Repository: JuliaML/TableTransforms.jl (julia)

Issue:
Revert of `CLR` assumes original data in `[0, 1]` range
We could show a warning to avoid silent bugs:

```julia
julia> t = [(a=10.,b=20.,c=70.)]
1-element Vector{@NamedTuple{a::Float64, b::Float64, c::Float64}}:
 (a = 10.0, b = 20.0, c = 70.0)

julia> n, c = apply(CLR(), t);

julia> revert(CLR(), n, c)
1-element Vector{@NamedTuple{a::Float64, b::Float64, c::Float64}}:
 (a = 0.09999999999999998, b = 0.19999999999999996, c = 0.7000000000000001)
```

or could rely on `Remainder` + `Closure` to pre-process the inputs.

Pull request:
Fix https://github.com/JuliaEarth/GeoStats.jl/issues/503

Patch:
diff --git a/src/transforms/logratio.jl b/src/transforms/logratio.jl
index 8453a1b0..47e59306 100644
--- a/src/transforms/logratio.jl
+++ b/src/transforms/logratio.jl
@@ -23,22 +23,27 @@ assertions(::LogRatio) = [scitypeassert(Continuous)]
 
 function applyfeat(transform::LogRatio, feat, prep)
   cols = Tables.columns(feat)
-  onames = Tables.columnnames(cols)
-  varnames = collect(onames)
+  names = Tables.columnnames(cols)
+  vars = collect(names)
+
+  # perform closure for full revertibility
+  cfeat, ccache = apply(Closure(), feat)
 
   # reference variable
-  rvar = refvar(transform, varnames)
-  _assert(rvar ∈ varnames, "invalid reference variable")
-  rind = findfirst(==(rvar), varnames)
+  rvar = refvar(transform, vars)
+  _assert(rvar ∈ vars, "invalid reference variable")
+
+  # reference index
+  rind = findfirst(==(rvar), vars)
 
   # permute columns if necessary
-  perm = rind ≠ lastindex(varnames)
+  perm = rind ≠ lastindex(vars)
   pfeat = if perm
-    popat!(varnames, rind)
-    push!(varnames, rvar)
-    feat |> Select(varnames)
+    popat!(vars, rind)
+    push!(vars, rvar)
+    cfeat |> Select(vars)
   else
-    feat
+    cfeat
   end
 
   # apply transform
@@ -46,33 +51,38 @@ function applyfeat(transform::LogRatio, feat, prep)
   Y = applymatrix(transform, X)
 
   # new variable names
-  newnames = newvars(transform, varnames)
+  newnames = newvars(transform, vars)
 
   # return same table type
   𝒯 = (; zip(newnames, eachcol(Y))...)
   newfeat = 𝒯 |> Tables.materializer(feat)
 
-  newfeat, (rind, perm, onames)
+  newfeat, (ccache, perm, rind, vars)
 end
 
 function revertfeat(transform::LogRatio, newfeat, fcache)
+  # retrieve cache
+  ccache, perm, rind, vars = fcache
+
   # revert transform
   Y = Tables.matrix(newfeat)
   X = revertmatrix(transform, Y)
-
-  # retrieve cache
-  rind, perm, onames = fcache
+  pfeat = (; zip(vars, eachcol(X))...)
 
   # revert the permutation if necessary
-  if perm
-    n = length(onames)
+  cfeat = if perm
+    n = length(vars)
     inds = collect(1:(n - 1))
     insert!(inds, rind, n)
-    X = X[:, inds]
+    pfeat |> Select(inds)
+  else
+    pfeat
   end
 
+  # revert closure for full revertibility
+  𝒯 = revert(Closure(), cfeat, ccache)
+
   # return same table type
-  𝒯 = (; zip(onames, eachcol(X))...)
   𝒯 |> Tables.materializer(newfeat)
 end
 


Test patch:
diff --git a/test/transforms/logratio.jl b/test/transforms/logratio.jl
index 9414d470..c0443202 100644
--- a/test/transforms/logratio.jl
+++ b/test/transforms/logratio.jl
@@ -12,21 +12,22 @@
   n, c = apply(T, t)
   @test Tables.schema(n).names == (:ARL1, :ARL2)
   @test n == t |> ALR(:c)
-  talr = revert(T, n, c)
+  r = revert(T, n, c)
+  @test Tables.matrix(r) ≈ Tables.matrix(t)
+
   T = CLR()
   n, c = apply(T, t)
   @test Tables.schema(n).names == (:CLR1, :CLR2, :CLR3)
-  tclr = revert(T, n, c)
+  r = revert(T, n, c)
+  @test Tables.matrix(r) ≈ Tables.matrix(t)
+
   T = ILR()
   n, c = apply(T, t)
   @test Tables.schema(n).names == (:ILR1, :ILR2)
   @test n == t |> ILR(:c)
-  tilr = revert(T, n, c)
-  @test Tables.matrix(talr) ≈ Tables.matrix(tclr)
-  @test Tables.matrix(tclr) ≈ Tables.matrix(tilr)
-  @test Tables.matrix(talr) ≈ Tables.matrix(tilr)
+  r = revert(T, n, c)
+  @test Tables.matrix(r) ≈ Tables.matrix(t)
 
-  # permute columns
   a = [1.0, 0.0, 1.0]
   b = [2.0, 2.0, 2.0]
   c = [3.0, 3.0, 0.0]
@@ -35,10 +36,23 @@
 
   T = ALR(:c)
   n1, c1 = apply(T, t1)
+  r1 = revert(T, n1, c1)
+  n2, c2 = apply(T, t2)
+  r2 = revert(T, n2, c2)
+  @test n1 == n2
+  @test Tables.matrix(r1) ≈ Tables.matrix(t1)
+  @test Tables.schema(r1).names == (:a, :c, :b)
+  @test Tables.matrix(r2) ≈ Tables.matrix(t2)
+  @test Tables.schema(r2).names == (:c, :a, :b)
+
+  T = ILR(:c)
+  n1, c1 = apply(T, t1)
+  r1 = revert(T, n1, c1)
   n2, c2 = apply(T, t2)
+  r2 = revert(T, n2, c2)
   @test n1 == n2
-  tₒ = revert(T, n1, c1)
-  @test Tables.schema(tₒ).names == (:a, :c, :b)
-  tₒ = revert(T, n2, c2)
-  @test Tables.schema(tₒ).names == (:c, :a, :b)
+  @test Tables.matrix(r1) ≈ Tables.matrix(t1)
+  @test Tables.schema(r1).names == (:a, :c, :b)
+  @test Tables.matrix(r2) ≈ Tables.matrix(t2)
+  @test Tables.schema(r2).names == (:c, :a, :b)
 end