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test_p2p_linkM_position.py
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test_p2p_linkM_position.py
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# _*_coding:utf-8 _*_
# @Time : 2022/7/16
# @Author : Lin
# node feature: position
# edge feature: link
import pickle
import torch
import torch.utils.data as Data
import torch.nn as nn
import numpy as np
import math
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
n_iter = 15
batch_size = 1
d_Q = 64
d_V = 64
num_of_node = 45
d_feature = 3
d_model = 256
n_layers = 6
n_head = 8
d_ff = 256
def position_normal(position):
position[:,0]=position[:,0]/max(position[:,0])
position[:, 1] = position[:,1] / max(position[:, 1])
position[:, 2] = position[:,2] / max(position[:, 2])
return position
def read_data(filename):
f = open(filename, 'rb')
data = pickle.load(f)
linkM = data['linkM']
position = data['position']
position=position_normal(position)
order=data['order']
output_pos=position[order.long()]
return linkM, position, output_pos,order
def shuffle_read(filename): #filename='shuffle.txt'
f=open(filename, 'r')
a=f.read().split(',')
sh_list=[]
for i in a:
sh_list.append(int(i))
new_list = list(reversed(sh_list))
return new_list
def make_data(n_iter):
filename_prefix = 'data/LEGO'
filename_suffix = '.data'
linkM = torch.empty(0, num_of_node, num_of_node)
position_enc_input = torch.empty(0, num_of_node, d_feature)
position_dec_input = torch.empty(0, num_of_node, d_feature)
position_dec_output = torch.empty(0, num_of_node, d_feature)
order=torch.empty(0,num_of_node).long()
for i in range(n_iter):
test=shuffle_read('shuffle.txt')
filename = filename_prefix + str(test[i]-1) + filename_suffix
linkM_item, position_item, output_pos_item, order_item = read_data(filename)
padding_len = num_of_node - linkM_item.shape[0]
order_added = np.ones([1,num_of_node])*-1
order_added[0,0:len(order_item)] = order_item
order_added=torch.tensor(order_added)
order_added = order_added
order=torch.cat([order,order_added.long()],dim=0)
linkM_right = np.zeros([linkM_item.shape[0], padding_len])
linkM_item = np.concatenate([linkM_item, linkM_right], axis=1)
linkM_bottom = np.zeros([padding_len, linkM_item.shape[1]])
linkM_item = np.concatenate([linkM_item, linkM_bottom], axis=0)
linkM = torch.cat([linkM, torch.tensor(linkM_item).unsqueeze(0)], dim=0)
position_added = torch.cat([position_item, torch.ones(padding_len, d_feature) * -1], dim=0)
position_enc_input = torch.cat([position_enc_input, position_added.float().unsqueeze(0)], dim=0)
position_added = torch.cat([torch.ones(1, d_feature) * -1, output_pos_item], dim=0)
position_added = torch.cat([position_added, torch.ones(padding_len - 1, d_feature) * -1], dim=0)
position_dec_input = torch.cat([position_dec_input, torch.tensor(position_added).float().unsqueeze(0)], dim=0)
position_added = torch.cat([output_pos_item, torch.ones(padding_len, d_feature) * -1], dim=0)
position_dec_output = torch.cat([position_dec_output, torch.tensor(position_added).float().unsqueeze(0)], dim=0)
return linkM, position_enc_input, position_dec_input, position_dec_output,order
class Mydataset(Data.Dataset):
def __init__(self, linkM, position_graph, position_seq, position_real, order):
super(Mydataset, self).__init__()
self.linkM = linkM.to(device)
self.position_graph = position_graph.to(device)
self.position_seq = position_seq.to(device)
self.position_real = position_real.to(device)
self.order=order.to(device)
def __len__(self):
return self.linkM.shape[0]
def __getitem__(self, idx):
return self.linkM[idx].to(device), self.position_graph[idx].to(device), self.position_seq[idx].to(device), \
self.position_real[idx].to(device), self.order[idx].to(device)
class position2embedding(nn.Module):
def __init__(self):
super(position2embedding, self).__init__()
self.fc = nn.Linear(d_feature, d_model)
def forward(self, position):
embedding = self.fc(position)
return embedding.to(device)
class ScaledDotProductAttention(nn.Module):
def __init__(self):
super(ScaledDotProductAttention, self).__init__()
def forward(self, Q, K, V, attn_mask):
score = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_Q)
score.masked_fill_(attn_mask, -1e9)
attn = nn.Softmax(-1)(score)
attn.masked_fill_(attn_mask, 0)
attn = torch.tensor(attn, requires_grad=True)
context = torch.matmul(attn, V)
return context.to(device)
class MultiHeadAttention(nn.Module):
def __init__(self):
super(MultiHeadAttention, self).__init__()
self.W_Q = nn.Linear(d_model, d_Q * n_head, bias=False)
self.W_K = nn.Linear(d_model, d_Q * n_head, bias=False)
self.W_V = nn.Linear(d_model, d_V * n_head, bias=False)
self.fc = nn.Linear(d_V * n_head, d_model, bias=False)
def forward(self, input_Q, input_K, input_V, attn_mask):
residual, batch_size = input_Q, input_Q.shape[0]
Q = self.W_Q(input_Q).view(batch_size, -1, n_head, d_Q).transpose(1, 2)
K = self.W_K(input_K).view(batch_size, -1, n_head, d_Q).transpose(1, 2)
V = self.W_V(input_V).view(batch_size, -1, n_head, d_V).transpose(1, 2)
attn_mask = attn_mask.unsqueeze(1).repeat(1, n_head, 1, 1)
context = ScaledDotProductAttention()(Q, K, V, attn_mask)
context = context.transpose(1, 2).reshape(batch_size, -1, d_Q * n_head)
out_put = self.fc(context)
return nn.LayerNorm(d_model).to(device)(out_put + residual)
class PoswiseFeedForwardNet(nn.Module):
def __init__(self):
super(PoswiseFeedForwardNet, self).__init__()
self.fc = nn.Sequential(
nn.Linear(d_model, d_ff, bias=False),
nn.ReLU(),
nn.Linear(d_ff, d_model, bias=False)
)
def forward(self, inputs):
residual = inputs
output = self.fc(inputs)
return nn.LayerNorm(d_model).to(device)(output + residual)
class EncoderLayer(nn.Module):
def __init__(self):
super(EncoderLayer, self).__init__()
self.enc_self_attn = MultiHeadAttention()
self.pos_ffn = PoswiseFeedForwardNet()
def forward(self, enc_embedding, enc_graph_attn_mask):
enc_embedding_out = self.enc_self_attn(enc_embedding, enc_embedding, enc_embedding,
enc_graph_attn_mask)
enc_embedding_out = self.pos_ffn(enc_embedding_out)
return enc_embedding_out.to(device)
def graph_attn_mask(linkM):
mask = linkM.data.eq(0)
return mask.to(device)
def get_attn_subsequence_mask(dec_embedding):
attn_shape = [dec_embedding.size(0), dec_embedding.size(1), dec_embedding.size(1)]
subsequence_mask = np.triu(np.ones(attn_shape), k=1)
subsequence_mask = torch.from_numpy(subsequence_mask).byte()
return subsequence_mask.to(device)
def get_attn_pad_mask(position_1, position_2):
len_q = position_1.shape[1]
len_k = position_2.shape[1]
a = position_1[:, :, 1].data.eq(-1).unsqueeze(1).transpose(-2, -1)
b = position_2[:, :, 1].data.eq(-1).unsqueeze(1)
a = a.repeat(1, 1, len_k)
b = b.repeat(1, len_q, 1)
return (a | b).to(device)
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.pos2emb = position2embedding()
self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])
def forward(self, position, linkM):
enc_embeddings = self.pos2emb(position)
enc_graph_attn_mask = graph_attn_mask(linkM)
for layer in self.layers:
enc_embeddings = layer(enc_embeddings, enc_graph_attn_mask)
return enc_embeddings.to(device)
class DecoderLayer(nn.Module):
def __init__(self):
super(DecoderLayer, self).__init__()
self.dec_self_attn = MultiHeadAttention()
self.dec_enc_attn = MultiHeadAttention()
self.pos_ffn = PoswiseFeedForwardNet()
def forward(self, dec_embedding, enc_embedding, dec_self_attn_mask, dec_enc_attn_mask):
dec_embedding = self.dec_self_attn(dec_embedding, dec_embedding, dec_embedding, dec_self_attn_mask)
dec_embedding = self.dec_enc_attn(dec_embedding, enc_embedding, enc_embedding, dec_enc_attn_mask)
dec_embedding = self.pos_ffn(dec_embedding)
return dec_embedding.to(device)
class PositionEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=5000):
super(PositionEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position_eco = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position_eco * div_term)
pe[:, 1::2] = torch.cos(position_eco * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:x.size(0), :]
return self.dropout(x)
class Decoder(nn.Module):
def __init__(self):
super(Decoder, self).__init__()
self.pos2emb = position2embedding()
self.pos_enc = PositionEncoding(d_model)
self.layers = nn.ModuleList([DecoderLayer() for _ in range(n_layers)])
def forward(self, position_seq, position_graph, enc_embedding):
dec_embedding = self.pos2emb(position_seq)
dec_embedding = self.pos_enc(dec_embedding.transpose(0, 1)).transpose(0, 1)
dec_self_attn_subsequence_mask = get_attn_subsequence_mask(dec_embedding)
dec_self_attn_pad_mask = get_attn_pad_mask(position_seq, position_seq)
dec_self_attn_mask = dec_self_attn_subsequence_mask | dec_self_attn_pad_mask
dec_enc_attn_mask = get_attn_pad_mask(position_seq, position_graph)
for layer in self.layers:
dec_embedding = layer(dec_embedding, enc_embedding, dec_self_attn_mask, dec_enc_attn_mask)
return dec_embedding.to(device)
class myGraphTransformer(nn.Module):
def __init__(self):
super(myGraphTransformer, self).__init__()
self.encoder = Encoder()
self.decoder = Decoder()
self.embedding2position = nn.Linear(d_model, d_feature)
def forward(self, position_graph, linkM, position_seq):
if hasattr(torch.cuda, 'empty_cache'):
torch.cuda.empty_cache()
enc_embedding = self.encoder(position_graph, linkM)
dec_embedding = self.decoder(position_seq, position_graph, enc_embedding)
position_output = self.embedding2position(dec_embedding)
return position_output.to(device)
def criterion(position_output, position_real):
loss = 0
for i in range(position_output.shape[0]):
padding_loc = torch.nonzero(position_real[i] == -1)
len_of_dec_input = padding_loc[0, 0]
diff = position_output[i, 0:len_of_dec_input, :] - position_real[i, 0:len_of_dec_input, :]
loss = loss + diff.pow(2).sum() / position_output.shape[0] / len_of_dec_input
return loss
def location_square_deviation(lst_1, lst_2=None):
n = len(lst_1)
lst = lst_1.copy()
if lst_2 is not None:
if n != len(lst_2):
return False
for i in range(n):
lst[lst_1.index(lst_2[i])] = i
s = 0
for i in range(n):
s += (lst[i]-i) ** 2
s= 3*s/n/(n-1)/(n+1)
return s
def kendall_tua(a,b):
Lens = len(a)
ties_onlyin_x = 0
ties_onlyin_y = 0
con_pair = 0
dis_pair = 0
for i in range(Lens-1):
for j in range(i+1,Lens):
test_tying_x = np.sign(a[i] - a[j])
test_tying_y = np.sign(b[i] - b[j])
panduan =test_tying_x * test_tying_y
if panduan == 1:
con_pair +=1
elif panduan == -1:
dis_pair +=1
if test_tying_y ==0 and test_tying_x != 0:
ties_onlyin_y += 1
elif test_tying_x == 0 and test_tying_y !=0:
ties_onlyin_x += 1
Kendall_tua = (con_pair - dis_pair)/np.sqrt((con_pair + dis_pair + ties_onlyin_x)*(dis_pair +con_pair + ties_onlyin_y))
return Kendall_tua
model = myGraphTransformer().to(device)
path = 'saved_model/p2p_linkM_position.pickle'
model.load_state_dict({k.replace('module.',''):v for k,v in torch.load(path).items()})
model.eval()
linkM, position_graph, position_seq, position_real,order = make_data(n_iter)
loader = Data.DataLoader(Mydataset(linkM, position_graph, position_seq, position_real,order), batch_size, True)
def top_k_in_masked(k, predicted_next_position, masked_kk, position_graph):
position_graph = position_graph.squeeze(0)
predicted_next_position = predicted_next_position.unsqueeze(0).repeat([position_graph.shape[0], 1])
diff = position_graph - predicted_next_position
mask_selected_padding=torch.ones(1,position_graph.shape[0]).to(device)
mask_selected_padding[0,masked_kk.long()]=1e9
EUdist2 = torch.pow(diff, 2).sum(-1).squeeze(0) * mask_selected_padding*(-1)
top_k_values,top_k_indices=torch.topk(EUdist2,k)
return top_k_values, top_k_indices
def ksqu2k(loss_cum, k):
A = torch.reshape(loss_cum,(-1,))
top_value, top_idx = torch.topk(A, k)
div = torch.div(top_idx, k, rounding_mode='floor')
mod = top_idx % k
loss_cum_new=torch.zeros_like(loss_cum)
for i in range(k):
loss_cum_new[i,:]=top_value[i]
return loss_cum_new, div, mod
def renew_masked_ID(masked_ID, index_k, row, col, k):
masked_ID_new=torch.zeros_like(masked_ID)
temp=torch.zeros([k,1])
for i in range(k):
masked_ID_new[i,:]=masked_ID[row[i],:]
temp[i,0] = index_k[row[i], col[i]]
masked_ID_new=torch.cat([masked_ID_new, temp],dim=1)
return masked_ID_new
for linkM, position_graph, position_seq, position_real, order in loader:
padding_loc = torch.nonzero(position_graph == -1)
realnum_of_node=padding_loc[0,1]
k=6
loss_cum=torch.zeros(k,k).to(device)
index_k=torch.zeros(k,k).to(device)
position_seq[0, 1: -1, :] = -1
position = model(position_graph, linkM, position_seq)
predicted_next_position=position[0,0,:]
# beam search
top_k_values, top_k_indices=top_k_in_masked(k, predicted_next_position,torch.tensor([]), position_graph)
position_seq = position_seq.repeat(k, 1, 1)
for kk in range(k):
loss_cum[kk,:] = top_k_values[0,kk]
position_seq[kk,1,:]=position_graph[0,top_k_indices[0,kk],:].squeeze(0)
masked_ID=torch.tensor([[top_k_indices[0,0]],[top_k_indices[0,1]],[top_k_indices[0,2]], [top_k_indices[0,3]],[top_k_indices[0,4]],[top_k_indices[0,5]]] )
for i in range(2,realnum_of_node+1):
for kk in range(k):
position = model(position_graph, linkM, position_seq[kk,:,:].unsqueeze(0))
predicted_next_position=position[0,i-1,:]
top_k_values, top_k_indices=top_k_in_masked(k, predicted_next_position, masked_ID[kk,:], position_graph)
loss_cum[kk,:] = loss_cum[kk,:] + top_k_values
index_k[kk,:]=top_k_indices
loss_cum, row, col=ksqu2k(loss_cum, k)
masked_ID=renew_masked_ID(masked_ID, index_k, row, col,k)
for kk in range(k):
next_ID=masked_ID[kk][-1].long()
position_seq[kk,i,:]=position_graph[0,next_ID,:]
idx_max=torch.argmax(loss_cum[:,0])
A=masked_ID[idx_max,:].cpu().tolist()
B=order.squeeze(0)[0:realnum_of_node].cpu().tolist()
lsd=location_square_deviation(A, B)
A_number=np.linspace(0,len(A)-1,len(A)).tolist()
C=[]
for i in range(len(A)):
C.append(A.index(B[i])+1)
ken=kendall_tua(A_number,C)
txt_file = open('test/p2p_linkM_position/lsd.txt', "a", encoding="utf-8")
txt_file.write(str(lsd))
txt_file.write("\n")
txt_file = open('test/p2p_linkM_position/tua.txt', "a", encoding="utf-8")
txt_file.write(str(ken))
txt_file.write("\n")