def metric(params, info, pre_res, **kwargs):
res = params_handler(params, info, pre_res)
# load node number
node_path=os.path.join(DATA_PATH, params["data"], "node.txt")
node_file=open(node_path, 'r')
nodes=node_file.readlines()
node_num=len(nodes)
node_file.close()
# load embeddings
if params["file_type"] == "txt":
embedding_path=os.path.join(DATA_PATH, "experiment", params["embeddings_file"])
X = dh.load_embedding(embedding_path, params["file_type"], node_num)
else:
embedding_path = os.path.join(DATA_PATH, "experiment", params["embeddings_file"])
X = dh.load_embedding(embedding_path, params["file_type"],node_num)
# results include: accuracy, micro f1, macro f1
metric_res = classification(X, params)
# insert into res
for k, v in metric_res.items():
res[k] = v
return res
def metric(params, info, pre_res, **kwargs):
res = params_handler(params, info)
# load embeddings
embedding_path = os.path.join(DATA_PATH, "experiment",
params["embeddings_file"])
X = dh.load_embedding(embedding_path, params["file_type"])
# results include: accuracy, micro f1, macro f1
metric_res = visualization(X, params)
# insert into res
for k, v in metric_res.items():
res[k] = v
return res