19 KiB
19 KiB
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Make sure you run this at the begining
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import os
import sys
import math
import numpy as np
import matplotlib.pyplot as plt
# Append template path to sys path
sys.path.append(os.getcwd() + "/template")
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from utils.load_data import load_data
from utils.visualize_tsp import plotTSP
from tsp import TSP_Bench_ONE
from tsp import TSP_Bench_PATH
from tsp import TSP_Bench_ALL
Workshop Starts Here¶
Get familiar with your dataset¶
There are problems at different levels. 3 easy, 2 medium, 1 difficult.
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for root, _, files in os.walk('./template/data'):
if(files):
for f in files:
print(str(root) + "/" + f)
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ulysses16 = np.array(load_data("./template/data/easy/ulysses16.tsp"))
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ulysses16[:]
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plt.scatter(ulysses16[:, 0], ulysses16[:, 1])
for i in range(0, 16):
plt.annotate(i, (ulysses16[i, 0], ulysses16[i, 1]+0.5))
Naive Solution: In Order¶
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simple_sequence = list(range(0, 16))
print(simple_sequence)
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plotTSP([simple_sequence], ulysses16, num_iters=1)
Naive Solution: Random Permutation¶
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random_permutation = np.random.permutation(16).tolist()
print(random_permutation)
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plotTSP([random_permutation], ulysses16, num_iters=1)
Best Solution¶
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best_ulysses16 = [0, 13, 12, 11, 6, 5, 14, 4, 10, 8, 9, 15, 2, 1, 3, 7]
plotTSP([best_ulysses16], ulysses16, num_iters=1)
Calculate Fitness (Sum of all Distances)¶
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def dist(node_0, node_1, coords):
"""
Euclidean distance between two nodes.
"""
coord_0, coord_1 = coords[node_0], coords[node_1]
return math.sqrt((coord_0[0] - coord_1[0]) ** 2 + (coord_0[1] - coord_1[1]) ** 2)
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print("Coordinate of City 0:", ulysses16[0])
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print("Coordinate of City 1:", ulysses16[1])
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print("Distance Between", dist(0, 1, ulysses16))
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def fitness(solution, coords):
N = len(coords)
cur_fit = 0
for i in range(len(solution)):
cur_fit += dist(solution[i % N], solution[(i + 1) % N], coords)
return cur_fit
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print ("Order Fitness:\t", fitness(simple_sequence, ulysses16))
print ("Random Fitness:\t", fitness(random_permutation, ulysses16))
print ("Best Fitness:\t", fitness(best_ulysses16, ulysses16))
Naive Random Model¶
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import math
import random
from model.base_model import Model
import numpy as np
class MyRandomModel(Model):
def __init__(self):
super().__init__()
def init(self, nodes):
"""
Put your initialization here.
"""
super().init(nodes)
def fit(self, max_it=1000):
"""
Put your iteration process here.
"""
random_solutions = []
for i in range(0, max_it):
solution = np.random.permutation(self.N).tolist()
random_solutions.append(solution)
self.fitness_list.append(self.fitness(solution))
self.best_solution = random_solutions[self.fitness_list.index(min(self.fitness_list))]
return self.best_solution, self.fitness_list
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tsp_file = './template/data/easy/ulysses16.tsp'
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best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyRandomModel)
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plt.plot(fitness_list, 'o-')
Genetic Algorithm¶
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import math
import random
from model.base_model import Model
from random import randint, sample
class Gene: # City
def __init__(self, name, lat, lng):
self.name = name
self.lat = lat
self.lng = lng
def get_distance_to(self, dest):
return math.sqrt( (self.lng - dest.lng) ** 2 + (self.lat - dest.lat) ** 2 )
class Individual: # Route: possible solution to TSP
def __init__(self, genes):
assert(len(genes) > 3)
self.genes = genes
self.__reset_params()
def swap(self, gene_1, gene_2):
self.genes[0]
a, b = self.genes.index(gene_1), self.genes.index(gene_2)
self.genes[b], self.genes[a] = self.genes[a], self.genes[b]
self.__reset_params()
def add(self, gene):
self.genes.append(gene)
self.__reset_params()
@property
def fitness(self):
if self.__fitness == 0:
self.__fitness = 1 / self.travel_cost # Normalize travel cost
return self.__fitness
@property
def travel_cost(self): # Get total travelling cost
if self.__travel_cost == 0:
for i in range(len(self.genes)):
origin = self.genes[i]
if i == len(self.genes) - 1:
dest = self.genes[0]
else:
dest = self.genes[i+1]
self.__travel_cost += origin.get_distance_to(dest)
return self.__travel_cost
def __reset_params(self):
self.__travel_cost = 0
self.__fitness = 0
class Population: # Population of individuals
def __init__(self, individuals):
self.individuals = individuals
@staticmethod
def gen_individuals(sz, genes):
individuals = []
for _ in range(sz):
individuals.append(Individual(sample(genes, len(genes))))
return Population(individuals)
def add(self, route):
self.individuals.append(route)
def rmv(self, route):
self.individuals.remove(route)
def get_fittest(self):
fittest = self.individuals[0]
for route in self.individuals:
if route.fitness > fittest.fitness:
fittest = route
return fittest
class MyGAModel(Model):
def __init__(self):
super().__init__()
self.iteration = 0
def init(self, nodes):
super().init(nodes)
def evolve(self, pop, tourn_size, mut_rate):
new_generation = Population([])
pop_size = len(pop.individuals)
elitism_num = pop_size // 4
# Elitism
for _ in range(elitism_num):
fittest = pop.get_fittest()
new_generation.add(fittest)
pop.rmv(fittest)
# Crossover
for _ in range(elitism_num, pop_size):
parent_1 = self.selection(new_generation, tourn_size)
parent_2 = self.selection(new_generation, tourn_size)
child = self.crossover(parent_1, parent_2)
new_generation.add(child)
# Mutation
for i in range(elitism_num, pop_size):
self.mutate(new_generation.individuals[i], mut_rate)
return new_generation
def crossover(self, parent_1, parent_2):
def fill_with_parent1_genes(child, parent, genes_n):
start_at = randint(0, len(parent.genes)-genes_n-1)
finish_at = start_at + genes_n
for i in range(start_at, finish_at):
child.genes[i] = parent_1.genes[i]
def fill_with_parent2_genes(child, parent):
j = 0
for i in range(0, len(parent.genes)):
if child.genes[i] == None:
while parent.genes[j] in child.genes:
j += 1
child.genes[i] = parent.genes[j]
j += 1
genes_n = len(parent_1.genes)
child = Individual([None for _ in range(genes_n)])
fill_with_parent1_genes(child, parent_1, genes_n // 2)
fill_with_parent2_genes(child, parent_2)
return child
def mutate(self, individual, rate):
for _ in range(len(individual.genes)):
if random.random() < rate:
sel_genes = sample(individual.genes, 2)
individual.swap(sel_genes[0], sel_genes[1])
def selection(self, population, competitors_n):
return Population(sample(population.individuals, competitors_n)).get_fittest()
def fit(self, max_it=100):
"""
Execute simulated annealing algorithm.
"""
pop_size = 1000
mut_rate = 0.9
tourn_size = 100
genes = [Gene(num, city[0], city[1]) for num, city in enumerate(self.coords)]
self.genes = genes
population = Population.gen_individuals(pop_size, genes)
for it in range(0, max_it):
mut_rate = mut_rate * 0.95
if mut_rate < 0.05:
mut_rate = 0.05
population = self.evolve(population, tourn_size, mut_rate)
cost = population.get_fittest().travel_cost
it += 1
self.fitness_list.append(cost)
print("[step] ", it, " [mut] ", mut_rate, " [best] ", self.fitness_list[self.fitness_list.index(min(self.fitness_list))])
self.best_solution = [gene.name for gene in population.get_fittest().genes]
return self.best_solution, self.fitness_list
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tsp_problem = "./template/data/easy/ulysses16.tsp"
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print("Genetic Algorithm")
best_solution, fitness_list, time = TSP_Bench_ONE(tsp_problem, MyGAModel, timeout=300)
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plt.plot(fitness_list, 'o-')
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plotTSP([best_solution], load_data(tsp_problem), num_iters=1)
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print ("Best Fitness:\t", fitness(best_solution, ulysses16))
print ("Best Fitness:\t", fitness(best_ulysses16, ulysses16))
Your Smart Model¶
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import math
import random
from model.base_model import Model
class MyModel(Model):
def __init__(self):
super().__init__()
def init(self, nodes):
"""
Put your initialization here.
"""
super().init(nodes)
self.log("Nothing to initialize in your model now")
def fit(self, max_it=1000):
"""
Put your iteration process here.
"""
self.best_solution = np.random.permutation(self.N).tolist()
self.fitness_list.append(self.fitness(self.best_solution))
return self.best_solution, self.fitness_list
Test your Model¶
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tsp_problem = './template/data/easy/ulysses16.tsp'
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best_solution, fitness_list, time = TSP_Bench_ONE(tsp_file, MyModel)
Test All Dataset¶
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tsp_path = './template'
for root, _, files in os.walk(tsp_path + '/data'):
if(files):
for f in files:
print(str(root) + "/" + f)
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def plot_results(best_solutions, times, title):
fig = plt.figure()
nodes = [len(s) for s in best_solutions]
data = np.array([[node, time] for node, time in sorted(zip(nodes, times))])
plt.plot(data[:, 0], data[:, 1], 'o-')
fig.suptitle(title, fontsize=20)
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print("Random Search")
best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyRandomModel)
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plot_results(best_solutions, times, "Random Model")
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print("Genetic Algorithm")
best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyGAModel)
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plot_results(best_solutions, times, "Genetic Algorithm")
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