Ctrl+K

spey.optimizer.core.fit

Contents

spey.optimizer.core.fit#

spey.optimizer.core.fit(func: Callable[[ndarray], ndarray], model_configuration: ModelConfig, do_grad: bool = False, hessian: Callable[[ndarray], ndarray] | None = None, initial_parameters: ndarray | None = None, bounds: List[Tuple[float, float]] | None = None, fixed_poi_value: float | None = None, logpdf: Callable[[List[float]], float] | None = None, constraints: List[Dict] | None = None, **options) → Tuple[float, ndarray][source]#

Operating the fitting of the given function.

Parameters:

  • func (Callable[[np.ndarray], np.ndarray]) – Function to be optimised. If do_grad=True, function is expected to return Tuple[np.ndarray, np.ndarray] where first is the value of the function and the second is gradients with respect to the nuisance parameters.

  • model_configuration (~spey.base.model_config.ModelConfig) – Model configuration.

  • do_grad (bool, default False) – boolean to set gradient on or off.

  • hessian (Optional[Callable[[np.ndarray], np.ndarray]], default None) – hessian of the function with respect to variational parameters. Currently not used.

  • initial_parameters (Optional[np.ndarray], default None) – Initial guess for the variational parameters.

  • bounds (Optional[List[Tuple[float, float]]], default None) – Bounds for the variational parameters each item has to have a lower and upper bound definition (<lower>, <upper>) in case of open boundary input can be None. Number of boundaries has to match with the number of parameters. They will match with respect to the index within the list.

  • fixed_poi_value (Optional[float], default None) – If float the poi index will be fixed.

  • logpdf (Optional[Callable[[List[float]], float]], default None) – If provided, log-probability distribution will be computed once the parameters are fitted. If not, the result of the func input will be returned.

  • constraints (Optional[List[Dict]], default None) – Constraints of the model. see scipy for details.

  • options (Dict) – extra options for the optimiser. see scipy minimiser for details.

Returns:

value and the parameters. log-probability value will be returned if logpdf argument is provided.

Return type:

Tuple[float, np.ndarray]

Contents