encoders ¶

CategoricalInputEncoderPerFeatureEncoderStep ¶

Bases: SeqEncStep

Expects input of size 1.

forward ¶

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

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`	`Any`	Additional keyword arguments passed to the encoder step.	`{}`

Returns:

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

FrequencyFeatureEncoderStep ¶

Bases: SeqEncStep

Encoder step to add frequency-based features to the input.

forward ¶

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

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`	`Any`	Additional keyword arguments passed to the encoder step.	`{}`

Returns:

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

InputEncoder ¶

Bases: Module

Base class for input encoders.

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

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`	The encoded tensor.

InputNormalizationEncoderStep ¶

Bases: SeqEncStep

Encoder step to normalize the input in different ways.

Can be used to normalize the input to a ranking, remove outliers, or normalize the input to have unit variance.

forward ¶

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

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`	`Any`	Additional keyword arguments passed to the encoder step.	`{}`

Returns:

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

reset_seed ¶

reset_seed() -> None

Reset the random seed.

LinearInputEncoder ¶

Bases: Module

A simple linear input encoder.

forward ¶

forward(*x: Tensor, **kwargs: Any) -> tuple[Tensor]

Apply the linear transformation to the input.

Parameters:

Name	Type	Description	Default
`*x`	`Tensor`	The input tensors to concatenate and transform.	`()`
`**kwargs`	`Any`	Unused keyword arguments.	`{}`

Returns:

Type	Description
`tuple[Tensor]`	A tuple containing the transformed tensor.

LinearInputEncoderStep ¶

Bases: SeqEncStep

A simple linear input encoder step.

forward ¶

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

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`	`Any`	Additional keyword arguments passed to the encoder step.	`{}`

Returns:

Type	Description
`dict`	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.

forward ¶

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

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`	`Any`	Additional keyword arguments passed to the encoder step.	`{}`

Returns:

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

RemoveDuplicateFeaturesEncoderStep ¶

Bases: SeqEncStep

Encoder step to remove duplicate features.

forward ¶

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

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`	`Any`	Additional keyword arguments passed to the encoder step.	`{}`

Returns:

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

RemoveEmptyFeaturesEncoderStep ¶

Bases: SeqEncStep

Encoder step to remove empty (constant) features.

forward ¶

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

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`	`Any`	Additional keyword arguments passed to the encoder step.	`{}`

Returns:

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

SeqEncStep ¶

Bases: Module

Abstract base class for sequential encoder steps.

SeqEncStep is a wrapper around a module that defines the expected input keys and the produced output keys. The outputs are assigned to the output keys in the order specified by out_keys.

Subclasses should either implement _forward or _fit and _transform. Subclasses that transform x should always use _fit and _transform, creating any state that depends on the train set in _fit and using it in _transform. This allows fitting on data first and doing inference later without refitting. Subclasses that work with y can alternatively use _forward instead.

forward ¶

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

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`	`Any`	Additional keyword arguments passed to the encoder step.	`{}`

Returns:

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

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.

forward ¶

forward(input: dict, **kwargs: Any) -> 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`	`Any`	Additional keyword arguments passed to each encoder step.	`{}`

Returns:

Type	Description
`Tensor`	The output of the final encoder step.

VariableNumFeaturesEncoderStep ¶

Bases: SeqEncStep

Encoder step to handle variable number of features.

Transforms the input to a fixed number of features by appending zeros. Also normalizes the input by the number of used features to keep the variance of the input constant, even when zeros are appended.

forward ¶

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

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`	`Any`	Additional keyword arguments passed to the encoder step.	`{}`

Returns:

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

normalize_data ¶

normalize_data(
    data: Tensor,
    *,
    normalize_positions: int = -1,
    return_scaling: bool = False,
    clip: bool = True,
    std_only: bool = False,
    mean: Tensor | None = None,
    std: Tensor | None = None
) -> Tensor | tuple[Tensor, tuple[Tensor, Tensor]]

Normalize data to mean 0 and std 1.

Parameters:

Name	Type	Description	Default
`data`	`Tensor`	The data to normalize. (T, B, H)	required
`normalize_positions`	`int`	If > 0, only use the first `normalize_positions` positions for normalization.	`-1`
`return_scaling`	`bool`	If True, return the scaling parameters as well (mean, std).	`False`
`std_only`	`bool`	If True, only divide by std.	`False`
`clip`	`bool`	If True, clip the data to [-100, 100].	`True`
`mean`	`Tensor \| None`	If given, use this value instead of computing it.	`None`
`std`	`Tensor \| None`	If given, use this value instead of computing it.	`None`

select_features ¶

select_features(x: Tensor, sel: Tensor) -> Tensor

Select features from the input tensor based on the selection mask.

Parameters:

Name	Type	Description	Default
`x`	`Tensor`	The input tensor.	required
`sel`	`Tensor`	The boolean selection mask indicating which features to keep.	required

Returns:

Type	Description
`Tensor`	The tensor with selected features.