https://github.com/rithinch/pytspsolver
🚚 Easy to use python package for rapid experimentation on the classic travelling salesman problem. Contains implementations of various optimization algorithms, cool visualizers and a plug-in architecture.
https://github.com/rithinch/pytspsolver
experiments flexible genetic-algorithm metaheuristics optimization-algorithms plug-and-play python3 travelling-salesman-problem visualizations
Last synced: 7 months ago
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🚚 Easy to use python package for rapid experimentation on the classic travelling salesman problem. Contains implementations of various optimization algorithms, cool visualizers and a plug-in architecture.
- Host: GitHub
- URL: https://github.com/rithinch/pytspsolver
- Owner: rithinch
- Created: 2019-03-05T09:58:33.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-07-16T07:32:12.000Z (about 1 year ago)
- Last Synced: 2025-03-10T08:04:15.660Z (7 months ago)
- Topics: experiments, flexible, genetic-algorithm, metaheuristics, optimization-algorithms, plug-and-play, python3, travelling-salesman-problem, visualizations
- Language: Python
- Homepage:
- Size: 412 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# :truck: pytspsolver
Easy to use package for rapid experimentation on the classic travelling salesman problem. Contains implementations of various optimization algorithms, cool visualizers and a plug-in architecture.
[](https://dev.azure.com/rithinchalumuri/pytspsolver/_build/latest?definitionId=7&branchName=master)
[](https://sonarcloud.io/dashboard?id=rithinch_pytspsolver)
[](https://snyk.io/test/github/rithinch/pytspsolver?targetFile=src/requirements.txt)
[](https://pypi.org/project/pytspsolver/)
[](https://saythanks.io/to/rithinch)
## Installation
```bash
> pip install pytspsolver
```
## Usage
This package is designed to provide an intutive pythonic interface; allowing you to conduct experiments with minimal code. 😅
Here's how you can kick-start a travelling salesman problem experiment:
```python
from pytspsolver.entities import TSProblem
from pytspsolver.experiments import Experiment
from pytspsolver.solvers import *
from pytspsolver.utilities import create_random_problem, get_tsp_lib_problem, Visualizer
import matplotlib.pyplot as plt
# Create a few tsp problems (represented as an adjacency matrix)
problems = [create_random_problem("UniqueProblemName"+str(i), i) for i in range(3,12)]
# Pass in the location of TSPLIB95 dataset file
tsp_prob = get_tsp_lib_problem("gr17.tsp")
# Create a new Experiment
experiment = Experiment()
# Add the problems to the experiment (single or list of problems)
experiment.add_problem(tsp_prob)
experiment.add_problems(problems)
# Add solvers to use in the experiment
experiment.add_solver(ExhaustiveSearch(time_limit=50))
experiment.add_solver(GreedySearch(time_limit=100))
# Run the experiment desired number of times
results = experiment.run(epoch=10)
# Set up Visualizer with experiment results
visualizer = Visualizer(results)
# Show visualizations - automatically averages the results from different epochs
visualizer.plot_n_vs_time_all(plt)
# Note: the visualizer has various plots available, they can be called in a similar fashion.
```
It comes with a plug in architecture, therefore it is very customizable.
## Local Setup (Development Purposes)
Contributions and pull requests are encouraged! 👏
Let's first create a new python environment with the name **your_env_name** using Anaconda Prompt/Terminal; this allows us to manage all package dependencies for this project in isolation.
```bash
> conda create -n your_env_name
```
We can now activate the created environment using the command below:
```bash
> conda activate your_env_name
```
We need to install a few dependencies. We can do this by running the following:
```bash
> conda install jupyter
> pip install -r ./src/requirements.txt
```
Then, we need to install the ```pytspsolver``` package. Everytime a code change is made to the package, this needs to be called. Otherwise newly added changes wouldn't reflect in places where this package is being used.
```bash
> pip install ./src
```
That's it, we're good to start developing now. :sunglasses:
## Additional Examples
A few examples have been implemented using jupyter notebooks; found in the **examples** folder. These notebooks can be accessed by launching jupyter notebook from your current conda environment.
```bash
> jupyter notebook
```
👉 Make sure you are in the right environment when launching jupyter notebook, otherwise, jupyter notebook kernel will be pointing to a different python version which won't have ```pytspsolver``` package installed.
## Contributors
* [Venkata Rithin Chalumuri](https://github.coventry.ac.uk/chalumuv)
* [Genaro Bedenko](https://github.coventry.ac.uk/bedenkog)
* [Ovidiu Mitroi](https://github.coventry.ac.uk/mitroio)
* [Rishi Mehangra](https://github.coventry.ac.uk/mehangrr)