Merge branch 'feature/adding-timed-visits'

backend/src/parameters/optimizer_parameters.yaml

@@ -2,5 +2,5 @@ detour_factor: 1.4
 detour_corridor_width: 300
 average_walking_speed: 4.8
 max_landmarks: 10
-max_landmarks_refiner: 20
-overshoot: 1.3
+max_landmarks_refiner: 30
+overshoot: 1.8

backend/src/structs/landmark.py

@@ -15,8 +15,11 @@ class Landmark(BaseModel) :
     attractiveness : int
     n_tags : int
     image_url : Optional[str] = None                            # TODO future
+    website_url : Optional[str] = None
+    wikipedia_url : Optional[str] = None
     description : Optional[str] = None                          # TODO future
     duration : Optional[int] = 0                                # TODO future
+    name_en : Optional[str] = None
 
     # Unique ID of a given landmark
     uuid: str = Field(default_factory=uuid4)                    # TODO implement this ASAP
@@ -33,7 +36,9 @@ 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}]'
-        return f'({self.type[:4]}),  score={self.attractiveness}\tmain:{not self.is_secondary}\tduration={self.duration}\t{time_to_next_str}\t{self.name}'
+        time_to_next_str = f", time_to_next={self.time_to_reach_next}" if self.time_to_reach_next else ""
+        is_secondary_str = f", secondary" if self.is_secondary else ""
+        type_str = '(' + self.type + ')'
+        if self.type in ["start", "finish", "nature", "shopping"] : type_str += '\t '
+        return f'Landmark{type_str}: [{self.name} @{self.location}, score={self.attractiveness}{time_to_next_str}{is_secondary_str}]'
     

backend/src/structs/linked_landmarks.py

@@ -52,7 +52,7 @@ class LinkedLandmarks:
         
         # Update 'is_secondary' for landmarks with attractiveness below the threshold score
         for landmark in self._landmarks:
-            if landmark.attractiveness < threshold_score:
+            if landmark.attractiveness < threshold_score and landmark.type not in ["start", "finish"]:
                 landmark.is_secondary = True
 
 

backend/src/tester.py

@@ -23,9 +23,8 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
         sightseeing=Preference(type='sightseeing', score = 5),
         nature=Preference(type='nature', score = 5),
         shopping=Preference(type='shopping', score = 5),
-
-        max_time_minute=300,
-        detour_tolerance_minute=15
+        max_time_minute=100,
+        detour_tolerance_minute=0
     )
 
     # Create start and finish 
@@ -64,10 +63,7 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
     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}")
+    logger.info(f"Estimated length of tour : {linked_tour.total_time} mintutes and visiting {len(linked_tour._landmarks)} landmarks.")
 
     # with open('linked_tour.yaml', 'w') as f:
     #     yaml.dump(linked_tour.asdict(), f)
@@ -78,6 +74,6 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
 # 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((48.5848435, 7.7332974)))      # Strasbourg Gare
+# test(tuple((45.758217, 4.831814)))      # Lyon Bellecour
+test(tuple((48.5848435, 7.7332974)))      # Strasbourg Gare
 # test(tuple((48.2067858, 16.3692340)))      # Vienne

backend/src/utils/landmarks_manager.py

@@ -81,19 +81,19 @@ class LandmarkManager:
         bbox = self.create_bbox(center_coordinates, reachable_bbox_side)
         # list for sightseeing
         if preferences.sightseeing.score != 0:
-            score_function = lambda score: int(score*10)*preferences.sightseeing.score/5   # self.count_elements_close_to(loc) +
+            score_function = lambda score: int(score*10*preferences.sightseeing.score/5)   # self.count_elements_close_to(loc) +
             L1 = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], preferences.sightseeing.type, score_function)
             L += L1
 
         # list for nature
         if preferences.nature.score != 0:
-            score_function = lambda score: int(score*10*self.nature_coeff)*preferences.nature.score/5   # self.count_elements_close_to(loc) +
+            score_function = lambda score: int(score*10*self.nature_coeff*preferences.nature.score/5)   # self.count_elements_close_to(loc) +
             L2 = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], preferences.nature.type, score_function)
             L += L2
 
         # list for shopping
         if preferences.shopping.score != 0:
-            score_function = lambda score: int(score*10)*preferences.shopping.score/5 # self.count_elements_close_to(loc) +
+            score_function = lambda score: int(score*10*preferences.shopping.score/5)   # self.count_elements_close_to(loc) +
             L3 = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, score_function)
             L += L3
 
@@ -290,7 +290,10 @@ class LandmarkManager:
                 elem_type = landmarktype            # Add the landmark type as 'sightseeing, 
                 n_tags = len(elem.tags().keys())    # Add number of tags
                 score = n_tags**self.tag_exponent   # Add score
-
+                website_url = None
+                wikpedia_url = None
+                image_url = None
+                name_en = None
 
                 # remove specific tags
                 skip = False
@@ -315,6 +318,7 @@ class LandmarkManager:
 
                     if "viewpoint" in tag:
                         score += self.viewpoint_bonus
+                        duration = 10
 
                     if "image" in tag:
                         score += self.image_bonus
@@ -331,26 +335,31 @@ class LandmarkManager:
                         if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
                             skip = True
                             break
+                    
+                    # Get additional information
+                    # if tag == 'wikipedia' :
+                    #     wikpedia_url = elem.tag('wikipedia')
+                    if tag in ['website', 'contact:website'] :
+                        website_url = elem.tag(tag)
+                    if tag == 'image' :
+                        image_url = elem.tag('image')
+                    if tag =='name:en' :
+                        name_en = elem.tag('name:en')
+
                 if skip:
                     continue
 
                 score = score_function(score)
                 if "place_of_worship" in elem.tags().values() :
                     score = int(score*self.church_coeff)
-                    duration = 20
+                    duration = 15
                 
                 elif "museum" in elem.tags().values() :
                     score = int(score*self.church_coeff)
                     duration = 60
                 
-                elif "fountain" in elem.tags().values() :
-                    duration = 5
-                
-                elif "park" in elem.tags().values() :
-                    duration = 30
-                
                 else : 
-                    duration = 15
+                    duration = 5
 
                 # Generate the landmark and append it to the list
                 landmark = Landmark(
@@ -362,7 +371,11 @@ class LandmarkManager:
                     attractiveness=score,
                     must_do=False,
                     n_tags=int(n_tags),
-                    duration = duration
+                    duration = duration, 
+                    name_en=name_en,
+                    image_url=image_url,
+                    # wikipedia_url=wikpedia_url,
+                    website_url=website_url
                 )
                 return_list.append(landmark)
         

backend/src/utils/optimizer.py

@@ -21,7 +21,7 @@ class Optimizer:
     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
+    overshoot: float                # overshoot to allow maxtime to overflow. Optimizer is a bit restrictive
 
 
     def __init__(self) :
@@ -178,7 +178,7 @@ class Optimizer:
 
         Args:
             landmarks (list[Landmark]): List of landmarks.
-            max_time (int): Maximum time allowed for tour.
+            max_time (int): Maximum time of visit allowed.
 
         Returns:
             Tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality constraint coefficients, and the right-hand side of the inequality constraint.
@@ -195,7 +195,7 @@ class Optimizer:
             for j, spot2 in enumerate(landmarks) :
                 t = get_time(spot1.location, spot2.location) + spot1.duration
                 dist_table[j] = t
-            closest = sorted(dist_table)[:22]
+            closest = sorted(dist_table)[:25]
             for i, dist in enumerate(dist_table) :
                 if dist not in closest :
                     dist_table[i] = 32700
@@ -476,7 +476,7 @@ class Optimizer:
         A, b = self.respect_start_finish(L)                  # Force start and finish positions
         A_eq = np.vstack((A_eq, A), dtype=np.int8)
         b_eq += b
-        A, b = self.respect_order(L)                         # Respect order of visit (only works when max_steps is limiting factor)
+        A, b = self.respect_order(L)                         # Respect order of visit (only works when max_time is limiting factor)
         A_eq = np.vstack((A_eq, A), dtype=np.int8)
         b_eq += b
         

backend/src/utils/refiner.py

@@ -214,7 +214,7 @@ class Refiner :
             if self.is_in_area(area, landmark.location) and landmark.name not in visited_names:
                 second_order_landmarks.append(landmark)
 
-        return take_most_important.take_most_important(second_order_landmarks, len(visited_landmarks))
+        return take_most_important.take_most_important(second_order_landmarks, int(self.max_landmarks_refiner*0.75))
 
 
     # Try fix the shortest path using shapely