Merge pull request #187 from JuliaAI/dev

For a 0.11.1 release

Author: ablaom

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 = "0.11.0"
+version = "0.11.1"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"

src/DecisionTree.jl

@@ -56,8 +56,9 @@ end
 is_leaf(l::Leaf) = true
 is_leaf(n::Node) = false
 
-zero(::Type{String}) = ""
-convert(::Type{Node{S, T}}, lf::Leaf{T}) where {S, T} = Node(0, zero(S), lf, Leaf(zero(T), [zero(T)]))
+_zero(::Type{String}) = ""
+_zero(x::Any) = zero(x)
+convert(::Type{Node{S, T}}, lf::Leaf{T}) where {S, T} = Node(0, _zero(S), lf, Leaf(_zero(T), [_zero(T)]))
 convert(::Type{Root{S, T}}, node::LeafOrNode{S, T}) where {S, T} = Root{S, T}(node, 0, Float64[])
 convert(::Type{LeafOrNode{S, T}}, tree::Root{S, T}) where {S, T} = tree.node
 promote_rule(::Type{Node{S, T}}, ::Type{Leaf{T}}) where {S, T} = Node{S, T}
@@ -95,21 +96,21 @@ depth(leaf::Leaf) = 0
 depth(tree::Node) = 1 + max(depth(tree.left), depth(tree.right))
 depth(tree::Root) = depth(tree.node)
 
-function print_tree(io::IO, leaf::Leaf, depth=-1, indent=0; feature_names=nothing)
+function print_tree(io::IO, leaf::Leaf, depth=-1, indent=0; sigdigits=4, feature_names=nothing)
     n_matches = count(leaf.values .== leaf.majority)
     ratio = string(n_matches, "/", length(leaf.values))
     println(io, "$(leaf.majority) : $(ratio)")
 end
-function print_tree(leaf::Leaf, depth=-1, indent=0; feature_names=nothing)
-    return print_tree(stdout, leaf, depth, indent; feature_names=feature_names)
+function print_tree(leaf::Leaf, depth=-1, indent=0; sigdigits=4, feature_names=nothing)
+    return print_tree(stdout, leaf, depth, indent; sigdigits, feature_names)
 end
 
 
-function print_tree(io::IO, tree::Root, depth=-1, indent=0; sigdigits=2, feature_names=nothing)
-    return print_tree(io, tree.node, depth, indent; sigdigits=sigdigits, feature_names=feature_names)
+function print_tree(io::IO, tree::Root, depth=-1, indent=0; sigdigits=4, feature_names=nothing)
+    return print_tree(io, tree.node, depth, indent; sigdigits, feature_names)
 end
-function print_tree(tree::Root, depth=-1, indent=0; sigdigits=2, feature_names=nothing)
-    return print_tree(stdout, tree, depth, indent; sigdigits=sigdigits, feature_names=feature_names)
+function print_tree(tree::Root, depth=-1, indent=0; sigdigits=4, feature_names=nothing)
+    return print_tree(stdout, tree, depth, indent; sigdigits, feature_names)
 end
 
 """
@@ -137,26 +138,26 @@ Feature 3 < -28.15 ?
 
 To facilitate visualisation of trees using third party packages, a `DecisionTree.Leaf` object,
 `DecisionTree.Node` object or  `DecisionTree.Root` object can be wrapped to obtain a tree structure implementing the 
-AbstractTrees.jl interface. See  [`wrap`](@ref)` for details. 
+AbstractTrees.jl interface. See  [`wrap`](@ref)` for details.
 """
 function print_tree(io::IO, tree::Node, depth=-1, indent=0; sigdigits=2, feature_names=nothing)
     if depth == indent
         println(io)
         return
     end
-    featval = round(tree.featval; sigdigits=sigdigits)
+    featval = round(tree.featval; sigdigits)
     if feature_names === nothing
         println(io, "Feature $(tree.featid) < $featval ?")
     else
         println(io, "Feature $(tree.featid): \"$(feature_names[tree.featid])\" < $featval ?")
     end
     print(io, "    " ^ indent * "├─ ")
-    print_tree(io, tree.left, depth, indent + 1; feature_names=feature_names)
+    print_tree(io, tree.left, depth, indent + 1; sigdigits, feature_names)
     print(io, "    " ^ indent * "└─ ")
-    print_tree(io, tree.right, depth, indent + 1; feature_names=feature_names)
+    print_tree(io, tree.right, depth, indent + 1; sigdigits, feature_names)
 end
-function print_tree(tree::Node, depth=-1, indent=0; sigdigits=2, feature_names=nothing)
-    return print_tree(stdout, tree, depth, indent; sigdigits=sigdigits, feature_names=feature_names)
+function print_tree(tree::Node, depth=-1, indent=0; sigdigits=4, feature_names=nothing)
+    return print_tree(stdout, tree, depth, indent; sigdigits, feature_names)
 end
 
 function show(io::IO, leaf::Leaf)

test/miscellaneous/convert.jl

@@ -32,4 +32,14 @@ push!(rv, nv[1])
 @test apply_tree(rv[1], [0]) == "A"
 @test apply_tree(rv[2], [0]) == "A"
 
-end # @testset
+end
+
+@testset "convert to text" begin
+    n, m = 10^3, 5;
+    features = rand(StableRNG(1), n, m);
+    weights = rand(StableRNG(1), -1:1, m);
+    labels = features * weights;
+    model = fit!(DecisionTreeRegressor(; rng=StableRNG(1)), features, labels)
+    # Smoke test.
+    print_tree(devnull, model)
+end

test/runtests.jl

@@ -9,8 +9,8 @@ using Statistics
 using Test
 using LinearAlgebra
 
-import DecisionTree: accuracy, R2, majority_vote, mean_squared_error
-import DecisionTree: confusion_matrix, ConfusionMatrix
+using DecisionTree: accuracy, R2, majority_vote, mean_squared_error
+using DecisionTree: confusion_matrix, ConfusionMatrix
 
 println("Julia version: ", VERSION)