lda implements latent Dirichlet allocation (LDA) using collapsed Gibbs
sampling. lda is fast and is tested on Linux, OS X, and Windows.
You can read more about lda in the documentation.
If you have NumPy installed,
pip install lda
Installation does not require a compiler on Windows or OS X.
lda.LDA implements latent Dirichlet allocation (LDA). The interface follows
conventions found in scikit-learn.
The following demonstrates how to inspect a model of a subset of the Reuters
news dataset. The input below, X, is a document-term matrix (sparse matrices
are accepted).
>>> import numpy as np
>>> import lda
>>> import lda.datasets
>>> X = lda.datasets.load_reuters()
>>> vocab = lda.datasets.load_reuters_vocab()
>>> titles = lda.datasets.load_reuters_titles()
>>> X.shape
(395, 4258)
>>> X.sum()
84010
>>> model = lda.LDA(n_topics=20, n_iter=1500, random_state=1)
>>> model.fit(X) # model.fit_transform(X) is also available
>>> topic_word = model.topic_word_ # model.components_ also works
>>> n_top_words = 8
>>> for i, topic_dist in enumerate(topic_word):
... topic_words = np.array(vocab)[np.argsort(topic_dist)][:-n_top_words:-1]
... print('Topic {}: {}'.format(i, ' '.join(topic_words)))
Topic 0: british churchill sale million major letters west
Topic 1: church government political country state people party
Topic 2: elvis king fans presley life concert young
Topic 3: yeltsin russian russia president kremlin moscow michael
Topic 4: pope vatican paul john surgery hospital pontiff
Topic 5: family funeral police miami versace cunanan city
Topic 6: simpson former years court president wife south
Topic 7: order mother successor election nuns church nirmala
Topic 8: charles prince diana royal king queen parker
Topic 9: film french france against bardot paris poster
Topic 10: germany german war nazi letter christian book
Topic 11: east peace prize award timor quebec belo
Topic 12: n't life show told very love television
Topic 13: years year time last church world people
Topic 14: mother teresa heart calcutta charity nun hospital
Topic 15: city salonika capital buddhist cultural vietnam byzantine
Topic 16: music tour opera singer israel people film
Topic 17: church catholic bernardin cardinal bishop wright death
Topic 18: harriman clinton u.s ambassador paris president churchill
Topic 19: city museum art exhibition century million churchesThe document-topic distributions are available in model.doc_topic_.
>>> doc_topic = model.doc_topic_
>>> for i in range(10):
... print("{} (top topic: {})".format(titles[i], doc_topic[i].argmax()))
0 UK: Prince Charles spearheads British royal revolution. LONDON 1996-08-20 (top topic: 8)
1 GERMANY: Historic Dresden church rising from WW2 ashes. DRESDEN, Germany 1996-08-21 (top topic: 13)
2 INDIA: Mother Teresa's condition said still unstable. CALCUTTA 1996-08-23 (top topic: 14)
3 UK: Palace warns British weekly over Charles pictures. LONDON 1996-08-25 (top topic: 8)
4 INDIA: Mother Teresa, slightly stronger, blesses nuns. CALCUTTA 1996-08-25 (top topic: 14)
5 INDIA: Mother Teresa's condition unchanged, thousands pray. CALCUTTA 1996-08-25 (top topic: 14)
6 INDIA: Mother Teresa shows signs of strength, blesses nuns. CALCUTTA 1996-08-26 (top topic: 14)
7 INDIA: Mother Teresa's condition improves, many pray. CALCUTTA, India 1996-08-25 (top topic: 14)
8 INDIA: Mother Teresa improves, nuns pray for "miracle". CALCUTTA 1996-08-26 (top topic: 14)
9 UK: Charles under fire over prospect of Queen Camilla. LONDON 1996-08-26 (top topic: 8)Python 2.7 or Python 3.3+ is required. The following packages are required
lda aims for simplicity. (It happens to be fast, as essential parts are
written in C via Cython.) If you are working with a very large corpus you may
wish to use more sophisticated topic models such as those implemented in hca
and MALLET. hca is written entirely in C and MALLET is written in Java.
Unlike lda, hca can use more than one processor at a time. Both MALLET and
hca implement topic models known to be more robust than standard latent
Dirichlet allocation.
Latent Dirichlet allocation is described in Blei et al. (2003) and Pritchard et al. (2000). Inference using collapsed Gibbs sampling is described in Griffiths and Steyvers (2004).
- Documentation: http://pythonhosted.org/lda
- Source code: https://github.com/ariddell/lda/
- Issue tracker: https://github.com/ariddell/lda/issues
lda is licensed under Version 2.0 of the Mozilla Public License.