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import numpy as np
import pytest
from pandas.errors import NumbaUtilError
import pandas.util._test_decorators as td
from pandas import (
DataFrame,
Series,
option_context,
to_datetime,
)
import pandas._testing as tm
pytestmark = pytest.mark.single_cpu
@pytest.fixture(params=["single", "table"])
def method(request):
"""method keyword in rolling/expanding/ewm constructor"""
return request.param
@pytest.fixture(
params=[
["sum", {}],
["mean", {}],
["median", {}],
["max", {}],
["min", {}],
["var", {}],
["var", {"ddof": 0}],
["std", {}],
["std", {"ddof": 0}],
]
)
def arithmetic_numba_supported_operators(request):
return request.param
@td.skip_if_no("numba")
@pytest.mark.filterwarnings("ignore")
# Filter warnings when parallel=True and the function can't be parallelized by Numba
class TestEngine:
@pytest.mark.parametrize("jit", [True, False])
def test_numba_vs_cython_apply(self, jit, nogil, parallel, nopython, center, step):
def f(x, *args):
arg_sum = 0
for arg in args:
arg_sum += arg
return np.mean(x) + arg_sum
if jit:
import numba
f = numba.jit(f)
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
args = (2,)
s = Series(range(10))
result = s.rolling(2, center=center, step=step).apply(
f, args=args, engine="numba", engine_kwargs=engine_kwargs, raw=True
)
expected = s.rolling(2, center=center, step=step).apply(
f, engine="cython", args=args, raw=True
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"data",
[
DataFrame(np.eye(5)),
DataFrame(
[
[5, 7, 7, 7, np.nan, np.inf, 4, 3, 3, 3],
[5, 7, 7, 7, np.nan, np.inf, 7, 3, 3, 3],
[np.nan, np.nan, 5, 6, 7, 5, 5, 5, 5, 5],
]
).T,
Series(range(5), name="foo"),
Series([20, 10, 10, np.inf, 1, 1, 2, 3]),
Series([20, 10, 10, np.nan, 10, 1, 2, 3]),
],
)
def test_numba_vs_cython_rolling_methods(
self,
data,
nogil,
parallel,
nopython,
arithmetic_numba_supported_operators,
step,
):
method, kwargs = arithmetic_numba_supported_operators
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
roll = data.rolling(3, step=step)
result = getattr(roll, method)(
engine="numba", engine_kwargs=engine_kwargs, **kwargs
)
expected = getattr(roll, method)(engine="cython", **kwargs)
tm.assert_equal(result, expected)
@pytest.mark.parametrize(
"data", [DataFrame(np.eye(5)), Series(range(5), name="foo")]
)
def test_numba_vs_cython_expanding_methods(
self, data, nogil, parallel, nopython, arithmetic_numba_supported_operators
):
method, kwargs = arithmetic_numba_supported_operators
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
data = DataFrame(np.eye(5))
expand = data.expanding()
result = getattr(expand, method)(
engine="numba", engine_kwargs=engine_kwargs, **kwargs
)
expected = getattr(expand, method)(engine="cython", **kwargs)
tm.assert_equal(result, expected)
@pytest.mark.parametrize("jit", [True, False])
def test_cache_apply(self, jit, nogil, parallel, nopython, step):
# Test that the functions are cached correctly if we switch functions
def func_1(x):
return np.mean(x) + 4
def func_2(x):
return np.std(x) * 5
if jit:
import numba
func_1 = numba.jit(func_1)
func_2 = numba.jit(func_2)
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
roll = Series(range(10)).rolling(2, step=step)
result = roll.apply(
func_1, engine="numba", engine_kwargs=engine_kwargs, raw=True
)
expected = roll.apply(func_1, engine="cython", raw=True)
tm.assert_series_equal(result, expected)
result = roll.apply(
func_2, engine="numba", engine_kwargs=engine_kwargs, raw=True
)
expected = roll.apply(func_2, engine="cython", raw=True)
tm.assert_series_equal(result, expected)
# This run should use the cached func_1
result = roll.apply(
func_1, engine="numba", engine_kwargs=engine_kwargs, raw=True
)
expected = roll.apply(func_1, engine="cython", raw=True)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"window,window_kwargs",
[
["rolling", {"window": 3, "min_periods": 0}],
["expanding", {}],
],
)
def test_dont_cache_args(
self, window, window_kwargs, nogil, parallel, nopython, method
):
# GH 42287
def add(values, x):
return np.sum(values) + x
engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
df = DataFrame({"value": [0, 0, 0]})
result = getattr(df, window)(method=method, **window_kwargs).apply(
add, raw=True, engine="numba", engine_kwargs=engine_kwargs, args=(1,)
)
expected = DataFrame({"value": [1.0, 1.0, 1.0]})
tm.assert_frame_equal(result, expected)
result = getattr(df, window)(method=method, **window_kwargs).apply(
add, raw=True, engine="numba", engine_kwargs=engine_kwargs, args=(2,)
)
expected = DataFrame({"value": [2.0, 2.0, 2.0]})
tm.assert_frame_equal(result, expected)
def test_dont_cache_engine_kwargs(self):
# If the user passes a different set of engine_kwargs don't return the same
# jitted function
nogil = False
parallel = True
nopython = True
def func(x):
return nogil + parallel + nopython
engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
df = DataFrame({"value": [0, 0, 0]})
result = df.rolling(1).apply(
func, raw=True, engine="numba", engine_kwargs=engine_kwargs
)
expected = DataFrame({"value": [2.0, 2.0, 2.0]})
tm.assert_frame_equal(result, expected)
parallel = False
engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
result = df.rolling(1).apply(
func, raw=True, engine="numba", engine_kwargs=engine_kwargs
)
expected = DataFrame({"value": [1.0, 1.0, 1.0]})
tm.assert_frame_equal(result, expected)
@td.skip_if_no("numba")
class TestEWM:
@pytest.mark.parametrize(
"grouper", [lambda x: x, lambda x: x.groupby("A")], ids=["None", "groupby"]
)
@pytest.mark.parametrize("method", ["mean", "sum"])
def test_invalid_engine(self, grouper, method):
df = DataFrame({"A": ["a", "b", "a", "b"], "B": range(4)})
with pytest.raises(ValueError, match="engine must be either"):
getattr(grouper(df).ewm(com=1.0), method)(engine="foo")
@pytest.mark.parametrize(
"grouper", [lambda x: x, lambda x: x.groupby("A")], ids=["None", "groupby"]
)
@pytest.mark.parametrize("method", ["mean", "sum"])
def test_invalid_engine_kwargs(self, grouper, method):
df = DataFrame({"A": ["a", "b", "a", "b"], "B": range(4)})
with pytest.raises(ValueError, match="cython engine does not"):
getattr(grouper(df).ewm(com=1.0), method)(
engine="cython", engine_kwargs={"nopython": True}
)
@pytest.mark.parametrize("grouper", ["None", "groupby"])
@pytest.mark.parametrize("method", ["mean", "sum"])
def test_cython_vs_numba(
self, grouper, method, nogil, parallel, nopython, ignore_na, adjust
):
df = DataFrame({"B": range(4)})
if grouper == "None":
grouper = lambda x: x
else:
df["A"] = ["a", "b", "a", "b"]
grouper = lambda x: x.groupby("A")
if method == "sum":
adjust = True
ewm = grouper(df).ewm(com=1.0, adjust=adjust, ignore_na=ignore_na)
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
result = getattr(ewm, method)(engine="numba", engine_kwargs=engine_kwargs)
expected = getattr(ewm, method)(engine="cython")
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("grouper", ["None", "groupby"])
def test_cython_vs_numba_times(self, grouper, nogil, parallel, nopython, ignore_na):
# GH 40951
df = DataFrame({"B": [0, 0, 1, 1, 2, 2]})
if grouper == "None":
grouper = lambda x: x
else:
grouper = lambda x: x.groupby("A")
df["A"] = ["a", "b", "a", "b", "b", "a"]
halflife = "23 days"
times = to_datetime(
[
"2020-01-01",
"2020-01-01",
"2020-01-02",
"2020-01-10",
"2020-02-23",
"2020-01-03",
]
)
ewm = grouper(df).ewm(
halflife=halflife, adjust=True, ignore_na=ignore_na, times=times
)
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
result = ewm.mean(engine="numba", engine_kwargs=engine_kwargs)
expected = ewm.mean(engine="cython")
tm.assert_frame_equal(result, expected)
@td.skip_if_no("numba")
def test_use_global_config():
def f(x):
return np.mean(x) + 2
s = Series(range(10))
with option_context("compute.use_numba", True):
result = s.rolling(2).apply(f, engine=None, raw=True)
expected = s.rolling(2).apply(f, engine="numba", raw=True)
tm.assert_series_equal(expected, result)
@td.skip_if_no("numba")
def test_invalid_kwargs_nopython():
with pytest.raises(NumbaUtilError, match="numba does not support kwargs with"):
Series(range(1)).rolling(1).apply(
lambda x: x, kwargs={"a": 1}, engine="numba", raw=True
)
@td.skip_if_no("numba")
@pytest.mark.slow
@pytest.mark.filterwarnings("ignore")
# Filter warnings when parallel=True and the function can't be parallelized by Numba
class TestTableMethod:
def test_table_series_valueerror(self):
def f(x):
return np.sum(x, axis=0) + 1
with pytest.raises(
ValueError, match="method='table' not applicable for Series objects."
):
Series(range(1)).rolling(1, method="table").apply(
f, engine="numba", raw=True
)
def test_table_method_rolling_methods(
self,
axis,
nogil,
parallel,
nopython,
arithmetic_numba_supported_operators,
step,
):
method, kwargs = arithmetic_numba_supported_operators
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
df = DataFrame(np.eye(3))
roll_table = df.rolling(2, method="table", axis=axis, min_periods=0, step=step)
if method in ("var", "std"):
with pytest.raises(NotImplementedError, match=f"{method} not supported"):
getattr(roll_table, method)(
engine_kwargs=engine_kwargs, engine="numba", **kwargs
)
else:
roll_single = df.rolling(
2, method="single", axis=axis, min_periods=0, step=step
)
result = getattr(roll_table, method)(
engine_kwargs=engine_kwargs, engine="numba", **kwargs
)
expected = getattr(roll_single, method)(
engine_kwargs=engine_kwargs, engine="numba", **kwargs
)
tm.assert_frame_equal(result, expected)
def test_table_method_rolling_apply(self, axis, nogil, parallel, nopython, step):
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
def f(x):
return np.sum(x, axis=0) + 1
df = DataFrame(np.eye(3))
result = df.rolling(
2, method="table", axis=axis, min_periods=0, step=step
).apply(f, raw=True, engine_kwargs=engine_kwargs, engine="numba")
expected = df.rolling(
2, method="single", axis=axis, min_periods=0, step=step
).apply(f, raw=True, engine_kwargs=engine_kwargs, engine="numba")
tm.assert_frame_equal(result, expected)
def test_table_method_rolling_weighted_mean(self, step):
def weighted_mean(x):
arr = np.ones((1, x.shape[1]))
arr[:, :2] = (x[:, :2] * x[:, 2]).sum(axis=0) / x[:, 2].sum()
return arr
df = DataFrame([[1, 2, 0.6], [2, 3, 0.4], [3, 4, 0.2], [4, 5, 0.7]])
result = df.rolling(2, method="table", min_periods=0, step=step).apply(
weighted_mean, raw=True, engine="numba"
)
expected = DataFrame(
[
[1.0, 2.0, 1.0],
[1.8, 2.0, 1.0],
[3.333333, 2.333333, 1.0],
[1.555556, 7, 1.0],
]
)[::step]
tm.assert_frame_equal(result, expected)
def test_table_method_expanding_apply(self, axis, nogil, parallel, nopython):
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
def f(x):
return np.sum(x, axis=0) + 1
df = DataFrame(np.eye(3))
result = df.expanding(method="table", axis=axis).apply(
f, raw=True, engine_kwargs=engine_kwargs, engine="numba"
)
expected = df.expanding(method="single", axis=axis).apply(
f, raw=True, engine_kwargs=engine_kwargs, engine="numba"
)
tm.assert_frame_equal(result, expected)
def test_table_method_expanding_methods(
self, axis, nogil, parallel, nopython, arithmetic_numba_supported_operators
):
method, kwargs = arithmetic_numba_supported_operators
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
df = DataFrame(np.eye(3))
expand_table = df.expanding(method="table", axis=axis)
if method in ("var", "std"):
with pytest.raises(NotImplementedError, match=f"{method} not supported"):
getattr(expand_table, method)(
engine_kwargs=engine_kwargs, engine="numba", **kwargs
)
else:
expand_single = df.expanding(method="single", axis=axis)
result = getattr(expand_table, method)(
engine_kwargs=engine_kwargs, engine="numba", **kwargs
)
expected = getattr(expand_single, method)(
engine_kwargs=engine_kwargs, engine="numba", **kwargs
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("data", [np.eye(3), np.ones((2, 3)), np.ones((3, 2))])
@pytest.mark.parametrize("method", ["mean", "sum"])
def test_table_method_ewm(self, data, method, axis, nogil, parallel, nopython):
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
df = DataFrame(data)
result = getattr(df.ewm(com=1, method="table", axis=axis), method)(
engine_kwargs=engine_kwargs, engine="numba"
)
expected = getattr(df.ewm(com=1, method="single", axis=axis), method)(
engine_kwargs=engine_kwargs, engine="numba"
)
tm.assert_frame_equal(result, expected)
@td.skip_if_no("numba")
def test_npfunc_no_warnings():
df = DataFrame({"col1": [1, 2, 3, 4, 5]})
with tm.assert_produces_warning(False):
df.col1.rolling(2).apply(np.prod, raw=True, engine="numba")
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