Hotfix: OLS ended() method

morl_baselines/multi_policy/linear_support/linear_support.py

@@ -50,6 +50,7 @@ def __init__(
         self.weight_support = []  # List of weight vectors for each value vector in the CCS
         self.queue = []
         self.iteration = 0
+        self.ols_ended = False
         self.verbose = verbose
         for w in extrema_weights(self.num_objectives):
             self.queue.append((float("inf"), w))
@@ -102,6 +103,7 @@ def next_weight(
         if len(self.queue) == 0:
             if self.verbose:
                 print("There are no corner weights in the queue. Returning None.")
+            self.ols_ended = True
             return None
         else:
             next_w = self.queue.pop(0)[1]
@@ -134,8 +136,14 @@ def get_corner_weights(self, top_k: Optional[int] = None) -> List[np.ndarray]:
             return weights
 
     def ended(self) -> bool:
-        """Returns True if the queue is empty."""
-        return len(self.queue) == 0
+        """Returns True if there are no more corner weights to test.
+
+        Warning: This method must be called AFTER calling next_weight().
+        Ex: w = ols.next_weight()
+            if ols.ended():
+                print("OLS ended.")
+        """
+        return self.ols_ended
 
     def add_solution(self, value: np.ndarray, w: np.ndarray) -> List[int]:
         """Add new value vector optimal to weight w.
@@ -375,10 +383,13 @@ def is_dominated(self, value: np.ndarray) -> bool:
     def _solve(w):
         return np.array(list(map(float, input().split())), dtype=np.float32)
 
-    num_objectives = 3
+    num_objectives = 2
     ols = LinearSupport(num_objectives=num_objectives, epsilon=0.0001, verbose=True)
-    while not ols.ended():
+    while True:
         w = ols.next_weight()
+        if ols.ended():
+            print("OLS ended.")
+            break
         print("w:", w)
         value = _solve(w)
         ols.add_solution(value, w)

tests/test_algos.py

@@ -113,8 +113,10 @@ def test_ols():
 
     ols = LinearSupport(num_objectives=2, epsilon=0.1, verbose=False)
     policies = []
-    while not ols.ended():
+    while True:
         w = ols.next_weight()
+        if ols.ended():
+            break
 
         new_policy = MOQLearning(
             env,