Adapter

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

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

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

get_config() → dict

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

Apply the transforms in the forward direction.

Parameters:

datadict

The data to be transformed.

**kwargsdict

Additional keyword arguments passed to each transform.

Returns:

dict

The transformed data.

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

Apply the transforms in the inverse direction.

Parameters:

datadict

The data to be transformed.

**kwargsdict

Additional keyword arguments passed to each transform.

Returns:

dict

The transformed data.

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

Apply the transforms in the given direction.

Parameters:

datadict

The data to be transformed.

inversebool, optional

If False, apply the forward transform, else apply the inverse transform (default False).

**kwargsdict

Additional keyword arguments passed to each transform.

Returns:

dict

The transformed data.

append(value: Transform) → Adapter

Append a transform to the list of transforms.

Parameters:

valueTransform

The transform to be added.

extend(values: Sequence[Transform]) → Adapter

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

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

Append a NumpyTransform to the adapter.

Parameters:

forward: callable, no lambda

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 should be used here.

inverse: callable, no lambda

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 should 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.

Notes

Important: This is only serializable if the forward and inverse functions are serializable. This most likely means you will have to pass the scope that the forward and inverse functions are contained in to the custom_objects argument of the deserialize function when deserializing this class.

as_set(keys: str | Sequence[str])

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])

Append an AsTimeSeries transform to the adapter.

Parameters:

keysstr or Sequence of str

The names of the variables to apply the transform to.

broadcast(keys: str | Sequence[str], *, to: str, expand: str | int | tuple = 'left', exclude: int | tuple = -1, squeeze: int | tuple = None)

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()

Remove all transforms from the adapter.

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

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)

Append a ConvertDType transform to the adapter.

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.

constrain(keys: str | Sequence[str], *, lower: int | float | ndarray = None, upper: int | float | ndarray = None, method: str = 'default', inclusive: str = 'both', epsilon: float = 1e-15)

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])

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)

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])

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)

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?

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

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)

Append a Rename transform to the adapter.

Parameters:

from_keystr

Variable name that should be renamed

to_keystr

New variable name

sqrt(keys: str | Sequence[str])

Append an Sqrt transform to the adapter.

Parameters:

keysstr or Sequence of str

The names of the variables to transform.

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

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

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.

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