[rax] add a light discount option for dcg2_lambdaweight

PiperOrigin-RevId: 605030074

rax/_src/lambdaweights.py

@@ -44,7 +44,7 @@ def labeldiff_lambdaweight(
     *,
     where: Optional[Array] = None,
     segments: Optional[Array] = None,
-    weights: Optional[Array] = None
+    weights: Optional[Array] = None,
 ) -> Array:
   r"""Absolute label difference lambdaweights.
 
@@ -96,7 +96,7 @@ def _generic_dcg_lambdaweight(
     topn: Optional[int] = None,
     normalize: bool = False,
     gain_fn: Callable[[Array], Array] = metrics.default_gain_fn,
-    discount_fn: Callable[[Array], Array] = metrics.default_discount_fn
+    discount_fn: Callable[[Array], Array] = metrics.default_discount_fn,
 ) -> Array:
   r"""Generic DCG lambdaweights with customized rank pair discount fn."""
 
@@ -148,7 +148,7 @@ def dcg_lambdaweight(
     topn: Optional[int] = None,
     normalize: bool = False,
     gain_fn: Callable[[Array], Array] = metrics.default_gain_fn,
-    discount_fn: Callable[[Array], Array] = metrics.default_discount_fn
+    discount_fn: Callable[[Array], Array] = metrics.default_discount_fn,
 ) -> Array:
   r"""DCG lambdaweights.
 
@@ -220,7 +220,8 @@ def dcg2_lambdaweight(
     topn: Optional[int] = None,
     normalize: bool = False,
     gain_fn: Callable[[Array], Array] = metrics.default_gain_fn,
-    discount_fn: Callable[[Array], Array] = metrics.default_discount_fn
+    discount_fn: Callable[[Array], Array] = metrics.default_discount_fn,
+    light_discount: bool = False,
 ) -> Array:
   r"""DCG v2 ("lambdaloss") lambdaweights.
 
@@ -231,6 +232,12 @@ def dcg2_lambdaweight(
                            |\op{discount}(|\op{rank}(s_i) - \op{rank}(s_j)|) -
                             \op{discount}(|\op{rank}(s_i) - \op{rank}(s_j)|+1)|
 
+  Or the following when ``light_discount`` is ``True``:
+
+  .. math::
+      \lambda_{ij}(s, y) = |\op{gain}(y_i) - \op{gain}(y_j)| \cdot
+                           |\op{discount}(|\op{rank}(s_i) - \op{rank}(s_j)|)|
+
   Args:
     scores: A ``[..., list_size]``-:class:`~jax.Array`, indicating the score of
       each item.
@@ -249,6 +256,7 @@ def dcg2_lambdaweight(
     normalize: Whether to use the normalized DCG formulation.
     gain_fn: A function mapping labels to gain values.
     discount_fn: A function mapping ranks to discount values.
+    light_discount: If ``True``, make the position discount light as above.
 
   Returns:
     DCG v2 ("lambdaloss") lambdaweights.
@@ -258,9 +266,12 @@ def _rank_pair_discount(discount_fn, ranks, valid_pairs, topn):
     ranks_abs_diffs = jnp.abs(utils.compute_pairs(ranks, operator.sub))
     ranks_max = utils.compute_pairs(ranks, jnp.maximum)
 
-    discounts = jnp.abs(
-        discount_fn(ranks_abs_diffs) - discount_fn(ranks_abs_diffs + 1)
-    )
+    if light_discount:
+      discounts = jnp.abs(discount_fn(ranks_abs_diffs))
+    else:
+      discounts = jnp.abs(
+          discount_fn(ranks_abs_diffs) - discount_fn(ranks_abs_diffs + 1)
+      )
     discounts = jnp.where(ranks_abs_diffs != 0, discounts, 0.0)
 
     if topn is not None:

rax/_src/lambdaweights_test.py

@@ -16,6 +16,7 @@
 """Tests for rax._src.weights."""
 
 import doctest
+import functools
 import math
 
 from absl.testing import absltest
@@ -67,6 +68,22 @@ class LambdaweightsTest(parameterized.TestCase):
               0.0,
           ],
       },
+      {
+          "lambdaweight_fn": functools.partial(
+              lambdaweights.dcg2_lambdaweight, light_discount=True
+          ),
+          "expected": [
+              0.0,
+              3.0 * abs(discount(1)) * abs(gain(0.0) - gain(1.0)),
+              3.0 * abs(discount(2)) * abs(gain(0.0) - gain(0.3)),
+              3.0 * abs(discount(1)) * abs(gain(1.0) - gain(0.0)),
+              0.0,
+              3.0 * abs(discount(1)) * abs(gain(1.0) - gain(0.3)),
+              3.0 * abs(discount(2)) * abs(gain(0.3) - gain(0.0)),
+              3.0 * abs(discount(1)) * abs(gain(0.3) - gain(1.0)),
+              0.0,
+          ],
+      },
   ])
   def test_computes_lambdaweights(self, lambdaweight_fn, expected):
     scores = jnp.array([0.0, 1.0, 2.0])