使用deeplearning4j训练Doc2Vec(文档向量)

心已赠人 2022-05-15 10:51 292阅读 0赞

本文作者：合肥工业大学 管理学院 钱洋 email：1563178220@qq.com 内容可能有不到之处，欢迎交流。

**未经本人允许禁止转载。**

# 训练文档向量 #

在上一小节中，本人介绍了使用DeepLearning4J训练得到词向量([https://blog.csdn.net/qy20115549/article/details/82152462][https_blog.csdn.net_qy20115549_article_details_82152462])。本篇主要介绍给定任意文本数据(分词后的数据)，如何使用DeepLearning4J训练得到文档的向量。

在平时的使用中，我们可以将文档转化成向量形式，进而**进行聚类分类等其他操作**。常用的将文档转化成向量形式的方法有one-hot编码、TF-IDF编码、主题模型(LDA)以及本篇要介绍的Doc2Vec操作。如下为笔者使用的文本数据：

![这里写图片描述][70]  
  
对应的操作程序如下：

package org.deeplearning4j.examples.nlp.paragraphvectors;
    
    import java.io.File;
    import java.util.ArrayList;
    import java.util.Arrays;
    import java.util.Collection;
    import java.util.List;
    import org.deeplearning4j.models.embeddings.loader.WordVectorSerializer;
    import org.deeplearning4j.models.paragraphvectors.ParagraphVectors;
    import org.deeplearning4j.models.word2vec.VocabWord;
    import org.deeplearning4j.models.word2vec.wordstore.inmemory.AbstractCache;
    import org.deeplearning4j.text.documentiterator.LabelsSource;
    import org.deeplearning4j.text.sentenceiterator.LineSentenceIterator;
    import org.deeplearning4j.text.sentenceiterator.SentenceIterator;
    import org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor;
    import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory;
    import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;
    import org.slf4j.Logger;
    import org.slf4j.LoggerFactory;
    
    
    public class Doc2VecTest {
        private static Logger log = LoggerFactory.getLogger(Doc2VecTest.class);
        //文档向量输出路径
        private static String outputPath = "E:/doc2vec.txt";
        public static void main(String[] args) throws Exception {
            //输入文本文件的目录
            File inputTxt = new File("E:/raw_sentences.txt");
            log.info("开始加载数据...."+inputTxt.getName());
            //加载数据
            SentenceIterator iter = new LineSentenceIterator(inputTxt);
            //切词操作
            TokenizerFactory token = new DefaultTokenizerFactory();
            //去除特殊符号及大小写转换操作
            token.setTokenPreProcessor(new CommonPreprocessor());
            AbstractCache<VocabWord> cache=new AbstractCache<>();
            //添加文档标签，这个一般从文件读取，为了方面我这里使用了数字
            List<String> labelList = new ArrayList<String>();
            for (int i = 0; i < 97162; i++) { 
                labelList.add("doc"+i);
            }
            //设置文档标签
            LabelsSource source = new LabelsSource(labelList);
            log.info("训练模型....");
            ParagraphVectors vec = new ParagraphVectors.Builder()
                    .minWordFrequency(1)
                    .iterations(5)
                    .epochs(1)
                    .layerSize(100)
                    .learningRate(0.025)
                    .labelsSource(source)
                    .windowSize(5)
                    .iterate(iter)
                    .trainWordVectors(false)
                    .vocabCache(cache)
                    .tokenizerFactory(token)
                    .sampling(0)
                    .build();
    
            vec.fit();
            log.info("相似的句子:");
            Collection<String> lst = vec.wordsNearest("doc0", 10);
            System.out.println(lst);
            log.info("输出文档向量....");
            WordVectorSerializer.writeWordVectors(vec, outputPath);
            //获取某词对应的向量
            log.info("向量获取:");
            double[] docVector = vec.getWordVector("doc0");
            System.out.println(Arrays.toString(docVector));
        }
    }

程序在控制台输出的结果为：

![这里写图片描述][70 1]  
  
另外，我们也成功的将每篇文档对应的向量输出到本地文件中，如下图所示为每篇文档对应的向量结果：  
  
![这里写图片描述][70 2]

# 改写输出方式 #

另外，我们也可以写一个操作方法，是的输出结果按照每个人的需求来，比如我个人的需求是：

> 文档内容 Tab键分割 向量内容

那么，上述的程序可以重写为：

package com.qian;
    
    import java.io.BufferedReader;
    import java.io.BufferedWriter;
    import java.io.File;
    import java.io.FileInputStream;
    import java.io.FileOutputStream;
    import java.io.IOException;
    import java.io.InputStreamReader;
    import java.io.OutputStreamWriter;
    import java.util.ArrayList;
    import java.util.Arrays;
    import java.util.Collection;
    import java.util.HashMap;
    import java.util.List;
    import java.util.Map;
    import org.deeplearning4j.models.paragraphvectors.ParagraphVectors;
    import org.deeplearning4j.models.word2vec.VocabWord;
    import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
    import org.deeplearning4j.models.word2vec.wordstore.inmemory.AbstractCache;
    import org.deeplearning4j.text.documentiterator.LabelsSource;
    import org.deeplearning4j.text.sentenceiterator.LineSentenceIterator;
    import org.deeplearning4j.text.sentenceiterator.SentenceIterator;
    import org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor;
    import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory;
    import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;
    import org.nd4j.linalg.api.ndarray.INDArray;
    import org.slf4j.Logger;
    import org.slf4j.LoggerFactory;
    
    
    public class Doc2VecTest {
        private static Logger log = LoggerFactory.getLogger(Doc2VecTest.class);
        //文档向量输出路径
        private static String outputPath = "data/doc2vec.txt";
        private static String inputPath = "data/raw_sentences.txt";
        public static void main(String[] args) throws Exception {
            //输入文本文件的目录
            File inputTxt = new File(inputPath);
            log.info("开始加载数据...." + inputTxt.getName());
            //加载数据
            SentenceIterator iter = new LineSentenceIterator(inputTxt);
            //切词操作
            TokenizerFactory token = new DefaultTokenizerFactory();
            //去除特殊符号及大小写转换操作
            token.setTokenPreProcessor(new CommonPreprocessor());
            AbstractCache<VocabWord> cache=new AbstractCache<>();
            //添加文档标签，这个一般从文件读取，为了方面我这里使用了数字
            List<String> labelList = new ArrayList<String>();
            for (int i = 1; i < 97163; i++) { 
                labelList.add("doc"+i);
            }
            //设置文档标签
            LabelsSource source = new LabelsSource(labelList);
            log.info("训练模型....");
            ParagraphVectors vec = new ParagraphVectors.Builder()
                    .minWordFrequency(1)
                    .iterations(5)
                    .epochs(1)
                    .layerSize(50)
                    .learningRate(0.025)
                    .labelsSource(source)
                    .windowSize(5)
                    .iterate(iter)
                    .trainWordVectors(false)
                    .vocabCache(cache)
                    .tokenizerFactory(token)
                    .sampling(0)
                    .build();
    
            vec.fit();
            log.info("相似的句子:");
            Collection<String> lst = vec.wordsNearest("doc1", 10);
            System.out.println(lst);
            log.info("输出文档向量....");
            writeDocVectors(vec,outputPath);
            //获取某词对应的向量
            log.info("向量获取:");
            double[] docVector = vec.getWordVector("doc1");
            System.out.println(Arrays.toString(docVector));
        }
        public static void writeDocVectors(ParagraphVectors vectors, String outpath) throws IOException {
            //写操作
            BufferedWriter bufferedWriter = new BufferedWriter(new OutputStreamWriter(new FileOutputStream(new File(outpath)),"gbk"));
            //读操作
            BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(new FileInputStream(new File(inputPath)), "gbk")); 
            String line = null;
            int i = 1;
            Map<String, String> keyToDoc = new HashMap<>();
            while ((line = bufferedReader.readLine())!=null) {
                keyToDoc.put("doc" + i, line);
                i++;
            }
            VocabCache<VocabWord> vocabCache = vectors.getVocab();
            for (VocabWord word : vocabCache.vocabWords()) {
                StringBuilder builder = new StringBuilder();
                //获取每个文档对应的标签
                INDArray vector = vectors.getWordVectorMatrix(word.getLabel());
                //向量添加
                for (int j = 0; j < vector.length(); j++) { 
                    builder.append(vector.getDouble(j));
                    if (j < vector.length() - 1) {
                        builder.append(" ");
                    }
                }
                //写入指定文件
                bufferedWriter.write(keyToDoc.get(word.getLabel()) + "\t" + builder.append("\n").toString());
            }
            bufferedWriter.close();
            bufferedReader.close();
        }
    }

程序的输出结果如下图所示，前面是文档后面是文档对应的向量：

![这里写图片描述][70 3]

[https_blog.csdn.net_qy20115549_article_details_82152462]: https://blog.csdn.net/qy20115549/article/details/82152462
[70]: /images/20220515/0b1a6eaca7cd40658bdc32f7c6a12a39.png
[70 1]: /images/20220515/1f4b9238047b461d802421d537daa460.png
[70 2]: /images/20220515/708c08c19b3146308672d16413427d4f.png
[70 3]: /images/20220515/38f37f3c872c4197b4c8fc53eb8ef7ca.png