better errorhandling, slimmed down optimizer
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@@ -29,9 +29,11 @@ def new_trip(preferences: Preferences, start: tuple[float, float], end: tuple[fl
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:return: the uuid of the first landmark in the optimized route
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'''
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if preferences is None:
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raise ValueError("Please provide preferences in the form of a 'Preference' BaseModel class.")
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raise HTTPException(status_code=406, detail="Preferences not provided")
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if preferences.shopping.score == 0 and preferences.sightseeing.score == 0 and preferences.nature.score == 0:
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raise HTTPException(status_code=406, detail="All preferences are 0.")
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if start is None:
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raise ValueError("Please provide the starting coordinates as a tuple of floats.")
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raise HTTPException(status_code=406, detail="Start coordinates not provided")
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if end is None:
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end = start
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logger.info("No end coordinates provided. Using start=end.")
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@@ -50,7 +52,12 @@ def new_trip(preferences: Preferences, start: tuple[float, float], end: tuple[fl
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landmarks_short.append(end_landmark)
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# First stage optimization
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base_tour = optimizer.solve_optimization(preferences.max_time_minute, landmarks_short)
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try:
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base_tour = optimizer.solve_optimization(preferences.max_time_minute, landmarks_short)
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except ArithmeticError:
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raise HTTPException(status_code=500, detail="No solution found")
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except TimeoutError:
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raise HTTPException(status_code=500, detail="Optimzation took too long")
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# Second stage optimization
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refined_tour = refiner.refine_optimization(landmarks, base_tour, preferences.max_time_minute, preferences.detour_tolerance_minute)
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@@ -1,6 +1,6 @@
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city_bbox_side: 5000 #m
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radius_close_to: 50
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church_coeff: 0.8
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park_coeff: 1.2
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park_coeff: 1.0
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tag_coeff: 10
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N_important: 40
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@@ -21,7 +21,6 @@ class LandmarkManager:
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logger = logging.getLogger(__name__)
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city_bbox_side: int # bbox side in meters
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radius_close_to: int # radius in meters
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church_coeff: float # coeff to adjsut score of churches
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park_coeff: float # coeff to adjust score of parks
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@@ -36,12 +35,17 @@ class LandmarkManager:
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with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
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parameters = yaml.safe_load(f)
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self.city_bbox_side = parameters['city_bbox_side']
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self.max_bbox_side = parameters['city_bbox_side']
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self.radius_close_to = parameters['radius_close_to']
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self.church_coeff = parameters['church_coeff']
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self.park_coeff = parameters['park_coeff']
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self.tag_coeff = parameters['tag_coeff']
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self.N_important = parameters['N_important']
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with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
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parameters = yaml.safe_load(f)
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self.walking_speed = parameters['average_walking_speed']
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self.detour_factor = parameters['detour_factor']
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self.overpass = Overpass()
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CachingStrategy.use(JSON, cacheDir=constants.OSM_CACHE_DIR)
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@@ -65,23 +69,26 @@ class LandmarkManager:
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- A list of the most important landmarks based on the user's preferences.
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"""
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max_walk_dist = (preferences.max_time_minute/2)/60*self.walking_speed*1000/self.detour_factor
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reachable_bbox_side = min(max_walk_dist, self.max_bbox_side)
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L = []
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bbox = self.create_bbox(center_coordinates)
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bbox = self.create_bbox(center_coordinates, reachable_bbox_side)
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# list for sightseeing
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if preferences.sightseeing.score != 0:
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score_function = lambda loc, n_tags: int((self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff) )*self.church_coeff)
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score_function = lambda loc, n_tags: int((((n_tags**1.2)*self.tag_coeff) )*self.church_coeff) # self.count_elements_close_to(loc) +
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L1 = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], preferences.sightseeing.type, score_function)
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L += L1
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# list for nature
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if preferences.nature.score != 0:
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score_function = lambda loc, n_tags: int((self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff) )*self.park_coeff)
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score_function = lambda loc, n_tags: int((((n_tags**1.2)*self.tag_coeff) )*self.park_coeff) # self.count_elements_close_to(loc) +
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L2 = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], preferences.nature.type, score_function)
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L += L2
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# list for shopping
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if preferences.shopping.score != 0:
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score_function = lambda loc, n_tags: int(self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff))
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score_function = lambda loc, n_tags: int(((n_tags**1.2)*self.tag_coeff)) # self.count_elements_close_to(loc) +
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L3 = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, score_function)
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L += L3
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@@ -183,12 +190,13 @@ class LandmarkManager:
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return 0
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def create_bbox(self, coordinates: tuple[float, float]) -> tuple[float, float, float, float]:
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def create_bbox(self, coordinates: tuple[float, float], reachable_bbox_side: int) -> tuple[float, float, float, float]:
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"""
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Create a bounding box around the given coordinates.
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Args:
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coordinates (tuple[float, float]): The latitude and longitude of the center of the bounding box.
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reachable_bbox_side (int): The side length of the bounding box in meters.
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Returns:
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tuple[float, float, float, float]: The minimum latitude, minimum longitude, maximum latitude, and maximum longitude
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@@ -199,7 +207,7 @@ class LandmarkManager:
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lon = coordinates[1]
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# Half the side length in km (since it's a square bbox)
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half_side_length_km = self.city_bbox_side / 2 / 1000
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half_side_length_km = reachable_bbox_side / 2 / 1000
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# Convert distance to degrees
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lat_diff = half_side_length_km / 111 # 1 degree latitude is approximately 111 km
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@@ -288,19 +296,24 @@ class LandmarkManager:
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break
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if "wikipedia" in tag:
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n_tags += 3 # wikipedia entries count more
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n_tags += 1 # wikipedia entries count more
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if tag == "wikidata":
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Q = elem.tag('wikidata')
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site = Site("wikidata", "wikidata")
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item = ItemPage(site, Q)
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item.get()
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n_languages = len(item.labels)
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n_tags += n_languages/10
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# if tag == "wikidata":
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# Q = elem.tag('wikidata')
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# site = Site("wikidata", "wikidata")
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# item = ItemPage(site, Q)
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# item.get()
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# n_languages = len(item.labels)
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# n_tags += n_languages/10
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if "viewpoint" in tag:
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n_tags += 10
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if elem_type != "nature":
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if "leisure" in tag and elem.tag('leisure') == "park":
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elem_type = "nature"
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if elem_type == "nature":
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n_tags += 1
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if landmarktype != "shopping":
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if "shop" in tag:
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@@ -310,7 +323,6 @@ class LandmarkManager:
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if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
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skip = True
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break
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if skip:
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continue
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