improve coverage

src/traits.jl

@@ -6,13 +6,6 @@ const DOC_UNKNOWN =
     "not overloaded the trait. "
 const DOC_ON_TYPE = "The value of the trait must depend only on the type of `algorithm`. "
 
-DOC_ONLY_ONE(func) =
-    "Ordinarily, at most one of the following should be overloaded for given "*
-    "algorithm "*
-    "`LearnAPI.$(func)_scitype`, `LearnAPI.$(func)_type`, "*
-    "`LearnAPI.$(func)_observation_scitype`, "*
-    "`LearnAPI.$(func)_observation_type`."
-
 const DOC_EXPLAIN_EACHOBS =
     """
 
@@ -82,7 +75,7 @@ value is non-empty.
 
 # New implementations
 
-All new implementations must overload this trait. Here's a checklist for elements in the
+All new implementations must implement this trait. Here's a checklist for elements in the
 return value:
 
 | expression                        | implementation compulsory? | include in returned tuple?         |
@@ -107,7 +100,6 @@ algorithm-specific ones. The LearnAPI.jl accessor functions are: $ACCESSOR_FUNCT
 (`LearnAPI.strip` is always included).
 
 """
-functions(::Any) = ()
 functions() = (
     :(LearnAPI.fit),
     :(LearnAPI.algorithm),
@@ -196,8 +188,6 @@ tags() = [
     "meta-algorithms"
 ]
 
-const DOC_TAGS_LIST = join(map(d -> "`\"$d\"`", tags()), ", ")
-
 """
     LearnAPI.tags(algorithm)
 
@@ -403,7 +393,7 @@ See also [`LearnAPI.target_observation_scitype`](@ref).
 
 # New implementations
 
-Optional. The fallback return value is `Union{}`. $(DOC_ONLY_ONE(:fit))
+Optional. The fallback return value is `Union{}`. 
 
 """
 fit_observation_scitype(::Any) = Union{}

test/accessor_functions.jl

@@ -0,0 +1,4 @@
+using Test
+using LearnAPI
+
+@test strip("junk") == "junk"

test/fit_update.jl

@@ -0,0 +1,14 @@
+using Test
+using LearnAPI
+
+struct Gander end
+
+LearnAPI.update(::Gander, data) = sum(data)
+LearnAPI.update_features(::Gander, data) = prod(data)
+
+@testset "update, update_features slurping" begin
+    @test update(Gander(), 2, 3, 4) == 9
+    @test update_features(Gander(), 2, 3, 4) == 24
+end
+
+true

test/runtests.jl

@@ -4,6 +4,8 @@ test_files = [
     "tools.jl",
     "traits.jl",
     "clone.jl",
+    "fit_update.jl",
+    "accessor_functions.jl",
     "predict_transform.jl",
     "patterns/regression.jl",
     "patterns/static_algorithms.jl",

test/traits.jl

@@ -44,6 +44,7 @@ small = SmallAlgorithm()
 @test LearnAPI.data_interface(small) == LearnAPI.RandomAccess()
 @test !(6 isa LearnAPI.fit_observation_scitype(small))
 @test 6 isa LearnAPI.target_observation_scitype(small)
+@test !LearnAPI.is_static(small)
 
 # DERIVED TRAITS