zfit.loss.
SimpleLoss
Bases: zfit.core.loss.BaseLoss
zfit.core.loss.BaseLoss
Loss from a (function returning a) Tensor.
This allows for a very generic loss function as the functions only restriction is that is should depend on zfit.Parameter.
func (Callable) – Callable that constructs the loss and returns a tensor without taking an argument.
Callable
deps (Iterable[ForwardRef]) – The dependents (independent zfit.Parameter) of the loss. Essentially the (free) parameters that the func depends on.
Iterable
ForwardRef
errordef (Optional[float]) – Definition of which change in the loss corresponds to a change of 1 sigma. For example, 1 for Chi squared, 0.5 for negative log-likelihood.
Optional
float
Usage:
import zfit from zfit import z param1 = zfit.Parameter('param1', 5, 1, 10) # we can build a model here if we want, but in principle, it's not necessary x = z.random.uniform(shape=(100,)) y = x * z.random.normal(mean=4, stddev=0.1, shape=x.shape) def squared_loss(): y_pred = x * param1 # this is very simple, but we can of course use any # zfit PDF or Func inside squared = (y_pred - y) ** 2 mse = tf.reduce_mean(squared) return mse loss = zfit.loss.SimpleLoss(squared_loss, param1)
which can then be used in conjunction with any zfit minimizer such as Minuit
minimizer = zfit.minize.Minuit() result = minimizer.minimize(loss)
add_cache_deps
Add dependencies that render the cache invalid if they change.
cache_deps (Union[ForwardRef, Iterable[ForwardRef]]) –
Union
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.
bool
TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.
dtype
The dtype of the object
DType
get_cache_deps
Return a set of all independent Parameter that this object depends on.
Parameter
only_floating (bool) – If True, only return floating Parameter
OrderedSet
get_dependencies
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
Recursively collect parameters that this object depends on according to the filter criteria.
parameters that are fixed.
True: only return parameters that fulfil this criterion
only parameters that are not floating.
floating (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True
floating()
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.
Set[ZfitParameter]
Set
ZfitParameter
register_cacher
Register a cacher that caches values produces by this instance; a dependent.
cacher (Union[ForwardRef, Iterable[ForwardRef]]) –
reset_cache_self
Clear the cache of self and all dependent cachers.