SimpleLoss#
- class zfit.loss.SimpleLoss(func, params=None, errordef=None, *, gradient=None, hessian=None, jit=None, deps=None, dependents=None)[source]#
Bases:
BaseLoss
Loss from a (function returning a) Tensor. (deprecated arguments)
Deprecated: SOME ARGUMENTS ARE DEPRECATED:
(deps)
. They will be removed in a future version. Instructions for updating: Use params instead.This allows for a very generic loss function as the functions only restriction is that is should depend on
zfit.Parameter
.- Parameters:
func (
Callable
) – Callable that evaluates the loss and takes the parameter values as a single, array-like argumentparams (
Iterable
[Parameter
] |None
) – The parameters (independentzfit.Parameter
) of the loss. Essentially the (free) parameters that thefunc
depends on.errordef (
float
|None
) – 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.gradient (
Callable
|str
|None
) – Function that calculates the gradient of the loss with respect to the parameters. If not given, the gradient will be calculated automatically. Can be a string to determine the method, either ‘num’ (default) to calculate the gradient numerically, or ‘zfit’ to use automatic differentiation. The latter requires that the whole function is written usingznp
ortf
operationshessian (
Callable
|str
|None
) – Function that calculates the hessian of the loss with respect to the parameters. If not given, the hessian will be calculated automatically.C an be a string to determine the method, either ‘num’ (default) to calculate the hessian numerically, or ‘zfit’ to use automatic differentiation. The latter requires that the whole function is written usingznp
ortf
operationsIf true, jit compiles the different functions. Only use if function is fully built using
znp
ortf
operations and no Python conditionals that depend on the concrete value of inputs.Defaults to
False
.
Usage:
import zfit import zfit.z.numpy as znp import tensorflow as tf param1 = zfit.Parameter('param1', 5, 1, 10) # we can build a model here if we want, but in principle, it's not necessary x = znp.random.uniform(size=(100,)) y = x * tf.random.normal(mean=4, stddev=0.1, shape=x.shape, dtype=znp.float64) def squared_loss(params): param1 = params[0] 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 = znp.mean(squared) return mse loss = zfit.loss.SimpleLoss(squared_loss, param1, errordef=1)
which can then be used in combination with any zfit minimizer such as Minuit
minimizer = zfit.minimize.Minuit() result = minimizer.minimize(loss)
- __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.full – If True, return the full loss value, otherwise allow for the removal of constants and only return the part that depends on the parameters. Constants don’t matter for the task of optimization, but they can greatly help with the numerical stability of the loss function.
- Return type:
array
- 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.
- add_constraints(constraints)#
DEPRECATED FUNCTION
Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use
create_new
instead and fill the constraints there.
- property dtype: DType#
The dtype of the object.
- get_params(floating=True, is_yield=None, extract_independent=True, *, autograd=None)#
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 (
bool
|None
) – if a parameter is floating, e.g. iffloating()
returnsTrue
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 aZfitIndependentParameter
.
- Return type:
set
[ZfitParameter
]
- gradient(params=None, *, numgrad=None, paramvals=None)#
Calculate the gradient of the loss with respect to the given parameters.
- Parameters:
params (
TypeVar
(ParamTypeInput
, zfit.core.interfaces.ZfitParameter,Union
[int
,float
,complex
,Tensor
, zfit.core.interfaces.ZfitParameter])) – The parameters with respect to which the gradient is calculated. IfNone
, all parameters are used.numgrad – If
True
, calculate the numerical gradient/Hessian instead of using the automatic one. This is usually slower if called repeatedly but can be used if the automatic gradient fails (e.g. if the model is not differentiable, written not in znp.* etc). Default will fall back to what the loss is set to.paramvals (
TypeVar
(ParamTypeInput
, zfit.core.interfaces.ZfitParameter,Union
[int
,float
,complex
,Tensor
, zfit.core.interfaces.ZfitParameter])) – Mapping of the parameter names to the actual values. The parameter names refer to the names of the parameters, typicallyParameter
, that is returned byget_params()
. If no params are given, the current default values of the parameters are used.
- Return type:
Tensor
- Returns:
The gradient of the loss with respect to the given parameters.
- hessian(params=None, hessian=None, *, numgrad=None, paramvals=None)#
Calculate the hessian of the loss with respect to the given parameters.
- Return type:
Tensor
Args: params: The parameters with respect to which the hessian is calculated. If
None
, all parametersare used.
hessian: Can be ‘full’ or ‘diag’. numgrad:If
True
, calculate the numerical gradient/Hessianinstead of using the automatic one. This is usually slower if called repeatedly but can be used if the automatic gradient fails (e.g. if the model is not differentiable, written not in znp.* etc). Default will fall back to what the loss is set to.
- register_cacher(cacher)#
Register a
cacher
that caches values produces by this instance; a dependent.- Parameters:
cacher (ztyping.CacherOrCachersType)
- reset_cache_self()#
Clear the cache of self and all dependent cachers.
- value(*, params=None, full=None)#
Calculate the loss value with the current values of the free parameters.
- Parameters:
params (
TypeVar
(ParamTypeInput
, zfit.core.interfaces.ZfitParameter,Union
[int
,float
,complex
,Tensor
, zfit.core.interfaces.ZfitParameter])) – Mapping of the parameter names to the actual values. The parameter names refer to the names of the parameters, typicallyParameter
, that is returned byget_params()
. If no params are given, the current default values of the parameters are used.full (
bool
|None
) – If True, return the full loss value, otherwise allow for the removal of constants and only return the part that depends on the parameters. Constants don’t matter for the task of optimization, but they can greatly help with the numerical stability of the loss function.
- Return type:
Tensor
- Returns:
Calculated loss value as a scalar.
- value_gradient(params=None, *, full=None, numgrad=None, paramvals=None)#
Calculate the loss value and the gradient with the current values of the free parameters.
- Parameters:
params (
TypeVar
(ParamTypeInput
, zfit.core.interfaces.ZfitParameter,Union
[int
,float
,complex
,Tensor
, zfit.core.interfaces.ZfitParameter])) – The parameters to calculate the gradient for. If not given, all free parameters are used.full (
bool
|None
) – If True, return the full loss value, otherwise allow for the removal of constants and only return the part that depends on the parameters. Constants don’t matter for the task of optimization, but they can greatly help with the numerical stability of the loss function.numgrad (
bool
|None
) – IfTrue
, calculate the numerical gradient/Hessian instead of using the automatic one. This is usually slower if called repeatedly but can be used if the automatic gradient fails (e.g. if the model is not differentiable, written not in znp.* etc). Default will fall back to what the loss is set to.paramvals (
TypeVar
(ParamTypeInput
, zfit.core.interfaces.ZfitParameter,Union
[int
,float
,complex
,Tensor
, zfit.core.interfaces.ZfitParameter])) – Mapping of the parameter names to the actual values. The parameter names refer to the names of the parameters, typicallyParameter
, that is returned byget_params()
. If no params are given, the current default values of the parameters are used.
- Return type:
tuple
[Tensor
,Tensor
]- Returns:
Calculated loss value as a scalar and the gradient as a tensor.
- value_gradient_hessian(params=None, *, hessian=None, full=None, numgrad=None, paramvals=None)#
Calculate the loss value, the gradient and the hessian with the current values of the free parameters.
- Parameters:
params (
TypeVar
(ParamTypeInput
, zfit.core.interfaces.ZfitParameter,Union
[int
,float
,complex
,Tensor
, zfit.core.interfaces.ZfitParameter])) – The parameters to calculate the gradient for. If not given, all free parameters are used.hessian – Can be ‘full’ or ‘diag’.
full (
bool
|None
) – If True, return the full loss value, otherwise allow for the removal of constants and only return the part that depends on the parameters. Constants don’t matter for the task of optimization, but they can greatly help with the numerical stability of the loss function.numgrad – If
True
, calculate the numerical gradient/Hessian instead of using the automatic one. This is usually slower if called repeatedly but can be used if the automatic gradient fails (e.g. if the model is not differentiable, written not in znp.* etc). Default will fall back to what the loss is set to.paramvals (
TypeVar
(ParamTypeInput
, zfit.core.interfaces.ZfitParameter,Union
[int
,float
,complex
,Tensor
, zfit.core.interfaces.ZfitParameter])) – Mapping of the parameter names to the actual values. The parameter names refer to the names of the parameters, typicallyParameter
, that is returned byget_params()
. If no params are given, the current default values of the parameters are used.
- Return type:
tuple
[Tensor
,Tensor
,Tensor
]- Returns:
Calculated loss value as a scalar, the gradient as a tensor and the hessian as a tensor.