Switch back to torchmetrics

pyproject.toml

@@ -40,6 +40,7 @@ dependencies = [
     "torch>=2.0.1",
     "torchvision>=0.15.2",
     "polars>=0.19.10",
+    "torchmetrics>=1.2.0",
     "stringcase>=1.2.0",
     "atpublic>=4.0",
     "pytest-pycharm>=0.7.0",

src/qusi/internal/light_curve_collection.py

@@ -156,6 +156,8 @@ def light_curve_iter(self) -> Iterator[LightCurve]:
         :return: The iterable of the light curves.
         """
         light_curve_paths = self.path_getter.get_shuffled_paths()
+        if len(light_curve_paths) == 0:
+            raise ValueError('LightCurveCollection returned no paths.')
         for light_curve_path in light_curve_paths:
             times, fluxes = self.load_times_and_fluxes_from_path_function(
                 light_curve_path
@@ -264,6 +266,8 @@ def observation_iter(self) -> Iterator[LightCurveObservation]:
         :return: The iterable of the light curves.
         """
         light_curve_paths = self.path_getter.get_shuffled_paths()
+        if len(light_curve_paths) == 0:
+            raise ValueError('LightCurveObservationCollection returned no paths.')
         for light_curve_path in light_curve_paths:
             times, fluxes = self.light_curve_collection.load_times_and_fluxes_from_path(
                 light_curve_path

src/qusi/internal/train_session.py

@@ -9,9 +9,10 @@
 from torch.nn import BCELoss, Module
 from torch.optim import AdamW
 from torch.utils.data import DataLoader
-from torcheval.metrics import BinaryAccuracy, BinaryAUROC
 
 import wandb
+from torchmetrics.classification import BinaryAccuracy, BinaryAUROC
+
 from qusi.internal.light_curve_dataset import InterleavedDataset, LightCurveDataset
 from qusi.internal.logging import set_up_default_logger
 from qusi.internal.train_hyperparameter_configuration import TrainHyperparameterConfiguration
@@ -51,14 +52,15 @@ def train_session(
     if metric_functions is None:
         metric_functions = [BinaryAccuracy(), BinaryAUROC()]
     set_up_default_logger()
+    sessions_directory = Path("sessions")
+    sessions_directory.mkdir(exist_ok=True)
     wandb_init(
         process_rank=0,
         project=logging_configuration.wandb_project,
         entity=logging_configuration.wandb_entity,
         settings=wandb.Settings(start_method="thread"),
+        dir=sessions_directory,
     )
-    sessions_directory = Path("sessions")
-    sessions_directory.mkdir(exist_ok=True)
     train_dataset = InterleavedDataset.new(*train_datasets)
     torch.multiprocessing.set_start_method("spawn")
     debug = False