[python] Fix training on subset constructed without params (#5213)

* Update basic.py

* Update test_engine.py

* Add return type annotation

python-package/lightgbm/basic.py

@@ -1348,12 +1348,12 @@ class Dataset:
         self._start_row += nrow
         return self
 
-    def get_params(self):
+    def get_params(self) -> Dict[str, Any]:
         """Get the used parameters in the Dataset.
 
         Returns
         -------
-        params : dict or None
+        params : dict
             The used parameters in this Dataset object.
         """
         if self.params is not None:
@@ -1380,6 +1380,8 @@ class Dataset:
                                                 "weight_column",
                                                 "zero_as_missing")
             return {k: v for k, v in self.params.items() if k in dataset_params}
+        else:
+            return {}
 
     def _free_handle(self):
         if self.handle is not None:

tests/python_package_test/test_engine.py

@@ -1481,6 +1481,18 @@ def test_init_with_subset():
     assert subset_data_4.get_data() == "lgb_train_data.bin"
 
 
+def test_training_on_constructed_subset_without_params():
+    X = np.random.random((100, 10))
+    y = np.random.random(100)
+    lgb_data = lgb.Dataset(X, y)
+    subset_indices = [1, 2, 3, 4]
+    subset = lgb_data.subset(subset_indices).construct()
+    bst = lgb.train({}, subset, num_boost_round=1)
+    assert subset.get_params() == {}
+    assert subset.num_data() == len(subset_indices)
+    assert bst.current_iteration() == 1
+
+
 def generate_trainset_for_monotone_constraints_tests(x3_to_category=True):
     number_of_dpoints = 3000
     x1_positively_correlated_with_y = np.random.random(size=number_of_dpoints)