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1535 lines
57 KiB
Python
1535 lines
57 KiB
Python
"""Test suite for statistics module, including helper NumericTestCase and
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approx_equal function.
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"""
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import collections
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import decimal
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import doctest
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import math
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import random
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import types
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import unittest
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from decimal import Decimal
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from fractions import Fraction
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# Module to be tested.
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import statistics
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# === Helper functions and class ===
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def _calc_errors(actual, expected):
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"""Return the absolute and relative errors between two numbers.
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>>> _calc_errors(100, 75)
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(25, 0.25)
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>>> _calc_errors(100, 100)
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(0, 0.0)
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Returns the (absolute error, relative error) between the two arguments.
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"""
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base = max(abs(actual), abs(expected))
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abs_err = abs(actual - expected)
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rel_err = abs_err/base if base else float('inf')
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return (abs_err, rel_err)
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def approx_equal(x, y, tol=1e-12, rel=1e-7):
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"""approx_equal(x, y [, tol [, rel]]) => True|False
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Return True if numbers x and y are approximately equal, to within some
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margin of error, otherwise return False. Numbers which compare equal
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will also compare approximately equal.
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x is approximately equal to y if the difference between them is less than
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an absolute error tol or a relative error rel, whichever is bigger.
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If given, both tol and rel must be finite, non-negative numbers. If not
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given, default values are tol=1e-12 and rel=1e-7.
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>>> approx_equal(1.2589, 1.2587, tol=0.0003, rel=0)
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True
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>>> approx_equal(1.2589, 1.2587, tol=0.0001, rel=0)
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False
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Absolute error is defined as abs(x-y); if that is less than or equal to
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tol, x and y are considered approximately equal.
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Relative error is defined as abs((x-y)/x) or abs((x-y)/y), whichever is
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smaller, provided x or y are not zero. If that figure is less than or
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equal to rel, x and y are considered approximately equal.
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Complex numbers are not directly supported. If you wish to compare to
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complex numbers, extract their real and imaginary parts and compare them
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individually.
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NANs always compare unequal, even with themselves. Infinities compare
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approximately equal if they have the same sign (both positive or both
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negative). Infinities with different signs compare unequal; so do
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comparisons of infinities with finite numbers.
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"""
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if tol < 0 or rel < 0:
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raise ValueError('error tolerances must be non-negative')
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# NANs are never equal to anything, approximately or otherwise.
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if math.isnan(x) or math.isnan(y):
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return False
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# Numbers which compare equal also compare approximately equal.
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if x == y:
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# This includes the case of two infinities with the same sign.
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return True
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if math.isinf(x) or math.isinf(y):
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# This includes the case of two infinities of opposite sign, or
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# one infinity and one finite number.
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return False
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# Two finite numbers.
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actual_error = abs(x - y)
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allowed_error = max(tol, rel*max(abs(x), abs(y)))
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return actual_error <= allowed_error
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# This class exists only as somewhere to stick a docstring containing
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# doctests. The following docstring and tests were originally in a separate
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# module. Now that it has been merged in here, I need somewhere to hang the.
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# docstring. Ultimately, this class will die, and the information below will
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# either become redundant, or be moved into more appropriate places.
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class _DoNothing:
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"""
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When doing numeric work, especially with floats, exact equality is often
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not what you want. Due to round-off error, it is often a bad idea to try
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to compare floats with equality. Instead the usual procedure is to test
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them with some (hopefully small!) allowance for error.
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The ``approx_equal`` function allows you to specify either an absolute
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error tolerance, or a relative error, or both.
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Absolute error tolerances are simple, but you need to know the magnitude
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of the quantities being compared:
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>>> approx_equal(12.345, 12.346, tol=1e-3)
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True
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>>> approx_equal(12.345e6, 12.346e6, tol=1e-3) # tol is too small.
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False
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Relative errors are more suitable when the values you are comparing can
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vary in magnitude:
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>>> approx_equal(12.345, 12.346, rel=1e-4)
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True
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>>> approx_equal(12.345e6, 12.346e6, rel=1e-4)
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True
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but a naive implementation of relative error testing can run into trouble
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around zero.
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If you supply both an absolute tolerance and a relative error, the
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comparison succeeds if either individual test succeeds:
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>>> approx_equal(12.345e6, 12.346e6, tol=1e-3, rel=1e-4)
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True
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"""
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pass
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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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class NumericTestCase(unittest.TestCase):
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"""Unit test class for numeric work.
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This subclasses TestCase. In addition to the standard method
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``TestCase.assertAlmostEqual``, ``assertApproxEqual`` is provided.
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"""
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# By default, we expect exact equality, unless overridden.
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tol = rel = 0
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def assertApproxEqual(
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self, first, second, tol=None, rel=None, msg=None
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):
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"""Test passes if ``first`` and ``second`` are approximately equal.
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This test passes if ``first`` and ``second`` are equal to
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within ``tol``, an absolute error, or ``rel``, a relative error.
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If either ``tol`` or ``rel`` are None or not given, they default to
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test attributes of the same name (by default, 0).
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The objects may be either numbers, or sequences of numbers. Sequences
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are tested element-by-element.
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>>> class MyTest(NumericTestCase):
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... def test_number(self):
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... x = 1.0/6
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... y = sum([x]*6)
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... self.assertApproxEqual(y, 1.0, tol=1e-15)
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... def test_sequence(self):
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... a = [1.001, 1.001e-10, 1.001e10]
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... b = [1.0, 1e-10, 1e10]
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... self.assertApproxEqual(a, b, rel=1e-3)
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...
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>>> import unittest
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>>> from io import StringIO # Suppress test runner output.
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>>> suite = unittest.TestLoader().loadTestsFromTestCase(MyTest)
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>>> unittest.TextTestRunner(stream=StringIO()).run(suite)
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<unittest.runner.TextTestResult run=2 errors=0 failures=0>
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"""
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if tol is None:
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tol = self.tol
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if rel is None:
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rel = self.rel
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if (
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isinstance(first, collections.Sequence) and
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isinstance(second, collections.Sequence)
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):
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check = self._check_approx_seq
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else:
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check = self._check_approx_num
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check(first, second, tol, rel, msg)
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def _check_approx_seq(self, first, second, tol, rel, msg):
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if len(first) != len(second):
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standardMsg = (
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"sequences differ in length: %d items != %d items"
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% (len(first), len(second))
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)
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msg = self._formatMessage(msg, standardMsg)
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raise self.failureException(msg)
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for i, (a,e) in enumerate(zip(first, second)):
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self._check_approx_num(a, e, tol, rel, msg, i)
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def _check_approx_num(self, first, second, tol, rel, msg, idx=None):
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if approx_equal(first, second, tol, rel):
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# Test passes. Return early, we are done.
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return None
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# Otherwise we failed.
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standardMsg = self._make_std_err_msg(first, second, tol, rel, idx)
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msg = self._formatMessage(msg, standardMsg)
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raise self.failureException(msg)
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@staticmethod
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def _make_std_err_msg(first, second, tol, rel, idx):
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# Create the standard error message for approx_equal failures.
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assert first != second
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template = (
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' %r != %r\n'
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' values differ by more than tol=%r and rel=%r\n'
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' -> absolute error = %r\n'
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' -> relative error = %r'
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)
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if idx is not None:
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header = 'numeric sequences first differ at index %d.\n' % idx
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template = header + template
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# Calculate actual errors:
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abs_err, rel_err = _calc_errors(first, second)
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return template % (first, second, tol, rel, abs_err, rel_err)
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# ========================
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# === Test the helpers ===
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# ========================
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# --- Tests for approx_equal ---
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class ApproxEqualSymmetryTest(unittest.TestCase):
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# Test symmetry of approx_equal.
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def test_relative_symmetry(self):
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# Check that approx_equal treats relative error symmetrically.
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# (a-b)/a is usually not equal to (a-b)/b. Ensure that this
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# doesn't matter.
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#
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# Note: the reason for this test is that an early version
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# of approx_equal was not symmetric. A relative error test
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# would pass, or fail, depending on which value was passed
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# as the first argument.
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#
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args1 = [2456, 37.8, -12.45, Decimal('2.54'), Fraction(17, 54)]
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args2 = [2459, 37.2, -12.41, Decimal('2.59'), Fraction(15, 54)]
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assert len(args1) == len(args2)
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for a, b in zip(args1, args2):
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self.do_relative_symmetry(a, b)
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def do_relative_symmetry(self, a, b):
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a, b = min(a, b), max(a, b)
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assert a < b
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delta = b - a # The absolute difference between the values.
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rel_err1, rel_err2 = abs(delta/a), abs(delta/b)
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# Choose an error margin halfway between the two.
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rel = (rel_err1 + rel_err2)/2
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# Now see that values a and b compare approx equal regardless of
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# which is given first.
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self.assertTrue(approx_equal(a, b, tol=0, rel=rel))
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self.assertTrue(approx_equal(b, a, tol=0, rel=rel))
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def test_symmetry(self):
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# Test that approx_equal(a, b) == approx_equal(b, a)
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args = [-23, -2, 5, 107, 93568]
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delta = 2
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for x in args:
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for type_ in (int, float, Decimal, Fraction):
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x = type_(x)*100
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y = x + delta
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r = abs(delta/max(x, y))
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# There are five cases to check:
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# 1) actual error <= tol, <= rel
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self.do_symmetry_test(x, y, tol=delta, rel=r)
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self.do_symmetry_test(x, y, tol=delta+1, rel=2*r)
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# 2) actual error > tol, > rel
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self.do_symmetry_test(x, y, tol=delta-1, rel=r/2)
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# 3) actual error <= tol, > rel
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self.do_symmetry_test(x, y, tol=delta, rel=r/2)
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# 4) actual error > tol, <= rel
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self.do_symmetry_test(x, y, tol=delta-1, rel=r)
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self.do_symmetry_test(x, y, tol=delta-1, rel=2*r)
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# 5) exact equality test
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self.do_symmetry_test(x, x, tol=0, rel=0)
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self.do_symmetry_test(x, y, tol=0, rel=0)
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def do_symmetry_test(self, a, b, tol, rel):
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template = "approx_equal comparisons don't match for %r"
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flag1 = approx_equal(a, b, tol, rel)
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flag2 = approx_equal(b, a, tol, rel)
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self.assertEqual(flag1, flag2, template.format((a, b, tol, rel)))
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class ApproxEqualExactTest(unittest.TestCase):
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# Test the approx_equal function with exactly equal values.
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# Equal values should compare as approximately equal.
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# Test cases for exactly equal values, which should compare approx
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# equal regardless of the error tolerances given.
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def do_exactly_equal_test(self, x, tol, rel):
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result = approx_equal(x, x, tol=tol, rel=rel)
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self.assertTrue(result, 'equality failure for x=%r' % x)
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result = approx_equal(-x, -x, tol=tol, rel=rel)
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self.assertTrue(result, 'equality failure for x=%r' % -x)
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def test_exactly_equal_ints(self):
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# Test that equal int values are exactly equal.
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for n in [42, 19740, 14974, 230, 1795, 700245, 36587]:
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self.do_exactly_equal_test(n, 0, 0)
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def test_exactly_equal_floats(self):
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# Test that equal float values are exactly equal.
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for x in [0.42, 1.9740, 1497.4, 23.0, 179.5, 70.0245, 36.587]:
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self.do_exactly_equal_test(x, 0, 0)
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def test_exactly_equal_fractions(self):
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# Test that equal Fraction values are exactly equal.
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F = Fraction
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for f in [F(1, 2), F(0), F(5, 3), F(9, 7), F(35, 36), F(3, 7)]:
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self.do_exactly_equal_test(f, 0, 0)
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def test_exactly_equal_decimals(self):
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# Test that equal Decimal values are exactly equal.
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D = Decimal
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for d in map(D, "8.2 31.274 912.04 16.745 1.2047".split()):
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self.do_exactly_equal_test(d, 0, 0)
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def test_exactly_equal_absolute(self):
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# Test that equal values are exactly equal with an absolute error.
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for n in [16, 1013, 1372, 1198, 971, 4]:
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# Test as ints.
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self.do_exactly_equal_test(n, 0.01, 0)
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# Test as floats.
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self.do_exactly_equal_test(n/10, 0.01, 0)
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# Test as Fractions.
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f = Fraction(n, 1234)
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self.do_exactly_equal_test(f, 0.01, 0)
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def test_exactly_equal_absolute_decimals(self):
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# Test equal Decimal values are exactly equal with an absolute error.
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self.do_exactly_equal_test(Decimal("3.571"), Decimal("0.01"), 0)
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self.do_exactly_equal_test(-Decimal("81.3971"), Decimal("0.01"), 0)
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def test_exactly_equal_relative(self):
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# Test that equal values are exactly equal with a relative error.
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for x in [8347, 101.3, -7910.28, Fraction(5, 21)]:
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self.do_exactly_equal_test(x, 0, 0.01)
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self.do_exactly_equal_test(Decimal("11.68"), 0, Decimal("0.01"))
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def test_exactly_equal_both(self):
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# Test that equal values are equal when both tol and rel are given.
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for x in [41017, 16.742, -813.02, Fraction(3, 8)]:
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self.do_exactly_equal_test(x, 0.1, 0.01)
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D = Decimal
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self.do_exactly_equal_test(D("7.2"), D("0.1"), D("0.01"))
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class ApproxEqualUnequalTest(unittest.TestCase):
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# Unequal values should compare unequal with zero error tolerances.
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# Test cases for unequal values, with exact equality test.
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def do_exactly_unequal_test(self, x):
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for a in (x, -x):
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result = approx_equal(a, a+1, tol=0, rel=0)
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self.assertFalse(result, 'inequality failure for x=%r' % a)
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def test_exactly_unequal_ints(self):
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# Test unequal int values are unequal with zero error tolerance.
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for n in [951, 572305, 478, 917, 17240]:
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self.do_exactly_unequal_test(n)
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def test_exactly_unequal_floats(self):
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# Test unequal float values are unequal with zero error tolerance.
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for x in [9.51, 5723.05, 47.8, 9.17, 17.24]:
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self.do_exactly_unequal_test(x)
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def test_exactly_unequal_fractions(self):
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# Test that unequal Fractions are unequal with zero error tolerance.
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F = Fraction
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for f in [F(1, 5), F(7, 9), F(12, 11), F(101, 99023)]:
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self.do_exactly_unequal_test(f)
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def test_exactly_unequal_decimals(self):
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# Test that unequal Decimals are unequal with zero error tolerance.
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for d in map(Decimal, "3.1415 298.12 3.47 18.996 0.00245".split()):
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self.do_exactly_unequal_test(d)
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class ApproxEqualInexactTest(unittest.TestCase):
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# Inexact test cases for approx_error.
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# Test cases when comparing two values that are not exactly equal.
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# === Absolute error tests ===
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def do_approx_equal_abs_test(self, x, delta):
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template = "Test failure for x={!r}, y={!r}"
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for y in (x + delta, x - delta):
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msg = template.format(x, y)
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self.assertTrue(approx_equal(x, y, tol=2*delta, rel=0), msg)
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self.assertFalse(approx_equal(x, y, tol=delta/2, rel=0), msg)
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def test_approx_equal_absolute_ints(self):
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# Test approximate equality of ints with an absolute error.
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for n in [-10737, -1975, -7, -2, 0, 1, 9, 37, 423, 9874, 23789110]:
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self.do_approx_equal_abs_test(n, 10)
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self.do_approx_equal_abs_test(n, 2)
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def test_approx_equal_absolute_floats(self):
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# Test approximate equality of floats with an absolute error.
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for x in [-284.126, -97.1, -3.4, -2.15, 0.5, 1.0, 7.8, 4.23, 3817.4]:
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self.do_approx_equal_abs_test(x, 1.5)
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self.do_approx_equal_abs_test(x, 0.01)
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self.do_approx_equal_abs_test(x, 0.0001)
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def test_approx_equal_absolute_fractions(self):
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# Test approximate equality of Fractions with an absolute error.
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delta = Fraction(1, 29)
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numerators = [-84, -15, -2, -1, 0, 1, 5, 17, 23, 34, 71]
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for f in (Fraction(n, 29) for n in numerators):
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self.do_approx_equal_abs_test(f, delta)
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self.do_approx_equal_abs_test(f, float(delta))
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def test_approx_equal_absolute_decimals(self):
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# Test approximate equality of Decimals with an absolute error.
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delta = Decimal("0.01")
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for d in map(Decimal, "1.0 3.5 36.08 61.79 7912.3648".split()):
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self.do_approx_equal_abs_test(d, delta)
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self.do_approx_equal_abs_test(-d, delta)
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def test_cross_zero(self):
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# Test for the case of the two values having opposite signs.
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self.assertTrue(approx_equal(1e-5, -1e-5, tol=1e-4, rel=0))
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# === Relative error tests ===
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def do_approx_equal_rel_test(self, x, delta):
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template = "Test failure for x={!r}, y={!r}"
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for y in (x*(1+delta), x*(1-delta)):
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msg = template.format(x, y)
|
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self.assertTrue(approx_equal(x, y, tol=0, rel=2*delta), msg)
|
|
self.assertFalse(approx_equal(x, y, tol=0, rel=delta/2), msg)
|
|
|
|
def test_approx_equal_relative_ints(self):
|
|
# Test approximate equality of ints with a relative error.
|
|
self.assertTrue(approx_equal(64, 47, tol=0, rel=0.36))
|
|
self.assertTrue(approx_equal(64, 47, tol=0, rel=0.37))
|
|
# ---
|
|
self.assertTrue(approx_equal(449, 512, tol=0, rel=0.125))
|
|
self.assertTrue(approx_equal(448, 512, tol=0, rel=0.125))
|
|
self.assertFalse(approx_equal(447, 512, tol=0, rel=0.125))
|
|
|
|
def test_approx_equal_relative_floats(self):
|
|
# Test approximate equality of floats with a relative error.
|
|
for x in [-178.34, -0.1, 0.1, 1.0, 36.97, 2847.136, 9145.074]:
|
|
self.do_approx_equal_rel_test(x, 0.02)
|
|
self.do_approx_equal_rel_test(x, 0.0001)
|
|
|
|
def test_approx_equal_relative_fractions(self):
|
|
# Test approximate equality of Fractions with a relative error.
|
|
F = Fraction
|
|
delta = Fraction(3, 8)
|
|
for f in [F(3, 84), F(17, 30), F(49, 50), F(92, 85)]:
|
|
for d in (delta, float(delta)):
|
|
self.do_approx_equal_rel_test(f, d)
|
|
self.do_approx_equal_rel_test(-f, d)
|
|
|
|
def test_approx_equal_relative_decimals(self):
|
|
# Test approximate equality of Decimals with a relative error.
|
|
for d in map(Decimal, "0.02 1.0 5.7 13.67 94.138 91027.9321".split()):
|
|
self.do_approx_equal_rel_test(d, Decimal("0.001"))
|
|
self.do_approx_equal_rel_test(-d, Decimal("0.05"))
|
|
|
|
# === Both absolute and relative error tests ===
|
|
|
|
# There are four cases to consider:
|
|
# 1) actual error <= both absolute and relative error
|
|
# 2) actual error <= absolute error but > relative error
|
|
# 3) actual error <= relative error but > absolute error
|
|
# 4) actual error > both absolute and relative error
|
|
|
|
def do_check_both(self, a, b, tol, rel, tol_flag, rel_flag):
|
|
check = self.assertTrue if tol_flag else self.assertFalse
|
|
check(approx_equal(a, b, tol=tol, rel=0))
|
|
check = self.assertTrue if rel_flag else self.assertFalse
|
|
check(approx_equal(a, b, tol=0, rel=rel))
|
|
check = self.assertTrue if (tol_flag or rel_flag) else self.assertFalse
|
|
check(approx_equal(a, b, tol=tol, rel=rel))
|
|
|
|
def test_approx_equal_both1(self):
|
|
# Test actual error <= both absolute and relative error.
|
|
self.do_check_both(7.955, 7.952, 0.004, 3.8e-4, True, True)
|
|
self.do_check_both(-7.387, -7.386, 0.002, 0.0002, True, True)
|
|
|
|
def test_approx_equal_both2(self):
|
|
# Test actual error <= absolute error but > relative error.
|
|
self.do_check_both(7.955, 7.952, 0.004, 3.7e-4, True, False)
|
|
|
|
def test_approx_equal_both3(self):
|
|
# Test actual error <= relative error but > absolute error.
|
|
self.do_check_both(7.955, 7.952, 0.001, 3.8e-4, False, True)
|
|
|
|
def test_approx_equal_both4(self):
|
|
# Test actual error > both absolute and relative error.
|
|
self.do_check_both(2.78, 2.75, 0.01, 0.001, False, False)
|
|
self.do_check_both(971.44, 971.47, 0.02, 3e-5, False, False)
|
|
|
|
|
|
class ApproxEqualSpecialsTest(unittest.TestCase):
|
|
# Test approx_equal with NANs and INFs and zeroes.
|
|
|
|
def test_inf(self):
|
|
for type_ in (float, Decimal):
|
|
inf = type_('inf')
|
|
self.assertTrue(approx_equal(inf, inf))
|
|
self.assertTrue(approx_equal(inf, inf, 0, 0))
|
|
self.assertTrue(approx_equal(inf, inf, 1, 0.01))
|
|
self.assertTrue(approx_equal(-inf, -inf))
|
|
self.assertFalse(approx_equal(inf, -inf))
|
|
self.assertFalse(approx_equal(inf, 1000))
|
|
|
|
def test_nan(self):
|
|
for type_ in (float, Decimal):
|
|
nan = type_('nan')
|
|
for other in (nan, type_('inf'), 1000):
|
|
self.assertFalse(approx_equal(nan, other))
|
|
|
|
def test_float_zeroes(self):
|
|
nzero = math.copysign(0.0, -1)
|
|
self.assertTrue(approx_equal(nzero, 0.0, tol=0.1, rel=0.1))
|
|
|
|
def test_decimal_zeroes(self):
|
|
nzero = Decimal("-0.0")
|
|
self.assertTrue(approx_equal(nzero, Decimal(0), tol=0.1, rel=0.1))
|
|
|
|
|
|
class TestApproxEqualErrors(unittest.TestCase):
|
|
# Test error conditions of approx_equal.
|
|
|
|
def test_bad_tol(self):
|
|
# Test negative tol raises.
|
|
self.assertRaises(ValueError, approx_equal, 100, 100, -1, 0.1)
|
|
|
|
def test_bad_rel(self):
|
|
# Test negative rel raises.
|
|
self.assertRaises(ValueError, approx_equal, 100, 100, 1, -0.1)
|
|
|
|
|
|
# --- Tests for NumericTestCase ---
|
|
|
|
# The formatting routine that generates the error messages is complex enough
|
|
# that it too needs testing.
|
|
|
|
class TestNumericTestCase(unittest.TestCase):
|
|
# The exact wording of NumericTestCase error messages is *not* guaranteed,
|
|
# but we need to give them some sort of test to ensure that they are
|
|
# generated correctly. As a compromise, we look for specific substrings
|
|
# that are expected to be found even if the overall error message changes.
|
|
|
|
def do_test(self, args):
|
|
actual_msg = NumericTestCase._make_std_err_msg(*args)
|
|
expected = self.generate_substrings(*args)
|
|
for substring in expected:
|
|
self.assertIn(substring, actual_msg)
|
|
|
|
def test_numerictestcase_is_testcase(self):
|
|
# Ensure that NumericTestCase actually is a TestCase.
|
|
self.assertTrue(issubclass(NumericTestCase, unittest.TestCase))
|
|
|
|
def test_error_msg_numeric(self):
|
|
# Test the error message generated for numeric comparisons.
|
|
args = (2.5, 4.0, 0.5, 0.25, None)
|
|
self.do_test(args)
|
|
|
|
def test_error_msg_sequence(self):
|
|
# Test the error message generated for sequence comparisons.
|
|
args = (3.75, 8.25, 1.25, 0.5, 7)
|
|
self.do_test(args)
|
|
|
|
def generate_substrings(self, first, second, tol, rel, idx):
|
|
"""Return substrings we expect to see in error messages."""
|
|
abs_err, rel_err = _calc_errors(first, second)
|
|
substrings = [
|
|
'tol=%r' % tol,
|
|
'rel=%r' % rel,
|
|
'absolute error = %r' % abs_err,
|
|
'relative error = %r' % rel_err,
|
|
]
|
|
if idx is not None:
|
|
substrings.append('differ at index %d' % idx)
|
|
return substrings
|
|
|
|
|
|
# =======================================
|
|
# === Tests for the statistics module ===
|
|
# =======================================
|
|
|
|
|
|
class GlobalsTest(unittest.TestCase):
|
|
module = statistics
|
|
expected_metadata = ["__doc__", "__all__"]
|
|
|
|
def test_meta(self):
|
|
# Test for the existence of metadata.
|
|
for meta in self.expected_metadata:
|
|
self.assertTrue(hasattr(self.module, meta),
|
|
"%s not present" % meta)
|
|
|
|
def test_check_all(self):
|
|
# Check everything in __all__ exists and is public.
|
|
module = self.module
|
|
for name in module.__all__:
|
|
# No private names in __all__:
|
|
self.assertFalse(name.startswith("_"),
|
|
'private name "%s" in __all__' % name)
|
|
# And anything in __all__ must exist:
|
|
self.assertTrue(hasattr(module, name),
|
|
'missing name "%s" in __all__' % name)
|
|
|
|
|
|
class DocTests(unittest.TestCase):
|
|
def test_doc_tests(self):
|
|
failed, tried = doctest.testmod(statistics)
|
|
self.assertGreater(tried, 0)
|
|
self.assertEqual(failed, 0)
|
|
|
|
class StatisticsErrorTest(unittest.TestCase):
|
|
def test_has_exception(self):
|
|
errmsg = (
|
|
"Expected StatisticsError to be a ValueError, but got a"
|
|
" subclass of %r instead."
|
|
)
|
|
self.assertTrue(hasattr(statistics, 'StatisticsError'))
|
|
self.assertTrue(
|
|
issubclass(statistics.StatisticsError, ValueError),
|
|
errmsg % statistics.StatisticsError.__base__
|
|
)
|
|
|
|
|
|
# === Tests for private utility functions ===
|
|
|
|
class ExactRatioTest(unittest.TestCase):
|
|
# Test _exact_ratio utility.
|
|
|
|
def test_int(self):
|
|
for i in (-20, -3, 0, 5, 99, 10**20):
|
|
self.assertEqual(statistics._exact_ratio(i), (i, 1))
|
|
|
|
def test_fraction(self):
|
|
numerators = (-5, 1, 12, 38)
|
|
for n in numerators:
|
|
f = Fraction(n, 37)
|
|
self.assertEqual(statistics._exact_ratio(f), (n, 37))
|
|
|
|
def test_float(self):
|
|
self.assertEqual(statistics._exact_ratio(0.125), (1, 8))
|
|
self.assertEqual(statistics._exact_ratio(1.125), (9, 8))
|
|
data = [random.uniform(-100, 100) for _ in range(100)]
|
|
for x in data:
|
|
num, den = statistics._exact_ratio(x)
|
|
self.assertEqual(x, num/den)
|
|
|
|
def test_decimal(self):
|
|
D = Decimal
|
|
_exact_ratio = statistics._exact_ratio
|
|
self.assertEqual(_exact_ratio(D("0.125")), (125, 1000))
|
|
self.assertEqual(_exact_ratio(D("12.345")), (12345, 1000))
|
|
self.assertEqual(_exact_ratio(D("-1.98")), (-198, 100))
|
|
|
|
|
|
class DecimalToRatioTest(unittest.TestCase):
|
|
# Test _decimal_to_ratio private function.
|
|
|
|
def testSpecialsRaise(self):
|
|
# Test that NANs and INFs raise ValueError.
|
|
# Non-special values are covered by _exact_ratio above.
|
|
for d in (Decimal('NAN'), Decimal('sNAN'), Decimal('INF')):
|
|
self.assertRaises(ValueError, statistics._decimal_to_ratio, d)
|
|
|
|
|
|
|
|
# === Tests for public functions ===
|
|
|
|
class UnivariateCommonMixin:
|
|
# Common tests for most univariate functions that take a data argument.
|
|
|
|
def test_no_args(self):
|
|
# Fail if given no arguments.
|
|
self.assertRaises(TypeError, self.func)
|
|
|
|
def test_empty_data(self):
|
|
# Fail when the data argument (first argument) is empty.
|
|
for empty in ([], (), iter([])):
|
|
self.assertRaises(statistics.StatisticsError, self.func, empty)
|
|
|
|
def prepare_data(self):
|
|
"""Return int data for various tests."""
|
|
data = list(range(10))
|
|
while data == sorted(data):
|
|
random.shuffle(data)
|
|
return data
|
|
|
|
def test_no_inplace_modifications(self):
|
|
# Test that the function does not modify its input data.
|
|
data = self.prepare_data()
|
|
assert len(data) != 1 # Necessary to avoid infinite loop.
|
|
assert data != sorted(data)
|
|
saved = data[:]
|
|
assert data is not saved
|
|
_ = self.func(data)
|
|
self.assertListEqual(data, saved, "data has been modified")
|
|
|
|
def test_order_doesnt_matter(self):
|
|
# Test that the order of data points doesn't change the result.
|
|
|
|
# CAUTION: due to floating point rounding errors, the result actually
|
|
# may depend on the order. Consider this test representing an ideal.
|
|
# To avoid this test failing, only test with exact values such as ints
|
|
# or Fractions.
|
|
data = [1, 2, 3, 3, 3, 4, 5, 6]*100
|
|
expected = self.func(data)
|
|
random.shuffle(data)
|
|
actual = self.func(data)
|
|
self.assertEqual(expected, actual)
|
|
|
|
def test_type_of_data_collection(self):
|
|
# Test that the type of iterable data doesn't effect the result.
|
|
class MyList(list):
|
|
pass
|
|
class MyTuple(tuple):
|
|
pass
|
|
def generator(data):
|
|
return (obj for obj in data)
|
|
data = self.prepare_data()
|
|
expected = self.func(data)
|
|
for kind in (list, tuple, iter, MyList, MyTuple, generator):
|
|
result = self.func(kind(data))
|
|
self.assertEqual(result, expected)
|
|
|
|
def test_range_data(self):
|
|
# Test that functions work with range objects.
|
|
data = range(20, 50, 3)
|
|
expected = self.func(list(data))
|
|
self.assertEqual(self.func(data), expected)
|
|
|
|
def test_bad_arg_types(self):
|
|
# Test that function raises when given data of the wrong type.
|
|
|
|
# Don't roll the following into a loop like this:
|
|
# for bad in list_of_bad:
|
|
# self.check_for_type_error(bad)
|
|
#
|
|
# Since assertRaises doesn't show the arguments that caused the test
|
|
# failure, it is very difficult to debug these test failures when the
|
|
# following are in a loop.
|
|
self.check_for_type_error(None)
|
|
self.check_for_type_error(23)
|
|
self.check_for_type_error(42.0)
|
|
self.check_for_type_error(object())
|
|
|
|
def check_for_type_error(self, *args):
|
|
self.assertRaises(TypeError, self.func, *args)
|
|
|
|
def test_type_of_data_element(self):
|
|
# Check the type of data elements doesn't affect the numeric result.
|
|
# This is a weaker test than UnivariateTypeMixin.testTypesConserved,
|
|
# because it checks the numeric result by equality, but not by type.
|
|
class MyFloat(float):
|
|
def __truediv__(self, other):
|
|
return type(self)(super().__truediv__(other))
|
|
def __add__(self, other):
|
|
return type(self)(super().__add__(other))
|
|
__radd__ = __add__
|
|
|
|
raw = self.prepare_data()
|
|
expected = self.func(raw)
|
|
for kind in (float, MyFloat, Decimal, Fraction):
|
|
data = [kind(x) for x in raw]
|
|
result = type(expected)(self.func(data))
|
|
self.assertEqual(result, expected)
|
|
|
|
|
|
class UnivariateTypeMixin:
|
|
"""Mixin class for type-conserving functions.
|
|
|
|
This mixin class holds test(s) for functions which conserve the type of
|
|
individual data points. E.g. the mean of a list of Fractions should itself
|
|
be a Fraction.
|
|
|
|
Not all tests to do with types need go in this class. Only those that
|
|
rely on the function returning the same type as its input data.
|
|
"""
|
|
def test_types_conserved(self):
|
|
# Test that functions keeps the same type as their data points.
|
|
# (Excludes mixed data types.) This only tests the type of the return
|
|
# result, not the value.
|
|
class MyFloat(float):
|
|
def __truediv__(self, other):
|
|
return type(self)(super().__truediv__(other))
|
|
def __sub__(self, other):
|
|
return type(self)(super().__sub__(other))
|
|
def __rsub__(self, other):
|
|
return type(self)(super().__rsub__(other))
|
|
def __pow__(self, other):
|
|
return type(self)(super().__pow__(other))
|
|
def __add__(self, other):
|
|
return type(self)(super().__add__(other))
|
|
__radd__ = __add__
|
|
|
|
data = self.prepare_data()
|
|
for kind in (float, Decimal, Fraction, MyFloat):
|
|
d = [kind(x) for x in data]
|
|
result = self.func(d)
|
|
self.assertIs(type(result), kind)
|
|
|
|
|
|
class TestSum(NumericTestCase, UnivariateCommonMixin, UnivariateTypeMixin):
|
|
# Test cases for statistics._sum() function.
|
|
|
|
def setUp(self):
|
|
self.func = statistics._sum
|
|
|
|
def test_empty_data(self):
|
|
# Override test for empty data.
|
|
for data in ([], (), iter([])):
|
|
self.assertEqual(self.func(data), 0)
|
|
self.assertEqual(self.func(data, 23), 23)
|
|
self.assertEqual(self.func(data, 2.3), 2.3)
|
|
|
|
def test_ints(self):
|
|
self.assertEqual(self.func([1, 5, 3, -4, -8, 20, 42, 1]), 60)
|
|
self.assertEqual(self.func([4, 2, 3, -8, 7], 1000), 1008)
|
|
|
|
def test_floats(self):
|
|
self.assertEqual(self.func([0.25]*20), 5.0)
|
|
self.assertEqual(self.func([0.125, 0.25, 0.5, 0.75], 1.5), 3.125)
|
|
|
|
def test_fractions(self):
|
|
F = Fraction
|
|
self.assertEqual(self.func([Fraction(1, 1000)]*500), Fraction(1, 2))
|
|
|
|
def test_decimals(self):
|
|
D = Decimal
|
|
data = [D("0.001"), D("5.246"), D("1.702"), D("-0.025"),
|
|
D("3.974"), D("2.328"), D("4.617"), D("2.843"),
|
|
]
|
|
self.assertEqual(self.func(data), Decimal("20.686"))
|
|
|
|
def test_compare_with_math_fsum(self):
|
|
# Compare with the math.fsum function.
|
|
# Ideally we ought to get the exact same result, but sometimes
|
|
# we differ by a very slight amount :-(
|
|
data = [random.uniform(-100, 1000) for _ in range(1000)]
|
|
self.assertApproxEqual(self.func(data), math.fsum(data), rel=2e-16)
|
|
|
|
def test_start_argument(self):
|
|
# Test that the optional start argument works correctly.
|
|
data = [random.uniform(1, 1000) for _ in range(100)]
|
|
t = self.func(data)
|
|
self.assertEqual(t+42, self.func(data, 42))
|
|
self.assertEqual(t-23, self.func(data, -23))
|
|
self.assertEqual(t+1e20, self.func(data, 1e20))
|
|
|
|
def test_strings_fail(self):
|
|
# Sum of strings should fail.
|
|
self.assertRaises(TypeError, self.func, [1, 2, 3], '999')
|
|
self.assertRaises(TypeError, self.func, [1, 2, 3, '999'])
|
|
|
|
def test_bytes_fail(self):
|
|
# Sum of bytes should fail.
|
|
self.assertRaises(TypeError, self.func, [1, 2, 3], b'999')
|
|
self.assertRaises(TypeError, self.func, [1, 2, 3, b'999'])
|
|
|
|
def test_mixed_sum(self):
|
|
# Mixed sums are allowed.
|
|
|
|
# Careful here: order matters. Can't mix Fraction and Decimal directly,
|
|
# only after they're converted to float.
|
|
data = [1, 2, Fraction(1, 2), 3.0, Decimal("0.25")]
|
|
self.assertEqual(self.func(data), 6.75)
|
|
|
|
|
|
class SumInternalsTest(NumericTestCase):
|
|
# Test internals of the sum function.
|
|
|
|
def test_ignore_instance_float_method(self):
|
|
# Test that __float__ methods on data instances are ignored.
|
|
|
|
# Python typically calls __dunder__ methods on the class, not the
|
|
# instance. The ``sum`` implementation calls __float__ directly. To
|
|
# better match the behaviour of Python, we call it only on the class,
|
|
# not the instance. This test will fail if somebody "fixes" that code.
|
|
|
|
# Create a fake __float__ method.
|
|
def __float__(self):
|
|
raise AssertionError('test fails')
|
|
|
|
# Inject it into an instance.
|
|
class MyNumber(Fraction):
|
|
pass
|
|
x = MyNumber(3)
|
|
x.__float__ = types.MethodType(__float__, x)
|
|
|
|
# Check it works as expected.
|
|
self.assertRaises(AssertionError, x.__float__)
|
|
self.assertEqual(float(x), 3.0)
|
|
# And now test the function.
|
|
self.assertEqual(statistics._sum([1.0, 2.0, x, 4.0]), 10.0)
|
|
|
|
|
|
class SumTortureTest(NumericTestCase):
|
|
def test_torture(self):
|
|
# Tim Peters' torture test for sum, and variants of same.
|
|
self.assertEqual(statistics._sum([1, 1e100, 1, -1e100]*10000), 20000.0)
|
|
self.assertEqual(statistics._sum([1e100, 1, 1, -1e100]*10000), 20000.0)
|
|
self.assertApproxEqual(
|
|
statistics._sum([1e-100, 1, 1e-100, -1]*10000), 2.0e-96, rel=5e-16
|
|
)
|
|
|
|
|
|
class SumSpecialValues(NumericTestCase):
|
|
# Test that sum works correctly with IEEE-754 special values.
|
|
|
|
def test_nan(self):
|
|
for type_ in (float, Decimal):
|
|
nan = type_('nan')
|
|
result = statistics._sum([1, nan, 2])
|
|
self.assertIs(type(result), type_)
|
|
self.assertTrue(math.isnan(result))
|
|
|
|
def check_infinity(self, x, inf):
|
|
"""Check x is an infinity of the same type and sign as inf."""
|
|
self.assertTrue(math.isinf(x))
|
|
self.assertIs(type(x), type(inf))
|
|
self.assertEqual(x > 0, inf > 0)
|
|
assert x == inf
|
|
|
|
def do_test_inf(self, inf):
|
|
# Adding a single infinity gives infinity.
|
|
result = statistics._sum([1, 2, inf, 3])
|
|
self.check_infinity(result, inf)
|
|
# Adding two infinities of the same sign also gives infinity.
|
|
result = statistics._sum([1, 2, inf, 3, inf, 4])
|
|
self.check_infinity(result, inf)
|
|
|
|
def test_float_inf(self):
|
|
inf = float('inf')
|
|
for sign in (+1, -1):
|
|
self.do_test_inf(sign*inf)
|
|
|
|
def test_decimal_inf(self):
|
|
inf = Decimal('inf')
|
|
for sign in (+1, -1):
|
|
self.do_test_inf(sign*inf)
|
|
|
|
def test_float_mismatched_infs(self):
|
|
# Test that adding two infinities of opposite sign gives a NAN.
|
|
inf = float('inf')
|
|
result = statistics._sum([1, 2, inf, 3, -inf, 4])
|
|
self.assertTrue(math.isnan(result))
|
|
|
|
def test_decimal_mismatched_infs_to_nan(self):
|
|
# Test adding Decimal INFs with opposite sign returns NAN.
|
|
inf = Decimal('inf')
|
|
data = [1, 2, inf, 3, -inf, 4]
|
|
with decimal.localcontext(decimal.ExtendedContext):
|
|
self.assertTrue(math.isnan(statistics._sum(data)))
|
|
|
|
def test_decimal_mismatched_infs_to_nan(self):
|
|
# Test adding Decimal INFs with opposite sign raises InvalidOperation.
|
|
inf = Decimal('inf')
|
|
data = [1, 2, inf, 3, -inf, 4]
|
|
with decimal.localcontext(decimal.BasicContext):
|
|
self.assertRaises(decimal.InvalidOperation, statistics._sum, data)
|
|
|
|
def test_decimal_snan_raises(self):
|
|
# Adding sNAN should raise InvalidOperation.
|
|
sNAN = Decimal('sNAN')
|
|
data = [1, sNAN, 2]
|
|
self.assertRaises(decimal.InvalidOperation, statistics._sum, data)
|
|
|
|
|
|
# === Tests for averages ===
|
|
|
|
class AverageMixin(UnivariateCommonMixin):
|
|
# Mixin class holding common tests for averages.
|
|
|
|
def test_single_value(self):
|
|
# Average of a single value is the value itself.
|
|
for x in (23, 42.5, 1.3e15, Fraction(15, 19), Decimal('0.28')):
|
|
self.assertEqual(self.func([x]), x)
|
|
|
|
def test_repeated_single_value(self):
|
|
# The average of a single repeated value is the value itself.
|
|
for x in (3.5, 17, 2.5e15, Fraction(61, 67), Decimal('4.9712')):
|
|
for count in (2, 5, 10, 20):
|
|
data = [x]*count
|
|
self.assertEqual(self.func(data), x)
|
|
|
|
|
|
class TestMean(NumericTestCase, AverageMixin, UnivariateTypeMixin):
|
|
def setUp(self):
|
|
self.func = statistics.mean
|
|
|
|
def test_torture_pep(self):
|
|
# "Torture Test" from PEP-450.
|
|
self.assertEqual(self.func([1e100, 1, 3, -1e100]), 1)
|
|
|
|
def test_ints(self):
|
|
# Test mean with ints.
|
|
data = [0, 1, 2, 3, 3, 3, 4, 5, 5, 6, 7, 7, 7, 7, 8, 9]
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), 4.8125)
|
|
|
|
def test_floats(self):
|
|
# Test mean with floats.
|
|
data = [17.25, 19.75, 20.0, 21.5, 21.75, 23.25, 25.125, 27.5]
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), 22.015625)
|
|
|
|
def test_decimals(self):
|
|
# Test mean with ints.
|
|
D = Decimal
|
|
data = [D("1.634"), D("2.517"), D("3.912"), D("4.072"), D("5.813")]
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), D("3.5896"))
|
|
|
|
def test_fractions(self):
|
|
# Test mean with Fractions.
|
|
F = Fraction
|
|
data = [F(1, 2), F(2, 3), F(3, 4), F(4, 5), F(5, 6), F(6, 7), F(7, 8)]
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), F(1479, 1960))
|
|
|
|
def test_inf(self):
|
|
# Test mean with infinities.
|
|
raw = [1, 3, 5, 7, 9] # Use only ints, to avoid TypeError later.
|
|
for kind in (float, Decimal):
|
|
for sign in (1, -1):
|
|
inf = kind("inf")*sign
|
|
data = raw + [inf]
|
|
result = self.func(data)
|
|
self.assertTrue(math.isinf(result))
|
|
self.assertEqual(result, inf)
|
|
|
|
def test_mismatched_infs(self):
|
|
# Test mean with infinities of opposite sign.
|
|
data = [2, 4, 6, float('inf'), 1, 3, 5, float('-inf')]
|
|
result = self.func(data)
|
|
self.assertTrue(math.isnan(result))
|
|
|
|
def test_nan(self):
|
|
# Test mean with NANs.
|
|
raw = [1, 3, 5, 7, 9] # Use only ints, to avoid TypeError later.
|
|
for kind in (float, Decimal):
|
|
inf = kind("nan")
|
|
data = raw + [inf]
|
|
result = self.func(data)
|
|
self.assertTrue(math.isnan(result))
|
|
|
|
def test_big_data(self):
|
|
# Test adding a large constant to every data point.
|
|
c = 1e9
|
|
data = [3.4, 4.5, 4.9, 6.7, 6.8, 7.2, 8.0, 8.1, 9.4]
|
|
expected = self.func(data) + c
|
|
assert expected != c
|
|
result = self.func([x+c for x in data])
|
|
self.assertEqual(result, expected)
|
|
|
|
def test_doubled_data(self):
|
|
# Mean of [a,b,c...z] should be same as for [a,a,b,b,c,c...z,z].
|
|
data = [random.uniform(-3, 5) for _ in range(1000)]
|
|
expected = self.func(data)
|
|
actual = self.func(data*2)
|
|
self.assertApproxEqual(actual, expected)
|
|
|
|
|
|
class TestMedian(NumericTestCase, AverageMixin):
|
|
# Common tests for median and all median.* functions.
|
|
def setUp(self):
|
|
self.func = statistics.median
|
|
|
|
def prepare_data(self):
|
|
"""Overload method from UnivariateCommonMixin."""
|
|
data = super().prepare_data()
|
|
if len(data)%2 != 1:
|
|
data.append(2)
|
|
return data
|
|
|
|
def test_even_ints(self):
|
|
# Test median with an even number of int data points.
|
|
data = [1, 2, 3, 4, 5, 6]
|
|
assert len(data)%2 == 0
|
|
self.assertEqual(self.func(data), 3.5)
|
|
|
|
def test_odd_ints(self):
|
|
# Test median with an odd number of int data points.
|
|
data = [1, 2, 3, 4, 5, 6, 9]
|
|
assert len(data)%2 == 1
|
|
self.assertEqual(self.func(data), 4)
|
|
|
|
def test_odd_fractions(self):
|
|
# Test median works with an odd number of Fractions.
|
|
F = Fraction
|
|
data = [F(1, 7), F(2, 7), F(3, 7), F(4, 7), F(5, 7)]
|
|
assert len(data)%2 == 1
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), F(3, 7))
|
|
|
|
def test_even_fractions(self):
|
|
# Test median works with an even number of Fractions.
|
|
F = Fraction
|
|
data = [F(1, 7), F(2, 7), F(3, 7), F(4, 7), F(5, 7), F(6, 7)]
|
|
assert len(data)%2 == 0
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), F(1, 2))
|
|
|
|
def test_odd_decimals(self):
|
|
# Test median works with an odd number of Decimals.
|
|
D = Decimal
|
|
data = [D('2.5'), D('3.1'), D('4.2'), D('5.7'), D('5.8')]
|
|
assert len(data)%2 == 1
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), D('4.2'))
|
|
|
|
def test_even_decimals(self):
|
|
# Test median works with an even number of Decimals.
|
|
D = Decimal
|
|
data = [D('1.2'), D('2.5'), D('3.1'), D('4.2'), D('5.7'), D('5.8')]
|
|
assert len(data)%2 == 0
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), D('3.65'))
|
|
|
|
|
|
class TestMedianDataType(NumericTestCase, UnivariateTypeMixin):
|
|
# Test conservation of data element type for median.
|
|
def setUp(self):
|
|
self.func = statistics.median
|
|
|
|
def prepare_data(self):
|
|
data = list(range(15))
|
|
assert len(data)%2 == 1
|
|
while data == sorted(data):
|
|
random.shuffle(data)
|
|
return data
|
|
|
|
|
|
class TestMedianLow(TestMedian, UnivariateTypeMixin):
|
|
def setUp(self):
|
|
self.func = statistics.median_low
|
|
|
|
def test_even_ints(self):
|
|
# Test median_low with an even number of ints.
|
|
data = [1, 2, 3, 4, 5, 6]
|
|
assert len(data)%2 == 0
|
|
self.assertEqual(self.func(data), 3)
|
|
|
|
def test_even_fractions(self):
|
|
# Test median_low works with an even number of Fractions.
|
|
F = Fraction
|
|
data = [F(1, 7), F(2, 7), F(3, 7), F(4, 7), F(5, 7), F(6, 7)]
|
|
assert len(data)%2 == 0
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), F(3, 7))
|
|
|
|
def test_even_decimals(self):
|
|
# Test median_low works with an even number of Decimals.
|
|
D = Decimal
|
|
data = [D('1.1'), D('2.2'), D('3.3'), D('4.4'), D('5.5'), D('6.6')]
|
|
assert len(data)%2 == 0
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), D('3.3'))
|
|
|
|
|
|
class TestMedianHigh(TestMedian, UnivariateTypeMixin):
|
|
def setUp(self):
|
|
self.func = statistics.median_high
|
|
|
|
def test_even_ints(self):
|
|
# Test median_high with an even number of ints.
|
|
data = [1, 2, 3, 4, 5, 6]
|
|
assert len(data)%2 == 0
|
|
self.assertEqual(self.func(data), 4)
|
|
|
|
def test_even_fractions(self):
|
|
# Test median_high works with an even number of Fractions.
|
|
F = Fraction
|
|
data = [F(1, 7), F(2, 7), F(3, 7), F(4, 7), F(5, 7), F(6, 7)]
|
|
assert len(data)%2 == 0
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), F(4, 7))
|
|
|
|
def test_even_decimals(self):
|
|
# Test median_high works with an even number of Decimals.
|
|
D = Decimal
|
|
data = [D('1.1'), D('2.2'), D('3.3'), D('4.4'), D('5.5'), D('6.6')]
|
|
assert len(data)%2 == 0
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), D('4.4'))
|
|
|
|
|
|
class TestMedianGrouped(TestMedian):
|
|
# Test median_grouped.
|
|
# Doesn't conserve data element types, so don't use TestMedianType.
|
|
def setUp(self):
|
|
self.func = statistics.median_grouped
|
|
|
|
def test_odd_number_repeated(self):
|
|
# Test median.grouped with repeated median values.
|
|
data = [12, 13, 14, 14, 14, 15, 15]
|
|
assert len(data)%2 == 1
|
|
self.assertEqual(self.func(data), 14)
|
|
#---
|
|
data = [12, 13, 14, 14, 14, 14, 15]
|
|
assert len(data)%2 == 1
|
|
self.assertEqual(self.func(data), 13.875)
|
|
#---
|
|
data = [5, 10, 10, 15, 20, 20, 20, 20, 25, 25, 30]
|
|
assert len(data)%2 == 1
|
|
self.assertEqual(self.func(data, 5), 19.375)
|
|
#---
|
|
data = [16, 18, 18, 18, 18, 20, 20, 20, 22, 22, 22, 24, 24, 26, 28]
|
|
assert len(data)%2 == 1
|
|
self.assertApproxEqual(self.func(data, 2), 20.66666667, tol=1e-8)
|
|
|
|
def test_even_number_repeated(self):
|
|
# Test median.grouped with repeated median values.
|
|
data = [5, 10, 10, 15, 20, 20, 20, 25, 25, 30]
|
|
assert len(data)%2 == 0
|
|
self.assertApproxEqual(self.func(data, 5), 19.16666667, tol=1e-8)
|
|
#---
|
|
data = [2, 3, 4, 4, 4, 5]
|
|
assert len(data)%2 == 0
|
|
self.assertApproxEqual(self.func(data), 3.83333333, tol=1e-8)
|
|
#---
|
|
data = [2, 3, 3, 4, 4, 4, 5, 5, 5, 5, 6, 6]
|
|
assert len(data)%2 == 0
|
|
self.assertEqual(self.func(data), 4.5)
|
|
#---
|
|
data = [3, 4, 4, 4, 5, 5, 5, 5, 6, 6]
|
|
assert len(data)%2 == 0
|
|
self.assertEqual(self.func(data), 4.75)
|
|
|
|
def test_repeated_single_value(self):
|
|
# Override method from AverageMixin.
|
|
# Yet again, failure of median_grouped to conserve the data type
|
|
# causes me headaches :-(
|
|
for x in (5.3, 68, 4.3e17, Fraction(29, 101), Decimal('32.9714')):
|
|
for count in (2, 5, 10, 20):
|
|
data = [x]*count
|
|
self.assertEqual(self.func(data), float(x))
|
|
|
|
def test_odd_fractions(self):
|
|
# Test median_grouped works with an odd number of Fractions.
|
|
F = Fraction
|
|
data = [F(5, 4), F(9, 4), F(13, 4), F(13, 4), F(17, 4)]
|
|
assert len(data)%2 == 1
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), 3.0)
|
|
|
|
def test_even_fractions(self):
|
|
# Test median_grouped works with an even number of Fractions.
|
|
F = Fraction
|
|
data = [F(5, 4), F(9, 4), F(13, 4), F(13, 4), F(17, 4), F(17, 4)]
|
|
assert len(data)%2 == 0
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), 3.25)
|
|
|
|
def test_odd_decimals(self):
|
|
# Test median_grouped works with an odd number of Decimals.
|
|
D = Decimal
|
|
data = [D('5.5'), D('6.5'), D('6.5'), D('7.5'), D('8.5')]
|
|
assert len(data)%2 == 1
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), 6.75)
|
|
|
|
def test_even_decimals(self):
|
|
# Test median_grouped works with an even number of Decimals.
|
|
D = Decimal
|
|
data = [D('5.5'), D('5.5'), D('6.5'), D('6.5'), D('7.5'), D('8.5')]
|
|
assert len(data)%2 == 0
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), 6.5)
|
|
#---
|
|
data = [D('5.5'), D('5.5'), D('6.5'), D('7.5'), D('7.5'), D('8.5')]
|
|
assert len(data)%2 == 0
|
|
random.shuffle(data)
|
|
self.assertEqual(self.func(data), 7.0)
|
|
|
|
def test_interval(self):
|
|
# Test median_grouped with interval argument.
|
|
data = [2.25, 2.5, 2.5, 2.75, 2.75, 3.0, 3.0, 3.25, 3.5, 3.75]
|
|
self.assertEqual(self.func(data, 0.25), 2.875)
|
|
data = [2.25, 2.5, 2.5, 2.75, 2.75, 2.75, 3.0, 3.0, 3.25, 3.5, 3.75]
|
|
self.assertApproxEqual(self.func(data, 0.25), 2.83333333, tol=1e-8)
|
|
data = [220, 220, 240, 260, 260, 260, 260, 280, 280, 300, 320, 340]
|
|
self.assertEqual(self.func(data, 20), 265.0)
|
|
|
|
|
|
class TestMode(NumericTestCase, AverageMixin, UnivariateTypeMixin):
|
|
# Test cases for the discrete version of mode.
|
|
def setUp(self):
|
|
self.func = statistics.mode
|
|
|
|
def prepare_data(self):
|
|
"""Overload method from UnivariateCommonMixin."""
|
|
# Make sure test data has exactly one mode.
|
|
return [1, 1, 1, 1, 3, 4, 7, 9, 0, 8, 2]
|
|
|
|
def test_range_data(self):
|
|
# Override test from UnivariateCommonMixin.
|
|
data = range(20, 50, 3)
|
|
self.assertRaises(statistics.StatisticsError, self.func, data)
|
|
|
|
def test_nominal_data(self):
|
|
# Test mode with nominal data.
|
|
data = 'abcbdb'
|
|
self.assertEqual(self.func(data), 'b')
|
|
data = 'fe fi fo fum fi fi'.split()
|
|
self.assertEqual(self.func(data), 'fi')
|
|
|
|
def test_discrete_data(self):
|
|
# Test mode with discrete numeric data.
|
|
data = list(range(10))
|
|
for i in range(10):
|
|
d = data + [i]
|
|
random.shuffle(d)
|
|
self.assertEqual(self.func(d), i)
|
|
|
|
def test_bimodal_data(self):
|
|
# Test mode with bimodal data.
|
|
data = [1, 1, 2, 2, 2, 2, 3, 4, 5, 6, 6, 6, 6, 7, 8, 9, 9]
|
|
assert data.count(2) == data.count(6) == 4
|
|
# Check for an exception.
|
|
self.assertRaises(statistics.StatisticsError, self.func, data)
|
|
|
|
def test_unique_data_failure(self):
|
|
# Test mode exception when data points are all unique.
|
|
data = list(range(10))
|
|
self.assertRaises(statistics.StatisticsError, self.func, data)
|
|
|
|
def test_none_data(self):
|
|
# Test that mode raises TypeError if given None as data.
|
|
|
|
# This test is necessary because the implementation of mode uses
|
|
# collections.Counter, which accepts None and returns an empty dict.
|
|
self.assertRaises(TypeError, self.func, None)
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# === Tests for variances and standard deviations ===
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class VarianceStdevMixin(UnivariateCommonMixin):
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# Mixin class holding common tests for variance and std dev.
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# Subclasses should inherit from this before NumericTestClass, in order
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# to see the rel attribute below. See testShiftData for an explanation.
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rel = 1e-12
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def test_single_value(self):
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# Deviation of a single value is zero.
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for x in (11, 19.8, 4.6e14, Fraction(21, 34), Decimal('8.392')):
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self.assertEqual(self.func([x]), 0)
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def test_repeated_single_value(self):
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# The deviation of a single repeated value is zero.
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for x in (7.2, 49, 8.1e15, Fraction(3, 7), Decimal('62.4802')):
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for count in (2, 3, 5, 15):
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data = [x]*count
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self.assertEqual(self.func(data), 0)
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def test_domain_error_regression(self):
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# Regression test for a domain error exception.
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# (Thanks to Geremy Condra.)
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data = [0.123456789012345]*10000
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# All the items are identical, so variance should be exactly zero.
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# We allow some small round-off error, but not much.
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result = self.func(data)
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self.assertApproxEqual(result, 0.0, tol=5e-17)
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self.assertGreaterEqual(result, 0) # A negative result must fail.
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def test_shift_data(self):
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# Test that shifting the data by a constant amount does not affect
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# the variance or stdev. Or at least not much.
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# Due to rounding, this test should be considered an ideal. We allow
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# some tolerance away from "no change at all" by setting tol and/or rel
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# attributes. Subclasses may set tighter or looser error tolerances.
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raw = [1.03, 1.27, 1.94, 2.04, 2.58, 3.14, 4.75, 4.98, 5.42, 6.78]
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expected = self.func(raw)
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# Don't set shift too high, the bigger it is, the more rounding error.
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shift = 1e5
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data = [x + shift for x in raw]
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self.assertApproxEqual(self.func(data), expected)
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def test_shift_data_exact(self):
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# Like test_shift_data, but result is always exact.
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raw = [1, 3, 3, 4, 5, 7, 9, 10, 11, 16]
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assert all(x==int(x) for x in raw)
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expected = self.func(raw)
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shift = 10**9
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data = [x + shift for x in raw]
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self.assertEqual(self.func(data), expected)
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def test_iter_list_same(self):
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# Test that iter data and list data give the same result.
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# This is an explicit test that iterators and lists are treated the
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# same; justification for this test over and above the similar test
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# in UnivariateCommonMixin is that an earlier design had variance and
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# friends swap between one- and two-pass algorithms, which would
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# sometimes give different results.
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data = [random.uniform(-3, 8) for _ in range(1000)]
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expected = self.func(data)
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self.assertEqual(self.func(iter(data)), expected)
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class TestPVariance(VarianceStdevMixin, NumericTestCase, UnivariateTypeMixin):
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# Tests for population variance.
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def setUp(self):
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self.func = statistics.pvariance
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def test_exact_uniform(self):
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# Test the variance against an exact result for uniform data.
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data = list(range(10000))
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random.shuffle(data)
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expected = (10000**2 - 1)/12 # Exact value.
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self.assertEqual(self.func(data), expected)
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def test_ints(self):
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# Test population variance with int data.
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data = [4, 7, 13, 16]
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exact = 22.5
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self.assertEqual(self.func(data), exact)
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def test_fractions(self):
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# Test population variance with Fraction data.
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F = Fraction
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data = [F(1, 4), F(1, 4), F(3, 4), F(7, 4)]
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exact = F(3, 8)
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result = self.func(data)
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self.assertEqual(result, exact)
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self.assertIsInstance(result, Fraction)
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def test_decimals(self):
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# Test population variance with Decimal data.
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D = Decimal
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data = [D("12.1"), D("12.2"), D("12.5"), D("12.9")]
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exact = D('0.096875')
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result = self.func(data)
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self.assertEqual(result, exact)
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self.assertIsInstance(result, Decimal)
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class TestVariance(VarianceStdevMixin, NumericTestCase, UnivariateTypeMixin):
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# Tests for sample variance.
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def setUp(self):
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self.func = statistics.variance
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def test_single_value(self):
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# Override method from VarianceStdevMixin.
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for x in (35, 24.7, 8.2e15, Fraction(19, 30), Decimal('4.2084')):
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self.assertRaises(statistics.StatisticsError, self.func, [x])
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def test_ints(self):
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# Test sample variance with int data.
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data = [4, 7, 13, 16]
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exact = 30
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self.assertEqual(self.func(data), exact)
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def test_fractions(self):
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# Test sample variance with Fraction data.
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F = Fraction
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data = [F(1, 4), F(1, 4), F(3, 4), F(7, 4)]
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exact = F(1, 2)
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result = self.func(data)
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self.assertEqual(result, exact)
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self.assertIsInstance(result, Fraction)
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def test_decimals(self):
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# Test sample variance with Decimal data.
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D = Decimal
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data = [D(2), D(2), D(7), D(9)]
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exact = 4*D('9.5')/D(3)
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result = self.func(data)
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self.assertEqual(result, exact)
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self.assertIsInstance(result, Decimal)
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class TestPStdev(VarianceStdevMixin, NumericTestCase):
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# Tests for population standard deviation.
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def setUp(self):
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self.func = statistics.pstdev
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def test_compare_to_variance(self):
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# Test that stdev is, in fact, the square root of variance.
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data = [random.uniform(-17, 24) for _ in range(1000)]
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expected = math.sqrt(statistics.pvariance(data))
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self.assertEqual(self.func(data), expected)
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class TestStdev(VarianceStdevMixin, NumericTestCase):
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# Tests for sample standard deviation.
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def setUp(self):
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self.func = statistics.stdev
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def test_single_value(self):
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# Override method from VarianceStdevMixin.
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for x in (81, 203.74, 3.9e14, Fraction(5, 21), Decimal('35.719')):
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self.assertRaises(statistics.StatisticsError, self.func, [x])
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def test_compare_to_variance(self):
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# Test that stdev is, in fact, the square root of variance.
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data = [random.uniform(-2, 9) for _ in range(1000)]
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expected = math.sqrt(statistics.variance(data))
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self.assertEqual(self.func(data), expected)
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# === Run tests ===
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def load_tests(loader, tests, ignore):
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"""Used for doctest/unittest integration."""
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tests.addTests(doctest.DocTestSuite())
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return tests
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if __name__ == "__main__":
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unittest.main()
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