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Merge pull request #120 from JuliaDecisionFocusedLearning/perturbed-overhaul
General overhaul
2 parents 9419111 + cd594ee commit 4d8c3ff

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‎.gitignore

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.DS_Store
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*.jl.*.cov
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*.jl.cov
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*.jl.mem

‎CITATION.bib

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@@ -2,7 +2,7 @@ @misc{InferOpt.jl
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author = {Guillaume Dalle, Léo Baty, Louis Bouvier and Axel Parmentier},
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title = {InferOpt.jl},
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url = {https://github.com/axelparmentier/InferOpt.jl},
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version = {v0.6.1},
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year = {2023},
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month = {9}
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version = {v0.7.0},
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year = {2025},
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month = {4}
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}

‎Project.toml

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name = "InferOpt"
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uuid = "4846b161-c94e-4150-8dac-c7ae193c601f"
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authors = ["Guillaume Dalle", "Léo Baty", "Louis Bouvier", "Axel Parmentier"]
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version = "0.6.1"
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version = "0.7.0"
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[deps]
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ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
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DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d"
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DifferentiableExpectations = "fc55d66b-b2a8-4ccc-9d64-c0c2166ceb36"
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DifferentiableFrankWolfe = "b383313e-5450-4164-a800-befbd27b574d"
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Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
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DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
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FrankWolfe = "f55ce6ea-fdc5-4628-88c5-0087fe54bd30"
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ImplicitDifferentiation = "57b37032-215b-411a-8a7c-41a003a55207"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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RequiredInterfaces = "97f35ef4-7bc5-4ec1-a41a-dcc69c7308c6"
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Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
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StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
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StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
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ThreadsX = "ac1d9e8a-700a-412c-b207-f0111f4b6c0d"
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[weakdeps]
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DifferentiableFrankWolfe = "b383313e-5450-4164-a800-befbd27b574d"
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FrankWolfe = "f55ce6ea-fdc5-4628-88c5-0087fe54bd30"
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ImplicitDifferentiation = "57b37032-215b-411a-8a7c-41a003a55207"
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[extensions]
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InferOptFrankWolfeExt = "DifferentiableFrankWolfe"
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InferOptFrankWolfeExt = ["DifferentiableFrankWolfe", "FrankWolfe", "ImplicitDifferentiation"]
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[compat]
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ChainRulesCore = "1"
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DensityInterface = "0.4.0"
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DifferentiableFrankWolfe = "0.2"
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DifferentiableExpectations = "0.2"
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DifferentiableFrankWolfe = "0.3"
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Distributions = "0.25"
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DocStringExtensions = "0.9"
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FrankWolfe = "0.3"
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ImplicitDifferentiation = "0.6"
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LinearAlgebra = "1"
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Random = "1"
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RequiredInterfaces = "0.1.3"
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Statistics = "1"
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StatsBase = "0.33, 0.34"
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StatsFuns = "1.3"
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ThreadsX = "0.1.11"
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julia = "1.10"
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[extras]
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Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
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GridGraphs = "dd2b58c7-5af7-4f17-9e46-57c68ac813fb"
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HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
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ImplicitDifferentiation = "57b37032-215b-411a-8a7c-41a003a55207"
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JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
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JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
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JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
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Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
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[targets]
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test = ["Aqua", "DifferentiableFrankWolfe", "Distributions", "Documenter", "FiniteDifferences", "Flux", "FrankWolfe", "Graphs", "GridGraphs", "HiGHS", "JET", "JuliaFormatter", "JuMP", "LinearAlgebra", "Literate", "Pkg", "ProgressMeter", "Random", "Revise", "Statistics", "Test", "TestItemRunner", "UnicodePlots", "Zygote"]
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test = ["Aqua", "DifferentiableFrankWolfe", "Distributions", "Documenter", "FiniteDifferences", "Flux", "FrankWolfe", "Graphs", "GridGraphs", "HiGHS", "ImplicitDifferentiation", "JET", "JuliaFormatter", "JuMP", "LinearAlgebra", "Literate", "Pkg", "ProgressMeter", "Random", "Revise", "Statistics", "Test", "TestItemRunner", "UnicodePlots", "Zygote"]

‎docs/make.jl

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@@ -6,11 +6,20 @@ DocMeta.setdocmeta!(InferOpt, :DocTestSetup, :(using InferOpt); recursive=true)
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# Copy README.md into docs/src/index.md (overwriting)
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cp(
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joinpath(dirname(@__DIR__), "README.md"),
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joinpath(@__DIR__, "src", "index.md");
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force=true,
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)
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open(joinpath(@__DIR__, "src", "index.md"), "w") do io
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println(
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io,
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"""
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```@meta
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EditURL = "https://github.com/JuliaDecisionFocusedLearning/InferOpt.jl/blob/main/README.md"
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```
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""",
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)
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# Write the contents out below the meta bloc
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for line in eachline(joinpath(dirname(@__DIR__), "README.md"))
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println(io, line)
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end
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end
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# Parse test/tutorial.jl into docs/src/tutorial.md (overwriting)
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@@ -21,8 +30,14 @@ Literate.markdown(tuto_jl_file, tuto_md_dir; documenter=true, execute=false)
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makedocs(;
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modules=[InferOpt],
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authors="Guillaume Dalle, Léo Baty, Louis Bouvier, Axel Parmentier",
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repo="https://github.com/JuliaDecisionFocusedLearning/InferOpt.jl/blob/{commit}{path}#{line}",
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sitename="InferOpt.jl",
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format=Documenter.HTML(),
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format=Documenter.HTML(;
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prettyurls=get(ENV, "CI", "false") == "true",
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canonical="https://juliadecisionfocusedlearning.github.io/InferOpt.jl",
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assets=String[],
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repolink="https://github.com/JuliaDecisionFocusedLearning/InferOpt.jl",
40+
),
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pages=[
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"Home" => "index.md",
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"Background" => "background.md",

‎ext/InferOptFrankWolfeExt.jl

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module InferOptFrankWolfeExt
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3+
using DifferentiableExpectations:
4+
DifferentiableExpectations, FixedAtomsProbabilityDistribution
35
using DifferentiableFrankWolfe: DifferentiableFrankWolfe, DiffFW
4-
using DifferentiableFrankWolfe: LinearMinimizationOracle # from FrankWolfe
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using DifferentiableFrankWolfe: IterativeLinearSolver # from ImplicitDifferentiation
6-
using InferOpt: InferOpt, RegularizedFrankWolfe, FixedAtomsProbabilityDistribution
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using InferOpt: compute_expectation, compute_probability_distribution
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using FrankWolfe: LinearMinimizationOracle
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using ImplicitDifferentiation: KrylovLinearSolver
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using InferOpt: InferOpt, RegularizedFrankWolfe
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using LinearAlgebra: dot
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"""
12+
RegularizedFrankWolfe(linear_maximizer; Ω, Ω_grad, frank_wolfe_kwargs=(;), implicit_kwargs=(; linear_solver=KrylovLinearSolver(; verbose=false)))
13+
14+
Construct a `RegularizedFrankWolfe` struct with a linear maximizer and the necessary components for the Frank-Wolfe algorithm.
15+
Set `implicit_kwargs` to `(; linear_solver=KrylovLinearSolver(; verbose=true))` if you want to see the solver potential warnings.
16+
"""
17+
function RegularizedFrankWolfe(
18+
linear_maximizer;
19+
Ω,
20+
Ω_grad,
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frank_wolfe_kwargs=NamedTuple(),
22+
implicit_kwargs=(; linear_solver=KrylovLinearSolver(; verbose=false)),
23+
)
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return RegularizedFrankWolfe(
25+
linear_maximizer, Ω, Ω_grad, frank_wolfe_kwargs, implicit_kwargs
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)
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end
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"""
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LinearMaximizationOracleWithKwargs{F,K}
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Wraps a linear maximizer as a `FrankWolfe.LinearMinimizationOracle` with a sign switch and predefined keyword arguments.
@@ -40,14 +59,17 @@ Keyword arguments are passed to the underlying linear maximizer.
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function InferOpt.compute_probability_distribution(
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regularized::RegularizedFrankWolfe, θ::AbstractArray; kwargs...
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)
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(; linear_maximizer, Ω, Ω_grad, frank_wolfe_kwargs) = regularized
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shape = size(θ)
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(; linear_maximizer, Ω, Ω_grad, frank_wolfe_kwargs, implicit_kwargs) = regularized
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f(y, θ) = Ω(y) - dot(θ, y)
4565
f_grad1(y, θ) = Ω_grad(y) - θ
46-
lmo = LinearMaximizationOracleWithKwargs(linear_maximizer, kwargs)
47-
implicit_kwargs = (; linear_solver=IterativeLinearSolver(; accept_inconsistent=true))
66+
maximizer(θ; shape, kwargs...) = vec(linear_maximizer(reshape(θ, shape); kwargs...))
67+
lmo = LinearMaximizationOracleWithKwargs(maximizer, (; shape, kwargs...))
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dfw = DiffFW(f, f_grad1, lmo; implicit_kwargs)
49-
weights, atoms = dfw.implicit(θ; frank_wolfe_kwargs=frank_wolfe_kwargs)
50-
probadist = FixedAtomsProbabilityDistribution(atoms, weights)
69+
weights, atoms = dfw.implicit(vec(θ); frank_wolfe_kwargs=frank_wolfe_kwargs)
70+
probadist = FixedAtomsProbabilityDistribution(
71+
map(atom -> reshape(atom, shape), atoms), weights
72+
)
5173
return probadist
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end
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‎src/InferOpt.jl

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@@ -10,71 +10,80 @@ module InferOpt
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using ChainRulesCore: ChainRulesCore, NoTangent, RuleConfig, Tangent, ZeroTangent
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using ChainRulesCore: rrule, rrule_via_ad, unthunk
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using DensityInterface: logdensityof
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using DifferentiableExpectations:
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DifferentiableExpectations, Reinforce, empirical_predistribution, empirical_distribution
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using Distributions:
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Distributions,
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ContinuousUnivariateDistribution,
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LogNormal,
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Normal,
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product_distribution,
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logpdf
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using DocStringExtensions: TYPEDEF, TYPEDFIELDS, TYPEDSIGNATURES
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using LinearAlgebra: dot
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using Random: AbstractRNG, GLOBAL_RNG, MersenneTwister, rand, seed!
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using Random: Random, AbstractRNG, GLOBAL_RNG, MersenneTwister, rand, seed!
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using Statistics: mean
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using StatsBase: StatsBase, sample
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using StatsFuns: logaddexp, softmax
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using ThreadsX: ThreadsX
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using RequiredInterfaces
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include("interface.jl")
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include("utils/utils.jl")
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include("utils/some_functions.jl")
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include("utils/probability_distribution.jl")
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include("utils/pushforward.jl")
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include("utils/generalized_maximizer.jl")
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include("utils/linear_maximizer.jl")
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include("utils/isotonic_regression/isotonic_l2.jl")
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include("utils/isotonic_regression/isotonic_kl.jl")
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include("utils/isotonic_regression/projection.jl")
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31-
include("simple/interpolation.jl")
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include("simple/identity.jl")
40+
# Layers
41+
include("layers/simple/interpolation.jl")
42+
include("layers/simple/identity.jl")
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34-
include("regularized/abstract_regularized.jl")
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include("regularized/soft_argmax.jl")
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include("regularized/sparse_argmax.jl")
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include("regularized/soft_rank.jl")
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include("regularized/regularized_frank_wolfe.jl")
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include("layers/perturbed/utils.jl")
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include("layers/perturbed/perturbation.jl")
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include("layers/perturbed/perturbed.jl")
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40-
include("perturbed/abstract_perturbed.jl")
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include("perturbed/additive.jl")
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include("perturbed/multiplicative.jl")
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include("perturbed/perturbed_oracle.jl")
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45-
include("imitation/spoplus_loss.jl")
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include("imitation/ssvm_loss.jl")
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include("imitation/fenchel_young_loss.jl")
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include("imitation/imitation_loss.jl")
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include("imitation/zero_one_loss.jl")
48+
include("layers/regularized/abstract_regularized.jl")
49+
include("layers/regularized/soft_argmax.jl")
50+
include("layers/regularized/sparse_argmax.jl")
51+
include("layers/regularized/soft_rank.jl")
52+
include("layers/regularized/regularized_frank_wolfe.jl")
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5154
if !isdefined(Base, :get_extension)
5255
include("../ext/InferOptFrankWolfeExt.jl")
5356
end
5457

58+
# Losses
59+
include("losses/fenchel_young_loss.jl")
60+
include("losses/spoplus_loss.jl")
61+
include("losses/ssvm_loss.jl")
62+
include("losses/zero_one_loss.jl")
63+
include("losses/imitation_loss.jl")
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65+
export compute_probability_distribution
66+
5567
export half_square_norm
5668
export shannon_entropy, negative_shannon_entropy
5769
export one_hot_argmax, ranking
58-
export GeneralizedMaximizer, objective_value
70+
export LinearMaximizer, apply_g, objective_value
5971

60-
export FixedAtomsProbabilityDistribution
61-
export compute_expectation
62-
export compute_probability_distribution
6372
export Pushforward
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6574
export IdentityRelaxation
6675
export Interpolation
6776

68-
export AbstractRegularized, AbstractRegularizedGeneralizedMaximizer
77+
export AbstractRegularized
6978
export SoftArgmax, soft_argmax
7079
export SparseArgmax, sparse_argmax
7180
export SoftRank, soft_rank, soft_rank_l2, soft_rank_kl
7281
export SoftSort, soft_sort, soft_sort_l2, soft_sort_kl
7382
export RegularizedFrankWolfe
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84+
export PerturbedOracle
7585
export PerturbedAdditive
7686
export PerturbedMultiplicative
77-
export PerturbedOracle
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export FenchelYoungLoss
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export StructuredSVMLoss

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