tfr.keras.losses.ClickEMLoss

Computes click EM loss between y_true and y_pred.

tfr.keras.losses.ClickEMLoss(
    reduction: tf.losses.Reduction = tf.losses.Reduction.AUTO,
    name: Optional[str] = None,
    exam_loss_weight: float = 1.0,
    rel_loss_weight: float = 1.0,
    ragged: bool = False
)

Implementation of click EM loss (Wang et al, 2018). This loss assumes that a click is generated by a factorized model \(P(\text{examination}) \cdot P(\text{relevance})\), which are latent variables determined by exam_logits and rel_logits respectively.

Note: This loss should be called with a logits tensor of shape [batch_size, list_size, 2]. The two elements in the last dimension of logits represent exam_logits and rel_logits respectively.

Standalone usage:

y_true = [[1., 0.]]
y_pred = [[[0.6, 0.9], [0.8, 0.2]]]
loss = tfr.keras.losses.ClickEMLoss()
loss(y_true, y_pred).numpy()
1.1462884

# Using ragged tensors
y_true = tf.ragged.constant([[1., 0.], [0., 1., 0.]])
y_pred = tf.ragged.constant([[[0.6, 0.9], [0.8, 0.2]],
    [[0.5, 0.9], [0.8, 0.2], [0.4, 0.8]]])
loss = tfr.keras.losses.ClickEMLoss(ragged=True)
loss(y_true, y_pred).numpy()
1.0770882

Usage with the compile() API:

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

References
Position Bias Estimation for Unbiased Learning to Rank in Personal Search, Wang et al, 2018.

Args
reduction	(Optional) The tf.keras.losses.Reduction to use (see tf.keras.losses.Loss).
name	(Optional) The name for the op.
exam_loss_weight	(Optional) Weight of examination logits.
rel_loss_weight	(Optional) Weight of relevance logits.
ragged	(Optional) If True, this loss will accept ragged tensors. If False, this loss will accept dense tensors.

Methods

from_config

@classmethod
from_config(
    config
)

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.