refactor: 🐛 Remove hidden property declarations

src/cellmap_data/multidataset.py

@@ -41,7 +41,7 @@ def __repr__(self) -> str:
     def class_counts(self):
         if (
             not hasattr(self, "_class_counts")
-            or self._class_counts is None
+            or self._class_counts is None  # This should be overkill...
             or len(self._class_counts) == 0
         ):
             class_counts = {}
@@ -60,71 +60,78 @@ def class_weights(self):
         """
         Returns the class weights for the multi-dataset based on the number of samples in each class.
         """
-        if len(self.classes) > 1:
-            class_counts = {}
-            class_count_sum = 0
-            for c in self.classes:
-                class_counts[c] = self.class_counts[c]["total"]
-                class_count_sum += class_counts[c]
-
-            class_weights = {
-                c: (
-                    1 - (class_counts[c] / class_count_sum)
-                    if class_counts[c] != class_count_sum
-                    else 0.1
-                )
-                for c in self.classes
-            }
-        else:
-            class_weights = {self.classes[0]: 0.1}  # less than 1 to avoid overflow
-        return class_weights
+        if not hasattr(self, "_class_weights"):
+            if len(self.classes) > 1:
+                class_counts = {}
+                class_count_sum = 0
+                for c in self.classes:
+                    class_counts[c] = self.class_counts[c]["total"]
+                    class_count_sum += class_counts[c]
+
+                class_weights = {
+                    c: (
+                        1 - (class_counts[c] / class_count_sum)
+                        if class_counts[c] != class_count_sum
+                        else 0.1
+                    )
+                    for c in self.classes
+                }
+            else:
+                class_weights = {self.classes[0]: 0.1}  # less than 1 to avoid overflow
+            self._class_weights = class_weights
+        return self._class_weights
 
     @property
     def dataset_weights(self):
         """
         Returns the weights for each dataset in the multi-dataset based on the number of samples in each dataset.
         """
-        class_weights = self.class_weights
-
-        dataset_weights = {}
-        for dataset in self.datasets:
-            dataset_weight = np.sum(
-                [
-                    dataset.class_counts["totals"][c] * class_weights[c]
-                    for c in self.classes
-                ]
-            )
-            dataset_weights[dataset] = dataset_weight
-        return dataset_weights
+        if not hasattr(self, "_dataset_weights"):
+            class_weights = self.class_weights
+
+            dataset_weights = {}
+            for dataset in self.datasets:
+                dataset_weight = np.sum(
+                    [
+                        dataset.class_counts["totals"][c] * class_weights[c]
+                        for c in self.classes
+                    ]
+                )
+                dataset_weights[dataset] = dataset_weight
+            self._dataset_weights = dataset_weights
+        return self._dataset_weights
 
     @property
     def sample_weights(self):
         """
         Returns the weights for each sample in the multi-dataset based on the number of samples in each dataset.
         """
-
-        dataset_weights = self.dataset_weights
-        sample_weights = []
-        for dataset, dataset_weight in dataset_weights.items():
-            sample_weights += [dataset_weight] * len(dataset)
-        return sample_weights
+        if not hasattr(self, "_sample_weights"):
+            dataset_weights = self.dataset_weights
+            sample_weights = []
+            for dataset, dataset_weight in dataset_weights.items():
+                sample_weights += [dataset_weight] * len(dataset)
+            self._sample_weights = sample_weights
+        return self._sample_weights
 
     @property
     def validation_indices(self) -> Sequence[int]:
         """
         Returns the indices of the validation set for each dataset in the multi-dataset.
         """
-        validation_indices = []
-        index_offset = 0
-        for dataset in self.datasets:
-            try:
-                validation_indices.extend(dataset.validation_indices)
-            except AttributeError:
-                UserWarning(
-                    f"Unable to get validation indices for dataset {dataset}\n skipping"
-                )
-            index_offset += len(dataset)
-        return list(np.array(validation_indices) + index_offset)
+        if not hasattr(self, "_validation_indices"):
+            validation_indices = []
+            index_offset = 0
+            for dataset in self.datasets:
+                try:
+                    validation_indices.extend(dataset.validation_indices)
+                except AttributeError:
+                    UserWarning(
+                        f"Unable to get validation indices for dataset {dataset}\n skipping"
+                    )
+                index_offset += len(dataset)
+            self._validation_indices = list(np.array(validation_indices) + index_offset)
+        return self._validation_indices
 
     def to(self, device: str):
         for dataset in self.datasets: