Upgraded refiner

backend/src/tester.py

@@ -5,7 +5,7 @@ from landmarks_manager import generate_landmarks
 from fastapi.encoders import jsonable_encoder
 
 from optimizer import solve_optimization
-from refiner import get_minor_landmarks
+from refiner import refine_optimization
 from structs.landmarks import Landmark
 from structs.landmarktype import LandmarkType
 from structs.preferences import Preferences, Preference
@@ -80,38 +80,32 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
                                   type=LandmarkType(landmark_type='shopping'),
                                   score = 5))
 
-    city_country = None
 
-    landmarks, landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
+    # Create start and finish 
+    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=coordinates, osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=coordinates, osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    
+    # Generate the landmarks from the start location
+    landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
     #write_data(landmarks, "landmarks.txt")
 
+    # Insert start and finish to the landmarks list
+    landmarks_short.insert(0, start)
+    landmarks_short.append(finish)
 
-    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-    finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8375946, 2.2949904), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-    
-    test = landmarks_short
-    
-    test.insert(0, start)
-    test.append(finish)
-
+    # TODO use these parameters in another way
     max_walking_time = 4    # hours
     detour = 30             # minutes
 
-    visited_list = solve_optimization(test, max_walking_time*60, True)
-    #visited_list = [Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags=0, time_to_reach=0), Landmark(name='Palais du Louvre', type=LandmarkType(landmark_type='sightseeing'), location=(48.8614768, 2.3351677), osm_type='relation', osm_id=3262297, attractiveness=32, must_do=False, n_tags=32, time_to_reach=85), Landmark(name='Musée du Louvre', type=LandmarkType(landmark_type='sightseeing'), location=(48.8611474, 2.3358637), osm_type='relation', osm_id=7515426, attractiveness=34, must_do=False, n_tags=33, time_to_reach=1), Landmark(name='Bourse de Commerce — Pinault Collection', type=LandmarkType(landmark_type='sightseeing'), location=(48.8628167, 2.3428183), osm_type='way', osm_id=19856722, attractiveness=32, must_do=False, n_tags=32, time_to_reach=12), Landmark(name='Centre Georges Pompidou', type=LandmarkType(landmark_type='sightseeing'), location=(48.8605235, 2.3524395), osm_type='way', osm_id=55503397, attractiveness=43, must_do=False, n_tags=43, time_to_reach=15), Landmark(name='Tour Saint-Jacques', type=LandmarkType(landmark_type='sightseeing'), location=(48.8579983, 2.3489178), osm_type='way', osm_id=20326709, attractiveness=33, must_do=False, n_tags=31, time_to_reach=8), Landmark(name='Hôtel de Ville', type=LandmarkType(landmark_type='sightseeing'), location=(48.8564265, 2.352527), osm_type='relation', osm_id=284089, attractiveness=34, must_do=False, n_tags=32, time_to_reach=7), Landmark(name='Cathédrale Notre-Dame de Paris', type=LandmarkType(landmark_type='sightseeing'), location=(48.8529372, 2.3498701), osm_type='way', osm_id=201611261, attractiveness=55, must_do=False, n_tags=54, time_to_reach=9), Landmark(name='Sainte-Chapelle', type=LandmarkType(landmark_type='sightseeing'), location=(48.8553966, 2.3450136), osm_type='relation', osm_id=3344870, attractiveness=57, must_do=False, n_tags=54, time_to_reach=10), Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8375946, 2.2949904), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags=0, time_to_reach=90)] 
-
-
-    minor_landmarks = get_minor_landmarks(landmarks, visited_list, 200)
-    #write_data(minor_landmarks, 'minor_landmarks.txt')
-    print("There are " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
-
-    full_set = visited_list[:-1] + minor_landmarks[:30]
-    full_set.append(finish)
-
-    new_route = solve_optimization(full_set, max_walking_time*60+detour, True)
+    # First stage optimization
+    base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
     
-    return new_route
+    # Second stage optimization
+    refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
+    
+    return refined_tour
 
 
-test4(tuple((48.8795156, 2.3660204)))
+#test4(tuple((48.8344400, 2.3220540)))       # Café Chez César 
+test4(tuple((48.8375946, 2.2949904)))       # Point random
 #test3('Vienna, Austria')