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Update utils.py #140

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25 changes: 12 additions & 13 deletions graphsage/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,23 +5,22 @@
import json
import sys
import os

import pdb
import networkx as nx
from networkx.readwrite import json_graph
version_info = list(map(int, nx.__version__.split('.')))
major = version_info[0]
minor = version_info[1]
assert (major <= 1) and (minor <= 11), "networkx major version > 1.11"

WALK_LEN=5
N_WALKS=50

def load_data(prefix, normalize=True, load_walks=False):
G_data = json.load(open(prefix + "-G.json"))
G = json_graph.node_link_graph(G_data)
if isinstance(G.nodes()[0], int):
conversion = lambda n : int(n)
else:
try:
if isinstance(G.nodes()[0], dict):
conversion = lambda n : int(n)
else:
print("Something wrong when load graph")
except:
conversion = lambda n : n

if os.path.exists(prefix + "-feats.npy"):
Expand All @@ -44,7 +43,7 @@ def load_data(prefix, normalize=True, load_walks=False):
## (necessary because of networkx weirdness with the Reddit data)
broken_count = 0
for node in G.nodes():
if not 'val' in G.node[node] or not 'test' in G.node[node]:
if not 'val' in G.nodes[node] or not 'test' in G.nodes[node]:
G.remove_node(node)
broken_count += 1
print("Removed {:d} nodes that lacked proper annotations due to networkx versioning issues".format(broken_count))
Expand All @@ -53,15 +52,15 @@ def load_data(prefix, normalize=True, load_walks=False):
## (some datasets might already have this..)
print("Loaded data.. now preprocessing..")
for edge in G.edges():
if (G.node[edge[0]]['val'] or G.node[edge[1]]['val'] or
G.node[edge[0]]['test'] or G.node[edge[1]]['test']):
if (G.nodes[edge[0]]['val'] or G.nodes[edge[1]]['val'] or
G.nodes[edge[0]]['test'] or G.nodes[edge[1]]['test']):
G[edge[0]][edge[1]]['train_removed'] = True
else:
G[edge[0]][edge[1]]['train_removed'] = False

if normalize and not feats is None:
from sklearn.preprocessing import StandardScaler
train_ids = np.array([id_map[n] for n in G.nodes() if not G.node[n]['val'] and not G.node[n]['test']])
train_ids = np.array([id_map[n] for n in G.nodes() if not G.nodes[n]['val'] and not G.nodes[n]['test']])
train_feats = feats[train_ids]
scaler = StandardScaler()
scaler.fit(train_feats)
Expand Down Expand Up @@ -97,7 +96,7 @@ def run_random_walks(G, nodes, num_walks=N_WALKS):
out_file = sys.argv[2]
G_data = json.load(open(graph_file))
G = json_graph.node_link_graph(G_data)
nodes = [n for n in G.nodes() if not G.node[n]["val"] and not G.node[n]["test"]]
nodes = [n for n in G.nodes() if not G.nodes[n]["val"] and not G.nodes[n]["test"]]
G = G.subgraph(nodes)
pairs = run_random_walks(G, nodes)
with open(out_file, "w") as fp:
Expand Down