Spaces:
Runtime error
Runtime error
File size: 6,159 Bytes
d2a8669 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 |
import dgl
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl.function as fn
from dgl.nn.functional import edge_softmax
import pandas as pd
class RHGNLayer(nn.Module):
def __init__(self,
in_dim,
out_dim,
node_dict,
edge_dict,
n_heads,
dropout = 0.2,
use_norm = False):
super(RHGNLayer, self).__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.node_dict = node_dict
self.edge_dict = edge_dict
self.num_types = len(node_dict)
self.num_relations = len(edge_dict)
self.total_rel = self.num_types * self.num_relations * self.num_types
self.n_heads = n_heads
self.d_k = out_dim // n_heads
self.sqrt_dk = math.sqrt(self.d_k)
self.att = None
self.k_linears = nn.ModuleList()
self.q_linears = nn.ModuleList()
self.v_linears = nn.ModuleList()
self.a_linears = nn.ModuleList()
self.norms = nn.ModuleList()
self.use_norm = use_norm
for t in range(self.num_types):
self.k_linears.append(nn.Linear(in_dim, out_dim))
self.q_linears.append(nn.Linear(in_dim, out_dim))
self.v_linears.append(nn.Linear(in_dim, out_dim))
self.a_linears.append(nn.Linear(out_dim, out_dim))
if use_norm:
self.norms.append(nn.LayerNorm(out_dim))
self.relation_pri = nn.Parameter(torch.ones(self.num_relations, self.n_heads))
self.relation_att = nn.Parameter(torch.Tensor(self.num_relations, n_heads, self.d_k, self.d_k))
self.relation_msg = nn.Parameter(torch.Tensor(self.num_relations, n_heads, self.d_k, self.d_k))
self.skip = nn.Parameter(torch.ones(self.num_types))
self.drop = nn.Dropout(dropout)
nn.init.xavier_uniform_(self.relation_att)
nn.init.xavier_uniform_(self.relation_msg)
def forward(self, G, h,is_batch=True,is_train=True,print_flag=False):
with G.local_scope():
node_dict, edge_dict = self.node_dict, self.edge_dict
for srctype, etype, dsttype in G.canonical_etypes:
sub_graph = G[srctype, etype, dsttype]
k_linear = self.k_linears[node_dict[srctype]]
v_linear = self.v_linears[node_dict[srctype]]
q_linear = self.q_linears[node_dict[dsttype]]
#k_linear = self.k_linears[0]
#v_linear = self.v_linears[0]
#q_linear = self.q_linears[0]
k = k_linear(h[srctype]).view(-1, self.n_heads, self.d_k)
v = v_linear(h[srctype]).view(-1, self.n_heads, self.d_k)
if is_batch:
q = q_linear(h[dsttype][:sub_graph.number_of_dst_nodes()]).view(-1, self.n_heads, self.d_k)
else:
q = q_linear(h[dsttype]).view(-1, self.n_heads, self.d_k)
e_id = self.edge_dict[etype]
relation_att = self.relation_att[e_id]
relation_pri = self.relation_pri[e_id]
relation_msg = self.relation_msg[e_id]
#relation_att = self.relation_att[0]
#relation_msg = self.relation_msg[0]
#relation_pri = self.relation_pri[0]
k = torch.einsum("bij,ijk->bik", k, relation_att)
v = torch.einsum("bij,ijk->bik", v, relation_msg)
sub_graph.srcdata['k'] = k
sub_graph.dstdata['q'] = q
sub_graph.srcdata['v_%d' % e_id] = v
sub_graph.apply_edges(fn.v_dot_u('q', 'k', 't'))
attn_score = sub_graph.edata.pop('t').sum(-1) * relation_pri / self.sqrt_dk
attn_score = edge_softmax(sub_graph, attn_score, norm_by='dst')
sub_graph.edata['t'] = attn_score.unsqueeze(-1)
'''
if print_flag==True:
# print('---------------',srctype,etype,dsttype,'---------------------')
srcnode=sub_graph.edges()[0].cpu().numpy()
dstnode=sub_graph.edges()[1].cpu().numpy()
attweight=attn_score.mean(dim=-1).cpu().detach().numpy()
# print(srcnode.shape,dstnode.shape,attweight.shape)
import time
if etype=='click':
df=pd.DataFrame({srctype:srcnode,dsttype:dstnode,etype:attweight})
df.to_csv('../data/attweight/{}.csv'.format(time.time()),index=False)
if etype=='purchase':
df = pd.DataFrame({srctype: srcnode, dsttype: dstnode, etype: attweight})
df.to_csv('../data/attweight/{}.csv'.format(time.time()), index=False)
'''
G.multi_update_all({etype : (fn.u_mul_e('v_%d' % e_id, 't', 'm'), fn.sum('m', 't')) for etype, e_id in edge_dict.items()},
cross_reducer = 'mean')
new_h = {}
for ntype in G.ntypes:
'''
Step 3: Target-specific Aggregation
x = norm( W[node_type] * gelu( Agg(x) ) + x )
'''
n_id = node_dict[ntype]
alpha = torch.sigmoid(self.skip[n_id])
if is_batch:
t = G.dstnodes[ntype].data['t'].view(-1, self.out_dim)
else:
t = G.nodes[ntype].data['t'].view(-1, self.out_dim)
trans_out = self.a_linears[n_id](t)
if is_train:
trans_out = self.drop(trans_out)
trans_out = trans_out * alpha + h[ntype][:G.number_of_dst_nodes(ntype)] * (1-alpha)
if self.use_norm:
new_h[ntype] = self.norms[n_id](trans_out)
else:
new_h[ntype] = trans_out
return new_h
|