Index: tools/telemetry/telemetry/util/statistics_unittest.py |
diff --git a/tools/telemetry/telemetry/util/statistics_unittest.py b/tools/telemetry/telemetry/util/statistics_unittest.py |
deleted file mode 100644 |
index 8468a165855be3ddcd5f3c60f133439b06870540..0000000000000000000000000000000000000000 |
--- a/tools/telemetry/telemetry/util/statistics_unittest.py |
+++ /dev/null |
@@ -1,219 +0,0 @@ |
-# 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 math |
-import random |
-import unittest |
- |
-from telemetry.util import statistics |
- |
- |
-def Relax(samples, iterations=10): |
- """Lloyd relaxation in 1D. |
- |
- Keeps the position of the first and last sample. |
- """ |
- for _ in xrange(0, iterations): |
- voronoi_boundaries = [] |
- for i in xrange(1, len(samples)): |
- voronoi_boundaries.append((samples[i] + samples[i-1]) * 0.5) |
- |
- relaxed_samples = [] |
- relaxed_samples.append(samples[0]) |
- for i in xrange(1, len(samples)-1): |
- relaxed_samples.append( |
- (voronoi_boundaries[i-1] + voronoi_boundaries[i]) * 0.5) |
- relaxed_samples.append(samples[-1]) |
- samples = relaxed_samples |
- return samples |
- |
-def CreateRandomSamples(num_samples): |
- samples = [] |
- position = 0.0 |
- samples.append(position) |
- for _ in xrange(1, num_samples): |
- position += random.random() |
- samples.append(position) |
- return samples |
- |
-class StatisticsUnitTest(unittest.TestCase): |
- |
- def testNormalizeSamples(self): |
- samples = [] |
- normalized_samples, scale = statistics.NormalizeSamples(samples) |
- self.assertEquals(normalized_samples, []) |
- self.assertEquals(scale, 1.0) |
- |
- samples = [0.0, 0.0] |
- normalized_samples, scale = statistics.NormalizeSamples(samples) |
- self.assertEquals(normalized_samples, [0.5, 0.5]) |
- self.assertEquals(scale, 1.0) |
- |
- samples = [0.0, 1.0/3.0, 2.0/3.0, 1.0] |
- normalized_samples, scale = statistics.NormalizeSamples(samples) |
- self.assertEquals(normalized_samples, [1.0/8.0, 3.0/8.0, 5.0/8.0, 7.0/8.0]) |
- self.assertEquals(scale, 0.75) |
- |
- samples = [1.0/8.0, 3.0/8.0, 5.0/8.0, 7.0/8.0] |
- normalized_samples, scale = statistics.NormalizeSamples(samples) |
- self.assertEquals(normalized_samples, samples) |
- self.assertEquals(scale, 1.0) |
- |
- def testDiscrepancyRandom(self): |
- """Tests NormalizeSamples and Discrepancy with random samples. |
- |
- Generates 10 sets of 10 random samples, computes the discrepancy, |
- relaxes the samples using Llloyd's algorithm in 1D, and computes the |
- discrepancy of the relaxed samples. Discrepancy of the relaxed samples |
- must be less than or equal to the discrepancy of the original samples. |
- """ |
- random.seed(1234567) |
- for _ in xrange(0, 10): |
- samples = CreateRandomSamples(10) |
- samples = statistics.NormalizeSamples(samples)[0] |
- d = statistics.Discrepancy(samples) |
- relaxed_samples = Relax(samples) |
- d_relaxed = statistics.Discrepancy(relaxed_samples) |
- self.assertTrue(d_relaxed <= d) |
- |
- def testDiscrepancyAnalytic(self): |
- """Computes discrepancy for sample sets with known statistics.""" |
- samples = [] |
- d = statistics.Discrepancy(samples) |
- self.assertEquals(d, 0.0) |
- |
- samples = [0.5] |
- d = statistics.Discrepancy(samples) |
- self.assertEquals(d, 0.5) |
- |
- samples = [0.0, 1.0] |
- d = statistics.Discrepancy(samples) |
- self.assertEquals(d, 1.0) |
- |
- samples = [0.5, 0.5, 0.5] |
- d = statistics.Discrepancy(samples) |
- self.assertEquals(d, 1.0) |
- |
- samples = [1.0/8.0, 3.0/8.0, 5.0/8.0, 7.0/8.0] |
- d = statistics.Discrepancy(samples) |
- self.assertEquals(d, 0.25) |
- |
- samples = [1.0/8.0, 5.0/8.0, 5.0/8.0, 7.0/8.0] |
- d = statistics.Discrepancy(samples) |
- self.assertEquals(d, 0.5) |
- |
- samples = [1.0/8.0, 3.0/8.0, 5.0/8.0, 5.0/8.0, 7.0/8.0] |
- d = statistics.Discrepancy(samples) |
- self.assertEquals(d, 0.4) |
- |
- samples = [0.0, 1.0/3.0, 2.0/3.0, 1.0] |
- d = statistics.Discrepancy(samples) |
- self.assertEquals(d, 0.5) |
- |
- samples = statistics.NormalizeSamples(samples)[0] |
- d = statistics.Discrepancy(samples) |
- self.assertEquals(d, 0.25) |
- |
- def testTimestampsDiscrepancy(self): |
- time_stamps = [] |
- d_abs = statistics.TimestampsDiscrepancy(time_stamps, True) |
- self.assertEquals(d_abs, 0.0) |
- |
- time_stamps = [4] |
- d_abs = statistics.TimestampsDiscrepancy(time_stamps, True) |
- self.assertEquals(d_abs, 0.5) |
- |
- time_stamps_a = [0, 1, 2, 3, 5, 6] |
- time_stamps_b = [0, 1, 2, 3, 5, 7] |
- time_stamps_c = [0, 2, 3, 4] |
- time_stamps_d = [0, 2, 3, 4, 5] |
- |
- d_abs_a = statistics.TimestampsDiscrepancy(time_stamps_a, True) |
- d_abs_b = statistics.TimestampsDiscrepancy(time_stamps_b, True) |
- d_abs_c = statistics.TimestampsDiscrepancy(time_stamps_c, True) |
- d_abs_d = statistics.TimestampsDiscrepancy(time_stamps_d, True) |
- d_rel_a = statistics.TimestampsDiscrepancy(time_stamps_a, False) |
- d_rel_b = statistics.TimestampsDiscrepancy(time_stamps_b, False) |
- d_rel_c = statistics.TimestampsDiscrepancy(time_stamps_c, False) |
- d_rel_d = statistics.TimestampsDiscrepancy(time_stamps_d, False) |
- |
- self.assertTrue(d_abs_a < d_abs_b) |
- self.assertTrue(d_rel_a < d_rel_b) |
- self.assertTrue(d_rel_d < d_rel_c) |
- self.assertAlmostEquals(d_abs_d, d_abs_c) |
- |
- def testDiscrepancyMultipleRanges(self): |
- samples = [[0.0, 1.2, 2.3, 3.3], [6.3, 7.5, 8.4], [4.2, 5.4, 5.9]] |
- d_0 = statistics.TimestampsDiscrepancy(samples[0]) |
- d_1 = statistics.TimestampsDiscrepancy(samples[1]) |
- d_2 = statistics.TimestampsDiscrepancy(samples[2]) |
- d = statistics.TimestampsDiscrepancy(samples) |
- self.assertEquals(d, max(d_0, d_1, d_2)) |
- |
- def testApproximateDiscrepancy(self): |
- """Tests approimate discrepancy implementation by comparing to exact |
- solution. |
- """ |
- random.seed(1234567) |
- for _ in xrange(0, 5): |
- samples = CreateRandomSamples(10) |
- samples = statistics.NormalizeSamples(samples)[0] |
- d = statistics.Discrepancy(samples) |
- d_approx = statistics.Discrepancy(samples, 500) |
- self.assertEquals(round(d, 2), round(d_approx, 2)) |
- |
- def testPercentile(self): |
- # The 50th percentile is the median value. |
- self.assertEquals(3, statistics.Percentile([4, 5, 1, 3, 2], 50)) |
- self.assertEquals(2.5, statistics.Percentile([5, 1, 3, 2], 50)) |
- # When the list of values is empty, 0 is returned. |
- self.assertEquals(0, statistics.Percentile([], 50)) |
- # When the given percentage is very low, the lowest value is given. |
- self.assertEquals(1, statistics.Percentile([2, 1, 5, 4, 3], 5)) |
- # When the given percentage is very high, the highest value is given. |
- self.assertEquals(5, statistics.Percentile([5, 2, 4, 1, 3], 95)) |
- # Linear interpolation between closest ranks is used. Using the example |
- # from <http://en.wikipedia.org/wiki/Percentile>: |
- self.assertEquals(27.5, statistics.Percentile([15, 20, 35, 40, 50], 40)) |
- |
- def testArithmeticMean(self): |
- # The ArithmeticMean function computes the simple average. |
- self.assertAlmostEquals(40/3.0, statistics.ArithmeticMean([10, 10, 20])) |
- self.assertAlmostEquals(15.0, statistics.ArithmeticMean([10, 20])) |
- # If the 'count' is zero, then zero is returned. |
- self.assertEquals(0, statistics.ArithmeticMean([])) |
- |
- def testDurationsDiscrepancy(self): |
- durations = [] |
- d = statistics.DurationsDiscrepancy(durations) |
- self.assertEquals(d, 0.0) |
- |
- durations = [4] |
- d = statistics.DurationsDiscrepancy(durations) |
- self.assertEquals(d, 4.0) |
- |
- durations_a = [1, 1, 1, 1, 1] |
- durations_b = [1, 1, 2, 1, 1] |
- durations_c = [1, 2, 1, 2, 1] |
- |
- d_a = statistics.DurationsDiscrepancy(durations_a) |
- d_b = statistics.DurationsDiscrepancy(durations_b) |
- d_c = statistics.DurationsDiscrepancy(durations_c) |
- |
- self.assertTrue(d_a < d_b < d_c) |
- |
- def testStandardDeviation(self): |
- self.assertAlmostEquals(math.sqrt(2/3.0), |
- statistics.StandardDeviation([1, 2, 3])) |
- self.assertEquals(0, statistics.StandardDeviation([1])) |
- self.assertEquals(0, statistics.StandardDeviation([])) |
- |
- def testTrapezoidalRule(self): |
- self.assertEquals(4, statistics.TrapezoidalRule([1, 2, 3], 1)) |
- self.assertEquals(2, statistics.TrapezoidalRule([1, 2, 3], .5)) |
- self.assertEquals(0, statistics.TrapezoidalRule([1, 2, 3], 0)) |
- self.assertEquals(-4, statistics.TrapezoidalRule([1, 2, 3], -1)) |
- self.assertEquals(3, statistics.TrapezoidalRule([-1, 2, 3], 1)) |
- self.assertEquals(0, statistics.TrapezoidalRule([1], 1)) |
- self.assertEquals(0, statistics.TrapezoidalRule([0], 1)) |