tweaks and corrections

Author: ablaom

docs/src/anatomy_of_an_implementation.md

@@ -411,7 +411,7 @@ specified by the trait* [`LearnAPI.data_interface(algorithm)`](@ref). Assuming t
 ```@example anatomy2
 Base.getindex(data::RidgeFitObs, I) =
     RidgeFitObs(data.A[:,I], data.names, y[I])
-Base.length(data::RidgeFitObs, I) = length(data.y)
+Base.length(data::RidgeFitObs) = length(data.y)
 ```
 
 We can do something similar for `predict`, but there's no need for a new type in this

docs/src/target_weights_features.md

@@ -28,13 +28,11 @@ training_loss = sum(ŷ .!= y)
 
 # Implementation guide
 
-The fallback returns `first(data)`, assuming `data` is a tuple, and `data` otherwise.
-
-| method                      | fallback          | compulsory?            |
-|:----------------------------|:-----------------:|------------------------|
-| [`LearnAPI.target`](@ref)   | returns `nothing` | no                     |
-| [`LearnAPI.weights`](@ref)  | returns `nothing` | no                     |
-| [`LearnAPI.features`](@ref) | see docstring     | only if fallback fails |
+| method                      | fallback          | compulsory?              |
+|:----------------------------|:-----------------:|--------------------------|
+| [`LearnAPI.target`](@ref)   | returns `nothing` | no                       |
+| [`LearnAPI.weights`](@ref)  | returns `nothing` | no                       |
+| [`LearnAPI.features`](@ref) | see docstring     | if fallback insufficient |
 
 
 # Reference

src/target_weights_features.jl

@@ -38,7 +38,7 @@ weights(::Any, data) = nothing
 
 Return, for each form of `data` supported in a call of the form [`fit(algorithm,
 data)`](@ref), the "features" part of `data` (as opposed to the target
-variable, for example). 
+variable, for example).
 
 The returned object `X` may always be passed to `predict` or `transform`, where
 implemented, as in the following sample workflow:
@@ -49,28 +49,25 @@ X = features(data)
 ŷ = predict(algorithm, kind_of_proxy, X) # eg, `kind_of_proxy = Point()`
 ```
 
-The return value has the same number of observations as `data` does. For supervised models
+The returned object has the same number of observations as `data`. For supervised models
 (i.e., where `:(LearnAPI.target) in LearnAPI.functions(algorithm)`) `ŷ` above is generally
 intended to be an approximate proxy for `LearnAPI.target(algorithm, data)`, the training
 target.
 
 
 # New implementations
 
-The only contract `features` must satisfy is the one about passability of the output to
-`predict` or `transform`, for each supported input `data`. The following fallbacks
-typically make overloading `LearnAPI.features` unnecessary:
-
-```julia
-LearnAPI.features(algorithm, data) = data
-LearnAPI.features(algorithm, data::Tuple) = first(data)
-```
+That the output can be passed to `predict` and/or `transform`, and has the same number of
+observations as `data`, are the only contracts. A fallback returns `first(data)` if `data`
+is a tuple, and otherwise returns `data`.
 
 Overloading may be necessary if [`obs(algorithm, data)`](@ref) is overloaded to return
 some algorithm-specific representation of training `data`. For density estimators, whose
 `fit` typically consumes *only* a target variable, you should overload this method to
 return `nothing`.
 
 """
-features(algorithm, data) = data
-features(algorithm, data::Tuple) = first(data)
+features(algorithm, data) = _first(data)
+_first(data) = data
+_first(data::Tuple) = first(data)
+# note the factoring above guards agains method ambiguities

test/integration/regression.jl

@@ -39,7 +39,7 @@ LearnAPI.algorithm(model::RidgeFitted) = model.algorithm
 
 Base.getindex(data::RidgeFitObs, I) =
     RidgeFitObs(data.A[:,I], data.names, data.y[I])
-Base.length(data::RidgeFitObs, I) = length(data.y)
+Base.length(data::RidgeFitObs) = length(data.y)
 
 # observations for consumption by `fit`:
 function LearnAPI.obs(::Ridge, data)