def batch_fn(self, batch_ex):
# batch_ex = [
# (node, label),
# (node, label),
# ]
#
batch_node = []
batch_label = []
for batch in batch_ex:
batch_node.append(batch[0])
batch_label.append(batch[1])
if len(batch_node) != self.batch_size:
if self.phase == "train":
return None #Skip
batch_node = np.array(batch_node, dtype="int64")
batch_label = np.array(batch_label, dtype="int64")
subgraphs = graphsage_sample(self.graph, batch_node, self.samples)
subgraphs[0].node_feat["index"] = subgraphs[
0].reindex_to_parrent_nodes(subgraphs[0].nodes).astype(np.int64)
subgraphs[0].node_feat["term_ids"] = self.term_ids[
subgraphs[0].node_feat["index"]].astype(np.int64)
feed_dict = {}
for i in range(self.num_layers):
feed_dict.update(self.graph_wrappers[i].to_feed(subgraphs[i]))
# only reindex from first subgraph
sub_node_idx = subgraphs[0].reindex_from_parrent_nodes(batch_node)
feed_dict["node_index"] = np.array(sub_node_idx, dtype="int64")
feed_dict["node_real_index"] = np.array(batch_node, dtype="int64")
feed_dict["label"] = np.array(batch_label, dtype="int64")
return feed_dict
def batch_fn(self, batch_ex):
# batch_ex = [
# (src, dst, neg),
# (src, dst, neg),
# (src, dst, neg),
# ]
#
batch_src = []
batch_dst = []
batch_neg = []
for batch in batch_ex:
batch_src.append(batch[0])
batch_dst.append(batch[1])
if len(batch) == 3: # default neg samples
batch_neg.append(batch[2])
if len(batch_src) != self.batch_size:
if self.phase == "train":
return None #Skip
if len(batch_neg) > 0:
batch_neg = np.unique(np.concatenate(batch_neg))
batch_src = np.array(batch_src, dtype="int64")
batch_dst = np.array(batch_dst, dtype="int64")
sampled_batch_neg = alias_sample(batch_dst.shape, self.alias,
self.events)
if len(batch_neg) > 0:
batch_neg = np.concatenate([batch_neg, sampled_batch_neg], 0)
else:
batch_neg = sampled_batch_neg
if self.phase == "train":
ignore_edges = set()
else:
ignore_edges = set()
nodes = np.unique(np.concatenate([batch_src, batch_dst, batch_neg], 0))
subgraphs = graphsage_sample(self.graph,
nodes,
self.samples,
ignore_edges=ignore_edges)
#subgraphs[0].reindex_to_parrent_nodes(subgraphs[0].nodes)
feed_dict = {}
for i in range(self.num_layers):
feed_dict.update(self.graph_wrappers[i].to_feed(subgraphs[i]))
# only reindex from first subgraph
sub_src_idx = subgraphs[0].reindex_from_parrent_nodes(batch_src)
sub_dst_idx = subgraphs[0].reindex_from_parrent_nodes(batch_dst)
sub_neg_idx = subgraphs[0].reindex_from_parrent_nodes(batch_neg)
feed_dict["user_index"] = np.array(sub_src_idx, dtype="int64")
feed_dict["item_index"] = np.array(sub_dst_idx, dtype="int64")
feed_dict["neg_item_index"] = np.array(sub_neg_idx, dtype="int64")
feed_dict["term_ids"] = self.term_ids[subgraphs[0].node_feat["index"]]
return feed_dict
def batch_fn(self, batch_ex):
batch_src = []
batch_dst = []
batch_neg = []
for batch in batch_ex:
batch_src.append(batch[0])
batch_dst.append(batch[1])
if len(batch) == 3: # default neg samples
batch_neg.append(batch[2])
if len(batch_src) != self.batch_size:
if self.phase == "train":
return None #Skip
if len(batch_neg) > 0:
batch_neg = np.unique(np.concatenate(batch_neg))
batch_src = np.array(batch_src, dtype="int64")
batch_dst = np.array(batch_dst, dtype="int64")
if self.neg_type == "batch_neg":
batch_neg = batch_dst
else:
# TODO user define shape of neg_sample
neg_shape = batch_dst.shape
sampled_batch_neg = alias_sample(neg_shape, self.alias,
self.events)
batch_neg = np.concatenate([batch_neg, sampled_batch_neg], 0)
if self.phase == "train":
# TODO user define ignore edges or not
#ignore_edges = np.concatenate([np.stack([batch_src, batch_dst], 1), np.stack([batch_dst, batch_src], 1)], 0)
ignore_edges = set()
else:
ignore_edges = set()
nodes = np.unique(np.concatenate([batch_src, batch_dst, batch_neg], 0))
subgraphs = graphsage_sample(self.graph,
nodes,
self.samples,
ignore_edges=ignore_edges)
subgraph = subgraphs[0]
subgraphs[0].node_feat["index"] = subgraphs[
0].reindex_to_parrent_nodes(subgraphs[0].nodes).astype(np.int64)
subgraphs[0].node_feat["term_ids"] = self.term_ids[
subgraphs[0].node_feat["index"]].astype(np.int64)
# only reindex from first subgraph
sub_src_idx = subgraphs[0].reindex_from_parrent_nodes(batch_src)
sub_dst_idx = subgraphs[0].reindex_from_parrent_nodes(batch_dst)
sub_neg_idx = subgraphs[0].reindex_from_parrent_nodes(batch_neg)
user_index = np.array(sub_src_idx, dtype="int64")
pos_item_index = np.array(sub_dst_idx, dtype="int64")
neg_item_index = np.array(sub_neg_idx, dtype="int64")
user_real_index = np.array(batch_src, dtype="int64")
pos_item_real_index = np.array(batch_dst, dtype="int64")
num_nodes = np.array([len(subgraph.nodes)], np.int32)
num_edges = np.array([len(subgraph.edges)], np.int32)
edges = subgraph.edges
node_feat = subgraph.node_feat
edge_feat = subgraph.edge_feat
# pairwise training with label 1.
fake_label = np.ones_like(user_index)
if self.phase == "train":
return num_nodes, num_edges, edges, node_feat["index"], node_feat["term_ids"], user_index, \
pos_item_index, neg_item_index, user_real_index, pos_item_real_index, fake_label
else:
return num_nodes, num_edges, edges, node_feat["index"], node_feat["term_ids"], user_index, \
pos_item_index, neg_item_index, user_real_index, pos_item_real_index