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job_availability.py
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job_availability.py
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from random import random, randrange, seed
from multiprocessing import Pool
from models import *
from methods import *
import argparse
num_professions = 2 # This is a constant; changing it requires
# further code modifications
num_agents = 100
prof1 = 50
prof2 = num_agents - prof1
professions = [0] * prof1 + [1] * prof2
num_localities = 10 # Fix localities to 10 localities with cap 10 each
locality_caps = [10] * 10
random_samples = 1000
Real_evaluation_samples=10000
def _distribute_jobs(prof1_jobs, prof2_jobs):
job_numbers = [(0, 0)] * num_localities
for _ in range(prof1_jobs):
l = randrange(num_localities)
prof1, prof2 = job_numbers[l]
job_numbers[l] = (prof1 + 1, prof2)
for _ in range(prof2_jobs):
l = randrange(num_localities)
prof1, prof2 = job_numbers[l]
job_numbers[l] = (prof1, prof2 + 1)
return job_numbers
class CorrectionFunctionWrapper():
def __init__(self, P):
self.P = P
def func(self, x):
return min(x, self.P)
def test_correction(prof1_jobs, prof2_jobs):
job_numbers = _distribute_jobs(prof1_jobs, prof2_jobs)
qualification_probabilities = \
[[random()] * num_localities for _ in range(num_agents)]
correction_functions = []
for p1, p2 in job_numbers:
correction_functions.append((CorrectionFunctionWrapper(p1), CorrectionFunctionWrapper(p2)))
model = RetroactiveCorrectionModel(num_agents, locality_caps,
num_professions, professions,
qualification_probabilities,
correction_functions,
random_samples,Real_evaluation_samples)
return model
def test_interview(prof1_jobs, prof2_jobs):
job_numbers = _distribute_jobs(prof1_jobs, prof2_jobs)
compatibility_probabilities = [random() for _ in range(num_agents)]
model = InterviewModel(num_agents, locality_caps, num_professions,
professions, job_numbers,
compatibility_probabilities, random_samples,Real_evaluation_samples)
return model
def test_coordination(prof1_jobs, prof2_jobs):
global prof1
global prof2
job_numbers = _distribute_jobs(prof1_jobs, prof2_jobs)
locality_num_jobs = [prof1 + prof2 for prof1, prof2 in job_numbers]
compatibility_probabilities = []
for _ in range(prof1):
competency = random()
compatibility_probabilities.append(
[[competency] * p1 + [0.] * p2 for p1, p2 in job_numbers])
for _ in range(prof2):
competency = random()
compatibility_probabilities.append(
[[0.] * p1 + [competency] * p2 for p1, p2 in job_numbers])
model = CoordinationModel(num_agents, locality_caps,
locality_num_jobs,
compatibility_probabilities,
random_samples,Real_evaluation_samples)
return model
def main(args):
settings = {"correction": test_correction, "interview": test_interview,
"coordination": test_coordination}
#重点
seed(args.seed)
m = settings[args.set](args.job_pro1,args.job_pro2)
print(args.variant)
if (args.variant == "True"):
flag = True
else:
flag = False
if args.alg == "add":
additive_optimization(m, args.dir, args.p, args.set, args.index)
elif args.alg == "greedy":
greedy_algorithm(m, args.dir, args.p, args.set, args.index)
elif args.alg == "nsga2":
nsga2_method(m, args.dir, args.p, args.set, args.index,False)
elif args.alg == "gsemo" or args.alg == "gsemo_col" or args.alg == "gsemo_row" or args.alg == "gsemo_row_col":
gsemo_algorithm(m, args.dir, args.p, args.set, args.index, args.alg, flag)
elif args.alg == "moead":
moead(m, args.dir, args.p, args.set, args.index)
else :
print("wrong algorithm name")
if __name__ == '__main__':
argparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
argparser.add_argument('-alg', type=str,default="moead")
argparser.add_argument('-dir', help="folder", type=str)
argparser.add_argument('-set', type=str, help="model name",default="correction")
argparser.add_argument('-p', help="folder",type=int,default="1")
argparser.add_argument('-index', help="instance",type=int,default="0")
argparser.add_argument('-seed',help="seed_of_instance",type=float,default=0)
argparser.add_argument('-job_pro1', help="prof1_jobs", type=int, default="1")
argparser.add_argument('-job_pro2', help="prof2_jobs", type=int, default="1")
argparser.add_argument('-variant', type=str, default="False")
args = argparser.parse_args()
main(args)