remove "Abstract" from detector types

Author: davnn

src/MLJModelInterface.jl

@@ -44,10 +44,10 @@ const ABSTRACT_MODEL_SUBTYPES =
      :UnsupervisedAnnotator,
      :SupervisedDetector,
      :UnsupervisedDetector,
-     :AbstractProbabilisticSupervisedDetector,
-     :AbstractProbabilisticUnsupervisedDetector,
-     :AbstractDeterministicSupervisedDetector,
-     :AbstractDeterministicUnsupervisedDetector]
+     :ProbabilisticSupervisedDetector,
+     :ProbabilisticUnsupervisedDetector,
+     :DeterministicSupervisedDetector,
+     :DeterministicUnsupervisedDetector]
 
 
 # ------------------------------------------------------------------------
@@ -147,11 +147,11 @@ abstract type UnsupervisedAnnotator <: Annotator end
 abstract type UnsupervisedDetector <: UnsupervisedAnnotator end
 abstract type SupervisedDetector   <: SupervisedAnnotator end
 
-abstract type AbstractProbabilisticSupervisedDetector   <: SupervisedDetector end
-abstract type AbstractProbabilisticUnsupervisedDetector <: UnsupervisedDetector end
+abstract type ProbabilisticSupervisedDetector   <: SupervisedDetector end
+abstract type ProbabilisticUnsupervisedDetector <: UnsupervisedDetector end
 
-abstract type AbstractDeterministicSupervisedDetector   <: SupervisedDetector end
-abstract type AbstractDeterministicUnsupervisedDetector <: UnsupervisedDetector end
+abstract type DeterministicSupervisedDetector   <: SupervisedDetector end
+abstract type DeterministicUnsupervisedDetector <: UnsupervisedDetector end
 
 # ------------------------------------------------------------------------
 # includes

src/model_api.jl

@@ -16,8 +16,8 @@ fit(::Static, ::Integer, data...) = (nothing, nothing, nothing)
 fit(m::Supervised, verbosity, X, y, w) = fit(m, verbosity, X, y)
 
 # fallback for unsupervised detectors when no "evaluation" labels appear:
-fit(m::Union{AbstractProbabilisticUnsupervisedDetector,
-             AbstractDeterministicUnsupervisedDetector},
+fit(m::Union{ProbabilisticUnsupervisedDetector,
+             DeterministicUnsupervisedDetector},
              verbosity,
              X,
              y) =  fit(m, verbosity, X)
@@ -154,27 +154,6 @@ function transform end
 """
 function inverse_transform end
 
-"""
-    augmented_transform
-
-If implemented, the same as `transform`, but with a return value
-augmented by the `transform`ation of the training data.
-
-For example, for if implemented for a `Supervised` model with a
-`transform` method, the return value of `transform_transform(model,
-fitresult, Xnew)` coincides with
-
-```julia
-(transform(model, fitresult, X), transform(model, fitresult, Xnew))
-```
-
-where `(X, y)` was the training data.
-
-Must be implemented by any `UnsupervisedDetector` or `SupervisedDetector`.
-
-"""
-function augmented_transform end
-
 # models can optionally overload these for enable serialization in a
 # custom format:
 function save end
@@ -184,4 +163,3 @@ function restore end
 some meta-models may choose to implement the `evaluate` operations
 """
 function evaluate end
-

src/model_traits.jl

@@ -2,10 +2,10 @@
 
 # unexported aliases:
 const Detector = Union{SupervisedDetector,UnsupervisedDetector}
-const ProbabilisticDetector = Union{AbstractProbabilisticSupervisedDetector,
-                                    AbstractProbabilisticUnsupervisedDetector}
-const DeterministicDetector = Union{AbstractDeterministicSupervisedDetector,
-                                    AbstractDeterministicUnsupervisedDetector}
+const ProbabilisticDetector = Union{ProbabilisticSupervisedDetector,
+                                    ProbabilisticUnsupervisedDetector}
+const DeterministicDetector = Union{DeterministicSupervisedDetector,
+                                    DeterministicUnsupervisedDetector}
 
 const StatTraits = StatisticalTraits
 
@@ -67,8 +67,8 @@ StatTraits.fit_data_scitype(M::Type{<:SupervisedAnnotator}) =
 # constructor will accept it as a valid argument, which then enables
 # *evaluation* of the detector with labeled data:
 StatTraits.fit_data_scitype(M::Type{<:Union{
-    AbstractProbabilisticUnsupervisedDetector,
-    AbstractDeterministicUnsupervisedDetector}}) =
+    ProbabilisticUnsupervisedDetector,
+    DeterministicUnsupervisedDetector}}) =
         Union{Tuple{input_scitype(M)},
               Tuple{input_scitype(M),target_scitype(M)}}