BaseLoss

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

Bases: zfit.core.interfaces.ZfitLoss, zfit.core.baseobject.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 (Union[ForwardRef, Iterable[ForwardRef]]) – The model or models to evaluate the data on

  • data (Union[ForwardRef, Iterable[ForwardRef]]) – Data to use

  • fit_range (Union[ForwardRef, Tensor, ndarray, Iterable[float], float, Tuple[float], List[float], bool, None]) – 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 (Union[Iterable[Union[ForwardRef, Callable]], ForwardRef, Callable, None]) – A Tensor representing a loss constraint. Using zfit.constraint.* allows for easy use of predefined constraints.

  • options (Optional[Mapping]) – 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.

gradients(*args, **kwargs)[source]

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use gradient instead.

value_gradients(*args, **kwargs)[source]

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use value_gradient instead.

value_gradients_hessian(*args, **kwargs)[source]

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use value_gradient_hessian instead.

add_cache_deps(cache_deps, allow_non_cachable=True)

Add dependencies that render the cache invalid if they change.

Parameters

  • cache_deps (Union[ForwardRef, Iterable[ForwardRef]]) –

  • allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.

Raises

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

property dtype: tensorflow.python.framework.dtypes.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

Warning: 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).

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 (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True

  • is_yield (Optional[bool]) – 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 (Optional[bool]) – 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[ForwardRef, Iterable[ForwardRef]]) –

reset_cache_self()

Clear the cache of self and all dependent cachers.

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SimpleLoss