Constrain

class bayesflow.adapters.transforms.Constrain(*, lower: int | float | ndarray = None, upper: int | float | ndarray = None, method: str = 'default', inclusive: str = 'both', epsilon: float = 1e-15)

Bases: ElementwiseTransform

Constrains neural network predictions of a data variable to specified bounds.

Parameters:

*str

String containing the name of the data variable to be transformed e.g. “sigma”. See examples below.

lowerint or float or np.darray, optional

Lower bound for named data variable.

upperint or float or np.darray, optional

Upper bound for named data variable.

methodstr, optional

Method by which to shrink the network predictions space to specified bounds. Choose from - Double bounded methods: sigmoid, expit, (default = sigmoid) - Lower bound only methods: softplus, exp, (default = softplus) - Upper bound only methods: softplus, exp, (default = softplus)

inclusive{‘both’, ‘lower’, ‘upper’, ‘none’}, optional

Indicates which bounds are inclusive (or exclusive). - “both” (default): Both lower and upper bounds are inclusive. - “lower”: Lower bound is inclusive, upper bound is exclusive. - “upper”: Lower bound is exclusive, upper bound is inclusive. - “none”: Both lower and upper bounds are exclusive.

epsilonfloat, optional

Small value to ensure inclusive bounds are not violated. Current default is 1e-15 as this ensures finite outcomes with the default transformations applied to data exactly at the boundaries.

Examples

1) Let sigma be the standard deviation of a normal distribution, then sigma should always be greater than zero.

>>> adapter = (
    bf.Adapter()
    .constrain("sigma", lower=0)
)

2) Suppose p is the parameter for a binomial distribution where p must be in [0,1] then we would constrain the neural network to estimate p in the following way.

>>> import bayesflow as bf
>>> adapter = bf.Adapter()
>>> adapter.constrain("p", lower=0, upper=1, method="sigmoid", inclusive="both")

classmethod from_config(config: dict, custom_objects=None) → Constrain

get_config() → dict

forward(data: ndarray, **kwargs) → ndarray

inverse(data: ndarray, **kwargs) → ndarray

__call__(data: ndarray, inverse: bool = False, **kwargs) → ndarray

Call self as a function.