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graph.py
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graph.py
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import logging
import numpy as np
from numba import jit
def make_label_data(src_l, dst_l, ts_l, val_flag, rand_sampler):
num = np.sum(val_flag)
val_src = src_l[val_flag]
val_dst = dst_l[val_flag]
val_ts = ts_l[val_flag]
val_src_l = np.hstack([val_src, val_src])
_, dst_fake = rand_sampler.sample(num)
val_dst_l = np.hstack([val_dst, dst_fake])
val_ts_l = np.hstack([val_ts, val_ts])
val_label_l = np.hstack([np.ones(num), np.zeros(num)])
return val_src_l, val_dst_l, val_ts_l, val_label_l
# @jit
def find_before_nb(src_idx, cut_time, off_set_l, node_idx_l, node_ts_l,
edge_idx_l):
neighbors_idx = node_idx_l[off_set_l[src_idx]:off_set_l[src_idx + 1]]
neighbors_ts = node_ts_l[off_set_l[src_idx]:off_set_l[src_idx + 1]]
neighbors_e_idx = edge_idx_l[off_set_l[src_idx]:off_set_l[src_idx + 1]]
if len(neighbors_idx) == 0 or len(neighbors_ts) == 0:
return neighbors_idx, neighbors_ts, neighbors_e_idx
right = np.searchsorted(neighbors_ts, cut_time, side="left")
return neighbors_idx[:right], neighbors_e_idx[:right], neighbors_ts[:right]
# @jit
def get_temporal_neighbor_nb(src_idx_l,
cut_time_l,
num_neighbors,
off_set_l,
node_idx_l,
node_ts_l,
edge_idx_l,
uniform=True):
assert (len(src_idx_l) == len(cut_time_l))
out_ngh_node_batch = np.zeros(
(len(src_idx_l), num_neighbors)).astype(np.int32)
out_ngh_t_batch = np.zeros(
(len(src_idx_l), num_neighbors)).astype(np.float32)
out_ngh_eidx_batch = np.zeros(
(len(src_idx_l), num_neighbors)).astype(np.int32)
for i, (src_idx, cut_time) in enumerate(zip(src_idx_l, cut_time_l)):
ngh_idx, ngh_eidx, ngh_ts = find_before_nb(src_idx, cut_time,
off_set_l, node_idx_l,
node_ts_l, edge_idx_l)
if len(ngh_idx) > 0:
if uniform:
sampled_idx = np.random.randint(0, len(ngh_idx), num_neighbors)
out_ngh_node_batch[i, :] = ngh_idx[sampled_idx]
out_ngh_t_batch[i, :] = ngh_ts[sampled_idx]
out_ngh_eidx_batch[i, :] = ngh_eidx[sampled_idx]
# resort based on time
pos = out_ngh_t_batch[i, :].argsort()
out_ngh_node_batch[i, :] = out_ngh_node_batch[i, :][pos]
out_ngh_t_batch[i, :] = out_ngh_t_batch[i, :][pos]
out_ngh_eidx_batch[i, :] = out_ngh_eidx_batch[i, :][pos]
else:
ngh_ts = ngh_ts[-num_neighbors:]
ngh_idx = ngh_idx[-num_neighbors:]
ngh_eidx = ngh_eidx[-num_neighbors:]
assert (len(ngh_idx) <= num_neighbors)
assert (len(ngh_ts) <= num_neighbors)
assert (len(ngh_eidx) <= num_neighbors)
out_ngh_node_batch[i, num_neighbors - len(ngh_idx):] = ngh_idx
out_ngh_t_batch[i, num_neighbors - len(ngh_ts):] = ngh_ts
out_ngh_eidx_batch[i,
num_neighbors - len(ngh_eidx):] = ngh_eidx
return out_ngh_node_batch, out_ngh_eidx_batch, out_ngh_t_batch
class NeighborFinder:
PRECISION = 5
def __init__(self, adj_list, uniform=False, exp=False, alpha=1.0):
"""
Params
------
node_idx_l: List[int]
node_ts_l: List[int]
off_set_l: List[int], such that node_idx_l[off_set_l[i]:off_set_l[i + 1]] = adjacent_list[i]
"""
node_idx_l, node_ts_l, edge_idx_l, off_set_l = self.init_off_set(
adj_list)
self.node_idx_l = node_idx_l
self.node_ts_l = node_ts_l
dt = node_ts_l.max() - node_ts_l.min()
self.norm_ts_l = (node_ts_l - node_ts_l.min()) / dt
self.edge_idx_l = edge_idx_l
self.off_set_l = off_set_l
# Backward capability.
assert not (uniform and exp)
self.uniform = uniform
if uniform:
self.sampling = "uniform"
elif exp:
self.sampling = "exp"
else:
self.sampling = "temporal"
if type(alpha) is float:
self.alpha = np.full_like(off_set_l, alpha)
elif type(alpha) is not np.ndarray:
self.alpha = np.zeros_like(off_set_l)
else:
self.alpha = alpha
self.cache = {}
numba_logger = logging.getLogger("numba")
numba_logger.setLevel(logging.WARNING)
def init_off_set(self, adj_list):
"""
Params
------
adj_list: List[List[int]]
"""
n_idx_l = []
n_ts_l = []
e_idx_l = []
off_set_l = [0]
for i in range(len(adj_list)):
curr = adj_list[i]
curr = sorted(curr, key=lambda x: x[1])
n_idx_l.extend([x[0] for x in curr])
e_idx_l.extend([x[1] for x in curr])
n_ts_l.extend([x[2] for x in curr])
off_set_l.append(len(n_idx_l))
n_idx_l = np.array(n_idx_l)
n_ts_l = np.array(n_ts_l)
e_idx_l = np.array(e_idx_l)
off_set_l = np.array(off_set_l)
assert len(n_idx_l) == len(n_ts_l)
assert off_set_l[-1] == len(n_ts_l)
return n_idx_l, n_ts_l, e_idx_l, off_set_l
def find_before(self, src_idx, cut_time, norm=False):
"""
Params
------
src_idx: int
cut_time: float
"""
node_idx_l = self.node_idx_l
node_ts_l = self.node_ts_l
norm_ts_l = self.norm_ts_l
edge_idx_l = self.edge_idx_l
off_set_l = self.off_set_l
neighbors_idx = node_idx_l[off_set_l[src_idx]:off_set_l[src_idx + 1]]
neighbors_ts = node_ts_l[off_set_l[src_idx]:off_set_l[src_idx + 1]]
neighbors_norm_ts = norm_ts_l[off_set_l[src_idx]:off_set_l[src_idx +
1]]
neighbors_e_idx = edge_idx_l[off_set_l[src_idx]:off_set_l[src_idx + 1]]
if len(neighbors_idx) == 0 or len(neighbors_ts) == 0:
if norm:
return neighbors_idx, neighbors_e_idx, neighbors_ts, neighbors_norm_ts
else:
return neighbors_idx, neighbors_e_idx, neighbors_ts
right = np.searchsorted(neighbors_ts, cut_time, side="left")
ngh_idx = neighbors_idx[:right]
ngh_eidx = neighbors_e_idx[:right]
ngh_ts = neighbors_ts[:right]
norm_ts = neighbors_norm_ts[:right]
if norm:
return ngh_idx, ngh_eidx, ngh_ts, norm_ts
else:
return ngh_idx, ngh_eidx, ngh_ts
def find_before_idx(self, src_idx, cut_time, norm=False):
node_ts_l = self.node_ts_l
off_set_l = self.off_set_l
neighbors_ts = node_ts_l[off_set_l[src_idx]:off_set_l[src_idx + 1]]
if len(neighbors_ts) == 0 or len(neighbors_ts) == 0:
return 0
right = np.searchsorted(neighbors_ts, cut_time, side="left")
return right
def get_temporal_neighbor(self, src_idx_l, cut_time_l, num_neighbors=20):
"""
Params
------
src_idx_l: List[int]
cut_time_l: List[float],
num_neighbors: int
"""
assert len(src_idx_l) == len(cut_time_l)
if self.sampling == "exp":
return self.exp_sampling(src_idx_l, cut_time_l, num_neighbors)
node_idx_l = self.node_idx_l
node_ts_l = self.node_ts_l
edge_idx_l = self.edge_idx_l
off_set_l = self.off_set_l
return get_temporal_neighbor_nb(src_idx_l, cut_time_l, num_neighbors,
off_set_l, node_idx_l, node_ts_l,
edge_idx_l, self.uniform)
def exp_sampling(self, src_idx_l, cut_time_l, num_neighbors=20):
out_ngh_node_batch = np.zeros(
(len(src_idx_l), num_neighbors)).astype(np.int32)
out_ngh_t_batch = np.zeros(
(len(src_idx_l), num_neighbors)).astype(np.float32)
out_ngh_eidx_batch = np.zeros(
(len(src_idx_l), num_neighbors)).astype(np.int32)
for i, (src_idx, cut_time) in enumerate(zip(src_idx_l, cut_time_l)):
right = self.find_before_idx(src_idx, cut_time)
result = self.check_cache(src_idx, right)
if result is not None:
out_ngh_node_batch[i] = result[0]
out_ngh_t_batch[i] = result[1]
out_ngh_eidx_batch[i] = result[2]
continue
ngh_idx, ngh_eidx, ngh_ts, norm_ts = self.find_before(src_idx,
cut_time,
norm=True)
if len(ngh_idx) <= 0:
continue
if len(ngh_idx) < num_neighbors:
right = len(ngh_idx)
out_ngh_node_batch[i, :right] = ngh_idx
out_ngh_t_batch[i, :right] = ngh_ts
out_ngh_eidx_batch[i, :right] = ngh_eidx
continue
ngh_dt = norm_ts - np.max(norm_ts)
ngh_logit = np.exp(self.alpha[src_idx] * ngh_dt)
prob = ngh_logit / np.sum(ngh_logit)
nonzero_num = (prob > 0).sum()
num = min(num_neighbors, nonzero_num)
sampled_idx = np.random.choice(len(ngh_ts),
size=num,
replace=False,
p=prob)
sampled_idx = np.sort(sampled_idx)
out_ngh_node_batch[i, :num] = ngh_idx[sampled_idx]
out_ngh_t_batch[i, :num] = ngh_ts[sampled_idx]
out_ngh_eidx_batch[i, :num] = ngh_eidx[sampled_idx]
# resort based on time
pos = out_ngh_t_batch[i, :].argsort()
out_ngh_node_batch[i, :] = out_ngh_node_batch[i, :][pos]
out_ngh_t_batch[i, :] = out_ngh_t_batch[i, :][pos]
out_ngh_eidx_batch[i, :] = out_ngh_eidx_batch[i, :][pos]
result = (out_ngh_node_batch[i], out_ngh_t_batch[i],
out_ngh_eidx_batch[i])
self.update_cache(src_idx, right, result)
return out_ngh_node_batch, out_ngh_eidx_batch, out_ngh_t_batch
def find_k_hop(self, k, src_idx_l, cut_time_l, num_neighbors=20):
"""Sampling the k-hop sub graph"""
x, y, z = self.get_temporal_neighbor(src_idx_l, cut_time_l,
num_neighbors)
node_records = [x]
eidx_records = [y]
t_records = [z]
for _ in range(k - 1):
ngn_node_est, ngh_t_est = (
node_records[-1],
t_records[-1],
) # [N, *([num_neighbors] * (k - 1))]
orig_shape = ngn_node_est.shape
ngn_node_est = ngn_node_est.flatten()
ngn_t_est = ngh_t_est.flatten()
(
out_ngh_node_batch,
out_ngh_eidx_batch,
out_ngh_t_batch,
) = self.get_temporal_neighbor(ngn_node_est, ngn_t_est,
num_neighbors)
out_ngh_node_batch = out_ngh_node_batch.reshape(
*orig_shape, num_neighbors) # [N, *([num_neighbors] * k)]
out_ngh_eidx_batch = out_ngh_eidx_batch.reshape(
*orig_shape, num_neighbors)
out_ngh_t_batch = out_ngh_t_batch.reshape(*orig_shape,
num_neighbors)
node_records.append(out_ngh_node_batch)
eidx_records.append(out_ngh_eidx_batch)
t_records.append(out_ngh_t_batch)
return node_records, eidx_records, t_records
def update_cache(self, node, idx, results):
key = (node, idx)
if key not in self.cache:
self.cache[key] = results
def check_cache(self, node, idx):
key = (node, idx)
return self.cache.get(key)