Update utils2.py

utils2.py

@@ -1,25 +1,25 @@
 from collections import Counter
 import pandas as pd
 import numpy as np
-from scipy.spatial import cKDTree
+# from scipy.spatial import cKDTree
 
 
-df_amenities = pd.read_csv("df_indonesia.csv").rename(
-    columns={"latitude":"lat", "longitude":"lon"}
-)
-df_banks = pd.read_csv("df_bank_indonesia.csv").rename(
-    columns={"latitude":"lat", "longitude":"lon"}
-)
+# df_amenities = pd.read_csv("df_indonesia.csv").rename(
+#     columns={"latitude":"lat", "longitude":"lon"}
+# )
+# df_banks = pd.read_csv("df_bank_indonesia.csv").rename(
+#     columns={"latitude":"lat", "longitude":"lon"}
+# )
 
-df_amenities["fsq_category_labels"] = df_amenities["fsq_category_labels"].apply(
-    lambda x: eval(x)
-)
+# df_amenities["fsq_category_labels"] = df_amenities["fsq_category_labels"].apply(
+#     lambda x: eval(x)
+# )
 
-bank_coords = df_banks[['lat','lon']].values
-tree_banks = cKDTree(bank_coords)
+# bank_coords = df_banks[['lat','lon']].values
+# tree_banks = cKDTree(bank_coords)
 
-amenity_coords = df_amenities[['lat','lon']].values
-tree_amenities = cKDTree(amenity_coords)
+# amenity_coords = df_amenities[['lat','lon']].values
+# tree_amenities = cKDTree(amenity_coords)
 
 DATASET_COLUMNS = [
     'Dining and Drinking', 'Community and Government', 'Retail',
@@ -29,55 +29,87 @@ DATASET_COLUMNS = [
        'Event'
 ]
 
-def compute_features(candidate_point, radius=0.005):
+import os
+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
+
+
+async def compute_places_count_with_api_key(api_key, lat, lng, radius, place_type):
+    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=[place_type]
+            included_types=place
+        )
+
+        # 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
 
-    # Banks
-    bank_idxs = tree_banks.query_ball_point([lat, lon], r=radius)
-
-    print("[BANK]", bank_idxs)
-    
-    n_banks = len(bank_idxs)
-    if n_banks > 0:
-        neighbors = df_banks.iloc[bank_idxs]
-        mean_dist_banks = np.mean(np.sqrt((neighbors['lat']-lat)**2 + (neighbors['lon']-lon)**2))
-        min_dist_bank = np.min(np.sqrt((neighbors['lat']-lat)**2 + (neighbors['lon']-lon)**2))
-    else:
-        mean_dist_banks = radius
-        min_dist_bank = radius
-
-    # Amenities
-    amenity_idxs = tree_amenities.query_ball_point([lat, lon], r=radius)
-    amenities = df_amenities.iloc[amenity_idxs]
-
-    total_amenities = len(amenities)
-
-    # Flatten all category IDs
-    # for cats in amenities['fsq_category_labels']:
-    all_category_ids = [cats[0].split(">")[0].strip() for cats in amenities['fsq_category_labels'] if len(cats)>0]
-    category_diversity = len(set(all_category_ids))
-
     features = {
-        'num_banks_in_radius': n_banks,
-        # 'mean_dist_banks': mean_dist_banks,
-        # 'min_dist_bank': min_dist_bank,
-        'total_amenities': total_amenities,
-        'category_diversity': category_diversity
+        'num_banks_in_radius':0,
+        'total_amenities':0,
+        'category_diversity':0
     }
 
-    # Count occurrences per category
-    print("[CATEGORIES]", all_category_ids)
-    count_per_category = Counter(all_category_ids)
-    for feat in DATASET_COLUMNS:
-        print("[FEAT]",feat)
-    # for cat, cnt in count_per_category.items():
-        features[f'num_{feat}'] = count_per_category.get(feat, 0)
-
+    for i,place in enumerate(GOOGLE_PLACE_TYPE_MAPPING):
+      total_count = await compute_places_count_with_api_key(
+          api_key,
+          lat,
+          lon,
+          radius,
+          place
+     )
+
+     features[f'num_{DATASET_COLUMNS[i]}'] = total_count
+
+
+    n_banks = compute_places_count_with_api_key(
+          api_key,
+          lat,
+          lon,
+          radius,
+          ['atm']
+    )
     
-    # # Count occurrences of first category
-    # first_categories = [cats[0] for cats in amenities['fsq_category_ids'] if len(cats)>0]
-    # count_first_category = Counter(first_categories)
-    # for cat, cnt in count_first_category.items():
-    #     features[f'num_first_{cat}'] = cnt
+
+    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())
+    })
 
     return features