def lstm_model(body_length, numb_epoch):
prepro = Preprocessing()
data = load_data()
# Loading train data from files
data.set_path(path='fnc-1-master')
train_stance_data = data.get_headline_body_stance()
train_bodies_data = data.get_body_id_text()
train_headlines, train_bodies, train_stances = data.get_mapped_id_body(train_stance_data, train_bodies_data)
# Removing punctuation and stop words from the headline and body of train data
train_headlines_cl = prepro.get_clean_data(train_headlines)
train_bodies_cl = prepro.get_clean_data(train_bodies)
train_stances_cl = prepro.get_clean_data(train_stances)
# Convert labels to integer
train_stances_in = prepro.convert_lable_int(train_stances_cl)
# Load the test data
data.set_name("test")
test_stance_data = data.get_headline_body_stance()
test_bodies_data = data.get_body_id_text()
test_headlines, test_bodies = data.get_mapped_id_body(test_stance_data, test_bodies_data, data_type="test")
# Removing punctuation and stop words from the headline and body of test data
test_headlines_cl = prepro.get_clean_data(test_headlines)
test_bodies_cl = prepro.get_clean_data(test_bodies)
# Set the tokenizer
total_text = train_headlines_cl + train_bodies_cl + test_headlines_cl + test_bodies_cl
token = Tokenizer(num_words=30000)
token.fit_on_texts(total_text)
print('Number of Unique words: ' + str(len(token.word_index.keys())))
# Convert headline and body to sequence
train_headlines_seq = token.texts_to_sequences(train_headlines_cl)
train_bodies_seq = token.texts_to_sequences(train_bodies_cl)
word_index = token.word_index
# Padding the headline and body
train_headlines_seq = pad_sequences(train_headlines_seq, maxlen=MAX_HEADLINE_LENGTH)
train_bodies_seq = pad_sequences(train_bodies_seq, maxlen=int(body_length))
# Converting the labels to one hot encoder
onehotencoder = OneHotEncoder()
train_stances_in = onehotencoder.fit_transform(train_stances_in).toarray()
# Splitting data to train and validation
train_headlines_final, headlines_val, train_bodies_final, bodies_val, train_stances_final, stances_val = \
train_test_split(train_headlines_seq, train_bodies_seq, train_stances_in, test_size=0.2, random_state=42)
# Convert headline and body to sequence
test_headlines_seq = token.texts_to_sequences(test_headlines_cl)
test_bodies_seq = token.texts_to_sequences(test_bodies_cl)
# Padding the headline and body
test_headlines_seq = pad_sequences(test_headlines_seq, maxlen=MAX_HEADLINE_LENGTH)
test_bodies_seq = pad_sequences(test_bodies_seq, maxlen=int(body_length))
# Getting embedding index
embeddings_index = models.get_embeddings_index(GLOVE_DIR)
print('Found %s word vectors.' % len(embeddings_index))
# Getting embedding matrix
embedding_matrix = models.get_embedding_matrix(embedding_dim=EMBEDDING_DIMENSION, embeddings_index=embeddings_index,
word_index=word_index)
# Getting the model
fake_nn = models.lstm_model(headline_length=MAX_HEADLINE_LENGTH, body_length=int(body_length),
embedding_dim=EMBEDDING_DIMENSION, word_index=word_index, embedding_matrix=embedding_matrix,
activation='relu',
drop_out=0.5, numb_layers=100, cells=200)
fake_nn.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
# Early stopping and model checkpoint
early_stopping = EarlyStopping(monitor='val_loss', patience=10)
bst_model_path = 'Fake_news_nlp.h5'
model_checkpoint = ModelCheckpoint(bst_model_path, save_best_only=True, save_weights_only=True)
# Fitting the model
fake_hist = fake_nn.fit([train_headlines_final, train_bodies_final], train_stances_final, batch_size=128,
epochs=int(numb_epoch), shuffle=True, validation_data=([headlines_val, bodies_val], stances_val),
callbacks=[early_stopping, model_checkpoint])
# Storing the training and validation accuracy and loss in file for plot
lstm_data = []
with open(os.path.join(OBJECT_DUMP, "lstm_seperate_headline_body_" + str(body_length) + ".txt"), 'wb') as bow_hist:
lstm_data.append(fake_hist.history['acc'])
lstm_data.append(fake_hist.history['val_acc'])
lstm_data.append(fake_hist.history['loss'])
lstm_data.append(fake_hist.history['val_loss'])
pickle.dump(lstm_data, bow_hist)
# Predict the labels for test data
result = fake_nn.predict([test_headlines_seq, test_bodies_seq], batch_size=128)
# Store the results in the result file
result_str = prepro.convert_lable_string(result)
with io.open(TEST_FILE, mode='r', encoding='utf8') as read_file:
test_stance = csv.DictReader(read_file)
with io.open(RESULT_FILE + "_" + str(body_length) + ".csv", mode='w',
encoding='utf8') as write_file:
writer = csv.DictWriter(write_file, fieldnames=['Headline', 'Body ID', 'Stance'])
writer.writeheader()
for sample, prediction in zip(test_stance, result_str):
writer.writerow({'Body ID': sample['Body ID'], 'Headline': sample['Headline'], 'Stance': prediction})
# Print the Accuracy, competition score and confusion matrix
print_result("fnc-1-master/competition_test_stances.csv", RESULT_FILE + "_" + str(body_length) + ".csv")
def feed_forward_model(numb_epocs):
prepro = Preprocessing()
data = load_data()
# Loading train data from files
data.set_path(path='fnc-1-master')
train_stance_data = data.get_headline_body_stance()
train_bodies_data = data.get_body_id_text()
train_headlines, train_bodies, train_stances = data.get_mapped_id_body(train_stance_data, train_bodies_data)
# Removing punctuation and stop words from the headline and body of train data
train_headlines_cl = prepro.get_clean_data(train_headlines)
train_bodies_cl = prepro.get_clean_data(train_bodies)
train_stances_cl = prepro.get_clean_data(train_stances)
# Convert labels to one hot encoder
train_stances_in = prepro.convert_lable_int(train_stances_cl)
onehotencoder = OneHotEncoder()
train_stances_in = onehotencoder.fit_transform(train_stances_in).toarray()
# Load test data
data.set_name("test")
test_stance_data = data.get_headline_body_stance()
test_bodies_data = data.get_body_id_text()
test_headlines, test_bodies = data.get_mapped_id_body(test_stance_data, test_bodies_data, data_type="test")
# Removing punctuation and stop words from the headline and body of test data
test_headlines_cl = prepro.get_clean_data(test_headlines)
test_bodies_cl = prepro.get_clean_data(test_bodies)
# Get all the text features
tfidf_vec = get_tfidf_vec(train_headlines_cl + train_bodies_cl + test_headlines_cl + test_bodies_cl)
train_global_feature = get_text_features('train', train_headlines, train_headlines_cl, train_bodies, train_bodies_cl,
tfidf_vec)
test_global_feature = get_text_features('test', test_headlines, test_headlines_cl, test_bodies, test_bodies_cl,
tfidf_vec)
# Headline Body vector representation
bow_vectorizer, tfreq_vectorizer = get_tfreq_vectorizer(train_headlines_cl, train_bodies_cl , lim_unigram=5000)
train_data = headline_body_vec('train', train_headlines_cl, train_bodies_cl, train_global_feature,
bow_vectorizer, tfreq_vectorizer)
test_data = headline_body_vec('test', test_headlines_cl, test_bodies_cl, test_global_feature,
bow_vectorizer, tfreq_vectorizer)
# Train validation split
train_data_final, train_val, train_stances_final, stances_val = \
train_test_split(train_data, train_stances_in, test_size=0.2, random_state=42)
# Get the Model
fake_nn = models.feed_forward_network(input_vector=(train_data.shape)[1], activation='relu', drop_out=0.5,
numb_layers=100)
# Early stopping and model checkpoint
early_stopping = EarlyStopping(monitor='val_loss', patience=10)
bst_model_path = 'Fake_news_nlp.h5'
model_checkpoint = ModelCheckpoint(bst_model_path, save_best_only=True, save_weights_only=True)
# Fitting the model
fake_hist = fake_nn.fit([train_data_final], train_stances_final, batch_size=128,
epochs=int(numb_epocs), shuffle=True, validation_data=([train_val], stances_val),
callbacks=[early_stopping, model_checkpoint])
# Storing the training and validation accuracy and loss in file for plot
bow_list_data = []
with open(os.path.join(OBJECT_DUMP, "feedforward_network" + ".txt"), 'wb') as bow_hist:
bow_list_data.append(fake_hist.history['acc'])
bow_list_data.append(fake_hist.history['val_acc'])
bow_list_data.append(fake_hist.history['loss'])
bow_list_data.append(fake_hist.history['val_loss'])
pickle.dump(bow_list_data, bow_hist)
# Predict the labels for test data
result = fake_nn.predict([test_data], batch_size=128)
# Store the results in the result file
result_str = prepro.convert_lable_string(result)
with io.open(TEST_FILE, mode='r', encoding='utf8') as read_file:
test_stance = csv.DictReader(read_file)
with io.open(RESULT_FILE + "feedforward_network" + ".csv", mode='w',
encoding='utf8') as write_file:
writer = csv.DictWriter(write_file, fieldnames=['Headline', 'Body ID', 'Stance'])
writer.writeheader()
for sample, prediction in zip(test_stance, result_str):
writer.writerow({'Body ID': sample['Body ID'], 'Headline': sample['Headline'], 'Stance': prediction})
# Print the Accuracy, competition score and confusion matrix
print_result("fnc-1-master/competition_test_stances.csv",
RESULT_FILE + "feedforward_network" + ".csv")
