def test_sample_ball():
p0 = np.ones(10)
std = np.ones(10)
sample = sampling_util.sample_ball(p0, std, size=10000, dist='normal')
mean = np.mean(sample, axis=0)
npt.assert_almost_equal(mean, p0, decimal=1)
sigma = np.std(sample, axis=0)
npt.assert_almost_equal(sigma, std, decimal=1)
sample = sampling_util.sample_ball(p0, std, size=10000, dist='uniform')
mean = np.mean(sample, axis=0)
npt.assert_almost_equal(mean, p0, decimal=1)
sigma = np.std(sample, axis=0)
npt.assert_almost_equal(sigma, std*0.607, decimal=1)
def mcmc_emcee(self, n_walkers, n_run, n_burn, mean_start, sigma_start, mpi=False, progress=False, threadCount=1):
numParam, _ = self.chain.param.num_param()
p0 = sampling_util.sample_ball(mean_start, sigma_start, n_walkers)
time_start = time.time()
if mpi is True:
pool = MPIPool()
if not pool.is_master():
pool.wait()
sys.exit(0)
is_master_pool = pool.is_master()
sampler = emcee.EnsembleSampler(n_walkers, numParam, self.chain.logL, pool=pool)
else:
is_master_pool = True
if threadCount > 1 :
pool = Pool(processes=threadCount)
else :
pool = None
sampler = emcee.EnsembleSampler(n_walkers, numParam, self.chain.likelihood, pool=pool)
sampler.run_mcmc(p0, n_burn + n_run, progress=progress)
flat_samples = sampler.get_chain(discard=n_burn, thin=1, flat=True)
dist = sampler.get_log_prob(flat=True, discard=n_burn, thin=1)
if is_master_pool:
print('Computing the MCMC...')
print('Number of walkers = ', n_walkers)
print('Burn-in iterations: ', n_burn)
print('Sampling iterations:', n_run)
time_end = time.time()
print(time_end - time_start, 'time taken for MCMC sampling')
return flat_samples, dist
def mcmc_emcee(self,
n_walkers,
n_run,
n_burn,
mean_start,
sigma_start,
mpi=False,
progress=False,
threadCount=1):
"""
Run MCMC with emcee.
For details, please have a look at the documentation of the emcee packager.
:param n_walkers: number of walkers in the emcee process
:type n_walkers: integer
:param n_run: number of sampling (after burn-in) of the emcee
:type n_run: integer
:param n_burn: number of burn-in iterations (those will not be saved in the output sample)
:type n_burn: integer
:param mean_start: mean of the parameter position of the initialising sample
:type mean_start: numpy array of length the number of parameters
:param sigma_start: spread of the parameter values (uncorrelated in each dimension) of the initialising sample
:type sigma_start: numpy array of length the number of parameters
:param mpi: if True, initializes an MPIPool to allow for MPI execution of the sampler
:type mpi: bool
:param progress: if True, prints the progress bar
:type progress: bool
:param threadCount: number of threats in multi-processing (not applicable for MPI)
:type threadCount: integer
:return: samples, ln likelihood value of samples
:rtype: numpy 2d array, numpy 1d array
"""
num_param, _ = self.chain.param.num_param()
p0 = sampling_util.sample_ball(mean_start, sigma_start, n_walkers)
time_start = time.time()
pool, is_master = choose_pool(mpi=mpi,
processes=threadCount,
use_dill=True)
sampler = emcee.EnsembleSampler(n_walkers,
num_param,
self.chain.logL,
pool=pool)
sampler.run_mcmc(p0, n_burn + n_run, progress=progress)
flat_samples = sampler.get_chain(discard=n_burn, thin=1, flat=True)
dist = sampler.get_log_prob(flat=True, discard=n_burn, thin=1)
if is_master:
print('Computing the MCMC...')
print('Number of walkers = ', n_walkers)
print('Burn-in iterations: ', n_burn)
print('Sampling iterations:', n_run)
time_end = time.time()
print(time_end - time_start, 'time taken for MCMC sampling')
return flat_samples, dist
def mcmc_emcee(self,
n_walkers,
n_burn,
n_run,
kwargs_mean_start,
kwargs_sigma_start,
continue_from_backend=False,
**kwargs_emcee):
"""
runs the EMCEE MCMC sampling
:param n_walkers: number of walkers
:param n_burn: number of iteration of burn in (not stored in the output sample
:param n_run: number of iterations (after burn in) to be sampled
:param kwargs_mean_start: keyword arguments of the mean starting position
:param kwargs_sigma_start: keyword arguments of the spread in the initial particles per parameter
:param continue_from_backend: bool, if True and 'backend' in kwargs_emcee, will continue a chain sampling from backend
:param kwargs_emcee: keyword argument for the emcee (e.g. to specify backend)
:return: samples of the EMCEE run
"""
num_param = self.param.num_param
sampler = emcee.EnsembleSampler(n_walkers,
num_param,
self.chain.likelihood,
args=(),
**kwargs_emcee)
mean_start = self.param.kwargs2args(**kwargs_mean_start)
sigma_start = self.param.kwargs2args(**kwargs_sigma_start)
p0 = sampling_util.sample_ball(mean_start, sigma_start, n_walkers)
backend = kwargs_emcee.get('backend', None)
if backend is not None:
if continue_from_backend:
p0 = None
else:
backend.reset(n_walkers, num_param)
sampler.run_mcmc(p0, n_burn + n_run, progress=True)
flat_samples = sampler.get_chain(discard=n_burn, thin=1, flat=True)
log_prob = sampler.get_log_prob(discard=n_burn, thin=1, flat=True)
return flat_samples, log_prob
def mcmc_emcee(self, n_walkers, n_run, n_burn, mean_start, sigma_start, mpi=False, progress=False, threadCount=1,
initpos=None, backup_filename=None, start_from_backup=False):
"""
Run MCMC with emcee.
For details, please have a look at the documentation of the emcee packager.
:param n_walkers: number of walkers in the emcee process
:type n_walkers: integer
:param n_run: number of sampling (after burn-in) of the emcee
:type n_run: integer
:param n_burn: number of burn-in iterations (those will not be saved in the output sample)
:type n_burn: integer
:param mean_start: mean of the parameter position of the initialising sample
:type mean_start: numpy array of length the number of parameters
:param sigma_start: spread of the parameter values (uncorrelated in each dimension) of the initialising sample
:type sigma_start: numpy array of length the number of parameters
:param mpi: if True, initializes an MPIPool to allow for MPI execution of the sampler
:type mpi: bool
:param progress: if True, prints the progress bar
:type progress: bool
:param threadCount: number of threats in multi-processing (not applicable for MPI)
:type threadCount: integer
:param initpos: initial walker position to start sampling (optional)
:type initpos: numpy array of size num param x num walkser
:param backup_filename: name of the HDF5 file where sampling state is saved (through emcee backend engine)
:type backup_filename: string
:param start_from_backup: if True, start from the state saved in `backup_filename`.
Otherwise, create a new backup file with name `backup_filename` (any already existing file is overwritten!).
:type start_from_backup: bool
:return: samples, ln likelihood value of samples
:rtype: numpy 2d array, numpy 1d array
"""
num_param, _ = self.chain.param.num_param()
if initpos is None:
initpos = sampling_util.sample_ball(mean_start, sigma_start, n_walkers, dist='normal')
pool = choose_pool(mpi=mpi, processes=threadCount, use_dill=True)
if backup_filename is not None:
backend = emcee.backends.HDFBackend(backup_filename, name="lenstronomy_mcmc_emcee")
if pool.is_master():
print("Warning: All samples (including burn-in) will be saved in backup file '{}'.".format(backup_filename))
if start_from_backup:
initpos = None
n_run_eff = n_run
else:
n_run_eff = n_burn + n_run
backend.reset(n_walkers, num_param)
if pool.is_master():
print("Warning: backup file '{}' has been reset!".format(backup_filename))
else:
backend = None
n_run_eff = n_burn + n_run
time_start = time.time()
sampler = emcee.EnsembleSampler(n_walkers, num_param, self.chain.logL,
pool=pool, backend=backend)
sampler.run_mcmc(initpos, n_run_eff, progress=progress)
flat_samples = sampler.get_chain(discard=n_burn, thin=1, flat=True)
dist = sampler.get_log_prob(flat=True, discard=n_burn, thin=1)
if pool.is_master():
print('Computing the MCMC...')
print('Number of walkers = ', n_walkers)
print('Burn-in iterations: ', n_burn)
print('Sampling iterations (in current run):', n_run_eff)
time_end = time.time()
print(time_end - time_start, 'time taken for MCMC sampling')
return flat_samples, dist
def test_raise(self):
with self.assertRaises(ValueError):
sampling_util.sample_ball(p0=np.ones(10), std=np.ones(10), size=1000, dist='BAD')
