124 lines
4.3 KiB
Python
124 lines
4.3 KiB
Python
import numpy as np
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import pytest
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from pandas.errors import NumbaUtilError
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import pandas.util._test_decorators as td
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from pandas import DataFrame, Series, option_context
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import pandas._testing as tm
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from pandas.core.util.numba_ import NUMBA_FUNC_CACHE
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@td.skip_if_no("numba", "0.46.0")
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@pytest.mark.filterwarnings("ignore:\\nThe keyword argument")
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# Filter warnings when parallel=True and the function can't be parallelized by Numba
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class TestRollingApply:
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@pytest.mark.parametrize("jit", [True, False])
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def test_numba_vs_cython(self, jit, nogil, parallel, nopython, center):
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def f(x, *args):
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arg_sum = 0
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for arg in args:
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arg_sum += arg
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return np.mean(x) + arg_sum
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if jit:
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import numba
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f = numba.jit(f)
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engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
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args = (2,)
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s = Series(range(10))
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result = s.rolling(2, center=center).apply(
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f, args=args, engine="numba", engine_kwargs=engine_kwargs, raw=True
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)
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expected = s.rolling(2, center=center).apply(
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f, engine="cython", args=args, raw=True
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)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("jit", [True, False])
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def test_cache(self, jit, nogil, parallel, nopython):
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# Test that the functions are cached correctly if we switch functions
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def func_1(x):
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return np.mean(x) + 4
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def func_2(x):
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return np.std(x) * 5
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if jit:
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import numba
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func_1 = numba.jit(func_1)
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func_2 = numba.jit(func_2)
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engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
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roll = Series(range(10)).rolling(2)
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result = roll.apply(
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func_1, engine="numba", engine_kwargs=engine_kwargs, raw=True
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)
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expected = roll.apply(func_1, engine="cython", raw=True)
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tm.assert_series_equal(result, expected)
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# func_1 should be in the cache now
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assert (func_1, "rolling_apply") in NUMBA_FUNC_CACHE
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result = roll.apply(
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func_2, engine="numba", engine_kwargs=engine_kwargs, raw=True
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)
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expected = roll.apply(func_2, engine="cython", raw=True)
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tm.assert_series_equal(result, expected)
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# This run should use the cached func_1
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result = roll.apply(
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func_1, engine="numba", engine_kwargs=engine_kwargs, raw=True
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)
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expected = roll.apply(func_1, engine="cython", raw=True)
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tm.assert_series_equal(result, expected)
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@td.skip_if_no("numba", "0.46.0")
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class TestGroupbyEWMMean:
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def test_invalid_engine(self):
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df = DataFrame({"A": ["a", "b", "a", "b"], "B": range(4)})
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with pytest.raises(ValueError, match="engine must be either"):
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df.groupby("A").ewm(com=1.0).mean(engine="foo")
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def test_invalid_engine_kwargs(self):
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df = DataFrame({"A": ["a", "b", "a", "b"], "B": range(4)})
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with pytest.raises(ValueError, match="cython engine does not"):
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df.groupby("A").ewm(com=1.0).mean(
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engine="cython", engine_kwargs={"nopython": True}
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)
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def test_cython_vs_numba(self, nogil, parallel, nopython, ignore_na, adjust):
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df = DataFrame({"A": ["a", "b", "a", "b"], "B": range(4)})
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gb_ewm = df.groupby("A").ewm(com=1.0, adjust=adjust, ignore_na=ignore_na)
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engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
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result = gb_ewm.mean(engine="numba", engine_kwargs=engine_kwargs)
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expected = gb_ewm.mean(engine="cython")
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tm.assert_frame_equal(result, expected)
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@td.skip_if_no("numba", "0.46.0")
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def test_use_global_config():
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def f(x):
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return np.mean(x) + 2
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s = Series(range(10))
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with option_context("compute.use_numba", True):
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result = s.rolling(2).apply(f, engine=None, raw=True)
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expected = s.rolling(2).apply(f, engine="numba", raw=True)
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tm.assert_series_equal(expected, result)
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@td.skip_if_no("numba", "0.46.0")
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def test_invalid_kwargs_nopython():
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with pytest.raises(NumbaUtilError, match="numba does not support kwargs with"):
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Series(range(1)).rolling(1).apply(
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lambda x: x, kwargs={"a": 1}, engine="numba", raw=True
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)
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