use colortimelog

setup.py

@@ -15,7 +15,7 @@
 
 import setuptools
 
-VERSION = '0.1.0'
+VERSION = '0.1.1'
 
 with open('README.md', 'r') as file_object:
  LONG_DESCRIPTION = file_object.read()

tests/integration_test.py

@@ -109,8 +109,8 @@ def test_four_clusters(self):
  predicted_label = model.predict(test_sequence, inference_args)
 
  # run evaluation
- model.logger.print(
- 3, 'Asserting the equivalence between'
+ model.logger.info(
+ 'Asserting the equivalence between'
  '\nGround truth: {}\nPredicted: {}'.format(
  test_cluster_id, predicted_label))
  accuracy = uisrnn.compute_sequence_match_accuracy(
@@ -125,8 +125,8 @@ def test_four_clusters(self):
  predicted_label = loaded_model.predict(test_sequence, inference_args)
 
  # run evaluation with loaded model
- model.logger.print(
- 3, 'Asserting the equivalence between'
+ model.logger.info(
+ 'Asserting the equivalence between'
  '\nGround truth: {}\nPredicted: {}'.format(
  test_cluster_id, predicted_label))
  accuracy = uisrnn.compute_sequence_match_accuracy(
@@ -140,13 +140,13 @@ def test_four_clusters(self):
  model.fit(train_sequence[:100, :], train_cluster_id[:100], training_args)
  transition_bias_2 = model.transition_bias
  self.assertNotAlmostEqual(transition_bias_1, transition_bias_2)
- model.logger.print(
- 3, 'Asserting transition_bias changed from {} to {}'.format(
+ model.logger.info(
+ 'Asserting transition_bias changed from {} to {}'.format(
  transition_bias_1, transition_bias_2))
 
  # run evaluation
- model.logger.print(
- 3, 'Asserting the equivalence between'
+ model.logger.info(
+ 'Asserting the equivalence between'
  '\nGround truth: {}\nPredicted: {}'.format(
  test_cluster_id, predicted_label))
  accuracy = uisrnn.compute_sequence_match_accuracy(

uisrnn/arguments.py

@@ -87,12 +87,12 @@ def parse_arguments():
  'value is None, we will estimate it from training data.')
  model_parser.add_argument(
  '--verbosity',
- default=2,
+ default=3,
  type=int,
  help='How verbose will the logging information be. Higher value '
  'represents more verbose information. A general guideline: '
- '0 for errors; 1 for finishing important steps; '
- '2 for finishing less important steps; 3 or above for debugging '
+ '0 for fatals; 1 for errors; 2 for finishing important steps; '
+ '3 for finishing less important steps; 4 or above for debugging '
  'information.')
  model_parser.add_argument(
  '--enable_cuda',

uisrnn/uisrnn.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 """The UIS-RNN model."""
 
+import colortimelog
 import functools
 import numpy as np
 import torch
@@ -103,7 +104,7 @@ def __init__(self, args):
  self.transition_bias = args.transition_bias
  self.transition_bias_denominator = 0.0
  self.crp_alpha = args.crp_alpha
- self.logger = utils.Logger(args.verbosity)
+ self.logger = colortimelog.Logger(args.verbosity)
 
  def _get_optimizer(self, optimizer, learning_rate):
  """Get optimizer for UISRNN.

uisrnn/utils.py

@@ -21,23 +21,6 @@
 from torch import autograd
 
 
-class Logger:
- """A class for printing logging information to screen."""
-
- def __init__(self, verbosity):
- self._verbosity = verbosity
-
- def print(self, level, message):
- """Print a message if level is not higher than verbosity.
-
- Args:
- level: the level of this message, smaller value means more important
- message: the message to be printed
- """
- if level <= self._verbosity:
- print(message)
-
-
 def generate_random_string(length=6):
  """Generate a random string of upper case letters and digits.