It seems to me that for a world with N coordinates, each ant will always move N times. Thus they will all finish at the same time.

There is no implementation of __copy__ or __deepcopy__ for Ants and Worlds.

• ACOpy version: 0.6.1
• Python version: 3.7.1, installed using homebrew
• Operating System: macOS Mojave (10.14)

Description

Currently (or as an isolated problem on my system), installing the package with pip does not also install the utils folder, which is required for the package to properly function.

My makeshift fix was simply moving the utils folder from this project into the site-packages folder where the package was stored. So far, this has solved/suppressed all errors, so it seems like the imported package is simply missing utils.

If the problem is unsolvable/unimportant/known, then this issue can be closed immediately, but I just wanted to bring it up in case it inconvenienced anyone.

What I Did

In terminal:

pip install acopy

In the python interpreter:

>>> import acopy
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.7/site-packages/acopy/__init__.py", line 2, in <module>
    from .ant import Ant  # noqa: F401
  File "/usr/local/lib/python3.7/site-packages/acopy/ant.py", line 7, in <module>
    from . import utils
ImportError: cannot import name 'utils' from 'acopy' (/usr/local/lib/python3.7/site-packages/acopy/__init__.py)

Moving acopy/utils from this repository into /usr/local/lib/python3.7/site-packages/acopy causes the above to give no error.

I like that you implemented an elite ant system, but it's rather rigid. I want to be able to choose whether the local best or global best solution is traced by the elite ants, as well as specify a particular number of them.

There is not a single @property that has a docstring. Fix this soon.

There is no setter/getter for t0

There are many use cases when starting node is fixed. The city to start the visiting is already given and according to that the route needs to be defined. Can this use case be solved with ACO using this library?
#enhancement

Sometimes I want the solver to return the best path from each iteration (local best) but other times I want the solver to simply return the best path from all iterations (global best). Can this be made into an option?

Also, it would be handy if the solver only yielded better and better solutions.

Was trying to run the acopy solver for fully connected graphs and it works fine but throws a key error whenever the graph is not fully connected.

I want to be able to create the world from an existing distance matrix. The problem with accepting only a list of coordinates is that it makes the assumption that ants can reach each coordinate from every other coordinate, but sometimes I'd like to restrict that assumption.

I'd like to keep the code as tidy as possible. Please keep lines under 80 chars (<=79).

I would like to use pants as a shell script by pointing it to a list of coordinates or some kind of file that describes a world. rho, Q, t0, alpha, beta, niters, and ant_count should be implemented as command line options.

I want to be able to use my csv files with pants. Pipes, file paths on the command line, etc.etc. You know the drill. I hate writing boilerplate code :-(

Your code is pretty clean but that main method at the bottom is a right mess! Clean that up man!

It doesn't make sense for the pheromone laid down by elite ants (in _trace_elite) to be done long after the main pheromone update (in _update_scent). Probably _update_scent should call _trace_elite.

When I clone an ant, it still has the same UID as the one from which I cloned it.

Currently, there are no unit tests. Although there is some test data, it is simply a global variable. It needs to be placed in its own module with a battery of unit tests.

• ACOpy version: 0.6.4
• Python version: 3.6.3
• Operating System: mac

Description

Hello.

I try to use acopy in the graph which was made by networkx.

For example

import acopy
import tsplib95
import networkx as nx
import random
import matplotlib.pyplot as plt


solver = acopy.Solver(rho=.03, q=1)
colony = acopy.Colony(alpha=1, beta=3)

// try to use graph made by networkx
G = nx.fast_gnp_random_graph(10, 0.9)
for (u, v) in G.edges():
    G.edges[u, v]['weight'] = random.randint(1, 1000)

tour = solver.solve(G, colony, limit=100)

nx.draw_networkx(G)
plt.show()


print(tour.cost)
print(tour.path)

But when I run this code, the error occured below.

Traceback (most recent call last):
  File "/Users/students/Desktop/keras_aco/main.py", line 15, in <module>
    tour = solver.solve(G, colony, limit=100)
  File "/Users/students/.pyenv/versions/keras_aco_env/lib/python3.6/site-packages/acopy/solvers.py", line 193, in solve
    for solution in self.optimize(*args, **kwargs):
  File "/Users/students/.pyenv/versions/keras_aco_env/lib/python3.6/site-packages/acopy/solvers.py", line 225, in optimize
    solutions = self.find_solutions(state.graph, state.ants)
  File "/Users/students/.pyenv/versions/keras_aco_env/lib/python3.6/site-packages/acopy/solvers.py", line 259, in find_solutions
    return [ant.tour(graph) for ant in ants]
  File "/Users/students/.pyenv/versions/keras_aco_env/lib/python3.6/site-packages/acopy/solvers.py", line 259, in <listcomp>
    return [ant.tour(graph) for ant in ants]
  File "/Users/students/.pyenv/versions/keras_aco_env/lib/python3.6/site-packages/acopy/ant.py", line 57, in tour
    node = self.choose_destination(graph, solution.current, unvisited)
  File "/Users/students/.pyenv/versions/keras_aco_env/lib/python3.6/site-packages/acopy/ant.py", line 110, in choose_destination
    scores = self.get_scores(graph, current, unvisited)
  File "/Users/students/.pyenv/versions/keras_aco_env/lib/python3.6/site-packages/acopy/ant.py", line 125, in get_scores
    edge = graph.edges[current, node]
  File "/Users/students/.pyenv/versions/keras_aco_env/lib/python3.6/site-packages/networkx/classes/reportviews.py", line 930, in __getitem__
    return self._adjdict[u][v]
KeyError: 0

I think that acopy's ant tried to use the edge which doesn't exists.
So, error occured.

What I Did

Can acopy deal with the network which isn't a complete graph?

And, Can I change the code
if Ant can't make a Hamiltonian path , then Ant trys to make a Hamiltonian path again.

Thank you, and sorry for bad english.

• ACOpy version: 0.6.4(dev)
• Python version: 3.7
• Operating System: Mac Catalina

Description

Now, acopy's ant can move on the complete graph certainly.
If the graph is a little sparse,

ant.py

    def tour(self, graph):
        """Find a solution to the given graph.

        :param graph: the graph to solve
        :type graph: :class:`networkx.Graph`
        :return: one solution
        :rtype: :class:`~acopy.solvers.Solution`
        """
        solution = self.initialize_solution(graph)
        unvisited = self.get_unvisited_nodes(graph, solution)
        while unvisited:
            node = self.choose_destination(graph, solution.current, unvisited)
            solution.add_node(node)
            unvisited.remove(node)
        solution.close()
        return solution

solution doesn't have all vertexes, because current node's adjacent nodes will be involved only.
And, If this process run, we try to get a edge which doesn't exist.

Proposal

    def tour(self, graph):
        """Find a solution to the given graph.

        :param graph: the graph to solve
        :type graph: :class:`networkx.Graph`
        :return: one solution
        :rtype: :class:`~acopy.solvers.Solution`
        """
        V = len(graph)
        solution = None

        while True:
            try:
                solution = self.initialize_solution(graph)
                while len(solution.nodes) < V:
                    unvisited_nodes = self.get_unvisited_nodes(graph, solution)
                    node = self.choose_destination(graph, solution.current, unvisited_nodes)
                    solution.add_node(node)
                solution.close()
                break
            except IndexError as e:
                print(e)
                continue
            except Exception as e:
                print(e)
                continue

        return solution

Above code is so ugly and not beautiful, but can handle the sparse graph.
Would you plan to create functions for sparse graphs, like above code(above code is not good...)?

Sorry, for bad english.
Thank you.

• ACOpy version: 0.6.4
• Python version: 3.7.3
• Operating System: Linux
  • uname -a: Linux AquaRing 4.19.44 #1-NixOS SMP Thu May 16 17:41:32 UTC 2019 x86_64 GNU/Linux

Description

I was trying to run a solver over a noncomplete graph. I expected this to work, but it didn't.

Alternatively, I would expect it to say loud and clear in the docs that this only works on complete graphs.

What I Did

Minimal failing example:

import acopy
import networkx as nx
import random

random.seed(0)

G = nx.cycle_graph(4)
for edge in G.edges:
    G.edges[edge]['weight'] = 1


solver = acopy.Solver()
colony = acopy.Colony()

tour = solver.solve(G, colony, limit=100)

Traceback:

Traceback (most recent call last):
  File "Untitled-1.py", line 15, in <module>
    tour = solver.solve(G, colony, limit=100)
  File "/nix/store/9yfdaw94lxrygpqqph8ry0vv2zd5dvqg-python3-3.7.3-env/lib/python3.7/site-packages/acopy/solvers.py", line 193, in solve
    for solution in self.optimize(*args, **kwargs):
  File "/nix/store/9yfdaw94lxrygpqqph8ry0vv2zd5dvqg-python3-3.7.3-env/lib/python3.7/site-packages/acopy/solvers.py", line 225, in optimize
    solutions = self.find_solutions(state.graph, state.ants)
  File "/nix/store/9yfdaw94lxrygpqqph8ry0vv2zd5dvqg-python3-3.7.3-env/lib/python3.7/site-packages/acopy/solvers.py", line 259, in find_solutions
    return [ant.tour(graph) for ant in ants]
  File "/nix/store/9yfdaw94lxrygpqqph8ry0vv2zd5dvqg-python3-3.7.3-env/lib/python3.7/site-packages/acopy/solvers.py", line 259, in <listcomp>
    return [ant.tour(graph) for ant in ants]
  File "/nix/store/9yfdaw94lxrygpqqph8ry0vv2zd5dvqg-python3-3.7.3-env/lib/python3.7/site-packages/acopy/ant.py", line 58, in tour
    solution.add_node(node)
  File "/nix/store/9yfdaw94lxrygpqqph8ry0vv2zd5dvqg-python3-3.7.3-env/lib/python3.7/site-packages/acopy/solvers.py", line 75, in add_node
    self._add_node(node)
  File "/nix/store/9yfdaw94lxrygpqqph8ry0vv2zd5dvqg-python3-3.7.3-env/lib/python3.7/site-packages/acopy/solvers.py", line 83, in _add_node
    data = self.graph.edges[edge]
  File "/nix/store/9yfdaw94lxrygpqqph8ry0vv2zd5dvqg-python3-3.7.3-env/lib/python3.7/site-packages/networkx/classes/reportviews.py", line 930, in __getitem__
    return self._adjdict[u][v]
KeyError: 2

(Sorry for the mangled paths, that's NixOS for ya)

• ACOpy version:
• Python version: 3.7
• Operating System: Ubuntu 19.1

Description

import acopy
import networkx as nx

g = nx.Graph()

g.add_edge(0, 1, weight = 2)
g.add_edge(1, 2, weight = 2)
g.add_edge(2, 3, weight = 2)
g.add_edge(3, 0, weight = 2)
g.add_edge(0, 2, weight = 1)
g.add_edge(1, 3, weight = 1)

solver = acopy.Solver(rho=.03, q=1)
colony = acopy.Colony(alpha=1, beta=3)
tour = solver.solve(g, colony, limit=100)

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Full output

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/workspaces/optimisation_shortest_path/notebooks/Clusterization.ipynb in 
      2 colony = acopy.Colony(alpha=1, beta=3)
      3 
----> 4 tour = solver.solve(tmp_g, colony, limit=100)

/usr/local/lib/python3.7/site-packages/acopy/solvers.py in solve(self, *args, **kwargs)

/usr/local/lib/python3.7/site-packages/acopy/solvers.py in optimize(self, graph, colony, gen_size, limit)

/usr/local/lib/python3.7/site-packages/acopy/solvers.py in find_solutions(self, graph, ants)

/usr/local/lib/python3.7/site-packages/acopy/solvers.py in (.0)

/usr/local/lib/python3.7/site-packages/acopy/ant.py in tour(self, graph)

/usr/local/lib/python3.7/site-packages/acopy/ant.py in choose_destination(self, graph, current, unvisited)

/usr/local/lib/python3.7/site-packages/acopy/ant.py in get_scores(self, graph, current, destinations)

/usr/local/lib/python3.7/site-packages/acopy/ant.py in score_edge(self, edge)

/usr/local/lib/python3.7/site-packages/pandas/core/generic.py in __nonzero__(self)
   1477     def __nonzero__(self):
   1478         raise ValueError(
-> 1479             f"The truth value of a {type(self).__name__} is ambiguous. "
   1480             "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
   1481         )

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

I think the methods attractiveness and trail_level should be renamed to something more generic. priori and posteriori would work well in my opinion. These are better descriptions of what the ant is trying to do in general, independent of the implementation, and therefore will work better for users trying to subclass Ant.

Some of the variables are specified differently in different places. For example, the World constructor takes arguments a and b, but the properties that hold those values have names alpha and beta. This potential confusion hurts readability and therefore maintainability.

• Acopy version:
• Python version:
• Operating System:

Description

I tried to run the algorithm on a larger tsp graph like att532.tsp but the algorithm diverge from the solution and is really slow.

What I Did

acopy solve att532.tsp --formt tsplib95 --limit 50

Question

How should I configure the parameter rho, q, alpha and beta foe better adaptation to the graph ? And make it faster ?

Would you please consider bumping the version of click to the 8th major version?

I have tested the following configuration in a vritual environment:

'click~=8.0'

Tests ran fine.