[python-package] Infer feature names from pyarrow.Table

python-package/lightgbm/basic.py

@@ -2126,6 +2126,8 @@ def _lazy_init(
                 categorical_feature=categorical_feature,
                 pandas_categorical=self.pandas_categorical,
             )
+        elif _is_pyarrow_table(data) and feature_name == "auto":
+            feature_name = data.column_names
 
         # process for args
         params = {} if params is None else params
@@ -2185,7 +2187,6 @@ def _lazy_init(
             self.__init_from_np2d(data, params_str, ref_dataset)
         elif _is_pyarrow_table(data):
             self.__init_from_pyarrow_table(data, params_str, ref_dataset)
-            feature_name = data.column_names
         elif isinstance(data, list) and len(data) > 0:
             if _is_list_of_numpy_arrays(data):
                 self.__init_from_list_np2d(data, params_str, ref_dataset)

tests/python_package_test/test_arrow.py

@@ -432,3 +432,25 @@ def test_predict_ranking():
         num_boost_round=5,
     )
     assert_equal_predict_arrow_pandas(booster, data)
+
+
+def test_arrow_feature_name_auto():
+    data = generate_dummy_arrow_table()
+    dataset = lgb.Dataset(
+        data, label=pa.array([0, 1, 0, 0, 1]), params=dummy_dataset_params(), categorical_feature=["a"]
+    )
+    booster = lgb.train({"num_leaves": 7}, dataset, num_boost_round=5)
+    assert booster.feature_name() == ["a", "b"]
+
+
+def test_arrow_feature_name_manual():
+    data = generate_dummy_arrow_table()
+    dataset = lgb.Dataset(
+        data,
+        label=pa.array([0, 1, 0, 0, 1]),
+        params=dummy_dataset_params(),
+        feature_name=["c", "d"],
+        categorical_feature=["c"],
+    )
+    booster = lgb.train({"num_leaves": 7}, dataset, num_boost_round=5)
+    assert booster.feature_name() == ["c", "d"]