This code can be used to reproduce all figures and results in the paper titled "Explainable Data-Driven Optimization: From Context to Decision and Back Again" by Alexandre Forel, Axel Parmentier and Thibaut Vidal published in the Proceedings of the fortieth International Conference on Machine Learning, 2023, in press.
Paper available here | Short video presentation
The project requires the Gurobi solver to be installed with an authorized license. Free academic licenses can be obtained at: https://www.gurobi.com/academia/academic-program-and-licenses/ .
The packages required are listed in the file environment.yml. A virtual python environment can be created using an Anaconda Distribution (https://www.anaconda.com/products/distribution) by using:
conda env create -f environment.yml
in the project root directory. This will automatically install all required packages and their dependencies.
The main scripts that generate data or results are included in the root folder. The folders have the following contents:
data: all data used for the experiments with Uber movement data,ocean: a copy of the OCEAN package taken from https://github.com/vidalt/OCEAN that has been adapted,src: all local functions needed to generate and analyze the experimental results.
All experiments are run using the scripts starting with the run prefix. The results are stored in the \output\ folder.
Running the scripts will generate the data for the following results:
run_uber_movement_path: Figure 1 and Table 3,run_synthetic_experiment: Tables 2, 4, 5, 7, and 8, and Figure 6,run_path_features_sensitivity: Figure 2 and 8,run_problem_complex_sens: Figures 3 and 4,run_forest_depth: Table 6,run_dual_formulation: Figure 9,run_spurious_experiment: Figures 10 and 11.
Run the script analyze_results to generate the figures, tables, and csv files used in the paper.