v0.7.1

• Add compatibility bounds for all dependent packages (#61)

• (Bug) Fix metadata macros so that they can be used in MJ model implementations written in external packages (#58, PR #59).

v0.7.0

• (Enhancement, breaking) Allow use of CategoricalArrays 7.0 (#44)

• (Enhancement) Add Brier score for data with a Finite scientific type

v0.6.0

• (Enhancement) Add isapprox method for UnivariateFinite
  distributions.

• (Code organization) Add metadata_* and @mlj_model from MLJModels (PR
  #47)

v0.5.0

v0.5.0

• Bump ScientificTypes requirement to v0.2.0

• (Enhancement) The performance measures API (built-in measures +
  adaptor for external measures) from MLJ has been migrated to MLJBase.
  MLJ.

• (Breaking) info, which returns a dictionary (needed for TOML
  serialization) is renamed to info_dic. In this way "info" is
  reserved for a method in MLJModels/MLJ that returns a
  more-convenient named-tuple

• (Breaking) The is_probabilistic model trait is replaced with
  prediction_type, which can have the values :deterministic,
  :probabilistic or :interval, to allow for models predicting real
  intervals, and for consistency with measures API.

• (Bug fix, mildly breaking) The package_license model trait is now included in
  info_dict in the case of unsupervisd models.

• (Enhancement, mildly breaking) Add new model traits hyperparameters,
  hyperparameter_types, docstring, and implemented_operations (fit, predict, inverse_transform, etc)
  (#36,
  #37,
  #38)

• (Enhancement) The MLJBase.table and MLJBase.matrix operations
  are now direct wraps of the corresponding Tables.jl operations for
  improved performance. In particular
  MLJBase.matrix(MLJBase.table(A)) is essentially a non-operation,
  and one can pass MLJBase.matrix the keyword argument
  transpose=... .

• (Breaking) The built-in dataset methods load_iris, load_boston,
  load_ames, load_reduced_ames, load_crabs return a raw
  DataFrame, instead of an MLJTask object, and continue to require
  import CSV to become available. However, macro versions
  @load_iris, etc, are always available, automatically triggering
  import CSV; these macros return a tuple (X, y) of input
  DataFrame and target vector y, with scitypes appropriately
  coerced. (MLJ
  #224)

• (Enhancement) selectrows now works for matrices. Needed to allow
  matrices as "node type" in MLJ learning networks; see MLJ
  #209.

• (Bug) Fix problem with == for MLJType objects
  (#35)

• (Breaking) Update requirement on ScientficTypes.jl to v0.2.0 to
  mitigate bug with coercion of column scitypes for tables that are
  also AbstractVectors, and to make coerce more convenient.

• (Enhancement) Add new method unpack for splitting tables, as in y, X = unpack(df,==(:target),!=(:dummy)). See doc-string for details.

• (Bug fix) Remove type piracy in get/setproperty! (#30)

v0.4.0

v0.4.0 (2019-08-19)

Diff since v0.3.0

Closed issues:

• Model interface defined? (#26)
• Register MLJBase 0.3.0 (#25)
• Default schema (#23)
• Implement the MLJ model API without needing to depend on external dependencies such as CSV.jl, CategoricalArrays.jl, etc. (#19)
• Register v0.2.6 (#18)

Merged pull requests:

Replace src/scitypes.jl with ScientificTypes.jl package (#28) (ablaom)

The first three changes below resolve MLJ issue #176

• (breaking for model implementations) Replace the model trait input_scitype_union with input_scitype. It's value is now some scitype defined by the new ScientificTypes package, eg Table(Continuous) (instead of Continuous) so that non-tabular data can be supported. See ScientificTypes documentation for more on the Table scientific type.

• (breaking for model implementations) Replace the model trait target_scitype_union with scitype_union with similar remarks applying.

• (breaking for model implementations) Eliminate target_is_multivariate and input_is_multivariate traits as this information is now implicit in value of above revamped traits

• (breaking) Eliminate scitypes method. The scientific types of a table are returned as part of ScientificTypes schema method, which is re-exported by MLJBase. (MLJBase.schema used to coincide with Tables.schema.)

• add package_license_trait (fallback value is "unknown")

• add supports_weights trait for measures and models

• Efficiency and other improvements to UnivariateFinite distribution (resolves #22), including implementation of Distributions.support. This is different from classes which returns all categorial values for the distribution, not just those with non-zero probability. This is breaking because classes used to return what support does now.

• Address DataFrames depreciation warning due to column access change in its API

• (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(). Addresses #19

v0.3.0

v0.3.0 (2019-07-19)

Diff since v0.2.6

Merged pull requests:

• Make CSV an optional dependency instead of a required dependency (#24) (DilumAluthge)
• (minor) Fixing "see also" links (#21) (tlienart)
• minor docstring fix (#20) (tlienart)

v0.2.6

v0.2.6 (2019-07-02)

Diff since v0.2.5

Closed issues:

• Register v0.2.5 (#16)
• Register v0.2.4 (#15)

Merged pull requests:

• Add rng argument to shuffle (#17) (ayush-1506)

v0.2.5

v0.2.5 (2019-07-01)

Diff since v0.2.4

v0.2.4

v0.2.4 (2019-06-25)

Diff since v0.2.3

Closed issues:

• Register v0.2.3 (#14)

v0.2.3

v0.2.3 (2019-06-12)

Diff since v0.2.2

Closed issues:

• Register v0.2.2 (#13)
• Deprecation message from CSV.jl (#11)
• Register v0.2.1 (#10)