tfr.keras.losses.ListMLELoss

Computes ListMLE loss between y_true and y_pred.

tfr.keras.losses.ListMLELoss(
    reduction: tf.losses.Reduction = tf.losses.Reduction.AUTO,
    name: Optional[str] = None,
    lambda_weight: Optional[losses_impl._LambdaWeight] = None,
    temperature: float = 1.0,
    ragged: bool = False
)

Implements ListMLE loss (Xia et al, 2008). For each list of scores s in y_pred and list of labels y in y_true:

loss = - log P(permutation_y | s)
P(permutation_y | s) = Plackett-Luce probability of permutation_y given s
permutation_y = permutation of items sorted by labels y.

Note: This loss is stochastic and may return different values for identical inputs.

Standalone usage:

tf.random.set_seed(42)
y_true = [[1., 0.]]
y_pred = [[0.6, 0.8]]
loss = tfr.keras.losses.ListMLELoss()
loss(y_true, y_pred).numpy()
0.7981389

# Using ragged tensors
tf.random.set_seed(42)
y_true = tf.ragged.constant([[1., 0.], [0., 1., 0.]])
y_pred = tf.ragged.constant([[0.6, 0.8], [0.5, 0.8, 0.4]])
loss = tfr.keras.losses.ListMLELoss(ragged=True)
loss(y_true, y_pred).numpy()
1.1613163

Usage with the compile() API:

model.compile(optimizer='sgd', loss=tfr.keras.losses.ListMLELoss())

Definition:

\[ \mathcal{L}(\{y\}, \{s\}) = - \log(P(\pi_y | s)) \]

where \(P(\pi_y | s)\) is the Plackett-Luce probability of a permutation \(\pi_y\) conditioned on scores \(s\). Here \(\pi_y\) represents a permutation of items ordered by the relevance labels \(y\) where ties are broken randomly.

References
Listwise approach to learning to rank: theory and algorithm, Xia et al, 2008

Args
reduction	(Optional) The tf.keras.losses.Reduction to use (see tf.keras.losses.Loss).
name	(Optional) The name for the op.
lambda_weight	(Optional) A lambdaweight to apply to the loss. Can be one of tfr.keras.losses.DCGLambdaWeight, tfr.keras.losses.NDCGLambdaWeight, tfr.keras.losses.PrecisionLambdaWeight, or, tfr.keras.losses.ListMLELambdaWeight.
temperature	(Optional) The temperature to use for scaling the logits.
ragged	(Optional) If True, this loss will accept ragged tensors. If False, this loss will accept dense tensors.

Methods

from_config

View source

@classmethod
from_config(
    config, custom_objects=None
)

Instantiates a Loss from its config (output of get_config()).

Args
config	Output of get_config().

Returns
A Loss instance.

get_config

View source

get_config() -> Dict[str, Any]

Returns the config dictionary for a Loss instance.

__call__

View source

__call__(
    y_true: tfr.keras.model.TensorLike,
    y_pred: tfr.keras.model.TensorLike,
    sample_weight: Optional[utils.TensorLike] = None
) -> tf.Tensor

See tf.keras.losses.Loss.