Ranking Losses (`rax.*_loss`)

Implementations of common ranking losses in JAX.

A ranking loss is a differentiable function that expresses the cost of a ranking induced by item scores compared to a ranking induced from relevance labels. Rax provides a number of ranking losses as JAX functions that are implemented according to the LossFn interface.

Loss functions are designed to operate on the last dimension of its inputs. The leading dimensions are considered batch dimensions. To compute per-list losses, for example to apply per-list weighting or for distributed computing of losses across devices, please use standard JAX transformations such as jax.vmap() or jax.pmap().

Standalone usage:

>>> scores = jnp.array([2., 1., 3.])
>>> labels = jnp.array([1., 0., 0.])
>>> print(rax.softmax_loss(scores, labels))
1.4076059

Usage with a batch of data and a mask to indicate valid items.

>>> scores = jnp.array([[2., 1., 0.], [1., 0.5, 1.5]])
>>> labels = jnp.array([[1., 0., 0.], [0., 0., 1.]])
>>> where = jnp.array([[True, True, False], [True, True, True]])
>>> print(rax.pairwise_hinge_loss(
...     scores, labels, where=where, reduce_fn=jnp.mean))
0.16666667

To compute gradients of each loss function, please use standard JAX transformations such as jax.grad() or jax.value_and_grad():

>>> scores = jnp.asarray([[0., 1., 3.], [1., 2., 0.]])
>>> labels = jnp.asarray([[0., 0., 1.], [1., 0., 0.]])
>>> print(jax.grad(rax.softmax_loss)(scores, labels, reduce_fn=jnp.mean))
[[ 0.02100503  0.0570976  -0.07810265]
 [-0.37763578  0.33262047  0.04501529]]

`pointwise_mse_loss`(scores, labels, *[, ...])	Mean squared error loss.
`pointwise_sigmoid_loss`(scores, labels, *[, ...])	Sigmoid cross entropy loss.
`pairwise_hinge_loss`(scores, labels, *[, ...])	Pairwise hinge loss.
`pairwise_logistic_loss`(scores, labels, *[, ...])	Pairwise logistic loss.
`pairwise_soft_zero_one_loss`(scores, labels, *)	Pairwise soft zero-one loss.
`pairwise_mse_loss`(scores, labels, *[, ...])	Pairwise mean squared error loss.
`pairwise_qr_loss`(scores, labels, *[, where, ...])	Pairwise quantile regression loss.
`softmax_loss`(scores, labels, *[, where, ...])	Softmax loss.
`listmle_loss`(scores, labels, *[, key, ...])	ListMLE Loss.
`poly1_softmax_loss`(scores, labels, *[, ...])	Poly1 softmax loss.
`unique_softmax_loss`(scores, labels, *[, ...])	Unique softmax loss.

rax.pointwise_mse_loss(scores, labels, *, where=None, segments=None, weights=None, reduce_fn=<function mean>)

Mean squared error loss.

Definition:

\[\ell(s, y) = \sum_i (y_i - s_i)^2 \]

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the loss. Items for which this is False will be ignored when computing the loss.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The loss will only be computed on items that share the same segment.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the loss values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The mean squared error loss.

rax.pointwise_sigmoid_loss(scores, labels, *, where=None, segments=None, weights=None, reduce_fn=<function mean>)

Sigmoid cross entropy loss.

Note

This loss clips label values so that 0 <= label <= 1.

Definition:

\[\ell(s, y) = \sum_i y_i * -\log(\op{sigmoid}(s_i)) + (1 - y_i) * -\log(1 - \op{sigmoid}(s_i)) \]

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional […, list_size]-Array, indicating which items are valid for computing the loss. Items for which this is False will be ignored when computing the loss.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The loss will only be computed on items that share the same segment.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the loss values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The sigmoid cross entropy loss.

rax.pairwise_hinge_loss(scores, labels, *, where=None, segments=None, weights=None, lambdaweight_fn=None, reduce_fn=<function mean>)

Pairwise hinge loss.

Definition:

\[\ell(s, y) = \sum_i \sum_j \II{y_i > y_j} \max(0, 1 - (s_i - s_j)) \]

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the loss. Items for which this is False will be ignored when computing the loss.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The loss will only be computed on items that share the same segment.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.
lambdaweight_fn (Optional[LambdaweightFn]) – An optional function that outputs lambdaweights.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the loss values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The pairwise hinge loss.

rax.pairwise_logistic_loss(scores, labels, *, where=None, segments=None, weights=None, lambdaweight_fn=None, reduce_fn=<function mean>)

Pairwise logistic loss.

Definition [Burges et al., 2005]:

\[\ell(s, y) = \sum_i \sum_j \II{y_i > y_j} \log(1 + \exp(-(s_i - s_j))) \]

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the loss. Items for which this is False will be ignored when computing the loss.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The loss will only be computed on items that share the same segment.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.
lambdaweight_fn (Optional[LambdaweightFn]) – An optional function that outputs lambdaweights.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the loss values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The pairwise logistic loss.

rax.pairwise_soft_zero_one_loss(scores, labels, *, where=None, segments=None, weights=None, lambdaweight_fn=None, reduce_fn=<function mean>)

Pairwise soft zero-one loss.

Definition:

\[\ell(s, y) = \sum_i \sum_j \II{y_i > y_j} sigmod(-(s_i - s_j)) \]

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the loss. Items for which this is False will be ignored when computing the loss.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The loss will only be computed on items that share the same segment.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.
lambdaweight_fn (Optional[LambdaweightFn]) – An optional function that outputs lambdaweights.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the loss values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The pairwise soft zero-one loss value.

rax.pairwise_mse_loss(scores, labels, *, where=None, segments=None, weights=None, lambdaweight_fn=None, reduce_fn=<function mean>)

Pairwise mean squared error loss.

Definition:

\[\ell(s, y) = \sum_i \sum_j ((y_i - y_j) - (s_i - s_j))^2 \]

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the loss. Items for which this is False will be ignored when computing the loss.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The loss will only be computed on items that share the same segment.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.
lambdaweight_fn (Optional[LambdaweightFn]) – An optional function that outputs lambdaweights.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the loss values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The pairwise mean squared error loss.

rax.pairwise_qr_loss(scores, labels, *, where=None, segments=None, weights=None, tau=0.5, squared=False, lambdaweight_fn=None, reduce_fn=<function mean>)

Pairwise quantile regression loss.

Definition:

\[\ell(s, y) = \sum_i \sum_j \II{y_i > y_j} \op{loss}_{ij} \\ \op{loss}_{ij} = \tau \max(0, (y_i - y_j) - (s_i - s_j)) + (1-\tau) \max(0, (s_i - s_j) - (y_i - y_j)) \]

When squared is True, each hinge loss is squared. Please note that only the pairs that have different labels are considered. When tau = 0.5, this boils down to median regression or mse with some small difference on the tied labels.

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the loss. Items for which this is False will be ignored when computing the loss.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The loss will only be computed on items that share the same segment.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.
tau (float) – A float in (0, 1.0] to define the quantile. When tau = 0.5, it becomes median regression.
squared (bool) – If True, square each individual pairwise loss value.
lambdaweight_fn (Optional[LambdaweightFn]) – An optional function that outputs lambdaweights.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the loss values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The pairwise quantile regression loss.

rax.softmax_loss(scores, labels, *, where=None, segments=None, weights=None, label_fn=<function <lambda>>, reduce_fn=<function mean>)

Softmax loss.

Definition:

\[\ell(s, y) = -\sum_i y_i \log \frac{\exp(s_i)}{\sum_j \exp(s_j)} \]

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the loss. Items for which this is False will be ignored when computing the loss.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The loss will only be computed on items that share the same segment.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.
label_fn (Callable[..., Array]) – A label function that maps labels to probabilities. Default keeps labels as-is.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the loss values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The softmax loss.

rax.listmle_loss(scores, labels, *, key=None, where=None, segments=None, reduce_fn=<function mean>)

ListMLE Loss.

Note

This loss performs sorting using the given labels. If the labels contain multiple identical values, you should provide a PRNGKey() to the key argument to make sure ties are broken randomly during the segments: Optional[Array] = None, sorting operation.

Definition [Xia et al., 2008]:

\[\ell(s, y) = -\sum_i \log \frac{\exp(s_i)} {\sum_j \II{\op{rank}(y_j) \ge \op{rank}(y_i)} \exp(s_j)} \]

where \(\op{rank}(y_i)\) indicates the rank of item \(i\) after sorting all labels \(y\).

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
key (Optional[Array]) – An optional PRNGKey() to perform random tie-breaking.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the loss. Items for which this is False will be ignored when computing the loss.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The loss will only be computed on items that share the same segment.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the loss values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The listmle loss.

rax.poly1_softmax_loss(scores, labels, *, epsilon=1.0, where=None, segments=None, weights=None, reduce_fn=<function mean>)

Poly1 softmax loss.

Definition [Leng et al., 2022]:

\[\ell(s, y) = \op{softmax}(s, y) + \epsilon * (1 - \op{pt}) \]

where \(\op{softmax}\) is the standard softmax loss as implemented in softmax_loss() and \(\op{pt}\) is the target softmax probability defined as:

\[\op{pt} = \sum_i \frac{y_i}{\sum_j y_j} \frac{\exp(s_i)}{\sum_j \exp(s_j)} \]

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
epsilon (float) – A float hyperparameter indicating the weight of the leading polynomial coefficient in the poly loss.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the loss. Items for which this is False will be ignored when computing the loss.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The loss will only be computed on items that share the same segment.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the loss values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The poly1 softmax loss.

rax.unique_softmax_loss(scores, labels, *, where=None, segments=None, weights=None, gain_fn=<function default_gain_fn>, reduce_fn=<function mean>)

Unique softmax loss.

Definition [Zhu and Klabjan, 2020]:

\[\ell(s, y) = -\sum_i \op{gain}(y_i) \log \frac{\exp(s_i)}{\exp(s_i) + \sum_{j : y_j < y_i} \exp(s_j)} \]

where \(\op{gain}(y_i)\) is a user-specified gain function applied to label \(y_i\) to boost items with higher relevance.

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the loss. Items for which this is False will be ignored when computing the loss.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The loss will only be computed on items that share the same segment.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.
gain_fn (Optional[Callable[[Array], Array]]) – An optional function that maps relevance labels to gain values. If provided, the per-item losses are multiplied by gain_fn(label) to boost the importance of relevant items.
reduce_fn (ReduceFn) – An optional function that reduces the loss values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The unique softmax loss.

Ranking Metrics (`rax.*_metric`)

Implementations of common ranking metrics in JAX.

A ranking metric expresses how well a ranking induced by item scores matches a ranking induced from relevance labels. Rax provides a number of ranking metrics as JAX functions that are implemented according to the MetricFn interface.

Metric functions are designed to operate on the last dimension of its inputs. The leading dimensions are considered batch dimensions. To compute per-list metrics, for example to apply per-list weighting or for distributed computing of metrics across devices, please use standard JAX transformations such as jax.vmap() or jax.pmap().

Standalone usage of a metric:

>>> import jax
>>> import rax
>>> scores = jnp.array([2., 1., 3.])
>>> labels = jnp.array([2., 0., 1.])
>>> print(rax.ndcg_metric(scores, labels))
0.79670763

Usage with a batch of data and a mask to indicate valid items:

>>> scores = jnp.array([[2., 1., 3.], [1., 0.5, 1.5]])
>>> labels = jnp.array([[2., 0., 1.], [0., 0., 1.]])
>>> where = jnp.array([[True, True, False], [True, True, True]])
>>> print(rax.ndcg_metric(scores, labels))
0.8983538

Usage with jax.vmap() batching and a mask to indicate valid items:

>>> scores = jnp.array([[2., 1., 0.], [1., 0.5, 1.5]])
>>> labels = jnp.array([[1., 0., 0.], [0., 0., 1.]])
>>> where = jnp.array([[True, True, False], [True, True, True]])
>>> print(jax.vmap(rax.ndcg_metric)(scores, labels, where=where))
[1. 1.]

`mrr_metric`(scores, labels, *[, where, ...])	Mean Reciprocal Rank (MRR).
`precision_metric`(scores, labels, *[, where, ...])	Precision.
`recall_metric`(scores, labels, *[, where, ...])	Recall.
`ap_metric`(scores, labels, *[, where, ...])	Average Precision.
`dcg_metric`(scores, labels, *[, where, ...])	Discounted cumulative gain (DCG).
`ndcg_metric`(scores, labels, *[, where, ...])	Normalized discounted cumulative gain (NDCG).

rax.mrr_metric(scores, labels, *, where=None, segments=None, topn=None, key=None, rank_fn=<function ranks>, cutoff_fn=<function cutoff>, reduce_fn=<function mean>)

Mean Reciprocal Rank (MRR).

Note

This metric converts graded relevance to binary relevance by considering items with label >= 1 as relevant and items with label < 1 as non-relevant.

Definition:

\[\op{mrr}(s, y) = \max_i \frac{y_i}{\op{rank}(s_i)} \]

where \(\op{rank}(s_i)\) indicates the rank of item \(i\) after sorting all scores \(s\) using rank_fn.

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item. Items for which the score is \(-\inf\) are treated as unranked items.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the metric.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The metric will only be computed on items that share the same segment.
topn (Optional[int]) – An optional integer value indicating at which rank the metric cuts off. If None, no cutoff is performed.
key (Optional[Array]) – An optional PRNGKey(). If provided, any random operations in this metric will be based on this key.
rank_fn (RankFn) – A function that maps scores to 1-based ranks.
cutoff_fn (CutoffFn) – A function that maps ranks and a cutoff integer to a binary array indicating which items are cutoff.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the metric values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The MRR metric.

rax.precision_metric(scores, labels, *, where=None, segments=None, topn=None, key=None, rank_fn=<function ranks>, cutoff_fn=<function cutoff>, reduce_fn=<function mean>)

Precision.

Note

This metric converts graded relevance to binary relevance by considering items with label >= 1 as relevant and items with label < 1 as non-relevant.

Definition:

\[\op{precision@n}(s, y) = \frac{1}{n} \sum_i y_i \cdot \II{\op{rank}(s_i) \leq n} \]

where \(\op{rank}(s_i)\) indicates the rank of item \(i\) after sorting all scores \(s\) using rank_fn.

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item. Items for which the score is \(-\inf\) are treated as unranked items.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the metric.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The metric will only be computed on items that share the same segment.
topn (Optional[int]) – An optional integer value indicating at which rank the metric cuts off. If None, no cutoff is performed.
key (Optional[Array]) – An optional PRNGKey(). If provided, any random operations in this metric will be based on this key.
rank_fn (RankFn) – A function that maps scores to 1-based ranks.
cutoff_fn (CutoffFn) – A function that maps ranks and a cutoff integer to a binary array indicating which items are cutoff.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the metric values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The precision metric.

rax.recall_metric(scores, labels, *, where=None, segments=None, topn=None, key=None, rank_fn=<function ranks>, cutoff_fn=<function cutoff>, reduce_fn=<function mean>)

Recall.

Note

This metric converts graded relevance to binary relevance by considering items with label >= 1 as relevant and items with label < 1 as non-relevant.

Definition:

\[\op{recall@n}(s, y) = \frac{1}{\sum_i y_i} \sum_i y_i \cdot \II{\op{rank}(s_i) \leq n} \]

where \(\op{rank}(s_i)\) indicates the rank of item \(i\) after sorting all scores \(s\) using rank_fn.

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item. Items for which the score is \(-\inf\) are treated as unranked items.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the metric.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The metric will only be computed on items that share the same segment.
topn (Optional[int]) – An optional integer value indicating at which rank the metric cuts off. If None, no cutoff is performed.
key (Optional[Array]) – An optional PRNGKey(). If provided, any random operations in this metric will be based on this key.
rank_fn (RankFn) – A function that maps scores to 1-based ranks.
cutoff_fn (CutoffFn) – A function that maps ranks and a cutoff integer to a binary array indicating which items are cutoff.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the metric values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The recall metric.

rax.ap_metric(scores, labels, *, where=None, segments=None, topn=None, key=None, rank_fn=<function ranks>, cutoff_fn=<function cutoff>, reduce_fn=<function mean>)

Average Precision.

Note

This metric converts graded relevance to binary relevance by considering items with label >= 1 as relevant and items with label < 1 as non-relevant.

Definition:

\[\op{ap}(s, y) = \frac{1}{\sum_i y_i} \sum_i y_i \op{precision@rank}_{s_i}(s, y) \]

where \(\op{precision@rank}_{s_i}(s, y)\) indicates the precision at the rank of item \(i\).

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item. Items for which the score is \(-\inf\) are treated as unranked items.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the metric.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The metric will only be computed on items that share the same segment.
topn (Optional[int]) – An optional integer value indicating at which rank the metric cuts off. If None, no cutoff is performed.
key (Optional[Array]) – An optional PRNGKey(). If provided, any random operations in this metric will be based on this key.
rank_fn (RankFn) – A function that maps scores to 1-based ranks.
cutoff_fn (CutoffFn) – A function that maps ranks and a cutoff integer to a binary array indicating which items are cutoff.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the metric values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The average precision metric.

rax.dcg_metric(scores, labels, *, where=None, segments=None, topn=None, weights=None, key=None, gain_fn=<function default_gain_fn>, discount_fn=<function default_discount_fn>, rank_fn=<function ranks>, cutoff_fn=<function cutoff>, reduce_fn=<function mean>)

Discounted cumulative gain (DCG).

Definition [Järvelin and Kekäläinen, 2002]:

\[\op{dcg}(s, y) = \sum_i \op{gain}(y_i) \cdot \op{discount}(\op{rank}(s_i)) \]

where \(\op{rank}(s_i)\) indicates the 1-based rank of item \(i\) as computed by rank_fn, \(\op{gain}(y)\) indicates the per-item gains as computed by gain_fn, and, \(\op{discount}(r)\) indicates the per-item rank discounts as computed by discount_fn.

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item. Items for which the score is \(-\inf\) are treated as unranked items.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the metric.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The metric will only be computed on items that share the same segment.
topn (Optional[int]) – An optional integer value indicating at which rank the metric cuts off. If None, no cutoff is performed.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the per-item weights.
key (Optional[Array]) – An optional PRNGKey(). If provided, any random operations in this metric will be based on this key.
gain_fn (Callable[[Array], Array]) – A function that maps relevance label to gain values.
discount_fn (Callable[[Array], Array]) – A function that maps 1-based ranks to discount values.
rank_fn (RankFn) – A function that maps scores to 1-based ranks.
cutoff_fn (CutoffFn) – A function that maps ranks and a cutoff integer to a binary array indicating which items are cutoff.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the metric values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The DCG metric.

rax.ndcg_metric(scores, labels, *, where=None, segments=None, topn=None, weights=None, key=None, gain_fn=<function default_gain_fn>, discount_fn=<function default_discount_fn>, rank_fn=<function ranks>, cutoff_fn=<function cutoff>, reduce_fn=<function mean>)

Normalized discounted cumulative gain (NDCG).

Definition [Järvelin and Kekäläinen, 2002]:

\[\op{ndcg}(s, y) = \op{dcg}(s, y) / \op{dcg}(y, y) \]

where \(\op{dcg}\) is the discounted cumulative gain metric.

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item. Items for which the score is \(-\inf\) are treated as unranked items.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the metric.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The metric will only be computed on items that share the same segment.
topn (Optional[int]) – An optional integer value indicating at which rank the metric cuts off. If None, no cutoff is performed.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the per-item weights.
key (Optional[Array]) – An optional PRNGKey(). If provided, any random operations in this metric will be based on this key.
gain_fn (Callable[[Array], Array]) – A function that maps relevance label to gain values.
discount_fn (Callable[[Array], Array]) – A function that maps 1-based ranks to discount values.
rank_fn (RankFn) – A function that maps scores to 1-based ranks.
cutoff_fn (CutoffFn) – A function that maps ranks and a cutoff integer to a binary array indicating which items are cutoff.
reduce_fn (Optional[ReduceFn]) – An optional function that reduces the metric values. Can be jax.numpy.sum() or jax.numpy.mean(). If None, no reduction is performed.

Return type:

Array

Returns:

The NDCG metric.

Function Transformations (`rax.*_t12n`)

Function transformations for ranking losses and metrics.

These function transformations can be used to transform the ranking metrics and losses. An example is approx_t12n which transforms a given ranking metric into a ranking loss by plugging in differentiable approximations to the rank and cutoff functions.

Example usage:

>>> scores = jnp.asarray([0., 1., 3., 2.])
>>> labels = jnp.asarray([0., 0., 1., 2.])
>>> approx_ndcg_loss_fn = rax.approx_t12n(rax.ndcg_metric)
>>> print(approx_ndcg_loss_fn(scores, labels))
-0.71789175

`approx_t12n`(metric_fn[, temperature])	Transforms `metric_fn` into an approximate differentiable loss.
`bound_t12n`(metric_fn)	Transforms `metric_fn` into a lower-bound differentiable loss.
`gumbel_t12n`(loss_or_metric_fn, *[, samples, ...])	Transforms `loss_or_metric_fn` to operate on Gumbel-sampled scores.
`segment_t12n`(loss_or_metric_fn)	Transforms `loss_or_metric_fn` to operate on segmented inputs.

rax.approx_t12n(metric_fn, temperature=1.0)

Transforms metric_fn into an approximate differentiable loss.

This transformation and uses a sigmoid approximation to compute ranks and indicators in metrics [Qin et al., 2010]. The returned approximate metric is mapped to negative values to be used as a loss.

Example usage:

>>> approx_mrr = rax.approx_t12n(rax.mrr_metric)
>>> scores = jnp.asarray([0., 1., 3., 2.])
>>> labels = jnp.asarray([0., 0., 1., 2.])
>>> print(approx_mrr(scores, labels))
-0.6965873

Example usage together with rax.gumbel_t12n():

>>> gumbel_approx_mrr = rax.gumbel_t12n(rax.approx_t12n(rax.mrr_metric))
>>> scores = jnp.asarray([0., 1., 3., 2.])
>>> labels = jnp.asarray([0., 0., 1., 2.])
>>> print(gumbel_approx_mrr(scores, labels, key=jax.random.PRNGKey(42)))
-0.71880937

Parameters:

metric_fn (MetricFn) – The metric function to convert to an approximate loss.
temperature (float) – The temperature parameter to use for the sigmoid approximation.

Return type:

LossFn

Returns:

A loss function that computes the approximate version of metric_fn.

rax.bound_t12n(metric_fn)

Transforms metric_fn into a lower-bound differentiable loss.

This transformation uses a hinge bound to compute ranks and indicators in metrics. The returned lower-bound of the metric is mapped to negative values to be used as a loss.

Example usage:

>>> bound_mrr = rax.bound_t12n(rax.mrr_metric)
>>> scores = jnp.asarray([0., 1., 3., 2.])
>>> labels = jnp.asarray([0., 1., 0., 1.])
>>> print(bound_mrr(scores, labels))
-0.33333334

Example usage together with rax.gumbel_t12n():

>>> gumbel_bound_mrr = rax.gumbel_t12n(rax.bound_t12n(rax.mrr_metric))
>>> scores = jnp.asarray([0., 1., 3., 2.])
>>> labels = jnp.asarray([0., 1., 0., 1.])
>>> print(gumbel_bound_mrr(scores, labels, key=jax.random.PRNGKey(42)))
-0.31619418

Parameters:: metric_fn (MetricFn) – The metric function to convert to a lower-bound loss.
Returns:: A loss function that computes the lower-bound version of metric_fn.

rax.gumbel_t12n(loss_or_metric_fn, *, samples=8, beta=1.0, smoothing_factor=None)

Transforms loss_or_metric_fn to operate on Gumbel-sampled scores.

This transformation changes given loss_or_metric_fn so that it samples scores from a Gumbel distribution prior to computing the loss or metric [Bruch et al., 2020]. The returned function requires a new key keyword argument.

Example usage:

>>> loss_fn = rax.gumbel_t12n(rax.softmax_loss)
>>> scores = jnp.asarray([0., 1., 3., 2.])
>>> labels = jnp.asarray([0., 0., 1., 2.])
>>> print(loss_fn(scores, labels, key=jax.random.PRNGKey(42)))
6.2066536
>>> print(loss_fn(scores, labels, key=jax.random.PRNGKey(79)))
5.0127797

Parameters:

loss_or_metric_fn (TypeVar(LossOrMetricFn, LossFn, MetricFn)) – A Rax loss or metric function.
samples (int) – Number of Gumbel samples to create.
beta (float) – Shape of the Gumbel distribution (default 1.0).
smoothing_factor (Optional[float]) – If supplied, this will apply an extra log(softmax(scores) + smoothing_factor) transformation to the scores. If set to 1e-20, this effectively makes the loss compatible with the TF-Ranking versions of Gumbel losses. If smoothing_factor <= 0, this may produce NaN values.

Return type:

TypeVar(LossOrMetricFn, LossFn, MetricFn)

Returns:

A new function that behaves the same as loss_or_metric_fn but which requires an additional key argument that will be used to randomly sample the scores from a Gumbel distribution.

rax.segment_t12n(loss_or_metric_fn)

Transforms loss_or_metric_fn to operate on segmented inputs.

Warning

This transformation incurs an additional \(O(n^2)\) computational cost (where \(n\) is the list size) if no specialized segmented implementation is available for the given loss_or_metric_fn.

This changes the loss_or_metric_fn to accept an additional keyword argument segments that is used to indicate segments to group lists together.

Parameters:: loss_or_metric_fn (TypeVar(LossOrMetricFn, LossFn, MetricFn)) – A Rax loss or metric function.
Return type:: TypeVar(LossOrMetricFn, LossFn, MetricFn)
Returns:: A new function that behaves the same as loss_or_metric_fn but which accepts an additional segments argument that is used to indicate segment identifiers.

Lambdaweights (`rax.*_lambdaweight`)

Implementations of lambdaweight functions for Rax pairwise losses.

Lambdaweight functions dynamically adjust the weights of a pairwise loss based on the scores and labels. Rax provides a number of lambdaweight functions as JAX functions that are implemented according to the LambdaweightFn interface.

Example usage:

>>> scores = jnp.array([1.2, 0.4, 1.9])
>>> labels = jnp.array([1.0, 2.0, 0.0])
>>> loss = rax.pairwise_logistic_loss(
...     scores, labels, lambdaweight_fn=rax.labeldiff_lambdaweight)
>>> print(loss)
1.8923712

`labeldiff_lambdaweight`(scores, labels, *[, ...])	Absolute label difference lambdaweights.
`dcg_lambdaweight`(scores, labels, *[, where, ...])	DCG lambdaweights.
`dcg2_lambdaweight`(scores, labels, *[, ...])	DCG v2 ("lambdaloss") lambdaweights.

rax.labeldiff_lambdaweight(scores, labels, *, where=None, segments=None, weights=None)

Absolute label difference lambdaweights.

Definition:

\[\lambda_{ij}(s, y) = |y_i - y_j| \]

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the lambdaweights. Items for which this is False will be ignored when computing the lambdaweights.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The lambdaweights will only be computed on items that share the same segment.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.

Return type:

Array

Returns:

Absolute label difference lambdaweights.

rax.dcg_lambdaweight(scores, labels, *, where=None, segments=None, weights=None, topn=None, normalize=False, gain_fn=<function default_gain_fn>, discount_fn=<function default_discount_fn>)

DCG lambdaweights.

Definition [Burges et al., 2006]:

\[\lambda_{ij}(s, y) = |\op{gain}(y_i) - \op{gain}(y_j)| \cdot |\op{discount}(\op{rank}(s_i)) - \op{discount}(\op{rank}(s_j))| \]

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the lambdaweights. Items for which this is False will be ignored when computing the lambdaweights.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The lambdaweights will only be computed on items that share the same segment.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.
topn (Optional[int]) – The topn cutoff. If None, no cutoff is performed.
normalize (bool) – Whether to use the normalized DCG formulation.
gain_fn (Callable[[Array], Array]) – A function mapping labels to gain values.
discount_fn (Callable[[Array], Array]) – A function mapping ranks to discount values.

Return type:

Array

Returns:

DCG lambdaweights.

rax.dcg2_lambdaweight(scores, labels, *, where=None, segments=None, weights=None, topn=None, normalize=False, gain_fn=<function default_gain_fn>, discount_fn=<function default_discount_fn>)

DCG v2 (“lambdaloss”) lambdaweights.

Definition [Wang et al., 2018]:

\[\lambda_{ij}(s, y) = |\op{gain}(y_i) - \op{gain}(y_j)| \cdot |\op{discount}(|\op{rank}(s_i) - \op{rank}(s_j)|) - \op{discount}(|\op{rank}(s_i) - \op{rank}(s_j)|+1)| \]

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the lambdaweights. Items for which this is False will be ignored when computing the lambdaweights.
segments (Optional[Array]) – An optional [..., list_size]-Array, indicating segments within each list. The lambdaweights will only be computed on items that share the same segment.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.
topn (Optional[int]) – The topn cutoff. If None, no cutoff is performed. Topn cutoff uses the method described in [Jagerman et al., 2022].
normalize (bool) – Whether to use the normalized DCG formulation.
gain_fn (Callable[[Array], Array]) – A function mapping labels to gain values.
discount_fn (Callable[[Array], Array]) – A function mapping ranks to discount values.

Return type:

Array

Returns:

DCG v2 (“lambdaloss”) lambdaweights.

Utilities

`ranks`(scores, *[, where, segments, axis, key])	Computes the ranks for given scores.
`cutoff`(a[, n, where, segments])	Computes a binary array to select the largest `n` values of `a`.
`approx_ranks`(scores, *[, where, segments, ...])	Computes approximate ranks.
`approx_cutoff`(a[, n, where, segments, step_fn])	Approximately select the largest `n` values of `a`.

rax.utils.ranks(scores, *, where=None, segments=None, axis=-1, key=None)

Computes the ranks for given scores.

Note that the ranks returned by this function are not differentiable due to the sort operation having no gradients.

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid.
segments (Optional[Array]) – A Array to indicate segments of items that should be grouped together. Like [0, 0, 1, 0, 2]. The segments may or may not be sorted.
axis (int) – The axis to sort on, by default this is the last axis.
key (Optional[Array]) – An optional jax.random.PRNGKey(). If provided, ties will be broken randomly using this key. If not provided, ties will retain the order of their appearance in the scores array.

Return type:

Array

Returns:

A Array with the same shape as scores that indicates the 1-based rank of each item.

rax.utils.cutoff(a, n=None, where=None, segments=None)

Computes a binary array to select the largest n values of a.

This function computes a binary Array that selects the n largest values of a across its last dimension.

Parameters:

a (Array) – The Array to select the topn from.
n (Optional[int]) – The cutoff value. If None, no cutoff is performed.
where (Optional[Array]) – A mask to indicate which values to include in the topn calculation.
segments (Optional[Array]) – A Array to indicate segments of items that should be grouped together. Like [0, 0, 1, 0, 2]. The segments may or may not be sorted.

Return type:

Array

Returns:

A Array of the same shape as a, where the n largest values are set to 1, and the smaller values are set to 0.

rax.utils.approx_ranks(scores, *, where=None, segments=None, key=None, step_fn=<PjitFunction of <function sigmoid>>)

Computes approximate ranks.

This can be used to construct differentiable approximations of metrics. For example:

>>> import functools
>>> approx_ndcg = functools.partial(
...     rax.ndcg_metric, rank_fn=rax.utils.approx_ranks)
>>> scores = jnp.asarray([-1., 1., 0.])
>>> labels = jnp.asarray([0., 0., 1.])
>>> print(approx_ndcg(scores, labels))
0.63092977
>>> print(jax.grad(approx_ndcg)(scores, labels))
[-0.03763788 -0.03763788  0.07527576]

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid.
segments (Optional[Array]) – A Array to indicate segments of items that should be grouped together. Like [0, 0, 1, 0, 2]. The segments may or may not be sorted.
key (Optional[Array]) – An optional jax.random.PRNGKey(). Unused by approx_ranks.
step_fn (Callable[[Array], Array]) – A callable that approximates the step function x >= 0.

Return type:

Array

Returns:

A Array of the same shape as scores, indicating the 1-based approximate rank of each item.

rax.utils.approx_cutoff(a, n=None, *, where=None, segments=None, step_fn=<PjitFunction of <function sigmoid>>)

Approximately select the largest n values of a.

This function computes a Array that is the probability of an item being in the n largest values of a across its last dimension.

Parameters:

a (Array) – The Array to select the topn from.
n (Optional[int]) – The cutoff value. If None, no cutoff is performed.
where (Optional[Array]) – A mask to indicate which values to include in the topn calculation.
segments (Optional[Array]) – A Array to indicate segments of items that should be grouped together. Like [0, 0, 1, 0, 2]. The segments may or may not be sorted.
step_fn (Callable[[Array], Array]) – A function that computes an approximation of x >= 0.

Return type:

Array

Returns:

A Array of the same shape as a.

Types

Rax-specific types and protocols.

Note

Types and protocols are provided for type-checking convenience only. You do not need to instantiate, subclass or extend them.

`CutoffFn`(args, *kwargs)	`typing.Protocol` for cutoff functions.
`LambdaweightFn`(args, *kwargs)	`typing.Protocol` for lambdaweight functions.
`LossFn`(args, *kwargs)	`typing.Protocol` for loss functions.
`MetricFn`(args, *kwargs)	`typing.Protocol` for metric functions.
`RankFn`(args, *kwargs)	`typing.Protocol` for rank functions.
`ReduceFn`(args, *kwargs)	`typing.Protocol` for reduce functions.

class rax.types.CutoffFn(*args, **kwargs)

typing.Protocol for cutoff functions.

__call__(a, n, segments=None)

Computes cutoffs based on the given array.

Parameters:

a (Array) – The array for which to compute the cutoffs.
n (Optional[int]) – The position of the cutoff.
segments (Optional[Array]) – An optional Array of the same shape as a that indicates which elements to group together.

Return type:

Array

Returns:

A binary Array of the same shape as a that represents which elements of a should be selected for the topn cutoff.

class rax.types.LambdaweightFn(*args, **kwargs)

typing.Protocol for lambdaweight functions.

__call__(scores, labels, *, where, weights, **kwargs)

Computes lambdaweights.

Parameters:

scores (Array) – A [..., list_size]-Array, indicating the score of each item.
labels (Array) – A [..., list_size]-Array, indicating the relevance label for each item.
where (Optional[Array]) – An optional [..., list_size]-Array, indicating which items are valid for computing the lambdaweights. Items for which this is False will be ignored when computing the lambdaweights.
weights (Optional[Array]) – An optional [..., list_size]-Array, indicating the weight for each item.
**kwargs – Optional lambdaweight-specific keyword arguments.

Return type:

Array

Returns:

A Array that represents the lambda weights.

class rax.types.LossFn(*args, **kwargs)

typing.Protocol for loss functions.

__call__(scores, labels, *, where, **kwargs)

Computes a loss.

Parameters:

scores (Array) – The score of each item.
labels (Array) – The label of each item.
where (Optional[Array]) – An optional Array of the same shape as scores that indicates which elements to include in the loss.
**kwargs – Optional loss-specific keyword arguments.

Return type:

Array

Returns:

A Array that represents the loss computed on the given scores and labels.

class rax.types.MetricFn(*args, **kwargs)

typing.Protocol for metric functions.

__call__(scores, labels, *, where, **kwargs)

Computes a metric.

Parameters:

scores (Array) – The score of each item.
labels (Array) – The label of each item.
where (Optional[Array]) – An optional Array of the same shape as scores that indicates which elements to include in the metric.
**kwargs – Optional metric-specific keyword arguments.

Return type:

Array

Returns:

A Array that represents the metric computed on the given scores and labels.

class rax.types.RankFn(*args, **kwargs)

typing.Protocol for rank functions.

__call__(scores, where, key, segments=None)

Computes 1-based ranks based on the given scores.

Parameters:

scores (Array) – The scores to compute the 1-based ranks for.
where (Optional[Array]) – An optional Array of the same shape as a that indicates which elements to rank. Other elements will be ranked last.
key (Optional[Array]) – An optional PRNGKey() used for random operations.
segments (Optional[Array]) – An optional Array of the same shape as a that indicates which elements to group together.

Return type:

Array

Returns:

A Array of the same shape as scores that represents the 1-based ranks.

class rax.types.ReduceFn(*args, **kwargs)

typing.Protocol for reduce functions.

__call__(a, where, axis)

Reduces an array across one or more dimensions.

Parameters:

a (Array) – The array to reduce.
where (Optional[Array]) – An optional Array of the same shape as a that indicates which elements to include in the reduction.
axis (Union[int, Tuple[int, ...], None]) – One or more axes to use for the reduction. If None this reduces across all available axes.

Return type:

Array

Returns:

A Array that represents the reduced result of a over given axis.

References

[BHBN20]

Sebastian Bruch, Shuguang Han, Michael Bendersky, and Marc Najork. A stochastic treatment of learning to rank scoring functions. In Proceedings of the 13th International Conference on Web Search and Data Mining, 61–69. 2020.

[BSR+05]

Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning, 89–96. 2005.

[BRL06]

Christopher Burges, Robert Ragno, and Quoc Le. Learning to rank with nonsmooth cost functions. In Advances in Neural Information Processing Systems, volume 19, 193–200. 2006.

[JQW+22]

Rolf Jagerman, Zhen Qin, Xuanhui Wang, Mike Bendersky, and Marc Najork. On optimizing top-k metrics for neural ranking models. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2303–2307. 2022.

[JarvelinKekalainen02] (1,2)

Kalervo Järvelin and Jaana Kekäläinen. Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems (TOIS), 20(4):422–446, 2002.

[LTL+22]

Zhaoqi Leng, Mingxing Tan, Chenxi Liu, Ekin Dogus Cubuk, Jay Shi, Shuyang Cheng, and Dragomir Anguelov. Polyloss: a polynomial expansion perspective of classification loss functions. In International Conference on Learning Representations. 2022.

[QLL10]

Tao Qin, Tie-Yan Liu, and Hang Li. A general approximation framework for direct optimization of information retrieval measures. Information retrieval, 13(4):375–397, 2010.

[WLG+18]

Xuanhui Wang, Cheng Li, Nadav Golbandi, Mike Bendersky, and Marc Najork. The lambdaloss framework for ranking metric optimization. In Proceedings of The 27th ACM International Conference on Information and Knowledge Management, 1313–1322. 2018.

[XLW+08]

Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. Listwise approach to learning to rank: theory and algorithm. In Proceedings of the 25th international conference on Machine learning, 1192–1199. 2008.

[ZK20]

Xiaofeng Zhu and Diego Klabjan. Listwise learning to rank by exploring unique ratings. In Proceedings of the 13th international conference on web search and data mining, 798–806. 2020.

Ranking Losses (rax.*_loss)

Ranking Metrics (rax.*_metric)

Function Transformations (rax.*_t12n)

Lambdaweights (rax.*_lambdaweight)

Utilities

Types

References

Ranking Losses (`rax.*_loss`)

Ranking Metrics (`rax.*_metric`)

Function Transformations (`rax.*_t12n`)

Lambdaweights (`rax.*_lambdaweight`)