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data.py
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data.py
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from nltk import word_tokenize
from collections import Counter
from nltk.corpus import stopwords
import numpy as np
import os
import xml.etree.ElementTree as ET
import html
import HTMLParser
import re
stop = set(stopwords.words('english'))
def load_embedding_file(embed_file_name, word_set):
''' loads embedding file and returns a dictionary (word -> embedding) for the words existing in the word_set '''
embeddings = {}
with open(embed_file_name, 'r') as embed_file:
for line in embed_file:
content = line.strip().split()
word = content[0]
if word in word_set:
embedding = np.array(content[1:], dtype=float)
embeddings[word] = embedding
return embeddings
def get_dataset_resources(data_file_name, sent_word2idx, target_word2idx, word_set, max_sent_len):
''' updates word2idx and word_set '''
if len(sent_word2idx) == 0:
sent_word2idx["<pad>"] = 0
word_count = []
sent_word_count = []
target_count = []
words = []
sentence_words = []
target_words = []
with open(data_file_name, 'r') as data_file:
lines = data_file.read().split('\n')
for line_no in range(0, len(lines)-1, 3):
sentence = lines[line_no]
target = lines[line_no+1]
sentence.replace("$T$", "")
sentence = sentence.lower()
target = target.lower()
max_sent_len = max(max_sent_len, len(sentence.split()))
sentence_words.extend(sentence.split())
target_words.extend([target])
words.extend(sentence.split() + target.split())
sent_word_count.extend(Counter(sentence_words).most_common())
target_count.extend(Counter(target_words).most_common())
word_count.extend(Counter(words).most_common())
for word, _ in sent_word_count:
if word not in sent_word2idx:
sent_word2idx[word] = len(sent_word2idx)
for target, _ in target_count:
if target not in target_word2idx:
target_word2idx[target] = len(target_word2idx)
for word, _ in word_count:
if word not in word_set:
word_set[word] = 1
return max_sent_len
def get_embedding_matrix(embeddings, sent_word2idx, target_word2idx, edim):
''' returns the word and target embedding matrix '''
word_embed_matrix = np.zeros([len(sent_word2idx), edim], dtype = float)
target_embed_matrix = np.zeros([len(target_word2idx), edim], dtype = float)
for word in sent_word2idx:
if word in embeddings:
word_embed_matrix[sent_word2idx[word]] = embeddings[word]
for target in target_word2idx:
for word in target:
if word in embeddings:
target_embed_matrix[target_word2idx[target]] += embeddings[word]
target_embed_matrix[target_word2idx[target]] /= max(1, len(target.split()))
print type(word_embed_matrix)
return word_embed_matrix, target_embed_matrix
def get_dataset(data_file_name, sent_word2idx, target_word2idx, embeddings):
''' returns the dataset'''
sentence_list = []
location_list = []
target_list = []
polarity_list = []
with open(data_file_name, 'r') as data_file:
lines = data_file.read().split('\n')
for line_no in range(0, len(lines)-1, 3):
sentence = lines[line_no].lower()
target = lines[line_no+1].lower()
polarity = int(lines[line_no+2])
sent_words = sentence.split()
target_words = target.split()
try:
target_location = sent_words.index("$t$")
except:
print "sentence does not contain target element tag"
exit()
is_included_flag = 1
id_tokenised_sentence = []
location_tokenised_sentence = []
for index, word in enumerate(sent_words):
if word == "$t$":
continue
try:
word_index = sent_word2idx[word]
except:
print "id not found for word in the sentence"
exit()
location_info = abs(index - target_location)
if word in embeddings:
id_tokenised_sentence.append(word_index)
location_tokenised_sentence.append(location_info)
# if word not in embeddings:
# is_included_flag = 0
# break
is_included_flag = 0
for word in target_words:
if word in embeddings:
is_included_flag = 1
break
try:
target_index = target_word2idx[target]
except:
print target
print "id not found for target"
exit()
if not is_included_flag:
print sentence
continue
sentence_list.append(id_tokenised_sentence)
location_list.append(location_tokenised_sentence)
target_list.append(target_index)
polarity_list.append(polarity)
return sentence_list, location_list, target_list, polarity_list