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https://github.com/lda-project/lda

Topic modeling with latent Dirichlet allocation using Gibbs sampling
https://github.com/lda-project/lda

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Topic modeling with latent Dirichlet allocation using Gibbs sampling

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README 

        
lda: Topic modeling with latent Dirichlet allocation

====================================================



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**NOTE: This package is in maintenance mode. Critical bugs will be fixed. No new features will be added.**



``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 `_.



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. The input below, ``X``, is a document-term matrix (sparse matrices

are accepted).



.. 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)

    >>> 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):-1]

    ...     print('Topic {}: {}'.format(i, ' '.join(topic_words)))



    Topic 0: british churchill sale million major letters west britain

    Topic 1: church government political country state people party against

    Topic 2: elvis king fans presley life concert young death

    Topic 3: yeltsin russian russia president kremlin moscow michael operation

    Topic 4: pope vatican paul john surgery hospital pontiff rome

    Topic 5: family funeral police miami versace cunanan city service

    Topic 6: simpson former years court president wife south church

    Topic 7: order mother successor election nuns church nirmala head

    Topic 8: charles prince diana royal king queen parker bowles

    Topic 9: film french france against bardot paris poster animal

    Topic 10: germany german war nazi letter christian book jews

    Topic 11: east peace prize award timor quebec belo leader

    Topic 12: n't life show told very love television father

    Topic 13: years year time last church world people say

    Topic 14: mother teresa heart calcutta charity nun hospital missionaries

    Topic 15: city salonika capital buddhist cultural vietnam byzantine show

    Topic 16: music tour opera singer israel people film israeli

    Topic 17: church catholic bernardin cardinal bishop wright death cancer

    Topic 18: harriman clinton u.s ambassador paris president churchill france

    Topic 19: city museum art exhibition century million churches set



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: 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)



Requirements

------------



Python ≥3.10 and NumPy.



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.



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)`_.



Important links

---------------



- Documentation: http://lda.readthedocs.org

- Source code: https://github.com/lda-project/lda/

- Issue tracker: https://github.com/lda-project/lda/issues



Other implementations

---------------------

- scikit-learn_'s `LatentDirichletAllocation `_ (uses online variational inference)

- `gensim `_ (uses online variational inference)



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: https://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



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