pmrf.fitting.FrequentistContext

class pmrf.fitting.FrequentistContext(model, measured, frequency, features, measured_features, output_path=None, output_root=None, sparam_kind=None, logger=None, cost_function=None)

Bases: FitContext

Context object for Frequentist fitting, containing the cost function.

Parameters:

• model (Model)

• measured (Network | NetworkCollection)

• frequency (Frequency)

• features (list[tuple[str, str, tuple[int, int]]])

• measured_features (ndarray)

• output_path (str | None)

• output_root (str | None)

• sparam_kind (str | None)

• logger (Logger | None)

• cost_function (Module | None)

cost_function

The sequence of functions defining the error metric.

Type:

eqx.Module or None

__init__(model, measured, frequency, features, measured_features, output_path=None, output_root=None, sparam_kind=None, logger=None, cost_function=None)

Parameters:

• model (Model)

• measured (Network | NetworkCollection)

• frequency (Frequency)

• features (list[tuple[str, str, tuple[int, int]]])

• measured_features (ndarray)

• output_path (str | None)

• output_root (str | None)

• sparam_kind (str | None)

• logger (Logger | None)

• cost_function (Module | None)

Return type:

None

Methods

__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(other)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__gt__(value, /)	Return self>value.
__init__(model, measured, frequency, ...[, ...])
__init_subclass__	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(**kwargs)
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
bounds()	Retrieve the lower and upper bounds for all model parameters.
make_cost_function([as_numpy])	Create the cost function to be minimized.
make_feature_function([as_numpy])	Create a JIT-compiled function to extract features from model parameters.
model_param_names()	Get the names of the flat parameters of the model.
settings([solver_kwargs, fitter_kwargs])	Create a FitSettings object from the current context.

Attributes

__annotations__
__dataclass_fields__
__dataclass_params__
__dict__
__doc__
__hash__
__match_args__
__module__
__weakref__	list of weak references to the object (if defined)
cost_function
logger
output_path
output_root
sparam_kind
model
measured
frequency
features
measured_features

cost_function: Module | None = None

make_cost_function(as_numpy=False)

Create the cost function to be minimized.

The cost function calculates the error between measured and model features, applies the defined cost_function transformation, and returns a scalar value.

Parameters:

as_numpy (bool, optional, default=False) – If True, returns a function compatible with NumPy arrays; otherwise JAX arrays.

Returns:

The JIT-compiled cost function taking flat parameters and returning a scalar cost.

Return type:

callable

bounds()

Retrieve the lower and upper bounds for all model parameters.

Returns:

A tuple containing (lower_bounds, upper_bounds).

Return type:

tuple of jnp.ndarray

Raises:

Exception – If any parameter is not associated with a parameter group.

__init__(model, measured, frequency, features, measured_features, output_path=None, output_root=None, sparam_kind=None, logger=None, cost_function=None)

Parameters:

• model (Model)

• measured (Network | NetworkCollection)

• frequency (Frequency)

• features (list[tuple[str, str, tuple[int, int]]])

• measured_features (ndarray)

• output_path (str | None)

• output_root (str | None)

• sparam_kind (str | None)

• logger (Logger | None)

• cost_function (Module | None)

Return type:

None