Add sampling without replacement strategy

backend/examples/assignment/strategies.py

@@ -1,5 +1,6 @@
 import abc
 import dataclasses
+import random
 from typing import List
 
 import numpy as np
@@ -17,7 +18,7 @@ class BaseStrategy(abc.ABC):
         ...
 
 
-class WeightedRandomStrategy:
+class WeightedRandomStrategy(BaseStrategy):
     def __init__(self, dataset_size: int, weights: List[int]):
         assert sum(weights) == 100
         self.dataset_size = dataset_size
@@ -27,3 +28,19 @@ class WeightedRandomStrategy:
         proba = np.array(self.weights) / 100
         assignees = np.random.choice(range(len(self.weights)), size=self.dataset_size, p=proba)
         return [Assignment(user=user, example=example) for example, user in enumerate(assignees)]
+
+
+class SamplingWithoutReplacementStrategy(BaseStrategy):
+    def __init__(self, dataset_size: int, weights: List[int]):
+        assert 0 <= sum(weights) <= 100 * len(weights)
+        self.dataset_size = dataset_size
+        self.weights = weights
+
+    def assign(self) -> List[Assignment]:
+        assignments = []
+        proba = np.array(self.weights) / 100
+        for user, p in enumerate(proba):
+            count = int(self.dataset_size * p)
+            examples = random.sample(range(self.dataset_size), count)
+            assignments.extend([Assignment(user=user, example=example) for example in examples])
+        return assignments