guidedlda 1.6.0.dev2

Topic modeling with Guided latent Dirichlet allocation

IN PROGRESS
===========

GuidedLDA: Guided Topic modeling with latent Dirichlet allocation
====================================================

``guidedlda`` implements latent Dirichlet allocation (LDA) using collapsed Gibbs
sampling. ``guidedlda`` is fast and is tested on Linux, OS X, and Windows.

You can read more about lda in `the documentation <https://guidedlda.readthedocs.io>`_.

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

``pip install guidedlda``

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

``guidedlda.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).

.. code-block:: python

>>> import numpy as np
>>> import guidedlda

>>> X = guidedlda.datasets.load_data(guidedlda.datasets.NYT)
>>> vocab = guidedlda.datasets.load_vocab(guidedlda.datasets.NYT)
>>> word2id = dict((v, idx) for idx, v in enumerate(vocab))

>>> X.shape
(8447, 3012)

>>> X.sum()
1221626

>>> model = guidedlda.GuidedLDA(n_topics=5, n_iter=100, random_state=7, refresh=20)
>>> model.fit(X)
>>> topic_word = model.topic_word_
>>> n_top_words = 8
INFO:guidedlda:n_documents: 8447
INFO:guidedlda:vocab_size: 3012
INFO:guidedlda:n_words: 1221626
INFO:guidedlda:n_topics: 5
INFO:guidedlda:n_iter: 100
WARNING:guidedlda:all zero column in document-term matrix found
INFO:guidedlda:<0> log likelihood: -11489265
INFO:guidedlda:<20> log likelihood: -9844667
INFO:guidedlda:<40> log likelihood: -9694223
INFO:guidedlda:<60> log likelihood: -9642506
INFO:guidedlda:<80> log likelihood: -9617962
INFO:guidedlda:<99> log likelihood: -9604031

>>> for i, topic_dist in enumerate(topic_word):
>>> topic_words = np.array(vocab)[np.argsort(topic_dist)][:-(n_top_words+1):-1]
>>> print('Topic {}: {}'.format(i, ' '.join(topic_words)))
Topic 0: company percent market business plan pay price increase
Topic 1: game play team win player season second start
Topic 2: life child write man school woman father family
Topic 3: place open small house music turn large play
Topic 4: official state government political states issue leader case

>>> seed_topic_list
>>> seed_topic_list = [['game', 'team', 'win', 'player', 'season', 'second', 'victory'],
>>> ['percent', 'company', 'market', 'price', 'sell', 'business', 'stock', 'share'],
>>> ['music', 'write', 'art', 'book', 'world', 'film'],
>>> ['political', 'government', 'leader', 'official', 'state', 'country', 'american','case', 'law', 'police', 'charge', 'officer', 'kill', 'arrest', 'lawyer']]

>>> model = guidedlda.GuidedLDA(n_topics=5, n_iter=100, random_state=7, refresh=20)

>>> seed_topics = {}
>>> for t_id, st in enumerate(seed_topic_list):
>>> for word in st:
>>> seed_topics[word2id[word]] = t_id

>>> model.fit(X, seed_topics=seed_topics, seed_confidence=0.15)
INFO:guidedlda:n_documents: 8447
INFO:guidedlda:vocab_size: 3012
INFO:guidedlda:n_words: 1221626
INFO:guidedlda:n_topics: 5
INFO:guidedlda:n_iter: 100
WARNING:guidedlda:all zero column in document-term matrix found
INFO:guidedlda:<0> log likelihood: -11486362
INFO:guidedlda:<20> log likelihood: -9767277
INFO:guidedlda:<40> log likelihood: -9663718
INFO:guidedlda:<60> log likelihood: -9624150
INFO:guidedlda:<80> log likelihood: -9601684
INFO:guidedlda:<99> log likelihood: -9587803


>>> n_top_words = 10
>>> topic_word = model.topic_word_
>>> for i, topic_dist in enumerate(topic_word):
>>> topic_words = np.array(vocab)[np.argsort(topic_dist)][:-(n_top_words+1):-1]
>>> print('Topic {}: {}'.format(i, ' '.join(topic_words)))
Topic 0: game play team win season player second point start victory
Topic 1: company percent market price business sell executive pay plan sale
Topic 2: play life man music place write turn woman old book
Topic 3: official government state political leader states issue case member country
Topic 4: school child city program problem student state study family group

The document-topic distributions should be retrived as: ``doc_topic = model.transform(X)``.

.. code-block:: python

>>> doc_topic = model.transform(X)
>>> for i in range(9):
>>> print("top topic: {} Document: {}".format(doc_topic[i].argmax(),
', '.join(np.array(vocab)[list(reversed(X[i,:].argsort()))[0:5]])))
top topic: 4 Document: plant, increase, food, increasingly, animal
top topic: 3 Document: explain, life, country, citizen, nation
top topic: 2 Document: thing, solve, problem, machine, carry
top topic: 2 Document: company, authority, opera, artistic, director
top topic: 3 Document: partner, lawyer, attorney, client, indict
top topic: 2 Document: roll, place, soon, treat, rating
top topic: 3 Document: city, drug, program, commission, report
top topic: 1 Document: company, comic, series, case, executive
top topic: 3 Document: son, scene, charge, episode, attack

Save the model for production or for running later:

.. code-block:: python
>>> from six.moves import cPickle as pickle
>>> # Uncomment next step if you want to lighten the model object
>>> # This step will delete some matrices inside the model.
>>> # you will be able to use model.transform(X) the same way as earlier.
>>> # you wont be able to use model.fit_transform(X_new)
>>> # model.purge_extra_matrices()
>>> with open('guidedlda_model.pickle', 'wb') as file_handle:
>>> pickle.dump(model, file_handle)
>>> # load the model for prediction
>>> with open('guidedlda_model.pickle', 'rb') as file_handle:
>>> model = pickle.load(file_handle)
>>> doc_topic = model.transform(X)


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

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

- numpy_
- pbr_

Caveat
------

``guidedlda`` aims for Guiding LDA. More often then not the topics we get from a LDA model are not to our setisfaction. GuidedLDA can give the topics a nudge in the direction we want it to converge. We have production trained it for half a million documents (We have a big machine). We have run predictions on millions and manually checked topics for thousands (we are sattisfied with the results).

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 ``guidedlda``, 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.

Notes
-----

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)`_. And Guided LDA is described in `Jagadeesh Jagarlamudi, Hal Daume III and Raghavendra Udupa (2012)`_


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

- Documentation: http://guidedlda.readthedocs.org
- Source code: https://github.com/vi3k6i5/guidedlda/
- Issue tracker: https://github.com/vi3k6i5/guidedlda/issues

Other implementations
---------------------
- scikit-learn_'s `LatentDirichletAllocation <http://scikit-learn.org/dev/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html>`_ (uses online variational inference)
- `gensim <https://pypi.python.org/pypi/gensim>`_ (uses online variational inference)

Credits
-------
I would like to thank Creators of LDA project: https://github.com/lda-project/lda.
I used the code from that LDA project as base to implement GuidedLDA on top of it.
Special thanks to : https://twitter.com/ariddell, https://twitter.com/tdhopper :)

License
-------

guidedlda 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/
.. _pbr: https://pypi.python.org/pypi/pbr
.. _Cython: http://cython.org
.. _Blei et al. (2003): http://jmlr.org/papers/v3/blei03a.html
.. _Pritchard et al. (2000): http://www.genetics.org/content/155/2/945.full
.. _Griffiths and Steyvers (2004): http://www.pnas.org/content/101/suppl_1/5228.abstract
.. _Jagadeesh Jagarlamudi Hal Daume III and Raghavendra Udupa (2012): http://www.umiacs.umd.edu/~jags/pdfs/GuidedLDA.pdf