from collections import defaultdict
from functools import partial
import logging
import os
import zipfile
import gzip
from urllib.request import urlretrieve
import torch
from tqdm import tqdm
import tarfile
from .utils import reporthook
from collections import Counter
logger = logging.getLogger(__name__)
[docs]class Vocab(object):
"""Defines a vocabulary object that will be used to numericalize a field.
Attributes:
freqs: A collections.Counter object holding the frequencies of tokens
in the data used to build the Vocab.
stoi: A collections.defaultdict instance mapping token strings to
numerical identifiers.
itos: A list of token strings indexed by their numerical identifiers.
"""
# TODO (@mttk): Populate classs with default values of special symbols
UNK = '<unk>'
[docs] def __init__(self, counter, max_size=None, min_freq=1, specials=('<unk>', '<pad>'),
vectors=None, unk_init=None, vectors_cache=None, specials_first=True):
"""Create a Vocab object from a collections.Counter.
Arguments:
counter: collections.Counter object holding the frequencies of
each value found in the data.
max_size: The maximum size of the vocabulary, or None for no
maximum. Default: None.
min_freq: The minimum frequency needed to include a token in the
vocabulary. Values less than 1 will be set to 1. Default: 1.
specials: The list of special tokens (e.g., padding or eos) that
will be prepended to the vocabulary. Default: ['<unk'>, '<pad>']
vectors: One of either the available pretrained vectors
or custom pretrained vectors (see Vocab.load_vectors);
or a list of aforementioned vectors
unk_init (callback): by default, initialize out-of-vocabulary word vectors
to zero vectors; can be any function that takes in a Tensor and
returns a Tensor of the same size. Default: 'torch.zeros'
vectors_cache: directory for cached vectors. Default: '.vector_cache'
specials_first: Whether to add special tokens into the vocabulary at first.
If it is False, they are added into the vocabulary at last.
Default: True.
"""
self.freqs = counter
counter = counter.copy()
min_freq = max(min_freq, 1)
self.itos = list()
self.unk_index = None
if specials_first:
self.itos = list(specials)
# only extend max size if specials are prepended
max_size = None if max_size is None else max_size + len(specials)
# frequencies of special tokens are not counted when building vocabulary
# in frequency order
for tok in specials:
del counter[tok]
# sort by frequency, then alphabetically
words_and_frequencies = sorted(counter.items(), key=lambda tup: tup[0])
words_and_frequencies.sort(key=lambda tup: tup[1], reverse=True)
for word, freq in words_and_frequencies:
if freq < min_freq or len(self.itos) == max_size:
break
self.itos.append(word)
if Vocab.UNK in specials: # hard-coded for now
unk_index = specials.index(Vocab.UNK) # position in list
# account for ordering of specials, set variable
self.unk_index = unk_index if specials_first else len(self.itos) + unk_index
self.stoi = defaultdict(self._default_unk_index)
else:
self.stoi = defaultdict()
if not specials_first:
self.itos.extend(list(specials))
# stoi is simply a reverse dict for itos
self.stoi.update({tok: i for i, tok in enumerate(self.itos)})
self.vectors = None
if vectors is not None:
self.load_vectors(vectors, unk_init=unk_init, cache=vectors_cache)
else:
assert unk_init is None and vectors_cache is None
def _default_unk_index(self):
return self.unk_index
def __getitem__(self, token):
return self.stoi.get(token, self.stoi.get(Vocab.UNK))
def __getstate__(self):
# avoid picking defaultdict
attrs = dict(self.__dict__)
# cast to regular dict
attrs['stoi'] = dict(self.stoi)
return attrs
def __setstate__(self, state):
if state.get("unk_index", None) is None:
stoi = defaultdict()
else:
stoi = defaultdict(self._default_unk_index)
stoi.update(state['stoi'])
state['stoi'] = stoi
self.__dict__.update(state)
def __eq__(self, other):
if self.freqs != other.freqs:
return False
if self.stoi != other.stoi:
return False
if self.itos != other.itos:
return False
if self.vectors != other.vectors:
return False
return True
def __len__(self):
return len(self.itos)
def lookup_indices(self, tokens):
indices = [self.__getitem__(token) for token in tokens]
return indices
def extend(self, v, sort=False):
words = sorted(v.itos) if sort else v.itos
for w in words:
if w not in self.stoi:
self.itos.append(w)
self.stoi[w] = len(self.itos) - 1
[docs] def load_vectors(self, vectors, **kwargs):
"""
Arguments:
vectors: one of or a list containing instantiations of the
GloVe, CharNGram, or Vectors classes. Alternatively, one
of or a list of available pretrained vectors:
charngram.100d
fasttext.en.300d
fasttext.simple.300d
glove.42B.300d
glove.840B.300d
glove.twitter.27B.25d
glove.twitter.27B.50d
glove.twitter.27B.100d
glove.twitter.27B.200d
glove.6B.50d
glove.6B.100d
glove.6B.200d
glove.6B.300d
Remaining keyword arguments: Passed to the constructor of Vectors classes.
"""
if not isinstance(vectors, list):
vectors = [vectors]
for idx, vector in enumerate(vectors):
if isinstance(vector, str):
# Convert the string pretrained vector identifier
# to a Vectors object
if vector not in pretrained_aliases:
raise ValueError(
"Got string input vector {}, but allowed pretrained "
"vectors are {}".format(
vector, list(pretrained_aliases.keys())))
vectors[idx] = pretrained_aliases[vector](**kwargs)
elif not isinstance(vector, Vectors):
raise ValueError(
"Got input vectors of type {}, expected str or "
"Vectors object".format(type(vector)))
tot_dim = sum(v.dim for v in vectors)
self.vectors = torch.Tensor(len(self), tot_dim)
for i, token in enumerate(self.itos):
start_dim = 0
for v in vectors:
end_dim = start_dim + v.dim
self.vectors[i][start_dim:end_dim] = v[token.strip()]
start_dim = end_dim
assert(start_dim == tot_dim)
[docs] def set_vectors(self, stoi, vectors, dim, unk_init=torch.Tensor.zero_):
"""
Set the vectors for the Vocab instance from a collection of Tensors.
Arguments:
stoi: A dictionary of string to the index of the associated vector
in the `vectors` input argument.
vectors: An indexed iterable (or other structure supporting __getitem__) that
given an input index, returns a FloatTensor representing the vector
for the token associated with the index. For example,
vector[stoi["string"]] should return the vector for "string".
dim: The dimensionality of the vectors.
unk_init (callback): by default, initialize out-of-vocabulary word vectors
to zero vectors; can be any function that takes in a Tensor and
returns a Tensor of the same size. Default: 'torch.zeros'
"""
self.vectors = torch.Tensor(len(self), dim)
for i, token in enumerate(self.itos):
wv_index = stoi.get(token, None)
if wv_index is not None:
self.vectors[i] = vectors[wv_index]
else:
self.vectors[i] = unk_init(self.vectors[i])
[docs]class SubwordVocab(Vocab):
[docs] def __init__(self, counter, max_size=None, specials=('<pad>'),
vectors=None, unk_init=torch.Tensor.zero_):
"""Create a revtok subword vocabulary from a collections.Counter.
Arguments:
counter: collections.Counter object holding the frequencies of
each word found in the data.
max_size: The maximum size of the subword vocabulary, or None for no
maximum. Default: None.
specials: The list of special tokens (e.g., padding or eos) that
will be prepended to the vocabulary in addition to an <unk>
token.
vectors: One of either the available pretrained vectors
or custom pretrained vectors (see Vocab.load_vectors);
or a list of aforementioned vectors
unk_init (callback): by default, initialize out-of-vocabulary word vectors
to zero vectors; can be any function that takes in a Tensor and
returns a Tensor of the same size. Default: 'torch.zeros
"""
try:
import revtok
except ImportError:
print("Please install revtok.")
raise
# Hardcode unk_index as subword_vocab has no specials_first argument
self.unk_index = (specials.index(SubwordVocab.UNK)
if SubwordVocab.UNK in specials else None)
if self.unk_index is None:
self.stoi = defaultdict()
else:
self.stoi = defaultdict(self._default_unk_index)
self.stoi.update({tok: i for i, tok in enumerate(specials)})
self.itos = specials.copy()
self.segment = revtok.SubwordSegmenter(counter, max_size)
max_size = None if max_size is None else max_size + len(self.itos)
# sort by frequency/entropy, then alphabetically
toks = sorted(self.segment.vocab.items(),
key=lambda tup: (len(tup[0]) != 1, -tup[1], tup[0]))
for tok, _ in toks:
if len(self.itos) == max_size:
break
self.itos.append(tok)
self.stoi[tok] = len(self.itos) - 1
if vectors is not None:
self.load_vectors(vectors, unk_init=unk_init)
def _infer_shape(f):
num_lines, vector_dim = 0, None
for line in f:
if vector_dim is None:
row = line.rstrip().split(b" ")
vector = row[1:]
# Assuming word, [vector] format
if len(vector) > 2:
# The header present in some (w2v) formats contains two elements.
vector_dim = len(vector)
num_lines += 1 # First element read
else:
num_lines += 1
f.seek(0)
return num_lines, vector_dim
[docs]class Vectors(object):
[docs] def __init__(self, name, cache=None,
url=None, unk_init=None, max_vectors=None):
"""
Arguments:
name: name of the file that contains the vectors
cache: directory for cached vectors
url: url for download if vectors not found in cache
unk_init (callback): by default, initialize out-of-vocabulary word vectors
to zero vectors; can be any function that takes in a Tensor and returns a Tensor of the same size
max_vectors (int): this can be used to limit the number of
pre-trained vectors loaded.
Most pre-trained vector sets are sorted
in the descending order of word frequency.
Thus, in situations where the entire set doesn't fit in memory,
or is not needed for another reason, passing `max_vectors`
can limit the size of the loaded set.
"""
cache = '.vector_cache' if cache is None else cache
self.itos = None
self.stoi = None
self.vectors = None
self.dim = None
self.unk_init = torch.Tensor.zero_ if unk_init is None else unk_init
self.cache(name, cache, url=url, max_vectors=max_vectors)
def __getitem__(self, token):
if token in self.stoi:
return self.vectors[self.stoi[token]]
else:
return self.unk_init(torch.Tensor(self.dim))
def cache(self, name, cache, url=None, max_vectors=None):
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
if os.path.isfile(name):
path = name
if max_vectors:
file_suffix = '_{}.pt'.format(max_vectors)
else:
file_suffix = '.pt'
path_pt = os.path.join(cache, os.path.basename(name)) + file_suffix
else:
path = os.path.join(cache, name)
if max_vectors:
file_suffix = '_{}.pt'.format(max_vectors)
else:
file_suffix = '.pt'
path_pt = path + file_suffix
if not os.path.isfile(path_pt):
if not os.path.isfile(path) and url:
logger.info('Downloading vectors from {}'.format(url))
if not os.path.exists(cache):
os.makedirs(cache)
dest = os.path.join(cache, os.path.basename(url))
if not os.path.isfile(dest):
with tqdm(unit='B', unit_scale=True, miniters=1, desc=dest) as t:
try:
urlretrieve(url, dest, reporthook=reporthook(t))
except KeyboardInterrupt as e: # remove the partial zip file
os.remove(dest)
raise e
logger.info('Extracting vectors into {}'.format(cache))
ext = os.path.splitext(dest)[1][1:]
if ext == 'zip':
with zipfile.ZipFile(dest, "r") as zf:
zf.extractall(cache)
elif ext == 'gz':
if dest.endswith('.tar.gz'):
with tarfile.open(dest, 'r:gz') as tar:
tar.extractall(path=cache)
if not os.path.isfile(path):
raise RuntimeError('no vectors found at {}'.format(path))
logger.info("Loading vectors from {}".format(path))
ext = os.path.splitext(path)[1][1:]
if ext == 'gz':
open_file = gzip.open
else:
open_file = open
vectors_loaded = 0
with open_file(path, 'rb') as f:
num_lines, dim = _infer_shape(f)
if not max_vectors or max_vectors > num_lines:
max_vectors = num_lines
itos, vectors, dim = [], torch.zeros((max_vectors, dim)), None
for line in tqdm(f, total=max_vectors):
# Explicitly splitting on " " is important, so we don't
# get rid of Unicode non-breaking spaces in the vectors.
entries = line.rstrip().split(b" ")
word, entries = entries[0], entries[1:]
if dim is None and len(entries) > 1:
dim = len(entries)
elif len(entries) == 1:
logger.warning("Skipping token {} with 1-dimensional "
"vector {}; likely a header".format(word, entries))
continue
elif dim != len(entries):
raise RuntimeError(
"Vector for token {} has {} dimensions, but previously "
"read vectors have {} dimensions. All vectors must have "
"the same number of dimensions.".format(word, len(entries),
dim))
try:
if isinstance(word, bytes):
word = word.decode('utf-8')
except UnicodeDecodeError:
logger.info("Skipping non-UTF8 token {}".format(repr(word)))
continue
vectors[vectors_loaded] = torch.tensor([float(x) for x in entries])
vectors_loaded += 1
itos.append(word)
if vectors_loaded == max_vectors:
break
self.itos = itos
self.stoi = {word: i for i, word in enumerate(itos)}
self.vectors = torch.Tensor(vectors).view(-1, dim)
self.dim = dim
logger.info('Saving vectors to {}'.format(path_pt))
if not os.path.exists(cache):
os.makedirs(cache)
torch.save((self.itos, self.stoi, self.vectors, self.dim), path_pt)
else:
logger.info('Loading vectors from {}'.format(path_pt))
self.itos, self.stoi, self.vectors, self.dim = torch.load(path_pt)
def __len__(self):
return len(self.vectors)
[docs] def get_vecs_by_tokens(self, tokens, lower_case_backup=False):
"""Look up embedding vectors of tokens.
Arguments:
tokens: a token or a list of tokens. if `tokens` is a string,
returns a 1-D tensor of shape `self.dim`; if `tokens` is a
list of strings, returns a 2-D tensor of shape=(len(tokens),
self.dim).
lower_case_backup : Whether to look up the token in the lower case.
If False, each token in the original case will be looked up;
if True, each token in the original case will be looked up first,
if not found in the keys of the property `stoi`, the token in the
lower case will be looked up. Default: False.
Examples:
>>> examples = ['chip', 'baby', 'Beautiful']
>>> vec = text.vocab.GloVe(name='6B', dim=50)
>>> ret = vec.get_vecs_by_tokens(tokens, lower_case_backup=True)
"""
to_reduce = False
if not isinstance(tokens, list):
tokens = [tokens]
to_reduce = True
if not lower_case_backup:
indices = [self[token] for token in tokens]
else:
indices = [self[token] if token in self.stoi
else self[token.lower()]
for token in tokens]
vecs = torch.stack(indices)
return vecs[0] if to_reduce else vecs
[docs]class GloVe(Vectors):
url = {
'42B': 'http://nlp.stanford.edu/data/glove.42B.300d.zip',
'840B': 'http://nlp.stanford.edu/data/glove.840B.300d.zip',
'twitter.27B': 'http://nlp.stanford.edu/data/glove.twitter.27B.zip',
'6B': 'http://nlp.stanford.edu/data/glove.6B.zip',
}
def __init__(self, name='840B', dim=300, **kwargs):
url = self.url[name]
name = 'glove.{}.{}d.txt'.format(name, str(dim))
super(GloVe, self).__init__(name, url=url, **kwargs)
[docs]class FastText(Vectors):
url_base = 'https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.{}.vec'
def __init__(self, language="en", **kwargs):
url = self.url_base.format(language)
name = os.path.basename(url)
super(FastText, self).__init__(name, url=url, **kwargs)
[docs]class CharNGram(Vectors):
name = 'charNgram.txt'
url = ('http://www.logos.t.u-tokyo.ac.jp/~hassy/publications/arxiv2016jmt/'
'jmt_pre-trained_embeddings.tar.gz')
def __init__(self, **kwargs):
super(CharNGram, self).__init__(self.name, url=self.url, **kwargs)
def __getitem__(self, token):
vector = torch.Tensor(1, self.dim).zero_()
if token == "<unk>":
return self.unk_init(vector)
chars = ['#BEGIN#'] + list(token) + ['#END#']
num_vectors = 0
for n in [2, 3, 4]:
end = len(chars) - n + 1
grams = [chars[i:(i + n)] for i in range(end)]
for gram in grams:
gram_key = '{}gram-{}'.format(n, ''.join(gram))
if gram_key in self.stoi:
vector += self.vectors[self.stoi[gram_key]]
num_vectors += 1
if num_vectors > 0:
vector /= num_vectors
else:
vector = self.unk_init(vector)
return vector
pretrained_aliases = {
"charngram.100d": partial(CharNGram),
"fasttext.en.300d": partial(FastText, language="en"),
"fasttext.simple.300d": partial(FastText, language="simple"),
"glove.42B.300d": partial(GloVe, name="42B", dim="300"),
"glove.840B.300d": partial(GloVe, name="840B", dim="300"),
"glove.twitter.27B.25d": partial(GloVe, name="twitter.27B", dim="25"),
"glove.twitter.27B.50d": partial(GloVe, name="twitter.27B", dim="50"),
"glove.twitter.27B.100d": partial(GloVe, name="twitter.27B", dim="100"),
"glove.twitter.27B.200d": partial(GloVe, name="twitter.27B", dim="200"),
"glove.6B.50d": partial(GloVe, name="6B", dim="50"),
"glove.6B.100d": partial(GloVe, name="6B", dim="100"),
"glove.6B.200d": partial(GloVe, name="6B", dim="200"),
"glove.6B.300d": partial(GloVe, name="6B", dim="300")
}
"""Mapping from string name to factory function"""
[docs]def build_vocab_from_iterator(iterator, num_lines=None):
"""
Build a Vocab from an iterator.
Arguments:
iterator: Iterator used to build Vocab. Must yield list or iterator of tokens.
num_lines: The expected number of elements returned by the iterator.
(Default: None)
Optionally, if known, the expected number of elements can be passed to
this factory function for improved progress reporting.
"""
counter = Counter()
with tqdm(unit_scale=0, unit='lines', total=num_lines) as t:
for tokens in iterator:
counter.update(tokens)
t.update(1)
word_vocab = Vocab(counter)
return word_vocab
