def q(pt):
#print('computing at point {}'.format(translator(pt)))
try:
ans = mdl_resid(tr(pt), return_format=['scaled residuals'])
except BaseException as a:
print('During optimization function evaluation failed at {}'.
format(pt))
print(a)
ans = np.array([1e6])
finally:
gc.collect()
return ans
'pmeet_35': 0.40800438661571153,
'preg_21': 0.07677173244887601,
'preg_28': 0.06061469851763078,
'preg_35': 0.01825557056243586,
'u_shift_mar': 1.7329041070973545,
'util_alp': 0.6182672481649074,
'util_kap': 0.8081836080864513,
'util_qbar': 0.5163163798943308,
'disutil_marry_sm_mal_coef': 14.446603934890161,
'disutil_shotgun_coef': 0.4904309002252879,
'taste_shock_mult': 4.116448914683272,
'high education': True,
'n_psi': npsi
}
tar = target_values('high education')
out, mdl, agents, res, mom = mdl_resid(
x=x,
targets=tar,
return_format=[
'distance', 'models', 'agents', 'scaled residuals', 'moments'
],
#load_from='mdl.pkl',
verbose=False,
draw=True,
cs_moments=False,
moments_repeat=2)
mdl[0].time_statistics()
print('Done. Residual in point x0 is {}'.format(out))
del ((out, mdl, agents, res, mom))
xfix = None
lb, ub, xdef, keys, translator = calibration_params(xfix=xfix)
print('calibration adjusts {}'.format(keys))
print('')
print('')
print('running tic tac...')
print('')
print('')
#Tik Tak Optimization
param=tiktak(xfix=xfix,N=20000,N_st=250,skip_local=False,skip_global=True,
resume_global=False,resume_local=False)
print('f is {} and x is {}'.format(param[0],param[1]))
#Now Re do the computation with graphs!
out, mdl = mdl_resid(param[1],return_format=['distance','model'],
verbose=True,draw=True)
def run(resume=False, high_e=True):
xinit, targ_mode = get_point(high_e, read_wisdom=False)
tar = target_values(targ_mode)
if resume:
x_load = filer('wisdom_refined.pkl', 0, 0)
prob_meet_load = x_load['pmeet_exo']
prob_preg_load = x_load['ppreg_exo']
xinit = x_load
out, mdl, agents, res, mom = mdl_resid(x=xinit,
targets=tar,
return_format=[
'distance', 'models', 'agents',
'scaled residuals', 'moments'
],
verbose=False)
print('initial distance is {}'.format(out))
if resume:
prob_meet_init = prob_meet_load
prob_preg_init = prob_preg_load
else:
prob_meet_init = np.array(
mdl[0].setup.pars['pmeet_t'][:mdl[0].setup.pars['Tmeet']])
prob_preg_init = np.array([
mdl[0].setup.upp_precomputed_fem[t][3]
for t in range(mdl[0].setup.pars['Tmeet'])
])
nopt = 10
yfactor = 1.5
for iopt in range(nopt):
print('running esimation round {}'.format(iopt))
print('estimating probabilities:')
prob_meet_est = 0.0
prob_preg_est = 0.0
nrep = 4 if iopt > 0 else 1
np.random.seed(12)
for rep in range(nrep):
o = AgentsEst(mdl, T=30, verbose=False, fix_seed=False)
prob_meet_est += (1 / nrep) * o.pmeet_exo.copy()
prob_preg_est += (1 / nrep) * o.ppreg_exo.copy()
print('estimated pmeet = {}'.format(prob_meet_est))
print('estimated ppreg = {}'.format(prob_preg_est))
# this does binary search
w = 1.0
factor = 0.5
ne = prob_meet_est.size
nw = 10
print('reference value is {}'.format(out))
y_previous = out
for i in range(nw):
prob_meet_w = w * prob_meet_est + (1 - w) * prob_meet_init[:ne]
prob_preg_w = w * prob_preg_est + (1 - w) * prob_preg_init[:ne]
xsearch = xinit.copy()
xsearch.update({
'pmeet_exo': prob_meet_w,
'ppreg_exo': prob_preg_w
})
out_w = mdl_resid(x=xsearch,
targets=tar,
return_format=['distance'],
verbose=False)
print('with weight = {}, distance is {}'.format(w, out_w))
if out_w < yfactor * out:
print('found a potentially imporving weight for yfactor = {}'.
format(yfactor))
break
else:
w = factor * w
if i < nw - 1:
print('trying new weight = {}'.format(w))
else:
print('no luck...')
xfix = {
k: xinit[k]
for k in [
'pmeet_21', 'pmeet_30', 'pmeet_40', 'preg_21', 'preg_28',
'preg_35'
]
}
lb, ub, _, keys, translator = calibration_params(xfix=xfix)
def tr(x):
xx = translator(x)
xx.update({'pmeet_exo': prob_meet_w, 'ppreg_exo': prob_preg_w})
return xx
x0 = [xinit[key] for key in keys]
x0, lb, ub = np.array(x0), np.array(lb), np.array(ub)
print('starting from {}'.format(tr(x0)))
tar = target_values('high education')
def q(pt):
#print('computing at point {}'.format(translator(pt)))
try:
ans = mdl_resid(tr(pt), return_format=['scaled residuals'])
except BaseException as a:
print('During optimization function evaluation failed at {}'.
format(pt))
print(a)
ans = np.array([1e6])
finally:
gc.collect()
return ans
res = dfols.solve(q,
x0,
rhobeg=0.02,
rhoend=1e-5,
maxfun=60,
bounds=(lb, ub),
scaling_within_bounds=True,
objfun_has_noise=False,
npt=len(x0) + 5,
user_params={
'restarts.use_restarts': True,
'restarts.rhoend_scale': 0.5,
'restarts.increase_npt': True
})
print(res)
print('Result is {}'.format(tr(res.x)))
filer('wisdom_refined.pkl', tr(res.x), True)
print('wrote to the file!')
xinit = tr(res.x)
out, mdl, agents, res, mom = mdl_resid(x=xinit,
return_format=[
'distance', 'models',
'agents',
'scaled residuals',
'moments'
])
if out > y_previous:
print('no reduction in function value obtained')
yfactor = 0.5 * yfactor + 0.5
y_previous = out
def run(adj_name, fix, educ_name, resume=False, noplot=False):
# at first this takes point x saved in estimates.py
# by calling get_point(high_e). x is a dict with parameter names and values
# then it applies adjustment fix (a dict) to the point x
# resulting file name is educ_name + adj_name
# adj_name -- name of adjustment
# fix -- dictionary containing parameters of setup.py to change
# educ_name -- name of education group ("col" or "hs")
# high_e -- input to get_point function, True for college, False for HS
high_e = (educ_name == 'col' or educ_name == 'college')
low_e = (educ_name == 'hs' or educ_name == 'high school')
assert (high_e or low_e), 'wrong specifier for education'
x, targ_mode = get_point(high_e, read_wisdom=False)
tar = target_values(targ_mode)
print('\n\n\n\n\n\n')
print('doing {} {}'.format(educ_name, adj_name))
x_new = x.copy()
fix = fix.copy()
if 'multiply' in fix:
mult = fix.pop('multiply')
for m in mult:
x_new[m] *= mult[m]
x_new.update(fix)
print(x_new)
name = '{} {}'.format(educ_name, adj_name)
fname = '{}.pkl'.format(name)
try:
if resume:
try:
mom = filer(fname, 0, 0, repeat=False)
skip = True
except:
skip = False
else:
skip = False
if not skip:
print("computing {}".format(fname))
out, mom = mdl_resid(x=x_new,
targets=tar,
return_format=['distance', 'moments'],
verbose=False,
draw=False,
cs_moments=False,
save_to='mdl for {}'.format(fname),
moments_save_name=name,
moments_repeat=5,
Tsim=42)
print("file {} saved".format(fname))
else:
print("file {} already exists".format(fname))
print('done, doing fit plots')
try:
if adj_name == 'baseline':
fp = FitPlots(targ_mode=targ_mode,
compare=None,
base='{} baseline.pkl'.format(educ_name),
compare_name='Data',
base_name='baseline',
moments_aux=None,
noplot=noplot)
else:
fp = FitPlots(targ_mode=targ_mode,
compare='{} baseline.pkl'.format(educ_name),
base=fname,
compare_name='baseline',
base_name=adj_name,
moments_aux=None,
noplot=noplot)
except:
print('something wrong with fit plots...')
except KeyboardInterrupt:
raise (KeyboardInterrupt)
except BaseException as a:
print("file {} {}.pkl could not be produced. Exception:".format(
name, adj_name))
print(a)
print(' ')
return mom, fp
lb, ub, x0, keys, translator = calibration_params(
) # bounds are set in a separate file
##### FIRST LET'S TRY TO RUN THE FUNCTION IN FEW POINTS
print('Testing the workers...')
from p_client import compute_for_values
pts = [lb + rs(lb.shape) * (ub - lb) for _ in range(1)]
pts = [('compute', translator(x)) for x in pts]
outs = compute_for_values(pts, timeout=72000.0)
print('Everything worked, output is {}'.format(outs))
print('')
print('')
print('running tic tac...')
print('')
print('')
#Tik Tak Optimization
param = tiktak(N=2000, N_st=50, skip_local=False, skip_global=False)
print('f is {} and x is {}'.format(param[0], param[1]))
#Now Re do the computation with graphs!
out, mdl = mdl_resid(param[1],
return_format=['distance', 'model'],
calibration_report=False,
verbose=True,
draw=True)
('no partners', {
'pmeet_21': 0.0,
'pmeet_28': 0.0,
'pmeet_35': 0.0
}), ('no home production', {
'util_kappa': 0.001
})]
for adj_name, fix in adjustments:
x_new = x.copy()
x_new.update(fix)
try:
out, mom = mdl_resid(x=x_new,
targets=tar,
return_format=['distance', 'moments'],
verbose=False,
draw=False,
cs_moments=False,
moments_save_name='{} {}'.format(
name, adj_name),
moments_repeat=5)
print("file {} {}.pkl saved".format(name, adj_name))
except KeyboardInterrupt:
raise (KeyboardInterrupt)
except BaseException as a:
print("file {} {}.pkl could not be produced. Exception:".format(
name, adj_name))
print(a)
print(' ')
['sigma_psi', 'sigma_psi_mult'],
['u_shift_mar', 'util_kap', 'util_alp'],
[
'sigma_psi', 'sigma_psi_mult', 'u_shift_mar',
'util_kap', 'util_alp'
]]
for rf in replace_fields:
xnew = x_base.copy()
for f in rf:
xnew[f] = x_comp[f]
xlist.append(xnew)
replace_fields = ['base'] + replace_fields + ['compare']
xlist.append(x_comp)
assert False
agents_list = list()
for x, rf in zip(xlist, replace_fields):
print(rf)
print(x)
out, agents = mdl_resid(x=x,
return_format=['distance', 'agents'],
verbose=False,
draw=False)
agents_list.append(agents)
print('Done. Residual in point x0 is {}'.format(out))
dill.dump((agents_list, replace_fields, xlist),
open('comparison.pkl', 'wb+'))
('trend and all probs', he + pmeets + ppregs),
('trend and abortions', he + abortions),
('all but trend', prefs + pmeets + ppregs + abortions)]
for name, rf in replace_fields:
xnew = x_base.copy()
for f in rf:
xnew[f] = x_comp[f]
xlist.append(xnew)
replace_fields = ['base'] + replace_fields + ['compare']
xlist.append(x_comp)
names = ['college'] + [f[0] for f in replace_fields] + ['high school']
names = ['educ comparison: ' + n for n in names]
for x, name in zip(xlist, names):
print(name)
print(x)
out, mom = mdl_resid(x=x,
targets=tar,
return_format=['distance', 'moments'],
verbose=False,
draw=False,
cs_moments=False,
save_to='mdl for {}'.format(name),
moments_save_name=name,
moments_repeat=5)
print('Done. Residual in point x0 is {}'.format(out))
def main(read_wisdom=False,erase=False):
high_e = True
if erase:
try:
os.remove('az_dist_fem.pkl')
print('removed')
except:
pass
try:
os.remove('az_dist_mal.pkl')
print('removed')
except:
pass
x, targ_mode = get_point(high_e,read_wisdom=read_wisdom)
tar = target_values(targ_mode)
this_name = 'college baseline' if high_e else 'high school baseline'
out, mdl, agents, res, mom = mdl_resid(x=x,targets=tar,
return_format=['distance','models','agents','scaled residuals','moments'],
#load_from='mdl.pkl',
verbose=True,draw=True,cs_moments=False,
moments_save_name = this_name,
moments_repeat=1)
#agents.sim_graphs()
mdl[0].time_statistics()
print('Done. Residual in point x0 is {}'.format(out))
from fit_plot import FitPlots
fp = FitPlots(targ_mode=targ_mode,
compare=None,
base=this_name+'.pkl',
compare_name='data',
base_name='college baseline',
moments_aux=None) #,moments_aux=moments_aux)
'''
from fit_plot import FitPlots
fp = FitPlots(targ_mode=targ_mode,
compare='col baseline.pkl',
base='col double social stigma.pkl',
compare_name='baseline',
base_name='double stigma',
moments_aux=None) #,moments_aux=moments_aux)
'''
mdl[0].mar_graphs(t = 4)
return locals()
0.662168, 0.778121, 1.95622, 0.300373, 0.586473, 0.020041, -0.0578633,
1.09095
])
#Name and location of files
if system() == 'Windows':
path = 'D:/blasutto/store_model'
else:
path = None
out, mdl, agents, res = mdl_resid(
x0,
return_format=['distance', 'models', 'agents', 'scaled residuals'],
#load_from=['mdl_save_bil.pkl','mdl_save_uni.pkl'],
solve_transition=True,
save_to=['mdl_save_bil.pkl', 'mdl_save_uni.pkl'],
store_path=path,
verbose=True,
calibration_report=False,
draw=graphs,
graphs=graphs,
welf=False) #Switch to true for decomposition of welfare analysis
print('Done. Residual in point x0 is {}'.format(out))
#assert False
#Indexes for the graphs
if graphs:
ai = 3
zfi = 3
zmi = 4
