def lstm_with_embedding_layer():
x_train, x_test, y_train, y_test = load_data.get_depression_data()
y_train_one_hot = preprocessing.class_one_hot(y_train)
embedding_matrix, word_index, tokenizer = preprocessing.get_embedding_matrix(
x_train)
x_train = preprocessing.vectorize_with_tokenizer(x_train, tokenizer)
x_test = preprocessing.vectorize_with_tokenizer(x_test, tokenizer)
model = get_lstm_model(True, word_index, embedding_matrix)
run(x_train, x_test, y_train_one_hot, y_test, model)
def lstm():
# x_train, x_test, y_train, y_test = load_data.get_depression_data()
# x_train, x_test, y_train, y_test = load_data.get_bipolar_disorder_data()
# x_train, y_train = load_data.get_rsdd_data(set_="train")
# x_test, y_test = load_data.get_rsdd_data(end_index=5, set_="validation")
x_train, y_train = load_data.get_smhd_data(set_="train")
x_test, y_test = load_data.get_smhd_data(end_index=5, set_="validation")
y_train_one_hot = preprocessing.class_one_hot(y_train)
vectorize_function = preprocessing.vectorize_data_glove
embedding_index = preprocessing.get_embeddings_index()
print(x_train[0])
x_train = preprocessing.add_features_and_vectorize(x_train,
vectorize_function,
embedding_index)
x_test = preprocessing.add_features_and_vectorize(x_test,
vectorize_function,
embedding_index)
model = get_lstm_model(use_embedding_layer=False,
input_shape=(x_train.shape[1], x_train.shape[2]))
run(x_train, x_test, y_train_one_hot, y_test, model)
def save_data(vectorize_function,
embedding_index,
data_per_iteration=2,
num_of_load_iterations=2):
x_train_filenames = []
y_train_filenames = []
num_of_train_batches = 0
for i in range(257, num_of_load_iterations):
start = time()
# x_train, y_train = load_data.get_rsdd_data(start_index=i * data_per_iteration,
# end_index=(i + 1) * data_per_iteration, set_="train")
x0, x1, _, _, _ = load_smhd_datasets.get_smhd_data_user_level(
start_index=i * data_per_iteration,
end_index=(i + 1) * data_per_iteration)
t1 = time()
print(t1 - start)
x_train, y_train = load_smhd_datasets.prepare_binary_data(x0, x1)
t2 = time()
print(t2 - t1)
x_train = preprocessing.add_features_and_vectorize(
x_train, vectorize_function, embedding_index)
t3 = time()
print(t3 - t2)
y_train_one_hot = preprocessing.class_one_hot(y_train, 2)
# print(x_train.shape)
# print(y_train_one_hot.shape)
np.save("x_train" + str(i) + ".npy", x_train)
np.save("y_train" + str(i) + ".npy", y_train_one_hot)
x_train_filenames.append("x_train" + str(i) + ".npy")
y_train_filenames.append("y_train" + str(i) + ".npy")
num_of_train_batches += len(x_train) // BATCH_SIZE
end = time()
print(end - t3)
f = open("num_of_train_batches.txt", "w")
f.write(str(num_of_train_batches))
f.close()
return x_train_filenames, y_train_filenames, num_of_train_batches
def multitask_memory_efficient():
vectorize_function = preprocessing.vectorize_data_glove
embedding_index = preprocessing.get_embeddings_index()
data_per_iteration = BATCH_SIZE
num_of_batches = TRAIN_SET_SIZE // data_per_iteration
num_of_train_batches = 0
x_train1_filenames = []
y_train1_filenames = []
x_train2_filenames = []
y_train2_filenames = []
for i in range(num_of_batches):
x_train1, y_train1 = load_data.get_depression_data(
start_index=i * data_per_iteration,
end_index=(i + 1) * data_per_iteration,
test_size=0)
x_train2, y_train2 = load_data.get_bipolar_disorder_data(
start_index=i * data_per_iteration // 2,
skiprows_start=(i + 1) * data_per_iteration // 2,
skiprows_end=(i + 1) * data_per_iteration // 2 + 10**7,
nrows=data_per_iteration,
test_size=0)
x_train1 = preprocessing.add_features_and_vectorize(
x_train1, vectorize_function, embedding_index)
x_train2 = preprocessing.add_features_and_vectorize(
x_train2, vectorize_function, embedding_index)
x_train1 = x_train1[:len(x_train2)]
y_train1 = y_train1[:len(y_train2)]
np.save("x_train1_" + str(i) + ".npy", x_train1)
y_train_one_hot1 = preprocessing.class_one_hot(y_train1, 2)
np.save("y_train1_" + str(i) + ".npy", y_train_one_hot1)
np.save("x_train2_" + str(i) + ".npy", x_train2)
y_train_one_hot2 = preprocessing.class_one_hot(y_train2, 2)
np.save("y_train2_" + str(i) + ".npy", y_train_one_hot2)
x_train1_filenames.append("x_train1_" + str(i) + ".npy")
y_train1_filenames.append("y_train1_" + str(i) + ".npy")
x_train2_filenames.append("x_train2_" + str(i) + ".npy")
y_train2_filenames.append("y_train2_" + str(i) + ".npy")
num_of_train_batches += len(x_train1) // BATCH_SIZE
x_test1, y_test1 = load_data.get_depression_data(start_index=0,
end_index=0,
test_size=500)
x_test2, y_test2 = load_data.get_bipolar_disorder_data(
start_index=num_of_batches * data_per_iteration // 2,
skiprows_start=(num_of_batches + 1) * data_per_iteration // 2 + 250,
skiprows_end=(num_of_batches + 1) * data_per_iteration // 2 + 10**7 +
250,
nrows=data_per_iteration,
test_size=1)
x_test1 = preprocessing.add_features_and_vectorize(x_test1,
vectorize_function,
embedding_index)
x_test2 = preprocessing.add_features_and_vectorize(x_test2,
vectorize_function,
embedding_index)
x_test1 = x_test1[:len(x_test2)]
y_test1 = y_test1[:len(y_test2)]
model = multitask1.get_multitask_model(
(x_test1.shape[1], x_test1.shape[2]))
multitask1.run_multitask(x_train1_filenames,
x_test1,
y_train1_filenames,
y_test1,
x_train2_filenames,
x_test2,
y_train2_filenames,
y_test2,
model,
fit_generator=True,
steps_per_epoch=num_of_train_batches)
def multitask_smhd_memory_efficient(reload_data=True,
data_per_iteration=2,
num_of_load_iterations=2,
num_of_train_batches=None,
user_level=True):
x_train1_filenames = []
y_train1_filenames = []
x_train2_filenames = []
y_train2_filenames = []
if reload_data:
vectorize_function = preprocessing.vectorize_data_glove
embedding_index = preprocessing.get_embeddings_index()
num_of_train_batches = 0
for i in range(num_of_load_iterations):
if user_level:
x0, x1, x2, x3, _ = load_smhd_datasets.get_smhd_data_user_level(
start_index=i * data_per_iteration,
end_index=(i + 1) * data_per_iteration)
else:
x0, x1, x2, x3, _ = load_smhd_datasets.get_smhd_data(
start_index=i * data_per_iteration,
end_index=(i + 1) * data_per_iteration)
x_train1, y_train1 = load_smhd_datasets.prepare_binary_data(
x0[:len(x0) // 3], x1)
x_train2, y_train2 = load_smhd_datasets.prepare_binary_data(
x0[len(x0) // 3:2 * len(x0) // 3], x2)
x_train3, y_train3 = load_smhd_datasets.prepare_binary_data(
x0[2 * len(x0) // 3:], x3)
x_train1 = preprocessing.add_features_and_vectorize(
x_train1, vectorize_function, embedding_index)
x_train2 = preprocessing.add_features_and_vectorize(
x_train2, vectorize_function, embedding_index)
x_train3 = preprocessing.add_features_and_vectorize(
x_train2, vectorize_function, embedding_index)
np.save("x_train1_" + str(i) + ".npy", x_train1)
y_train_one_hot1 = preprocessing.class_one_hot(y_train1, 2)
np.save("y_train1_" + str(i) + ".npy", y_train_one_hot1)
np.save("x_train2_" + str(i) + ".npy", x_train2)
y_train_one_hot2 = preprocessing.class_one_hot(y_train2, 2)
np.save("y_train2_" + str(i) + ".npy", y_train_one_hot2)
np.save("x_train3_" + str(i) + ".npy", x_train3)
y_train_one_hot3 = preprocessing.class_one_hot(y_train3, 2)
np.save("y_train3_" + str(i) + ".npy", y_train_one_hot3)
x_train1_filenames.append("x_train1_" + str(i) + ".npy")
y_train1_filenames.append("y_train1_" + str(i) + ".npy")
x_train2_filenames.append("x_train2_" + str(i) + ".npy")
y_train2_filenames.append("y_train2_" + str(i) + ".npy")
num_of_train_batches += len(x_train1) // BATCH_SIZE
f = open("num_of_train_batches.txt", "w")
f.write(num_of_train_batches)
f.close()
if user_level:
x0, x1, x2, _, _ = load_smhd_datasets.get_smhd_data_user_level(
set_='validation')
else:
x0, x1, x2, _, _ = load_smhd_datasets.get_smhd_data(
set_='validation')
x_test1, y_test1 = load_smhd_datasets.prepare_binary_data(
x0[:len(x0) // 2], x1)
x_test2, y_test2 = load_smhd_datasets.prepare_binary_data(
x0[len(x0) // 2:], x2)
x_test1 = preprocessing.add_features_and_vectorize(
x_test1, vectorize_function, embedding_index)
x_test2 = preprocessing.add_features_and_vectorize(
x_test2, vectorize_function, embedding_index)
x_test1 = x_test1[:len(x_test2)]
y_test1 = y_test1[:len(y_test2)]
x_test2 = x_test2[:len(x_test1)]
y_test2 = y_test2[:len(y_test1)]
print(len(y_test1))
print(len(y_test2))
np.save("x_test1.npy", x_test1)
np.save("y_test1.npy", y_test1)
np.save("x_test2.npy", x_test2)
np.save("y_test2.npy", y_test2)
else:
for i in range(num_of_load_iterations):
x_train1_filenames.append("x_train1_" + str(i) + ".npy")
y_train1_filenames.append("y_train1_" + str(i) + ".npy")
x_train2_filenames.append("x_train2_" + str(i) + ".npy")
y_train2_filenames.append("y_train2_" + str(i) + ".npy")
if isinstance(num_of_train_batches, str):
num_of_train_batches = eval(
open(num_of_train_batches, "r").readlines()[0])
x_test1 = np.load("x_test1.npy")
y_test1 = np.load("y_test1.npy")
x_test2 = np.load("x_test2.npy")
y_test2 = np.load("y_test2.npy")
model = multitask1.get_multitask_model(
(x_test1.shape[1], x_test1.shape[2]))
acc1, f11, acc2, f12 = multitask1.run_multitask(
x_train1_filenames,
x_test1,
y_train1_filenames,
y_test1,
x_train2_filenames,
x_test2,
y_train2_filenames,
y_test2,
model,
fit_generator=True,
steps_per_epoch=num_of_train_batches)
return acc1, f11, acc2, f12
def lstm_memory_efficient():
vectorize_function = preprocessing.vectorize_data_glove
embedding_index = preprocessing.get_embeddings_index()
data_per_iteration = 5 # BATCH_SIZE * 10
count = 0
i = 0
x_train_filenames = []
y_train_filenames = []
while count < TRAIN_SET_SIZE // BATCH_SIZE:
i += 1
# x_train, y_train = load_data.get_depression_data(start_index=i*data_per_iteration,
# end_index=(i+1)*data_per_iteration, test_size=0)
# x_train, y_train = load_data.get_bipolar_disorder_data(start_index=i * data_per_iteration // 2,
# skiprows_start=(i+1) * data_per_iteration // 2,
# skiprows_end=(i+1) * data_per_iteration // 2 + 10**7,
# nrows=data_per_iteration, test_size=0)
x_train, y_train = load_data.get_rsdd_data(
start_index=i * data_per_iteration,
end_index=(i + 1) * data_per_iteration,
set_="train")
x_train = preprocessing.add_features_and_vectorize(
x_train, vectorize_function, embedding_index)
y_train_one_hot = preprocessing.class_one_hot(y_train, 2)
print(x_train.shape)
print(y_train_one_hot.shape)
for j in range(len(x_train) // BATCH_SIZE):
np.save("x_train" + str(count) + ".npy",
x_train[j * BATCH_SIZE:(j + 1) * BATCH_SIZE])
np.save("y_train" + str(count) + ".npy",
y_train_one_hot[j * BATCH_SIZE:(j + 1) * BATCH_SIZE])
x_train_filenames.append("x_train" + str(count) + ".npy")
y_train_filenames.append("y_train" + str(count) + ".npy")
count += 1
# x_test, y_test = load_data.get_bipolar_disorder_data(start_index=num_of_batches * data_per_iteration // 2,
# skiprows_start=(num_of_batches+1) * data_per_iteration // 2,
# skiprows_end=(num_of_batches+1) * data_per_iteration // 2 + 10**7,
# nrows=data_per_iteration, test_size=1)
# x_test, y_test = load_data.get_depression_data(start_index=0, end_index=0, test_size=500)
x_test, y_test = load_data.get_rsdd_data(end_index=5, set_="validation")
x_test = preprocessing.add_features_and_vectorize(x_test,
vectorize_function,
embedding_index)
np.save("x_test.npy", x_test)
np.save("y_test.npy", y_test)
x_test = np.load("x_test.npy")
y_test = np.load("y_test.npy")
model = get_lstm_model(use_embedding_layer=False,
input_shape=(x_test.shape[1], x_test.shape[2]))
return run(x_train_filenames,
x_test,
y_train_filenames,
y_test,
model,
True,
steps_per_epoch=20)
def lstm_model_hyperparameters_random_search(x, y, n_iterations=100):
x_train, x_validation, y_train, y_validation = train_test_split(
x, y, test_size=0.2)
y_train = preprocessing.class_one_hot(y_train)
best_acc = 0
best_f1 = 0
best_avg = 0
best_model = None
for _ in range(n_iterations):
if random() < 0.5:
nb_epoch = randint(5, 50)
else:
nb_epoch = randint(
5,
30) # model will not lose to much time for large epoch number
if random() < 0.5:
lr = random() * 10**(randint(0, 3) - 4)
else:
lr = random() * 10**-3
r = random()
if r < 0.2:
broj_neurona_u_sloju = randint(100, 500)
elif r < 0.4:
broj_neurona_u_sloju = 2**randint(2, 10)
else:
broj_neurona_u_sloju = EMBEDDING_DIM
r = random()
if r < 0.3:
dropout_rate = r
elif r < 0.5:
dropout_rate = random() / 10
elif r < 0.7:
dropout_rate = 0.2
else:
dropout_rate = 0
r = random()
if r < 0.2:
recurrent_dropout = r
elif r < 0.4:
recurrent_dropout = random() / 10
elif r < 0.6:
recurrent_dropout = 0.2
elif r < 0.8:
recurrent_dropout = 0
else:
recurrent_dropout = dropout_rate
if random() < 0.5:
bidirectional = True
else:
bidirectional = False
r = random()
if r < 0.3:
activation = "sigmoid"
elif r < 0.6:
activation = "relu"
else:
activation = "None"
model = Sequential()
if bidirectional:
model.add(
Bidirectional(LSTM(units=broj_neurona_u_sloju,
dropout=dropout_rate,
recurrent_dropout=recurrent_dropout),
input_shape=(x_train.shape[1],
x_train.shape[2])))
else:
model.add(
LSTM(units=broj_neurona_u_sloju,
dropout=dropout_rate,
recurrent_dropout=recurrent_dropout,
input_shape=(x_train.shape[1], x_train.shape[2])))
if activation == "sigmoid":
model.add(Activation('sigmoid'))
elif activation == "relu":
model.add(Activation('relu'))
model.add(Dense(output_dim=2))
model.add(Activation('softmax'))
adam = Adam(lr=lr)
model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=['accuracy'])
model.fit(x_train,
y_train,
nb_epoch=nb_epoch,
batch_size=100,
verbose=0)
pred = model.predict_classes(x_validation, 100)
acc_score = metrics.accuracy_score(pred, y_validation)
f1_score = metrics.f1_score(pred, y_validation, average='macro')
avg_score = (acc_score + f1_score) / 2
if avg_score > best_avg:
print('acc_score: ', acc_score)
print('f1_score: ', f1_score)
print('avg_score: ', avg_score)
print('broj_neurona: ', broj_neurona_u_sloju)
print('lr: ', lr)
print('nb_epoch: ', nb_epoch)
print('recurrent_dropout: ', recurrent_dropout)
print('bidirectional: ', bidirectional)
print('dropout_rate: ', dropout_rate)
print('activation: ', activation)
print()
best_avg = avg_score
best_model = model
if acc_score > best_acc:
best_acc = acc_score
if f1_score > best_f1:
best_f1 = f1_score
elif acc_score > best_acc:
print('acc_score: ', acc_score)
print('f1_score: ', f1_score)
print('avg_score: ', avg_score)
print('broj_neurona: ', broj_neurona_u_sloju)
print('lr: ', lr)
print('nb_epoch: ', nb_epoch)
print('recurrent_dropout: ', recurrent_dropout)
print('bidirectional: ', bidirectional)
print('dropout_rate: ', dropout_rate)
print('activation: ', activation)
print()
best_acc = acc_score
if f1_score > best_f1:
best_f1 = f1_score
if f1_score > best_f1:
print('acc_score: ', acc_score)
print('f1_score: ', f1_score)
print('avg_score: ', avg_score)
print('broj_neurona: ', broj_neurona_u_sloju)
print('lr: ', lr)
print('nb_epoch: ', nb_epoch)
print('recurrent_dropout: ', recurrent_dropout)
print('bidirectional: ', bidirectional)
print('dropout_rate: ', dropout_rate)
print('activation: ', activation)
print()
best_f1 = f1_score
return best_model
