better clusters

backend/src/utils/cluster_manager.py

@@ -12,6 +12,10 @@ from ..utils.get_time_separation import get_distance
 from ..constants import OSM_CACHE_DIR
 
 
+# silence the overpass logger
+logging.getLogger('OSMPythonTools').setLevel(level=logging.CRITICAL)
+
+
 class Cluster(BaseModel):
     """"
     A class representing an interesting area for shopping or sightseeing.
@@ -102,7 +106,6 @@ class ClusterManager:
                 points.append(coords)
 
             self.all_points = np.array(points)
-            self.valid = True
 
             # Apply DBSCAN to find clusters. Choose different settings for different cities.
             if self.cluster_type == 'shopping' and len(self.all_points) > 200 :
@@ -114,12 +117,17 @@ class ClusterManager:
 
             labels = dbscan.fit_predict(self.all_points)
 
-            # Separate clustered points and noise points
-            self.cluster_points = self.all_points[labels != -1]
-            self.cluster_labels = labels[labels != -1]
+            # Check that there are at least 2 different clusters
+            if len(set(labels)) > 2 :
+                self.logger.debug(f"Found {len(set(labels))} different clusters.")
+                # Separate clustered points and noise points
+                self.cluster_points = self.all_points[labels != -1]
+                self.cluster_labels = labels[labels != -1]
+                self.filter_clusters()      # ValueError here sometimes. I dont know why. # Filter the clusters to keep only the largest ones.
+                self.valid = True
 
-            # filter the clusters to keep only the largest ones
-            self.filter_clusters()        
+            else : 
+                self.valid = False      
 
 
     def generate_clusters(self) -> list[Landmark]:
@@ -224,6 +232,9 @@ class ClusterManager:
             for elem in result.elements():
                 location = (elem.centerLat(), elem.centerLon())
 
+                # Skip if element has neither name or location
+                if elem.tag('name') is None : 
+                    continue
                 if location[0] is None : 
                     location = (elem.lat(), elem.lon())
                     if location[0] is None : 
@@ -277,6 +288,6 @@ class ClusterManager:
             filtered_cluster_labels.append(np.full((label_counts[label],), label))  # Replicate the label
 
         # update the cluster points and labels with the filtered data
-        self.cluster_points = np.vstack(filtered_cluster_points)
+        self.cluster_points = np.vstack(filtered_cluster_points)        # ValueError here
         self.cluster_labels = np.concatenate(filtered_cluster_labels)
 

backend/src/utils/landmarks_manager.py

@@ -210,7 +210,7 @@ class LandmarkManager:
         # caution, when applying a list of selectors, overpass will search for elements that match ALL selectors simultaneously
         # we need to split the selectors into separate queries and merge the results
         for sel in dict_to_selector_list(amenity_selector):
-            self.logger.debug(f"Current selector: {sel}")
+            # self.logger.debug(f"Current selector: {sel}")
 
             # query_conditions = ['count_tags()>5']
             # if landmarktype == 'shopping' :       # use this later for shopping clusters
@@ -232,7 +232,7 @@ class LandmarkManager:
                 includeCenter = True,
                 out = 'center'
                 )
-            self.logger.debug(f"Query: {query}")
+            # self.logger.debug(f"Query: {query}")
 
             try:
                 result = self.overpass.query(query)