Mercurial > hg-stable
annotate tests/test-ancestor.py @ 23334:59e6e5dd3605
ancestor.missingancestors: turn into a state-keeping class
This allows multiple efficient missing ancestor queries against the same set of
bases. In upcoming patches we'll also define ways to grow the set of bases.
The fact that the test output hasn't changed establishes this patch's
correctness.
author | Siddharth Agarwal <sid0@fb.com> |
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date | Fri, 14 Nov 2014 23:44:38 -0800 |
parents | 3b1b8f25443e |
children | 3f28e8cb3066 |
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ancestor.deepest: ignore ninteresting while building result (issue3984)
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1 from mercurial import ancestor, commands, hg, ui, util |
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2 from mercurial.node import nullrev |
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3 import binascii, getopt, math, os, random, sys, time |
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4 |
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5 def buildgraph(rng, nodes=100, rootprob=0.05, mergeprob=0.2, prevprob=0.7): |
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6 '''nodes: total number of nodes in the graph |
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7 rootprob: probability that a new node (not 0) will be a root |
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8 mergeprob: probability that, excluding a root a node will be a merge |
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9 prevprob: probability that p1 will be the previous node |
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10 |
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11 return value is a graph represented as an adjacency list. |
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12 ''' |
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13 graph = [None] * nodes |
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14 for i in xrange(nodes): |
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15 if i == 0 or rng.random() < rootprob: |
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16 graph[i] = [nullrev] |
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17 elif i == 1: |
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18 graph[i] = [0] |
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19 elif rng.random() < mergeprob: |
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20 if i == 2 or rng.random() < prevprob: |
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21 # p1 is prev |
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22 p1 = i - 1 |
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23 else: |
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24 p1 = rng.randrange(i - 1) |
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25 p2 = rng.choice(range(0, p1) + range(p1 + 1, i)) |
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26 graph[i] = [p1, p2] |
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27 elif rng.random() < prevprob: |
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28 graph[i] = [i - 1] |
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29 else: |
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30 graph[i] = [rng.randrange(i - 1)] |
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31 |
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32 return graph |
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33 |
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34 def buildancestorsets(graph): |
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35 ancs = [None] * len(graph) |
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36 for i in xrange(len(graph)): |
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37 ancs[i] = set([i]) |
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38 if graph[i] == [nullrev]: |
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39 continue |
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40 for p in graph[i]: |
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41 ancs[i].update(ancs[p]) |
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42 return ancs |
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43 |
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44 def naivemissingancestors(ancs, revs, bases): |
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45 res = set() |
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46 for rev in revs: |
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47 if rev != nullrev: |
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48 res.update(ancs[rev]) |
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49 for base in bases: |
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50 if base != nullrev: |
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51 res.difference_update(ancs[base]) |
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52 return sorted(res) |
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53 |
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54 def test_missingancestors(seed, rng): |
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55 # empirically observed to take around 1 second |
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56 graphcount = 100 |
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57 testcount = 100 |
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58 nerrs = [0] |
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59 # the default mu and sigma give us a nice distribution of mostly |
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60 # single-digit counts (including 0) with some higher ones |
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61 def lognormrandom(mu, sigma): |
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62 return int(math.floor(rng.lognormvariate(mu, sigma))) |
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63 |
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64 def samplerevs(nodes, mu=1.1, sigma=0.8): |
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65 count = min(lognormrandom(mu, sigma), len(nodes)) |
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66 return rng.sample(nodes, count) |
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67 |
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68 def err(seed, graph, bases, revs, output, expected): |
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69 if nerrs[0] == 0: |
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70 print >> sys.stderr, 'seed:', hex(seed)[:-1] |
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71 if gerrs[0] == 0: |
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72 print >> sys.stderr, 'graph:', graph |
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73 print >> sys.stderr, '* bases:', bases |
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74 print >> sys.stderr, '* revs: ', revs |
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75 print >> sys.stderr, '* output: ', output |
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76 print >> sys.stderr, '* expected:', expected |
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77 nerrs[0] += 1 |
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78 gerrs[0] += 1 |
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79 |
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80 for g in xrange(graphcount): |
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81 graph = buildgraph(rng) |
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82 ancs = buildancestorsets(graph) |
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83 gerrs = [0] |
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84 for _ in xrange(testcount): |
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85 # start from nullrev to include it as a possibility |
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86 graphnodes = range(nullrev, len(graph)) |
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87 bases = samplerevs(graphnodes) |
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88 revs = samplerevs(graphnodes) |
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89 |
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90 # fast algorithm |
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91 inc = ancestor.incrementalmissingancestors(graph.__getitem__, bases) |
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92 h = inc.missingancestors(revs) |
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93 # reference slow algorithm |
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94 r = naivemissingancestors(ancs, revs, bases) |
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95 if h != r: |
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96 err(seed, graph, bases, revs, h, r) |
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97 |
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98 # graph is a dict of child->parent adjacency lists for this graph: |
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99 # o 13 |
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100 # | |
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101 # | o 12 |
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102 # | | |
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103 # | | o 11 |
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104 # | | |\ |
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105 # | | | | o 10 |
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106 # | | | | | |
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107 # | o---+ | 9 |
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108 # | | | | | |
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109 # o | | | | 8 |
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110 # / / / / |
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111 # | | o | 7 |
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112 # | | | | |
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113 # o---+ | 6 |
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114 # / / / |
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115 # | | o 5 |
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116 # | |/ |
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117 # | o 4 |
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118 # | | |
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119 # o | 3 |
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120 # | | |
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121 # | o 2 |
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122 # |/ |
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123 # o 1 |
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124 # | |
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125 # o 0 |
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126 |
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127 graph = {0: [-1], 1: [0], 2: [1], 3: [1], 4: [2], 5: [4], 6: [4], |
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128 7: [4], 8: [-1], 9: [6, 7], 10: [5], 11: [3, 7], 12: [9], |
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129 13: [8]} |
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130 |
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131 def genlazyancestors(revs, stoprev=0, inclusive=False): |
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132 print ("%% lazy ancestor set for %s, stoprev = %s, inclusive = %s" % |
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133 (revs, stoprev, inclusive)) |
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134 return ancestor.lazyancestors(graph.get, revs, stoprev=stoprev, |
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135 inclusive=inclusive) |
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136 |
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137 def printlazyancestors(s, l): |
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138 print 'membership: %r' % [n for n in l if n in s] |
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139 print 'iteration: %r' % list(s) |
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140 |
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141 def test_lazyancestors(): |
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142 # Empty revs |
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143 s = genlazyancestors([]) |
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144 printlazyancestors(s, [3, 0, -1]) |
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145 |
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146 # Standard example |
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147 s = genlazyancestors([11, 13]) |
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148 printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0]) |
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149 |
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150 # Standard with ancestry in the initial set (1 is ancestor of 3) |
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151 s = genlazyancestors([1, 3]) |
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152 printlazyancestors(s, [1, -1, 0]) |
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153 |
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154 # Including revs |
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155 s = genlazyancestors([11, 13], inclusive=True) |
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156 printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0]) |
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157 |
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158 # Test with stoprev |
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159 s = genlazyancestors([11, 13], stoprev=6) |
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160 printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0]) |
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161 s = genlazyancestors([11, 13], stoprev=6, inclusive=True) |
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162 printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0]) |
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163 |
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164 |
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165 # The C gca algorithm requires a real repo. These are textual descriptions of |
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166 # DAGs that have been known to be problematic. |
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167 dagtests = [ |
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168 '+2*2*2/*3/2', |
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169 '+3*3/*2*2/*4*4/*4/2*4/2*2', |
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170 ] |
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171 def test_gca(): |
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172 u = ui.ui() |
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173 for i, dag in enumerate(dagtests): |
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174 repo = hg.repository(u, 'gca%d' % i, create=1) |
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175 cl = repo.changelog |
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176 if not util.safehasattr(cl.index, 'ancestors'): |
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177 # C version not available |
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178 return |
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179 |
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180 commands.debugbuilddag(u, repo, dag) |
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181 # Compare the results of the Python and C versions. This does not |
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182 # include choosing a winner when more than one gca exists -- we make |
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183 # sure both return exactly the same set of gcas. |
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184 for a in cl: |
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185 for b in cl: |
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186 cgcas = sorted(cl.index.ancestors(a, b)) |
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187 pygcas = sorted(ancestor.ancestors(cl.parentrevs, a, b)) |
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188 if cgcas != pygcas: |
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189 print "test_gca: for dag %s, gcas for %d, %d:" % (dag, a, b) |
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190 print " C returned: %s" % cgcas |
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191 print " Python returned: %s" % pygcas |
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192 |
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193 def main(): |
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194 seed = None |
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195 opts, args = getopt.getopt(sys.argv[1:], 's:', ['seed=']) |
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196 for o, a in opts: |
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197 if o in ('-s', '--seed'): |
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198 seed = long(a, base=0) # accepts base 10 or 16 strings |
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199 |
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200 if seed is None: |
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201 try: |
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202 seed = long(binascii.hexlify(os.urandom(16)), 16) |
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203 except AttributeError: |
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204 seed = long(time.time() * 1000) |
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205 |
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206 rng = random.Random(seed) |
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207 test_missingancestors(seed, rng) |
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208 test_lazyancestors() |
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209 test_gca() |
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210 |
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211 if __name__ == '__main__': |
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212 main() |