (@ablaom)
• Move EPFL course up the list on "Learning MLJ" page (#931) (@ablaom)
• Add OneRuleClassifier to list of models in manual (#932) (@ablaom)
• For a 0.18.2 release (#935) (@ablaom)

MLJ v0.18.1

Diff since v0.18.0

• Re-export doc from MLJModels and bump compat of same

Merged pull requests:

• For a 0.18.1 release (#926) (@ablaom)

MLJ v0.18.0

Diff since v0.17.3

This release supports changes appearing in the upstream package releases listed below (click on package for detailed release notes).

The principal change, which is breaking, is how model serialization works. The previous MLJ.save method still works, but you can only save to Julia JLS files, and the format is new and not backwards compatible. A new workflow allows for serialization using any generic serializer; serialization now plays nicely with model composition and model wrappers, such as TunedModel and EnsembleModel (even with non-Julia atomic models), and training data will not be inadvertently serialized.

Refer to this manual page details.

• MLJBase 0.20
• MLJTuning 0.7
• MLJEnsembles 0.3
• MLJIteration 0.5

The package MLJSerialization has been dropped as a dependency as serialization functionality has moved to MLJBase.

Closed issues:

• Use of ScientificTypes and CategoricalArrays in native model (#907)
• Broken tutorial link (#917)
• For a 0.18 release (#920)

Merged pull requests:

• More doc updates and new example (#914) (@ablaom)
• Documentation update. No new release. (#916) (@ablaom)
• Address changes in MLJBase 0.20 (#921) (@ablaom)
• Update manual to reflect changes in MLJBase 0.20 (#922) (@ablaom)
• For a 0 point 18 release (#924) (@ablaom)
• For a 0.18 release (#925) (@ablaom)