45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
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import pytest
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import numpy as np
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from numpy.testing import assert_allclose
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from scipy.stats import _boost
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type_char_to_type_tol = {'f': (np.float32, 32*np.finfo(np.float32).eps),
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'd': (np.float64, 32*np.finfo(np.float64).eps),
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'g': (np.longdouble, 32*np.finfo(np.longdouble).eps)}
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# Each item in this list is
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# (func, args, expected_value)
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# All the values can be represented exactly, even with np.float32.
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#
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# This is not an exhaustive test data set of all the functions!
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# It is a spot check of several functions, primarily for
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# checking that the different data types are handled correctly.
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test_data = [
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(_boost._beta_cdf, (0.5, 2, 3), 0.6875),
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(_boost._beta_ppf, (0.6875, 2, 3), 0.5),
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(_boost._beta_pdf, (0.5, 2, 3), 1.5),
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(_boost._beta_sf, (0.5, 2, 1), 0.75),
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(_boost._beta_isf, (0.75, 2, 1), 0.5),
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(_boost._binom_cdf, (1, 3, 0.5), 0.5),
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(_boost._binom_pdf, (1, 4, 0.5), 0.25),
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(_boost._hypergeom_cdf, (2, 3, 5, 6), 0.5),
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(_boost._nbinom_cdf, (1, 4, 0.25), 0.015625),
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(_boost._ncf_mean, (10, 12, 2.5), 1.5),
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]
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@pytest.mark.filterwarnings('ignore::RuntimeWarning')
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@pytest.mark.parametrize('func, args, expected', test_data)
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def test_stats_boost_ufunc(func, args, expected):
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type_sigs = func.types
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type_chars = [sig.split('->')[-1] for sig in type_sigs]
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for type_char in type_chars:
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typ, rtol = type_char_to_type_tol[type_char]
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args = [typ(arg) for arg in args]
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value = func(*args)
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assert isinstance(value, typ)
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assert_allclose(value, expected, rtol=rtol)
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