fusionlab.nn.components.AdaptiveQuantileLoss

class fusionlab.nn.components.AdaptiveQuantileLoss

Bases: Loss, NNLearner

Adaptive Quantile Loss layer that computes quantile loss for given quantiles [1].

The layer expects y_true of shape \((B, H, O)\), where B is batch size, H is horizon, and O is output dimension, and y_pred of shape \((B, H, Q, O)\), where Q is the number of quantiles if they are specified.

\[\text{QuantileLoss}(\hat{y}, y) = \max(q \cdot (y - \hat{y}),\, (q - 1) \cdot (y - \hat{y}))\]

The final loss is the mean across batch, time, quantiles, and output dimension.

Parameters:

quantiles (list of float, optional) – A list of quantiles used to compute quantile loss. If set to 'auto', defaults to [0.1, 0.5, 0.9]. If None, the loss returns 0.0 (no quantile loss).

Notes

For quantile regression, each quantile penalizes under- and over-estimates differently, encouraging a robust modeling of the distribution of possible outcomes.

call(`y_true`, `y_pred`, training=False)

Compute the quantile loss.

get_config()

Returns configuration for serialization.

from_config(`config`)

Creates a new instance from config dict.

Examples

>>> from fusionlab.nn.components import AdaptiveQuantileLoss
>>> import tensorflow as tf
>>> # Suppose y_true is (B, H, O)
... # y_pred is (B, H, Q, O)
>>> y_true = tf.random.normal((32, 10, 1))
>>> y_pred = tf.random.normal((32, 10, 3, 1))
>>> # Instantiate with custom quantiles
>>> aq_loss = AdaptiveQuantileLoss([0.2, 0.5, 0.8])
>>> # Forward pass (loss calculation)
>>> loss_value = aq_loss(y_true, y_pred)

See also

MultiObjectiveLoss

Can combine this quantile loss with an anomaly loss.

AnomalyLoss

Computes anomaly-based loss, complementary to quantile loss.

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__(quantiles, name='AdaptiveQuantileLoss')

Parameters:

quantiles (List[float] | None)

Methods

__init__(quantiles[, name])
call(y_true, y_pred)	Compute quantile loss.
from_config(config)	Creates a new instance from a config dict.
get_config()	Configuration for serialization.
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__(quantiles, name='AdaptiveQuantileLoss')

Parameters:

quantiles (List[float] | None)

call(y_true, y_pred)

Compute quantile loss.

Parameters:

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

• y_pred (tf.Tensor) – Predicted values of shape (B, H, Q, O) if quantiles is not None.

• training (bool, optional) – Unused parameter, included for consistency. Defaults to False.

Returns:

A scalar representing the mean quantile loss. 0.0 if quantiles is None.

Return type:

tf.Tensor

get_config()

Configuration for serialization.

Returns:

Dictionary with ‘quantiles’.

Return type:

dict

classmethod from_config(config)

Creates a new instance from a config dict.

Parameters:

config (dict) – Configuration dictionary.

Returns:

A new instance of the layer.

Return type:

AdaptiveQuantileLoss

help(**kwargs)

my_params = AdaptiveQuantileLoss(quantiles, name='AdaptiveQuantileLoss')