Add new traits (#11)

* Add new traits * Implement scitype for Dates * Rename Geometric --> Geometrical * Add more examples to Temporal docstring * Add scitype for Distribution * Add tests for Dates * Apply suggestions from code review Co-authored-by: Elias Carvalho <[email protected]> * Apply suggestions --------- Co-authored-by: Elias Carvalho <[email protected]>
JuliaML · Nov 29, 2023 · 7e12489 · 7e12489
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commit 7e12489
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diff --git a/Project.toml b/Project.toml
@@ -3,21 +3,31 @@ uuid = "6cb2f572-2d2b-4ba6-bdb3-e710fa044d6c"
 authors = ["Elias Carvalho <[email protected]> and contributors"]
 version = "0.1.0"
 
+[deps]
+Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
+
 [weakdeps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
 CoDa = "5900dafe-f573-5c72-b367-76665857777b"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DynamicQuantities = "06fc5a27-2a28-4c7c-a15d-362465fb6821"
+Meshes = "eacbb407-ea5a-433e-ab97-5258b1ca43fa"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 
 [extensions]
 DataScienceTraitsCategoricalArraysExt = "CategoricalArrays"
 DataScienceTraitsCoDaExt = "CoDa"
+DataScienceTraitsDistributionsExt = "Distributions"
 DataScienceTraitsDynamicQuantitiesExt = "DynamicQuantities"
+DataScienceTraitsMeshesExt = "Meshes"
 DataScienceTraitsUnitfulExt = "Unitful"
 
 [compat]
 CategoricalArrays = "0.10"
 CoDa = "1.2"
+Dates = "1.9"
+Distributions = "0.25"
 DynamicQuantities = "0.7"
+Meshes = "0.36"
 Unitful = "1.17"
 julia = "1.9"
diff --git a/ext/DataScienceTraitsDistributionsExt.jl b/ext/DataScienceTraitsDistributionsExt.jl
@@ -0,0 +1,12 @@
+# -----------------------------------------------------------------
+# Licensed under the MIT License. See LICENSE in the project root.
+# -----------------------------------------------------------------
+
+module DataScienceTraitsDistributionsExt
+
+using DataScienceTraits
+using Distributions: Distribution
+
+DataScienceTraits.scitype(::Type{<:Distribution}) = DataScienceTraits.Distributional
+
+end
diff --git a/ext/DataScienceTraitsMeshesExt.jl b/ext/DataScienceTraitsMeshesExt.jl
@@ -0,0 +1,12 @@
+# -----------------------------------------------------------------
+# Licensed under the MIT License. See LICENSE in the project root.
+# -----------------------------------------------------------------
+
+module DataScienceTraitsMeshesExt
+
+using DataScienceTraits
+using Meshes: Geometry
+
+DataScienceTraits.scitype(::Type{<:Geometry}) = DataScienceTraits.Geometrical
+
+end
diff --git a/src/DataScienceTraits.jl b/src/DataScienceTraits.jl
@@ -4,6 +4,8 @@
 
 module DataScienceTraits
 
+using Dates: TimeType
+
 """
     SciType
 
@@ -32,6 +34,27 @@ Scientific type of compositional data (See CoDa.jl).
 """
 abstract type Compositional <: SciType end
 
+"""
+    Distributional
+
+Scientific type of distributional data (See Distributions.jl)
+"""
+abstract type Distributional <: SciType end
+
+"""
+    Geometrical
+
+Scientific type of geometrical data (See Meshes.jl)
+"""
+abstract type Geometrical <: SciType end
+
+"""
+    Temporal
+
+Scientific type of temporal data (e.g. Date, Time, DateTime).
+"""
+abstract type Temporal <: SciType end
+
 """
     Unknown
 
@@ -91,6 +114,7 @@ scitype(::Type{<:Number}) = Continuous
 scitype(::Type{<:Integer}) = Categorical
 scitype(::Type{<:AbstractChar}) = Categorical
 scitype(::Type{<:AbstractString}) = Categorical
+scitype(::Type{<:TimeType}) = Temporal
 scitype(::Type{Union{T,Missing}}) where {T} = scitype(T)
 
 sciconvert(::Type{Continuous}, x::Integer) = float(x)

diff --git a/test/Project.toml b/test/Project.toml
@@ -1,6 +1,9 @@
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
 CoDa = "5900dafe-f573-5c72-b367-76665857777b"
+Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DynamicQuantities = "06fc5a27-2a28-4c7c-a15d-362465fb6821"
+Meshes = "eacbb407-ea5a-433e-ab97-5258b1ca43fa"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
diff --git a/test/runtests.jl b/test/runtests.jl
@@ -1,8 +1,11 @@
 using DataScienceTraits
 using CategoricalArrays
+using Distributions
+using Meshes
+using CoDa
 import DynamicQuantities
 import Unitful
-using CoDa
+using Dates
 using Test
 
 const DST = DataScienceTraits
@@ -235,4 +238,31 @@ const DST = DataScienceTraits
     carr = categorical([1, 3, 2], ordered=true)
     @test DST.isordered(carr)
   end
+
+  @testset "Distributions" begin
+    @test scitype(Normal()) <: DST.Distributional
+    @test scitype(Exponential()) <: DST.Distributional
+    @test elscitype(fill(Normal(), 3)) <: DST.Distributional
+    @test elscitype(fill(Exponential(), 3)) <: DST.Distributional
+    @test elscitype([Normal(), missing, Normal()]) <: DST.Distributional
+    @test elscitype([Exponential(), missing, Exponential()]) <: DST.Distributional
+  end
+
+  @testset "Meshes" begin
+    @test scitype(rand(Point2)) <: DST.Geometrical
+    @test scitype(rand(Triangle{2,Float64})) <: DST.Geometrical
+    @test scitype(rand(Triangle{2,Float64})) <: DST.Geometrical
+    @test elscitype(rand(Point2, 3)) <: DST.Geometrical
+    @test elscitype(rand(Triangle{2,Float64}, 3)) <: DST.Geometrical
+    @test elscitype([Point(0, 0), missing, Point(1, 1)]) <: DST.Geometrical
+    @test elscitype([Triangle((0, 0), (1, 0), (1, 1)), missing, Point(1, 1)]) <: DST.Geometrical
+  end
+
+  @testset "Dates" begin
+    @test scitype(Date(2023, 1, 1)) <: DST.Temporal
+    @test scitype(Time(1, 0, 0)) <: DST.Temporal
+    @test scitype(DateTime(2023, 1, 1)) <: DST.Temporal
+    @test elscitype(fill(Date(2023, 1, 1), 3)) <: DST.Temporal
+    @test elscitype([Date(2023, 1, 1), missing, Time(1, 0, 0)]) <: DST.Temporal
+  end
 end