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.Cachable, 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_dependents(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)

Add dependents that render the cache invalid if they change.

Parameters
  • cache_dependents (ZfitCachable) –

  • 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, name: str = 'analytic_integrate') → Union[float, tensorflow.python.framework.ops.Tensor][source]

Analytical integration over function and raise Error if not possible.

Parameters
  • limits (tuple, Space) – the limits to integrate over

  • norm_range (tuple, Space, False) – the limits to normalize over

  • name (str) –

Returns

the integral value

Return type

Tensor

Raises
  • NotImplementedError – 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.

property axes

Return the axes.

convert_sort_space(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None, limits: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool] = None) → Optional[zfit.core.limits.Space][source]

Convert the inputs (using eventually obs, axes) to Space 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, name: str = 'create_sampler') → 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.

  • () (name) – 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.

property dtype

The dtype of the object

get_dependents(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_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[ZfitParameter]

Return the parameters. If it is empty, automatically return all floating variables.

Parameters
  • () (names) – If True, return only the floating parameters.

  • () – The names of the parameters to return.

Returns

Return type

list(ZfitParameters)

abstract 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 = []
integrate(**kwargs)
property n_obs

Return the number of observables.

property 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, name: str = 'numeric_integrate') → Union[float, tensorflow.python.framework.ops.Tensor][source]

Numerical integration over the model.

Parameters
  • limits (tuple, Space) – the limits to integrate over

  • norm_range (tuple, Space, False) – the limits to normalize over

  • name (str) –

Returns

the integral value

Return type

Tensor

property obs

Return the observables.

old_graph_caching_methods = []
property params
partial_analytic_integrate(**kwargs)
partial_integrate(**kwargs)
partial_numeric_integrate(x: Union[float, tensorflow.python.framework.ops.Tensor], limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'partial_numeric_integrate') → Union[float, tensorflow.python.framework.ops.Tensor][source]

Force numerical partial integration of the function over the limits and evaluate it at x.

Dimension of limits and x have to add up to the full dimension and be therefore equal to the dimensions of norm_range (if not False)

Parameters
  • x (numerical) – The value at which the partially integrated function will be evaluated

  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes

  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes

  • name (str) –

Returns

the value of the partially integrated function evaluated at x.

Return type

Tensor

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 (Space): the limits to integrate over.

    • norm_range (Space, 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.ZfitCachable)
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, name: str = 'sample') → 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, Space) – In which region to sample in

  • name (str) –

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.

property space

Return the Space object that defines the dimensionality of the object.

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))