/
backup_tool.py
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/
backup_tool.py
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import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
def Evaluation(Y_hat,Y):
#print(Y_hat)
#print(Y)
from sklearn.metrics import f1_score
TP,FP,FN,TN = 0,0,0,0
for i in range(len(Y_hat)):
if int(Y_hat[i]) == 1 and int(Y[i]) ==1:
TP+=1
elif int(Y_hat[i]) == 1 and int(Y[i]) ==0:
FP +=1
elif int(Y_hat[i]) == 0 and int(Y[i]) ==1:
FN +=1
elif int(Y_hat[i]) == 0 and int(Y[i]) ==0:
TN +=1
else:
print('[ERROR]')
Accuracy = (TP+TN+1)/(TP+FP+FN+TN+1)
Precision = (TP+1)/(TP+FP+1)
Recall = (TP+1)/(TP+FN+1)
F1 = f1_score(Y, Y_hat)
print('Accuracy:',Accuracy)
print('Precision:',Precision)
print('Recall:',Recall)
print('F1:',F1)
def batch_str2batch_tensor(batch_X,batch_Y,Sent_BERT_model):
batch_Y_tensor = torch.tensor(batch_Y,dtype=torch.long).cuda()
batch_X_tensor = list()
for i in range(batch_X.size()[0]):
batch_X_tensor.append([torch.tensor(batch_X[i,0,:]).cuda(),torch.tensor(batch_X[i,1,:]).cuda()])
return batch_X_tensor,batch_Y_tensor
def Train_Eval_Process_Layer(train_X,train_Y,test_X,test_Y,Sent_BERT_model):
# RetaGNN + Self Attention
# train_X = [batch,batch,...] where batch = [batch_num,L,dim]_tensor
# train_Y = [batch,batch,...] where batch = [batch_num,1]_tensor
# test_X = [one_batch]where one_batch = [1,L,dim]_tensor
# test_Y = list
import pyprind
import pickle
epoch_num = 15
model = NLI_model(input_dim=256).cuda()
#model.aux_logits = False
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()
for epoch_ in range(epoch_num):
model.train()
for i in pyprind.prog_bar(range(len(train_X))):
batch_X,batch_Y = train_X[i],train_Y[i]
batch_X = torch.tensor(batch_X).cuda()
batch_Y = torch.tensor(batch_Y).cuda()
#batch_X,batch_Y = batch_str2batch_tensor(batch_X,batch_Y,Sent_BERT_model)
batch_Y_hat,_ = model(batch_X)
loss = criterion(batch_Y_hat, batch_Y)#.float())
#loss = F.cross_entropy(batch_Y_hat, batch_Y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
#print('loss:',loss)
model.eval()
pred_Y_list,true_Y_list = list(),list()
for i in range(len(test_X)):
batch_X,batch_Y = train_X[i],train_Y[i]
batch_X = torch.tensor(batch_X).cuda()
batch_Y = torch.tensor(batch_Y).cuda()
#batch_X,batch_Y = batch_str2batch_tensor(batch_X,batch_Y,Sent_BERT_model)
pred_Y,_ = model(batch_X)
pred_Y = pred_Y.argmax(dim=1)
pred_Y = list(pred_Y.cpu().data.numpy())
true_Y = list(batch_Y.cpu().data.numpy())
pred_Y_list += pred_Y
true_Y_list += true_Y
Evaluation(pred_Y_list,true_Y_list)
torch.save(model.state_dict(), 'demo2.pkl')
class NLI_model(nn.Module):
def __init__(self,input_dim):
super(NLI_model,self).__init__()
hidden_dim = input_dim
self.input_dim = input_dim
self.nli_embedding = nn.Linear(input_dim*3, hidden_dim)
self.fc = nn.Linear(input_dim, 3)
self.m = nn.LogSoftmax()
def forward(self,s1_s2):
s1,s2 = s1_s2[:,0,:],s1_s2[:,1,:]
s1_minus_s2_abs = torch.abs(s1-s2)
s1_2 = torch.cat([s1,s2,s1_minus_s2_abs],1)
embedding = self.nli_embedding(s1_2)
pred_Y = self.fc(embedding)
return pred_Y,embedding
class Cross_NLI_Model(nn.Module):
def __init__(self):
super(Cross_NLI_Model,self).__init__()
input_dim = 256
self.input_dim = 256
self.nli_model = NLI_model(input_dim=256).cuda()
self.D2D_FC = nn.Linear(input_dim,input_dim)
self.softmax = nn.Softmax(dim=-1)
self.fc = nn.Linear(input_dim,1)
self.sigmoid_layer = nn.Sigmoid()
def forward(self,X,sent_embedding):
sent_embedding = nn.Embedding.from_pretrained(sent_embedding)
X = sent_embedding(X) #bz,l,d,2
pair_embedding_list = list()
for i in range(X.size()[0]):
X_i = X[i,:,:,:]
_,pair_embedding = self.nli_model(X_i) #l ,d
pair_embedding = pair_embedding.view(1,-1,self.input_dim)
pair_embedding_list.append(pair_embedding)
pair_embedding_tensor = torch.cat(pair_embedding_list,0)
#'bz_pair_embedding_torch: torch.Size([32, 1225, 256])'
attention = self.softmax(torch.sum(self.D2D_FC(pair_embedding_tensor),-1))
#'attention: torch.Size([32, 1225])'
out = torch.sum(torch.matmul(attention,pair_embedding_tensor),1)
#'out: torch.Size([32, 256])'
pred_Y = self.sigmoid_layer(self.fc(out))
return pred_Y
def Train_Eval_Process_Layer_for_CROSS_NLI(train_X,train_Y,test_X,test_Y):
import pyprind
import pickle
epoch_num = 15
model = Cross_NLI_Model().cuda()
optimizer = optim.Adam(model.parameters())
criterion = nn.BCELoss()
for epoch_ in range(epoch_num):
model.train()
for i in pyprind.prog_bar(range(len(train_X))):
batch_X,batch_Y = train_X[i],train_Y[i]
bz_L_2,sent_embedding = batch_X[0],batch_X[1]
bz_L_2 = torch.tensor(bz_L_2).cuda()
sent_embedding = torch.FloatTensor(sent_embedding).cuda()
batch_Y = torch.tensor(batch_Y).cuda()
batch_Y_hat = model(bz_L_2,sent_embedding)
loss = criterion(batch_Y_hat, batch_Y.float())
optimizer.zero_grad()
loss.backward()
optimizer.step()
#print('loss:',loss)
return model
class One_Sent2Other_Sent(nn.Module):
def __init__(self):
super(One_Sent2Other_Sent,self).__init__()
input_dim = 256
self.input_dim = 256
#self.nli_model = NLI_model(input_dim=256).cuda()
self.D2D_FC = nn.Linear(input_dim,input_dim)
self.softmax = nn.Softmax(dim=-1)
self.single_sent2other_sent_FCL = nn.Linear(input_dim*3,input_dim)
self.sigmoid_layer = nn.Sigmoid()
self.relu = nn.ReLU()
self.rnn = nn.LSTM(input_dim, input_dim)
self.attention_weight = nn.Linear(self.input_dim, 1)
self.fc = nn.Linear(self.input_dim, 1)
def Attention_Layer(self,hidden_vec):
self.attn = self.attention_weight(hidden_vec)
out = torch.sum(hidden_vec * self.attn,1)
return out
def forward(self,X):
single_sent2other_info = list()
for i in range(X.size()[0]):
X_i = X[i,:]
print(X_i)
X_i = X_i.view(1,-1)
if i != 0 and i != int(X.size()[0] -1):
X1 = X[:i,:].view(-1,256)
X2 = X[i+1:,:].view(-1,256)
X_other = torch.cat([X1,X2],0)
elif i == int(X.size()[0] -1):
X_other = X[:i-1,:]
elif i == 0 :
X_other = X[i+1:,:]
else:
print('[ERROR]: nan index exist.')
X_other_len = X_other.size()[0]
if X_other_len !=0:
X_i_long_tensor = torch.cat([X_i for j in range(X_other_len)],0)
X_i_ohter_residual = torch.cat([X_i_long_tensor,X_other,torch.abs(X_i_long_tensor-X_other)],1)
X_i_ohter_residual = self.relu(self.single_sent2other_sent_FCL(X_i_ohter_residual))
X_i_ohter_residual = torch.max(X_i_ohter_residual,0)[0].view(1,-1)
single_sent2other_info.append(X_i_ohter_residual)
single_sent2other_info = torch.cat(single_sent2other_info,0).view(1,-1,self.input_dim)
hidden_vec, (h_n, c_n) = self.rnn(single_sent2other_info)
attn_layer_out = self.Attention_Layer(hidden_vec)
out = self.fc(attn_layer_out)
pred_Y = self.sigmoid_layer(out)
return pred_Y
import pyprind
import torch
import torch.nn as nn
import torch.optim as optim
#train_X = [(b,l,d),(b,l,d),...] ; train_Y = [(b,),(b,),...]
#test_X = (N,l,d) test_Y = (N,)
def Train_Eval_Process_Layer_v2(train_X,train_Y,test_X,test_Y):
# LSTM
epoch_num = 10
#model = LSTM_model(input_dim=8,hidden_dim=8)
model = One_Sent2Other_Sent()
optimizer = optim.Adam(model.parameters())
criterion = nn.BCELoss()
for epoch_ in pyprind.prog_bar(range(epoch_num)):
model.train()
for i in range(len(train_X)):
X = torch.tensor(train_X[i])#.cuda()
pred_train_Y = model(X)
Y = torch.tensor([train_Y[i]])#.cuda()
true_train_Y = Y.squeeze(dim=-1)
loss = criterion(pred_train_Y, true_train_Y.float())
optimizer.zero_grad()
loss.backward()
optimizer.step()
#print('loss:',loss)
model.eval()
pred_test_Y = list()
for i in range(len(test_X)):
X = torch.tensor(test_X[i])#.cuda()
pred_test_Y_i = model(X).cpu().data.numpy().reshape(1,1)
pred_test_Y.append(pred_test_Y_i)
test_Y_hat = np.concatenate(pred_test_Y,0)
test_Y_hat_list = list()
for i in range(test_Y_hat.shape[0]):
if test_Y_hat[i,0] >= 0.5:
test_Y_hat_list.append(1)
else:
test_Y_hat_list.append(0)
Evaluation(test_Y_hat_list,test_Y)
def Evaluation(Y_hat,Y):
from sklearn.metrics import f1_score
TP,FP,FN,TN = 0.0001,0.0001,0.0001,0.0001
for i in range(len(Y_hat)):
if int(Y_hat[i]) == 1 and int(Y[i]) ==1:
TP+=1
elif int(Y_hat[i]) == 1 and int(Y[i]) ==0:
FP +=1
elif int(Y_hat[i]) == 0 and int(Y[i]) ==1:
FN +=1
elif int(Y_hat[i]) == 0 and int(Y[i]) ==0:
TN +=1
else:
print('[ERROR]')
Accuracy = (TP+TN)/(TP+FP+FN+TN)
Precision = (TP)/(TP+FP)
Recall = (TP)/(TP+FN)
F1 = f1_score(Y, Y_hat)
print('Accuracy:',Accuracy)
print('Precision:',Precision)
print('Recall:',Recall)
print('F1:',F1)