def run_test(seqs, label_seqs, sess, preds_T, input_PHs, label_PHs, mask_PHs,
seq_length_PH, loss_T, options):
all_losses = []
all_preds = []
all_labels = []
batch_size = options['batch_size']
for idx in xrange(len(label_seqs) / batch_size):
batch_x = seqs[idx * batch_size:(idx + 1) * batch_size]
batch_y = label_seqs[idx * batch_size:(idx + 1) * batch_size]
inputs, _, masks, seq_length = mime_util.st_preprocess_hf_aux(
batch_x, options)
preds, loss = sess.run(
[preds_T, loss_T],
feed_dict={
input_PHs[0]: inputs[0],
input_PHs[1]: inputs[1],
input_PHs[2]: inputs[2],
mask_PHs[0]: masks[0],
mask_PHs[1]: masks[1],
mask_PHs[2]: masks[2],
label_PHs[-1]: batch_y,
seq_length_PH: seq_length,
})
all_losses.append(loss)
all_preds.extend(list(preds))
all_labels.extend(batch_y)
auc = roc_auc_score(all_labels, all_preds)
aucpr = average_precision_score(all_labels, all_preds)
accuracy = (np.array(all_labels) == np.squeeze(
binarize(np.array(all_preds).reshape(-1, 1), threshold=.5))).mean()
return np.mean(all_losses), auc, aucpr
def train(
input_path='',
batch_size=100,
num_iter=100,
eval_period=10,
num_eval=100,
rnn_size=256,
output_size=1,
learning_rate=1e-3,
output_path='',
random_seed=1234,
split_seed=1234,
emb_activation='sigmoid',
order_activation='sigmoid',
visit_activation='sigmoid',
num_dx=100,
num_rx=100,
num_pr=100,
dx_emb_size=128,
rx_emb_size=128,
pr_emb_size=128,
dxobj_emb_size=128,
visit_emb_size=128,
max_dx_per_visit=29,
max_rx_per_dx=17,
max_pr_per_dx=10,
regularize=1e-3,
aux_lambda=0.1,
min_threshold=5,
max_threshold=150,
train_ratio=1.0,
association_threshold=0.0,
):
options = locals().copy()
input_PHs, label_PHs, mask_PHs, loss_Ts, seq_length_PH, preds_T = build_model(
options)
all_vars = tf.trainable_variables()
L2_loss = tf.constant(0.0, dtype=tf.float32)
for var in all_vars:
if len(var.shape) < 2:
continue
L2_loss += tf.reduce_sum(var**2)
optimizer = tf.train.AdamOptimizer(learning_rate=options['learning_rate'])
loss_T = options['aux_lambda'] * (loss_Ts[0] + loss_Ts[1] +
loss_Ts[2]) + loss_Ts[3]
minimize_op = optimizer.minimize(loss_T + regularize * L2_loss)
train_seqs, train_labels, valid_seqs, valid_labels, test_seqs, test_labels = mime_util.load_data(
options['input_path'],
min_threshold=options['min_threshold'],
max_threshold=options['max_threshold'],
seed=options['split_seed'],
train_ratio=options['train_ratio'],
association_threshold=options['association_threshold'])
saver = tf.train.Saver(max_to_keep=1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
best_valid_loss = 100000.0
best_test_loss = 100000.0
best_valid_auc = 0.0
best_test_auc = 0.0
best_valid_aucpr = 0.0
best_test_aucpr = 0.0
for train_iter in xrange(options['num_iter'] + 1):
batch_x, batch_y = mime_util.sample_batch(train_seqs, train_labels,
options['batch_size'])
inputs, labels, masks, seq_length = mime_util.st_preprocess_hf_aux(
batch_x, options)
_, preds, losses = sess.run(
[minimize_op, preds_T, loss_Ts],
feed_dict={
input_PHs[0]: inputs[0],
input_PHs[1]: inputs[1],
input_PHs[2]: inputs[2],
mask_PHs[0]: masks[0],
mask_PHs[1]: masks[1],
mask_PHs[2]: masks[2],
label_PHs[0]: labels[0],
label_PHs[1]: labels[1],
label_PHs[2]: labels[2],
label_PHs[3]: batch_y,
seq_length_PH: seq_length,
})
if train_iter > 0 and train_iter % options['eval_period'] == 0:
valid_loss, valid_auc, valid_aucpr = run_test(
valid_seqs, valid_labels, sess, preds_T, input_PHs,
label_PHs, mask_PHs, seq_length_PH, loss_Ts[-1], options)
if valid_loss < best_valid_loss:
test_loss, test_auc, test_aucpr = run_test(
test_seqs, test_labels, sess, preds_T, input_PHs,
label_PHs, mask_PHs, seq_length_PH, loss_Ts[-1],
options)
best_valid_loss = valid_loss
best_valid_auc = valid_auc
best_valid_aucpr = valid_aucpr
best_test_loss = test_loss
best_test_auc = test_auc
best_test_aucpr = test_aucpr
savePath = saver.save(sess,
output_path + '/r' +
str(random_seed) + 's' +
str(split_seed) + '/model',
global_step=train_iter)
print('round:%d, valid_loss:%f, valid_auc:%f, valid_aucpr:%f' %
(train_iter, valid_loss, valid_auc, valid_aucpr))
return best_valid_loss, best_test_loss, best_valid_auc, best_test_auc, best_valid_aucpr, best_test_aucpr