Implement cost bar for DVSP plots

Project.toml

@@ -10,6 +10,7 @@ ConstrainedShortestPaths = "b3798467-87dc-4d99-943d-35a1bd39e395"
 DataDeps = "124859b0-ceae-595e-8997-d05f6a7a8dfe"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
+FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
@@ -40,6 +41,7 @@ ConstrainedShortestPaths = "0.6.0"
 DataDeps = "0.7"
 Distributions = "0.25"
 DocStringExtensions = "0.9"
+FileIO = "1.17.0"
 Flux = "0.14, 0.15, 0.16"
 Graphs = "1.11"
 HiGHS = "1.9"

README.md

@@ -6,6 +6,10 @@
 [![Coverage](https://codecov.io/gh/JuliaDecisionFocusedLearning/DecisionFocusedLearningBenchmarks.jl/branch/main/graph/badge.svg)](https://app.codecov.io/gh/JuliaDecisionFocusedLearning/DecisionFocusedLearningBenchmarks.jl)
 [![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/JuliaDiff/BlueStyle)
 
+!!! warning 
+    This package is currently under active development. The API may change in future releases.
+    Please refer to the [documentation](https://JuliaDecisionFocusedLearning.github.io/DecisionFocusedLearningBenchmarks.jl/stable/) for the latest updates.
+
 ## What is Decision-Focused Learning?
 
 Decision-focused learning (DFL) is a paradigm that integrates machine learning prediction with combinatorial optimization to make better decisions under uncertainty. Unlike traditional "predict-then-optimize" approaches that optimize prediction accuracy independently of downstream decision quality, DFL directly optimizes end-to-end decision performance.

docs/src/benchmark_interfaces.md

@@ -26,13 +26,13 @@ The package defines a hierarchy of three abstract types:
 
 ```
 AbstractBenchmark
-├── AbstractStochasticBenchmark{exogenous}
+└── AbstractStochasticBenchmark{exogenous}
     └── AbstractDynamicBenchmark{exogenous}
 ```
 
 - **`AbstractBenchmark`**: static, single-stage optimization problems
 - **`AbstractStochasticBenchmark{exogenous}`**: stochastic, single stage optimization problems
- **`AbstractDynamicBenchmark{exogenous}`**: multi-stage sequential decision problems
+- **`AbstractDynamicBenchmark{exogenous}`**: multi-stage sequential decision-making problems
 
 The `{exogenous}` type parameter indicates whether uncertainty distribution comes from external sources (`true`) or is influenced by decisions (`false`), which affects available methods.
 

src/DynamicVehicleScheduling/DynamicVehicleScheduling.jl

@@ -13,7 +13,7 @@ using IterTools: partition
 using JSON
 using JuMP
 using Plots: plot, plot!, scatter!, @animate, Plots, gif
-using Printf: @printf
+using Printf: @printf, @sprintf
 using Random: Random, AbstractRNG, MersenneTwister, seed!, randperm
 using Requires: @require
 using Statistics: mean, quantile

src/DynamicVehicleScheduling/anticipative_solver.jl

@@ -54,6 +54,7 @@ function anticipative_solver(
 )
     if reset_env
         reset!(env; reset_rng=true, seed)
+        scenario = env.scenario
     end
 
     @assert !is_terminated(env)
@@ -213,13 +214,15 @@ function anticipative_solver(
             current_epoch=epoch,
         )
 
+        reward = -cost(state, decode_bitmatrix_to_routes(y_true))
+
         x = if two_dimensional_features
             compute_2D_features(state, env.instance)
         else
             compute_features(state, env.instance)
         end
 
-        return DataSample(; instance=state, y_true, x)
+        return DataSample(; instance=(; state, reward), y_true, x)
     end
 
     return obj, dataset