📅 最后修改于: 2020-10-16 02:29:40 🧑 作者: Mango
本章将解释什么是潜在狄利克雷分配（LDA）槌模型，以及如何在Gensim中创建该模型。
在上一节中，我们实现了LDA模型，并从20Newsgroup数据集的文档中获取主题。那是Gensim的LDA算法的内置版本。也有Gensim的槌状版本，可以提供更好的主题质量。在这里，我们将在已经实现的上一个示例中应用Mallet的LDA。
Mallet是一个开放源代码工具包，由Andrew McCullum编写。它基本上是一个基于Java的软件包，用于NLP，文档分类，聚类，主题建模以及许多其他机器学习文本应用程序。它为我们提供了Mallet主题建模工具包，其中包含高效，基于采样的LDA实现以及分层LDA。
Mallet2.0是MALLET（Java主题建模工具包）的最新版本。在将其与Gensim for LDA结合使用之前，必须先在系统上下载mallet-2.0.8.zip软件包并解压缩。安装并解压缩后，请手动或通过我们将提供的代码将环境变量％MALLET_HOME％设置为指向MALLET目录的位置，然后再通过Mallet实现LDA。
Python为潜在的狄利克雷分配（LDA）提供了Gensim包装器。该包装的语法为gensim.models.wrappers.LdaMallet 。该模块是来自MALLET的折叠式Gibbs采样，允许从训练语料库估计LDA模型，还可以推断未见过的新文档的主题分布。
我们将在以前建立的LDA模型上使用LDA Mallet，并将通过计算相关性得分来检查性能差异。
在上一个示例中构建的语料库上应用Mallet LDA模型之前，我们必须必须更新环境变量并提供Mallet文件的路径。可以在以下代码的帮助下完成-
import os
from gensim.models.wrappers import LdaMallet
os.environ.update({'MALLET_HOME':r'C:/mallet-2.0.8/'})
#You should update this path as per the path of Mallet directory on your system.
mallet_path = r'C:/mallet-2.0.8/bin/mallet'
#You should update this path as per the path of Mallet directory on your system.
一旦提供了Mallet文件的路径，我们现在就可以在语料库上使用它了。可以在ldamallet.show_topics（）函数的帮助下完成以下操作-
ldamallet = gensim.models.wrappers.LdaMallet(
mallet_path, corpus=corpus, num_topics=20, id2word=id2word
)
pprint(ldamallet.show_topics(formatted=False))
[
(4,
[('gun', 0.024546225966016102),
('law', 0.02181426826996709),
('state', 0.017633545129043606),
('people', 0.017612848479831116),
('case', 0.011341763768445888),
('crime', 0.010596684396796159),
('weapon', 0.00985160502514643),
('person', 0.008671896020034356),
('firearm', 0.00838214293105946),
('police', 0.008257963035784506)]),
(9,
[('make', 0.02147966482730431),
('people', 0.021377478029838543),
('work', 0.018557122419783363),
('money', 0.016676885346413244),
('year', 0.015982015123646026),
('job', 0.012221540976905783),
('pay', 0.010239117106069897),
('time', 0.008910688739014919),
('school', 0.0079092581238504),
('support', 0.007357449417535254)]),
(14,
[('power', 0.018428398507941996),
('line', 0.013784244460364121),
('high', 0.01183271164249895),
('work', 0.011560979224821522),
('ground', 0.010770484918850819),
('current', 0.010745781971789235),
('wire', 0.008399002000938712),
('low', 0.008053160742076529),
('water', 0.006966231071366814),
('run', 0.006892122230182061)]),
(0,
[('people', 0.025218349201353372),
('kill', 0.01500904870564167),
('child', 0.013612400660948935),
('armenian', 0.010307655991816822),
('woman', 0.010287984892595798),
('start', 0.01003226060272248),
('day', 0.00967818081674404),
('happen', 0.009383114328428673),
('leave', 0.009383114328428673),
('fire', 0.009009363443229208)]),
(1,
[('file', 0.030686386604212003),
('program', 0.02227713642901929),
('window', 0.01945561169918489),
('set', 0.015914874783314277),
('line', 0.013831003577619592),
('display', 0.013794120901412606),
('application', 0.012576992586582082),
('entry', 0.009275993066056873),
('change', 0.00872275292295209),
('color', 0.008612104894331132)]),
(12,
[('line', 0.07153810971508515),
('buy', 0.02975597944523662),
('organization', 0.026877236406682988),
('host', 0.025451316957679788),
('price', 0.025182275552207485),
('sell', 0.02461728860071565),
('mail', 0.02192687454599263),
('good', 0.018967419085797303),
('sale', 0.017998870026097017),
('send', 0.013694207538540181)]),
(11,
[('thing', 0.04901329901329901),
('good', 0.0376018876018876),
('make', 0.03393393393393394),
('time', 0.03326898326898327),
('bad', 0.02664092664092664),
('happen', 0.017696267696267698),
('hear', 0.015615615615615615),
('problem', 0.015465465465465466),
('back', 0.015143715143715144),
('lot', 0.01495066495066495)]),
(18,
[('space', 0.020626317374284855),
('launch', 0.00965716006366413),
('system', 0.008560244332602057),
('project', 0.008173097603991913),
('time', 0.008108573149223556),
('cost', 0.007764442723792318),
('year', 0.0076784101174345075),
('earth', 0.007484836753129436),
('base', 0.0067535595990880545),
('large', 0.006689035144319697)]),
(5,
[('government', 0.01918437232469453),
('people', 0.01461203206475212),
('state', 0.011207097828624796),
('country', 0.010214802708381975),
('israeli', 0.010039691804809714),
('war', 0.009436532025838587),
('force', 0.00858043427504086),
('attack', 0.008424780138532182),
('land', 0.0076659662230523775),
('world', 0.0075103120865437)]),
(2,
[('car', 0.041091194044470564),
('bike', 0.015598981291017729),
('ride', 0.011019688510138114),
('drive', 0.010627877363110981),
('engine', 0.009403467528651191),
('speed', 0.008081104907434616),
('turn', 0.007738270153785875),
('back', 0.007738270153785875),
('front', 0.007468899990204721),
('big', 0.007370947203447938)])
]
现在我们还可以通过如下计算相干分数来评估其性能：
ldamallet = gensim.models.wrappers.LdaMallet(
mallet_path, corpus=corpus, num_topics=20, id2word=id2word
)
pprint(ldamallet.show_topics(formatted=False))
Coherence Score: 0.5842762900901401