From 6cef9a08e9e6bfc54b32da2b1108949d2f1a5de2 Mon Sep 17 00:00:00 2001
From: mzzhang95 <48953222+mzzhang95@users.noreply.github.com>
Date: Wed, 1 Mar 2023 19:24:12 -0500
Subject: [PATCH] [pyspark] Update eval_metric validation to support list of
 strings (#8826)

---
 python-package/xgboost/spark/core.py              | 15 +++++++++++++--
 .../test_with_spark/test_spark_local.py           | 10 ++++++++++
 2 files changed, 23 insertions(+), 2 deletions(-)

diff --git a/python-package/xgboost/spark/core.py b/python-package/xgboost/spark/core.py
index 6d9733817..8a13e88cc 100644
--- a/python-package/xgboost/spark/core.py
+++ b/python-package/xgboost/spark/core.py
@@ -314,8 +314,19 @@ class _SparkXGBParams(
                 raise ValueError("Only string type 'objective' param is allowed.")
 
         if self.getOrDefault(self.eval_metric) is not None:
-            if not isinstance(self.getOrDefault(self.eval_metric), str):
-                raise ValueError("Only string type 'eval_metric' param is allowed.")
+            if not (
+                isinstance(self.getOrDefault(self.eval_metric), str)
+                or (
+                    isinstance(self.getOrDefault(self.eval_metric), List)
+                    and all(
+                        isinstance(metric, str)
+                        for metric in self.getOrDefault(self.eval_metric)
+                    )
+                )
+            ):
+                raise ValueError(
+                    "Only string type or list of string type 'eval_metric' param is allowed."
+                )
 
         if self.getOrDefault(self.early_stopping_rounds) is not None:
             if not (
diff --git a/tests/test_distributed/test_with_spark/test_spark_local.py b/tests/test_distributed/test_with_spark/test_spark_local.py
index 27f1ef06f..b86a1930f 100644
--- a/tests/test_distributed/test_with_spark/test_spark_local.py
+++ b/tests/test_distributed/test_with_spark/test_spark_local.py
@@ -730,6 +730,16 @@ class TestPySparkLocal:
         train_params = py_cls._get_distributed_train_params(clf_data.cls_df_train)
         assert train_params["tree_method"] == "gpu_hist"
 
+    def test_classifier_with_list_eval_metric(self, clf_data: ClfData) -> None:
+        classifier = SparkXGBClassifier(eval_metric=["auc", "rmse"])
+        model = classifier.fit(clf_data.cls_df_train)
+        model.transform(clf_data.cls_df_test).collect()
+
+    def test_classifier_with_string_eval_metric(self, clf_data: ClfData) -> None:
+        classifier = SparkXGBClassifier(eval_metric="auc")
+        model = classifier.fit(clf_data.cls_df_train)
+        model.transform(clf_data.cls_df_test).collect()
+
 
 class XgboostLocalTest(SparkTestCase):
     def setUp(self):