def train(embedding='bert', trial='trial', ckpt=None, verbose=False, plot=False):
x_train = np.load('data/x_train_' + embedding + '.npy', allow_pickle=True)
y_train = np.load('data/y_train_' + embedding + '.npy', allow_pickle=True)
x_val = np.load('data/x_dev_' + embedding + '.npy', allow_pickle=True)
y_val = np.load('data/y_dev_' + embedding + '.npy', allow_pickle=True)
if verbose:
print(y_train.shape)
print(x_train.shape)
model = lstm_model(_SEQ_SHAPE, load_weights=ckpt, verbose=verbose)
save_best_model = ModelCheckpoint('models/'+trial+'_weights.{epoch:02d}-{val_loss:.2f}.hdf5',
monitor='val_loss', verbose=0, save_best_only=True, period=1)
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0.01, patience=3, verbose=0)
#tensorboard = TensorBoard(log_dir='logs', write_graph=True)
out = model.fit(x_train, y_train, epochs=20, callbacks=[save_best_model, early_stopping], #, tensorboard],
batch_size=64, validation_data=(x_val, y_val), verbose=2 if verbose==True else 0)
if plot:
# Summarize history for loss
plt.figure()
plt.plot(out.history['loss'])
plt.plot(out.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
def train(sequence_length,
image_shape,
batch_size,
nb_epoch):
filepath = os.path.join('data', 'checkpoints', 'ConvLSTM.{epoch:03d}-{mse:.5f}.hdf5')
# helper: save model
checkpointer = ModelCheckpoint(filepath = filepath,
monitor='mse',
verbose=2,
save_best_only=True,
save_weights_only=False,
mode='auto')
# helper: stop training when model converges
early_stopper = EarlyStopping(monitor='mse',
min_delta=0,
patience=10,
restore_best_weights=True)
# Get the training data
data = DataSet(
sequence_length=sequence_length,
image_shape=image_shape)
# Get samples per epoch.
# Multiply by 0.7 to estimate how much data is the train set
steps_per_epoch = (len(data.data) * 0.70) // batch_size
# Multiply by 0.3 to estimate how much data is the validation set
validation_steps = (len(data.data) * 0.30) // batch_size
# Data generators
generator = data.frame_generator(batch_size, 'train', augment = True)
val_generator = data.frame_generator(batch_size, 'test', augment = False)
# Get the model
model = lstm_model()
# Train the model
history = model.fit_generator(generator=generator,
steps_per_epoch=steps_per_epoch,
epochs=nb_epoch,
verbose=0,
callbacks=[early_stopper, checkpointer],
validation_data=val_generator,
validation_steps=validation_steps)
# Close GPU session
session.close()
def test(embedding='bert', ckpt=None, verbose=False):
if ckpt is not None:
latest_ckpt = ckpt
else:
list_of_files = glob.glob('models/*.hdf5')
latest_ckpt = max(list_of_files, key=os.path.getatime)
x_test = np.load('data/x_test_' + embedding + '.npy')
y_test = np.load('data/y_test_' + embedding + '.npy')
model = lstm_model(_SEQ_SHAPE, load_weights=latest_ckpt, verbose=verbose)
scores = model.evaluate(x_test, y_test, verbose=0)
return scores
def class_report(embedding='bert', ckpt=None, verbose=False):
if ckpt is not None:
latest_ckpt = ckpt
else:
list_of_files = glob.glob('models/*.hdf5')
latest_ckpt = max(list_of_files, key=os.path.getatime)
x_test = np.load('data/x_test_' + embedding + '.npy')
y_test = np.load('data/y_test_' + embedding + '.npy')
model = lstm_model(_SEQ_SHAPE, load_weights=latest_ckpt, verbose=verbose)
# Confution Matrix and Classification Report
Y_pred = model.predict(x_test, batch_size=64)
y_pred = np.argmax(Y_pred, axis=1)
y_true = np.argmax(y_test, axis=1)
#print(y_true)
print('Confusion Matrix')
confusion = confusion_matrix(y_true, y_pred, labels=[*range(5)], normalize='true')
print(confusion)
print('Classification Report')
print(classification_report(y_true, y_pred, labels=[*range(5)]))
return confusion
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 run(ycol=0, method='gru'):
'''
运行主程序
'''
start = time.time()
predict_win = 1
# 数据处理
if 'lstm' in method or 'gru' in method:
timesteps = 1
elif 'rnn' in method:
timesteps = 8
stock_win = dp.createWinData(price, ycol, timesteps, predict_win)
train, test = dp.splitData(stock_win, ratio=0.9)
scaler = StandardScaler()
scaler.fit(train)
train_norm = scaler.transform(train)
test_norm = scaler.transform(test)
#last_value = train[-1, -predict_win:] # 测试集上一个数据
#np.random.shuffle(train_norm) # 打乱训练数据
#y_tr = dp.transUpDown(train_norm[:,-predict_win:], last_value)
y_tr = train_norm[:, -1]
x_tr = train_norm[:, :-1]
#y_te = dp.transUpDown(test_norm[:,-predict_win:], last_value)
y_te = test_norm[:, -1]
x_te = test_norm[:, :-1]
x_tr = np.reshape(
x_tr, (x_tr.shape[0], timesteps, int(x_tr.shape[1] / timesteps)))
x_te = np.reshape(
x_te, (x_te.shape[0], timesteps, int(x_te.shape[1] / timesteps)))
y_tr = np.reshape(y_tr, (y_tr.shape[0], 1))
y_te = np.reshape(y_te, (y_te.shape[0], 1))
layers = [x_tr.shape[0], x_tr.shape[1], x_tr.shape[2], predict_win]
# 模型建立
if 'lstm' in method:
model = models.lstm_model(layers)
elif 'rnn' in method:
model = models.rnn_model(layers)
elif 'gru' in method:
model = models.gru_model(timesteps, 28)
# 模型训练
earlystop = EarlyStopping(monitor='val_loss',
patience=10,
verbose=0,
mode='min')
history = LossHistory()
model.fit(x_tr,
y_tr,
epochs=200,
batch_size=16,
shuffle=True,
verbose=1,
validation_split=0.1,
callbacks=[earlystop, history])
score = model.evaluate(x_te, y_te, batch_size=16)
print('test score:', score)
#history.loss_plot('epoch')
# 模型测试
#y_tr_pre = model.predict_classes(x_tr, batch_size = 32)
y_te_pre = model.predict(x_te, batch_size=32)
true = y_te * scaler.scale_[-1] + scaler.mean_[-1]
predict = y_te_pre * scaler.scale_[-1] + scaler.mean_[-1]
#y = np.concatenate((y_tr, y_te))
#y = y.reshape(y.shape[0])
#dp.plotCompare(y, y_tr_pre.reshape(y_tr_pre.shape[0]),
# y_te_pre.reshape(y_te_pre.shape[0]), scaler)
dp.plotTestCompare(true, predict)
dp.errorPercentage(true, predict)
print('the stock:', ycol)
print('the method:', method)
print('timesteps:', timesteps)
# 预测结果
#print('train')
#dp.plotUpDown(y_tr, y_tr_pre)
#print('test')
#dp.plotUpDown(y_te, y_te_pre)
'''
test_len = len(y_te)
diff, diff_pre = [], []
for i in range(test_len):
if i == 0:
diff.append((true[0] - last_value) * 100 / last_value)
diff_pre.append((predict[0] - last_value) * 100 / last_value)
else:
diff.append((true[i] - true[i - 1]) * 100 / true[i - 1])
diff_pre.append((predict[i] - true[i - 1]) * 100 / true[i - 1])
plt.scatter(diff, diff_pre)
plt.grid(True)
plt.xlabel('diff')
plt.ylabel('diff_pre')
plt.title('Scatter Plot')
plt.show()
diff_label = dp.transUpDown1(diff)
diff_pre_label = dp.transUpDown1(diff_pre)
dp.plotUpDown(diff_label, diff_pre_label)
'''
print('time cost:', time.time() - start)
TrueCount2 = secondWord.value_counts().index.tolist()[:10]
#FalseCount2 = secondWordFalse2.value_counts().index.tolist()[:10]
TrueCountNGram = []
for x in itertools.product(TrueCount, TrueCount2):
k = ' '.join(x)
TrueCountNGram.append(k)
numOfWords = 2000
tokenizerPos = Tokenizer(num_words=numOfWords)
gen = Generator(df1, TrueCount, TrueCountNGram, stop)
#genT = Generator(dfV)
X_input = gen.generate(6).__next__()
epochs = 5
model = lstm_model()
model.fit_generator(gen.generate(200),
epochs=epochs,
verbose=1,
steps_per_epoch=500)
model_yaml = model.to_yaml()
with open("model.yaml", "w") as yaml_file:
yaml_file.write(model_yaml)
# serialize weights to HDF5
model.save_weights("model.h5")
print("Saved model to disk")
data_dir = os.path.join(init_path, 'datasets')
train_test_path = os.path.join(data_dir, train_test_name)
stopwords_path = os.path.join(data_dir, stopwords_name)
save_dir = os.path.join(init_path, 'saved_models')
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
model_path = os.path.join(save_dir, model_name)
tokenizer_path = os.path.join(save_dir, tokenizer_name)
# Model
last_layer_options = m.get_last_layer_options(num_classes)
model = m.lstm_model(last_layer_options=last_layer_options,
num_features=max_features,
embedding_dim=embedding_dim,
sequence_len=sequence_len,
lstm_units=lstm_units,
dropout_rate=dropout_rate,
fc_units=fc_units)
model.summary()
model.compile(optimizer=optimizer, loss=last_layer_options[2], metrics=metric)
# Data
df_data = pd.read_csv(train_test_path, header=0)
print(f'原始数据集的形状为: {df_data.shape}')
df_data['review'] = df_data['review'].astype('str')
print('计算每个类别评论长度的中位数、平均值、标准差：')
df_data['len'] = df_data['review'].map(lambda x: len(x))
print(df_data[['cat', 'len']].groupby(by='cat').agg(
['median', 'mean', 'max', 'min', 'std']))
from models import lstm_model
from models import train_model
from preprocess import load_data
from preprocess import load_fast_text_embedding
from preprocess import get_embed_weights
max_len = 50
embedding_dim = 300
tokenizer, x_train, y_train, x_test, y_test, vocab_size = load_data(
'sarcasm_v2.csv')
embedding_index = load_fast_text_embedding('wiki-news-300d-1M-subword.vec')
embedding_matrix = get_embed_weights(embedding_index, tokenizer)
model = lstm_model(vocab_size, embedding_matrix)
train_model(model, x_train, y_train, x_test, y_test)
#half embeddingd
# Validation Loss:1.128701367992565 Validation Accuracy:0.6840490793889286
# Validation Accuracy:68.40% (+/- 0.00%)
# Train Loss:1.1293133928731907 Train Accuracy:68.40490797546013
# Test Loss:1.110706667958593 Test Accuracy:71.16564420834641
if args == "1":
print('hola2')
# Preprocessing of data
X_train, y_train, X_val, y_val = data_prep_task1(timesteps=60)
# Hyperparameters
lr = 0.001
epochs = 100
n_batches = 16
# call model, compile and fit
Model = lstm_model(40,
dropout=True,
dr=0.2,
n_batches=16,
input_size=X_train.shape[1],
input_dimension=1,
bidirectional=False)
Model.compile(loss='mse', optimizer='adam', metrics=['mae'])
History = Model.fit(X_train,
y_train,
batch_size=n_batches,
epochs=epochs,
validation_data=(X_val, y_val),
verbose=1,
shuffle=False)
# plot evaluation of results
#plot_model(History)
