v0.12.0

MLJ v0.12.0

Diff since v0.11.5

This release provides updates to breaking changes in MLJBase 0.14.0, and MLJModels 0.11.0 and MLJTuning 0.4.0. For a complete list of changes, closed issues and pull requests, refer to the linked release notes.

It also updates the MLJ documentation to reflect the new changes.

Summary

Main breaking changes:

• Adds restrictions to acceleration options - nesting
  distributed processes within multithread process is now disallowed.

• Adds more user-friendly interface for inspecting training reports and
  fitted parameters of composite models. For example, if composite = @pipeline OneHotEncoder KNNRegressor and mach = machine(composite, X, y), then access the fitted parameters of the machine associated
  with KNNRegressor using fitted_params(mach).knn_regressor.

• The @from_network syntax has been changed to make it more
  expressive. In particular, through the new concept of learning
  network machines it is possible to export a learning network to a
  composite type supporting multiple operations (e.g., predict and
  transform, as in clustering). See the
  manual
  for details. The old syntax is no longer supported.

Other enhancements of note:

• Adds MLJFlux
  models to the registry for incorporating neural network models.

• A more economic @pipeline syntax has been introduced. For example,
  pipe = @pipeline OneHotEncoder PCA(maxoutdim=3) defines model
  pipe with automatically generated field names and model type name. Target inverse
  transformations now ooccur immediately after the supervised model in
  a @pipeline, instead of at the end, unless invert_last=true. The old syntax is
  available but deprecated.

• It is now possible to simulataneously load model code for models
  having the same name but from different packages, when using @load
  or load.

• Removes the requirement to specify the kind of source node, as in
  source(y, kind=:target). The role of source nodes in learning
  networks is now inferred from the order in which they appear in
  learning network machine constructors (see above).

Deprecations:

• The old @pipeline syntax.

• The specification of kind when constructing a source node.

• The use of fitresults() when exporting learning networks "by
  hand". See the manual for the new way to do this.

Closed issues:

• Integrate flux models (#33)
• Conflict w Scikitlearn.jl (#502)
• Accessing nested machines is (still) awkward (#553)
• Add note at MLJ landing page that MLJ wraps a majority of sk-learn models (#573)
• OpenML integration: Kmeans is not fitting (#580)

Merged pull requests:

• export brier_score (#572) (@OkonSamuel)
• Add binder notebooks to the repo (#577) (@darenasc)
• Add MLJFlux to Model List, alphabetize model list (#578) (@azev77)
• For a 0.12.0 release (#581) (@ablaom)
• For a 0.12.0 release (#582) (@ablaom)