Add CoDa as direct dependency

JuliaML · Nov 30, 2023 · 9fd3649 · 9fd3649
1 parent 4f197f2
commit 9fd3649
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Showing 5 changed files with 7 additions and 26 deletions.
diff --git a/Project.toml b/Project.toml
@@ -5,17 +5,12 @@ version = "0.4.1"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
+CoDa = "5900dafe-f573-5c72-b367-76665857777b"
 DataScienceTraits = "6cb2f572-2d2b-4ba6-bdb3-e710fa044d6c"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
-[weakdeps]
-CoDa = "5900dafe-f573-5c72-b367-76665857777b"
-
-[extensions]
-TableDistancesCoDaExt = "CoDa"
-
 [compat]
 CategoricalArrays = "0.10"
 CoDa = "1.0"

diff --git a/ext/TableDistancesCoDaExt.jl b/ext/TableDistancesCoDaExt.jl
diff --git a/src/TableDistances.jl b/src/TableDistances.jl
@@ -4,16 +4,17 @@
 
 module TableDistances
 
-# basic imports
 using Tables
 using Distances
 using Statistics
 using DataScienceTraits
-using CategoricalArrays
 
-using DataScienceTraits: Continuous, Categorical
+using DataScienceTraits: Continuous
+using DataScienceTraits: Categorical
+using DataScienceTraits: Compositional
 using DataScienceTraits: isordered
 using CategoricalArrays: levelcode
+using CoDa: Aitchison, norm
 
 import Distances: pairwise, result_type
 

diff --git a/src/distances.jl b/src/distances.jl
@@ -17,6 +17,7 @@ result_type(::UnorderedCategDistance, x, y) = Bool
 default_distance(x) = default_distance(elscitype(x), x)
 default_distance(::Type{Continuous}, x) = Euclidean()
 default_distance(::Type{Categorical}, x) = isordered(x) ? OrederedCategDistance() : UnorderedCategDistance()
+default_distance(::Type{Compositional}, x) = Aitchison()
 
 function default_distances(table)
  cols = Tables.columns(table)

diff --git a/src/normalizations.jl b/src/normalizations.jl
@@ -4,6 +4,7 @@
 
 default_normalization(::Type) = nothing
 default_normalization(::Type{Continuous}) = x -> (quantile(x, 0.75) - quantile(x, 0.25))
+default_normalization(::Type{Compositional}) = x -> maximum(norm.(x))
 
 function normalize(tables...)
  rtables = Tables.rowtable.(tables)