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corpora.bleicorpus – Corpus in Blei’s LDA-C format

corpora.bleicorpus – Corpus in Blei’s LDA-C format

Blei’s LDA-C format.

class gensim.corpora.bleicorpus.BleiCorpus(fname, fname_vocab=None)

Corpus in Blei’s LDA-C format.

The corpus is represented as two files: one describing the documents, and another describing the mapping between words and their ids.

Each document is one line:

N fieldId1:fieldValue1 fieldId2:fieldValue2 ... fieldIdN:fieldValueN

The vocabulary is a file with words, one word per line; word at line K has an implicit id=K.

Initialize the corpus from a file.

fname_vocab is the file with vocabulary; if not specified, it defaults to fname.vocab.

docbyoffset(offset)

Return the document stored at file position offset.

classmethod 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.

save(*args, **kwargs)

Save the object to file (also see load).

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.

static save_corpus(fname, corpus, id2word=None, metadata=False)

Save a corpus in the LDA-C format.

There are actually two files saved: fname and fname.vocab, where fname.vocab is the vocabulary file.

This function is automatically called by BleiCorpus.serialize; don’t call it directly, call serialize instead.

classmethod serialize(fname, corpus, id2word=None, index_fname=None, progress_cnt=None, labels=None, metadata=False)

Iterate through the document stream corpus, saving the documents to fname and recording byte offset of each document. Save the resulting index structure to file index_fname (or fname.index is not set).

This relies on the underlying corpus class serializer providing (in addition to standard iteration):

  • save_corpus method that returns a sequence of byte offsets, one for

    each saved document,

  • the docbyoffset(offset) method, which returns a document positioned at offset bytes within the persistent storage (file).

Example:

>>> MmCorpus.serialize('test.mm', corpus)
>>> mm = MmCorpus('test.mm') # `mm` document stream now has random access
>>> print(mm[42]) # retrieve document no. 42, etc.