Merge pull request #28 from JuliaAI/dev

For a 0.1.6 release

Author: ablaom

Project.toml

@@ -1,23 +1,23 @@
 name = "CategoricalDistributions"
 uuid = "af321ab8-2d2e-40a6-b165-3d674595d28e"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.1.5"
+version = "0.1.6"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Missings = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-ScientificTypesBase = "30f210dd-8aff-4c5f-94ba-8e64358c1161"
+ScientificTypes = "321657f4-b219-11e9-178b-2701a2544e81"
 UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
 
 [compat]
 CategoricalArrays = "0.9, 0.10"
 Distributions = "0.25"
 Missings = "0.4, 1"
 OrderedCollections = "1.1"
-ScientificTypesBase = "2, 3"
+ScientificTypes = "3.0"
 UnicodePlots = "2"
 julia = "1.3"
 

src/CategoricalDistributions.jl

@@ -1,7 +1,14 @@
 module CategoricalDistributions
 
 import Distributions
-import ScientificTypesBase: Finite, Multiclass, OrderedFactor
+import ScientificTypes:
+    Finite,
+    Multiclass,
+    OrderedFactor,
+    DefaultConvention,
+    Density,
+    ScientificTypesBase
+
 using OrderedCollections
 using CategoricalArrays
 import Missings
@@ -15,6 +22,7 @@ import Distributions: pdf, logpdf, support, mode
 
 include("utilities.jl")
 include("types.jl")
+include("scitypes.jl")
 include("methods.jl")
 include("arrays.jl")
 include("arithmetic.jl")

src/scitypes.jl

@@ -0,0 +1,11 @@
+const STB = ScientificTypesBase
+
+STB.scitype(::UnivariateFinite{S}, ::DefaultConvention) where S = Density{S}
+STB.scitype(
+    ::UnivariateFiniteArray{S,V,R,P,N},
+    ::DefaultConvention
+) where {S,V,R,P,N} = AbstractArray{Density{S},N}
+
+# For performance for ordinary arrays of `UnivariateFinite` elements:
+STB.Scitype(::Type{<:UnivariateFinite{S}}, ::DefaultConvention) where S =
+    Density{S}

test/arrays.jl

@@ -8,7 +8,7 @@ import Distributions
 using StableRNGs
 import Random
 using Missings
-using ScientificTypesBase
+using ScientificTypes
 
 import CategoricalDistributions: classes
 import CategoricalArrays.unwrap

test/methods.jl

@@ -7,7 +7,7 @@ import Distributions
 using StableRNGs
 import Random
 rng = StableRNG(123)
-using ScientificTypesBase
+using ScientificTypes
 
 import CategoricalDistributions: classes
 

test/runtests.jl

@@ -3,6 +3,7 @@ using CategoricalDistributions
 using CategoricalArrays
 using Distributions
 using Random
+using ScientificTypes
 using StableRNGs # for RNGs stable across all julia versions
 rng = StableRNGs.StableRNG(123)
 
@@ -16,6 +17,10 @@ end
      @test include("types.jl")
 end
 
+@testset "scitypes" begin
+     @test include("scitypes.jl")
+end
+
 @testset "methods" begin
      @test include("methods.jl")
 end

test/scitypes.jl

@@ -0,0 +1,17 @@
+@testset "scitype" begin
+    d = UnivariateFinite([0.1, 0.2, 0.7], pool=missing)
+    @test scitype(d)  == Density{Multiclass{3}}
+    d2 = UnivariateFinite([0.1, 0.2, 0.7], pool=missing, ordered=true)
+    @test scitype(d2)  == Density{OrderedFactor{3}}
+    d = UnivariateFinite(rand(3), pool=missing)
+
+    v = UnivariateFinite(rand(3), augment=true, pool=missing)
+    @test scitype(v) == AbstractVector{Density{Multiclass{2}}}
+    v2 = UnivariateFinite(rand(3), augment=true, pool=missing, ordered=true)
+    @test scitype(v2) == AbstractVector{Density{OrderedFactor{2}}}
+
+    @test scitype([d, d]) == AbstractVector{Density{Multiclass{3}}}
+    @test scitype([d2, d2]) == AbstractVector{Density{OrderedFactor{3}}}
+end
+
+true

test/types.jl

@@ -4,7 +4,7 @@ using Test
 using CategoricalDistributions
 using CategoricalArrays
 using StableRNGs
-using ScientificTypesBase
+using ScientificTypes
 import Random
 
 # coverage of constructor testing is expanded in the other test files