Remove "svd"s in comment

invenia · Feb 19, 2020 · 000d50e · 000d50e
1 parent c3c0dec
commit 000d50e
Showing 1 changed file with 2 additions and 7 deletions.
diff --git a/test/runtests.jl b/test/runtests.jl
@@ -529,29 +529,24 @@ end
     @testset "KNN" begin
         # Test a case with few variable
         # (e.g., only a few variables, only )
-        @testset "Data - few variables" begin
+        @testset "Data with few variables" begin
             data = Matrix(dataset("Ecdat", "Electricity"))
             X = add_missings(data)
 
             knn_imputed = impute(copy(X), Impute.KNN(; num_nn=3, dist=Euclidean(), context=Context(; limit = 1.0)))
             mean_imputed = impute(copy(X), Fill(; context=Context(; limit=1.0)))
 
-            # If we don't have enough variables then SVD imputation will probably perform
-            # about as well as mean imputation.
             @test nrmsd(knn_imputed, data) > nrmsd(mean_imputed, data) * 0.9
         end
 
-        @testset "Data - random variables" begin
+        @testset "Data with random values" begin
             M = rand(100, 200)
             data = M * M'
             X = add_missings(data)
 
             knn_imputed = impute(copy(X), Impute.KNN(; num_nn=3, dist=Euclidean(), context=Context(; limit = 1.0)))
             mean_imputed = impute(copy(X), Fill(; context=Context(; limit=1.0)))
 
-            # If most of the variance in the original data can't be explained by a small
-            # subset of the eigen values in the svd decomposition then our low rank approximations
-            # won't perform very well.
             @test nrmsd(knn_imputed, data) > nrmsd(mean_imputed, data) * 0.9
         end
     end