def forward(self, formula, queries, source_nodes):
if formula.query_type == "1-chain":
# a chain is simply a call to the path decoder
return self.path_dec.forward(
self.enc.forward(source_nodes, formula.target_mode),
self.enc.forward([query.anchor_nodes[0] for query in queries], formula.anchor_modes[0]),
formula.rels)
elif formula.query_type == "2-inter" or formula.query_type == "3-inter":
target_embeds = self.enc(source_nodes, formula.target_mode)
embeds1 = self.enc([query.anchor_nodes[0] for query in queries], formula.anchor_modes[0])
embeds1 = self.path_dec.project(embeds1, _reverse_relation(formula.rels[0]))
embeds2 = self.enc([query.anchor_nodes[1] for query in queries], formula.anchor_modes[1])
if len(formula.rels[1]) == 2:
for i_rel in formula.rels[1][::-1]:
embeds2 = self.path_dec.project(embeds2, _reverse_relation(i_rel))
else:
embeds2 = self.path_dec.project(embeds2, _reverse_relation(formula.rels[1]))
scores1 = self.cos(target_embeds, embeds1)
scores2 = self.cos(target_embeds, embeds2)
if formula.query_type == "3-inter":
embeds3 = self.enc([query.anchor_nodes[2] for query in queries], formula.anchor_modes[2])
embeds3 = self.path_dec.project(embeds3, _reverse_relation(formula.rels[2]))
scores3 = self.cos(target_embeds, embeds2)
scores = scores1 * scores2 * scores3
else:
scores = scores1 * scores2
return scores
else:
raise Exception("Query type not supported for this model.")
def evaluate_metapath_auc(test_metapaths, graph, enc_dec, batch_size=512):
predictions = []
labels = []
for rels in test_metapaths:
print "Testing on", rels
if rels[0] == rels[1]:
continue
rels_pos_metapaths = test_metapaths[rels]
if len(rels_pos_metapaths) > 0:
node_set = set(graph.adj_lists[_reverse_relation(rels[1])].keys())
rels_all_metapaths = graph.get_metapath_byrels(rels)
rels_neg_metapaths = []
for e in rels_pos_metapaths:
sample_space = list(node_set - set(rels_all_metapaths[e[0]]))
if len(sample_space) > 0:
rels_neg_metapaths.append(
(e[0], np.random.choice(sample_space)))
labels.extend([1 for _ in rels_pos_metapaths] +
[0 for _ in rels_neg_metapaths])
metapaths = rels_pos_metapaths + rels_neg_metapaths
splits = len(metapaths) / batch_size + 1
for metapath_split in np.array_split(metapaths, splits):
scores = enc_dec.forward([e[0] for e in metapath_split],
[e[1] for e in metapath_split], rels)
predictions.extend(scores.data.tolist())
return roc_auc_score(labels, predictions)
def forward(self, formula, queries, target_nodes):
if formula.query_type == "1-chain" or formula.query_type == "2-chain" or formula.query_type == "3-chain":
# a chain is simply a call to the path decoder
return self.path_dec.forward(
self.enc.forward(target_nodes, formula.target_mode),
self.enc.forward([query.anchor_nodes[0] for query in queries],
formula.anchor_modes[0]), formula.rels)
elif formula.query_type == "2-inter" or formula.query_type == "3-inter" or formula.query_type == "3-inter_chain":
target_embeds = self.enc(target_nodes, formula.target_mode)
embeds1 = self.enc([query.anchor_nodes[0] for query in queries],
formula.anchor_modes[0])
embeds1 = self.path_dec.project(embeds1,
_reverse_relation(formula.rels[0]))
embeds2 = self.enc([query.anchor_nodes[1] for query in queries],
formula.anchor_modes[1])
if len(formula.rels[1]) == 2:
for i_rel in formula.rels[1][::-1]:
embeds2 = self.path_dec.project(embeds2,
_reverse_relation(i_rel))
else:
embeds2 = self.path_dec.project(
embeds2, _reverse_relation(formula.rels[1]))
if formula.query_type == "3-inter":
embeds3 = self.enc(
[query.anchor_nodes[2] for query in queries],
formula.anchor_modes[2])
embeds3 = self.path_dec.project(
embeds3, _reverse_relation(formula.rels[2]))
query_intersection = self.inter_dec(embeds1, embeds2,
formula.target_mode,
embeds3)
else:
query_intersection = self.inter_dec(embeds1, embeds2,
formula.target_mode)
scores = self.cos(target_embeds, query_intersection)
return scores
elif formula.query_type == "3-chain_inter":
target_embeds = self.enc(target_nodes, formula.target_mode)
embeds1 = self.enc([query.anchor_nodes[0] for query in queries],
formula.anchor_modes[0])
embeds1 = self.path_dec.project(
embeds1, _reverse_relation(formula.rels[1][0]))
embeds2 = self.enc([query.anchor_nodes[1] for query in queries],
formula.anchor_modes[1])
embeds2 = self.path_dec.project(
embeds2, _reverse_relation(formula.rels[1][1]))
query_intersection = self.inter_dec(embeds1, embeds2,
formula.rels[0][-1])
query_intersection = self.path_dec.project(
query_intersection, _reverse_relation(formula.rels[0]))
scores = self.cos(target_embeds, query_intersection)
return scores
def evaluate_edge_auc(test_edges, graph, enc_dec, batch_size=512):
predictions = []
labels = []
for rel in test_edges:
print "Testing on", rel
node_set = set(graph.adj_lists[rel].keys())
rel_pos_edges = test_edges[rel]
rel_neg_edges = [(np.random.choice(
list(node_set -
set(graph.adj_lists[_reverse_relation(rel)][e[1]]))), e[1])
for e in rel_pos_edges]
labels.extend([1 for _ in rel_pos_edges] + [0 for _ in rel_neg_edges])
edges = rel_pos_edges + rel_neg_edges
splits = len(edges) / batch_size + 1
for edge_split in np.array_split(edges, splits):
scores = enc_dec.forward([e[0] for e in edge_split],
[e[1] for e in edge_split], [rel])
predictions.extend(scores.data.tolist())
return roc_auc_score(labels, predictions)
