def evaluate(self, jobs):
'''
Evaluates a given list of jobs, avoiding calculating duplicates.
:param jobs: list of jobs with scores to be computed
:return: list of individuals with scores computed
'''
self.results = []
job_keys = set(map(lambda x: x.get_key(), jobs))
inner_jobs = []
for phenotype, epochs, seed in job_keys:
individual = Individual(phenotype)
j = Job(individual, epochs, seed)
inner_jobs.append(j)
inner_results = {}
expected_results = len(inner_jobs)
while len(inner_results) < expected_results:
# makes adding new workers while the script
# is running possible
self.__add_workers()
for worker in self.workers[:]:
if worker.is_available() and inner_jobs:
job = inner_jobs.pop()
print("Worker available. Assigning job ({}/{}): {}".format(
expected_results - len(inner_jobs), expected_results,
job))
success = worker.assign_job(job)
if not success:
print("Worker %s broken in assigning job,"
" removing from pool and reasigning job" %
worker.ip)
inner_jobs.append(worker.get_current_job())
self.workers.remove(worker)
for worker in self.workers[:]:
try:
if not worker.is_available():
if worker.has_result():
result = worker.get_result()
job = worker.get_current_job()
inner_results[job.get_key()] = result
worker.free_worker()
except Exception as e:
print(e)
print("Worker %s broken in result collection,"
" removing from pool and reasigning job" % worker.ip)
inner_jobs.append(worker.get_current_job())
self.workers.remove(worker)
time.sleep(1)
for job in jobs:
key = job.get_key()
result_copy = copy.deepcopy(inner_results[key])
self.results.append(result_copy)
return self.results
def evaluate_scores(population,
workmanager,
computed_scores,
m=5,
layers=4,
seed=1337):
jobs = []
computed = []
for individual in population:
get_already_computed(individual, computed_scores, m, layers)
if not individual.score:
jobs.append(Job(individual, 1, seed))
else:
computed.append(individual)
computed.extend(workmanager.evaluate(jobs))
return computed
from master.queue_handling import WorkManager
from common.helper_classes import Individual, Job
wm = WorkManager()
i1 = Individual((1,1,1,1))
i2 = Individual((1,1,1,1))
j1 = Job(i1)
j2 = Job(i2)
l = [j1, j2]
a = wm.evaluate(l)
for r in a:
print(r)
from common.helper_classes import Job, Individual
from queue_handling import WorkManager
i = Individual((1, 1, 1, 1))
j = Job(i)
wm = WorkManager()
scores = []
jobs = []
for i in range(100):
jobs.append(j)
population = wm.evaluate(jobs)
with open("variance.txt", 'w') as f:
for individual in population:
f.write("%s\n" % individual.score)
import sys
from common.helper_classes import Job, Individual
import master.queue_handling as qh
from master.csv_handling import *
sys.path.append("..")
if __name__ == '__main__':
filename = "cnn1.csv"
codes_to_calculate = get_set_of_codes_from_filename(filename, N=100)
jobs = []
for code in set(codes_to_calculate):
jobs.append(Job(Individual(genotype_to_phenotype(code, n=4, m=5)), epochs=10, seed=1337))
wm = qh.WorkManager()
calculated = wm.evaluate(jobs)
save_csv_and_history("zbiorczy.csv", calculated, iteration=0, n=4, m=5)