BaseLoss#

class zfit.loss.BaseLoss(model, data, fit_range=None, constraints=None, options=None)[source]#

Bases: ZfitLoss, BaseNumeric

A “simultaneous fit” can be performed by giving one or more model, data, fit_range to the loss. The length of each has to match the length of the others.

Parameters

model (ztyping.ModelsInputType) – The model or models to evaluate the data on
data (ztyping.DataInputType) – Data to use
fit_range (ztyping.LimitsTypeInput) – The fitting range. It’s the norm_range for the models (if they have a norm_range) and the data_range for the data.
constraints (ztyping.ConstraintsTypeInput) – A Tensor representing a loss constraint. Using zfit.constraint.* allows for easy use of predefined constraints.
options (Mapping | None) – Different options for the loss calculation.

__call__(_x=None)[source]#

Calculate the loss value with the given input for the free parameters.

Parameters: *positional* – Array-like argument to set the parameters. The order of the values correspond to the position of the parameters in get_params() (called without any arguments). For more detailed control, it is always possible to wrap value() and set the desired parameters manually.
Return type: Tensor
Returns: Calculated loss value as a scalar.

add_cache_deps(cache_deps, allow_non_cachable=True)#

Add dependencies that render the cache invalid if they change.

Parameters

cache_deps (Union[zfit.core.interfaces.ZfitGraphCachable, Iterable[zfit.core.interfaces.ZfitGraphCachable]]) –
allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitGraphCachable will raise an error.

Raises

TypeError – if one of the cache_dependents is not a ZfitGraphCachable _and_ allow_non_cachable if False.

property dtype: DType#

The dtype of the object.

Return type: DType

get_cache_deps(only_floating=True)#

Return a set of all independent Parameter that this object depends on.

Parameters: only_floating (bool) – If True, only return floating Parameter
Return type: OrderedSet

get_dependencies(only_floating=True)#

DEPRECATED FUNCTION

Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use get_params instead if you want to retrieve the independent parameters or get_cache_deps in case you need the numerical cache dependents (advanced).

Return type: OrderedSet

get_params(floating=True, is_yield=None, extract_independent=True, only_floating=<class 'zfit.util.checks.NotSpecified'>)#

Recursively collect parameters that this object depends on according to the filter criteria.

Which parameters should be included can be steered using the arguments as a filter.

None: do not filter on this. E.g. floating=None will return parameters that are floating as well as
parameters that are fixed.
True: only return parameters that fulfil this criterion
False: only return parameters that do not fulfil this criterion. E.g. floating=False will return
only parameters that are not floating.

Parameters

floating (bool | None) – if a parameter is floating, e.g. if floating() returns True
is_yield (bool | None) – if a parameter is a yield of the _current_ model. This won’t be applied recursively, but may include yields if they do also represent a parameter parametrizing the shape. So if the yield of the current model depends on other yields (or also non-yields), this will be included. If, however, just submodels depend on a yield (as their yield) and it is not correlated to the output of our model, they won’t be included.
extract_independent (bool | None) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type

set[ZfitParameter]

register_cacher(cacher)#

Register a cacher that caches values produces by this instance; a dependent.

Parameters: cacher (Union[zfit.core.interfaces.ZfitGraphCachable, Iterable[zfit.core.interfaces.ZfitGraphCachable]]) –

reset_cache_self()#: Clear the cache of self and all dependent cachers.