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models.ldamodel – Latent Dirichlet Allocation

models.ldamodel – Latent Dirichlet Allocation

For a faster implementation of LDA (parallelized for multicore machines), see gensim.models.ldamulticore.

Latent Dirichlet Allocation (LDA) in Python.

This module allows both LDA model estimation from a training corpus and inference of topic distribution on new, unseen documents. The model can also be updated with new documents for online training.

The core estimation code is based on the script by M. Hoffman [1], see Hoffman, Blei, Bach: Online Learning for Latent Dirichlet Allocation, NIPS 2010.

The algorithm:

  • is streamed: training documents may come in sequentially, no random access required,
  • runs in constant memory w.r.t. the number of documents: size of the training corpus does not affect memory footprint, can process corpora larger than RAM, and
  • is distributed: makes use of a cluster of machines, if available, to speed up model estimation.
class gensim.models.ldamodel.LdaModel(corpus=None, num_topics=100, id2word=None, distributed=False, chunksize=2000, passes=1, update_every=1, alpha='symmetric', eta=None, decay=0.5, offset=1.0, eval_every=10, iterations=50, gamma_threshold=0.001, minimum_probability=0.01, random_state=None, ns_conf={}, minimum_phi_value=0.01, per_word_topics=False)

Bases: gensim.interfaces.TransformationABC, gensim.models.basemodel.BaseTopicModel

The constructor estimates Latent Dirichlet Allocation model parameters based on a training corpus:

>>> lda = LdaModel(corpus, num_topics=10)

You can then infer topic distributions on new, unseen documents, with

>>> doc_lda = lda[doc_bow]

The model can be updated (trained) with new documents via

>>> lda.update(other_corpus)

Model persistency is achieved through its load/save methods.

If given, start training from the iterable corpus straight away. If not given, the model is left untrained (presumably because you want to call update() manually).

num_topics is the number of requested latent topics to be extracted from the training corpus.

id2word is a mapping from word ids (integers) to words (strings). It is used to determine the vocabulary size, as well as for debugging and topic printing.

alpha and eta are hyperparameters that affect sparsity of the document-topic (theta) and topic-word (lambda) distributions. Both default to a symmetric 1.0/num_topics prior.

alpha can be set to an explicit array = prior of your choice. It also support special values of ‘asymmetric’ and ‘auto’: the former uses a fixed normalized asymmetric 1.0/topicno prior, the latter learns an asymmetric prior directly from your data.

eta can be a scalar for a symmetric prior over topic/word distributions, or a vector of shape num_words, which can be used to impose (user defined) asymmetric priors over the word distribution. It also supports the special value ‘auto’, which learns an asymmetric prior over words directly from your data. eta can also be a matrix of shape num_topics x num_words, which can be used to impose asymmetric priors over the word distribution on a per-topic basis (can not be learned from data).

Turn on distributed to force distributed computing (see the web tutorial on how to set up a cluster of machines for gensim).

Calculate and log perplexity estimate from the latest mini-batch every eval_every model updates (setting this to 1 slows down training ~2x; default is 10 for better performance). Set to None to disable perplexity estimation.

decay and offset parameters are the same as Kappa and Tau_0 in Hoffman et al, respectively.

minimum_probability controls filtering the topics returned for a document (bow).

random_state can be a np.random.RandomState object or the seed for one


>>> lda = LdaModel(corpus, num_topics=100)  # train model
>>> print(lda[doc_bow]) # get topic probability distribution for a document
>>> lda.update(corpus2) # update the LDA model with additional documents
>>> print(lda[doc_bow])
>>> lda = LdaModel(corpus, num_topics=50, alpha='auto', eval_every=5)  # train asymmetric alpha from data
bound(corpus, gamma=None, subsample_ratio=1.0)

Estimate the variational bound of documents from corpus: E_q[log p(corpus)] - E_q[log q(corpus)]

gamma are the variational parameters on topic weights for each corpus document (=2d matrix=what comes out of inference()). If not supplied, will be inferred from the model.


Clear model state (free up some memory). Used in the distributed algo.

do_estep(chunk, state=None)

Perform inference on a chunk of documents, and accumulate the collected sufficient statistics in state (or self.state if None).

do_mstep(rho, other, extra_pass=False)

M step: use linear interpolation between the existing topics and collected sufficient statistics in other to update the topics.

get_document_topics(bow, minimum_probability=None, minimum_phi_value=None, per_word_topics=False)

Return topic distribution for the given document bow, as a list of (topic_id, topic_probability) 2-tuples.

Ignore topics with very low probability (below minimum_probability).

If per_word_topics is True, it also returns a list of topics, sorted in descending order of most likely topics for that word. It also returns a list of word_ids and each words corresponding topics’ phi_values, multiplied by feature length (i.e, word count)

get_term_topics(word_id, minimum_probability=None)

Returns most likely topics for a particular word in vocab.

get_topic_terms(topicid, topn=10)

Return a list of (word_id, probability) 2-tuples for the most probable words in topic topicid.

Only return 2-tuples for the topn most probable words (ignore the rest).

inference(chunk, collect_sstats=False)

Given a chunk of sparse document vectors, estimate gamma (parameters controlling the topic weights) for each document in the chunk.

This function does not modify the model (=is read-only aka const). The whole input chunk of document is assumed to fit in RAM; chunking of a large corpus must be done earlier in the pipeline.

If collect_sstats is True, also collect sufficient statistics needed to update the model’s topic-word distributions, and return a 2-tuple (gamma, sstats). Otherwise, return (gamma, None). gamma is of shape len(chunk) x self.num_topics.

Avoids computing the phi variational parameter directly using the optimization presented in Lee, Seung: Algorithms for non-negative matrix factorization, NIPS 2001.

init_dir_prior(prior, name)
classmethod load(fname, *args, **kwargs)

Load a previously saved object from file (also see save).

Large arrays can be memmap’ed back as read-only (shared memory) by setting mmap=’r’:

>>> LdaModel.load(fname, mmap='r')
log_perplexity(chunk, total_docs=None)

Calculate and return per-word likelihood bound, using the chunk of documents as evaluation corpus. Also output the calculated statistics. incl. perplexity=2^(-bound), to log at INFO level.

print_topic(topicno, topn=10)

Return a single topic as a formatted string. See show_topic() for parameters.

>>> lsimodel.print_topic(10, topn=5)
'-0.340 * "category" + 0.298 * "$M$" + 0.183 * "algebra" + -0.174 * "functor" + -0.168 * "operator"'
print_topics(num_topics=20, num_words=10)

Alias for show_topics() that prints the num_words most probable words for topics number of topics to log. Set topics=-1 to print all topics.

save(fname, ignore=['state', 'dispatcher'], separately=None, *args, **kwargs)

Save the model to file.

Large internal arrays may be stored into separate files, with fname as prefix.

separately can be used to define which arrays should be stored in separate files.

ignore parameter can be used to define which variables should be ignored, i.e. left out from the pickled lda model. By default the internal state is ignored as it uses its own serialisation not the one provided by LdaModel. The state and dispatcher will be added to any ignore parameter defined.

Note: do not save as a compressed file if you intend to load the file back with mmap.

Note: If you intend to use models across Python 2/3 versions there are a few things to keep in mind:

  1. The pickled Python dictionaries will not work across Python versions
  2. The save method does not automatically save all np arrays using np, only those ones that exceed sep_limit set in The main concern here is the alpha array if for instance using alpha=’auto’.

Please refer to the wiki recipes section ( for an example on how to work around these issues.

show_topic(topicid, topn=10)

Return a list of (word, probability) 2-tuples for the most probable words in topic topicid.

Only return 2-tuples for the topn most probable words (ignore the rest).

show_topics(num_topics=10, num_words=10, log=False, formatted=True)

For num_topics number of topics, return num_words most significant words (10 words per topic, by default).

The topics are returned as a list – a list of strings if formatted is True, or a list of (word, probability) 2-tuples if False.

If log is True, also output this result to log.

Unlike LSA, there is no natural ordering between the topics in LDA. The returned num_topics <= self.num_topics subset of all topics is therefore arbitrary and may change between two LDA training runs.

top_topics(corpus, num_words=20)

Calculate the Umass topic coherence for each topic. Algorithm from Mimno, Wallach, Talley, Leenders, McCallum: Optimizing Semantic Coherence in Topic Models, CEMNLP 2011.

update(corpus, chunksize=None, decay=None, offset=None, passes=None, update_every=None, eval_every=None, iterations=None, gamma_threshold=None, chunks_as_numpy=False)

Train the model with new documents, by EM-iterating over corpus until the topics converge (or until the maximum number of allowed iterations is reached). corpus must be an iterable (repeatable stream of documents),

In distributed mode, the E step is distributed over a cluster of machines.

This update also supports updating an already trained model (self) with new documents from corpus; the two models are then merged in proportion to the number of old vs. new documents. This feature is still experimental for non-stationary input streams.

For stationary input (no topic drift in new documents), on the other hand, this equals the online update of Hoffman et al. and is guaranteed to converge for any decay in (0.5, 1.0>. Additionally, for smaller corpus sizes, an increasing offset may be beneficial (see Table 1 in Hoffman et al.)


corpus (gensim corpus): The corpus with which the LDA model should be updated.

chunks_as_numpy (bool): Whether each chunk passed to .inference should be a np
array of not. np can in some settings turn the term IDs into floats, these will be converted back into integers in inference, which incurs a performance hit. For distributed computing it may be desirable to keep the chunks as np arrays.

For other parameter settings, see LdaModel constructor.

update_alpha(gammat, rho)

Update parameters for the Dirichlet prior on the per-document topic weights alpha given the last gammat.

update_eta(lambdat, rho)

Update parameters for the Dirichlet prior on the per-topic word weights eta given the last lambdat.

class gensim.models.ldamodel.LdaState(eta, shape)

Bases: gensim.utils.SaveLoad

Encapsulate information for distributed computation of LdaModel objects.

Objects of this class are sent over the network, so try to keep them lean to reduce traffic.

blend(rhot, other, targetsize=None)

Given LdaState other, merge it with the current state. Stretch both to targetsize documents before merging, so that they are of comparable magnitude.

Merging is done by average weighting: in the extremes, rhot=0.0 means other is completely ignored; rhot=1.0 means self is completely ignored.

This procedure corresponds to the stochastic gradient update from Hoffman et al., algorithm 2 (eq. 14).

blend2(rhot, other, targetsize=None)

Alternative, more simple blend.

load(fname, mmap=None)

Load a previously saved object from file (also see save).

If the object was saved with large arrays stored separately, you can load these arrays via mmap (shared memory) using mmap=’r’. Default: don’t use mmap, load large arrays as normal objects.

If the file being loaded is compressed (either ‘.gz’ or ‘.bz2’), then mmap=None must be set. Load will raise an IOError if this condition is encountered.


Merge the result of an E step from one node with that of another node (summing up sufficient statistics).

The merging is trivial and after merging all cluster nodes, we have the exact same result as if the computation was run on a single node (no approximation).


Prepare the state for a new EM iteration (reset sufficient stats).

save(fname_or_handle, separately=None, sep_limit=10485760, ignore=frozenset([]), pickle_protocol=2)

Save the object to file (also see load).

fname_or_handle is either a string specifying the file name to save to, or an open file-like object which can be written to. If the object is a file handle, no special array handling will be performed; all attributes will be saved to the same file.

If separately is None, automatically detect large numpy/scipy.sparse arrays in the object being stored, and store them into separate files. This avoids pickle memory errors and allows mmap’ing large arrays back on load efficiently.

You can also set separately manually, in which case it must be a list of attribute names to be stored in separate files. The automatic check is not performed in this case.

ignore is a set of attribute names to not serialize (file handles, caches etc). On subsequent load() these attributes will be set to None.

pickle_protocol defaults to 2 so the pickled object can be imported in both Python 2 and 3.

gensim.models.ldamodel.update_dir_prior(prior, N, logphat, rho)

Updates a given prior using Newton’s method, described in Huang: Maximum Likelihood Estimation of Dirichlet Distribution Parameters.