def test_slogdet(self):
np.random.seed(mpirank)
arr = np.random.rand(2, 2 * mpisize)
sign, logdet = mpi_slogdet(arr)
full_arr = np.vstack(comm.allgather(arr))
test_sign, test_logdet = np.linalg.slogdet(full_arr)
self.assertEqual(sign, test_sign)
self.assertAlmostEqual(logdet, test_logdet)
def test_slogdet_odd(self):
cols = 32
rows = mpi_arrange(cols)[1] - mpi_arrange(cols)[0]
arr = np.random.rand(rows, cols)
sign, logdet = mpi_slogdet(arr)
full_arr = np.vstack(comm.allgather(arr))
test_sign, test_logdet = np.linalg.slogdet(full_arr)
self.assertEqual(sign, test_sign)
self.assertAlmostEqual(logdet, test_logdet)
def pslogdet(data):
"""
:py:func:`imagine.tools.mpi_helper.mpi_slogdet` or :py:func:`numpy.linalg.slogdet`
depending on :py:data:`imagine.rc['distributed_arrays']`.
"""
if rc['distributed_arrays']:
return m.mpi_slogdet(data)
else:
return np.linalg.slogdet(data)
def mpi_slogdet_timing(data_size):
local_row_size = mpi_arrange(data_size)[1] - mpi_arrange(data_size)[0]
random_data = np.random.rand(local_row_size, data_size)
tmr = Timer()
tmr.tick('mpi_slogdet')
sign, logdet = mpi_slogdet(random_data)
tmr.tock('mpi_slogdet')
if not mpirank:
print('@ tools_profiles::mpi_slogdet_timing with ' + str(mpisize) +
' nodes')
print('global matrix size (' + str(data_size) + ',' + str(data_size) +
')')
print('elapse time ' + str(tmr.record['mpi_slogdet']) + '\n')
def __call__(self, observable_dict):
"""
SimpleLikelihood object call function
Parameters
----------
observable_dict : imagine.observables.observable_dict.Simulations
Simulations object
Returns
------
likelicache : float
log-likelihood value (copied to all nodes)
"""
log.debug('@ simple_likelihood::__call__')
assert isinstance(observable_dict, Simulations)
# check dict entries
assert (observable_dict.keys() == self._measurement_dict.keys())
likelicache = np.float64(0)
if self._covariance_dict is None: # no covariance matrix
for name in self._measurement_dict.keys():
obs_mean = deepcopy(observable_dict[name].ensemble_mean
) # use mpi_mean, copied to all nodes
data = deepcopy(
self._measurement_dict[name].data) # to distributed data
diff = np.nan_to_num(data - obs_mean)
likelicache += -float(0.5) * float(np.vdot(
diff, diff)) # copied to all nodes
else: # with covariance matrix
for name in self._measurement_dict.keys():
obs_mean = deepcopy(observable_dict[name].ensemble_mean
) # use mpi_mean, copied to all nodes
data = deepcopy(
self._measurement_dict[name].data) # to distributed data
diff = np.nan_to_num(data - obs_mean)
if name in self._covariance_dict.keys(
): # not all measreuments have cov
cov = deepcopy(self._covariance_dict[name].data
) # to distributed data
(sign, logdet) = mpi_slogdet(cov * 2. * np.pi)
likelicache += -0.5 * (
np.vdot(diff, mpi_lu_solve(cov, diff)) + sign * logdet)
else:
likelicache += -0.5 * np.vdot(diff, diff)
return likelicache
def testfield(measure_size, simulation_size, make_plots=True, debug=False):
if debug:
log.basicConfig(filename='imagine_li_dynesty.log', level=log.DEBUG)
else:
log.basicConfig(filename='imagine_li_dynesty.log')
"""
:return:
log.basicConfig(filename='imagine.log', level=log.INFO)
"""
"""
# step 0, set 'a' and 'b', 'mea_std'
TestField in LiSimulator is modeled as
field = gaussian_random(mean=a,std=b)_x * cos(x)
where x in (0,2pi)
for generating mock data we need
true values of a and b: true_a, true_b, mea_seed
measurement uncertainty: mea_std
measurement points, positioned in (0,2pi) evenly, due to TestField modelling
"""
true_a = 3.
true_b = 6.
mea_std = 0.1 # std of gaussian measurement error
mea_seed = 233
truths = [true_a, true_b] # will be used in visualizing posterior
"""
# step 1, prepare mock data
"""
"""
# 1.1, generate measurements
mea_field = signal_field + noise_field
"""
x = np.linspace(0, 2. * np.pi, measure_size) # data points in measurements
np.random.seed(mea_seed) # seed for signal field
signal_field = np.multiply(
np.cos(x), np.random.normal(loc=true_a,
scale=true_b,
size=measure_size))
mea_field = np.vstack([
signal_field +
np.random.normal(loc=0., scale=mea_std, size=measure_size)
])
"""
# 1.2, generate covariances
what's the difference between pre-define dan re-estimated?
"""
# re-estimate according to measurement error
mea_repeat = np.zeros((simulation_size, measure_size))
for i in range(simulation_size): # times of repeated measurements
mea_repeat[i, :] = signal_field + np.random.normal(
loc=0., scale=mea_std, size=measure_size)
mea_cov = oas_mcov(mea_repeat)[1]
print(mpirank, 're-estimated: \n', mea_cov, 'slogdet',
mpi_slogdet(mea_cov))
# pre-defined according to measurement error
mea_cov = (mea_std**2) * mpi_eye(measure_size)
print(mpirank, 'pre-defined: \n', mea_cov, 'slogdet', mpi_slogdet(mea_cov))
"""
# 1.3 assemble in imagine convention
"""
mock_data = Measurements() # create empty Measrurements object
mock_cov = Covariances() # create empty Covariance object
# pick up a measurement
mock_data.append(('test', 'nan', str(measure_size), 'nan'), mea_field,
True)
mock_cov.append(('test', 'nan', str(measure_size), 'nan'), mea_cov, True)
"""
# 1.4, visualize mock data
"""
if mpirank == 0 and make_plots:
plt.plot(x,
mock_data[('test', 'nan', str(measure_size), 'nan')].data[0])
plt.savefig('testfield_mock_li.pdf')
"""
# step 2, prepare pipeline and execute analysis
"""
"""
# 2.1, ensemble likelihood
"""
likelihood = EnsembleLikelihood(
mock_data, mock_cov) # initialize likelihood with measured info
"""
# 2.2, field factory list
"""
factory = TestFieldFactory(
active_parameters=('a', 'b')) # factory with single active parameter
factory.parameter_ranges = {
'a': (0, 10),
'b': (0, 10)
} # adjust parameter range for Bayesian analysis
factory_list = [factory] # likelihood requires a list/tuple of factories
"""
# 2.3, flat prior
"""
prior = FlatPrior()
"""
# 2.4, simulator
"""
simer = LiSimulator(mock_data)
"""
# 2.5, pipeline
"""
pipe = DynestyPipeline(simer, factory_list, likelihood, prior,
simulation_size)
pipe.random_type = 'controllable' # 'fixed' random_type doesnt work for Dynesty pipeline, yet
pipe.seed_tracer = int(23)
pipe.sampling_controllers = {'nlive': 400}
tmr = Timer()
tmr.tick('test')
results = pipe()
tmr.tock('test')
if mpirank == 0:
print('\n elapse time ' + str(tmr.record['test']) + '\n')
"""
# step 3, visualize (with corner package)
"""
if mpirank == 0 and make_plots:
samples = results['samples']
for i in range(len(
pipe.active_parameters)): # convert variables into parameters
low, high = pipe.active_ranges[pipe.active_parameters[i]]
for j in range(samples.shape[0]):
samples[j, i] = unity_mapper(samples[j, i], low, high)
# corner plot
corner.corner(samples[:, :len(pipe.active_parameters)],
range=[0.99] * len(pipe.active_parameters),
quantiles=[0.02, 0.5, 0.98],
labels=pipe.active_parameters,
show_titles=True,
title_kwargs={"fontsize": 15},
color='steelblue',
truths=truths,
truth_color='firebrick',
plot_contours=True,
hist_kwargs={'linewidth': 2},
label_kwargs={'fontsize': 20})
plt.savefig('testfield_posterior_li_dynesty.pdf')
def __call__(self, observable_dict):
"""
EnsembleLikelihood class call function
Parameters
----------
observable_dict : imagine.observables.observable_dict.Simulations
Simulations object
Returns
------
likelicache : float
log-likelihood value (copied to all nodes)
"""
log.debug('@ ensemble_likelihood::__call__')
assert isinstance(observable_dict, Simulations)
# check dict entries
assert (observable_dict.keys() == self._measurement_dict.keys())
likelicache = float(0)
if self._covariance_dict is None:
for name in self._measurement_dict.keys():
obs_mean, obs_cov = oas_mcov(
observable_dict[name].data) # to distributed data
data = deepcopy(
self._measurement_dict[name].data) # to distributed data
diff = np.nan_to_num(data - obs_mean)
if (mpi_trace(obs_cov) <
1E-28): # zero will not be reached, at most E-32
likelicache += -0.5 * np.vdot(diff, diff)
else:
sign, logdet = mpi_slogdet(obs_cov * 2. * np.pi)
likelicache += -0.5 * (np.vdot(
diff, mpi_lu_solve(obs_cov, diff)) + sign * logdet)
else:
for name in self._measurement_dict.keys():
obs_mean, obs_cov = oas_mcov(
observable_dict[name].data) # to distributed data
data = deepcopy(
self._measurement_dict[name].data) # to distributed data
diff = np.nan_to_num(data - obs_mean)
if name in self._covariance_dict.keys(
): # not all measurements have cov
full_cov = deepcopy(
self._covariance_dict[name].data) + obs_cov
if (mpi_trace(full_cov) <
1E-28): # zero will not be reached, at most E-32
likelicache += -0.5 * np.vdot(diff, diff)
else:
sign, logdet = mpi_slogdet(full_cov * 2. * np.pi)
likelicache += -0.5 * (
np.vdot(diff, mpi_lu_solve(full_cov, diff)) +
sign * logdet)
else:
if (mpi_trace(obs_cov) <
1E-28): # zero will not be reached, at most E-32
likelicache += -0.5 * np.vdot(diff, diff)
else:
sign, logdet = mpi_slogdet(obs_cov * 2. * np.pi)
likelicache += -0.5 * (np.vdot(
diff, mpi_lu_solve(obs_cov, diff)) + sign * logdet)
return likelicache
