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bpo-38490: statistics: Add covariance, Pearson's correlation, and simple linear regression (#16813)

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Co-authored-by: Tymoteusz Wołodźko <twolodzko+gitkraken@gmail.com
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Tymoteusz Wołodźko 2021-04-25 13:45:09 +02:00 • committed by GitHub
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78
Lib/test/test_statistics.py
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@ -2407,6 +2407,84 @@ class TestQuantiles(unittest.TestCase):
quantiles([10, None, 30], n=4) # data is non-numeric
class TestBivariateStatistics(unittest.TestCase):
def test_unequal_size_error(self):
for x, y in [
([1, 2, 3], [1, 2]),
([1, 2], [1, 2, 3]),
]:
with self.assertRaises(statistics.StatisticsError):
statistics.covariance(x, y)
with self.assertRaises(statistics.StatisticsError):
statistics.correlation(x, y)
with self.assertRaises(statistics.StatisticsError):
statistics.linear_regression(x, y)
def test_small_sample_error(self):
for x, y in [
([], []),
([], [1, 2,]),
([1, 2,], []),
([1,], [1,]),
([1,], [1, 2,]),
([1, 2,], [1,]),
]:
with self.assertRaises(statistics.StatisticsError):
statistics.covariance(x, y)
with self.assertRaises(statistics.StatisticsError):
statistics.correlation(x, y)
with self.assertRaises(statistics.StatisticsError):
statistics.linear_regression(x, y)
class TestCorrelationAndCovariance(unittest.TestCase):
def test_results(self):
for x, y, result in [
([1, 2, 3], [1, 2, 3], 1),
([1, 2, 3], [-1, -2, -3], -1),
([1, 2, 3], [3, 2, 1], -1),
([1, 2, 3], [1, 2, 1], 0),
([1, 2, 3], [1, 3, 2], 0.5),
]:
self.assertAlmostEqual(statistics.correlation(x, y), result)
self.assertAlmostEqual(statistics.covariance(x, y), result)
def test_different_scales(self):
x = [1, 2, 3]
y = [10, 30, 20]
self.assertAlmostEqual(statistics.correlation(x, y), 0.5)
self.assertAlmostEqual(statistics.covariance(x, y), 5)
y = [.1, .2, .3]
self.assertAlmostEqual(statistics.correlation(x, y), 1)
self.assertAlmostEqual(statistics.covariance(x, y), 0.1)
class TestLinearRegression(unittest.TestCase):
def test_constant_input_error(self):
x = [1, 1, 1,]
y = [1, 2, 3,]
with self.assertRaises(statistics.StatisticsError):
statistics.linear_regression(x, y)
def test_results(self):
for x, y, true_intercept, true_slope in [
([1, 2, 3], [0, 0, 0], 0, 0),
([1, 2, 3], [1, 2, 3], 0, 1),
([1, 2, 3], [100, 100, 100], 100, 0),
([1, 2, 3], [12, 14, 16], 10, 2),
([1, 2, 3], [-1, -2, -3], 0, -1),
([1, 2, 3], [21, 22, 23], 20, 1),
([1, 2, 3], [5.1, 5.2, 5.3], 5, 0.1),
]:
intercept, slope = statistics.linear_regression(x, y)
self.assertAlmostEqual(intercept, true_intercept)
self.assertAlmostEqual(slope, true_slope)
class TestNormalDist:
# General note on precision: The pdf(), cdf(), and overlap() methods