def load_graph(params):
"""
params: data file path
Return:
en2oid: the dictionary mapping str to original id
tag2oid: the dictionary mapping str to original id
edge_lst: entity relations
mix_edge_lst: entity and tag relations
"""
en2oid = dh.load_name(os.path.join(params["name"], params["entity"]))
tag2oid = dh.load_name(os.path.join(params["name"], params["tag"]))
edge_lst = dh.load_edge(os.path.join(params["name"], params["edge"]))
mix_edge_lst = dh.load_edge(os.path.join(params["name"], params["mix_edge"]))
return en2oid, tag2oid, edge_lst, mix_edge_lst
def metric(params, info, pre_res, **kwargs):
res = params_handler(params, info)
mus = dh.load_as_pickle(params["mus"])
sigs = dh.load_as_pickle(params["sigs"])
std_sigs = np.sqrt(sigs)
row2name = dh.load_name(
os.path.join(info["network_folder"]["name"],
info["network_folder"]["tag"]))
assert len(mus) > 0, "The mus file has no data"
N = len(mus)
M = len(mus[0])
# sigs is spherical or diagonal
if len(std_sigs[0]) == 1:
ones = np.ones_like(mus)
tmp = std_sigs.reshape(N, 1)
std_sigs = ones * tmp
# dimension reduction
if M > 2:
mus, std_sigs = ct.reduce_dist_dim(mus, std_sigs, 2)
if params["draw_ellipse"]:
res["res_path"] = os.path.join(params["res_home"], "dist_ellipse.pdf")
dg.draw_ellipse(mus, std_sigs, row2name, res["res_path"],
params["timesOfSigma"], params["filter"],
params["notation"])
else:
res["res_path"] = os.path.join(params["res_home"], "dist_scatter.pdf")
dg.draw_scatter(mus, std_sigs, row2name, res["res_path"],
params["timesOfSigma"], params["filter"],
params["notation"])
res["scatter_path"] = os.path.join(params["res_home"], "scatter.pdf")
dg.draw_scatter(mus, std_sigs, row2name, res["scatter_path"])
return res