data4co provides convenient dataset generators for the combinatorial optimization problem
A data generator tool for Combinatorial Optimization (CO) problems, enabling customizable, diverse, and scalable datasets for benchmarking optimization algorithms.
data
| Problem | First | Impl. | Second | Impl. | Third | Impl. |
|---|---|---|---|---|---|---|
| TSP | TSPLIB(Origin) | ✔ | Uniform | ✔ | Multi. Distros. | 📆 |
| MIS | SATLIB | ✔ | KaMIS | 📆 | -- | -- |
draw
| Problem | problem | Impl. | solution | Impl. |
|---|---|---|---|---|
| TSP | draw_tsp_problem | ✔ | draw_tsp_soluton | ✔ |
| MIS | draw_mis_problem | ✔ | draw_mis_soluton | ✔ |
evaluator
| Problem | Base | Impl. | First | Impl. | Second | Impl. | Third | Impl. |
|---|---|---|---|---|---|---|---|---|
| TSP | TSPEvaluator | ✔ | TSPLIB(Origin) | ✔ | Uniform | ✔ | Multi. Distros. | 📆 |
| MIS | MISEvaluator | 📆 | SATLIBEvaluator | 📆 | -- | -- | -- | -- |
generator
| Problem | Type1 | Impl. | Type2 | Impl. | Type3 | Impl. | Type4 | Impl. |
|---|---|---|---|---|---|---|---|---|
| TSP | uniform | ✔ | gaussian | ✔ | cluster | ✔ | w/regret | ✔ |
| MIS | ER | ✔ | BA | ✔ | HK | ✔ | WS | ✔ |
solver
| Problem | Base | Impl. | First | Impl. | Second | Impl. | Third | Impl. |
|---|---|---|---|---|---|---|---|---|
| TSP | TSPSolver | ✔ | LKH | ✔ | Concorde | ✔ | Concorde(Large) | ✔ |
| MIS | MISSolver | ✔ | KaMIS | ✔ | Gurobi | ✔ | -- | -- |
✔: Supported; 📆: Planned for future versions (contributions welcomed!).
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
requests>=2.31.0
aiohttp>=3.9.3
async_timeout>=4.0.3
If you want to obtain complete data4co's functions, like drawing, the following packages need to be installed by pip:
matplotlib>=3.7.5
PyPI
It is very convenient to directly use the following commands
pip install data4co
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")
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()
test lkh
>>> from data4co.evaluate import TSPLIBOriginEvaluator
>>> from data4co.solver import TSPLKHSolver
>>> lkh_solver = TSPLKHSolver(scale=1)
>>> eva = TSPLIBOriginEvaluator()
>>> eva.evaluate(lkh_solver, norm="EUC_2D")
solved_costs gt_costs gaps
att48 33523.708507 33523.708507 0.000000e+00
eil51 429.983312 429.983312 0.000000e+00
berlin52 7544.365902 7544.365902 3.616585e-14
st70 678.557469 678.597452 -5.892021e-03
eil76 545.229738 545.387552 -2.893612e-02
pr76 108159.438274 108159.438274 -1.345413e-14
kroA100 21285.443182 21285.443182 0.000000e+00
kroC100 20750.762504 20750.762504 0.000000e+00
kroD100 21294.290821 21294.290821 3.416858e-14
rd100 7910.396210 7910.396210 0.000000e+00
eil101 642.856874 642.309536 8.521414e-02
lin105 14382.995933 14382.995933 0.000000e+00
ch130 6110.900592 6110.860950 6.487238e-04
ch150 6530.902722 6532.280933 -2.109847e-02
tsp225 3859.000000 3859.000000 0.000000e+00
a280 2588.301213 2586.769648 5.920765e-02
pr1002 260277.189980 259066.663053 4.672646e-01
pr2392 384469.093320 378062.826191 1.694498e+00
AVG 50054.634253 49631.448887 1.250504e-01
>>> eva.evaluate(lkh_solver, norm="GEO")
solved_costs gt_costs gaps
ulysses16 6859.0 6859.0 0.000000
ulysses22 7013.0 7013.0 0.000000
gr96 55209.0 55209.0 0.000000
gr202 40160.0 40160.0 0.000000
gr666 295012.0 294358.0 0.222178
AVG 80850.6 80719.8 0.044436
test concorde
>>> from data4co.evaluate import TSPLIBOriginEvaluator
>>> from data4co.solver import TSPConcordeSolver
>>> eva = TSPLIBOriginEvaluator()
>>> con_solver = TSPConcordeSolver(scale=1)
>>> eva.evaluate(con_solver, norm="EUC_2D")
solved_costs gt_costs gaps
att48 33523.708507 33523.708507 2.170392e-14
eil51 429.117939 429.983312 -2.012573e-01
berlin52 7544.365902 7544.365902 0.000000e+00
st70 678.583751 678.597452 -2.019036e-03
eil76 544.652026 545.387552 -1.348631e-01
pr76 108159.438274 108159.438274 -1.345413e-14
kroA100 21285.443182 21285.443182 -1.709139e-14
kroC100 20750.762504 20750.762504 0.000000e+00
kroD100 21294.290821 21294.290821 3.416858e-14
rd100 7910.396210 7910.396210 0.000000e+00
eil101 641.690973 642.309536 -9.630290e-02
lin105 14382.995933 14382.995933 0.000000e+00
ch130 6110.739012 6110.860950 -1.995428e-03
ch150 6532.280933 6532.280933 0.000000e+00
tsp225 3859.000000 3859.000000 0.000000e+00
a280 2587.930486 2586.769648 4.487600e-02
pr1002 259066.663053 259066.663053 -1.123411e-14
pr2392 378062.826191 378062.826191 0.000000e+00
AVG 49631.382539 49631.448887 -2.175343e-02
>>> eva.evaluate(con_solver, norm="GEO")
solved_costs gt_costs gaps
ulysses16 6859.0 6859.0 0.0
ulysses22 7013.0 7013.0 0.0
gr96 55209.0 55209.0 0.0
gr202 40160.0 40160.0 0.0
gr666 294358.0 294358.0 0.0
AVG 80719.8 80719.8 0.0
TSP
from data4co.solver import TSPConcordeSolver
from data4co.draw.tsp import draw_tsp_solution, draw_tsp_problem
# use TSPConcordeSolver to solve the problem
solver = TSPConcordeSolver(scale=1)
solver.from_tsp("docs/kroA150.tsp")
solver.solve(norm="EUC_2D")
# draw
draw_tsp_problem(
save_path="docs/kroA150_problem.png",
points=solver.ori_points,
)
draw_tsp_solution(
save_path="docs/kroA150_solution.png",
points=solver.ori_points,
tours=solver.tours
)
MIS
from data4co.solver import KaMISSolver
from data4co import draw_mis_problem, draw_mis_solution
# use KaMISSolver to solve the problem
mis_solver = KaMISSolver()
mis_solver.solve(src="docs/mis_example")
# draw
draw_mis_problem(
save_path="docs/mis_problem.png",
ckle_path="docs/mis_example/mis_example.gpickle"
)
draw_mis_solution(
save_path="docs/mis_solution.png",
gpickle_path="docs/mis_example/mis_example.gpickle",
result_path="docs/mis_example/solve/mis_example_unweighted.result"
)
FAQs
data4co provides convenient dataset generators for the combinatorial optimization problem
We found that data4co demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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