From 58110200faeae703cca4d89fcb1fb55cd6e607fc Mon Sep 17 00:00:00 2001
From: salbert83 <salbert83@hotmail.com>
Date: Fri, 8 Jul 2022 20:51:19 -0400
Subject: [PATCH] Unit tests for multithreading

---
 test/classification/adult.jl         |  4 ++++
 test/classification/digits.jl        |  4 ++++
 test/classification/heterogeneous.jl |  4 ++++
 test/classification/iris.jl          |  6 ++++++
 test/classification/low_precision.jl |  9 +++++++++
 test/classification/random.jl        | 10 ++++++++++
 6 files changed, 37 insertions(+)

diff --git a/test/classification/adult.jl b/test/classification/adult.jl
index 00ee7878..6c61d1b3 100644
--- a/test/classification/adult.jl
+++ b/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);
diff --git a/test/classification/digits.jl b/test/classification/digits.jl
index d8c6bc06..096f1947 100644
--- a/test/classification/digits.jl
+++ b/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,
diff --git a/test/classification/heterogeneous.jl b/test/classification/heterogeneous.jl
index 004d89b2..f3afbfa9 100644
--- a/test/classification/heterogeneous.jl
+++ b/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)
diff --git a/test/classification/iris.jl b/test/classification/iris.jl
index a8ffb3b8..a4eb0b05 100644
--- a/test/classification/iris.jl
+++ b/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
diff --git a/test/classification/low_precision.jl b/test/classification/low_precision.jl
index 4bdcfbf8..4ee31de6 100644
--- a/test/classification/low_precision.jl
+++ b/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}
diff --git a/test/classification/random.jl b/test/classification/random.jl
index a91ebac6..8b6502ed 100644
--- a/test/classification/random.jl
+++ b/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)