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https://github.com/tahoe-lafs/tahoe-lafs.git
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Move statistics tests out.
This commit is contained in:
Itamar Turner-Trauring
parent
e681ca1cb9
commit
b140d1f1af
147
src/allmydata/test/test_statistics.py
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147
src/allmydata/test/test_statistics.py
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"""
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Tests for allmydata.util.statistics.
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"""
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from io import StringIO
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from twisted.trial import unittest
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from allmydata.util import statistics
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class Statistics(unittest.TestCase):
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def should_assert(self, msg, func, *args, **kwargs):
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try:
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func(*args, **kwargs)
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self.fail(msg)
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except AssertionError:
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pass
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def failUnlessListEqual(self, a, b, msg = None):
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self.failUnlessEqual(len(a), len(b))
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for i in range(len(a)):
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self.failUnlessEqual(a[i], b[i], msg)
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def failUnlessListAlmostEqual(self, a, b, places = 7, msg = None):
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self.failUnlessEqual(len(a), len(b))
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for i in range(len(a)):
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self.failUnlessAlmostEqual(a[i], b[i], places, msg)
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def test_binomial_coeff(self):
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f = statistics.binomial_coeff
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self.failUnlessEqual(f(20, 0), 1)
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self.failUnlessEqual(f(20, 1), 20)
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self.failUnlessEqual(f(20, 2), 190)
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self.failUnlessEqual(f(20, 8), f(20, 12))
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self.should_assert("Should assert if n < k", f, 2, 3)
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def test_binomial_distribution_pmf(self):
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f = statistics.binomial_distribution_pmf
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pmf_comp = f(2, .1)
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pmf_stat = [0.81, 0.18, 0.01]
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self.failUnlessListAlmostEqual(pmf_comp, pmf_stat)
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# Summing across a PMF should give the total probability 1
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self.failUnlessAlmostEqual(sum(pmf_comp), 1)
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self.should_assert("Should assert if not 0<=p<=1", f, 1, -1)
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self.should_assert("Should assert if n < 1", f, 0, .1)
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out = StringIO()
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statistics.print_pmf(pmf_comp, out=out)
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lines = out.getvalue().splitlines()
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self.failUnlessEqual(lines[0], "i=0: 0.81")
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self.failUnlessEqual(lines[1], "i=1: 0.18")
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self.failUnlessEqual(lines[2], "i=2: 0.01")
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def test_survival_pmf(self):
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f = statistics.survival_pmf
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# Cross-check binomial-distribution method against convolution
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# method.
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p_list = [.9999] * 100 + [.99] * 50 + [.8] * 20
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pmf1 = statistics.survival_pmf_via_conv(p_list)
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pmf2 = statistics.survival_pmf_via_bd(p_list)
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self.failUnlessListAlmostEqual(pmf1, pmf2)
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self.failUnlessTrue(statistics.valid_pmf(pmf1))
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self.should_assert("Should assert if p_i > 1", f, [1.1]);
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self.should_assert("Should assert if p_i < 0", f, [-.1]);
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def test_repair_count_pmf(self):
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survival_pmf = statistics.binomial_distribution_pmf(5, .9)
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repair_pmf = statistics.repair_count_pmf(survival_pmf, 3)
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# repair_pmf[0] == sum(survival_pmf[0,1,2,5])
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# repair_pmf[1] == survival_pmf[4]
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# repair_pmf[2] = survival_pmf[3]
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self.failUnlessListAlmostEqual(repair_pmf,
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[0.00001 + 0.00045 + 0.0081 + 0.59049,
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.32805,
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.0729,
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0, 0, 0])
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def test_repair_cost(self):
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survival_pmf = statistics.binomial_distribution_pmf(5, .9)
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bwcost = statistics.bandwidth_cost_function
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cost = statistics.mean_repair_cost(bwcost, 1000,
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survival_pmf, 3, ul_dl_ratio=1.0)
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self.failUnlessAlmostEqual(cost, 558.90)
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cost = statistics.mean_repair_cost(bwcost, 1000,
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survival_pmf, 3, ul_dl_ratio=8.0)
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self.failUnlessAlmostEqual(cost, 1664.55)
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# I haven't manually checked the math beyond here -warner
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cost = statistics.eternal_repair_cost(bwcost, 1000,
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survival_pmf, 3,
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discount_rate=0, ul_dl_ratio=1.0)
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self.failUnlessAlmostEqual(cost, 65292.056074766246)
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cost = statistics.eternal_repair_cost(bwcost, 1000,
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survival_pmf, 3,
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discount_rate=0.05,
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ul_dl_ratio=1.0)
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self.failUnlessAlmostEqual(cost, 9133.6097158191551)
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def test_convolve(self):
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f = statistics.convolve
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v1 = [ 1, 2, 3 ]
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v2 = [ 4, 5, 6 ]
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v3 = [ 7, 8 ]
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v1v2result = [ 4, 13, 28, 27, 18 ]
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# Convolution is commutative
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r1 = f(v1, v2)
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r2 = f(v2, v1)
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self.failUnlessListEqual(r1, r2, "Convolution should be commutative")
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self.failUnlessListEqual(r1, v1v2result, "Didn't match known result")
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# Convolution is associative
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r1 = f(f(v1, v2), v3)
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r2 = f(v1, f(v2, v3))
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self.failUnlessListEqual(r1, r2, "Convolution should be associative")
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# Convolution is distributive
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r1 = f(v3, [ a + b for a, b in zip(v1, v2) ])
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tmp1 = f(v3, v1)
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tmp2 = f(v3, v2)
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r2 = [ a + b for a, b in zip(tmp1, tmp2) ]
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self.failUnlessListEqual(r1, r2, "Convolution should be distributive")
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# Convolution is scalar multiplication associative
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tmp1 = f(v1, v2)
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r1 = [ a * 4 for a in tmp1 ]
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tmp2 = [ a * 4 for a in v1 ]
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r2 = f(tmp2, v2)
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self.failUnlessListEqual(r1, r2, "Convolution should be scalar multiplication associative")
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def test_find_k(self):
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f = statistics.find_k
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g = statistics.pr_file_loss
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plist = [.9] * 10 + [.8] * 10 # N=20
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t = .0001
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k = f(plist, t)
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self.failUnlessEqual(k, 10)
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self.failUnless(g(plist, k) < t)
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def test_pr_file_loss(self):
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f = statistics.pr_file_loss
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plist = [.5] * 10
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self.failUnlessEqual(f(plist, 3), .0546875)
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def test_pr_backup_file_loss(self):
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f = statistics.pr_backup_file_loss
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plist = [.5] * 10
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self.failUnlessEqual(f(plist, .5, 3), .02734375)
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@ -4,7 +4,6 @@ import six
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import os, time, sys
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import yaml
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from six.moves import StringIO
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from twisted.trial import unittest
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from twisted.internet import defer, reactor
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from twisted.python.failure import Failure
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@ -12,7 +11,7 @@ from twisted.python.failure import Failure
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from allmydata.util import idlib, mathutil
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from allmydata.util import fileutil
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from allmydata.util import limiter, pollmixin
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from allmydata.util import statistics, dictutil, yamlutil
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from allmydata.util import dictutil, yamlutil
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from allmydata.util import log as tahoe_log
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from allmydata.util.fileutil import EncryptedTemporaryFile
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from allmydata.test.common_util import ReallyEqualMixin
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@ -34,143 +33,6 @@ class Math(unittest.TestCase):
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f = mathutil.round_sigfigs
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self.failUnlessEqual(f(22.0/3, 4), 7.3330000000000002)
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class Statistics(unittest.TestCase):
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def should_assert(self, msg, func, *args, **kwargs):
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try:
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func(*args, **kwargs)
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self.fail(msg)
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except AssertionError:
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pass
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def failUnlessListEqual(self, a, b, msg = None):
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self.failUnlessEqual(len(a), len(b))
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for i in range(len(a)):
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self.failUnlessEqual(a[i], b[i], msg)
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def failUnlessListAlmostEqual(self, a, b, places = 7, msg = None):
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self.failUnlessEqual(len(a), len(b))
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for i in range(len(a)):
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self.failUnlessAlmostEqual(a[i], b[i], places, msg)
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def test_binomial_coeff(self):
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f = statistics.binomial_coeff
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self.failUnlessEqual(f(20, 0), 1)
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self.failUnlessEqual(f(20, 1), 20)
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self.failUnlessEqual(f(20, 2), 190)
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self.failUnlessEqual(f(20, 8), f(20, 12))
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self.should_assert("Should assert if n < k", f, 2, 3)
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def test_binomial_distribution_pmf(self):
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f = statistics.binomial_distribution_pmf
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pmf_comp = f(2, .1)
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pmf_stat = [0.81, 0.18, 0.01]
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self.failUnlessListAlmostEqual(pmf_comp, pmf_stat)
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# Summing across a PMF should give the total probability 1
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self.failUnlessAlmostEqual(sum(pmf_comp), 1)
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self.should_assert("Should assert if not 0<=p<=1", f, 1, -1)
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self.should_assert("Should assert if n < 1", f, 0, .1)
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out = StringIO()
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statistics.print_pmf(pmf_comp, out=out)
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lines = out.getvalue().splitlines()
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self.failUnlessEqual(lines[0], "i=0: 0.81")
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self.failUnlessEqual(lines[1], "i=1: 0.18")
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self.failUnlessEqual(lines[2], "i=2: 0.01")
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def test_survival_pmf(self):
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f = statistics.survival_pmf
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# Cross-check binomial-distribution method against convolution
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# method.
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p_list = [.9999] * 100 + [.99] * 50 + [.8] * 20
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pmf1 = statistics.survival_pmf_via_conv(p_list)
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pmf2 = statistics.survival_pmf_via_bd(p_list)
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self.failUnlessListAlmostEqual(pmf1, pmf2)
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self.failUnlessTrue(statistics.valid_pmf(pmf1))
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self.should_assert("Should assert if p_i > 1", f, [1.1]);
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self.should_assert("Should assert if p_i < 0", f, [-.1]);
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def test_repair_count_pmf(self):
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survival_pmf = statistics.binomial_distribution_pmf(5, .9)
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repair_pmf = statistics.repair_count_pmf(survival_pmf, 3)
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# repair_pmf[0] == sum(survival_pmf[0,1,2,5])
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# repair_pmf[1] == survival_pmf[4]
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# repair_pmf[2] = survival_pmf[3]
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self.failUnlessListAlmostEqual(repair_pmf,
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[0.00001 + 0.00045 + 0.0081 + 0.59049,
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.32805,
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.0729,
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0, 0, 0])
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def test_repair_cost(self):
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survival_pmf = statistics.binomial_distribution_pmf(5, .9)
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bwcost = statistics.bandwidth_cost_function
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cost = statistics.mean_repair_cost(bwcost, 1000,
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survival_pmf, 3, ul_dl_ratio=1.0)
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self.failUnlessAlmostEqual(cost, 558.90)
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cost = statistics.mean_repair_cost(bwcost, 1000,
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survival_pmf, 3, ul_dl_ratio=8.0)
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self.failUnlessAlmostEqual(cost, 1664.55)
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# I haven't manually checked the math beyond here -warner
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cost = statistics.eternal_repair_cost(bwcost, 1000,
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survival_pmf, 3,
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discount_rate=0, ul_dl_ratio=1.0)
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self.failUnlessAlmostEqual(cost, 65292.056074766246)
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cost = statistics.eternal_repair_cost(bwcost, 1000,
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survival_pmf, 3,
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discount_rate=0.05,
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ul_dl_ratio=1.0)
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self.failUnlessAlmostEqual(cost, 9133.6097158191551)
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def test_convolve(self):
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f = statistics.convolve
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v1 = [ 1, 2, 3 ]
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v2 = [ 4, 5, 6 ]
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v3 = [ 7, 8 ]
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v1v2result = [ 4, 13, 28, 27, 18 ]
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# Convolution is commutative
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r1 = f(v1, v2)
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r2 = f(v2, v1)
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self.failUnlessListEqual(r1, r2, "Convolution should be commutative")
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self.failUnlessListEqual(r1, v1v2result, "Didn't match known result")
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# Convolution is associative
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r1 = f(f(v1, v2), v3)
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r2 = f(v1, f(v2, v3))
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self.failUnlessListEqual(r1, r2, "Convolution should be associative")
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# Convolution is distributive
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r1 = f(v3, [ a + b for a, b in zip(v1, v2) ])
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tmp1 = f(v3, v1)
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tmp2 = f(v3, v2)
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r2 = [ a + b for a, b in zip(tmp1, tmp2) ]
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self.failUnlessListEqual(r1, r2, "Convolution should be distributive")
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# Convolution is scalar multiplication associative
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tmp1 = f(v1, v2)
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r1 = [ a * 4 for a in tmp1 ]
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tmp2 = [ a * 4 for a in v1 ]
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r2 = f(tmp2, v2)
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self.failUnlessListEqual(r1, r2, "Convolution should be scalar multiplication associative")
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def test_find_k(self):
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f = statistics.find_k
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g = statistics.pr_file_loss
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plist = [.9] * 10 + [.8] * 10 # N=20
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t = .0001
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k = f(plist, t)
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self.failUnlessEqual(k, 10)
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self.failUnless(g(plist, k) < t)
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def test_pr_file_loss(self):
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f = statistics.pr_file_loss
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plist = [.5] * 10
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self.failUnlessEqual(f(plist, 3), .0546875)
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def test_pr_backup_file_loss(self):
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f = statistics.pr_backup_file_loss
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plist = [.5] * 10
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self.failUnlessEqual(f(plist, .5, 3), .02734375)
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class FileUtil(ReallyEqualMixin, unittest.TestCase):
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def mkdir(self, basedir, path, mode=0o777):
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