models.doc2vec – Deep learning with paragraph2vec

参考：

用 Doc2Vec 得到文档／段落／句子的向量表达

https://radimrehurek.com/gensim/models/doc2vec.html

Gensim Doc2vec Tutorial on the IMDB Sentiment Dataset

基于gensim的Doc2Vec简析

Gensim进阶教程：训练word2vec与doc2vec模型

用gensim doc2vec计算文本相似度

转自：

gensim doc2vec + sklearn kmeans 做文本聚类

原文显示太乱 为方便看摘录过来。。

用doc2vec做文本相似度，模型可以找到输入句子最相似的句子，然而分析大量的语料时，不可能一句一句的输入，语料数据大致怎么分类也不能知晓。于是决定做文本聚类。
选择kmeans作为聚类方法。前面doc2vec可以将每个段文本的向量计算出来，然后用kmeans就很好操作了。
选择sklearn库中的KMeans类。

程序如下：

# coding:utf-8

import sys
import gensim
import numpy as np

from gensim.models.doc2vec import Doc2Vec, LabeledSentence
from sklearn.cluster import KMeans

TaggededDocument = gensim.models.doc2vec.TaggedDocument

def get_datasest():
    with open("out/text_dict_cut.txt", 'r') as cf:
        docs = cf.readlines()
        print len(docs)

    x_train = []
    #y = np.concatenate(np.ones(len(docs)))
    for i, text in enumerate(docs):
        word_list = text.split(' ')
        l = len(word_list)
        word_list[l-1] = word_list[l-1].strip()
        document = TaggededDocument(word_list, tags=[i])
        x_train.append(document)

    return x_train

def train(x_train, size=200, epoch_num=1):
    model_dm = Doc2Vec(x_train,min_count=1, window = 3, size = size, sample=1e-3, negative=5, workers=4)
    model_dm.train(x_train, total_examples=model_dm.corpus_count, epochs=100)
    model_dm.save('model/model_dm')

    return model_dm

def cluster(x_train):
    infered_vectors_list = []
    print "load doc2vec model..."
    model_dm = Doc2Vec.load("model/model_dm")
    print "load train vectors..."
    i = 0
    for text, label in x_train:
        vector = model_dm.infer_vector(text)
        infered_vectors_list.append(vector)
        i += 1

    print "train kmean model..."
    kmean_model = KMeans(n_clusters=15)
    kmean_model.fit(infered_vectors_list)
    labels= kmean_model.predict(infered_vectors_list[0:100])
    cluster_centers = kmean_model.cluster_centers_

    with open("out/own_claasify.txt", 'w') as wf:
        for i in range(100):
            string = ""
            text = x_train[i][0]
            for word in text:
                string = string + word
            string = string + '\t'
            string = string + str(labels[i])
            string = string + '\n'
            wf.write(string)

    return cluster_centers

if __name__ == '__main__':
    x_train = get_datasest()
    model_dm = train(x_train)
    cluster_centers = cluster(x_train)