ConditionalPDFV1#
- class zfit.pdf.ConditionalPDFV1(pdf, cond, *, name='ConditionalPDF', extended=None, norm=None, use_vectorized_map=False, sample_with_replacement=True)[source]#
Bases:
BaseFunctor
EXPERIMENTAL! Implementation of a Conditional PDF, rather slow and for research purpose.
As an example, a Gaussian is wrapped in order to make ‘sigma’ conditional.
- Parameters:
pdf (
ZfitPDF
) – PDF that will be wrapped. Convert one or several parameters of pdf to a conditional parameter, meaning that the parameter param in thecond
mapping will now be determined by the data in theSpace
, the value of thecond
.cond (
Mapping
[ZfitIndependentParameter
,ZfitSpace
]) – Mapping of parameter to input data.name (
str
) – Human-readable name or label of the PDF for better identification. Has no programmatical functional purpose as identification.extended (
Union
[bool
,TypeVar
(ParamTypeInput
, zfit.core.interfaces.ZfitParameter,Union
[int
,float
,complex
,Tensor
, zfit.core.interfaces.ZfitParameter]),None
]) – The overall yield of the PDF. If this is parameter-like, it will be used as the yield, the expected number of events, and the PDF will be extended. An extended PDF has additional functionality, such as theext_*
methods and thecounts
(for binned PDFs).norm (
Optional
[Space
]) – |@doc:model.init.norm| The normalization of the PDF. |@docend:model.init.norm|() (sample_with_replacement) –
() –
- add_cache_deps(cache_deps, allow_non_cachable=True)#
Add dependencies that render the cache invalid if they change.
- analytic_integrate(limits, norm=None, *, norm_range=None)#
Analytical integration over function and raise Error if not possible.
- Parameters:
limits (
Union
[Tuple
[Tuple
[float
,...
]],Tuple
[float
,...
],bool
,Space
]) – Limits of the integration.norm (
Union
[Tuple
[Tuple
[float
,...
]],Tuple
[float
,...
],bool
,Space
]) – Normalization of the integration. By default, this is the same as the default space of the PDF.False
means no normalization and returns the unnormed integral.
- Return type:
- Returns:
The integral value
- Raises:
AnalyticIntegralNotImplementedError – If no analytical integral is available (for this limits).
NormRangeNotImplementedError – if the norm argument is not supported. This means that no analytical normalization is available, explicitly: the analytical integral over the limits = norm is not available.
- as_func(norm=False, *, norm_range=None)#
Return a
Function
with the functionmodel(x, norm=norm)
.
- create_extended(yield_, name=None, *, name_addition=None)#
Return an extended version of this pdf with yield
yield_
. The parameters are shared.- Parameters:
yield – Yield (expected number of events) of the PDF. This is the expected number of events. If this is parameter-like, it will be used as the yield, the expected number of events, and the PDF will be extended. An extended PDF has additional functionality, such as the
ext_*
methods and thecounts
(for binned PDFs).name (
str
|None
) – New name of the PDF. IfNone
, the name of the PDF with a trailing “_ext” is used.
- Returns:
a new PDF that is extended
- Return type:
ZfitPDF
- create_projection_pdf(limits, *, options=None, limits_to_integrate=None)#
Create a PDF projection by integrating out some dimensions.
The new projection pdf is still fully dependent on the pdf it was created with.
- Parameters:
limits (
Union
[ZfitLimit
,Tensor
,ndarray
,Iterable
[float
],float
,Tuple
[float
],List
[float
],bool
,None
]) – |@doc:pdf.partial_integrate.limits||@docend:pdf.partial_integrate.limit|options –
Options for the integration. Additional options for the integration. Currently supported options are: - type: one of (
bins
)This hints that bins are integrated. A method that is vectorizable, non-dynamic and therefore less suitable for complicated functions is chosen.
- Return type:
ZfitPDF
- Returns:
A pdf without the dimensions from
limits
.
- create_sampler(n=None, limits=None, fixed_params=True)#
Create a
Sampler
that acts asData
but can be resampled, also with changed parameters and n.If
limits
is not specified,space
is used (if the space contains limits). Ifn
is None and the model is an extended pdf, ‘extended’ is used by default.- Parameters:
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 (
Union
[Tuple
[Tuple
[float
,...
]],Tuple
[float
,...
],bool
,Space
]) – From which space to sample.fixed_params (
bool
|list
[ZfitParameter
] |tuple
[ZfitParameter
]) – A list ofParameters
that will be fixed during severalresample
calls. If True, all are fixed, if False, all are floating. If aParameter
is not fixed and its value gets updated (e.g. by aParameter.set_value()
call), this will be reflected inresample
. If fixed, the Parameter will still have the same value as theSampler
has been created with when it resamples.
- Return type:
Sampler
- Returns:
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: DType#
The dtype of the object.
- property extended: Parameter | None#
Return the yield (only for extended models).
- Returns:
The yield of the current model or None
- 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: bool = True) ztyping.DependentsType #
DEPRECATED FUNCTION
Deprecated: 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). :rtype:OrderedSet
- 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 (
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
]
- get_yield()#
Return the yield (only for extended models).
- log_pdf(x, norm=None, *, norm_range=None)#
Log probability density function normalized over
norm_range
.- Parameters:
x (
Union
[float
,Tensor
]) – Data to evaluate the method on. Should beZfitData
or a mapping of obs to numpy-like arrays. If an array is given, the first dimension is interpreted as the events while the second is meant to be the dimensionality of a single event.norm (
Union
[Tuple
[Tuple
[float
,...
]],Tuple
[float
,...
],bool
,Space
]) – Normalization of the function. By default, this is thenorm
of the PDF (which by default is the same as the space of the PDF). Should beZfitSpace
to define the space to normalize over.
- Return type:
- Returns:
A
Tensor
of typeself.dtype
.
- property norm: Space | None | bool#
Return the current normalization range. If None and the
obs
have limits, they are returned.- Returns:
The current normalization range.
- property norm_range: Space | None | bool#
Return the current normalization range. If None and the
obs
have limits, they are returned. (deprecated)Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use the
norm
attribute instead.- Returns:
The current normalization range.
- normalization(norm, *, options=None, limits=None)#
Return the normalization of the function (usually the integral over
norm
). (deprecated arguments)Deprecated: SOME ARGUMENTS ARE DEPRECATED:
(limits)
. They will be removed in a future version. Instructions for updating: Usenorm
instead.- Parameters:
norm (
Union
[Tuple
[Tuple
[float
,...
]],Tuple
[float
,...
],bool
,Space
]) – Normalization of the function. By default, this is thenorm
of the PDF (which by default is the same as the space of the PDF). Should beZfitSpace
to define the space to normalize over.() (options) – |@doc:pdf.param.options||@docend:pdf.param.options|
- Return type:
- Returns:
The normalization value
- numeric_integrate(limits, norm=None, *, options=None, norm_range=None)#
Numerical integration over the model.
- Parameters:
limits (
Union
[Tuple
[Tuple
[float
,...
]],Tuple
[float
,...
],bool
,Space
]) – Limits of the integration.norm (
Union
[Tuple
[Tuple
[float
,...
]],Tuple
[float
,...
],bool
,Space
]) – Normalization of the integration. By default, this is the same as the default space of the PDF.False
means no normalization and returns the unnormed integral.options –
Options for the integration. Additional options for the integration. Currently supported options are: - type: one of (
bins
)This hints that bins are integrated. A method that is vectorizable, non-dynamic and therefore less suitable for complicated functions is chosen.
- Return type:
- Returns:
The integral value
- classmethod register_additional_repr(**kwargs)#
Register an additional attribute to add to the repr.
- Parameters:
an (any keyword argument. The value has to be gettable from the instance (has to be) –
self. (attribute or callable method of) –
- classmethod register_analytic_integral(cls, func, limits=None, priority=50, *, supports_norm=None, supports_norm_range=None, supports_multiple_limits=None)#
Register an analytic integral with the class. (deprecated arguments)
Deprecated: SOME ARGUMENTS ARE DEPRECATED:
(supports_norm_range)
. They will be removed in a future version. Instructions for updating: Usesupports_norm
instead.- 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.
- x (
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.
- norm_range (
params (Dict[param_name,
zfit.Parameters
]): The parameters of the model.model (
ZfitModel
):The model that is being integrated.
limits (
Union
[Tuple
[Tuple
[float
,...
]],Tuple
[float
,...
],bool
,Space
]) – If aSpace
is given, it is used as limits. Otherwise arguments to instantiate a Range class can be given as follows.|limits_init|priority (
int
|float
) – Priority of the function. If multiple functions cover the same space, the one with the highest priority will be used.supports_multiple_limits (
bool
|None
) – IfTrue
, the ``limits` given to the integration function can have multiple limits. IfFalse
, only simple limits will pass through and multiple limits will be auto-handled.supports_norm (
bool
|None
) – IfTrue
,norm
argument to the function may not beNone
. IfFalse
,norm
will always beNone
and care is taken of the normalization automatically.
- Return type:
- register_cacher(cacher)#
Register a
cacher
that caches values produces by this instance; a dependent.- Parameters:
cacher (ztyping.CacherOrCachersType) –
- classmethod register_inverse_analytic_integral(func)#
Register an inverse analytical integral, the inverse (unnormalized) cdf.
- reset_cache_self()#
Clear the cache of self and all dependent cachers.
- sample(n=None, limits=None, x=None)#
Sample
n
points withinlimits
from the model.If
limits
is not specified,space
is used (if the space contains limits). Ifn
is None and the model is an extended pdf, ‘extended’ is used by default.- Parameters:
- Return type:
SampleData
- 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.
- set_norm_range(norm: ztyping.LimitsTypeInput)#
Set the normalization range (temporarily if used with contextmanager). (deprecated)
Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer to create a new PDF with
norm
set.
- set_yield(value)#
Make the model extended inplace by setting a yield. If possible, prefer to use
create_extended
.This does not alter the general behavior of the PDF. The
pdf
andintegrate
and similar methods will continue to return the same - normalized to 1 - values. However, not only can this parameter be accessed viaget_yield
, the methodsext_pdf
andext_integral
provide a version ofpdf
andintegrate
respecetively that is multiplied by the yield.These can be useful for plotting and for binned likelihoods.
- Parameters:
value – Yield (expected number of events) of the PDF. This is the expected number of events. If this is parameter-like, it will be used as the yield, the expected number of events, and the PDF will be extended. An extended PDF has additional functionality, such as the
ext_*
methods and thecounts
(for binned PDFs).
- to_binned(space, *, extended=None, norm=None)#
Convert to binned pdf, returns self if already binned.
- to_unbinned()#
Convert to unbinned pdf, returns self if already unbinned.
- unnormalized_pdf(x: ztyping.XType) ztyping.XType #
PDF “unnormalized”. Use
functions
for unnormalized pdfs. this is only for performance in special cases. (deprecated)Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use
pdf(norm=False)
instead- Parameters:
x (
Union
[float
,Tensor
]) – Data to evaluate the method on. Should beZfitData
or a mapping of obs to numpy-like arrays. If an array is given, the first dimension is interpreted as the events while the second is meant to be the dimensionality of a single event.- Return type:
- Returns:
1-dimensional
tf.Tensor
containing the unnormalized pdf.
- update_integration_options(draws_per_dim=None, mc_sampler=None, tol=None, max_draws=None, draws_simpson=None)#
Set the integration options.
- Parameters:
max_draws (default ~1'000'000) – Maximum number of draws when integrating . Typically 500’000 - 5’000’000.
tol – Tolerance on the error of the integral. typically 1e-4 to 1e-8
draws_per_dim – The draws for MC integration to do per iteration. Can be set to
'auto
’.draws_simpson – Number of points in one dimensional Simpson integration. Can be set to
'auto'
.