Broadcast

class bayesflow.adapters.transforms.Broadcast(keys: Sequence[str], *, to: str, expand: str | int | tuple = 'left', exclude: int | tuple = -1, squeeze: int | tuple = None)

Bases: Transform

Broadcasts arrays or scalars to the shape of a given other array.

Parameters:

keyssequence of str,

Input a list of strings, where the strings are the names of data variables.

tostr

Name of the data variable to broadcast to.

expandstr or int or tuple, optional

Where should new dimensions be added to match the number of dimensions in to? Can be “left”, “right”, or an integer or tuple containing the indices of the new dimensions. The latter is needed if we want to include a dimension in the middle, which will be required for more advanced cases. By default we expand left.

excludeint or tuple, optional

Which dimensions (of the dimensions after expansion) should retain their size, rather than being broadcasted to the corresponding dimension size of to? By default we exclude the last dimension (usually the data dimension) from broadcasting the size.

squeezeint or tuple, optional

Axis to squeeze after broadcasting.

Notes

Important: Do not broadcast to variables that are used as inference variables (i.e., parameters to be inferred by the networks). The adapter will work during training but then fail during inference because the variable being broadcasted to is not available.

Examples

shape (1, ) array:

>>> a = np.array((1,))

shape (2, 3) array:

>>> b = np.array([[1, 2, 3], [4, 5, 6]])

shape (2, 2, 3) array:

>>> c = np.array([[[1, 2, 3], [4, 5, 6]], [[4, 5, 6], [1, 2, 3]]])

>>> dat = dict(a=a, b=b, c=c)
>>> bc = bf.adapters.transforms.Broadcast("a", to="b")
>>> new_dat = bc.forward(dat)
>>> new_dat["a"].shape
(2, 1)

>>> bc = bf.adapters.transforms.Broadcast("a", to="b", exclude=None)
>>> new_dat = bc.forward(dat)
>>> new_dat["a"].shape
(2, 3)

>>> bc = bf.adapters.transforms.Broadcast("b", to="c", expand=1)
>>> new_dat = bc.forward(dat)
>>> new_dat["b"].shape
(2, 2, 3)

It is recommended to precede this transform with a bayesflow.adapters.transforms.ToArray transform.

get_config() → dict

forward(data: dict[str, ndarray], **kwargs) → dict[str, ndarray]

inverse(data: dict[str, ndarray], **kwargs) → dict[str, ndarray]

__call__(data: dict[str, ndarray], *, inverse: bool = False, **kwargs) → dict[str, ndarray]

Call self as a function.

extra_repr() → str

classmethod from_config(config: dict, custom_objects=None)