from kameleon_rks.experiments.tools import store_samples
result_fname = os.path.splitext(os.path.basename(__file__))[0] + ".txt"
if __name__ == '__main__':
import time
from kameleon_rks.examples.plotting import visualise_pairwise_marginals
from kameleon_rks.proposals.Metropolis import AdaptiveIndependentMetropolis
from kameleon_rks.samplers.mini_pmc import mini_pmc
from kameleon_rks.tools.log import Log
import numpy as np
from smc2.sv_models import SVoneSP500Model
logger = Log.get_logger()
def get_AdaptiveIndependentMetropolis_instance(D, target_log_pdf):
gamma2 = 0.1
# # run 1 (initial parameters from Ingmar's pilot runs)
# proposal_mu = np.array([ -0.17973298, 0.11796741, 0.86733172, 0.52834129, -3.32354247])
# proposal_var = np.array([ 0.01932092, 0.02373668, 0.02096583, 0.1566503 , 0.46316933])
# proposal_L_C = np.diag(np.sqrt(proposal_var))
# proposal_L_C *= 2
# # resuts:
# # mean: array([ 0.08820961, -0.03436691, 0.7178945, 0.54124475, -3.77499049])
# # var: array([ 0.01514925, 0.01407534, 0.056966, 0.37502436, 0.5887545])
# # np.mean(var): 0.201
# # np.linalg.norm(mean): 3.882
import time
from kameleon_rks.tools.log import Log
import numpy as np
logger = Log.get_logger()
def mini_mcmc(transition_kernel,
start,
num_iter,
D,
recompute_log_pdf=False,
time_budget=None):
# MCMC results
samples = np.zeros((num_iter, D)) + np.nan
proposals = np.zeros((num_iter, D)) + np.nan
accepted = np.zeros(num_iter) + np.nan
acc_prob = np.zeros(num_iter) + np.nan
log_pdf = np.zeros(num_iter) + np.nan
step_sizes = np.zeros(num_iter) + np.nan
# timings for output and time limit
times = np.zeros(num_iter)
last_time_printed = time.time()
# for adaptive transition kernels
avg_accept = 0.
# init MCMC (first iteration)
current = start
return instance
def get_AdaptiveLangevin(D, target_log_pdf, grad, prec=False, step_size=1):
acc_star = 0.574
gamma2 = 0.1
instance = AdaptiveLangevin(D, target_log_pdf, grad, step_size,
one_over_4th_root_t_schedule, acc_star)
instance.do_preconditioning = prec
instance.manual_gradient_step_size = 1
return instance
if __name__ == '__main__':
Log.set_loglevel(20)
max_moment = 8
D = 10
pop_size = 10
bananicity = 0.03
V = 100
Z = sample_banana(3000, D, bananicity, V)
moments = np.array([(Z**i).mean(0) for i in range(1, max_moment)])
pk = PolynomialKernel(3)
print(moments)
true_correct = np.linalg.inv(np.cov(Z.T))
target_log_pdf = lambda x: log_banana_pdf(
x, bananicity, V, compute_grad=False)
target_grad = lambda x: log_banana_pdf(x, bananicity, V, compute_grad=True)
samplers = [
import time
from kameleon_rks.tools.log import Log
import numpy as np
logger = Log.get_logger()
def mini_mcmc(transition_kernel, start, num_iter, D, recompute_log_pdf=False, time_budget=None):
# MCMC results
samples = np.zeros((num_iter, D)) + np.nan
proposals = np.zeros((num_iter, D)) + np.nan
accepted = np.zeros(num_iter) + np.nan
acc_prob = np.zeros(num_iter) + np.nan
log_pdf = np.zeros(num_iter) + np.nan
step_sizes = np.zeros(num_iter) + np.nan
# timings for output and time limit
times = np.zeros(num_iter)
last_time_printed = time.time()
# for adaptive transition kernels
avg_accept = 0.
# init MCMC (first iteration)
current = start
current_log_pdf = None
current_kwargs = {}
logger.info("Starting MCMC using %s in D=%d dimensions" % \
(transition_kernel.get_name(), D,))
return instance
def get_AM_5(D, target_log_pdf):
step_size = 5.
acc_star = 0.234
gamma2 = 0.1
instance = AdaptiveMetropolis(D, target_log_pdf, step_size, gamma2, one_over_4th_root_t_schedule, acc_star)
return instance
if __name__ == '__main__':
Log.set_loglevel(20)
mdl = SVoneSP500Model()
#the following is the number of particles used for integrating out the latent variables
mdl.mdl_param.NX = mdl.mdl_param.NX * 1
D = mdl.dim
pop_size=50
target_log_pdf = mdl.get_logpdf_closure()
samplers = [
# get_StaticMetropolis_instance(D, target_log_pdf),
# get_AdaptiveMetropolis_instance(D, target_log_pdf),
# get_OracleKameleon_instance(D, target_log_pdf),
# get_Kameleon_instance(D, target_log_pdf),
# get_StaticLangevin_instance(D, target_log_pdf, target_grad),
return instance
def get_KernelAdaptiveLangevin_instance(D, target_log_pdf, grad):
step_size = 1.
schedule = one_over_sqrt_t_schedule
acc_star = 0.574
m = 500
surrogate = KernelExpFiniteGaussian(sigma=10, lmbda=1., m=m, D=D)
instance = KernelAdaptiveLangevin(D, target_log_pdf, surrogate, step_size, schedule, acc_star)
return instance
if __name__ == '__main__':
Log.set_loglevel(20)
D = 2
bananicity = 0.03
V = 100
target_log_pdf = lambda x: log_banana_pdf(x, bananicity, V, compute_grad=False)
target_grad = lambda x: log_banana_pdf(x, bananicity, V, compute_grad=True)
samplers = [
get_StaticMetropolis_instance(D, target_log_pdf),
get_AdaptiveMetropolis_instance(D, target_log_pdf),
get_OracleKameleon_instance(D, target_log_pdf),
get_Kameleon_instance(D, target_log_pdf),
get_StaticLangevin_instance(D, target_log_pdf, target_grad),
get_AdaptiveLangevin_instance(D, target_log_pdf, target_grad),
return instance
def get_AM_5(D, target_log_pdf):
step_size = 5.
acc_star = 0.234
gamma2 = 0.1
instance = AdaptiveMetropolis(D, target_log_pdf, step_size, gamma2, one_over_4th_root_t_schedule, acc_star)
return instance
if __name__ == '__main__':
Log.set_loglevel(20)
D = 2
pop_size=10
bananicity = 0.03
V = 100
Z = sample_banana(700, D, bananicity, V)
moments = np.array([(Z**i).mean(0) for i in range(1,4)])
target_log_pdf = lambda x: log_banana_pdf(x, bananicity, V, compute_grad=False)
target_grad = lambda x: log_banana_pdf(x, bananicity, V, compute_grad=True)
samplers = [
# get_StaticMetropolis_instance(D, target_log_pdf),
# get_AdaptiveMetropolis_instance(D, target_log_pdf),
# get_OracleKameleon_instance(D, target_log_pdf),
# get_Kameleon_instance(D, target_log_pdf),
# get_StaticLangevin_instance(D, target_log_pdf, target_grad),
return instance
def get_AM_5(D, target_log_pdf):
step_size = 5.
acc_star = 0.234
gamma2 = 0.1
instance = AdaptiveMetropolis(D, target_log_pdf, step_size, gamma2,
one_over_4th_root_t_schedule, acc_star)
return instance
if __name__ == '__main__':
Log.set_loglevel(20)
mdl = SVoneSP500Model()
#the following is the number of particles used for integrating out the latent variables
mdl.mdl_param.NX = mdl.mdl_param.NX * 1
D = mdl.dim
pop_size = 50
target_log_pdf = mdl.get_logpdf_closure()
samplers = [
# get_StaticMetropolis_instance(D, target_log_pdf),
# get_AdaptiveMetropolis_instance(D, target_log_pdf),
# get_OracleKameleon_instance(D, target_log_pdf),
# get_Kameleon_instance(D, target_log_pdf),
# get_StaticLangevin_instance(D, target_log_pdf, target_grad),
return instance
def get_AdaptiveLangevin(D, target_log_pdf, grad, prec = False, step_size=1):
acc_star = 0.574
gamma2 = 0.1
instance = AdaptiveLangevin(D, target_log_pdf, grad, step_size, one_over_4th_root_t_schedule, acc_star)
instance.do_preconditioning = prec
instance.manual_gradient_step_size = 1
return instance
if __name__ == '__main__':
Log.set_loglevel(20)
max_moment = 8
D = 10
pop_size=10
bananicity = 0.03
V = 100
Z = sample_banana(3000, D, bananicity, V)
moments = np.array([(Z**i).mean(0) for i in range(1, max_moment)])
pk = PolynomialKernel(3)
print(moments)
true_correct = np.linalg.inv(np.cov(Z.T))
target_log_pdf = lambda x: log_banana_pdf(x, bananicity, V, compute_grad=False)
target_grad = lambda x: log_banana_pdf(x, bananicity, V, compute_grad=True)
samplers = [
#
