revert to 0.2 for now

Project.toml

@@ -27,7 +27,7 @@ InferOptFrankWolfeExt = "DifferentiableFrankWolfe"
 ChainRulesCore = "1"
 DensityInterface = "0.4.0"
 DifferentiableExpectations = "0.2"
-DifferentiableFrankWolfe = "0.3"
+DifferentiableFrankWolfe = "0.2"
 Distributions = "0.25"
 DocStringExtensions = "0.9.3"
 LinearAlgebra = "<0.0.1,1"

src/utils/linear_maximizer.jl

@@ -45,7 +45,6 @@ end
 # default is oracles of the form argmax_y θᵀy
 objective_value(::Any, θ, y; kwargs...) = dot(θ, y)
 apply_g(::Any, y; kwargs...) = y
-# apply_h(::Any, y; kwargs...) = zero(eltype(y)) is not needed
 
 """
 $TYPEDSIGNATURES
@@ -65,12 +64,3 @@ Applies the function `g` of the LinearMaximizer `f` to `y`.
 function apply_g(f::LinearMaximizer, y; kwargs...)
     return f.g(y; kwargs...)
 end
-
-# """
-# $TYPEDSIGNATURES
-
-# Applies the function `h` of the LinearMaximizer `f` to `y`.
-# """
-# function apply_h(f::LinearMaximizer, y; kwargs...)
-#     return f.h(y; kwargs...)
-# end

src/utils/pushforward.jl

@@ -1,18 +1,17 @@
 """
-    Pushforward <: AbstractLayer
+$TYPEDEF
 
 Differentiable pushforward of a probabilistic optimization layer with an arbitrary function post-processing function.
 
 `Pushforward` can be used for direct regret minimization (aka learning by experience) when the post-processing returns a cost.
 
 # Fields
-- `optimization_layer::AbstractOptimizationLayer`: probabilistic optimization layer
-- `post_processing`: callable
-
-See also: `FixedAtomsProbabilityDistribution`.
+$TYPEDFIELDS
 """
 struct Pushforward{O<:AbstractOptimizationLayer,P} <: AbstractLayer
+    "probabilistic optimization layer"
     optimization_layer::O
+    "callable"
     post_processing::P
 end
 
@@ -22,13 +21,11 @@ function Base.show(io::IO, pushforward::Pushforward)
 end
 
 """
-    (pushforward::Pushforward)(θ; kwargs...)
+$TYPEDSIGNATURES
 
 Output the expectation of `pushforward.post_processing(X)`, where `X` follows the distribution defined by `pushforward.optimization_layer` applied to `θ`.
 
 This function is differentiable, even if `pushforward.post_processing` isn't.
-
-See also: `compute_expectation`.
 """
 function (pushforward::Pushforward)(θ::AbstractArray; kwargs...)
     (; optimization_layer, post_processing) = pushforward

src/utils/some_functions.jl

@@ -1,26 +1,26 @@
 """
-    positive_part(x)
+$TYPEDSIGNATURES
 
-Compute `max(x,0)`.
+Compute `max(x, 0)`.
 """
 positive_part(x) = x >= zero(x) ? x : zero(x)
 
 """
-    isproba(x)
+$TYPEDSIGNATURES
 
 Check whether `x ∈ [0,1]`.
 """
 isproba(x::Real) = zero(x) <= x <= one(x)
 
 """
-    isprobadist(p)
+$TYPEDSIGNATURES
 
 Check whether the elements of `p` are nonnegative and sum to 1.
 """
 isprobadist(p::AbstractVector{R}) where {R<:Real} = all(isproba, p) && sum(p) ≈ one(R)
 
 """
-    half_square_norm(x)
+$TYPEDSIGNATURES
 
 Compute the squared Euclidean norm of `x` and divide it by 2.
 """
@@ -29,7 +29,7 @@ function half_square_norm(x::AbstractArray)
 end
 
 """
-    shannon_entropy(p)
+$TYPEDSIGNATURES
 
 Compute the Shannon entropy of a probability distribution: `H(p) = -∑ pᵢlog(pᵢ)`.
 """
@@ -46,7 +46,7 @@ end
 negative_shannon_entropy(p::AbstractVector) = -shannon_entropy(p)
 
 """
-    one_hot_argmax(z)
+$TYPEDSIGNATURES
 
 One-hot encoding of the argmax function.
 """
@@ -57,7 +57,7 @@ function one_hot_argmax(z::AbstractVector{R}; kwargs...) where {R<:Real}
 end
 
 """
-    ranking(θ[; rev])
+$TYPEDSIGNATURES
 
 Compute the vector `r` such that `rᵢ` is the rank of `θᵢ` in `θ`.
 """