fusionlab.nn.components.MultiObjectiveLoss

class fusionlab.nn.components.MultiObjectiveLoss

Bases: Loss, NNLearner

Multi-Objective Loss layer combining quantile loss and anomaly loss [1].

This layer expects: 1. y_true: \((B, H, O)\) 2. y_pred: \((B, H, Q, O)\), if

quantiles are used (or (B, H, 1, O) for a single quantile).

3. anomaly_scores: \((B, H, D)\), optional.

\[\text{Loss} = \text{QuantileLoss} + \text{AnomalyLoss}\]

If anomaly_scores is None, only quantile loss is returned.

Parameters:

• quantile_loss_fn (Layer) – A callable implementing quantile loss, e.g. AdaptiveQuantileLoss.

• anomaly_loss_fn (Layer) – A callable implementing anomaly loss, e.g. AnomalyLoss.

Notes

This layer allows multi-task learning by combining two objectives: forecasting accuracy (quantile loss) and anomaly detection (anomaly loss).

call(`y_true`, `y_pred`, `anomaly_scores`=None,

training=False)

Compute the combined loss.

get_config()

Returns configuration for serialization.

from_config(`config`)

Rebuilds the layer from a config dict.

Examples

>>> from fusionlab.nn.components import (
...     MultiObjectiveLoss,
...     AdaptiveQuantileLoss,
...     AnomalyLoss
... )
>>> import tensorflow as tf
>>> # Suppose y_true is (B, H, O),
... # and y_pred is (B, H, Q, O).
>>> y_true = tf.random.normal((32, 10, 1))
>>> y_pred = tf.random.normal((32, 10, 3, 1))
>>> anomaly_scores = tf.random.normal((32, 10, 8))
>>> # Instantiate loss components
>>> q_loss_fn = AdaptiveQuantileLoss([0.2, 0.5, 0.8])
>>> a_loss_fn = AnomalyLoss(weight=2.0)
>>> # Combine them
>>> mo_loss = MultiObjectiveLoss(q_loss_fn, a_loss_fn)
>>> # Compute the combined loss
>>> total_loss = mo_loss(y_true, y_pred, anomaly_scores)

See also

AdaptiveQuantileLoss

Implements quantile loss to handle uncertainty in predictions.

AnomalyLoss

Computes anomaly-based MSE for anomaly detection tasks.

References

[1]

Lim, B., & Zohren, S. (2021). “Time-series forecasting with deep learning: a survey.” Philosophical Transactions of the Royal Society A, 379(2194), 20200209.

__init__(quantile_loss_fn, anomaly_loss_fn, anomaly_scores=None, name='MultiObjectiveLoss')

Methods

__init__(quantile_loss_fn, anomaly_loss_fn)
call(y_true, y_pred)	Compute combined quantile and anomaly loss.
from_config(config)	Creates a new MultiObjectiveLoss from the config dictionary.
get_config()	Returns configuration dictionary, including configs of the sub-layers.
get_params([deep])	Get the parameters for this learner.
help(**kwargs)
load(file_path[, format])	Load the learner's state from a specified file in the desired format.
save([file_path, format, overwrite, ...])	Save the learner's state to a specified file in the desired format.
set_params(**params)	Set the parameters of this learner.
summary()	Provide a summary of the learner's parameters.

Attributes

dtype
my_params

__init__(quantile_loss_fn, anomaly_loss_fn, anomaly_scores=None, name='MultiObjectiveLoss')

call(y_true, y_pred)

Compute combined quantile and anomaly loss.

Parameters:

• y_true (tf.Tensor) – Ground truth of shape (B, H, O).

• y_pred (tf.Tensor) – Predictions of shape (B, H, Q, O) (or (B, H, 1, O) if Q=1).

• anomaly_scores (tf.Tensor or None, optional) – Tensor of shape (B, H, D). If None, anomaly loss is omitted.

• training (bool, optional) – Indicates training mode. Defaults to False.

Returns:

A scalar representing the sum of quantile loss and anomaly loss (if anomaly_scores is provided).

Return type:

tf.Tensor

get_config()

Returns configuration dictionary, including configs of the sub-layers.

Returns:

Contains serialized configs of quantile_loss_fn and anomaly_loss_fn.

Return type:

dict

classmethod from_config(config)

Creates a new MultiObjectiveLoss from the config dictionary.

Parameters:

config (dict) – Configuration dictionary with sub-layer configs.

Returns:

A new instance combining quantile and anomaly losses.

Return type:

MultiObjectiveLoss

help(**kwargs)

my_params = MultiObjectiveLoss(quantile_loss_fn, anomaly_loss_fn, anomaly_scores=None, name='MultiObjectiveLoss')