Package: guidedlda
GuidedLDA: Guided Topic modeling with latent Dirichlet allocation====================================================.. image:: https://readthedocs.org/projects/guidedlda/badge/?version=latest:target: http://guidedlda.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image:: https://badge.fury.io/py/guidedlda.svg:target: https://badge.fury.io/py/guidedlda:alt: Package version``GuidedLDA`` OR ``SeededLDA`` implements latent Dirichlet allocation (LDA) using collapsed Gibbs sampling. ``GuidedLDA`` can be guided by setting some seed words per topic. Which will make the topics converge in that direction.You can read more about guidedlda in `the documentation <https://guidedlda.readthedocs.io>`_.Installation------------::pip install guidedldaIf pip install does not work, then try the next step:::https://github.com/vi3k6i5/GuidedLDAcd GuidedLDAsh build_dist.shpython setup.py sdistpip install -e .If the above step also does not work, please raise an `issue <https://github.com/vi3k6i5/guidedlda/issues>`_ with details of your workstation's OS version, Python version, architecture etc. and I will try my best to fix it ASAP.Getting started---------------``guidedlda.GuidedLDA`` implements latent Dirichlet allocation (LDA). The interface followsconventions found in scikit-learn_.`Example Code <https://github.com/vi3k6i5/GuidedLDA/blob/master/examples/example_seeded_lda.py>`_.The following demonstrates how to inspect a model of a subset of the NYTnews dataset. The input below, ``X``, is a document-term matrix (sparse matricesare 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>>> # Normal LDA without seeding>>> model = guidedlda.GuidedLDA(n_topics=5, n_iter=100, random_state=7, refresh=20)>>> model.fit(X)INFO:guidedlda:n_documents: 8447INFO:guidedlda:vocab_size: 3012INFO:guidedlda:n_words: 1221626INFO:guidedlda:n_topics: 5INFO:guidedlda:n_iter: 100WARNING:guidedlda:all zero column in document-term matrix foundINFO:guidedlda:<0> log likelihood: -11489265INFO:guidedlda:<20> log likelihood: -9844667INFO:guidedlda:<40> log likelihood: -9694223INFO:guidedlda:<60> log likelihood: -9642506INFO:guidedlda:<80> log likelihood: -9617962INFO:guidedlda:<99> log likelihood: -9604031>>> topic_word = model.topic_word_>>> 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: company percent market business plan pay price increaseTopic 1: game play team win player season second startTopic 2: life child write man school woman father familyTopic 3: place open small house music turn large playTopic 4: official state government political states issue leader case>>> # Guided LDA with seed topics.>>> 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: 8447INFO:guidedlda:vocab_size: 3012INFO:guidedlda:n_words: 1221626INFO:guidedlda:n_topics: 5INFO:guidedlda:n_iter: 100WARNING:guidedlda:all zero column in document-term matrix foundINFO:guidedlda:<0> log likelihood: -11486362INFO:guidedlda:<20> log likelihood: -9767277INFO:guidedlda:<40> log likelihood: -9663718INFO:guidedlda:<60> log likelihood: -9624150INFO:guidedlda:<80> log likelihood: -9601684INFO: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 victoryTopic 1: company percent market price business sell executive pay plan saleTopic 2: play life man music place write turn woman old bookTopic 3: official government state political leader states issue case member countryTopic 4: school child city program problem student state study family groupThe 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, animaltop topic: 3 Document: explain, life, country, citizen, nationtop topic: 2 Document: thing, solve, problem, machine, carrytop topic: 2 Document: company, authority, opera, artistic, directortop topic: 3 Document: partner, lawyer, attorney, client, indicttop topic: 2 Document: roll, place, soon, treat, ratingtop topic: 3 Document: city, drug, program, commission, reporttop topic: 1 Document: company, comic, series, case, executivetop topic: 3 Document: son, scene, charge, episode, attackSave 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 satisfaction. 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 satisfied 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 `Pritchardet 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/issuesOther 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 the 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.Thanks to : `Allen Riddell <https://twitter.com/ariddell>`_ and `Tim Hopper <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