rename fit.* -> fit_update.* and descriptors -> tags

Author: ablaom

docs/make.jl

@@ -15,7 +15,7 @@ makedocs(
         "Anatomy of an Implementation" => "anatomy_of_an_implementation.md",
         "Reference" => [
             "Overview" => "reference.md",
-            "fit/update" => "fit.md",
+            "fit/update" => "fit_update.md",
             "predict/transform" => "predict_transform.md",
             "Kinds of Target Proxy" => "kinds_of_target_proxy.md",
             "minimize" => "minimize.md",

docs/src/anatomy_of_an_implementation.md

@@ -220,7 +220,7 @@ A macro provides a shortcut, convenient when multiple traits are to be defined:
     Ridge,
     constructor = Ridge,
     kinds_of_proxy=(LiteralTarget(),),
-    descriptors = (:regression,),
+    tags = (:regression,),
     functions = (
         :(LearnAPI.fit),
         :(LearnAPI.algorithm),
@@ -327,7 +327,7 @@ LearnAPI.minimize(model::RidgeFitted) =
     Ridge,
     constructor = Ridge,
     kinds_of_proxy=(LiteralTarget(),),
-    descriptors = (:regression,),
+    tags = (:regression,),
     functions = (
         :(LearnAPI.fit),
         :(LearnAPI.algorithm),

docs/src/fit.md renamed to docs/src/fit_update.md

@@ -25,7 +25,7 @@ package [ScientificTypesBase.jl](https://github.com/JuliaAI/ScientificTypesBase.
 | [`LearnAPI.constructor`](@ref)`(algorithm)`                           | constructor for generating new or modified versions of `algorithm`                                                               | (no fallback)                                         | `RidgeRegressor`                                         |
 | [`LearnAPI.functions`](@ref)`(algorithm)`                             | functions you can apply to `algorithm` or associated model (traits excluded)                                                     | `()`                                                  | `(:fit, :predict, :minimize, :(LearnAPI.algorithm), :obs)`      |
 | [`LearnAPI.kinds_of_proxy`](@ref)`(algorithm)`                        | instances `kind` of `KindOfProxy` for which an implementation of `LearnAPI.predict(algorithm, kind, ...)` is guaranteed.         | `()`                                                  | `(Distribution(), Interval())`                           |
-| [`LearnAPI.descriptors`](@ref)`(algorithm)`                           | lists one or more suggestive algorithm descriptors from `LearnAPI.descriptors()`                                                 | `()`                                                  | (:regression, :probabilistic)                            |
+| [`LearnAPI.tags`](@ref)`(algorithm)`                           | lists one or more suggestive algorithm tags from `LearnAPI.tags()`                                                 | `()`                                                  | (:regression, :probabilistic)                            |
 | [`LearnAPI.is_pure_julia`](@ref)`(algorithm)`                         | `true` if implementation is 100% Julia code                                                                                      | `false`                                               | `true`                                                   |
 | [`LearnAPI.pkg_name`](@ref)`(algorithm)`                              | name of package providing core code (may be different from package providing LearnAPI.jl implementation)                         | `"unknown"`                                           | `"DecisionTree"`                                         |
 | [`LearnAPI.pkg_license`](@ref)`(algorithm)`                           | name of license of package providing core code                                                                                   | `"unknown"`                                           | `"MIT"`                                                  |
@@ -114,7 +114,7 @@ informative (as in `LearnAPI.predict_type(algorithm) = Any`).
 LearnAPI.constructor
 LearnAPI.functions
 LearnAPI.kinds_of_proxy
-LearnAPI.descriptors
+LearnAPI.tags
 LearnAPI.is_pure_julia
 LearnAPI.pkg_name
 LearnAPI.pkg_license

docs/src/traits.md

@@ -5,7 +5,7 @@ import InteractiveUtils.subtypes
 include("tools.jl")
 include("types.jl")
 include("predict_transform.jl")
-include("fit.jl")
+include("fit_update.jl")
 include("minimize.jl")
 include("target_weights_features.jl")
 include("obs.jl")

src/LearnAPI.jl

@@ -16,15 +16,15 @@ Overload a number of traits for algorithms of type `TypeEx`. For example, the co
 ```julia
 @trait(
     RidgeRegressor,
-    descriptors = ("regression", ),
+    tags = ("regression", ),
     doc_url = "https://some.cool.documentation",
 )
 ```
 
 is equivalent to
 
 ```julia
-LearnAPI.descriptors(::RidgeRegressor) = ("regression", ),
+LearnAPI.tags(::RidgeRegressor) = ("regression", ),
 LearnAPI.doc_url(::RidgeRegressor) = "https://some.cool.documentation",
 ```
 

src/fit.jl renamed to src/fit_update.jl

@@ -27,7 +27,7 @@ const TRAITS = [
     :constructor,
     :functions,
     :kinds_of_proxy,
-    :descriptors,
+    :tags,
     :is_pure_julia,
     :pkg_name,
     :pkg_license,
@@ -179,7 +179,7 @@ For more on target variables and target proxies, refer to the LearnAPI documenta
 """
 kinds_of_proxy(::Any) = ()
 
-descriptors() = [
+tags() = [
     "regression",
     "classification",
     "clustering",
@@ -206,12 +206,12 @@ descriptors() = [
     "image processing",
 ]
 
-const DOC_DESCRIPTORS_LIST = join(map(d -> "`\"$d\"`", descriptors()), ", ")
+const DOC_TAGS_LIST = join(map(d -> "`\"$d\"`", tags()), ", ")
 
 """
-    LearnAPI.descriptors(algorithm)
+    LearnAPI.tags(algorithm)
 
-Lists one or more suggestive algorithm descriptors. Do `LearnAPI.descriptors()` to list
+Lists one or more suggestive algorithm tags. Do `LearnAPI.tags()` to list
 all possible.
 
 !!! warning
@@ -223,7 +223,7 @@ all possible.
 This trait should return a tuple of strings, as in `("classifier", "text analysis")`.
 
 """
-descriptors(::Any) = ()
+tags(::Any) = ()
 
 """
     LearnAPI.is_pure_julia(algorithm)

src/tools.jl

@@ -104,7 +104,7 @@ LearnAPI.minimize(model::RidgeFitted) =
     Ridge,
     constructor = Ridge,
     kinds_of_proxy = (LiteralTarget(),),
-    descriptors = ("regression",)
+    tags = ("regression",)
     functions = (
         :(LearnAPI.fit),
         :(LearnAPI.algorithm),
@@ -237,7 +237,7 @@ LearnAPI.minimize(model::BabyRidgeFitted) =
     BabyRidge,
     constructor = BabyRidge,
     kinds_of_proxy = (LiteralTarget(),),
-    descriptors = ("regression",)
+    tags = ("regression",)
     functions = (
         :(LearnAPI.fit),
         :(LearnAPI.algorithm),

src/traits.jl

@@ -39,7 +39,7 @@ end
 @trait(
     Selector,
     constructor = Selector,
-    descriptors = ("feature engineering",)
+    tags = ("feature engineering",)
     functions = (
         :(LearnAPI.fit),
         :(LearnAPI.algorithm),
@@ -105,7 +105,7 @@ end
     Selector2,
     constructor = Selector2,
     predict_or_transform_mutates = true,
-    descriptors = ("feature engineering",)
+    tags = ("feature engineering",)
     functions = (
         :(LearnAPI.fit),
         :(LearnAPI.algorithm),