252 lines
11 KiB
Python
252 lines
11 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import logging
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import numpy as np
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import os
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import sys
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import tensorflow as tf
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import time
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import yaml
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from lib import utils, log_helper, metrics
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from lib.utils import StandardScaler, DataLoader
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from model.dcrnn_model import DCRNNModel
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class DCRNNSupervisor(object):
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"""
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Do experiments using Graph Random Walk RNN model.
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"""
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def __init__(self, adj_mx, **kwargs):
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self._kwargs = kwargs
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self._data_kwargs = kwargs.get('data')
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self._model_kwargs = kwargs.get('model')
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self._train_kwargs = kwargs.get('train')
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# logging.
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self._log_dir = self._get_log_dir(kwargs)
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log_helper.config_logging(log_dir=self.log_dir, log_filename='info.log', level=logging.DEBUG)
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self._writer = tf.summary.FileWriter(self._log_dir)
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logging.info(kwargs)
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# Data preparation
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self._data = self._prepare_data(**self._data_kwargs)
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# Build models.
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scaler = self._data['scaler']
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self._epoch = 0
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with tf.name_scope('Train'):
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with tf.variable_scope('DCRNN', reuse=False):
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self._train_model = DCRNNModel(is_training=True, scaler=scaler,
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batch_size=self._data_kwargs['batch_size'],
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adj_mx=adj_mx, **self._model_kwargs)
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with tf.name_scope('Val'):
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with tf.variable_scope('DCRNN', reuse=True):
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self._val_model = DCRNNModel(is_training=False, scaler=scaler,
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batch_size=self._data_kwargs['batch_size'],
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adj_mx=adj_mx, **self._model_kwargs)
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with tf.name_scope('Test'):
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with tf.variable_scope('DCRNN', reuse=True):
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self._test_model = DCRNNModel(is_training=False, scaler=scaler,
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batch_size=self._data_kwargs['test_batch_size'],
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adj_mx=adj_mx, **self._model_kwargs)
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# Log model statistics.
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total_trainable_parameter = utils.get_total_trainable_parameter_size()
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logging.info('Total number of trainable parameters: %d' % total_trainable_parameter)
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for var in tf.global_variables():
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logging.info('%s, %s' % (var.name, var.get_shape()))
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@staticmethod
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def _get_log_dir(kwargs):
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log_dir = kwargs['train'].get('log_dir')
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if log_dir is None:
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batch_size = kwargs['data'].get('batch_size')
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learning_rate = kwargs['train'].get('base_lr')
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max_diffusion_step = kwargs['model'].get('max_diffusion_step')
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num_rnn_layers = kwargs['model'].get('num_rnn_layers')
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rnn_units = kwargs['model'].get('rnn_units')
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structure = '-'.join(
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['%d' % rnn_units for _ in range(num_rnn_layers)])
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horizon = kwargs['model'].get('horizon')
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filter_type = kwargs['model'].get('filter_type')
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filter_type_abbr = 'L'
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if filter_type == 'random_walk':
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filter_type_abbr = 'R'
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elif filter_type == 'dual_random_walk':
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filter_type_abbr = 'DR'
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run_id = 'dcrnn_%s_%d_h_%d_%s_lr_%g_bs_%d_%s/' % (
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filter_type_abbr, max_diffusion_step, horizon,
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structure, learning_rate, batch_size,
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time.strftime('%m%d%H%M%S'))
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base_dir = kwargs.get('base_dir')
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log_dir = os.path.join(base_dir, run_id)
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if not os.path.exists(log_dir):
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os.makedirs(log_dir)
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return log_dir
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@staticmethod
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def _prepare_data(dataset_dir, **kwargs):
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data = {}
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for category in ['train', 'val', 'test']:
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cat_data = np.load(os.path.join(dataset_dir, category + '.npz'))
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data['x_' + category] = cat_data['x']
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data['y_' + category] = cat_data['y']
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scaler = StandardScaler(mean=data['x_train'][..., 0].mean(), std=data['x_train'][..., 0].std())
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# Data format
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for category in ['train', 'val', 'test']:
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data['x_' + category][..., 0] = scaler.transform(data['x_' + category][..., 0])
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data['y_' + category][..., 0] = scaler.transform(data['y_' + category][..., 0])
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for k, v in data.items():
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logging.info((k, v.shape))
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data['train_loader'] = DataLoader(data['x_train'], data['y_train'], kwargs['batch_size'], shuffle=True)
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data['val_loader'] = DataLoader(data['x_val'], data['y_val'], kwargs['val_batch_size'], shuffle=False)
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data['test_loader'] = DataLoader(data['x_test'], data['y_test'], kwargs['test_batch_size'], shuffle=False)
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data['scaler'] = scaler
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return data
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def train(self, sess, **kwargs):
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kwargs.update(self._train_kwargs)
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return self._train(sess, **kwargs)
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def _train(self, sess, base_lr, epoch, steps, patience=50, epochs=100,
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min_learning_rate=2e-6, lr_decay_ratio=0.1, save_model=1,
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test_every_n_epochs=10, **train_kwargs):
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history = []
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min_val_loss = float('inf')
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wait = 0
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max_to_keep = train_kwargs.get('max_to_keep', 100)
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saver = tf.train.Saver(tf.global_variables(), max_to_keep=max_to_keep)
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model_filename = train_kwargs.get('model_filename')
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if model_filename is not None:
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saver.restore(sess, model_filename)
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self._epoch = epoch + 1
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else:
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sess.run(tf.global_variables_initializer())
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while self._epoch <= epochs:
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# Learning rate schedule.
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new_lr = max(min_learning_rate, base_lr * (lr_decay_ratio ** np.sum(self._epoch >= np.array(steps))))
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self._train_model.set_lr(sess=sess, lr=new_lr)
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sys.stdout.flush()
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start_time = time.time()
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train_results = self._train_model.run_epoch_generator(sess, self._train_model,
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self._data['train_loader'].get_iterator(),
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train_op=self._train_model.train_op,
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writer=self._writer)
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train_loss, train_mae = train_results['loss'], train_results['mae']
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if train_loss > 1e5:
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logging.warning('Gradient explosion detected. Ending...')
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break
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global_step = sess.run(tf.train.get_or_create_global_step())
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# Compute validation error.
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val_results = self._val_model.run_epoch_generator(sess, self._val_model,
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self._data['val_loader'].get_iterator(),
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train_op=None)
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val_loss, val_mae = val_results['loss'], val_results['mae']
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utils.add_simple_summary(self._writer,
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['loss/train_loss', 'metric/train_mae', 'loss/val_loss', 'metric/val_mae'],
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[train_loss, train_mae, val_loss, val_mae], global_step=global_step)
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end_time = time.time()
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message = 'Epoch [{}] ({}) train_mae: {:.4f}, val_mae: {:.4f} lr:{:.6f} {:.1f}s'.format(
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self._epoch, global_step, train_mae, val_mae, new_lr, (end_time - start_time))
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logging.info(message)
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if self._epoch % test_every_n_epochs == test_every_n_epochs - 1:
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self.test_and_write_result(sess, global_step)
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if val_loss <= min_val_loss:
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wait = 0
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if save_model > 0:
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model_filename = self.save_model(sess, saver, val_loss)
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logging.info(
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'Val loss decrease from %.4f to %.4f, saving to %s' % (min_val_loss, val_loss, model_filename))
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min_val_loss = val_loss
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else:
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wait += 1
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if wait > patience:
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logging.warning('Early stopping at epoch: %d' % self._epoch)
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break
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history.append(val_mae)
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# Increases epoch.
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self._epoch += 1
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sys.stdout.flush()
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return np.min(history)
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def test_and_write_result(self, sess, global_step, **kwargs):
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test_results = self._test_model.run_epoch_generator(sess, self._test_model,
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self._data['test_loader'].get_iterator(),
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return_output=True, train_op=None)
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# y_preds: a list of (batch_size, horizon, num_nodes, output_dim)
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test_loss, y_preds = test_results['loss'], test_results['outputs']
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utils.add_simple_summary(self._writer, ['loss/test_loss'], [test_loss], global_step=global_step)
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y_preds = np.concatenate(y_preds, axis=0)
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scaler = self._data['scaler']
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outputs = []
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for horizon_i in range(self._data['y_test'].shape[1]):
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y_truth = np.concatenate(self._data['y_test'][:, horizon_i, :, 0], axis=0)
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y_truth = scaler.inverse_transform(y_truth)
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y_pred = np.concatenate(y_preds[:, horizon_i, :, 0], axis=0)
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y_pred = y_pred[:y_truth.shape[0], ...] # Only take the batch number
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y_pred = scaler.inverse_transform(y_pred)
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outputs.append(y_pred)
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mae = metrics.masked_mae_np(y_pred, y_truth, null_val=0)
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mape = metrics.masked_mape_np(y_pred, y_truth, null_val=0)
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rmse = metrics.masked_rmse_np(y_pred, y_truth, null_val=0)
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logging.info(
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"Horizon {:02d}, MAE: {:.2f}, MAPE: {:.4f}, RMSE: {:.2f}".format(
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horizon_i + 1, mae, mape, rmse
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)
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)
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utils.add_simple_summary(self._writer,
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['%s_%d' % (item, horizon_i + 1) for item in
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['metric/rmse', 'metric/mape', 'metric/mae']],
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[rmse, mape, mae],
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global_step=global_step)
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return y_preds
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@staticmethod
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def restore(sess, config):
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"""
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Restore from saved model.
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:param sess:
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:param config:
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:return:
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"""
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model_filename = config['train'].get('model_filename')
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max_to_keep = config['train'].get('max_to_keep', 100)
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saver = tf.train.Saver(tf.global_variables(), max_to_keep=max_to_keep)
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saver.restore(sess, model_filename)
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def save_model(self, sess, saver, val_loss):
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config_filename = 'config_{}.yaml'.format(self._epoch)
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config = dict(self._kwargs)
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global_step = np.asscalar(sess.run(tf.train.get_or_create_global_step()))
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config['train']['epoch'] = self._epoch
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config['train']['global_step'] = global_step
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config['train']['log_dir'] = self._log_dir
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config['train']['model_filename'] = saver.save(sess,
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os.path.join(self._log_dir, 'models-{:.4f}'.format(val_loss)),
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global_step=global_step, write_meta_graph=False)
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with open(os.path.join(self._log_dir, config_filename), 'w') as f:
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yaml.dump(config, f, default_flow_style=False)
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return config['train']['model_filename']
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@property
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def log_dir(self):
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return self._log_dir
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