https://github.com/graph-com/dig_milp
The official implementation of DIG-MILP
https://github.com/graph-com/dig_milp
Last synced: 6 months ago
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The official implementation of DIG-MILP
- Host: GitHub
- URL: https://github.com/graph-com/dig_milp
- Owner: Graph-COM
- Created: 2023-10-15T14:36:00.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-27T01:58:24.000Z (over 2 years ago)
- Last Synced: 2024-06-09T01:28:21.890Z (over 1 year ago)
- Language: Python
- Size: 2.32 MB
- Stars: 10
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# DIG_MILP
The official implementation of DIG-MILP: A Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee
## Environment Requirement
To implement the code, the environment below is required:
```
SCIP version 7.0.3
ecole 0.7.3
matplotlib 3.7.2
networkx 3.0
numpy 1.24.4
PySCIPOpt 3.5.0
PyYAML 6.0.1
scikit-learn 1.3.0
scipy 1.10.1
torch 2.0.0+cu117
torch-geometric 2.3.1
torchaudio 2.0.1+cu117
torchvision 0.15.1+cu117
tqdm 4.65.0
wandb 0.15.5
```
The corresponding CUDA version is 11.7 and CUDNN version is 8500.
To install [PYSCIPOpt](https://github.com/scipopt/PySCIPOpt), please first install [SCIP](https://scipopt.org/doc).
## Code implementation
We include the code implementation for all the four tasks (set covering, combinatorial auctions, CVS and IIS).
### Combinatorial auctions (CA) (synthetic)
#### Data pre-process
1. run ```_1_generate_lp_data_mixed.py``` to generate graphs for combinatorial auctions. Here the generation follows that in [Exact Combinatorial Optimization with Graph Convolutional Neural Networks](https://github.com/ds4dm/learn2branch).
2. run ```_2_infer_scip_primal.py``` to get a set of primal solution/slack to the original problems.
3. run ```_3_generate_dual.py``` to get the dual format of linear relaxation to the original problems.
4. run ```_4_1_infer_scip_dual_solution.py``` and ```_4_2_infer_scip_dual_slack.py``` to infer a set of solutions / slack variables for the dual format of linear relaxation.
5. run ```_5_normalize_nodes.py``` to normalize the node inputs of the dataset.
6. run ```_6_normalize_weight.py``` to normalize the edge weight in the dataset.
7. run ```_7_generate_vcgraph.py``` to generate the VC-bipartite graph in torch-geometric in-memory dataset.
8. run```_8_normalize_degree.py``` to normalize the node degrees.
#### DIG-MILP training and inference
9. run ```sh 9_train.sh``` to train the model. The pre-trained DIG-MILP model is in the train_files.
10. run ```sh 10_generate.sh``` to generate new instances.
#### downstream task - parameter tuning without sharing data
enter /downstream_parameter/,
11. run ```sh 17_infer_log.sh``` and ```sh 17_infer_log2.sh``` to infer the 45 different hyper-parameter seeds for SCIP.
run the script for three times and change the target output path to 'time_log0', 'time_log1', and 'time_log2'.
12. run ``paint_similarity.py``` to paint the Pearson correlation figure and get the score.
* We include the SCIP solution time for all the four problems in the time_log folder and the corresponding figure in the paint folder.
#### downstream task - train machine learning models
enter / downstream_train_ml/,
13. run ```_1_infer_generate_primal.py``` to get the optimal solution for the generated MILP instances.
14. run ```_2_generate_train_data.py``` to generate the training data for downstream task.
15. run ```_7_generate_val_lp.py``` and ```_8_generate_val_data.py``` to generate the validation data, run ```_4_generate_test_lp.py``` amd ```_5_generate_test_graph.py``` to generate the testing data.
16. run ```sh 3_train.sh``` to train the downstream machine learning model.
17. run ```_6_test.py``` to test the model.
* The implementation of Set covering (SC), CVS and IIS is similar to CA. But there could be some difference in data pre-processing:
### CVS and IIS
#### Data pre-process
To pre-process the data downloaded from [MIPLIB2017](https://miplib.zib.de/tag_benchmark.html), we provide the script to pre-process and we also directly include the pre-processed version (in primal_format folder).