ERR: Raise ValueError when BaseIndexer start & end bounds are unequal length (#44497)

Author: mroeschke

doc/source/whatsnew/v1.4.0.rst

@@ -679,8 +679,8 @@ Groupby/resample/rolling
 - Fixed bug in :meth:`Series.rolling` and :meth:`DataFrame.rolling` not calculating window bounds correctly for the first row when ``center=True`` and index is decreasing (:issue:`43927`)
 - Fixed bug in :meth:`Series.rolling` and :meth:`DataFrame.rolling` for centered datetimelike windows with uneven nanosecond (:issue:`43997`)
 - Bug in :meth:`GroupBy.nth` failing on ``axis=1`` (:issue:`43926`)
-- Fixed bug in :meth:`Series.rolling` and :meth:`DataFrame.rolling` not respecting right bound on centered datetime-like windows, if the index contain duplicates (:issue:`#3944`)
-
+- Fixed bug in :meth:`Series.rolling` and :meth:`DataFrame.rolling` not respecting right bound on centered datetime-like windows, if the index contain duplicates (:issue:`3944`)
+- Bug in :meth:`Series.rolling` and :meth:`DataFrame.rolling` when using a :class:`pandas.api.indexers.BaseIndexer` subclass that returned unequal start and end arrays would segfault instead of raising a ``ValueError`` (:issue:`44470`)
 
 Reshaping
 ^^^^^^^^^

pandas/core/window/ewm.py

@@ -417,6 +417,13 @@ def __init__(
                 self.alpha,
             )
 
+    def _check_window_bounds(
+        self, start: np.ndarray, end: np.ndarray, num_vals: int
+    ) -> None:
+        # emw algorithms are iterative with each point
+        # ExponentialMovingWindowIndexer "bounds" are the entire window
+        pass
+
     def _get_window_indexer(self) -> BaseIndexer:
         """
         Return an indexer class that will compute the window start and end bounds

pandas/core/window/rolling.py

@@ -227,6 +227,20 @@ def _validate(self) -> None:
         if self.method not in ["table", "single"]:
             raise ValueError("method must be 'table' or 'single")
 
+    def _check_window_bounds(
+        self, start: np.ndarray, end: np.ndarray, num_vals: int
+    ) -> None:
+        if len(start) != len(end):
+            raise ValueError(
+                f"start ({len(start)}) and end ({len(end)}) bounds must be the "
+                f"same length"
+            )
+        elif len(start) != num_vals:
+            raise ValueError(
+                f"start and end bounds ({len(start)}) must be the same length "
+                f"as the object ({num_vals})"
+            )
+
     def _create_data(self, obj: NDFrameT) -> NDFrameT:
         """
         Split data into blocks & return conformed data.
@@ -311,10 +325,7 @@ def __iter__(self):
             center=self.center,
             closed=self.closed,
         )
-
-        assert len(start) == len(
-            end
-        ), "these should be equal in length from get_window_bounds"
+        self._check_window_bounds(start, end, len(obj))
 
         for s, e in zip(start, end):
             result = obj.iloc[slice(s, e)]
@@ -565,9 +576,7 @@ def calc(x):
                     center=self.center,
                     closed=self.closed,
                 )
-                assert len(start) == len(
-                    end
-                ), "these should be equal in length from get_window_bounds"
+                self._check_window_bounds(start, end, len(x))
 
                 return func(x, start, end, min_periods, *numba_args)
 
@@ -608,6 +617,7 @@ def _numba_apply(
             center=self.center,
             closed=self.closed,
         )
+        self._check_window_bounds(start, end, len(values))
         aggregator = executor.generate_shared_aggregator(
             func, engine_kwargs, numba_cache_key_str
         )
@@ -1544,10 +1554,7 @@ def cov_func(x, y):
                 center=self.center,
                 closed=self.closed,
             )
-
-            assert len(start) == len(
-                end
-            ), "these should be equal in length from get_window_bounds"
+            self._check_window_bounds(start, end, len(x_array))
 
             with np.errstate(all="ignore"):
                 mean_x_y = window_aggregations.roll_mean(
@@ -1588,10 +1595,7 @@ def corr_func(x, y):
                 center=self.center,
                 closed=self.closed,
             )
-
-            assert len(start) == len(
-                end
-            ), "these should be equal in length from get_window_bounds"
+            self._check_window_bounds(start, end, len(x_array))
 
             with np.errstate(all="ignore"):
                 mean_x_y = window_aggregations.roll_mean(

pandas/tests/window/test_base_indexer.py

@@ -452,3 +452,46 @@ def test_rolling_groupby_with_fixed_forward_many(group_keys, window_size):
     manual = manual.set_index(["a", "c"])["b"]
 
     tm.assert_series_equal(result, manual)
+
+
+def test_unequal_start_end_bounds():
+    class CustomIndexer(BaseIndexer):
+        def get_window_bounds(self, num_values, min_periods, center, closed):
+            return np.array([1]), np.array([1, 2])
+
+    indexer = CustomIndexer()
+    roll = Series(1).rolling(indexer)
+    match = "start"
+    with pytest.raises(ValueError, match=match):
+        roll.mean()
+
+    with pytest.raises(ValueError, match=match):
+        next(iter(roll))
+
+    with pytest.raises(ValueError, match=match):
+        roll.corr(pairwise=True)
+
+    with pytest.raises(ValueError, match=match):
+        roll.cov(pairwise=True)
+
+
+def test_unequal_bounds_to_object():
+    # GH 44470
+    class CustomIndexer(BaseIndexer):
+        def get_window_bounds(self, num_values, min_periods, center, closed):
+            return np.array([1]), np.array([2])
+
+    indexer = CustomIndexer()
+    roll = Series([1, 1]).rolling(indexer)
+    match = "start and end"
+    with pytest.raises(ValueError, match=match):
+        roll.mean()
+
+    with pytest.raises(ValueError, match=match):
+        next(iter(roll))
+
+    with pytest.raises(ValueError, match=match):
+        roll.corr(pairwise=True)
+
+    with pytest.raises(ValueError, match=match):
+        roll.cov(pairwise=True)