Switch to horizontal type hierarchy

Author: BatyLeo

docs/src/custom_benchmarks.md

@@ -9,14 +9,14 @@ problems to the benchmark suite or integrate their own domains.
 ## Type hierarchy
 
 ```
-AbstractBenchmark
+AbstractStaticBenchmark
 └── AbstractStochasticBenchmark{exogenous}
     └── AbstractDynamicBenchmark{exogenous}
 ```
 
 | Type | Use case |
 |------|----------|
-| `AbstractBenchmark` | Static, single-stage optimization (e.g. shortest path, portfolio) |
+| `AbstractStaticBenchmark` | Static, single-stage optimization (e.g. shortest path, portfolio) |
 | `AbstractStochasticBenchmark{true}` | Single-stage with exogenous uncertainty (scenarios drawn independently of decisions) |
 | `AbstractStochasticBenchmark{false}` | Single-stage with endogenous uncertainty |
 | `AbstractDynamicBenchmark{true}` | Multi-stage sequential decisions with exogenous uncertainty |
@@ -41,7 +41,7 @@ repeatedly and applies `target_policy` to each result.
 
 ---
 
-## `AbstractBenchmark`: required methods
+## `AbstractStaticBenchmark`: required methods
 
 ### Data generation (choose one strategy)
 

docs/src/index.md

@@ -43,7 +43,7 @@ Where:
 
 The package organizes benchmarks into three main categories based on their problem structure:
 
-### Static Benchmarks (`AbstractBenchmark`)
+### Static Benchmarks (`AbstractStaticBenchmark`)
 Single-stage optimization problems with no randomness involved:
 - [`ArgmaxBenchmark`](@ref): argmax toy problem
 - [`Argmax2DBenchmark`](@ref): 2D argmax toy problem

docs/src/using_benchmarks.md

@@ -8,7 +8,7 @@ This guide covers everything you need to work with existing benchmarks in Decisi
 
 A benchmark bundles a problem family (an instance generator, a combinatorial solver, and a statistical model architecture) into a single object. It provides everything needed to run a Decision-Focused Learning experiment out of the box, without having to create each component from scratch.
 Three abstract types cover the main settings:
-- **`AbstractBenchmark`**: static problems (one instance, one decision)
+- **`AbstractStaticBenchmark`**: static problems (one instance, one decision)
 - **`AbstractStochasticBenchmark{exogenous}`**: stochastic problems (type parameter indicates whether uncertainty is exogenous)
 - **`AbstractDynamicBenchmark{exogenous}`**: sequential / multi-stage problems
 
@@ -65,7 +65,7 @@ sample.scenario   # looks up :scenario in context first, then in extra
 
 ### Static benchmarks
 
-For static benchmarks (`<:AbstractBenchmark`), `generate_dataset` may compute a default ground-truth label `y` if the benchmark implements it:
+For static benchmarks (`<:AbstractStaticBenchmark`), `generate_dataset` may compute a default ground-truth label `y` if the benchmark implements it:
 
 ```julia
 bench = ArgmaxBenchmark()

src/Argmax/Argmax.jl

@@ -15,7 +15,7 @@ Basic benchmark problem with an argmax as the CO algorithm.
 # Fields
 $TYPEDFIELDS
 """
-struct ArgmaxBenchmark{E} <: AbstractBenchmark
+struct ArgmaxBenchmark{E} <: AbstractStaticBenchmark
     "instances dimension, total number of classes"
     instance_dim::Int
     "number of features"

src/Argmax2D/Argmax2D.jl

@@ -16,7 +16,7 @@ Argmax becnhmark on a 2d polytope.
 # Fields
 $TYPEDFIELDS
 """
-struct Argmax2DBenchmark{E,R} <: AbstractBenchmark
+struct Argmax2DBenchmark{E,R} <: AbstractStaticBenchmark
     "number of features"
     nb_features::Int
     "true mapping between features and costs"

src/FixedSizeShortestPath/FixedSizeShortestPath.jl

@@ -21,7 +21,7 @@ Data is generated using the process described in: <https://arxiv.org/abs/2307.13
 # Fields
 $TYPEDFIELDS
 """
-struct FixedSizeShortestPathBenchmark <: AbstractBenchmark
+struct FixedSizeShortestPathBenchmark <: AbstractStaticBenchmark
     "grid graph instance"
     graph::SimpleDiGraph{Int64}
     "grid size of graphs"

src/PortfolioOptimization/PortfolioOptimization.jl

@@ -19,7 +19,7 @@ Data is generated using the process described in: <https://arxiv.org/abs/2307.13
 # Fields
 $TYPEDFIELDS
 """
-struct PortfolioOptimizationBenchmark <: AbstractBenchmark
+struct PortfolioOptimizationBenchmark <: AbstractStaticBenchmark
     "number of assets"
     d::Int
     "size of feature vectors"

src/Ranking/Ranking.jl

@@ -15,7 +15,7 @@ Basic benchmark problem with ranking as the CO algorithm.
 # Fields
 $TYPEDFIELDS
 """
-struct RankingBenchmark{E} <: AbstractBenchmark
+struct RankingBenchmark{E} <: AbstractStaticBenchmark
     "instances dimension, total number of classes"
     instance_dim::Int
     "number of features"

src/SubsetSelection/SubsetSelection.jl

@@ -18,7 +18,7 @@ without knowing their values, but only observing some features.
 # Fields
 $TYPEDFIELDS
 """
-struct SubsetSelectionBenchmark{M} <: AbstractBenchmark
+struct SubsetSelectionBenchmark{M} <: AbstractStaticBenchmark
     "total number of items"
     n::Int
     "number of items to select"

src/Utils/Utils.jl

@@ -15,7 +15,10 @@ include("data_sample.jl")
 include("maximizers.jl")
 include("environment.jl")
 include("policy.jl")
-include("interface.jl")
+include("interface/abstract_benchmark.jl")
+include("interface/static_benchmark.jl")
+include("interface/stochastic_benchmark.jl")
+include("interface/dynamic_benchmark.jl")
 include("grid_graph.jl")
 include("misc.jl")
 include("model_builders.jl")
@@ -26,7 +29,8 @@ export TopKMaximizer, one_hot_argmax
 
 export AbstractEnvironment, get_seed, is_terminated, observe, reset!, step!
 
-export AbstractBenchmark, AbstractStochasticBenchmark, AbstractDynamicBenchmark
+export AbstractBenchmark,
+    AbstractStaticBenchmark, AbstractStochasticBenchmark, AbstractDynamicBenchmark
 export ExogenousStochasticBenchmark,
     EndogenousStochasticBenchmark, ExogenousDynamicBenchmark, EndogenousDynamicBenchmark
 export generate_instance, generate_sample, generate_dataset