Tests.

Author: trivialfis

python-package/xgboost/testing/data.py

@@ -361,9 +361,7 @@ class ClickFold:
 
 
 class RelDataCV(NamedTuple):
-    """Simple data struct for holding a train-test split of a learning to rank dataset.
-
-    """
+    """Simple data struct for holding a train-test split of a learning to rank dataset."""
 
     train: RelData
     test: RelData

python-package/xgboost/testing/metrics.py

@@ -1,9 +1,61 @@
 """Tests for evaluation metrics."""
-from typing import Dict
+from typing import Dict, List
 
 import numpy as np
+import pytest
 
 import xgboost as xgb
+from xgboost.compat import concat
+from xgboost.core import _parse_eval_str
+
+
+def check_precision_score(tree_method: str) -> None:
+    """Test for precision with ranking and classification."""
+    datasets = pytest.importorskip("sklearn.datasets")
+
+    X, y = datasets.make_classification(
+        n_samples=1024, n_features=4, n_classes=2, random_state=2023
+    )
+    qid = np.zeros(shape=y.shape)  # same group
+
+    ltr = xgb.XGBRanker(n_estimators=2, tree_method=tree_method)
+    ltr.fit(X, y, qid=qid)
+
+    # re-generate so that XGBoost doesn't evaluate the result to 1.0
+    X, y = datasets.make_classification(
+        n_samples=512, n_features=4, n_classes=2, random_state=1994
+    )
+
+    ltr.set_params(eval_metric="pre@32")
+    result = _parse_eval_str(
+        ltr.get_booster().eval_set(evals=[(xgb.DMatrix(X, y), "Xy")])
+    )
+    score_0 = result[1][1]
+
+    X_list = []
+    y_list = []
+    n_query_groups = 3
+    q_list: List[np.ndarray] = []
+    for i in range(n_query_groups):
+        # same for all groups
+        X, y = datasets.make_classification(
+            n_samples=512, n_features=4, n_classes=2, random_state=1994
+        )
+        X_list.append(X)
+        y_list.append(y)
+        q = np.full(shape=y.shape, fill_value=i, dtype=np.uint64)
+        q_list.append(q)
+
+    qid = concat(q_list)
+    X = concat(X_list)
+    y = concat(y_list)
+
+    result = _parse_eval_str(
+        ltr.get_booster().eval_set(evals=[(xgb.DMatrix(X, y, qid=qid), "Xy")])
+    )
+    assert result[1][0].endswith("pre@32")
+    score_1 = result[1][1]
+    assert score_1 == score_0
 
 
 def check_quantile_error(tree_method: str) -> None:

tests/ci_build/lint_python.py

@@ -92,7 +92,7 @@ def check_cmd_print_failure_assistance(cmd: List[str]) -> bool:
 
     subprocess.run([cmd[0], "--version"])
     msg = """
-Please run the following command on your machine to address the formatting error:
+Please run the following command on your machine to address the error:
 
     """
     msg += " ".join(cmd)

tests/python-gpu/test_gpu_eval_metrics.py

@@ -5,7 +5,7 @@
 
 import xgboost
 from xgboost import testing as tm
-from xgboost.testing.metrics import check_quantile_error
+from xgboost.testing.metrics import check_precision_score, check_quantile_error
 
 sys.path.append("tests/python")
 import test_eval_metrics as test_em  # noqa
@@ -59,6 +59,9 @@ def test_pr_auc_multi(self):
     def test_pr_auc_ltr(self):
         self.cpu_test.run_pr_auc_ltr("gpu_hist")
 
+    def test_precision_score(self):
+        check_precision_score("gpu_hist")
+
     @pytest.mark.skipif(**tm.no_sklearn())
     def test_quantile_error(self) -> None:
         check_quantile_error("gpu_hist")

tests/python/test_eval_metrics.py

@@ -3,7 +3,7 @@
 
 import xgboost as xgb
 from xgboost import testing as tm
-from xgboost.testing.metrics import check_quantile_error
+from xgboost.testing.metrics import check_precision_score, check_quantile_error
 
 rng = np.random.RandomState(1337)
 
@@ -318,24 +318,7 @@ def test_pr_auc_ltr(self):
         self.run_pr_auc_ltr("hist")
 
     def test_precision_score(self):
-        from sklearn.metrics import precision_score
-        from sklearn.datasets import make_classification
-
-        x, y = make_classification(n_samples=128, n_features=4, n_classes=2)
-        qid = np.zeros(shape=y.shape)  # same group
-
-        ltr = xgb.XGBRanker()
-        ltr.fit(x, y, qid=qid)
-        p = ltr.predict(x)
-        sorted_idx = np.argsort(p)
-
-        score_0 = precision_score(y, y[sorted_idx])
-        print(score_0)
-
-        Xy = xgb.DMatrix(x, y)
-        ltr.set_params(eval_metric="pre")
-        e = ltr.get_booster().eval_set(evals=[(Xy, "Xy")])
-        print(e)
+        check_precision_score("hist")
 
     @pytest.mark.skipif(**tm.no_sklearn())
     def test_quantile_error(self) -> None: