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nelder_mead_nltax.py
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nelder_mead_nltax.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_nltax import Economy, split_shock
import pickle
p_init = float(args[1])
rc_init = float(args[2])
ome_init = float(args[3])
varpi_init = float(args[4])
num_core = args[5]
print('the code is running with ', num_core, 'cores...')
prices_init = [p_init, rc_init, ome_init, varpi_init]
nd_log_file = './log/log.txt'
detailed_output_file = './log/detail.txt'
f = open(detailed_output_file, 'w')
f.close()
dist_min = 10000000.0
econ_save = None
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., 3., 2., 30)
zgrid2 = np.load('./input_data/zgrid.npy') ** 2.
prob = np.load('./DeBacker/prob_epsz.npy') #DeBacker
# prob = np.load('./input_data/.npy')
taub = np.array([0.137, 0.185, 0.202, 0.238, 0.266, 0.28]) * 0.50 #large one
psib = np.array([0.12543758, 0.13944768, 0.15, 0.20772159, 0.3213201, 0.40113872])
path_to_data_i_s = './tmp/data_i_s'
alpha = 0.3 #new!
theta = 0.41
# ynb = 0.451
ynb_p_gdp = 0.25
xnb_p_gdp = 0.105
g_p_gdp = 0.13
pure_sweat_share = 0.10 #target
s_emp_share = 0.30 #target
yc_init = 0.8679
# 0.76
# GDP_implied = (1.-alpha + s_emp_share/(1. - s_emp_share)*(1.-theta)*yc_init + (1.-alpha)*ynb)/(1.-alpha - pure_sweat_share)
GDP_implied = (1.-alpha + s_emp_share/(1. - s_emp_share)*(1.-theta))/((1.-alpha)*(1. - ynb_p_gdp) - pure_sweat_share)*yc_init
def target(prices):
global dist_min
global econ_save
p_ = prices[0]
rc_ = prices[1]
ome_ = prices[2]
varpi_ = prices[3]
# print('computing for the case w = {:f}, p = {:f}, rc = {:f}'.format(w_, p_, rc_), end = ', ')
print('computing for the case p = {:f}, rc = {:f}'.format(p_, rc_), end = ', ')
###set any additional condition/parameters
### alpha = 0.4 as default, and nu = 1. - phi - alpha
#econ = Economy(agrid = agrid2, zgrid = zgrid2, path_to_data_i_s = path_to_data_i_s, rho = 0.01, ome = 0.6, varpi = 0.1)
econ = Economy(alpha = alpha, theta = theta, yn = ynb_p_gdp*GDP_implied, xnb = xnb_p_gdp*GDP_implied, g = g_p_gdp*GDP_implied,
scaling_n = GDP_implied, scaling_b = GDP_implied,
agrid = agrid2, kapgrid = kapgrid2, zgrid = zgrid2, rho = 0.01, upsilon = 0.50, prob = prob, la = 0.7,
taub = taub, psib = psib,
ome = ome_, varpi = varpi_, path_to_data_i_s = path_to_data_i_s)
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', num_core, '--machinefile=node.hf' ,'python', 'SCEconomy_s_emp.py'], stdout=subprocess.PIPE)
result = subprocess.run(['mpiexec', '-n', num_core ,'python', 'SCEconomy_nltax.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)
p = econ.p
rc = econ.rc
ome = econ.ome
varpi = econ.varpi
moms = econ.moms
# mom0 = comm.bcast(mom0) #1. - Ecs/Eys
# mom1 = comm.bcast(mom1) # 1. - (Ecc + Ex+ (grate + delk)*(kc + Eks) + g + xnb - yn)/yc
# mom2 = comm.bcast(mom2) # 1. - (tax_rev - tran - netb)/g
# mom3 = comm.bcast(mom3) # 0.0
# mom4 = comm.bcast(mom4) # Ens/En
# mom5 = comm.bcast(mom5) # (p*Eys - (rs+delk)*Eks - w*Ens)/GDP
# mom6 = comm.bcast(mom6) # nc
# mom7 = comm.bcast(mom7) # 1. - EIc
# dist = np.sqrt(moms[0]**2.0 + moms[1]**2.0 + moms[2]**2.0) #mom3 should be missing.
# dist = np.sqrt(moms[0]**2.0 + moms[1]**2.0 + moms[2]**2.0 + 100.0*(moms[4] - 0.3)**2.0 +500.* (moms[5]-0.09)**2.0 + 100.*(moms[6] - 0.11)**2.0) #mom3 should be missing.
# dist = np.sqrt(moms[0]**2.0 + moms[1]**2.0 + 100.0*(moms[4] - s_emp_share)**2.0 +500.* (moms[5]-pure_sweat_share)**2.0 + 100.*(moms[7] - 0.37)**2.0) #mom3 should be missing.
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) #mom3 should be missing.
if p != p_ or rc != rc_ or ome != ome_ or varpi != varpi_:
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('varpi = ', varpi, ', varpi_ = ', varpi_)
print('dist = {:f}'.format(dist))
f = open(nd_log_file, 'a')
f.writelines(str(p) + ', ' + str(rc) + ', ' + str(ome) + ', ' + str(varpi) + ', ' + str(dist) + ', ' + str(moms[0]) + ', ' + str(moms[1]) + ', ' + str(moms[2]) + ', ' + str(moms[4]) + ', ' + str(moms[5]) + ', ' + str(moms[7]) + '\n')
# f.writelines(str(p) + ', ' + str(rc) + ', ' + str(varpi) + ', ' + str(ome) + ', ' + str(theta) + ', ' + str(dist) + ', ' +\
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('w, p, rc, dist, mom0, mom1, mom2, mom3\n')
f.writelines('p, rc, ome, varpi, dist, mom0, mom1, mom2, mom4, mom5, mom7\n')
f.close()
from markov import calc_trans, Stationary
num_pop = 100_000
sim_time = 3_000
data_i_s = np.ones((num_pop, sim_time), dtype = int)
#need to set initial state for zp
data_i_s[:, 0] = 7
np.random.seed(0)
data_rand = np.random.rand(num_pop, sim_time)
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)
### check
f = open(nd_log_file, 'w')
f.writelines(np.array_str(np.bincount(data_i_s[:,0]) / np.sum(np.bincount(data_i_s[:,0])), precision = 4, suppress_small = True) + '\n')
f.writelines(np.array_str(Stationary(prob), precision = 4, suppress_small = True) + '\n')
f.writelines('yc_init = ' + str(yc_init) + '\n')
f.writelines('GDP_implied = ' + str(GDP_implied) + '\n')
f.close()
del data_i_s
split_shock(path_to_data_i_s, 100_000, int(num_core))
nm_result = None
from scipy.optimize import minimize
tol_nm = 1.0e-4
for i in range(5):
nm_result = minimize(target, prices_init, method='Nelder-Mead', tol = tol_nm)
if nm_result.fun < tol_nm: #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)
e = econ
print('')
print('agrid')
print(e.agrid)
print('kapgrid')
print(e.kapgrid)
print('zgrid')
print(e.zgrid)
print('epsgrid')
print(e.epsgrid)
print('prob')
print(e.prob)
print('yc_init = ', yc_init)
print('GDP Implied = ', GDP_implied)
e.print_parameters()
e.calc_moments()
#
#econ.calc_sweat_eq_value()
#econ.simulate_other_vars()
#econ.save_result()