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bpo-36324: NormalDist() add more tests and update comments (GH-12476)
* Improve coverage. * Note inherent limitations of the accuracy tests https://bugs.python.org/issue36324
This commit is contained in:
Raymond Hettinger
Miss Islington (bot)
parent
aa3ecb8041
commit
2afb598618
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@ -2040,6 +2040,13 @@ def test_compare_to_variance(self):
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class TestNormalDist(unittest.TestCase):
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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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# explicit accuracy guarantees. Accordingly, some of the accuracy
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# tests below may fail if the underlying math functions are
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# inaccurate. There isn't much we can do about this short of
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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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with self.assertRaises(TypeError):
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@ -2062,6 +2069,12 @@ def test_instantiation_and_attributes(self):
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with self.assertRaises(statistics.StatisticsError):
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statistics.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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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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data = [96, 107, 90, 92, 110]
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@ -2077,6 +2090,12 @@ def test_alternative_constructor(self):
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with self.assertRaises(statistics.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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class NewNormalDist(NormalDist):
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pass
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nnd = NewNormalDist.from_samples(data)
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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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mu, sigma = 10_000, 3.0
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@ -2099,12 +2118,6 @@ def test_sample_generation(self):
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self.assertEqual(data2, data4)
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self.assertNotEqual(data1, data2)
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# verify that subclass type is honored
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class NewNormalDist(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_pdf(self):
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NormalDist = statistics.NormalDist
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X = NormalDist(100, 15)
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@ -2151,8 +2164,8 @@ def test_cdf(self):
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self.assertEqual(set(map(type, cdfs)), {float})
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# Verify montonic
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self.assertEqual(cdfs, sorted(cdfs))
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# Verify center
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self.assertAlmostEqual(X.cdf(100), 0.50)
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# Verify center (should be exact)
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self.assertEqual(X.cdf(100), 0.50)
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# Check against a table of known values
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# https://en.wikipedia.org/wiki/Standard_normal_table#Cumulative
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Z = NormalDist()
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@ -2216,10 +2229,11 @@ def test_inv_cdf(self):
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p = 1.0 - p
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self.assertAlmostEqual(iq.cdf(iq.inv_cdf(p)), p)
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# Now apply cdf() first. At six sigmas, the round-trip
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# loses a lot of precision, so only check to 6 places.
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for x in range(10, 190):
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self.assertAlmostEqual(iq.inv_cdf(iq.cdf(x)), x, places=6)
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# Now apply cdf() first. Near the tails, the round-trip loses
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# precision and is ill-conditioned (small changes in the inputs
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# give large changes in the output), so only check to 5 places.
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for x in range(200):
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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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@ -2237,6 +2251,9 @@ def test_inv_cdf(self):
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iq.sigma = -0.1 # sigma under zero
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iq.inv_cdf(0.5)
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# Special values
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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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@ -2275,6 +2292,7 @@ def overlap_numeric(X, Y, *, steps=8_192, z=5):
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(NormalDist(-100, 15), NormalDist(110, 15)),
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(NormalDist(-100, 15), NormalDist(-110, 15)),
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# Misc cases with unequal standard deviations
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(NormalDist(100, 12), NormalDist(100, 15)),
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(NormalDist(100, 12), NormalDist(110, 15)),
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(NormalDist(100, 12), NormalDist(150, 15)),
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(NormalDist(100, 12), NormalDist(150, 35)),
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@ -2305,18 +2323,6 @@ def test_properties(self):
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self.assertEqual(X.stdev, 15)
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self.assertEqual(X.variance, 225)
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def test_unary_operations(self):
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NormalDist = statistics.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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self.assertEqual(X.mu, Y.mu)
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self.assertEqual(X.sigma, Y.sigma)
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Y = -X
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self.assertIsNot(X, Y)
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self.assertEqual(X.mu, -Y.mu)
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self.assertEqual(X.sigma, Y.sigma)
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def test_same_type_addition_and_subtraction(self):
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NormalDist = statistics.NormalDist
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X = NormalDist(100, 12)
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@ -2340,13 +2346,27 @@ def test_translation_and_scaling(self):
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with self.assertRaises(TypeError): # __rtruediv__
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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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X = NormalDist(100, 12)
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Y = +X
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self.assertIsNot(X, Y)
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self.assertEqual(X.mu, Y.mu)
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self.assertEqual(X.sigma, Y.sigma)
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Y = -X
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self.assertIsNot(X, Y)
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self.assertEqual(X.mu, -Y.mu)
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self.assertEqual(X.sigma, Y.sigma)
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def test_equality(self):
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NormalDist = statistics.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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nd4 = NormalDist(2, 4)
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self.assertNotEqual(nd1, nd2)
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self.assertEqual(nd1, nd3)
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self.assertEqual(nd2, nd4)
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# Test NotImplemented when types are different
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class A:
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