From da921171e9fc8df1e412d1d0815e43844a346f3a Mon Sep 17 00:00:00 2001
From: Helldragon67 <kilian.scheidecker@orange.fr>
Date: Mon, 12 Aug 2024 15:58:30 +0200
Subject: [PATCH] more balanced scores
---
.../src/parameters/landmark_parameters.yaml | 15 ++--
.../src/parameters/optimizer_parameters.yaml | 3 +-
backend/src/structs/linked_landmarks.py | 21 ++++++
backend/src/tester.py | 20 +++---
backend/src/utils/landmarks_manager.py | 71 +++++++++++--------
backend/src/utils/optimizer.py | 10 ++-
backend/src/utils/refiner.py | 11 +--
7 files changed, 97 insertions(+), 54 deletions(-)
diff --git a/backend/src/parameters/landmark_parameters.yaml b/backend/src/parameters/landmark_parameters.yaml
index 777c18f..983b9c8 100644
--- a/backend/src/parameters/landmark_parameters.yaml
+++ b/backend/src/parameters/landmark_parameters.yaml
@@ -1,6 +1,11 @@
-city_bbox_side: 5000 #m
+city_bbox_side: 7500 #m
radius_close_to: 50
-church_coeff: 0.8
-park_coeff: 1.0
-tag_coeff: 10
-N_important: 40
+church_coeff: 0.75
+nature_coeff: 1.25
+overall_coeff: 10
+tag_exponent: 1.15
+image_bonus: 10
+viewpoint_bonus: 15
+wikipedia_bonus: 6
+N_important: 50
+pay_bonus: -1
diff --git a/backend/src/parameters/optimizer_parameters.yaml b/backend/src/parameters/optimizer_parameters.yaml
index 2bf4f32..6dc49d2 100644
--- a/backend/src/parameters/optimizer_parameters.yaml
+++ b/backend/src/parameters/optimizer_parameters.yaml
@@ -1,4 +1,5 @@
detour_factor: 1.4
detour_corridor_width: 200
average_walking_speed: 4.8
-max_landmarks: 7
+max_landmarks: 10
+max_landmarks_refiner: 20
diff --git a/backend/src/structs/linked_landmarks.py b/backend/src/structs/linked_landmarks.py
index 7d350f5..6c74c74 100644
--- a/backend/src/structs/linked_landmarks.py
+++ b/backend/src/structs/linked_landmarks.py
@@ -25,6 +25,11 @@ class LinkedLandmarks:
"""
Create the links between the landmarks in the list by setting their .next_uuid and the .time_to_next attributes.
"""
+
+ # Mark secondary landmarks as such
+ self.update_secondary_landmarks()
+
+
for i, landmark in enumerate(self._landmarks[:-1]):
landmark.next_uuid = self._landmarks[i + 1].uuid
time_to_next = get_time(landmark.location, self._landmarks[i + 1].location)
@@ -34,6 +39,22 @@ class LinkedLandmarks:
self._landmarks[-1].next_uuid = None
self._landmarks[-1].time_to_reach_next = 0
+ def update_secondary_landmarks(self) -> None:
+ # Extract the attractiveness scores and sort them in descending order
+ scores = sorted([landmark.attractiveness for landmark in self._landmarks], reverse=True)
+
+ # Determine the 10th highest score
+ if len(scores) >= 10:
+ threshold_score = scores[9]
+ else:
+ # If there are fewer than 10 landmarks, use the lowest score in the list as the threshold
+ threshold_score = min(scores) if scores else 0
+
+ # Update 'is_secondary' for landmarks with attractiveness below the threshold score
+ for landmark in self._landmarks:
+ if landmark.attractiveness < threshold_score:
+ landmark.is_secondary = True
+
def __getitem__(self, index: int) -> Landmark:
return self._landmarks[index]
diff --git a/backend/src/tester.py b/backend/src/tester.py
index f83c4a7..acfec75 100644
--- a/backend/src/tester.py
+++ b/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=180,
- detour_tolerance_minute=30
+ max_time_minute=1000,
+ detour_tolerance_minute=0
)
# Create start and finish
@@ -60,7 +60,9 @@ 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)
- logger.info(f"Optimized route: {linked_tour}")
+ logger.info("Optimized route : ")
+ for l in linked_tour :
+ logger.info(f"{l}")
# with open('linked_tour.yaml', 'w') as f:
# yaml.dump(linked_tour.asdict(), f)
@@ -68,9 +70,9 @@ 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((48.8375946, 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((48.2067858, 16.3692340))) # Vienne
+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((48.2067858, 16.3692340))) # Vienne
diff --git a/backend/src/utils/landmarks_manager.py b/backend/src/utils/landmarks_manager.py
index 6cde21c..67fb086 100644
--- a/backend/src/utils/landmarks_manager.py
+++ b/backend/src/utils/landmarks_manager.py
@@ -23,8 +23,8 @@ class LandmarkManager:
radius_close_to: int # radius in meters
church_coeff: float # coeff to adjsut score of churches
- park_coeff: float # coeff to adjust score of parks
- tag_coeff: float # coeff to adjust weight of tags
+ nature_coeff: float # coeff to adjust score of parks
+ overall_coeff: float # coeff to adjust weight of tags
N_important: int # number of important landmarks to consider
@@ -38,8 +38,13 @@ class LandmarkManager:
self.max_bbox_side = parameters['city_bbox_side']
self.radius_close_to = parameters['radius_close_to']
self.church_coeff = parameters['church_coeff']
- self.park_coeff = parameters['park_coeff']
- self.tag_coeff = parameters['tag_coeff']
+ self.nature_coeff = parameters['nature_coeff']
+ self.overall_coeff = parameters['overall_coeff']
+ self.tag_exponent = parameters['tag_exponent']
+ self.image_bonus = parameters['image_bonus']
+ self.wikipedia_bonus = parameters['wikipedia_bonus']
+ self.viewpoint_bonus = parameters['viewpoint_bonus']
+ self.pay_bonus = parameters['pay_bonus']
self.N_important = parameters['N_important']
with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
@@ -76,25 +81,25 @@ class LandmarkManager:
bbox = self.create_bbox(center_coordinates, reachable_bbox_side)
# list for sightseeing
if preferences.sightseeing.score != 0:
- score_function = lambda loc, n_tags: int((((n_tags**1.2)*self.tag_coeff) )*self.church_coeff) # 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 loc, n_tags: int((((n_tags**1.2)*self.tag_coeff) )*self.park_coeff) # 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 loc, n_tags: int(((n_tags**1.2)*self.tag_coeff)) # 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
L = self.remove_duplicates(L)
- self.correct_score(L, preferences)
+ # self.correct_score(L, preferences)
L_constrained = take_most_important(L, self.N_important)
self.logger.info(f'Generated {len(L)} landmarks around {center_coordinates}, and constrained to {len(L_constrained)} most important ones.')
@@ -273,8 +278,8 @@ class LandmarkManager:
continue
# skip if unused
- if 'disused:leisure' in elem.tags().keys():
- continue
+ # if 'disused:leisure' in elem.tags().keys():
+ # continue
# skip if part of another building
if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
@@ -284,19 +289,20 @@ class LandmarkManager:
osm_id = elem.id() # Add OSM id
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
# remove specific tags
skip = False
for tag in elem.tags().keys():
if "pay" in tag:
- n_tags -= 1 # discard payment options for tags
+ score += self.pay_bonus # discard payment options for tags
if "disused" in tag:
skip = True # skip disused amenities
break
- if "wikipedia" in tag:
- n_tags += 1 # wikipedia entries count more
+ if "wiki" in tag:
+ score += self.wikipedia_bonus # wikipedia entries count more
# if tag == "wikidata":
# Q = elem.tag('wikidata')
@@ -305,15 +311,16 @@ class LandmarkManager:
# item.get()
# n_languages = len(item.labels)
# n_tags += n_languages/10
+
if "viewpoint" in tag:
- n_tags += 10
+ score += self.viewpoint_bonus
+
+ if "image" in tag:
+ score += self.image_bonus
if elem_type != "nature":
if "leisure" in tag and elem.tag('leisure') == "park":
elem_type = "nature"
-
- if elem_type == "nature":
- n_tags += 1
if landmarktype != "shopping":
if "shop" in tag:
@@ -326,20 +333,22 @@ class LandmarkManager:
if skip:
continue
- score = score_function(location, n_tags)
- if score != 0:
- # Generate the landmark and append it to the list
- landmark = Landmark(
- name=name,
- type=elem_type,
- location=location,
- osm_type=osm_type,
- osm_id=osm_id,
- attractiveness=score,
- must_do=False,
- n_tags=int(n_tags)
- )
- return_list.append(landmark)
+ score = score_function(score)
+ if "place_of_worship" in elem.tags().values() :
+ score = int(score*self.church_coeff)
+
+ # Generate the landmark and append it to the list
+ landmark = Landmark(
+ name=name,
+ type=elem_type,
+ location=location,
+ osm_type=osm_type,
+ osm_id=osm_id,
+ attractiveness=score,
+ must_do=False,
+ n_tags=int(n_tags)
+ )
+ return_list.append(landmark)
self.logger.debug(f"Fetched {len(return_list)} landmarks of type {landmarktype} in {bbox}")
diff --git a/backend/src/utils/optimizer.py b/backend/src/utils/optimizer.py
index 931a3d2..947c0dc 100644
--- a/backend/src/utils/optimizer.py
+++ b/backend/src/utils/optimizer.py
@@ -203,7 +203,7 @@ class Optimizer:
return c, A_ub, [max_steps]
- def respect_number(self, L: int):
+ def respect_number(self, L, max_landmarks: int):
"""
Generate constraints to ensure each landmark is visited only once and cap the total number of visited landmarks.
@@ -224,7 +224,7 @@ class Optimizer:
b.append(1)
A = np.vstack((A, ones*L))
- b.append(self.max_landmarks+1)
+ b.append(max_landmarks+1)
return A, b
@@ -433,6 +433,7 @@ class Optimizer:
self,
max_time: int,
landmarks: list[Landmark],
+ max_landmarks: int = None
) -> list[Landmark]:
"""
Main optimization pipeline to solve the landmark visiting problem.
@@ -443,15 +444,18 @@ class Optimizer:
Args:
max_time (int): Maximum time allowed for the tour in minutes.
landmarks (list[Landmark]): List of landmarks to visit.
+ max_landmarks (int): Maximum number of landmarks visited
Returns:
list[Landmark]: The optimized tour of landmarks with updated travel times, or None if no valid solution is found.
"""
+ if max_landmarks is None :
+ max_landmarks = self.max_landmarks
L = len(landmarks)
# SET CONSTRAINTS FOR INEQUALITY
c, A_ub, b_ub = self.init_ub_dist(landmarks, max_time) # Add the distances from each landmark to the other
- A, b = self.respect_number(L) # Respect max number of visits (no more possible stops than landmarks).
+ A, b = self.respect_number(L, max_landmarks) # Respect max number of visits (no more possible stops than landmarks).
A_ub = np.vstack((A_ub, A), dtype=np.int16)
b_ub += b
A, b = self.break_sym(L) # break the 'zig-zag' symmetry
diff --git a/backend/src/utils/refiner.py b/backend/src/utils/refiner.py
index c8f0aa5..871e332 100644
--- a/backend/src/utils/refiner.py
+++ b/backend/src/utils/refiner.py
@@ -17,7 +17,7 @@ class Refiner :
detour_factor: float # detour factor of straight line vs real distance in cities
detour_corridor_width: float # width of the corridor around the path
average_walking_speed: float # average walking speed of adult
- max_landmarks: int # max number of landmarks to visit
+ max_landmarks_refiner: int # max number of landmarks to visit
optimizer: Optimizer # optimizer object
def __init__(self, optimizer: Optimizer) :
@@ -29,7 +29,7 @@ class Refiner :
self.detour_factor = parameters['detour_factor']
self.detour_corridor_width = parameters['detour_corridor_width']
self.average_walking_speed = parameters['average_walking_speed']
- self.max_landmarks = parameters['max_landmarks'] + 4
+ self.max_landmarks_refiner = parameters['max_landmarks_refiner']
def create_corridor(self, landmarks: list[Landmark], width: float) :
@@ -308,8 +308,8 @@ class Refiner :
"""
# No need to refine if no detour is taken
- if detour == 0:
- return base_tour
+ # if detour == 0:
+ # return base_tour
minor_landmarks = self.get_minor_landmarks(all_landmarks, base_tour, self.detour_corridor_width)
@@ -322,7 +322,8 @@ class Refiner :
# get a new tour
new_tour = self.optimizer.solve_optimization(
max_time = max_time + detour,
- landmarks = full_set
+ landmarks = full_set,
+ max_landmarks = self.max_landmarks_refiner
)
if new_tour is None: