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Improve quantile performance v2 #91

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Improve quantile performance v2 #91

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b6ceb83
Improve quantile performance v2
bkamins Oct 17, 2021
7943e97
Update src/Statistics.jl
bkamins Oct 17, 2021
af561f2
Update src/Statistics.jl
bkamins Oct 17, 2021
6e233de
Update src/Statistics.jl
bkamins Oct 17, 2021
d84deda
Update runtests.jl
bkamins Oct 17, 2021
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Update runtests.jl
bkamins Oct 17, 2021
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27 changes: 13 additions & 14 deletions src/Statistics.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -937,8 +937,7 @@ function quantile!(q::AbstractArray, v::AbstractVector, p::AbstractArray;
end
isempty(q) && return q

minp, maxp = extrema(p)
_quantilesort!(v, sorted, minp, maxp)
_quantilesort!(v, sorted, p)

for (i, j) in zip(eachindex(p), eachindex(q))
@inbounds q[j] = _quantile(v,p[i], alpha=alpha, beta=beta)
Expand All @@ -949,35 +948,35 @@ end
function quantile!(v::AbstractVector, p::Union{AbstractArray, Tuple{Vararg{Real}}};
sorted::Bool=false, alpha::Real=1., beta::Real=alpha)
if !isempty(p)
minp, maxp = extrema(p)
_quantilesort!(v, sorted, minp, maxp)
_quantilesort!(v, sorted, p isa Tuple : collect(p) ? p)
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end
return map(x->_quantile(v, x, alpha=alpha, beta=beta), p)
end

quantile!(v::AbstractVector, p::Real; sorted::Bool=false, alpha::Real=1., beta::Real=alpha) =
_quantile(_quantilesort!(v, sorted, p, p), p, alpha=alpha, beta=beta)
_quantile(_quantilesort!(v, sorted, [p]), p, alpha=alpha, beta=beta)

# Function to perform partial sort of v for quantiles in given range
function _quantilesort!(v::AbstractArray, sorted::Bool, minp::Real, maxp::Real)
function _quantilesort!(v::AbstractArray, sorted::Bool, p::AbstractVector{<:Real})
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isempty(v) && throw(ArgumentError("empty data vector"))
require_one_based_indexing(v)

if !sorted
lv = length(v)
lo = floor(Int,minp*(lv))
hi = ceil(Int,1+maxp*(lv))

# only need to perform partial sort
sort!(v, 1, lv, Base.Sort.PartialQuickSort(lo:hi), Base.Sort.Forward)
start = 1
for pv in sort(p)
lv = length(v)
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Move this out of the loop? BTW, better use lastindex even if we call require_one_based_indexing(v).

lo = floor(Int,pv*(lv))
hi = ceil(Int,1+pv*(lv))
sort!(v, start, lv, Base.Sort.PartialQuickSort(lo:hi), Base.Sort.Forward)
start = hi + 1
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Are you completely sure of the +1? Is that still correct if p contains duplicates? That would be worth testing.

end
end
if (sorted && (ismissing(v[end]) || (v[end] isa Number && isnan(v[end])))) ||
any(x -> ismissing(x) || (x isa Number && isnan(x)), v)
throw(ArgumentError("quantiles are undefined in presence of NaNs or missing values"))
end
return v
end

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# Core quantile lookup function: assumes `v` sorted
@inline function _quantile(v::AbstractVector, p::Real; alpha::Real=1.0, beta::Real=alpha)
0 <= p <= 1 || throw(ArgumentError("input probability out of [0,1] range"))
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