v0.4.0

• (Enhancment) Update to MLJBase 0.5.0 and MLJModels 0.4.0. The
  following new scikit-learn models are thereby made available:

• ScikitLearn.jl
• SVM: SVMClassifier, SVMRegressor, SVMNuClassifier,
  SVMNuRegressor, SVMLClassifier, SVMLRegressor,
• Linear Models (regressors): ARDRegressor,
  BayesianRidgeRegressor, ElasticNetRegressor,
  ElasticNetCVRegressor, HuberRegressor, LarsRegressor,
  LarsCVRegressor, LassoRegressor, LassoCVRegressor,
  LassoLarsRegressor, LassoLarsCVRegressor,
  LassoLarsICRegressor, LinearRegressor,
  OrthogonalMatchingPursuitRegressor,
  OrthogonalMatchingPursuitCVRegressor,
  PassiveAggressiveRegressor, RidgeRegressor,
  RidgeCVRegressor, SGDRegressor, TheilSenRegressor

• (New feature) The macro @pipeline allows one to construct linear
  (non-branching) pipeline composite models with one line of code. One
  may include static transformations (ordinary functions) in the
  pipeline, as well as target transformations for the supervised case
  (when one component model is supervised).

• (Breaking) Source nodes (type Source) now have a kind field,
  which is either :input,:target or :other, with :input the
  default value in the source constructor. If building a learning
  network, and the network is to be exported as a standalone model,
  then it is now necessary to tag the source nodes accordingly, as in
  Xs = source(X) and ys = source(y, kind=:target).

• (Breaking) By virtue of the preceding change, the syntax for
  exporting a learning network is simplified. Do?@from_network for
  details. Also, one now uses fitresults(N) instead of fit results(N, X, y) and fitresults(N, X) when exporting a learning
  network N "by hand"; see the updated
  manual
  for details.

• (Breaking) One must explicitly state if a supervised learning
  network being exported with @from_network is probabilistic by
  adding is_probablistic=true to the macro expression. Before, this
  information was unreliably inferred from the network.

• (Enhancement) Add macro-free method for loading model code into an arbitrary
  module. Do ?load for details.

• (Enhancement) @load now returns a mode instance with default
  hyperparameters (instead of nothing), as in tree_model = @load DecisionTreeRegressor

• (Breaking) info("PCA") now returns a named-tuple, instead of a
  dictionary, of the properties of a the model named "PCA"

• (Breaking) The list returned by models(conditional) is now a list
  of complete metadata entries (named-tuples, as returned by
  info). An entry proxy appears in the list exactly when
  conditional(proxy) == true. Model query is simplified; for
  example models() do model model.is_supervised && model.is_pure_julia end finds all pure julia supervised models.

• (Bug fix) Introduce new private methods to avoid relying on MLJBase
  type piracy MLJBase
  #30.

• (Enhancement) If composite is a a learning network exported as a
  model, and m = machine(composite, args...) then report(m)
  returns the reports for each machine in the learning network, and
  similarly for fitted_params(m).

• (Enhancement) MLJ.table, vcat and hcat now overloaded for
  AbstractNode, so that they can immediately be used in defining
  learning networks. For example, if X = source(rand(20,3)) and
  y=source(rand(20)) then MLJ.table(X) and vcat(y, y) both make
  sense and define new nodes.

• (Enhancement) pretty(X) prints a pretty version of any table X,
  complete with types and scitype annotations. Do ?pretty for
  options. A wrap of pretty_table from PrettyTables.jl.

• (Enhancement) std is re-exported from Statistics

• (Enhancement) The manual and MLJ
  cheatsheet
  have been updated.

• Performance measures have been migrated to MLJBase, while the model
  registry and model load/search facilities have migrated to
  MLJModels. As relevant methods are re-exported to MLJ, this is
  unlikely to effect many users.

v0.3.0

v0.3.0 (2019-08-21)

Diff since v0.2.5

• Introduction of traits for measures (loss functions, etc); see top
  of /src/measures.jl for definitions. This

  • allows user to use loss functions from LossFunctions.jl,
  • enables improved measure checks and error message reporting with measures
  • allows evaluate! to report per-observation measures when
    available (for later use by Bayesian optimisers, for example)
  • allows support for sample-weighted measures playing nicely
    with rest of API

• Improvements to resampling:

  • evaluate! method now reports per-observation measures when
    available
  • sample weights can be passed to evaluate! for use by measures
    that support weights
  • user can pass a list of train/evaluation pairs of row indices
    directly to evaluate!, in place of a ResamplingStrategy
    object
  • implementing a new ResamplingStrategy is now straightforward (see docs)
  • one can call evaluate (no exclamation mark) directly on
    model + data without first constructing a machine, if desired

• Doc strings and the
  manual have
  been revised and updated. The manual includes a new section "Tuning
  models", and extra material under "Learning networks" explaining how
  to export learning networks as stand-alone models using the
  @from_network macro.

• Improved checks and error-reporting for binding models to data in
  machines.

• (Breaking) CSV is now an optional dependency, which means you now
  need to import CSV before you can load tasks with load_boston(),
  load_iris(), load_crabs(), load_ames(), load_reduced_ames()

• Added schema method for tables (re-exported from
  ScientificTypes.jl). Returns a named tuple with keys :names,
  :types, :scitypes and :nrows.

• (Breaking) Eliminate scitypes method. The scientific types of a
  table are returned as part of ScientificTypes schema method (see
  above)

Closed issues:

• Migrate @load macro to MLJBase.jl? (#208)
• Loss functions in MLJ (#205)
• Missing package dependency? (#204)
• Test for MLJModels/Clustering.jl gives warning "implicit dims=2... (#202)
• TunedModel objects not displaying correctly (#197)
• DecisionTreeRegressor fails to predict (#193)
• Control verbosity of @load macro (#192)
• How to know which models are regression models? (#191)
• Error loading the package (#190)
• Data science and ML ontologies in MLJ (#189)
• Machine fit! from the model not working (#187)
• How can I extract a fitresult that does not contain any of the original data? (#186)
• @from_network not working if MLJBase not in load path. (#184)
• Improve nested parameter specification in tuning (#180)
• Resampling strategies should have option for independent RNG (#178)
• Fitting SVC machine changes hyperparameters (#172)
• range(SVC, :gamma, ...) returns NominalRange instead of NumericRange (#170)
• Local support at the ATI? (#169)
• range(tree, :n, ...) not working for tree=DecisionTreeRegressorClassfier (#168)
• Add some transformers from MultivariateStats.jl (#167)
• Use of MLJRegistry (#165)
• No method matching build_tree (#164)
• Multiple learning curves just repeating the first curve (#163)
• Register v0.2.5 (#162)
• Register v0.2.4 (#160)
• Issue with Documentation/Example - DecisionTreeClassifier again... (#156)
• Convert example/xgboost.jl into notebook (#148)
• GSoC Project Proposal (#78)
• Implement MLJ interface for linear models (#35)

Merged pull requests:

• Update to MLJBase 0.4.0 (#212) (ablaom)
• Improve resampling/evaluation; add measures API (incl. LossFunctions) (#206) (ablaom)
• Fix link to ipynb tour (#203) (Kryohi)
• Fix197b (#201) (tlienart)
• Fix #192: Add verbosity option to @load macro (#196) (juliohm)
• Update bug_report.md (#194) (juliohm)
• Make CSV a test dependency (#185) (DilumAluthge)
• Minor fixes + docstrings (#183) (tlienart)
• typo fix in learning networks docs (#182) (tlienart)
• Add rng to resampling methods (cv and holdout) (#179) (ayush-1506)
• Change get_type implementation (#171) (oleskiewicz)

v0.2.5

v0.2.5 (2019-06-15)

Diff since v0.2.4

Closed issues:

• Register v0.2.3 (#154)

v0.2.4

v0.2.4 (2019-06-13)

Diff since v0.2.3

Closed issues:

• Allow ability to call predict\(mach, \*\) on a task (as well as new input data)? (#158)
• misclassification_rate StackOverflow error (#133)
• Add task interface (#68)
• Treatment of supervised models predicting an ordered categorical (#48)
• Proposal for metadata (#22)
• Boosting Packages (#21)
• Literature discussion (#10)

v0.2.3

v0.2.3 (2019-06-05)

Diff since v0.2.2

Closed issues:

• Classification task: no models (#153)
• Register v0.2.2 (#149)
• Announcing MLJ + sktime tutorial and development sprint (#143)

v0.2.2

v0.2.2 (2019-05-30)

Diff since v0.2.1

Closed issues:

• specifying new rows in calls to fit! on a Node not triggering retraining. (#147)
• fit! of Node sometimes calls update on model when it should call fit on model (#146)
• MultivariateStats.PCA has wrong load_path (#141)
• Error running the tour.ipynb notebook (#140)
• For reproducibility, include a Manifest.toml file with all examples. (#137)
• Coveralls activation (#131)
• Register v0.2.1 (#127)
• Migrate MultivariateStats: MLJ -> MLJModels (#125)
• Register v0.2.0 (#124)
• Wrap Scikitlearn.jl Elastic Net algorithms (#112)

Merged pull requests:

• Improve coverage of other files (#145) (giordano)
• add manifest.toml, project.toml for example notebooks, update tour.ipynb (#142) (ayush-1506)
• Simplify methods of load macro by reducing code duplication (#136) (giordano)
• Improve test coverage of tasks.jl (#135) (giordano)
• Move docs dependencies to Project.toml (#130) (giordano)
• Adding SimpleRidgeRegressor (#129) (ayush-1506)
• Send code coverage to Coveralls and Codecov (#128) (giordano)

v0.2.0

bump to 0.2.0