August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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非监督学习
：
主题建模与聚类
｜
255
文档中，但包含该单词的文档数量不超过
70%
。正如第
2
章的介绍，这些参数可以
被优化，且需要一些实验。
在
Gensim
中，这一步可以通过一个过滤器来实现，其参数为
no_below
和
no_above
（相当于
scikit-learn
中的
min_df
和
max_df
）：
dict_gensim_para.filter_extremes(no_below=5, no_above=0.7)
在读入词汇表之后，我们可以使用
Gensim
计算词袋矩阵（在
Gensim
中称之为语料
库，但在这里我们继续使用当前的术语）：
bow_gensim_para = [dict_gensim_para.doc2bow(paragraph) \
for paragraph in gensim_paragraphs]
最后，我们执行
TF-IDF
转换。第一行代码拟合词袋模型，而第二行是转换权重：
from gensim.models import TfidfModel
tfidf_gensim_para = TfidfModel(bow_gensim_para)
vectors_gensim_para = tfidf_gensim_para[bow_gensim_para]
下面的
Gensim
主题建模任务都将使用
vectors_gensim_para
矩阵。
8.8.2
案例：利用
Gensim
执行非负矩阵分解
首先，我们来检查一下
NMF
的结果，看看是否可以再现
scikit-learn ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.