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https://github.com/python/cpython
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bpo-37798: Test both Python and C versions in test_statistics.py (GH-15453)
This commit is contained in:
Dong-hee Na
Raymond Hettinger
parent
edd21129dd
commit
8ad22a4226
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@ -18,6 +18,7 @@
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from decimal import Decimal
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from fractions import Fraction
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from test import support
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# Module to be tested.
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@ -178,6 +179,23 @@ class _DoNothing:
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# We prefer this for testing numeric values that may not be exactly equal,
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# and avoid using TestCase.assertAlmostEqual, because it sucks :-)
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py_statistics = support.import_fresh_module('statistics', blocked=['_statistics'])
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c_statistics = support.import_fresh_module('statistics', fresh=['_statistics'])
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class TestModules(unittest.TestCase):
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func_names = ['_normal_dist_inv_cdf']
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def test_py_functions(self):
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for fname in self.func_names:
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self.assertEqual(getattr(py_statistics, fname).__module__, 'statistics')
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@unittest.skipUnless(c_statistics, 'requires _statistics')
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def test_c_functions(self):
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for fname in self.func_names:
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self.assertEqual(getattr(c_statistics, fname).__module__, '_statistics')
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class NumericTestCase(unittest.TestCase):
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"""Unit test class for numeric work.
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@ -2314,7 +2332,7 @@ def test_error_cases(self):
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quantiles([10, None, 30], n=4) # data is non-numeric
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class TestNormalDist(unittest.TestCase):
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class TestNormalDist:
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# General note on precision: The pdf(), cdf(), and overlap() methods
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# depend on functions in the math libraries that do not make
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@ -2324,35 +2342,35 @@ class TestNormalDist(unittest.TestCase):
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# implementing our own implementations from scratch.
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def test_slots(self):
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nd = statistics.NormalDist(300, 23)
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nd = self.module.NormalDist(300, 23)
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with self.assertRaises(TypeError):
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vars(nd)
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self.assertEqual(tuple(nd.__slots__), ('_mu', '_sigma'))
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def test_instantiation_and_attributes(self):
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nd = statistics.NormalDist(500, 17)
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nd = self.module.NormalDist(500, 17)
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self.assertEqual(nd.mean, 500)
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self.assertEqual(nd.stdev, 17)
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self.assertEqual(nd.variance, 17**2)
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# default arguments
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nd = statistics.NormalDist()
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nd = self.module.NormalDist()
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self.assertEqual(nd.mean, 0)
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self.assertEqual(nd.stdev, 1)
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self.assertEqual(nd.variance, 1**2)
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# error case: negative sigma
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with self.assertRaises(statistics.StatisticsError):
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statistics.NormalDist(500, -10)
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with self.assertRaises(self.module.StatisticsError):
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self.module.NormalDist(500, -10)
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# verify that subclass type is honored
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class NewNormalDist(statistics.NormalDist):
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class NewNormalDist(self.module.NormalDist):
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pass
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nnd = NewNormalDist(200, 5)
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self.assertEqual(type(nnd), NewNormalDist)
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def test_alternative_constructor(self):
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NormalDist = statistics.NormalDist
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NormalDist = self.module.NormalDist
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data = [96, 107, 90, 92, 110]
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# list input
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self.assertEqual(NormalDist.from_samples(data), NormalDist(99, 9))
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@ -2361,9 +2379,9 @@ def test_alternative_constructor(self):
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# iterator input
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self.assertEqual(NormalDist.from_samples(iter(data)), NormalDist(99, 9))
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# error cases
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with self.assertRaises(statistics.StatisticsError):
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with self.assertRaises(self.module.StatisticsError):
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NormalDist.from_samples([]) # empty input
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with self.assertRaises(statistics.StatisticsError):
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with self.assertRaises(self.module.StatisticsError):
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NormalDist.from_samples([10]) # only one input
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# verify that subclass type is honored
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@ -2373,7 +2391,7 @@ class NewNormalDist(NormalDist):
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self.assertEqual(type(nnd), NewNormalDist)
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def test_sample_generation(self):
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NormalDist = statistics.NormalDist
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NormalDist = self.module.NormalDist
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mu, sigma = 10_000, 3.0
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X = NormalDist(mu, sigma)
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n = 1_000
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@ -2381,7 +2399,7 @@ def test_sample_generation(self):
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self.assertEqual(len(data), n)
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self.assertEqual(set(map(type, data)), {float})
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# mean(data) expected to fall within 8 standard deviations
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xbar = statistics.mean(data)
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xbar = self.module.mean(data)
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self.assertTrue(mu - sigma*8 <= xbar <= mu + sigma*8)
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# verify that seeding makes reproducible sequences
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@ -2395,7 +2413,7 @@ def test_sample_generation(self):
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self.assertNotEqual(data1, data2)
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def test_pdf(self):
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NormalDist = statistics.NormalDist
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NormalDist = self.module.NormalDist
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X = NormalDist(100, 15)
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# Verify peak around center
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self.assertLess(X.pdf(99), X.pdf(100))
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@ -2426,7 +2444,7 @@ def test_pdf(self):
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self.assertAlmostEqual(Z.pdf(-x / 100.0), px, places=4)
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# Error case: variance is zero
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Y = NormalDist(100, 0)
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with self.assertRaises(statistics.StatisticsError):
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with self.assertRaises(self.module.StatisticsError):
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Y.pdf(90)
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# Special values
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self.assertEqual(X.pdf(float('-Inf')), 0.0)
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@ -2434,7 +2452,7 @@ def test_pdf(self):
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self.assertTrue(math.isnan(X.pdf(float('NaN'))))
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def test_cdf(self):
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NormalDist = statistics.NormalDist
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NormalDist = self.module.NormalDist
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X = NormalDist(100, 15)
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cdfs = [X.cdf(x) for x in range(1, 200)]
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self.assertEqual(set(map(type, cdfs)), {float})
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@ -2456,7 +2474,7 @@ def test_cdf(self):
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self.assertAlmostEqual(Z.cdf(-z), 1.0 - cum_prob, places=5)
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# Error case: variance is zero
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Y = NormalDist(100, 0)
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with self.assertRaises(statistics.StatisticsError):
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with self.assertRaises(self.module.StatisticsError):
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Y.cdf(90)
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# Special values
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self.assertEqual(X.cdf(float('-Inf')), 0.0)
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@ -2465,7 +2483,7 @@ def test_cdf(self):
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@support.skip_if_pgo_task
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def test_inv_cdf(self):
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NormalDist = statistics.NormalDist
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NormalDist = self.module.NormalDist
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# Center case should be exact.
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iq = NormalDist(100, 15)
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@ -2513,15 +2531,15 @@ def test_inv_cdf(self):
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self.assertAlmostEqual(iq.inv_cdf(iq.cdf(x)), x, places=5)
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# Error cases:
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with self.assertRaises(statistics.StatisticsError):
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with self.assertRaises(self.module.StatisticsError):
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iq.inv_cdf(0.0) # p is zero
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with self.assertRaises(statistics.StatisticsError):
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with self.assertRaises(self.module.StatisticsError):
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iq.inv_cdf(-0.1) # p under zero
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with self.assertRaises(statistics.StatisticsError):
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with self.assertRaises(self.module.StatisticsError):
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iq.inv_cdf(1.0) # p is one
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with self.assertRaises(statistics.StatisticsError):
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with self.assertRaises(self.module.StatisticsError):
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iq.inv_cdf(1.1) # p over one
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with self.assertRaises(statistics.StatisticsError):
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with self.assertRaises(self.module.StatisticsError):
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iq = NormalDist(100, 0) # sigma is zero
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iq.inv_cdf(0.5)
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@ -2529,7 +2547,7 @@ def test_inv_cdf(self):
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self.assertTrue(math.isnan(Z.inv_cdf(float('NaN'))))
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def test_overlap(self):
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NormalDist = statistics.NormalDist
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NormalDist = self.module.NormalDist
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# Match examples from Imman and Bradley
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for X1, X2, published_result in [
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@ -2586,26 +2604,26 @@ def overlap_numeric(X, Y, *, steps=8_192, z=5):
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X.overlap(X, X) # too may arguments
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with self.assertRaises(TypeError):
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X.overlap(None) # right operand not a NormalDist
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with self.assertRaises(statistics.StatisticsError):
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with self.assertRaises(self.module.StatisticsError):
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X.overlap(NormalDist(1, 0)) # right operand sigma is zero
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with self.assertRaises(statistics.StatisticsError):
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with self.assertRaises(self.module.StatisticsError):
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NormalDist(1, 0).overlap(X) # left operand sigma is zero
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def test_properties(self):
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X = statistics.NormalDist(100, 15)
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X = self.module.NormalDist(100, 15)
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self.assertEqual(X.mean, 100)
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self.assertEqual(X.stdev, 15)
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self.assertEqual(X.variance, 225)
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def test_same_type_addition_and_subtraction(self):
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NormalDist = statistics.NormalDist
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NormalDist = self.module.NormalDist
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X = NormalDist(100, 12)
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Y = NormalDist(40, 5)
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self.assertEqual(X + Y, NormalDist(140, 13)) # __add__
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self.assertEqual(X - Y, NormalDist(60, 13)) # __sub__
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def test_translation_and_scaling(self):
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NormalDist = statistics.NormalDist
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NormalDist = self.module.NormalDist
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X = NormalDist(100, 15)
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y = 10
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self.assertEqual(+X, NormalDist(100, 15)) # __pos__
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@ -2621,7 +2639,7 @@ def test_translation_and_scaling(self):
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y / X
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def test_unary_operations(self):
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NormalDist = statistics.NormalDist
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NormalDist = self.module.NormalDist
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X = NormalDist(100, 12)
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Y = +X
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self.assertIsNot(X, Y)
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@ -2633,7 +2651,7 @@ def test_unary_operations(self):
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self.assertEqual(X.stdev, Y.stdev)
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def test_equality(self):
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NormalDist = statistics.NormalDist
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NormalDist = self.module.NormalDist
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nd1 = NormalDist()
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nd2 = NormalDist(2, 4)
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nd3 = NormalDist()
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@ -2673,7 +2691,7 @@ def __init__(self, mu, sigma):
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self.assertNotEqual(nd, lnd)
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def test_pickle_and_copy(self):
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nd = statistics.NormalDist(37.5, 5.625)
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nd = self.module.NormalDist(37.5, 5.625)
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nd1 = copy.copy(nd)
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self.assertEqual(nd, nd1)
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nd2 = copy.deepcopy(nd)
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@ -2682,14 +2700,36 @@ def test_pickle_and_copy(self):
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self.assertEqual(nd, nd3)
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def test_hashability(self):
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ND = statistics.NormalDist
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ND = self.module.NormalDist
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s = {ND(100, 15), ND(100.0, 15.0), ND(100, 10), ND(95, 15), ND(100, 15)}
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self.assertEqual(len(s), 3)
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def test_repr(self):
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nd = statistics.NormalDist(37.5, 5.625)
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nd = self.module.NormalDist(37.5, 5.625)
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self.assertEqual(repr(nd), 'NormalDist(mu=37.5, sigma=5.625)')
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# Swapping the sys.modules['statistics'] is to solving the
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# _pickle.PicklingError:
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# Can't pickle <class 'statistics.NormalDist'>:
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# it's not the same object as statistics.NormalDist
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class TestNormalDistPython(unittest.TestCase, TestNormalDist):
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module = py_statistics
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def setUp(self):
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sys.modules['statistics'] = self.module
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def tearDown(self):
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sys.modules['statistics'] = statistics
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@unittest.skipUnless(c_statistics, 'requires _statistics')
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class TestNormalDistC(unittest.TestCase, TestNormalDist):
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module = c_statistics
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def setUp(self):
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sys.modules['statistics'] = self.module
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def tearDown(self):
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sys.modules['statistics'] = statistics
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# === Run tests ===
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@ -0,0 +1,2 @@
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Update test_statistics.py to verify that the statistics module works well
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for both C and Python implementations. Patch by Dong-hee Na
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