lda 0.2.0

lda: Topic modeling with latent Dirichlet allocation
====================================================

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Topic modeling with latent Dirichlet allocation. ``lda`` aims for simplicity.

``lda`` implements latent Dirichlet allocation (LDA) using collapsed Gibbs
sampling. LDA is described in `Blei et al. (2003)`_ and `Pritchard et al. (2000)`_.

Installation
------------

``pip install lda``

Getting started
---------------

``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.

.. code-block:: python

>>> 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)
>>> model = lda.LDA(n_topics=20, n_iter=500, random_state=1)
>>> model.fit(X)
>>> 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: church people told years last year time
Topic 1: elvis music fans york show concert king
Topic 2: pope trip mass vatican poland health john
Topic 3: film french against france festival magazine quebec
Topic 4: king michael romania president first service romanian
Topic 5: police family versace miami cunanan west home
Topic 6: germany german war political government minister nazi
Topic 7: harriman u.s clinton churchill ambassador paris british
Topic 8: yeltsin russian russia president kremlin moscow operation
Topic 9: prince queen bowles church king royal public
Topic 10: simpson million years south irish churches says
Topic 11: charles diana parker camilla marriage family royal
Topic 12: east peace prize president award catholic timor
Topic 13: order nuns india successor election roman sister
Topic 14: pope vatican hospital surgery rome roman doctors
Topic 15: mother teresa heart calcutta missionaries hospital charity
Topic 16: bernardin cardinal cancer church life catholic chicago
Topic 17: died funeral church city death buddhist israel
Topic 18: museum kennedy cultural city culture greek byzantine
Topic 19: art exhibition century city tour works madonna

The document-topic distributions are available in ``model.doc_topic_``.

.. code-block:: python

>>> 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: 11)
1 GERMANY: Historic Dresden church rising from WW2 ashes. DRESDEN, Germany 1996-08-21 (top topic: 0)
2 INDIA: Mother Teresa's condition said still unstable. CALCUTTA 1996-08-23 (top topic: 15)
3 UK: Palace warns British weekly over Charles pictures. LONDON 1996-08-25 (top topic: 11)
4 INDIA: Mother Teresa, slightly stronger, blesses nuns. CALCUTTA 1996-08-25 (top topic: 15)
5 INDIA: Mother Teresa's condition unchanged, thousands pray. CALCUTTA 1996-08-25 (top topic: 15)
6 INDIA: Mother Teresa shows signs of strength, blesses nuns. CALCUTTA 1996-08-26 (top topic: 15)
7 INDIA: Mother Teresa's condition improves, many pray. CALCUTTA, India 1996-08-25 (top topic: 15)
8 INDIA: Mother Teresa improves, nuns pray for "miracle". CALCUTTA 1996-08-26 (top topic: 15)
9 UK: Charles under fire over prospect of Queen Camilla. LONDON 1996-08-26 (top topic: 0)

Requirements
------------

Python 2.7 or Python 3.3+ is required. The following packages are required

- numpy_
- scipy_
- pbr_

Caveat
------

``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.

Important links
---------------

- Documentation: http://pythonhosted.org/lda
- Source code: https://github.com/ariddell/lda/
- Issue tracker: https://github.com/ariddell/lda/issues

License
-------

lda is licensed under Version 2.0 of the Mozilla Public License.

.. _Python: http://www.python.org/
.. _scikit-learn: http://scikit-learn.org
.. _hca: http://www.mloss.org/software/view/527/
.. _MALLET: http://mallet.cs.umass.edu/
.. _numpy: http://www.numpy.org/
.. _scipy: http://docs.scipy.org/doc/
.. _pbr: https://pypi.python.org/pypi/pbr
.. _Blei et al. (2003): http://jmlr.org/papers/v3/blei03a.html
.. _Pritchard et al. (2000): http://www.genetics.org/content/164/4/1567.full


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