update Anatomy of an Implementation re verbosity

Author: ablaom

docs/src/anatomy_of_an_implementation.md

@@ -124,7 +124,7 @@ Note that we also include `learner` in the struct, for it must be possible to re
 The implementation of `fit` looks like this:
 
 ```@example anatomy
-function LearnAPI.fit(learner::Ridge, data; verbosity=1)
+function LearnAPI.fit(learner::Ridge, data; verbosity=LearnAPI.default_verbosity())
     X, y = data
 
     # data preprocessing:
@@ -503,7 +503,7 @@ methods - one to handle "regular" input, and one to handle the pre-processed dat
 (observations) which appears first below:
 
 ```@example anatomy2
-function LearnAPI.fit(learner::Ridge, observations::RidgeFitObs; verbosity=1)
+function LearnAPI.fit(learner::Ridge, observations::RidgeFitObs; verbosity=LearnAPI.default_verbosity())
 
     lambda = learner.lambda
 

docs/src/common_implementation_patterns.md

@@ -11,7 +11,7 @@ which introduces the main interface objects and terminology.
 Although an implementation is defined purely by the methods and traits it implements, many
 implementations fall into one (or more) of the informally understood patterns or tasks
 below. While some generally fall into one of the core `Descriminative`, `Generative` or
-`Static` patterns detailed [here](@id kinds of learner), there are exceptions (such as
+`Static` patterns detailed [here](@id kinds_of_learner), there are exceptions (such as
 clustering, which has both `Descriminative` and `Static` variations).
 
 - [Regression](@ref): Supervised learners for continuous targets

docs/src/examples.md

@@ -32,7 +32,7 @@ struct RidgeFitted{T,F}
     named_coefficients::F
 end
 
-function LearnAPI.fit(learner::Ridge, data; verbosity=1)
+function LearnAPI.fit(learner::Ridge, data; verbosity=LearnAPI.default_verbosity())
     X, y = data
 
     # data preprocessing:
@@ -129,7 +129,11 @@ function LearnAPI.obs(::Ridge, data)
 end
 LearnAPI.obs(::Ridge, observations::RidgeFitObs) = observations
 
-function LearnAPI.fit(learner::Ridge, observations::RidgeFitObs; verbosity=1)
+function LearnAPI.fit(
+    learner::Ridge,
+    observations::RidgeFitObs;
+    verbosity=LearnAPI.default_verbosity(),
+    )
 
     lambda = learner.lambda
 
@@ -226,7 +230,7 @@ frontend = FrontEnds.Saffron()
 LearnAPI.obs(learner::Ridge, data) = FrontEnds.fitobs(learner, data, frontend)
 LearnAPI.obs(model::RidgeFitted, data) = obs(model, data, frontend)
 
-function LearnAPI.fit(learner::Ridge, observations::FrontEnds.Obs; verbosity=1)
+function LearnAPI.fit(learner::Ridge, observations::FrontEnds.Obs; verbosity=LearnAPI.default_verbosity())
 
     lambda = learner.lambda
 

docs/src/fit_update.md

@@ -14,10 +14,12 @@ returns one of:
 - [`LearnAPI.Generative()`](@ref)
 
 ```
-fit(learner; verbosity=1) -> static_model 
+fit(learner; verbosity=...) -> static_model
 ```
 
-This pattern applies in the case [`LearnAPI.kind_of(learner)`](@ref) returns [`LearnAPI.Static()`](@ref). 
+This pattern applies in the case [`LearnAPI.kind_of(learner)`](@ref) returns:
+
+- [`LearnAPI.Static()`](@ref)
 
 Examples appear below.
 

docs/src/reference.md

@@ -217,17 +217,18 @@ Most learners will also implement [`predict`](@ref) and/or [`transform`](@ref).
 
 ## Utilities
 
-
 - [`LearnAPI.is_learner`](@ref)
 - [`clone`](@ref): for cloning a learner with specified hyperparameter replacements.
 - [`@trait`](@ref): for simultaneously declaring multiple traits
 - [`@functions`](@ref): for listing functions available for use with a learner 
+- [`LearnAPI.default_verbosity`](@ref): get/reset the default verbosity
 
 ```@docs
 LearnAPI.is_learner
 clone
 @trait
 @functions
+LearnAPI.default_verbosity
 ```
 
 ---

docs/src/traits.md

@@ -1,4 +1,4 @@
-2# [Learner Traits](@id traits)
+# [Learner Traits](@id traits)
 
 Learner traits are simply functions whose sole argument is a learner.