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# -*- coding: utf-8 -*-
"""
Created on Tue Oct 11 16:31:58 2022
@author: gita
"""
import random
import numpy as np
import copy
class upsampling_ner:
def __init__(self, path_data, entities, pos_labels):
"""
Parameters
----------
path_data : str
Path of the dataset in format CONLL.
entities : List
List of the senten.
pos_labels : Dict
Dictionary where the keys are the kind of labels, and the values
are the position of the labels in one line
Returns
-------
None.
"""
self.__path_data = path_data
self.__entities = entities
self.__search_factor = 1000
self.__pos_labels = pos_labels
self.__get_data_variables()
def __get_data_variables(self):
"""
Takes the data path and turn the senteces into a matrix of shape
(Sentences, tokens of each sentence).
Also executes the __get_total_mentions.
Returns
-------
None.
"""
col = self.__pos_labels['ner']
self.__dataset = []
self.__data_labels = []
data_temp = []
labels_temp = []
with open(self.__path_data, mode='r', encoding='utf-8') as f:
for line in f.readlines():
if line != '\n':
data_temp.append(line.split(' ')[0])
labels_temp.append(line.split(' ')[col][:-1])
#print('si')
else:
self.__dataset.append(data_temp)
self.__data_labels.append(labels_temp)
data_temp = []
labels_temp = []
self.__get_total_mentions_and_tokens()
def get_mentions(self, sentence, labels):
"""
Divide sentence to a dictionary of mentions and a dictionary of labels
of the mentions
Parameters
----------
sentence : List
List of the tokens of the sentence.
labels : List
List of the labels of each token.
Returns
-------
dict_mentions : Dictionary
sentece divided by its entities mentions key=number of mention,
value= set of tokens in the mention.
dict_label_mentions : Dictionary
labels corresponding of the mentions in the same order as token
mentions. key= number of mention, value= label of the mention.
"""
dict_mentions = {}
dict_label_mentions = {}
mention = 0
#print(sentence)
dict_mentions[mention] = [sentence[0]]
dict_label_mentions[mention] = labels[0]
for i,label in enumerate(labels[1:]):
if label == labels[i]:
dict_mentions[mention].append(sentence[i+1])
else:
mention += 1
dict_mentions[mention] = [sentence[i+1]]
dict_label_mentions[mention] = labels[i+1]
return dict_mentions, dict_label_mentions
def __get_total_mentions_and_tokens(self):
"""
Takes the dataset and divide ach sentence in mentions and it store it
in __all_mentions
Returns
-------
None.
"""
self.__all_mentions = {}
self.__tokens_per_entity = {}
for key in self.__entities:
self.__all_mentions[key] = []
self.__tokens_per_entity[key] = []
for i,sentence in enumerate(self.__dataset):
if sentence:
for j,word in enumerate(sentence):
self.__tokens_per_entity[self.__data_labels[i][j]].append(word)
mentions,label_mentions = self.get_mentions(sentence, self.__data_labels[i])
for n,label in enumerate(label_mentions.values()):
if mentions[n] not in self.__all_mentions[label]: self.__all_mentions[label].append(mentions[n]);
def get_mentions_dict(self):
"Return all the mentions in the dataset"
return self.__all_mentions
def get_dataset(self):
"Return the dataset"
return self.__dataset, self.__data_labels
def Label_wise_token_replacement(self, token_mentions, label_mentions, labels, p):
"""
Do the Label wise token replacement to a sentence divided in mentions
Parameters
----------
token_mentions : Dictionary
sentece divided by its entities mentions key=number of mention,
value= set of tokens in the mention.
label_mentions : Dictionary
labels corresponding of the mentions in the same order as token
mentions. key= number of mention, value= label of the mention
labels : List
list of entities to be upsampled.
p : float
probability upsampled a mention selected.
Returns
-------
token_mentions : Dictionary
token mentions but with mention replacement.
"""
p = 1-p
for i in token_mentions.keys():
if label_mentions[i] in labels:
for j,token in enumerate(token_mentions[i]):
umbral=np.random.uniform(0,1)
if umbral>=p:
token_selected = random.choice(self.__tokens_per_entity[label_mentions[i]])
search = 0
while token_selected == token and search <= self.__search_factor:
token_selected = random.choice(self.__tokens_per_entity[label_mentions[i]])
search += 1
token_mentions[i][j] = token_selected
return token_mentions
def synonym_replacement(self, token_mentions, label_mentions, labels, p):
"""
Do the synonym_replacement to a sentence divided in mentions
Parameters
----------
token_mentions : Dictionary
sentece divided by its entities mentions key=number of mention,
value= set of tokens in the mention.
label_mentions : Dictionary
labels corresponding of the mentions in the same order as token
mentions. key= number of mention, value= label of the mention
labels : List
list of entities to be upsampled.
p : float
probability upsampled a mention selected.
Returns
-------
token_mentions : Dictionary
token mentions but with shuffled.
"""
import requests
from bs4 import BeautifulSoup
url='http://www.wordreference.com/sinonimos/'
p = 1-p
for i in token_mentions.keys():
if label_mentions[i] in labels:
for j,token in enumerate(token_mentions[i]):
umbral=np.random.uniform(0,1)
if umbral>=p:
buscar=url+token
resp=requests.get(buscar)
bs=BeautifulSoup(resp.text,'lxml')
try:
lista=bs.find(class_='trans clickable')
sino=lista.find('li')
list_synonyms = sino.next_element.split(', ')
except:
list_synonyms = False
if list_synonyms:
synonym_selected = random.choice(list_synonyms)
search = 0
while synonym_selected == token_mentions[i][j] and search <= self.__search_factor:
synonym_selected = random.choice(list_synonyms)
search += 1
token_mentions[i][j] = synonym_selected
return token_mentions
def mention_replacement(self, token_mentions, label_mentions, labels, p):
"""
Do the mentions replacement to a sentence divided in mentions
Parameters
----------
token_mentions : Dictionary
sentece divided by its entities mentions key=number of mention,
value= set of tokens in the mention.
label_mentions : Dictionary
labels corresponding of the mentions in the same order as token
mentions. key= number of mention, value= label of the mention
labels : List
list of entities to be upsampled.
p : float
probability upsampled a mention selected.
Returns
-------
token_mentions : Dictionary
token mentions but with mention replacement.
"""
p = 1-p
for i in token_mentions.keys():
if label_mentions[i] in labels:
umbral=np.random.uniform(0,1)
if umbral>=p:
set_of_mentions = self.__all_mentions[label_mentions[i]]
mention_selected = random.choice(set_of_mentions)
search = 0
while token_mentions[i] == mention_selected and search <= self.__search_factor:
mention_selected = random.choice(set_of_mentions)
search += 1
token_mentions[i] = mention_selected
return token_mentions
def shuffle_within_segments(self, token_mentions, label_mentions, labels, p):
"""
Do the shuffle within segments to a sentence divided in mentions
Parameters
----------
token_mentions : Dictionary
sentece divided by its entities mentions key=number of mention,
value= set of tokens in the mention.
label_mentions : Dictionary
labels corresponding of the mentions in the same order as token
mentions. key= number of mention, value= label of the mention
labels : List
list of entities to be upsampled.
p : float
probability upsampled a mention selected.
Returns
-------
token_mentions : Dictionary
token mentions but with shuffled.
"""
p = 1-p
for i in token_mentions.keys():
if label_mentions[i] in labels:
umbral=np.random.uniform(0,1)
if umbral>=p: random.shuffle(token_mentions[i])
return token_mentions
def mention_back_traslation(self, token_mentions, label_mentions, labels, p):
"""
Do the back traslation to each mention in a sentence divided in mentions
Parameters
----------
token_mentions : Dictionary
sentece divided by its entities mentions key=number of mention,
value= set of tokens in the mention.
label_mentions : Dictionary
labels corresponding of the mentions in the same order as token
mentions. key= number of mention, value= label of the mention
labels : List
list of entities to be upsampled.
p : float
probability upsampled a mention selected.
Returns
-------
token_mentions : Dictionary
token mentions but with mention brack traslation.
}
"""
from deep_translator import GoogleTranslator
from nltk.tokenize import word_tokenize
p = 1-p
for i in token_mentions.keys():
if label_mentions[i] in labels:
umbral=np.random.uniform(0,1)
if umbral>=p:
try:
language = random.choice(['en', 'sv', 'fr', 'ja', 'ko', 'af', 'sq', 'cs', 'es', 'el', 'ga'])
to_translate = " ".join(token_mentions[i])
#print("to_trans: ", to_translate[:20])
translateden = GoogleTranslator(source='auto', target=language).translate(to_translate)
#print("Trans: ",translateden[:20])
translatedes = GoogleTranslator(source='auto', target='de').translate(translateden)
#print("back Trans: ",translatedes[:20])
mention_selected = word_tokenize(translatedes)
token_mentions[i] = mention_selected
except:
pass
return token_mentions
def upsampling(self, labels, p, methods=None):
if methods is None:
print("Not upsampling required")
else:
new_mentions = []
new_labels = []
for i,sentence in enumerate(self.__dataset):
if sentence:
sentence_mentions,label_mentions = self.get_mentions(sentence, self.__data_labels[i])
if "SiS" in methods:
new_mentions_temp = self.shuffle_within_segments(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
new_mentions.append(new_mentions_temp)
new_labels.append(label_mentions)
if "LwTR" in methods:
new_mentions_temp = self.Label_wise_token_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
new_mentions.append(new_mentions_temp)
new_labels.append(label_mentions)
if "MR" in methods:
new_mentions_temp = self.mention_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
new_mentions.append(new_mentions_temp)
new_labels.append(label_mentions)
if "SR" in methods:
new_mentions_temp = self.synonym_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
new_mentions.append(new_mentions_temp)
new_labels.append(label_mentions)
if "MBT" in methods:
new_mentions_temp = self.mention_back_traslation(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
new_mentions.append(new_mentions_temp)
new_labels.append(label_mentions)
#Turn the mentions into sentences
new_samples_generated = []
new_labels_generated = []
for i,mentions in enumerate(new_mentions):
new_labels_temp = new_labels[i]
sample_temp = []
labels_temp = []
for key in mentions.keys():
sample_temp += mentions[key]
labels_temp += [new_labels_temp[key]]*len(mentions[key])
new_samples_generated.append(sample_temp)
new_labels_generated.append(labels_temp)
return new_samples_generated, new_labels_generated
def mention_to_sentence(self, mentions, labels):
sample_temp = []
labels_temp = []
for key in mentions.keys():
sample_temp += mentions[key]
labels_temp += [labels[key]]*len(mentions[key])
return sample_temp, labels_temp
def upsampling_by_sentence(self, labels, p, methods=None):
if methods is None:
print("Not upsampling required")
else:
new_mentions = []
new_labels = []
map_sentences = []
map_labels = []
sentences_upsampled = []
labels_upsampled = []
for i,sentence in enumerate(self.__dataset):
sentences_upsampled_temp = {}
labels_upsampled_temp = {}
sentences_upsampled_temp["Original"] = sentence
labels_upsampled_temp["Original"] = self.__data_labels[i]
sentence_mentions,label_mentions = self.get_mentions(sentence, self.__data_labels[i])
if "SiS" in methods:
new_mentions_temp = self.shuffle_within_segments(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
sentences_upsampled_temp["SiS"], labels_upsampled_temp["SiS"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
if "LwTR" in methods:
new_mentions_temp = self.Label_wise_token_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
sentences_upsampled_temp["LwTR"], labels_upsampled_temp["LwTR"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
if "MR" in methods:
new_mentions_temp = self.mention_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
sentences_upsampled_temp["MR"], labels_upsampled_temp["MR"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
if "SR" in methods:
new_mentions_temp = self.synonym_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
sentences_upsampled_temp["SR"], labels_upsampled_temp["SR"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
if "MBT" in methods:
new_mentions_temp = self.mention_back_traslation(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
sentences_upsampled_temp["MBT"], labels_upsampled_temp["MBT"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
if len(sentences_upsampled_temp)>1:
print(len(sentences_upsampled_temp))
sentences_upsampled.append(sentences_upsampled_temp)
labels_upsampled.append(labels_upsampled_temp)
return sentences_upsampled, labels_upsampled
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