Fix spark test.

Author: trivialfis

tests/test_distributed/test_with_spark/test_spark_local.py

@@ -1359,14 +1359,42 @@ def ltr_data(spark: SparkSession) -> Generator[LTRData, None, None]:
         ],
         ["features", "label", "qid"],
     )
+    X_train = np.array(
+        [
+            [1.0, 2.0, 3.0],
+            [4.0, 5.0, 6.0],
+            [9.0, 4.0, 8.0],
+            [np.NaN, 1.0, 5.5],
+            [np.NaN, 6.0, 7.5],
+            [np.NaN, 8.0, 9.5],
+        ]
+    )
+    qid_train = np.array([0, 0, 0, 1, 1, 1])
+    y_train = np.array([0, 1, 2, 0, 1, 2])
+
+    X_test = np.array(
+        [
+            [1.5, 2.0, 3.0],
+            [4.5, 5.0, 6.0],
+            [9.0, 4.5, 8.0],
+            [np.NaN, 1.0, 6.0],
+            [np.NaN, 6.0, 7.0],
+            [np.NaN, 8.0, 10.5],
+        ]
+    )
+
+    ltr = xgb.XGBRanker(tree_method="approx", objective="rank:pairwise")
+    ltr.fit(X_train, y_train, qid=qid_train)
+    predt = ltr.predict(X_test)
+
     ranker_df_test = spark.createDataFrame(
         [
-            (Vectors.dense(1.5, 2.0, 3.0), 0, -1.75218),
-            (Vectors.dense(4.5, 5.0, 6.0), 0, -0.34192949533462524),
-            (Vectors.dense(9.0, 4.5, 8.0), 0, 1.7251298427581787),
-            (Vectors.sparse(3, {1: 1.0, 2: 6.0}), 1, -1.7521828413009644),
-            (Vectors.sparse(3, {1: 6.0, 2: 7.0}), 1, -1.0988065004348755),
-            (Vectors.sparse(3, {1: 8.0, 2: 10.5}), 1, 1.632674217224121),
+            (Vectors.dense(1.5, 2.0, 3.0), 0, float(predt[0])),
+            (Vectors.dense(4.5, 5.0, 6.0), 0, float(predt[1])),
+            (Vectors.dense(9.0, 4.5, 8.0), 0, float(predt[2])),
+            (Vectors.sparse(3, {1: 1.0, 2: 6.0}), 1, float(predt[3])),
+            (Vectors.sparse(3, {1: 6.0, 2: 7.0}), 1, float(predt[4])),
+            (Vectors.sparse(3, {1: 8.0, 2: 10.5}), 1, float(predt[5])),
         ],
         ["features", "qid", "expected_prediction"],
     )