Unit tests for multithreading

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)