rm redundant is_static method (forgotten in v 2.0)

Author: ablaom

docs/src/traits.md

@@ -31,7 +31,6 @@ In the examples column of the table below, `Continuous` is a name owned the pack
 | [`LearnAPI.data_interface`](@ref)`(learner)`             | Interface implemented by objects returned by [`obs`](@ref)                                                             | `Base.HasLength()` (supports `MLCore.getobs/numobs`) | `Base.SizeUnknown()` (supports `iterate`)                      |
 | [`LearnAPI.fit_scitype`](@ref)`(learner)`                | upper bound on `scitype(data)` ensuring `fit(learner, data)` works                                                     | `Union{}`                                            | `Tuple{AbstractVector{Continuous}, Continuous}`                |
 | [`LearnAPI.target_observation_scitype`](@ref)`(learner)` | upper bound on the scitype of each observation of the targget                                                          | `Any`                                                | `Continuous`                                                   |
-| [`LearnAPI.is_static`](@ref)`(learner)`                  | `true` if `fit` consumes no data                                                                                       | `false`                                              | `true`                                                         |
 
 ### Derived Traits
 
@@ -109,7 +108,6 @@ LearnAPI.data_interface
 LearnAPI.iteration_parameter
 LearnAPI.fit_scitype
 LearnAPI.target_observation_scitype
-LearnAPI.is_static
 ```
 
 ```@docs

src/traits.jl

@@ -389,32 +389,6 @@ tables, and tuples of these. See the doc-string for details.
 """
 data_interface(::Any) = LearnAPI.RandomAccess()
 
-"""
-    LearnAPI.is_static(learner)
-
-Returns `true` if [`fit`](@ref) is called with no data arguments, as in
-`fit(learner)`. That is, `learner` does not generalize to new data, and data is only
-provided at the `predict` or `transform` step.
-
-For example, some clustering algorithms are applied with this workflow, to assign labels
-to the observations in `X`:
-
-```julia
-model = fit(learner) # no training data
-labels = predict(model, X) # may mutate `model`!
-
-# extract some byproducts of the clustering algorithm (e.g., outliers):
-LearnAPI.extras(model)
-```
-
-# New implementations
-
-This trait, falling back to `false`, may only be overloaded when `fit` has no data
-arguments. See more at [`fit`](@ref).
-
-"""
-is_static(::Any) = false
-
 """
     LearnAPI.iteration_parameter(learner)
 

test/traits.jl

@@ -46,7 +46,6 @@ small = SmallLearner()
 @test LearnAPI.data_interface(small) == LearnAPI.RandomAccess()
 @test !(6 isa LearnAPI.fit_scitype(small))
 @test 6 isa LearnAPI.target_observation_scitype(small)
-@test !LearnAPI.is_static(small)
 
 # DERIVED TRAITS