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nelder_mead_LSC_ns_lifecycle.py
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nelder_mead_LSC_ns_lifecycle.py
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import sys
args = sys.argv
import numpy as np
import time
import subprocess
### use modified version of SCEConomy module
from SCEconomy_LSC_ns_lifecycle import Economy, split_shock
import pickle
p_init = float(args[1])
rc_init = float(args[2])
#ome_init = float(args[3])
#nu_init = float(args[4])
num_core = int(args[3])
ome_init = 0.4561128052733918
# varpi_init = 0.559952019068588
theta_init = 0.5071181286751945
ome_ = ome_init
# varpi_ = varpi_init
theta_ = theta_init
print('the code is running with ', num_core, 'cores...')
# prices_init = [p_init, rc_init, ome_init, nu_init]
prices_init = [p_init, rc_init]
nd_log_file = '/cluster/shared/yaoxx366/log2/log.txt'
detailed_output_file = '/cluster/shared/yaoxx366/log2/detail.txt'
f = open(detailed_output_file, 'w')
f.close()
dist_min = 10000000.0
econ_save = None
zgrid2 = np.load('./input_data/zgrid.npy') ** 2.0
prob = np.load('./DeBacker/prob_epsz.npy') #DeBacker
path_to_data_i_s = './tmp/data_i_s'
def curvedspace(begin, end, curve, num=100):
import numpy as np
ans = np.linspace(0, (end - begin)**(1.0/curve), num) ** (curve) + begin
ans[-1] = end #so that the last element is exactly end
return ans
from markov import calc_trans, Stationary
#generate shock sequene
path_to_data_i_s = './tmp/data_i_s'
path_to_data_is_o = './tmp/data_is_o'
num_pop = 100_000
sim_time = 3_000
prob = np.load('./DeBacker/prob_epsz.npy')
prob_yo = np.array([[44./45., 1./45.], [3./45., 42./45.]]) #[[y -> y, y -> o], [o -> y, o ->o]]
def curvedspace(begin, end, curve, num=100):
import numpy as np
ans = np.linspace(0, (end - begin)**(1.0/curve), num) ** (curve) + begin
ans[-1] = end #so that the last element is exactly end
return ans
agrid2 = curvedspace(0., 200., 2., 40)
kapgrid2 = curvedspace(0., 2., 2., 30)
zgrid2 = np.load('./input_data/zgrid.npy') ** 2.
# epsgrid2 = (np.load('./input_data/epsgrid.npy') ** 1.75) * 0.8
prob = np.load('./DeBacker/prob_epsz.npy') #DeBacker
pure_sweat_share = 0.090 # target
s_emp_share = 0.33 # target
xc_share = 0.134 # target
#w*nc/GDP = 0.22
GDP_guess = 3.20
taup = 0.36 *(1.0 - 0.278)
taub = np.array([0.137, 0.185, 0.202, 0.238, 0.266, 0.28]) *(1.0 - 0.506) #large one
psib = np.array([-0.010393600000000013, 0.012646399999999981, 0.03, 0.12492479999999993, 0.3117408000000001,0.4430048000000002])
taun = np.array([0.293, 0.317, 0.324, 0.343, 0.39, 0.405, 0.408, 0.419])
psin = np.array([-0.10037472000000003, -0.08685792000000002, -0.08193888000000002, -0.06546208, 0.0011951999999999727, 0.03, 0.04398335999999975, 0.14192735999999984])
def target(prices):
global dist_min
global econ_save
p_ = prices[0]
rc_ = prices[1]
# ome_ = prices[2]
# nu_ = prices[3]
print('computing for the case p = {:f}, rc = {:f}'.format(p_, rc_), end = ', ')
###set any additional condition/parameters
econ = Economy(sim_time = 1000, num_total_pop = num_pop,
agrid = agrid2, zgrid = zgrid2, rho = 0.01, prob = prob,
ome = ome_, theta = theta_,
path_to_data_i_s = path_to_data_i_s, path_to_data_is_o = path_to_data_is_o,
scaling_n = GDP_guess, scaling_b = GDP_guess, g = 0.133*GDP_guess, yn = 0.266*GDP_guess, xnb = 0.110*GDP_guess,
delk = 0.041, delkap = 0.041, veps = 0.418, vthet = 1.0 - 0.418,
tauc = 0.065, taud = 0.133,
taup = taup, taub = taub , psib = psib
#, epsgrid = epsgrid2
)
econ.set_prices(p = p_, rc = rc_)
with open('econ.pickle', mode='wb') as f: pickle.dump(econ, f)
#with open('econ.pickle', mode='rb') as f: econ = pickle.load(f)
t0 = time.time()
result = subprocess.run(['mpiexec', '-n', str(num_core), 'python', 'SCEconomy_LSC_ns_lifecycle.py'], stdout=subprocess.PIPE)
t1 = time.time()
f = open(detailed_output_file, 'ab') #use byte mode
f.write(result.stdout)
f.close()
print('etime: {:f}'.format(t1 - t0), end = ', ')
time.sleep(1)
with open('econ.pickle', mode='rb') as f: econ = pickle.load(f)
w = econ.w
p = econ.p
rc = econ.rc
ome = econ.ome
theta = econ.theta
moms = econ.moms
dist = np.sqrt(moms[0]**2.0 + moms[1]**2.0)
# dist = np.sqrt(moms[0]**2.0 + moms[1]**2.0 + (moms[4]/s_emp_share - 1.)**2.0 + (moms[5]/pure_sweat_share - 1.)**2.0)
if p != p_ or rc != rc_ or ome != ome_ or theta != theta_:
print('err: input prices and output prices do not coincide.')
print('p = ', p, ', p_ = ', p_)
print('rc = ', rc, ', rc_ = ', rc_)
print('ome = ', ome, ', ome_ = ', ome_)
print('theta = ', theta, ', theta_ = ', theta_)
# return
print('dist = {:f}'.format(dist))
f = open(nd_log_file, 'a')
f.writelines(str(p) + ', ' + str(rc) + ', ' + str(ome) + ', ' + str(theta) + ', ' + str(dist) + ', ' + str(moms[0]) + ', ' + str(moms[1]) + ', ' + str(moms[2]) + ', ' + str(moms[3]) + '\n')
f.close()
if dist < dist_min:
econ_save = econ
dist_min = dist
return dist
if __name__ == '__main__':
f = open(nd_log_file, 'w')
f.writelines('p, rc, ome, theta, dist, mom0, mom1, mom2, mom3\n')
f.writelines('GDP_guess = ' + str(GDP_guess) + '\n')
f.close()
### generate shocks ###
np.random.seed(0)
data_rand = np.random.rand(num_pop, sim_time)
data_i_s = np.ones((num_pop, sim_time), dtype = int)
data_i_s[:, 0] = 7 #initial state. it does not matter if simulation is long enough.
calc_trans(data_i_s, data_rand, prob)
data_i_s = data_i_s[:, 2000:]
np.save(path_to_data_i_s + '.npy' , data_i_s)
split_shock(path_to_data_i_s, num_pop, num_core)
del data_rand
np.random.seed(2)
data_rand = np.random.rand(num_pop, sim_time+1) #+1 is added since this matters in calculation
data_is_o = np.ones((num_pop, sim_time+1), dtype = int)
data_is_o[:, 0] = 0 #initial state. it does not matter if simulation is long enough.
calc_trans(data_is_o, data_rand, prob_yo)
data_is_o = data_is_o[:, 2000:]
np.save(path_to_data_is_o + '.npy' , data_is_o)
split_shock(path_to_data_is_o, num_pop, num_core)
del data_rand
### end generate shocks ###
nm_result = None
from scipy.optimize import minimize
for i in range(5):
nm_result = minimize(target, prices_init, method='Nelder-Mead')
if nm_result.fun < 1.0e-3:
break
else:
prices_init = nm_result.x #restart
f = open(nd_log_file, 'a')
f.write(str(nm_result))
f.close()
###calculate other important variables###
econ = econ_save
with open('econ.pickle', mode='wb') as f: pickle.dump(econ, f)
#
#econ.calc_sweat_eq_value()
#econ.simulate_other_vars()
#econ.save_result()