craftbeerpi4-pione/venv3/lib/python3.7/site-packages/pandas/tests/window/test_numba.py

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2021-03-03 23:49:41 +01:00
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import 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 TestApply:
@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")
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)