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.
I published an article about it on freecodecamp Medium blog.
pip install guidedlda
If pip install does not work, then try the next step:
https://github.com/vi3k6i5/GuidedLDA cd GuidedLDA sh build_dist.sh python setup.py sdist pip install -e .
If the above step also does not work, please raise an issue with details of your workstation's OS version, Python version, architecture etc. and I will try my best to fix it ASAP.
guidedlda.GuidedLDA 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 NYT
news dataset. The input below, X, is a document-term matrix (sparse matrices
are accepted).
>>> 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: 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
>>> 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 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
>>> # 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: 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 groupThe document-topic distributions should be retrived as: doc_topic = model.transform(X).
>>> 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, attackOptionally, reduce the model by purging additional matrices:
>>> # Next step will 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()Save the model for production or for running later:
>>> from six.moves import cPickle as pickle
>>> 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)Python 2.7 or Python 3.3+ is required. The following packages are required
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.
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)
- Documentation: http://guidedlda.readthedocs.org
- Source code: https://github.com/vi3k6i5/guidedlda/
- Issue tracker: https://github.com/vi3k6i5/guidedlda/issues
- scikit-learn's LatentDirichletAllocation (uses online variational inference)
- gensim (uses online variational inference)
I would like to thank the creators of LDA project. I used the code from that LDA project as base to implement GuidedLDA on top of it.
Thanks to : Allen Riddell and Tim Hopper. :)
guidedlda is licensed under Version 2.0 of the Mozilla Public License.