better time management for optimizer

backend/src/parameters/landmark_parameters.yaml

@@ -7,5 +7,5 @@ tag_exponent: 1.15
 image_bonus: 10
 viewpoint_bonus: 15
 wikipedia_bonus: 6
-N_important: 50
+N_important: 40
 pay_bonus: -1

backend/src/parameters/optimizer_parameters.yaml

@@ -1,5 +1,6 @@
 detour_factor: 1.4
-detour_corridor_width: 200
+detour_corridor_width: 300
 average_walking_speed: 4.8
 max_landmarks: 10
 max_landmarks_refiner: 20
+overshoot: 1.3

backend/src/structs/landmark.py

@@ -33,6 +33,7 @@ class Landmark(BaseModel) :
         return self.uuid.int
     
     def __str__(self) -> str:
-        time_to_next_str = f", time_to_next={self.time_to_reach_next}" if self.time_to_reach_next else ""
-        return f'Landmark({self.type}): [{self.name} @{self.location}, score={self.attractiveness}{time_to_next_str}]'
+        time_to_next_str = f"time_to_next={self.time_to_reach_next}" if self.time_to_reach_next else ""
+        # return f'Landmark({self.type}): [{self.name} @{self.location}, score={self.attractiveness}{time_to_next_str}]'
+        return f'({self.type[:4]}),  score={self.attractiveness}\tmain:{not self.is_secondary}\tduration={self.duration}\t{time_to_next_str}\t{self.name}'
     

backend/src/tester.py

@@ -24,8 +24,8 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
         nature=Preference(type='nature', score = 5),
         shopping=Preference(type='shopping', score = 5),
 
-        max_time_minute=1000,
-        detour_tolerance_minute=0
+        max_time_minute=300,
+        detour_tolerance_minute=15
     )
 
     # Create start and finish 
@@ -60,9 +60,14 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
     refined_tour = refiner.refine_optimization(all_landmarks=landmarks, base_tour=base_tour, max_time = preferences.max_time_minute, detour = preferences.detour_tolerance_minute)
 
     linked_tour = LinkedLandmarks(refined_tour)
+    total_time = 0           
     logger.info("Optimized route : ")
     for l in linked_tour :
         logger.info(f"{l}")
+        total_time += l.duration
+        total_time += l.time_to_reach_next
+
+    logger.info(f"Total time: {total_time}")
 
     # with open('linked_tour.yaml', 'w') as f:
     #     yaml.dump(linked_tour.asdict(), f)
@@ -70,9 +75,9 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
     return linked_tour
 
 
-test(tuple((48.8344400, 2.3220540)))       # Café Chez César 
+# test(tuple((48.8344400, 2.3220540)))       # Café Chez César 
 # test(tuple((48.8375946, 2.2949904)))       # Point random
 # test(tuple((47.377859, 8.540585)))         # Zurich HB
-# test(tuple((45.7576485, 4.8330241)))      # Lyon Bellecour
+test(tuple((45.7576485, 4.8330241)))      # Lyon Bellecour
 # test(tuple((48.5848435, 7.7332974)))      # Strasbourg Gare
 # test(tuple((48.2067858, 16.3692340)))      # Vienne

backend/src/utils/landmarks_manager.py

@@ -343,8 +343,14 @@ class LandmarkManager:
                     score = int(score*self.church_coeff)
                     duration = 60
                 
-                else : 
+                elif "fountain" in elem.tags().values() :
+                    duration = 5
+                
+                elif "park" in elem.tags().values() :
                     duration = 30
+                
+                else : 
+                    duration = 15
 
                 # Generate the landmark and append it to the list
                 landmark = Landmark(

backend/src/utils/optimizer.py

@@ -17,10 +17,11 @@ class Optimizer:
 
     logger = logging.getLogger(__name__)
 
-    detour: int = None          # accepted max detour time (in minutes)
-    detour_factor: float        # detour factor of straight line vs real distance in cities
-    average_walking_speed: float        # average walking speed of adult
-    max_landmarks: int          # max number of landmarks to visit
+    detour: int = None              # accepted max detour time (in minutes)
+    detour_factor: float            # detour factor of straight line vs real distance in cities
+    average_walking_speed: float    # average walking speed of adult
+    max_landmarks: int              # max number of landmarks to visit
+    overshoot: float                # experimentally determined overshoot possibility to return long enough tours
 
 
     def __init__(self) :
@@ -31,6 +32,7 @@ class Optimizer:
             self.detour_factor = parameters['detour_factor']
             self.average_walking_speed = parameters['average_walking_speed']
             self.max_landmarks = parameters['max_landmarks']
+            self.overshoot = parameters['overshoot']
         
 
 
@@ -167,7 +169,7 @@ class Optimizer:
 
 
 
-    def init_ub_dist(self, landmarks: list[Landmark], max_steps: int):
+    def init_ub_dist(self, landmarks: list[Landmark], max_time: int):
         """
         Initialize the objective function coefficients and inequality constraints for the optimization problem.
 
@@ -176,7 +178,7 @@ class Optimizer:
 
         Args:
             landmarks (list[Landmark]): List of landmarks.
-            max_steps (int): Maximum number of steps allowed.
+            max_time (int): Maximum time allowed for tour.
 
         Returns:
             Tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality constraint coefficients, and the right-hand side of the inequality constraint.
@@ -200,7 +202,7 @@ class Optimizer:
             A_ub += dist_table
         c = c*len(landmarks)
 
-        return c, A_ub, [max_steps]
+        return c, A_ub, [max_time*self.overshoot]
 
 
     def respect_number(self, L, max_landmarks: int):