Fixing Bank Geocoding For Accurate Optimization

Nov 6, 2025 by Admin 48 views

Hey guys! Let's dive into how we're fixing the bank geocoding issue to make sure our optimizer is picking the right banks. This is all about getting the optimizer in promoter_app.py to play nice without breaking anything. Currently, it’s choosing banks that are costing us more money, and we need to fix that ASAP!

The Core Problem: Why Wrong Banks Are Being Selected

The main issue is that the optimizer in promoter_app.py was selecting banks like Nunthorpe and Stokesley, which are in the "far" tier, instead of Chilterns, which is in the "local" tier. This seemingly small mistake leads to a significant cost difference: £136k compared to £127k in app.py. Understanding why this happens requires a bit of digging into the logic and data used by the optimizer. It all boils down to how the system determines which tier a bank belongs to and the geographical data it uses to make that determination.

The tier_for_bank logic is responsible for assigning a tier to each bank based on its location. Initially, the logic was flawed, but it has since been corrected to use an OR condition: a bank is considered "local" if either its Local Planning Authority (LPA) or National Character Area (NCA) matches the criteria. However, the underlying issue persisted because the bank geography data itself was incomplete. Specifically, the Banks table had empty lpa_name and nca_name fields. This meant that the system couldn't accurately determine the correct tier for each bank, leading to suboptimal selections.

In the original app.py, this problem was cleverly sidestepped through a process that runs at startup. The enrich_banks_geography() function in app.py geocodes each bank using its postcode, looks up the LPA and NCA for each bank location, and updates the backend dictionary in memory. This enriched data is then used throughout the session. This dynamic enrichment ensures that the system always has the most accurate geographical information for each bank, allowing it to make informed decisions about tier assignments and bank selections. The key is that this enrichment happens in memory and not directly in the database, which avoids potential issues with data consistency and performance.

The Solution: Mimicking app.py's Approach

To solve this, we're copying the exact approach used in app.py. We will implement the enrich_banks_with_geography() function to ensure accurate bank selection. The goal is to get the optimizer to correctly identify and prioritize local banks, bringing the total cost back down to £127k.

The Code

Here’s the Python code we’ll use in optimizer_core.py:

def enrich_banks_with_geography(banks_df):
    """
    Geocode banks and add lpa_name/nca_name columns.
    This matches app.py's enrich_banks_geography() function.
    Only geocodes banks with empty lpa_name or nca_name.
    """
    enriched_banks = []
    
    for idx, row in banks_df.iterrows():
        bank = row.to_dict()
        
        # Skip if already has geography data
        if pd.notna(bank.get('lpa_name')) and pd.notna(bank.get('nca_name')):
            enriched_banks.append(bank)
            continue
            
        # Get postcode
        postcode = bank.get('postcode', '').strip()
        if not postcode:
            enriched_banks.append(bank)
            continue
        
        try:
            # Geocode postcode to lat/lon
            lat, lon = get_postcode_info(postcode)
            if lat and lon:
                # Look up LPA/NCA
                lpa_name, nca_name = get_lpa_nca_for_point(lat, lon)
                bank['lpa_name'] = lpa_name
                bank['nca_name'] = nca_name
            
            time.sleep(0.15)  # Rate limit
        except Exception as e:
            print(f"Failed to geocode bank {bank.get('bank_name')}: {e}")
        
        enriched_banks.append(bank)
    
    return pd.DataFrame(enriched_banks)

# In optimise() function, right after loading backend:
backend = repo.get_backend()
backend["Banks"] = enrich_banks_with_geography(backend["Banks"])

Key Steps

Don't update Supabase: We're enriching the data in memory, just like app.py does.
Add import time: Make sure to include this at the top of the file.
Call enrich before prepare functions: This must happen before prepare_options, prepare_hedgerow_options, and prepare_watercourse_options.
Match app.py exactly: Both apps should have identical enriched bank data to ensure consistency.

Files to Modify

optimizer_core.py: Add the enrich_banks_with_geography() function and call it in optimise().
promoter_app.py: This should already be created and should work once optimizer_core is fixed.
pdf_generator_promoter.py: Stub (already created).
email_notification.py: Stub (already created).

Critical Points: Avoiding Common Mistakes

Several key points must be adhered to for this solution to work effectively and avoid the pitfalls encountered in previous attempts. Let's highlight them:

In-Memory Enrichment: The enrichment of bank geography data should occur in memory, just like in app.py. This means that the enrich_banks_with_geography() function updates the backend dictionary directly without attempting to persist these changes to Supabase or any other database. This approach is crucial for avoiding the infinite loop issue that plagued previous attempts. By keeping the enrichment process isolated to the current session, we ensure that the data is consistent and up-to-date without risking unintended side effects.
Rate Limiting: When geocoding the banks, it's essential to implement rate limiting to avoid overwhelming the geocoding service and potentially getting rate-limited or blocked. The provided code includes a time.sleep(0.15) call after each geocoding request, which introduces a small delay to prevent excessive requests. This simple measure can significantly improve the reliability of the geocoding process and ensure that it completes successfully without interruption.
Order of Operations: The order in which functions are called is critical for the correct functioning of the optimizer. The enrich_banks_with_geography() function must be called before any of the prepare functions, such as prepare_options, prepare_hedgerow_options, and prepare_watercourse_options. These prepare functions rely on the enriched bank data to make informed decisions about bank selections and tier assignments. If the data is not enriched before these functions are called, the optimizer will fall back to using the incomplete data from the Banks table, leading to suboptimal results.
Data Consistency: Ensuring that both app.py and promoter_app.py have identical enriched bank data is paramount. This means that the enrich_banks_with_geography() function should produce the same results in both applications. To achieve this, it's crucial to use the same geocoding service, the same logic for looking up LPA and NCA information, and the same data sources. Any discrepancies in the enriched data can lead to different bank selections and cost estimations, undermining the purpose of the optimization process.

By adhering to these critical points, we can ensure that the bank geocoding issue is resolved effectively, leading to accurate bank selections and cost optimization.

Expected Outcome: The Right Bank at the Right Price

After these changes, we expect:

Chilterns bank (postcode HP4 3QQ) to geocode to Chilterns NCA.
tier_for_bank(Chilterns) to return "local" because the NCA matches.
The optimizer to select Chilterns bank, resulting in a total cost of £127k.
Both apps producing identical results, meaning consistency across the board.

What NOT To Do: Common Mistakes to Avoid

Don't try to persist to Supabase: This caused the infinite loop issue, so let’s avoid it.
Don't add complex caching: Just enrich once per optimise() call. Keep it simple.
Don't overthink it: Copy app.py's straightforward approach. No need to reinvent the wheel.

Testing: Verifying the Fix

To verify the fix, we'll check the allocation. It should show:

Chilterns bank, local tier, £20k-£29k per unit pricing = £127k total

And definitely not:

Nunthorpe/Stokesley banks, far tier, £26k-£30k per unit pricing = £136k total

Let's get this done and make sure our optimizer is working perfectly! By focusing on these steps, we can ensure that the system selects the correct banks, leading to significant cost savings and improved overall performance. Keep it simple, keep it consistent, and let's make it happen!