Merge pull request #997 from JuliaAI/update-compats-2025-03-25

Update some compats and dump Julia 1.6 support in favor of new LTS release

Author: ablaom

.github/workflows/ci.yml

@@ -22,7 +22,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6' 
+          - '1.10' 
           - '1' # automatically expands to the latest stable 1.x release of Julia.
         os:
           - ubuntu-latest

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJBase"
 uuid = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "1.7.0"
+version = "1.7.1"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -26,7 +26,6 @@ RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 ScientificTypes = "321657f4-b219-11e9-178b-2701a2544e81"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
-StatisticalMeasures = "a19d573c-0a75-4610-95b3-7071388c7541"
 StatisticalMeasuresBase = "c062fc1d-0d66-479b-b6ac-8b44719de4cc"
 StatisticalTraits = "64bff920-2084-43da-a3e6-9bb72801c0c9"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
@@ -46,7 +45,7 @@ ComputationalResources = "0.3"
 DelimitedFiles = "1"
 Distributions = "0.25.3"
 InvertedIndices = "1"
-LearnAPI = "0.1"
+LearnAPI = "1"
 MLJModelInterface = "1.11"
 Missings = "0.4, 1"
 OrderedCollections = "1.1"
@@ -56,13 +55,13 @@ ProgressMeter = "1.7.1"
 RecipesBase = "1"
 Reexport = "1.2"
 ScientificTypes = "3"
-StatisticalMeasures = "0.1.1"
+StatisticalMeasures = "0.2"
 StatisticalMeasuresBase = "0.1.1"
 StatisticalTraits = "3.4"
 Statistics = "1"
 StatsBase = "0.32, 0.33, 0.34"
 Tables = "0.2, 1.0"
-julia = "1.6"
+julia = "1.10"
 
 [extras]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"

src/MLJBase.jl

@@ -310,10 +310,4 @@ export default_measure
 export pdf, sampler, mode, median, mean, shuffle!, categorical, shuffle,
    levels, levels!, std, Not, support, logpdf, LittleDict
 
-# for julia < 1.9
-if !isdefined(Base, :get_extension)
-    include(joinpath("..","ext", "DefaultMeasuresExt.jl"))
-    @reexport using .DefaultMeasuresExt.StatisticalMeasures
-end
-
 end # module

src/hyperparam/one_dimensional_ranges.jl

@@ -88,7 +88,7 @@ ERROR_AMBIGUOUS_UNION = ArgumentError(
     " in the example, `range(Int, :dummy, lower=1, upper=10)`.")
 
 """
-    r = range(model, :hyper; values=nothing)
+    r = range(model, :hyper; values=...)
 
 Define a one-dimensional `NominalRange` object for a field `hyper` of
 `model`. Note that `r` is not directly iterable but `iterator(r)` is.
@@ -97,19 +97,14 @@ A nested hyperparameter is specified using dot notation. For example,
 `:(atom.max_depth)` specifies the `max_depth` hyperparameter of
 the submodel `model.atom`.
 
-    r = range(model, :hyper; upper=nothing, lower=nothing,
-              scale=nothing, values=nothing)
+    r = range(model, :hyper; upper=..., lower..., unit=..., origin=...,
+              scale=nothing)
 
-Assuming `values` is not specified, define a one-dimensional
-`NumericRange` object for a `Real` field `hyper` of `model`.  Note
-that `r` is not directly iteratable but `iterator(r, n)`is an iterator
-of length `n`. To generate random elements from `r`, instead apply
-`rand` methods to `sampler(r)`. The supported scales are `:linear`,`
-:log`, `:logminus`, `:log10`, `:log10minus`, `:log2`, or a callable
-object.
-
-Note that `r` is not directly iterable, but `iterator(r, n)` is, for
-given resolution (length) `n`.
+Define a one-dimensional `NumericRange` object for a `Real` property `hyper` of `model`.
+Note that `r` is not directly iteratable but `iterator(r, n)`is an iterator of length
+`n`. To generate random elements from `r`, instead apply `rand` methods to
+`sampler(r)`. The supported scales are `:linear`,` :log`, `:logminus`, `:log10`,
+`:log10minus`, `:log2`, or a callable object.
 
 By default, the behaviour of the constructed object depends on the
 type of the value of the hyperparameter `:hyper` at `model` *at the
@@ -119,12 +114,22 @@ behaviour is determined by the value of the specified type.
 
 A nested hyperparameter is specified using dot notation (see above).
 
+# keyword options
+
 If `scale` is unspecified, it is set to `:linear`, `:log`,
 `:log10minus`, or `:linear`, according to whether the interval
 `(lower, upper)` is bounded, right-unbounded, left-unbounded, or
 doubly unbounded, respectively.  Note `upper=Inf` and `lower=-Inf` are
 allowed.
 
+You must specify at least two of the keyword arguments `upper`, `lower`, `unit` and
+`origin`. The last two parameters are used when fitting some distributions to
+unbounded `NumericRange` objects. See [`Distributions.fit`](@ref) for details.
+
+A range is *unbounded* if `lower=-Inf` or `upper=Inf`, or one of these is left
+unspecified. In this case, both `unit` and `origin` must be specified. In the bounded
+case, these have the radius and midpoint of `[lower, upper]` as fallbacks.
+
 If `values` is specified, the other keyword arguments are ignored and
 a `NominalRange` object is returned (see above).
 

src/resampling.jl

@@ -1,4 +1,4 @@
- # TYPE ALIASES
+# TYPE ALIASES
 
 const AbstractRow = Union{AbstractVector{<:Integer}, Colon}
 const TrainTestPair = Tuple{AbstractRow,AbstractRow}
@@ -873,7 +873,7 @@ function _check_measure(measure, operation, model, y)
     end
 
     model isa Deterministic &&
-        StatisticalMeasuresBase.kind_of_proxy(measure) != LearnAPI.LiteralTarget() &&
+        StatisticalMeasuresBase.kind_of_proxy(measure) != LearnAPI.Point() &&
         throw(ERR_MEASURES_DETERMINISTIC(measure))
 
     return true
@@ -962,7 +962,7 @@ function _actual_operations(operation::Nothing,
         # trait. But it's values are instances of LearnAPI.KindOfProxy, instead of
         # symbols:
         #
-        # `LearnAPI.LiteralTarget()` ~ `:deterministic` (`model isa Deterministic`)
+        # `LearnAPI.Point()`        ~ `:deterministic` (`model isa Deterministic`)
         # `LearnAPI.Distribution()` ~ `:probabilistic` (`model isa Deterministic`)
         #
         kind_of_proxy = StatisticalMeasuresBase.kind_of_proxy(m)
@@ -983,7 +983,7 @@ function _actual_operations(operation::Nothing,
         if MLJBase.prediction_type(model) === :probabilistic
             if kind_of_proxy === LearnAPI.Distribution()
                 return predict
-            elseif kind_of_proxy === LearnAPI.LiteralTarget()
+            elseif kind_of_proxy === LearnAPI.Point()
                 if observation_scitype <: Union{Missing,Finite}
                     return predict_mode
                 elseif observation_scitype <:Union{Missing,Infinite}
@@ -997,7 +997,7 @@ function _actual_operations(operation::Nothing,
         elseif MLJBase.prediction_type(model) === :deterministic
             if kind_of_proxy === LearnAPI.Distribution()
                 throw(err_incompatible_prediction_types(model, m))
-            elseif kind_of_proxy === LearnAPI.LiteralTarget()
+            elseif kind_of_proxy === LearnAPI.Point()
                 return predict
             else
                 throw(err_ambiguous_operation(model, m))

src/utilities.jl

@@ -475,11 +475,9 @@ generate_name!(model, existing_names; kwargs...) =
 
 *Private method.*
 
-Tries to infer the per-observation scitype from the scitype of `S`, when `S` is
-known to be the scitype of some container with multiple observations; here we
-view the scitype for one row of a table to be the scitype of the row converted
-to a vector. Return `Unknown` if unable to draw reliable inferrence.
-
+Tries to infer the per-observation scitype from the scitype of `S`, when `S` is known to
+be the scitype of some container with multiple observations. Return `Unknown` if unable to
+draw reliable inferrence.
 
 The observation scitype for a table is here understood as the scitype of a row
 converted to a vector.

test/resampling.jl

@@ -31,7 +31,7 @@ dummy_measure_det(yhat, y) = 42
 API.@trait(
     typeof(dummy_measure_det),
     observation_scitype = MLJBase.Textual,
-    kind_of_proxy = LearnAPI.LiteralTarget(),
+    kind_of_proxy = LearnAPI.Point(),
 )
 
 dummy_measure_interval(yhat, y) = 42
@@ -256,7 +256,7 @@ end
 @everywhere begin
     user_rms(yhat, y) = mean((yhat -y).^2) |> sqrt
     # deliberately omitting `consumes_multiple_observations` trait:
-    API.@trait typeof(user_rms) kind_of_proxy=LearnAPI.LiteralTarget()
+    API.@trait typeof(user_rms) kind_of_proxy=LearnAPI.Point()
 end
 
 @testset_accelerated "folds specified" accel begin