def testWeisfeilerLehman(self):
wl_state_exp = {
"labels": {
"0": "wl_0.0",
"1": "wl_0.1",
"a": "wl_0.2",
"b": "wl_0.3",
"wl_0.0;in(wl_0.3)": "wl_1.0",
"wl_0.0;any(wl_0.2),in(wl_0.2)": "wl_1.1",
"wl_0.1;any(wl_0.2),out(wl_0.2,wl_0.3)": "wl_1.2",
"wl_0.1;any(wl_0.2),out(wl_0.2)": "wl_1.3",
"wl_0.2;in(wl_0.1),out(wl_0.0)": "wl_1.4",
"wl_0.2;any(wl_0.0,wl_0.1)": "wl_1.5",
"wl_0.3;in(wl_0.1),out(wl_0.0)": "wl_1.6",
"wl_1.0;in(wl_1.6)": "wl_2.0",
"wl_1.1;any(wl_1.5),in(wl_1.4)": "wl_2.1",
"wl_1.2;any(wl_1.5),out(wl_1.4,wl_1.6)": "wl_2.2",
"wl_1.3;any(wl_1.5),out(wl_1.4)": "wl_2.3",
"wl_1.4;in(wl_1.2),out(wl_1.1)": "wl_2.4",
"wl_1.4;in(wl_1.3),out(wl_1.1)": "wl_2.5",
"wl_1.5;any(wl_1.1,wl_1.2)": "wl_2.6",
"wl_1.5;any(wl_1.1,wl_1.3)": "wl_2.7",
"wl_1.6;in(wl_1.2),out(wl_1.0)": "wl_2.8",
"wl_2.0;in(wl_2.8)": "wl_3.0",
"wl_2.1;any(wl_2.7),in(wl_2.4)": "wl_3.1",
"wl_2.1;any(wl_2.6),in(wl_2.5)": "wl_3.2",
"wl_2.2;any(wl_2.6),out(wl_2.4,wl_2.8)": "wl_3.3",
"wl_2.3;any(wl_2.7),out(wl_2.5)": "wl_3.4",
"wl_2.4;in(wl_2.2),out(wl_2.1)": "wl_3.5",
"wl_2.5;in(wl_2.3),out(wl_2.1)": "wl_3.6",
"wl_2.6;any(wl_2.1,wl_2.2)": "wl_3.7",
"wl_2.7;any(wl_2.1,wl_2.3)": "wl_3.8",
"wl_2.8;in(wl_2.2),out(wl_2.0)": "wl_3.9"
},
"next_labels": {
0: 4,
1: 7,
2: 9,
3: 10
}
}
hyper_dummy_wl = Hypergraph(example_graphs.gt_dummy_wl)
hyper_dummy_wl, wl_state = weisfeiler_lehman.init(hyper_dummy_wl,
test_mode=True)
i = 1
while True:
new_hyper_dummy_wl, wl_state = weisfeiler_lehman.iterate(
hyper_dummy_wl, wl_state, i, test_mode=True)
if weisfeiler_lehman.is_stable(hyper_dummy_wl, new_hyper_dummy_wl,
i):
break
hyper_dummy_wl = new_hyper_dummy_wl
i += 1
self.assertEqual(
wl_state_exp, wl_state,
"The multi-sets of labels computed by Weisfeiler-Lehman are not correct."
)
def testWeisfeilerLehman(self):
wl_state_exp = {
"labels": {
"0": "wl_0.0",
"1": "wl_0.1",
"a": "wl_0.2",
"b": "wl_0.3",
"wl_0.0;in(wl_0.3)": "wl_1.0",
"wl_0.0;any(wl_0.2),in(wl_0.2)": "wl_1.1",
"wl_0.1;any(wl_0.2),out(wl_0.2,wl_0.3)": "wl_1.2",
"wl_0.1;any(wl_0.2),out(wl_0.2)": "wl_1.3",
"wl_0.2;in(wl_0.1),out(wl_0.0)": "wl_1.4",
"wl_0.2;any(wl_0.0,wl_0.1)": "wl_1.5",
"wl_0.3;in(wl_0.1),out(wl_0.0)": "wl_1.6",
"wl_1.0;in(wl_1.6)": "wl_2.0",
"wl_1.1;any(wl_1.5),in(wl_1.4)": "wl_2.1",
"wl_1.2;any(wl_1.5),out(wl_1.4,wl_1.6)": "wl_2.2",
"wl_1.3;any(wl_1.5),out(wl_1.4)": "wl_2.3",
"wl_1.4;in(wl_1.2),out(wl_1.1)": "wl_2.4",
"wl_1.4;in(wl_1.3),out(wl_1.1)": "wl_2.5",
"wl_1.5;any(wl_1.1,wl_1.2)": "wl_2.6",
"wl_1.5;any(wl_1.1,wl_1.3)": "wl_2.7",
"wl_1.6;in(wl_1.2),out(wl_1.0)": "wl_2.8",
"wl_2.0;in(wl_2.8)": "wl_3.0",
"wl_2.1;any(wl_2.7),in(wl_2.4)": "wl_3.1",
"wl_2.1;any(wl_2.6),in(wl_2.5)": "wl_3.2",
"wl_2.2;any(wl_2.6),out(wl_2.4,wl_2.8)": "wl_3.3",
"wl_2.3;any(wl_2.7),out(wl_2.5)": "wl_3.4",
"wl_2.4;in(wl_2.2),out(wl_2.1)": "wl_3.5",
"wl_2.5;in(wl_2.3),out(wl_2.1)": "wl_3.6",
"wl_2.6;any(wl_2.1,wl_2.2)": "wl_3.7",
"wl_2.7;any(wl_2.1,wl_2.3)": "wl_3.8",
"wl_2.8;in(wl_2.2),out(wl_2.0)": "wl_3.9"
},
"next_labels": {
0: 4,
1: 7,
2: 9,
3: 10
}
}
hyper_dummy_wl = Hypergraph(example_graphs.gt_dummy_wl)
hyper_dummy_wl, wl_state = weisfeiler_lehman.init(hyper_dummy_wl, test_mode=True)
i = 1
while True:
new_hyper_dummy_wl, wl_state = weisfeiler_lehman.iterate(hyper_dummy_wl, wl_state, i, test_mode=True)
if weisfeiler_lehman.is_stable(hyper_dummy_wl, new_hyper_dummy_wl, i):
break
hyper_dummy_wl = new_hyper_dummy_wl
i += 1
self.assertEqual(wl_state_exp, wl_state, "The multi-sets of labels computed by Weisfeiler-Lehman are not correct.")
def extract_canon_repr_for_each_wl_iter(hypergraph, wl_iterations=0, wl_state=None, accumulate_wl_results=True):
for i in range(wl_iterations + 1):
if i == 1:
hypergraph, wl_state = weisfeiler_lehman.init(hypergraph, wl_state)
if i >= 1:
hypergraph, wl_state = weisfeiler_lehman.iterate(hypergraph, wl_state, i)
if i == wl_iterations or accumulate_wl_results:
canon_str = arnborg_proskurowski.get_canonical_representation(hypergraph)
if canon_str == u"Tree-width > 3":
# TODO: How to handle graphs with larger tree-width?
# for now ignore the graph
raise StopIteration
yield i, canon_str, wl_state
def extract_features_for_each_wl_iter(hypergraph, wl_iterations=0, wl_state=None, accumulate_wl_shingles=True):
raw_features = arnborg_proskurowski.get_reduced_features(hypergraph)
# if tw == -1:
# # TODO: How to handle graphs with larger tree-width?
# # for now collect all possible features
# print "The hypergraph has tree-width > 3."
for i in range(wl_iterations + 1):
if i == 1:
hypergraph, wl_state = weisfeiler_lehman.init(hypergraph, wl_state)
if i >= 1:
# old_hypergraph = hypergraph
hypergraph, wl_state = weisfeiler_lehman.iterate(hypergraph, wl_state, i)
if i == wl_iterations or accumulate_wl_shingles:
new_features = [process_raw_feature(raw_feature, hypergraph) for raw_feature in raw_features]
yield i, itertools.chain(*new_features), wl_state
