def test_reload_of_csvfile(self):
temp_dir = os.path.join(self.get_temp_dir(), 'test_dir')
csv_file = os.path.join(temp_dir, 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
csv_mngr.update_metrics(0, _create_scalar_metrics())
csv_mngr.update_metrics(5, _create_scalar_metrics())
new_csv_mngr = csv_manager.CSVMetricsManager(csv_file)
fieldnames, metrics = new_csv_mngr.get_metrics()
self.assertCountEqual(fieldnames, ['round_num', 'a/b', 'a/c'])
self.assertLen(metrics, 2, 'There should be 2 rows (for rounds 0 and 5).')
self.assertEqual(5, metrics[-1]['round_num'],
'Last metrics are for round 5.')
self.assertEqual(set(os.listdir(temp_dir)), set(['metrics.csv']))
def test_update_metrics_adds_empty_str_if_previous_column_not_provided(self): csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv') csv_mngr = csv_manager.CSVMetricsManager(csv_file) csv_mngr.update_metrics(0, _create_scalar_metrics_with_extra_column()) csv_mngr.update_metrics(1, _create_scalar_metrics()) _, metrics = csv_mngr.get_metrics() self.assertEqual(metrics[1]['a/d'], '')
def test_save_metrics_adds_column(self, save_mode):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file, save_mode=save_mode)
csv_mngr.save_metrics(_create_scalar_metrics(), 0)
fieldnames, metrics = csv_mngr.get_metrics()
self.assertCountEqual(fieldnames, ['round_num', 'a/b', 'a/c'])
self.assertNotIn('a/d', metrics[0].keys())
csv_mngr.save_metrics(_create_scalar_metrics_with_extra_column(), 1)
fieldnames, metrics = csv_mngr.get_metrics()
self.assertCountEqual(fieldnames, ['round_num', 'a/b', 'a/c', 'a/d'])
expected_round_0_metrics = {
'round_num': 0,
'a/b': 1.0,
'a/c': 2.0,
'a/d': ''
}
self.assertDictEqual(metrics[0], expected_round_0_metrics)
expected_round_1_metrics = {
'round_num': 1,
'a/b': 1.0,
'a/c': 2.0,
'a/d': 3.0
}
self.assertDictEqual(metrics[1], expected_round_1_metrics)
def test_rows_are_cleared_and_last_round_num_is_reset(self):
csv_file = os.path.join(self.get_temp_dir(), 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
csv_mngr.update_metrics(0, _create_scalar_metrics())
csv_mngr.update_metrics(5, _create_scalar_metrics())
csv_mngr.update_metrics(10, _create_scalar_metrics())
_, metrics = csv_mngr.get_metrics()
self.assertLen(metrics, 3,
'There should be 3 rows (for rounds 0, 5, and 10).')
csv_mngr.clear_rounds_after(last_valid_round_num=7)
_, metrics = csv_mngr.get_metrics()
self.assertLen(
metrics, 2,
'After clearing all rounds after last_valid_round_num=7, should be 2 '
'rows of metrics (for rounds 0 and 5).')
self.assertEqual(5, metrics[-1]['round_num'],
'Last metrics retained are for round 5.')
# The internal state of the manager knows the last round number is 7, so it
# raises an exception if a user attempts to add new metrics at round 7, ...
with self.assertRaises(ValueError):
csv_mngr.update_metrics(7, _create_scalar_metrics())
# ... but allows a user to add new metrics at a round number greater than 7.
csv_mngr.update_metrics(8, _create_scalar_metrics()) # (No exception.)
def test_update_metrics_raises_value_error_if_round_num_is_out_of_order(
self):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
csv_mngr.update_metrics(1, _create_scalar_metrics())
with self.assertRaises(ValueError):
csv_mngr.update_metrics(0, _create_scalar_metrics())
def test_column_names_with_list(self):
metrics_to_append = {'a': [3, 4, 5], 'b': 6}
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
csv_mngr.update_metrics(0, metrics_to_append)
fieldnames, _ = csv_mngr.get_metrics()
self.assertCountEqual(['a/0', 'a/1', 'a/2', 'b', 'round_num'], fieldnames)
def test_rows_are_cleared_is_reflected_in_saved_file(self):
temp_dir = self.get_temp_dir()
csv_file = os.path.join(temp_dir, 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
csv_mngr.update_metrics(0, _create_scalar_metrics())
csv_mngr.update_metrics(5, _create_scalar_metrics())
csv_mngr.update_metrics(10, _create_scalar_metrics())
filename = os.path.join(temp_dir, 'metrics.csv')
with tf.io.gfile.GFile(filename, 'r') as csvfile:
num_lines_before = len(csvfile.readlines())
# The CSV file should have 4 lines, one for the fieldnames, and 3 for each
# call to `update_metrics`.
self.assertEqual(num_lines_before, 4)
csv_mngr.clear_rounds_after(last_valid_round_num=7)
with tf.io.gfile.GFile(filename, 'r') as csvfile:
num_lines_after = len(csvfile.readlines())
# The CSV file should have 3 lines, one for the fieldnames, and 2 for the
# calls to `update_metrics` with round_nums less <= 7.
self.assertEqual(num_lines_after, 3)
def test_clear_rounds_after_raises_value_error_if_round_num_is_negative(
self):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
csv_mngr.update_metrics(0, _create_scalar_metrics())
with self.assertRaises(ValueError):
csv_mngr.clear_rounds_after(round_num=-1)
def test_clear_rounds_after_raises_runtime_error_if_no_metrics(self):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
# Clear is allowed with no metrics if no rounds have yet completed.
csv_mngr.clear_rounds_after(round_num=0)
with self.assertRaises(RuntimeError):
# Raise exception with no metrics if no rounds have yet completed.
csv_mngr.clear_rounds_after(round_num=1)
def test_get_metrics_with_nonscalars_returns_list_of_lists(self):
metrics_to_append = {
'a': tf.ones([1], dtype=tf.int32),
'b': tf.zeros([2, 2], dtype=tf.int32)
}
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
csv_mngr.update_metrics(0, metrics_to_append)
_, metrics = csv_mngr.get_metrics()
self.assertEqual(metrics[0]['a'], '[1]')
self.assertEqual(metrics[0]['b'], '[[0, 0], [0, 0]]')
def test_update_metrics_returns_flat_dict_with_scalars(self):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
input_data_dict = _create_scalar_metrics()
appended_data_dict = csv_mngr.update_metrics(0, input_data_dict)
self.assertEqual(
collections.OrderedDict({
'a/b': 1.0,
'a/c': 2.0,
'round_num': 0.0
}), appended_data_dict)
def test_update_metrics_adds_column_if_previously_unseen_metric_added(self):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
csv_mngr.update_metrics(0, _create_scalar_metrics())
fieldnames, metrics = csv_mngr.get_metrics()
self.assertCountEqual(fieldnames, ['round_num', 'a/b', 'a/c'])
self.assertNotIn('a/d', metrics[0].keys())
csv_mngr.update_metrics(1, _create_scalar_metrics_with_extra_column())
fieldnames, metrics = csv_mngr.get_metrics()
self.assertCountEqual(fieldnames, ['round_num', 'a/b', 'a/c', 'a/d'])
self.assertEqual(metrics[0]['a/d'], '')
def test_clear_metrics_with_round_zero_removes_all_metrics(self):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
csv_mngr.save_metrics(0, _create_scalar_metrics())
csv_mngr.save_metrics(5, _create_scalar_metrics())
csv_mngr.save_metrics(10, _create_scalar_metrics())
_, metrics = csv_mngr.get_metrics()
self.assertLen(metrics, 3,
'There should be 3 rows (for rounds 0, 5, and 10).')
csv_mngr.clear_metrics(round_num=0)
_, metrics = csv_mngr.get_metrics()
self.assertEmpty(metrics)
self.assertIsNone(csv_mngr._latest_round_num)
def test_nonscalar_metrics_are_saved(self, save_mode):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file, save_mode=save_mode)
_, metrics = csv_mngr.get_metrics()
self.assertEmpty(metrics)
csv_mngr.save_metrics(_create_nonscalar_metrics(), 0)
_, metrics = csv_mngr.get_metrics()
self.assertLen(metrics, 1)
csv_mngr.save_metrics(_create_nonscalar_metrics(), 1)
_, metrics = csv_mngr.get_metrics()
self.assertLen(metrics, 2)
def test_nonscalar_metrics_are_appended(self):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
_, metrics = csv_mngr.get_metrics()
self.assertEmpty(metrics)
csv_mngr.update_metrics(0, _create_nonscalar_metrics())
_, metrics = csv_mngr.get_metrics()
self.assertLen(metrics, 1)
csv_mngr.update_metrics(1, _create_nonscalar_metrics())
_, metrics = csv_mngr.get_metrics()
self.assertLen(metrics, 2)
def test_constructor_raises_value_error_if_csvfile_is_invalid(self):
metrics_missing_round_num = _create_scalar_metrics()
temp_dir = self.get_temp_dir()
# This csvfile is 'invalid' in that it was not originally created by an
# instance of CSVMetricsManager, and is missing a column for
# round_num.
invalid_csvfile = os.path.join(temp_dir, 'invalid_metrics.csv')
with tf.io.gfile.GFile(invalid_csvfile, 'w') as csvfile:
writer = csv.DictWriter(
csvfile, fieldnames=metrics_missing_round_num.keys())
writer.writeheader()
writer.writerow(metrics_missing_round_num)
with self.assertRaises(ValueError):
csv_manager.CSVMetricsManager(invalid_csvfile)
def test_save_metrics_with_alternating_fieldnames(self, save_mode):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file, save_mode=save_mode)
csv_mngr.save_metrics({'a': 1}, 0)
csv_mngr.save_metrics({'b': 3}, 1)
csv_mngr.save_metrics({'a': 2}, 2)
fieldnames, metrics = csv_mngr.get_metrics()
self.assertCountEqual(fieldnames, ['round_num', 'a', 'b'])
expected_round_0_metrics = {'round_num': 0, 'a': 1, 'b': ''}
self.assertDictEqual(metrics[0], expected_round_0_metrics)
expected_round_1_metrics = {'round_num': 1, 'a': '', 'b': 3}
self.assertDictEqual(metrics[1], expected_round_1_metrics)
expected_round_2_metrics = {'round_num': 2, 'a': 2, 'b': ''}
self.assertDictEqual(metrics[2], expected_round_2_metrics)
def test_update_metrics_returns_flat_dict_with_nonscalars(self):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
input_data_dict = _create_nonscalar_metrics()
appended_data_dict = csv_mngr.update_metrics(0, input_data_dict)
expected_dict = collections.OrderedDict({
'a/b': tf.ones([1]),
'a/c': tf.zeros([2, 2]),
'round_num': 0.0
})
self.assertListEqual(list(expected_dict.keys()),
list(appended_data_dict.keys()))
self.assertEqual(expected_dict['round_num'],
appended_data_dict['round_num'])
self.assertAllEqual(expected_dict['a/b'], appended_data_dict['a/b'])
self.assertAllEqual(expected_dict['a/c'], appended_data_dict['a/c'])
def test_clear_metrics_removes_rounds_equal_to_input_arg(self, save_mode):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file, save_mode=save_mode)
csv_mngr.save_metrics(_create_scalar_metrics(), 0)
csv_mngr.save_metrics(_create_scalar_metrics(), 5)
csv_mngr.save_metrics(_create_scalar_metrics(), 10)
_, metrics = csv_mngr.get_metrics()
self.assertLen(metrics, 3,
'There should be 3 rows (for rounds 0, 5, and 10).')
csv_mngr.clear_metrics(round_num=5)
_, metrics = csv_mngr.get_metrics()
self.assertLen(
metrics, 1, 'After clearing all rounds starting at round_num=5, there '
'should be 1 row of metrics (for round 0).')
self.assertEqual(0, metrics[-1]['round_num'],
'Last metrics retained are for round 0.')
self.assertEqual(0, csv_mngr._latest_round_num)
def test_clear_metrics_removes_rounds_after_input_arg(self, save_mode):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file, save_mode=save_mode)
csv_mngr.save_metrics(_create_scalar_metrics(), 0)
csv_mngr.save_metrics(_create_scalar_metrics(), 5)
csv_mngr.save_metrics(_create_scalar_metrics(), 10)
_, metrics = csv_mngr.get_metrics()
self.assertLen(metrics, 3,
'There should be 3 rows (for rounds 0, 5, and 10).')
csv_mngr.clear_metrics(round_num=7)
_, metrics = csv_mngr.get_metrics()
self.assertLen(
metrics, 2, 'After clearing all rounds after round_num=7, should be 2 '
'rows of metrics (for rounds 0 and 5).')
self.assertEqual(5, metrics[-1]['round_num'],
'Last metrics retained are for round 5.')
self.assertEqual(5, csv_mngr._latest_round_num)
def test_rows_are_cleared_and_last_round_num_is_reset(self):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
csv_mngr.update_metrics(0, _create_scalar_metrics())
csv_mngr.update_metrics(5, _create_scalar_metrics())
csv_mngr.update_metrics(10, _create_scalar_metrics())
_, metrics = csv_mngr.get_metrics()
self.assertLen(metrics, 3,
'There should be 3 rows (for rounds 0, 5, and 10).')
csv_mngr.clear_rounds_after(round_num=7)
_, metrics = csv_mngr.get_metrics()
self.assertLen(
metrics, 2,
'After clearing all rounds after round_num=7, should be 2 '
'rows of metrics (for rounds 0 and 5).')
self.assertEqual(5, metrics[-1]['round_num'],
'Last metrics retained are for round 5.')
self.assertEqual(5, csv_mngr._latest_round_num)
def test_save_in_append_mode_appends_and_does_not_read_metrics(
self, mock_dict_writer, mock_dict_reader, mock_write_to_csv):
mock_reader = mock.MagicMock()
mock_reader.fieldnames = ['round_num']
mock_reader.__iter__.return_value = [{'round_num': 0}]
mock_dict_reader.return_value = mock_reader
mock_writer = mock.MagicMock()
mock_dict_writer.return_value = mock_writer
csv_file = os.path.join(self.get_temp_dir(), 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(
csv_file, save_mode=csv_manager.SaveMode.APPEND)
# After construction, list(csv.DictReader) should be called once, and
# _write_to_csv should have no calls.
self.assertEqual(mock_reader.__iter__.call_count, 1)
self.assertEqual(mock_write_to_csv.call_count, 0)
# After saving, list(csv.DictReader) should be called still just once, and
# _write_to_csv should have no calls.
csv_mngr.save_metrics({}, 1)
self.assertEqual(mock_reader.__iter__.call_count, 1)
self.assertEqual(mock_write_to_csv.call_count, 0)
def test_clear_metrics_raises_if_round_num_is_negative(self, save_mode):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file, save_mode=save_mode)
csv_mngr.save_metrics(_create_scalar_metrics(), 0)
with self.assertRaises(ValueError):
csv_mngr.clear_metrics(round_num=-1)
def test_constructor_raises_if_save_mode_is_invalid(self):
csv_file = os.path.join(self.get_temp_dir(), 'metrics.csv')
with self.assertRaises(ValueError):
csv_manager.CSVMetricsManager(csv_file, save_mode='invalid_mode')
def test_column_names(self):
csv_file = os.path.join(self.get_temp_dir(), 'test_dir', 'metrics.csv')
csv_mngr = csv_manager.CSVMetricsManager(csv_file)
csv_mngr.update_metrics(0, _create_scalar_metrics())
fieldnames, _ = csv_mngr.get_metrics()
self.assertCountEqual(['a/b', 'a/c', 'round_num'], fieldnames)
def test_csvfile_is_saved(self):
temp_dir = os.path.join(self.get_temp_dir(), 'test_dir')
csv_file = os.path.join(temp_dir, 'metrics.csv')
csv_manager.CSVMetricsManager(csv_file)
self.assertEqual(set(os.listdir(temp_dir)), set(['metrics.csv']))
