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pso_collab_mp_run7.py
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pso_collab_mp_run7.py
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import numpy as np
import time
import os
import sch
import writer_data_porv50 as writer_data
import calc_economic
import pyDOE
from copy import deepcopy
import json
import multiprocessing
def re100(x,j):
input = x.vars
problem = x.cid
particle = j
iteration = x.itr
os.chdir("/private/hsut/private/opt/egg_base_collab_porv50/include/schedule")
sch.write_file(input, problem, particle, iteration)
os.chdir("/private/hsut/private/opt/egg_base_collab_porv50/model")
writer_data.write_file(input, problem, particle, iteration)
init=f"{problem}_{iteration}_{particle}"
deck=f"/private/hsut/private/opt/egg_base_collab_porv50/model/EGG_{problem}_{iteration}_{particle}.DATA"
runner= (f"runeclipse -p eclipse -q mr {deck}")
os.system(runner)
time.sleep(15)
prt=f"/private/hsut/private/opt/egg_base_collab_porv50/model/EGG_{problem}_{iteration}_{particle}.PRT"
while not os.path.exists(prt):
print(f"rerun eclipse problem {problem} iteration {iteration} particle {particle} ")
os.system(runner)
time.sleep(60)
while True:
with open(prt, "r") as file:
lines = file.readlines()
if len(lines)>0:
#print(f"found PRT problem {problem} iteration {iteration} particle {particle} ")
complete_line = lines[-1]
errors_line = lines[-4].strip()
bugs_line = lines[-3].strip()
if "Total number of time steps" in complete_line:
errors = int(errors_line.split()[1])
bugs = int(bugs_line.split()[1])
if errors > 0 or bugs > 0:
print(lines[-10:-1])
print(f"rerun error eclipse problem {problem} iteration {iteration} particle {particle} ")
os.system(runner)
time.sleep(10)
else:
#print(f"calculate co2 problem {problem} iteration {iteration} particle {particle} ")
simulation_result = calc_economic.NPV(deck, 10, 40, 0, init)
return simulation_result
#break
def calculatorco2(simulation_result,co2,gasprice):
npv=simulation_result.npv(0.15,60,gasprice,co2)
z=-npv
return z
out = "/private/hsut/private/opt/egg_base_collab_porv50/trialpso"
if not os.path.isdir(out):
#shutil.rmtree(out)
os.mkdir(out)
coop = "True" # This argument will decide to run either the collaborative or the non-collaborative PSO.
run = 13 #change from sys arg
out = out + "/C-PSO"
if not os.path.isdir(out):
os.mkdir(out)
out = out + "/Run" + str(run)
if not os.path.isdir(out):
os.mkdir(out)
nv = 15 #number of dimension / variable
nprob = 16 #number of problem
list_OF = [calculatorco2,calculatorco2,calculatorco2,calculatorco2,calculatorco2,calculatorco2,calculatorco2,calculatorco2,calculatorco2,calculatorco2,calculatorco2,calculatorco2,calculatorco2,calculatorco2,calculatorco2,calculatorco2]
#modifier in objective function
co2sens = [0,75,200,750,0,75,200,750,0,75,200,750,0,75,200,750] #USD / tonne
gassens = [1.5,1.5,1.5,1.5,2.5,2.5,2.5,2.5,4.0,4.0,4.0,4.0,7.5,7.5,7.5,7.5] #USD / MMBTU
list_args = []
#[10000,10000,10000,10000,10000,0.1,0.1,0.1,0.1,0.1,0,0,0,0,0]
lb = [1000,1000,1000,1000,1000,0,0,0,0,0,0,0,0,0,0]
ub = [10000,10000,10000,10000,10000,4,4,4,4,4,2,2,2,2,2]
fb = 0.5
N = 30 # number of particle depend on dimension
w = 0.5
cp = 2.0
cg = 2.0
vs = 0.5 #vmax definition
max_itr = 25
np.random.seed(run)
#initialize first position
init_vars = []
for i in range(nv):
lhs = pyDOE.lhs(1, samples=(nprob * N))
lhs = lhs.flatten()
lhs = lhs.tolist()
init = []
for j in range(nprob):
cntry = []
for k in range(N):
for l in range(len(lhs)):
rand = lhs[l]
if k / N <= rand < (k + 1) / N:
cntry.append(0 if rand * (ub[i] - lb[i]) + lb[i] < 0.001 else rand * (ub[i] - lb[i]) + lb[i]) #prevent eclipse from breaking down for < 0.001
lhs.remove(rand)
break
np.random.shuffle(cntry)
init.append(cntry)
init_vars.append(init)
#initialize velocity
vmax = vs * (np.array(ub) - np.array(lb))
vmax = vmax.tolist()
init_vels = []
for i in range(nv):
init = []
for j in range(nprob):
cntry = [] #country, 1 population
for k in range(N):
rand = np.random.rand()
cntry.append(- vmax[i] + rand * 2 * vmax[i])
init.append(cntry)
init_vels.append(init)
class Indv(object):
def __init__(self, itr, cid, vels, vars, ofvs, stat, src):
self.itr = itr
self.cid = cid
self.vels = vels
self.vars = vars
self.ofvs = ofvs
self.stat = stat
self.src = src
# Initialization
wrld = []
for i in range(nprob):
cntry = [] #initialize the one country
for j in range(N): #loop for all particle
itr = 0
cid = i
vels = []
vars = []
for k in range(nv): #loop for all dimensions
vels.append(init_vels[k][i][j])
vars.append(init_vars[k][i][j])
stat = "init"
src = -1
ofv = None
cntry.append(Indv(itr, cid, vels, vars, ofv, stat, src))
wrld.append(cntry)
#run heavy function
def runeclipse_wrapper(wrld, i, j):
#wrld, i, j = args
return re100(wrld[i][j],j) #world, problem, particle
#eclipse_result =[]
#for i in range(nprob):
# for j in range(N):
# result = runeclipse_wrapper(wrld, i, j)
# eclipse_result.append(result)
#print(0)
def runeclipse_parallel(wrld):
# Define the number of processes
num_processes = 30
# Create a Pool of processes
pool = multiprocessing.Pool(processes=num_processes)
# Create a list of tuples with the arguments for each process
args_list = [(wrld, i, j) for i in range(nprob) for j in range(N)]
#print(args_list)
# Apply the function to each argument tuple in parallel
heavy_function = []
for i, args in enumerate(args_list):
# Apply the function to the argument tuple in parallel
result = pool.apply_async(runeclipse_wrapper, args_list[i] )
heavy_function.append(result)
time.sleep(0.2)
#heavy_function = pool.map(runeclipse_wrapper, args_list)
#time.sleep(3)
heavy_function = [result.get() for result in heavy_function]
#heavy_function = list(heavy_function)
pool.close()
pool.join()
return heavy_function
eclipse_result = runeclipse_parallel(wrld)
os.system("rm -rf /private/hsut/private/opt/egg_base_collab_porv50/model/EGG_*_0_*")
print("finished heavy function")
mapped_lists = [eclipse_result[i:i+N] for i in range(0, len(eclipse_result), N)]
for i in range(nprob):
for j in range(N):
obv_fun = []
for k in range(nprob):
cal = mapped_lists[i][j].npv(0.15,60,gassens[k] ,co2sens[k] )
obv_fun.append(cal)
#print(f"calculating npv problem {i} particle {j} objective {k}")
wrld[i][j].ofvs = obv_fun
pbest = deepcopy(wrld)
gbest = []
for i in range(nprob):
cntry = wrld[i]
best = deepcopy(cntry[0])
for j in range(N):
indv = deepcopy(cntry[j])
if indv.ofvs[i] > best.ofvs[i]:
best = indv
gbest.append(best)
wrld_hst = deepcopy(wrld)
best_hst = []
for i in range(nprob):
best = deepcopy(gbest[i])
best_hst.append([best])
shrng_hst = []
for i in range(nprob):
shrng_hst.append([])
def update_pbest():
for i in range(nprob):
for j in range(N):
if wrld[i][j].ofvs[i] > pbest[i][j].ofvs[i]:
pbest[i][j] = deepcopy(wrld[i][j])
def list_cndts(p):
cndts = [] #list candidates
for i in range(nprob):
if i != p:
cntry = wrld[i]
for j in range(N):
indv = cntry[j]
cndts.append(deepcopy(indv))
return cndts
def sort(pop, p):
tuples = []
for i in range(len(pop)):
indv = pop[i]
ofv = indv.ofvs[p]
tuples.append((indv, ofv))
sorted_tuples = sorted(tuples, key=lambda item: item[1], reverse=True)
sorted_pop = []
for i in range(len(pop)):
indv = sorted_tuples[i][0]
sorted_pop.append(deepcopy(indv))
return sorted_pop
def improve_gbest():
for i in range(nprob):
cndts = list_cndts(i)
sorted_cndts = sort(cndts, i)
best = sorted_cndts[0]
if best.ofvs[i] > gbest[i].ofvs[i]:
gbest[i] = deepcopy(best)
def update_src():
for i in range(nprob):
indv = gbest[i]
if indv.cid != i:
indv.src = indv.cid
def update_cid():
for i in range(nprob):
indv = gbest[i]
if indv.cid != i:
indv.cid = i
def record_shrng():
for i in range(nprob):
indv = deepcopy(gbest[i])
if indv.src != -1:
shrng_hst[i].append([indv])
else:
shrng_hst[i].append([])
def reset_src():
for i in range(nprob):
indv = gbest[i]
indv.src = -1
def update_gbest():
for i in range(nprob):
cntry = wrld[i]
best = deepcopy(cntry[0])
for j in range(N):
indv = deepcopy(cntry[j])
if indv.ofvs[i] > best.ofvs[i]:
best = indv
if best.ofvs[i] > gbest[i].ofvs[i]:
gbest[i] = deepcopy(best)
if coop:
improve_gbest()
update_src()
update_cid()
record_shrng()
reset_src()
def update_itr(itr):
for i in range(nprob):
cntry = wrld[i]
for j in range(N):
indv = cntry[j]
indv.itr = itr
print(f"update iteration problem {i} ")
def update_vels():
for i in range(nprob):
cntry = wrld[i]
for j in range(N):
indv = cntry[j]
indv.vels = w * np.array(indv.vels)
r = []
for k in range(nv):
r.append(np.random.rand())
indv.vels = indv.vels + cp * np.array(r) * (np.array(pbest[i][j].vars) - np.array(indv.vars))
r = []
for k in range(nv):
r.append(np.random.rand())
indv.vels = indv.vels + cg * np.array(r) * (np.array(gbest[i].vars) - np.array(indv.vars))
indv.vels = indv.vels.tolist()
def ensure_vels_feasibility():
for i in range(nprob):
cntry = wrld[i]
for j in range(N):
indv = cntry[j]
for k in range(nv):
if indv.vels[k] < - vmax[k]:
indv.vels[k] = - vmax[k]
elif indv.vels[k] > vmax[k]:
indv.vels[k] = vmax[k]
def update_vars():
for i in range(nprob):
cntry = wrld[i]
for j in range(N):
indv = cntry[j]
indv.vars = np.array(indv.vars) + np.array(indv.vels)
indv.vars = np.where(indv.vars<0.001,0,indv.vars) #avoid eclipse error
indv.vars = indv.vars.tolist()
def ensure_vars_feasibility():
for i in range(nprob):
cntry = wrld[i]
for j in range(N):
indv = cntry[j]
for k in range(nv):
if indv.vars[k] < lb[k]:
indv.vars[k] = lb[k] + fb * abs(indv.vars[k] - lb[k])
elif indv.vars[k] > ub[k]:
indv.vars[k] = ub[k] - fb * abs(indv.vars[k] - ub[k])
def update_stat(stat):
for i in range(nprob):
cntry = wrld[i]
for j in range(N):
indv = cntry[j]
indv.stat = stat
def update_ofvs():
eclipse_result = runeclipse_parallel(wrld)
os.system(f"rm -rf /private/hsut/private/opt/egg_base_collab_porv50/model/EGG_*_{wrld[0][0].itr}_*")
print("finished update heavy function")
mapped_lists = [eclipse_result[i:i+N] for i in range(0, len(eclipse_result), N)]
for i in range(nprob):
for j in range(N):
obv_fun = []
for k in range(nprob):
cal = mapped_lists[i][j].npv(0.15,60,gassens[k] ,co2sens[k] )
obv_fun.append(cal)
wrld[i][j].ofvs = obv_fun
def record_wrld():
for i in range(nprob):
cntry = deepcopy(wrld[i])
wrld_hst[i].extend(cntry)
def record_best():
for i in range(nprob):
cntry = wrld[i]
best = deepcopy(cntry[0])
for j in range(N):
indv = deepcopy(cntry[j])
if indv.ofvs[i] > best.ofvs[i]:
best = indv
if gbest[i].ofvs[i] > best.ofvs[i]:
best = deepcopy(gbest[i])
best_hst[i].append(best)
def move():
update_vels()
ensure_vels_feasibility()
update_vars()
ensure_vars_feasibility()
update_stat("move")
update_ofvs()
record_wrld()
record_best()
def write_log():
log_input = {}
log_input["out"] = out
log_input["coop"] = coop
log_input["run"] = run
log_input["nv"] = nv
log_input["npar"] = nprob
log_input["list_OF"] = [i.__name__ for i in list_OF]
log_input["list_args"] = list_args
log_input["lb"] = lb
log_input["ub"] = ub
log_input["fb"] = fb
log_input["N"] = N
log_input["w"] = w
log_input["cp"] = cp
log_input["cg"] = cg
log_input["vs"] = vs
log_input["max_itr"] = max_itr
file = open(out + "/log_input.json", "w")
json.dump(log_input, file, indent=4, separators=(",", ": "))
file.close()
log_cases = []
for i in range(len(wrld_hst)):
line = []
for j in range(nprob):
cntry_hst = wrld_hst[j]
indv = cntry_hst[i]
itr = indv.itr
cid = indv.cid
vels = indv.vels
vars = indv.vars
ofv = indv.ofvs[j]
stat = indv.stat
src = indv.src
line.extend([itr, cid])
line.extend(vels)
line.extend(vars)
line.extend([ofv, stat, src])
log_cases.append(line)
pre = "itr,cid"
for i in range(nv):
pre = pre + ",vels[" + str(i) + "]"
for i in range(nv):
pre = pre + ",vars[" + str(i) + "]"
post = "stat,src"
for i in range(nprob):
if i == 0:
hdr = pre + ",ofvs[" + str(i) + "]," + post
else:
hdr = hdr + "," + pre + ",ofvs[" + str(i) + "]," + post
file = open(out + "/log_cases.txt", "w")
file.write(hdr + "\n")
for line in log_cases:
for i in range(len(line)):
if i == 0:
file.write(str(line[i]))
else:
file.write("," + str(line[i]))
file.write("\n")
file.close()
log_optimization = []
for i in range(len(best_hst[0])):
line = []
for j in range(nprob):
cntry_hst = best_hst[j]
best = cntry_hst[i]
itr = i
cid = best.cid
vels = best.vels
vars = best.vars
ofv = best.ofvs[j]
stat = best.stat
src = best.src
line.extend([itr, cid])
line.extend(vels)
line.extend(vars)
line.extend([ofv, stat, src])
log_optimization.append(line)
file = open(out + "/log_optimization.txt", "w")
file.write(hdr + "\n")
for line in log_optimization:
for i in range(len(line)):
if i == 0:
file.write(str(line[i]))
else:
file.write("," + str(line[i]))
file.write("\n")
file.close()
def generate_sharing_data(shared_pop):
sharing_data = [0] * nprob
for i in range(len(shared_pop)):
indv = shared_pop[i]
src = indv.src
sharing_data[src] = sharing_data[src] + 1
return sharing_data
log_sharing = []
for i in range(len(shrng_hst[0])):
line = []
for j in range(nprob):
cntry_hst = shrng_hst[j]
shared_pop = cntry_hst[i]
itr = i + 1
cid = j
sharing_data = generate_sharing_data(shared_pop)
totl = len(shared_pop)
line.extend([itr, cid])
line.extend(sharing_data)
line.append(totl)
log_sharing.append(line)
pre = "itr,cid"
for i in range(nprob):
if i == 0:
mid = "src=" + str(i)
else:
mid = mid + "," + "src=" + str(i)
post = "totl"
for i in range(nprob):
if i == 0:
hdr = pre + "," + mid + "," + post
else:
hdr = hdr + "," + pre + "," + mid + "," + post
file = open(out + "/log_sharing.txt", "w")
file.write(hdr + "\n")
for line in log_sharing:
for i in range(len(line)):
if i == 0:
file.write(str(line[i]))
else:
file.write("," + str(line[i]))
file.write("\n")
file.close()
# Iterative process
for i in range(max_itr):
update_pbest()
update_gbest()
update_itr(i + 1)
write_log()
move()
os.system(f"rm -rf /private/hsut/private/opt/egg_base_collab_porv50/model/EGG_*_{wrld[0][0].itr}_*")
write_log()