160 lines
6.1 KiB
Python
160 lines
6.1 KiB
Python
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import numpy as np
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from numpy.testing import assert_equal, assert_allclose
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import pytest
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from scipy.stats import variation
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from scipy._lib._util import AxisError
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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):
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x = np.arange(9.0)
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assert_allclose(variation(x, ddof=1), np.sqrt(60/8)/4)
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@pytest.mark.parametrize('sgn', [1, -1])
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def test_sign(self, sgn):
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x = np.array([1, 2, 3, 4, 5])
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v = variation(sgn*x)
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expected = sgn*np.sqrt(2)/3
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assert_allclose(v, expected, rtol=1e-10)
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def test_scalar(self):
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# A scalar is treated like a 1-d sequence with length 1.
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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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def test_variation_nan(self, nan_policy, expected):
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x = np.arange(10.)
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x[9] = np.nan
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assert_allclose(variation(x, nan_policy=nan_policy), expected)
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def test_nan_policy_raise(self):
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x = np.array([1.0, 2.0, np.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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def test_bad_nan_policy(self):
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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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def test_keepdims(self):
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x = np.arange(10).reshape(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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assert_allclose(y, expected)
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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):
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x = np.zeros((5, 0))
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y = variation(x, axis=axis, keepdims=True)
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assert_equal(y, expected)
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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):
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x = np.array([[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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assert_equal(y, np.full((2, 1), fill_value=expected_fill))
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def test_propagate_nan(self):
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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 = np.arange(8).reshape(2, -1).astype(float)
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a[1, 0] = np.nan
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v = variation(a, axis=1, nan_policy="propagate")
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assert_allclose(v, [np.sqrt(5/4)/1.5, np.nan], atol=1e-15)
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def test_axis_none(self):
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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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assert_allclose(y, np.sqrt(5/4)/1.5)
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def test_bad_axis(self):
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# Check that an invalid axis raises np.exceptions.AxisError.
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x = np.array([[1, 2, 3], [4, 5, 6]])
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with pytest.raises(AxisError):
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variation(x, axis=10)
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def test_mean_zero(self):
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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 = np.array([10, -3, 1, -4, -4])
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y = variation(x)
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assert_equal(y, np.inf)
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x2 = np.array([x, -10*x])
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y2 = variation(x2, axis=1)
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assert_equal(y2, [np.inf, np.inf])
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@pytest.mark.parametrize('x', [np.zeros(5), [], [1, 2, np.inf, 9]])
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def test_return_nan(self, x):
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# Test some cases where `variation` returns nan.
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y = variation(x)
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assert_equal(y, np.nan)
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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):
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x = np.empty((3, 0))
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y = variation(x, axis=axis)
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assert_equal(y, expected)
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def test_neg_inf(self):
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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 = np.array([-3, -5])
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assert_equal(variation(x1, ddof=2), -np.inf)
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x2 = np.array([[np.nan, 1, -10, np.nan],
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[-20, -3, np.nan, np.nan]])
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assert_equal(variation(x2, axis=1, ddof=2, nan_policy='omit'),
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[-np.inf, -np.inf])
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@pytest.mark.parametrize("nan_policy", ['propagate', 'omit'])
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def test_combined_edge_cases(self, nan_policy):
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x = np.array([[0, 10, np.nan, 1],
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[0, -5, np.nan, 2],
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[0, -5, np.nan, 3]])
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y = variation(x, axis=0, nan_policy=nan_policy)
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assert_allclose(y, [np.nan, np.inf, np.nan, np.sqrt(2/3)/2])
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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):
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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 = np.nan
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x = np.array([[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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v = variation(x, axis=1, ddof=ddof, nan_policy='omit')
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assert_allclose(v, expected)
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def test_variation_ddof(self):
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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 = np.array([1, 2, 3, 4, 5])
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nan_a = np.array([1, 2, 3, np.nan, 4, 5, np.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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assert_allclose(y, np.sqrt(5/2)/3)
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assert y == nan_y
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