Merge pull request #19 from axelparmentier/giom

Prepare for v0.3.0

Author: gdalle

CITATION.bib

@@ -2,7 +2,7 @@ @misc{InferOpt.jl
 	author  = {Guillaume Dalle, Léo Baty, Louis Bouvier and Axel Parmentier},
 	title   = {InferOpt.jl},
 	url     = {https://github.com/axelparmentier/InferOpt.jl},
-	version = {v0.2.0},
+	version = {v0.3.0},
 	year    = {2022},
-	month   = {6}
+	month   = {7}
 }

Project.toml

@@ -1,7 +1,7 @@
 name = "InferOpt"
 uuid = "4846b161-c94e-4150-8dac-c7ae193c601f"
 authors = ["Guillaume Dalle", "Léo Baty", "Louis Bouvier", "Axel Parmentier"]
-version = "0.2.0"
+version = "0.3.0"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

docs/src/tutorial.md

@@ -121,7 +121,7 @@ Thanks to this smoothing, we can now train our model with a standard gradient op
 
 ````@example tutorial
 encoder = deepcopy(initial_encoder)
-opt = ADAM();
+opt = Adam();
 losses = Float64[]
 for epoch in 1:200
     l = 0.0

src/InferOpt.jl

@@ -16,7 +16,7 @@ using StatsBase: StatsBase, sample
 using Test
 
 include("utils/probability_distribution.jl")
-include("utils/composition.jl")
+include("utils/pushforward.jl")
 
 include("interpolation/interpolation.jl")
 
@@ -42,8 +42,9 @@ include("ssvm/isbaseloss.jl")
 include("ssvm/zeroone_baseloss.jl")
 include("ssvm/ssvm_loss.jl")
 
-export FixedAtomsProbabilityDistribution, sample, compute_expectation
-export ProbabilisticComposition
+export FixedAtomsProbabilityDistribution
+export compute_expectation, compress_distribution!
+export Pushforward
 export compute_probability_distribution
 
 export Interpolation

src/utils/probability_distribution.jl

@@ -46,11 +46,13 @@ end
 Base.rand(probadist::FixedAtomsProbabilityDistribution) = rand(GLOBAL_RNG, probadist)
 
 """
-    compress!(probadist[; atol])
+    compress_distribution!(probadist[; atol])
 
 Remove duplicated atoms in `probadist` (up to a tolerance on equality).
 """
-function compress!(probadist::FixedAtomsProbabilityDistribution{A,W}; atol=0) where {A,W}
+function compress_distribution!(
+    probadist::FixedAtomsProbabilityDistribution{A,W}; atol=0
+) where {A,W}
     (; atoms, weights) = probadist
     to_delete = Int[]
     for i in length(probadist):-1:1

src/utils/composition.jl renamed to src/utils/pushforward.jl

@@ -1,24 +1,24 @@
 """
-    ProbabilisticComposition{L,G}
+    Pushforward{L,G}
 
 Differentiable composition of a probabilistic `layer` with an arbitrary function `post_processing`.
 
-`ProbabilisticComposition` can be used for direct regret minimization (aka learning by experience) when the post-processing returns a cost.
+`Pushforward` can be used for direct regret minimization (aka learning by experience) when the post-processing returns a cost.
 
 # Fields
 - `layer::L`: anything that implements `compute_probability_distribution(layer, θ; kwargs...)`
 - `post_processing::P`: callable
 
 See also: [`FixedAtomsProbabilityDistribution`](@ref).
 """
-struct ProbabilisticComposition{L,P}
+struct Pushforward{L,P}
     layer::L
     post_processing::P
 end
 
-function Base.show(io::IO, composition::ProbabilisticComposition)
+function Base.show(io::IO, composition::Pushforward)
     (; layer, post_processing) = composition
-    return print(io, "ProbabilisticComposition($layer, $post_processing)")
+    return print(io, "Pushforward($layer, $post_processing)")
 end
 
 """
@@ -30,25 +30,23 @@ This function is not differentiable if `composition.post_processing` isn't.
 
 See also: [`apply_on_atoms`](@ref).
 """
-function compute_probability_distribution(
-    composition::ProbabilisticComposition, θ; kwargs...
-)
+function compute_probability_distribution(composition::Pushforward, θ; kwargs...)
     (; layer, post_processing) = composition
     probadist = compute_probability_distribution(layer, θ; kwargs...)
     post_processed_probadist = apply_on_atoms(post_processing, probadist; kwargs...)
     return post_processed_probadist
 end
 
 """
-    (composition::ProbabilisticComposition)(θ)
+    (composition::Pushforward)(θ)
 
 Output the expectation of `composition.post_processing(X)`, where `X` follows the distribution defined by `composition.layer` applied to `θ`.
 
 Unlike [`compute_probability_distribution(composition, θ)`](@ref), this function is differentiable, even if `composition.post_processing` isn't.
 
 See also: [`compute_expectation`](@ref).
 """
-function (composition::ProbabilisticComposition)(θ::AbstractArray{<:Real}; kwargs...)
+function (composition::Pushforward)(θ::AbstractArray{<:Real}; kwargs...)
     (; layer, post_processing) = composition
     probadist = compute_probability_distribution(layer, θ; kwargs...)
     return compute_expectation(probadist, post_processing; kwargs...)

test/argmax.jl

@@ -77,21 +77,19 @@ pipelines_experience = [
     (
         encoder=encoder_factory(),
         maximizer=identity,
-        loss=ProbabilisticComposition(
-            PerturbedAdditive(true_maximizer; ε=1.0, nb_samples=10), cost
-        ),
+        loss=Pushforward(PerturbedAdditive(true_maximizer; ε=1.0, nb_samples=10), cost),
     ),
     (
         encoder=encoder_factory(),
         maximizer=identity,
-        loss=ProbabilisticComposition(
+        loss=Pushforward(
             PerturbedMultiplicative(true_maximizer; ε=1.0, nb_samples=10), cost
         ),
     ),
     (
         encoder=encoder_factory(),
         maximizer=identity,
-        loss=ProbabilisticComposition(
+        loss=Pushforward(
             RegularizedGeneric(true_maximizer, half_square_norm, identity), cost
         ),
     ),

test/paths.jl

@@ -79,21 +79,19 @@ pipelines_experience = [
     (
         encoder=encoder_factory(),
         maximizer=identity,
-        loss=ProbabilisticComposition(
-            PerturbedAdditive(true_maximizer; ε=1.0, nb_samples=10), cost
-        ),
+        loss=Pushforward(PerturbedAdditive(true_maximizer; ε=1.0, nb_samples=10), cost),
     ),
     (
         encoder=encoder_factory(),
         maximizer=identity,
-        loss=ProbabilisticComposition(
+        loss=Pushforward(
             PerturbedMultiplicative(true_maximizer; ε=1.0, nb_samples=10), cost
         ),
     ),
     (
         encoder=encoder_factory(),
         maximizer=identity,
-        loss=ProbabilisticComposition(
+        loss=Pushforward(
             RegularizedGeneric(true_maximizer, half_square_norm, identity), cost
         ),
     ),

test/ranking.jl

@@ -71,21 +71,19 @@ pipelines_experience = [
     (
         encoder=encoder_factory(),
         maximizer=identity,
-        loss=ProbabilisticComposition(
-            PerturbedAdditive(true_maximizer; ε=1.0, nb_samples=10), cost
-        ),
+        loss=Pushforward(PerturbedAdditive(true_maximizer; ε=1.0, nb_samples=10), cost),
     ),
     (
         encoder=encoder_factory(),
         maximizer=identity,
-        loss=ProbabilisticComposition(
+        loss=Pushforward(
             PerturbedMultiplicative(true_maximizer; ε=1.0, nb_samples=10), cost
         ),
     ),
     (
         encoder=encoder_factory(),
         maximizer=identity,
-        loss=ProbabilisticComposition(
+        loss=Pushforward(
             RegularizedGeneric(true_maximizer, half_square_norm, identity), cost
         ),
     ),

test/tutorial.jl

@@ -111,7 +111,7 @@ Thanks to this smoothing, we can now train our model with a standard gradient op
 =#
 
 encoder = deepcopy(initial_encoder)
-opt = ADAM();
+opt = Adam();
 losses = Float64[]
 for epoch in 1:200
     l = 0.0