Merge pull request #14 from JuliaAI/dev

For a 0.1.2 release

Author: ablaom

.github/workflows/ci.yml

@@ -17,7 +17,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.0'
+          - '1.3'
           - '1' # automatically expands to the latest stable 1.x release of Julia.
         os:
           - ubuntu-latest

Project.toml

@@ -1,7 +1,7 @@
 name = "CategoricalDistributions"
 uuid = "af321ab8-2d2e-40a6-b165-3d674595d28e"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.1.1"
+version = "0.1.2"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -19,7 +19,7 @@ Missings = "0.4, 1"
 OrderedCollections = "1.1"
 ScientificTypesBase = "2"
 UnicodePlots = "2"
-julia = "1.0"
+julia = "1.3"
 
 [extras]
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"

README.md

@@ -73,7 +73,7 @@ levels(d2)
 julia> pdf(d2, "maybe")
 0.0
 
-julia> pdf(d2, "okay")https://github.com/JuliaAI/CategoricalDistributions.jl#measures-over-finite-labeled-sets
+julia> pdf(d2, "okay")
 ERROR: DomainError with Value okay not in pool. :
 ```
 
@@ -122,10 +122,10 @@ julia> pdf(v, L)
 ## Measures over finite labeled sets
 
 There is, in fact, no enforcement that probabilities in a
-`UnivariateFinite` distribution sum to one, only that they be
-non-negative. Thus `UnivariateFinite` objects can be more properly
-understood as an implementation of arbitrary non-negative measures
-over finite labeled sets.
+`UnivariateFinite` distribution sum to one, only that they be belong
+to a type `T` for which `zero(T)` is defined. In particular
+`UnivariateFinite` objects implement arbitrary non-negative, signed,
+or complex measures over a finite labeled set.
 
 
 ## What does this package provide?

src/CategoricalDistributions.jl

@@ -9,13 +9,15 @@ using Random
 using UnicodePlots
 
 const Dist = Distributions
+const MAX_NUM_LEVELS_TO_SHOW_BARS = 12
 
 import Distributions: pdf, logpdf, support, mode
 
 include("utilities.jl")
 include("types.jl")
 include("methods.jl")
 include("arrays.jl")
+include("arithmetic.jl")
 
 export UnivariateFinite, UnivariateFiniteArray
 

src/arithmetic.jl

@@ -0,0 +1,55 @@
+# ## ARITHMETIC
+
+const ERR_DIFFERENT_SAMPLE_SPACES = ArgumentError(
+    "Adding two `UnivariateFinite` objects whose "*
+    "sample spaces have different labellings is not allowed. ")
+
+import Base: +, *, /, -
+
+pdf_matrix(d::UnivariateFinite, L) = pdf.(d, L)
+pdf_matrix(d::AbstractArray{<:UnivariateFinite}, L) = pdf(d, L)
+
+function +(d1::U, d2::U) where U <: SingletonOrArray
+    L = classes(d1)
+    L == classes(d2) || throw(ERR_DIFFERENT_SAMPLE_SPACES)
+    return UnivariateFinite(L, pdf_matrix(d1, L) + pdf_matrix(d2, L))
+end
+
+function _minus(d, T)
+    S = d.scitype
+    decoder = d.decoder
+    prob_given_ref = copy(d.prob_given_ref)
+    for ref in keys(prob_given_ref)
+        prob_given_ref[ref] = -prob_given_ref[ref]
+    end
+    return T(S, decoder, prob_given_ref)
+end
+-(d::UnivariateFinite) = _minus(d, UnivariateFinite)
+-(d::UnivariateFiniteArray) = _minus(d, UnivariateFiniteArray)
+
+function -(d1::U, d2::U) where U <: SingletonOrArray
+    L = classes(d1)
+    L == classes(d2) || throw(ERR_DIFFERENT_SAMPLE_SPACES)
+    return UnivariateFinite(L, pdf_matrix(d1, L) - pdf_matrix(d2, L))
+end
+
+# It seems that the restriction `x::Number` below (applying only to the
+# array case) is unavoidable because of a method ambiguity with
+# `Base.*(::AbstractArray, ::Number)`.
+
+function _times(d, x, T)
+    S = d.scitype
+    decoder = d.decoder
+    prob_given_ref = copy(d.prob_given_ref)
+    for ref in keys(prob_given_ref)
+        prob_given_ref[ref] *= x
+    end
+    return T(d.scitype, decoder, prob_given_ref)
+end
+*(d::UnivariateFinite, x) = _times(d, x, UnivariateFinite)
+*(d::UnivariateFiniteArray, x::Number) = _times(d, x, UnivariateFiniteArray)
+
+*(x, d::UnivariateFinite) = d*x
+*(x::Number, d::UnivariateFiniteArray) = d*x
+/(d::UnivariateFinite, x) = d*inv(x)
+/(d::UnivariateFiniteArray, x::Number) = d*inv(x)

src/arrays.jl

@@ -254,6 +254,7 @@ function Base.Broadcast.broadcasted(::typeof(mode),
     return reshape(mode_flat, size(u))
 end
 
+
 ## EXTENSION OF CLASSES TO ARRAYS OF UNIVARIATE FINITE
 
 # We already have `classes(::UnivariateFininiteArray)
@@ -266,3 +267,5 @@ function classes(yhat::AbstractArray{<:Union{Missing,UnivariateFinite}})
     i === nothing && throw(ERR_EMPTY_UNIVARIATE_FINITE)
     return classes(yhat[i])
 end
+
+

src/methods.jl

@@ -79,11 +79,22 @@ function Base.show(stream::IO, d::UnivariateFinite)
     print(stream, "UnivariateFinite{$(d.scitype)}($arg_str)")
 end
 
+Base.show(io::IO, mime::MIME"text/plain",
+          d::UnivariateFinite) = show(io, d)
+
+# in common case of `Real` probabilities we can do a pretty bar plot:
 function Base.show(io::IO, mime::MIME"text/plain",
-                   d::UnivariateFinite{S}) where S
+                   d::UnivariateFinite{<:Finite{K},V,R,P}) where {K,V,R,P<:Real}
+    show_bars = false
+    if K <= MAX_NUM_LEVELS_TO_SHOW_BARS &&
+        all(>=(0), values(d.prob_given_ref))
+        show_bars = true
+    end
+    show_bars || return show(io, d)
     s = support(d)
     x = string.(CategoricalArrays.DataAPI.unwrap.(s))
     y = pdf.(d, s)
+    S = d.scitype
     plt = barplot(x, y, title="UnivariateFinite{$S}")
     show(io, mime, plt)
 end
@@ -125,58 +136,6 @@ end
 
 # TODO: It would be useful to define == as well.
 
-# TODO: Now that `UnivariateFinite` is any finite measure, we can
-# replace the following nonsense with an overloading of `+`. I think
-# it is only used in MLJEnsembles.jl - but need to check. I believe
-# this is a private method we can easily remove
-
-function average(dvec::AbstractVector{UnivariateFinite{S,V,R,P}};
-                 weights=nothing) where {S,V,R,P}
-
-    n = length(dvec)
-
-    Dist.@check_args(UnivariateFinite, weights == nothing || n==length(weights))
-
-    # check all distributions have consistent pool:
-    first_index = first(dvec).decoder.classes
-    for d in dvec
-        d.decoder.classes == first_index ||
-            error("Averaging UnivariateFinite distributions with incompatible"*
-                  " pools. ")
-    end
-
-    # get all refs:
-    refs = reduce(union, [keys(d.prob_given_ref) for d in dvec]) |> collect
-
-    # initialize the prob dictionary for the distribution sum:
-    prob_given_ref = LittleDict{R,P}([refs...], zeros(P, length(refs)))
-
-    # make vector of all the distributions dicts padded to have same common keys:
-    prob_given_ref_vec = map(dvec) do d
-        merge(prob_given_ref, d.prob_given_ref)
-    end
-
-    # sum up:
-    if weights == nothing
-        scale = 1/n
-        for x in refs
-            for k in 1:n
-                prob_given_ref[x] += scale*prob_given_ref_vec[k][x]
-            end
-        end
-    else
-        scale = 1/sum(weights)
-        for x in refs
-            for k in 1:n
-                prob_given_ref[x] +=
-                    weights[k]*prob_given_ref_vec[k][x]*scale
-            end
-        end
-    end
-    d1 = first(dvec)
-    return UnivariateFinite(sample_scitype(d1), d1.decoder, prob_given_ref)
-end
-
 """
     Dist.pdf(d::UnivariateFinite, x)
 
@@ -371,3 +330,9 @@ function Dist.fit(d::Type{<:UnivariateFinite},
 end
 
 
+# # BROADCASTING OVER SINGLE UNIVARIATE FINITE
+
+# This mirrors behaviour assigned Distributions.Distribution objects,
+# which allows `pdf.(d::UnivariateFinite, support(d))` to work.
+
+Broadcast.broadcastable(d::UnivariateFinite) = Ref(d)