laywerrobot/lib/python3.6/site-packages/gensim/utils.py
2020-08-27 21:55:39 +02:00

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2010 Radim Rehurek <radimrehurek@seznam.cz>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
"""Various general utility functions."""
from __future__ import with_statement
from contextlib import contextmanager
import collections
import logging
import warnings
try:
from html.entities import name2codepoint as n2cp
except ImportError:
from htmlentitydefs import name2codepoint as n2cp
try:
import cPickle as _pickle
except ImportError:
import pickle as _pickle
import re
import unicodedata
import os
import random
import itertools
import tempfile
from functools import wraps
import multiprocessing
import shutil
import sys
import subprocess
import inspect
import numpy as np
import numbers
import scipy.sparse
from six import iterkeys, iteritems, u, string_types, unichr
from six.moves import xrange
from smart_open import smart_open
if sys.version_info[0] >= 3:
unicode = str
logger = logging.getLogger(__name__)
PAT_ALPHABETIC = re.compile(r'(((?![\d])\w)+)', re.UNICODE)
RE_HTML_ENTITY = re.compile(r'&(#?)([xX]?)(\w{1,8});', re.UNICODE)
def get_random_state(seed):
"""Generate :class:`numpy.random.RandomState` based on input seed.
Parameters
----------
seed : {None, int, array_like}
Seed for random state.
Returns
-------
:class:`numpy.random.RandomState`
Random state.
Raises
------
AttributeError
If seed is not {None, int, array_like}.
Notes
-----
Method originally from `maciejkula/glove-python <https://github.com/maciejkula/glove-python>`_
and written by `@joshloyal <https://github.com/joshloyal>`_.
"""
if seed is None or seed is np.random:
return np.random.mtrand._rand
if isinstance(seed, (numbers.Integral, np.integer)):
return np.random.RandomState(seed)
if isinstance(seed, np.random.RandomState):
return seed
raise ValueError('%r cannot be used to seed a np.random.RandomState instance' % seed)
def 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/.
"""
def _synched(func):
@wraps(func)
def _synchronizer(self, *args, **kwargs):
tlock = getattr(self, tlockname)
logger.debug("acquiring lock %r for %s", tlockname, func.__name__)
with tlock: # use lock as a context manager to perform safe acquire/release pairs
logger.debug("acquired lock %r for %s", tlockname, func.__name__)
result = func(self, *args, **kwargs)
logger.debug("releasing lock %r for %s", tlockname, func.__name__)
return result
return _synchronizer
return _synched
def 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
-------
file-like object
An open file, positioned at the beginning.
"""
if isinstance(input, string_types):
# input was a filename: open as file
return smart_open(input)
else:
# input already a file-like object; just reset to the beginning
input.seek(0)
return input
@contextmanager
def 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).
"""
mgr = file_or_filename(input)
exc = False
try:
yield mgr
except Exception:
# Handling any unhandled exceptions from the code nested in 'with' statement.
exc = True
if not isinstance(input, string_types) or not mgr.__exit__(*sys.exc_info()):
raise
# Try to introspect and silence errors.
finally:
if not exc and isinstance(input, string_types):
mgr.__exit__(None, None, None)
def deaccent(text):
"""Remove letter accents from the given string.
Parameters
----------
text : str
Input string.
Returns
-------
str
Unicode string without accents.
Examples
--------
>>> from gensim.utils import deaccent
>>> deaccent("Šéf chomutovských komunistů dostal poštou bílý prášek")
u'Sef chomutovskych komunistu dostal postou bily prasek'
"""
if not isinstance(text, unicode):
# assume utf8 for byte strings, use default (strict) error handling
text = text.decode('utf8')
norm = unicodedata.normalize("NFD", text)
result = u('').join(ch for ch in norm if unicodedata.category(ch) != 'Mn')
return unicodedata.normalize("NFC", result)
def copytree_hardlink(source, dest):
"""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
Warnings
--------
Available on UNIX systems only.
"""
copy2 = shutil.copy2
try:
shutil.copy2 = os.link
shutil.copytree(source, dest)
finally:
shutil.copy2 = copy2
def 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 :func:`~gensim.utils.deaccent`?
encoding : str, optional
Encoding of input string, used as parameter for :func:`~gensim.utils.to_unicode`.
errors : str, optional
Error handling behaviour, used as parameter for :func:`~gensim.utils.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 :func:`~gensim.utils.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']
"""
lowercase = lowercase or to_lower or lower
text = to_unicode(text, encoding, errors=errors)
if lowercase:
text = text.lower()
if deacc:
text = deaccent(text)
return simple_tokenize(text)
def simple_tokenize(text):
"""Tokenize input test using :const:`gensim.utils.PAT_ALPHABETIC`.
Parameters
----------
text : str
Input text.
Yields
------
str
Tokens from `text`.
"""
for match in PAT_ALPHABETIC.finditer(text):
yield match.group()
def 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 :func:`~gensim.utils.tokenize` internally.
Parameters
----------
doc : str
Input document.
deacc : bool, optional
Remove accent marks from tokens using :func:`~gensim.utils.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
-------
list of str
Tokens extracted from `doc`.
"""
tokens = [
token for token in tokenize(doc, lower=True, deacc=deacc, errors='ignore')
if min_len <= len(token) <= max_len and not token.startswith('_')
]
return tokens
def 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
-------
str
Bytestring in utf8.
"""
if isinstance(text, unicode):
return text.encode('utf8')
# do bytestring -> unicode -> utf8 full circle, to ensure valid utf8
return unicode(text, encoding, errors=errors).encode('utf8')
to_utf8 = any2utf8
def 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
-------
str
Unicode version of `text`.
"""
if isinstance(text, unicode):
return text
return unicode(text, encoding, errors=errors)
to_unicode = any2unicode
def 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.
"""
raise AttributeError('This method should be called on a class object.')
class SaveLoad(object):
"""Serialize/deserialize object from disk, by equipping objects with the save()/load() methods.
Warnings
--------
This uses pickle internally (among other techniques), so objects must not contain unpicklable attributes
such as lambda functions etc.
"""
@classmethod
def load(cls, fname, mmap=None):
"""Load an object previously saved using :meth:`~gensim.utils.SaveLoad.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
--------
:meth:`~gensim.utils.SaveLoad.save`
Save object to file.
Returns
-------
object
Object loaded from `fname`.
Raises
------
AttributeError
When called on an object instance instead of class (this is a class method).
"""
logger.info("loading %s object from %s", cls.__name__, fname)
compress, subname = SaveLoad._adapt_by_suffix(fname)
obj = unpickle(fname)
obj._load_specials(fname, mmap, compress, subname)
logger.info("loaded %s", fname)
return obj
def _load_specials(self, fname, mmap, compress, subname):
"""Load attributes that were stored separately, and give them the same opportunity
to recursively load using the :class:`~gensim.utils.SaveLoad` interface.
Parameters
----------
fname : str
Input file path.
mmap : {None, r+, r, w+, c}
Memory-map options. See `numpy.load(mmap_mode)
<https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.load.html>`_.
compress : bool
Is the input file compressed?
subname : str
Attribute name. Set automatically during recursive processing.
"""
def mmap_error(obj, filename):
return IOError(
'Cannot mmap compressed object %s in file %s. ' % (obj, filename) +
'Use `load(fname, mmap=None)` or uncompress files manually.'
)
for attrib in getattr(self, '__recursive_saveloads', []):
cfname = '.'.join((fname, attrib))
logger.info("loading %s recursively from %s.* with mmap=%s", attrib, cfname, mmap)
getattr(self, attrib)._load_specials(cfname, mmap, compress, subname)
for attrib in getattr(self, '__numpys', []):
logger.info("loading %s from %s with mmap=%s", attrib, subname(fname, attrib), mmap)
if compress:
if mmap:
raise mmap_error(attrib, subname(fname, attrib))
val = np.load(subname(fname, attrib))['val']
else:
val = np.load(subname(fname, attrib), mmap_mode=mmap)
setattr(self, attrib, val)
for attrib in getattr(self, '__scipys', []):
logger.info("loading %s from %s with mmap=%s", attrib, subname(fname, attrib), mmap)
sparse = unpickle(subname(fname, attrib))
if compress:
if mmap:
raise mmap_error(attrib, subname(fname, attrib))
with np.load(subname(fname, attrib, 'sparse')) as f:
sparse.data = f['data']
sparse.indptr = f['indptr']
sparse.indices = f['indices']
else:
sparse.data = np.load(subname(fname, attrib, 'data'), mmap_mode=mmap)
sparse.indptr = np.load(subname(fname, attrib, 'indptr'), mmap_mode=mmap)
sparse.indices = np.load(subname(fname, attrib, 'indices'), mmap_mode=mmap)
setattr(self, attrib, sparse)
for attrib in getattr(self, '__ignoreds', []):
logger.info("setting ignored attribute %s to None", attrib)
setattr(self, attrib, None)
@staticmethod
def _adapt_by_suffix(fname):
"""Get compress setting and filename for numpy file compression.
Parameters
----------
fname : str
Input filename.
Returns
-------
(bool, function)
First argument will be True if `fname` compressed.
"""
compress, suffix = (True, 'npz') if fname.endswith('.gz') or fname.endswith('.bz2') else (False, 'npy')
return compress, lambda *args: '.'.join(args + (suffix,))
def _smart_save(self, fname, separately=None, sep_limit=10 * 1024**2, ignore=frozenset(), pickle_protocol=2):
"""Save the object to a file. Used internally by :meth:`gensim.utils.SaveLoad.save()`.
Parameters
----------
fname : str
Path to file.
separately : list, optional
Iterable of attributes than need to store distinctly.
sep_limit : int, optional
Limit for separation.
ignore : frozenset, optional
Attributes that shouldn't be store.
pickle_protocol : int, optional
Protocol number for pickle.
Notes
-----
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.
"""
logger.info("saving %s object under %s, separately %s", self.__class__.__name__, fname, separately)
compress, subname = SaveLoad._adapt_by_suffix(fname)
restores = self._save_specials(fname, separately, sep_limit, ignore, pickle_protocol,
compress, subname)
try:
pickle(self, fname, protocol=pickle_protocol)
finally:
# restore attribs handled specially
for obj, asides in restores:
for attrib, val in iteritems(asides):
setattr(obj, attrib, val)
logger.info("saved %s", fname)
def _save_specials(self, fname, separately, sep_limit, ignore, pickle_protocol, compress, subname):
"""Save aside any attributes that need to be handled separately, including
by recursion any attributes that are themselves :class:`~gensim.utils.SaveLoad` instances.
Parameters
----------
fname : str
Output filename.
separately : list or None
List of attributes to store separately.
sep_limit : int
Don't store arrays smaller than this separately. In bytes.
ignore : iterable of str
Attributes that shouldn't be stored at all.
pickle_protocol : int
Protocol number for pickle.
compress : bool
If True - compress output with :func:`numpy.savez_compressed`.
subname : function
Produced by :meth:`~gensim.utils.SaveLoad._adapt_by_suffix`
Returns
-------
list of (obj, {attrib: value, ...})
Settings that the caller should use to restore each object's attributes that were set aside
during the default :func:`~gensim.utils.pickle`.
"""
asides = {}
sparse_matrices = (scipy.sparse.csr_matrix, scipy.sparse.csc_matrix)
if separately is None:
separately = []
for attrib, val in iteritems(self.__dict__):
if isinstance(val, np.ndarray) and val.size >= sep_limit:
separately.append(attrib)
elif isinstance(val, sparse_matrices) and val.nnz >= sep_limit:
separately.append(attrib)
# whatever's in `separately` or `ignore` at this point won't get pickled
for attrib in separately + list(ignore):
if hasattr(self, attrib):
asides[attrib] = getattr(self, attrib)
delattr(self, attrib)
recursive_saveloads = []
restores = []
for attrib, val in iteritems(self.__dict__):
if hasattr(val, '_save_specials'): # better than 'isinstance(val, SaveLoad)' if IPython reloading
recursive_saveloads.append(attrib)
cfname = '.'.join((fname, attrib))
restores.extend(val._save_specials(cfname, None, sep_limit, ignore, pickle_protocol, compress, subname))
try:
numpys, scipys, ignoreds = [], [], []
for attrib, val in iteritems(asides):
if isinstance(val, np.ndarray) and attrib not in ignore:
numpys.append(attrib)
logger.info("storing np array '%s' to %s", attrib, subname(fname, attrib))
if compress:
np.savez_compressed(subname(fname, attrib), val=np.ascontiguousarray(val))
else:
np.save(subname(fname, attrib), np.ascontiguousarray(val))
elif isinstance(val, (scipy.sparse.csr_matrix, scipy.sparse.csc_matrix)) and attrib not in ignore:
scipys.append(attrib)
logger.info("storing scipy.sparse array '%s' under %s", attrib, subname(fname, attrib))
if compress:
np.savez_compressed(
subname(fname, attrib, 'sparse'),
data=val.data,
indptr=val.indptr,
indices=val.indices
)
else:
np.save(subname(fname, attrib, 'data'), val.data)
np.save(subname(fname, attrib, 'indptr'), val.indptr)
np.save(subname(fname, attrib, 'indices'), val.indices)
data, indptr, indices = val.data, val.indptr, val.indices
val.data, val.indptr, val.indices = None, None, None
try:
# store array-less object
pickle(val, subname(fname, attrib), protocol=pickle_protocol)
finally:
val.data, val.indptr, val.indices = data, indptr, indices
else:
logger.info("not storing attribute %s", attrib)
ignoreds.append(attrib)
self.__dict__['__numpys'] = numpys
self.__dict__['__scipys'] = scipys
self.__dict__['__ignoreds'] = ignoreds
self.__dict__['__recursive_saveloads'] = recursive_saveloads
except Exception:
# restore the attributes if exception-interrupted
for attrib, val in iteritems(asides):
setattr(self, attrib, val)
raise
return restores + [(self, asides)]
def save(self, fname_or_handle, separately=None, sep_limit=10 * 1024**2, 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 <https://en.wikipedia.org/wiki/Mmap>`_ 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
--------
:meth:`~gensim.utils.SaveLoad.load`
Load object from file.
"""
try:
_pickle.dump(self, fname_or_handle, protocol=pickle_protocol)
logger.info("saved %s object", self.__class__.__name__)
except TypeError: # `fname_or_handle` does not have write attribute
self._smart_save(fname_or_handle, separately, sep_limit, ignore, pickle_protocol=pickle_protocol)
def identity(p):
"""Identity fnc, for flows that don't accept lambda (pickling etc).
Parameters
----------
p : object
Input parameter.
Returns
-------
object
Same as `p`.
"""
return p
def 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
------
int
Highest feature id.
Notes
-----
For empty `corpus` return -1.
"""
maxid = -1
for document in corpus:
maxid = max(maxid, max([-1] + [fieldid for fieldid, _ in document])) # [-1] to avoid exceptions from max(empty)
return maxid
class FakeDict(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.
"""
def __init__(self, num_terms):
"""
Parameters
----------
num_terms : int
Number of terms.
"""
self.num_terms = num_terms
def __str__(self):
return "FakeDict(num_terms=%s)" % self.num_terms
def __getitem__(self, val):
if 0 <= val < self.num_terms:
return str(val)
raise ValueError("internal id out of bounds (%s, expected <0..%s))" % (val, self.num_terms))
def iteritems(self):
"""Iterate over all keys and values.
Yields
------
(int, str)
Pair of (id, token).
"""
for i in xrange(self.num_terms):
yield i, str(i)
def keys(self):
"""Override the `dict.keys()`, which is used to determine the maximum internal id of a corpus,
i.e. the vocabulary dimensionality.
Returns
-------
list of int
Highest id, packed in list.
Notes
-----
To avoid materializing the whole `range(0, self.num_terms)`,
this returns the highest id = `[self.num_terms - 1]` only.
"""
return [self.num_terms - 1]
def __len__(self):
return self.num_terms
def get(self, val, default=None):
if 0 <= val < self.num_terms:
return str(val)
return default
def 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 : :class:`~gensim.utils.FakeDict`
"Fake" mapping which maps each `word_id` -> `str(word_id)`.
Warnings
--------
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.
"""
num_terms = 1 + get_max_id(corpus)
id2word = FakeDict(num_terms)
return id2word
def 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
-------
(bool, object)
Pair of (is `obj` a corpus, `obj` with peeked element restored)
Examples
--------
>>> from gensim.utils import is_corpus
>>> corpus = [[(1, 1.0)], [(2, -0.3), (3, 0.12)]]
>>> corpus_or_not, corpus = is_corpus(corpus)
Warnings
--------
An "empty" corpus (empty input sequence) is ambiguous, so in this case
the result is forcefully defined as (False, `obj`).
"""
try:
if 'Corpus' in obj.__class__.__name__: # the most common case, quick hack
return True, obj
except Exception:
pass
try:
if hasattr(obj, 'next') or hasattr(obj, '__next__'):
# the input is an iterator object, meaning once we call next()
# that element could be gone forever. we must be careful to put
# whatever we retrieve back again
doc1 = next(obj)
obj = itertools.chain([doc1], obj)
else:
doc1 = next(iter(obj)) # empty corpus is resolved to False here
if len(doc1) == 0: # sparse documents must have a __len__ function (list, tuple...)
return True, obj # the first document is empty=>assume this is a corpus
# if obj is a 1D numpy array(scalars) instead of 2-tuples, it resolves to False here
id1, val1 = next(iter(doc1))
id1, val1 = int(id1), float(val1) # must be a 2-tuple (integer, float)
except Exception:
return False, obj
return True, obj
def get_my_ip():
"""Try to obtain our external ip (from the Pyro4 nameserver's point of view)
Returns
-------
str
IP address.
Warnings
--------
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.
"""
import socket
try:
from Pyro4.naming import locateNS
# we know the nameserver must exist, so use it as our anchor point
ns = locateNS()
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect((ns._pyroUri.host, ns._pyroUri.port))
result, port = s.getsockname()
except Exception:
try:
# see what ifconfig says about our default interface
import commands
result = commands.getoutput("ifconfig").split("\n")[1].split()[1][5:]
if len(result.split('.')) != 4:
raise Exception()
except Exception:
# give up, leave the resolution to gethostbyname
result = socket.gethostbyname(socket.gethostname())
return result
class RepeatCorpus(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)]]
"""
def __init__(self, corpus, reps):
"""
Parameters
----------
corpus : iterable of iterable of (int, numeric)
Input corpus.
reps : int
Number of repeats for documents from corpus.
"""
self.corpus = corpus
self.reps = reps
def __iter__(self):
return itertools.islice(itertools.cycle(self.corpus), self.reps)
class RepeatCorpusNTimes(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)], []]
"""
def __init__(self, corpus, n):
"""
Parameters
----------
corpus : iterable of iterable of (int, numeric)
Input corpus.
n : int
Number of repeats for corpus.
"""
self.corpus = corpus
self.n = n
def __iter__(self):
for _ in xrange(self.n):
for document in self.corpus:
yield document
class ClippedCorpus(SaveLoad):
"""Wrap a `corpus` and return `max_doc` element from it."""
def __init__(self, corpus, max_docs=None):
"""
Parameters
----------
corpus : iterable of iterable of (int, numeric)
Input corpus.
max_docs : int
Maximum number of documents in the wrapped corpus.
Warnings
--------
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.
"""
self.corpus = corpus
self.max_docs = max_docs
def __iter__(self):
return itertools.islice(self.corpus, self.max_docs)
def __len__(self):
return min(self.max_docs, len(self.corpus))
class SlicedCorpus(SaveLoad):
"""Wrap `corpus` and return a slice of it."""
def __init__(self, corpus, slice_):
"""
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.
"""
self.corpus = corpus
self.slice_ = slice_
self.length = None
def __iter__(self):
if hasattr(self.corpus, 'index') and len(self.corpus.index) > 0:
return (self.corpus.docbyoffset(i) for i in self.corpus.index[self.slice_])
return itertools.islice(self.corpus, self.slice_.start, self.slice_.stop, self.slice_.step)
def __len__(self):
# check cached length, calculate if needed
if self.length is None:
if isinstance(self.slice_, (list, np.ndarray)):
self.length = len(self.slice_)
elif isinstance(self.slice_, slice):
(start, end, step) = self.slice_.indices(len(self.corpus.index))
diff = end - start
self.length = diff // step + (diff % step > 0)
else:
self.length = sum(1 for x in self)
return self.length
def 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
-------
string
Unicode string of character
"""
try:
return unichr(intval)
except ValueError:
# ValueError: unichr() arg not in range(0x10000) (narrow Python build)
s = "\\U%08x" % intval
# return UTF16 surrogate pair
return s.decode('unicode-escape')
def 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
<http://github.com/sku/python-twitter-ircbot/blob/321d94e0e40d0acc92f5bf57d126b57369da70de/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
"""
def substitute_entity(match):
try:
ent = match.group(3)
if match.group(1) == "#":
# decoding by number
if match.group(2) == '':
# number is in decimal
return safe_unichr(int(ent))
elif match.group(2) in ['x', 'X']:
# number is in hex
return safe_unichr(int(ent, 16))
else:
# they were using a name
cp = n2cp.get(ent)
if cp:
return safe_unichr(cp)
else:
return match.group()
except Exception:
# in case of errors, return original input
return match.group()
return RE_HTML_ENTITY.sub(substitute_entity, text)
def chunkize_serial(iterable, chunksize, as_numpy=False, dtype=np.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]]
"""
it = iter(iterable)
while True:
if as_numpy:
# convert each document to a 2d numpy array (~6x faster when transmitting
# chunk data over the wire, in Pyro)
wrapped_chunk = [[np.array(doc, dtype=dtype) for doc in itertools.islice(it, int(chunksize))]]
else:
wrapped_chunk = [list(itertools.islice(it, int(chunksize)))]
if not wrapped_chunk[0]:
break
# memory opt: wrap the chunk and then pop(), to avoid leaving behind a dangling reference
yield wrapped_chunk.pop()
grouper = chunkize_serial
class InputQueue(multiprocessing.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.
"""
def __init__(self, q, corpus, chunksize, maxsize, as_numpy):
"""
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.
"""
super(InputQueue, self).__init__()
self.q = q
self.maxsize = maxsize
self.corpus = corpus
self.chunksize = chunksize
self.as_numpy = as_numpy
def run(self):
it = iter(self.corpus)
while True:
chunk = itertools.islice(it, self.chunksize)
if self.as_numpy:
# HACK XXX convert documents to numpy arrays, to save memory.
# This also gives a scipy warning at runtime:
# "UserWarning: indices array has non-integer dtype (float64)"
wrapped_chunk = [[np.asarray(doc) for doc in chunk]]
else:
wrapped_chunk = [list(chunk)]
if not wrapped_chunk[0]:
self.q.put(None, block=True)
break
try:
qsize = self.q.qsize()
except NotImplementedError:
qsize = '?'
logger.debug("prepared another chunk of %i documents (qsize=%s)", len(wrapped_chunk[0]), qsize)
self.q.put(wrapped_chunk.pop(), block=True)
if os.name == 'nt':
warnings.warn("detected Windows; aliasing chunkize to chunkize_serial")
def chunkize(corpus, chunksize, maxsize=0, as_numpy=False):
"""Split `corpus` into fixed-sized chunks, using :func:`~gensim.utils.chunkize_serial`.
Parameters
----------
corpus : iterable of object
An iterable.
chunksize : int
Split `corpus` into chunks of this size.
maxsize : int, optional
Ignored. For interface compatibility only.
as_numpy : bool, optional
Yield chunks as `np.ndarray`s instead of lists?
Yields
------
list OR np.ndarray
"chunksize"-ed chunks of elements from `corpus`.
"""
for chunk in chunkize_serial(corpus, chunksize, as_numpy=as_numpy):
yield chunk
else:
def chunkize(corpus, chunksize, maxsize=0, as_numpy=False):
"""Split `corpus` into fixed-sized chunks, using :func:`~gensim.utils.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 :func:`~gensim.utils.chunkize_serial` (no I/O optimizations).
Yields
------
list of object OR np.ndarray
Groups based on `iterable`
"""
assert chunksize > 0
if maxsize > 0:
q = multiprocessing.Queue(maxsize=maxsize)
worker = InputQueue(q, corpus, chunksize, maxsize=maxsize, as_numpy=as_numpy)
worker.daemon = True
worker.start()
while True:
chunk = [q.get(block=True)]
if chunk[0] is None:
break
yield chunk.pop()
else:
for chunk in chunkize_serial(corpus, chunksize, as_numpy=as_numpy):
yield chunk
def 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
-------
str
New path to file with `ext` appended.
Examples
--------
>>> from gensim.utils import smart_extension
>>> smart_extension("my_file.pkl.gz", ".vectors")
'my_file.pkl.vectors.gz'
"""
fname, oext = os.path.splitext(fname)
if oext.endswith('.bz2'):
fname = fname + oext[:-4] + ext + '.bz2'
elif oext.endswith('.gz'):
fname = fname + oext[:-3] + ext + '.gz'
else:
fname = fname + oext + ext
return fname
def 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.
"""
with smart_open(fname, 'wb') as fout: # 'b' for binary, needed on Windows
_pickle.dump(obj, fout, protocol=protocol)
def 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
-------
object
Python object loaded from `fname`.
"""
with smart_open(fname, 'rb') as f:
# Because of loading from S3 load can't be used (missing readline in smart_open)
if sys.version_info > (3, 0):
return _pickle.load(f, encoding='latin1')
else:
return _pickle.loads(f.read())
def revdict(d):
"""Reverse a dictionary mapping, i.e. `{1: 2, 3: 4}` -> `{2: 1, 4: 3}`.
Parameters
----------
d : dict
Input dictionary.
Returns
-------
dict
Reversed dictionary mapping.
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}
"""
return {v: k for (k, v) in iteritems(dict(d))}
def 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
-------
function
Decorated function
"""
if isinstance(reason, string_types):
def decorator(func):
fmt = "Call to deprecated `{name}` ({reason})."
@wraps(func)
def new_func1(*args, **kwargs):
warnings.warn(
fmt.format(name=func.__name__, reason=reason),
category=DeprecationWarning,
stacklevel=2
)
return func(*args, **kwargs)
return new_func1
return decorator
elif inspect.isclass(reason) or inspect.isfunction(reason):
func = reason
fmt = "Call to deprecated `{name}`."
@wraps(func)
def new_func2(*args, **kwargs):
warnings.warn(
fmt.format(name=func.__name__),
category=DeprecationWarning,
stacklevel=2
)
return func(*args, **kwargs)
return new_func2
else:
raise TypeError(repr(type(reason)))
@deprecated("Function will be removed in 4.0.0")
def 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 :mod:`gensim.similarity.docsim`.
Return
------
list
a list of 3-tuples (docid, doc's similarity to the query, texts[docid])
"""
sims = index[query] # perform a similarity query against the corpus
sims = sorted(enumerate(sims), key=lambda item: -item[1])
return [(topid, topcosine, texts[topid]) for topid, topcosine in sims[:n]] # only consider top-n most similar docs
def randfname(prefix='gensim'):
"""Generate a random filename in temp.
Parameters
----------
prefix : str
Prefix of filename.
Returns
-------
str
Full path in the in system's temporary folder, ending in a random filename.
"""
randpart = hex(random.randint(0, 0xffffff))[2:]
return os.path.join(tempfile.gettempdir(), prefix + randpart)
@deprecated("Function will be removed in 4.0.0")
def 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.
"""
start = 0
for chunk in grouper(docs, chunksize):
end = start + len(chunk)
logger.info("uploading documents %i-%i", start, end - 1)
if preprocess is not None:
pchunk = []
for doc in chunk:
doc['tokens'] = preprocess(doc['text'])
del doc['text']
pchunk.append(doc)
chunk = pchunk
server.buffer(chunk)
start = end
def 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
-------
:class:`Pyro4.core.Proxy`
Proxy from Pyro4.
"""
import Pyro4
try:
return Pyro4.locateNS(host, port, broadcast, hmac_key)
except Pyro4.errors.NamingError:
raise RuntimeError("Pyro name server not found")
def 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.
"""
if ns_conf is None:
ns_conf = {}
if random_suffix:
name += '.' + hex(random.randint(0, 0xffffff))[2:]
import Pyro4
with getNS(**ns_conf) as ns:
with Pyro4.Daemon(ip or get_my_ip(), port or 0) as daemon:
# register server for remote access
uri = daemon.register(obj, name)
ns.remove(name)
ns.register(name, uri)
logger.info("%s registered with nameserver (URI '%s')", name, uri)
daemon.requestLoop()
def has_pattern():
"""Check whether the `pattern <https://github.com/clips/pattern>`_ package is installed.
Returns
-------
bool
Is `pattern` installed?
"""
try:
from pattern.en import parse # noqa:F401
return True
except ImportError:
return False
def lemmatize(content, allowed_tags=re.compile(r'(NN|VB|JJ|RB)'), light=False,
stopwords=frozenset(), min_length=2, max_length=15):
"""Use the English lemmatizer from `pattern <https://github.com/clips/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 : :class:`_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 of str
List with tokens with POS tags.
Warnings
--------
This function is only available when the optional `pattern <https://github.com/clips/pattern>`_ is installed.
Raises
------
ImportError
If `pattern <https://github.com/clips/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']
"""
if not has_pattern():
raise ImportError(
"Pattern library is not installed. Pattern library is needed in order to use lemmatize function"
)
from pattern.en import parse
if light:
import warnings
warnings.warn("The light flag is no longer supported by pattern.")
# tokenization in `pattern` is weird; it gets thrown off by non-letters,
# producing '==relate/VBN' or '**/NN'... try to preprocess the text a little
# FIXME this throws away all fancy parsing cues, including sentence structure,
# abbreviations etc.
content = u(' ').join(tokenize(content, lower=True, errors='ignore'))
parsed = parse(content, lemmata=True, collapse=False)
result = []
for sentence in parsed:
for token, tag, _, _, lemma in sentence:
if min_length <= len(lemma) <= max_length and not lemma.startswith('_') and lemma not in stopwords:
if allowed_tags.match(tag):
lemma += "/" + tag[:2]
result.append(lemma.encode('utf8'))
return result
def 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
-------
list of (int, float)
Vector in BoW format.
"""
nnz = np.random.uniform(size=(dim,))
return [(i, float(np.random.poisson(lam=lam) + 1.0)) for i in xrange(dim) if nnz[i] < prob_nnz]
def mock_data(n_items=1000, dim=1000, prob_nnz=0.5, lam=1.0):
"""Create a random Gensim-style corpus (BoW), using :func:`~gensim.utils.mock_data_row`.
Parameters
----------
n_items : int
Size of corpus
dim : int
Dimension of vector, used for :func:`~gensim.utils.mock_data_row`.
prob_nnz : float, optional
Probability of each coordinate will be nonzero, will be drawn from Poisson distribution,
used for :func:`~gensim.utils.mock_data_row`.
lam : float, optional
Parameter for Poisson distribution, used for :func:`~gensim.utils.mock_data_row`.
Returns
-------
list of list of (int, float)
Gensim-style corpus.
"""
return [mock_data_row(dim=dim, prob_nnz=prob_nnz, lam=lam) for _ in xrange(n_items)]
def 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 : int
Sum of all counts that were pruned.
"""
result = 0
old_len = len(vocab)
for w in list(vocab): # make a copy of dict's keys
if not keep_vocab_item(w, vocab[w], min_reduce, trim_rule): # vocab[w] <= min_reduce:
result += vocab[w]
del vocab[w]
logger.info(
"pruned out %i tokens with count <=%i (before %i, after %i)",
old_len - len(vocab), min_reduce, old_len, len(vocab)
)
return result
def qsize(queue):
"""Get the (approximate) queue size where available.
Parameters
----------
queue : :class:`queue.Queue`
Input queue.
Returns
-------
int
Queue size, -1 if `qsize` method isn't implemented (OS X).
"""
try:
return queue.qsize()
except NotImplementedError:
# OS X doesn't support qsize
return -1
RULE_DEFAULT = 0
RULE_DISCARD = 1
RULE_KEEP = 2
def 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
-------
bool
True if `word` should stay, False otherwise.
"""
default_res = count >= min_count
if trim_rule is None:
return default_res
else:
rule_res = trim_rule(word, count, min_count)
if rule_res == RULE_KEEP:
return True
elif rule_res == RULE_DISCARD:
return False
else:
return default_res
def check_output(stdout=subprocess.PIPE, *popenargs, **kwargs):
r"""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 :mod:`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.
"""
try:
logger.debug("COMMAND: %s %s", popenargs, kwargs)
process = subprocess.Popen(stdout=stdout, *popenargs, **kwargs)
output, unused_err = process.communicate()
retcode = process.poll()
if retcode:
cmd = kwargs.get("args")
if cmd is None:
cmd = popenargs[0]
error = subprocess.CalledProcessError(retcode, cmd)
error.output = output
raise error
return output
except KeyboardInterrupt:
process.terminate()
raise
def 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
-------
list of (object, object)
Selected items from dictionary, as a list.
"""
selected_keys = random.sample(list(d), min(len(d), n)) if use_random else itertools.islice(iterkeys(d), n)
return [(key, d[key]) for key in selected_keys]
def 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
-------
numpy.ndarray
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.
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]])
"""
ndarray = np.asarray(ndarray)
if window_size == ndarray.shape[0]:
return np.array([ndarray])
elif window_size > ndarray.shape[0]:
return np.ndarray((0, 0))
stride = ndarray.strides[0]
return np.lib.stride_tricks.as_strided(
ndarray, shape=(ndarray.shape[0] - window_size + 1, window_size),
strides=(stride, stride))
def 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?
"""
for doc_num, document in enumerate(texts):
for window in _iter_windows(document, window_size, copy, ignore_below_size):
if include_doc_num:
yield (doc_num, window)
else:
yield window
def _iter_windows(document, window_size, copy=False, ignore_below_size=True):
doc_windows = strided_windows(document, window_size)
if doc_windows.shape[0] == 0:
if not ignore_below_size:
yield document.copy() if copy else document
else:
for doc_window in doc_windows:
yield doc_window.copy() if copy else doc_window
def flatten(nested_list):
"""Recursively flatten a nested sequence of elements.
Parameters
----------
nested_list : iterable
Possibly nested sequence of elements to flatten.
Returns
-------
list
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 list(lazy_flatten(nested_list))
def lazy_flatten(nested_list):
"""Lazy version of :func:`~gensim.utils.flatten`.
Parameters
----------
nested_list : list
Possibly nested list.
Yields
------
object
Element of list
"""
for el in nested_list:
if isinstance(el, collections.Iterable) and not isinstance(el, string_types):
for sub in flatten(el):
yield sub
else:
yield el