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Weighted mean with function #886

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Weighted mean with function #886

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d6a36af
Weighted mean with function
itsdebartha Aug 1, 2023
2acfbfa
Added more tests for coverage
itsdebartha Aug 1, 2023
b8be0a5
Minor modifications
itsdebartha Aug 3, 2023
7653f2c
Corrections
itsdebartha Aug 23, 2023
448e6ee
Try to use Broadcast
itsdebartha Sep 3, 2023
6a6997a
Fixings and finalising
itsdebartha Oct 6, 2023
f9984f8
Changes as requested by @devmotion
itsdebartha Dec 3, 2023
c1768a6
Remove internal method `_funcweightedmean`
itsdebartha Dec 17, 2023
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25 changes: 25 additions & 0 deletions src/weights.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -682,6 +682,31 @@ function mean(A::AbstractArray, w::UnitWeights; dims::Union{Colon,Int}=:)
return mean(A, dims=dims)
end

"""
mean(f, A::AbstractArray, w::AbstractWeights[, dims::Int])
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Suggested change
mean(f, A::AbstractArray, w::AbstractWeights[, dims::Int])
mean(f, A::AbstractArray, w::AbstractWeights[; dims])

dims shouldn't be required to be an integer.


Compute the weighted mean of array `A`, after transforming it'S
contents with the function `f`, with weight vector `w` (of type
`AbstractWeights`). If `dim` is provided, compute the
weighted mean along dimension `dims`.

# Examples
```julia
n = 20
x = rand(n)
w = rand(n)
mean(√, x, weights(w))
```
"""
mean(f, A::AbstractArray, w::AbstractWeights; dims::Union{Colon,Int}=:) =
_mean(f.(A), w, dims)
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Avoid memory allocation by using mean(f, A) instead of mean(f.(A)). Remember that f.(A) creates an extra array, which is slow. Memory access is usually the biggest bottleneck on modern CPUs. mean(f.(A)) is 2 separate operations: The first one creates a new array, f.(A), and the second calculates its mean. mean(f, A) calculates the mean of (f(x) for x in A) directly, as one operation, without creating a new array.

Suggested change
_mean(f.(A), w, dims)
_mean(f, A; dims)

I'd also suggest making it slightly more generic, as

Suggested change
_mean(f.(A), w, dims)
_mean(f, A; kwargs...)

(See below for more details.)


function mean(f, A::AbstractArray, w::UnitWeights; dims::Union{Colon,Int}=:)
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This is a slightly more generic version of the same thing, which is less likely to require maintenance in the future (if we add additional keyword arguments to mean). I suggest using this pattern when you can.

Suggested change
function mean(f, A::AbstractArray, w::UnitWeights; dims::Union{Colon,Int}=:)
function mean(f, A::AbstractArray, w::UnitWeights; kwargs...)

a = (dims === :) ? length(A) : size(A, dims)
a != length(w) && throw(DimensionMismatch("Inconsistent array dimension."))
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return mean(f.(A), dims=dims)
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Avoid memory allocation by using mean(f, A) instead of mean(f.(A)). Remember that f.(A) creates an extra array, which is slow. Memory access is usually the biggest bottleneck on modern CPUs. mean(f.(A)) is 2 separate operations: The first one creates a new array, f.(A), and the second calculates its mean. mean(f, A) calculates the mean of (f(x) for x in A) directly, as one operation, without creating a new array.

Suggested change
return mean(f.(A), dims=dims)
return mean(f, A; dims)

I'd also suggest making it slightly more generic, as

Suggested change
return mean(f.(A), dims=dims)
return mean(f, A; kwargs...)

end

##### Weighted quantile #####

"""
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21 changes: 21 additions & 0 deletions test/weights.jl
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Expand Up @@ -270,6 +270,27 @@ end
@test mean(a, f(wt), dims=3) ≈ sum(a.*reshape(wt, 1, 1, length(wt)), dims=3)/sum(wt)
@test_throws ErrorException mean(a, f(wt), dims=4)
end

@test mean(√, [1:3;], f([1.0, 1.0, 0.5])) ≈ 1.3120956
@test mean(√, 1:3, f([1.0, 1.0, 0.5])) ≈ 1.3120956
@test mean(√, [1 + 2im, 4 + 5im], f([1.0, 0.5])) ≈ 1.60824421 + 0.88948688im
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for wt in ([1.0, 1.0, 1.0], [1.0, 0.2, 0.0], [0.2, 0.0, 1.0])
@test mean(√, a, f(wt), dims=1) ≈ sum(sqrt.(a).*reshape(wt, length(wt), 1, 1), dims=1)/sum(wt)
@test mean(√, a, f(wt), dims=2) ≈ sum(sqrt.(a).*reshape(wt, 1, length(wt), 1), dims=2)/sum(wt)
@test mean(√, a, f(wt), dims=3) ≈ sum(sqrt.(a).*reshape(wt, 1, 1, length(wt)), dims=3)/sum(wt)
@test_throws ErrorException mean(√, a, f(wt), dims=4)
end
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b = reshape(1.0:9.0, 3, 3)
w = UnitWeights{Float64}(3)
@test mean(√, b, w; dims=1) ≈ reshape(w, :, 3) * sqrt.(b) / sum(w)
@test mean(√, b, w; dims=2) ≈ sqrt.(b) * w / sum(w)

c = 1.0:9.0
w = UnitWeights{Float64}(9)
@test mean(√, c, w) ≈ sum(sqrt.(c)) / length(c)
@test_throws DimensionMismatch mean(√, c, UnitWeights{Float64}(6))
end

@testset "Quantile fweights" begin
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