For a 0.21.12 release

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJBase"
 uuid = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
-version = "0.21.11"
+version = "0.21.12"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -33,6 +33,7 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 CategoricalArrays = "0.9, 0.10"
 CategoricalDistributions = "0.1"
 ComputationalResources = "0.3"
+DelimitedFiles = "1"
 Distributions = "0.25.3"
 InvertedIndices = "1"
 LossFunctions = "0.10"
@@ -47,3 +48,17 @@ StatisticalTraits = "3.2"
 StatsBase = "0.32, 0.33, 0.34"
 Tables = "0.2, 1.0"
 julia = "1.6"
+
+[extras]
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+DecisionTree = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb"
+Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
+Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
+MultivariateStats = "6f286f6a-111f-5878-ab1e-185364afe411"
+NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+TypedTables = "9d95f2ec-7b3d-5a63-8d20-e2491e220bb9"
+
+[targets]
+test = ["DataFrames", "DecisionTree", "Distances", "Logging", "MultivariateStats", "NearestNeighbors", "StableRNGs", "Test", "TypedTables"]

README.md

@@ -29,9 +29,7 @@ repository provides core functionality for MLJ, including:
   [MLJModelInterface](https://github.com/JuliaAI/MLJModelInterface.jl) (/src/interface)
 
 - definition of **machines** and their associated methods, such as
-  `fit!` and `predict`/`transform` (src/machines). Serialization of machines,
-  however, now lives in
-  [MLJSerialization](https://github.com/JuliaAI/MLJSerialization.jl).
+  `fit!` and `predict`/`transform` (src/machines).
 
 - MLJ's **model composition** interface, including **learning
   networks**, **pipelines**, **stacks**, **target transforms** (/src/composition)

src/MLJBase.jl

@@ -292,11 +292,15 @@ export coerce, coerce!, autotype, schema, info
 export UnivariateFiniteArray, UnivariateFiniteVector
 
 # -----------------------------------------------------------------------
-# abstract model types defined in MLJModelInterface.jl and extended here:
+# re-export from MLJModelInterface.jl
+
+#abstract model types defined in MLJModelInterface.jl and extended here:
 for T in EXTENDED_ABSTRACT_MODEL_TYPES
     @eval(export $T)
 end
 
+export params
+
 # -------------------------------------------------------------------
 # exports from this module, MLJBase
 
@@ -306,9 +310,6 @@ export default_resource
 # one_dimensional_ranges.jl:
 export ParamRange, NumericRange, NominalRange, iterator, scale
 
-# parameter_inspection.jl:
-export params # note this is *not* an extension of StatsBase.params
-
 # data.jl:
 export partition, unpack, complement, restrict, corestrict
 

src/machines.jl

@@ -533,7 +533,7 @@ last_model(mach) = isdefined(mach, :old_model) ? mach.old_model : nothing
         rows=nothing,
         verbosity=1,
         force=false,
-        composite=nothing
+        composite=nothing,
     )
 
 Without mutating any other machine on which it may depend, perform one of the following
@@ -553,7 +553,7 @@ data to `rows` if specified:
    increment `mach.state`.
 
 If the model, `model`, bound to `mach` is a symbol, then instead perform the action using
-the true model `getproperty(composite, model)`.
+the true model given by `getproperty(composite, model)`. See also [`machine`](@ref).
 
 
 ### Training action logic
@@ -762,13 +762,13 @@ end
 
 """
 
-    fit!(mach::Machine, rows=nothing, verbosity=1, force=false)
+    fit!(mach::Machine, rows=nothing, verbosity=1, force=false, composite=nothing)
 
 Fit the machine `mach`. In the case that `mach` has `Node` arguments,
 first train all other machines on which `mach` depends.
 
 To attempt to fit a machine without touching any other machine, use
-`fit_only!`. For more on the internal logic of fitting see
+`fit_only!`. For more on options and the the internal logic of fitting see
 [`fit_only!`](@ref)
 
 """

src/operations.jl

@@ -138,7 +138,7 @@ for operation in OPERATIONS
             get!(ret, $quoted_operation, mach)
         end
 
-        function $operation(mach::Machine{<:Static}, Xraw, Xraw_more...)
+        function $operation(mach::Machine, Xraw, Xraw_more...)
             _check_and_fit_if_warranted!(mach)
             ret = $(operation)(
                 last_model(mach),
@@ -153,10 +153,11 @@ for operation in OPERATIONS
         $operation(mach::Machine, X::AbstractNode) =
             node($(operation), mach, X)
 
-        $operation(mach::Machine{<:Static},
-                   X::AbstractNode,
-                   Xmore::AbstractNode...) =
-                       node($(operation), mach, X, Xmore...)
+        $operation(
+            mach::Machine,
+            X::AbstractNode,
+            Xmore::AbstractNode...,
+        ) = node($(operation), mach, X, Xmore...)
     end
     eval(ex)
 end