214 lines
9.0 KiB
Python
214 lines
9.0 KiB
Python
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import math
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import numpy as np
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import pytest
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from numpy.testing import suppress_warnings
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from scipy.stats import variation
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from scipy._lib._util import AxisError
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from scipy.conftest import array_api_compatible
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from scipy._lib._array_api import xp_assert_equal, xp_assert_close, is_numpy
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from scipy.stats._axis_nan_policy import (too_small_nd_omit, too_small_nd_not_omit,
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SmallSampleWarning)
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pytestmark = [array_api_compatible, pytest.mark.usefixtures("skip_xp_backends")]
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skip_xp_backends = pytest.mark.skip_xp_backends
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class TestVariation:
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"""
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Test class for scipy.stats.variation
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"""
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def test_ddof(self, xp):
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x = xp.arange(9.0)
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xp_assert_close(variation(x, ddof=1), xp.asarray(math.sqrt(60/8)/4))
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@pytest.mark.parametrize('sgn', [1, -1])
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def test_sign(self, sgn, xp):
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x = xp.asarray([1., 2., 3., 4., 5.])
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v = variation(sgn*x)
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expected = xp.asarray(sgn*math.sqrt(2)/3)
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xp_assert_close(v, expected, rtol=1e-10)
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def test_scalar(self, xp):
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# A scalar is treated like a 1-d sequence with length 1.
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xp_assert_equal(variation(4.0), 0.0)
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@pytest.mark.parametrize('nan_policy, expected',
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[('propagate', np.nan),
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('omit', np.sqrt(20/3)/4)])
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@skip_xp_backends(np_only=True,
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reasons=['`nan_policy` only supports NumPy backend'])
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def test_variation_nan(self, nan_policy, expected, xp):
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x = xp.arange(10.)
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x[9] = xp.nan
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xp_assert_close(variation(x, nan_policy=nan_policy), expected)
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@skip_xp_backends(np_only=True,
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reasons=['`nan_policy` only supports NumPy backend'])
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def test_nan_policy_raise(self, xp):
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x = xp.asarray([1.0, 2.0, xp.nan, 3.0])
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with pytest.raises(ValueError, match='input contains nan'):
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variation(x, nan_policy='raise')
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@skip_xp_backends(np_only=True,
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reasons=['`nan_policy` only supports NumPy backend'])
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def test_bad_nan_policy(self, xp):
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with pytest.raises(ValueError, match='must be one of'):
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variation([1, 2, 3], nan_policy='foobar')
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@skip_xp_backends(np_only=True,
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reasons=['`keepdims` only supports NumPy backend'])
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def test_keepdims(self, xp):
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x = xp.reshape(xp.arange(10), (2, 5))
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y = variation(x, axis=1, keepdims=True)
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expected = np.array([[np.sqrt(2)/2],
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[np.sqrt(2)/7]])
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xp_assert_close(y, expected)
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@skip_xp_backends(np_only=True,
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reasons=['`keepdims` only supports NumPy backend'])
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@pytest.mark.parametrize('axis, expected',
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[(0, np.empty((1, 0))),
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(1, np.full((5, 1), fill_value=np.nan))])
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def test_keepdims_size0(self, axis, expected, xp):
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x = xp.zeros((5, 0))
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if axis == 1:
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with pytest.warns(SmallSampleWarning, match=too_small_nd_not_omit):
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y = variation(x, axis=axis, keepdims=True)
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else:
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y = variation(x, axis=axis, keepdims=True)
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xp_assert_equal(y, expected)
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@skip_xp_backends(np_only=True,
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reasons=['`keepdims` only supports NumPy backend'])
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@pytest.mark.parametrize('incr, expected_fill', [(0, np.inf), (1, np.nan)])
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def test_keepdims_and_ddof_eq_len_plus_incr(self, incr, expected_fill, xp):
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x = xp.asarray([[1, 1, 2, 2], [1, 2, 3, 3]])
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y = variation(x, axis=1, ddof=x.shape[1] + incr, keepdims=True)
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xp_assert_equal(y, xp.full((2, 1), fill_value=expected_fill))
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@skip_xp_backends(np_only=True,
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reasons=['`nan_policy` only supports NumPy backend'])
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def test_propagate_nan(self, xp):
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# Check that the shape of the result is the same for inputs
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# with and without nans, cf gh-5817
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a = xp.reshape(xp.arange(8, dtype=float), (2, -1))
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a[1, 0] = xp.nan
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v = variation(a, axis=1, nan_policy="propagate")
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xp_assert_close(v, [math.sqrt(5/4)/1.5, xp.nan], atol=1e-15)
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@skip_xp_backends(np_only=True, reasons=['Python list input uses NumPy backend'])
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def test_axis_none(self, xp):
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# Check that `variation` computes the result on the flattened
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# input when axis is None.
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y = variation([[0, 1], [2, 3]], axis=None)
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xp_assert_close(y, math.sqrt(5/4)/1.5)
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def test_bad_axis(self, xp):
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# Check that an invalid axis raises np.exceptions.AxisError.
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x = xp.asarray([[1, 2, 3], [4, 5, 6]])
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with pytest.raises((AxisError, IndexError)):
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variation(x, axis=10)
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def test_mean_zero(self, xp):
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# Check that `variation` returns inf for a sequence that is not
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# identically zero but whose mean is zero.
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x = xp.asarray([10., -3., 1., -4., -4.])
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y = variation(x)
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xp_assert_equal(y, xp.asarray(xp.inf))
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x2 = xp.stack([x, -10.*x])
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y2 = variation(x2, axis=1)
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xp_assert_equal(y2, xp.asarray([xp.inf, xp.inf]))
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@pytest.mark.parametrize('x', [[0.]*5, [1, 2, np.inf, 9]])
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def test_return_nan(self, x, xp):
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x = xp.asarray(x)
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# Test some cases where `variation` returns nan.
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y = variation(x)
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xp_assert_equal(y, xp.asarray(xp.nan, dtype=x.dtype))
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@pytest.mark.parametrize('axis, expected',
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[(0, []), (1, [np.nan]*3), (None, np.nan)])
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def test_2d_size_zero_with_axis(self, axis, expected, xp):
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x = xp.empty((3, 0))
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with suppress_warnings() as sup:
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# torch
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sup.filter(UserWarning, "std*")
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if axis != 0:
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if is_numpy(xp):
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with pytest.warns(SmallSampleWarning, match="See documentation..."):
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y = variation(x, axis=axis)
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else:
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y = variation(x, axis=axis)
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else:
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y = variation(x, axis=axis)
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xp_assert_equal(y, xp.asarray(expected))
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def test_neg_inf(self, xp):
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# Edge case that produces -inf: ddof equals the number of non-nan
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# values, the values are not constant, and the mean is negative.
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x1 = xp.asarray([-3., -5.])
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xp_assert_equal(variation(x1, ddof=2), xp.asarray(-xp.inf))
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@skip_xp_backends(np_only=True,
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reasons=['`nan_policy` only supports NumPy backend'])
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def test_neg_inf_nan(self, xp):
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x2 = xp.asarray([[xp.nan, 1, -10, xp.nan],
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[-20, -3, xp.nan, xp.nan]])
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xp_assert_equal(variation(x2, axis=1, ddof=2, nan_policy='omit'),
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[-xp.inf, -xp.inf])
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@skip_xp_backends(np_only=True,
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reasons=['`nan_policy` only supports NumPy backend'])
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@pytest.mark.parametrize("nan_policy", ['propagate', 'omit'])
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def test_combined_edge_cases(self, nan_policy, xp):
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x = xp.array([[0, 10, xp.nan, 1],
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[0, -5, xp.nan, 2],
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[0, -5, xp.nan, 3]])
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if nan_policy == 'omit':
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with pytest.warns(SmallSampleWarning, match=too_small_nd_omit):
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y = variation(x, axis=0, nan_policy=nan_policy)
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else:
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y = variation(x, axis=0, nan_policy=nan_policy)
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xp_assert_close(y, [xp.nan, xp.inf, xp.nan, math.sqrt(2/3)/2])
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@skip_xp_backends(np_only=True,
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reasons=['`nan_policy` only supports NumPy backend'])
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@pytest.mark.parametrize(
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'ddof, expected',
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[(0, [np.sqrt(1/6), np.sqrt(5/8), np.inf, 0, np.nan, 0.0, np.nan]),
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(1, [0.5, np.sqrt(5/6), np.inf, 0, np.nan, 0, np.nan]),
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(2, [np.sqrt(0.5), np.sqrt(5/4), np.inf, np.nan, np.nan, 0, np.nan])]
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)
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def test_more_nan_policy_omit_tests(self, ddof, expected, xp):
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# The slightly strange formatting in the follow array is my attempt to
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# maintain a clean tabular arrangement of the data while satisfying
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# the demands of pycodestyle. Currently, E201 and E241 are not
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# disabled by the `noqa` annotation.
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nan = xp.nan
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x = xp.asarray([[1.0, 2.0, nan, 3.0],
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[0.0, 4.0, 3.0, 1.0],
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[nan, -.5, 0.5, nan],
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[nan, 9.0, 9.0, nan],
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[nan, nan, nan, nan],
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[3.0, 3.0, 3.0, 3.0],
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[0.0, 0.0, 0.0, 0.0]])
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with pytest.warns(SmallSampleWarning, match=too_small_nd_omit):
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v = variation(x, axis=1, ddof=ddof, nan_policy='omit')
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xp_assert_close(v, expected)
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@skip_xp_backends(np_only=True,
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reasons=['`nan_policy` only supports NumPy backend'])
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def test_variation_ddof(self, xp):
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# test variation with delta degrees of freedom
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# regression test for gh-13341
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a = xp.asarray([1., 2., 3., 4., 5.])
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nan_a = xp.asarray([1, 2, 3, xp.nan, 4, 5, xp.nan])
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y = variation(a, ddof=1)
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nan_y = variation(nan_a, nan_policy="omit", ddof=1)
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xp_assert_close(y, math.sqrt(5/2)/3)
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assert y == nan_y
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