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cleanup-backend #13

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remoll merged 8 commits from cleanup-backend into main 2024-07-27 12:09:54 +00:00
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backend/src/landmarks_manager.py
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@@ -1,128 +1,104 @@
import math as m import math as m
import json, os import yaml
import logging
from typing import List, Tuple, Optional from typing import List, Tuple
from OSMPythonTools.overpass import Overpass, overpassQueryBuilder from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
from OSMPythonTools import cachingStrategy
from pywikibot import ItemPage, Site from pywikibot import ItemPage, Site
from pywikibot import config from pywikibot import config
config.put_throttle = 0 config.put_throttle = 0
config.maxlag = 0 config.maxlag = 0
from structs.landmarks import Landmark, LandmarkType
from structs.preferences import Preferences, Preference from structs.preferences import Preferences, Preference
from structs.landmarks import Landmark
from utils import take_most_important
import constants
SIGHTSEEING = LandmarkType(landmark_type='sightseeing') SIGHTSEEING = 'sightseeing'
NATURE = LandmarkType(landmark_type='nature') NATURE = 'nature'
SHOPPING = LandmarkType(landmark_type='shopping') SHOPPING = 'shopping'
# Include the json here
# Create a list of all things to visit given some preferences and a city. Ready for the optimizer
def generate_landmarks(preferences: Preferences, coordinates: Tuple[float, float]) :
l_sights, l_nature, l_shop = get_amenities() class LandmarkManager:
logger = logging.getLogger(__name__)
city_bbox_side: int # bbox side in meters
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
N_important: int # number of important landmarks to consider
preferences: Preferences # preferences of visit
location: Tuple[float] # coordinates around which to find a path
def __init__(self, preferences: Preferences, coordinates: Tuple[float, float]) -> None:
with constants.AMENITY_SELECTORS_PATH.open('r') as f:
self.amenity_selectors = yaml.safe_load(f)
with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
parameters = yaml.safe_load(f)
self.city_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.N_important = parameters['N_important']
self.preferences = preferences
self.location = coordinates
def generate_landmarks_list(self) -> Tuple[List[Landmark], List[Landmark]] :
"""
Generate and prioritize a list of landmarks based on user preferences.
This method fetches landmarks from various categories (sightseeing, nature, shopping) based on the user's preferences
and current location. It scores and corrects these landmarks, removes duplicates, and then selects the most important
landmarks based on a predefined criterion.
Returns:
Tuple[List[Landmark], List[Landmark]]:
- A list of all existing landmarks.
- A list of the most important landmarks based on the user's preferences.
"""
L = [] L = []
# List for sightseeing # List for sightseeing
if preferences.sightseeing.score != 0 : if self.preferences.sightseeing.score != 0 :
L1 = get_landmarks(l_sights, SIGHTSEEING, coordinates=coordinates) L1 = self.fetch_landmarks(self.amenity_selectors['sightseeing'], SIGHTSEEING, coordinates=self.location)
correct_score(L1, preferences.sightseeing) self.correct_score(L1, self.preferences.sightseeing)
L += L1 L += L1
# List for nature # List for nature
if preferences.nature.score != 0 : if self.preferences.nature.score != 0 :
L2 = get_landmarks(l_nature, NATURE, coordinates=coordinates) L2 = self.fetch_landmarks(self.amenity_selectors['nature'], NATURE, coordinates=self.location)
correct_score(L2, preferences.nature) self.correct_score(L2, self.preferences.nature)
L += L2 L += L2
# List for shopping # List for shopping
if preferences.shopping.score != 0 : if self.preferences.shopping.score != 0 :
L3 = get_landmarks(l_shop, SHOPPING, coordinates=coordinates) L3 = self.fetch_landmarks(self.amenity_selectors['shopping'], SHOPPING, coordinates=self.location)
correct_score(L3, preferences.shopping) self.correct_score(L3, self.preferences.shopping)
L += L3 L += L3
L = remove_duplicates(L) L = self.remove_duplicates(L)
return L, take_most_important(L) return L, take_most_important(L, self.N_important)
# Helper function to gather the amenities list def remove_duplicates(self, landmarks: List[Landmark]) -> List[Landmark] :
def get_amenities() -> List[List[str]] :
# Get the list of amenities from the files
sightseeing = get_list('/amenities/sightseeing.am')
nature = get_list('/amenities/nature.am')
shopping = get_list('/amenities/shopping.am')
return sightseeing, nature, shopping
# Helper function to read a .am file and generate the corresponding list
def get_list(path: str) -> List[str] :
with open(os.path.dirname(os.path.abspath(__file__)) + path) as f :
content = f.readlines()
amenities = []
for line in content :
if not line.startswith('#') :
amenities.append(line.strip('\n'))
return amenities
# Take the most important landmarks from the list
def take_most_important(L: List[Landmark], N = 0) -> List[Landmark] :
# Read the parameters from the file
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
parameters = json.loads(f.read())
N_important = parameters['N important']
L_copy = []
L_clean = []
scores = [0]*len(L)
names = []
name_id = {}
for i, elem in enumerate(L) :
if elem.name not in names :
names.append(elem.name)
name_id[elem.name] = [i]
L_copy.append(elem)
else :
name_id[elem.name] += [i]
scores = []
for j in name_id[elem.name] :
scores.append(L[j].attractiveness)
best_id = max(range(len(scores)), key=scores.__getitem__)
t = name_id[elem.name][best_id]
if t == i :
for old in L_copy :
if old.name == elem.name :
old.attractiveness = L[t].attractiveness
scores = [0]*len(L_copy)
for i, elem in enumerate(L_copy) :
scores[i] = elem.attractiveness
res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-(N_important-N):]
for i, elem in enumerate(L_copy) :
if i in res :
L_clean.append(elem)
return L_clean
# Remove duplicate elements and elements with low score
def remove_duplicates(L: List[Landmark]) -> List[Landmark] :
""" """
Removes duplicate landmarks based on their names from the given list. Removes duplicate landmarks based on their names from the given list. Only retains the landmark with highest score
Parameters: Parameters:
L (List[Landmark]): A list of Landmark objects. landmarks (List[Landmark]): A list of Landmark objects.
Returns: Returns:
List[Landmark]: A list of unique Landmark objects based on their names. List[Landmark]: A list of unique Landmark objects based on their names.
@@ -131,11 +107,9 @@ def remove_duplicates(L: List[Landmark]) -> List[Landmark] :
L_clean = [] L_clean = []
names = [] names = []
for landmark in L : for landmark in landmarks :
if landmark.name in names: if landmark.name in names:
continue continue
else : else :
names.append(landmark.name) names.append(landmark.name)
L_clean.append(landmark) L_clean.append(landmark)
@@ -143,26 +117,52 @@ def remove_duplicates(L: List[Landmark]) -> List[Landmark] :
return L_clean return L_clean
# Correct the score of a list of landmarks by taking into account preference settings def correct_score(self, landmarks: List[Landmark], preference: Preference) :
def correct_score(L: List[Landmark], preference: Preference) : """
Adjust the attractiveness score of each landmark in the list based on user preferences.
if len(L) == 0 : This method updates the attractiveness of each landmark by scaling it according to the user's preference score.
The score adjustment is computed using a simple linear transformation based on the preference score.
Args:
landmarks (List[Landmark]): A list of landmarks whose scores need to be corrected.
preference (Preference): The user's preference settings that influence the attractiveness score adjustment.
Raises:
TypeError: If the type of any landmark in the list does not match the expected type in the preference.
"""
if len(landmarks) == 0 :
return return
if L[0].type != preference.type : if landmarks[0].type != preference.type :
raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {L[0].name}") raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {landmarks[0].name}")
for elem in L : for elem in landmarks :
elem.attractiveness = int(elem.attractiveness*preference.score/5) # arbitrary computation elem.attractiveness = int(elem.attractiveness*preference.score/5) # arbitrary computation
# Function to count elements within a certain radius of a location def count_elements_close_to(self, coordinates: Tuple[float, float]) -> int:
def count_elements_within_radius(coordinates: Tuple[float, float], radius: int) -> int: """
Count the number of OpenStreetMap elements (nodes, ways, relations) within a specified radius of the given location.
This function constructs a bounding box around the specified coordinates based on the radius. It then queries
OpenStreetMap data to count the number of elements within that bounding box.
Args:
coordinates (Tuple[float, float]): The latitude and longitude of the location to search around.
Returns:
int: The number of elements (nodes, ways, relations) within the specified radius. Returns 0 if no elements
are found or if an error occurs during the query.
"""
lat = coordinates[0] lat = coordinates[0]
lon = coordinates[1] lon = coordinates[1]
alpha = (180*radius)/(6371000*m.pi) radius = self.radius_close_to
alpha = (180*radius) / (6371000*m.pi)
bbox = {'latLower':lat-alpha,'lonLower':lon-alpha,'latHigher':lat+alpha,'lonHigher': lon+alpha} bbox = {'latLower':lat-alpha,'lonLower':lon-alpha,'latHigher':lat+alpha,'lonHigher': lon+alpha}
# Build the query to find elements within the radius # Build the query to find elements within the radius
@@ -177,19 +177,27 @@ def count_elements_within_radius(coordinates: Tuple[float, float], radius: int)
if N_elem is None : if N_elem is None :
return 0 return 0
return N_elem return N_elem
except : except :
return 0 return 0
# Creates a bounding box around given coordinates, side_length in meters def create_bbox(self, coordinates: Tuple[float, float]) -> Tuple[float, float, float, float]:
def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[float, float, float, float]: """
Create a bounding box around the given coordinates.
Args:
coordinates (Tuple[float, float]): The latitude and longitude of the center of the bounding box.
Returns:
Tuple[float, float, float, float]: The minimum latitude, minimum longitude, maximum latitude, and maximum longitude
defining the bounding box.
"""
lat = coordinates[0] lat = coordinates[0]
lon = coordinates[1] lon = coordinates[1]
# Half the side length in km (since it's a square bbox) # Half the side length in km (since it's a square bbox)
half_side_length_km = side_length / 2 / 1000 half_side_length_km = self.city_bbox_side / 2 / 1000
# Convert distance to degrees # Convert distance to degrees
lat_diff = half_side_length_km / 111 # 1 degree latitude is approximately 111 km lat_diff = half_side_length_km / 111 # 1 degree latitude is approximately 111 km
@@ -204,19 +212,28 @@ def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[flo
return min_lat, min_lon, max_lat, max_lon return min_lat, min_lon, max_lat, max_lon
def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: Tuple[float, float]) -> List[Landmark] : def fetch_landmarks(self, list_amenity: list, landmarktype: str, coordinates: Tuple[float, float]) -> List[Landmark] :
"""
Fetches landmarks of a specified type from OpenStreetMap (OSM) within a bounding box centered on given coordinates.
# Read the parameters from the file Args:
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f : list_amenity (list): A list of OSM amenity queries to be used for fetching landmarks.
parameters = json.loads(f.read()) These queries are typically used to filter results (e.g., [''amenity'='place_of_worship']).
tag_coeff = parameters['tag coeff'] landmarktype (str): The type of the landmark (e.g., 'sightseeing', 'nature', 'shopping').
park_coeff = parameters['park coeff'] coordinates (Tuple[float, float]): The central coordinates (latitude, longitude) for the bounding box.
church_coeff = parameters['church coeff']
radius = parameters['radius close to'] Returns:
bbox_side = parameters['city bbox side'] List[Landmark]: A list of Landmark objects that were fetched and filtered based on the provided criteria.
Notes:
- The bounding box is created around the given coordinates with a side length defined by `self.city_bbox_side`.
- Landmarks are fetched using Overpass API queries.
- Landmarks are filtered based on various conditions including tags and type.
- Scores are assigned to landmarks based on their attributes and surrounding elements.
"""
# Create bbox around start location # Create bbox around start location
bbox = create_bbox(coordinates, bbox_side) bbox = self.create_bbox(coordinates)
# Initialize some variables # Initialize some variables
N = 0 N = 0
@@ -272,9 +289,9 @@ def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: T
n_languages = len(item.labels) n_languages = len(item.labels)
n_tags += n_languages/10 n_tags += n_languages/10
if elem_type != LandmarkType(landmark_type="nature") : if elem_type != "nature" :
if "leisure" in tag and elem.tag('leisure') == "park": if "leisure" in tag and elem.tag('leisure') == "park":
elem_type = LandmarkType(landmark_type="nature") elem_type = "nature"
if amenity not in ["'shop'='department_store'", "'shop'='mall'"] : if amenity not in ["'shop'='department_store'", "'shop'='mall'"] :
if "shop" in tag : if "shop" in tag :
@@ -290,14 +307,15 @@ def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: T
# Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
if amenity == "'amenity'='place_of_worship'" : if amenity == "'amenity'='place_of_worship'" :
#score = int((count_elements_within_radius(location, radius) + (n_tags*tag_coeff) )*church_coeff) #score = int((count_elements_close_to(location, radius) + (n_tags*tag_coeff) )*church_coeff)
score = int((count_elements_within_radius(location, radius) + ((n_tags**1.2)*tag_coeff) )*church_coeff) score = int((self.count_elements_close_to(location) + ((n_tags**1.2)*self.tag_coeff) )*self.church_coeff)
elif amenity == "'leisure'='park'" : elif amenity == "'leisure'='park'" :
score = int((count_elements_within_radius(location, radius) + ((n_tags**1.2)*tag_coeff) )*park_coeff) score = int((self.count_elements_close_to(location) + ((n_tags**1.2)*self.tag_coeff) )*self.park_coeff)
else : else :
score = int(count_elements_within_radius(location, radius) + ((n_tags**1.2)*tag_coeff)) score = int(self.count_elements_close_to(location) + ((n_tags**1.2)*self.tag_coeff))
if score is not None : if score is not None :
# Generate the landmark and append it to the list # Generate the landmark and append it to the list
#print(f"There are {n_tags} tags on this Landmark. Total score : {score}\n") #print(f"There are {n_tags} tags on this Landmark. Total score : {score}\n")
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)) 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))

570
backend/src/optimizer.py Normal file
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@@ -0,0 +1,570 @@
import yaml, logging
import numpy as np
from typing import List, Tuple
from scipy.optimize import linprog
from collections import defaultdict, deque
from geopy.distance import geodesic
from structs.landmarks import Landmark
import constants
class Optimizer:
logger = logging.getLogger(__name__)
landmarks: List[Landmark] # list of landmarks
max_time: int = None # max visit time (in minutes)
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
def __init__(self, max_time: int, landmarks: List[Landmark]) :
self.max_time = max_time
self.landmarks = landmarks
# load parameters from file
with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
parameters = yaml.safe_load(f)
self.detour_factor = parameters['detour_factor']
self.average_walking_speed = parameters['average_walking_speed']
self.max_landmarks = parameters['max_landmarks']
def print_res(self, L: List[Landmark]):
"""
Print the suggested order of landmarks to visit and log the total time walked.
Args:
L (List[Landmark]): List of landmarks in the suggested visit order.
"""
self.logger.info(f'The following order is suggested : ')
dist = 0
for elem in L :
if elem.time_to_reach_next is not None :
print('- ' + elem.name + ', time to reach next = ' + str(elem.time_to_reach_next))
dist += elem.time_to_reach_next
else :
print('- ' + elem.name)
self.logger.info(f'Minutes walked : {dist}')
self.logger.info(f'Visited {len(L)-2} landmarks')
# Prevent the use of a particular solution
def prevent_config(self, resx):
"""
Prevent the use of a particular solution by adding constraints to the optimization.
Args:
resx (List[float]): List of edge weights.
Returns:
Tuple[List[int], List[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
"""
for i, elem in enumerate(resx):
resx[i] = round(elem)
N = len(resx) # Number of edges
L = int(np.sqrt(N)) # Number of landmarks
nonzeroind = np.nonzero(resx)[0] # the return is a little funky so I use the [0]
nonzero_tup = np.unravel_index(nonzeroind, (L,L))
ind_a = nonzero_tup[0].tolist()
vertices_visited = ind_a
vertices_visited.remove(0)
ones = [1]*L
h = [0]*N
for i in range(L) :
if i in vertices_visited :
h[i*L:i*L+L] = ones
return h, [len(vertices_visited)-1]
# Prevents the creation of the same circle (both directions)
def prevent_circle(self, circle_vertices: list, L: int) :
"""
Prevent circular paths by by adding constraints to the optimization.
Args:
circle_vertices (list): List of vertices forming a circle.
L (int): Number of landmarks.
Returns:
Tuple[np.ndarray, List[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
"""
l1 = [0]*L*L
l2 = [0]*L*L
for i, node in enumerate(circle_vertices[:-1]) :
next = circle_vertices[i+1]
l1[node*L + next] = 1
l2[next*L + node] = 1
s = circle_vertices[0]
g = circle_vertices[-1]
l1[g*L + s] = 1
l2[s*L + g] = 1
return np.vstack((l1, l2)), [0, 0]
def is_connected(self, resx) :
"""
Determine the order of visits and detect any circular paths in the given configuration.
Args:
resx (list): List of edge weights.
Returns:
Tuple[List[int], Optional[List[List[int]]]]: A tuple containing the visit order and a list of any detected circles.
"""
# first round the results to have only 0-1 values
for i, elem in enumerate(resx):
resx[i] = round(elem)
N = len(resx) # length of res
L = int(np.sqrt(N)) # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
nonzero_tup = np.unravel_index(nonzeroind, (L,L))
ind_a = nonzero_tup[0].tolist()
ind_b = nonzero_tup[1].tolist()
# Step 1: Create a graph representation
graph = defaultdict(list)
for a, b in zip(ind_a, ind_b):
graph[a].append(b)
# Step 2: Function to perform BFS/DFS to extract journeys
def get_journey(start):
journey_nodes = []
visited = set()
stack = deque([start])
while stack:
node = stack.pop()
if node not in visited:
visited.add(node)
journey_nodes.append(node)
for neighbor in graph[node]:
if neighbor not in visited:
stack.append(neighbor)
return journey_nodes
# Step 3: Extract all journeys
all_journeys_nodes = []
visited_nodes = set()
for node in ind_a:
if node not in visited_nodes:
journey_nodes = get_journey(node)
all_journeys_nodes.append(journey_nodes)
visited_nodes.update(journey_nodes)
for l in all_journeys_nodes :
if 0 in l :
order = l
all_journeys_nodes.remove(l)
break
if len(all_journeys_nodes) == 0 :
return order, None
return order, all_journeys_nodes
def get_time(self, p1: Tuple[float, float], p2: Tuple[float, float]) -> int :
"""
Calculate the time in minutes to travel from one location to another.
Args:
p1 (Tuple[float, float]): Coordinates of the starting location.
p2 (Tuple[float, float]): Coordinates of the destination.
Returns:
int: Time to travel from p1 to p2 in minutes.
"""
# Compute the straight-line distance in km
if p1 == p2 :
return 0
else:
dist = geodesic(p1, p2).kilometers
# Consider the detour factor for average cityto deterline walking distance (in km)
walk_dist = dist*self.detour_factor
# Time to walk this distance (in minutes)
walk_time = walk_dist/self.average_walking_speed*60
return round(walk_time)
def init_ub_dist(self, landmarks: List[Landmark], max_steps: int):
"""
Initialize the objective function coefficients and inequality constraints for the optimization problem.
This function computes the distances between all landmarks and stores their attractiveness to maximize sightseeing.
The goal is to maximize the objective function subject to the constraints A*x < b and A_eq*x = b_eq.
Args:
landmarks (List[Landmark]): List of landmarks.
max_steps (int): Maximum number of steps allowed.
Returns:
Tuple[List[float], List[float], List[int]]: Objective function coefficients, inequality constraint coefficients, and the right-hand side of the inequality constraint.
"""
# Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
c = []
# Coefficients of inequality constraints (left-hand side)
A_ub = []
for spot1 in landmarks :
dist_table = [0]*len(landmarks)
c.append(-spot1.attractiveness)
for j, spot2 in enumerate(landmarks) :
t = self.get_time(spot1.location, spot2.location)
dist_table[j] = t
closest = sorted(dist_table)[:22]
for i, dist in enumerate(dist_table) :
if dist not in closest :
dist_table[i] = 32700
A_ub += dist_table
c = c*len(landmarks)
return c, A_ub, [max_steps]
def respect_number(self, L: int):
"""
Generate constraints to ensure each landmark is visited only once and cap the total number of visited landmarks.
Args:
L (int): Number of landmarks.
Returns:
Tuple[np.ndarray, List[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
"""
ones = [1]*L
zeros = [0]*L
A = ones + zeros*(L-1)
b = [1]
for i in range(L-1) :
h_new = zeros*i + ones + zeros*(L-1-i)
A = np.vstack((A, h_new))
b.append(1)
A = np.vstack((A, ones*L))
b.append(self.max_landmarks+1)
return A, b
# Constraint to not have d14 and d41 simultaneously. Does not prevent cyclic paths with more elements
def break_sym(self, L):
"""
Generate constraints to prevent simultaneous travel between two landmarks in both directions.
Args:
L (int): Number of landmarks.
Returns:
Tuple[np.ndarray, List[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
"""
upper_ind = np.triu_indices(L,0,L)
up_ind_x = upper_ind[0]
up_ind_y = upper_ind[1]
A = [0]*L*L
b = [1]
for i, _ in enumerate(up_ind_x[1:]) :
l = [0]*L*L
if up_ind_x[i] != up_ind_y[i] :
l[up_ind_x[i]*L + up_ind_y[i]] = 1
l[up_ind_y[i]*L + up_ind_x[i]] = 1
A = np.vstack((A,l))
b.append(1)
return A, b
def init_eq_not_stay(self, L: int):
"""
Generate constraints to prevent staying in the same position (e.g., removing d11, d22, d33, etc.).
Args:
L (int): Number of landmarks.
Returns:
Tuple[List[np.ndarray], List[int]]: Equality constraint coefficients and the right-hand side of the equality constraints.
"""
l = [0]*L*L
for i in range(L) :
for j in range(L) :
if j == i :
l[j + i*L] = 1
l = np.array(np.array(l), dtype=np.int8)
return [l], [0]
def respect_user_must_do(self, landmarks: List[Landmark]) :
"""
Generate constraints to ensure that landmarks marked as 'must_do' are included in the optimization.
Args:
landmarks (List[Landmark]): List of landmarks, where some are marked as 'must_do'.
Returns:
Tuple[np.ndarray, List[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
"""
L = len(landmarks)
A = [0]*L*L
b = [0]
for i, elem in enumerate(landmarks[1:]) :
if elem.must_do is True and elem.name not in ['finish', 'start']:
l = [0]*L*L
l[i*L:i*L+L] = [1]*L # set mandatory departures from landmarks tagged as 'must_do'
A = np.vstack((A,l))
b.append(1)
return A, b
def respect_user_must_avoid(self, landmarks: List[Landmark]) :
"""
Generate constraints to ensure that landmarks marked as 'must_avoid' are skipped in the optimization.
Args:
landmarks (List[Landmark]): List of landmarks, where some are marked as 'must_avoid'.
Returns:
Tuple[np.ndarray, List[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
"""
L = len(landmarks)
A = [0]*L*L
b = [0]
for i, elem in enumerate(landmarks[1:]) :
if elem.must_avoid is True and elem.name not in ['finish', 'start']:
l = [0]*L*L
l[i*L:i*L+L] = [1]*L
A = np.vstack((A,l))
b.append(0) # prevent departures from landmarks tagged as 'must_do'
return A, b
# Constraint to ensure start at start and finish at goal
def respect_start_finish(self, L: int):
"""
Generate constraints to ensure that the optimization starts at the designated start landmark and finishes at the goal landmark.
Args:
L (int): Number of landmarks.
Returns:
Tuple[np.ndarray, List[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
"""
l_start = [1]*L + [0]*L*(L-1) # sets departures only for start (horizontal ones)
l_start[L-1] = 0 # prevents the jump from start to finish
l_goal = [0]*L*L # sets arrivals only for finish (vertical ones)
l_L = [0]*L*(L-1) + [1]*L # prevents arrivals at start and departures from goal
for k in range(L-1) : # sets only vertical ones for goal (go to)
l_L[k*L] = 1
if k != 0 :
l_goal[k*L+L-1] = 1
A = np.vstack((l_start, l_goal))
b = [1, 1]
A = np.vstack((A,l_L))
b.append(0)
return A, b
def respect_order(self, L: int):
"""
Generate constraints to tie the optimization problem together and prevent stacked ones, although this does not fully prevent circles.
Args:
L (int): Number of landmarks.
Returns:
Tuple[np.ndarray, List[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
"""
A = [0]*L*L
b = [0]
for i in range(L-1) : # Prevent stacked ones
if i == 0 or i == L-1: # Don't touch start or finish
continue
else :
l = [0]*L
l[i] = -1
l = l*L
for j in range(L) :
l[i*L + j] = 1
A = np.vstack((A,l))
b.append(0)
return A, b
def link_list(self, order: List[int], landmarks: List[Landmark])->List[Landmark] :
"""
Compute the time to reach from each landmark to the next and create a list of landmarks with updated travel times.
Args:
order (List[int]): List of indices representing the order of landmarks to visit.
landmarks (List[Landmark]): List of all landmarks.
Returns:
List[Landmark]]: The updated linked list of landmarks with travel times
"""
L = []
j = 0
while j < len(order)-1 :
# get landmarks involved
elem = landmarks[order[j]]
next = landmarks[order[j+1]]
# get attributes
elem.time_to_reach_next = self.get_time(elem.location, next.location)
elem.must_do = True
elem.location = (round(elem.location[0], 5), round(elem.location[1], 5))
elem.next_uuid = next.uuid
L.append(elem)
j += 1
next.location = (round(next.location[0], 5), round(next.location[1], 5))
next.must_do = True
L.append(next)
return L
# Main optimization pipeline
def solve_optimization (self) :
"""
Main optimization pipeline to solve the landmark visiting problem.
This method sets up and solves a linear programming problem with constraints to find an optimal tour of landmarks,
considering user-defined must-visit landmarks, start and finish points, and ensuring no cycles are present.
Returns:
List[Landmark]: The optimized tour of landmarks with updated travel times, or None if no valid solution is found.
"""
L = len(self.landmarks)
# SET CONSTRAINTS FOR INEQUALITY
c, A_ub, b_ub = self.init_ub_dist(self.landmarks, self.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_ub = np.vstack((A_ub, A), dtype=np.int16)
b_ub += b
A, b = self.break_sym(L) # break the 'zig-zag' symmetry
A_ub = np.vstack((A_ub, A), dtype=np.int16)
b_ub += b
# SET CONSTRAINTS FOR EQUALITY
A_eq, b_eq = self.init_eq_not_stay(L) # Force solution not to stay in same place
A, b = self.respect_user_must_do(self.landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
A_eq = np.vstack((A_eq, A), dtype=np.int8)
b_eq += b
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_eq = np.vstack((A_eq, A), dtype=np.int8)
b_eq += b
# SET BOUNDS FOR DECISION VARIABLE (x can only be 0 or 1)
x_bounds = [(0, 1)]*L*L
# Solve linear programming problem
res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
# Raise error if no solution is found
if not res.success :
raise ArithmeticError("No solution could be found, the problem is overconstrained. Please adapt your must_dos")
# If there is a solution, we're good to go, just check for connectiveness
else :
order, circles = self.is_connected(res.x)
#nodes, edges = is_connected(res.x)
i = 0
timeout = 80
while circles is not None and i < timeout:
A, b = self.prevent_config(res.x)
A_ub = np.vstack((A_ub, A))
b_ub += b
#A_ub, b_ub = prevent_circle(order, len(landmarks), A_ub, b_ub)
for circle in circles :
A, b = self.prevent_circle(circle, L)
A_eq = np.vstack((A_eq, A))
b_eq += b
res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
if not res.success :
self.logger.info("Solving failed because of overconstrained problem")
return None
order, circles = self.is_connected(res.x)
#nodes, edges = is_connected(res.x)
if circles is None :
break
print(i)
i += 1
if i == timeout :
raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
# Add the times to reach and stop optimizing
tour = self.link_list(order, self.landmarks)
# logging
if i != 0 :
self.logger.info(f"Neded to recompute paths {i} times because of unconnected loops...")
self.print_res(tour) # how to do better ?
self.logger.info(f"Total score : {int(-res.fun)}")
return tour

446
backend/src/optimizer_v4.py
View File

@@ -1,446 +0,0 @@
import numpy as np
import json, os
from typing import List, Tuple
from scipy.optimize import linprog
from collections import defaultdict, deque
from geopy.distance import geodesic
from structs.landmarks import Landmark
# Function to print the result
def print_res(L: List[Landmark]):
print('The following order is suggested : ')
dist = 0
for elem in L :
if elem.time_to_reach_next is not None :
print('- ' + elem.name + ', time to reach next = ' + str(elem.time_to_reach_next))
dist += elem.time_to_reach_next
else :
print('- ' + elem.name)
print("\nMinutes walked : " + str(dist))
print(f"Visited {len(L)-2} landmarks")
# Prevent the use of a particular solution
def prevent_config(resx):
for i, elem in enumerate(resx):
resx[i] = round(elem)
N = len(resx) # Number of edges
L = int(np.sqrt(N)) # Number of landmarks
nonzeroind = np.nonzero(resx)[0] # the return is a little funky so I use the [0]
nonzero_tup = np.unravel_index(nonzeroind, (L,L))
ind_a = nonzero_tup[0].tolist()
vertices_visited = ind_a
vertices_visited.remove(0)
ones = [1]*L
h = [0]*N
for i in range(L) :
if i in vertices_visited :
h[i*L:i*L+L] = ones
return h, [len(vertices_visited)-1]
# Prevents the creation of the same circle (both directions)
def prevent_circle(circle_vertices: list, L: int) :
l1 = [0]*L*L
l2 = [0]*L*L
for i, node in enumerate(circle_vertices[:-1]) :
next = circle_vertices[i+1]
l1[node*L + next] = 1
l2[next*L + node] = 1
s = circle_vertices[0]
g = circle_vertices[-1]
l1[g*L + s] = 1
l2[s*L + g] = 1
return np.vstack((l1, l2)), [0, 0]
# Returns the order of visit as well as any circles if there are some
def is_connected(resx) :
# first round the results to have only 0-1 values
for i, elem in enumerate(resx):
resx[i] = round(elem)
N = len(resx) # length of res
L = int(np.sqrt(N)) # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
n_edges = resx.sum() # number of edges
nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
nonzero_tup = np.unravel_index(nonzeroind, (L,L))
ind_a = nonzero_tup[0].tolist()
ind_b = nonzero_tup[1].tolist()
# Step 1: Create a graph representation
graph = defaultdict(list)
for a, b in zip(ind_a, ind_b):
graph[a].append(b)
# Step 2: Function to perform BFS/DFS to extract journeys
def get_journey(start):
journey_nodes = []
visited = set()
stack = deque([start])
while stack:
node = stack.pop()
if node not in visited:
visited.add(node)
journey_nodes.append(node)
for neighbor in graph[node]:
if neighbor not in visited:
stack.append(neighbor)
return journey_nodes
# Step 3: Extract all journeys
all_journeys_nodes = []
visited_nodes = set()
for node in ind_a:
if node not in visited_nodes:
journey_nodes = get_journey(node)
all_journeys_nodes.append(journey_nodes)
visited_nodes.update(journey_nodes)
for l in all_journeys_nodes :
if 0 in l :
order = l
all_journeys_nodes.remove(l)
break
if len(all_journeys_nodes) == 0 :
return order, None
return order, all_journeys_nodes
# Function that returns the time in minutes from one location to another
def get_time(p1: Tuple[float, float], p2: Tuple[float, float], detour: float, speed: float) :
# Compute the straight-line distance in km
if p1 == p2 :
return 0
else:
dist = geodesic(p1, p2).kilometers
# Consider the detour factor for average cityto deterline walking distance (in km)
walk_dist = dist*detour
# Time to walk this distance (in minutes)
walk_time = walk_dist/speed*60
return round(walk_time)
# Initialize A and c. Compute the distances from all landmarks to each other and store attractiveness
# We want to maximize the sightseeing : max(c) st. A*x < b and A_eq*x = b_eq
def init_ub_dist(landmarks: List[Landmark], max_steps: int):
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
parameters = json.loads(f.read())
detour = parameters['detour factor']
speed = parameters['average walking speed']
# Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
c = []
# Coefficients of inequality constraints (left-hand side)
A_ub = []
for spot1 in landmarks :
dist_table = [0]*len(landmarks)
c.append(-spot1.attractiveness)
for j, spot2 in enumerate(landmarks) :
t = get_time(spot1.location, spot2.location, detour, speed)
dist_table[j] = t
closest = sorted(dist_table)[:22]
for i, dist in enumerate(dist_table) :
if dist not in closest :
dist_table[i] = 32700
A_ub += dist_table
c = c*len(landmarks)
return c, A_ub, [max_steps]
# Constraint to respect only one travel per landmark. Also caps the total number of visited landmarks
def respect_number(L: int, max_landmarks: int):
ones = [1]*L
zeros = [0]*L
A = ones + zeros*(L-1)
b = [1]
for i in range(L-1) :
h_new = zeros*i + ones + zeros*(L-1-i)
A = np.vstack((A, h_new))
b.append(1)
if max_landmarks is None :
# Read the parameters from the file
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
parameters = json.loads(f.read())
max_landmarks = parameters['max landmarks']
A = np.vstack((A, ones*L))
b.append(max_landmarks+1)
return A, b
# Constraint to not have d14 and d41 simultaneously. Does not prevent cyclic paths with more elements
def break_sym(L):
upper_ind = np.triu_indices(L,0,L)
up_ind_x = upper_ind[0]
up_ind_y = upper_ind[1]
A = [0]*L*L # useless row to prevent overhead ? better solution welcomed
# A[up_ind_x[0]*L + up_ind_y[0]] = 1
# A[up_ind_y[0]*L + up_ind_x[0]] = 1
b = [1]
for i, _ in enumerate(up_ind_x[1:]) :
l = [0]*L*L
if up_ind_x[i] != up_ind_y[i] :
l[up_ind_x[i]*L + up_ind_y[i]] = 1
l[up_ind_y[i]*L + up_ind_x[i]] = 1
A = np.vstack((A,l))
b.append(1)
return A, b
# Constraint to not stay in position. Removes d11, d22, d33, etc.
def init_eq_not_stay(L: int):
l = [0]*L*L
for i in range(L) :
for j in range(L) :
if j == i :
l[j + i*L] = 1
l = np.array(np.array(l), dtype=np.int8)
return [l], [0]
# Go through the landmarks and force the optimizer to use landmarks marked as must_do
def respect_user_must_do(landmarks: List[Landmark]) :
L = len(landmarks)
A = [0]*L*L
b = [0]
for i, elem in enumerate(landmarks[1:]) :
if elem.must_do is True and elem.name not in ['finish', 'start']:
l = [0]*L*L
l[i*L:i*L+L] = [1]*L # set mandatory departures from landmarks tagged as 'must_do'
A = np.vstack((A,l))
b.append(1)
return A, b
# Constraint to ensure start at start and finish at goal
def respect_start_finish(L: int):
l_start = [1]*L + [0]*L*(L-1) # sets departures only for start (horizontal ones)
l_start[L-1] = 0 # prevents the jump from start to finish
l_goal = [0]*L*L # sets arrivals only for finish (vertical ones)
l_L = [0]*L*(L-1) + [1]*L # prevents arrivals at start and departures from goal
for k in range(L-1) : # sets only vertical ones for goal (go to)
l_L[k*L] = 1
if k != 0 :
l_goal[k*L+L-1] = 1
A = np.vstack((l_start, l_goal))
b = [1, 1]
A = np.vstack((A,l_L))
b.append(0)
return A, b
# Constraint to tie the problem together. Necessary but not sufficient to avoid circles
def respect_order(L: int):
A = [0]*L*L # useless row to reduce overhead ? better solution is welcome
b = [0]
for i in range(L-1) : # Prevent stacked ones
if i == 0 or i == L-1: # Don't touch start or finish
continue
else :
l = [0]*L
l[i] = -1
l = l*L
for j in range(L) :
l[i*L + j] = 1
A = np.vstack((A,l))
b.append(0)
return A, b
# Computes the time to reach from each landmark to the next
def link_list(order: List[int], landmarks: List[Landmark])->List[Landmark] :
# Read the parameters from the file
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
parameters = json.loads(f.read())
detour_factor = parameters['detour factor']
speed = parameters['average walking speed']
L = []
j = 0
total_dist = 0
while j < len(order)-1 :
elem = landmarks[order[j]]
next = landmarks[order[j+1]]
d = get_time(elem.location, next.location, detour_factor, speed)
elem.time_to_reach_next = d
elem.must_do = True
elem.location = (round(elem.location[0], 5), round(elem.location[1], 5))
L.append(elem)
j += 1
total_dist += d
next.location = (round(next.location[0], 5), round(next.location[1], 5))
L.append(next)
return L, total_dist
# Same as link_list but does it on a already ordered list
def link_list_simple(ordered_visit: List[Landmark])-> List[Landmark] :
# Read the parameters from the file
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
parameters = json.loads(f.read())
detour_factor = parameters['detour factor']
speed = parameters['average walking speed']
L = []
j = 0
total_dist = 0
while j < len(ordered_visit)-1 :
elem = ordered_visit[j]
next = ordered_visit[j+1]
elem.next_uuid = next.uuid
d = get_time(elem.location, next.location, detour_factor, speed)
elem.time_to_reach_next = d
if elem.name not in ['start', 'finish'] :
elem.must_do = True
L.append(elem)
j += 1
total_dist += d
L.append(next)
return L, total_dist
# Main optimization pipeline
def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_details: bool, max_landmarks = None) :
L = len(landmarks)
# SET CONSTRAINTS FOR INEQUALITY
c, A_ub, b_ub = init_ub_dist(landmarks, max_steps) # Add the distances from each landmark to the other
A, b = 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 = break_sym(L) # break the 'zig-zag' symmetry
A_ub = np.vstack((A_ub, A), dtype=np.int16)
b_ub += b
# SET CONSTRAINTS FOR EQUALITY
A_eq, b_eq = init_eq_not_stay(L) # Force solution not to stay in same place
A, b = respect_user_must_do(landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
A_eq = np.vstack((A_eq, A), dtype=np.int8)
b_eq += b
A, b = respect_start_finish(L) # Force start and finish positions
A_eq = np.vstack((A_eq, A), dtype=np.int8)
b_eq += b
A, b = respect_order(L) # Respect order of visit (only works when max_steps is limiting factor)
A_eq = np.vstack((A_eq, A), dtype=np.int8)
b_eq += b
# SET BOUNDS FOR DECISION VARIABLE (x can only be 0 or 1)
x_bounds = [(0, 1)]*L*L
# Solve linear programming problem
res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
# Raise error if no solution is found
if not res.success :
raise ArithmeticError("No solution could be found, the problem is overconstrained. Please adapt your must_dos")
# If there is a solution, we're good to go, just check for connectiveness
else :
order, circles = is_connected(res.x)
#nodes, edges = is_connected(res.x)
i = 0
timeout = 80
while circles is not None and i < timeout:
A, b = prevent_config(res.x)
A_ub = np.vstack((A_ub, A))
b_ub += b
#A_ub, b_ub = prevent_circle(order, len(landmarks), A_ub, b_ub)
for circle in circles :
A, b = prevent_circle(circle, len(landmarks))
A_eq = np.vstack((A_eq, A))
b_eq += b
res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
if not res.success :
print("Solving failed because of overconstrained problem")
return None
order, circles = is_connected(res.x)
#nodes, edges = is_connected(res.x)
if circles is None :
break
print(i)
i += 1
if i == timeout :
raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
# Add the times to reach and stop optimizing
L, _ = link_list(order, landmarks)
if printing_details is True :
if i != 0 :
print(f"Neded to recompute paths {i} times because of unconnected loops...")
print_res(L)
print("\nTotal score : " + str(int(-res.fun)))
return L

8
backend/src/parameters/landmark_parameters.yaml
View File

@@ -1,6 +1,6 @@
city_bbox_side: 5000 #m city_bbox_side: 5000 #m
radius_close_to: 30 radius_close_to: 50
church_coeff: 0.6 church_coeff: 0.8
park_coeff: 1.5 park_coeff: 1.2
tag_coeff: 100 tag_coeff: 10
N_important: 40 N_important: 40

238
backend/src/refiner.py
View File

@@ -1,45 +1,110 @@
import os, json import yaml, logging
from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
from typing import List, Tuple from typing import List, Tuple
from math import pi from math import pi
from structs.landmarks import Landmark from structs.landmarks import Landmark
from landmarks_manager import take_most_important from optimizer import Optimizer
from optimizer_v4 import solve_optimization, link_list_simple, print_res, get_time from utils import get_time, link_list_simple, take_most_important
import constants
# Create corridor from tour
def create_corridor(landmarks: List[Landmark], width: float) : class Refiner :
logger = logging.getLogger(__name__)
all_landmarks: List[Landmark] # list of all landmarks
base_tour: List[Landmark] # base tour that needs to be refined
max_time: int = None # max visit time (in minutes)
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
def __init__(self, max_time: int, detour: int, all_landmarks: List[Landmark], base_tour: List[Landmark]) :
self.max_time = max_time
self.detour = detour
self.all_landmarks = all_landmarks
self.base_tour = base_tour
# load parameters from file
with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
parameters = yaml.safe_load(f)
self.detour_factor = parameters['detour_factor']
self.average_walking_speed = parameters['average_walking_speed']
self.max_landmarks = parameters['max_landmarks'] + 4
def create_corridor(self, landmarks: List[Landmark], width: float) :
"""
Create a corridor around the path connecting the landmarks.
Args:
landmarks (List[Landmark]): the landmark path around which to create the corridor
width (float): Width of the corridor in meters.
Returns:
Geometry: A buffered geometry object representing the corridor around the path.
"""
corrected_width = (180*width)/(6371000*pi) corrected_width = (180*width)/(6371000*pi)
path = create_linestring(landmarks) path = self.create_linestring(landmarks)
obj = buffer(path, corrected_width, join_style="mitre", cap_style="square", mitre_limit=2) obj = buffer(path, corrected_width, join_style="mitre", cap_style="square", mitre_limit=2)
return obj return obj
# Create linestring from tour def create_linestring(self, tour: List[Landmark])->LineString :
def create_linestring(landmarks: List[Landmark])->List[Point] : """
Create a `LineString` object from a tour.
Args:
tour (List[Landmark]): An ordered sequence of landmarks that represents the visiting order.
Returns:
LineString: A `LineString` object representing the path through the landmarks.
"""
points = [] points = []
for landmark in tour :
for landmark in landmarks :
points.append(Point(landmark.location)) points.append(Point(landmark.location))
return LineString(points) return LineString(points)
# Check if some coordinates are in area. Used for the corridor # Check if some coordinates are in area. Used for the corridor
def is_in_area(area: Polygon, coordinates) -> bool : def is_in_area(self, area: Polygon, coordinates) -> bool :
"""
Check if a given point is within a specified area.
Args:
area (Polygon): The polygon defining the area.
coordinates (Tuple[float, float]): The coordinates of the point to check.
Returns:
bool: True if the point is within the area, otherwise False.
"""
point = Point(coordinates) point = Point(coordinates)
return point.within(area) return point.within(area)
# Function to determine if two landmarks are close to each other # Function to determine if two landmarks are close to each other
def is_close_to(location1: Tuple[float], location2: Tuple[float]): def is_close_to(self, location1: Tuple[float], location2: Tuple[float]):
"""Determine if two locations are close by rounding their coordinates to 3 decimals.""" """
Determine if two locations are close to each other by rounding their coordinates to 3 decimal places.
Args:
location1 (Tuple[float, float]): The coordinates of the first location.
location2 (Tuple[float, float]): The coordinates of the second location.
Returns:
bool: True if the locations are within 0.001 degrees of each other, otherwise False.
"""
absx = abs(location1[0] - location2[0]) absx = abs(location1[0] - location2[0])
absy = abs(location1[1] - location2[1]) absy = abs(location1[1] - location2[1])
@@ -47,36 +112,55 @@ def is_close_to(location1: Tuple[float], location2: Tuple[float]):
#return (round(location1[0], 3), round(location1[1], 3)) == (round(location2[0], 3), round(location2[1], 3)) #return (round(location1[0], 3), round(location1[1], 3)) == (round(location2[0], 3), round(location2[1], 3))
# Rearrange some landmarks in the order of visit to group visit def rearrange(self, tour: List[Landmark]) -> List[Landmark]:
def rearrange(landmarks: List[Landmark]) -> List[Landmark]: """
Rearrange landmarks to group nearby visits together.
This function reorders landmarks so that nearby landmarks are adjacent to each other in the list,
while keeping 'start' and 'finish' landmarks in their original positions.
Args:
tour (List[Landmark]): Ordered list of landmarks to be rearranged.
Returns:
List[Landmark]: The rearranged list of landmarks with grouped nearby visits.
"""
i = 1 i = 1
while i < len(landmarks): while i < len(tour):
j = i+1 j = i+1
while j < len(landmarks): while j < len(tour):
if is_close_to(landmarks[i].location, landmarks[j].location) and landmarks[i].name not in ['start', 'finish'] and landmarks[j].name not in ['start', 'finish']: if self.is_close_to(tour[i].location, tour[j].location) and tour[i].name not in ['start', 'finish'] and tour[j].name not in ['start', 'finish']:
# If they are not adjacent, move the j-th element to be adjacent to the i-th element # If they are not adjacent, move the j-th element to be adjacent to the i-th element
if j != i + 1: if j != i + 1:
landmarks.insert(i + 1, landmarks.pop(j)) tour.insert(i + 1, tour.pop(j))
break # Move to the next i-th element after rearrangement break # Move to the next i-th element after rearrangement
j += 1 j += 1
i += 1 i += 1
return landmarks return tour
# Simple nearest neighbour planner to try to fix the path def find_shortest_path_through_all_landmarks(self, landmarks: List[Landmark]) -> Tuple[List[Landmark], Polygon]:
def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> Tuple[List[Landmark], Polygon]: """
Find the shortest path through all landmarks using a nearest neighbor heuristic.
# Read from data This function constructs a path that starts from the 'start' landmark, visits all other landmarks in the order
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f : of their proximity, and ends at the 'finish' landmark. It returns both the ordered list of landmarks and a
parameters = json.loads(f.read()) polygon representing the path.
detour = parameters['detour factor']
speed = parameters['average walking speed'] Args:
landmarks (List[Landmark]): List of all landmarks including 'start' and 'finish'.
Returns:
Tuple[List[Landmark], Polygon]: A tuple where the first element is the list of landmarks in the order they
should be visited, and the second element is a `Polygon` representing
the path connecting all landmarks.
"""
# Step 1: Find 'start' and 'finish' landmarks # Step 1: Find 'start' and 'finish' landmarks
start_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'start') start_idx = next(i for i, lm in enumerate(landmarks) if lm.type == 'start')
finish_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'finish') finish_idx = next(i for i, lm in enumerate(landmarks) if lm.type == 'finish')
start_landmark = landmarks[start_idx] start_landmark = landmarks[start_idx]
finish_landmark = landmarks[finish_idx] finish_landmark = landmarks[finish_idx]
@@ -93,7 +177,7 @@ def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> Tuple
# Step 4: Use nearest neighbor heuristic to visit all landmarks # Step 4: Use nearest neighbor heuristic to visit all landmarks
while unvisited_landmarks: while unvisited_landmarks:
nearest_landmark = min(unvisited_landmarks, key=lambda lm: get_time(current_landmark.location, lm.location, detour, speed)) nearest_landmark = min(unvisited_landmarks, key=lambda lm: get_time(current_landmark.location, lm.location))
path.append(nearest_landmark) path.append(nearest_landmark)
coordinates.append(nearest_landmark.location) coordinates.append(nearest_landmark.location)
current_landmark = nearest_landmark current_landmark = nearest_landmark
@@ -108,25 +192,52 @@ def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> Tuple
return path, path_poly return path, path_poly
# Returns a list of minor landmarks around the planned path to enhance experience # Returns a list of minor landmarks around the planned path to enhance experience
def get_minor_landmarks(all_landmarks: List[Landmark], visited_landmarks: List[Landmark], width: float) -> List[Landmark] : def get_minor_landmarks(self, all_landmarks: List[Landmark], visited_landmarks: List[Landmark], width: float) -> List[Landmark] :
"""
Identify landmarks within a specified corridor that have not been visited yet.
This function creates a corridor around the path defined by visited landmarks and then finds landmarks that fall
within this corridor. It returns a list of these landmarks, excluding those already visited, sorted by their importance.
Args:
all_landmarks (List[Landmark]): List of all available landmarks.
visited_landmarks (List[Landmark]): List of landmarks that have already been visited.
width (float): Width of the corridor around the visited landmarks.
Returns:
List[Landmark]: List of important landmarks within the corridor that have not been visited yet.
"""
second_order_landmarks = [] second_order_landmarks = []
visited_names = [] visited_names = []
area = create_corridor(visited_landmarks, width) area = self.create_corridor(visited_landmarks, width)
for visited in visited_landmarks : for visited in visited_landmarks :
visited_names.append(visited.name) visited_names.append(visited.name)
for landmark in all_landmarks : for landmark in all_landmarks :
if is_in_area(area, landmark.location) and landmark.name not in visited_names: if self.is_in_area(area, landmark.location) and landmark.name not in visited_names:
second_order_landmarks.append(landmark) second_order_landmarks.append(landmark)
return take_most_important(second_order_landmarks, len(visited_landmarks)) return take_most_important(second_order_landmarks, len(visited_landmarks))
# Try fix the shortest path using shapely # Try fix the shortest path using shapely
def fix_using_polygon(tour: List[Landmark])-> List[Landmark] : def fix_using_polygon(self, tour: List[Landmark])-> List[Landmark] :
"""
Improve the tour path using geometric methods to ensure it follows a more optimal shape.
This function creates a polygon from the given tour and attempts to refine it using a concave hull. It reorders
the landmarks to fit within this refined polygon and adjusts the tour to ensure the 'start' landmark is at the
beginning. It also checks if the final polygon is simple and rearranges the tour if necessary.
Args:
tour (List[Landmark]): List of landmarks representing the current tour path.
Returns:
List[Landmark]: Refined list of landmarks in the order of visit to produce a better tour path.
"""
coords = [] coords = []
coords_dict = {} coords_dict = {}
@@ -173,18 +284,24 @@ def fix_using_polygon(tour: List[Landmark])-> List[Landmark] :
# Rearrange only if polygon still not simple # Rearrange only if polygon still not simple
if not better_tour_poly.is_simple : if not better_tour_poly.is_simple :
better_tour = rearrange(better_tour) better_tour = self.rearrange(better_tour)
return better_tour return better_tour
# Second stage of the optimization. Use linear programming again to refine the path # Second stage of the optimization. Use linear programming again to refine the path
def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, detour: int, print_infos: bool) -> List[Landmark] : def refine_optimization(self) -> List[Landmark] :
"""
Refine the initial tour path by considering additional minor landmarks and optimizing the path.
# Read from the file This method evaluates the need for further optimization based on the initial tour. If a detour is required, it adds
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f : minor landmarks around the initial predicted path and solves a new optimization problem to find a potentially better
parameters = json.loads(f.read()) tour. It then links the new tour and adjusts it using a nearest neighbor heuristic and polygon-based methods to
max_landmarks = parameters['max landmarks'] + 4 ensure a valid path. The final tour is chosen based on the shortest distance.
Returns:
List[Landmark]: The refined list of landmarks representing the optimized tour path.
"""
# No need to refine if no detour is taken # No need to refine if no detour is taken
# if detour == 0 : # if detour == 0 :
@@ -192,18 +309,20 @@ def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], ma
new_tour = base_tour new_tour = base_tour
else : else :
minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200) minor_landmarks = self.get_minor_landmarks(self.all_landmarks, self.base_tour, 200)
if print_infos : print("Using " + str(len(minor_landmarks)) + " minor landmarks around the predicted path") self.logger.info(f"Using {len(minor_landmarks)} minor landmarks around the predicted path")
# full set of visitable landmarks # full set of visitable landmarks
full_set = base_tour[:-1] + minor_landmarks # create full set of possible landmarks (without finish) full_set = self.base_tour[:-1] + minor_landmarks # create full set of possible landmarks (without finish)
full_set.append(base_tour[-1]) # add finish back full_set.append(self.base_tour[-1]) # add finish back
# get a new tour # get a new tour
new_tour = solve_optimization(full_set, max_time+detour, False, max_landmarks) optimizer = Optimizer(self.max_time + self.detour, full_set)
new_tour = optimizer.solve_optimization()
if new_tour is None : if new_tour is None :
new_tour = base_tour new_tour = self.base_tour
# Link the new tour # Link the new tour
new_tour, new_dist = link_list_simple(new_tour) new_tour, new_dist = link_list_simple(new_tour)
@@ -213,11 +332,11 @@ def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], ma
return new_tour return new_tour
# Find shortest path using the nearest neighbor heuristic # Find shortest path using the nearest neighbor heuristic
better_tour, better_poly = find_shortest_path_through_all_landmarks(new_tour) better_tour, better_poly = self.find_shortest_path_through_all_landmarks(new_tour)
# Fix the tour using Polygons if the path looks weird # Fix the tour using Polygons if the path looks weird
if base_tour[0].location == base_tour[-1].location and not better_poly.is_valid : if self.base_tour[0].location == self.base_tour[-1].location and not better_poly.is_valid :
better_tour = fix_using_polygon(better_tour) better_tour = self.fix_using_polygon(better_tour)
# Link the tour again # Link the tour again
better_tour, better_dist = link_list_simple(better_tour) better_tour, better_dist = link_list_simple(better_tour)
@@ -228,16 +347,7 @@ def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], ma
else : else :
final_tour = better_tour final_tour = better_tour
if print_infos : self.logger.info("Refined tour (result of second stage optimization): ")
print("\n\n\nRefined tour (result of second stage optimization): ")
print_res(final_tour)
total_score = 0
for elem in final_tour :
total_score += elem.attractiveness
print("\nTotal score : " + str(total_score))
return final_tour return final_tour

6
backend/src/structs/landmarks.py
View File

@@ -8,7 +8,7 @@ class Landmark(BaseModel) :
# Properties of the landmark # Properties of the landmark
name : str name : str
type: Literal['sightseeing', 'nature', 'shopping'] type: Literal['sightseeing', 'nature', 'shopping', 'start', 'finish']
location : tuple location : tuple
osm_type : str osm_type : str
osm_id : int osm_id : int
@@ -22,8 +22,8 @@ class Landmark(BaseModel) :
uuid: str = Field(default_factory=uuid4) # TODO implement this ASAP uuid: str = Field(default_factory=uuid4) # TODO implement this ASAP
# Additional properties depending on specific tour # Additional properties depending on specific tour
must_do : bool must_do : Optional[bool] = False
must_avoid : bool must_avoid : Optional[bool] = False
is_secondary : Optional[bool] = False # TODO future is_secondary : Optional[bool] = False # TODO future
time_to_reach_next : Optional[int] = 0 # TODO fix this in existing code time_to_reach_next : Optional[int] = 0 # TODO fix this in existing code

2
backend/src/structs/preferences.py
View File

@@ -3,7 +3,7 @@ from typing import Optional, Literal
class Preference(BaseModel) : class Preference(BaseModel) :
name: str name: str
type: Literal['sightseeing', 'nature', 'shopping'] type: Literal['sightseeing', 'nature', 'shopping', 'start', 'finish']
score: int # score could be from 1 to 5 score: int # score could be from 1 to 5
# Input for optimization # Input for optimization

55
backend/src/tester.py
View File

@@ -4,10 +4,9 @@ from typing import List
from landmarks_manager import LandmarkManager from landmarks_manager import LandmarkManager
from fastapi.encoders import jsonable_encoder from fastapi.encoders import jsonable_encoder
from optimizer_v4 import solve_optimization from optimizer import Optimizer
from refiner import refine_optimization from refiner import Refiner
from structs.landmarks import Landmark from structs.landmarks import Landmark
from structs.landmarktype import LandmarkType
from structs.preferences import Preferences, Preference from structs.preferences import Preferences, Preference
@@ -24,58 +23,66 @@ def write_data(L: List[Landmark], file_name: str):
data.to_json(file_name, indent = 2, force_ascii=False) data.to_json(file_name, indent = 2, force_ascii=False)
def test4(coordinates: tuple[float, float]) -> List[Landmark]: def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] = None) -> List[Landmark]:
manager = LandmarkManager()
preferences = Preferences( preferences = Preferences(
sightseeing=Preference( sightseeing=Preference(
name='sightseeing', name='sightseeing',
type=LandmarkType(landmark_type='sightseeing'), type='sightseeing',
score = 5), score = 5),
nature=Preference( nature=Preference(
name='nature', name='nature',
type=LandmarkType(landmark_type='nature'), type='nature',
score = 0), score = 0),
shopping=Preference( shopping=Preference(
name='shopping', name='shopping',
type=LandmarkType(landmark_type='shopping'), type='shopping',
score = 0)) score = 0),
max_time_minute=180,
detour_tolerance_minute=0
)
# Create start and finish # 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=False, n_tags = 0) if finish_coords is None :
finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=coordinates, osm_type='finish', osm_id=0, attractiveness=0, must_do=False, n_tags = 0) finish_coords = start_coords
start = Landmark(name='start', type='start', location=start_coords, osm_type='', osm_id=0, attractiveness=0, n_tags = 0)
finish = Landmark(name='finish', type='start', location=finish_coords, osm_type='', osm_id=0, attractiveness=0, n_tags = 0)
#finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8777055, 2.3640967), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0) #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8777055, 2.3640967), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
#start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.847132, 2.312359), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0) #start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.847132, 2.312359), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
#finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.843185, 2.344533), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0) #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.843185, 2.344533), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
#finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.847132, 2.312359), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0) #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.847132, 2.312359), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
manager = LandmarkManager(preferences=preferences, coordinates=start_coords)
# Generate the landmarks from the start location # Generate the landmarks from the start location
landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location) landmarks, landmarks_short = manager.generate_landmarks_list()
# Store data to file for debug purposes
#write_data(landmarks, "landmarks_Wien.txt") #write_data(landmarks, "landmarks_Wien.txt")
# Insert start and finish to the landmarks list # Insert start and finish to the landmarks list
landmarks_short.insert(0, start) landmarks_short.insert(0, start)
landmarks_short.append(finish) landmarks_short.append(finish)
max_walking_time = 180 # minutes
detour = 0 # minutes
# First stage optimization # First stage optimization
base_tour = solve_optimization(landmarks_short, max_walking_time, True) optimizer = Optimizer(max_time=preferences.max_time_minute, landmarks=landmarks_short)
base_tour = optimizer.solve_optimization()
# Second stage using linear optimization # Second stage using linear optimization
refiner = Refiner(max_time = preferences.max_time_minute, detour = preferences.detour_tolerance_minute, all_landmarks=landmarks, base_tour=base_tour)
refined_tour = refine_optimization(landmarks, base_tour, max_walking_time, detour, True) refined_tour = refiner.refine_optimization()
return refined_tour return refined_tour
#test4(tuple((48.8344400, 2.3220540))) # Café Chez César #test(tuple((48.8344400, 2.3220540))) # Café Chez César
#test4(tuple((48.8375946, 2.2949904))) # Point random #test(tuple((48.8375946, 2.2949904))) # Point random
#test4(tuple((47.377859, 8.540585))) # Zurich HB #test(tuple((47.377859, 8.540585))) # Zurich HB
#test4(tuple((45.7576485, 4.8330241))) # Lyon Bellecour #test(tuple((45.7576485, 4.8330241))) # Lyon Bellecour
test4(tuple((48.5848435, 7.7332974))) # Strasbourg Gare test(tuple((48.5848435, 7.7332974))) # Strasbourg Gare
#test4(tuple((48.2067858, 16.3692340))) # Vienne #test(tuple((48.2067858, 16.3692340))) # Vienne

106
backend/src/utils.py Normal file
View File

@@ -0,0 +1,106 @@
import yaml
from typing import List, Tuple
from geopy.distance import geodesic
from structs.landmarks import Landmark
import constants
def get_time(p1: Tuple[float, float], p2: Tuple[float, float]) -> int :
"""
Calculate the time in minutes to travel from one location to another.
Args:
p1 (Tuple[float, float]): Coordinates of the starting location.
p2 (Tuple[float, float]): Coordinates of the destination.
detour (float): Detour factor affecting the distance.
speed (float): Walking speed in kilometers per hour.
Returns:
int: Time to travel from p1 to p2 in minutes.
"""
with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
parameters = yaml.safe_load(f)
detour_factor = parameters['detour_factor']
average_walking_speed = parameters['average_walking_speed']
# Compute the straight-line distance in km
if p1 == p2 :
return 0
else:
dist = geodesic(p1, p2).kilometers
# Consider the detour factor for average cityto deterline walking distance (in km)
walk_dist = dist*detour_factor
# Time to walk this distance (in minutes)
walk_time = walk_dist/average_walking_speed*60
return round(walk_time)
# Same as link_list but does it on a already ordered list
def link_list_simple(ordered_visit: List[Landmark])-> List[Landmark] :
L = []
j = 0
total_dist = 0
while j < len(ordered_visit)-1 :
elem = ordered_visit[j]
next = ordered_visit[j+1]
elem.next_uuid = next.uuid
d = get_time(elem.location, next.location)
elem.time_to_reach_next = d
if elem.name not in ['start', 'finish'] :
elem.must_do = True
L.append(elem)
j += 1
total_dist += d
L.append(next)
return L, total_dist
# Take the most important landmarks from the list
def take_most_important(landmarks: List[Landmark], N_important) -> List[Landmark] :
L = len(landmarks)
L_copy = []
L_clean = []
scores = [0]*len(landmarks)
names = []
name_id = {}
for i, elem in enumerate(landmarks) :
if elem.name not in names :
names.append(elem.name)
name_id[elem.name] = [i]
L_copy.append(elem)
else :
name_id[elem.name] += [i]
scores = []
for j in name_id[elem.name] :
scores.append(L[j].attractiveness)
best_id = max(range(len(scores)), key=scores.__getitem__)
t = name_id[elem.name][best_id]
if t == i :
for old in L_copy :
if old.name == elem.name :
old.attractiveness = L[t].attractiveness
scores = [0]*len(L_copy)
for i, elem in enumerate(L_copy) :
scores[i] = elem.attractiveness
res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-(N_important-L):]
for i, elem in enumerate(L_copy) :
if i in res :
L_clean.append(elem)
return L_clean