Save best model

.github/workflows/tests.yml

@@ -42,7 +42,7 @@ jobs:
       run: |
         pytest --cov=casanovo tests/
     - name: Upload coverage to codecov
-      uses: codecov/codecov-action@v3
+      uses: codecov/codecov-action@v4
       with:
         token: ${{ secrets.CODECOV_TOKEN }}
         fail_ci_if_error: true

CHANGELOG.md

@@ -15,6 +15,10 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 - Precursor charges are exported as integers instead of floats in the mzTab output file, in compliance with the mzTab specification.
 
+### Removed
+
+- Removed the `save_top_k` option from the Casanovo config, the model with the lowest validation loss during training will now be saved to a fixed filename `best.ckpt`. 
+
 ## [4.2.1] - 2024-06-25
 
 ### Fixed

casanovo/config.py

@@ -18,6 +18,7 @@
 _config_deprecated = dict(
     every_n_train_steps="val_check_interval",
     max_iters="cosine_schedule_period_iters",
+    save_top_k=None,
 )
 
 
@@ -74,7 +75,6 @@
         top_match=int,
         max_epochs=int,
         num_sanity_val_steps=int,
-        save_top_k=int,
         model_save_folder_path=str,
         val_check_interval=int,
         calculate_precision=bool,
@@ -96,10 +96,22 @@
                 # Remap deprecated config entries.
                 for old, new in _config_deprecated.items():
                     if old in self._user_config:
-                        self._user_config[new] = self._user_config.pop(old)
+                        warning_msg = (
+                            f"Depreciated config option '{old}' "
+                            "is no longer in use"
+                        )
+
+                        if new is not None:
+                            self._user_config[new] = self._user_config.pop(old)
+                            warning_msg = (
+                                f"Deprecated config option '{old}' "
+                                f"remapped to '{new}'"
+                            )
+                        else:
+                            del self._user_config[old]
+
                         warnings.warn(
-                            f"Deprecated config option '{old}' remapped to "
-                            f"'{new}'",
+                            warning_msg,
                             DeprecationWarning,
                         )
                 # Check for missing entries in config file.

casanovo/config.yaml

@@ -42,9 +42,6 @@ random_seed: 454
 n_log: 1
 # Tensorboard directory to use for keeping track of training metrics.
 tb_summarywriter:
-# Save the top k model checkpoints during training. -1 saves all, and leaving
-# this field empty saves none.
-save_top_k: 5
 # Path to saved checkpoints.
 model_save_folder_path: ""
 # Model validation and checkpointing frequency in training steps.

casanovo/denovo/model_runner.py

@@ -59,19 +59,14 @@ def __init__(
             ModelCheckpoint(
                 dirpath=config.model_save_folder_path,
                 save_on_train_epoch_end=True,
-            )
+            ),
+            ModelCheckpoint(
+                dirpath=config.model_save_folder_path,
+                monitor="valid_CELoss",
+                filename="best",
+            ),
         ]
 
-        if config.save_top_k is not None:
-            self.callbacks.append(
-                ModelCheckpoint(
-                    dirpath=config.model_save_folder_path,
-                    monitor="valid_CELoss",
-                    mode="min",
-                    save_top_k=config.save_top_k,
-                )
-            )
-
     def __enter__(self):
         """Enter the context manager"""
         self.tmp_dir = tempfile.TemporaryDirectory()

tests/conftest.py

@@ -208,7 +208,6 @@ def tiny_config(tmp_path):
         "random_seed": 454,
         "n_log": 1,
         "tb_summarywriter": None,
-        "save_top_k": 5,
         "n_peaks": 150,
         "min_mz": 50.0,
         "max_mz": 2500.0,

tests/test_integration.py

@@ -32,8 +32,10 @@ def test_train_and_run(
 
     result = run(train_args)
     model_file = tmp_path / "epoch=19-step=20.ckpt"
+    best_model = tmp_path / "best.ckpt"
     assert result.exit_code == 0
     assert model_file.exists()
+    assert best_model.exists()
 
     # Try evaluating:
     eval_args = [

tests/unit_tests/test_runner.py

@@ -168,7 +168,7 @@ def test_save_final_model(tmp_path, mgf_small, tiny_config):
 
     # Test checkpoint saving when val_check_interval is not a factor of training steps
     config.val_check_interval = 15
-    validation_file = tmp_path / "epoch=14-step=15.ckpt"
+    validation_file = tmp_path / "best.ckpt"
     with ModelRunner(config) as runner:
         runner.train([mgf_small], [mgf_small])