For a 0.12.1 release

Project.toml

@@ -2,7 +2,7 @@ name = "DecisionTree"
 uuid = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb"
 license = "MIT"
 desc = "Julia implementation of Decision Tree (CART) and Random Forest algorithms"
-version = "0.12.0"
+version = "0.12.1"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"

src/abstract_trees.jl

@@ -28,6 +28,11 @@ apart from the two points mentioned.
 In analogy to the type definitions of `DecisionTree`, the generic type `S` is 
 the type of the feature values used within a node as a threshold for the splits
 between its children and `T` is the type of the classes given (these might be ids or labels).
+
+    !!! note
+    You may only add lacking class labels. It's not possible to overwrite existing labels
+    with this mechanism. In case you want add class labels, the generic type `T` must 
+    be a subtype of `Integer`.
 """
 struct InfoNode{S, T} <: AbstractTrees.AbstractNode{DecisionTree.Node{S,T}}
     node    :: DecisionTree.Node{S, T}
@@ -89,8 +94,8 @@ AbstractTrees.children(node::InfoNode) = (
 AbstractTrees.children(node::InfoLeaf) = ()
 
 """
-    printnode(io::IO, node::InfoNode)
-    printnode(io::IO, leaf::InfoLeaf)
+    printnode(io::IO, node::InfoNode; sigdigits=4)
+    printnode(io::IO, leaf::InfoLeaf; sigdigits=4)
 
 Write a printable representation of `node` or `leaf` to output-stream `io`.
 
@@ -108,23 +113,28 @@ For the condition of the form `feature < value` which gets printed in the `print
 variant for `InfoNode`, the left subtree is the 'yes-branch' and the right subtree
 accordingly the 'no-branch'. `AbstractTrees.print_tree` outputs the left subtree first
 and then below the right subtree.
+
+`value` gets rounded to `sigdigits` significant digits.
 """
-function AbstractTrees.printnode(io::IO, node::InfoNode)
+function AbstractTrees.printnode(io::IO, node::InfoNode; sigdigits=4)
+    featval = round(node.node.featval; sigdigits)
     if :featurenames ∈ keys(node.info) 
-        print(io, node.info.featurenames[node.node.featid], " < ", node.node.featval)
+        print(io, node.info.featurenames[node.node.featid], " < ", featval)
     else
-	    print(io, "Feature: ", node.node.featid, " < ", node.node.featval)
+	    print(io, "Feature: ", node.node.featid, " < ", featval)
     end
 end
 
-function AbstractTrees.printnode(io::IO, leaf::InfoLeaf)
+function AbstractTrees.printnode(io::IO, leaf::InfoLeaf; sigdigits=4)
     dt_leaf = leaf.leaf
     matches     = findall(dt_leaf.values .== dt_leaf.majority)
 	match_count = length(matches)
 	val_count   = length(dt_leaf.values)
     if :classlabels ∈ keys(leaf.info)
+        @assert dt_leaf.majority isa Integer "classes must be represented as Integers"
         print(io, leaf.info.classlabels[dt_leaf.majority], " ($match_count/$val_count)")
     else
-	    print(io, "Class: ", dt_leaf.majority, " ($match_count/$val_count)")
+	    print(io, dt_leaf.majority isa Integer ? "Class: " : "", 
+            dt_leaf.majority, " ($match_count/$val_count)")
     end
 end

test/miscellaneous/abstract_trees_test.jl

@@ -81,4 +81,23 @@ end
 traverse_tree(leaf::InfoLeaf) = nothing
 
 traverse_tree(wrapped_tree)
+end
+
+@testset "abstract_trees - test misuse" begin
+
+    @info("Test misuse of `classlabel` information") 
+
+    @info("Create test data - a decision tree based on the iris data set") 
+    features, labels = load_data("iris") 
+    features = float.(features)
+    labels   = string.(labels)
+    model = DecisionTreeClassifier()
+    fit!(model, features, labels)    
+
+    @info("Try to replace the exisitng class labels")
+    class_labels = unique(labels)
+    dtree = model.root.node
+    wt = DecisionTree.wrap(dtree, (classlabels = class_labels,))
+    @test_throws AssertionError AbstractTrees.print_tree(wt)
+
 end