basemodel

Baseclass for a Model. Handle integration and sampling

class zfit.core.basemodel.BaseModel(obs: Union[str, Iterable[str], zfit.Space], params: Optional[Dict[str, zfit.core.interfaces.ZfitParameter]] = None, name: str = 'BaseModel', dtype=tf.float64, **kwargs)[source]

Bases: zfit.core.baseobject.BaseNumeric, zfit.util.cache.GraphCachable, zfit.core.dimension.BaseDimensional, zfit.core.interfaces.ZfitModel

Base class for any generic model.

# TODO instructions on how to use

The base model to inherit from and overwrite _unnormalized_pdf.

Parameters:
  • dtype (DType) – the dtype of the model
  • name (str) – the name of the model
  • params (Dict(str, Parameter)) – A dictionary with the internal name of the parameter and the parameters itself the model depends on
add_cache_deps(cache_deps: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)

Add dependencies that render the cache invalid if they change.

Parameters:
  • cache_deps (ZfitGraphCachable) –
  • 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.
analytic_integrate(limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None) → Union[float, tensorflow.python.framework.ops.Tensor][source]

Analytical integration over function and raise Error if not possible.

Parameters:
  • limits (tuple, ZfitSpace) – the limits to integrate over
  • norm_range (tuple, ZfitSpace, False) – the limits to normalize over
Returns:

the integral value
Return type:

Tensor
Raises:
  • AnalyticIntegralNotImplementedError – If no analytical integral is available (for this limits).
  • NormRangeNotImplementedError – if the norm_range argument is not supported. This means that no analytical normalization is available, explicitly: the analytical integral over the limits = norm_range is not available.
axes

Return the axes, integer based identifier(indices) for the coordinate system.

convert_sort_space(obs: Union[str, Iterable[str], zfit.Space, zfit.core.interfaces.ZfitLimit, tensorflow.python.framework.ops.Tensor, numpy.ndarray, Iterable[float], float, Tuple[float], List[float], bool, None] = None, axes: Union[int, Iterable[int]] = None, limits: Union[zfit.core.interfaces.ZfitLimit, tensorflow.python.framework.ops.Tensor, numpy.ndarray, Iterable[float], float, Tuple[float], List[float], bool, None] = None) → Optional[zfit.core.interfaces.ZfitSpace][source]

Convert the inputs (using eventually obs, axes) to ZfitSpace and sort them according to own obs.

Parameters:
  • () (limits) –
  • () –
  • () –

Returns:

copy(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject
create_sampler(n: Union[int, tensorflow.python.framework.ops.Tensor, str] = None, limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, fixed_params: Union[bool, List[zfit.core.interfaces.ZfitParameter], Tuple[zfit.core.interfaces.ZfitParameter]] = True) → zfit.core.data.Sampler[source]

Create a Sampler that acts as Data but can be resampled, also with changed parameters and n.

If limits is not specified, space is used (if the space contains limits). If n is None and the model is an extended pdf, ‘extended’ is used by default.
Parameters:
  • n (int, tf.Tensor, str) –

    The number of samples to be generated. Can be a Tensor that will be or a valid string. Currently implemented:

    • ’extended’: samples poisson(yield) from each pdf that is extended.
  • () (fixed_params) – From which space to sample.
  • () – A list of Parameters that will be fixed during several resample calls. If True, all are fixed, if False, all are floating. If a Parameter is not fixed and its value gets updated (e.g. by a Parameter.set_value() call), this will be reflected in resample. If fixed, the Parameter will still have the same value as the Sampler has been created with when it resamples.
Returns:

py:class:~`zfit.core.data.Sampler`
Raises:
  • NotExtendedPDFError – if ‘extended’ is chosen (implicitly by default or explicitly) as an option for n but the pdf itself is not extended.
  • ValueError – if n is an invalid string option.
  • InvalidArgumentError – if n is not specified and pdf is not extended.
dtype

The dtype of the object

get_cache_deps(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])

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

Parameters:only_floating (bool) – If True, only return floating Parameter
get_dependencies(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])

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: Optional[bool] = True, is_yield: Optional[bool] = None, extract_independent: Optional[bool] = True, only_floating=<class 'zfit.util.checks.NotSpecified'>) → Set[zfit.core.interfaces.ZfitParameter]

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 – if a parameter is floating, e.g. if floating() returns True
  • is_yield – 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 – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.
gradients(x: Union[float, tensorflow.python.framework.ops.Tensor], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], params: Optional[Iterable[zfit.core.interfaces.ZfitParameter]] = None)[source]
graph_caching_methods = [<function FunctionWrapperRegistry.__call__.<locals>.concrete_func>, <function FunctionWrapperRegistry.__call__.<locals>.concrete_func>, <function FunctionWrapperRegistry.__call__.<locals>.concrete_func>, <function FunctionWrapperRegistry.__call__.<locals>.concrete_func>, <function FunctionWrapperRegistry.__call__.<locals>.concrete_func>, <function FunctionWrapperRegistry.__call__.<locals>.concrete_func>]
instances = <_weakrefset.WeakSet object>
integrate(**kwargs)
n_obs

Return the number of observables, the dimensionality. Corresponds to the last dimension.

name

The name of the object.

numeric_integrate(limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None) → Union[float, tensorflow.python.framework.ops.Tensor][source]

Numerical integration over the model.

Parameters:
  • limits (tuple, ZfitSpace) – the limits to integrate over
  • norm_range (tuple, ZfitSpace, False) – the limits to normalize over
Returns:

the integral value
Return type:

Tensor
obs

Return the observables, string identifier for the coordinate system.

params
partial_analytic_integrate(**kwargs)
partial_integrate(**kwargs)
partial_numeric_integrate(**kwargs)
classmethod register_additional_repr(**kwargs)[source]

Register an additional attribute to add to the repr.

Parameters:
  • keyword argument. The value has to be gettable from the instance (has to be an (any) –
  • or callable method of self. (attribute) –
classmethod register_analytic_integral(func: Callable, limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, priority: Union[int, float] = 50, *, supports_norm_range: bool = False, supports_multiple_limits: bool = False) → None[source]

Register an analytic integral with the class.

Parameters:
  • func (callable) –

    A function that calculates the (partial) integral over the axes limits. The signature has to be the following:

    • x (ZfitData, None): the data for the remaining axes in a partial
      integral. If it is not a partial integral, this will be None.
    • limits (ZfitSpace): the limits to integrate over.
    • norm_range (ZfitSpace, None): Normalization range of the integral.
      If not supports_supports_norm_range, this will be None.
    • params (Dict[param_name, zfit.Parameters]): The parameters of the model.
    • model (ZfitModel):The model that is being integrated.
  • () (limits) – |limits_arg_descr|
  • priority (int) – Priority of the function. If multiple functions cover the same space, the one with the highest priority will be used.
  • supports_multiple_limits (bool) – If True, the limits given to the integration function can have multiple limits. If False, only simple limits will pass through and multiple limits will be auto-handled.
  • supports_norm_range (bool) – If True, norm_range argument to the function may not be None. If False, norm_range will always be None and care is taken of the normalization automatically.
register_cacher(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])

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

Parameters:() (cacher) –
classmethod register_inverse_analytic_integral(func: Callable) → None[source]

Register an inverse analytical integral, the inverse (unnormalized) cdf.

Parameters:() (func) –
reset_cache(reseter: zfit.util.cache.ZfitGraphCachable)
reset_cache_self()

Clear the cache of self and all dependent cachers.

sample(n: Union[int, tensorflow.python.framework.ops.Tensor, str] = None, limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None) → zfit.core.data.SampleData[source]

Sample n points within limits from the model.

If limits is not specified, space is used (if the space contains limits). If n is None and the model is an extended pdf, ‘extended’ is used by default.

Parameters:
  • n (int, tf.Tensor, str) –

    The number of samples to be generated. Can be a Tensor that will be or a valid string. Currently implemented:

    • ’extended’: samples poisson(yield) from each pdf that is extended.
  • limits (tuple, ZfitSpace) – In which region to sample in
Returns:

SampleData(n_obs, n_samples)
Raises:
  • NotExtendedPDFError – if ‘extended’ is (implicitly by default or explicitly) chosen as an option for n but the pdf itself is not extended.
  • ValueError – if n is an invalid string option.
  • InvalidArgumentError – if n is not specified and pdf is not extended.
space
update_integration_options(draws_per_dim=None, mc_sampler=None)[source]

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.core.basemodel.SimpleModelSubclassMixin(*args, **kwargs)[source]

Bases: object

Subclass a model: implement the corresponding function and specify _PARAMS.

In order to create a custom model, two things have to be implemented: the class attribute _PARAMS has to be a list containing the names of the parameters and the corresponding function (_unnormalized_pdf/_func) has to be overridden.

Example:

class MyPDF(zfit.pdf.ZPDF):
    _PARAMS = ['mu', 'sigma']

    def _unnormalized_pdf(self, x):
        mu = self.params['mu']
        sigma = self.params['sigma']
        x = z.unstack_x(x)
        return z.exp(-z.square((x - mu) / sigma))