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DCRNN
Commit d78463c4 by xlwang
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deleted unused module

parent 211679fd
Showing with 0 additions and 318 deletions
model/dcrnn_supervisor.py deleted 100644 → 0
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import os
import sys
import tensorflow as tf
import time
import yaml
from lib import utils, metrics
from lib.AMSGrad import AMSGrad
from lib.metrics import masked_mae_loss
from model.dcrnn_model import DCRNNModel
class DCRNNSupervisor(object):
"""
Do experiments using Graph Random Walk RNN model.
"""
def __init__(self, adj_mx, **kwargs):
self._kwargs = kwargs
self._data_kwargs = kwargs.get('data')
self._model_kwargs = kwargs.get('model')
self._train_kwargs = kwargs.get('train')
# logging.
self._log_dir = self._get_log_dir(kwargs)
log_level = self._kwargs.get('log_level', 'INFO')
self._logger = utils.get_logger(self._log_dir, __name__, 'info.log', level=log_level)
self._writer = tf.summary.FileWriter(self._log_dir)
self._logger.info(kwargs)
# Data preparation
self._data = utils.load_dataset(**self._data_kwargs)
for k, v in self._data.items():
if hasattr(v, 'shape'):
self._logger.info((k, v.shape))
# Build models.
scaler = self._data['scaler']
with tf.name_scope('Train'):
with tf.variable_scope('DCRNN', reuse=False):
self._train_model = DCRNNModel(is_training=True, scaler=scaler,
batch_size=self._data_kwargs['batch_size'],
adj_mx=adj_mx, **self._model_kwargs)
with tf.name_scope('Test'):
with tf.variable_scope('DCRNN', reuse=True):
self._test_model = DCRNNModel(is_training=False, scaler=scaler,
batch_size=self._data_kwargs['test_batch_size'],
adj_mx=adj_mx, **self._model_kwargs)
# Learning rate.
self._lr = tf.get_variable('learning_rate', shape=(), initializer=tf.constant_initializer(0.01),
trainable=False)
self._new_lr = tf.placeholder(tf.float32, shape=(), name='new_learning_rate')
self._lr_update = tf.assign(self._lr, self._new_lr, name='lr_update')
# Configure optimizer
optimizer_name = self._train_kwargs.get('optimizer', 'adam').lower()
epsilon = float(self._train_kwargs.get('epsilon', 1e-3))
optimizer = tf.train.AdamOptimizer(self._lr, epsilon=epsilon)
if optimizer_name == 'sgd':
optimizer = tf.train.GradientDescentOptimizer(self._lr, )
elif optimizer_name == 'amsgrad':
optimizer = AMSGrad(self._lr, epsilon=epsilon)
# Calculate loss
output_dim = self._model_kwargs.get('output_dim') # output_dim =1
preds = self._train_model.outputs # (batch_size, horizon, num_nodes, output_dim=1)
labels = self._train_model.labels[..., :output_dim]
null_val = 0.
self._loss_fn = masked_mae_loss(scaler, null_val) # return a masked loss function
self._train_loss = self._loss_fn(preds=preds, labels=labels)
tvars = tf.trainable_variables()
grads = tf.gradients(self._train_loss, tvars)
max_grad_norm = kwargs['train'].get('max_grad_norm', 1.)
grads, _ = tf.clip_by_global_norm(grads, max_grad_norm)
global_step = tf.train.get_or_create_global_step()
self._train_op = optimizer.apply_gradients(zip(grads, tvars), global_step=global_step, name='train_op')
max_to_keep = self._train_kwargs.get('max_to_keep', 100)
self._epoch = 0
self._saver = tf.train.Saver(tf.global_variables(), max_to_keep=max_to_keep)
# Log model statistics.
total_trainable_parameter = utils.get_total_trainable_parameter_size()
self._logger.info('Total number of trainable parameters: {:d}'.format(total_trainable_parameter))
for var in tf.global_variables():
self._logger.debug('{}, {}'.format(var.name, var.get_shape()))
@staticmethod
def _get_log_dir(kwargs):
log_dir = kwargs['train'].get('log_dir')
if log_dir is None:
batch_size = kwargs['data'].get('batch_size')
learning_rate = kwargs['train'].get('base_lr')
max_diffusion_step = kwargs['model'].get('max_diffusion_step')
num_rnn_layers = kwargs['model'].get('num_rnn_layers')
rnn_units = kwargs['model'].get('rnn_units')
structure = '-'.join(
['%d' % rnn_units for _ in range(num_rnn_layers)])
horizon = kwargs['model'].get('horizon')
filter_type = kwargs['model'].get('filter_type')
filter_type_abbr = 'L'
if filter_type == 'random_walk':
filter_type_abbr = 'R'
elif filter_type == 'dual_random_walk':
filter_type_abbr = 'DR'
run_id = 'dcrnn_%s_%d_h_%d_%s_lr_%g_bs_%d_%s/' % (
filter_type_abbr, max_diffusion_step, horizon,
structure, learning_rate, batch_size,
time.strftime('%m%d%H%M%S'))
base_dir = kwargs.get('base_dir')
log_dir = os.path.join(base_dir, run_id)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
return log_dir
def run_epoch_generator(self, sess, model, data_generator, return_output=False, training=False, writer=None):
losses = []
maes = []
outputs = []
output_dim = self._model_kwargs.get('output_dim')
preds = model.outputs
labels = model.labels[..., :output_dim]
loss = self._loss_fn(preds=preds, labels=labels)
fetches = {
'loss': loss,
'mae': loss,
'global_step': tf.train.get_or_create_global_step()
}
if training:
fetches.update({
'train_op': self._train_op
})
merged = model.merged
if merged is not None:
fetches.update({'merged': merged})
if return_output:
fetches.update({
'outputs': model.outputs
})
for _, (x, y) in enumerate(data_generator):
feed_dict = {
model.inputs: x,
model.labels: y,
}
vals = sess.run(fetches, feed_dict=feed_dict)
losses.append(vals['loss'])
maes.append(vals['mae'])
if writer is not None and 'merged' in vals:
writer.add_summary(vals['merged'], global_step=vals['global_step'])
if return_output:
outputs.append(vals['outputs'])
results = {
'loss': np.mean(losses),
'mae': np.mean(maes)
}
if return_output:
results['outputs'] = outputs
return results
def get_lr(self, sess):
return np.asscalar(sess.run(self._lr))
def set_lr(self, sess, lr):
sess.run(self._lr_update, feed_dict={
self._new_lr: lr
})
def train(self, sess, **kwargs):
kwargs.update(self._train_kwargs)
return self._train(sess, **kwargs)
def _train(self, sess, base_lr, epoch, steps, patience=50, epochs=100,
min_learning_rate=2e-6, lr_decay_ratio=0.1, save_model=1,
test_every_n_epochs=10, **train_kwargs):
history = []
min_val_loss = float('inf')
wait = 0
max_to_keep = train_kwargs.get('max_to_keep', 100)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=max_to_keep)
model_filename = train_kwargs.get('model_filename')
if model_filename is not None:
saver.restore(sess, model_filename)
self._epoch = epoch + 1
else:
sess.run(tf.global_variables_initializer())
self._logger.info('Start training ...')
while self._epoch <= epochs:
# Learning rate schedule.
new_lr = max(min_learning_rate, base_lr * (lr_decay_ratio ** np.sum(self._epoch >= np.array(steps))))
self.set_lr(sess=sess, lr=new_lr)
start_time = time.time()
train_results = self.run_epoch_generator(sess, self._train_model,
self._data['train_loader'].get_iterator(),
training=True,
writer=self._writer)
train_loss, train_mae = train_results['loss'], train_results['mae']
if train_loss > 1e5:
self._logger.warning('Gradient explosion detected. Ending...')
break
global_step = sess.run(tf.train.get_or_create_global_step())
# Compute validation error.
val_results = self.run_epoch_generator(sess, self._test_model,
self._data['val_loader'].get_iterator(),
training=False)
val_loss, val_mae = np.asscalar(val_results['loss']), np.asscalar(val_results['mae'])
utils.add_simple_summary(self._writer,
['loss/train_loss', 'metric/train_mae', 'loss/val_loss', 'metric/val_mae'],
[train_loss, train_mae, val_loss, val_mae], global_step=global_step)
end_time = time.time()
message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, val_mae: {:.4f} lr:{:.6f} {:.1f}s'.format(
self._epoch, epochs, global_step, train_mae, val_mae, new_lr, (end_time - start_time))
self._logger.info(message)
if self._epoch % test_every_n_epochs == test_every_n_epochs - 1:
self.evaluate(sess)
if val_loss <= min_val_loss:
wait = 0
if save_model > 0:
model_filename = self.save(sess, val_loss)
self._logger.info(
'Val loss decrease from %.4f to %.4f, saving to %s' % (min_val_loss, val_loss, model_filename))
min_val_loss = val_loss
else:
wait += 1
if wait > patience:
self._logger.warning('Early stopping at epoch: %d' % self._epoch)
break
history.append(val_mae)
# Increases epoch.
self._epoch += 1
sys.stdout.flush()
return np.min(history)
def evaluate(self, sess, **kwargs):
global_step = sess.run(tf.train.get_or_create_global_step())
test_results = self.run_epoch_generator(sess, self._test_model,
self._data['test_loader'].get_iterator(),
return_output=True,
training=False)
# y_preds: a list of (batch_size, horizon, num_nodes, output_dim)
test_loss, y_preds = test_results['loss'], test_results['outputs']
utils.add_simple_summary(self._writer, ['loss/test_loss'], [test_loss], global_step=global_step)
y_preds = np.concatenate(y_preds, axis=0)
scaler = self._data['scaler']
predictions = []
y_truths = []
for horizon_i in range(self._data['y_test'].shape[1]):
y_truth = scaler.inverse_transform(self._data['y_test'][:, horizon_i, :, 0])
y_truths.append(y_truth)
y_pred = scaler.inverse_transform(y_preds[:y_truth.shape[0], horizon_i, :, 0])
predictions.append(y_pred)
mae = metrics.masked_mae_np(y_pred, y_truth, null_val=0)
mape = metrics.masked_mape_np(y_pred, y_truth, null_val=0)
rmse = metrics.masked_rmse_np(y_pred, y_truth, null_val=0)
self._logger.info(
"Horizon {:02d}, MAE: {:.2f}, MAPE: {:.4f}, RMSE: {:.2f}".format(
horizon_i + 1, mae, mape, rmse
)
)
utils.add_simple_summary(self._writer,
['%s_%d' % (item, horizon_i + 1) for item in
['metric/rmse', 'metric/mape', 'metric/mae']],
[rmse, mape, mae],
global_step=global_step)
outputs = {
'predictions': predictions,
'groundtruth': y_truths
}
return outputs
def load(self, sess, model_filename):
"""
Restore from saved model.
:param sess:
:param model_filename:
:return:
"""
self._saver.restore(sess, model_filename)
def save(self, sess, val_loss):
config = dict(self._kwargs)
global_step = np.asscalar(sess.run(tf.train.get_or_create_global_step()))
prefix = os.path.join(self._log_dir, 'models-{:.4f}'.format(val_loss))
config['train']['epoch'] = self._epoch
config['train']['global_step'] = global_step
config['train']['log_dir'] = self._log_dir
config['train']['model_filename'] = self._saver.save(sess, prefix, global_step=global_step,
write_meta_graph=False)
config_filename = 'config_{}.yaml'.format(self._epoch)
with open(os.path.join(self._log_dir, config_filename), 'w') as f:
yaml.dump(config, f, default_flow_style=False)
return config['train']['model_filename']
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