gensim绘制二维图

# from gensim.models import Word2Vec
# model = Word2Vec.load('baike.model')
# print (indexes = model.cosine('like'))
import pandas as pd
df=pd.read_csv(r'D:\程序\test_18.model.txt',sep=' ',header=None,engine='python')
print(df.iloc[:][0])
# coding=utf-8
import numpy as np
import matplotlib
import matplotlib.pyplot as plt

from sklearn.decomposition import PCA
# from gensim.models import word2vec
from gensim.models.word2vec import Word2Vec

# plot the result
#zhfont = matplotlib.font_manager.FontProperties(fname='/usr/share/fonts/truetype/wqy/wqy-microhei.ttc')
fig = plt.figure()
# ax = fig.add_subplot(111)
#fontproperties=zhfont,

X = df.values[:,1:]#np.array([[-1,2,66,-1], [-2,6,58,-1], [-3,8,45,-2], [1,9,36,1], [2,10,62,1], [3,5,83,2]])  #导入数据，维度为4

pca = PCA(n_components=2)   #降到2维
pca.fit(X)                  #训练
newX=pca.fit_transform(X)   #降维后的数据
# PCA(copy=True, n_components=2, whiten=False)

#添加一个判断词性的脚本然后绘制散点图

plt.scatter(newX[:,0],newX[:,1],label=df.iloc[:][0].values.tolist())
plt.show()
#
# plt.show()

# -*- coding: UTF-8 -*-
__author__ = 'ZhengXiang'
__time__ = '2019/10/15 16:56'

import jieba
# jieba.load_userdict("F:/Python/weibo/201906/userdict.txt")
from gensim.models import word2vec
import pandas as pd
import re

# 读取文件
# d = pd.read_csv('F:/Python/weibo/201906/08/weibo0812_clean.csv', usecols=['co_clean2'])
# re_clean = d['co_clean2'].values.tolist()
# print(type(re_clean))
# print(re_clean)
path="1.txt"


def stopwordslist(filepath):
    stopwords = [line.strip() for line in open(filepath, 'r', encoding='utf-8').readlines()]
    return stopwords

# def movestopwords(sentence):
#     stopwords = stopwordslist('F:/Python/weibo/201906/08/stopwords.txt')  # 这里加载停用词的路径
#     santi_words =[x for x in sentence if len(x) >1 and x not in stopwords]
#     #santi_words= re.sub('[（）：:？“”《》，。！·、\d ]+', ' ', santi_words)
#     return santi_words

seg_lists=[]
with open(path, encoding="utf-8") as f:
    for i in f.readlines():
        #i = jieba.cut(i)  # 默认是精确模式
        seg_list = (jieba.lcut(i, cut_all=False))
        # 去停用词
        # tmp=movestopwords(seg_list)
        # print(tmp)
        tmp=' '.join(seg_list)
        # print(tmp)
        seg_lists.append(tmp)
    print(seg_lists)

# 分完词后保存到新的txt中
with open('fenci_0225.txt','w',encoding='utf-8') as f:
    for i in seg_lists:
        if i =='':
            pass
        else:
            f.write(i)
            # f.write('\n')
print("分词结果保存成功")

# 用 word2vec 进行训练
sentences=word2vec.Text8Corpus('fenci_0225.txt')
# #用来处理按文本分词语料
print(sentences)
model = word2vec.Word2Vec(sentences, size=300,window=5,min_count=2,workers=5,sg=1,hs=1)  #训练模型就这一句话  去掉出现频率小于2的词
# model = word2vec.Word2Vec(sentences,sg=1,size=100,window=5,min_count=5,negative=3,sample=0.001,hs=1,workers=4)
# http://blog.csdn.net/szlcw1/article/details/52751314 训练skip-gram模型; 第一个参数是训练预料，min_count是小于该数的单词会被踢出，默认值为5，size是神经网络的隐藏层单元数，在保存的model.txt中会显示size维的向量值。默认是100。默认window=5
# # 第一个参数是训练语料，第二个参数是小于该数的单词会被剔除，默认值为5, 第三个参数是神经网络的隐藏层单元数，默认为100
# model=word2vec.Word2Vec(sentences,min_count=3, size=50, window=5, workers=4)

# 保存模型，以便重用
model.save("test_18.model")   #保存模型
model.wv.save_word2vec_format('test_18.model.txt','test_18.vocab.txt',binary=False) # 将模型保存成文本，model.wv.save_word2vec_format()来进行模型的保存的话，会生成一个模型文件。里边存放着模型中所有词的词向量。这个文件中有多少行模型中就有多少个词向量。
model.wv.save_word2vec_format('test_18.model.bin',binary=True)

# y2=model.similarity(u"不错", u"好吃") #计算两个词之间的余弦距离
# print(y2)

# for i in model.most_similar(u"好吃"): #计算余弦距离最接近“滋润”的10个词
#     print(i[0],i[1])

# 训练词向量时传入的两个参数也对训练效果有很大影响，需要根据语料来决定参数的选择，好的词向量对NLP的分类、聚类、相似度判别等任务有重要意义

本文地址：https://blog.csdn.net/qq_34069180/article/details/107321898