fix/wandb-final-metrics-skipped (#212)

* fix

* sklearn scorer proper name

* fix typing errors

* try to fix pydantic errors

Author: voorhs

autointent/_callbacks/tensorboard.py

@@ -16,7 +16,7 @@ def __init__(self) -> None:
         Raises an ImportError if neither are installed.
         """
         try:
-            from torch.utils.tensorboard import SummaryWriter
+            from torch.utils.tensorboard import SummaryWriter  # type: ignore[attr-defined]
 
             self.writer = SummaryWriter
         except ImportError:

autointent/_callbacks/wandb.py

@@ -89,6 +89,11 @@ def log_metrics(self, metrics: dict[str, Any]) -> None:
         """
         self.wandb.log(metrics)
 
+    def _close_current_run(self) -> None:
+        """Close the current W&B run if open."""
+        if self.wandb.run is not None:
+            self.wandb.finish()
+
     def log_final_metrics(self, metrics: dict[str, Any]) -> None:
         """Logs final evaluation metrics to W&B.
 
@@ -97,6 +102,8 @@ def log_final_metrics(self, metrics: dict[str, Any]) -> None:
         Args:
             metrics: A dictionary of final performance metrics.
         """
+        self._close_current_run()
+
         wandb_run_init_args = {
             "project": self.project_name,
             "group": self.group,

autointent/_dump_tools.py

@@ -229,15 +229,15 @@ def load(  # noqa: C901, PLR0912, PLR0915
             elif child.name == Dumper.ptuning_models:
                 for model_dir in child.iterdir():
                     try:
-                        model = AutoModelForSequenceClassification.from_pretrained(model_dir / "base_model")
+                        model = AutoModelForSequenceClassification.from_pretrained(model_dir / "base_model")  # type: ignore[no-untyped-call]
                         hf_models[model_dir.name] = PeftModel.from_pretrained(model, model_dir / "peft")
                     except Exception as e:  # noqa: PERF203
                         msg = f"Error loading PeftModel {model_dir.name}: {e}"
                         logger.exception(msg)
             elif child.name == Dumper.hf_models:
                 for model_dir in child.iterdir():
                     try:
-                        hf_models[model_dir.name] = AutoModelForSequenceClassification.from_pretrained(model_dir)
+                        hf_models[model_dir.name] = AutoModelForSequenceClassification.from_pretrained(model_dir)  # type: ignore[no-untyped-call]
                     except Exception as e:  # noqa: PERF203
                         msg = f"Error loading HF model {model_dir.name}: {e}"
                         logger.exception(msg)

autointent/modules/base/_base.py

@@ -32,6 +32,11 @@ class BaseModule(ABC):
     name: str
     """Name of the module."""
 
+    @property
+    def trial_name(self) -> str:
+        """Name of the module for logging."""
+        return self.name
+
     @abstractmethod
     def fit(self, *args: tuple[Any], **kwargs: dict[str, Any]) -> None:
         """Fit the model.

autointent/modules/scoring/_bert.py

@@ -76,7 +76,7 @@ def _initialize_model(self) -> Any:  # noqa: ANN401
         label2id = {i: i for i in range(self._n_classes)}
         id2label = {i: i for i in range(self._n_classes)}
 
-        return AutoModelForSequenceClassification.from_pretrained(
+        return AutoModelForSequenceClassification.from_pretrained(  # type: ignore[no-untyped-call]
             self.classification_model_config.model_name,
             trust_remote_code=self.classification_model_config.trust_remote_code,
             num_labels=self._n_classes,

autointent/modules/scoring/_sklearn/sklearn_scorer.py

@@ -79,6 +79,10 @@ def __init__(
             logger.error(msg)
             raise ValueError(msg)
 
+    @property
+    def trial_name(self) -> str:
+        return f"sklearn_{self.clf_name}"
+
     @classmethod
     def from_context(
         cls,

autointent/nodes/_node_optimizer.py

@@ -131,7 +131,7 @@ def objective(
         module = self.node_info.modules_available[module_name].from_context(context, **config)
         config.update(module.get_implicit_initialization_params())
 
-        context.callback_handler.start_module(module_name=module_name, num=self._counter, module_kwargs=config)
+        context.callback_handler.start_module(module_name=module.trial_name, num=self._counter, module_kwargs=config)
 
         self._logger.debug("Scoring %s module...", module_name)