Renamed wrap to wrapdn ("decision node") as new "wrap" function in Base

src/Trees/AbstractTrees_BetaML_interface.jl

@@ -11,7 +11,7 @@ For more information see [JuliaAI/DecisionTree.jl](https://github.com/JuliaAI/De
 The file `src/abstract_trees.jl` in that repo serves as a model implementation.
 """
 
-export InfoNode, InfoLeaf, wrap, DecisionNode, Leaf
+export InfoNode, InfoLeaf, wrapdn, DecisionNode, Leaf
 
 """
 These types are introduced so that additional information currently not present in 
@@ -31,27 +31,27 @@ end
 AbstractTrees.nodevalue(l::InfoLeaf) = l.leaf # round(l.leaf,sigdigits=4)
 
 """
-    wrap(node:: DecisionNode, ...)
+    wrapdn(node:: DecisionNode, ...)
 
 Called on the root node of a `DecsionTree` `dc` in order to add visualization information.
 In case of a `BetaML/DecisionTree` this is typically a list of feature names as follows:
 
-`wdc = wrap(dc, featurenames = ["Colour","Size"])`
+`wdc = wrapdn(dc, featurenames = ["Colour","Size"])`
 """
 
-wrap(node::DecisionNode, info::NamedTuple = NamedTuple()) = InfoNode(node, info)
-wrap(leaf::Leaf,         info::NamedTuple = NamedTuple()) = InfoLeaf(leaf, info)
-wrap(mod::DecisionTreeEstimator, info::NamedTuple = NamedTuple()) = wrap(mod.par.tree, info)
-wrap(m::Union{DecisionNode,Leaf,DecisionTreeEstimator};featurenames=[]) = wrap(m,(featurenames=featurenames,))
+wrapdn(node::DecisionNode, info::NamedTuple = NamedTuple()) = InfoNode(node, info)
+wrapdn(leaf::Leaf,         info::NamedTuple = NamedTuple()) = InfoLeaf(leaf, info)
+wrapdn(mod::DecisionTreeEstimator, info::NamedTuple = NamedTuple()) = wrapdn(mod.par.tree, info)
+wrapdn(m::Union{DecisionNode,Leaf,DecisionTreeEstimator};featurenames=[]) = wrapdn(m,(featurenames=featurenames,))
 
 
 
 
 #### Implementation of the `AbstractTrees`-interface
 
 AbstractTrees.children(node::InfoNode) = (
-    wrap(node.node.trueBranch, node.info),
-    wrap(node.node.falseBranch, node.info)
+    wrapdn(node.node.trueBranch, node.info),
+    wrapdn(node.node.falseBranch, node.info)
 )
 AbstractTrees.children(node::InfoLeaf) = ()
 

src/Trees/DecisionTrees.jl

@@ -254,7 +254,7 @@ Dict{String, Any}("job_is_regression" => 1, "fitted_records" => 6, "max_reached_
 
 julia> using Plots, TreeRecipe, AbstractTrees
 julia> featurenames = ["Something", "Som else"];
-julia> wrapped_tree   = wrap(dtree, featurenames = featurenames); # featurenames is otional
+julia> wrapped_tree   = wrapdn(dtree, featurenames = featurenames); # featurenames is otional
 julia> print_tree(wrapped_tree)
 Som else >= 18.0?
 ├─ Som else >= 31.0?

test/Trees_tests.jl

@@ -57,7 +57,7 @@ x = [1 0.1; 2 0.2; 3 0.3; 4 0.4; 5 0.5]
 y = ["a","a","b","b","b"]
 @test findbestgain_sortedvector(x,y,2,x[:,2];mCols=[],currentUncertainty=gini(y),splitting_criterion=gini,rng=copy(TESTRNG)) == 0.3
 
-wrappedNode = BetaML.wrap(myTree)
+wrappedNode = BetaML.wrapdn(myTree)
 print("Node printing: ")
 printnode(stdout,wrappedNode)
 println("")
@@ -83,12 +83,12 @@ X = [1.8 2.5; 0.5 20.5; 0.6 18; 0.7 22.8; 0.4 31; 1.7 3.7];
 y = 2 .* X[:,1] .- X[:,2] .+ 3;
 mod = DecisionTreeEstimator(max_depth=2)
 ŷ   = fit!(mod,X,y);
-wmod = wrap(mod,featurenames=["dim1","dim2"])
+wmod = wrapdn(mod,featurenames=["dim1","dim2"])
 print_tree(wmod)
 y2 = ["a","b","b","c","b","a"]
 mod2 = DecisionTreeEstimator(max_depth=2)
 ŷ2   = fit!(mod2,X,y2);
-wmod2 = wrap(mod2,featurenames=["dim1","dim2"])
+wmod2 = wrapdn(mod2,featurenames=["dim1","dim2"])
 print_tree(wmod2)
 
 #print(myTree)

test/Trees_tests_additional.jl

@@ -58,7 +58,7 @@ tree = Tree()
 
     println("--> add information about feature names")
     featurenames = ["Color", "Size"]
-    wrapped_tree = wrap(model, featurenames = featurenames)
+    wrapped_tree = wrapdn(model, featurenames = featurenames)
 
     println("--> plot the tree using the `TreeRecipe`")
     plt = plot(wrapped_tree)        # this calls automatically the `TreeRecipe`