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')
def evaluate_model_cnn(x, y, learn_path, k=10):
print(len(y), len(y[0]))
# create a k fold with no unique classes
count = 0
while True:
count += 1
# print(count, 'Finding a proper KF...')
kf = list(
KFold(n_splits=k, shuffle=True,
random_state=randint(0, 100000)).split(x))
good_folds = True
for train_index, test_index in kf:
for i in range(len(y[0])):
if len(np.unique(
y[train_index,
i])) < 2: # or len(np.unique(y[test_index, i])) < 2:
# print(y[train_index, i],np.unique(y[train_index, i]))
print(i)
good_folds = False
break
if not good_folds:
break
if good_folds:
break
print('Found a good KF in', count, 'try!')
with open(learn_path + 'topic_classifier-folds.pkl', 'wb') as out_file:
pickle.dump(kf, out_file)
fold_num = 0
stats = QuickDataFrame([
'Jaccard (normalised)', 'Accuracy (normalised)', 'Accuracy',
'F1_score (micro averaged)', 'F1_score (macro averaged by labels)',
'F1_score (averaged by samples)', 'Hamming loss', 'Label Ranking loss:'
])
txt_cnns = [TextCNN() for _ in range(y.shape[1])]
prog = Progresser(k)
for train_index, test_index in kf:
# print(train_index, test_index)
print('___________________________________________________')
x_train, x_test = x[train_index], x[test_index]
y_train, y_test = y[train_index], y[test_index]
# cls = SVC(kernel='linear')
# cls = SVC(kernel='poly', probability=True, tol=1e-5)
# cls = GaussianNB()
# cls = RandomForestClassifier(max_features='auto', random_state=1)
# topic_classifier = BinaryRelevance(classifier=cls, require_dense=[True, True])
# try:
# topic_classifier.fit(x_train, y_train)
for i in range(y.shape[1]):
txt_cnns[i].train_cnn(train_index, i)
# except Exception as e:
# print('\nfit error!:', e)
# continue
# with open(learn_path + 'topic_classifier-SVC' + str(fold_num) + '.pkl', 'wb') as out_file:
# pickle.dump(topic_classifier, out_file)
# try:
predictions = np.zeros((len(x_test), y.shape[1]))
for i in range(y.shape[1]):
predictions[:, i] = np.array(txt_cnns[i].predict_text(test_index))
s = [
jaccard_score(y_test, predictions, average='micro'),
accuracy_score(y_test, predictions, normalize=True),
accuracy_score(y_test, predictions, normalize=False),
f1_score(y_test, predictions, average='micro'),
f1_score(y_test, predictions, average='macro'),
f1_score(y_test, predictions, average='samples'),
hamming_loss(y_test, predictions),
label_ranking_loss(y_test, predictions)
]
stats.append(s)
print(stats[stats.length - 1])
# except Exception as e:
# print('Eval error!:', e)
fold_num += 1
prog.count()
for col in stats.cols:
print(col, np.mean(stats[col]))