Create utils.py

utils.py

@@ -0,0 +1,49 @@
+from collections import Counter
+import pandas as pd
+import numpy as np
+
+
+def compute_features(candidate_point, radius=0.005):
+    lat, lon = candidate_point
+
+    # Banks
+    bank_idxs = tree_banks.query_ball_point([lat, lon], r=radius)
+    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
+    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
+    }
+
+    # Count occurrences per category
+    count_per_category = Counter(all_category_ids)
+    for cat, cnt in count_per_category.items():
+        features[f'num_{cat}'] = cnt
+
+    # # 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
+
+    return features