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sampler.py
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sampler.py
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import random
import numpy as np
import torch
from graph_util import Graph, GraphWithAnswer, IndexedGraph
r"""
edge_index: Tensor of shape [2, E]
"""
def ladies_sampler_np(node_list, samp_num_list, num_nodes, lap_matrix):
r"""
LADIES_Sampler: Sample a fixed number of nodes per layer. The sampling probability (importance)
is computed adaptively according to the nodes sampled in the upper layer.
Currently it only returns one single large graph.
Sample nodes from top to bottom, based on the probability computed adaptively (layer-dependent).
References:
* https://github.com/acbull/LADIES/blob/master/pytorch_ladies.py
"""
for d in samp_num_list:
# Row-select the lap_matrix (U) by previously sampled nodes
u = lap_matrix[node_list, :]
# Only use the upper layer's neighborhood to calculate the probability.
pi = np.array(np.sum(u.multiply(u), axis=0))[0]
del u
# What's chosen should not be considered again
pi[node_list] = 0
s_num = np.min([np.sum(pi > 0), d])
if s_num == 0:
break
pi = pi / np.sum(pi)
# Sample the next layer's nodes based on the adaptively probability (p).
after_nodes = np.random.choice(num_nodes, s_num, p=pi, replace=False)
node_list = np.unique(np.concatenate((node_list, after_nodes)))
del after_nodes
return node_list
def sample_meta_tree(origin_node, igraph: IndexedGraph, edge_add_ratio=0.8) -> Graph:
device = igraph.edge_index.device
dfn = {origin_node: 0}
idx = [origin_node]
import numpy as np
num_iter = np.random.randint(5, 16)
ei0 = []
ei1 = []
ea = []
for i in range(num_iter):
c = np.random.choice(idx)
adj_node_list = igraph.get_adj_nodes(c)
if len(adj_node_list) == 0:
break
adj_node = np.random.choice(adj_node_list)
adj_ei, adj_ea = igraph.get_edges_between(c, adj_node)
e_id = np.random.choice(len(adj_ea))
a, p, b = adj_ei[0][e_id].item(), adj_ea[e_id].item(), adj_ei[1][e_id].item()
assert a == c or b == c
if a in dfn and b in dfn:
continue
if a not in dfn:
idx.append(a)
dfn[a] = len(dfn)
if b not in dfn:
idx.append(b)
dfn[b] = len(dfn)
ei0.append(a)
ei1.append(b)
ea.append(p)
g = Graph(
x=igraph.x,
edge_index=torch.tensor([ei0, ei1], dtype=torch.long),
edge_attr=torch.tensor(ea, dtype=torch.long),
)
idx = torch.tensor(idx, dtype=torch.long, device=device)
g = sample_induced_edges(igraph, g, idx, edge_add_ratio)
from graph_util import relabel_nodes
g = relabel_nodes(g, idx)
return g
def sample_from_origin(node_list, igraph: IndexedGraph, lap_matrix, ladies_size, induced_edge_prob=1.0):
device = igraph.edge_index.device
num_nodes = igraph.num_nodes
# Sampling around the nodes to be predicted
import random
def rand_partition(n_ele, n_parts):
assert n_parts <= n_ele
from random import randint
if n_ele == 0:
return []
bar = [randint(0, n_ele - n_parts) for _ in range(n_parts - 1)]
bar += [0, n_ele - n_parts]
bar.sort()
return [bar[i] - bar[i - 1] + 1 for i in range(1, n_parts + 1)]
n_ele = random.randint(1, ladies_size)
n_parts = random.randint(0, n_ele)
node_list = ladies_sampler_np(
node_list.numpy(), rand_partition(n_ele, n_parts),
num_nodes, lap_matrix)
node_list = torch.from_numpy(node_list).to(device).contiguous()
graph = Graph(
x=igraph.x,
edge_index=torch.tensor([[], []], dtype=torch.long, device=device),
edge_attr=torch.tensor([], dtype=torch.long, device=device),
)
graph = sample_induced_edges(igraph, graph, node_list, induced_edge_prob)
from graph_util import relabel_nodes
graph = relabel_nodes(graph, node_list)
return graph
def gen_ans_by_masking(graph: Graph, num_nodes, relation_cnt, config) -> GraphWithAnswer:
device = graph.edge_index.device
mask_ratio = config['pretrain_mask_ratio']
mask_type_ratio = config['pretrain_mask_type_ratio']
# Choose the nodes to predict again
def mask_num(size):
import random
assert size >= 0
size_map = {
0: [0, 0],
1: [1, 1], # Edge only!
2: [1, 1],
3: [1, 1],
4: [1, 2],
5: [1, 2],
6: [1, 3],
7: [2, 3],
8: [2, 3],
9: [2, 3],
10: [2, 4],
11: [2, 4],
12: [3, 4],
13: [3, 4],
14: [3, 5],
15: [3, 5],
16: [4, 5],
17: [4, 6],
18: [4, 6],
19: [5, 7],
20: [5, 7],
21: [6, 7],
22: [6, 7],
23: [6, 8],
24: [6, 8],
25: [7, 8],
26: [7, 9],
27: [7, 9],
28: [8, 9],
29: [8, 9],
30: [8, 10],
31: [8, 10],
32: [9, 10]
}
from math import floor
return random.randint(*size_map.get(size, [floor(size * mask_ratio[0]), floor(size * mask_ratio[1])]))
from math import floor
nmask = floor((mask_type_ratio[0] / (mask_type_ratio[0] + mask_type_ratio[1])) * graph.num_nodes)
emask = floor(
(mask_type_ratio[1] / (mask_type_ratio[0] + mask_type_ratio[1])) * graph.num_nodes) # still use node_size here
emask = min(emask, graph.num_edges)
num_node_mask = mask_num(nmask)
num_edge_mask = mask_num(emask)
x_query = torch.from_numpy(np.random.choice(graph.num_nodes, num_node_mask, replace=False)).to(device)
edge_query = torch.from_numpy(np.random.choice(graph.num_edges, num_edge_mask, replace=False)).to(device)
x_ans = graph.x[x_query]
edge_ans = graph.edge_attr[edge_query]
def do_mask(arr, list_predict, num_nodes) -> torch.LongTensor:
randseed = random.random()
if randseed < config['mask_ratio']:
arr[list_predict] = -1
elif config['mask_ratio'] <= randseed < (config['mask_ratio'] + 1) / 2:
arr[list_predict] = torch.randint(low=0, high=num_nodes, size=list_predict.shape, device=device)
else:
pass
return arr
return GraphWithAnswer(
edge_index=graph.edge_index,
x=do_mask(graph.x, x_query, num_nodes),
edge_attr=do_mask(graph.edge_attr, edge_query, relation_cnt),
x_query=x_query,
x_ans=x_ans,
edge_query=edge_query,
edge_ans=edge_ans,
joint_nodes=torch.tensor([], device=device, dtype=torch.long),
union_query=torch.tensor([], device=device, dtype=torch.long)
)
def sample_induced_edges(igraph: IndexedGraph, graph: Graph, node_list, add_prob) -> Graph:
subg = igraph.get_induced_subgraph(node_list)
existing_edge = set()
for a, b, p in zip(graph.edge_index[0], graph.edge_index[1], graph.edge_attr):
existing_edge.add((a.item(), b.item(), p.item()))
import random
idx = []
for i in range(subg.num_edges):
if random.random() > add_prob:
continue
if (subg.edge_index[0][i].item(), subg.edge_index[1][i].item(), subg.edge_attr[i].item()) in existing_edge:
continue
idx.append(i)
newg = Graph(
x=graph.x,
edge_index=torch.cat([graph.edge_index, subg.edge_index[:, idx]], dim=1),
edge_attr=torch.cat([graph.edge_attr, subg.edge_attr[idx]]),
)
return newg
def sample_n_p(igraph: IndexedGraph, target_node, n, config):
dfn = {target_node: 0}
idx = [target_node]
cur_node = target_node
edge_index0 = []
edge_index1 = []
edge_attr = []
sampled_node = [target_node]
for i in range(n):
adj_nodes = list(igraph.get_adj_nodes(cur_node))
if not adj_nodes:
return None
nxt_node = np.random.choice(adj_nodes)
dfn[nxt_node] = len(dfn)
idx.append(nxt_node)
ei, ea = igraph.get_edges_between(cur_node, nxt_node)
ei = list(ei)
ea = list(ea)
p = np.random.choice(len(ea))
e0 = ei[0][p]
e1 = ei[1][p]
if e0 != nxt_node and e1 != nxt_node:
while True:
p = np.random.choice(len(ea))
e0 = ei[0][p]
e1 = ei[1][p]
if e0 == nxt_node or e1 == nxt_node:
break
if e0 == idx[-1]:
edge_index0.append(len(idx) - 1)
edge_index1.append(len(idx) - 2)
elif e1 == idx[-1]:
edge_index0.append(len(idx) - 2)
edge_index1.append(len(idx) - 1)
else:
assert False
edge_attr.append(ea[p])
sampled_node.append(nxt_node)
cur_node = nxt_node
result = Graph(
x=igraph.x[idx],
edge_index=torch.tensor([edge_index0, edge_index1]),
edge_attr=torch.tensor(edge_attr),
)
idx = torch.tensor(idx)
randseed = random.random()
if randseed <= config['p_mask_ratio']:
x_node = torch.tensor([-1] * n + [idx[-1]])
ans = torch.tensor([target_node])
elif config['p_mask_ratio'] < randseed <= (config['mask_ratio'] + 1) / 2:
x_node = torch.tensor([-1] * n + [idx[-1]])
ans = torch.randint(low=0, high=igraph.num_nodes, size=(1,), dtype=torch.long)
else:
x_node = torch.tensor([target_node] + [-1] * (n - 1) + [idx[-1]])
ans = torch.tensor([target_node])
result = GraphWithAnswer(
x=x_node,
edge_index=result.edge_index,
edge_attr=result.edge_attr,
x_query=torch.tensor([0], dtype=torch.long),
x_ans=ans,
edge_query=torch.tensor([], dtype=torch.long),
edge_ans=torch.tensor([], dtype=torch.long),
joint_nodes=torch.tensor([], dtype=torch.long),
union_query=torch.tensor([], dtype=torch.long)
)
return result
def sample_one_edge_between(igraph, a, b):
ei, ea = igraph.get_edges_between(a, b)
ptr = np.random.choice(len(ea))
return ei[0, ptr], ei[1, ptr], ea[ptr]
def sample_n_i(igraph: IndexedGraph, target_node, n, edge_drop_out_rate):
full_adj_node = igraph.get_adj_nodes(target_node)
if len(full_adj_node) < n:
return None
adj_node = np.random.choice(full_adj_node, size=n, replace=False).tolist()
edge_index0 = []
edge_index1 = []
edge_attr = []
idx = [target_node] + list(adj_node)
for ind, i in enumerate(adj_node):
a, b, p = sample_one_edge_between(igraph, target_node, i)
if a != target_node and b != target_node:
while True:
a, b, p = sample_one_edge_between(igraph, target_node, i)
if a == target_node or b == target_node:
break
if a == target_node:
edge_index0.append(0)
edge_index1.append(ind + 1)
elif b == target_node:
edge_index0.append(ind + 1)
edge_index1.append(0)
else:
assert False
edge_attr.append(p)
g = Graph(
x=igraph.x,
edge_index=torch.tensor([edge_index0, edge_index1], dtype=torch.long),
edge_attr=torch.tensor(edge_attr),
)
g = GraphWithAnswer(
x=torch.tensor([-1] + adj_node),
edge_index=g.edge_index,
edge_attr=g.edge_attr,
x_query=torch.tensor([0], dtype=torch.long),
x_ans=torch.tensor([target_node], dtype=torch.long),
edge_query=torch.tensor([], dtype=torch.long),
edge_ans=torch.tensor([], dtype=torch.long),
joint_nodes=torch.tensor([], dtype=torch.long),
union_query=torch.tensor([], dtype=torch.long)
)
return g
def cap_edges(g: GraphWithAnswer, max_num_edges):
if g.num_edges <= max_num_edges:
return g
g = g.clone()
new_edges = torch.multinomial(torch.ones([g.num_edges]), max_num_edges, replacement=False)
chosen = torch.zeros([g.num_edges], dtype=torch.bool)
chosen[new_edges] = True
g.edge_index = g.edge_index[:, chosen]
g.edge_attr = g.edge_attr[chosen]
if hasattr(g, 'edge_query'):
q_mask = chosen[g.edge_query]
g.edge_query = g.edge_query[q_mask]
g.edge_ans = g.edge_ans[q_mask]
return g
def mini_sampler(igraph: IndexedGraph, target_nodes, lap_matrix, relation_cnt, config) -> GraphWithAnswer:
target_node = np.random.choice(target_nodes)
d: dict = config['pretrain_sampler_ratio']
edge_drop_out_rate = config['edge_drop_out_rate']
a = list(d.keys())
p = np.array(list(map(d.__getitem__, a)), dtype=np.float32)
p = p / p.sum()
g_type = np.random.choice(a=a, p=p)
g = None
for n in range(config['sample_retries']):
if g_type == '1p':
g = sample_n_p(igraph, target_node, 1, config)
elif g_type == '2p':
g = sample_n_p(igraph, target_node, 2, config)
elif g_type == '3p':
g = sample_n_p(igraph, target_node, 3, config)
elif g_type == '2i':
g = sample_n_i(igraph, target_node, 2, edge_drop_out_rate)
elif g_type == '3i':
g = sample_n_i(igraph, target_node, 3, edge_drop_out_rate)
elif g_type == 'meta_tree':
g = sample_meta_tree(target_node, igraph, edge_add_ratio=config['induced_edge_prob'])
elif g_type == 'ladies':
g = sample_from_origin(
node_list=target_nodes,
igraph=igraph,
lap_matrix=lap_matrix,
ladies_size=config['ladies_size'],
induced_edge_prob=config['induced_edge_prob'],
)
else:
raise NotImplementedError(f'Sample type "{g_type}" not implemented')
if g is not None:
break
if isinstance(g, Graph) and not isinstance(g, GraphWithAnswer):
g = gen_ans_by_masking(
graph=g,
num_nodes=igraph.num_nodes,
relation_cnt=relation_cnt,
config=config,
)
if g is not None:
assert isinstance(g, GraphWithAnswer)
g = cap_edges(g, 50)
return g