Adjust CPython "magic constants" in KDE tests

jackoconnordev · jackoconnordev · commit bdf71bdfb025 · 2025-07-24T23:21:39.000+01:00
## test_kde

I'm not too sure why but this one takes a few seconds to run the second
for loop which calculates the cumulative distribution and does a rough
calculation of the area under the curve.

## test_kde_random

I have a lower bound for RustPython to sort a random list of 1_000_000
numbers on my laptop of &gt; 1 hour. By dropping n to 30_000 sort will not
take an egregious amount of time to run. It is then necessary to lower
the tolerance for the math.isclose check, or the computed values may
**randomly** fail due to the higher variance caused by the smaller
sample size.
diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py
@@ -2493,7 +2493,8 @@ def test_kde_random(self):
 
         data = [-2.1, -1.3, -0.4, 1.9, 5.1, 6.2, 7.8, 14.3, 15.1, 15.3, 15.8, 17.0]
         xarr = [x / 10 for x in range(-100, 250)]
-        n = 1_000_000
+        # TODO: RUSTPYTHON - n originally 1_000_000 in CPython implementation but sorting is slow
+        n = 30_000
         h = 1.75
         dx = 0.1
 
@@ -2515,7 +2516,8 @@ def p_expected(x):
                 F_hat = statistics.kde(data, h, kernel, cumulative=True)
 
                 for x in xarr:
-                    self.assertTrue(math.isclose(p_observed(x), p_expected(x), abs_tol=0.0005))
+                    # TODO : RUSTPYTHON - abs_tol=0.0005 in CPython implementation but smaller `n` increases variance
+                    self.assertTrue(math.isclose(p_observed(x), p_expected(x), abs_tol=0.005))
 
         # Test online updates to data
 
