Redesign the 'Learn' transform syntax (#3)

Author: eliascarv

src/learn.jl

@@ -3,7 +3,7 @@
 # ------------------------------------------------------------------
 
 """
-    Learn(model, train, incols, outcols)
+    Learn(train, model, incols => outcols)
 
 Fits the statistical learning `model` using the input columns, selected by `incols`,
 and the output columns, selected by `outcols`, from the `train` table.
@@ -14,19 +14,19 @@ a collection of identifiers or a regular expression (regex).
 # Examples
 
 ```julia
-Learn(model, train, [1, 2, 3], "d")
-Learn(model, train, [:a, :b, :c], :d)
-Learn(model, train, ["a", "b", "c"], 4)
-Learn(model, train, [1, 2, 3], [:d, :e])
-Learn(model, train, r"[abc]", ["d", "e"])
+Learn(train, model, [1, 2, 3] => "d")
+Learn(train, model, [:a, :b, :c] => :d)
+Learn(train, model, ["a", "b", "c"] => 4)
+Learn(train, model, [1, 2, 3] => [:d, :e])
+Learn(train, model, r"[abc]" => ["d", "e"])
 ```
 """
 struct Learn{M<:FittedModel} <: StatelessFeatureTransform
   model::M
   input::Vector{Symbol}
 end
 
-function Learn(model, train, incols, outcols)
+function Learn(train, model, (incols, outcols)::Pair)
   if !Tables.istable(train)
     throw(ArgumentError("training data must be a table"))
   end

test/runtests.jl

@@ -13,8 +13,8 @@ const SLM = StatsLearnModels
     iris = DataFrame(load_iris())
     input = iris[:, Not(:target)]
     output = iris[:, [:target]]
-    Tree = @load(DecisionTreeClassifier, pkg=DecisionTree, verbosity=0)
     train, test = partition(1:nrow(input), 0.7, rng=123)
+    Tree = @load(DecisionTreeClassifier, pkg=DecisionTree, verbosity=0)
     fmodel = SLM.fit(Tree(), input[train, :], output[train, :])
     pred = SLM.predict(fmodel, input[test, :])
     accuracy = count(pred.target .== output.target[test]) / length(test)
@@ -26,16 +26,16 @@ const SLM = StatsLearnModels
     iris = DataFrame(load_iris())
     outcol = :target
     incols = setdiff(propertynames(iris), [outcol])
-    Tree = @load(DecisionTreeClassifier, pkg=DecisionTree, verbosity=0)
     train, test = partition(1:nrow(iris), 0.7, rng=123)
-    transform = Learn(Tree(), iris[train, :], incols, outcol)
+    Tree = @load(DecisionTreeClassifier, pkg=DecisionTree, verbosity=0)
+    transform = Learn(iris[train, :], Tree(), incols => outcol)
     @test !isrevertible(transform)
     pred = transform(iris[test, :])
     accuracy = count(pred.target .== iris.target[test]) / length(test)
     @test accuracy > 0.9
 
     # throws
     # training data is not a table
-    @test_throws ArgumentError Learn(Tree(), nothing, incols, outcol)
+    @test_throws ArgumentError Learn(nothing, Tree(), incols => outcol)
   end
 end