ExtendedUnbinnedNLL¶
- class zfit.loss.ExtendedUnbinnedNLL(model, data, fit_range=None, constraints=None, options=None)[source]¶
Bases:
zfit.core.loss.UnbinnedNLL
An Unbinned Negative Log Likelihood with an additional poisson term for the number of events in the dataset.
The unbinned log likelihood can be written as
\[\mathcal{L}_{non-extended}(x | \theta) = \prod_{i} f_{\theta} (x_i)\]where \(x_i\) is a single event from the dataset data and f is the model.
The extended likelihood has an additional term
\[\mathcal{L}_{extended term} = poiss(N_{tot}, N_{data}) = N_{data}^{N_{tot}} \frac{e^{- N_{data}}}{N_{tot}!}\]and the extended likelihood is the product of both.
A simultaneous fit can be performed by giving one or more
model
,data
, to the loss. The length of each has to match the length of the others\[\mathcal{L}_{simultaneous}(\theta | {data_0, data_1, ..., data_n}) = \prod_{i} \mathcal{L}(\theta_i, data_i)\]where \(\theta_i\) is a set of parameters and a subset of \(\theta\)
For optimization purposes, it is often easier to minimize a function and to use a log transformation. The actual loss is given by
\[\mathcal{L} = - \sum_{i}^{n} ln(f(\theta|x_i))\]and therefore being called “negative log …”
If the dataset has weights, a weighted likelihood will be constructed instead
\[\mathcal{L} = - \sum_{i}^{n} w_i \cdot ln(f(\theta|x_i))\]Note that this is not a real likelihood anymore! Calculating uncertainties can be done with hesse (as it has a correction) but will yield wrong results with profiling methods. The minimum is however fully valid.
- __call__(_x=None)¶
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 wrapvalue()
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
[ForwardRef
,Iterable
[ForwardRef
]]) –allow_non_cachable (
bool
) – IfTrue
, allowcache_dependents
to be non-cachables. IfFalse
, anycache_dependents
that is not aZfitCachable
will raise an error.
- Raises
TypeError – if one of the
cache_dependents
is not aZfitCachable
_and_allow_non_cachable
ifFalse
.
- create_new(model=None, data=None, fit_range=None, constraints=None, options=None)¶
Create a new loss from the current loss and replacing what is given as the arguments.
This creates a “copy” of the current loss but replacing any argument that is explicitly given. Equivalent to creating a new instance but with some arguments taken.
A loss has more than a model and data (and constraints), it can have internal optimizations and more that may do alter the behavior of a naive re-instantiation in unpredictable ways.
- Parameters
model (
Union
[ZfitPDF
,Iterable
[ZfitPDF
],None
]) –- If not given, the current one will be used.
PDFs that return the normalized probability for
data under the given parameters. If multiple model and data are given, they will be used in the same order to do a simultaneous fit.
data (
Union
[ZfitData
,Iterable
[ZfitData
],None
]) –- If not given, the current one will be used.
Dataset that will be given to the model.
If multiple model and data are given, they will be used in the same order to do a simultaneous fit.
fit_range –
constraints –
- If not given, the current one will be used.
Auxiliary measurements (“constraints”)
that add a likelihood term to the loss.
\[\mathcal{L}(\theta) = \mathcal{L}_{unconstrained} \prod_{i} f_{constr_i}(\theta)\]Usually, an auxiliary measurement – by its very nature -S should only be added once to the loss. zfit does not automatically deduplicate constraints if they are given multiple times, leaving the freedom for arbitrary constructs.
Constraints can also be used to restrict the loss by adding any kinds of penalties.
options –
- If not given, the current one will be used.
Additional options (as a dict) for the loss.
Current possibilities include:
’subtr_const’ (default True): subtract from each points log probability density a constant that is approximately equal to the average log probability density in the very first evaluation before the summation. This brings the initial loss value closer to 0 and increases, especially for large datasets, the numerical stability.
The value will be stored ith ‘subtr_const_value’ and can also be given directly.
The subtraction should not affect the minimum as the absolute value of the NLL is meaningless. However, with this switch on, one cannot directly compare different likelihoods ablolute value as the constant may differ! Use
create_new
in order to have a comparable likelihood between different losses
These settings may extend over time. In order to make sure that a loss is the same under the same data, make sure to use
create_new
instead of instantiating a new loss as the former will automatically overtake any relevant constants and behavior.
- 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
) – IfTrue
, only return floatingParameter
- 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 orget_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.
- None: do not filter on this. E.g.
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.
- False: only return parameters that do not fulfil this criterion. E.g.
- Parameters
floating (
Optional
[bool
]) – if a parameter is floating, e.g. iffloating()
returnsTrue
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 aZfitIndependentParameter
.
- Return type
Set
[ZfitParameter
]
- gradients(*args, **kwargs)¶
DEPRECATED FUNCTION
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use
gradient
instead.
- 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.
- value_gradients(*args, **kwargs)¶
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)¶
DEPRECATED FUNCTION
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use
value_gradient_hessian
instead.