You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
462 lines
16 KiB
462 lines
16 KiB
1 year ago
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from pandas.compat import (
|
||
|
|
is_ci_environment,
|
||
|
|
is_platform_mac,
|
||
|
|
is_platform_windows,
|
||
|
|
)
|
||
|
|
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.mark.skipif(
|
||
|
|
is_ci_environment() and (is_platform_windows() or is_platform_mac()),
|
||
|
|
reason="On GHA CI, Windows can fail with "
|
||
|
|
"'Windows fatal exception: stack overflow' "
|
||
|
|
"and macOS can timeout",
|
||
|
|
),
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
@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)
|