def divide_name(handles, handles2names):
names = [handles2names[handle] for handle in handles]
names_idx, names_len = embedAndPack(names)
print(names_idx)
names_idx = to_self_cuda(names_idx)
names_len = names_len.flatten()
return names_idx, names_len
def eval_model(model, test_names):
idx = 0
y_hat_test_class = []
y_test = []
model.eval()
while idx < len(test_names):
batch_test_names = test_names[idx:idx + batch_size]
X_batch_test = []
X_batch_test_emoji = []
seq_lens = []
for i in range(len(batch_test_names)):
handle = batch_test_names[i][:-4].lower()
pick_emd = txn.get(handle.encode())
temp = pickle.loads(pick_emd)
dates = [val[0] for val in temp][::-1]
tweet_emb = [val[1:] for val in temp]
#tweet_emb = pickle.loads(txn.get(handle.encode()))[:fix_seq_len]
seq_lens.append(len(tweet_emb))
while len(tweet_emb) < fix_seq_len:
tweet_emb.append([0 for i in range(768)])
emoji_idx = process_tweet(handle, dates, longest_emoji_len,
word2idx, fix_seq_len)[::-1]
X_batch_test_emoji.append(emoji_idx)
X_batch_test.append(tweet_emb)
y_test.append(map_attribute(handle))
X_batch_test = to_float_cuda(X_batch_test)
X_batch_test = X_batch_test.permute(1, 0, 2)
X_batch_test_emoji = to_self_cuda(X_batch_test_emoji)
y_hat_test = model(fix_seq_len, None, X_batch_test, seq_lens,
[(X_batch_test_emoji, None)])
#print (y_hat_test)
#print (y_hat_test.cpu().detach().numpy())
if inference_type == InferenceType.age:
target = np.argmax(y_hat_test.cpu().detach().numpy(), axis=1)
for i in range(len(target)):
y_hat_test_class.append(target[i])
else:
target = np.where(y_hat_test.cpu().detach().numpy() < 0.5, 0, 1)
for i in range(len(target)):
y_hat_test_class.append(target[i][0])
#print ("epoch "+str(idx))
idx += batch_size
print(y_test, y_hat_test_class)
f1 = f1_score(y_test, y_hat_test_class, average='macro')
#auc = roc_auc_score(y_test, y_hat_test_class )
return f1
def process_tbn(X_train, y_train, X_test, y_test):
# divide into train and corresponding handles
X_train, handles_train = X_train[:, 1:].astype(
np.float), X_train[:, :1].flatten()
X_test, handles_test = X_test[:, 1:].astype(
np.float), X_test[:, :1].flatten()
numerical_train, embedding_bio_train, embedding_tweet_train, embedding_network_train = divide_data(
X_train, bioLen, numLen, tweetLen)
#print (embedding_bio_train.shape, embedding_tweet_train.shape)
y_train = to_float_cuda(
y_train
) #to_float_cuda(y_train.reshape(-1, 1)) if sourceType != SourceType.age else to_self_cuda(y_train)
#embedding_train = cat_embeddings(numerical, embedding_bio, embedding_tweet)
numerical_test, embedding_bio_test, embedding_tweet_test, embedding_network_test = divide_data(
X_test, bioLen, numLen, tweetLen)
y_test = to_self_cuda(
y_test
) #to_float_cuda(y_test.reshape(-1, 1)) if sourceType == SourceType.age else to_self_cuda(y_test)
#embedding_test = cat_embeddings(numerical, embedding_bio, embedding_tweet)
bin_label = True #False if sourceType == SourceType.age else True
if processType == ProcessType.mlp:
embedding_train = cat_embeddings(numerical_train, embedding_bio_train,
embedding_tweet_train)
embedding_test = cat_embeddings(numerical_test, embedding_bio_test,
embedding_tweet_test)
index = y_train[:] > 1
embedding_train_greater = embedding_train[index]
y_train_greater = y_train[index]
y_train_greater = transfer_y(y_train_greater, True, 2, 1, 0)
y_train_greater = to_float_cuda(y_train_greater)
counter = [0, 0]
model1 = MyMLP(embedding_train.shape[1], 20, D_out, bin_label)
to_cuda(model1)
train_model(model1, embedding_train_greater, y_train_greater)
print("--------1")
index = y_train[:] <= 1
embedding_train_smaller = embedding_train[index]
y_train_smaller = y_train[index]
#y_train_smaller = transfer_y(y_train_smaller, False, )
#print (y_train_smaller.shape)
model2 = MyMLP(embedding_train.shape[1], 20, D_out, bin_label)
to_cuda(model2)
train_model(model2, embedding_train_smaller, y_train_smaller)
#auc = eval_model(model2, embedding_train_smaller, y_train_smaller)
#print (y_train_smaller)
#print (auc)
print("--------2")
y_train_root = transfer_y(y_train, True, 1, 1, 0)
model = MyMLP(embedding_train.shape[1], 20, D_out, bin_label)
to_cuda(model)
train_model(model, embedding_train, y_train_root)
y_test_root = transfer_y(y_test, True, 1, 1, 0)
#auc = eval_model(model, embedding_test, y_test_root)
#print (auc)
print("--------3")
auc = han_eval_model([model1, model2, model], embedding_test, y_test)
elif processType == ProcessType.name_c_tbn:
l_out = 8
embedding_train = cat_embeddings(numerical_train, embedding_bio_train,
embedding_tweet_train)
embedding_test = cat_embeddings(numerical_test, embedding_bio_test,
embedding_tweet_test)
#print (handles_train)
train_names_idx, train_names_len = divide_name(handles_train,
handles2names)
lstm_model = NameLstmAttention(batch_size, hidden_size,
embedding_length, l_out)
model = LstmAttentionEnsemble(
embedding_train.shape[1] + hidden_size,
int(embedding_train.shape[1] + hidden_size / 2), D_out, lstm_model,
bin_label)
to_cuda(model)
train_model(model, embedding_train, train_names_idx, train_names_len,
y_train)
test_names_idx, test_names_len = divide_name(handles_test,
handles2names)
auc = eval_model(model, embedding_test, test_names_idx, test_names_len,
y_test)
elif processType == ProcessType.tbn_att:
l_out = 8
embedding_train = torch.stack(
(embedding_bio_train, embedding_tweet_train), axis=1)
embedding_test = torch.stack(
(embedding_bio_test, embedding_tweet_test), axis=1)
index = y_train[:] > 1
embedding_train_greater, numerical_train_greater = embedding_train[
index], numerical_train[index]
y_train_greater = y_train[index]
y_train_greater = transfer_y(y_train_greater, True, 2, 1, 0)
counter = [0, 0]
lstm_model1 = LstmAttention(batch_size, hidden_size, embedding_length,
l_out)
#model1 = LstmAttentionEnsemble(numerical_train.shape[1]+l_out, int(numerical_train.shape[1]+l_out/2), D_out, lstm_model1, bin_label)
model1 = Attention(768, 100, D_out, 768, bin_label)
to_cuda(lstm_model1)
to_cuda(model1)
print(y_train_greater)
train_model(model1, numerical_train_greater, embedding_train_greater,
y_train_greater)
print("--------1")
index = y_train[:] <= 1
embedding_train_smaller, numerical_train_smaller = embedding_train[
index], numerical_train[index]
y_train_smaller = y_train[index]
lstm_model2 = LstmAttention(batch_size, hidden_size, embedding_length,
l_out)
#model2 = LstmAttentionEnsemble(numerical_train.shape[1]+l_out, int(numerical_train.shape[1]+l_out/2), D_out, lstm_model2, bin_label)
model2 = Attention(768, 100, D_out, 768, bin_label)
to_cuda(lstm_model2)
to_cuda(model2)
train_model(model2, numerical_train_smaller, embedding_train_smaller,
y_train_smaller)
print("--------2")
y_train_root = transfer_y(y_train, True, 1, 1, 0)
lstm_model = LstmAttention(batch_size, hidden_size, embedding_length,
l_out)
#model = LstmAttentionEnsemble(numerical_train.shape[1]+l_out, int(numerical_train.shape[1]+l_out/2), D_out, lstm_model, bin_label)
model = Attention(768, 100, D_out, 768, bin_label)
to_cuda(lstm_model)
to_cuda(model)
train_model(model, numerical_train, embedding_train, y_train_root)
y_test_root = transfer_y(y_test, True, 1, 1, 0)
#auc = eval_model(model, embedding_test, y_test_root)
#print (auc)
print("--------3")
auc = han_eval_model([model1, model2, model], numerical_test,
embedding_test, y_test)
elif processType == ProcessType.name:
l_out = 8
train_names_idx, train_names_len = divide_name(handles_train,
handles2names)
print(train_names_idx, train_names_len)
lstm_model = NameLstmAttention(batch_size, hidden_size,
embedding_length, l_out)
model = LstmAttentionEnsemble(hidden_size, int(hidden_size / 2), D_out,
lstm_model, bin_label)
to_cuda(model)
train_model(model, train_names_idx, train_names_len, y_train)
test_names_idx, test_names_len = divide_name(handles_test,
handles2names)
auc = eval_model(model, test_names_idx, test_names_len, y_test)
elif processType == ProcessType.tbn_c_name_att:
l_out = 8
embedding_train = torch.stack(
(embedding_bio_train, embedding_tweet_train), axis=1)
embedding_test = torch.stack(
(embedding_bio_test, embedding_tweet_test), axis=1)
train_names_idx, train_names_len = divide_name(handles_train,
handles2names)
lstm_sub_model = NameLstmAttention(batch_size, 768, 1000, l_out)
lstm_model = LstmAttention(batch_size, hidden_size, embedding_length,
l_out)
model = MulLstmAttentionEnsemble(
numerical_train.shape[1] + l_out,
int(embedding_train.shape[1] + hidden_size / 2), D_out,
[lstm_sub_model], lstm_model, bin_label)
to_cuda(lstm_sub_model)
to_cuda(lstm_model)
to_cuda(model)
train_model(model, numerical_train, train_names_idx, embedding_train,
train_names_len, y_train)
test_names_idx, test_names_len = divide_name(handles_test,
handles2names)
auc = eval_model(model, numerical_test, test_names_idx, embedding_test,
test_names_len, y_test)
elif processType == ProcessType.tbnn_att:
l_out = 8
embedding_train = torch.stack(
(embedding_bio_train, embedding_tweet_train,
embedding_network_train),
axis=1)
embedding_test = torch.stack(
(embedding_bio_test, embedding_tweet_test, embedding_network_test),
axis=1)
lstm_model = LstmAttention(batch_size, hidden_size, embedding_length,
l_out)
model = LstmAttentionEnsemble(
numerical_train.shape[1] + l_out,
int(numerical_train.shape[1] + l_out / 2), D_out, lstm_model,
bin_label)
to_cuda(model)
train_model(model, numerical_train, embedding_train, y_train)
auc = eval_model(model, numerical_test, embedding_test, y_test)
elif processType == ProcessType.tbnn_e_att:
#emoji_embeddings, emoji_input_ids, dim = get_handle2idx_embeddings("/home/yaguang/db/wiki_sort_emoji_hashtag/")
l_out = 8
embedding_train = torch.stack(
(embedding_bio_train, embedding_tweet_train,
embedding_network_train),
axis=1)
embedding_test = torch.stack(
(embedding_bio_test, embedding_tweet_test, embedding_network_test),
axis=1)
#emoji
train_emoji_idx = divide_emojis(handles_train, emoji_input_ids)
test_emoji_idx = divide_emojis(handles_test, emoji_input_ids)
#print (emoji_embeddings)
emoji_cnn_model = CNN_NLP(pretrained_embedding=emoji_embeddings,
dropout=0.5)
#emoji_cnn_model = CNN_NLP(vocab_size=dim)
#lstm_sub_model = NameLstmAttention(batch_size, 768, 1000, l_out)
lstm_model = LstmAttention(batch_size, hidden_size, embedding_length,
l_out)
model = MulLstmAttentionEnsemble(
numerical_train.shape[1] + l_out,
int(embedding_train.shape[1] + hidden_size / 2), D_out,
[emoji_cnn_model], lstm_model, bin_label)
to_cuda(emoji_cnn_model)
to_cuda(lstm_model)
to_cuda(model)
train_model(model, numerical_train, train_emoji_idx, embedding_train,
y_train)
test_names_idx, test_names_len = divide_name(handles_test,
handles2names)
auc = eval_model(model, numerical_test, test_emoji_idx, embedding_test,
y_test)
elif processType == ProcessType.tbn_real_att:
embedding_train = torch.stack(
(embedding_bio_train, embedding_tweet_train), axis=1)
embedding_test = torch.stack(
(embedding_bio_test, embedding_tweet_test), axis=1)
l_out = 8
#model = Attention(numerical_train.shape[1]+l_out, int(numerical_train.shape[1]+l_out/2), D_out, 768, bin_label)
model = Attention(768, 100, D_out, 768, bin_label)
to_cuda(model)
train_model(model, numerical_train, embedding_train, y_train)
auc = eval_model(model, numerical_test, embedding_test, y_test)
return auc
def train_model(model, train_names, test_names):
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
loss_fn = nn.CrossEntropyLoss(
) if inference_type == InferenceType.age else nn.BCELoss()
#model.train()
for epoch in range(epochs):
idx = 0
counter = [0, 0, 0, 0, 0]
model.train()
train_names = random.sample(train_names, len(train_names))
while idx < len(train_names):
batch_train_names = train_names[idx:idx + batch_size]
X_batch_train = []
y_batch_train = []
X_batch_train_emoji = []
seq_lens = []
for i in range(len(batch_train_names)):
handle = batch_train_names[i][:-4].lower()
#print (handle)
pick_emd = txn.get(handle.encode())
temp = pickle.loads(pick_emd)
#print (temp)
dates = [val[0] for val in temp][::-1]
tweet_emb = [val[1:] for val in temp]
seq_lens.append(len(tweet_emb))
while len(tweet_emb) < fix_seq_len:
tweet_emb.append([0 for i in range(768)])
emoji_idx = process_tweet(handle, dates, longest_emoji_len,
word2idx, fix_seq_len)[::-1]
X_batch_train.append(tweet_emb)
X_batch_train_emoji.append(emoji_idx)
y_batch_train.append(map_attribute(handle))
counter[map_attribute(handle)] += 1
X_batch_train = to_float_cuda(X_batch_train)
X_batch_train_emoji = to_self_cuda(X_batch_train_emoji)
#print ("X_batch_train")
#print (X_batch_train.shape)
X_batch_train = X_batch_train.permute(1, 0, 2)
#print (X_batch_train.shape)
#X_batch_train = pack_padded_sequence(X_batch_train)
y_batch_train = to_float_cuda(y_batch_train).reshape(
-1,
1) if inference_type != InferenceType.age else to_self_cuda(
y_batch_train)
#y_batch_train = to_float_cuda(y_batch_train).reshape(-1, 1)
y_pred = model(fix_seq_len, None, X_batch_train, seq_lens,
[(X_batch_train_emoji, None)])
loss = loss_fn(y_pred, y_batch_train)
#print (y_pred, y_batch_train)
loss.backward()
optimizer.step()
optimizer.zero_grad()
idx += batch_size
print("epoch " + str(epoch) + " with batch " +
str(int(idx / batch_size)) + " is " + str(loss.item()))
#print (eval_mem_model(model,X_batch_train, y_batch_train, seq_lens))
#break
auc = eval_model(model, X_test_names)
print(auc)
return loss.item()
def process_tbn(X_train, y_train, X_test, y_test, X_test_mic, y_test_mic):
# divide into train and corresponding handles
X_train, handles_train = X_train[:, 1:].astype(
np.float), X_train[:, :1].flatten()
X_test, handles_test = X_test[:, 1:].astype(
np.float), X_test[:, :1].flatten()
numerical_train, embedding_bio_train, embedding_tweet_train, embedding_network_train = divide_data(
X_train, bioLen, numLen, tweetLen)
#print (embedding_bio_train.shape, embedding_tweet_train.shape)
y_train = to_float_cuda(y_train.reshape(
-1, 1)) if sourceType != SourceType.age else to_self_cuda(y_train)
#embedding_train = cat_embeddings(numerical, embedding_bio, embedding_tweet)
numerical_test, embedding_bio_test, embedding_tweet_test, embedding_network_test = divide_data(
X_test, bioLen, numLen, tweetLen)
y_test = to_float_cuda(y_test.reshape(
-1, 1)) if sourceType == SourceType.age else to_self_cuda(y_test)
#embedding_test = cat_embeddings(numerical, embedding_bio, embedding_tweet)
bin_label = False if sourceType == SourceType.age else True
if processType == ProcessType.mlp:
embedding_train = cat_embeddings(numerical_train, embedding_bio_train,
embedding_tweet_train)
embedding_test = cat_embeddings(numerical_test, embedding_bio_test,
embedding_tweet_test)
model = MyMLP(embedding_train.shape[1], 20, D_out)
to_cuda(model)
train_model(model, embedding_train, y_train)
auc = eval_model(model, embedding_test, y_test)
elif processType == ProcessType.name_c_tbn:
l_out = 8
embedding_train = cat_embeddings(numerical_train, embedding_bio_train,
embedding_tweet_train)
embedding_test = cat_embeddings(numerical_test, embedding_bio_test,
embedding_tweet_test)
print(handles_train)
train_names_idx, train_names_len = divide_name(handles_train,
handles2names)
lstm_model = NameLstmAttention(batch_size, hidden_size,
embedding_length, l_out)
model = LstmAttentionEnsemble(
embedding_train.shape[1] + hidden_size,
int(embedding_train.shape[1] + hidden_size / 2), D_out, lstm_model,
bin_label)
to_cuda(model)
train_model(model, embedding_train, train_names_idx, train_names_len,
y_train)
test_names_idx, test_names_len = divide_name(handles_test,
handles2names)
auc = eval_model(model, embedding_test, test_names_idx, test_names_len,
y_test)
elif processType == ProcessType.tbn_att:
embedding_train = torch.stack(
(embedding_bio_train, embedding_tweet_train), axis=1)
embedding_test = torch.stack(
(embedding_bio_test, embedding_tweet_test), axis=1)
#embedding_test_mic = torch.stack((embedding_bio_test_mic, embedding_tweet_test_mic), axis=1)
l_out = 8
lstm_model = LstmAttention(batch_size, hidden_size, embedding_length,
l_out)
model = LstmAttentionEnsemble(l_out, int(l_out / 2), D_out, lstm_model,
bin_label)
to_cuda(model)
train_model(model, numerical_train, embedding_train, y_train)
auc = eval_model(model, numerical_test, embedding_test, y_test)
print(auc)
auc = eval_model(model, numerical_test_mic, embedding_test_mic,
y_test_mic)
print(auc)
elif processType == ProcessType.name:
l_out = 8
train_names_idx, train_names_len = divide_name(handles_train,
handles2names)
lstm_model = NameLstmAttention(batch_size, hidden_size,
embedding_length, l_out)
model = LstmAttentionEnsemble(hidden_size, int(hidden_size / 2), D_out,
lstm_model, bin_label)
to_cuda(model)
train_model(model, train_names_idx, train_names_len, y_train)
test_names_idx, test_names_len = divide_name(handles_test,
handles2names)
auc = eval_model(model, test_names_idx, test_names_len, y_test)
elif processType == ProcessType.tbn_c_name_att:
l_out = 8
embedding_train = torch.stack(
(embedding_bio_train, embedding_tweet_train), axis=1)
embedding_test = torch.stack(
(embedding_bio_test, embedding_tweet_test), axis=1)
train_names_idx, train_names_len = divide_name(handles_train,
handles2names)
lstm_sub_model = NameLstmAttention(batch_size, 768, 1000, l_out)
lstm_model = LstmAttention(batch_size, hidden_size, embedding_length,
l_out)
model = MulLstmAttentionEnsemble(
numerical_train.shape[1] + l_out,
int(embedding_train.shape[1] + hidden_size / 2), D_out,
[lstm_sub_model], lstm_model, bin_label)
to_cuda(lstm_sub_model)
to_cuda(lstm_model)
to_cuda(model)
train_model(model, numerical_train, train_names_idx, embedding_train,
train_names_len, y_train)
test_names_idx, test_names_len = divide_name(handles_test,
handles2names)
auc = eval_model(model, numerical_test, test_names_idx, embedding_test,
test_names_len, y_test)
elif processType == ProcessType.tbnn_att:
l_out = 8
embedding_train = torch.stack(
(embedding_bio_train, embedding_tweet_train,
embedding_network_train),
axis=1)
embedding_test = torch.stack(
(embedding_bio_test, embedding_tweet_test, embedding_network_test),
axis=1)
lstm_model = LstmAttention(batch_size, hidden_size, embedding_length,
l_out)
model = LstmAttentionEnsemble(
numerical_train.shape[1] + l_out,
int(numerical_train.shape[1] + l_out / 2), D_out, lstm_model,
bin_label)
to_cuda(model)
train_model(model, numerical_train, embedding_train, y_train)
auc = eval_model(model, numerical_test, embedding_test, y_test)
elif processType == ProcessType.tbnn_e_att:
emoji_embeddings, emoji_input_ids, dim = get_handle2idx_embeddings(
"/home/yaguang/pattern/db/wiki_sort_emoji_hashtag/")
l_out = 8
embedding_train = torch.stack(
(embedding_bio_train, embedding_tweet_train,
embedding_network_train),
axis=1)
embedding_test = torch.stack(
(embedding_bio_test, embedding_tweet_test, embedding_network_test),
axis=1)
#emoji
train_emoji_idx = divide_emojis(handles_train, emoji_input_ids)
test_emoji_idx = divide_emojis(handles_test, emoji_input_ids)
#emoji_cnn_model = CNN_NLP(pretrained_embedding=emoji_embeddings, dropout=0.5)
emoji_cnn_model = CNN_NLP(vocab_size=dim)
#lstm_sub_model = NameLstmAttention(batch_size, 768, 1000, l_out)
lstm_model = LstmAttention(batch_size, hidden_size, embedding_length,
l_out)
model = MulLstmAttentionEnsemble(
numerical_train.shape[1] + l_out,
int(embedding_train.shape[1] + hidden_size / 2), D_out,
[emoji_cnn_model], lstm_model, bin_label)
to_cuda(emoji_cnn_model)
to_cuda(lstm_model)
to_cuda(model)
train_model(model, numerical_train, train_emoji_idx, embedding_train,
y_train)
test_names_idx, test_names_len = divide_name(handles_test,
handles2names)
auc = eval_model(model, numerical_test, test_emoji_idx, embedding_test,
test_names_len, y_test)
return auc
def divide_emojis(handles, input_ids):
handle_ids = [input_ids[handle] for handle in handles]
#print (handles[:4])
#print ([val.shape for val in handle_ids[:4]])
return to_self_cuda(np.concatenate(handle_ids, axis=0))