v0.11.1

MLJ v0.11.1

Diff since v0.11.0

Minor issues only:

• #497
• Revise cheatsheet (#474)

Closed issues:

• Add sample-weight interface point? (#177)
• Add default_measure to learning_curve! (#283)
• Flush out unsupervised models in "Adding models for general use" section of manual (#285)
• is_probabilistic=true in @pipeline syntax is clunky (#305)
• [suggestions] Unroll the network in @from_network (#311)
• Towards stabilisation of the core API (#318)
• failure on nightly (1.4) (#384)
• Documentation of extracting best fitted params (#386)
• incorporate input_scitype and target_scitype declarations for @pipeline models (#412)
• "Supervised" models with no predict method (#460)
• Use OpenML.load to iris data set in the Getting Started page of docs? (#461)
• Review cheatsheet (#474)
• Re-export UnsupervisedNetwork from MLJBase (#497)
• Broken link for MLJ tour in documentation (#501)

Merged pull requests:

• For a 0.11.1 release (#506) (@ablaom)

v0.11.0

MLJ v0.11.0

Diff since v0.10.3

Make compatibility updates to MLJBase and MLJModels to effect the following changes to MLJ (see the linked release notes for links to the issues/PRs)):

• (new model) Add LightGBM models LightGBMClassifier and
  LightGBMRegressor

• (new model) Add new built-in model, ContinuousEncoder, for
  transforming all features of a table to Continuous scitype,
  dropping any features that cannot be so transformed

• (new model) Add ParallelKMeans model, KMeans, loaded with
  @load KMeans pkg=ParallelKMeans

• (mildly breaking enhancement) Arrange for the CV
  resampling strategyto spread fold "remainders" evenly among folds in
  train_test_pairs(::CV, ...) (a small change only noticeable in
  small datasets)

• (breaking) Restyle report and fitted_params for exported
  learning networks (e.g., pipelines) to include a dictionary of reports or
  fitted_params, keyed on the machines in the underlying learning
  network. New doc-strings detail the new behaviour.

• (enhancement) Allow calling of transform on machines with Static models without
  first calling fit!

• Allow machine constructor to work on supervised models that take nothing for
  the input features X (for models that simply fit a
  sampler/distribution to the target data y) (#51)

Also:

• (documentation) In the "Adding New Models for General Use"
  section of the manual, add detail on how to wrap unsupervised
  models, as well as models that fit a sampler/distribution to data

• (documentation) Expand the "Transformers" sections of the
  manual, including more material on static transformers and
  transformers that implement predict (#393)

Closed issues:

• Add tuning by stochastic search (#37)
• Improve documentation around static transformers (#393)
• Error in docs for model search (#478)
• Update [compat] StatsBase="^0.32,^0.33" (#481)
• For a 0.10.3 release (#483)
• Help with coercing strings for binary data into Continuous variables (#489)
• EvoTree Error (#490)
• Add info with workaround to avoid MKL error (#491)
• LogisticClassifier pkg = MLJLinearModels computes a number of coefficients but not the same number of mean_and_std_given_feature (#492)
• MethodError: no method matching... (#493)
• For a 0.10.4 release (#495)
• Error: fitted_params(LogisticModel) (#498)

Merged pull requests:

• Fixes the docs for model search (#486) (@ven-k)
• Readme update (#494) (@ablaom)
• paper added to MLJ repo (#496) (@darenasc)
• For a 0.11.0 release (#499) (@ablaom)
• For a 0.11.0 release (#500) (@ablaom)

v0.10.3

MLJ v0.10.3

Diff since v0.10.2

• Allow MLJ to use StatsBase v0.33 (PR #484 , #481)

• Enable use of RandomSearch tuning strategy (PR #482, #37)

Merged pull requests:

• Enable hyper-parameter tuning using random search (#482) (@ablaom)
• Extend [compat] StatsBase = "^0.32,^0.33" (#484) (@ablaom)
• For a 0.10.3 release (#485) (@ablaom)

v0.10.2

MLJ v0.10.2

Diff since v0.10.1

• Extend [compat] Distributions = "^0.21,^0.22,^0.23"

• Minor doc fixes

Closed issues:

• Task design discussion (#166)
• Non-normalized versions of measures (#445)
• Overload model traits to work on the named-tuple "proxies" for models listed by models() (#464)
• Multiprocess issue (#468)
• Julia v1.4.0 is downloading MLJ v0.2.3 instead of MLJ v0.10.1 (#476)

Merged pull requests:

• Added IRIS example in docs (#475) (@ashryaagr)
• For a 0.10.2 release (#479) (@ablaom)

v0.10.1

MLJ v0.10.1

Diff since v0.10.0

(enhancement) Add serialization for machines. Serialization is model-specific, with a fallback implementation using JLSO. The user serializes with MLJBase.save(path, mach) and de-serializes with machine(path) (#138, #292)

Closed issues:

• Adhere by Invenia's bluestyle (#434)
• Update list of scikitlearn models in readme table. (#469)

Merged pull requests:

• updated list of ScikitLearn models in Readme (#472) (@OkonSamuel)
• For a 0.10.1 release (#473) (@ablaom)

v0.10.0

MLJ v0.10.0

Diff since v0.9.3

Upgrade to MLJBase 0.12.0 and MLJModels 0.9.0 to effect the following changes:

• (breaking) suppress normalisation of measure weights (MLJBase PR #208)

• (breaking) Shift the optional rng argument of iterator to first position (MLJBase #215)

• (mildly breaking) Let all models (supervised and unsupervised) share a common set of traits. So, for example, unsupervised models now have the target_scitype trait (usually taking the value Unknown). For a list of the common traits, do models()[1] |> keys |> collect (JuliaAI/MLJBase.jl#163).

• (enhancement) Add sampler wrapper for one-dimensional ranges, for random sampling from ranges using rand (MLJBase #213)

• Change default value of num_round in XGBoost models from 1 to 100 (MLJModels PR #201)

Closed issues:

• Help with loading code on multiple processes for paralleled tuning of a pipeline (#440)
• Re-export CPU1, CPUProcesses, CPUThreads (#447)
• Taking loss functions seriously (#450)
• @pipeline to accept multiple Supervised models (#455)
• What parts of MLJBase should be reexported in MLJ (#462)
• unpack not working (#465)
• Automatic Ensembling options (#466)

Merged pull requests:

• Fix model table link in README.md (#467) (@pshashk)
• For a 0.10.0 release (#471) (@ablaom)

v0.9.3

MLJ v0.9.3

Diff since v0.9.2

Merged pull requests:

• re-instate [compat] declarations for MLJ* (#453) (@ablaom)
• Documentation fixes (#454) (@ablaom)
• Patch 2 (#458) (@OkonSamuel)
• Update README.md (#459) (@azev77)
• For 0.9.3 release (#463) (@ablaom)

v0.9.2

MLJ v0.9.2

• (enhancement) Update Tables requirement to "^1.0" (#444)

• (new models) Add the pure-julia gradient boosted tree models from EvoTrees: EvoRegressor, EvoTreeCount, EvoTreeGaussian, EvoTreeCount (#122)

• (documentation) Update README.md and some documentation errors

Diff since v0.9.1

Closed issues:

• Implementing MLJ model interface for EvoTrees.jl (#122)
• Improve the tuning strategy interface (#315)
• Re-organizing the MLJ stack (#317)
• Add Tables 1.0 (#444)

Merged pull requests:

• Further updates to the manual to reflect light interface restructuring (#441) (@ablaom)
• Patch 2 (#443) (@OkonSamuel)
• Tablesv1 (#448) (@tlienart)
• Readme review (#451) (@ablaom)
• For 0.9.2 release (#452) (@ablaom)
• re-instate [compat] declarations for MLJ* (#453) (@ablaom)

v0.9.1

MLJ v0.9.1

Diff since v0.9.0

• (enhancement) Enable dataset loading from OpenML using OpenML.load(id).

• (documentation) Update the MLJ manual with missing measure docstrings; and to reflect use of MLJScientificTypes in place of ScientificTypes

• (documentation - developers) Update manual to reflect split of MLJBase into MLJBase and MLJModelInterface

Closed issues:

• Evaluation error logs on loading model (#433)

v0.9.0

Minor release with the light interface (#439)