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models.wrappers.ldamallet – Latent Dirichlet Allocation via Mallet

models.wrappers.ldamallet – Latent Dirichlet Allocation via Mallet

Python wrapper for Latent Dirichlet Allocation (LDA) from MALLET, the Java topic modelling toolkit

This module allows both LDA model estimation from a training corpus and inference of topic distribution on new, unseen documents, using an (optimized version of) collapsed gibbs sampling from MALLET.


MALLET’s LDA training requires O(corpus\_words) of memory, keeping the entire corpus in RAM. If you find yourself running out of memory, either decrease the workers constructor parameter, or use gensim.models.ldamodel.LdaModel or gensim.models.ldamulticore.LdaMulticore which needs only O(1) memory. The wrapped model can NOT be updated with new documents for online training – use LdaModel or LdaMulticore for that.


Use official guide or this one

sudo apt-get install default-jdk
sudo apt-get install ant
git clone
cd Mallet/


>>> from gensim.test.utils import common_corpus, common_dictionary
>>> from gensim.models.wrappers import LdaMallet
>>> path_to_mallet_binary = "/path/to/mallet/binary"
>>> model = LdaMallet(path_to_mallet_binary, corpus=common_corpus, num_topics=20, id2word=common_dictionary)
>>> vector = model[common_corpus[0]]  # LDA topics of a documents
class gensim.models.wrappers.ldamallet.LdaMallet(mallet_path, corpus=None, num_topics=100, alpha=50, id2word=None, workers=4, prefix=None, optimize_interval=0, iterations=1000, topic_threshold=0.0, random_seed=0)

Bases: gensim.utils.SaveLoad, gensim.models.basemodel.BaseTopicModel

Python wrapper for LDA using MALLET.

Communication between MALLET and Python takes place by passing around data files on disk and calling Java with


This is only python wrapper for MALLET LDA, you need to install original implementation first and pass the path to binary to mallet_path.

  • mallet_path (str) – Path to the mallet binary, e.g. /home/username/mallet-2.0.7/bin/mallet.
  • corpus (iterable of iterable of (int, int), optional) – Collection of texts in BoW format.
  • num_topics (int, optional) – Number of topics.
  • alpha (int, optional) – Alpha parameter of LDA.
  • id2word (Dictionary, optional) – Mapping between tokens ids and words from corpus, if not specified - will be inferred from corpus.
  • workers (int, optional) – Number of threads that will be used for training.
  • prefix (str, optional) – Prefix for produced temporary files.
  • optimize_interval (int, optional) – Optimize hyperparameters every optimize_interval iterations (sometimes leads to Java exception 0 to switch off hyperparameter optimization).
  • iterations (int, optional) – Number of training iterations.
  • topic_threshold (float, optional) – Threshold of the probability above which we consider a topic.
  • random_seed (int, optional) – Random seed to ensure consistent results, if 0 - use system clock.
convert_input(corpus, infer=False, serialize_corpus=True)

Convert corpus to Mallet format and save it to a temporary text file.

  • corpus (iterable of iterable of (int, int)) – Collection of texts in BoW format.
  • infer (bool, optional) –

  • serialize_corpus (bool, optional) –

corpus2mallet(corpus, file_like)

Convert corpus to Mallet format and write it to file_like descriptor.


document id[SPACE]label (not used)[SPACE]whitespace delimited utf8-encoded tokens[NEWLINE]
  • corpus (iterable of iterable of (int, int)) – Collection of texts in BoW format.
  • file_like (file-like object) – Opened file.

Get path to corpus.mallet file.

Returns:Path to corpus.mallet file.
Return type:str

Get path to corpus text file.

Returns:Path to corpus text file.
Return type:str

Get path to document topic text file.

Returns:Path to document topic text file.
Return type:str

Get path to inferencer.mallet file.

Returns:Path to inferencer.mallet file.
Return type:str

Get path to temporary file.

Returns:Path to file.
Return type:str

Get path to topic keys text file.

Returns:Path to topic keys text file.
Return type:str

Get path to word weight file.

Returns:Path to word weight file.
Return type:str

Get topics X words matrix.

Returns:Topics X words matrix, shape num_topics x vocabulary_size.
Return type:numpy.ndarray

“Get the version of Mallet.

Parameters:direc_path (str) – Path to mallet archive.
Returns:Version of mallet.
Return type:str
classmethod load(*args, **kwargs)

Load a previously saved LdaMallet class. Handles backwards compatibility from older LdaMallet versions which did not use random_seed parameter.


Load document topics from gensim.models.wrappers.ldamallet.LdaMallet.fdoctopics() file. Shortcut for gensim.models.wrappers.ldamallet.LdaMallet.read_doctopics().

Returns:Sequence of LDA vectors for documents.
Return type:iterator of list of (int, float)

Load words X topics matrix from gensim.models.wrappers.ldamallet.LdaMallet.fstate() file.

Returns:Matrix words X topics.
Return type:numpy.ndarray
print_topic(topicno, topn=10)

Get a single topic as a formatted string.

  • topicno (int) – Topic id.
  • topn (int) – Number of words from topic that will be used.

String representation of topic, like ‘-0.340 * “category” + 0.298 * “$M$” + 0.183 * “algebra” + … ‘.

Return type:


print_topics(num_topics=20, num_words=10)

Get the most significant topics (alias for show_topics() method).

  • num_topics (int, optional) – The number of topics to be selected, if -1 - all topics will be in result (ordered by significance).
  • num_words (int, optional) – The number of words to be included per topics (ordered by significance).

Sequence with (topic_id, [(word, value), … ]).

Return type:

list of (int, list of (str, float))

read_doctopics(fname, eps=1e-06, renorm=True)

Get document topic vectors from MALLET’s “doc-topics” format, as sparse gensim vectors.

  • fname (str) – Path to input file with document topics.
  • eps (float, optional) – Threshold for probabilities.
  • renorm (bool, optional) – If True - explicitly re-normalize distribution.

RuntimeError – If any line in invalid format.


list of (int, float) – LDA vectors for document.

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

Save the object to a file.

  • fname_or_handle (str or file-like) – Path to output file or already opened file-like object. If the object is a file handle, no special array handling will be performed, all attributes will be saved to the same file.
  • separately (list of str or None, optional) –

    If None, automatically detect large numpy/scipy.sparse arrays in the object being stored, and store them into separate files. This prevent memory errors for large objects, and also allows memory-mapping the large arrays for efficient loading and sharing the large arrays in RAM between multiple processes.

    If list of str: store these attributes into separate files. The automated size check is not performed in this case.

  • sep_limit (int, optional) – Don’t store arrays smaller than this separately. In bytes.
  • ignore (frozenset of str, optional) – Attributes that shouldn’t be stored at all.
  • pickle_protocol (int, optional) – Protocol number for pickle.

See also

Load object from file.
show_topic(topicid, topn=10, num_words=None)

Get num_words most probable words for the given topicid.

  • topicid (int) – Id of topic.
  • topn (int, optional) – Top number of topics that you’ll receive.
  • num_words (int, optional) – DEPRECATED PARAMETER, use topn instead.

Sequence of probable words, as a list of (word, word_probability) for topicid topic.

Return type:

list of (str, float)

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

Get the num_words most probable words for num_topics number of topics.

  • num_topics (int, optional) – Number of topics to return, set -1 to get all topics.
  • num_words (int, optional) – Number of words.
  • log (bool, optional) – If True - write topic with logging too, used for debug proposes.
  • formatted (bool, optional) – If True - return the topics as a list of strings, otherwise as lists of (weight, word) pairs.

  • list of str – Topics as a list of strings (if formatted=True) OR
  • list of (float, str) – Topics as list of (weight, word) pairs (if formatted=False)


Train Mallet LDA.

Parameters:corpus (iterable of iterable of (int, int)) – Corpus in BoW format
gensim.models.wrappers.ldamallet.malletmodel2ldamodel(mallet_model, gamma_threshold=0.001, iterations=50)

Convert LdaMallet to LdaModel.

This works by copying the training model weights (alpha, beta…) from a trained mallet model into the gensim model.

  • mallet_model (LdaMallet) – Trained Mallet model
  • gamma_threshold (float, optional) – To be used for inference in the new LdaModel.
  • iterations (int, optional) – Number of iterations to be used for inference in the new LdaModel.

Gensim native LDA.

Return type: