Merge pull request #189 from JuliaAI/dev

For a 0.11.2 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 = "0.11.1"
+version = "0.11.2"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"

src/classification/main.jl

@@ -449,11 +449,21 @@ function apply_forest(forest::Ensemble{S, T}, features::AbstractVector{S}) where
     end
 end
 
-function apply_forest(forest::Ensemble{S, T}, features::AbstractMatrix{S}) where {S, T}
+function apply_forest(
+        forest::Ensemble{S, T},
+        features::AbstractMatrix{S};
+        use_multithreading = false
+    ) where {S, T}
     N = size(features,1)
     predictions = Array{T}(undef, N)
-    for i in 1:N
-        predictions[i] = apply_forest(forest, features[i, :])
+    if use_multithreading
+        Threads.@threads for i in 1:N
+            predictions[i] = apply_forest(forest, @view(features[i, :]))
+        end
+    else
+        for i in 1:N
+            predictions[i] = apply_forest(forest, @view(features[i, :]))
+        end
     end
     return predictions
 end

test/classification/adult.jl

@@ -22,6 +22,10 @@ cm = confusion_matrix(labels, preds)
 f1 = impurity_importance(model)
 p1 = permutation_importance(model, labels, features, (model, y, X)->accuracy(y, apply_forest(model, X)), rng=StableRNG(1)).mean
 
+preds_MT = apply_forest(model, features, use_multithreading = true)
+cm_MT = confusion_matrix(labels, preds_MT)
+@test cm_MT.accuracy > 0.9
+
 n_iterations = 15
 model, coeffs = build_adaboost_stumps(labels, features, n_iterations; rng=StableRNG(1));
 preds = apply_adaboost_stumps(model, coeffs, features);

test/classification/digits.jl

@@ -86,6 +86,10 @@ preds = apply_forest(model, X)
 cm = confusion_matrix(Y, preds)
 @test cm.accuracy > 0.95
 
+preds_MT = apply_forest(model, X, use_multithreading = true)
+cm_MT = confusion_matrix(Y, preds_MT)
+@test cm_MT.accuracy > 0.95
+
 n_iterations        = 100
 model, coeffs = DecisionTree.build_adaboost_stumps(
         Y, X,

test/classification/heterogeneous.jl

@@ -26,6 +26,10 @@ preds = apply_forest(model, features)
 cm = confusion_matrix(labels, preds)
 @test cm.accuracy > 0.9
 
+preds_MT = apply_forest(model, features, use_multithreading = true)
+cm_MT = confusion_matrix(labels, preds_MT)
+@test cm_MT.accuracy > 0.9
+
 n_subfeatures = 7
 model, coeffs = build_adaboost_stumps(labels, features, n_subfeatures; rng=StableRNG(1))
 preds = apply_adaboost_stumps(model, coeffs, features)

test/classification/iris.jl

@@ -79,6 +79,12 @@ cm = confusion_matrix(labels, preds)
 probs = apply_forest_proba(model, features, classes)
 @test reshape(sum(probs, dims=2), n) ≈ ones(n)
 
+preds_MT = apply_forest(model, features, use_multithreading = true)
+cm_MT = confusion_matrix(labels, preds_MT)
+@test cm_MT.accuracy > 0.95
+@test typeof(preds_MT) == Vector{String}
+@test sum(preds .!= preds_MT) == 0
+
 # run n-fold cross validation for forests
 println("\n##### nfoldCV Classification Forest #####")
 n_subfeatures = 2

test/classification/low_precision.jl

@@ -48,6 +48,11 @@ cm = confusion_matrix(labels, preds)
 @test typeof(preds) == Vector{Int32}
 @test cm.accuracy > 0.9
 
+preds_MT = apply_forest(model, features, use_multithreading = true)
+cm_MT = confusion_matrix(labels, preds_MT)
+@test typeof(preds_MT) == Vector{Int32}
+@test cm_MT.accuracy > 0.9
+
 n_iterations        = Int32(25)
 model, coeffs = build_adaboost_stumps(labels, features, n_iterations; rng=StableRNG(1));
 preds = apply_adaboost_stumps(model, coeffs, features);
@@ -116,6 +121,10 @@ model = build_forest(labels, features)
 preds = apply_forest(model, features)
 @test typeof(preds) == Vector{Int8}
 
+preds_MT = apply_forest(model, features, use_multithreading = true)
+@test typeof(preds_MT) == Vector{Int8}
+@test sum(abs.(preds .- preds_MT)) == zero(Int8)
+
 model = build_tree(labels, features)
 preds = apply_tree(model, features)
 @test typeof(preds) == Vector{Int8}

test/classification/random.jl

@@ -55,6 +55,12 @@ cm = confusion_matrix(labels, preds)
 @test cm.accuracy > 0.9
 @test typeof(preds) == Vector{Int}
 
+preds_MT = apply_forest(model, features, use_multithreading = true)
+cm_MT = confusion_matrix(labels, preds_MT)
+@test cm_MT.accuracy > 0.9
+@test typeof(preds_MT) == Vector{Int}
+@test sum(abs.(preds .- preds_MT)) == zero(Int)
+
 n_subfeatures       = 3
 n_trees             = 9
 partial_sampling    = 0.7
@@ -77,6 +83,10 @@ cm = confusion_matrix(labels, preds)
 @test cm.accuracy > 0.6
 @test length(model) == n_trees
 
+preds_MT = apply_forest(model, features, use_multithreading = true)
+cm_MT = confusion_matrix(labels, preds_MT)
+@test cm_MT.accuracy > 0.9
+
 # test n_subfeatures
 n_subfeatures       = 0
 m_partial = build_forest(labels, features; rng=StableRNG(1)) # default sqrt(n_features)

test/regression/digits.jl

@@ -80,6 +80,10 @@ model = build_forest(
 preds = apply_forest(model, X)
 @test R2(Y, preds) > 0.8
 
+preds_MT = apply_forest(model, X, use_multithreading = true)
+@test R2(Y, preds_MT) > 0.8
+@test sum(abs.(preds .- preds_MT)) < 1e-8
+
 println("\n##### 3 foldCV Regression Tree #####")
 n_folds = 5
 r2 = nfoldCV_tree(Y, X, n_folds; rng=StableRNG(1), verbose=false);

test/regression/low_precision.jl

@@ -47,6 +47,11 @@ preds = apply_forest(model, features)
 @test R2(labels, preds) > 0.9
 @test typeof(preds) <: Vector{Float64}
 
+preds_MT = apply_forest(model, features, use_multithreading=true)
+@test R2(labels, preds_MT) > 0.9
+@test typeof(preds_MT) <: Vector{Float64}
+@test sum(abs.(preds .- preds_MT)) < 1.0e-8
+
 println("\n##### nfoldCV Regression Tree #####")
 n_folds             = Int32(3)
 pruning_purity      = 1.0
@@ -102,6 +107,10 @@ model = build_forest(labels, features)
 preds = apply_forest(model, features)
 @test typeof(preds) == Vector{Float16}
 
+preds_MT = apply_forest(model, features, use_multithreading = true)
+@test typeof(preds_MT) == Vector{Float16}
+@test sum(abs.(preds .- preds_MT)) < 1.0e-8
+
 model = build_tree(labels, features)
 preds = apply_tree(model, features)
 @test typeof(preds) == Vector{Float16}