1st attempt to implement AbstractTrees-interface

src/Api.jl

@@ -10,6 +10,8 @@ export BetaMLModel, BetaMLSupervisedModel, BetaMLUnsupervisedModel,
        BetaMLOptionsSet, BetaMLHyperParametersSet, BetaMLLearnableParametersSet,
        predict, fit, fit!, train!, partition, info
 
+export InfoNode, InfoLeaf, wrap
+
 abstract type BetaMLModel end
 abstract type BetaMLSupervisedModel <: BetaMLModel end
 abstract type BetaMLUnsupervisedModel <: BetaMLModel end

src/Trees/Trees.jl

@@ -44,6 +44,7 @@ Acknowlegdments: originally based on the [Josh Gordon's code](https://www.youtub
 module Trees
 
 using LinearAlgebra, Random, Statistics, Reexport, CategoricalArrays
+using AbstractTrees
 
 using  ForceImport
 @force using ..Api
@@ -56,5 +57,6 @@ import Base.show
 include("DecisionTrees.jl") # Decision Trees algorithm and API
 include("RandomForests.jl") # Random Forests algorithm and API
 include("Trees_MLJ.jl")     # MLJ interface
+include("abstract_trees.jl")
 
 end # end module

src/Trees/abstract_trees.jl

@@ -0,0 +1,28 @@
+abstract type AbstractNode end      # will be placed elsewhere in the future
+
+struct InfoNode{T} <: AbstractNode
+    node    :: DecisionNode{T}
+    info    :: NamedTuple
+end
+
+struct InfoLeaf{T} <: AbstractNode
+    leaf    :: Leaf{T}
+    info    :: NamedTuple
+end
+
+wrap(node::DecisionNode, info::NamedTuple = NamedTuple()) = InfoNode(node, info)
+wrap(leaf::Leaf,         info::NamedTuple = NamedTuple()) = InfoLeaf(leaf, info)
+
+AbstractTrees.children(node::InfoNode) = (
+    wrap(node.node.trueBranch, node.info),
+    wrap(node.node.falseBranch, node.info)
+)
+AbstractTrees.children(node::InfoLeaf) = ()
+
+function AbstractTrees.printnode(io::IO, node::InfoNode)
+    print(io, node.node)
+end
+
+function AbstractTrees.printnode(io::IO, leaf::InfoLeaf)
+    print(io, leaf.leaf)
+end