def main_old():
parser = argparse.ArgumentParser(
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('--operation',
type=str,
default="all",
help="[all | init | train | metric | draw]")
parser.add_argument('--conf', type=str, default="default")
args = parser.parse_args()
params = dh.load_json_file(os.path.join(CONF_PATH, args.conf + ".json"))
metric_path_pre = os.path.join(RES_PATH, args.conf)
if os.path.exists(metric_path_pre) == False:
os.mkdir(metric_path_pre)
output_path = os.path.join(metric_path_pre, dh.get_time_str())
metric_path = output_path + "_metric"
def metric(embeddings):
if "metrics" not in params:
return
for metric in params["metrics"]:
res = getattr(Metric, metric["func"])(embeddings, metric)
dh.append_to_file(metric_path, str(res) + "\n")
print res
dh.symlink(metric_path, os.path.join(metric_path_pre, "new_metric"))
if "drawers" in params:
draw_path = output_path + "_draw"
if os.path.exists(draw_path) == False:
os.mkdir(draw_path)
draw_cnt = [0]
def draw(embeddings):
if "drawers" not in params:
return
for drawer in params['drawers']:
getattr(Metric, drawer["func"])(embeddings, drawer, draw_path,
draw_cnt[0])
draw_cnt[0] += 1
if args.operation == "all":
G, embeddings, weights = __import__("init." + params["init"]["func"],
fromlist=["init"]).init(
params["init"], metric,
output_path, draw)
__import__("dynamic_loop." + params["main_loop"]["func"],
fromlist=["dynamic_loop"]).loop(params["main_loop"], G,
embeddings, weights, metric,
output_path, draw)
elif args.operation == "init":
G, embeddings, weights = __import__("init." + params["init"]["func"],
fromlist=["init"]).init(
params["init"], metric,
output_path, draw)
elif args.operation == "draw":
pass
else:
print "Not Support!"
def train_model(params):
g_mat, tree = extract_tree(params)
handlers = {}
handlers["get_network"] = gn(g_mat, params["get_network_hierarchy"])
handlers["embedding_model"] = __import__(
'node_embedding.' + params["embedding_model"]["func"],
fromlist=["node_embedding"]).NodeEmbedding
handlers["transfer_embeddings"] = __import__(
'transfer_embeddings.' + params["transfer_embeddings"]["func"],
fromlist=["transfer_embeddings"]).TransferEmbedding
res_coordinates = [None] * len(tree)
res_coordinates[len(tree) - 1] = np.zeros(
params["embedding_model"]["embedding_size"], dtype=np.float32)
res_radius = [None] * len(tree)
res_radius[len(tree) - 1] = float(params["init_radius"])
dfs(len(tree) - 1, tree, handlers, params, res_radius, res_coordinates)
res_path = params["train_output"]
dh.symlink(res_path, os.path.join(RES_PATH, "new_train_res"))
dh.append_to_file(
res_path,
json.dumps({
"radius": np.array(res_radius).tolist(),
"coordinates": np.array(res_coordinates).tolist()
}))
return res_coordinates, res_radius
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('--operation',
type=str,
default="all",
help="[all | init | train]")
parser.add_argument('--conf', type=str, default="amherst0.25")
args = parser.parse_args()
params = dh.load_json_file(os.path.join(CONF_PATH, args.conf + ".json"))
fw = open('my_embedding.txt', 'w')
metric_path_pre = os.path.join(RES_PATH, args.conf)
if os.path.exists(metric_path_pre) == False:
os.mkdir(metric_path_pre)
output_path = os.path.join(metric_path_pre, dh.get_time_str())
print(output_path)
metric_path = output_path + "_metric"
def metric(embeddings):
if "metrics" not in params:
return
for metric in params["metrics"]:
print("[] Start node classification...")
res = getattr(Metric, metric["func"])(embeddings, metric)
dh.append_to_file(metric_path, str(res) + "\n")
print("[+] Metric: " + str(res))
dh.symlink(metric_path, os.path.join(metric_path_pre, "new_metric"))
if args.operation == "all":
G, embeddings, weights = __import__("init." + params["init"]["func"],
fromlist=["init"]).init(
params["init"], metric,
output_path)
__import__("dynamic_loop." + params["main_loop"]["func"],
fromlist=["dynamic_loop"]).loop(params["main_loop"], G,
embeddings, weights, metric,
output_path)
print(embeddings)
elif args.operation == "init":
G, embeddings, weights = __import__("init." + params["init"]["func"],
fromlist=["init"]).init(
params["init"], metric,
output_path)
else:
print("Not Support!")
def metric(params):
js = json.loads(open(params["metric_input"]).read())
coordinates = np.array(js["coordinates"])
radius = np.array(js["radius"])
res_path = params["metric_output"]
dh.symlink(res_path, os.path.join(RES_PATH, "new_metric_res"))
ret = []
for metric in params["metric_function"]:
if metric["metric_func"] == "draw_circle_2D":
pic_path = os.path.join(PIC_PATH, "draw_circle_" + str(int(time.time() * 1000.0)) + ".pdf")
dh.symlink(pic_path, os.path.join(PIC_PATH, "new_draw_circle"))
getattr(Metric, metric["metric_func"])(coordinates, radius, metric, params["num_nodes"], pic_path)
else:
origin_coordinates = coordinates[: params["num_nodes"]]
res = getattr(Metric, metric["metric_func"])(origin_coordinates, metric)
ret.append((metric["metric_func"], res))
dh.append_to_file(res_path, json.dumps(ret))
return ret