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# Copyright 2014 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import unittest
# Special import necessary because filename contains dash characters.
bisect_perf_module = __import__('bisect-perf-regression')
class BisectPerfRegressionTest(unittest.TestCase):
"""Test case for top-level functions in the bisect-perf-regrssion module."""
def setUp(self):
"""Sets up the test environment before each test method."""
pass
def tearDown(self):
"""Cleans up the test environment after each test method."""
pass
def testCalculateTruncatedMeanRaisesError(self):
"""CalculateTrunctedMean raises an error when passed an empty list."""
with self.assertRaises(TypeError):
bisect_perf_module.CalculateTruncatedMean([], 0)
def testCalculateMeanSingleNum(self):
"""Tests the CalculateMean function with a single number."""
self.assertEqual(3.0, bisect_perf_module.CalculateMean([3]))
def testCalculateMeanShortList(self):
"""Tests the CalculateMean function with a short list."""
self.assertEqual(0.5, bisect_perf_module.CalculateMean([-3, 0, 1, 4]))
def testCalculateMeanCompareAlternateImplementation(self):
"""Tests CalculateMean by comparing against an alternate implementation."""
def AlternateMeanFunction(values):
"""Simple arithmetic mean function."""
return sum(values) / float(len(values))
test_values_lists = [[1], [5, 6.5, 1.2, 3], [-3, 0, 1, 4],
[-3, -1, 0.12, 0.752, 3.33, 8, 16, 32, 439]]
for values in test_values_lists:
self.assertEqual(
AlternateMeanFunction(values),
bisect_perf_module.CalculateMean(values))
def testCalculateConfidence(self):
"""Tests the confidence calculation."""
bad_values = [[0, 1], [1, 2]]
good_values = [[6, 7], [7, 8]]
# Closest means are mean(1, 2) and mean(6, 7).
distance = 6.5 - 1.5
# Standard deviation of [n-1, n, n, n+1] is 0.8165.
stddev_sum = 0.8165 + 0.8165
# Expected confidence is an int in the range [0, 100].
expected_confidence = min(100, int(100 * distance / float(stddev_sum)))
self.assertEqual(
expected_confidence,
bisect_perf_module.CalculateConfidence(bad_values, good_values))
def testCalculateConfidence0(self):
"""Tests the confidence calculation when it's expected to be 0."""
bad_values = [[0, 1], [1, 2], [4, 5], [0, 2]]
good_values = [[4, 5], [6, 7], [7, 8]]
# Both groups have value lists with means of 4.5, which means distance
# between groups is zero, and thus confidence is zero.
self.assertEqual(
0, bisect_perf_module.CalculateConfidence(bad_values, good_values))
def testCalculateConfidence100(self):
"""Tests the confidence calculation when it's expected to be 100."""
bad_values = [[1, 1], [1, 1]]
good_values = [[1.2, 1.2], [1.2, 1.2]]
# Standard deviation in both groups is zero, so confidence is 100.
self.assertEqual(
100, bisect_perf_module.CalculateConfidence(bad_values, good_values))
if __name__ == '__main__':
unittest.main()
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