minor improvements

app/anomaly-detector/anomaly_detect.py

@@ -186,9 +186,8 @@ async def connect_to_database(dbname, dbuser, dbpass, dbhost, dbport):
     """
     conn = f"host={dbhost} port={dbport} dbname={dbname} user={dbuser} password={dbpass}"
     try:
-        pool = AsyncConnectionPool(conn, min_size=1, max_size=20)
-        await pool.open()
-        return pool
+        async with AsyncConnectionPool(conn, min_size=1, max_size=20) as pool:
+            return pool
     except Exception as e:
         print(f"Failed to connect to database: {e}")
         return None

app/anomaly-detector/anomaly_detector.py

@@ -150,14 +150,14 @@ def create_embeddings(df):
         for unit in ['year', 'month', 'day', 'hour', 'minute', 'second']:
             df[f"{col}_{unit}"] = getattr(df[col].dt, unit)
 
-    # new_timestamp_features = [
-    #     float(col) for col in df.columns
-    #     if col.startswith("EVENT_TIMESTAMP_") or col.startswith("LABEL_TIMESTAMP_")
-    # ]
+    new_timestamp_features = [
+        col for col in df.columns
+        if col.startswith("EVENT_TIMESTAMP_") or col.startswith("LABEL_TIMESTAMP_")
+    ]
 
     # Combine numerical, categorical and timestamp features
     combined_features = concat([
-        df[numerical_features], encoded_categorical_features #, df[new_timestamp_features]
+        df[numerical_features], encoded_categorical_features, df[new_timestamp_features]
     ], axis=1)
     print(combined_features.head())