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.
> pip install pytspsolverThis 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:
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.
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.
> conda create -n your_env_nameWe can now activate the created environment using the command below:
> conda activate your_env_nameWe need to install a few dependencies. We can do this by running the following:
> conda install jupyter
> pip install -r ./src/requirements.txtThen, 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.
> pip install ./srcThat's it, we're good to start developing now. 😎
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.
> 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.