Create utils.py

utils.py

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+from google.maps import areainsights_v1
+from google.maps.areainsights_v1.types import ComputeInsightsRequest, Filter, LocationFilter, Insight
+from google.type import latlng_pb2
+import asyncio
+
+# DATASET_COLUMNS = [
+#     'Dining and Drinking', 'Community and Government', 'Retail',
+#        'Business and Professional Services', 'Landmarks and Outdoors',
+#        'Arts and Entertainment', 'Health and Medicine',
+#        'Travel and Transportation', 'Sports and Recreation',
+#        'Event'
+# ]
+
+DATASET_COLUMNS = [
+    'restaurant',
+    'bookstore',
+    'fast_food',
+    'clinic',
+    'school',
+    'bakery',
+    'convenience_store',
+    'shopping_mall',
+    'atm',
+    'bank',
+    'dentist',
+    'pharmacy',
+    'cafe',
+    'supermarket',
+    'hospital',
+    'gym',
+    'jewelry_store',
+    'charging_station',
+    'electronics_store',
+    'clothing_store',
+    'department_store',
+    'hotel',
+    'yoga',
+    'attraction',
+    'college',
+    'co_working',
+    'university',
+    'hostel',
+    'viewpoint'
+]
+
+
+GOOGLE_PLACE_TYPE_MAPPING = [
+    # Dining and Drinking
+    [
+        'restaurant', 'bar', 'cafe', 'bakery', 'night_club'
+    ],
+    # Community and Government
+    [
+        'government_office', 'local_government_office', 'city_hall',
+        'courthouse', 'police', 'fire_station', 'post_office', 'library'
+    ],
+    # Retail
+    [
+        'store', 'shopping_mall', 'grocery_store', 'pharmacy',
+        'supermarket', 'drugstore'
+    ],
+    # Business and Professional Services
+    [
+        'bank', 'atm', 'corporate_office', 'accounting', 'lawyer',
+        # 'establishment'
+    ],
+    # Landmarks and Outdoors
+    [
+        'park', 'tourist_attraction', 'national_park',
+        'historical_landmark', 'cultural_landmark'
+    ],
+    # Arts and Entertainment
+    [
+        'movie_theater', 'museum', 'art_gallery',
+        'performing_arts_theater', 'amusement_park', 'aquarium', 'zoo'
+    ],
+    # Health and Medicine
+    [
+        'hospital', 'doctor', 'dentist', 'pharmacy',
+        'physiotherapist', 'spa'
+    ],
+    # Travel and Transportation
+    [
+        'airport', 'bus_station', 'train_station', 'transit_station',
+        'subway_station', 'parking', 'lodging'
+    ],
+    # Sports and Recreation
+    [
+        'gym', 'stadium', 'bowling_alley', 'fitness_center',
+        'park', 'amusement_center'
+    ],
+    # Event (Mapped to common event venues)
+    [
+        'event_venue', 'convention_center', 'banquet_hall', 'stadium'
+    ]
+]
+
+# import os
+
+GOOGLE_PLACE_TYPE_MAPPING = [
+    # Original Column: 'restaurant'
+    ['restaurant'],
+    # Original Column: 'bookstore'
+    ['book_store'],
+    # Original Column: 'fast_food'
+    ['fast_food_restaurant'],
+    # Original Column: 'clinic'
+    ['dental_clinic', 'doctor', 'medical_lab', 'skin_care_clinic'],
+    # Original Column: 'school'
+    ['school', 'primary_school', 'secondary_school', 'preschool'],
+    # Original Column: 'bakery'
+    ['bakery'],
+    # Original Column: 'convenience_store'
+    ['convenience_store'],
+    # Original Column: 'shopping_mall'
+    ['shopping_mall'],
+    # Original Column: 'atm'
+    ['atm'],
+    # Original Column: 'bank'
+    ['bank'],
+    # Original Column: 'dentist'
+    ['dentist', 'dental_clinic'],
+    # Original Column: 'pharmacy'
+    ['pharmacy', 'drugstore'],
+    # Original Column: 'cafe'
+    ['cafe', 'coffee_shop'],
+    # Original Column: 'supermarket'
+    ['supermarket'],
+    # Original Column: 'hospital'
+    ['hospital'],
+    # Original Column: 'gym'
+    ['gym', 'wellness_center'],
+    # Original Column: 'jewelry_store'
+    ['jewelry_store'],
+    # Original Column: 'charging_station'
+    ['electric_vehicle_charging_station'],
+    # Original Column: 'electronics_store'
+    ['electronics_store'],
+    # Original Column: 'clothing_store'
+    ['clothing_store', 'department_store'],
+    # Original Column: 'department_store'
+    ['department_store'],
+    # Original Column: 'hotel'
+    ['hotel', 'motel', 'resort_hotel'],
+    # Original Column: 'yoga'
+    ['yoga_studio'],
+    # Original Column: 'attraction'
+    ['tourist_attraction', 'amusement_park', 'museum', 'cultural_landmark'],
+    # Original Column: 'college'
+    ['university'],
+    # Original Column: 'co_working' (No direct Place Type exists, using closest fit)
+    ['corporate_office'],
+    # Original Column: 'university'
+    ['university'],
+    # Original Column: 'hostel'
+    ['hostel', 'budget_japanese_inn'],
+    # Original Column: 'viewpoint' (No direct Place Type exists, using closest fit)
+    ['tourist_attraction', 'observation_deck']
+]
+
+
+
+async def compute_places_count_with_api_key(api_key, lat, lng, radius, places):
+    try:
+        client = areainsights_v1.AreaInsightsAsyncClient(
+            client_options={"api_key": api_key}
+        )
+
+        # 1. Define the geographic filter (a circle)
+        location_filter = LocationFilter(
+            circle=LocationFilter.Circle(
+                lat_lng=latlng_pb2.LatLng(latitude=lat, longitude=lng),
+                radius=radius
+            )
+        )
+
+        # 2. Define the place type filter
+        type_filter = areainsights_v1.TypeFilter(
+            included_types=places
+        )
+
+        # 3. Assemble the main request body
+        request = ComputeInsightsRequest(
+            # We want the total count of matching places
+            insights=[Insight.INSIGHT_COUNT],
+            filter=Filter(
+                location_filter=location_filter,
+                type_filter=type_filter
+            )
+        )
+
+        response = await client.compute_insights(request=request)
+
+        count = int(response.count)
+
+        return count
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return None
+        
+
+def compute_features(candidate_point, api_key, radius=5000):
+    lat, lon = candidate_point
+
+    features = {
+        'num_banks_in_radius':0,
+        # 'total_amenities':0,
+        # 'category_diversity':0
+    }
+
+    for i,places in enumerate(GOOGLE_PLACE_TYPE_MAPPING):
+      total_count = asyncio.run(compute_places_count_with_api_key(
+          api_key,
+          lat,
+          lon,
+          radius,
+          places
+      ))
+
+      features[f'num_{DATASET_COLUMNS[i]}'] = total_count
+
+
+    n_banks = asyncio.run(compute_places_count_with_api_key(
+          api_key,
+          lat,
+          lon,
+          radius,
+          ['atm']
+    ))
+    
+
+    features.update({
+        'num_banks_in_radius': n_banks,
+        # 'total_amenities': sum(v for v in features.values()),
+        # 'category_diversity': sum(bool(v) for v in features.values())
+    })
+
+    print(features)
+
+    return features