corpora.ucicorpus – Corpus in UCI format¶Corpus in UCI format.
gensim.corpora.ucicorpus.UciCorpus(fname, fname_vocab=None)¶Bases: gensim.corpora.ucicorpus.UciReader, gensim.corpora.indexedcorpus.IndexedCorpus
Corpus in the UCI bag-of-words format.
fname (str) – Path to corpus in UCI format.
fname_vocab (bool, optional) – Path to vocab.
Examples
>>> from gensim.corpora import UciCorpus
>>> from gensim.test.utils import datapath
>>>
>>> corpus = UciCorpus(datapath('testcorpus.uci'))
>>> for document in corpus:
... pass
create_dictionary()¶Generate gensim.corpora.dictionary.Dictionary directly from the corpus and vocabulary data.
Dictionary, based on corpus.
Examples
>>> from gensim.corpora.ucicorpus import UciCorpus
>>> from gensim.test.utils import datapath
>>> ucc = UciCorpus(datapath('testcorpus.uci'))
>>> dictionary = ucc.create_dictionary()
docbyoffset(self, offset)¶Get the document at file offset offset (in bytes).
offset (int) – File offset, in bytes, of the desired document.
Document in sparse bag-of-words format.
list of (int, str)
input¶object
input
load(fname, mmap=None)¶Load an object previously saved using save() from a file.
fname (str) – Path to file that contains needed object.
mmap (str, optional) – Memory-map option. If the object was saved with large arrays stored separately, you can load these arrays via mmap (shared memory) using mmap=’r’. If the file being loaded is compressed (either ‘.gz’ or ‘.bz2’), then `mmap=None must be set.
See also
save()Save object to file.
Object loaded from fname.
object
AttributeError – When called on an object instance instead of class (this is a class method).
num_docs¶‘long long’
num_docs
num_nnz¶‘long long’
num_nnz
num_terms¶‘long long’
num_terms
save(*args, **kwargs)¶Saves corpus in-memory state.
Warning
This save only the “state” of a corpus class, not the corpus data!
For saving data use the serialize method of the output format you’d like to use
(e.g. gensim.corpora.mmcorpus.MmCorpus.serialize()).
save_corpus(fname, corpus, id2word=None, progress_cnt=10000, metadata=False)¶Save a corpus in the UCI Bag-of-Words format.
Warning
This function is automatically called by :meth`gensim.corpora.ucicorpus.UciCorpus.serialize`, don’t call it directly, call :meth`gensim.corpora.ucicorpus.UciCorpus.serialize` instead.
fname (str) – Path to output file.
corpus (iterable of iterable of (int, int)) – Corpus in BoW format.
id2word ({dict of (int, str), gensim.corpora.dictionary.Dictionary}, optional) – Mapping between words and their ids. If None - will be inferred from corpus.
progress_cnt (int, optional) – Progress counter, write log message each progress_cnt documents.
metadata (bool, optional) – THIS PARAMETER WILL BE IGNORED.
Notes
There are actually two files saved: fname and fname.vocab, where fname.vocab is the vocabulary file.
serialize(fname, corpus, id2word=None, index_fname=None, progress_cnt=None, labels=None, metadata=False)¶Serialize corpus with offset metadata, allows to use direct indexes after loading.
fname (str) – Path to output file.
corpus (iterable of iterable of (int, float)) – Corpus in BoW format.
id2word (dict of (str, str), optional) – Mapping id -> word.
index_fname (str, optional) – Where to save resulting index, if None - store index to fname.index.
progress_cnt (int, optional) – Number of documents after which progress info is printed.
labels (bool, optional) – If True - ignore first column (class labels).
metadata (bool, optional) – If True - ensure that serialize will write out article titles to a pickle file.
Examples
>>> from gensim.corpora import MmCorpus
>>> from gensim.test.utils import get_tmpfile
>>>
>>> corpus = [[(1, 0.3), (2, 0.1)], [(1, 0.1)], [(2, 0.3)]]
>>> output_fname = get_tmpfile("test.mm")
>>>
>>> MmCorpus.serialize(output_fname, corpus)
>>> mm = MmCorpus(output_fname) # `mm` document stream now has random access
>>> print(mm[1]) # retrieve document no. 42, etc.
[(1, 0.1)]
skip_headers(input_file)¶Skip headers in input_file.
input_file (file) – File object.
transposed¶‘bool’
transposed
gensim.corpora.ucicorpus.UciReader(input)¶Bases: gensim.corpora._mmreader.MmReader
Reader of UCI format for gensim.corpora.ucicorpus.UciCorpus.
input (str) – Path to file in UCI format.
docbyoffset(self, offset)¶Get the document at file offset offset (in bytes).
offset (int) – File offset, in bytes, of the desired document.
Document in sparse bag-of-words format.
list of (int, str)
input¶object
input
num_docs¶‘long long’
num_docs
num_nnz¶‘long long’
num_nnz
num_terms¶‘long long’
num_terms
skip_headers(input_file)¶Skip headers in input_file.
input_file (file) – File object.
transposed¶‘bool’
transposed
gensim.corpora.ucicorpus.UciWriter(fname)¶Bases: gensim.matutils.MmWriter
Writer of UCI format for gensim.corpora.ucicorpus.UciCorpus.
Notes
This corpus format is identical to Matrix Market format<http://math.nist.gov/MatrixMarket/formats.html>, except for different file headers. There is no format line, and the first three lines of the file contain `number_docs, num_terms, and num_nnz, one value per line.
fname (str) – Path to output file.
FAKE_HEADER = b' \n'¶HEADER_LINE = b'%%MatrixMarket matrix coordinate real general\n'¶MAX_HEADER_LENGTH = 20¶close()¶Close self.fout file.
fake_headers(num_docs, num_terms, num_nnz)¶Write “fake” headers to file, to be rewritten once we’ve scanned the entire corpus.
num_docs (int) – Number of documents in corpus.
num_terms (int) – Number of term in corpus.
num_nnz (int) – Number of non-zero elements in corpus.
update_headers(num_docs, num_terms, num_nnz)¶Update headers with actual values.
write_corpus(fname, corpus, progress_cnt=1000, index=False)¶Write corpus in file.
fname (str) – Path to output file.
corpus (iterable of list of (int, int)) – Corpus in BoW format.
progress_cnt (int, optional) – Progress counter, write log message each progress_cnt documents.
index (bool, optional) – If True - return offsets, otherwise - nothing.
Sequence of offsets to documents (in bytes), only if index=True.
list of int
write_headers()¶Write blank header lines. Will be updated later, once corpus stats are known.
write_vector(docno, vector)¶Write a single sparse vector to the file.
docno (int) – Number of document.
vector (list of (int, number)) – Document in BoW format.
Max word index in vector and len of vector. If vector is empty, return (-1, 0).
(int, int)
