# -*- coding: utf-8 -*-
# Copyright (C) 2016-2023 PyThaiNLP Project
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Tuple
import itertools
[docs]class Word2VecAug:
[docs] def __init__(
self, model: str, tokenize: object, type: str = "file"
) -> None:
"""
:param str model: path model
:param object tokenize: tokenize function
:param str type: moodel type (file, binary)
"""
import gensim.models.keyedvectors as word2vec
self.tokenizer = tokenize
if type == "file":
self.model = word2vec.KeyedVectors.load_word2vec_format(model)
elif type == "binary":
self.model = word2vec.KeyedVectors.load_word2vec_format(
model, binary=True, unicode_errors="ignore"
)
else:
self.model = model
self.dict_wv = list(self.model.key_to_index.keys())
[docs] def modify_sent(self, sent: str, p: float = 0.7) -> List[List[str]]:
"""
:param str sent: text sentence
:param float p: probability
:rtype: List[List[str]]
"""
list_sent_new = []
for i in sent:
if i in self.dict_wv:
w = [j for j, v in self.model.most_similar(i) if v >= p]
if w == []:
list_sent_new.append([i])
else:
list_sent_new.append(w)
else:
list_sent_new.append([i])
return list_sent_new
[docs] def augment(
self, sentence: str, n_sent: int = 1, p: float = 0.7
) -> List[Tuple[str]]:
"""
:param str sentence: text sentence
:param int n_sent: max number for synonyms sentence
:param int p: probability
:return: list of synonyms
:rtype: List[Tuple[str]]
"""
self.sentence = self.tokenizer(sentence)
self.list_synonym = self.modify_sent(self.sentence, p=p)
new_sentences = []
for x in list(itertools.product(*self.list_synonym))[0:n_sent]:
new_sentences.append(x)
return new_sentences