def infer(params, info, pre_res, **kwargs):
res = params_handler(params, info, pre_res)
embeds = dh.get_tagonehot(
os.path.join(info["network_folder"]["name"],
info["network_folder"]["mix_edge"]))
dh.save_as_pickle(embeds, res["entity_embedding_path"])
return res
def infer(params, info, pre_res, **kwargs):
res, G_entity, G_tag, features = params_handler(params, info, pre_res)
model_handler = __import__("model." + params["embedding_model"]["func"],
fromlist=["model"])
model = model_handler.TagConditionedEmbedding(params["embedding_model"],
features)
bs_handler = __import__("batch_strategy." + params["batch_strategy"],
fromlist=["batch_strategy"])
bs = bs_handler.BatchStrategy(G_tag, G_entity, params)
embeds = model.infer(bs.get_all(), params["embedding_model"]["model_path"])
dh.save_as_pickle(embeds, res["entity_embedding_path"])
return res
def optimize(params, info, pre_res, **kwargs):
#pdb.set_trace()
res, G_entity, G_tag = params_handler(params, info, pre_res)
# get features
gf_handler = __import__("get_features." + params["get_features"]["func"], fromlist = ["sget_features"])
# if "dim" not in params["get_features"]:
# params["get_features"]["dim"] = params["tag_num"]
features = gf_handler.get_features(params["get_features"], info) # return numpy
if "dim" not in params["get_features"]:
params["get_features"]["dim"] = features.shape[1]
# model init
print ("[+] The embedding model is model.%s" % (params["embedding_model"]["func"]))
info["logger"].info("[+] The embedding model is model.%s" % (params["embedding_model"]["func"]))
params["embedding_model"]["aggregator"]["feature_num"] = params["get_features"]["dim"]
model_handler = __import__("model." + params["embedding_model"]["func"], fromlist=["model"])
model = model_handler.TagConditionedEmbedding(params["embedding_model"], features)
# batch generator
print ("[+] The batch strategy is batch_strategy.%s" % (params["batch_strategy"]))
info["logger"].info("[+] The batch strategy is batch_strategy.%s\n" % (params["batch_strategy"]))
bs_handler = __import__("batch_strategy." + params["batch_strategy"], fromlist=["batch_strategy"])
bs = bs_handler.BatchStrategy(G_tag, G_entity, params)
# train model
res["model_path"] = model.train(bs.get_batch)
if info["debug_level"] == "DEBUG":
pdb.set_trace()
# infer model
embeds = model.infer(bs.get_all(), res["model_path"])
dh.save_as_pickle(embeds, res["entity_embedding_path"])
print ("[+] The entity embedding result is saved at %s" % (res["entity_embedding_path"]))
info["logger"].info("[+] The entity embedding result is saved at %s" % (res["entity_embedding_path"]))
return res