NMF和LDA主题提取简介
非负矩阵分解，即Non-negative Matrix Factorization，简写为NMF。
潜在狄利克雷分布，即Latent Dirichlet Allocation， 简写为LDA。
本文是应用sklearn.decomposition.NMF和sklearn.decomposition.LatentDirichletAllocation的一个例子，在示例中对一个文档语料库进行分析，并提取该语料库主题结构的附加模型。输出是主题列表，每个主题都表示为单词列表。
非负矩阵分解应用于两个不同的目标函数：Frobenius范数和广义Kullback-Leibler散度。后者等效于概率潜在语义索引（PLSA）。
使用默认参数(n_samples /n_features /n_components)的情况下，示例在几十秒内即可运行完成。您可以尝试增加维度，但是要注意时间复杂度以免运行过长时间：NMF的时间复杂度是多项式的；而LDA时间复杂度与(n_samples * iterations，样本数乘以迭代次数)成正比。
代码实现[Python]
# -*- coding: utf-8 -*-
# Author: Olivier Grisel
# Lars Buitinck
# Chyi-Kwei Yau
# License: BSD 3 clause
from time import time
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import NMF, LatentDirichletAllocation
from sklearn.datasets import fetch_20newsgroups
n_samples = 2000
n_features = 1000
n_components = 10
n_top_words = 20
def print_top_words(model, feature_names, n_top_words):
for topic_idx, topic in enumerate(model.components_):
message = "Topic #%d: " % topic_idx
message += " ".join([feature_names[i]
for i in topic.argsort()[:-n_top_words - 1:-1]])
print(message)
print()
# 加载20个新闻组数据集并将其向量化。我们使用一些启发式方法尽早过滤掉无用的项：删掉帖子中的标题、页脚和引用的回复、以及常见的英语单词，
# 删除了只出现在一个文档的单词以及出现于至少95％的文档的单词。
print("Loading dataset...")
t0 = time()
dataset = fetch_20newsgroups(shuffle=True, random_state=1,
remove=('headers', 'footers', 'quotes'))
data_samples = dataset.data[:n_samples]
print("done in %0.3fs." % (time() - t0))
# 在NMF中使用tf-idf特征。
print("Extracting tf-idf features for NMF...")
tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2,
max_features=n_features,
stop_words='english')
t0 = time()
tfidf = tfidf_vectorizer.fit_transform(data_samples)
print("done in %0.3fs." % (time() - t0))
# 在LDA中使用tf（raw term count）特征Use。
print("Extracting tf features for LDA...")
tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2,
max_features=n_features,
stop_words='english')
t0 = time()
tf = tf_vectorizer.fit_transform(data_samples)
print("done in %0.3fs." % (time() - t0))
print()
# 拟合NMF模型
print("Fitting the NMF model (Frobenius norm) with tf-idf features, "
"n_samples=%d and n_features=%d..."
% (n_samples, n_features))
t0 = time()
nmf = NMF(n_components=n_components, random_state=1,
alpha=.1, l1_ratio=.5).fit(tfidf)
print("done in %0.3fs." % (time() - t0))
print("\nTopics in NMF model (Frobenius norm):")
tfidf_feature_names = tfidf_vectorizer.get_feature_names()
print_top_words(nmf, tfidf_feature_names, n_top_words)
# 拟合NMF模型
print("Fitting the NMF model (generalized Kullback-Leibler divergence) with "
"tf-idf features, n_samples=%d and n_features=%d..."
% (n_samples, n_features))
t0 = time()
nmf = NMF(n_components=n_components, random_state=1,
beta_loss='kullback-leibler', solver='mu', max_iter=1000, alpha=.1,
l1_ratio=.5).fit(tfidf)
print("done in %0.3fs." % (time() - t0))
print("\nTopics in NMF model (generalized Kullback-Leibler divergence):")
tfidf_feature_names = tfidf_vectorizer.get_feature_names()
print_top_words(nmf, tfidf_feature_names, n_top_words)
print("Fitting LDA models with tf features, "
"n_samples=%d and n_features=%d..."
% (n_samples, n_features))
lda = LatentDirichletAllocation(n_components=n_components, max_iter=5,
learning_method='online',
learning_offset=50.,
random_state=0)
t0 = time()
lda.fit(tf)
print("done in %0.3fs." % (time() - t0))
print("\nTopics in LDA model:")
tf_feature_names = tf_vectorizer.get_feature_names()
print_top_words(lda, tf_feature_names, n_top_words)
代码执行
代码运行时间大约:0分13.781秒。
运行代码输出的文本内容如下。注意：输出中显示了每个主题对应的单词，但是略去了每个单词对应的权重。
Loading dataset... done in 7.911s. Extracting tf-idf features for NMF... done in 0.268s. Extracting tf features for LDA... done in 0.254s. Fitting the NMF model (Frobenius norm) with tf-idf features, n_samples=2000 and n_features=1000... done in 0.406s. Topics in NMF model (Frobenius norm): Topic #0: just people don think like know time good make way really say right ve want did ll new use years Topic #1: windows use dos using window program os drivers application help software pc running ms screen files version card code work Topic #2: god jesus bible faith christian christ christians does heaven sin believe lord life church mary atheism belief human love religion Topic #3: thanks know does mail advance hi info interested email anybody looking card help like appreciated information send list video need Topic #4: car cars tires miles 00 new engine insurance price condition oil power speed good 000 brake year models used bought Topic #5: edu soon com send university internet mit ftp mail cc pub article information hope program mac email home contact blood Topic #6: file problem files format win sound ftp pub read save site help image available create copy running memory self version Topic #7: game team games year win play season players nhl runs goal hockey toronto division flyers player defense leafs bad teams Topic #8: drive drives hard disk floppy software card mac computer power scsi controller apple mb 00 pc rom sale problem internal Topic #9: key chip clipper keys encryption government public use secure enforcement phone nsa communications law encrypted security clinton used legal standard Fitting the NMF model (generalized Kullback-Leibler divergence) with tf-idf features, n_samples=2000 and n_features=1000... done in 1.769s. Topics in NMF model (generalized Kullback-Leibler divergence): Topic #0: just people don like did know make really right think say things time look way didn ve course probably good Topic #1: help thanks windows know hi need using does looking anybody appreciated card mail software use info email ftp available pc Topic #2: does god believe know mean true christians read point jesus christian church come people fact says religion say agree bible Topic #3: know thanks mail interested like new just bike email edu advance want contact really list heard com post hear information Topic #4: 10 new 30 12 20 50 11 sale 16 15 time 14 old power ago good 100 great offer cost Topic #5: number 1993 data subject government new numbers provide information space following com research include large note group major time talk Topic #6: edu problem file com remember try soon article mike files code program sun free send think cases manager little called Topic #7: game year team games world fact second case won said win division play best clearly claim allow example used doesn Topic #8: think don drive hard need bit mac make sure read apple going comes disk computer case pretty drives software ve Topic #9: good just use like doesn got way don ll going does chip better doing bad key want sure bit car Fitting LDA models with tf features, n_samples=2000 and n_features=1000... done in 3.167s. Topics in LDA model: Topic #0: edu com mail send graphics ftp pub available contact university list faq ca information cs 1993 program sun uk mit Topic #1: don like just know think ve way use right good going make sure ll point got need really time doesn Topic #2: christian think atheism faith pittsburgh new bible radio games alt lot just religion like book read play time subject believe Topic #3: drive disk windows thanks use card drives hard version pc software file using scsi help does new dos controller 16 Topic #4: hiv health aids disease april medical care research 1993 light information study national service test led 10 page new drug Topic #5: god people does just good don jesus say israel way life know true fact time law want believe make think Topic #6: 55 10 11 18 15 team game 19 period play 23 12 13 flyers 20 25 22 17 24 16 Topic #7: car year just cars new engine like bike good oil insurance better tires 000 thing speed model brake driving performance Topic #8: people said did just didn know time like went think children came come don took years say dead told started Topic #9: key space law government public use encryption earth section security moon probe enforcement keys states lunar military crime surface technology
源码下载
- Python版源码文件: plot_topics_extraction_with_nmf_lda.py
- Jupyter Notebook版源码文件: plot_topics_extraction_with_nmf_lda.ipynb