Feature/#1032 working veto system

server/ladder_service/veto_system.py

@@ -260,21 +260,20 @@ def _is_valid_veto_config_for_queue(
 ) -> bool:
     num_maps = len(queue_config.map_pool.maps)
 
+    if (queue_config.minimum_maps_after_veto > num_maps):
+        return False
+
     if (
         queue_config.max_tokens_per_map == 0
-        and queue_config.minimum_maps_after_veto >= num_maps
+        and queue_config.minimum_maps_after_veto == num_maps
+        and queue_config.veto_tokens_per_player > 0
     ):
         return False
 
     if queue_config.max_tokens_per_map != 0:
         total_players = queue.team_size * 2
         vetoable_maps_per_player = queue_config.veto_tokens_per_player / queue_config.max_tokens_per_map
         vetoable_maps = total_players * vetoable_maps_per_player
-
-        # tokens/map > number of maps that may be vetoed
-        # TODO: because vetoable_maps >= 0 this inequality also implies
-        # queue_config.minimum_maps_after_veto > num_maps
-        # Why is it strictly greater than here but greater than or equal to above?
         if vetoable_maps > num_maps - queue_config.minimum_maps_after_veto:
             return False
 

server/matchmaker/map_pool.py

@@ -38,11 +38,16 @@ def apply_antirepetition_adjustment(self, initial_weights: dict[int, float], pla
         adjusted_weights = notzero_weights.copy()
 
         def get_notrepeated_weight_transfer_targets(current_weight, rep_count):
-            for base_threshold in base_thresholds:
+            thresholds = list(base_thresholds)
+            factor = repeat_factor ** (rep_count - 1)
+            if factor < 1:
+                thresholds.extend(t * factor for t in base_thresholds if t * factor < base_thresholds[-1])
+
+            for threshold in thresholds:
                 targets = [
                     target_id for target_id in notzero_weights
                     if repetition_counts.get(target_id, 0) == 0 and
-                    notzero_weights[target_id] >= base_threshold * repeat_factor ** (rep_count - 1) * current_weight
+                    notzero_weights[target_id] >= threshold * current_weight
                 ]
                 if targets:
                     return targets
@@ -100,7 +105,7 @@ def choose_map(self, played_map_ids: Iterable[int] = (), initial_weights: Option
         self._logger.debug("______map_list________________: %s", map_list)
         final_weights = [adjusted_weights.get(mp_mv_id, 0) * m.weight for mp_mv_id, m in map_list]
         self._logger.debug("______final_weights________________: %s", final_weights)
-        return random.choices([map for _, map in map_list], weights=final_weights, k=1)[0].get_map()
+        return random.choices([map for _, map in map_list], weights=final_weights, k=1)[0].get_map()  # nosec B311
 
     def __repr__(self) -> str:
         return f"MapPool({self.id}, {self.name}, {list(self.maps.values())})"

tests/unit_tests/test_map_pool.py

@@ -250,7 +250,7 @@ def test_choose_map_raises_on_empty_map_pool(map_pool_factory):
             {1: 0, 2: 1.5, 3: 1.5}
         ),
         # Really complex redistribution test
-        # 1 -> [5,6], [2,3] -> [5,6,8], [7,9] -> 5
+        # 1 -> [5], [2,3] -> [5,6,8], [7,9] -> 5
         (
             {1: 0.9, 2: 0.6, 3: 0.4, 4: 0.2, 5: 0.75, 6: 0.6, 7: 1, 8: 0.5, 9: 1},
             [1, 1, 2, 3, 7, 9],
@@ -261,8 +261,8 @@ def test_choose_map_raises_on_empty_map_pool(map_pool_factory):
                 2: 0,
                 3: 0,
                 4: 0.2,
-                5: pytest.approx(3.655405, rel=1e-6),
-                6: pytest.approx(1.324324, rel=1e-6),
+                5: pytest.approx(4.055405, rel=1e-6),
+                6: pytest.approx(0.924324, rel=1e-6),
                 7: 0,
                 8: pytest.approx(0.770270, rel=1e-6),
                 9: 0
@@ -279,8 +279,8 @@ def test_choose_map_raises_on_empty_map_pool(map_pool_factory):
                 2: 0,
                 3: 0,
                 4: 0.2,
-                5: pytest.approx(3.655405, rel=1e-6),
-                6: pytest.approx(1.324324, rel=1e-6),
+                5: pytest.approx(4.055405, rel=1e-6),
+                6: pytest.approx(0.924324, rel=1e-6),
                 7: 0,
                 8: pytest.approx(0.770270, rel=1e-6),
                 9: 0