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com2014-template
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[new] Implement container

This commit is contained in:
Wu Han 2020-12-19 22:32:38 +00:00
parent e120708d8d
commit 89d429f69b
24 changed files with 135 additions and 115 deletions
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.dockerignore Normal file
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.vscode/

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.dockerignore.txt
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.vscode/
*.json
*.txt

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.gitignore vendored
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.vscode/
__pycache__/
output/

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Dockerfile Normal file
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FROM python:3.8
ADD ./ /tsp
WORKDIR /tsp
CMD ["/bin/bash", "-c", "pip install -r requirements.txt && python main.py; cp -r output/* /output"]

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README.md
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## Windows
docker container run -v $PWD/requirements.txt:/tsp/requirements.txt -v $PWD/output/:/output/ com2014-tsp
docker container run --rm -v /f/COM2014/template:/tsp python:3.8 /bin/bash -c "cd /tsp; ./run_all.sh"
## Linux
docker container run --rm -v $pwd:/tsp python:3.8 /bin/bash -c "cd /tsp; ./run_all.sh"
docker container run -v $PWD/requirements.txt:/tsp/requirements.txt -v $PWD/output/:/output/ com2014-tsp

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main.py
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import os
import argparse
import signal
import json
import matplotlib
# matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from utils import visualize_tsp
from model.anneal_model import SimAnneal
from model.my_model import MyModel
def write_to_file(solution, fitness):
with open('output/' + os.path.splitext(os.path.basename(args.tsp_file))[0] + '.txt', "w") as outfile:
outfile.write(", ".join(str(item) for item in solution))
outfile.write("\n")
outfile.write(str(fitness))
def plot_learning(fitness_list):
"""
Plot the fitness through iterations.
"""
plt.plot([i for i in range(len(fitness_list))], fitness_list)
plt.ylabel("Fitness")
plt.xlabel("Iteration")
plt.show()
def log(msg):
print('[*] {msg}'.format(msg=msg))
def load_data(file):
coords = []
@ -42,57 +23,99 @@ def load_data(file):
coords.append(line)
return coords
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='TSP Solver')
parser.add_argument(
'tsp_file',
type=str,
help='Path to tsp file.'
)
parser.add_argument(
'--plot',
action="store_true",
help='Plot results'
)
args = parser.parse_args()
def timeout_handler(signum, frame):
print("Timeout")
raise Exception("Timeout")
def TSP(tsp_file, model):
coords = load_data(args.tsp_file)
best_solution = []
fitness_list = []
# model = MyModel(coords)
# best_solution, fitness_list = model.fit(max_it=100000)
coords = load_data(tsp_file)
sa = SimAnneal(coords)
best_solution, fitness_list = sa.fit(max_it=100000)
# Set timeout
signal.signal(signal.SIGALRM, timeout_handler)
signal.alarm(60)
if best_solution:
if args.plot:
visualize_tsp.plotTSP([best_solution], coords)
if fitness_list:
plot_learning(fitness_list)
print("Writing the best solution to file" )
write_to_file(best_solution, sa.fitness(best_solution))
data = {}
data['nodes'] = []
for i in range(len(coords)):
data['nodes'].append({
'title': str(i),
'id': i,
'x': int(coords[i][0]),
'y': int(coords[i][1])
})
data['edges'] = []
for i in range(len(best_solution)):
if i == len(best_solution)-1:
data['edges'].append({
'source': best_solution[i],
'target': best_solution[0]
})
# Try your algorithm
try:
model.init(coords)
best_solution, fitness_list = model.fit(max_it=100000)
except Exception as exc:
if(str(exc) == "Timeout"):
log("Timeout -3")
with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
outfile.write("-3")
else:
data['edges'].append({
'source': best_solution[i],
'target': best_solution[i+1]
print(exc)
log("Unkown -4")
with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
outfile.write("-4")
# Collect results
if(len(fitness_list) > 0):
log("Best: " + str(fitness_list[-1]))
if (len(best_solution) == 0):
log("No Answer -1")
with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
outfile.write("-1")
elif (len(best_solution) != len(coords)):
log("Invalid -2")
with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
outfile.write("-2")
else:
log("Writing the best solution to file" )
with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
outfile.write(str(model.fitness(best_solution)))
outfile.write("\n")
outfile.write(", ".join(str(item) for item in best_solution))
# Write to JSON
data = {}
data['nodes'] = []
for i in range(len(coords)):
data['nodes'].append({
'title': str(i),
'id': i,
'x': int(coords[i][0]),
'y': int(coords[i][1])
})
with open('output/' + os.path.splitext(os.path.basename(args.tsp_file))[0] + '.json', 'w') as outfile:
json.dump(data, outfile)
data['edges'] = []
for i in range(len(best_solution)):
if i == len(best_solution)-1:
data['edges'].append({
'source': best_solution[i],
'target': best_solution[0]
})
else:
data['edges'].append({
'source': best_solution[i],
'target': best_solution[i+1]
})
with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.json', 'w') as outfile:
json.dump(data, outfile)
if __name__ == "__main__":
for root, _, files in os.walk('./data'):
if(files):
for f in files:
# Get input file name
tsp_file = str(root) + '/' + str(f)
log(tsp_file)
# Your Model
# model = MyModel()
# Simulated Annealing
model = SimAnneal()
TSP(tsp_file, model)
print()

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model/anneal_model.py
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import math
import random
import matplotlib.pyplot as plt
from model.base_model import Model
class SimAnneal(Model):
def __init__(self, coords, T=-1, alpha=-1, stopping_T=-1):
super().__init__(coords)
def __init__(self, T=-1, alpha=-1, stopping_T=-1):
super().__init__()
self.iteration = 0
self.T = math.sqrt(self.N) if T == -1 else T
self.T_save = self.T # save inital T to reset if batch annealing is used
self.T = T
self.alpha = 0.995 if alpha == -1 else alpha
self.stopping_temperature = 1e-8 if stopping_T == -1 else stopping_T
def init(self, coords):
super().init(coords)
if (self.T == -1):
self.T = math.sqrt(self.N)
self.T_save = self.T # save inital T to reset if batch annealing is used
def initial_solution(self):
"""
Greedy algorithm to get an initial solution (closest-neighbour).

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model/base_model.py
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class Model:
def __init__(self, coords):
def __init__(self):
self.best_solution = []
self.best_fitness = float("Inf")
self.fitness_list = []
def init(self, coords):
self.coords = coords
self.N = len(coords)
self.nodes = [i for i in range(self.N)]
self.best_solution = None
self.best_fitness = float("Inf")
self.fitness_list = []
def dist(self, node_0, node_1):
"""
Euclidean distance between two nodes.

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model/my_model.py
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@ -3,11 +3,14 @@ import random
from model.base_model import Model
class MyModel(Model):
def __init__(self, coords):
super().__init__(coords)
def __init__(self):
super().__init__()
def init(self, coords):
"""
Put your initialization here.
"""
super().init(coords)
self.log("Nothing to initialize in your model now")
def fit(self, max_it=1000, visualize=False):

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34767.13377984314

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output/st70.json
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output/st70.txt
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63, 10, 66, 47, 53, 32, 59, 51, 9, 4, 52, 20, 16, 42, 40, 3, 17, 41, 5, 11, 33, 61, 60, 39, 38, 24, 44, 45, 26, 8, 67, 43, 29, 19, 13, 27, 7, 25, 48, 54, 18, 6, 31, 2, 1, 23, 14, 56, 62, 65, 21, 58, 37, 22, 12, 68, 30, 34, 69, 28, 35, 0, 15, 46, 36, 57, 49, 50, 55, 64

1
output/ulysses16.json
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{"nodes": [{"title": "0", "id": 0, "x": 38, "y": 20}, {"title": "1", "id": 1, "x": 39, "y": 26}, {"title": "2", "id": 2, "x": 40, "y": 25}, {"title": "3", "id": 3, "x": 36, "y": 23}, {"title": "4", "id": 4, "x": 33, "y": 10}, {"title": "5", "id": 5, "x": 37, "y": 12}, {"title": "6", "id": 6, "x": 38, "y": 13}, {"title": "7", "id": 7, "x": 37, "y": 20}, {"title": "8", "id": 8, "x": 41, "y": 9}, {"title": "9", "id": 9, "x": 41, "y": 13}, {"title": "10", "id": 10, "x": 36, "y": -5}, {"title": "11", "id": 11, "x": 38, "y": 15}, {"title": "12", "id": 12, "x": 38, "y": 15}, {"title": "13", "id": 13, "x": 37, "y": 15}, {"title": "14", "id": 14, "x": 35, "y": 14}, {"title": "15", "id": 15, "x": 39, "y": 19}], "edges": [{"source": 7, "target": 3}, {"source": 3, "target": 1}, {"source": 1, "target": 2}, {"source": 2, "target": 0}, {"source": 0, "target": 15}, {"source": 15, "target": 12}, {"source": 12, "target": 11}, {"source": 11, "target": 6}, {"source": 6, "target": 5}, {"source": 5, "target": 9}, {"source": 9, "target": 8}, {"source": 8, "target": 10}, {"source": 10, "target": 4}, {"source": 4, "target": 14}, {"source": 14, "target": 13}, {"source": 13, "target": 7}]}

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output/ulysses16.txt
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7, 3, 1, 2, 0, 15, 12, 11, 6, 5, 9, 8, 10, 4, 14, 13

4
requirements.txt
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matplotlib
scikit-learn
scipy
numpy

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run_all.sh
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#!/bin/bash
pip install -r requirements.txt
python main.py data/simple/ulysses16.tsp
python main.py data/simple/att48.tsp
python main.py data/simple/st70.tsp
python main.py data/medium/a280.tsp
python main.py data/medium/pcb442.tsp
python main.py data/hard/dsj1000.tsp

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utils/visualize_tsp.py
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import matplotlib.pyplot as plt
def plot_learning(fitness_list):
"""
Plot the fitness through iterations.
"""
plt.plot([i for i in range(len(fitness_list))], fitness_list)
plt.ylabel("Fitness")
plt.xlabel("Iteration")
plt.show()
def plotTSP(paths, points, num_iters=1):
"""