Skip to content

Commit

Permalink
clean the README.md
Browse files Browse the repository at this point in the history
  • Loading branch information
ubuntu committed Apr 11, 2024
1 parent 2f47464 commit 88768b5
Showing 1 changed file with 1 addition and 239 deletions.
240 changes: 1 addition & 239 deletions README.md
View file
Original file line number Diff line number Diff line change
@@ -1,239 +1 @@
## Data4CO

[![PyPi version](https://badgen.net/pypi/v/data4co/)](https://pypi.org/pypi/data4co/)
[![PyPI pyversions](https://img.shields.io/badge/dynamic/json?color=blue&label=python&query=info.requires_python&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fdata4co%2Fjson)](https://pypi.python.org/pypi/data4co/)
[![Downloads](https://static.pepy.tech/badge/data4co)](https://pepy.tech/project/data4co)
[![codecov](https://codecov.io/gh/heatingma/ML4CO-Kit/graph/badge.svg?token=N18LNMQNKS)](https://codecov.io/gh/heatingma/ML4CO-Kit)
[![GitHub stars](https://img.shields.io/github/stars/heatingma/Data4CO.svg?style=social&label=Star&maxAge=8640)](https://GitHub.com/heatingma/Data4CO/stargazers/)

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 (Original) | ✔ | Uniform | ✔ | Multi. Distros. | 📆 |
| MIS | SATLIB | ✔ | KaMIS | 📆 | -- | -- |

**Visualization**
| 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 (Original) | ✔ | 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!).

### 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

```python
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

```python
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**
```python
>>> from data4co.evaluate import TSPLIBOriEvaluator
>>> from data4co.solver import TSPLKHSolver

>>> lkh_solver = TSPLKHSolver(scale=1)
>>> eva = TSPLIBOriEvaluator()
>>> eva.evaluate(lkh_solver, norm="EUC_2D")
solved_costs ref_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 ref_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**
```python
>>> from data4co.evaluate import TSPLIBOriEvaluator
>>> from data4co.solver import TSPConcordeSolver

>>> eva = TSPLIBOriEvaluator()
>>> con_solver = TSPConcordeSolver(scale=1)
>>> eva.evaluate(con_solver, norm="EUC_2D")
solved_costs ref_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 ref_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**
```python
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
)
```
<details>
<summary>Click to view the drawing results</summary>
<img src="docs/kroA150_problem.png" width="35%" alt="" />
<img src="docs/kroA150_solution.png" width="35%" alt="" />
</details>

**MIS**
```python
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",
gpickle_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"
)
```
<details>
<summary>Click to view the drawing results</summary>
<img src="docs/mis_problem.png" width="35%" alt="" />
<img src="docs/mis_solution.png" width="35%" alt="" />
</details>
## ML4CO-kit

0 comments on commit 88768b5

Please sign in to comment.