fitresult¶
-
class
zfit.minimizers.fitresult.
FitResult
(params: Dict[zfit.core.interfaces.ZfitParameter, float], edm: float, fmin: float, status: int, converged: bool, info: dict, loss: zfit.core.interfaces.ZfitLoss, minimizer: zfit.minimizers.interface.ZfitMinimizer)[source]¶ Bases:
zfit.minimizers.interface.ZfitResult
Create a FitResult from a minimization. Store parameter values, minimization infos and calculate errors.
Any errors calculated are saved under self.params dictionary with {parameter: {error_name1: {‘low’: value ‘high’: value or similar}}
Parameters: params (OrderedDict[ Parameter
, float]) – Result of the fit where each:param
Parameter
key has the value: from the minimum found by the minimizer. :param edm: The estimated distance to minimum, estimated by the minimizer (if available) :type edm: Union[int, float] :param fmin: The minimum of the function found by the minimizer :type fmin: Union[numpy.float64, float] :param status: A status code (if available) :type status: int :param converged: Whether the fit has successfully converged or not. :type converged: bool :param info: Additional information (if available) like number of function calls and theoriginal minimizer return message.Parameters: - loss (Union[ZfitLoss]) – The loss function that was minimized. Contains also the pdf, data etc.
- minimizer (ZfitMinimizer) – Minimizer that was used to obtain this FitResult and will be used to calculate certain errors. If the minimizer is state-based (like “iminuit”), then this is a copy and the state of other FitResults or of the actual minimizer that performed the minimization won’t be altered.
-
converged
¶
-
covariance
(params: Optional[Iterable[zfit.core.interfaces.ZfitParameter]] = None, as_dict: bool = False)[source]¶ Calculate the covariance matrix for params.
Parameters: Returns: 2D numpy.array of shape (N, N); dict`(param1, param2) -> covariance if `as_dict == True.
-
edm
¶ The estimated distance to the minimum.
Returns: numeric
-
error
(params: Optional[Iterable[zfit.core.interfaces.ZfitParameter]] = None, method: Union[str, Callable] = 'minuit_minos', error_name: str = None, sigma: float = 1.0) → collections.OrderedDict[source]¶ Calculate and set for params the asymmetric error using the set error method.
Parameters: - params (list(
Parameter
or str)) – The parameters or their names to calculate the errors. If params is None, use all floating parameters. - method (str or Callable) – The method to use to calculate the errors. Valid choices are {‘minuit_minos’} or a Callable.
- sigma (float) –
Errors are calculated with respect to sigma std deviations. The definition of 1 sigma depends on the loss function and is defined there.
For example, the negative log-likelihood (without the factor of 2) has a correspondents of \(\Delta\) NLL of 1 corresponds to 1 std deviation.
- error_name (str) – The name for the error in the dictionary.
Returns: - A OrderedDict containing as keys the parameter names and as value a dict which
contains (next to probably more things) two keys ‘lower’ and ‘upper’, holding the calculated errors. Example: result[‘par1’][‘upper’] -> the asymmetric upper error of ‘par1’
Return type: OrderedDict
- params (list(
-
fmin
¶ Function value at the minimum.
Returns: numeric
-
hesse
(params: Optional[Iterable[zfit.core.interfaces.ZfitParameter]] = None, method: Union[str, Callable] = 'minuit_hesse', error_name: Optional[str] = None, sigma=1.0) → collections.OrderedDict[source]¶ Calculate for params the symmetric error using the Hessian matrix.
Parameters: Returns: - Result of the hessian (symmetric) error as dict with each parameter holding
the error dict {‘error’: sym_error}.
So given param_a (from zfit.Parameter(.)) error_a = result.hesse(params=param_a)[param_a][‘error’] error_a is the hessian error.
Return type: OrderedDict
-
info
¶
-
loss
¶
-
minimizer
¶
-
params
¶
-
status
¶