NLP（三）：word2vec + siameseLSTM

一、词嵌入

import jieba
from gensim.models import Word2Vec
import torch
import gensim
import numpy as np
model = gensim.models.KeyedVectors.load_word2vec_format('model\\word2vec.bin', binary=True)
class WordEmbedding(object):
    def __init__(self):
        pass
    def sentenceTupleToEmbedding(self, data1, data2):
        aCutListMaxLen = max([len(list(jieba.cut(sentence_a))) for sentence_a in data1])
        bCutListMaxLen = max([len(list(jieba.cut(sentence_a))) for sentence_a in data2])
        maxLen = max(aCutListMaxLen,bCutListMaxLen)
        seq_len = maxLen
        a = self.sqence_vec(data1, seq_len) #batch_size, sqence, embedding
        b = self.sqence_vec(data2, seq_len)
        return torch.FloatTensor(a), torch.FloatTensor(b)
    def sqence_vec(self, data, seq_len):
        data_a_vec = []
        for sequence_a in data:
            sequence_vec = []  # sequence * 128
            for word_a in jieba.cut(sequence_a):
                if word_a in model:
                    sequence_vec.append(model[word_a])
            sequence_vec = np.array(sequence_vec)
            add = np.zeros((seq_len - sequence_vec.shape[0], 128))
            sequenceVec = np.vstack((sequence_vec, add))
            data_a_vec.append(sequenceVec)
        a_vec = np.array(data_a_vec)
        return a_vec

二、dataSet设置

import torch.utils.data as data
import torch

class DatasetIterater(data.Dataset):
    def __init__(self, texta, textb, label):
        self.texta = texta
        self.textb = textb
        self.label = label
    def __getitem__(self, item):
        texta = self.texta[item]
        textb = self.textb[item]
        label = self.label[item]
        return texta, textb, label
    def __len__(self):
        return len(self.texta)

三、SiameseLSTM

import torch
from torch import nn
class SiameseLSTM(nn.Module):
    def __init__(self, input_size):
        super(SiameseLSTM, self).__init__()
        self.lstm = nn.LSTM(input_size=input_size, hidden_size=10, num_layers=4, batch_first=True)
        self.fc = nn.Linear(10, 1)
    def forward(self, data1, data2):
        out1, (h1, c1) = self.lstm(data1)
        out2, (h2, c2) = self.lstm(data2)
        pre1 = out1[:, -1, :]
        pre2 = out2[:, -1, :]
        dis = torch.abs(pre1 - pre2)
        out = self.fc(dis)
        return out

四、mainProcess

import torch
from torch import nn
from torch.utils.data import DataLoader
import pandas as pd
from datasetIterater import DatasetIterater
import jieba
from wordEmbedding import WordEmbedding
from siameseLSTM import  SiameseLSTM

learning_rate = 0.001
train_texta = pd.read_csv("data/POI/negtive.csv")["address_1"]
train_textb = pd.read_csv("data/POI/negtive.csv")["address_2"]
train_label = pd.read_csv("data/POI/negtive.csv")["tag"]
train_data = DatasetIterater(train_texta,train_textb,train_label)
train_iter = DataLoader(dataset=train_data,batch_size=32,shuffle=True)

net = SiameseLSTM(128)
criterion = nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)
word = WordEmbedding()
train_loss = []
for epoch in range(10):
    for batch_id, (data1, data2, label) in enumerate(train_iter):
        a, b = word.sentenceTupleToEmbedding(data1, data2)
        distence = net(a, b)
        loss = criterion(distence, label.float().unsqueeze(-1))
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        train_loss.append(loss.item())
        if batch_id % 10==0:
            print(epoch, batch_id, loss.item())