mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2025-01-04 20:01:44 +01:00
123 lines
4.3 KiB
Python
123 lines
4.3 KiB
Python
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
|
|
import pandas._testing as tm
|
|
from pandas.core.util.numba_ import NUMBA_FUNC_CACHE
|
|
|
|
|
|
@td.skip_if_no("numba", "0.46.0")
|
|
@pytest.mark.filterwarnings("ignore:\\nThe keyword argument")
|
|
# Filter warnings when parallel=True and the function can't be parallelized by Numba
|
|
class TestRollingApply:
|
|
@pytest.mark.parametrize("jit", [True, False])
|
|
def test_numba_vs_cython(self, jit, nogil, parallel, nopython, center):
|
|
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).apply(
|
|
f, args=args, engine="numba", engine_kwargs=engine_kwargs, raw=True
|
|
)
|
|
expected = s.rolling(2, center=center).apply(
|
|
f, engine="cython", args=args, raw=True
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("jit", [True, False])
|
|
def test_cache(self, jit, nogil, parallel, nopython):
|
|
# 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)
|
|
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)
|
|
|
|
# func_1 should be in the cache now
|
|
assert (func_1, "rolling_apply") in NUMBA_FUNC_CACHE
|
|
|
|
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)
|
|
|
|
|
|
@td.skip_if_no("numba", "0.46.0")
|
|
class TestGroupbyEWMMean:
|
|
def test_invalid_engine(self):
|
|
df = DataFrame({"A": ["a", "b", "a", "b"], "B": range(4)})
|
|
with pytest.raises(ValueError, match="engine must be either"):
|
|
df.groupby("A").ewm(com=1.0).mean(engine="foo")
|
|
|
|
def test_invalid_engine_kwargs(self):
|
|
df = DataFrame({"A": ["a", "b", "a", "b"], "B": range(4)})
|
|
with pytest.raises(ValueError, match="cython engine does not"):
|
|
df.groupby("A").ewm(com=1.0).mean(
|
|
engine="cython", engine_kwargs={"nopython": True}
|
|
)
|
|
|
|
def test_cython_vs_numba(self, nogil, parallel, nopython, ignore_na, adjust):
|
|
df = DataFrame({"A": ["a", "b", "a", "b"], "B": range(4)})
|
|
gb_ewm = df.groupby("A").ewm(com=1.0, adjust=adjust, ignore_na=ignore_na)
|
|
|
|
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
|
|
result = gb_ewm.mean(engine="numba", engine_kwargs=engine_kwargs)
|
|
expected = gb_ewm.mean(engine="cython")
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@td.skip_if_no("numba", "0.46.0")
|
|
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", "0.46.0")
|
|
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
|
|
)
|