data4co provides convenient dataset generators for the combinatorial optimization problem
Project description
Data4CO
A data generator tool for Combinatorial Optimization (CO) problems, enabling customizable, diverse, and scalable datasets for benchmarking optimization algorithms.
Current support
data
Problem | First | Impl. | Second | Impl. | Third | Impl. |
---|---|---|---|---|---|---|
TSP | tsplib | ✔ | LKH | ✔ | Concorde | ✔ |
MIS | satlib | ✔ | KaMIS | 📆 | -- | -- |
evaluator
Problem | First | Impl. | Second | Impl. |
---|---|---|---|---|
TSP | tsplib | ✔ | uniform | ✔ |
MIS | satlib | ✔ | ER | 📆 |
generator
Problem | Type1 | Impl. | Type2 | Impl. | Type3 | Impl. | Type4 | Impl. |
---|---|---|---|---|---|---|---|---|
TSP | uniform | ✔ | gaussian | ✔ | cluster | 📆 | -- | -- |
MIS | ER | ✔ | BA | ✔ | HK | ✔ | WS | ✔ |
solver
Problem | Base | Impl. | First | Impl. | Second | Impl. |
---|---|---|---|---|---|---|
TSP | TSPSolver | ✔ | LKH | ✔ | Concorde | ✔ |
MIS | MISSolver | ✔ | KaMIS | ✔ | Gurobi | ✔ |
✔: Supported; 📆: Planned for future versions (contributions welcomed!).
How to Install
Github
Clone with the url https://github.com/heatingma/Data4CO.git , and the following packages are required, and shall be automatically installed by pip
:
Python >= 3.8
numpy>=1.24.4
networkx==2.8.8
tsplib95==0.7.1
tqdm>=4.66.1
pulp>=2.8.0,
pandas>=2.0.0,
scipy>=1.10.1
PyPI It is very convenient to directly use the following commands
pip install data4co
How to Use Solver (TSPLKHSolver as example)
from data4co.solver import TSPLKHSolver
tsp_lkh_solver = TSPLKHSolver(lkh_max_trials=500)
tsp_lkh_solver.from_txt("path/to/read/file.txt")
tsp_lkh_solver.solve()
tsp_lkh_solver.evaluate()
tsp_lkh_solver.to_txt("path/to/write/file.txt")
How to Use Generator (TSPDataGenerator as example)
from data4co import TSPDataGenerator
tsp_data_lkh = TSPDataGenerator(
num_threads=8,
nodes_num=50,
data_type="uniform",
solver="lkh",
train_samples_num=16,
val_samples_num=16,
test_samples_num=16,
save_path="path/to/save/"
)
tsp_data_lkh.generate()
How to Use Evaluator (TSPLIBEvaluator as example)
>>> from data4co.evaluate import TSPLIBEvaluator
>>> from data4co.solver import TSPLKHSolver, TSPConcordeSolver
# test LKH
>>> lkh_solver = TSPLKHSolver(lkh_scale=1e2)
>>> eva = TSPLIBEvaluator()
>>> eva.evaluate(lkh_solver)
solved_costs gt_costs gaps
a280 2586.769648 2586.769648 0.000000e+00
att48 33523.708507 33523.708507 0.000000e+00
berlin52 7544.365902 7544.365902 3.616585e-14
ch130 6110.722200 6110.860950 -2.270541e-03
ch150 6530.902722 6532.280933 -2.109847e-02
eil101 640.211591 642.309536 -3.266252e-01
eil51 428.871756 429.983312 -2.585113e-01
eil76 544.369053 545.387552 -1.867479e-01
kroA100 21285.443182 21285.443182 0.000000e+00
kroC100 20750.762504 20750.762504 0.000000e+00
kroD100 21294.290821 21294.290821 3.416858e-14
lin105 14382.995933 14382.995933 0.000000e+00
pr1002 260047.681630 259066.663053 3.786742e-01
pr2392 383849.940441 378062.826191 1.530728e+00
pr76 108159.438274 108159.438274 -1.345413e-14
rd100 7910.396210 7910.396210 0.000000e+00
st70 677.109609 678.597452 -2.192526e-01
tsp225 3859.000000 3859.000000 0.000000e+00
AVG 50007.054444 49631.448887 4.971646e-02
# test concorde
>>> con_solver = TSPConcordeSolver(concorde_scale=1e2)
>>> eva.evaluate(con_solver)
solved_costs gt_costs gaps
a280 2586.769648 2586.769648 -1.757974e-14
att48 33523.708507 33523.708507 2.170392e-14
berlin52 7544.365902 7544.365902 0.000000e+00
ch130 6110.722200 6110.860950 -2.270541e-03
ch150 6530.902722 6532.280933 -2.109847e-02
eil101 640.211591 642.309536 -3.266252e-01
eil51 428.871756 429.983312 -2.585113e-01
eil76 544.369053 545.387552 -1.867479e-01
kroA100 21285.443182 21285.443182 -1.709139e-14
kroC100 20750.762504 20750.762504 0.000000e+00
kroD100 21294.290821 21294.290821 3.416858e-14
lin105 14382.995933 14382.995933 -1.264680e-14
pr1002 259066.663053 259066.663053 0.000000e+00
pr2392 378062.696085 378062.826191 -3.441403e-05
pr76 108159.438274 108159.438274 -1.345413e-14
rd100 7910.396210 7910.396210 -3.449238e-14
st70 677.109609 678.597452 -2.192526e-01
tsp225 3859.000000 3859.000000 0.000000e+00
AVG 49631.039836 49631.448887 -5.636336e-02
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