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"""
Shared loads word embeddings and related distances
=====================================================
"""
from abc import ABC, abstractmethod
from collections import defaultdict
import os
import pickle
import numpy as np
import torch
from textattack.shared import utils
class AbstractWordEmbedding(utils.ReprMixin, ABC):
"""Abstract class representing word embedding used by TextAttack.
This class specifies all the methods that is required to be defined
so that it can be used for transformation and constraints. For
custom word embedding not supported by TextAttack, please create a
class that inherits this class and implement the required methods.
However, please first check if you can use `WordEmbedding` class,
which has a lot of internal methods implemented.
"""
@abstractmethod
def __getitem__(self, index):
"""Gets the embedding vector for word/id
Args:
index (Union[str|int]): `index` can either be word or integer representing the id of the word.
Returns:
vector (ndarray): 1-D embedding vector. If corresponding vector cannot be found for `index`, returns `None`.
"""
raise NotImplementedError()
@abstractmethod
def get_mse_dist(self, a, b):
"""Return MSE distance between vector for word `a` and vector for word
`b`.
Since this is a metric, `get_mse_dist(a,b)` and `get_mse_dist(b,a)` should return the same value.
Args:
a (Union[str|int]): Either word or integer presenting the id of the word
b (Union[str|int]): Either word or integer presenting the id of the word
Returns:
distance (float): MSE (L2) distance
"""
raise NotImplementedError()
@abstractmethod
def get_cos_sim(self, a, b):
"""Return cosine similarity between vector for word `a` and vector for
word `b`.
Since this is a metric, `get_mse_dist(a,b)` and `get_mse_dist(b,a)` should return the same value.
Args:
a (Union[str|int]): Either word or integer presenting the id of the word
b (Union[str|int]): Either word or integer presenting the id of the word
Returns:
distance (float): cosine similarity
"""
raise NotImplementedError()
@abstractmethod
def word2index(self, word):
"""
Convert between word to id (i.e. index of word in embedding matrix)
Args:
word (str)
Returns:
index (int)
"""
raise NotImplementedError()
@abstractmethod
def index2word(self, index):
"""
Convert index to corresponding word
Args:
index (int)
Returns:
word (str)
"""
raise NotImplementedError()
@abstractmethod
def nearest_neighbours(self, index, topn):
"""
Get top-N nearest neighbours for a word
Args:
index (int): ID of the word for which we're finding the nearest neighbours
topn (int): Used for specifying N nearest neighbours
Returns:
neighbours (list[int]): List of indices of the nearest neighbours
"""
raise NotImplementedError()
class WordEmbedding(AbstractWordEmbedding):
"""Object for loading word embeddings and related distances for TextAttack.
This class has a lot of internal components (e.g. get consine similarity)
implemented. Consider using this class if you can provide the appropriate
input data to create the object.
Args:
emedding_matrix (ndarray): 2-D array of shape N x D where N represents size of vocab and D is the dimension of embedding vectors.
word2index (Union[dict|object]): dictionary (or a similar object) that maps word to its index with in the embedding matrix.
index2word (Union[dict|object]): dictionary (or a similar object) that maps index to its word.
nn_matrix (ndarray): Matrix for precomputed nearest neighbours. It should be a 2-D integer array of shape N x K
where N represents size of vocab and K is the top-K nearest neighbours. If this is set to `None`, we have to compute nearest neighbours
on the fly for `nearest_neighbours` method, which is costly.
"""
PATH = "word_embeddings"
def __init__(self, embedding_matrix, word2index, index2word, nn_matrix=None):
self.embedding_matrix = embedding_matrix
self._word2index = word2index
self._index2word = index2word
self.nn_matrix = nn_matrix
# Dictionary for caching results
self._mse_dist_mat = defaultdict(dict)
self._cos_sim_mat = defaultdict(dict)
self._nn_cache = {}
def __getitem__(self, index):
"""Gets the embedding vector for word/id
Args:
index (Union[str|int]): `index` can either be word or integer representing the id of the word.
Returns:
vector (ndarray): 1-D embedding vector. If corresponding vector cannot be found for `index`, returns `None`.
"""
if isinstance(index, str):
try:
index = self._word2index[index]
except KeyError:
return None
try:
return self.embedding_matrix[index]
except IndexError:
# word embedding ID out of bounds
return None
def word2index(self, word):
"""
Convert between word to id (i.e. index of word in embedding matrix)
Args:
word (str)
Returns:
index (int)
"""
return self._word2index[word]
def index2word(self, index):
"""
Convert index to corresponding word
Args:
index (int)
Returns:
word (str)
"""
return self._index2word[index]
def get_mse_dist(self, a, b):
"""Return MSE distance between vector for word `a` and vector for word
`b`.
Since this is a metric, `get_mse_dist(a,b)` and `get_mse_dist(b,a)` should return the same value.
Args:
a (Union[str|int]): Either word or integer presenting the id of the word
b (Union[str|int]): Either word or integer presenting the id of the word
Returns:
distance (float): MSE (L2) distance
"""
if isinstance(a, str):
a = self._word2index[a]
if isinstance(b, str):
b = self._word2index[b]
a, b = min(a, b), max(a, b)
try:
mse_dist = self._mse_dist_mat[a][b]
except KeyError:
e1 = self.embedding_matrix[a]
e2 = self.embedding_matrix[b]
e1 = torch.tensor(e1).to(utils.device)
e2 = torch.tensor(e2).to(utils.device)
mse_dist = torch.sum((e1 - e2) ** 2).item()
self._mse_dist_mat[a][b] = mse_dist
return mse_dist
def get_cos_sim(self, a, b):
"""Return cosine similarity between vector for word `a` and vector for
word `b`.
Since this is a metric, `get_mse_dist(a,b)` and `get_mse_dist(b,a)` should return the same value.
Args:
a (Union[str|int]): Either word or integer presenting the id of the word
b (Union[str|int]): Either word or integer presenting the id of the word
Returns:
distance (float): cosine similarity
"""
if isinstance(a, str):
a = self._word2index[a]
if isinstance(b, str):
b = self._word2index[b]
a, b = min(a, b), max(a, b)
try:
cos_sim = self._cos_sim_mat[a][b]
except KeyError:
e1 = self.embedding_matrix[a]
e2 = self.embedding_matrix[b]
e1 = torch.tensor(e1).to(utils.device)
e2 = torch.tensor(e2).to(utils.device)
cos_sim = torch.nn.CosineSimilarity(dim=0)(e1, e2).item()
self._cos_sim_mat[a][b] = cos_sim
return cos_sim
def nearest_neighbours(self, index, topn):
"""
Get top-N nearest neighbours for a word
Args:
index (int): ID of the word for which we're finding the nearest neighbours
topn (int): Used for specifying N nearest neighbours
Returns:
neighbours (list[int]): List of indices of the nearest neighbours
"""
if isinstance(index, str):
index = self._word2index[index]
if self.nn_matrix is not None:
nn = self.nn_matrix[index][1 : (topn + 1)]
else:
try:
nn = self._nn_cache[index]
except KeyError:
embedding = torch.tensor(self.embedding_matrix).to(utils.device)
vector = torch.tensor(self.embedding_matrix[index]).to(utils.device)
dist = torch.norm(embedding - vector, dim=1, p=None)
# Since closest neighbour will be the same word, we consider N+1 nearest neighbours
nn = dist.topk(topn + 1, largest=False)[1:].tolist()
self._nn_cache[index] = nn
return nn
@staticmethod
def counterfitted_GLOVE_embedding():
"""Returns a prebuilt counter-fitted GLOVE word embedding proposed by
"Counter-fitting Word Vectors to Linguistic Constraints" (Mrkšić et
al., 2016)"""
if (
"textattack_counterfitted_GLOVE_embedding" in utils.GLOBAL_OBJECTS
and isinstance(
utils.GLOBAL_OBJECTS["textattack_counterfitted_GLOVE_embedding"],
WordEmbedding,
)
):
# avoid recreating same embedding (same memory) and instead share across different components
return utils.GLOBAL_OBJECTS["textattack_counterfitted_GLOVE_embedding"]
word_embeddings_folder = "paragramcf"
word_embeddings_file = "paragram.npy"
word_list_file = "wordlist.pickle"
mse_dist_file = "mse_dist.p"
cos_sim_file = "cos_sim.p"
nn_matrix_file = "nn.npy"
# Download embeddings if they're not cached.
word_embeddings_folder = os.path.join(
WordEmbedding.PATH, word_embeddings_folder
).replace("\\", "/")
word_embeddings_folder = utils.download_from_s3(word_embeddings_folder)
# Concatenate folder names to create full path to files.
word_embeddings_file = os.path.join(
word_embeddings_folder, word_embeddings_file
)
word_list_file = os.path.join(word_embeddings_folder, word_list_file)
mse_dist_file = os.path.join(word_embeddings_folder, mse_dist_file)
cos_sim_file = os.path.join(word_embeddings_folder, cos_sim_file)
nn_matrix_file = os.path.join(word_embeddings_folder, nn_matrix_file)
# loading the files
embedding_matrix = np.load(word_embeddings_file)
word2index = np.load(word_list_file, allow_pickle=True)
index2word = {}
for word, index in word2index.items():
index2word[index] = word
nn_matrix = np.load(nn_matrix_file)
embedding = WordEmbedding(embedding_matrix, word2index, index2word, nn_matrix)
with open(mse_dist_file, "rb") as f:
mse_dist_mat = pickle.load(f)
with open(cos_sim_file, "rb") as f:
cos_sim_mat = pickle.load(f)
embedding._mse_dist_mat = mse_dist_mat
embedding._cos_sim_mat = cos_sim_mat
utils.GLOBAL_OBJECTS["textattack_counterfitted_GLOVE_embedding"] = embedding
return embedding
class GensimWordEmbedding(AbstractWordEmbedding):
"""Wraps Gensim's `models.keyedvectors` module
(https://radimrehurek.com/gensim/models/keyedvectors.html)"""
def __init__(self, keyed_vectors):
gensim = utils.LazyLoader("gensim", globals(), "gensim")
if isinstance(keyed_vectors, gensim.models.KeyedVectors):
self.keyed_vectors = keyed_vectors
else:
raise ValueError(
"`keyed_vectors` argument must be a "
"`gensim.models.keyedvectors.WordEmbeddingsKeyedVectors` object"
)
self.keyed_vectors.init_sims()
self._mse_dist_mat = defaultdict(dict)
self._cos_sim_mat = defaultdict(dict)
def __getitem__(self, index):
"""Gets the embedding vector for word/id
Args:
index (Union[str|int]): `index` can either be word or integer representing the id of the word.
Returns:
vector (ndarray): 1-D embedding vector. If corresponding vector cannot be found for `index`, returns `None`.
"""
if isinstance(index, str):
try:
index = self.keyed_vectors.key_to_index.get(index)
except KeyError:
return None
try:
return self.keyed_vectors.get_normed_vectors()[index]
except IndexError:
# word embedding ID out of bounds
return None
def word2index(self, word):
"""
Convert between word to id (i.e. index of word in embedding matrix)
Args:
word (str)
Returns:
index (int)
"""
vocab = self.keyed_vectors.key_to_index.get(word)
if vocab is None:
raise KeyError(word)
return vocab
def index2word(self, index):
"""
Convert index to corresponding word
Args:
index (int)
Returns:
word (str)
"""
try:
# this is a list, so the error would be IndexError
return self.keyed_vectors.index_to_key[index]
except IndexError:
raise KeyError(index)
def get_mse_dist(self, a, b):
"""Return MSE distance between vector for word `a` and vector for word
`b`.
Since this is a metric, `get_mse_dist(a,b)` and `get_mse_dist(b,a)` should return the same value.
Args:
a (Union[str|int]): Either word or integer presenting the id of the word
b (Union[str|int]): Either word or integer presenting the id of the word
Returns:
distance (float): MSE (L2) distance
"""
try:
mse_dist = self._mse_dist_mat[a][b]
except KeyError:
e1 = self.keyed_vectors.get_normed_vectors()[a]
e2 = self.keyed_vectors.get_normed_vectors()[b]
e1 = torch.tensor(e1).to(utils.device)
e2 = torch.tensor(e2).to(utils.device)
mse_dist = torch.sum((e1 - e2) ** 2).item()
self._mse_dist_mat[a][b] = mse_dist
return mse_dist
def get_cos_sim(self, a, b):
"""Return cosine similarity between vector for word `a` and vector for
word `b`.
Since this is a metric, `get_mse_dist(a,b)` and `get_mse_dist(b,a)` should return the same value.
Args:
a (Union[str|int]): Either word or integer presenting the id of the word
b (Union[str|int]): Either word or integer presenting the id of the word
Returns:
distance (float): cosine similarity
"""
if not isinstance(a, str):
a = self.keyed_vectors.index_to_key[a]
if not isinstance(b, str):
b = self.keyed_vectors.index_to_key[b]
cos_sim = self.keyed_vectors.similarity(a, b)
return cos_sim
def nearest_neighbours(self, index, topn, return_words=True):
"""
Get top-N nearest neighbours for a word
Args:
index (int): ID of the word for which we're finding the nearest neighbours
topn (int): Used for specifying N nearest neighbours
Returns:
neighbours (list[int]): List of indices of the nearest neighbours
"""
word = self.keyed_vectors.index_to_key[index]
return [
self.word2index(i[0])
for i in self.keyed_vectors.similar_by_word(word, topn)
]
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