class TestLikelihoodBase(unittest.TestCase):
def setUp(self):
self.likelihood = Likelihood()
def tearDown(self):
del self.likelihood
def test_repr(self):
self.likelihood = Likelihood(parameters=["a", "b"])
expected = "Likelihood(parameters=['a', 'b'])"
self.assertEqual(expected, repr(self.likelihood))
def test_base_log_likelihood(self):
self.assertTrue(np.isnan(self.likelihood.log_likelihood()))
def test_base_noise_log_likelihood(self):
self.assertTrue(np.isnan(self.likelihood.noise_log_likelihood()))
def test_base_log_likelihood_ratio(self):
self.assertTrue(np.isnan(self.likelihood.log_likelihood_ratio()))
def test_meta_data_unset(self):
self.assertEqual(self.likelihood.meta_data, None)
def test_meta_data_set_fail(self):
with self.assertRaises(ValueError):
self.likelihood.meta_data = 10
def test_meta_data(self):
meta_data = dict(x=1, y=2)
self.likelihood.meta_data = meta_data
self.assertEqual(self.likelihood.meta_data, meta_data)
def __init__(
self,
interferometers,
waveform_generator,
priors=None,
distance_marginalization=True,
phase_marginalization=True,
time_marginalization=False,
):
"""
A likelihood object, able to compute the likelihood of the data given
some model parameters
The simplest frequency-domain gravitational wave transient likelihood.
Does not include time/phase marginalization.
Parameters
----------
interferometers: list
A list of `bilby.gw.detector.Interferometer` instances - contains
the detector data and power spectral densities
waveform_generator: bilby.gw.waveform_generator.WaveformGenerator
An object which computes the frequency-domain strain of the signal,
given some set of parameters
"""
Likelihood.__init__(self, dict())
self.interferometers = interferometers
self.waveform_generator = waveform_generator
self._noise_log_l = np.nan
self.psds = dict()
self.strain = dict()
self._data_to_gpu()
if priors is None:
self.priors = priors
else:
self.priors = priors.copy()
self.distance_marginalization = distance_marginalization
self.phase_marginalization = phase_marginalization
if self.distance_marginalization:
self._setup_distance_marginalization()
priors["luminosity_distance"] = priors["luminosity_distance"].minimum
if self.phase_marginalization:
priors["phase"] = 0.0
self.time_marginalization = False
class TestLikelihoodBase(unittest.TestCase):
def setUp(self):
self.likelihood = Likelihood()
def tearDown(self):
del self.likelihood
def test_repr(self):
self.likelihood = Likelihood(parameters=['a', 'b'])
expected = 'Likelihood(parameters=[\'a\', \'b\'])'
self.assertEqual(expected, repr(self.likelihood))
def test_base_log_likelihood(self):
self.assertTrue(np.isnan(self.likelihood.log_likelihood()))
def test_base_noise_log_likelihood(self):
self.assertTrue(np.isnan(self.likelihood.noise_log_likelihood()))
def test_base_log_likelihood_ratio(self):
self.assertTrue(np.isnan(self.likelihood.log_likelihood_ratio()))
def __init__(self,
posteriors,
hyper_prior,
sampling_prior=None,
ln_evidences=None,
max_samples=1e100,
selection_function=lambda args: 1,
conversion_function=lambda args: (args, None),
cupy=True):
"""
Parameters
----------
posteriors: list
An list of pandas data frames of samples sets of samples.
Each set may have a different size.
These can contain a `prior` column containing the original prior
values.
hyper_prior: `bilby.hyper.model.Model`
The population model, this can alternatively be a function.
sampling_prior: `bilby.hyper.model.Model` *DEPRECATED*
The sampling prior, this can alternatively be a function.
ln_evidences: list, optional
Log evidences for single runs to ensure proper normalisation
of the hyperparameter likelihood. If not provided, the original
evidences will be set to 0. This produces a Bayes factor between
the sampling power_prior and the hyperparameterised model.
selection_function: func
Function which evaluates your population selection function.
conversion_function: func
Function which converts a dictionary of sampled parameter to a
dictionary of parameters of the population model.
max_samples: int, optional
Maximum number of samples to use from each set.
cupy: bool
If True and a compatible CUDA environment is available,
cupy will be used for performance.
Note: this requires setting up your hyper_prior properly.
"""
if cupy and not CUPY_LOADED:
logger.warning('Cannot import cupy, falling back to numpy.')
self.samples_per_posterior = max_samples
self.data = self.resample_posteriors(posteriors,
max_samples=max_samples)
if not isinstance(hyper_prior, Model):
hyper_prior = Model([hyper_prior])
self.hyper_prior = hyper_prior
Likelihood.__init__(self, hyper_prior.parameters)
if sampling_prior is not None:
logger.warning('Passing a sampling_prior is deprecated. This '
'should be passed as a column in the posteriors.')
if not isinstance(sampling_prior, Model):
sampling_prior = Model([sampling_prior])
self.sampling_prior = sampling_prior.prob(self.data)
elif 'prior' in self.data:
self.sampling_prior = self.data.pop('prior')
else:
logger.info('No prior values provided, defaulting to 1.')
self.sampling_prior = 1
if ln_evidences is not None:
self.total_noise_evidence = np.sum(ln_evidences)
else:
self.total_noise_evidence = np.nan
self.conversion_function = conversion_function
self.selection_function = selection_function
self.n_posteriors = len(posteriors)
self.samples_factor =\
- self.n_posteriors * np.log(self.samples_per_posterior)
def __init__(
self,
posteriors,
hyper_prior,
sampling_prior=None,
ln_evidences=None,
max_samples=1e100,
selection_function=lambda args: 1,
conversion_function=lambda args: (args, None),
cupy=True,
):
"""
Parameters
----------
posteriors: list
An list of pandas data frames of samples sets of samples.
Each set may have a different size.
These can contain a `prior` column containing the original prior
values.
hyper_prior: `bilby.hyper.model.Model`
The population model, this can alternatively be a function.
sampling_prior: array-like *DEPRECATED*
The sampling prior, this can alternatively be a function.
THIS WILL BE REMOVED IN THE NEXT RELEASE.
ln_evidences: list, optional
Log evidences for single runs to ensure proper normalisation
of the hyperparameter likelihood. If not provided, the original
evidences will be set to 0. This produces a Bayes factor between
the sampling power_prior and the hyperparameterised model.
selection_function: func
Function which evaluates your population selection function.
conversion_function: func
Function which converts a dictionary of sampled parameter to a
dictionary of parameters of the population model.
max_samples: int, optional
Maximum number of samples to use from each set.
cupy: bool
If True and a compatible CUDA environment is available,
cupy will be used for performance.
Note: this requires setting up your hyper_prior properly.
"""
if cupy and not CUPY_LOADED:
logger.warning("Cannot import cupy, falling back to numpy.")
self.samples_per_posterior = max_samples
self.data = self.resample_posteriors(posteriors,
max_samples=max_samples)
if isinstance(hyper_prior, types.FunctionType):
hyper_prior = Model([hyper_prior])
elif not (hasattr(hyper_prior, 'parameters')
and callable(getattr(hyper_prior, 'prob'))):
raise AttributeError(
"hyper_prior must either be a function, "
"or a class with attribute 'parameters' and method 'prob'")
self.hyper_prior = hyper_prior
Likelihood.__init__(self, hyper_prior.parameters)
if sampling_prior is not None:
raise ValueError(
"Passing a sampling_prior is deprecated and will be removed "
"in the next release. This should be passed as a 'prior' "
"column in the posteriors.")
elif "prior" in self.data:
self.sampling_prior = self.data.pop("prior")
else:
logger.info("No prior values provided, defaulting to 1.")
self.sampling_prior = 1
if ln_evidences is not None:
self.total_noise_evidence = np.sum(ln_evidences)
else:
self.total_noise_evidence = np.nan
self.conversion_function = conversion_function
self.selection_function = selection_function
self.n_posteriors = len(posteriors)
def test_repr(self):
self.likelihood = Likelihood(parameters=["a", "b"])
expected = "Likelihood(parameters=['a', 'b'])"
self.assertEqual(expected, repr(self.likelihood))
def setUp(self):
self.likelihood = Likelihood()
def test_repr(self):
self.likelihood = Likelihood(parameters=['a', 'b'])
expected = 'Likelihood(parameters=[\'a\', \'b\'])'
self.assertEqual(expected, repr(self.likelihood))