def populate(self, *args, **kwargs):
"""
"""
user = kwargs.pop('user_data')
social = kwargs.pop('socialbar_list')
taverns = kwargs.pop('taverns')
city = kwargs.pop('city_map')
resources = kwargs.pop('resources')
for key in ['player_id', 'user_name']:
setattr(self, key, user[key])
for key in kwargs.keys():
kwargs.pop(key)
# City
if not self.city:
self.city = City(account=self)
self.city.update(**city)
# Players
for raw_player in social:
player = self.session.query(Player).filter_by(
player_id=raw_player['player_id']).first()
if not player:
player = Player(account=self)
player.update(**raw_player)
# Taverns
for raw_tavern in taverns:
tavern = self.session.query(Tavern).filter_by(
ownerId=raw_tavern['ownerId']).first()
if not tavern:
tavern = Tavern(account=self)
tavern.update(**raw_tavern)
# Resources
if not self.resources:
self.resources = Resources(account=self)
self.resources.update(**resources)
return super(Account, self).populate(*args, **kwargs)
def empty_activity(self):
return Activity(index=0,
start=0,
duration=0,
precedence=[0],
resources=Resources([0, 0, 0]),
active=False,
completed=False,
eleg=False,
end=0)
def set_pob_from_json(self):
## Test: D:\VS\Proyecto SGS UIS\SGS Proy UIS\Resources\instancej301_2.json
print('\n')
self.experimental = False
self.file_name = input(
'Ingrese la instancia a ejecutar (ej. j603_5): ')
instance: Instance = futils.get_instance_from_file(file_name)
self.resources: Resources = Resources(instance.resources)
for i in range(self.nPob):
activities = copy.deepcopy(instance.activities)
[act.set_default_values() for act in activities]
[act.set_duration_beta() for act in activities]
genes = self.get_single_sgs_serie(activities)
self.chromosomes.append(Chromosome(genes=genes))
# TODO: Take the activities on the list and produce the rest of chromosomes.
print('Json loaded')
def sgs_parallel():
print('\n|------- Ejecutando Modelo SGS en Paralelo ------ |')
j30 = J30()
instances = {
1: ['j3014_3', j30.j3014_3],
2: ['j3019_5', j30.j3019_5],
3: ['j301_10', j30.j301_10],
4: ['j301_2', j30.j301_2],
5: ['j3020_7', j30.j3020_7],
6: ['j3023_9', j30.j3023_9],
7: ['j3037_8', j30.j3037_8],
8: ['j3044_9', j30.j3044_9],
9: ['j3047_6', j30.j3047_6],
10: ['j306_3', j30.j306_3],
11: ['j6011_8', j30.j6011_8],
12: ['j6012_10', j30.j6012_10],
13: ['j6014_2', j30.j6014_2],
14: ['j6017_6', j30.j6017_6],
15: ['j601_2', j30.j601_2],
16: ['j601_6', j30.j601_6],
17: ['j6022_10', j30.j6022_10],
18: ['j603_5', j30.j603_5],
19: ['j604_6', j30.j604_6],
20: ['j607_7', j30.j607_7]
}
keys = list(instances.values())
for i in range(len(keys)):
print(f'{i}. {(keys[i])[0]}')
option = int(input('Seleccione la instancia a evaluar'))
la = prepare_instance(instances.get(option))
paral = Parallel(la[0], Resources(la[1]), with_logs=True, single_esc=False)
paral.run()
class Genetic:
chromosomes: List[Chromosome]
## List of the last activity that indicates the makespan of each
## choromosome
makespan: List[Activity]
## Best makespan obtained so far.
best_makespan: List[Activity]
## Fitness ending time of the best last activity
fitness_reference: Activity
## Number of generations
generations: int
##
nPob: int
## mutation rate
mutation_rate: float
## Resources default
resources = Resources([10, 10, 5])
## Experimental?
experimental = True
file_name: str
def __init__(self, nPob: int, generations, mutation_rate: float = 0.3):
if (not nPob % 2 == 0 or not nPob > 2):
raise Exception(
f'The populations must be an even number {nPob} and greater than 2'
)
self.nPob = nPob
self.chromosomes = []
self.makespan = []
self.best_makespan = []
self.generations = generations
self.mutation_rate = mutation_rate
self.fitness_reference = Activity.empty_activity()
def set_experimental_pob(self):
""" Sets the list of chromosomes in the genetic algorithm """
for i in range(0, self.nPob):
print(f'Chromosome: {i}')
chromosome = self.set_single_chromosome()
prnt.print_activities(chromosome.genes)
self.chromosomes.append(chromosome)
def set_pob_from_json(self):
## Test: D:\VS\Proyecto SGS UIS\SGS Proy UIS\Resources\instancej301_2.json
print('\n')
self.experimental = False
self.file_name = input(
'Ingrese la instancia a ejecutar (ej. j603_5): ')
instance: Instance = futils.get_instance_from_file(file_name)
self.resources: Resources = Resources(instance.resources)
for i in range(self.nPob):
activities = copy.deepcopy(instance.activities)
[act.set_default_values() for act in activities]
[act.set_duration_beta() for act in activities]
genes = self.get_single_sgs_serie(activities)
self.chromosomes.append(Chromosome(genes=genes))
# TODO: Take the activities on the list and produce the rest of chromosomes.
print('Json loaded')
def set_single_chromosome(self) -> Chromosome:
"""Creates a single chromosome, this process runs 1000 times to get a good average"""
## Initial list, range goes from 0 to 999 discounting this initial
## asignment
chromosome = Chromosome(genes=self.get_single_sgs_serie())
## Division by 1001 due to the extra sample that is being taken
## (initial list).
for act in chromosome.genes:
act.start /= 1001
initialTime = time.time()
for i in range(0, 5):
print(f'Progress: {(i+1)/20}%')
res: List[Chromosome] = self.multi_threading(threads=2)
print(f'Number of lists: {len(res)}')
for ch in res:
for j in range(0, len(ch.genes)):
chromosome.genes[j].start += ch.genes[j].start / 101
print()
print(
f'Time elapsed for creating one Chromosome: \n {time.time()-initialTime}s\n'
)
return chromosome
def get_single_sgs_serie(self,
activities: List[Activity]) -> List[Activity]:
"""Runs the SGS Algorithm once, returns a list of activities representing it."""
serie = Serie(activities,
self.resources,
False,
experimental=self.experimental)
return serie.run()
def get_sgs_serie(self) -> List[List[Activity]]:
print('Thread started')
res: List[List[Activity]] = []
for i in range(0, 10):
activities = gen_act.gen_activities()
res.append(self.get_single_sgs_serie(activities))
print('Thread ended')
return res
def multi_threading(self, threads: int = 3) -> List[Chromosome]:
thread_list = []
for i in range(0, threads):
t = ThreadWithReturn(target=self.get_sgs_serie)
t.start()
thread_list.append(t)
res: List[Chromosome] = []
for t in thread_list:
for items in t.join():
res.append(Chromosome(genes=items))
return res
def run_parallel(self):
self.makespan.clear()
chromosomes: List[Chromosome] = []
for chromosome in self.chromosomes:
genes = copy.deepcopy(chromosome.genes)
parallel = Parallel(activities=genes,
resources=self.resources,
with_logs=False)
scheduled: List[Activity] = parallel.run()
self.makespan.append(scheduled[len(scheduled) - 1])
chromosome.genes = copy.deepcopy(scheduled)
chromosomes.append(chromosome)
del parallel
self.chromosomes.clear()
self.chromosomes = chromosomes
def set_best_makespan(self):
## List sorted to get the best end
makespan_sorted = sorted(self.makespan, key=self.get_end, reverse=True)
current_best: Activity = Activity.empty_activity()
current_best.end = 1000000
index_current_best = 0
if not len(self.best_makespan) == 0:
current_best: Activity = self.best_makespan[len(self.best_makespan)
- 1]
new_best: Activity = makespan_sorted[0]
is_better = new_best.end < current_best.end
if is_better:
index: int = self.makespan.index(new_best)
self.best_makespan = copy.deepcopy(self.chromosomes[index].genes)
self.fitness_reference = copy.deepcopy(new_best)
for chromosome in self.chromosomes:
act = max(chromosome.genes, key=self.get_end)
new_fitness = float(new_best.end) / float(act.end)
chromosome.fitness = new_fitness
#print(f'new fitness {new_fitness}')
print('\n\nCurrent best: ')
current_best.print_activity()
print('New best: ')
self.best_makespan[len(self.best_makespan) - 1].print_activity()
print('\n')
def get_end(self, act: Activity):
return act.end
def parent_selection() -> List[List[Activity]]:
"""Gets the best 50% of the population"""
parents: List[List]
def cross_pob(self):
print('### Starting crossing.... #####')
new_pob: List[Chromosome] = []
for i in range(0, len(self.chromosomes), 2):
pair = self.get_crossed(self.chromosomes[i],
self.chromosomes[i + 1])
for chromosome in pair:
new_pob.append(chromosome)
return new_pob
def get_crossed(self, chromosome1: Chromosome, chromosome2: Chromosome):
genes_len = len(chromosome1.genes) - 1
cross_point1 = random.randint(1, genes_len)
cross_point2 = random.randint(cross_point1, genes_len)
mutation_index = random.randint(0, 1)
chromosome1.genes.sort(key=operator.attrgetter('index'))
chromosome2.genes.sort(key=operator.attrgetter('index'))
#prnt.print_activities(chromosome1.genes)
#prnt.print_activities(chromosome2.genes)
#print(f'Cross Point 1: {cross_point1}, Cross Point 2: {cross_point2}')
if mutation_index == 0:
self.mutate_chromosome(chromosome1)
if mutation_index == 1:
self.mutate_chromosome(chromosome2)
for i in range(cross_point1, cross_point2):
genes1 = copy.deepcopy(chromosome1.genes[i])
genes2 = copy.deepcopy(chromosome2.genes[i])
chromosome1.genes[i] = genes2
chromosome2.genes[i] = genes1
#print('Chromosome 1: ')
#prnt.print_activities(chromosome1.genes)
#print('Chromosome 2: ')
#prnt.print_activities(chromosome2.genes)
return [chromosome1, chromosome2]
def mutate_chromosome(self, chromosome: Chromosome, places: int = 1):
"""Mutates a chromosome by moving 10% of the activities a random position between 1-3 places forward
chromosome -- List[Activitiy] Chromosome to be mutated.
places -- int Upper limit of places the activity will be moved. 3 by default
"""
chromosome_to_mutate = copy.deepcopy(chromosome)
## Determine how many places each gene is going to be moved
position = random.randint(1, places)
chromosome_len: int = len(chromosome_to_mutate.genes)
## Number of genes to be moved.
number_of_genes = round(chromosome_len * self.mutation_rate)
for i in range(0, number_of_genes):
gene_index = random.randint(1, chromosome_len - 2)
new_index = gene_index + position
## Checks that the new index doesn't surpass the len of the array
if (new_index >= chromosome_len - 1):
new_index = new_index - chromosome_len + 2 # Moves to extra positions to avoid first activity
gene1 = chromosome_to_mutate.genes[gene_index]
gene2 = chromosome_to_mutate.genes[new_index]
## Updates the new indexes
gene1.index = new_index + 1
gene2.index = gene_index + 1
## Sets the genes in the chromosome
chromosome_to_mutate.genes[new_index] = gene1
chromosome_to_mutate.genes[gene_index] = gene2
#print('After mutation: ')
#prnt.print_activities(chromosome_to_mutate.genes)
if (not chromosome_to_mutate.is_a_valid_chromosome()):
#print('Chromosome not valid')
return
# print('After mutation: ')
# prnt.print_activities(chromosome.genes)
chromosome = chromosome_to_mutate
def menu(self):
print('\n')
print(
'| Running genetic algorithm, select one of the following options')
print(
'| 1. Run with simulated data ................................. |')
print(
'| 2. Run with model from JsonFile .............................|')
print(
'| 3. Exit .....................................................|')
print('\n')
option = int(input('Option: '))
if option <= 0 and option > 2:
sys.exit()
evaluate = {
1: self.set_experimental_pob,
2: self.set_pob_from_json,
3: sys.exit
}
result = evaluate.get(option)
result()
def set_risk(self, activities: List[Activity]):
n_activities = []
r1 = []
r2 = []
r3 = []
risk_1 = 0.5
risk_2 = 0.6
if (len(activities) == 32):
r1 = [3]
r2 = [5, 16, 22, 29, 18]
r3 = [4, 9, 17, 29]
elif (len(activities) == 62):
r1 = [2, 3, 6, 11, 27, 30, 33, 41, 45, 61]
r2 = [12, 14, 20, 29, 38, 50, 51, 56]
r3 = [25, 43]
else:
[act.set_risk3(0, 0) for act in activities]
for i in r1:
[act.set_risk1(risk_1) for act in activities if act.index == i]
for i in r2:
[act.set_risk2(risk_2) for act in activities if act.index == i]
for i in r3:
[
act.set_risk3(risk_1, risk_2) for act in activities
if act.index == i
]
def run_genetic(self):
print('......')
self.menu()
for i in range(0, self.generations):
## Each chromosome is run using the parallel mode.
self.run_parallel()
## Sets the Best Makespan
self.set_best_makespan()
## Choose the parents
## Crossing
crossed_and_mutated = copy.deepcopy(self.cross_pob())
self.chromosomes.clear()
self.chromosomes = crossed_and_mutated
print(f'Current Generation: {i}')
print(f'Current fitness: {self.fitness_reference.end}')
# Termina genético
self.set_risk(self.best_makespan)
p = Parallel(self.best_makespan,
self.resources,
with_logs=True,
single_esc=False)
p.run()
import utils.print as prt
import utils.activities_utils as gen_act
import utils.file_uitls as futils
from solutions.j30 import J30
from scipy.stats import beta
import copy
#Genetics
import utils.activities_utils as acts
#SGS
from sgs.parallel import Parallel
from sgs.serie import Serie
from genetics.genetic import Genetic
resources = Resources([10, 10, 5])
def sgs_serie():
print('|------- Ejecutando Modelo SGS en Serie ------ |')
activitties = gen_act.gen_activities()
serie = Serie(activitties, resources, True)
serie.run()
def prepare_instance(instance):
original_instance = futils.get_instance_from_file(instance[0])
for i in range(len(instance[1])):
act: Activity = ([act for act in instance[1] if act.index == i + 1])[0]
act_or: Activity = [
act for act in original_instance.activities if act.index == i + 1
class Account(Model):
"""
"""
REQUEST_CLASS = "StartupService"
__tablename__ = 'account'
# Attributes
# ---------------------------------------------------------
player_id = Column(Integer, primary_key=True, default=0)
id = Column(String, default=0)
user_name = Column(String, default='', unique=True)
# Back-refs
# ---------------------------------------------------------
# Containers
# ---------------------------------------------------------
city = relationship(City,
backref=backref('account', uselist=False),
uselist=False)
players = relationship(Player, backref=backref('account', uselist=False))
taverns = relationship(Tavern, backref=backref('account', uselist=False))
resources = relationship(Resources,
backref=backref('account', uselist=False),
uselist=False)
def __init__(self, *args, **kwargs):
"""
"""
return super(Account, self).__init__(*args, **kwargs)
def __repr__(self):
"""
"""
return "Account %s (%s)" % (self.player_id, self.user_name)
def fetch(self):
"""
Does a HTTP request to get the start up blob for the city, then populates the models
"""
print "%s fetching..." % (self)
timer = time.time()
data = self.request('getData', [])
account = Request.service(data, 'StartupService')
account['taverns'] = Request.method(data, 'getOtherTavernStates')
account['resources'] = Request.method(
data, 'getPlayerResources')['resources']
self.update(**account)
print "%s fetched in %.2fs" % (self, time.time() - timer)
return self
def updateFromResponse(self, data):
"""
"""
if not data:
return self
resources = Request.method(data, 'getPlayerResources')['resources']
if resources:
self.resources.update(**resources)
return self
def populate(self, *args, **kwargs):
"""
"""
user = kwargs.pop('user_data')
social = kwargs.pop('socialbar_list')
taverns = kwargs.pop('taverns')
city = kwargs.pop('city_map')
resources = kwargs.pop('resources')
for key in ['player_id', 'user_name']:
setattr(self, key, user[key])
for key in kwargs.keys():
kwargs.pop(key)
# City
if not self.city:
self.city = City(account=self)
self.city.update(**city)
# Players
for raw_player in social:
player = self.session.query(Player).filter_by(
player_id=raw_player['player_id']).first()
if not player:
player = Player(account=self)
player.update(**raw_player)
# Taverns
for raw_tavern in taverns:
tavern = self.session.query(Tavern).filter_by(
ownerId=raw_tavern['ownerId']).first()
if not tavern:
tavern = Tavern(account=self)
tavern.update(**raw_tavern)
# Resources
if not self.resources:
self.resources = Resources(account=self)
self.resources.update(**resources)
return super(Account, self).populate(*args, **kwargs)
