pypomp.core.results.PanelPompTrainResult

class pypomp.core.results.PanelPompTrainResult(method: str, execution_time: float | None, key: Array, timestamp: Timestamp = <factory>, theta: PanelParameters | None = None, shared_traces: DataArray = <factory>, unit_traces: DataArray = <factory>, logLiks: DataArray = <factory>, optimizer: Optimizer = <factory>, J: int = 0, M: int = 0, eta: LearningRate | None = None, alpha: float = 0.97, alpha_cooling: float = 1.0)

Bases: PanelPompEstimationTracesMixin, PanelPompBaseResult

Result from PanelPomp.train() method.

Methods

CLL([average])	Return conditional log-likelihoods as a DataFrame.
ESS([average])	Return Effective Sample Size as a DataFrame.
__init__(method, execution_time, key, ...)
merge(*results)	Merge multiple result objects of the same type.
print_summary([n])	Print a summary of this result.
to_dataframe([ignore_nan])	Convert panel result to DataFrame.
traces()	Return panel result formatted as traces (long format).

Attributes

J	The number of particles used for filtering.
M	The number of iterations performed.
alpha	The discount factor for the gradient moving average.
alpha_cooling	The cooling factor for the discount factor.
eta	The learning rate object.
theta	The panel parameter object used for the computation.
shared_traces	Shared parameter traces across iterations.
unit_traces	Unit-specific parameter traces across iterations.
logLiks	Log-likelihoods for each unit across iterations.
optimizer	The optimizer used for training.
method	The name of the method that produced this result (e.g., 'pfilter', 'mif').
execution_time	Total execution time in seconds.
key	The JAX random key used for this execution.
timestamp	The date and time when the result was created.