def test_os3e_weighted(self):
'''Ensure unit-weighted version of graph yields same availability.'''
link_fail_prob = 0.01
g = OS3EGraph()
g_unit = set_unit_weights(g.copy())
apsp = nx.all_pairs_shortest_path_length(g)
apsp_paths = nx.all_pairs_shortest_path(g)
combos = [["Sunnyvale, CA", "Boston"],
["Portland"],
["Sunnyvale, CA", "Salt Lake City"],
["Seattle", "Boston"],
["Seattle", "Portland"]]
for combo in combos:
for max_failures in range(1, 2):
a, c = availability_one_combo(g, combo, apsp, apsp_paths,
False, link_fail_prob, max_failures)
a_u, c_u = availability_one_combo(g_unit, combo, apsp,
apsp_paths, True, link_fail_prob, max_failures)
self.assertAlmostEqual(a, a_u)
self.assertAlmostEqual(c, c_u)
def test_os3e(self):
'''Validate coverage and availability are reasonable.'''
link_fail_prob = 0.01
g = OS3EGraph()
apsp = nx.all_pairs_shortest_path_length(g)
apsp_paths = nx.all_pairs_shortest_path(g)
combos = [["Sunnyvale, CA", "Boston"],
["Portland"],
["Sunnyvale, CA", "Salt Lake City"],
["Seattle", "Boston"],
["Seattle", "Portland"]]
weighted = False
'''
34 nodes
41 edges
pfail = 0.01
p(good) =
1 * p(success) * p(success) * ... 41 = 0.99 ** 41 =
0.66228204098398347
p(1 fail) =
(41 choose 1) * (p(success) ** (41 - 1)) * p(fail) =
41 * (0.99 ** 40) * 0.01 =
0.27427842101356892
p(2 fail) =
(41 choose 2) * (p(success) ** (41 - 2)) * (p(fail) ** 2) =
820 * (0.99 ** 39) * (0.01 ** 2) =
0.055409782022943221
p(3 fail) =
(41 choose 3) * (p(success) ** (41 - 2)) * (p(fail) ** 2) =
10660 * (0.99 ** 38) * (0.01 ** 3) =
0.0072760319828107265
sum (0..1 failures) =
0.66228204098398347 + 0.27427842101356892 =
0.93656046199755238
error bar =
0.063439538002447615
sum (0..2 failures) =
0.66228204098398347 + 0.27427842101356892 + 0.055409782022943221 =
0.99197024402049561
error bar =
0.00802
sum (0..3 failures) =
0.66228204098398347 + 0.27427842101356892 + 0.055409782022943221 +
0.0072760319828107265 =
0.99924627600330629
error bar = 0.00075
'''
exp_coverage = {
0: 0.66228204098398347,
1: 0.27427842101356892,
2: 0.055409782022943221,
3: 0.0072760319828107265
}
def get_coverage(f):
'''Return expected coverage for given number of failures.'''
return sum([v for k, v in exp_coverage.iteritems() if k <= f])
for combo in combos:
for max_failures in range(3):
a, c = availability_one_combo(g, combo, apsp, apsp_paths,
weighted, link_fail_prob,
max_failures)
self.assertTrue(a < 1.0)
self.assertTrue(c < 1.0)
self.assertAlmostEqual(get_coverage(max_failures), c)
# A link of average length should have equivalent failure probability
# to the input prob.
avg_link_fail_prob = sum(distances) / float(g_w.number_of_edges()) * weighted_link_fail_prob
assert abs(link_fail_prob - avg_link_fail_prob) < 0.000001
apsp = nx.all_pairs_shortest_path_length(g)
apsp_paths = nx.all_pairs_shortest_path(g)
apsp_w = nx.all_pairs_dijkstra_path_length(g_w)
apsp_w_paths = nx.all_pairs_dijkstra_path(g_w)
combos = [["Portland"],
["Sunnyvale, CA", "Boston"],
["Sunnyvale, CA", "Salt Lake City"],
["Seattle", "Boston"],
["Seattle", "Portland"]]
# These should be roughly equal, given our construction of weighted link
# failure probability.
for combo in combos:
print "combo: %s" % combo
for max_failures in range(1, 4):
print "\tfailures: %s" % max_failures
a, c = availability_one_combo(g, combo, apsp, apsp_paths,
False, link_fail_prob, max_failures)
a_w, c_w = availability_one_combo(g_w, combo, apsp_w,
apsp_w_paths, True, weighted_link_fail_prob, max_failures)
print "\t\tunweighted availability, coverage: %0.6f, %0.6f" % (a, c)
print "\t\t weighted availability, coverage: %0.6f, %0.6f" % (a_w, c_w)