def from_results_internal(
cls,
results_internal: np.ndarray,
log_posterior_list: np.ndarray,
model: AbstractPriorModel,
total_iterations: int,
time: Optional[float] = None,
):
"""
The `Samples` classes in **PyAutoFit** provide an interface between the results of a `NonLinearSearch` (e.g.
as files on your hard-disk) and Python.
To create a `Samples` object after an `pyswarms` model-fit the results must be converted from the
native format used by `pyswarms` (which are numpy ndarrays) to lists of values, the format used by
the **PyAutoFit** `Samples` objects.
This classmethod performs this conversion before creating a `PySwarmsSamples` object.
Parameters
----------
results_internal
The Pyswarms results in their native internal format from which the samples are computed.
log_posterior_list
The log posterior of the PySwarms accepted samples.
model
Maps input vectors of unit parameter values to physical values and model instances via priors.
total_iterations
The total number of PySwarms iterations, which cannot be estimated from the sample list (which contains
only accepted samples).
time
The time taken to perform the model-fit, which is passed around `Samples` objects for outputting
information on the overall fit.
"""
parameter_lists = [
param.tolist() for parameters in results_internal
for param in parameters
]
log_prior_list = model.log_prior_list_from(
parameter_lists=parameter_lists)
log_likelihood_list = [
lp - prior for lp, prior in zip(log_posterior_list, log_prior_list)
]
weight_list = len(log_likelihood_list) * [1.0]
sample_list = Sample.from_lists(
model=model,
parameter_lists=[
parameters.tolist()[0] for parameters in results_internal
],
log_likelihood_list=log_likelihood_list,
log_prior_list=log_prior_list,
weight_list=weight_list)
return PySwarmsSamples(
model=model,
sample_list=sample_list,
total_iterations=total_iterations,
time=time,
results_internal=results_internal,
)
def from_results_internal(
cls,
results_internal: Results,
model: AbstractPriorModel,
number_live_points: int,
unconverged_sample_size: int = 100,
time: Optional[float] = None,
):
"""
The `Samples` classes in **PyAutoFit** provide an interface between the results of a `NonLinearSearch` (e.g.
as files on your hard-disk) and Python.
To create a `Samples` object after a `dynesty` model-fit the results must be converted from the
native format used by `dynesty` to lists of values, the format used by the **PyAutoFit** `Samples` objects.
This classmethod performs this conversion before creating a `DynestySamples` object.
Parameters
----------
results_internal
The `dynesty` results in their native internal format from which the samples are computed.
model
Maps input vectors of unit parameter values to physical values and model instances via priors.
number_live_points
The number of live points used by the `dynesty` search.
unconverged_sample_size
If the samples are for a search that is yet to convergence, a reduced set of samples are used to provide
a rough estimate of the parameters. The number of samples is set by this parameter.
time
The time taken to perform the model-fit, which is passed around `Samples` objects for outputting
information on the overall fit.
"""
parameter_lists = results_internal.samples.tolist()
log_prior_list = model.log_prior_list_from(parameter_lists=parameter_lists)
log_likelihood_list = list(results_internal.logl)
try:
weight_list = list(
np.exp(np.asarray(results_internal.logwt) - results_internal.logz[-1])
)
except:
weight_list = results_internal["weights"]
sample_list = Sample.from_lists(
model=model,
parameter_lists=parameter_lists,
log_likelihood_list=log_likelihood_list,
log_prior_list=log_prior_list,
weight_list=weight_list,
)
return DynestySamples(
model=model,
sample_list=sample_list,
number_live_points=number_live_points,
unconverged_sample_size=unconverged_sample_size,
time=time,
results_internal=results_internal,
)
def from_results_internal(
cls,
results_internal,
model: AbstractPriorModel,
auto_correlation_settings: AutoCorrelationsSettings,
unconverged_sample_size: int = 100,
time: Optional[float] = None,
):
"""
The `Samples` classes in **PyAutoFit** provide an interface between the results of a `NonLinearSearch` (e.g.
as files on your hard-disk) and Python.
To create a `Samples` object after an `Zeus` model-fit the results must be converted from the
native format used by `Zeus` (which is a HDFBackend) to lists of values, the format used by the **PyAutoFit**
`Samples` objects.
This classmethod performs this conversion before creating a `ZeusSamples` object.
Parameters
----------
results_internal
The MCMC results in their native internal format from which the samples are computed.
model
Maps input vectors of unit parameter values to physical values and model instances via priors.
auto_correlations_settings
Customizes and performs auto correlation calculations performed during and after the search.
unconverged_sample_size
If the samples are for a search that is yet to convergence, a reduced set of samples are used to provide
a rough estimate of the parameters. The number of samples is set by this parameter.
time
The time taken to perform the model-fit, which is passed around `Samples` objects for outputting
information on the overall fit.
"""
parameter_lists = results_internal.get_chain(flat=True).tolist()
log_posterior_list = results_internal.get_log_prob(flat=True).tolist()
log_prior_list = model.log_prior_list_from(
parameter_lists=parameter_lists)
log_likelihood_list = [
log_posterior - log_prior for log_posterior, log_prior in zip(
log_posterior_list, log_prior_list)
]
weight_list = len(log_likelihood_list) * [1.0]
sample_list = Sample.from_lists(
model=model,
parameter_lists=parameter_lists,
log_likelihood_list=log_likelihood_list,
log_prior_list=log_prior_list,
weight_list=weight_list)
return ZeusSamples(
model=model,
sample_list=sample_list,
auto_correlation_settings=auto_correlation_settings,
unconverged_sample_size=unconverged_sample_size,
time=time,
results_internal=results_internal,
)
