voiage.backends.advanced_integration.JaxAdvancedBackend

Extended JAX backend with advanced features.

Methods

evppi_advanced

evppi_advanced([positional or keyword] self: None = None, [positional or keyword] net_benefit_array: np.ndarray = None, [positional or keyword] parameter_samples: np.ndarray | Mapping[str, np.ndarray] = None, [positional or keyword] parameters_of_interest: Sequence[str] = None, [positional or keyword] method: str = 'polynomial', [positional or keyword] degree: int = 2, [positional or keyword] cv_folds: int = 5, [positional or keyword] regularization: float = 1e-06) -> float

Advanced EVPPI calculation with enhanced regression.

Parameters:

• self
• net_benefit_array np.ndarray
• parameter_samples np.ndarray | Mapping[str, np.ndarray]
• parameters_of_interest Sequence[str]
• method str (default: 'polynomial')
• degree int (default: 2)
• cv_folds int (default: 5)
• regularization float (default: 1e-06)

Returns: float

get_gpu_info

get_gpu_info([positional or keyword] self: None = None) -> object

Get GPU information for optimization.

Parameters:

• self

Returns: object

profile_evppi

profile_evppi([positional or keyword] self: None = None, [positional or keyword] net_benefit_array: np.ndarray = None, [positional or keyword] parameter_samples: np.ndarray | Mapping[str, np.ndarray] = None, [positional or keyword] parameters_of_interest: Sequence[str] = None) -> dict[str, object]

Profile EVPPI calculation performance.

Parameters:

• self
• net_benefit_array np.ndarray
• parameter_samples np.ndarray | Mapping[str, np.ndarray]
• parameters_of_interest Sequence[str]

Returns: dict[str, object]