Merge log_helper into utils and change logging mechanism.

lib/log_helper.py

@@ -1,45 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import sys
-
-import logging
-import os
-
-
-def get_logger(log_dir, name):
-    logger = logging.getLogger(name)
-    logger.setLevel(logging.DEBUG)
-    # Add file handler and stdout handler
-    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-    file_handler = logging.FileHandler(os.path.join(log_dir, 'info.log'))
-    file_handler.setFormatter(formatter)
-    # Add console handler.
-    console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
-    console_handler = logging.StreamHandler(sys.stdout)
-    console_handler.setFormatter(console_formatter)
-    logger.addHandler(file_handler)
-    logger.addHandler(console_handler)
-    # Add google cloud log handler
-    logger.info('Log directory: %s', log_dir)
-    return logger
-
-
-def config_logging(log_dir, log_filename='info.log', level=logging.INFO):
-    # Add file handler and stdout handler
-    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-    # Create the log directory if necessary.
-    try:
-        os.makedirs(log_dir)
-    except OSError:
-        pass
-    file_handler = logging.FileHandler(os.path.join(log_dir, log_filename))
-    file_handler.setFormatter(formatter)
-    file_handler.setLevel(level=level)
-    # Add console handler.
-    console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
-    console_handler = logging.StreamHandler(sys.stdout)
-    console_handler.setFormatter(console_formatter)
-    console_handler.setLevel(level=level)
-    logging.basicConfig(handlers=[file_handler, console_handler], level=level)

lib/utils.py

@@ -1,6 +1,9 @@
+import logging
 import numpy as np
+import os
 import pickle
 import scipy.sparse as sp
+import sys
 import tensorflow as tf
 
 from scipy.sparse import linalg
@@ -123,6 +126,43 @@ def calculate_scaled_laplacian(adj_mx, lambda_max=2, undirected=True):
     return L.astype(np.float32)
 
 
+def config_logging(log_dir, log_filename='info.log', level=logging.INFO):
+    # Add file handler and stdout handler
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    # Create the log directory if necessary.
+    try:
+        os.makedirs(log_dir)
+    except OSError:
+        pass
+    file_handler = logging.FileHandler(os.path.join(log_dir, log_filename))
+    file_handler.setFormatter(formatter)
+    file_handler.setLevel(level=level)
+    # Add console handler.
+    console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setFormatter(console_formatter)
+    console_handler.setLevel(level=level)
+    logging.basicConfig(handlers=[file_handler, console_handler], level=level)
+
+
+def get_logger(log_dir, name, log_filename='info.log'):
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.DEBUG)
+    # Add file handler and stdout handler
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    file_handler = logging.FileHandler(os.path.join(log_dir, log_filename))
+    file_handler.setFormatter(formatter)
+    # Add console handler.
+    console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setFormatter(console_formatter)
+    logger.addHandler(file_handler)
+    logger.addHandler(console_handler)
+    # Add google cloud log handler
+    logger.info('Log directory: %s', log_dir)
+    return logger
+
+
 def get_total_trainable_parameter_size():
     """
     Calculates the total number of trainable parameters in the current graph.

model/dcrnn_supervisor.py

@@ -2,7 +2,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import logging
 import numpy as np
 import os
 import sys
@@ -10,7 +9,7 @@ import tensorflow as tf
 import time
 import yaml
 
-from lib import utils, log_helper, metrics
+from lib import utils, metrics
 from lib.utils import StandardScaler, DataLoader
 
 from model.dcrnn_model import DCRNNModel
@@ -30,12 +29,15 @@ class DCRNNSupervisor(object):
 
         # logging.
         self._log_dir = self._get_log_dir(kwargs)
-        log_helper.config_logging(log_dir=self.log_dir, log_filename='info.log', level=logging.DEBUG)
+        self._logger = utils.get_logger(self.log_dir, __name__, 'info.log')
         self._writer = tf.summary.FileWriter(self._log_dir)
-        logging.info(kwargs)
+        self._logger.info(kwargs)
 
         # Data preparation
         self._data = self._prepare_data(**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']
@@ -61,9 +63,9 @@ class DCRNNSupervisor(object):
 
         # Log model statistics.
         total_trainable_parameter = utils.get_total_trainable_parameter_size()
-        logging.info('Total number of trainable parameters: %d' % total_trainable_parameter)
+        self._logger.info('Total number of trainable parameters: %d' % total_trainable_parameter)
         for var in tf.global_variables():
-            logging.info('%s, %s' % (var.name, var.get_shape()))
+            self._logger.info('%s, %s' % (var.name, var.get_shape()))
 
     @staticmethod
     def _get_log_dir(kwargs):
@@ -105,8 +107,6 @@ class DCRNNSupervisor(object):
         for category in ['train', 'val', 'test']:
             data['x_' + category][..., 0] = scaler.transform(data['x_' + category][..., 0])
             data['y_' + category][..., 0] = scaler.transform(data['y_' + category][..., 0])
-        for k, v in data.items():
-            logging.info((k, v.shape))
         data['train_loader'] = DataLoader(data['x_train'], data['y_train'], kwargs['batch_size'], shuffle=True)
         data['val_loader'] = DataLoader(data['x_val'], data['y_val'], kwargs['val_batch_size'], shuffle=False)
         data['test_loader'] = DataLoader(data['x_test'], data['y_test'], kwargs['test_batch_size'], shuffle=False)
@@ -146,7 +146,7 @@ class DCRNNSupervisor(object):
                                                                   writer=self._writer)
             train_loss, train_mae = train_results['loss'], train_results['mae']
             if train_loss > 1e5:
-                logging.warning('Gradient explosion detected. Ending...')
+                self._logger.warning('Gradient explosion detected. Ending...')
                 break
 
             global_step = sess.run(tf.train.get_or_create_global_step())
@@ -162,20 +162,20 @@ class DCRNNSupervisor(object):
             end_time = time.time()
             message = 'Epoch [{}] ({}) train_mae: {:.4f}, val_mae: {:.4f} lr:{:.6f} {:.1f}s'.format(
                 self._epoch, global_step, train_mae, val_mae, new_lr, (end_time - start_time))
-            logging.info(message)
+            self._logger.info(message)
             if self._epoch % test_every_n_epochs == test_every_n_epochs - 1:
                 self.test_and_write_result(sess, global_step)
             if val_loss <= min_val_loss:
                 wait = 0
                 if save_model > 0:
                     model_filename = self.save_model(sess, saver, val_loss)
-                logging.info(
+                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:
-                    logging.warning('Early stopping at epoch: %d' % self._epoch)
+                    self._logger.warning('Early stopping at epoch: %d' % self._epoch)
                     break
 
             history.append(val_mae)
@@ -207,7 +207,7 @@ class DCRNNSupervisor(object):
             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)
-            logging.info(
+            self._logger.info(
                 "Horizon {:02d}, MAE: {:.2f}, MAPE: {:.4f}, RMSE: {:.2f}".format(
                     horizon_i + 1, mae, mape, rmse
                 )

scripts/generate_training_data.py

@@ -8,8 +8,6 @@ import numpy as np
 import os
 import pandas as pd
 
-from lib.utils import DataLoader, StandardScaler
-
 
 def generate_graph_seq2seq_io_data(
         df, x_offsets, y_offsets, add_time_in_day=True, add_day_in_week=False, scaler=None