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Rename from VecchiaMLE.jl to NonparametricVecchia.jl (#135)
1 parent dab461b commit 5155a01

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+107
-102
lines changed

‎.github/workflows/Aqua.yml‎

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@@ -15,4 +15,4 @@ jobs:
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version: 'lts'
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- name: Aqua.jl
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run: julia --color=yes -e 'using Pkg; Pkg.add("Aqua"); Pkg.develop(path=".");
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using Aqua, VecchiaMLE; Aqua.test_all(VecchiaMLE)'
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using Aqua, NonparametricVecchia; Aqua.test_all(NonparametricVecchia)'

‎.github/workflows/moonshot.yml‎

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@@ -36,12 +36,12 @@ jobs:
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Pkg.activate(".")
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path_HSL_jll = "/scratch/github-actions/actions_runner_hsl/HSL_jll.jl.v" * ENV["HSL_VERSION"]
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Pkg.develop(path=path_HSL_jll)
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- name: Test VecchiaMLE.jl
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- name: Test NonparametricVecchia.jl
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shell: julia --color=yes {0}
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run: |
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using Pkg
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Pkg.develop(path=".")
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Pkg.test("VecchiaMLE")
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Pkg.test("NonparametricVecchia")
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- uses: julia-actions/julia-processcoverage@v1
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- uses: codecov/codecov-action@v5
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with:

‎Project.toml‎

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@@ -1,4 +1,4 @@
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name = "VecchiaMLE"
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name = "NonparametricVecchia"
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uuid = "499233a1-c46f-47cc-ab67-5a37788e9870"
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version = "0.1.0"
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authors = ["Caleb Derrickson (@CalebDerrickson), Alexis Montoison (@amontoison)", "Chris Geoga (@cgeoga)"]
@@ -15,8 +15,8 @@ StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
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Vecchia = "8d73829f-f4b0-474a-9580-cecc8e084068"
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[extensions]
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VecchiaMLECUDAExt = ["CUDA", "KernelAbstractions"]
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VecchiaMLEVecchiaExt = ["StaticArrays", "Vecchia"]
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NonparametricVecchiaCUDAExt = ["CUDA", "KernelAbstractions"]
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NonparametricVecchiaVecchiaExt = ["StaticArrays", "Vecchia"]
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[compat]
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CUDA = "5.8.2"

‎README.md‎

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@@ -1,24 +1,28 @@
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# VecchiaMLE
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# NonparametricVecchia
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[![docs-dev][docs-dev-img]][docs-dev-url] [![ci][ci-img]][ci-url]
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[docs-dev-img]: https://img.shields.io/badge/docs-dev-purple.svg
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[docs-dev-url]: https://exanauts.github.io/VecchiaMLE.jl/dev
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[ci-img]: https://github.com/exanauts/VecchiaMLE.jl/actions/workflows/CI.yml/badge.svg
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[ci-url]: https://github.com/exanauts/VecchiaMLE.jl/actions/workflows/CI.yml
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[docs-dev-url]: https://exanauts.github.io/NonparametricVecchia.jl/dev
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[ci-img]: https://github.com/exanauts/NonparametricVecchia.jl/actions/workflows/CI.yml/badge.svg
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[ci-url]: https://github.com/exanauts/NonparametricVecchia.jl/actions/workflows/CI.yml
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## Overview
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**VecchiaMLE.jl** is a Julia package that approximates the inverse cholesky factor of a Gaussian process covariance matrix via a nonparametric optimization process.
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The nonzero entries of the inverse cholesky, `L`, are determined via the Vecchia Approximation.
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The values of `L` are recovered via optimizing the joint probability distribution function (mean zero) to best match the given samples.
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**NonparametricVecchia.jl** is a Julia package that approximates the inverse
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cholesky factor of a Gaussian process covariance matrix via a nonparametric
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optimization process. The nonzero entries of the inverse cholesky, `L`, are
15+
determined via the Vecchia Approximation. The values of `L` are recovered via
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optimizing the joint probability distribution function (mean zero) to best match
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the given samples.
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## Installation
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This package is not registered but can be installed and tested through the Julia package manager:
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This package is not registered but can be installed and tested through the Julia
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package manager:
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```julia
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julia> ]
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pkg> add https://github.com/exanauts/VecchiaMLE.jl.git
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pkg> test VecchiaMLE
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pkg> add https://github.com/exanauts/NonparametricVecchia.jl.git
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pkg> test NonparametricVecchia
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```

‎docs/Project.toml‎

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@@ -3,7 +3,7 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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MadNLP = "2621e9c9-9eb4-46b1-8089-e8c72242dfb6"
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NLPModelsIpopt = "f4238b75-b362-5c4c-b852-0801c9a21d71"
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StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
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VecchiaMLE = "499233a1-c46f-47cc-ab67-5a37788e9870"
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NonparametricVecchia = "499233a1-c46f-47cc-ab67-5a37788e9870"
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Vecchia = "8d73829f-f4b0-474a-9580-cecc8e084068"
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[compat]

‎docs/make.jl‎

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using Documenter, VecchiaMLE
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using Documenter, NonparametricVecchia
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makedocs(
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modules = [VecchiaMLE],
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modules = [NonparametricVecchia],
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doctest = true,
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linkcheck = true,
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format = Documenter.HTML(ansicolor = true,
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prettyurls = get(ENV, "CI", nothing) == "true",
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collapselevel = 1),
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sitename = "VecchiaMLE.jl",
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sitename = "NonparametricVecchia.jl",
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pages = ["Home" => "index.md"], # "Tutorials" => "vecchia_model.md"
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)
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deploydocs(
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repo = "github.com/exanauts/VecchiaMLE.jl.git",
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repo = "github.com/exanauts/NonparametricVecchia.jl.git",
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push_preview = true,
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devbranch = "master",
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devbranch = "main",
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)

‎docs/src/index.md‎

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# [VecchiaMLE.jl documentation](@id Home)
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# [NonparametricVecchia.jl documentation](@id Home)
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## Overview
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@@ -9,6 +9,6 @@ The Cholesky factor is computed via the Vecchia approximation, which is sparse a
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```julia
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julia> ]
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pkg> add https://github.com/exanauts/VecchiaMLE.jl.git
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pkg> test VecchiaMLE
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pkg> add https://github.com/exanauts/NonparametricVecchia.jl.git
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pkg> test NonparametricVecchia
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```

‎docs/src/vecchia_model.md‎

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```@example VecchiaModel_L
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using VecchiaMLE
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using NonparametricVecchia
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using LinearAlgebra
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using SparseArrays
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using NLPModelsIpopt
@@ -18,7 +18,7 @@ L = recover_factor(nlp_L, output.solution)
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```
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```@example VecchiaModel_U
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using VecchiaMLE
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using NonparametricVecchia
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using LinearAlgebra
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using SparseArrays
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using NLPModelsIpopt
@@ -37,7 +37,7 @@ U = recover_factor(nlp_U, output.solution)
3737
```
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3939
```@example Vecchia
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using VecchiaMLE
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using NonparametricVecchia
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using Vecchia
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using StaticArrays
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using LinearAlgebra

ext/VecchiaMLECUDAExt.jl renamed to ext/NonparametricVecchiaCUDAExt.jl

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@@ -1,17 +1,18 @@
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module VecchiaMLECUDAExt
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module NonparametricVecchiaCUDAExt
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using LinearAlgebra
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using NLPModels
5-
using VecchiaMLE
5+
using NonparametricVecchia
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using CUDA
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using CUDA.CUSPARSE
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using KernelAbstractions
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10-
function VecchiaMLE.VecchiaModel(I::Vector{Int}, J::Vector{Int}, samples::CuMatrix{T};
11-
lvar_diag::Union{Nothing,CuVector{T}}=nothing, uvar_diag::Union{Nothing,CuVector{T}}=nothing,
12-
lambda::Real=0, format::Symbol=:coo, uplo::Symbol=:L) where T
10+
function NonparametricVecchia.VecchiaModel(I::Vector{Int}, J::Vector{Int}, samples::CuMatrix{T};
11+
lvar_diag::Union{Nothing,CuVector{T}}=nothing,
12+
uvar_diag::Union{Nothing,CuVector{T}}=nothing,
13+
lambda::Real=0, format::Symbol=:coo, uplo::Symbol=:L) where T
1314
S = CuArray{T, 1, CUDA.DeviceMemory}
14-
cache = VecchiaMLE.create_vecchia_cache(I, J, samples, T(lambda), format, uplo)
15+
cache = NonparametricVecchia.create_vecchia_cache(I, J, samples, T(lambda), format, uplo)
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1617
nvar = length(cache.rowsL) + length(cache.colptrL) - 1
1718
ncon = length(cache.colptrL) - 1
@@ -60,8 +61,8 @@ function VecchiaMLE.VecchiaModel(I::Vector{Int}, J::Vector{Int}, samples::CuMatr
6061
end
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63-
function VecchiaMLE.create_vecchia_cache(I::Vector{Int}, J::Vector{Int}, samples::CuMatrix{T},
64-
lambda::T, format::Symbol, uplo::Symbol) where {T}
64+
function NonparametricVecchia.create_vecchia_cache(I::Vector{Int}, J::Vector{Int}, samples::CuMatrix{T},
65+
lambda::T, format::Symbol, uplo::Symbol) where {T}
6566
S = CuArray{T, 1, CUDA.DeviceMemory}
6667
Msamples, n = size(samples)
6768

@@ -95,7 +96,7 @@ function VecchiaMLE.create_vecchia_cache(I::Vector{Int}, J::Vector{Int}, samples
9596
m = CuVector{Int}(m)
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9798
hess_obj_vals = S(undef, nnzh_tri_obj)
98-
VecchiaMLE.vecchia_build_B!(B, samples, lambda, rowsL, colptrL, hess_obj_vals, n, m)
99+
NonparametricVecchia.vecchia_build_B!(B, samples, lambda, rowsL, colptrL, hess_obj_vals, n, m)
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100101
if uplo == :L
101102
diagL = colptrL[1:n]
@@ -107,7 +108,7 @@ function VecchiaMLE.create_vecchia_cache(I::Vector{Int}, J::Vector{Int}, samples
107108
end
108109
buffer = S(undef, nnzL)
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110-
return VecchiaMLE.VecchiaCache{eltype(S), S, typeof(rowsL), typeof(B[1])}(
111+
return NonparametricVecchia.VecchiaCache{eltype(S), S, typeof(rowsL), typeof(B[1])}(
111112
n, Msamples, nnzL,
112113
colptrL, rowsL, diagL,
113114
m, offsets, B, nnzh_tri_obj,
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116117
)
117118
end
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119-
function VecchiaMLE.recover_factor(nlp::VecchiaModel{T,<:CuVector{T}}, solution::CuVector{T}) where T
120+
function NonparametricVecchia.recover_factor(nlp::VecchiaModel{T,<:CuVector{T}}, solution::CuVector{T}) where T
120121
n = nlp.cache.n
121122
colptr = nlp.cache.colptrL
122123
rowval = nlp.cache.rowsL
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126127
return factor
127128
end
128129

129-
function VecchiaMLE.vecchia_mul!(y::CuVector{T}, B::Vector{<:CuMatrix{T}}, hess_obj_vals::CuVector{T},
130-
x::CuVector{T}, n::Int, m::CuVector{Int}, offsets::CuVector{Int}) where T <: AbstractFloat
130+
function NonparametricVecchia.vecchia_mul!(y::CuVector{T}, B::Vector{<:CuMatrix{T}}, hess_obj_vals::CuVector{T},
131+
x::CuVector{T}, n::Int, m::CuVector{Int}, offsets::CuVector{Int}) where T <: AbstractFloat
131132
# Reset the vector y
132133
fill!(y, zero(T))
133134

@@ -139,9 +140,9 @@ function VecchiaMLE.vecchia_mul!(y::CuVector{T}, B::Vector{<:CuMatrix{T}}, hess_
139140
return y
140141
end
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142-
function VecchiaMLE.vecchia_build_B!(B::Vector{<:CuMatrix{T}}, samples::CuMatrix{T}, lambda::T,
143-
rowsL::CuVector{Int}, colptrL::CuVector{Int}, hess_obj_vals::CuVector{T},
144-
n::Int, m::CuVector{Int}) where T <: AbstractFloat
143+
function NonparametricVecchia.vecchia_build_B!(B::Vector{<:CuMatrix{T}}, samples::CuMatrix{T}, lambda::T,
144+
rowsL::CuVector{Int}, colptrL::CuVector{Int}, hess_obj_vals::CuVector{T},
145+
n::Int, m::CuVector{Int}) where T <: AbstractFloat
145146
# Launch the kernel
146147
backend = KernelAbstractions.get_backend(samples)
147148
r = size(samples, 1)
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151152
return nothing
152153
end
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154-
function VecchiaMLE.vecchia_generate_hess_tri_structure!(nnzh::Int, n::Int, colptr_diff::CuVector{Int},
155-
hrows::CuVector{Int}, hcols::CuVector{Int})
155+
function NonparametricVecchia.vecchia_generate_hess_tri_structure!(nnzh::Int, n::Int, colptr_diff::CuVector{Int},
156+
hrows::CuVector{Int}, hcols::CuVector{Int})
156157
# reset hrows, hcols
157158
fill!(hrows, one(Int))
158159
fill!(hcols, one(Int))

‎ext/NonparametricVecchiaVecchiaExt.jl‎

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module NonparametricVecchiaVecchiaExt
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3+
using NonparametricVecchia, Vecchia, StaticArrays
4+
5+
function NonparametricVecchia.VecchiaModel(pts::Vector{SVector{D,Float64}},
6+
data::Matrix{Float64},
7+
ordering, conditioning;
8+
lvar_diag=fill(1e-10, length(pts)),
9+
uvar_diag=fill(1e10, length(pts)),
10+
lambda=0.0) where{D}
11+
va = VecchiaApproximation(pts, nothing, data; ordering=ordering,
12+
conditioning=conditioning)
13+
VecchiaModel(va; lvar_diag=lvar_diag, uvar_diag=uvar_diag, lambda=lambda)
14+
end
15+
16+
function NonparametricVecchia.VecchiaModel(va::VecchiaApproximation;
17+
lvar_diag=fill(1e-10, length(pts)),
18+
uvar_diag=fill(1e10, length(pts)),
19+
lambda=0.0)
20+
(condsets, perm) = (va.condix, va.perm)
21+
n = length(condsets)
22+
nnz = sum(length, condsets) + length(condsets)
23+
IJ = Vector{Tuple{Int64, Int64}}()
24+
sizehint!(IJ, nnz)
25+
for (j, cj) in enumerate(condsets)
26+
foreach(k->push!(IJ, (k, j)), cj)
27+
push!(IJ, (j,j))
28+
end
29+
(I, J) = (getindex.(IJ, 1), getindex.(IJ, 2))
30+
(perm, VecchiaModel(I, J, permutedims(va.data);
31+
uplo=:U, lvar_diag=lvar_diag,
32+
uvar_diag=uvar_diag, lambda=lambda))
33+
end
34+
35+
end

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