mirror of
https://github.com/aykhans/AzSuicideDataVisualization.git
synced 2025-04-22 10:28:02 +00:00
412 lines
15 KiB
Python
412 lines
15 KiB
Python
import numpy as np
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import pytest
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from pandas.compat import (
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is_ci_environment,
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is_platform_mac,
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is_platform_windows,
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)
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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 (
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DataFrame,
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Series,
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option_context,
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to_datetime,
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)
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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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# TODO(GH#44584): Mark these as pytest.mark.single_cpu
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pytestmark = pytest.mark.skipif(
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is_ci_environment() and (is_platform_windows() or is_platform_mac()),
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reason="On Azure CI, Windows can fail with "
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"'Windows fatal exception: stack overflow' "
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"and MacOS can timeout",
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)
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@td.skip_if_no("numba")
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@pytest.mark.filterwarnings("ignore:\n")
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# Filter warnings when parallel=True and the function can't be parallelized by Numba
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class TestEngine:
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@pytest.mark.parametrize("jit", [True, False])
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def test_numba_vs_cython_apply(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(
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"data", [DataFrame(np.eye(5)), Series(range(5), name="foo")]
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)
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def test_numba_vs_cython_rolling_methods(
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self, data, nogil, parallel, nopython, arithmetic_numba_supported_operators
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):
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method, kwargs = arithmetic_numba_supported_operators
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engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
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roll = data.rolling(2)
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result = getattr(roll, method)(
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engine="numba", engine_kwargs=engine_kwargs, **kwargs
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)
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expected = getattr(roll, method)(engine="cython", **kwargs)
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# Check the cache
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if method not in ("mean", "sum", "var", "std", "max", "min"):
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assert (
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getattr(np, f"nan{method}"),
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"Rolling_apply_single",
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) in NUMBA_FUNC_CACHE
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tm.assert_equal(result, expected)
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@pytest.mark.parametrize(
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"data", [DataFrame(np.eye(5)), Series(range(5), name="foo")]
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)
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def test_numba_vs_cython_expanding_methods(
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self, data, nogil, parallel, nopython, arithmetic_numba_supported_operators
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):
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method, kwargs = arithmetic_numba_supported_operators
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engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
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data = DataFrame(np.eye(5))
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expand = data.expanding()
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result = getattr(expand, method)(
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engine="numba", engine_kwargs=engine_kwargs, **kwargs
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)
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expected = getattr(expand, method)(engine="cython", **kwargs)
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# Check the cache
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if method not in ("mean", "sum", "var", "std", "max", "min"):
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assert (
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getattr(np, f"nan{method}"),
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"Expanding_apply_single",
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) in NUMBA_FUNC_CACHE
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tm.assert_equal(result, expected)
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@pytest.mark.parametrize("jit", [True, False])
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def test_cache_apply(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_single") 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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@pytest.mark.parametrize(
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"window,window_kwargs",
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[
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["rolling", {"window": 3, "min_periods": 0}],
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["expanding", {}],
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],
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)
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def test_dont_cache_args(
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self, window, window_kwargs, nogil, parallel, nopython, method
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):
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# GH 42287
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def add(values, x):
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return np.sum(values) + x
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engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
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df = DataFrame({"value": [0, 0, 0]})
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result = getattr(df, window)(method=method, **window_kwargs).apply(
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add, raw=True, engine="numba", engine_kwargs=engine_kwargs, args=(1,)
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)
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expected = DataFrame({"value": [1.0, 1.0, 1.0]})
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tm.assert_frame_equal(result, expected)
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result = getattr(df, window)(method=method, **window_kwargs).apply(
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add, raw=True, engine="numba", engine_kwargs=engine_kwargs, args=(2,)
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)
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expected = DataFrame({"value": [2.0, 2.0, 2.0]})
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tm.assert_frame_equal(result, expected)
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@td.skip_if_no("numba")
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class TestEWM:
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@pytest.mark.parametrize(
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"grouper", [lambda x: x, lambda x: x.groupby("A")], ids=["None", "groupby"]
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)
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@pytest.mark.parametrize("method", ["mean", "sum"])
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def test_invalid_engine(self, grouper, method):
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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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getattr(grouper(df).ewm(com=1.0), method)(engine="foo")
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@pytest.mark.parametrize(
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"grouper", [lambda x: x, lambda x: x.groupby("A")], ids=["None", "groupby"]
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)
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@pytest.mark.parametrize("method", ["mean", "sum"])
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def test_invalid_engine_kwargs(self, grouper, method):
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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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getattr(grouper(df).ewm(com=1.0), method)(
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engine="cython", engine_kwargs={"nopython": True}
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)
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@pytest.mark.parametrize("grouper", ["None", "groupby"])
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@pytest.mark.parametrize("method", ["mean", "sum"])
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def test_cython_vs_numba(
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self, grouper, method, nogil, parallel, nopython, ignore_na, adjust
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):
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if grouper == "None":
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grouper = lambda x: x
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warn = FutureWarning
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else:
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grouper = lambda x: x.groupby("A")
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warn = None
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if method == "sum":
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adjust = True
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df = DataFrame({"A": ["a", "b", "a", "b"], "B": range(4)})
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ewm = grouper(df).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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with tm.assert_produces_warning(warn, match="nuisance"):
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# GH#42738
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result = getattr(ewm, method)(engine="numba", engine_kwargs=engine_kwargs)
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expected = getattr(ewm, method)(engine="cython")
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("grouper", ["None", "groupby"])
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def test_cython_vs_numba_times(self, grouper, nogil, parallel, nopython, ignore_na):
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# GH 40951
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if grouper == "None":
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grouper = lambda x: x
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warn = FutureWarning
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else:
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grouper = lambda x: x.groupby("A")
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warn = None
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halflife = "23 days"
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times = to_datetime(
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[
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"2020-01-01",
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"2020-01-01",
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"2020-01-02",
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"2020-01-10",
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"2020-02-23",
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"2020-01-03",
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]
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)
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df = DataFrame({"A": ["a", "b", "a", "b", "b", "a"], "B": [0, 0, 1, 1, 2, 2]})
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ewm = grouper(df).ewm(
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halflife=halflife, adjust=True, ignore_na=ignore_na, times=times
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)
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engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
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with tm.assert_produces_warning(warn, match="nuisance"):
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# GH#42738
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result = ewm.mean(engine="numba", engine_kwargs=engine_kwargs)
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expected = ewm.mean(engine="cython")
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tm.assert_frame_equal(result, expected)
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@td.skip_if_no("numba")
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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")
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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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@td.skip_if_no("numba")
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@pytest.mark.slow
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@pytest.mark.filterwarnings("ignore:\n")
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# Filter warnings when parallel=True and the function can't be parallelized by Numba
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class TestTableMethod:
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def test_table_series_valueerror(self):
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def f(x):
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return np.sum(x, axis=0) + 1
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with pytest.raises(
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ValueError, match="method='table' not applicable for Series objects."
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):
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Series(range(1)).rolling(1, method="table").apply(
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f, engine="numba", raw=True
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)
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def test_table_method_rolling_methods(
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self, axis, nogil, parallel, nopython, arithmetic_numba_supported_operators
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):
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method, kwargs = arithmetic_numba_supported_operators
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engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
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df = DataFrame(np.eye(3))
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roll_table = df.rolling(2, method="table", axis=axis, min_periods=0)
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if method in ("var", "std"):
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with pytest.raises(NotImplementedError, match=f"{method} not supported"):
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getattr(roll_table, method)(
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engine_kwargs=engine_kwargs, engine="numba", **kwargs
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)
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else:
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roll_single = df.rolling(2, method="single", axis=axis, min_periods=0)
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result = getattr(roll_table, method)(
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engine_kwargs=engine_kwargs, engine="numba", **kwargs
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)
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expected = getattr(roll_single, method)(
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engine_kwargs=engine_kwargs, engine="numba", **kwargs
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)
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tm.assert_frame_equal(result, expected)
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def test_table_method_rolling_apply(self, axis, nogil, parallel, nopython):
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engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
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def f(x):
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return np.sum(x, axis=0) + 1
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df = DataFrame(np.eye(3))
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result = df.rolling(2, method="table", axis=axis, min_periods=0).apply(
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f, raw=True, engine_kwargs=engine_kwargs, engine="numba"
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)
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expected = df.rolling(2, method="single", axis=axis, min_periods=0).apply(
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f, raw=True, engine_kwargs=engine_kwargs, engine="numba"
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)
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tm.assert_frame_equal(result, expected)
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def test_table_method_rolling_weighted_mean(self):
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def weighted_mean(x):
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arr = np.ones((1, x.shape[1]))
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arr[:, :2] = (x[:, :2] * x[:, 2]).sum(axis=0) / x[:, 2].sum()
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return arr
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df = DataFrame([[1, 2, 0.6], [2, 3, 0.4], [3, 4, 0.2], [4, 5, 0.7]])
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result = df.rolling(2, method="table", min_periods=0).apply(
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weighted_mean, raw=True, engine="numba"
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)
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expected = DataFrame(
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[
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[1.0, 2.0, 1.0],
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[1.8, 2.0, 1.0],
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[3.333333, 2.333333, 1.0],
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[1.555556, 7, 1.0],
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]
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)
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tm.assert_frame_equal(result, expected)
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def test_table_method_expanding_apply(self, axis, nogil, parallel, nopython):
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engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
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def f(x):
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return np.sum(x, axis=0) + 1
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df = DataFrame(np.eye(3))
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result = df.expanding(method="table", axis=axis).apply(
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f, raw=True, engine_kwargs=engine_kwargs, engine="numba"
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)
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expected = df.expanding(method="single", axis=axis).apply(
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f, raw=True, engine_kwargs=engine_kwargs, engine="numba"
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)
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tm.assert_frame_equal(result, expected)
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def test_table_method_expanding_methods(
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self, axis, nogil, parallel, nopython, arithmetic_numba_supported_operators
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):
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method, kwargs = arithmetic_numba_supported_operators
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engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
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df = DataFrame(np.eye(3))
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expand_table = df.expanding(method="table", axis=axis)
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if method in ("var", "std"):
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with pytest.raises(NotImplementedError, match=f"{method} not supported"):
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getattr(expand_table, method)(
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engine_kwargs=engine_kwargs, engine="numba", **kwargs
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)
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else:
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expand_single = df.expanding(method="single", axis=axis)
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result = getattr(expand_table, method)(
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engine_kwargs=engine_kwargs, engine="numba", **kwargs
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)
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expected = getattr(expand_single, method)(
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engine_kwargs=engine_kwargs, engine="numba", **kwargs
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)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("data", [np.eye(3), np.ones((2, 3)), np.ones((3, 2))])
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@pytest.mark.parametrize("method", ["mean", "sum"])
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def test_table_method_ewm(self, data, method, axis, nogil, parallel, nopython):
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engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
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df = DataFrame(data)
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result = getattr(df.ewm(com=1, method="table", axis=axis), method)(
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engine_kwargs=engine_kwargs, engine="numba"
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)
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expected = getattr(df.ewm(com=1, method="single", axis=axis), method)(
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engine_kwargs=engine_kwargs, engine="numba"
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)
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tm.assert_frame_equal(result, expected)
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