Support custom metric in sklearn ranker.

python-package/xgboost/sklearn.py

@@ -4,6 +4,7 @@
 import json
 import os
 import warnings
+from concurrent.futures import ThreadPoolExecutor
 from typing import (
     Any,
     Callable,
@@ -127,6 +128,49 @@ def inner(y_score: np.ndarray, dmatrix: DMatrix) -> Tuple[str, float]:
     return inner
 
 
+def ltr_metric_decorator(func: Callable, n_jobs: Optional[int]) -> Metric:
+    """Decorate a learning to rank metric."""
+
+    def inner(y_score: np.ndarray, dmatrix: DMatrix) -> Tuple[str, float]:
+        y_true = dmatrix.get_label()
+        group_ptr = dmatrix.get_uint_info("group_ptr")
+        if group_ptr.size < 2:
+            raise ValueError(
+                "Invalid `group_ptr`. Likely caused by invalid qid or group."
+            )
+        scores = np.empty(group_ptr.size - 1)
+        futures = []
+        weight = dmatrix.get_group()
+        no_weight = weight.size == 0
+
+        def task(i: int) -> float:
+            begin = group_ptr[i - 1]
+            end = group_ptr[i]
+            gy = y_true[begin:end]
+            gp = y_score[begin:end]
+            if gy.size == 1:
+                # Maybe there's a better default? 1.0 because many ranking score
+                # functions have output in range [0, 1].
+                return 1.0
+            return func(gy, gp)
+
+        workers = n_jobs if n_jobs is not None else os.cpu_count()
+        with ThreadPoolExecutor(max_workers=workers) as executor:
+            for i in range(1, group_ptr.size):
+                f = executor.submit(task, i)
+                futures.append(f)
+
+            for i, f in enumerate(futures):
+                scores[i] = f.result()
+
+        if no_weight:
+            return func.__name__, scores.mean()
+
+        return func.__name__, np.average(scores, weights=weight)
+
+    return inner
+
+
 __estimator_doc = """
     n_estimators : int
         Number of gradient boosted trees.  Equivalent to number of boosting
@@ -868,7 +912,10 @@ def _duplicated(parameter: str) -> None:
                 metric = eval_metric
             elif callable(eval_metric):
                 # Parameter from constructor or set_params
-                metric = _metric_decorator(eval_metric)
+                if self._get_type() == "ranker":
+                    metric = ltr_metric_decorator(eval_metric, self.n_jobs)
+                else:
+                    metric = _metric_decorator(eval_metric)
             else:
                 params.update({"eval_metric": eval_metric})
 
@@ -1979,10 +2026,6 @@ def fit(
             ) = self._configure_fit(
                 xgb_model, eval_metric, params, early_stopping_rounds, callbacks
             )
-            if callable(metric):
-                raise ValueError(
-                    "Custom evaluation metric is not yet supported for XGBRanker."
-                )
 
             self._Booster = train(
                 params,

tests/python/test_with_sklearn.py

@@ -154,6 +154,32 @@ def test_ranking():
     np.testing.assert_almost_equal(pred, pred_orig)
 
 
+def test_ranking_metric() -> None:
+    from sklearn.metrics import roc_auc_score
+
+    X, y, qid, w = tm.make_ltr(512, 4, 3, 2)
+    # use auc for test as ndcg_score in sklearn works only on label gain instead of exp
+    # gain.
+    # note that the auc in sklearn is different from the one in XGBoost. The one in
+    # sklearn compares the number of mis-classified docs, while the one in xgboost
+    # compares the number of mis-classified pairs.
+    ltr = xgb.XGBRanker(
+        eval_metric=roc_auc_score, n_estimators=10, tree_method="hist", max_depth=2
+    )
+    ltr.fit(
+        X,
+        y,
+        qid=qid,
+        sample_weight=w,
+        eval_set=[(X, y)],
+        eval_qid=[qid],
+        sample_weight_eval_set=[w],
+        verbose=True,
+    )
+    results = ltr.evals_result()
+    assert results["validation_0"]["roc_auc_score"][-1] > 0.6
+
+
 def test_stacking_regression():
     from sklearn.datasets import load_diabetes
     from sklearn.ensemble import RandomForestRegressor, StackingRegressor
@@ -1426,10 +1452,10 @@ def test_weighted_evaluation_metric():
     X_train, X_test = X[:1600], X[1600:]
     y_train, y_test = y[:1600], y[1600:]
     weights_eval_set = np.random.choice([1, 2], len(X_test))
-    
+
     np.random.seed(0)
     weights_train = np.random.choice([1, 2], len(X_train))
-    
+
     clf = xgb.XGBClassifier(
         tree_method="hist",
         eval_metric=log_loss,