InputEncoder

Bases: Module

Base class for input encoders.

All input encoders should subclass this class and implement the forward method.

__init__

__init__()

forward

forward(x: Tensor, single_eval_pos: int) -> Tensor

Encode the input tensor.

Parameters:

Name	Type	Description	Default
x	Tensor	The input tensor to encode.	required
single_eval_pos	int	The position to use for single evaluation.	required

Returns:

Type	Description
Tensor	torch.Tensor: The encoded tensor.

SequentialEncoder

Bases: Sequential, InputEncoder

An encoder that applies a sequence of encoder steps.

SequentialEncoder allows building an encoder from a sequence of EncoderSteps. The input is passed through each step in the provided order.

__init__

__init__(*args, output_key: str = 'output', **kwargs)

Initialize the SequentialEncoder.

Parameters:

Name	Type	Description	Default
*args		A list of SeqEncStep instances to apply in order.	()
output_key	str	The key to use for the output of the encoder in the state dict. Defaults to "output", i.e. state["output"] will be returned.	'output'
**kwargs		Additional keyword arguments passed to torch.nn.Sequential.	{}

forward

forward(input: dict, **kwargs) -> Tensor

Apply the sequence of encoder steps to the input.

Parameters:

Name	Type	Description	Default
input	dict	The input state dictionary. If the input is not a dict and the first layer expects one input key, the input tensor is mapped to the key expected by the first layer.	required
**kwargs		Additional keyword arguments passed to each encoder step.	{}

Returns:

Type	Description
Tensor	torch.Tensor: The output of the final encoder step.

LinearInputEncoderStep

Bases: SeqEncStep

A simple linear input encoder step.

__init__

__init__(
    num_features: int,
    emsize: int,
    replace_nan_by_zero: bool = False,
    bias: bool = True,
    in_keys: tuple[str] = ("main"),
    out_keys: tuple[str] = ("output"),
)

Initialize the LinearInputEncoderStep.

Parameters:

Name	Type	Description	Default
num_features	int	The number of input features.	required
emsize	int	The embedding size, i.e. the number of output features.	required
replace_nan_by_zero	bool	Whether to replace NaN values in the input by zero. Defaults to False.	False
bias	bool	Whether to use a bias term in the linear layer. Defaults to True.	True
in_keys	tuple[str]	The keys of the input tensors. Defaults to ("main",).	('main')
out_keys	tuple[str]	The keys to assign the output tensors to. Defaults to ("output",).	('output')

forward

forward(
    state: dict,
    cache_trainset_representation: bool = False,
    **kwargs
)

Perform the forward pass of the encoder step.

Parameters:

Name	Type	Description	Default
state	dict	The input state dictionary containing the input tensors.	required
cache_trainset_representation	bool	Whether to cache the training set representation. Only supported for _fit and _transform (not _forward).	False
**kwargs		Additional keyword arguments passed to the encoder step.	{}

Returns:

Type	Description
	The updated state dictionary with the output tensors assigned to the output keys.

NanHandlingEncoderStep

Bases: SeqEncStep

Encoder step to handle NaN and infinite values in the input.

__init__

__init__(
    keep_nans: bool = True,
    in_keys: tuple[str] = ("main"),
    out_keys: tuple[str] = ("main", "nan_indicators"),
)

Initialize the NanHandlingEncoderStep.

Parameters:

Name	Type	Description	Default
keep_nans	bool	Whether to keep NaN values as separate indicators. Defaults to True.	True
in_keys	tuple[str]	The keys of the input tensors. Must be a single key.	('main')
out_keys	tuple[str]	The keys to assign the output tensors to. Defaults to ("main", "nan_indicators").	('main', 'nan_indicators')

forward

forward(
    state: dict,
    cache_trainset_representation: bool = False,
    **kwargs
)

Perform the forward pass of the encoder step.

Parameters:

Name	Type	Description	Default
state	dict	The input state dictionary containing the input tensors.	required
cache_trainset_representation	bool	Whether to cache the training set representation. Only supported for _fit and _transform (not _forward).	False
**kwargs		Additional keyword arguments passed to the encoder step.	{}

Returns:

Type	Description
	The updated state dictionary with the output tensors assigned to the output keys.