For a 0.1.2 release

Project.toml

@@ -1,7 +1,7 @@
 name = "StatisticalMeasuresBase"
 uuid = "c062fc1d-0d66-479b-b6ac-8b44719de4cc"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.1.1"
+version = "0.1.2"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"

README.md

@@ -9,6 +9,7 @@ A Julia package for building production-ready measures (metrics) for statistics
 Related:
 [StatisticalMeasures.jl](https://juliaai.github.io/StatisticalMeasures.jl/dev/)
 
+[List](https://github.com/FluxML/FluxML-Community-Call-Minutes/discussions/38) of Julia packages providing metrics.
 
 ## The main idea
 
@@ -47,18 +48,18 @@ julia> multitarget_L1(ŷ, y, weights)
 ```
 
 ```julia
-using Tables
-t = y' |> Tables.table |> Tables.rowtable
-t̂ = ŷ' |> Tables.table |> Tables.rowtable
+using DataFrames
+df    = DataFrame(y', :auto)
+df̂    = DataFrame(ŷ', :auto)
 
-julia> multitarget_L1(t̂, t, weights)
+julia> multitarget_L1(df̂, df, weights)
 39
 ```
 
-Generate measurements *for each observation* with the `measurement` method:
+Generate measurements *for each observation* with the `measurements` method:
 
 ```julia
-julia> measurements(multitarget_L1, t̂, t, weights)
+julia> measurements(multitarget_L1, df̂, df, weights)
 3-element Vector{Int64}:
   3
   9

docs/Project.toml

@@ -1,4 +1,5 @@
 [deps]
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 ScientificTypesBase = "30f210dd-8aff-4c5f-94ba-8e64358c1161"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

docs/src/index.md

@@ -47,16 +47,16 @@ multitarget_L1(ŷ, y, weights)
 ```
 
 ```@example 01
-using Tables
-t = y' |> Tables.table |> Tables.rowtable
-t̂ = ŷ' |> Tables.table |> Tables.rowtable
-multitarget_L1(t̂, t, weights)
+using DataFrames
+df    = DataFrame(y', :auto)
+df̂    = DataFrame(ŷ', :auto)
+multitarget_L1(df̂, df, weights)
 ```
 
-Generate measurements *for each observation* with the `measurement` method:
+Generate measurements *for each observation* with the `measurements` method:
 
 ```@example 01
-measurements(multitarget_L1, t̂, t, weights)
+measurements(multitarget_L1, df̂, df, weights)
 ```
 
 # Overview
@@ -69,7 +69,7 @@ measures. For a package that does, based on this interface, see
 can also be applied to measures provided by other packages, such as
 [LossFunctions.jl](https://github.com/JuliaML/LossFunctions.jl).
 
-Specically, this package provides:
+Specifically, this package provides:
 
 - A measure wrapper [`multimeasure`](@ref) that leverages MLUtils.jl to broadcast a simple
   measure over multiple observations; the main use case is for extending a measure (e.g.,

docs/src/tutorial.md

@@ -115,10 +115,10 @@ instead of an aggregate.
 Mutli-targets (as tables):
 
 ```@example 42
-using Tables
-t    = y' |> Tables.table |> Tables.rowtable
-t̂    = ŷ' |> Tables.table |> Tables.rowtable
-@assert MultitargetHuberLoss()(t̂, t, weights) ≈ MultitargetHuberLoss()(ŷ, y, weights)
+using DataFrames
+df    = DataFrame(y', :auto)
+df̂    = DataFrame(ŷ', :auto)
+@assert MultitargetHuberLoss()(df̂, df, weights) ≈ MultitargetHuberLoss()(df̂, df, weights)
 ```
 
 Multi-dimensional arrays:

src/fussy_measure.jl

@@ -31,7 +31,7 @@ Return a new measure, `fussy`, with the same behavior as `measure`, except that
   `extra_check==nothing`. Note the first argument here is `measure`, not `atomic_measure`.
 
 Do not use `fussy_measure` unless both `y` and `ŷ` are expected to implement the
-MLUtils.jl `getobs`/`numbos` interface (e.g., are `AbstractArray`s)
+MLUtils.jl `getobs`/`numobs` interface (e.g., are `AbstractArray`s)
 
 See also [`$API.measurements`](@ref), [`$API.is_measure`](@ref)
 

src/multimeasure.jl

@@ -67,7 +67,7 @@ Advanced Examples below.
 using StatisticalMeasuresBase
 
 # define an atomic measure:
-struct L2OnScalars
+struct L2OnScalars end
 (::L2OnScalars)(ŷ, y) = (ŷ - y)^2
 
 julia> $API.external_aggregation_mode(L2OnScalars())

src/traits.jl

@@ -131,8 +131,8 @@ can_report_unaggregated(measure) = false
 Return the kind of proxy `ŷ` for target predictions expected in calls of the form
 `measure(ŷ, y, args...; kwargs...)`.
 
-Typical return values are `LearnAPI.LiteralTarget()`, when `ŷ` is expected to have the
-same form as `ŷ`, or `LearnAPI.Distribution()`, when the observations in `ŷ` are expected
+Typical return values are `LearnAPI.Point()`, when `ŷ` is expected to have the
+same form as `y`, or `LearnAPI.Distribution()`, when the observations in `ŷ` are expected
 to represent probability density/mass functions. For other kinds of proxy, see the
 [LearnAPI.jl](https://juliaai.github.io/LearnAPI.jl/dev/) documentation.
 
@@ -176,7 +176,7 @@ value has no meaning.
 
 # New implementations
 
-Optional but strongly recommended for measure than consume multiple observations. The
+Optional but strongly recommended for measures that consume multiple observations. The
 fallback returns `Union{}`.
 
 Examples of return values are `Union{Finite,Missing}`, for `CategoricalValue` observations

test/integration.jl

@@ -84,7 +84,6 @@ const HuberLossType = API.Multimeasure{
 @trait(
     HuberLossType,
     observation_scitype = Union{ST.Continuous,Missing},
-#   kind_of_proxy = LearnAPI.LiteralTarget(),
     human_name = "Huber loss",
 )
 
@@ -93,7 +92,6 @@ const MultitargetHuberLossType = API.FussyMeasure{<:API.Multimeasure{<:HuberLoss
     MultitargetHuberLossType,
     observation_scitype = AbstractArray{<:Union{Continuous,Missing}},
     can_consume_tables = true,
-#   kind_of_proxy = LearnAPI.LiteralTarget(),
     human_name = "multi-target Huber loss",
 )
 

test/multimeasure.jl

@@ -4,7 +4,6 @@ struct MAEOnScalars end
 
 @trait(
     MAEOnScalars,
-    kind_of_proxy = LiteralTarget(),
     orientation = Loss(),
 )