data4co 0.0.1a19

Data4CO

A data generator tool for Combinatorial Optimization (CO) problems, enabling customizable, diverse, and scalable datasets for benchmarking optimization algorithms.

Current support

data

draw

evaluator

generator

solver

✔: 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
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

Solver 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")

Generator 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()

Evaluator Example

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

Draw Example

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
)

Click to view the drawing results

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"
)

Click to view the drawing results