def simulate(
self,
source=None,
mask=None,
pars=None,
resid=None,
init=None,
operation=np.multiply,
linear=False,
debug=False,
verbose=False,
**args
):
"""Simulate time series given a series of exogenous innovations.
Parameters
----------
source : dict
Dict of `extract` results
mask : array
Mask for eps. Each non-None element will be replaced.
"""
pars = pars if pars is not None else source["pars"]
resi = resid if resid is not None else source["resid"]
init = init if init is not None else source["init"]
sample = pars, resi, init
if verbose:
st = time.time()
self.debug |= debug
if hasattr(self, "pool"):
from .estimation import create_pool
create_pool(self)
pickled_self = dill.dumps(self, recurse=True)
def runner(arg):
pelf = dill.loads(pickled_self)
superflag = False
par, eps, state = arg
if mask is not None:
eps = np.where(np.isnan(mask), eps, operation(np.array(mask), eps))
if pelf.set_par is not None:
_, vv = pelf.set_par(par, return_vv=True, **args)
if not np.all(vv == pelf.vv):
raise Exception(
"The ordering of variables has changed given different parameters."
)
X = [state]
L, K = [], []
for eps_t in eps:
state, (l, k), flag = pelf.t_func(
state, eps_t, return_k=True, linear=linear
)
superflag |= flag
X.append(state)
L.append(l)
K.append(k)
X = np.array(X)
LK = np.array((L, K))
K = np.array(K)
return X, LK, superflag
wrap = tqdm.tqdm if verbose else (lambda x, **kwarg: x)
res = wrap(
self.mapper(runner, zip(*sample)),
unit=" sample(s)",
total=len(source["pars"]),
dynamic_ncols=True,
)
X, LK, flags = map2arr(res)
if verbose > 1:
print(
"[simulate:]".ljust(15, " ") + "Simulation took ",
time.time() - st,
" seconds.",
)
if np.any(flags) and verbose:
print("[simulate:]".ljust(15, " ") + "No OBC solution found (at least once).")
return X, (LK[..., 0, :], LK[..., 1, :]), flags
def prior_draw(self, nsample, seed=None, ncores=None, verbose=False):
"""Draw parameters from prior. Drawn parameters have a finite likelihood.
Parameters
----------
nsample : int
Size of the prior sample
ncores : int
Number of cores used for prior sampling. Defaults to the number of available processors
Returns
-------
array
Numpy array of parameters
"""
import pathos
import warnings
import tqdm
import cloudpickle as cpickle
from grgrlib import map2arr
if seed is None:
seed = 0
if verbose:
print('[get_par:]'.ljust(15, ' ') +
'Drawing from the pior and checking likelihood.')
if not hasattr(self, 'ndim'):
self.prep_estim(load_R=True, verbose=verbose)
if ncores > 1:
lprob_dump = cpickle.dumps(self.lprob)
lprob = cpickle.loads(lprob_dump)
else:
lprob = self.lprob
frozen_prior = self.fdict['frozen_prior']
def runner(locseed):
np.random.seed(seed + locseed)
draw_prob = -np.inf
while np.isinf(draw_prob):
with warnings.catch_warnings(record=False):
try:
np.warnings.filterwarnings('error')
rst = np.random.randint(2**16)
pdraw = [pl.rvs(random_state=rst) for pl in frozen_prior]
draw_prob = lprob(pdraw, None, verbose)
except:
pass
return pdraw
if ncores is None:
ncores = pathos.multiprocessing.cpu_count()
mapper = map
if ncores > 1:
loc_pool = pathos.pools.ProcessPool(ncores)
loc_pool.clear()
mapper = loc_pool.imap
print('[get_cand:]'.ljust(15, ' ') + 'Sampling parameters from prior...')
pmap_sim = tqdm.tqdm(mapper(runner, range(nsample)), total=nsample)
## to circumvent mem overflow
if ncores > 1:
loc_pool.close()
loc_pool.join()
return map2arr(pmap_sim)
def prior_sampler(self,
nsamples,
seed=0,
test_lprob=False,
lks=None,
verbose=True,
debug=False,
**args):
"""Draw parameters from prior.
Parameters
----------
nsamples : int
Size of the prior sample
seed : int, optional
Set the random seed (0 by default)
test_lprob : bool, optional
Whether to ensure that drawn parameters have a finite likelihood (False by default)
verbose : bool, optional
debug : bool, optional
Returns
-------
array
Numpy array of parameters
"""
import tqdm
from grgrlib import map2arr, serializer
from .stats import get_prior
store_reduce_sys = np.copy(self.fdict['reduce_sys'])
l_max, k_max = lks or (None, None)
# if not store_reduce_sys:
# self.get_sys(reduce_sys=True, verbose=verbose > 1, **args)
if test_lprob and not hasattr(self, 'ndim'):
self.prep_estim(load_R=True, verbose=verbose > 2)
frozen_prior = get_prior(self.prior, verbose=verbose)[0]
self.debug |= debug
if hasattr(self, 'pool'):
from .estimation import create_pool
create_pool(self)
set_par = serializer(self.set_par)
get_par = serializer(self.get_par)
lprob = serializer(self.lprob) if test_lprob else None
def runner(locseed):
np.random.seed(seed + locseed)
done = False
no = 0
while not done:
no += 1
with np.warnings.catch_warnings(record=False):
try:
np.warnings.filterwarnings('error')
rst = np.random.randint(2**31) # win explodes with 2**32
pdraw = [
pl.rvs(random_state=rst + sn)
for sn, pl in enumerate(frozen_prior)
]
if test_lprob:
draw_prob = lprob(pdraw,
linear=None,
verbose=verbose > 1)
done = not np.isinf(draw_prob)
else:
set_par(pdraw)
done = True
except Exception as e:
if verbose > 1:
print(str(e) + '(%s) ' % no)
return pdraw, no
if verbose > 1:
print('[prior_sample:]'.ljust(15, ' ') + ' Sampling from the pior...')
wrapper = tqdm.tqdm if verbose < 2 else (lambda x, **kwarg: x)
pmap_sim = wrapper(self.mapper(runner, range(nsamples)), total=nsamples)
draws, nos = map2arr(pmap_sim)
# if not store_reduce_sys:
# self.get_sys(reduce_sys=False, verbose=verbose > 1, **args)
if verbose:
smess = ''
if test_lprob:
smess = 'of zero likelihood, '
print(
'[prior_sample:]'.ljust(15, ' ') +
' Sampling done. %2.2f%% of the prior is either %sindetermined or explosive.'
% (100 * (sum(nos) - nsamples) / sum(nos), smess))
return draws
def irfs(
self,
shocklist,
pars=None,
state=None,
T=30,
linear=False,
set_k=False,
force_init_equil=None,
verbose=True,
debug=False,
**args
):
"""Simulate impulse responses
Parameters
----------
shocklist : tuple or list of tuples
Tuple of (shockname, size, period)
T : int, optional
Simulation horizon. (default: 30)
linear : bool, optional
Simulate linear model (default: False)
set_k: int, optional
Enforce a `k` (defaults to False)
force_init_equil:
If set to `False`, the equilibrium will be recalculated every iteration. This may be problematic if there is multiplicity because the algoritm selects the equilibrium with the lowest (l,k) (defaults to True)
verbose : bool or int, optional
Level of verbosity (default: 1)
Returns
-------
DataFrame, tuple(int,int)
The simulated series as a pandas.DataFrame object and the expected durations at the constraint
"""
self.debug |= debug
if force_init_equil is None:
force_init_equil = not bool(np.any(set_k))
if not isinstance(shocklist, list):
shocklist = [
shocklist,
]
if hasattr(self, "pool"):
from .estimation import create_pool
create_pool(self)
st = time.time()
nstates = self.dimx
l_max, k_max = self.lks
# accept all sorts of inputs
new_shocklist = []
for vec in shocklist:
if isinstance(vec, str):
vec = (vec, 1, 0)
elif len(vec) == 2:
vec += (0,)
new_shocklist.append(vec)
pickled_self = dill.dumps(self, recurse=True)
def runner(par):
pelf = dill.loads(pickled_self)
X = np.empty((T, nstates))
K = np.empty(T)
L = np.empty(T)
if np.any(par):
try:
pelf.set_par(par, **args)
except ValueError:
X[:] = np.nan
K[:] = np.nan
L[:] = np.nan
return X, K, L, 4
st_vec = state if state is not None else np.zeros(nstates)
supererrflag = False
supermultflag = False
l, k = 0, 0
for t in range(T):
shk_vec = np.zeros(len(pelf.shocks))
for vec in new_shocklist:
if vec[2] == t:
shock = vec[0]
shocksize = vec[1]
shock_arg = pelf.shocks.index(shock)
shk_vec[shock_arg] = shocksize
# force_init_equil will force recalculation of l,k only if the shock vec is not empty
# but loop must be skipped if no shock is provided at all (e.g. if samplinf from a state distribution)
if force_init_equil and not np.any(shk_vec) and np.any([s[2] for s in new_shocklist]):
set_k_eff = (l - 1, k) if l else (l, max(k - 1, 0))
_, (l_endo, k_endo), flag = pelf.t_func(
st_vec[-(pelf.dimq - pelf.dimeps) :],
shk_vec,
set_k=None,
linear=linear,
return_k=True,
)
multflag = l_endo != set_k_eff[0] or k_endo != set_k_eff[1]
supermultflag |= multflag
if verbose > 1 and multflag:
print(
"[irfs:]".ljust(15, " ")
+ "Multiplicity found in period %s: new eql. %s coexits with old eql. %s."
% (t, (l_endo, k_endo), set_k_eff)
)
elif set_k is None:
set_k_eff = None
elif isinstance(set_k, tuple):
set_l_eff, set_k_eff = set_k
if set_l_eff - t >= 0:
set_k_eff = set_l_eff - t, set_k_eff
else:
set_k_eff = 0, max(set_k_eff + set_l_eff - t, 0)
elif set_k:
set_k_eff = 0, max(set_k - t, 0)
else:
set_k_eff = set_k
if set_k_eff:
if set_k_eff[0] > l_max or set_k_eff[1] > k_max:
raise IndexError(
"set_k exceeds l_max (%s vs. %s)." % (set_k_eff, (l_max, k_max))
)
st_vec, (l, k), flag = pelf.t_func(
st_vec[-(pelf.dimq - pelf.dimeps) :],
shk_vec,
set_k=set_k_eff,
linear=linear,
return_k=True,
)
if flag and verbose > 1:
print(
"[irfs:]".ljust(15, " ")
+ "No OBC solution found in period %s (error flag %s)." % (t, flag)
)
supererrflag |= flag
X[t, :] = st_vec
L[t] = l
K[t] = k
return X, L, K, supererrflag, supermultflag
if pars is not None and np.ndim(pars) > 1:
res = self.mapper(runner, pars)
X, L, K, flag, multflag = map2arr(res)
else:
X, L, K, flag, multflag = runner(pars)
X = pd.DataFrame(X, columns=self.vv)
if verbose == 1:
if np.any(flag):
print("[irfs:]".ljust(14, " ") + " No OBC solution(s) found.")
elif np.any(multflag):
print("[irfs:]".ljust(14, " ") + " Multiplicity/Multiplicities found.")
if verbose > 2:
print(
"[irfs:]".ljust(15, " ") + "Simulation took ",
np.round((time.time() - st), 5),
" seconds.",
)
return X, np.vstack((L, K)), flag
def irfs(self, shocklist, pars=None, T=30, linear=False, verbose=False):
"""Simulate impulse responses
Parameters
----------
shocklist : tuple or list of tuples
Tuple of (shockname, size, period)
T : int
Simulation horizon. (default: 30)
Returns
-------
DataFrame, tuple(int,int)
The simulated series as a pandas.DataFrame object and the expected durations at the constraint
"""
if isinstance(shocklist, tuple):
shocklist = [shocklist,]
st = time.time()
def irfs_runner(par):
if np.any(par):
self.set_par(par, autocompile=False)
st_vec = np.zeros(len(self.vv))
X = np.empty((T, len(self.vv)))
K = np.empty(T)
L = np.empty(T)
superflag = False
for t in range(T):
shk_vec = np.zeros(len(self.shocks))
for vec in shocklist:
if vec[2] == t:
shock = vec[0]
shocksize = vec[1]
shock_arg = self.shocks.index(shock)
shk_vec[shock_arg] = shocksize
st_vec, (l, k), flag = self.t_func(
st_vec, shk_vec, linear=linear, return_k=True)
superflag |= flag
X[t,:] = st_vec
L[t] = l
K[t] = k
return X, K, L, superflag
if np.any(pars):
res = self.mapper(irfs_runner, pars)
X, K, L, flag = map2arr(res)
else:
X, K, L, flag = irfs_runner(pars)
X = pd.DataFrame(X, columns=self.vv)
if np.any(flag) and verbose:
print('[irfs:]'.ljust(15, ' ') + 'No rational expectations solution found at least once.')
if verbose:
print('[irfs:]'.ljust(15, ' ') + 'Simulation took ', np.round((time.time() - st), 5), ' seconds.')
return X, (K, L)
def simulate(self,
source=None,
mask=None,
pars=None,
resid=None,
init=None,
operation=np.multiply,
linear=False,
debug=False,
verbose=False,
**args):
"""Simulate time series given a series of exogenous innovations.
Parameters
----------
source : dict
Dict of `extract` results
mask : array
Mask for eps. Each non-None element will be replaced.
"""
from grgrlib.core import serializer
pars = pars if pars is not None else source['pars']
resi = resid if resid is not None else source['resid']
init = init if init is not None else source['init']
sample = pars, resi, init
if verbose:
st = time.time()
self.debug |= debug
if hasattr(self, 'pool'):
from .estimation import create_pool
create_pool(self)
if self.set_par is not None:
set_par = serializer(self.set_par)
else:
set_par = None
t_func = serializer(self.t_func)
obs = serializer(self.obs)
vv_orig = self.vv.copy()
def runner(arg):
superflag = False
par, eps, state = arg
if mask is not None:
eps = np.where(np.isnan(mask), eps, operation(np.array(mask), eps))
if set_par is not None:
_, vv = set_par(par, return_vv=True, **args)
if not np.all(vv == vv_orig):
raise Exception(
'The ordering of variables has changed given different parameters.'
)
X = [state]
L, K = [], []
for eps_t in eps:
state, (l, k), flag = t_func(state,
eps_t,
return_k=True,
linear=linear)
superflag |= flag
X.append(state)
L.append(l)
K.append(k)
X = np.array(X)
LK = np.array((L, K))
K = np.array(K)
return X, LK, superflag
wrap = tqdm.tqdm if verbose else (lambda x, **kwarg: x)
res = wrap(self.mapper(runner, zip(*sample)),
unit=' sample(s)',
total=len(source['pars']),
dynamic_ncols=True)
X, LK, flags = map2arr(res)
if verbose > 1:
print('[simulate:]'.ljust(15, ' ') + 'Simulation took ',
time.time() - st, ' seconds.')
if np.any(flags) and verbose:
print('[simulate:]'.ljust(15, ' ') +
'No rational expectations solution found (at least once).')
return X, (LK[..., 0, :], LK[..., 1, :]), flags
def extract(self,
sample=None,
nsamples=1,
init_cov=None,
precalc=True,
seed=0,
nattemps=4,
accept_failure=False,
verbose=True,
debug=False,
l_max=None,
k_max=None,
**npasargs):
"""Extract the timeseries of (smoothed) shocks.
Parameters
----------
sample : array, optional
Provide one or several parameter vectors used for which the smoothed shocks are calculated (default is the current `self.par`)
nsamples : int, optional
Number of `npas`-draws for each element in `sample`. Defaults to 1
nattemps : int, optional
Number of attemps per sample to crunch the sample with a different seed. Defaults to 4
Returns
-------
tuple
The result(s)
"""
import tqdm
import os
from grgrlib import map2arr
if sample is None:
if type(self).__name__ == "DSGE_DUMMY":
sample = None
else:
sample = self.par
if np.ndim(sample) <= 1:
sample = [sample]
np.random.seed(seed)
fname = self.filter.name
verbose = max(verbose, debug)
if hasattr(self, "pool"):
from .estimation import create_pool
create_pool(self)
run_filter = serializer(self.run_filter)
if fname == "ParticleFilter":
raise NotImplementedError
elif fname == "KalmanFilter":
if nsamples > 1:
print(
"[extract:]".ljust(15, " ") +
" Setting `nsamples` to 1 as the linear filter does not rely on sampling."
)
nsamples = 1
debug = not hasattr(self, "debug") or self.debug
self.debug = True
else:
if self.filter.reduced_form:
self.create_filter(R=self.filter.R,
N=self.filter.N,
reduced_form=False)
print(
"[extract:]".ljust(15, " ") +
" Extraction requires filter in non-reduced form. Recreating filter instance."
)
run_filter, npas = serializer(self.run_filter, self.filter.npas)
self.debug |= debug
if sample[0] is not None:
set_par = serializer(self.set_par)
t_func = serializer(self.t_func)
edim = len(self.shocks)
xdim = len(self.vv)
odim = len(self.observables)
obs_func = serializer(self.obs)
filter_get_eps = serializer(self.get_eps_lin)
dimeps = self.dimeps
dimp = self.dimp
# win explodes with 2**32
seeds = np.random.randint(2**31, size=nsamples)
sample = [(x, y) for x in sample for y in seeds]
def runner(arg):
par, seed_loc = arg
if par is not None:
set_par(par, l_max=l_max, k_max=k_max)
res = run_filter(verbose=verbose > 2, seed=seed_loc, init_cov=init_cov)
if fname == "KalmanFilter":
means, covs = res
res = means.copy()
resid = np.empty((means.shape[0] - 1, dimeps))
for t, x in enumerate(means[1:]):
resid[t] = filter_get_eps(x, res[t])
res[t + 1] = t_func(res[t], resid[t], linear=True)[0]
return par, res[0], resid, 0
np.random.shuffle(res)
sample = np.dstack((obs_func(res), res[..., dimp:]))
inits = res[:, 0, :]
def t_func_loc(states, eps):
(q, pobs), flag = t_func(states, eps, get_obs=True)
return np.hstack((pobs, q)), flag
for natt in range(nattemps):
try:
init, resid, flags = npas(func=t_func_loc,
X=sample,
init_states=inits,
verbose=max(
len(sample) == 1, verbose - 1),
seed=seed_loc,
nsamples=1,
**npasargs)
return par, init, resid[0], flags
except Exception as e:
raised_error = e
if accept_failure:
print("[extract:]".ljust(15, " ") +
"got an error: '%s' (after %s unsuccessful attemps)." %
(raised_error, natt + 1))
return None
else:
import sys
raise type(raised_error)(str(raised_error) +
" (after %s unsuccessful attemps)." %
(natt + 1)).with_traceback(
sys.exc_info()[2])
wrap = tqdm.tqdm if (verbose
and len(sample) > 1) else (lambda x, **kwarg: x)
res = wrap(
self.mapper(runner, sample),
unit=" sample(s)",
total=len(sample),
dynamic_ncols=True,
)
pars, init, resid, flags = map2arr(res)
if hasattr(self, "pool") and self.pool:
self.pool.close()
if fname == "KalmanFilter":
self.debug = debug
if resid.shape[0] == 1:
resid[0] = pd.DataFrame(resid[0],
index=self.data.index[:-1],
columns=self.shocks)
edict = {"pars": pars, "init": init, "resid": resid, "flags": flags}
return edict
def run(self):
"""update the evolution paths and the distribution parameters m,
sigma, and C within CMA-ES.
"""
par = self.params
self.eigenvalues, self.eigenbasis = np.linalg.eigh(self.C)
self.condition_number = np.linalg.cond(self.C)
self.invsqrt = np.linalg.inv(np.linalg.cholesky(self.C))
z = self.sigma * self.eigenvalues**0.5 * self.randn()
y = self.eigenbasis @ z.T
xs = self.xmean + y.T
res = self.map(self.objective_fct, xs)
fs = map2arr(res)
# interrupt if things don't go nicely
if np.isinf(fs).all():
raise ValueError("Sample returns infs only.")
self.counteval += len(fs)
N = len(self.xmean)
xold = self.xmean.copy()
# sort by fitness
xs = xs[fs.argsort()]
self.fs = np.sort(fs)
self.xs = xs
self.best.update(xs[0], self.fs[0], self.counteval)
# compute new weighted mean value via recombination
self.xmean = xs[0:par.mu_mean].T @ par.weights_mean[:par.mu_mean]
# update evolution paths via cumulation:
y = self.xmean - xold
z = self.invsqrt @ y
csn = (par.cs * (2 - par.cs) * par.mueff)**0.5 / self.sigma
# update evolution path
self.ps = (1 - par.cs) * self.ps + csn * z
ccn = (par.cc * (2 - par.cc) * par.mueff)**0.5 / self.sigma
# turn off rank-one accumulation when sigma increases quickly
hsig = par.cs and (np.sum(self.ps**2) / N
# account for initial value of ps
/ (1 - (1 - par.cs)**(2 * self.counteval / par.lam))
< 2 + 4 / (N + 1))
self.pc = (1 - par.cc) * self.pc + ccn * hsig * y
# covariance matrix adaption:
# minor adjustment for the variance loss from hsig
c1a = par.c1 * (1 - (1 - hsig**2) * par.cc * (2 - par.cc))
self.C *= 1 - c1a - par.cmu * np.sum(par.weights)
# rank-one update
self.C += par.c1 * np.outer(self.pc, self.pc)
# rank-mu update
for k, wk in enumerate(par.weights):
# guaranty positive definiteness
if wk < 0:
mahalano = np.sum((self.invsqrt @ (xs[k] - xold))**2)
wk *= N * self.sigma**2 / mahalano
self.C += (wk * par.cmu / self.sigma**2 *
np.outer(xs[k] - xold, xs[k] - xold))
# adapt step-size
cn, sum_square_ps = par.cs / par.damps, np.sum(self.ps**2)
self.sigma *= np.exp(np.minimum(1, cn * (sum_square_ps / N - 1) / 2))
def prior_sampler(self, nsample, seed=None, test_lprob=False, verbose=True):
"""Draw parameters from prior. Drawn parameters have a finite likelihood.
Parameters
----------
nsample : int
Size of the prior sample
Returns
-------
array
Numpy array of parameters
"""
import tqdm
from grgrlib import map2arr, serializer
if seed is None:
seed = 0
reduce_sys = np.copy(self.fdict['reduce_sys'])
if not reduce_sys:
self.get_sys(reduce_sys=True, verbose=verbose > 1)
if test_lprob and not hasattr(self, 'ndim'):
self.prep_estim(load_R=True, verbose=verbose > 2)
if not 'frozen_prior' in self.fdict.keys():
from .stats import get_prior
self.fdict['frozen_prior'] = get_prior(self.prior)[0]
frozen_prior = self.fdict['frozen_prior']
if not hasattr(self, 'mapper'):
self.mapper = map
if hasattr(self, 'pool'):
self.pool.clear()
get_par = serializer(self.get_par)
get_sys = serializer(self.get_sys)
def runner(locseed):
np.random.seed(seed + locseed)
done = False
no = 0
while not done:
no += 1
with np.warnings.catch_warnings(record=False):
try:
np.warnings.filterwarnings('error')
rst = np.random.randint(32**2 - 2)
pdraw = [
pl.rvs(random_state=rst + sn)
for sn, pl in enumerate(frozen_prior)
]
if test_lprob:
draw_prob = lprob(pdraw, None, verbose > 1)
done = not np.isinf(draw_prob)
else:
pdraw_full = get_par(pdraw, asdict=False, full=True)
get_sys(par=pdraw_full, reduce_sys=True)
done = True
except Exception as e:
if verbose > 1:
print(str(e) + '(%s) ' % no)
return pdraw, no
if verbose > 1:
print('[prior_sample:]'.ljust(15, ' ') + ' sampling from the pior...')
wrapper = tqdm.tqdm if verbose < 2 else (lambda x, **kwarg: x)
pmap_sim = wrapper(self.mapper(runner, range(nsample)), total=nsample)
draws, nos = map2arr(pmap_sim)
if not reduce_sys:
self.get_sys(reduce_sys=False, verbose=verbose > 1)
if verbose:
print(
'[prior_sample:]'.ljust(15, ' ') +
' sampling done. %2.2f%% of the prior are either indetermined or explosive.'
% (100 * (sum(nos) - nsample) / nsample))
return draws
def irfs(self,
shocklist,
pars=None,
state=None,
T=30,
linear=False,
verbose=True):
"""Simulate impulse responses
Parameters
----------
shocklist : tuple or list of tuples
Tuple of (shockname, size, period)
T : int
Simulation horizon. (default: 30)
Returns
-------
DataFrame, tuple(int,int)
The simulated series as a pandas.DataFrame object and the expected durations at the constraint
"""
from grgrlib.core import serializer
if isinstance(shocklist, tuple):
shocklist = [
shocklist,
]
if hasattr(self, 'pool'):
from .estimation import create_pool
create_pool(self)
st = time.time()
set_par = serializer(self.set_par)
t_func = serializer(self.t_func)
shocks = self.shocks
nstates = len(self.vv)
def runner(par):
X = np.empty((T, nstates))
K = np.empty(T)
L = np.empty(T)
if np.any(par):
try:
set_par(par)
except ValueError:
X[:] = np.nan
K[:] = np.nan
L[:] = np.nan
return X, K, L, 4
st_vec = state if state is not None else np.zeros(nstates)
superflag = False
for t in range(T):
shk_vec = np.zeros(len(shocks))
for vec in shocklist:
if vec[2] == t:
shock = vec[0]
shocksize = vec[1]
shock_arg = shocks.index(shock)
shk_vec[shock_arg] = shocksize
st_vec, (l, k), flag = t_func(st_vec,
shk_vec,
linear=linear,
return_k=True)
superflag |= flag
X[t, :] = st_vec
L[t] = l
K[t] = k
return X, K, L, superflag
if pars is not None and np.ndim(pars) > 1:
res = self.mapper(runner, pars)
X, K, L, flag = map2arr(res)
else:
X, K, L, flag = runner(pars)
X = pd.DataFrame(X, columns=self.vv)
if np.any(flag) and verbose:
print('[irfs:]'.ljust(15, ' ') +
'No rational expectations solution found at least once.')
if verbose > 1:
print('[irfs:]'.ljust(15, ' ') + 'Simulation took ',
np.round((time.time() - st), 5), ' seconds.')
return X, (K, L)
def simulate(self,
source,
mask=None,
linear=False,
debug=False,
verbose=False):
"""Simulate time series given a series of exogenous innovations.
Parameters
----------
source : dict
Dict of `extract` results
mask : array
Mask for eps. Each non-None element will be replaced.
"""
from grgrlib.core import serializer
sample = zip(source['pars'], source['resid'],
[s[0] for s in source['means']])
if verbose:
st = time.time()
self.debug |= debug
if hasattr(self, 'pool'):
from .estimation import create_pool
create_pool(self)
set_par = serializer(self.set_par)
t_func = serializer(self.t_func)
def runner(arg):
superflag = False
par, eps, state = arg
if mask is not None:
eps = np.where(np.isnan(mask), eps, np.array(mask) * eps)
set_par(par)
X = [state]
K = []
L = []
for eps_t in eps:
state, (l, k), flag = t_func(state,
noise=eps_t,
return_k=True,
linear=linear)
superflag |= flag
X.append(state)
L.append(l)
K.append(k)
X = np.array(X)
L = np.array(L)
K = np.array(K)
return X, (L, K), superflag
wrap = tqdm.tqdm if verbose else (lambda x, **kwarg: x)
res = wrap(self.mapper(runner, sample),
unit=' sample(s)',
total=len(source['pars']),
dynamic_ncols=True)
res = map2arr(res)
superflag = res[-1].any()
if verbose:
print('[simulate:]'.ljust(15, ' ') + 'Simulation took ',
time.time() - st, ' seconds.')
if superflag and verbose:
print('[simulate:]'.ljust(15, ' ') +
'No rational expectations solution found.')
X, LK, flags = res
return X, (LK[:, 0, :], LK[:, 1, :]), flags
