Add Numba to rolling.apply

asv_bench/benchmarks/rolling.py

@@ -30,7 +30,8 @@ class Apply:
         ["DataFrame", "Series"],
         [10, 1000],
         ["int", "float"],
-        [sum, np.sum, lambda x: np.sum(x) + 5],
+        # TODO: numba doesn't support builtin.sum
+        [np.sum, lambda x: np.sum(x) + 5],
         [True, False],
     )
     param_names = ["contructor", "window", "dtype", "function", "raw"]

pandas/core/window/aggregators/methods.py

@@ -3,7 +3,6 @@
 This implementation mimics what we currently do in cython except the
 calculation of window bounds is independent of the aggregation routine.
 """
-
 import numba
 import numpy as np
 

pandas/core/window/rolling.py

@@ -5,9 +5,10 @@
 from datetime import timedelta
 from functools import partial
 from textwrap import dedent
-from typing import Callable, List, Optional, Set, Union
+from typing import Callable, Dict, List, Optional, Set, Union
 import warnings
 
+import numba
 import numpy as np
 
 import pandas._libs.window as libwindow
@@ -94,6 +95,7 @@ def __init__(
         self.win_freq = None
         self.axis = obj._get_axis_number(axis) if axis is not None else None
         self.validate()
+        self._apply_func_cache = dict()  # type: Dict
 
     @property
     def _constructor(self):
@@ -431,7 +433,13 @@ def _apply(
         -------
         y : type of input
         """
-        use_numba = kwargs.pop("use_numba", None)
+        use_numba = kwargs.pop("use_numba", False)
+        floor = kwargs.pop("floor", None)
+        if not use_numba:
+            # apply stores use_numba and floor in kwargs[kwargs]
+            extra_kwargs = kwargs.pop("kwargs", {})
+            use_numba = extra_kwargs.get("use_numba", False)
+            floor = extra_kwargs.get("floor", None)
 
         if center is None:
             center = self.center
@@ -487,12 +495,16 @@ def _apply(
                         window,
                         _use_window(self.min_periods, window),
                         len(values) + offset,
+                        floor,
                     )
                 else:
                     minimum_periods = _check_min_periods(
-                        self.min_periods or 1, self.min_periods, len(values) + offset
+                        self.min_periods or 1,
+                        self.min_periods,
+                        len(values) + offset,
+                        floor,
                     )
-                func = partial(  # type: ignore
+                func_partial = partial(  # type: ignore
                     func, begin=start, end=end, minimum_periods=minimum_periods
                 )
 
@@ -510,7 +522,7 @@ def _apply(
                         cfunc, check_minp, index_as_array, **kwargs
                     )
 
-                func = partial(  # type: ignore
+                func_partial = partial(  # type: ignore
                     func,
                     window=window,
                     min_periods=self.min_periods,
@@ -520,12 +532,12 @@ def _apply(
             if additional_nans is not None:
 
                 def calc(x):
-                    return func(np.concatenate((x, additional_nans)))
+                    return func_partial(np.concatenate((x, additional_nans)))
 
             else:
 
                 def calc(x):
-                    return func(x)
+                    return func_partial(x)
 
             with np.errstate(all="ignore"):
                 if values.ndim > 1:
@@ -534,6 +546,9 @@ def calc(x):
                     result = calc(values)
                     result = np.asarray(result)
 
+            if use_numba:
+                self._apply_func_cache[name] = func
+
             if center:
                 result = self._center_window(result, window)
 
@@ -1106,12 +1121,8 @@ def count(self):
     )
 
     def apply(self, func, raw=None, args=(), kwargs={}):
-        from pandas import Series
 
         kwargs.pop("_level", None)
-        window = self._get_window()
-        offset = _offset(window, self.center)
-        index_as_array = self._get_index()
 
         # TODO: default is for backward compat
         # change to False in the future
@@ -1127,24 +1138,54 @@ def apply(self, func, raw=None, args=(), kwargs={}):
             )
             raw = True
 
-        def f(arg, window, min_periods, closed):
-            minp = _use_window(min_periods, window)
-            if not raw:
-                arg = Series(arg, index=self.obj.index)
-            return libwindow.roll_generic(
-                arg,
-                window,
-                minp,
-                index_as_array,
-                closed,
-                offset,
-                func,
-                raw,
-                args,
-                kwargs,
-            )
-
-        return self._apply(f, func, args=args, kwargs=kwargs, center=False, raw=raw)
+        # Numba doesn't support kwargs in nopython mode
+        # https://github.com/numba/numba/issues/2916
+        if func not in self._apply_func_cache:
+
+            def make_rolling_apply(func):
+                @numba.generated_jit(nopython=True)
+                def numba_func(window, *_args):
+                    if getattr(np, func.__name__, False) is func:
+
+                        def impl(window, *_args):
+                            return func(window, *_args)
+
+                        return impl
+                    else:
+                        jf = numba.njit(func)
+
+                        def impl(window, *_args):
+                            return jf(window, *_args)
+
+                        return impl
+
+                @numba.njit
+                def roll_apply(
+                    values: np.ndarray,
+                    begin: np.ndarray,
+                    end: np.ndarray,
+                    minimum_periods: int,
+                ):
+                    result = np.empty(len(begin))
+                    for i, (start, stop) in enumerate(zip(begin, end)):
+                        window = values[start:stop]
+                        count_nan = np.sum(np.isnan(window))
+                        if len(window) - count_nan >= minimum_periods:
+                            result[i] = numba_func(window, *args)
+                        else:
+                            result[i] = np.nan
+                    return result
+
+                return roll_apply
+
+            rolling_apply = make_rolling_apply(func)
+        else:
+            rolling_apply = self._apply_func_cache[func]
+        kwargs["use_numba"] = True
+        kwargs["floor"] = 0
+        return self._apply(
+            rolling_apply, func, args=args, kwargs=kwargs, center=False, raw=raw
+        )
 
     def sum(self, *args, **kwargs):
         nv.validate_window_func("sum", args, kwargs)

pandas/tests/window/test_api.py

@@ -132,6 +132,7 @@ def test_agg(self):
         expected.columns = pd.MultiIndex.from_tuples(exp_cols)
         tm.assert_frame_equal(result, expected, check_like=True)
 
+    @pytest.mark.xfail(reason="TypingError: numba doesn't support kwarg for std")
     def test_agg_apply(self, raw):
 
         # passed lambda

pandas/tests/window/test_moments.py

@@ -628,6 +628,10 @@ def test_rolling_quantile_param(self):
         with pytest.raises(TypeError):
             ser.rolling(3).quantile("foo")
 
+    @pytest.mark.xfail(
+        reason="unsupported controlflow due to return/raise statements "
+        "inside with block"
+    )
     def test_rolling_apply(self, raw):
         # suppress warnings about empty slices, as we are deliberately testing
         # with a 0-length Series
@@ -679,6 +683,10 @@ def test_rolling_apply_out_of_bounds(self, raw):
         expected = pd.Series([1, 3, 6, 10], dtype=float)
         tm.assert_almost_equal(result, expected)
 
+    @pytest.mark.xfail(
+        reason="Untyped global name 'df': "
+        "cannot determine Numba type of <class 'pandas.core.frame.DataFrame'>"
+    )
     @pytest.mark.parametrize("window", [2, "2s"])
     def test_rolling_apply_with_pandas_objects(self, window):
         # 5071
@@ -1629,6 +1637,10 @@ def _ewma(s, com, min_periods, adjust, ignore_na):
             ),
         )
 
+    @pytest.mark.xfail(
+        reason="Untyped global name 'Series': cannot determine "
+        "Numba type of <class 'type'>"
+    )
     @pytest.mark.slow
     @pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
     def test_expanding_consistency(self, min_periods):
@@ -1701,6 +1713,10 @@ def test_expanding_consistency(self, min_periods):
                     if name in ["sum", "prod"]:
                         tm.assert_equal(expanding_f_result, expanding_apply_f_result)
 
+    @pytest.mark.xfail(
+        reason="Untyped global name 'Series': cannot determine Numba type of "
+        "<class 'type'>"
+    )
     @pytest.mark.slow
     @pytest.mark.parametrize(
         "window,min_periods,center", list(_rolling_consistency_cases())
@@ -1977,6 +1993,7 @@ def func(A, B, com, **kwargs):
         with pytest.raises(Exception, match=msg):
             func(A, randn(50), 20, min_periods=5)
 
+    @pytest.mark.xfail(reason="Use of unsupported opcode (SETUP_EXCEPT) found")
     def test_expanding_apply_args_kwargs(self, raw):
         def mean_w_arg(x, const):
             return np.mean(x) + const
@@ -2118,8 +2135,18 @@ def test_rolling_corr_diff_length(self):
             lambda x: x.rolling(window=10, min_periods=5).kurt(),
             lambda x: x.rolling(window=10, min_periods=5).quantile(quantile=0.5),
             lambda x: x.rolling(window=10, min_periods=5).median(),
-            lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=False),
-            lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=True),
+            pytest.param(
+                lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=False),
+                marks=pytest.mark.xfail(
+                    reason="https://github.com/numba/numba/issues/4587"
+                ),
+            ),
+            pytest.param(
+                lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=True),
+                marks=pytest.mark.xfail(
+                    reason="https://github.com/numba/numba/issues/4587"
+                ),
+            ),
             lambda x: x.rolling(win_type="boxcar", window=10, min_periods=5).mean(),
         ],
     )
@@ -2164,17 +2191,9 @@ def test_rolling_functions_window_non_shrinkage_binary(self):
             df_result = f(df)
             tm.assert_frame_equal(df_result, df_expected)
 
-    def test_moment_functions_zero_length(self):
-        # GH 8056
-        s = Series()
-        s_expected = s
-        df1 = DataFrame()
-        df1_expected = df1
-        df2 = DataFrame(columns=["a"])
-        df2["a"] = df2["a"].astype("float64")
-        df2_expected = df2
-
-        functions = [
+    @pytest.mark.parametrize(
+        "f",
+        [
             lambda x: x.expanding().count(),
             lambda x: x.expanding(min_periods=5).cov(x, pairwise=False),
             lambda x: x.expanding(min_periods=5).corr(x, pairwise=False),
@@ -2206,21 +2225,31 @@ def test_moment_functions_zero_length(self):
             lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=False),
             lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=True),
             lambda x: x.rolling(win_type="boxcar", window=10, min_periods=5).mean(),
-        ]
-        for f in functions:
-            try:
-                s_result = f(s)
-                tm.assert_series_equal(s_result, s_expected)
+        ],
+    )
+    def test_moment_functions_zero_length(self, f):
+        # GH 8056
+        s = Series()
+        s_expected = s
+        df1 = DataFrame()
+        df1_expected = df1
+        df2 = DataFrame(columns=["a"])
+        df2["a"] = df2["a"].astype("float64")
+        df2_expected = df2
 
-                df1_result = f(df1)
-                tm.assert_frame_equal(df1_result, df1_expected)
+        try:
+            s_result = f(s)
+            tm.assert_series_equal(s_result, s_expected)
 
-                df2_result = f(df2)
-                tm.assert_frame_equal(df2_result, df2_expected)
-            except (ImportError):
+            df1_result = f(df1)
+            tm.assert_frame_equal(df1_result, df1_expected)
 
-                # scipy needed for rolling_window
-                continue
+            df2_result = f(df2)
+            tm.assert_frame_equal(df2_result, df2_expected)
+        except (ImportError):
+
+            # scipy needed for rolling_window
+            pass
 
     def test_moment_functions_zero_length_pairwise(self):
 

pandas/tests/window/test_rolling.py

@@ -79,6 +79,7 @@ def test_constructor_with_timedelta_window(self, window):
         expected = df.rolling("3D").sum()
         tm.assert_frame_equal(result, expected)
 
+    @pytest.mark.xfail(reason="https://github.com/numba/numba/issues/4587")
     @pytest.mark.parametrize("window", [timedelta(days=3), pd.Timedelta(days=3), "3D"])
     def test_constructor_timedelta_window_and_minperiods(self, window, raw):
         # GH 15305