Fix issue with single-column tables

Author: juliohm

src/TableTransforms.jl

@@ -7,7 +7,7 @@ module TableTransforms
 using Tables
 using ScientificTypes
 using Distributions: Normal
-using Transducers: Map, foldxt
+using Transducers: tcollect
 using LinearAlgebra
 using Statistics
 

src/transforms.jl

@@ -142,7 +142,7 @@ function apply(transform::Colwise, table)
   end
 
   # parallel map with multiple threads
-  vals = foldxt(vcat, Map(colfunc), names)
+  vals = tcollect(colfunc(n) for n in names)
 
   # new table with transformed columns
   𝒯 = (; first.(vals)...) |> Tables.materializer(table)
@@ -172,7 +172,7 @@ function revert(transform::Colwise, newtable, cache)
   end
 
   # parallel map with multiple threads
-  vals = foldxt(vcat, Map(colfunc), 1:length(names))
+  vals = tcollect(colfunc(i) for i in 1:length(names))
 
   # new table with transformed columns
   (; vals...) |> Tables.materializer(newtable)

src/transforms/parallel.jl

@@ -16,7 +16,7 @@ isrevertible(p::Parallel) = any(isrevertible, p.transforms)
 function apply(p::Parallel, table)
   # apply transforms in parallel
   f(transform) = apply(transform, table)
-  vals = foldxt(vcat, Map(f), p.transforms)
+  vals = tcollect(f(t) for t in p.transforms)
 
   # retrieve tables and caches
   tables = first.(vals)

test/transforms.jl

@@ -339,4 +339,16 @@
     n₂ = P₂(t)
     @test Tables.matrix(n₁) ≈ Tables.matrix(n₂)
   end
+
+  @testset "Miscellaneous" begin
+    # make sure transforms work with
+    # single-column tables
+    t = Table(x=rand(10000))
+    n, c = apply(ZScore(), t)
+    r = revert(ZScore(), n, c)
+    @test isapprox(mean(n.x), 0.0, atol=1e-8)
+    @test isapprox(std(n.x), 1.0, atol=1e-8)
+    @test isapprox(mean(r.x), mean(t.x), atol=1e-8)
+    @test isapprox(std(r.x), std(t.x), atol=1e-8)
+  end
 end