switch to using self.rng and non-optional seed

src/utils/datasets/transformed_datasets/base.py

@@ -6,10 +6,6 @@
 
 
 class TransformedDataset(Dataset):
-    @staticmethod
-    def _identity(x: Any) -> Any:
-        return x
-
     def __init__(
         self,
         dataset: torch.utils.data.Dataset,
@@ -19,7 +15,7 @@ def __init__(
         # If isinstance(subset_idx,int): perturb this class with probability p,
         # if isinstance(subset_idx,List[int]): perturb datapoints with these indices with probability p
         p: float = 1.0,
-        seed: Optional[int] = None,
+        seed: int = 42,
         device: str = "cpu",
         sample_fn: Optional[Callable] = None,
         label_fn: Optional[Callable] = None,
@@ -33,36 +29,40 @@ def __init__(
         if sample_fn is not None:
             self.sample_fn = sample_fn
         else:
-            self.sample_fn = TransformedDataset._identity
+            self.sample_fn = self._identity
         if label_fn is not None:
             self.label_fn = label_fn
         else:
-            self.label_fn = TransformedDataset._identity
+            self.label_fn = self._identity
         self.seed = seed
-        if self.seed is not None:
-            random.seed(self.seed)
+        self.rng = random.Random()
+        self.rng.seed(self.seed)
         self.samples_to_perturb = []
         for i in range(self.__len__()):
             x, y = dataset[i]
             perturb_sample = (self.cls_idx is None) or (y == self.cls_idx)
-            p_condition = (random.random() <= self.p) if self.p < 1.0 else True
+            p_condition = (self.rng.random() <= self.p) if self.p < 1.0 else True
             perturb_sample = p_condition and perturb_sample
             if perturb_sample:
                 self.samples_to_perturb.append(i)
 
-    def __len__(self):
+    def __len__(self) -> int:
         if isinstance(self.dataset, Sized):
             return len(self.dataset)
         dl = torch.utils.data.DataLoader(self.dataset, batch_size=1)
         return len(dl)
 
-    def __getitem__(self, index):
+    def __getitem__(self, index) -> Any:
         x, y = self.dataset[index]
         xx = self.sample_fn(x)
         yy = self.label_fn(y)
 
         return xx, yy if index in self.samples_to_perturb else x, y
 
-    def _get_original_label(self, index):
+    def _get_original_label(self, index) -> int:
         _, y = self.dataset[index]
         return y
+
+    @staticmethod
+    def _identity(x: Any) -> Any:
+        return x

src/utils/datasets/transformed_datasets/label_grouping.py

@@ -1,5 +1,4 @@
-import random
-from typing import Dict, Literal, Optional, Union, cast
+from typing import Dict, Literal, Union
 
 import torch
 
@@ -13,7 +12,7 @@ def __init__(
         self,
         dataset: torch.utils.data.Dataset,
         n_classes: int,
-        seed: Optional[int] = None,
+        seed: int = 42,
         device: str = "cpu",
         n_groups: int = 2,
         class_to_group: Union[ClassToGroupLiterals, Dict[int, int]] = "random",
@@ -33,7 +32,7 @@ def __init__(
         self.groups = list(range(n_groups))
         if class_to_group == "random":
             # create a dictionary of class groups that assigns each class to a group
-            group_assignments = [random.randint(0, n_groups - 1) for _ in range(n_classes)]
+            group_assignments = [self.rng.randint(0, n_groups - 1) for _ in range(n_classes)]
             self.class_to_group = {}
             for i in range(n_classes):
                 self.class_to_group[i] = group_assignments[i]

src/utils/datasets/transformed_datasets/label_poisoning.py

@@ -1,4 +1,3 @@
-import random
 from typing import Optional
 
 import torch
@@ -13,7 +12,7 @@ def __init__(
         n_classes: int,
         cls_idx: Optional[int] = None,
         p: float = 1.0,  # TODO: decide on default value vis-à-vis subset_idx
-        seed: Optional[int] = None,
+        seed: int = 42,
         device: str = "cpu",
     ):
 
@@ -25,7 +24,7 @@ def __init__(
 
     def _poison(self, original_label):
         label_arr = [i for i in range(self.n_classes) if original_label != i]
-        label_idx = random.randint(0, len(label_arr))
+        label_idx = self.rng.randint(0, len(label_arr))
         return label_arr[label_idx]
 
     def __getitem__(self, index):