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dump default_verbosity as likely introduces precompilation issues
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docs/src/anatomy_of_an_implementation.md

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@@ -112,7 +112,7 @@ Note that we also include `learner` in the struct, for it must be possible to re
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The implementation of `fit` looks like this:
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```@example anatomy
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function LearnAPI.fit(learner::Ridge, data; verbosity=LearnAPI.default_verbosity())
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function LearnAPI.fit(learner::Ridge, data; verbosity=1)
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X, y = data
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(observations) which appears first below:
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```@example anatomy2
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function LearnAPI.fit(learner::Ridge, observations::RidgeFitObs; verbosity=LearnAPI.default_verbosity())
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function LearnAPI.fit(learner::Ridge, observations::RidgeFitObs; verbosity=1)
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lambda = learner.lambda
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docs/src/fit_update.md

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### Training
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```julia
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fit(learner, data; verbosity=LearnAPI.default_verbosity()) -> model
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fit(learner; verbosity=LearnAPI.default_verbosity()) -> static_model
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fit(learner, data; verbosity=1) -> model
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fit(learner; verbosity=1) -> static_model
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```
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A "static" algorithm is one that does not generalize to new observations (e.g., some
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Exactly one of the following must be implemented:
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| method | fallback |
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|:-----------------------------------------------------------------------|:---------|
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| [`fit`](@ref)`(learner, data; verbosity=LearnAPI.default_verbosity())` | none |
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| [`fit`](@ref)`(learner; verbosity=LearnAPI.default_verbosity())` | none |
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| method | fallback |
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|:--------------------------------------------|:---------|
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| [`fit`](@ref)`(learner, data; verbosity=1)` | none |
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| [`fit`](@ref)`(learner; verbosity=1)` | none |
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### Updating
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| method | fallback | compulsory? |
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|:-------------------------------------------------------------------------------------|:---------|-------------|
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| [`update`](@ref)`(model, data; verbosity=..., hyperparameter_updates...)` | none | no |
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| [`update_observations`](@ref)`(model, new_data; verbosity=..., hyperparameter_updates...)` | none | no |
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| [`update_features`](@ref)`(model, new_data; verbosity=..., hyperparameter_updates...)` | none | no |
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| [`update`](@ref)`(model, data; verbosity=1, hyperparameter_updates...)` | none | no |
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| [`update_observations`](@ref)`(model, new_data; verbosity=1, hyperparameter_updates...)` | none | no |
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| [`update_features`](@ref)`(model, new_data; verbosity=1, hyperparameter_updates...)` | none | no |
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There are some contracts governing the behaviour of the update methods, as they relate to
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a previous `fit` call. Consult the document strings for details.
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update
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update_observations
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update_features
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LearnAPI.default_verbosity
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```

src/LearnAPI.jl

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module LearnAPI
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include("types.jl")
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include("verbosity.jl")
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include("tools.jl")
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include("predict_transform.jl")
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include("fit_update.jl")

src/fit_update.jl

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# # FIT
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"""
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fit(learner, data; verbosity=LearnAPI.default_verbosity())
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fit(learner; verbosity=LearnAPI.default_verbosity())
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fit(learner, data; verbosity=1)
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fit(learner; verbosity=1)
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Execute the machine learning or statistical algorithm with configuration `learner` using
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the provided training `data`, returning an object, `model`, on which other methods, such
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Use `verbosity=0` for warnings only, and `-1` for silent training.
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See also [`LearnAPI.default_verbosity`](@ref), [`predict`](@ref), [`transform`](@ref),
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See also [`predict`](@ref), [`transform`](@ref),
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[`inverse_transform`](@ref), [`LearnAPI.functions`](@ref), [`obs`](@ref).
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# Extended help
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verbosity=...)` is implemented, then the trait [`LearnAPI.is_static`](@ref) must be
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overloaded to return `true`.
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The signature must include `verbosity` with [`LearnAPI.default_verbosity()`](@ref) as
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default.
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The signature must include `verbosity` with `1` as default.
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If `data` encapsulates a *target* variable, as defined in LearnAPI.jl documentation, then
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If `data` encapsulates a *target* variable, as defined in LearnAPI.jl documentati[on, then
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[`LearnAPI.target(data)`](@ref) must be overloaded to return it. If [`predict`](@ref) or
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[`transform`](@ref) are implemented and consume data, then
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[`LearnAPI.features(data)`](@ref) must return something that can be passed as data to

src/verbosity.jl

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test/runtests.jl

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test_files = [
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"tools.jl",
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"verbosity.jl",
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"traits.jl",
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"clone.jl",
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"predict_transform.jl",

test/verbosity.jl

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