utils – Various utility functions

Various general utility functions.

class gensim.utils.ClippedCorpus(corpus, max_docs=None)

Bases: gensim.utils.SaveLoad

Wrap a corpus and return max_doc element from it.

Parameters
  • corpus (iterable of iterable of (int, numeric)) – Input corpus.

  • max_docs (int) – Maximum number of documents in the wrapped corpus.

Warning

Any documents after max_docs are ignored. This effectively limits the length of the returned corpus to <= max_docs. Set max_docs=None for “no limit”, effectively wrapping the entire input corpus.

classmethod load(fname, mmap=None)

Load an object previously saved using save() from a file.

Parameters
  • 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.

Returns

Object loaded from fname.

Return type

object

Raises

AttributeError – When called on an object instance instead of class (this is a class method).

save(fname_or_handle, separately=None, sep_limit=10485760, ignore=frozenset({}), pickle_protocol=2)

Save the object to a file.

Parameters
  • 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()

Load object from file.

class gensim.utils.FakeDict(num_terms)

Bases: object

Objects of this class act as dictionaries that map integer->str(integer), for a specified range of integers <0, num_terms).

This is meant to avoid allocating real dictionaries when num_terms is huge, which is a waste of memory.

Parameters

num_terms (int) – Number of terms.

get(val, default=None)
iteritems()

Iterate over all keys and values.

Yields

(int, str) – Pair of (id, token).

keys()

Override the dict.keys(), which is used to determine the maximum internal id of a corpus, i.e. the vocabulary dimensionality.

Returns

Highest id, packed in list.

Return type

list of int

Notes

To avoid materializing the whole range(0, self.num_terms), this returns the highest id = [self.num_terms - 1] only.

class gensim.utils.InputQueue(q, corpus, chunksize, maxsize, as_numpy)

Bases: multiprocessing.context.Process

Populate a queue of input chunks from a streamed corpus.

Useful for reading and chunking corpora in the background, in a separate process, so that workers that use the queue are not starved for input chunks.

Parameters
  • q (multiprocessing.Queue) – Enqueue chunks into this queue.

  • corpus (iterable of iterable of (int, numeric)) – Corpus to read and split into “chunksize”-ed groups

  • chunksize (int) – Split corpus into chunks of this size.

  • as_numpy (bool, optional) – Enqueue chunks as numpy.ndarray instead of lists.

property authkey
property daemon

Return whether process is a daemon

property exitcode

Return exit code of process or None if it has yet to stop

property ident

Return identifier (PID) of process or None if it has yet to start

is_alive()

Return whether process is alive

join(timeout=None)

Wait until child process terminates

property name
property pid

Return identifier (PID) of process or None if it has yet to start

run()

Method to be run in sub-process; can be overridden in sub-class

property sentinel

Return a file descriptor (Unix) or handle (Windows) suitable for waiting for process termination.

start()

Start child process

terminate()

Terminate process; sends SIGTERM signal or uses TerminateProcess()

gensim.utils.NO_CYTHON = RuntimeError("Cython extensions are unavailable. Without them, this gensim functionality is disabled. If you've installed from a package, ask the package maintainer to include Cython extensions. If you're building gensim from source yourself, run `python setup.py build_ext --inplace` and retry. ",)

An exception that gensim code raises when Cython extensions are unavailable.

class gensim.utils.RepeatCorpus(corpus, reps)

Bases: gensim.utils.SaveLoad

Wrap a corpus as another corpus of length reps. This is achieved by repeating documents from corpus over and over again, until the requested length len(result) == reps is reached. Repetition is done on-the-fly=efficiently, via itertools.

Examples

>>> from gensim.utils import RepeatCorpus
>>>
>>> corpus = [[(1, 2)], []]  # 2 documents
>>> list(RepeatCorpus(corpus, 5))  # repeat 2.5 times to get 5 documents
[[(1, 2)], [], [(1, 2)], [], [(1, 2)]]
Parameters
  • corpus (iterable of iterable of (int, numeric)) – Input corpus.

  • reps (int) – Number of repeats for documents from corpus.

classmethod load(fname, mmap=None)

Load an object previously saved using save() from a file.

Parameters
  • 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.

Returns

Object loaded from fname.

Return type

object

Raises

AttributeError – When called on an object instance instead of class (this is a class method).

save(fname_or_handle, separately=None, sep_limit=10485760, ignore=frozenset({}), pickle_protocol=2)

Save the object to a file.

Parameters
  • 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()

Load object from file.

class gensim.utils.RepeatCorpusNTimes(corpus, n)

Bases: gensim.utils.SaveLoad

Wrap a corpus and repeat it n times.

Examples

>>> from gensim.utils import RepeatCorpusNTimes
>>>
>>> corpus = [[(1, 0.5)], []]
>>> list(RepeatCorpusNTimes(corpus, 3))  # repeat 3 times
[[(1, 0.5)], [], [(1, 0.5)], [], [(1, 0.5)], []]
Parameters
  • corpus (iterable of iterable of (int, numeric)) – Input corpus.

  • n (int) – Number of repeats for corpus.

classmethod load(fname, mmap=None)

Load an object previously saved using save() from a file.

Parameters
  • 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.

Returns

Object loaded from fname.

Return type

object

Raises

AttributeError – When called on an object instance instead of class (this is a class method).

save(fname_or_handle, separately=None, sep_limit=10485760, ignore=frozenset({}), pickle_protocol=2)

Save the object to a file.

Parameters
  • 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()

Load object from file.

class gensim.utils.SaveLoad

Bases: object

Serialize/deserialize object from disk, by equipping objects with the save()/load() methods.

Warning

This uses pickle internally (among other techniques), so objects must not contain unpicklable attributes such as lambda functions etc.

classmethod load(fname, mmap=None)

Load an object previously saved using save() from a file.

Parameters
  • 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.

Returns

Object loaded from fname.

Return type

object

Raises

AttributeError – When called on an object instance instead of class (this is a class method).

save(fname_or_handle, separately=None, sep_limit=10485760, ignore=frozenset({}), pickle_protocol=2)

Save the object to a file.

Parameters
  • 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()

Load object from file.

class gensim.utils.SlicedCorpus(corpus, slice_)

Bases: gensim.utils.SaveLoad

Wrap corpus and return a slice of it.

Parameters
  • corpus (iterable of iterable of (int, numeric)) – Input corpus.

  • slice (slice or iterable) – Slice for corpus.

Notes

Negative slicing can only be used if the corpus is indexable, otherwise, the corpus will be iterated over. Slice can also be a np.ndarray to support fancy indexing.

Calculating the size of a SlicedCorpus is expensive when using a slice as the corpus has to be iterated over once. Using a list or np.ndarray does not have this drawback, but consumes more memory.

classmethod load(fname, mmap=None)

Load an object previously saved using save() from a file.

Parameters
  • 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.

Returns

Object loaded from fname.

Return type

object

Raises

AttributeError – When called on an object instance instead of class (this is a class method).

save(fname_or_handle, separately=None, sep_limit=10485760, ignore=frozenset({}), pickle_protocol=2)

Save the object to a file.

Parameters
  • 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()

Load object from file.

gensim.utils.any2unicode(text, encoding='utf8', errors='strict')

Convert text (bytestring in given encoding or unicode) to unicode.

Parameters
  • text (str) – Input text.

  • errors (str, optional) – Error handling behaviour if text is a bytestring.

  • encoding (str, optional) – Encoding of text if it is a bytestring.

Returns

Unicode version of text.

Return type

str

gensim.utils.any2utf8(text, errors='strict', encoding='utf8')

Convert a unicode or bytes string in the given encoding into a utf8 bytestring.

Parameters
  • text (str) – Input text.

  • errors (str, optional) – Error handling behaviour if text is a bytestring.

  • encoding (str, optional) – Encoding of text if it is a bytestring.

Returns

Bytestring in utf8.

Return type

str

gensim.utils.call_on_class_only(*args, **kwargs)

Helper to raise AttributeError if a class method is called on an instance. Used internally.

Parameters
  • *args – Variable length argument list.

  • **kwargs – Arbitrary keyword arguments.

Raises

AttributeError – If a class method is called on an instance.

gensim.utils.check_output(stdout=-1, *popenargs, **kwargs)

Run OS command with the given arguments and return its output as a byte string.

Backported from Python 2.7 with a few minor modifications. Widely used for gensim.models.wrappers. Behaves very similar to https://docs.python.org/2/library/subprocess.html#subprocess.check_output.

Examples

>>> from gensim.utils import check_output
>>> check_output(args=['echo', '1'])
'1\n'
Raises

KeyboardInterrupt – If Ctrl+C pressed.

gensim.utils.chunkize(corpus, chunksize, maxsize=0, as_numpy=False)

Split corpus into fixed-sized chunks, using chunkize_serial().

Parameters
  • corpus (iterable of object) – An iterable.

  • chunksize (int) – Split corpus into chunks of this size.

  • maxsize (int, optional) – If > 0, prepare chunks in a background process, filling a chunk queue of size at most maxsize.

  • as_numpy (bool, optional) – Yield chunks as np.ndarray instead of lists?

Yields

list OR np.ndarray – “chunksize”-ed chunks of elements from corpus.

Notes

Each chunk is of length chunksize, except the last one which may be smaller. A once-only input stream (corpus from a generator) is ok, chunking is done efficiently via itertools.

If maxsize > 0, don’t wait idly in between successive chunk yields, but rather keep filling a short queue (of size at most maxsize) with forthcoming chunks in advance. This is realized by starting a separate process, and is meant to reduce I/O delays, which can be significant when corpus comes from a slow medium like HDD, database or network.

If maxsize == 0, don’t fool around with parallelism and simply yield the chunksize via chunkize_serial() (no I/O optimizations).

Yields

list of object OR np.ndarray – Groups based on iterable

gensim.utils.chunkize_serial(iterable, chunksize, as_numpy=False, dtype=<class 'numpy.float32'>)

Yield elements from iterable in “chunksize”-ed groups.

The last returned element may be smaller if the length of collection is not divisible by chunksize.

Parameters
  • iterable (iterable of object) – An iterable.

  • chunksize (int) – Split iterable into chunks of this size.

  • as_numpy (bool, optional) – Yield chunks as np.ndarray instead of lists.

Yields

list OR np.ndarray – “chunksize”-ed chunks of elements from iterable.

Examples

>>> print(list(grouper(range(10), 3)))
[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]

Recursively copy a directory ala shutils.copytree, but hardlink files instead of copying.

Parameters
  • source (str) – Path to source directory

  • dest (str) – Path to destination directory

Warning

Available on UNIX systems only.

gensim.utils.deaccent(text)

Remove letter accents from the given string.

Parameters

text (str) – Input string.

Returns

Unicode string without accents.

Return type

str

Examples

>>> from gensim.utils import deaccent
>>> deaccent("Šéf chomutovských komunistů dostal poštou bílý prášek")
u'Sef chomutovskych komunistu dostal postou bily prasek'
gensim.utils.decode_htmlentities(text)

Decode all HTML entities in text that are encoded as hex, decimal or named entities. Adapted from python-twitter-ircbot/html_decode.py.

Parameters

text (str) – Input HTML.

Examples

>>> from gensim.utils import decode_htmlentities
>>>
>>> u = u'E tu vivrai nel terrore - L&#x27;aldil&#xE0; (1981)'
>>> print(decode_htmlentities(u).encode('UTF-8'))
E tu vivrai nel terrore - L'aldilà (1981)
>>> print(decode_htmlentities("l&#39;eau"))
l'eau
>>> print(decode_htmlentities("foo &lt; bar"))
foo < bar
gensim.utils.default_prng = Generator(PCG64) at 0x11B687C78

A default, shared numpy-Generator-based PRNG for any/all uses that don’t require seeding

gensim.utils.deprecated(reason)

Decorator to mark functions as deprecated.

Calling a decorated function will result in a warning being emitted, using warnings.warn. Adapted from https://stackoverflow.com/a/40301488/8001386.

Parameters

reason (str) – Reason of deprecation.

Returns

Decorated function

Return type

function

gensim.utils.dict_from_corpus(corpus)

Scan corpus for all word ids that appear in it, then construct a mapping which maps each word_id -> str(word_id).

Parameters

corpus (iterable of iterable of (int, numeric)) – Collection of texts in BoW format.

Returns

id2word – “Fake” mapping which maps each word_id -> str(word_id).

Return type

FakeDict

Warning

This function is used whenever words need to be displayed (as opposed to just their ids) but no word_id -> word mapping was provided. The resulting mapping only covers words actually used in the corpus, up to the highest word_id found.

gensim.utils.effective_n_jobs(n_jobs)

Determines the number of jobs can run in parallel.

Just like in sklearn, passing n_jobs=-1 means using all available CPU cores.

Parameters

n_jobs (int) – Number of workers requested by caller.

Returns

Number of effective jobs.

Return type

int

gensim.utils.file_or_filename(input)

Open a filename for reading with smart_open, or seek to the beginning if input is an already open file.

Parameters

input (str or file-like) – Filename or file-like object.

Returns

An open file, positioned at the beginning.

Return type

file-like object

gensim.utils.flatten(nested_list)

Recursively flatten a nested sequence of elements.

Parameters

nested_list (iterable) – Possibly nested sequence of elements to flatten.

Returns

Flattened version of nested_list where any elements that are an iterable (collections.Iterable) have been unpacked into the top-level list, in a recursive fashion.

Return type

list

gensim.utils.getNS(host=None, port=None, broadcast=True, hmac_key=None)

Get a Pyro4 name server proxy.

Parameters
  • host (str, optional) – Name server hostname.

  • port (int, optional) – Name server port.

  • broadcast (bool, optional) – Use broadcast mechanism? (i.e. reach out to all Pyro nodes in the network)

  • hmac_key (str, optional) – Private key.

Raises

RuntimeError – When Pyro name server is not found.

Returns

Proxy from Pyro4.

Return type

Pyro4.core.Proxy

gensim.utils.get_max_id(corpus)

Get the highest feature id that appears in the corpus.

Parameters

corpus (iterable of iterable of (int, numeric)) – Collection of texts in BoW format.

Returns

Highest feature id.

Return type

int

Notes

For empty corpus return -1.

gensim.utils.get_my_ip()

Try to obtain our external ip (from the Pyro4 nameserver’s point of view)

Returns

IP address.

Return type

str

Warning

This tries to sidestep the issue of bogus /etc/hosts entries and other local misconfiguration, which often mess up hostname resolution. If all else fails, fall back to simple socket.gethostbyname() lookup.

gensim.utils.get_random_state(seed)

Generate numpy.random.RandomState based on input seed.

Parameters

seed ({None, int, array_like}) – Seed for random state.

Returns

Random state.

Return type

numpy.random.RandomState

Raises

AttributeError – If seed is not {None, int, array_like}.

Notes

Method originally from maciejkula/glove-python and written by @joshloyal.

gensim.utils.grouper(iterable, chunksize, as_numpy=False, dtype=<class 'numpy.float32'>)

Yield elements from iterable in “chunksize”-ed groups.

The last returned element may be smaller if the length of collection is not divisible by chunksize.

Parameters
  • iterable (iterable of object) – An iterable.

  • chunksize (int) – Split iterable into chunks of this size.

  • as_numpy (bool, optional) – Yield chunks as np.ndarray instead of lists.

Yields

list OR np.ndarray – “chunksize”-ed chunks of elements from iterable.

Examples

>>> print(list(grouper(range(10), 3)))
[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
gensim.utils.has_pattern()

Check whether the pattern package is installed.

Returns

Is pattern installed?

Return type

bool

gensim.utils.identity(p)

Identity fnc, for flows that don’t accept lambda (pickling etc).

Parameters

p (object) – Input parameter.

Returns

Same as p.

Return type

object

gensim.utils.ignore_deprecation_warning()

Contextmanager for ignoring DeprecationWarning.

gensim.utils.is_corpus(obj)

Check whether obj is a corpus, by peeking at its first element. Works even on streamed generators. The peeked element is put back into a object returned by this function, so always use that returned object instead of the original obj.

Parameters

obj (object) – An iterable of iterable that contains (int, numeric).

Returns

Pair of (is obj a corpus, obj with peeked element restored)

Return type

(bool, object)

Examples

>>> from gensim.utils import is_corpus
>>> corpus = [[(1, 1.0)], [(2, -0.3), (3, 0.12)]]
>>> corpus_or_not, corpus = is_corpus(corpus)

Warning

An “empty” corpus (empty input sequence) is ambiguous, so in this case the result is forcefully defined as (False, obj).

gensim.utils.iter_windows(texts, window_size, copy=False, ignore_below_size=True, include_doc_num=False)

Produce a generator over the given texts using a sliding window of window_size.

The windows produced are views of some subsequence of a text. To use deep copies instead, pass copy=True.

Parameters
  • texts (list of str) – List of string sentences.

  • window_size (int) – Size of sliding window.

  • copy (bool, optional) – Produce deep copies.

  • ignore_below_size (bool, optional) – Ignore documents that are not at least window_size in length?

  • include_doc_num (bool, optional) – Yield the text position with texts along with each window?

gensim.utils.keep_vocab_item(word, count, min_count, trim_rule=None)

Should we keep word in the vocab or remove it?

Parameters
  • word (str) – Input word.

  • count (int) – Number of times that word appeared in a corpus.

  • min_count (int) – Discard words with frequency smaller than this.

  • trim_rule (function, optional) – Custom function to decide whether to keep or discard this word. If a custom trim_rule is not specified, the default behaviour is simply count >= min_count.

Returns

True if word should stay, False otherwise.

Return type

bool

gensim.utils.lazy_flatten(nested_list)

Lazy version of flatten().

Parameters

nested_list (list) – Possibly nested list.

Yields

object – Element of list

gensim.utils.lemmatize(content, allowed_tags=re.compile('(NN|VB|JJ|RB)'), light=False, stopwords=frozenset({}), min_length=2, max_length=15)

Use the English lemmatizer from pattern to extract UTF8-encoded tokens in their base form aka lemma, e.g. “are, is, being” becomes “be” etc.

This is a smarter version of stemming, taking word context into account.

Parameters
  • content (str) – Input string

  • allowed_tags (_sre.SRE_Pattern, optional) – Compiled regexp to select POS that will be used. Only considers nouns, verbs, adjectives and adverbs by default (=all other lemmas are discarded).

  • light (bool, optional) – DEPRECATED FLAG, DOESN’T SUPPORT BY pattern.

  • stopwords (frozenset, optional) – Set of words that will be removed from output.

  • min_length (int, optional) – Minimal token length in output (inclusive).

  • max_length (int, optional) – Maximal token length in output (inclusive).

Returns

List with tokens with POS tags.

Return type

list of str

Warning

This function is only available when the optional pattern is installed.

Raises

ImportError

If pattern not installed.

Examples

>>> from gensim.utils import lemmatize
>>> lemmatize('Hello World! How is it going?! Nonexistentword, 21')
['world/NN', 'be/VB', 'go/VB', 'nonexistentword/NN']

Note the context-dependent part-of-speech tags between these two examples:

>>> lemmatize('The study ranks high.')
['study/NN', 'rank/VB', 'high/JJ']

>>> lemmatize('The ranks study hard.')
['rank/NN', 'study/VB', 'hard/RB']
gensim.utils.merge_counts(dict1, dict2)

Merge dict1 of (word, freq1) and dict2 of (word, freq2) into dict1 of (word, freq1+freq2). :param dict1: First dictionary. :type dict1: dict of (str, int) :param dict2: Second dictionary. :type dict2: dict of (str, int)

Returns

result – Merged dictionary with sum of frequencies as values.

Return type

dict

gensim.utils.mock_data(n_items=1000, dim=1000, prob_nnz=0.5, lam=1.0)

Create a random Gensim-style corpus (BoW), using mock_data_row().

Parameters
  • n_items (int) – Size of corpus

  • dim (int) – Dimension of vector, used for mock_data_row().

  • prob_nnz (float, optional) – Probability of each coordinate will be nonzero, will be drawn from Poisson distribution, used for mock_data_row().

  • lam (float, optional) – Parameter for Poisson distribution, used for mock_data_row().

Returns

Gensim-style corpus.

Return type

list of list of (int, float)

gensim.utils.mock_data_row(dim=1000, prob_nnz=0.5, lam=1.0)

Create a random gensim BoW vector, with the feature counts following the Poisson distribution.

Parameters
  • dim (int, optional) – Dimension of vector.

  • prob_nnz (float, optional) – Probability of each coordinate will be nonzero, will be drawn from the Poisson distribution.

  • lam (float, optional) – Lambda parameter for the Poisson distribution.

Returns

Vector in BoW format.

Return type

list of (int, float)

gensim.utils.open_file(input)

Provide “with-like” behaviour without closing the file object.

Parameters

input (str or file-like) – Filename or file-like object.

Yields

file – File-like object based on input (or input if this already file-like).

gensim.utils.pickle(obj, fname, protocol=2)

Pickle object obj to file fname, using smart_open so that fname can be on S3, HDFS, compressed etc.

Parameters
  • obj (object) – Any python object.

  • fname (str) – Path to pickle file.

  • protocol (int, optional) – Pickle protocol number. Default is 2 in order to support compatibility across python 2.x and 3.x.

gensim.utils.prune_vocab(vocab, min_reduce, trim_rule=None)

Remove all entries from the vocab dictionary with count smaller than min_reduce.

Modifies vocab in place, returns the sum of all counts that were pruned.

Parameters
  • vocab (dict) – Input dictionary.

  • min_reduce (int) – Frequency threshold for tokens in vocab.

  • trim_rule (function, optional) – Function for trimming entities from vocab, default behaviour is vocab[w] <= min_reduce.

Returns

result – Sum of all counts that were pruned.

Return type

int

gensim.utils.pyro_daemon(name, obj, random_suffix=False, ip=None, port=None, ns_conf=None)

Register an object with the Pyro name server.

Start the name server if not running yet and block until the daemon is terminated. The object is registered under name, or name`+ some random suffix if `random_suffix is set.

gensim.utils.qsize(queue)

Get the (approximate) queue size where available.

Parameters

queue (queue.Queue) – Input queue.

Returns

Queue size, -1 if qsize method isn’t implemented (OS X).

Return type

int

gensim.utils.randfname(prefix='gensim')

Generate a random filename in temp.

Parameters

prefix (str) – Prefix of filename.

Returns

Full path in the in system’s temporary folder, ending in a random filename.

Return type

str

gensim.utils.revdict(d)

Reverse a dictionary mapping, i.e. {1: 2, 3: 4} -> {2: 1, 4: 3}.

Parameters

d (dict) – Input dictionary.

Returns

Reversed dictionary mapping.

Return type

dict

Notes

When two keys map to the same value, only one of them will be kept in the result (which one is kept is arbitrary).

Examples

>>> from gensim.utils import revdict
>>> d = {1: 2, 3: 4}
>>> revdict(d)
{2: 1, 4: 3}
gensim.utils.safe_unichr(intval)

Create a unicode character from its integer value. In case unichr fails, render the character as an escaped U<8-byte hex value of intval> string.

Parameters

intval (int) – Integer code of character

Returns

Unicode string of character

Return type

string

gensim.utils.sample_dict(d, n=10, use_random=True)

Selected n (possibly random) items from the dictionary d.

Parameters
  • d (dict) – Input dictionary.

  • n (int, optional) – Number of items to select.

  • use_random (bool, optional) – Select items randomly (without replacement), instead of by the natural dict iteration order?

Returns

Selected items from dictionary, as a list.

Return type

list of (object, object)

gensim.utils.save_as_line_sentence(corpus, filename)

Save the corpus in LineSentence format, i.e. each sentence on a separate line, tokens are separated by space.

Parameters

corpus (iterable of iterables of strings) –

gensim.utils.simple_preprocess(doc, deacc=False, min_len=2, max_len=15)

Convert a document into a list of lowercase tokens, ignoring tokens that are too short or too long.

Uses tokenize() internally.

Parameters
  • doc (str) – Input document.

  • deacc (bool, optional) – Remove accent marks from tokens using deaccent()?

  • min_len (int, optional) – Minimum length of token (inclusive). Shorter tokens are discarded.

  • max_len (int, optional) – Maximum length of token in result (inclusive). Longer tokens are discarded.

Returns

Tokens extracted from doc.

Return type

list of str

gensim.utils.simple_tokenize(text)

Tokenize input test using gensim.utils.PAT_ALPHABETIC.

Parameters

text (str) – Input text.

Yields

str – Tokens from text.

gensim.utils.smart_extension(fname, ext)

Append a file extension ext to fname, while keeping compressed extensions like .bz2 or .gz (if any) at the end.

Parameters
  • fname (str) – Filename or full path.

  • ext (str) – Extension to append before any compression extensions.

Returns

New path to file with ext appended.

Return type

str

Examples

>>> from gensim.utils import smart_extension
>>> smart_extension("my_file.pkl.gz", ".vectors")
'my_file.pkl.vectors.gz'
gensim.utils.strided_windows(ndarray, window_size)

Produce a numpy.ndarray of windows, as from a sliding window.

Parameters
  • ndarray (numpy.ndarray) – Input array

  • window_size (int) – Sliding window size.

Returns

Subsequences produced by sliding a window of the given size over the ndarray. Since this uses striding, the individual arrays are views rather than copies of ndarray. Changes to one view modifies the others and the original.

Return type

numpy.ndarray

Examples

>>> from gensim.utils import strided_windows
>>> strided_windows(np.arange(5), 2)
array([[0, 1],
       [1, 2],
       [2, 3],
       [3, 4]])
>>> strided_windows(np.arange(10), 5)
array([[0, 1, 2, 3, 4],
       [1, 2, 3, 4, 5],
       [2, 3, 4, 5, 6],
       [3, 4, 5, 6, 7],
       [4, 5, 6, 7, 8],
       [5, 6, 7, 8, 9]])
gensim.utils.synchronous(tlockname)

A decorator to place an instance-based lock around a method.

Notes

Adapted from http://code.activestate.com/recipes/577105-synchronization-decorator-for-class-methods/.

gensim.utils.to_unicode(text, encoding='utf8', errors='strict')

Convert text (bytestring in given encoding or unicode) to unicode.

Parameters
  • text (str) – Input text.

  • errors (str, optional) – Error handling behaviour if text is a bytestring.

  • encoding (str, optional) – Encoding of text if it is a bytestring.

Returns

Unicode version of text.

Return type

str

gensim.utils.to_utf8(text, errors='strict', encoding='utf8')

Convert a unicode or bytes string in the given encoding into a utf8 bytestring.

Parameters
  • text (str) – Input text.

  • errors (str, optional) – Error handling behaviour if text is a bytestring.

  • encoding (str, optional) – Encoding of text if it is a bytestring.

Returns

Bytestring in utf8.

Return type

str

gensim.utils.tokenize(text, lowercase=False, deacc=False, encoding='utf8', errors='strict', to_lower=False, lower=False)

Iteratively yield tokens as unicode strings, optionally removing accent marks and lowercasing it.

Parameters
  • text (str or bytes) – Input string.

  • deacc (bool, optional) – Remove accentuation using deaccent()?

  • encoding (str, optional) – Encoding of input string, used as parameter for to_unicode().

  • errors (str, optional) – Error handling behaviour, used as parameter for to_unicode().

  • lowercase (bool, optional) – Lowercase the input string?

  • to_lower (bool, optional) – Same as lowercase. Convenience alias.

  • lower (bool, optional) – Same as lowercase. Convenience alias.

Yields

str – Contiguous sequences of alphabetic characters (no digits!), using simple_tokenize()

Examples

>>> from gensim.utils import tokenize
>>> list(tokenize('Nic nemůže letět rychlostí vyšší, než 300 tisíc kilometrů za sekundu!', deacc=True))
[u'Nic', u'nemuze', u'letet', u'rychlosti', u'vyssi', u'nez', u'tisic', u'kilometru', u'za', u'sekundu']
gensim.utils.toptexts(query, texts, index, n=10)

Debug fnc to help inspect the top n most similar documents (according to a similarity index index), to see if they are actually related to the query.

Parameters
  • query ({list of (int, number), numpy.ndarray}) – vector OR BoW (list of tuples)

  • texts (str) – object that can return something insightful for each document via texts[docid], such as its fulltext or snippet.

  • index (any) – A instance from from gensim.similarity.docsim.

Returns

a list of 3-tuples (docid, doc’s similarity to the query, texts[docid])

Return type

list

gensim.utils.trim_vocab_by_freq(vocab, topk, trim_rule=None)

Retain topk most frequent words in vocab. If there are more words with the same frequency as topk-th one, they will be kept. Modifies vocab in place, returns nothing.

Parameters
  • vocab (dict) – Input dictionary.

  • topk (int) – Number of words with highest frequencies to keep.

  • trim_rule (function, optional) – Function for trimming entities from vocab, default behaviour is vocab[w] <= min_count.

gensim.utils.unpickle(fname)

Load object from fname, using smart_open so that fname can be on S3, HDFS, compressed etc.

Parameters

fname (str) – Path to pickle file.

Returns

Python object loaded from fname.

Return type

object

gensim.utils.upload_chunked(server, docs, chunksize=1000, preprocess=None)

Memory-friendly upload of documents to a SimServer (or Pyro SimServer proxy).

Notes

Use this function to train or index large collections – avoid sending the entire corpus over the wire as a single Pyro in-memory object. The documents will be sent in smaller chunks, of chunksize documents each.