Remove data slurping fallbacks

docs/src/anatomy_of_an_implementation.md

@@ -1,20 +1,27 @@
 # Anatomy of an Implementation
 
-This section explains a detailed implementation of the LearnAPI for naive [ridge
+This section explains a detailed implementation of the LearnAPI.jl for naive [ridge
 regression](https://en.wikipedia.org/wiki/Ridge_regression) with no intercept. The kind of
 workflow we want to enable has been previewed in [Sample workflow](@ref). Readers can also
 refer to the [demonstration](@ref workflow) of the implementation given later.
 
-A transformer ordinarily implements `transform` instead of
-`predict`. For more on `predict` versus `transform`, see [Predict or transform?](@ref)
+The core LearnAPI.jl pattern looks like this:
+
+```julia
+model = fit(algorithm, data)
+predict(model, newdata)
+```
+
+A transformer ordinarily implements `transform` instead of `predict`. For more on
+`predict` versus `transform`, see [Predict or transform?](@ref)
 
 !!! note
 
     New implementations of `fit`, `predict`, etc,
-    always have a *single* `data` argument, as in
-    `LearnAPI.fit(algorithm, data; verbosity=1) = ...`.
-    For convenience, user-calls, such as `fit(algorithm, X, y)`, automatically fallback
-    to `fit(algorithm, (X, y))`.
+    always have a *single* `data` argument as above.
+    For convenience, a signature such as `fit(algorithm, X, y)`, calling
+    `fit(algorithm, (X, y))`, can be added, but the LearnAPI.jl specification is
+    silent on the meaning or existence of signatures with extra arguments.
 
 !!! note
 
@@ -52,7 +59,7 @@ nothing # hide
 
 Instances of `Ridge` will be [algorithms](@ref algorithms), in LearnAPI.jl parlance.
 
-Associated with each new type of LearnAPI [algorithm](@ref algorithms) will be a keyword
+Associated with each new type of LearnAPI.jl [algorithm](@ref algorithms) will be a keyword
 argument constructor, providing default values for all properties (struct fields) that are
 not other algorithms, and we must implement [`LearnAPI.constructor(algorithm)`](@ref), for
 recovering the constructor from an instance:
@@ -244,6 +251,14 @@ in LearnAPI.functions(algorithm)`, for every instance `algorithm`. With [some
 exceptions](@ref trait_contract), the value of a trait should depend only on the *type* of
 the argument.
 
+## Signatures added for convenience
+
+We add one `fit` signature for user-convenience only. The LearnAPI.jl specification has
+nothing to say about `fit` signatures with more than two positional arguments.
+
+```@example anatomy
+LearnAPI.fit(algorithm::Ridge, X, y; kwargs...) = fit(algorithm, (X, y); kwargs...)
+```
 
 ## [Demonstration](@id workflow)
 
@@ -466,6 +481,14 @@ overload the trait, [`LearnAPI.data_interface(algorithm)`](@ref). See [Data
 interfaces](@ref data_interfaces) for details.
 
 
+### Addition of signatures for user convenience
+
+As above, we add a signature which plays no role vis-à-vis LearnAPI.jl.
+
+```@example anatomy2
+LearnAPI.fit(algorithm::Ridge, X, y; kwargs...)  = fit(algorithm, (X, y); kwargs...)
+```
+
 ## Demonstration of an advanced `obs` workflow
 
 We now can train and predict using internal data representations, resampled using the

docs/src/common_implementation_patterns.md

@@ -1,5 +1,7 @@
 # Common Implementation Patterns
 
+!!! warning
+
 !!! warning
 
 	This section is only an implementation guide. The definitive specification of the

docs/src/fit_update.md

@@ -11,8 +11,6 @@ A "static" algorithm is one that does not generalize to new observations (e.g.,
 clustering algorithms); there is no trainiing data and the algorithm is executed by
 `predict` or `transform` which receive the data. See example below.
 
-When `fit` expects a tuple form of argument, `data = (X1, ..., Xn)`, then the signature
-`fit(algorithm, X1, ..., Xn)` is also provided.
 
 ### Updating
 
@@ -32,7 +30,7 @@ Supposing `Algorithm` is some supervised classifier type, with an iteration para
 
 ```julia
 algorithm = Algorithm(n=100)
-model = fit(algorithm, (X, y)) # or `fit(algorithm, X, y)`
+model = fit(algorithm, (X, y))
 
 # Predict probability distributions:
 ŷ = predict(model, Distribution(), Xnew) 
@@ -76,6 +74,22 @@ labels = predict(algorithm, X)
 LearnAPI.extras(model)
 ```
 
+### Density estimation
+
+In density estimation, `fit` consumes no features, only a target variable; `predict`,
+which consumes no data, returns the learned density:
+
+```julia
+model = fit(algorithm, y) # no features
+predict(model)            # shortcut for  `predict(model, Distribution())`
+```
+
+A one-liner will typically be implemented as well:
+
+```julia
+predict(algorithm, y)
+```
+
 ## Implementation guide
 
 ### Training

docs/src/predict_transform.md

@@ -6,16 +6,15 @@ transform(model, data)
 inverse_transform(model, data)
 ```
 
-When a method expects a tuple form of argument, `data = (X1, ..., Xn)`, then a slurping
-signature is also provided, as in `transform(model, X1, ..., Xn)`.
-
+Versions without the `data` argument may also appear, for example in [Density
+estimation](@ref).
 
 ## [Typical worklows](@id predict_workflow)
 
 Train some supervised `algorithm`:
 
 ```julia
-model = fit(algorithm, (X, y)) # or `fit(algorithm, X, y)`
+model = fit(algorithm, (X, y))
 ```
 
 Predict probability distributions:

src/fit_update.jl

@@ -14,13 +14,10 @@ The second signature is provided by algorithms that do not generalize to new obs
 ..., data)` carries out the actual algorithm execution, writing any byproducts of that
 operation to the mutable object `model` returned by `fit`.
 
-Whenever `fit` expects a tuple form of argument, `data = (X1, ..., Xn)`, then the
-signature `fit(algorithm, X1, ..., Xn)` is also provided.
-
-For example, a supervised classifier will typically admit this workflow:
+For example, a supervised classifier might have a workflow like this:
 
 ```julia
-model = fit(algorithm, (X, y)) # or `fit(algorithm, X, y)`
+model = fit(algorithm, (X, y))
 ŷ = predict(model, Xnew)
 ```
 
@@ -33,24 +30,22 @@ See also [`predict`](@ref), [`transform`](@ref), [`inverse_transform`](@ref),
 
 # New implementations
 
-Implementation is compulsory. The signature must include `verbosity`. Fallbacks provide
-the data slurping versions.  A fallback for the first signature calls the second, ignoring
-`data`:
+Implementation of exactly one of the signatures is compulsory. If `fit(algorithm;
+verbosity=1)` is implemented, then the trait [`LearnAPI.is_static`](@ref) must be
+overloaded to return `true`.
 
-```julia
-fit(algorithm, data; kwargs...) = fit(algorithm; kwargs...)
-```
+The signature must include `verbosity`.
 
-If only the `fit(algorithm)` signature is expliclty implemented, then the trait
-[`LearnAPI.is_static`](@ref) must be overloaded to return `true`.
+The LearnAPI.jl specification has nothing to say regarding `fit` signatures with more than
+two arguments. For convenience, for example, an algorithm is free to implement a slurping
+signature, such as `fit(algorithm, X, y, extras...) = fit(algorithm, (X, y, extras...))` but
+LearnAPI.jl does not guarantee such signatures are actually implemented.
 
 $(DOC_DATA_INTERFACE(:fit))
 
 """
-fit(algorithm, data; kwargs...) =
-    fit(algorithm; kwargs...)
-fit(algorithm, data1, datas...; kwargs...) =
-    fit(algorithm, (data1, datas...); kwargs...)
+function fit end
+
 
 # # UPDATE AND COUSINS
 
@@ -91,7 +86,7 @@ Implementation is optional. The signature must include
 See also [`LearnAPI.clone`](@ref)
 
 """
-update(model, data1, datas...; kwargs...) = update(model, (data1, datas...); kwargs...)
+function update end
 
 """
     update_observations(model, new_data; verbosity=1, parameter_replacements...)
@@ -127,8 +122,7 @@ Implementation is optional. The signature must include
 See also [`LearnAPI.clone`](@ref).
 
 """
-update_observations(algorithm, data1, datas...; kwargs...) =
-    update_observations(algorithm, (data1, datas...); kwargs...)
+function update_observations end
 
 """
     update_features(model, new_data; verbosity=1, parameter_replacements...)
@@ -154,5 +148,4 @@ Implementation is optional. The signature must include
 See also [`LearnAPI.clone`](@ref).
 
 """
-update_features(algorithm, data1, datas...; kwargs...) =
-    update_features(algorithm, (data1, datas...); kwargs...)
+function update_features end

src/predict_transform.jl

@@ -13,12 +13,20 @@ DOC_MUTATION(op) =
 
     """
 
+DOC_SLURPING(op) =
+    """
+
+    An algorithm is free to implement `$op` signatures with additional positional
+    arguments (eg., data-slurping signatures) but LearnAPI.jl is silent about their
+    interpretation or existence.
+
+    """
 
 DOC_MINIMIZE(func) =
     """
 
-    If, additionally, [`LearnAPI.strip(model)`](@ref) is overloaded, then the following identity
-    must hold:
+    If, additionally, [`LearnAPI.strip(model)`](@ref) is overloaded, then the following
+    identity must hold:
 
     ```julia
     $func(LearnAPI.strip(model), args...) = $func(model, args...)
@@ -63,7 +71,7 @@ which lists all supported target proxies.
 The argument `model` is anything returned by a call of the form `fit(algorithm, ...)`.
 
 If `LearnAPI.features(LearnAPI.algorithm(model)) == nothing`, then argument `data` is
-omitted. An example is density estimators.
+omitted in both signatures. An example is density estimators.
 
 # Example
 
@@ -79,20 +87,20 @@ See also [`fit`](@ref), [`transform`](@ref), [`inverse_transform`](@ref).
 
 # Extended help
 
-If `predict` supports data in the form of a tuple `data = (X1, ..., Xn)`, then a slurping
-signature is also provided, as in `predict(model, X1, ..., Xn)`.
-
-Note `predict ` does not mutate any argument, except in the special case
+Note `predict ` must not mutate any argument, except in the special case
 `LearnAPI.is_static(algorithm) == true`.
 
 # New implementations
 
 If there is no notion of a "target" variable in the LearnAPI.jl sense, or you need an
 operation with an inverse, implement [`transform`](@ref) instead.
 
-Implementation is optional. Only the first signature is implemented, but each
-`kind_of_proxy` that gets an implementation must be added to the list returned by
-[`LearnAPI.kinds_of_proxy`](@ref).
+Implementation is optional. Only the first signature (with or without the `data` argument)
+is implemented, but each `kind_of_proxy` that gets an implementation must be added to the
+list returned by [`LearnAPI.kinds_of_proxy`](@ref).
+
+If `data` is not present in the implemented signature (eg., for density estimators) then
+[`LearnAPI.features(algorithm, data)`](@ref) must return `nothing`.
 
 $(DOC_IMPLEMENTED_METHODS(":(LearnAPI.predict)"))
 
@@ -106,23 +114,12 @@ $(DOC_DATA_INTERFACE(:predict))
 predict(model, data) = predict(model, kinds_of_proxy(algorithm(model)) |> first, data)
 predict(model) = predict(model, kinds_of_proxy(algorithm(model)) |> first)
 
-# automatic slurping of multiple data arguments:
-predict(model, k::KindOfProxy, data1, data2, datas...; kwargs...) =
-    predict(model, k, (data1, data2, datas...); kwargs...)
-predict(model, data1, data2, datas...; kwargs...) =
-    predict(model, (data1, data2, datas...); kwargs...)
-
-
-
 """
     transform(model, data)
 
 Return a transformation of some `data`, using some `model`, as returned by
 [`fit`](@ref).
 
-For `data` that consists of a tuple, a slurping version is also provided, i.e., you can do
-`transform(model, X1, X2, X3)` in place of `transform(model, (X1, X2, X3))`.
-
 # Example
 
 Below, `X` and `Xnew` are data of the same form.
@@ -157,8 +154,10 @@ See also [`fit`](@ref), [`predict`](@ref),
 
 # New implementations
 
-Implementation for new LearnAPI.jl algorithms is optional. A fallback provides the
-slurping version. $(DOC_IMPLEMENTED_METHODS(":(LearnAPI.transform)"))
+Implementation for new LearnAPI.jl algorithms is
+optional. $(DOC_IMPLEMENTED_METHODS(":(LearnAPI.transform)"))
+
+$(DOC_SLURPING(:transform))
 
 $(DOC_MINIMIZE(:transform))
 
@@ -167,8 +166,8 @@ $(DOC_MUTATION(:transform))
 $(DOC_DATA_INTERFACE(:transform))
 
 """
-transform(model, data1, data2, datas...; kwargs...) =
-    transform(model, (data1, data2, datas...); kwargs...) # automatic slurping
+function transform end
+
 
 """
     inverse_transform(model, data)

test/patterns/ensembling.jl

@@ -160,6 +160,13 @@ LearnAPI.strip(model::EnsembleFitted) = EnsembleFitted(
    )
 )
 
+# convenience method:
+LearnAPI.fit(algorithm::Ensemble, X, y, extras...; kwargs...) =
+    fit(algorithm, (X, y, extras...); kwargs...)
+LearnAPI.update(algorithm::EnsembleFitted, X, y, extras...; kwargs...) =
+    update(algorithm, (X, y, extras...); kwargs...)
+
+
 # synthetic test data:
 N = 10 # number of observations
 train = 1:6

test/patterns/gradient_descent.jl

@@ -227,9 +227,9 @@ function LearnAPI.update_observations(
     )
 
     # unpack data:
-    X = observations.X
-    y_hot = observations.y_hot
-    classes = observations.classes
+    X = observations_new.X
+    y_hot = observations_new.y_hot
+    classes = observations_new.classes
     nclasses = length(classes)
 
     classes == model.classes || error("New training target has incompatible classes.")
@@ -328,6 +328,16 @@ LearnAPI.training_losses(model::PerceptronClassifierFitted) = model.losses
 )
 
 
+# ### Convenience methods
+
+LearnAPI.fit(algorithm::PerceptronClassifier, X, y; kwargs...) =
+    fit(algorithm, (X, y); kwargs...)
+LearnAPI.update_observations(algorithm::PerceptronClassifier, X, y; kwargs...) =
+    update_observations(algorithm, (X, y); kwargs...)
+LearnAPI.update(algorithm::PerceptronClassifier, X, y; kwargs...) =
+    update(algorithm, (X, y); kwargs...)
+
+
 # ## Tests
 
 # synthetic test data: