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Add support for MultivariateStats #204

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Add support for MultivariateStats #204

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b4ce53e
semver: up 0.0.9 for multivariate stats
tpoisot Oct 9, 2023
5a554b9
feat: Whitening (untested)
tpoisot Oct 9, 2023
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4 changes: 3 additions & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
name = "SpeciesDistributionToolkit"
uuid = "72b53823-5c0b-4575-ad0e-8e97227ad13b"
authors = ["Timothée Poisot <timothee.poisot@umontreal.ca>"]
version = "0.0.8"
version = "0.0.9"

[deps]
ArchGDAL = "c9ce4bd3-c3d5-55b8-8973-c0e20141b8c3"
Expand All @@ -10,11 +10,13 @@ Fauxcurrences = "a2d61402-033a-4ca9-aef4-652d70cf7c9c"
GBIF = "ee291a33-5a6c-5552-a3c8-0f29a1181037"
GDAL = "add2ef01-049f-52c4-9ee2-e494f65e021a"
MakieCore = "20f20a25-4f0e-4fdf-b5d1-57303727442b"
MultivariateStats = "6f286f6a-111f-5878-ab1e-185364afe411"
OffsetArrays = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
Phylopic = "c889285c-44aa-4473-b1e1-56f5d4e3ccf5"
Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
SimpleSDMDatasets = "2c7d61d0-5c73-410d-85b2-d2e7fbbdcefa"
SimpleSDMLayers = "2c645270-77db-11e9-22c3-0f302a89c64c"
StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"

[compat]
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6 changes: 6 additions & 0 deletions src/SpeciesDistributionToolkit.jl
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Expand Up @@ -12,6 +12,9 @@ using MakieCore
import StatsBase
import OffsetArrays

import MultivariateStats
import StatsAPI

# We make ample use of re-export
using Reexport

Expand All @@ -34,6 +37,9 @@ include("integrations/gbif_phylopic.jl")
# Plotting
include("integrations/makie.jl")

# MultivariateStats
include("integrations/multivariatestats.jl")

# Functions for IO
include("io/geotiff.jl")
include("io/ascii.jl")
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17 changes: 17 additions & 0 deletions src/integrations/multivariatestats.jl
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Original file line number Diff line number Diff line change
@@ -0,0 +1,17 @@
function _partial_array_from_layers(layers::Vector{T}; samples::Int=10_000) where {T <: SimpleSDMLayers.SimpleSDMLayer}
all_keys = unique(vcat(keys.(layers)...))
samples = max(length(all_keys), samples)
selected_keys = StatsBase.sample(all_keys, samples; replace=false)
X = Vector(Float64, (length(selected_keys), length(layers)))
for i in axes(layers, 1)
for j in axes(selected_keys, 1)
X[j,i] = layers[i][selected_keys[j]]
end
end
return X
end

function StatsAPI.fit(MultivariateStats.Whitening, X::Vector{T}; samples::Int=10_000, kwargs...) where {T <: SimpleSDMLayers.SimpleSDMLayer}
Y = SpeciesDistributionToolkit._partial_array_from_layers(X; samples=samples)
return StatsAPI.fit(MultivariateStats.Whitening, Y; kwargs...)
end