def create_w2v_vectors():
# with open('./word2vec/IRBlog/w2v_per_300.pkl', 'rb') as infile:
with open('./word2vec/Mixed/w2v_per.pkl', 'rb') as infile:
w2v = pickle.load(infile)
w2v_length = 100 # 300
stop_words = set(
pd.read_csv('./Primary_data/PersianStopWordList.txt', header=None)[0])
questions = pd.read_csv('./Primary_data/result_filtered.csv',
delimiter=';')
train = QuickDataFrame(['w' + str(i) for i in range(0, w2v_length)])
prog = Progresser(questions.shape[0])
# build the train data
for i, qrow in questions.iterrows():
prog.count()
sum_array = np.zeros(w2v_length)
number_of_words = 0
for word in tokenise(qrow['sentence']):
if word not in stop_words and word in w2v:
number_of_words += 1
sum_array += w2v[word]
if i != len(train):
print('wat?!!')
train.append(list(sum_array / number_of_words))
train.to_csv('./Primary_data/w2v-100_vector_Q.csv')
def build_w2v_vectors():
with open('./word2vec/word2vec-En.pkl', 'rb') as infile:
w2v = pickle.load(infile)
w2v_length = 300
stop_words = set()
for w in stopwords.words('english'):
stop_words.add(w)
id_mappings = QuickDataFrame.read_csv(
'./EurLex_data/eurlex_ID_mappings.csv', sep='\t')
# create DataFrame
cols_list = ['doc_id'] + ['w' + str(i) for i in range(0, w2v_length)]
train = QuickDataFrame(columns=cols_list)
prog = Progresser(len(id_mappings))
for i in range(len(id_mappings)):
prog.count()
# read the file
try:
with open('./EurLex_data/lem_txt/' + str(id_mappings['DocID'][i]) +
'-lem.txt',
'r',
encoding="utf8") as infile:
doc_text = infile.read()
except IOError:
continue
try:
sum_array = np.zeros(w2v_length)
number_of_words = 0
for word in word_tokenize(doc_text):
if word not in stop_words and word in w2v:
number_of_words += 1
sum_array += w2v[word]
if number_of_words > 0:
sum_array = sum_array / number_of_words
train.append([id_mappings['DocID'][i]] + list(sum_array))
except Exception as e:
print(e)
train.to_csv('./EurLex_data/w2v_vector_Q.csv')
def build_all_vectors():
id_mappings = QuickDataFrame.read_csv(
'./EurLex_data/eurlex_ID_mappings.csv', sep='\t')
subject_data = QuickDataFrame.read_csv(
'./EurLex_data/eurlex_id2class/id2class_eurlex_subject_matter.qrels',
header=False,
columns=['sub', 'doc_id', 'col2'],
sep=' ')
words_vector = QuickDataFrame.read_csv('./EurLex_data/1000words.csv',
header=False,
columns=['term'])
topics = QuickDataFrame.read_csv('./EurLex_data/tags.csv')
# train = QuickDataFrame.read_csv('./EurLex_data/w2v_vector_Q.csv')
# train.set_index(train['doc_id'], unique=True)
# create DataFrame
cols_list = ['doc_id'] + list(words_vector['term'])
train = QuickDataFrame(columns=cols_list)
# filling word columns
prog = Progresser(len(id_mappings))
for i in range(len(id_mappings)):
prog.count()
try:
# read the file
try:
with open('./EurLex_data/lem_txt/' +
str(id_mappings['DocID'][i]) + '-lem.txt',
'r',
encoding="utf8") as infile:
doc_text = infile.read()
except IOError:
continue
# add a new row
train.append(value=0)
# complete the data in that row
train['doc_id'][len(train) - 1] = id_mappings['DocID'][i]
for word in word_tokenize(doc_text):
if word in train.data:
train[word][len(train) - 1] = 1
except Exception as e:
print(e)
# index by doc id
train.set_index(train['doc_id'], unique=True)
# rename word columns
rename_dict = dict()
index = 0
for wrd in list(words_vector['term']):
rename_dict[wrd] = 'wrd' + str(index)
index += 1
train.rename(columns=rename_dict)
# add topic columns
for col in list(topics['term']):
train.add_column(name=col, value=0)
# filling topic columns
for i in range(len(subject_data)):
try:
sub = subject_data['sub'][i]
doc_id = subject_data['doc_id'][i]
train[sub, doc_id] = 1
except Exception as e:
print(e)
# rename topic columns
rename_dict = dict()
index = 0
for tpc in list(topics['term']):
rename_dict[tpc] = 'tpc' + str(index)
index += 1
train.rename(columns=rename_dict)
# write to file
print('\nWriting to file...')
# train.to_csv('./EurLex_data/eurlex_combined_vectors.csv')
train.to_csv('./EurLex_data/eurlex_combined_vectors-w2v.csv')