lint : fix F541 f-string with no placeholder

Author: Sicara

snorkel/classification/training/loggers/checkpointer.py

@@ -124,7 +124,7 @@ def checkpoint(
         elif not self.checkpoint_condition_met and iteration >= self.checkpoint_runway:
             self.checkpoint_condition_met = True
             logging.info(
-                f"checkpoint_runway condition has been met. Start checkpointing."
+                "checkpoint_runway condition has been met. Start checkpointing."
             )
 
         checkpoint_path = f"{self.checkpoint_dir}/checkpoint_{iteration}.pth"
@@ -187,7 +187,7 @@ def load_best_model(self, model: MultitaskClassifier) -> MultitaskClassifier:
         """Load the best model from the checkpoint."""
         metric = list(self.checkpoint_metric.keys())[0]
         if metric not in self.best_metric_dict:  # pragma: no cover
-            logging.info(f"No best model found, use the original model.")
+            logging.info("No best model found, use the original model.")
         else:
             # Load the best model of checkpoint_metric
             best_model_path = (

snorkel/classification/training/trainer.py

@@ -179,7 +179,7 @@ def fit(
         # Set to training mode
         model.train()
 
-        logging.info(f"Start training...")
+        logging.info("Start training...")
 
         self.metrics: Dict[str, float] = dict()
         self._reset_losses()
@@ -372,7 +372,7 @@ def _set_warmup_scheduler(self) -> None:
         if self.config.lr_scheduler_config.warmup_steps:
             warmup_steps = self.config.lr_scheduler_config.warmup_steps
             if warmup_steps < 0:
-                raise ValueError(f"warmup_steps much greater or equal than 0.")
+                raise ValueError("warmup_steps much greater or equal than 0.")
             warmup_unit = self.config.lr_scheduler_config.warmup_unit
             if warmup_unit == "epochs":
                 self.warmup_steps = int(warmup_steps * self.n_batches_per_epoch)

snorkel/labeling/model/label_model.py

@@ -577,7 +577,7 @@ def _set_class_balance(
     def _set_constants(self, L: np.ndarray) -> None:
         self.n, self.m = L.shape
         if self.m < 3:
-            raise ValueError(f"L_train should have at least 3 labeling functions")
+            raise ValueError("L_train should have at least 3 labeling functions")
         self.t = 1
 
     def _create_tree(self) -> None:
@@ -679,7 +679,7 @@ def _set_warmup_scheduler(self) -> None:
         if self.train_config.lr_scheduler_config.warmup_steps:
             warmup_steps = self.train_config.lr_scheduler_config.warmup_steps
             if warmup_steps < 0:
-                raise ValueError(f"warmup_steps much greater or equal than 0.")
+                raise ValueError("warmup_steps much greater or equal than 0.")
             warmup_unit = self.train_config.lr_scheduler_config.warmup_unit
             if warmup_unit == "epochs":
                 self.warmup_steps = int(warmup_steps)