Adapter#

class bayesflow.adapters.Adapter(transforms: Sequence[Transform] | None = None)[source]#

Bases: MutableSequence[Transform]

Defines an adapter to apply various transforms to data.

Where possible, the transforms also supply an inverse transform.

Parameters:

transformsSequence[Transform], optional: The sequence of transforms to execute.

static create_default(inference_variables: Sequence[str]) → Adapter[source]#

Create an adapter with a set of default transforms.

Parameters:

inference_variablesSequence of str: The names of the variables to be inferred by an estimator.

Returns:

An initialized Adapter with a set of default transforms.

classmethod from_config(config: dict, custom_objects=None) → Adapter[source]#

get_config() → dict[source]#

forward(data: dict[str, any], *, stage: str = 'inference', log_det_jac: bool = False, **kwargs) → dict[str, ndarray] | tuple[dict[str, ndarray], dict[str, ndarray]][source]#

Apply the transforms in the forward direction.

Parameters:

datadict: The data to be transformed.
stagestr, one of [“training”, “validation”, “inference”]: The stage the function is called in.
log_det_jac: bool, optional: Whether to return the log determinant of the Jacobian of the transforms.
**kwargsdict: Additional keyword arguments passed to each transform.

Returns:

dict | tuple[dict, dict]: The transformed data or tuple of transformed data and log determinant of the Jacobian.

inverse(data: dict[str, ndarray], *, stage: str = 'inference', log_det_jac: bool = False, **kwargs) → dict[str, ndarray] | tuple[dict[str, ndarray], dict[str, ndarray]][source]#

Apply the transforms in the inverse direction.

Parameters:

datadict: The data to be transformed.
stagestr, one of [“training”, “validation”, “inference”]: The stage the function is called in.
log_det_jac: bool, optional: Whether to return the log determinant of the Jacobian of the transforms.
**kwargsdict: Additional keyword arguments passed to each transform.

Returns:

dict | tuple[dict, dict]: The transformed data or tuple of transformed data and log determinant of the Jacobian.

__call__(data: Mapping[str, any], *, inverse: bool = False, stage='inference', **kwargs) → dict[str, ndarray] | tuple[dict[str, ndarray], dict[str, ndarray]][source]#

Apply the transforms in the given direction.

Parameters:

dataMapping[str, any]: The data to be transformed.
inversebool, optional: If False, apply the forward transform, else apply the inverse transform (default False).
stagestr, one of [“training”, “validation”, “inference”]: The stage the function is called in.
**kwargs: Additional keyword arguments passed to each transform.

Returns:

dict | tuple[dict, dict]: The transformed data or tuple of transformed data and log determinant of the Jacobian.

append(value: Transform) → Adapter[source]#

Append a transform to the list of transforms.

Parameters:

valueTransform: The transform to be added.

extend(values: Sequence[Transform]) → Adapter[source]#

Extend the adapter with a sequence of transforms.

Parameters:

valuesSequence of Transform: The additional transforms to extend the adapter.

insert(index: int, value: Transform | Sequence[Transform]) → Adapter[source]#

Insert a transform at a given index.

Parameters:

indexint: The index to insert at.
valueTransform or Sequence of Transform: The transform or transforms to insert.

add_transform(value: Transform) → Adapter#

Append a transform to the list of transforms.

Parameters:

valueTransform: The transform to be added.

apply(include: str | Sequence[str] = None, *, forward: ufunc | str, inverse: ufunc | str = None, predicate: Predicate = None, exclude: str | Sequence[str] = None, **kwargs)[source]#

Append a NumpyTransform to the adapter.

Parameters:

forwardstr or np.ufunc: The name of the NumPy function to use for the forward transformation.
inversestr or np.ufunc, optional: The name of the NumPy function to use for the inverse transformation. By default, the inverse is inferred from the forward argument for supported methods. You can find the supported methods in INVERSE_METHODS.
predicatePredicate, optional: Function that indicates which variables should be transformed.
includestr or Sequence of str, optional: Names of variables to include in the transform.
excludestr or Sequence of str, optional: Names of variables to exclude from the transform.
**kwargsdict: Additional keyword arguments passed to the transform.

apply_serializable(include: str | Sequence[str] = None, *, forward: Callable[[ndarray, ...], ndarray], inverse: Callable[[ndarray, ...], ndarray], predicate: Predicate = None, exclude: str | Sequence[str] = None, **kwargs)[source]#

Append a SerializableCustomTransform to the adapter.

Parameters:

forwardfunction, no lambda: Registered serializable function to transform the data in the forward pass. For the adapter to be serializable, this function has to be serializable as well (see Notes). Therefore, only proper functions and no lambda functions can be used here.
inversefunction, no lambda: Registered serializable function to transform the data in the inverse pass. For the adapter to be serializable, this function has to be serializable as well (see Notes). Therefore, only proper functions and no lambda functions can be used here.
predicatePredicate, optional: Function that indicates which variables should be transformed.
includestr or Sequence of str, optional: Names of variables to include in the transform.
excludestr or Sequence of str, optional: Names of variables to exclude from the transform.
**kwargsdict: Additional keyword arguments passed to the transform.

Raises:

ValueError: When the provided functions are not registered serializable functions.

Notes

Important: The forward and inverse functions have to be registered with Keras. To do so, use the @keras.saving.register_keras_serializable decorator. They must also be registered (and identical) when loading the adapter at a later point in time.

Examples

The example below shows how to use the keras.saving.register_keras_serializable decorator to register functions with Keras. Note that for this simple example, one usually would use the simpler apply() method.

>>> import keras
>>>
>>> @keras.saving.register_keras_serializable("custom")
>>> def forward_fn(x):
>>>     return x**2
>>>
>>> @keras.saving.register_keras_serializable("custom")
>>> def inverse_fn(x):
>>>     return x**0.5
>>>
>>> adapter = bf.Adapter().apply_serializable(
>>>     "x",
>>>     forward=forward_fn,
>>>     inverse=inverse_fn,
>>> )

as_set(keys: str | Sequence[str])[source]#

Append an AsSet transform to the adapter.

Parameters:

keysstr or Sequence of str: The names of the variables to apply the transform to.

as_time_series(keys: str | Sequence[str])[source]#

Append an AsTimeSeries transform to the adapter.

Parameters:

keysstr or Sequence of str: The names of the variables to apply the transform to.

Append a Broadcast transform to the adapter.

Parameters:

keysstr or Sequence of str: The names of the variables to apply the transform to.
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.

clear()[source]#: Remove all transforms from the adapter.

concatenate(keys: str | Sequence[str], *, into: str, axis: int = -1)[source]#

Append a Concatenate transform to the adapter.

Parameters:

keysstr or Sequence of str: The names of the variables to concatenate.
intostr: The name of the resulting variable.
axisint, optional: Along which axis to concatenate the keys. The last axis is used by default.

convert_dtype(from_dtype: str, to_dtype: str, *, predicate: Predicate = None, include: str | Sequence[str] = None, exclude: str | Sequence[str] = None)[source]#

Append a ConvertDType transform to the adapter. See also map_dtype().

Parameters:

from_dtypestr: Original dtype
to_dtypestr: Target dtype
predicatePredicate, optional: Function that indicates which variables should be transformed.
includestr or Sequence of str, optional: Names of variables to include in the transform.
excludestr or Sequence of str, optional: Names of variables to exclude from the transform.

Append a Constrain transform to the adapter.

Parameters:

keysstr or Sequence of str: The names of the variables to constrain.
lower: int 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.

drop(keys: str | Sequence[str])[source]#

Append a Drop transform to the adapter.

Parameters:

keysstr or Sequence of str: The names of the variables to drop.

expand_dims(keys: str | Sequence[str], *, axis: int | tuple)[source]#

Append an ExpandDims transform to the adapter.

Parameters:

keysstr or Sequence of str: The names of the variables to expand.
axisint or tuple: The axis to expand.

keep(keys: str | Sequence[str])[source]#

Append a Keep transform to the adapter.

Parameters:

keysstr or Sequence of str: The names of the variables to keep.

log(keys: str | Sequence[str], *, p1: bool = False)[source]#

Append an Log transform to the adapter.

Parameters:

keysstr or Sequence of str: The names of the variables to transform.
p1boolean: Add 1 to the input before taking the logarithm?

map_dtype(keys: str | Sequence[str], to_dtype: str)[source]#

Append a ConvertDType transform to the adapter. See also convert_dtype().

Parameters:

keysstr or Sequence of str: The names of the variables to transform.
to_dtypestr: Target dtype

one_hot(keys: str | Sequence[str], num_classes: int)[source]#

Append a OneHot transform to the adapter.

Parameters:

keysstr or Sequence of str: The names of the variables to transform.
num_classesint: Number of classes for the encoding.

rename(from_key: str, to_key: str)[source]#

Append a Rename transform to the adapter.

Parameters:

from_keystr: Variable name that should be renamed
to_keystr: New variable name

scale(keys: str | Sequence[str], by: float | ndarray)[source]#

shift(keys: str | Sequence[str], by: float | ndarray)[source]#

split(key: str, *, into: Sequence[str], indices_or_sections: int | Sequence[int] = None, axis: int = -1)[source]#

sqrt(keys: str | Sequence[str])[source]#

Append an Sqrt transform to the adapter.

Parameters:

keysstr or Sequence of str: The names of the variables to transform.

standardize(include: str | Sequence[str] = None, *, predicate: Predicate = None, exclude: str | Sequence[str] = None, **kwargs)[source]#

Append a Standardize transform to the adapter.

Parameters:

predicatePredicate, optional: Function that indicates which variables should be transformed.
includestr or Sequence of str, optional: Names of variables to include in the transform.
excludestr or Sequence of str, optional: Names of variables to exclude from the transform.
**kwargsdict: Additional keyword arguments passed to the transform.

to_array(include: str | Sequence[str] = None, *, predicate: Predicate = None, exclude: str | Sequence[str] = None, **kwargs)[source]#

Append a ToArray transform to the adapter.

Parameters:

predicatePredicate, optional: Function that indicates which variables should be transformed.
includestr or Sequence of str, optional: Names of variables to include in the transform.
excludestr or Sequence of str, optional: Names of variables to exclude from the transform.
**kwargsdict: Additional keyword arguments passed to the transform.

to_dict()[source]#

count(value) → integer -- return number of occurrences of value#

index(value[, start[, stop]]) → integer -- return first index of value.#

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

pop([index]) → item -- remove and return item at index (default last).#: Raise IndexError if list is empty or index is out of range.

remove(value)#: S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.

reverse()#: S.reverse() – reverse IN PLACE