[Python-checkins] bpo-36018: Test idempotence. Test two methods against one-another. (GH-13021)
Raymond Hettinger
webhook-mailer at python.org
Tue Apr 30 02:47:37 EDT 2019
https://github.com/python/cpython/commit/b0a2c0fa83f9b79616ccf451687096542de1e6f8
commit: b0a2c0fa83f9b79616ccf451687096542de1e6f8
branch: master
author: Raymond Hettinger <rhettinger at users.noreply.github.com>
committer: GitHub <noreply at github.com>
date: 2019-04-29T23:47:33-07:00
summary:
bpo-36018: Test idempotence. Test two methods against one-another. (GH-13021)
files:
M Lib/test/test_statistics.py
diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py
index 0a967055f0fe..903ee8f343cb 100644
--- a/Lib/test/test_statistics.py
+++ b/Lib/test/test_statistics.py
@@ -2158,6 +2158,20 @@ def test_specific_cases(self):
result = quantiles(map(datatype, data), n=n)
self.assertTrue(all(type(x) == datatype) for x in result)
self.assertEqual(result, list(map(datatype, expected)))
+ # Quantiles should be idempotent
+ if len(expected) >= 2:
+ self.assertEqual(quantiles(expected, n=n), expected)
+ # Cross-check against other methods
+ if len(data) >= n:
+ # After end caps are added, method='inclusive' should
+ # give the same result as method='exclusive' whenever
+ # there are more data points than desired cut points.
+ padded_data = [min(data) - 1000] + data + [max(data) + 1000]
+ self.assertEqual(
+ quantiles(data, n=n),
+ quantiles(padded_data, n=n, method='inclusive'),
+ (n, data),
+ )
# Invariant under tranlation and scaling
def f(x):
return 3.5 * x - 1234.675
@@ -2219,6 +2233,15 @@ def f(x):
actual = quantiles(statistics.NormalDist(), n=n, method="inclusive")
self.assertTrue(all(math.isclose(e, a, abs_tol=0.0001)
for e, a in zip(expected, actual)))
+ # Whenever n is smaller than the number of data points, running
+ # method='inclusive' should give the same result as method='exclusive'
+ # after the two included extreme points are removed.
+ data = [random.randrange(10_000) for i in range(501)]
+ actual = quantiles(data, n=32, method='inclusive')
+ data.remove(min(data))
+ data.remove(max(data))
+ expected = quantiles(data, n=32)
+ self.assertEqual(expected, actual)
def test_equal_inputs(self):
quantiles = statistics.quantiles
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