In verbose mode, make the prefix of the progress bar a fixed-length

deepecho/models/base.py

@@ -6,6 +6,17 @@
 from deepecho.sequences import assemble_sequences
 
 
+def _format_score(score):
+    """Format a score as a fixed-length string ``±XX.XX``.
+
+    Values are clipped to the range ``[-99.99, +99.99]`` so the result
+    is always exactly 6 characters.
+    """
+    score = max(-99.99, min(99.99, score))
+    sign = '+' if score >= 0 else '-'
+    return f'{sign}{abs(score):05.2f}'
+
+
 class DeepEcho:
     """The base class for DeepEcho models."""
 

deepecho/models/basic_gan.py

@@ -7,7 +7,7 @@
 import torch
 from tqdm import tqdm
 
-from deepecho.models.base import DeepEcho
+from deepecho.models.base import DeepEcho, _format_score
 
 LOGGER = logging.getLogger(__name__)
 
@@ -547,7 +547,10 @@ def fit_sequences(self, sequences, context_types, data_types):
             if self._verbose:
                 d_loss = discriminator_score.item()
                 g_loss = generator_score.item()
-                iterator.set_description(f'Epoch {epoch + 1} | D Loss {d_loss} | G Loss {g_loss}')
+                iterator.set_description(
+                    f'Epoch {epoch + 1} | D Loss {_format_score(d_loss)}'
+                    f' | G Loss {_format_score(g_loss)}'
+                )
 
     def sample_sequence(self, context, sequence_length=None):
         """Sample a single sequence conditioned on context.

deepecho/models/par.py

@@ -7,7 +7,7 @@
 import torch
 from tqdm import tqdm
 
-from deepecho.models.base import DeepEcho
+from deepecho.models.base import DeepEcho, _format_score
 
 LOGGER = logging.getLogger(__name__)
 
@@ -336,8 +336,8 @@ def fit_sequences(self, sequences, context_types, data_types):
 
         iterator = tqdm(range(self.epochs), disable=(not self.verbose))
         if self.verbose:
-            pbar_description = 'Loss ({loss:.3f})'
-            iterator.set_description(pbar_description.format(loss=0))
+            pbar_description = 'Loss ({loss})'
+            iterator.set_description(pbar_description.format(loss=_format_score(0)))
 
         # Reset loss_values dataframe
         self.loss_values = pd.DataFrame(columns=['Epoch', 'Loss'])
@@ -364,7 +364,7 @@ def fit_sequences(self, sequences, context_types, data_types):
                 self.loss_values = epoch_loss_df
 
             if self.verbose:
-                iterator.set_description(pbar_description.format(loss=loss.item()))
+                iterator.set_description(pbar_description.format(loss=_format_score(loss.item())))
 
             optimizer.step()
 

tests/unit/test_base.py

@@ -0,0 +1,26 @@
+"""Unit tests for the ``base`` module."""
+
+import pytest
+
+from deepecho.models.base import _format_score
+
+
+@pytest.mark.parametrize(
+    'score, expected',
+    [
+        (0, '+00.00'),
+        (1.233434, '+01.23'),
+        (-0.93, '-00.93'),
+        (0.01, '+00.01'),
+        (-1.21, '-01.21'),
+        (99.99, '+99.99'),
+        (-99.99, '-99.99'),
+        (150, '+99.99'),
+        (-200, '-99.99'),
+    ],
+)
+def test__format_score(score, expected):
+    """Test the ``_format_score`` method."""
+    result = _format_score(score)
+    assert result == expected
+    assert len(result) == 6