#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (C) 2010 Radim Rehurek # 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.abc import logging import warnings import numbers from html.entities import name2codepoint as n2cp 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 heapq from copy import deepcopy from datetime import datetime import platform import types import numpy as np import scipy.sparse from smart_open import open from gensim import __version__ as gensim_version logger = logging.getLogger(__name__) # When pickling objects for persistence, use this protocol by default. # Note that users won't be able to load models saved with high protocols on older environments that do # not support that protocol (e.g. Python 2). # In the rare cases where this matters, users can explicitly pass `model.save(pickle_protocol=2)`. # See also https://github.com/RaRe-Technologies/gensim/pull/3065 PICKLE_PROTOCOL = 4 PAT_ALPHABETIC = re.compile(r'(((?![\d])\w)+)', re.UNICODE) RE_HTML_ENTITY = re.compile(r'&(#?)([xX]?)(\w{1,8});', re.UNICODE) NO_CYTHON = RuntimeError( "Compiled extensions are unavailable. " "If you've installed from a package, ask the package maintainer to include compiled extensions. " "If you're building Gensim from source yourself, install Cython and a C compiler, and then " "run `python setup.py build_ext --inplace` to retry. " ) """An exception that gensim code raises when Cython extensions are unavailable.""" #: A default, shared numpy-Generator-based PRNG for any/all uses that don't require seeding default_prng = np.random.default_rng() 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 `_ and written by `@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, str): # input was a filename: open as file return open(input, 'rb') 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, str) or not mgr.__exit__(*sys.exc_info()): raise # Try to introspect and silence errors. finally: if not exc and isinstance(input, str): 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 -------- .. sourcecode:: pycon >>> 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, str): # assume utf8 for byte strings, use default (strict) error handling text = text.decode('utf8') norm = unicodedata.normalize("NFD", text) result = ''.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 -------- .. sourcecode:: pycon >>> 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, str): return text.encode('utf8') # do bytestring -> unicode -> utf8 full circle, to ensure valid utf8 return str(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, str): return text return str(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: """Serialize/deserialize objects from disk, by equipping them 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. """ def add_lifecycle_event(self, event_name, log_level=logging.INFO, **event): """ Append an event into the `lifecycle_events` attribute of this object, and also optionally log the event at `log_level`. Events are important moments during the object's life, such as "model created", "model saved", "model loaded", etc. The `lifecycle_events` attribute is persisted across object's :meth:`~gensim.utils.SaveLoad.save` and :meth:`~gensim.utils.SaveLoad.load` operations. It has no impact on the use of the model, but is useful during debugging and support. Set `self.lifecycle_events = None` to disable this behaviour. Calls to `add_lifecycle_event()` will not record events into `self.lifecycle_events` then. Parameters ---------- event_name : str Name of the event. Can be any label, e.g. "created", "stored" etc. event : dict Key-value mapping to append to `self.lifecycle_events`. Should be JSON-serializable, so keep it simple. Can be empty. This method will automatically add the following key-values to `event`, so you don't have to specify them: - `datetime`: the current date & time - `gensim`: the current Gensim version - `python`: the current Python version - `platform`: the current platform - `event`: the name of this event log_level : int Also log the complete event dict, at the specified log level. Set to False to not log at all. """ # See also https://github.com/RaRe-Technologies/gensim/issues/2863 event_dict = deepcopy(event) event_dict['datetime'] = datetime.now().isoformat() event_dict['gensim'] = gensim_version event_dict['python'] = sys.version event_dict['platform'] = platform.platform() event_dict['event'] = event_name if not hasattr(self, 'lifecycle_events'): # Avoid calling str(self), the object may not be fully initialized yet at this point. logger.debug("starting a new internal lifecycle event log for %s", self.__class__.__name__) self.lifecycle_events = [] if log_level: logger.log(log_level, "%s lifecycle event %s", self.__class__.__name__, event_dict) if self.lifecycle_events is not None: self.lifecycle_events.append(event_dict) @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) obj.add_lifecycle_event("loaded", fname=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) `_. 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) with ignore_deprecation_warning(): 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) with ignore_deprecation_warning(): 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) with ignore_deprecation_warning(): setattr(self, attrib, sparse) for attrib in getattr(self, '__ignoreds', []): logger.info("setting ignored attribute %s to None", attrib) with ignore_deprecation_warning(): 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=PICKLE_PROTOCOL, ): """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. """ 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 asides.items(): with ignore_deprecation_warning(): 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 self.__dict__.items(): 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) with ignore_deprecation_warning(): # 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 self.__dict__.items(): 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 asides.items(): 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 asides.items(): 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=PICKLE_PROTOCOL, ): """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 -------- :meth:`~gensim.utils.SaveLoad.load` Load object from file. """ self.add_lifecycle_event( "saving", fname_or_handle=str(fname_or_handle), separately=str(separately), sep_limit=sep_limit, ignore=ignore, ) 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: if document: maxid = max(maxid, max(fieldid for fieldid, _ in document)) return maxid class FakeDict: """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 "%s" % (self.__class__.__name__, 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 __contains__(self, val): return 0 <= val < self.num_terms def iteritems(self): """Iterate over all keys and values. Yields ------ (int, str) Pair of (id, token). """ for i in range(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 -------- .. sourcecode:: pycon >>> 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 -------- .. sourcecode:: pycon >>> 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 -------- .. sourcecode:: pycon >>> 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 range(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 chr(intval) except ValueError: # ValueError: chr() 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 `_. Parameters ---------- text : str Input HTML. Examples -------- .. sourcecode:: pycon >>> from gensim.utils import decode_htmlentities >>> >>> u = u'E tu vivrai nel terrore - L'aldilà (1981)' >>> print(decode_htmlentities(u).encode('UTF-8')) E tu vivrai nel terrore - L'aldilà (1981) >>> print(decode_htmlentities("l'eau")) l'eau >>> print(decode_htmlentities("foo < 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 -------- .. sourcecode:: pycon >>> 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) # Multiprocessing on Windows (and on OSX with python3.8+) uses "spawn" mode, which # causes issues with pickling. # So for these two platforms, use simpler serial processing in `chunkize`. # See https://github.com/RaRe-Technologies/gensim/pull/2800#discussion_r410890171 if os.name == 'nt' or (sys.platform == "darwin" and sys.version_info >= (3, 8)): 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`. """ if maxsize > 0: entity = "Windows" if os.name == 'nt' else "OSX with python3.8+" warnings.warn("detected %s; aliasing chunkize to chunkize_serial" % entity) 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 -------- .. sourcecode:: pycon >>> 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=PICKLE_PROTOCOL): """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. """ with 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 open(fname, 'rb') as f: return _pickle.load(f, encoding='latin1') # needed because loading from S3 doesn't support readline() 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 -------- .. sourcecode:: pycon >>> from gensim.utils import revdict >>> d = {1: 2, 3: 4} >>> revdict(d) {2: 1, 4: 3} """ return {v: k for (k, v) in dict(d).items()} 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, str): 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))) @contextmanager def ignore_deprecation_warning(): """Contextmanager for ignoring DeprecationWarning.""" with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=DeprecationWarning) yield @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.abc -- 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 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 range(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 range(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 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`. """ if topk >= len(vocab): return min_count = heapq.nlargest(topk, vocab.values())[-1] prune_vocab(vocab, min_count, trim_rule=trim_rule) def merge_counts(dict1, dict2): """Merge `dict1` of (word, freq1) and `dict2` of (word, freq2) into `dict1` of (word, freq1+freq2). Parameters ---------- dict1 : dict of (str, int) First dictionary. dict2 : dict of (str, int) Second dictionary. Returns ------- result : dict Merged dictionary with sum of frequencies as values. """ for word, freq in dict2.items(): if word in dict1: dict1[word] += freq else: dict1[word] = freq return dict1 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. Used in word2vec/glove2word2vec tests. Behaves very similar to https://docs.python.org/2/library/subprocess.html#subprocess.check_output. Examples -------- .. sourcecode:: pycon >>> 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(d.keys(), 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 -------- .. sourcecode:: pycon >>> 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.abc.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.abc.Iterable) and not isinstance(el, str): for sub in flatten(el): yield sub else: yield el def 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 """ with open(filename, mode='wb', encoding='utf8') as fout: for sentence in corpus: line = any2unicode(' '.join(sentence) + '\n') fout.write(line) def 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 ------- int Number of effective jobs. """ if n_jobs == 0: raise ValueError('n_jobs == 0 in Parallel has no meaning') elif n_jobs is None: return 1 elif n_jobs < 0: n_jobs = max(multiprocessing.cpu_count() + 1 + n_jobs, 1) return n_jobs def is_empty(corpus): """Is the corpus (an iterable or a scipy.sparse array) empty?""" if scipy.sparse.issparse(corpus): return corpus.shape[1] == 0 # by convention, scipy.sparse documents are columns if isinstance(corpus, types.GeneratorType): return False # don't try to guess emptiness of generators, may lose elements irretrievably try: # list, numpy array etc first_doc = next(iter(corpus)) # noqa: F841 (ignore unused variable) return False # first document exists => not empty except StopIteration: return True except Exception: return False