path_prefix = './'
import warnings
warnings.filterwarnings('ignore')
import torch
import numpy as np
import pandas as pd
import torch.optim as optim
import torch.nn.functional as F
# 汉字分词或者单个文字
def load_training_data(path='training_label.txt'):
# 把training時需要的data讀進來
# 如果是'training_label.txt'，需要讀取label，如果是'training_nolabel.txt'，不需要讀取label
if 'training_label' in path:
with open(path, 'r', encoding='utf-8') as f:
lines = f.readlines()
lines = [list(line.strip('\n')) for line in lines if line]
lines = [i for i in lines if i]
x = [line[4:] for line in lines if line]
y = [line[0] for line in lines if line]
return x, y
else:
with open(path, 'r', encoding='utf-8') as f:
lines = f.readlines()
print(lines)
x = [list(line.strip('\n')) for line in lines if len(line)!=0]
x=[i for i in x if i]
return x
def load_testing_data(path='testing_data'):
# 把testing時需要的data讀進來
with open(path, 'r', encoding='utf-8') as f:
lines = f.readlines()
X = ["".join(line.strip('\n').split(",")[1:]).strip() for line in lines[1:]]
X = [sen.split(' ') for sen in X]
return X
def evaluation(outputs, labels):
#outputs => probability (float)
#labels => labels
outputs[outputs>=0.5] = 1 # 大於等於0.5為有惡意
outputs[outputs<0.5] = 0 # 小於0.5為無惡意
correct = torch.sum(torch.eq(outputs, labels)).item()
return correct
import os
import numpy as np
import pandas as pd
import argparse
from gensim.models import word2vec
def train_word2vec(x):
# 訓練word to vector 的 word embedding #skip-gram or c。词嵌入
model = word2vec.Word2Vec(x, size=250, window=5, min_count=5, workers=12, iter=10, sg=1)
return model
if __name__ == "__main__":
print("loading training data ...")
train_x, y = load_training_data('training_label.txt')
train_x_no_label = load_training_data('training_nolabel.txt')
print("loading testing data ...")
test_x = load_testing_data('testing_data.txt')
model = train_word2vec(train_x + train_x_no_label + test_x)
print("saving model ...")
model.save(os.path.join(path_prefix, 'w2v_all.model'))
from gensim.models import Word2Vec
embedding = Word2Vec.load("w2v_all.model")
embedding['我']
from torch import nn
from gensim.models import Word2Vec
class Preprocess():
def __init__(self, sentences, sen_len, w2v_path="./w2v_all.model"):
self.w2v_path = w2v_path
self.sentences = sentences
self.sen_len = sen_len
self.idx2word = []
self.word2idx = {}
self.embedding_matrix = []
def get_w2v_model(self):
# 把之前訓練好的word to vec 模型讀進來
self.embedding = Word2Vec.load(self.w2v_path)
self.embedding_dim = self.embedding.vector_size
def add_embedding(self, word):
# 把word加進embedding，並賦予他一個隨機生成的representation vector
# word只會是"<PAD>"或"<UNK>"
vector = torch.empty(1, self.embedding_dim)
torch.nn.init.uniform_(vector)
self.word2idx[word] = len(self.word2idx)
self.idx2word.append(word)
self.embedding_matrix = torch.cat([self.embedding_matrix, vector], 0)
def make_embedding(self, load=True):
print("Get embedding ...")
# 取得訓練好的 Word2vec word embedding
if load:
print("loading word to vec model ...")
self.get_w2v_model()
else:
raise NotImplementedError
# 製作一個 word2idx 的 dictionary
# 製作一個 idx2word 的 list
# 製作一個 word2vector 的 list
for i, word in enumerate(self.embedding.wv.vocab):
print('get words #{}'.format(i+1), end='\r')
#e.g. self.word2index['魯'] = 1
#e.g. self.index2word[1] = '魯'
#e.g. self.vectors[1] = '魯' vector
self.word2idx[word] = len(self.word2idx)
self.idx2word.append(word)
self.embedding_matrix.append(self.embedding[word])
self.embedding_matrix = torch.tensor(self.embedding_matrix)
# 將"<PAD>"跟"<UNK>"加進embedding裡面
self.add_embedding("<PAD>")
self.add_embedding("<UNK>")
print("total words: {}".format(len(self.embedding_matrix)))
return self.embedding_matrix
def pad_sequence(self, sentence):
# 將每個句子變成一樣的長度
if len(sentence) > self.sen_len:
sentence = sentence[:self.sen_len]
else:
pad_len = self.sen_len - len(sentence)
for _ in range(pad_len):
sentence.append(self.word2idx["<PAD>"])
assert len(sentence) == self.sen_len
return sentence
def sentence_word2idx(self):
# 把句子裡面的字轉成相對應的index
sentence_list = []
for i, sen in enumerate(self.sentences):
print('sentence count #{}'.format(i+1), end='\r')
sentence_idx = []
for word in sen:
if word in self.word2idx.keys():
sentence_idx.append(self.word2idx[word])
else:
sentence_idx.append(self.word2idx["<UNK>"])
# 將每個句子變成一樣的長度
sentence_idx = self.pad_sequence(sentence_idx)
sentence_list.append(sentence_idx)
return torch.LongTensor(sentence_list)
def labels_to_tensor(self, y):
# 把labels轉成tensor
y = [int(label) for label in y]
return torch.LongTensor(y)
# data.py
# 實作了dataset所需要的'__init__', '__getitem__', '__len__'
# 好讓dataloader能使用
import torch
from torch.utils import data
class TwitterDataset(data.Dataset):
"""
Expected data shape like:(data_num, data_len)
Data can be a list of numpy array or a list of lists
input data shape : (data_num, seq_len, feature_dim)
"""
def __init__(self, X, y):
self.data = X
self.label = y
def __getitem__(self, idx):
if self.label is None: return self.data[idx]
return self.data[idx], self.label[idx]
def __len__(self):
return len(self.data)
import torch
from torch import nn
class LSTM_Net(nn.Module):
def __init__(self, embedding, embedding_dim, hidden_dim, num_layers, dropout=0.5, fix_embedding=True):
super(LSTM_Net, self).__init__()
# 製作 embedding layer
self.embedding = torch.nn.Embedding(embedding.size(0), embedding.size(1))
self.embedding.weight = torch.nn.Parameter(embedding)
# 是否將 embedding fix住，如果fix_embedding為False，在訓練過程中，embedding也會跟著被訓練
self.embedding.weight.requires_grad = False if fix_embedding else True
self.embedding_dim = embedding.size(1)
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.dropout = dropout
self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=num_layers, batch_first=True)
self.classifier = nn.Sequential( nn.Dropout(dropout),
nn.Linear(hidden_dim, 1),
nn.Sigmoid() )
def forward(self, inputs):
inputs = self.embedding(inputs)
x, _ = self.lstm(inputs, None)
# x 的 dimension (batch, seq_len, hidden_size)
# 取用 LSTM 最後一層的 hidden state
x = x[:, -1, :]
x = self.classifier(x)
return x
import torch
from torch import nn
import torch.optim as optim
import torch.nn.functional as F
def training(batch_size, n_epoch, lr, model_dir, train, valid, model, device):
total = sum(p.numel() for p in model.parameters())
trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('\nstart training, parameter total:{}, trainable:{}\n'.format(total, trainable))
model.train() # 將model的模式設為train，這樣optimizer就可以更新model的參數
criterion = nn.BCELoss() # 定義損失函數，這裡我們使用binary cross entropy loss
t_batch = len(train)
v_batch = len(valid)
optimizer = optim.Adam(model.parameters(), lr=lr) # 將模型的參數給optimizer，並給予適當的learning rate
total_loss, total_acc, best_acc = 0, 0, 0
for epoch in range(n_epoch):
total_loss, total_acc = 0, 0
# 這段做training
for i, (inputs, labels) in enumerate(train):
inputs = inputs.to(device, dtype=torch.long) # device為"cuda"，將inputs轉成torch.cuda.LongTensor
labels = labels.to(device, dtype=torch.float) # device為"cuda"，將labels轉成torch.cuda.FloatTensor，因為等等要餵進criterion，所以型態要是float
optimizer.zero_grad() # 由於loss.backward()的gradient會累加，所以每次餵完一個batch後需要歸零
outputs = model(inputs) # 將input餵給模型
outputs = outputs.squeeze() # 去掉最外面的dimension，好讓outputs可以餵進criterion()
loss = criterion(outputs, labels) # 計算此時模型的training loss
loss.backward() # 算loss的gradient
optimizer.step() # 更新訓練模型的參數
correct = evaluation(outputs, labels) # 計算此時模型的training accuracy
total_acc += (correct / batch_size)
total_loss += loss.item()
print('[ Epoch{}: {}/{} ] loss:{:.3f} acc:{:.3f} '.format(
epoch+1, i+1, t_batch, loss.item(), correct*100/batch_size), end='\r')
print('\nTrain | Loss:{:.5f} Acc: {:.3f}'.format(total_loss/t_batch, total_acc/t_batch*100))
# 這段做validation
model.eval() # 將model的模式設為eval，這樣model的參數就會固定住
with torch.no_grad():
total_loss, total_acc = 0, 0
for i, (inputs, labels) in enumerate(valid):
inputs = inputs.to(device, dtype=torch.long) # device為"cuda"，將inputs轉成torch.cuda.LongTensor
labels = labels.to(device, dtype=torch.float) # device為"cuda"，將labels轉成torch.cuda.FloatTensor，因為等等要餵進criterion，所以型態要是float
outputs = model(inputs) # 將input餵給模型
outputs = outputs.squeeze() # 去掉最外面的dimension，好讓outputs可以餵進criterion()
loss = criterion(outputs, labels) # 計算此時模型的validation loss
correct = evaluation(outputs, labels) # 計算此時模型的validation accuracy
total_acc += (correct / batch_size)
total_loss += loss.item()
print("Valid | Loss:{:.5f} Acc: {:.3f} ".format(total_loss/v_batch, total_acc/v_batch*100))
if total_acc > best_acc:
# 如果validation的結果優於之前所有的結果，就把當下的模型存下來以備之後做預測時使用
best_acc = total_acc
#torch.save(model, "{}/val_acc_{:.3f}.model".format(model_dir,total_acc/v_batch*100))
torch.save(model, "{}/ckpt.model".format(model_dir))
print('saving model with acc {:.3f}'.format(total_acc/v_batch*100))
print('-----------------------------------------------')
model.train() # 將model的模式設為train，這樣optimizer就可以更新model的參數（因為剛剛轉成eval模式）
import torch
from torch import nn
import torch.optim as optim
import torch.nn.functional as F
def testing(batch_size, test_loader, model, device):
model.eval()
ret_output = []
with torch.no_grad():
for i, inputs in enumerate(test_loader):
inputs = inputs.to(device, dtype=torch.long)
outputs = model(inputs)
outputs = outputs.squeeze()
outputs[outputs>=0.5] = 1 # 大於等於0.5為負面
outputs[outputs<0.5] = 0 # 小於0.5為正面
ret_output += outputs.int().tolist()
return ret_output
# main.py
import os
import torch
import argparse
import numpy as np
from torch import nn
from gensim.models import word2vec
from sklearn.model_selection import train_test_split
# 通過torch.cuda.is_available()的回傳值進行判斷是否有使用GPU的環境，如果有的話device就設為"cuda"，沒有的話就設為"cpu"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 處理好各個data的路徑
train_with_label = os.path.join(path_prefix, 'training_label.txt')
train_no_label = os.path.join(path_prefix, 'training_nolabel.txt')
testing_data = os.path.join(path_prefix, 'testing_data.txt')
w2v_path = os.path.join(path_prefix, 'w2v_all.model') # 處理word to vec model的路徑
# 定義句子長度、要不要固定embedding、batch大小、要訓練幾個epoch、learning rate的值、model的資料夾路徑
sen_len = 30
fix_embedding = True # fix embedding during training
batch_size = 128
epoch =50
lr = 0.001
model_dir = path_prefix # model directory for checkpoint model
print("loading data ...") # 把'training_label.txt'跟'training_nolabel.txt'讀進來
train_x, y = load_training_data(train_with_label)
train_x_no_label = load_training_data(train_no_label)
# 對input跟labels做預處理
preprocess = Preprocess(train_x, sen_len, w2v_path=w2v_path)
embedding = preprocess.make_embedding(load=True)
train_x = preprocess.sentence_word2idx()
y = preprocess.labels_to_tensor(y)
# 製作一個model的對象
model = LSTM_Net(embedding, embedding_dim=250, hidden_dim=250, num_layers=1, dropout=0.5, fix_embedding=fix_embedding)
model = model.to(device) # device為"cuda"，model使用GPU來訓練(餵進去的inputs也需要是cuda tensor)
# 把data分為training data跟validation data(將一部份training data拿去當作validation data)
X_train, X_val, y_train, y_val = train_x[:8], train_x[8:], y[:8], y[8:]
# 把data做成dataset供dataloader取用
train_dataset = TwitterDataset(X=X_train, y=y_train)
val_dataset = TwitterDataset(X=X_val, y=y_val)
# 把data 轉成 batch of tensors
train_loader = torch.utils.data.DataLoader(dataset = train_dataset,
batch_size = batch_size,
shuffle = True,
num_workers = 8)
val_loader = torch.utils.data.DataLoader(dataset = val_dataset,
batch_size = batch_size,
shuffle = False,
num_workers = 8)
# 開始訓練
training(batch_size, epoch, lr, model_dir, train_loader, val_loader, model, device)
# 開始測試模型並做預測
print("loading testing data ...")
test_x = load_testing_data(testing_data)
preprocess = Preprocess(test_x, sen_len, w2v_path=w2v_path)
embedding = preprocess.make_embedding(load=True)
test_x = preprocess.sentence_word2idx()
test_dataset = TwitterDataset(X=test_x, y=None)
test_loader = torch.utils.data.DataLoader(dataset = test_dataset,
batch_size = batch_size,
shuffle = False,
num_workers = 8)
print('\nload model ...')
model = torch.load(os.path.join(model_dir, 'ckpt.model'))
outputs = testing(batch_size, test_loader, model, device)
# 寫到csv檔案供上傳kaggle
tmp = pd.DataFrame({"id":[str(i) for i in range(len(test_x))],"label":outputs})
print("save csv ...")
tmp.to_csv(os.path.join(path_prefix, 'predict.csv'), index=False)
print("Finish Predicting")
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