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model.py
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model.py
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'''
Descripttion: model
version: 1.0
Author: Assassin 567
Date: 2021-11-03 10:40:02
LastEditors: Hello KG
LastEditTime: 2021-11-06 11:14:26
'''
import os
import sys
import time
from typing import List
from collections import Counter, defaultdict
import numpy as np
import torch
from torch import nn
from torch.nn import Parameter
from torch_scatter import scatter_max, scatter_sum, scatter_softmax,scatter_add, scatter_mean
PackageDir = os.path.dirname(__file__)
sys.path.insert(1, PackageDir)
class TimeEncode(torch.nn.Module):
'''
Description: the embedding of each entity is composed of three parts: static embedding; time-specific embedding-1 ; time-specific embedding-2
function: the time-specific embedding for entities (time-specific embedding-2)
'''
def __init__(self, expand_dim, entity_specific=False, num_entities=None, num_timestamps=None, device='cpu'):
super(TimeEncode, self).__init__()
self.time_dim = expand_dim
self.entity_specific = entity_specific
self.base_num = 50
self.num_entities = num_entities
self.num_timestamps = num_timestamps
if entity_specific:
self.basis_freq = torch.nn.Parameter(
torch.from_numpy(np.random.randn(num_timestamps, self.base_num)).float())
self.phase = torch.nn.Parameter(torch.zeros(self.base_num, self.time_dim).float()) #
else:
self.basis_freq = torch.nn.Parameter(
torch.from_numpy(1 / 10 ** np.linspace(0, 9, self.time_dim)).float()) # shape: num_entities * time_dim
self.phase = torch.nn.Parameter(torch.zeros(self.time_dim).float())
self.ent_time_compare_base = nn.Linear(2*self.time_dim, self.time_dim, bias=False)
self.ent_time_compare_base.weight.data[:,:self.time_dim] = torch.eye(self.time_dim)
self.ent_time_compare_base.weight.data[:,self.time_dim:] = -1*torch.eye(self.time_dim)
self.ent_time_act = torch.nn.LeakyReLU() #negative_slope=0.2
def forward(self, ts, ts_base, entities=None):
if self.entity_specific:
assert sum(ts[0]<0)+sum(ts[1]<0)+sum(ts[2]<0) == 0
assert np.max(ts[2]-ts[0])==1 and np.max(ts[0]-ts[1])==1
harmonic = torch.mm(self.basis_freq[ts[0]],self.phase) # W * base_vec base_vec*256
reg_time = torch.norm(torch.mm(self.basis_freq[ts[1]]-self.basis_freq[ts[0]],self.phase),p=2,dim=1)+ \
torch.norm(torch.mm(self.basis_freq[ts[2]]-self.basis_freq[ts[0]],self.phase),p=2,dim=1)
harmonic_base = torch.mm(self.basis_freq[ts_base[0]],self.phase) # W * base_vec base_vec*256
reg_time_base = torch.norm(torch.mm(self.basis_freq[ts_base[1]]-self.basis_freq[ts_base[0]],self.phase),p=2,dim=1)+ \
torch.norm(torch.mm(self.basis_freq[ts_base[2]]-self.basis_freq[ts_base[0]],self.phase),p=2,dim=1)
harmonic = self.ent_time_act(self.ent_time_compare_base(torch.cat((harmonic,harmonic_base),dim=1)))
reg_time = torch.cat([reg_time, reg_time_base], axis=0)
else:
batch_size = ts.size(0)
seq_len = ts.size(1)
ts = torch.unsqueeze(ts, dim=2)
map_ts = ts * self.basis_freq.view(1, 1, -1) # [batch_size, 1, time_dim]
map_ts += self.phase.view(1, 1, -1)
harmonic = torch.cos(map_ts)
reg_time = 0
return harmonic, reg_time
class TimeEncode_ori(torch.nn.Module):
'''
This class implemented the Bochner's time embedding
expand_dim: int, dimension of temporal entity embeddings
enitity_specific: bool, whether use entith specific freuency and phase.
num_entities: number of entities.
function: the time-specific embedding for entities (time-specific embedding-1)
ref : xERTE: Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs.
https://github.com/TemporalKGTeam/xERTE
'''
def __init__(self, expand_dim, entity_specific=False, num_entities=None, device='cpu'):
"""
:param expand_dim: number of samples draw from p(w), which are used to estimate kernel based on MCMC
:param entity_specific: if use entity specific time embedding
:param num_entities: number of entities
refer to Self-attention with Functional Time Representation Learning for more detail
"""
super(TimeEncode_ori, self).__init__()
self.time_dim = expand_dim
self.entity_specific = entity_specific
if entity_specific:
self.basis_freq = torch.nn.Parameter(
torch.from_numpy(1 / 10 ** np.linspace(0, 9, self.time_dim)).float().unsqueeze(dim=0).repeat(
num_entities, 1))
self.phase = torch.nn.Parameter(torch.zeros(self.time_dim).float().unsqueeze(dim=0).repeat(num_entities, 1))
else:
self.basis_freq = torch.nn.Parameter(
torch.from_numpy(1 / 10 ** np.linspace(0, 9, self.time_dim)).float()) # shape: num_entities * time_dim
self.phase = torch.nn.Parameter(torch.zeros(self.time_dim).float())
def forward(self, ts, entities=None):
'''
:param ts: [batch_size, seq_len]
:param entities: which entities do we extract their time embeddings.
:return: [batch_size, seq_len, time_dim]
'''
batch_size = ts.size(0)
seq_len = ts.size(1)
ts = torch.unsqueeze(ts, dim=2)
if self.entity_specific:
map_ts = ts * self.basis_freq[entities].unsqueeze(
dim=1) #
map_ts += self.phase[entities].unsqueeze(dim=1)
else:
map_ts = ts * self.basis_freq.view(1, 1, -1) #
map_ts += self.phase.view(1, 1, -1)
harmonic = torch.cos(map_ts)
return harmonic
class G3(torch.nn.Module):
def __init__(self, dim_in, dim_out):
"""[summary]
bilinear mapping along last dimension of x and y:
output = MLP_1(x)^T A MLP_2(y), where A is two-dimenion matrix
Arguments:
left_dims {[type]} -- input dims of MLP_1
right_dims {[type]} -- input dims of MLP_2
output_dims {[type]} -- [description]
"""
super(G3, self).__init__()
self.dim_out = dim_out
self.query_proj = nn.Linear(dim_in, 1, bias=False)
nn.init.normal_(self.query_proj.weight, mean=0, std=np.sqrt(2.0 / (dim_in)))
self.key_proj = nn.Linear(dim_out, dim_out//3, bias=False)
nn.init.normal_(self.key_proj.weight, mean=0, std=np.sqrt(2.0 / (dim_out)))
self.act_leaky = nn.Sigmoid()
self.layernorm = torch.nn.LayerNorm(normalized_shape=dim_in,elementwise_affine=True)
def forward(self, inputs):
obj_later = inputs[1]
triple_loss = torch.norm(inputs[3] + inputs[2] - obj_later,p=1 ,dim=1) #
rel_loss = torch.norm(inputs[4] - inputs[2],p=1 ,dim=1) #
return rel_loss, triple_loss #
class AttentionFlow(nn.Module):
def __init__(self, n_dims_in, n_dims_out, ratio_update=0, node_score_aggregation='sum',
device='cpu'):
"""[summary]
Arguments:
n_dims -- int, dimension of entity and relation embedding
n_dims_sm -- int, smaller than n_dims to reduce the compuation consumption of calculating attention score
ratio_update -- new node representation = ratio*self+(1-ratio)\sum{aggregation of neighbors' representation}
"""
super(AttentionFlow, self).__init__()
self.transition_fn = G3(6 * n_dims_in, 3 * n_dims_in)
self.linear_between_steps = nn.Linear(n_dims_in, n_dims_out, bias=True)
torch.nn.init.xavier_normal_(self.linear_between_steps.weight)
self.linear_between_steps_time = nn.Linear(n_dims_in, n_dims_out, bias=True)
torch.nn.init.xavier_normal_(self.linear_between_steps_time.weight)
self.act_between_steps = torch.nn.LeakyReLU(negative_slope=0.2)
self.node_score_aggregation = node_score_aggregation
self.ratio_update = torch.nn.Parameter(torch.tensor(ratio_update)) #
self.query_src_ts_emb = None
self.query_rel_emb = None
self.device = device
self.linear_src_update = nn.Linear(n_dims_in*5 + n_dims_in*3, n_dims_in, bias=True) #MLP
torch.nn.init.xavier_normal_(self.linear_src_update.weight)
self.act_src_update = torch.nn.LeakyReLU(negative_slope=0.02) #act
self.linear_rel_update = nn.Linear(n_dims_in*5 + n_dims_in*3 , n_dims_in, bias=True)
torch.nn.init.xavier_normal_(self.linear_src_update.weight)
self.act_rel_update = torch.nn.LeakyReLU(negative_slope=0.02)
self.linear_src_update_1 = nn.Linear(n_dims_in*4, 1, bias=False) #MLP
torch.nn.init.xavier_normal_(self.linear_src_update_1.weight)
self.linear_rel_update_1 = nn.Linear(n_dims_in*4, 1, bias=False)
torch.nn.init.xavier_normal_(self.linear_rel_update_1.weight)
self.act_sigmoid = nn.Sigmoid()
self.linear_trans = nn.ModuleList(nn.Linear(n_dims_in*3, n_dims_in, bias=False) for i in range(3))
for ss in range(3):
torch.nn.init.xavier_normal_(self.linear_trans[ss].weight)
self.time_weight = nn.Linear(n_dims_in*7, 1, bias=True)
torch.nn.init.xavier_normal_(self.time_weight.weight)
self.step_score_add = torch.nn.Parameter(torch.ones(3, 1).float())
self.query_obj_answer = nn.Linear(n_dims_in*7, 1, bias=True) # act_rel_update
torch.nn.init.xavier_normal_(self.query_obj_answer.weight)
self.layernorm_1 = torch.nn.LayerNorm(normalized_shape=n_dims_in*7, elementwise_affine=True)
def set_query_emb(self, query_src_ts_emb, query_rel_emb):
self.query_src_ts_emb, self.query_rel_emb = query_src_ts_emb, query_rel_emb
def set_query_reg(self, reg_query):
self.reg_query = reg_query
def set_query_time(self, query_ts_emb_special):
self.query_ts_emb_special = query_ts_emb_special
def _topk_att_score(self, edges, logits, k: int, tc=None):
"""
:param edges: numpy array, (eg_idx, vi, ti, vj, tj, rel, node_idx_i, node_idx_j), dtype np.int32
:param logits: tensor, same length as edges, dtype=torch.float32
:param k: number of nodes in attended-from horizon
:return:
pruned_edges, numpy.array, (eg_idx, vi, ts)
pruned_logits, tensor, same length as pruned_edges
origin_indices
"""
if tc:
t_start = time.time()
res_edges = []
res_logits = []
res_indices = []
for eg_idx in sorted(set(edges[:, 0])):
mask = edges[:, 0] == eg_idx
orig_indices = np.arange(len(edges))[mask]
masked_edges = edges[mask]
masked_edges_logits = logits[mask]
if masked_edges.shape[0] <= k: #
res_edges.append(masked_edges)
res_logits.append(masked_edges_logits)
res_indices.append(orig_indices)
else: #
topk_edges_logits, indices = torch.topk(masked_edges_logits, k)
res_indices.append(orig_indices[indices.cpu().numpy()])
try:
res_edges.append(masked_edges[indices.cpu().numpy()])
except Exception as e:
print(indices.cpu().numpy())
print(max(indices.cpu().numpy()))
print(str(e))
raise KeyError
res_logits.append(topk_edges_logits)
if tc:
tc['graph']['topk'] += time.time() - t_start
return np.concatenate(res_edges, axis=0), torch.cat(res_logits, dim=0), np.concatenate(res_indices, axis=0)
def _cal_attention_score(self, edges, memorized_embedding, rel_emb, src_ts_emb_special_1):
"""
calculating node attention from memorized embedding
"""
hidden_vi_orig = memorized_embedding[0] #
hidden_vj_orig = memorized_embedding[2] #
return self.cal_attention_score(edges[:, 8], hidden_vi_orig, hidden_vj_orig, rel_emb, src_ts_emb_special_1) #
def cal_attention_score(self, query_idx, hidden_vi, hidden_vj, rel_emb, src_ts_emb_special):
"""
calculate attention score between two nodes of edges
wraped as a separate method so that it can be used for calculating attention between a node and it's full
neighborhood, attention is used to select important nodes from the neighborhood
:param query_idx: indicating in subgraph for which query the edge lies.
"""
query_src_ts_emb_repeat = torch.index_select(self.query_src_ts_emb, dim=0,
index=torch.from_numpy(query_idx).long().to(
self.device)) #
query_rel_emb_repeat = torch.index_select(self.query_rel_emb, dim=0,
index=torch.from_numpy(query_idx).long().to(
self.device))#
transition_logits, loght_emb = self.transition_fn(
(hidden_vi, hidden_vj, rel_emb, query_src_ts_emb_repeat, query_rel_emb_repeat, \
src_ts_emb_special[0], src_ts_emb_special[1], src_ts_emb_special[2])) #
return transition_logits, loght_emb
def forward(self, step_score_add_all, visited_node_score, visited_nodes_reg, src_ts_emb_special_set,sample_loss, selected_edges_l=None, visited_node_representation=None, rel_emb_l=None,
max_edges=10, analysis=False, tc=None):
"""calculate attention score
Arguments:
node_attention {tensor, num_edges} -- src_attention of selected_edges, node_attention[i] is the attention score
of (selected_edge[i, 1], selected_edge[i, 2]) in eg_idx==selected_edge[i, 0]
Keyword Arguments:
selected_edges {numpy.array, num_edges x 9} -- (eg_idx, vi, ti, vj, tj, rel, idx_eg_vi_ti, idx_eg_vj_tj,query-id) (default: {None})
contain selfloop
memorized_embedding torch.Tensor,
return:
pruned_edges, orig_indices
updated_memorized_embedding:
updated_node_score: Tensor, shape: n_new_node
:param attended_nodes:
"""
transition_logits, loght_emb = self._cal_attention_score(selected_edges_l[-1], visited_node_representation, rel_emb_l[-1], src_ts_emb_special_set)#
transition_logits = torch.mm(torch.stack((transition_logits , sample_loss , \
loght_emb ), dim=-1), -1*torch.abs(step_score_add_all)) # torch.zeros_like(transition_logits)
target_score = transition_logits.squeeze()
pruned_edges, _, orig_indices = self._topk_att_score(selected_edges_l[-1], target_score,
max_edges) #
loght_emb = loght_emb.unsqueeze(1)
return target_score, visited_nodes_reg, pruned_edges, orig_indices
def _update_node_representation_along_edges_old(self, edges, memorized_embedding, transition_logits):
num_nodes = len(memorized_embedding)
# update representation of nodes with neighbors
# 1. message passing and aggregation
sparse_index_rep = torch.from_numpy(edges[:, [6, 7]]).to(torch.int64).to(self.device)
sparse_value_rep = transition_logits
trans_matrix_sparse_rep = torch.sparse.FloatTensor(sparse_index_rep.t(), sparse_value_rep,
torch.Size([num_nodes, num_nodes])).to(self.device)
updated_memorized_embedding = torch.sparse.mm(trans_matrix_sparse_rep, memorized_embedding)
# 2. linear
updated_memorized_embedding = self.act_between_steps(self.linear_between_steps(updated_memorized_embedding))
# 3. pass representation of nodes without neighbors, i.e. not updated
sparse_index_identical = torch.from_numpy(np.setdiff1d(np.arange(num_nodes), edges[:, 6])).unsqueeze(
1).repeat(1, 2).to(self.device)
sparse_value_identical = torch.ones(len(sparse_index_identical)).to(self.device)
trans_matrix_sparse_identical = torch.sparse.FloatTensor(sparse_index_identical.t(), sparse_value_identical,
torch.Size([num_nodes, num_nodes])).to(self.device)
identical_memorized_embedding = torch.sparse.mm(trans_matrix_sparse_identical, memorized_embedding)
updated_memorized_embedding = updated_memorized_embedding + identical_memorized_embedding
return updated_memorized_embedding
def _update_node_representation_along_edges(self, edges, node_representation, transition_logits, linear_act=True):
"""
:param edges:
:param memorized_embedding:
:param transition_logits:
:param linear_act: whether apply linear and activation layer after message aggregation
:return:
"""
num_nodes = len(node_representation)
sparse_index_rep = torch.from_numpy(edges[:, [6, 7]]).to(torch.int64).to(self.device) #1307*2
sparse_value_rep = (1 - self.ratio_update) * transition_logits #1307
sparse_index_identical = torch.from_numpy(np.setdiff1d(np.arange(num_nodes), edges[:, 6])).unsqueeze(
1).repeat(1, 2).to(self.device)
sparse_value_identical = torch.ones(len(sparse_index_identical)).to(self.device)
sparse_index_self = torch.from_numpy(np.unique(edges[:, 6])).unsqueeze(1).repeat(1, 2).to(self.device)
sparse_value_self = self.ratio_update * torch.ones(len(sparse_index_self)).to(self.device)
sparse_index = torch.cat([sparse_index_rep, sparse_index_identical, sparse_index_self], axis=0)
sparse_value = torch.cat([sparse_value_rep, sparse_value_identical, sparse_value_self])
trans_matrix_sparse = torch.sparse.FloatTensor(sparse_index.t(), sparse_value,
torch.Size([num_nodes, num_nodes])).to(self.device)
updated_node_representation = torch.sparse.mm(trans_matrix_sparse, node_representation)
if linear_act:
updated_node_representation = self.act_between_steps(self.linear_between_steps(updated_node_representation))
return updated_node_representation
def _update_node_representation_along_edges_new(self, edges, node_representation, pruned_rel_emb, transition_logits, linear_act=True):
"""
:param edges:
:param memorized_embedding:
:param transition_logits:
:param linear_act: whether apply linear and activation layer after message aggregation
:return:
"""
num_nodes = len(node_representation)
input_rel = pruned_rel_emb# rel
input_ent_1 = node_representation[edges[:, 6],:] # src
input_ent_2 = node_representation[edges[:, 7],:] # obj
out_ent = [[] for i in range(3)]
for i in range(3):
out_ent[i] = self.linear_trans[i](torch.cat([input_rel, input_ent_1, input_ent_2], axis=1))#
out_ent[i] = self.act_between_steps(out_ent[i]) # LeakyReLU
out_ent = sum(out_ent)/3
out_ent = out_ent*transition_logits.unsqueeze(1) #
sparse_index_rep = torch.from_numpy(edges[:, [6]]).to(torch.int64).to(self.device)
sparse_index_identical = torch.from_numpy(np.setdiff1d(np.arange(num_nodes), edges[:, 6])).unsqueeze(1).to(self.device) #
sparse_index = torch.cat([sparse_index_rep, sparse_index_identical], axis=0) #
spply_ent = node_representation[sparse_index_identical[:,0],:] #
sparse_value = torch.cat([out_ent, spply_ent]) #
updated_node_representation = scatter_add(sparse_value, sparse_index.squeeze(), dim=0) #
updated_node_representation = (1 - self.ratio_update)*updated_node_representation + self.ratio_update*node_representation #
if linear_act:
updated_node_representation = self.act_between_steps(self.linear_between_steps(updated_node_representation))
return updated_node_representation
def bypass_forward(self, embedding):
return embedding
def bypass_forward_time_encode(self, embedding):
return embedding
def query_src_update(self, new_query, rel_pass, nodes_src, nodes_obj, \
src_ts_emb_special, src_ts_emb_special_new, obj_ts_emb_special):
'''
function:update the src of each query for each path
# self.linear_src_update = nn.Linear(n_dims_in*5, n_dims_out, bias=True) #MLP
# self.act_src_update = torch.nn.LeakyReLU(negative_slope=0.2) #act
'''
# query update
old_query_src = torch.index_select(self.query_src_ts_emb, dim=0,index=torch.from_numpy(new_query).long().to(self.device)) #query-src
old_query_rel = torch.index_select(self.query_rel_emb, dim=0,index=torch.from_numpy(new_query).long().to(self.device)) #query-rel
query_src_ts_emb = old_query_src + rel_pass #
query_rel_emb = old_query_rel - rel_pass #
return self.bypass_forward(query_src_ts_emb), self.bypass_forward(query_rel_emb) #
def query_rel_update(self, new_query, rel_pass, nodes_pass):
old_query_src = torch.index_select(self.query_src_ts_emb, dim=0,index=torch.from_numpy(new_query[:,3]).long().to(self.device)) #src
old_query_rel = torch.index_select(self.query_rel_emb, dim=0,index=torch.from_numpy(new_query[:,3]).long().to(self.device)) #rel
query_cat_rel = torch.cat([old_query_src, old_query_rel, rel_pass, nodes_pass], axis=1) #concat
return self.linear_rel_update(query_cat_rel)
def cal_query_time_weight(self, query_idx, triple_loss, answer_embed):
'''
function:
'''
query_rel_emb_repeat = torch.index_select(self.query_rel_emb, dim=0,
index=torch.from_numpy(query_idx).long().to(
self.device))#找出问题剩余的关系(关系累计误差)
query_src_emb_repeat = torch.index_select(self.query_src_ts_emb, dim=0,
index=torch.from_numpy(query_idx).long().to(
self.device))#找出问题的剩余的主语 (与实际求得的答案相减,为实体误差累计加上关系误差)
return torch.mm(torch.stack((torch.norm(query_rel_emb_repeat,p=1 ,dim=1) , triple_loss, \
torch.norm(query_src_emb_repeat-answer_embed,p=1 ,dim=1) ), dim=-1), -1*torch.abs(self.step_score_add))
class xERTE(torch.nn.Module):
def __init__(self, ngh_finder, num_entity=None, num_rel=None, timestamps=None, ent_time_set=None, emb_dim: List[int] = None,
DP_num_edges=40, DP_steps=3,
emb_static_ratio=1, diac_embed=False,
node_score_aggregation='sum', ent_score_aggregation='sum', max_attended_edges=20, ratio_update=0,
device='cpu', analysis=False, use_time_embedding=True, loss_margin=0, **kwargs):
"""[summary]
Arguments:
ngh_finder {[type]} -- an instance of NeighborFinder, find neighbors of a node from temporal KG
according to TGAN scheme
Keyword Arguments:
num_entity {[type]} -- [description] (default: {None})
num_rel {[type]} -- [description] (default: {None})
embed_dim {[type]} -- [dimension of ERTKG embedding] (default: {None})
attn_mode {str} -- [currently only prod is supported] (default: {'prod'})
use_time {str} -- [use time embedding] (default: {'time'})
agg_method {str} -- [description] (default: {'attn'})
tgan_num_layers {int} -- [description] (default: {2})
tgan_n_head {int} -- [description] (default: {4})
null_idx {int} -- [description] (default: {0})
drop_out {float} -- [description] (default: {0.1})
seq_len {[type]} -- [description] (default: {None})
max_attended_nodes {int} -- [max number of nodes in attending-from horizon] (default: {20})
ratio_update: new node representation = ratio*self+(1-ratio)\sum{aggregation of neighbors' representation}
device {str} -- [description] (default: {'cpu'})
"""
super(xERTE, self).__init__()
self.smooth_label = 0.01 #0.1 #0.01 #0.001 #
print('label smooth : ', self.smooth_label )
self.step_score_add_all = torch.nn.Parameter(torch.ones(3, 1).float())
self.self_triple_ent_transform = nn.Linear(3*emb_dim[0], emb_dim[0], bias=False)
torch.nn.init.xavier_normal_(self.self_triple_ent_transform.weight)
self.self_triple_ent_transform.to(device)
self.self_act_relu = torch.nn.LeakyReLU()
emb_dim.append(emb_dim[-1]) #
self.DP_num_edges = DP_num_edges #
self.DP_steps = DP_steps #
self.use_time_embedding = use_time_embedding
self.ngh_finder = ngh_finder
self.temporal_embed_dim = [int(emb_dim[_] * 2 / (1 + emb_static_ratio)) for _ in range(DP_steps)] #
self.static_embed_dim = [emb_dim[_] * 2 - self.temporal_embed_dim[_] for _ in range(DP_steps)] #
self.entity_raw_embed = torch.nn.Embedding(num_entity, self.static_embed_dim[0]).cpu() #
nn.init.xavier_normal_(self.entity_raw_embed.weight)
self.relation_raw_embed = torch.nn.Embedding(num_rel + 1, emb_dim[0]).cpu() #
nn.init.xavier_normal_(self.relation_raw_embed.weight)
self.selfloop = num_rel # index of relation "selfloop"
self.att_flow_list = nn.ModuleList([AttentionFlow(emb_dim[_], emb_dim[_ + 1],
node_score_aggregation=node_score_aggregation,
ratio_update=ratio_update, device=device,)
for _ in range(DP_steps+1)])
if use_time_embedding:
self.node_emb_proj = nn.Linear(2 * emb_dim[0], emb_dim[0])
self.time_emb_comp = nn.Linear(2 * emb_dim[0], emb_dim[0])
self.static_time_comb = nn.Linear(2 * emb_dim[0], emb_dim[0])
else:
self.node_emb_proj = nn.Linear(emb_dim[0], emb_dim[0])
nn.init.xavier_normal_(self.node_emb_proj.weight)
self.max_attended_edges = max_attended_edges
self.timestamps_dic = np.zeros(max(timestamps)+1,dtype=np.int32)
self.timestamps_dic[:] = -1
for idx,ss in enumerate(timestamps):
self.timestamps_dic[ss] = idx
self.timestamps_dic = np.zeros((num_entity,max(timestamps)+1),dtype=np.int32)
self.timestamps_former_dic = np.zeros((num_entity,max(timestamps)+1),dtype=np.int32)
self.timestamps_later_dic = np.zeros((num_entity,max(timestamps)+1),dtype=np.int32)
self.timestamps_dic[:] = -1
self.timestamps_former_dic[:] = -1
self.timestamps_later_dic[:] = -1
for idx,ss in enumerate(ent_time_set): #
self.timestamps_dic[ss[0],ss[1]] = idx
if ss[0]==ent_time_set[idx-1][0]: #
self.timestamps_former_dic[ss[0],ss[1]] = idx-1
else:
self.timestamps_former_dic[ss[0],ss[1]] = idx
try:
if ss[0]==ent_time_set[idx+1][0]: #
self.timestamps_later_dic[ss[0],ss[1]] = idx+1
else:
self.timestamps_later_dic[ss[0],ss[1]] = idx
except: #
self.timestamps_later_dic[ss[0],ss[1]] = idx
x_label,y_label = np.where(self.timestamps_dic!=-1)
x_former = -1
for idx in range(len(x_label)):
if x_label[idx]==x_former:#
y_former = y_later
if x_label[idx+1]==x_former: #
y_later = y_label[idx+1]
else:
y_later = max(timestamps)+1 #
else: #
x_former = x_label[idx]
y_former = 0
try:
if x_label[idx+1]==x_label[idx]: #
y_later = y_label[idx+1]
else: #
y_later = max(timestamps)+1
except:
if idx+1==len(y_label): #
y_later = max(timestamps)+1
self.timestamps_dic[x_former, y_former:y_later] = self.timestamps_dic[x_label[idx], y_label[idx]]
x_former = -1
for idx in range(len(x_label)):
if x_label[idx]==x_former: #
y_former = y_later
if x_label[idx+1]==x_former:
y_later = y_label[idx+1]+1
else:
y_later = max(timestamps)+1
else: #
x_former = x_label[idx]
y_former = 0
try:
if x_label[idx+1]==x_label[idx]:
y_later = y_label[idx+1]+1
else:
y_later = max(timestamps)+1
except:
if idx+1==len(y_label):
y_later = max(timestamps)+1
self.timestamps_former_dic[x_former, y_former:y_later] = self.timestamps_dic[x_label[idx], y_label[idx]]
x_former = -1
for idx in range(len(x_label)):
if x_label[idx]==x_former: #
y_former = y_later
if x_label[idx+1]==x_label[idx]:
y_later = y_label[idx]
else:
y_later = max(timestamps)+1
else: #
x_former = x_label[idx]
y_former = 0
try:
if x_label[idx+1]==x_label[idx]:
y_later = y_label[idx]
else:
y_later = max(timestamps)+1
except:
if idx+1==len(y_label):
y_later = max(timestamps)+1
self.timestamps_later_dic[x_former, y_former:y_later] = self.timestamps_dic[x_label[idx], y_label[idx]]
if use_time_embedding:
self.time_encoder = TimeEncode(expand_dim=self.temporal_embed_dim[0], entity_specific=diac_embed,
num_entities=num_entity, num_timestamps= len(ent_time_set), device=device) #
self.time_encoder_ori = TimeEncode_ori(expand_dim=self.temporal_embed_dim[0], entity_specific=diac_embed,
num_entities=num_entity, device=device) #
self.ent_spec_time_embed = diac_embed
self.device = device
self.analysis = analysis
self.ent_score_aggregation = ent_score_aggregation
self.res2query_node_score = nn.Linear(2, 1, bias=False)
torch.nn.init.xavier_normal_(self.res2query_node_score.weight)
self.act_res2query_node = torch.nn.LeakyReLU(negative_slope=0.02)
self.param = Parameter(torch.Tensor((0.3,))) #
self.loss_margin = loss_margin
def set_init(self, src_idx_l, rel_idx_l, cut_time_l, train_flag, target_idx_l):
self.src_idx_l = src_idx_l
self.rel_idx_l = rel_idx_l
self.cut_time_l = cut_time_l
self.sampled_edges_l = []
self.rel_emb_l = []
self.node2index = {(i, src, ts): i for i, (src, rel, ts) in
enumerate(zip(src_idx_l, rel_idx_l, cut_time_l))} # (eg_idx, ent, ts) -> node_idx
self.num_existing_nodes = len(src_idx_l)
query_src_emb = self.get_ent_emb(self.src_idx_l, self.device) #static-query-src
query_rel_emb = self.get_rel_emb(self.rel_idx_l, self.device) # query-rel
if self.use_time_embedding: #
if self.ent_spec_time_embed:
query_ts_emb_ori = self.time_encoder_ori(
torch.zeros(len(self.cut_time_l), 1).to(torch.float32).to(self.device),
entities=self.src_idx_l)
cut_time_l_in = [self.timestamps_dic[self.src_idx_l,self.cut_time_l], \
self.timestamps_former_dic[self.src_idx_l,self.cut_time_l], \
self.timestamps_later_dic[self.src_idx_l,self.cut_time_l]]
query_ts_emb_special, reg_query = self.time_encoder(cut_time_l_in, cut_time_l_in)
query_ts_emb = query_ts_emb_ori.squeeze() #
else:
query_ts_emb = self.time_encoder(
torch.zeros(len(self.cut_time_l), 1).to(torch.float32).to(self.device))
query_ts_emb = torch.squeeze(query_ts_emb, 1) #
# new
query_src_ts_emb = self.self_triple_ent_transform(torch.cat((query_src_emb, query_ts_emb, query_ts_emb_special,), dim=-1))#
query_src_ts_emb = self.self_act_relu(query_src_ts_emb) #
self.query_time_emb = query_ts_emb
else:
query_src_ts_emb = self.node_emb_proj(query_src_emb)
self.att_flow_list[0].set_query_emb(query_src_ts_emb, query_rel_emb) #
if train_flag:
cut_time_l_in = [self.timestamps_dic[target_idx_l,self.cut_time_l], \
self.timestamps_former_dic[target_idx_l,self.cut_time_l], \
self.timestamps_later_dic[target_idx_l,self.cut_time_l]]
_, reg_query_target = self.time_encoder(cut_time_l_in,cut_time_l_in) #
reg_query = torch.cat((reg_query, reg_query_target)) #
self.att_flow_list[0].set_query_reg(reg_query)
else:
self.att_flow_list[0].set_query_reg(reg_query) #
self.att_flow_list[0].set_query_time(query_ts_emb_special)
return query_ts_emb_special
def initialize(self):
"""get initial node (entity+time) embedding and initial node score
Returns:
attending_nodes, np.array -- n_attending_nodes x 3, (eg_idx, entity_id, ts)
attending_node_attention, np,array -- n_attending_nodes, (1,)
memorized_embedding, dict ((entity_id, ts): TGAN_embedding)
"""
eg_idx_l = np.arange(len(self.src_idx_l), dtype=np.int32)
att_score = np.ones_like(self.src_idx_l, dtype=np.float32) * (1 - 1e-8)
attended_nodes = np.stack([eg_idx_l, self.src_idx_l, self.cut_time_l, np.arange(len(self.src_idx_l)), np.arange(len(eg_idx_l))], axis=1) #
visited_nodes_score = torch.from_numpy(att_score).to(self.device) #
visited_nodes = attended_nodes[:,:4] #
visited_node_representation = self.att_flow_list[0].query_src_ts_emb #
visited_nodes_reg = self.att_flow_list[0].reg_query #
return attended_nodes, visited_nodes, visited_nodes_score, visited_node_representation, visited_nodes_reg
def forward(self, sample, train_flag=False, p2o=None):
src_idx_l, rel_idx_l, cut_time_l = sample.src_idx, sample.rel_idx, sample.ts #
if train_flag: #
self.target_loss = self.get_target_loss(src_idx_l, rel_idx_l, cut_time_l,sample.target_idx) #
else:
self.target_loss = self.get_target_loss(src_idx_l, rel_idx_l, cut_time_l,[])
p2o_set = None
query_idx_l = sample.rel_idx #
if p2o!=None:
p2o_set = [] #
for s in rel_idx_l:
p2o_set.append(list(set(p2o[s])))
src_ts_emb_special = self.set_init(src_idx_l, rel_idx_l, cut_time_l, train_flag, sample.target_idx) #
attended_nodes, visited_nodes, visited_node_score, visited_node_representation, visited_nodes_reg = self.initialize()
for step in range(self.DP_steps): #
attended_nodes, visited_nodes, no_pruned_score, no_pruned_edge_l, orig_indices, visited_nodes_reg, src_ts_emb_special, \
query_src_new_emb, query_rel_new_emb, loss_error = \
self._flow(attended_nodes, visited_nodes, visited_node_score, visited_nodes_reg, src_ts_emb_special, step, p2o_set, \
query_idx_l=query_idx_l,cut_time_l=cut_time_l) #
self.att_flow_list[step+1].set_query_emb(query_src_new_emb, query_rel_new_emb) #
query_rel_emb_repeat_pruned = no_pruned_score.unsqueeze(1) #采样后的路径的打分(没有经过裁剪)
error_loss = loss_error
if step==0:
attended_nodes_all = no_pruned_edge_l[:,[0, 3, 4, 7,8]] # attended_nodes 节点
error_loss_all = error_loss
query_rel_emb_repeat_all = query_rel_emb_repeat_pruned #得分
else:
attended_nodes_all = np.concatenate((attended_nodes_all, no_pruned_edge_l[:,[0, 3, 4, 7,8]]),axis=0)
query_rel_emb_repeat_all = torch.cat((query_rel_emb_repeat_all, query_rel_emb_repeat_pruned),dim=0)
if train_flag:
error_loss_all = torch.cat((error_loss_all, error_loss),dim=0) # error_loss
torch.cuda.empty_cache()
logits_rel,que_ent_idx = scatter_max(query_rel_emb_repeat_all.squeeze(), torch.LongTensor(attended_nodes_all[:, 3]).to(self.device))
que_ent_idx_edx = torch.where(que_ent_idx < len(attended_nodes_all))[0] #找出存在值的索引
logits_rel = logits_rel[que_ent_idx_edx] #找出存在的得分
attended_nodes_all = attended_nodes_all[np.array(que_ent_idx[que_ent_idx_edx].cpu()),:4] #找出的index
logits_rel_idx = attended_nodes_all[:, 0] # np.concatenate((attended_nodes_all[:, 0],np.array([128])))[que_ent_idx.cpu()] #记录对应的query idx(设定一个不存在的值)
logits_rel_idx_tensor = torch.LongTensor(logits_rel_idx).to(self.device)
logits_rel_mean = scatter_mean(logits_rel, logits_rel_idx_tensor) #(按照query索引)求各个batch的均值
logits_rel_max, _ = scatter_max(logits_rel, logits_rel_idx_tensor) #求各个batch的最大值
# logits_rel_num = scatter_sum(torch.ones_like(logits_rel), torch.LongTensor(logits_rel_idx).to(self.device))
variance = torch.pow(logits_rel-logits_rel_mean[logits_rel_idx],2) #误差的平方
variance = scatter_mean(variance, logits_rel_idx_tensor) #误差平方的均值 => 作为方差
logits_rel = (logits_rel-logits_rel_max[logits_rel_idx])/ torch.pow(variance[logits_rel_idx]+1e-30,0.5) #减去最大值 / 方差
logits_rel = scatter_softmax(logits_rel, logits_rel_idx_tensor) #softmax,
torch.cuda.empty_cache()
entity_att_score, entities = self.get_entity_attn_score(logits_rel, attended_nodes_all) #
if train_flag:
return entity_att_score, entities , torch.mean(visited_nodes_reg) , torch.mean(error_loss_all)# train
else:
return entity_att_score, entities , torch.mean(visited_nodes_reg) # train
def _flow(self, attended_nodes, visited_nodes, visited_node_score, visited_nodes_reg, src_ts_emb_special, \
step, p2o, tc=None, query_idx_l=None, cut_time_l=None):
"""[summary]
Arguments:
visited_nodes {numpy.array} -- num_nodes_visited x 4 (eg_idx, entity_id, ts, node_idx), dtype: numpy.int32, sort (eg_idx, ts, entity_id)
all nodes visited during the expansion
visited_node_score {Tensor} -- num_nodes_visited, dtype: torch.float32
visited_node_representation {Tensor} -- num_nodes_visited x emb_dim_l[step]
visited_node_score[node_idx] is the prediction score of node_idx
visited_node_representation[node_idx] is the hidden representation of node_idx
return:
pruned_node {numpy.array} -- num_nodes_ x 4 (eg_idx, entity_id, ts, node_idx) sorted by (eg_idx, ts, entity_id)
new_node_score {Tensor} -- new num_nodes_visited
so that new_node_score[i] is the node prediction score of??
updated_visited_node_representation: Tensor -- num_nodes_visited x emb_dim_l[step+1]
"""
sampled_edges, new_sampled_nodes = self._get_sampled_edges(attended_nodes, cut_time_l,
num_neighbors=self.DP_num_edges[step],
step=step,
add_self_loop=True, tc=tc,p2o=p2o,query_idx_l=query_idx_l)#
self.sampled_edges_l.append(sampled_edges) #
sample_loss, loss_error, sample_embedding = self.get_query_answer_loss(self.target_loss, sampled_edges[:,3], sampled_edges[:,4], sampled_edges[:,0])
rel_emb = self.get_rel_emb(sampled_edges[:, 5], self.device) #
self.rel_emb_l.append(rel_emb) #
src_ts_emb_special_former = src_ts_emb_special[sampled_edges[:, 9]]
src_ts_emb_special_new, obj_ts_emb_special , ent_time_reg = self.get_time_emb(sampled_edges[:,1], sampled_edges[:,4], sampled_edges[:,3], \
self.target_loss[3][sampled_edges[:, 0]]) #
src_ts_emb_special_set = [src_ts_emb_special_former, src_ts_emb_special_new, obj_ts_emb_special , ent_time_reg]
visited_node_representation = self.get_visited_node_representation(self.sampled_edges_l[-1], cut_time_l[self.sampled_edges_l[-1][:,0]], src_ts_emb_special_set)
# pruned
no_pruned_score,visited_nodes_reg, pruned_edges, orig_indices = \
self.att_flow_list[step](self.step_score_add_all, visited_node_score, visited_nodes_reg, src_ts_emb_special_set, sample_loss,
selected_edges_l=self.sampled_edges_l,
visited_node_representation=visited_node_representation,
rel_emb_l=self.rel_emb_l,
max_edges=self.max_attended_edges, tc=tc)#
assert len(pruned_edges) == len(orig_indices) #
no_pruned_edge_l = sampled_edges
self.sampled_edges_l[-1] = pruned_edges
self.rel_emb_l[-1] = self.rel_emb_l[-1][orig_indices]
#
src_ts_emb_special_former = src_ts_emb_special_former[orig_indices]
src_ts_emb_special_new = src_ts_emb_special_new[orig_indices]
obj_ts_emb_special = obj_ts_emb_special[orig_indices]
# ent_time_reg = ent_time_reg[orig_indices]
visited_nodes_reg = torch.cat([visited_nodes_reg, ent_time_reg], axis=0)
query_index = np.arange(len(pruned_edges)) #
updated_attended_nodes = np.concatenate([pruned_edges[:, [0, 3, 4, 7]], query_index.reshape(-1,1)], axis=1) #
rel_pass = self.rel_emb_l[-1] #
nodes_src = visited_node_representation[0][orig_indices] #
nodes_obj = visited_node_representation[2][orig_indices] #
query_src_new_emb, query_rel_new_emb \
= self.att_flow_list[step].query_src_update(pruned_edges[:, 8], rel_pass, nodes_src, nodes_obj, \
src_ts_emb_special_former, src_ts_emb_special_new, obj_ts_emb_special) #
return updated_attended_nodes, visited_nodes, no_pruned_score, no_pruned_edge_l, orig_indices, visited_nodes_reg, obj_ts_emb_special, \
query_src_new_emb, query_rel_new_emb, loss_error
def loss(self, entity_att_score, entities, target_idx_l, batch_size, gradient_iters_per_update=1, loss_fn='BCE'):
one_hot_label = torch.from_numpy(
np.array([int(v == target_idx_l[eg_idx]) for eg_idx, v in entities], dtype=np.float32)).to(self.device) #
label_average = 1/scatter_sum(torch.ones_like(one_hot_label), torch.LongTensor(entities[:,0]).to(self.device))[entities[:,0]]
try:
assert gradient_iters_per_update > 0
one_hot_label = one_hot_label*(1-self.smooth_label) + self.smooth_label*label_average #inv Label smooth
if loss_fn == 'BCE':
if gradient_iters_per_update == 1:
loss = torch.nn.BCELoss()(entity_att_score, one_hot_label)
else:
loss = torch.nn.BCELoss(reduction='sum')(entity_att_score, one_hot_label)
loss /= gradient_iters_per_update * batch_size
else:
# CE has problems
if gradient_iters_per_update == 1:
loss = torch.nn.NLLLoss()(entity_att_score, one_hot_label)
else:
loss = torch.nn.NLLLoss(reduction='sum')(entity_att_score, one_hot_label)
loss /= gradient_iters_per_update * batch_size
except:
print(entity_att_score)
entity_att_score_np = entity_att_score.cpu().detach().numpy()
print("all entity score smaller than 1:", all(entity_att_score_np < 1))
print("all entity score greater than 0:", all(entity_att_score_np > 0))
raise ValueError("Check if entity score in (0,1)")
return loss
def get_time_emb(self, src_idx_l, cut_time_l, obj_idx_l, query_base_time):
query_base_time_in = [self.timestamps_dic[src_idx_l,query_base_time], \
self.timestamps_former_dic[src_idx_l,query_base_time], \
self.timestamps_later_dic[src_idx_l,query_base_time] ]
cut_time_l_in = [self.timestamps_dic[src_idx_l,cut_time_l], \
self.timestamps_former_dic[src_idx_l,cut_time_l], \
self.timestamps_later_dic[src_idx_l,cut_time_l] ]
src_hidden_time, src_reg = self.time_encoder(cut_time_l_in, query_base_time_in) #
cut_time_l_in = [self.timestamps_dic[obj_idx_l,cut_time_l], \
self.timestamps_former_dic[obj_idx_l,cut_time_l], \
self.timestamps_later_dic[obj_idx_l,cut_time_l] ]
query_base_time_in = [self.timestamps_dic[obj_idx_l,query_base_time], \
self.timestamps_former_dic[obj_idx_l,query_base_time], \
self.timestamps_later_dic[obj_idx_l,query_base_time] ]
obj_hidden_time, obj_reg = self.time_encoder(cut_time_l_in, query_base_time_in) #
return src_hidden_time ,obj_hidden_time, torch.cat([src_reg, obj_reg],dim=-1) #
def get_time_emb_2(self,obj_idx_l, obj_time, base_time):
cut_time_l_in = [self.timestamps_dic[obj_idx_l,obj_time], \
self.timestamps_former_dic[obj_idx_l,obj_time], \
self.timestamps_later_dic[obj_idx_l,obj_time] ]
cut_time_l_base = [self.timestamps_dic[obj_idx_l,base_time], \
self.timestamps_former_dic[obj_idx_l,base_time], \
self.timestamps_later_dic[obj_idx_l,base_time] ]
obj_hidden_time, obj_reg = self.time_encoder(cut_time_l_in,cut_time_l_base) #
return obj_hidden_time #
def get_target_loss(self, src_idx, rel_idx, cut_time_l,target_idx):
#
cut_time_l_ori = cut_time_l - cut_time_l #query时间误差
src_time_ori = self.time_encoder_ori(torch.from_numpy(cut_time_l_ori[:, np.newaxis]).to(self.device),
entities=src_idx).squeeze()
src_node = self.get_ent_emb(src_idx, self.device)
src_hidden_time = self.get_time_emb_2(src_idx, cut_time_l, cut_time_l) #target-time
# rel
rel_emb = self.get_rel_emb(rel_idx, self.device)
# new
query_input_emb = self.self_triple_ent_transform(torch.cat((src_node, src_time_ori, src_hidden_time), dim=-1))
query_input_emb = self.self_act_relu(query_input_emb) #src的表征
# answer
if len(target_idx)>0:
obj_time_ori = self.time_encoder_ori(torch.from_numpy(cut_time_l_ori[:, np.newaxis]).to(self.device),
entities=target_idx).squeeze()
obj_node = self.get_ent_emb(target_idx, self.device)
obj_hidden_time = self.get_time_emb_2(target_idx, cut_time_l, cut_time_l)
# new
answer_embed = self.self_triple_ent_transform(torch.cat((obj_node, obj_time_ori, obj_hidden_time), dim=-1))
answer_embed = self.self_act_relu(answer_embed)
triple_loss = query_input_emb + rel_emb- answer_embed
triple_loss = torch.norm(triple_loss,p=1 ,dim=1)
else:
triple_loss = []
return triple_loss, query_input_emb, rel_emb, cut_time_l #, src_hidden_time_1 # torch.cat((answer_embed, answer_ts_emb, obj_hidden_time), dim=-1)
def get_query_answer_loss(self, target_loss, obj_idx, obj_time, query_idx):
cut_time_l_ori = obj_time - target_loss[3][query_idx] #obj时间误差
obj_time_ori = self.time_encoder_ori(torch.from_numpy(cut_time_l_ori[:, np.newaxis]).to(self.device),
entities=obj_idx).squeeze()
obj_node = self.get_ent_emb(obj_idx, self.device)
# rel
obj_hidden_time = self.get_time_emb_2(obj_idx, obj_time, target_loss[3][query_idx])
# 5-2-12
rel_emb = target_loss[1][query_idx] #query rel
query_input_emb = target_loss[2][query_idx] #query src
# new
answer_embed = self.self_triple_ent_transform(torch.cat((obj_node, obj_time_ori, obj_hidden_time), dim=-1))
answer_embed = self.self_act_relu(answer_embed)
triple_loss = torch.norm(query_input_emb + rel_emb- answer_embed , p=1 ,dim=1)