add CV tests

bensadeghi · Sep 17, 2020 · 971ebb9 · 971ebb9
1 parent 6d73208
commit 971ebb9
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Showing 2 changed files with 35 additions and 19 deletions.
diff --git a/test/classification/adult.jl b/test/classification/adult.jl
@@ -10,6 +10,9 @@ preds = apply_tree(model, features)
 cm = confusion_matrix(labels, preds)
 @test cm.accuracy > 0.99
 
+features = string.(features)
+labels   = string.(labels)
+
 n_subfeatures = 3
 n_trees = 5
 model = build_forest(labels, features, n_subfeatures, n_trees)
@@ -23,24 +26,24 @@ preds = apply_adaboost_stumps(model, coeffs, features);
 cm = confusion_matrix(labels, preds);
 @test cm.accuracy > 0.8
 
-# println("\n##### 3 foldCV Classification Tree #####")
-# pruning_purity = 0.9
-# nfolds = 3
-# accuracy = nfoldCV_tree(labels, features, nfolds, pruning_purity);
-# @test mean(accuracy) > 0.8
-
-# println("\n##### 3 foldCV Classification Forest #####")
-# n_subfeatures = 2
-# n_trees = 10
-# n_folds = 3
-# partial_sampling = 0.5
-# accuracy = nfoldCV_forest(labels, features, nfolds, n_subfeatures, n_trees, partial_sampling)
-# @test mean(accuracy) > 0.8
-
-# println("\n##### nfoldCV Classification Adaboosted Stumps #####")
-# n_iterations = 15
-# nfolds = 3
-# accuracy = nfoldCV_stumps(labels, features, nfolds, n_iterations);
-# @test mean(accuracy) > 0.8
+println("\n##### 3 foldCV Classification Tree #####")
+pruning_purity = 0.9
+nfolds = 3
+accuracy = nfoldCV_tree(labels, features, nfolds, pruning_purity; verbose=false);
+@test mean(accuracy) > 0.8
+
+println("\n##### 3 foldCV Classification Forest #####")
+n_subfeatures = 2
+n_trees = 10
+n_folds = 3
+partial_sampling = 0.5
+accuracy = nfoldCV_forest(labels, features, n_folds, n_subfeatures, n_trees, partial_sampling; verbose=false)
+@test mean(accuracy) > 0.8
+
+println("\n##### nfoldCV Classification Adaboosted Stumps #####")
+n_iterations = 15
+n_folds = 3
+accuracy = nfoldCV_stumps(labels, features, n_folds, n_iterations; verbose=false);
+@test mean(accuracy) > 0.8
 
 end # @testset
diff --git a/test/regression/digits.jl b/test/regression/digits.jl
@@ -79,4 +79,17 @@ model = build_forest(
 preds = apply_forest(model, X)
 @test R2(Y, preds) > 0.8
 
+println("\n##### 3 foldCV Regression Tree #####")
+n_folds = 5
+r2 = nfoldCV_tree(Y, X, n_folds; verbose=false);
+@test mean(r2) > 0.6
+
+println("\n##### 3 foldCV Regression Forest #####")
+n_subfeatures = 2
+n_trees = 10
+n_folds = 5
+partial_sampling = 0.5
+r2 = nfoldCV_forest(Y, X, n_folds, n_subfeatures, n_trees, partial_sampling; verbose=false)
+@test mean(r2) > 0.6
+
 end # @testset