Remove "svd"s in comment

invenia · Feb 24, 2020 · e51cfb5 · e51cfb5
1 parent e4cd596
commit e51cfb5
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Showing 2 changed files with 3 additions and 13 deletions.
diff --git a/src/imputors/knn.jl b/src/imputors/knn.jl
@@ -24,11 +24,6 @@ function KNN(; num_nn=1,
     KNN(num_nn, dist, context)
 end
 
-"""
-    impute!(imp::KNN, data::AbstractMatrix)
-
-data : N x D matrix
-"""
 function impute!(data::AbstractMatrix{<:Union{T, Missing}},
     imp::KNN) where T<:Real
 
@@ -51,7 +46,7 @@ function impute!(data::AbstractMatrix{<:Union{T, Missing}},
         # index of columns
         idxs, dists = NearestNeighbors.knn(kdtree, transposed, imp.num_nn, true)
         invWdist(i) = dists[i] == 0 ? transposed[idxs[i]] : dists[i]
-        # TODO : going to parallel?
+
         for (i, x) in enumerate(mdata)
             if ndims(transposed) != 1
                 # ndims(dataT) == 1 means there is there is only single row.

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