Add quantile metric. (#8761)

python-package/xgboost/testing/metrics.py

@@ -0,0 +1,27 @@
+"""Tests for evaluation metrics."""
+from typing import Dict
+
+import numpy as np
+
+import xgboost as xgb
+
+
+def check_quantile_error(tree_method: str) -> None:
+    """Test for the `quantile` loss."""
+    from sklearn.datasets import make_regression
+    from sklearn.metrics import mean_pinball_loss
+
+    rng = np.random.RandomState(19)
+    # pylint: disable=unbalanced-tuple-unpacking
+    X, y = make_regression(128, 3, random_state=rng)
+    Xy = xgb.QuantileDMatrix(X, y)
+    evals_result: Dict[str, Dict] = {}
+    booster = xgb.train(
+        {"tree_method": tree_method, "eval_metric": "quantile", "quantile_alpha": 0.3},
+        Xy,
+        evals=[(Xy, "Train")],
+        evals_result=evals_result,
+    )
+    predt = booster.inplace_predict(X)
+    loss = mean_pinball_loss(y, predt, alpha=0.3)
+    np.testing.assert_allclose(evals_result["Train"]["quantile"][-1], loss)