MLJ News

Development news for MLJ and its satellite packages, MLJBase, MLJRegistry and MLJModels

MLJBase v0.4.0

Release notes

MLJBase v0.2.4

• Add ColorImage and GreyImage scitypes

• Overload in method for subtypes of Model (apparently causing Julia crashes in an untagged commit, because of a method signature ambiguity, now resolved).

MLJ v0.2.5

• Add MLJ cheatsheet

• Allow models to query specific traits, in addition to tasks. Query ?models for details

• add @from_networks macro for exporting learning networks as models (experimental).

MLJModels v0.2.4

• Add compatibility requirement MLJBase="0.2.3"

MLJBase v0.2.3

• Small changes on definitions of == and isequal for MLJType objects. In particular, fields that are random number generators may change state without effecting an object's == equivalence class.
• Add @set_defaults macro for generating keywork constructors for Model subtypes.

• Add abstract type UnsupervisedNetwork <: Unsupervised.

MLJ v0.2.3

• Fixed bug in models(::MLJTask) method which excluded some relevant models. (#153)

• Fixed some broken links to the tour.ipynb.

MLJ v0.2.2

• Resolved these isssues:

  • 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)](https://github.com/alan-turing-institute/MLJ.jl/issues/146)

- Error running the tour.ipynb notebook
  [(#140)](https://github.com/alan-turing-institute/MLJ.jl/issues/140)

- For reproducibility, include a Manifest.toml file with all
  examples. [(#137)](https://github.com/alan-turing-institute/MLJ.jl/issues/137)

• Activated overalls code coverage (#131)

• Removed local version of MultivariateStats (now in MLJModels, see below).

• Minor changes to OneHotEncoder, in line with scitype philosophy.

MLJBase v0.2.2

• Fix some minor bugs.

• Added compatibility requirement CSV v0.5 or higher to allow removal of allowmissing keyword in CSV.read, which is to be depreciated.

Announcement: MLJ tutorial and development sprint

• Details here Applications close May 29th 5pm (GMTT + 1 = London)

MLJModels v0.2.3

• The following support vector machine models from LIBSVM.jl have been added: EpsilonSVR, LinearSVC, NuSVR, NuSVC, SVC, OneClassSVM.

MLJModels v0.2.2

• MulitivariateStats models RidgeRegressor and PCA migrated here from MLJ. Addresses: MLJ #125.

MLJModels v0.2.1

• ScikitLearn wraps ElasticNet and ElasticNetCV now available (and registered at MLJRegistry). Resolves: MLJ #112

MLJ v0.2.1

• Fix a bug and related problem in "Getting Started" docs: [#126](https://github.com/alan-turing-institute/MLJ.jl/issues/126 .

MLJBase 0.2.0, MLJModels 0.2.0, MLJ 0.2.0

• Model API refactored to resolve #93 and #119 and hence simplify the model interface. This breaks all implementations of supervised models, and some scitype methods. However, for the regular user the effects are restricted to: (i) no more target_type hyperparameter for some models; (ii) Deterministic{Node} is now DeterministicNetwork and Probabillistic{Node} is now ProbabilisticNetwork when exporting learning networks as models.
• New feature: Task constructors now allow the user to explicitly specify scitypes of features/target. There is a coerce method for vectors and tables for the user who wants to do this manually. Resolves: #119

Official registered versions of MLJBase 0.1.1, MLJModels 0.1.1, MLJ 0.1.1 released

• Minor revisions to the repos, doc updates, and a small breaking change around scitype method names and associated traits. Resolves: #119

unversioned commits 12 April 2019 (around 00:10, GMT)

• Added out-of-bag estimates for performance in homogeneous ensembles. Resolves: #77

unversioned commits 11 April 2019 (before noon, GMT)

• Removed dependency on unregistered package TOML.jl (using, Pkg.TOML instead). Resolves #113

unversioned commits 8 April 2019 (some time after 20:00 GMT)

• Addition of XGBoost models XGBoostRegressor, XGBoostClassifier and XGBoostCount. Resolves #65.

• Documentation reorganized as GitHub pages. Includes some additions but still a work in progress.

unversioned commits 1 March 2019 (some time after 03:50 GMT)

• Addition of "scientific type" hierarchy, including Continuous, Discrete, Multiclass, and Other subtypes of Found (to complement Missing). See Getting Started for more one this. Resolves: #86

• Revamp of model traits to take advantage of scientific types, with output_kind replaced with target_scitype_union, input_kind replaced with input_scitype. Also, output_quantity dropped, input_quantity replaced with Bool-valued input_is_multivariate, and is_pure_julia made Bool-valued. Trait definitions in all model implementations and effected meta-algorithms have been updated. Related: #81

• Substantial update of the core guide Adding New Models to reflect above changes and in response to new model implementer queries. Some design "decisions" regarding multivariate targets now explict there.

• the order the y and yhat arguments of measures (aka loss functions) have been reversed. Progress on: #91

• Update of Standardizer and OneHotEncoder to mesh with new scitypes.

• New improved task constructors infer task metadata from data scitypes. This brings us close to a simple implementation of basic task-model matching. Query the doc-strings for SupervisedTask and UnsupervisedTask for details. Machines can now dispatch on tasks instead of X and y. A task, task, is now callable: task() returns (X, y) for supervised models, and X for unsupervised models. Progress on: #86

• the data in the load_ames() test task has been replaced by the full data set, and load_reduced_ames() now loads a reduced set.