From 57f56a0b07cd195256a9e5f8c591a7e360274d32 Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 15:36:41 +0100
Subject: [PATCH 1/8] Add firm data

---
 .../Annual_UK_VAT_Statistics_2023-24.xls      |  Bin 0 -> 259072 bytes
 .../raw_data/ukbusinessworkbook2024.xlsx      |  Bin 0 -> 1058370 bytes
 .../datasets/firm/generate_synthetic_data.py  | 1288 +++++++++++++++++
 3 files changed, 1288 insertions(+)
 create mode 100644 policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/raw_data/Annual_UK_VAT_Statistics_2023-24.xls
 create mode 100644 policyengine_uk_data/datasets/firm/ONS_UK_business_data/raw_data/ukbusinessworkbook2024.xlsx
 create mode 100644 policyengine_uk_data/datasets/firm/generate_synthetic_data.py

diff --git a/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/raw_data/Annual_UK_VAT_Statistics_2023-24.xls b/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/raw_data/Annual_UK_VAT_Statistics_2023-24.xls
new file mode 100644
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diff --git a/policyengine_uk_data/datasets/firm/generate_synthetic_data.py b/policyengine_uk_data/datasets/firm/generate_synthetic_data.py
new file mode 100644
index 00000000..88f93bc7
--- /dev/null
+++ b/policyengine_uk_data/datasets/firm/generate_synthetic_data.py
@@ -0,0 +1,1288 @@
+#!/usr/bin/env python3
+"""
+Synthetic firm data generation for UK business population.
+
+This script generates individual firm records from ONS business structure data,
+calibrated to HMRC VAT registration statistics for accurate representation.
+"""
+
+import logging
+from pathlib import Path
+from typing import Dict, Tuple
+import sys
+
+import numpy as np
+import pandas as pd
+import torch
+from torch import Tensor
+
+# Add parent directories to path for imports
+sys.path.insert(0, str(Path(__file__).parent.parent.parent))
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent))
+
+try:
+    from policyengine_uk_data.storage import STORAGE_FOLDER
+except ImportError:
+    # If can't import, use a default storage location
+    STORAGE_FOLDER = Path(__file__).parent.parent.parent / "storage"
+    if not STORAGE_FOLDER.exists():
+        STORAGE_FOLDER.mkdir(parents=True, exist_ok=True)
+
+logger = logging.getLogger(__name__)
+
+
+class SyntheticFirmGenerator:
+    """
+    Synthetic firm data generator for UK business population analysis.
+    
+    Generates complete UK firm population calibrated to official data sources:
+    - ONS data for population structure and employment distribution
+    - HMRC data for VAT registration and sector targets
+    - Assigns VAT flags to identify HMRC-visible firms
+    """
+    
+    def __init__(
+        self,
+        device: str = "cpu",
+        random_seed: int = 42
+    ):
+        """Initialize the synthetic firm generator.
+        
+        Args:
+            device: Computing device ('cpu', 'cuda', 'mps')
+            random_seed: Random seed for reproducibility
+        """
+        self.device = device
+        self.random_seed = random_seed
+        
+        # Set random seeds
+        torch.manual_seed(random_seed)
+        np.random.seed(random_seed)
+        
+        logger.info(f"Initialized firm generator on device: {device}")
+    
+    def load_data(self) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, int]:
+        """Load ONS and HMRC data files from standardized CSV sources.
+        
+        Returns:
+            Tuple containing ONS turnover data, ONS employment data, 
+            HMRC turnover bands, HMRC sector data, VAT liability by sector, 
+            VAT liability by turnover band, and ONS total firm count
+        """
+        logger.info("Loading data files...")
+        
+        # Define paths to data files
+        project_root = Path(__file__).parent
+        ons_path = project_root / 'ONS_UK_business_data' / 'firm_turnover.csv'
+        ons_employment_path = project_root / 'ONS_UK_business_data' / 'firm_employment.csv'
+        hmrc_turnover_path = project_root / 'HMRC_VAT_annual_statistics' / 'vat_population_by_turnover_band.csv'
+        hmrc_sector_path = project_root / 'HMRC_VAT_annual_statistics' / 'vat_population_by_sector.csv'
+        vat_liability_sector_path = project_root / 'HMRC_VAT_annual_statistics' / 'vat_liability_by_sector.csv'
+        vat_liability_band_path = project_root / 'HMRC_VAT_annual_statistics' / 'vat_liability_by_turnover_band.csv'
+        
+        # Load CSV files
+        ons_df = pd.read_csv(ons_path)
+        ons_employment_df = pd.read_csv(ons_employment_path)
+        hmrc_turnover_df = pd.read_csv(hmrc_turnover_path)
+        hmrc_sector_df = pd.read_csv(hmrc_sector_path)
+        vat_liability_sector_df = pd.read_csv(vat_liability_sector_path)
+        vat_liability_band_df = pd.read_csv(vat_liability_band_path)
+        
+        logger.info(f"Loaded ONS turnover data: {len(ons_df)} rows")
+        logger.info(f"Loaded ONS employment data: {len(ons_employment_df)} rows")
+        logger.info(f"Loaded HMRC turnover data: {len(hmrc_turnover_df)} rows")
+        logger.info(f"Loaded HMRC sector data: {len(hmrc_sector_df)} rows")
+        logger.info(f"Loaded VAT liability by sector data: {len(vat_liability_sector_df)} rows")
+        logger.info(f"Loaded VAT liability by band data: {len(vat_liability_band_df)} rows")
+        
+        # Extract ONS total
+        ons_total_row = ons_df[ons_df['SIC Code'].isna() | (ons_df['SIC Code'] == '')]
+        if len(ons_total_row) > 0:
+            ons_total = ons_total_row.iloc[0]['Total']
+        else:
+            sector_rows = ons_df[~ons_df['Description'].str.contains('Total', na=False)]
+            ons_total = sector_rows['Total'].sum()
+        
+        logger.info(f"ONS total firms: {ons_total:,}")
+        
+        return ons_df, ons_employment_df, hmrc_turnover_df, hmrc_sector_df, vat_liability_sector_df, vat_liability_band_df, ons_total
+    
+    def generate_base_firms(self, ons_df: pd.DataFrame) -> Tuple[Tensor, Tensor]:
+        """Generate base firm records using efficient batch processing.
+        
+        Args:
+            ons_df: ONS turnover data
+            
+        Returns:
+            Tuple of (sic_codes, turnover_values)
+        """
+        logger.info("Generating base firms using efficient batch processing...")
+        
+        # ONS turnover band parameters (min, max, midpoint in £k)
+        band_params = {
+            '0-49': (0, 49, 24.5),
+            '50-99': (50, 99, 74.5),
+            '100-249': (100, 249, 174.5),
+            '250-499': (250, 499, 374.5),
+            '500-999': (500, 999, 749.5),
+            '1000-4999': (1000, 4999, 2999.5),
+            '5000+': (5000, 50000, 15000)
+        }
+        
+        all_sic_codes = []
+        all_turnovers = []
+        
+        # Process each sector
+        for _, row in ons_df.iterrows():
+            sic_code = row['SIC Code']
+            
+            # Skip summary rows
+            if pd.isna(sic_code) or sic_code == '' or sic_code == 'Total':
+                continue
+            
+            sic_formatted = str(int(sic_code)).zfill(5)
+            
+            # Generate firms for each turnover band
+            for band, (min_val, max_val, midpoint) in band_params.items():
+                if band in row and pd.notna(row[band]) and row[band] > 0:
+                    count = int(row[band])
+                    
+                    if count > 0:
+                        # Generate turnover values with noise smoothing
+                        turnovers = self._generate_turnover_values(
+                            band, count, min_val, max_val, midpoint
+                        )
+                        
+                        # Store results
+                        all_sic_codes.extend([sic_formatted] * count)
+                        all_turnovers.extend(turnovers.cpu().numpy())
+        
+        # Convert to efficient data structures
+        sic_codes_tensor = torch.tensor([int(sic) for sic in all_sic_codes], 
+                                       dtype=torch.int64, device=self.device)
+        turnover_tensor = torch.tensor(all_turnovers, 
+                                      dtype=torch.float32, device=self.device)
+        
+        logger.info(f"Generated {len(all_sic_codes):,} base firms")
+        
+        return sic_codes_tensor, turnover_tensor
+    
+    def _generate_turnover_values(self, band_name: str, firm_count: int, 
+                                        min_turnover: float, max_turnover: float, 
+                                        midpoint_turnover: float) -> Tensor:
+        """Generate turnover values within a band with noise smoothing.
+        
+        Args:
+            band_name: ONS turnover band name (e.g., '0-49', '50-99')
+            firm_count: Number of firms to generate in this band
+            min_turnover: Minimum turnover value (£k)
+            max_turnover: Maximum turnover value (£k)  
+            midpoint_turnover: Midpoint turnover for distribution (£k)
+            
+        Returns:
+            Generated turnover values with noise smoothing applied
+        """
+        if firm_count == 0:
+            return torch.empty(0, device=self.device)
+        
+        # Generate turnover values with noise smoothing across the full band range
+        band_width = max_turnover - min_turnover
+        
+        # Use uniform noise for distribution smoothing
+        noise_std = max(25.0, band_width * 0.2)  # Uniform noise: 25k minimum or 20% of band width
+        
+        # Generate uniform base values across the full band range
+        uniform_values = torch.rand(firm_count, device=self.device)
+        base_turnover_values = min_turnover + uniform_values * band_width
+        
+        # Add Gaussian noise for smoothing
+        noise = torch.normal(0, noise_std, (firm_count,), device=self.device)
+        turnover_values = base_turnover_values + noise
+        
+        # Ensure all turnover values are positive
+        turnover_values = torch.clamp(turnover_values, min=0.1)
+        
+        return turnover_values
+    
+    def assign_vat_registration_flags(self, turnover_values: Tensor, hmrc_bands: Dict[str, int]) -> Tensor:
+        """Assign VAT registration flags to identify HMRC-visible firms.
+        
+        VAT registration triggers:
+        1. Mandatory: Annual turnover > £85k
+        2. Voluntary: Random subset of firms below threshold (calculated from HMRC data)
+        
+        Args:
+            turnover_values: Array of turnover values
+            hmrc_bands: HMRC band data to calculate voluntary rate
+            
+        Returns:
+            Boolean array indicating VAT registration status
+        """
+        logger.info("Assigning VAT registration flags...")
+        
+        # Calculate voluntary VAT rate from Target/Synthetic ratio for £1_to_Threshold
+        hmrc_target_1_to_threshold = hmrc_bands['£1_to_Threshold']  # HMRC target: 678,350
+        synthetic_1_to_threshold = ((turnover_values > 0) & (turnover_values <= 85.0)).sum().item()  # Current synthetic count
+        voluntary_rate = hmrc_target_1_to_threshold / synthetic_1_to_threshold if synthetic_1_to_threshold > 0 else 0.15
+        
+        logger.info(f"Calculated voluntary VAT rate: {voluntary_rate:.3f} (Target: {hmrc_target_1_to_threshold:,} / Synthetic: {synthetic_1_to_threshold:,})")
+        
+        # Mandatory registration above threshold
+        mandatory_vat = turnover_values > 85.0
+        
+        # Voluntary registration below threshold (but above 0)
+        below_threshold = (turnover_values > 0) & (turnover_values <= 85.0)
+        n_below_threshold = below_threshold.sum().item()
+        
+        if n_below_threshold > 0:
+            # Random selection for voluntary VAT registration using calculated rate
+            voluntary_mask = torch.rand(len(turnover_values), device=self.device) < voluntary_rate
+            voluntary_vat = below_threshold & voluntary_mask
+        else:
+            voluntary_vat = torch.zeros_like(below_threshold)
+        
+        vat_registered = mandatory_vat | voluntary_vat
+        
+        logger.info(f"VAT registration: {mandatory_vat.sum():.0f} mandatory + {voluntary_vat.sum():.0f} voluntary = {vat_registered.sum():.0f} total")
+        logger.info(f"Non-VAT registered: {(~vat_registered).sum():.0f} firms")
+        
+        return vat_registered
+    
+    def create_comprehensive_target_matrix(self, turnover_values: Tensor, sic_codes: Tensor, input_values: Tensor,
+                                      hmrc_bands: Dict[str, int], hmrc_sector_df: pd.DataFrame, ons_employment_df: pd.DataFrame, 
+                                      vat_liability_sector_df: pd.DataFrame, vat_liability_band_df: pd.DataFrame, ons_total: int) -> Tuple[Tensor, Tensor]:
+        """Create comprehensive target matrix for calibration.
+        
+        Creates targets for all HMRC turnover bands, sector targets, and VAT liability targets.
+        The optimization will determine which firms contribute to which targets 
+        based on VAT registration flags.
+        
+        Args:
+            turnover_values: Array of turnover values
+            sic_codes: Array of SIC codes
+            input_values: Array of input values
+            hmrc_bands: Dictionary of all HMRC targets by band
+            hmrc_sector_df: HMRC sector data for ratio targets
+            ons_employment_df: ONS employment data for ratio targets
+            vat_liability_sector_df: VAT liability data by sector
+            vat_liability_band_df: VAT liability data by turnover band
+            ons_total: Total firm count target from ONS
+            
+        Returns:
+            Tuple of (target_matrix, target_values)
+        """
+        logger.info("Creating comprehensive target matrix for calibration...")
+        
+        n_firms = len(turnover_values)
+        # Get sector data and calculate ratios
+        sector_rows = hmrc_sector_df[hmrc_sector_df['Trade_Sector'] != 'Total'].copy()
+        hmrc_total = hmrc_sector_df[hmrc_sector_df['Trade_Sector'] == 'Total']['2023-24'].iloc[0]
+        n_sectors = len(sector_rows)
+        
+        # Get VAT liability sector data (excluding total)
+        vat_liability_sector_rows = vat_liability_sector_df[vat_liability_sector_df['Trade_Sector'] != 'Total'].copy()
+        n_vat_sectors = len(vat_liability_sector_rows)
+        
+        # Get VAT liability by turnover band data
+        vat_liability_band_latest = vat_liability_band_df.iloc[-1]  # Get 2023-24 data
+        vat_liability_bands = {
+            'Negative_or_Zero': vat_liability_band_latest['Negative_or_Zero'],
+            '£1_to_Threshold': vat_liability_band_latest['£1_to_Threshold'],
+            '£Threshold_to_£150k': vat_liability_band_latest['£Threshold_to_£150k'],
+            '£150k_to_£300k': vat_liability_band_latest['£150k_to_£300k'],
+            '£300k_to_£500k': vat_liability_band_latest['£300k_to_£500k'],
+            '£500k_to_£1m': vat_liability_band_latest['£500k_to_£1m'],
+            '£1m_to_£10m': vat_liability_band_latest['£1m_to_£10m'],
+            'Greater_than_£10m': vat_liability_band_latest['Greater_than_£10m']
+        }
+        n_vat_bands = 7  # Number of VAT liability bands (excluding Total and Negative_or_Zero)
+        
+        # Get employment data and calculate ratios
+        emp_bands = ['0-4', '5-9', '10-19', '20-49', '50-99', '100-249', '250+']
+        n_employment_bands = len(emp_bands)
+        
+        n_targets = 7 + n_sectors + n_employment_bands + n_vat_sectors + n_vat_bands  # 7 turnover + sector + employment + VAT liability by sector + VAT liability by band
+        
+        # Initialize target matrix  
+        target_matrix = torch.zeros(n_targets, n_firms, device=self.device)
+        
+        # Map all firms to HMRC bands
+        all_band_indices = self._map_to_hmrc_bands(turnover_values)
+        
+        # Rows 0-6: Turnover band targets
+        # Row 0: £1_to_Threshold - keep ONS structure
+        firms_in_threshold = (all_band_indices == 1)
+        target_matrix[0, firms_in_threshold] = 1.0
+        
+        # Row 1: £Threshold_to_£150k - calibrate to HMRC target  
+        firms_150k = (all_band_indices == 2)
+        target_matrix[1, firms_150k] = 1.0
+        
+        # Rows 2-6: Individual HMRC bands above £150k
+        for i, band_idx in enumerate([3, 4, 5, 6, 7], start=2):  # £150k_to_£300k, etc.
+            firms_in_band = (all_band_indices == band_idx)
+            target_matrix[i, firms_in_band] = 1.0
+        
+        # Rows 7 to 7+n_sectors-1: Sector targets (for VAT-registered firms only)
+        for i, (_, sector_row) in enumerate(sector_rows.iterrows(), start=7):
+            trade_sector = sector_row['Trade_Sector']
+            # Convert HMRC Trade_Sector to SIC code (00001 -> 1, 00002 -> 2, etc.)
+            sic_code = int(trade_sector)
+            
+            # Find firms in this sector
+            firms_in_sector = (sic_codes == sic_code)
+            target_matrix[i, firms_in_sector] = 1.0
+        
+        # Generate employment assignments for target matrix (temporary assignments for optimization)
+        employment_values = self.assign_employment(n_firms, ons_employment_df)
+        
+        # Map employment to bands
+        def map_employment_to_band_idx(emp_val):
+            if emp_val <= 4:
+                return 0  # '0-4'
+            elif emp_val <= 9:
+                return 1  # '5-9'
+            elif emp_val <= 19:
+                return 2  # '10-19'
+            elif emp_val <= 49:
+                return 3  # '20-49'
+            elif emp_val <= 99:
+                return 4  # '50-99'
+            elif emp_val <= 249:
+                return 5  # '100-249'
+            else:
+                return 6  # '250+'
+        
+        employment_band_indices = torch.tensor([map_employment_to_band_idx(emp.item()) for emp in employment_values], 
+                                             dtype=torch.long, device=self.device)
+        
+        # Rows 7+n_sectors to 7+n_sectors+n_employment_bands-1: Employment ratio targets  
+        emp_start_row = 7 + n_sectors
+        for band_idx, _ in enumerate(emp_bands):
+            row_idx = emp_start_row + band_idx
+            firms_in_emp_band = (employment_band_indices == band_idx)
+            target_matrix[row_idx, firms_in_emp_band] = 1.0
+        
+        # Calculate VAT liability for each firm (output - input)
+        vat_liability_values = turnover_values - input_values  # in £k
+        
+        # Rows 7+n_sectors+n_employment_bands to 7+n_sectors+n_employment_bands+n_vat_sectors-1: VAT liability targets by sector
+        vat_start_row = 7 + n_sectors + n_employment_bands
+        for i, (_, vat_row) in enumerate(vat_liability_sector_rows.iterrows()):
+            row_idx = vat_start_row + i
+            sic_code = int(vat_row['Trade_Sector'])
+            
+            # Find firms in this sector and calculate their VAT liability contribution
+            firms_in_sector = (sic_codes == sic_code)
+            # Weight by VAT liability for this sector's target
+            target_matrix[row_idx, firms_in_sector] = vat_liability_values[firms_in_sector]
+        
+        # Rows 7+n_sectors+n_employment_bands+n_vat_sectors to end: VAT liability targets by turnover band (excluding Negative_or_Zero)
+        vat_band_start_row = 7 + n_sectors + n_employment_bands + n_vat_sectors
+        for i, band_name in enumerate(['£1_to_Threshold', '£Threshold_to_£150k', 
+                                       '£150k_to_£300k', '£300k_to_£500k', '£500k_to_£1m', 
+                                       '£1m_to_£10m', 'Greater_than_£10m']):
+            row_idx = vat_band_start_row + i
+            
+            # Map firms to this turnover band
+            if band_name == '£1_to_Threshold':
+                firms_in_band = (turnover_values > 0) & (turnover_values <= 85)
+            elif band_name == '£Threshold_to_£150k':
+                firms_in_band = (turnover_values > 85) & (turnover_values <= 150)
+            elif band_name == '£150k_to_£300k':
+                firms_in_band = (turnover_values > 150) & (turnover_values <= 300)
+            elif band_name == '£300k_to_£500k':
+                firms_in_band = (turnover_values > 300) & (turnover_values <= 500)
+            elif band_name == '£500k_to_£1m':
+                firms_in_band = (turnover_values > 500) & (turnover_values <= 1000)
+            elif band_name == '£1m_to_£10m':
+                firms_in_band = (turnover_values > 1000) & (turnover_values <= 10000)
+            else:  # Greater_than_£10m
+                firms_in_band = turnover_values > 10000
+            
+            # Weight by VAT liability for this band's target
+            target_matrix[row_idx, firms_in_band] = vat_liability_values[firms_in_band]
+        
+        # Calculate targets
+        # £1_to_Threshold: Use ONS structure (current count from generation)
+        ons_threshold_count = firms_in_threshold.sum().item()
+        
+        # Turnover band targets (absolute numbers)
+        turnover_targets = [
+            ons_threshold_count,                     # £1_to_Threshold: Keep ONS structure
+            hmrc_bands['£Threshold_to_£150k'],       # £Threshold_to_£150k: HMRC
+            hmrc_bands['£150k_to_£300k'],            # £150k_to_£300k: HMRC
+            hmrc_bands['£300k_to_£500k'],            # £300k_to_£500k: HMRC
+            hmrc_bands['£500k_to_£1m'],              # £500k_to_£1m: HMRC
+            hmrc_bands['£1m_to_£10m'],               # £1m_to_£10m: HMRC
+            hmrc_bands['Greater_than_£10m']          # Greater_than_£10m: HMRC
+        ]
+        
+        # Sector targets (direct HMRC targets for VAT-registered firms)
+        sector_targets = []
+        for _, sector_row in sector_rows.iterrows():
+            sector_count = sector_row['2023-24']  # Direct HMRC target
+            sector_targets.append(sector_count)
+        
+        # Employment count targets (direct ONS employment counts)
+        employment_targets = []
+        # Get ONS employment totals
+        ons_emp_totals = {}
+        for band in emp_bands:
+            if band in ons_employment_df.columns:
+                sector_rows_emp = ons_employment_df[~ons_employment_df['Description'].str.contains('Total', na=False)]
+                ons_emp_totals[band] = sector_rows_emp[band].fillna(0).sum()
+            else:
+                ons_emp_totals[band] = 0
+        
+        # Use direct ONS employment counts as targets
+        for band in emp_bands:
+            emp_count = ons_emp_totals[band]
+            employment_targets.append(emp_count)
+        
+        # VAT liability targets by sector (in millions £, convert to £k)
+        vat_liability_sector_targets = []
+        for _, vat_row in vat_liability_sector_rows.iterrows():
+            vat_liability_millions = vat_row['2023-24']  # in millions £
+            vat_liability_k = vat_liability_millions * 1000  # convert to £k
+            vat_liability_sector_targets.append(vat_liability_k)
+        
+        # VAT liability targets by turnover band (in millions £, convert to £k) - excluding Negative_or_Zero
+        vat_liability_band_targets = []
+        for band_name in ['£1_to_Threshold', '£Threshold_to_£150k', 
+                          '£150k_to_£300k', '£300k_to_£500k', '£500k_to_£1m', 
+                          '£1m_to_£10m', 'Greater_than_£10m']:
+            vat_liability_millions = vat_liability_bands[band_name]  # in millions £
+            vat_liability_k = vat_liability_millions * 1000  # convert to £k
+            vat_liability_band_targets.append(vat_liability_k)
+        
+        target_values_list = turnover_targets + sector_targets + employment_targets + vat_liability_sector_targets + vat_liability_band_targets
+        
+        target_values = torch.tensor(
+            target_values_list, 
+            dtype=torch.float32, device=self.device
+        )
+        
+        logger.info(f"Target matrix shape: {target_matrix.shape}")
+        logger.info(f"Calibration targets (turnover + sector + employment):")
+        
+        # Log turnover targets
+        turnover_names = ['£1_to_Threshold (ONS)', '£Threshold_to_£150k (HMRC)', 
+                         '£150k_to_£300k (HMRC)', '£300k_to_£500k (HMRC)', 
+                         '£500k_to_£1m (HMRC)', '£1m_to_£10m (HMRC)', 'Greater_than_£10m (HMRC)']
+        for target_name, target_val in zip(turnover_names, turnover_targets):
+            logger.info(f"  {target_name}: {target_val:,.0f}")
+        
+        # Log sector and employment targets (summary)
+        logger.info(f"  Sector targets: {n_sectors} sectors from HMRC data (VAT-registered)")
+        logger.info(f"  Employment count targets: {n_employment_bands} bands from ONS data (direct counts)")
+        logger.info(f"  VAT liability sector targets: {n_vat_sectors} sectors from HMRC data (in £millions)")
+        logger.info(f"  VAT liability band targets: {n_vat_bands} bands from HMRC data (in £millions, 2x weight)")
+        logger.info(f"  Total targets: {len(target_values_list)} (7 turnover + {n_sectors} sector + {n_employment_bands} employment + {n_vat_sectors} VAT liability sector + {n_vat_bands} VAT liability band)")
+        logger.info(f"  Negative_or_Zero: MANUAL (ONS doesn't have them)")
+        
+        return target_matrix, target_values
+    
+    def optimize_weights(self, target_matrix: Tensor, target_values: Tensor,
+                               n_iterations: int = 300, lr: float = 0.01) -> Tensor:
+        """Optimize weights to match multiple targets simultaneously.
+        
+        Uses symmetric relative error loss for robust calibration.
+        
+        Args:
+            target_matrix: Matrix where A[i,j] = contribution of firm j to target i
+            target_values: Vector of target values to match
+            n_iterations: Number of optimization iterations
+            lr: Learning rate
+            
+        Returns:
+            Optimized weights
+        """
+        logger.info("Starting multi-objective weight optimization...")
+        
+        _, n_firms = target_matrix.shape
+        
+        # Initialize log-weights (ensures positive weights)
+        log_weights = torch.zeros(n_firms, device=self.device, requires_grad=True)
+        optimizer = torch.optim.Adam([log_weights], lr=lr)
+        
+        best_loss = float('inf')
+        patience = 100
+        patience_counter = 0
+        
+        for iteration in range(n_iterations):
+            optimizer.zero_grad()
+            
+            # Convert to positive weights
+            weights = torch.exp(log_weights)
+            
+            # Apply 15% dropout during training
+            if log_weights.requires_grad:  # Only during training
+                dropout_mask = torch.rand_like(weights) > 0.05  # Keep 95%, drop 15%
+                weights = weights * dropout_mask
+            
+            # Calculate predictions: target_matrix @ weights
+            predictions = torch.matmul(target_matrix, weights)
+            
+            # Symmetric relative error loss (robust to different target scales)
+            epsilon = 1e-6
+            pred_adj = predictions + epsilon
+            target_adj = target_values + epsilon
+            
+            # Symmetric relative error: min(|pred/target - 1|^2, |target/pred - 1|^2)
+            error_1 = ((pred_adj / target_adj) - 1) ** 2
+            error_2 = ((target_adj / pred_adj) - 1) ** 2
+            sre_loss = torch.minimum(error_1, error_2)
+            
+            # Apply importance weights based on target type
+            importance_weights = torch.ones_like(sre_loss)
+            importance_weights[:7] = 5.0  # 5x weight for turnover targets
+            
+            # Calculate indices for different target types
+            # Structure: 7 turnover + n_sectors + n_employment_bands + n_vat_sectors + n_vat_bands
+            n_total_targets = len(sre_loss)
+            
+            # We need to dynamically calculate the section sizes from the actual target matrix
+            # For now, use fixed estimates based on typical data structure
+            n_employment = 7  # Fixed: 7 employment bands
+            
+            # The remaining targets are split between sectors and VAT liability
+            n_remaining = n_total_targets - 7 - n_employment  # Remove turnover and employment
+            n_est_sectors = n_remaining // 3  # Rough estimate (sectors, vat_sectors, vat_bands)
+            n_est_vat_sectors = n_est_sectors
+            n_est_vat_bands = n_remaining - n_est_sectors - n_est_vat_sectors
+            
+            sector_start_idx = 7
+            emp_start_idx = 7 + n_est_sectors
+            vat_sector_start_idx = emp_start_idx + n_employment
+            vat_band_start_idx = vat_sector_start_idx + n_est_vat_sectors
+            
+            # Set importance weights
+            importance_weights[sector_start_idx:emp_start_idx] = 1.0  # 1x weight for sector targets
+            importance_weights[emp_start_idx:vat_sector_start_idx] = 1.0  # 1x weight for employment targets
+            importance_weights[vat_sector_start_idx:vat_band_start_idx] = 1.0  # 1x weight for VAT liability sector targets
+            importance_weights[vat_band_start_idx:] = 2.0  # 2x weight for VAT liability band targets
+            
+            weighted_loss = sre_loss * importance_weights
+            total_loss = torch.mean(weighted_loss)
+            
+            # Add regularization to prevent extreme weights
+            reg_loss = 0.01 * torch.mean(torch.abs(log_weights))
+            total_loss += reg_loss
+            
+            total_loss.backward()
+            
+            # Gradient clipping for stability
+            torch.nn.utils.clip_grad_norm_([log_weights], max_norm=1.0)
+            
+            optimizer.step()
+            
+            # Early stopping
+            if total_loss.item() < best_loss:
+                best_loss = total_loss.item()
+                patience_counter = 0
+            else:
+                patience_counter += 1
+            
+            if iteration % 100 == 0:
+                logger.info(f"Iteration {iteration}: Loss = {total_loss.item():.6f}")
+            
+            if patience_counter > patience:
+                logger.info(f"Early stopping at iteration {iteration}")
+                break
+        
+        final_weights = torch.exp(log_weights)
+        final_predictions = torch.matmul(target_matrix, final_weights)
+        
+        logger.info("Optimization complete:")
+        target_names = ['Negative_or_Zero', '£1_to_Threshold', '£Threshold_to_£150k', '£150k_to_£300k', 
+                       '£300k_to_£500k', '£500k_to_£1m', '£1m_to_£10m', 'Greater_than_£10m']
+        for i, (pred, target, name) in enumerate(zip(final_predictions, target_values, target_names)):
+            if target > 0:  # Only log targets we're actually trying to match
+                accuracy = 1 - abs(pred - target) / target
+                logger.info(f"  {name}: {pred:.0f} vs {target:.0f} ({accuracy:.1%})")
+            else:
+                logger.info(f"  {name}: SKIPPED (pred: {pred:.0f})")
+        
+        return final_weights.detach()
+    
+    def apply_final_calibration(self, base_sic_codes: Tensor, base_turnover: Tensor, base_input: Tensor,
+                                     weights_tensor: Tensor, hmrc_bands: Dict[str, int]) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
+        """Apply final calibration adjustments.
+        
+        Key calibration steps:
+        1. Keep ALL base firms (no sampling)
+        2. Add zero/negative turnover firms manually
+        3. Apply calibration weights to match targets
+        
+        Args:
+            base_sic_codes: Original base SIC codes
+            base_turnover: Original base turnover values
+            base_input: Original base input values
+            weights_tensor: Calibration weights from optimization
+            hmrc_bands: HMRC target bands
+            
+        Returns:
+            Tuple of final (sic_codes, turnover, input, weights) tensors
+        """
+        logger.info("Applying final calibration adjustments...")
+        
+        all_final_firms = []
+        
+        # Step 1: Add ALL base firms with their weights
+        hmrc_band_indices = self._map_to_hmrc_bands(base_turnover)
+        
+        logger.info(f"Adding {len(base_sic_codes):,} base firms...")
+        for i in range(len(base_sic_codes)):
+            band_idx = hmrc_band_indices[i].item()
+            
+            # Use calibrated weights from optimization for all bands
+            weight = weights_tensor[i].item()
+            
+            all_final_firms.append({
+                'sic_code': base_sic_codes[i].item(),
+                'annual_turnover_k': base_turnover[i].item(),
+                'annual_input_k': base_input[i].item(),
+                'weight': weight
+            })
+        
+        # Step 2: Manually add zero/negative turnover firms
+        negative_zero_target = hmrc_bands['Negative_or_Zero']
+        
+        if negative_zero_target > 0:
+            # Get current sector distribution for proportional allocation
+            unique_sics, counts = torch.unique(base_sic_codes, return_counts=True)
+            total_current_firms = len(base_sic_codes)
+            
+            logger.info(f"Manually adding {negative_zero_target:,} zero turnover firms from HMRC...")
+            zero_firms_count = 0
+            
+            for sic, count in zip(unique_sics, counts):
+                sector_weight = count.float() / total_current_firms
+                firms_for_sector = int((negative_zero_target * sector_weight).item())
+                if firms_for_sector > 0:
+                    for _ in range(firms_for_sector):
+                        all_final_firms.append({
+                            'sic_code': sic.item(),
+                            'annual_turnover_k': 0.0,
+                            'annual_input_k': 0.0,
+                            'weight': 1.0
+                        })
+                        zero_firms_count += 1
+            
+            logger.info(f"Added {zero_firms_count:,} firms with zero turnover")
+        
+        # Convert back to tensors
+        if all_final_firms:
+            final_sics = torch.tensor([f['sic_code'] for f in all_final_firms], 
+                                    dtype=torch.int64, device=self.device)
+            final_turnover = torch.tensor([f['annual_turnover_k'] for f in all_final_firms], 
+                                        dtype=torch.float32, device=self.device)
+            final_input = torch.tensor([f['annual_input_k'] for f in all_final_firms],
+                                     dtype=torch.float32, device=self.device)
+            final_weights = torch.tensor([f['weight'] for f in all_final_firms], 
+                                       dtype=torch.float32, device=self.device)
+            
+            logger.info(f"Final dataset: {len(all_final_firms):,} firms")
+            logger.info(f"Total weighted population: {final_weights.sum():.0f}")
+        else:
+            # Empty tensors if no firms generated
+            final_sics = torch.empty(0, dtype=torch.int64, device=self.device)
+            final_turnover = torch.empty(0, dtype=torch.float32, device=self.device)
+            final_input = torch.empty(0, dtype=torch.float32, device=self.device)
+            final_weights = torch.empty(0, dtype=torch.float32, device=self.device)
+        
+        return final_sics, final_turnover, final_input, final_weights
+    
+    def _map_to_hmrc_bands(self, turnover_values: Tensor) -> Tensor:
+        """Map turnover values to HMRC band indices.
+        
+        Args:
+            turnover_values: Array of turnover values
+            
+        Returns:
+            HMRC band indices (0-7 for 8 HMRC bands)
+        """
+        # Initialize with highest band (Greater_than_£10m = 7)
+        band_indices = torch.full_like(turnover_values, 7, dtype=torch.long)
+        
+        # Assign bands based on turnover thresholds
+        band_indices = torch.where(turnover_values <= 0, 0, band_indices)  # Negative_or_Zero
+        band_indices = torch.where((turnover_values > 0) & (turnover_values <= 85), 1, band_indices)  # £1_to_Threshold
+        band_indices = torch.where((turnover_values > 85) & (turnover_values <= 150), 2, band_indices)  # £Threshold_to_£150k
+        band_indices = torch.where((turnover_values > 150) & (turnover_values <= 300), 3, band_indices)  # £150k_to_£300k
+        band_indices = torch.where((turnover_values > 300) & (turnover_values <= 500), 4, band_indices)  # £300k_to_£500k
+        band_indices = torch.where((turnover_values > 500) & (turnover_values <= 1000), 5, band_indices)  # £500k_to_£1m
+        band_indices = torch.where((turnover_values > 1000) & (turnover_values <= 10000), 6, band_indices)  # £1m_to_£10m
+        
+        return band_indices
+    
+    def _print_validation_section(self, title: str, width: int = 65):
+        """Print a formatted validation section header."""
+        print(f"\n{title}:")
+        print("-" * width)
+    
+    def _print_accuracy_breakdown(self, accuracies: list, n_items: int, label: str):
+        """Print standardized accuracy breakdown."""
+        accuracy_95_plus = sum(1 for acc in accuracies if acc >= 0.95)
+        accuracy_90_95 = sum(1 for acc in accuracies if 0.90 <= acc < 0.95)
+        accuracy_80_90 = sum(1 for acc in accuracies if 0.80 <= acc < 0.90)
+        accuracy_below_80 = sum(1 for acc in accuracies if acc < 0.80)
+        
+        overall_accuracy = np.mean(accuracies) if accuracies else 0.0
+        print(f"{label} OVERALL ACCURACY: {overall_accuracy:.1%}")
+        print(f"Accuracy breakdown: ≥95%: {accuracy_95_plus}/{n_items}, "
+              f"90-95%: {accuracy_90_95}/{n_items}, "
+              f"80-90%: {accuracy_80_90}/{n_items}, "
+              f"<80%: {accuracy_below_80}/{n_items}")
+        return overall_accuracy
+    
+    def generate_input_values(self, turnover_values: Tensor, sic_codes: Tensor) -> Tensor:
+        """Generate input values for firms directly from distributions.
+        
+        Input values are drawn from beta distributions that allow for:
+        - Most firms: inputs 60-95% of turnover (positive VAT liability)
+        - Some firms: inputs > turnover (negative VAT liability)
+        
+        Args:
+            turnover_values: Array of turnover values (output)
+            sic_codes: Array of SIC codes (used for sector-specific variations)
+            
+        Returns:
+            Array of input values in £k
+        """
+        logger.info("Generating input values for firms from distributions...")
+        
+        n_firms = len(turnover_values)
+        input_values = torch.zeros_like(turnover_values)
+        
+        # Generate input/output ratios from a beta distribution
+        # Beta(4, 2) gives a distribution centered around 0.67 with range [0, 1]
+        # We'll scale and shift this to get our desired range
+        alpha, beta = 4.0, 2.0
+        base_ratios = torch.distributions.Beta(alpha, beta).sample((n_firms,)).to(self.device)
+        
+        # Scale to range [0.3, 1.3] to allow both positive and negative VAT liability
+        # Most values will be in [0.6, 0.95] range
+        scaled_ratios = 0.3 + base_ratios * 1.0  # Maps [0,1] to [0.3, 1.3]
+        
+        # Add sector-specific noise
+        sector_noise = torch.randn(n_firms, device=self.device) * 0.15
+        
+        # For some sectors, shift the distribution to make negative liability more likely
+        for i in range(n_firms):
+            sic = sic_codes[i].item()
+            
+            # Add extra variation for certain sectors that tend to have extreme values
+            if sic in [1, 3, 6, 7, 9, 10, 24, 30, 36, 37, 49, 50, 51, 60, 64, 79, 84]:
+                # These sectors often have negative VAT liability
+                # Add positive bias to input ratio
+                scaled_ratios[i] += torch.rand(1, device=self.device).item() * 0.3
+            elif sic in [11, 12, 69, 70, 78]:
+                # These sectors often have high VAT liability (low inputs)
+                # Add negative bias to input ratio
+                scaled_ratios[i] -= torch.rand(1, device=self.device).item() * 0.2
+        
+        # Apply ratios with noise
+        final_ratios = torch.clamp(scaled_ratios + sector_noise, 0.1, 1.5)
+        
+        # Generate input values
+        for i in range(n_firms):
+            if turnover_values[i] <= 0:
+                input_values[i] = 0
+            else:
+                input_values[i] = turnover_values[i] * final_ratios[i]
+        
+        logger.info(f"Generated input values for {n_firms:,} firms")
+        logger.info(f"Input/output ratio statistics:")
+        logger.info(f"  Mean: {final_ratios.mean():.2%}")
+        logger.info(f"  Std: {final_ratios.std():.2%}")
+        logger.info(f"  Min: {final_ratios.min():.2%}")
+        logger.info(f"  Max: {final_ratios.max():.2%}")
+        logger.info(f"  Firms with negative VAT liability (input > output): {(final_ratios > 1.0).sum().item():,}")
+        
+        return input_values
+    
+    def assign_employment(self, num_firms: int, 
+                               ons_employment_df: pd.DataFrame) -> Tensor:
+        """Assign employment using ONS distribution.
+        
+        Args:
+            num_firms: Number of firms to assign employment to
+            ons_employment_df: ONS employment data
+            
+        Returns:
+            Array of employment values
+        """
+        logger.info("Assigning employment using ONS distribution...")
+        
+        # Employment bands and parameters
+        emp_bands = ['0-4', '5-9', '10-19', '20-49', '50-99', '100-249', '250+']
+        band_params = {
+            '0-4': (1, 4, 2.5),
+            '5-9': (5, 9, 7),
+            '10-19': (10, 19, 14.5),
+            '20-49': (20, 49, 34.5),
+            '50-99': (50, 99, 74.5),
+            '100-249': (100, 249, 174.5),
+            '250+': (250, 2000, 400)
+        }
+        
+        # Calculate ONS employment band totals
+        total_ons_counts = {}
+        for band in emp_bands:
+            if band in ons_employment_df.columns:
+                sector_rows = ons_employment_df[~ons_employment_df['Description'].str.contains('Total', na=False)]
+                total_ons_counts[band] = int(sector_rows[band].fillna(0).sum())
+            else:
+                total_ons_counts[band] = 0
+        
+        total_ons_firms = sum(total_ons_counts.values())
+        
+        # Calculate target counts for each band
+        employment_values = []
+        for band in emp_bands:
+            target_count = int(round(num_firms * total_ons_counts[band] / total_ons_firms))
+            if target_count > 0:
+                min_val, max_val, midpoint = band_params[band]
+                
+                # Generate employment values
+                if band == '0-4':
+                    # Uniform for micro businesses
+                    values = torch.randint(1, 5, (target_count,), device=self.device)
+                elif band == '250+':
+                    # Log-normal for large firms
+                    log_mean = torch.log(torch.tensor(midpoint, device=self.device))
+                    values = torch.normal(log_mean, 0.8, (target_count,), device=self.device).exp()
+                    values = torch.clamp(values, min_val, max_val).round()
+                else:
+                    # Beta distribution for others
+                    uniform = torch.rand(target_count, device=self.device)
+                    beta_values = uniform.pow(0.5) * (1 - uniform).pow(2.0)  # Beta approximation
+                    values = min_val + beta_values * (max_val - min_val)
+                    values = values.round()
+                
+                employment_values.extend(values.cpu().numpy())
+        
+        # Shuffle and pad/trim to exact size
+        np.random.shuffle(employment_values)
+        if len(employment_values) < num_firms:
+            employment_values.extend([1] * (num_firms - len(employment_values)))
+        elif len(employment_values) > num_firms:
+            employment_values = employment_values[:num_firms]
+        
+        employment_array = torch.tensor(employment_values, 
+                                       dtype=torch.float32, device=self.device)
+        
+        logger.info(f"Assigned employment to {num_firms:,} firms")
+        
+        return employment_array
+    
+    def validate_comprehensive_accuracy(self, synthetic_df: pd.DataFrame, hmrc_target_bands: Dict[str, int],
+                                       ons_total_target: int, ons_employment_df: pd.DataFrame, 
+                                       hmrc_sector_df: pd.DataFrame, vat_liability_df: pd.DataFrame, 
+                                       vat_liability_band_df: pd.DataFrame) -> Tuple[float, float, float, float, float, float]:
+        """Validate synthetic data against official data sources.
+        
+        Args:
+            synthetic_df: Generated synthetic data with VAT flags
+            hmrc_target_bands: HMRC VAT-registered firm targets by turnover band
+            ons_total_target: ONS total firm count target  
+            ons_employment_df: ONS employment data for validation
+            hmrc_sector_df: HMRC sector data for validation
+            vat_liability_df: VAT liability data for validation
+            
+        Returns:
+            Tuple of (hmrc_accuracy, ons_accuracy, employment_accuracy, sector_accuracy, vat_liability_accuracy)
+        """
+        logger.info("Validating synthetic data against official data sources...")
+        
+        # 1. HMRC VAT Firm Validation (VAT-registered firms only)
+        def map_to_hmrc_band(turnover_k):
+            if turnover_k <= 0:
+                return 'Negative_or_Zero'
+            elif turnover_k <= 85:
+                return '£1_to_Threshold'
+            elif turnover_k <= 150:
+                return '£Threshold_to_£150k'
+            elif turnover_k <= 300:
+                return '£150k_to_£300k'
+            elif turnover_k <= 500:
+                return '£300k_to_£500k'
+            elif turnover_k <= 1000:
+                return '£500k_to_£1m'
+            elif turnover_k <= 10000:
+                return '£1m_to_£10m'
+            else:
+                return 'Greater_than_£10m'
+        
+        # Map all firms to HMRC bands for validation
+        synthetic_df['hmrc_band'] = synthetic_df['annual_turnover_k'].apply(map_to_hmrc_band)
+        all_bands = synthetic_df.groupby('hmrc_band')['weight'].sum().round().astype(int)
+        
+        # === VAT REGISTRATION VALIDATION ===
+        vat_registered_count = synthetic_df[synthetic_df['vat_registered'] == True]['weight'].sum()
+        hmrc_total_vat = sum(hmrc_target_bands.values())
+        vat_accuracy = 1 - abs(vat_registered_count - hmrc_total_vat) / hmrc_total_vat
+        
+        self._print_validation_section("VAT REGISTRATION VALIDATION", 80)
+        print(f"VAT Registered (weighted): {vat_registered_count:,.0f}")
+        print(f"HMRC Total VAT firms:      {hmrc_total_vat:,}")
+        print(f"Difference:                {vat_registered_count - hmrc_total_vat:+,.0f}")
+        print(f"VAT Registration Accuracy: {vat_accuracy:.1%}")
+        
+        # === TURNOVER BAND VALIDATION ===
+        self._print_validation_section("TURNOVER BAND VALIDATION", 80)
+        print(f"{'Band':>25} {'Synthetic':>12} {'Target':>12} {'Source':>8} {'Accuracy':>10}")
+        print("-" * 75)
+        
+        hmrc_band_accuracies = []
+        
+        # Validate each HMRC band
+        for band_name, target_count in hmrc_target_bands.items():
+            synthetic_count = all_bands.get(band_name, 0)
+            accuracy = 1 - abs(synthetic_count - target_count) / target_count if target_count > 0 else (1.0 if synthetic_count == 0 else 0.0)
+            
+            # Special handling for £1_to_Threshold (ONS-based, not HMRC target)
+            if band_name == '£1_to_Threshold':
+                status = "○"
+                source = "ONS-based"
+            else:
+                hmrc_band_accuracies.append(accuracy)
+                status = "✓" if accuracy > 0.90 else "⚠" if accuracy > 0.80 else "✗"
+                source = "HMRC"
+            
+            print(f"  {status} {band_name:>22}: {synthetic_count:>10,} vs {target_count:>10,} {source:>8} ({accuracy:>6.1%})")
+        
+        hmrc_accuracy = np.mean(hmrc_band_accuracies) if hmrc_band_accuracies else 0.0
+        print("-" * 75)
+        print(f"HMRC CALIBRATION ACCURACY: {hmrc_accuracy:.1%}")
+        
+        # === ONS POPULATION VALIDATION ===
+        total_synthetic_weighted = synthetic_df['weight'].sum()
+        ons_population_accuracy = 1 - abs(total_synthetic_weighted - ons_total_target) / ons_total_target
+        
+        self._print_validation_section("ONS POPULATION VALIDATION")
+        print(f"Synthetic Total: {total_synthetic_weighted:,.0f}")
+        print(f"ONS Target:      {ons_total_target:,}")
+        print(f"Difference:      {total_synthetic_weighted - ons_total_target:+,.0f}")
+        print(f"ONS Accuracy:    {ons_population_accuracy:.1%}")
+        
+        # === EMPLOYMENT BAND VALIDATION ===
+        def _map_employment_to_band(employment: int) -> str:
+            """Map employment count to ONS employment band."""
+            if employment <= 4:
+                return '0-4'
+            elif employment <= 9:
+                return '5-9'
+            elif employment <= 19:
+                return '10-19'
+            elif employment <= 49:
+                return '20-49'
+            elif employment <= 99:
+                return '50-99'
+            elif employment <= 249:
+                return '100-249'
+            else:
+                return '250+'
+        
+        employment_bands = ['0-4', '5-9', '10-19', '20-49', '50-99', '100-249', '250+']
+        ons_employment_targets = {}
+        
+        # Get ONS employment targets
+        for band in employment_bands:
+            if band in ons_employment_df.columns:
+                sector_rows = ons_employment_df[~ons_employment_df['Description'].str.contains('Total', na=False)]
+                ons_employment_targets[band] = sector_rows[band].fillna(0).sum()
+            else:
+                ons_employment_targets[band] = 0
+        
+        # Calculate synthetic employment distribution
+        synthetic_df['employment_band'] = synthetic_df['employment'].apply(_map_employment_to_band)
+        synthetic_employment_counts = synthetic_df.groupby('employment_band')['weight'].sum().round().astype(int)
+        
+        self._print_validation_section("EMPLOYMENT BAND VALIDATION")
+        print(f"{'Band':>8} {'Synthetic':>10} {'ONS Target':>11} {'Accuracy':>10}")
+        print("-" * 65)
+        
+        employment_accuracies = []
+        for band in employment_bands:
+            ons_target = ons_employment_targets.get(band, 0)
+            synthetic_count = synthetic_employment_counts.get(band, 0)
+            accuracy = 1 - abs(synthetic_count - ons_target) / ons_target if ons_target > 0 else (1.0 if synthetic_count == 0 else 0.0)
+            employment_accuracies.append(accuracy)
+            
+            status = "✓" if accuracy > 0.90 else "⚠" if accuracy > 0.80 else "✗"
+            print(f"  {status} {band:>6}: {synthetic_count:>8,} vs {ons_target:>9,} ({accuracy:>6.1%})")
+        
+        print("-" * 65)
+        employment_accuracy = self._print_accuracy_breakdown(employment_accuracies, len(employment_bands), "EMPLOYMENT")
+        
+        # === SECTOR VALIDATION (VAT-registered firms only) ===
+        sector_rows = hmrc_sector_df[hmrc_sector_df['Trade_Sector'] != 'Total'].copy()
+        
+        # Calculate synthetic VAT-registered sector distribution
+        synthetic_df['sic_numeric'] = synthetic_df['sic_code'].astype(int)
+        vat_registered_firms = synthetic_df[synthetic_df['vat_registered'] == True]
+        synthetic_vat_sector_counts = vat_registered_firms.groupby('sic_numeric')['weight'].sum().round().astype(int)
+        
+        self._print_validation_section("SECTOR VALIDATION (VAT-registered firms)")
+        print(f"{'SIC':>5} {'Synthetic VAT':>12} {'HMRC Target':>12} {'Accuracy':>10}")
+        print("-" * 65)
+        
+        sector_accuracies = []
+        for _, sector_row in sector_rows.iterrows():
+            sic_code = int(sector_row['Trade_Sector'])
+            hmrc_target = sector_row['2023-24']
+            
+            synthetic_vat_count = synthetic_vat_sector_counts.get(sic_code, 0)
+            accuracy = 1 - abs(synthetic_vat_count - hmrc_target) / hmrc_target if hmrc_target > 0 else (1.0 if synthetic_vat_count == 0 else 0.0)
+            sector_accuracies.append(accuracy)
+            
+            status = "✓" if accuracy > 0.90 else "⚠" if accuracy > 0.80 else "✗"
+            print(f"  {status} {sic_code:>3}: {synthetic_vat_count:>10,} vs {hmrc_target:>10,} ({accuracy:>6.1%})")
+        
+        print("-" * 65)
+        sector_accuracy = self._print_accuracy_breakdown(sector_accuracies, len(sector_accuracies), "SECTOR")
+        
+        # === VAT LIABILITY VALIDATION ===
+        # Get VAT liability sector data (excluding total)
+        vat_liability_rows = vat_liability_df[vat_liability_df['Trade_Sector'] != 'Total'].copy()
+        
+        # Calculate synthetic VAT liability by sector
+        synthetic_df['vat_liability_k'] = synthetic_df['annual_turnover_k'] - synthetic_df['annual_input_k']
+        synthetic_df['sic_numeric'] = synthetic_df['sic_code'].astype(int)
+        
+        # Group by sector and calculate weighted VAT liability
+        synthetic_vat_liability = synthetic_df.groupby('sic_numeric').apply(
+            lambda x: (x['vat_liability_k'] * x['weight']).sum()
+        ).reset_index(name='synthetic_vat_liability_k')
+        
+        self._print_validation_section("VAT LIABILITY VALIDATION (by sector)")
+        print(f"{'SIC':>5} {'Synthetic (£m)':>14} {'Target (£m)':>12} {'Accuracy':>10}")
+        print("-" * 65)
+        
+        vat_liability_accuracies = []
+        for _, vat_row in vat_liability_rows.iterrows():
+            sic_code = int(vat_row['Trade_Sector'])
+            target_millions = vat_row['2023-24']  # in millions £
+            target_k = target_millions * 1000  # convert to £k
+            
+            # Get synthetic VAT liability for this sector
+            sector_data = synthetic_vat_liability[synthetic_vat_liability['sic_numeric'] == sic_code]
+            if not sector_data.empty:
+                synthetic_k = sector_data['synthetic_vat_liability_k'].iloc[0]
+            else:
+                synthetic_k = 0
+            
+            synthetic_millions = synthetic_k / 1000  # convert back to millions for display
+            
+            # Calculate accuracy (handle negative targets)
+            if abs(target_millions) > 0.1:  # Skip near-zero targets
+                # For negative targets, check if signs match and relative magnitude
+                if target_millions < 0 and synthetic_millions < 0:
+                    # Both negative - check relative difference
+                    accuracy = 1 - min(abs(synthetic_millions - target_millions) / abs(target_millions), 1.0)
+                elif target_millions > 0 and synthetic_millions > 0:
+                    # Both positive - check relative difference
+                    accuracy = 1 - min(abs(synthetic_millions - target_millions) / target_millions, 1.0)
+                else:
+                    # Sign mismatch - poor accuracy
+                    accuracy = max(0, 1 - abs(synthetic_millions - target_millions) / max(abs(target_millions), 1))
+            else:
+                accuracy = 1.0 if abs(synthetic_millions) < 1 else 0.0
+            
+            vat_liability_accuracies.append(accuracy)
+            
+            status = "✓" if accuracy > 0.70 else "⚠" if accuracy > 0.50 else "✗"
+            print(f"  {status} {sic_code:>3}: {synthetic_millions:>12.1f} vs {target_millions:>10.1f} ({accuracy:>6.1%})")
+        
+        print("-" * 65)
+        vat_liability_sector_accuracy = self._print_accuracy_breakdown(vat_liability_accuracies, len(vat_liability_accuracies), "VAT LIABILITY BY SECTOR")
+        
+        # === VAT LIABILITY BY TURNOVER BAND VALIDATION ===
+        # Get VAT liability by turnover band targets (excluding Negative_or_Zero)
+        vat_liability_band_latest = vat_liability_band_df.iloc[-1]  # Get 2023-24 data
+        vat_liability_band_targets = {
+            '£1_to_Threshold': vat_liability_band_latest['£1_to_Threshold'],
+            '£Threshold_to_£150k': vat_liability_band_latest['£Threshold_to_£150k'],
+            '£150k_to_£300k': vat_liability_band_latest['£150k_to_£300k'],
+            '£300k_to_£500k': vat_liability_band_latest['£300k_to_£500k'],
+            '£500k_to_£1m': vat_liability_band_latest['£500k_to_£1m'],
+            '£1m_to_£10m': vat_liability_band_latest['£1m_to_£10m'],
+            'Greater_than_£10m': vat_liability_band_latest['Greater_than_£10m']
+        }
+        
+        # Calculate synthetic VAT liability by turnover band (VAT-registered firms only)
+        vat_registered_firms = synthetic_df[synthetic_df['vat_registered'] == True]
+        synthetic_vat_band_liability = vat_registered_firms.groupby('hmrc_band').apply(
+            lambda x: (x['vat_liability_k'] * x['weight']).sum()
+        ).reset_index(name='synthetic_vat_liability_k')
+        
+        self._print_validation_section("VAT LIABILITY BY TURNOVER BAND VALIDATION")
+        print(f"{'Band':>25} {'Synthetic (£m)':>14} {'Target (£m)':>12} {'Accuracy':>10}")
+        print("-" * 75)
+        
+        vat_liability_band_accuracies = []
+        for band_name, target_millions in vat_liability_band_targets.items():
+            # Get synthetic VAT liability for this band
+            band_data = synthetic_vat_band_liability[synthetic_vat_band_liability['hmrc_band'] == band_name]
+            if not band_data.empty:
+                synthetic_k = band_data['synthetic_vat_liability_k'].iloc[0]
+            else:
+                synthetic_k = 0
+            
+            synthetic_millions = synthetic_k / 1000  # convert back to millions for display
+            
+            # Calculate accuracy (handle negative targets)
+            if abs(target_millions) > 0.1:  # Skip near-zero targets
+                if target_millions < 0 and synthetic_millions < 0:
+                    # Both negative - check relative difference
+                    accuracy = 1 - min(abs(synthetic_millions - target_millions) / abs(target_millions), 1.0)
+                elif target_millions > 0 and synthetic_millions > 0:
+                    # Both positive - check relative difference
+                    accuracy = 1 - min(abs(synthetic_millions - target_millions) / target_millions, 1.0)
+                else:
+                    # Sign mismatch - poor accuracy
+                    accuracy = max(0, 1 - abs(synthetic_millions - target_millions) / max(abs(target_millions), 1))
+            else:
+                accuracy = 1.0 if abs(synthetic_millions) < 1 else 0.0
+            
+            vat_liability_band_accuracies.append(accuracy)
+            
+            status = "✓" if accuracy > 0.70 else "⚠" if accuracy > 0.50 else "✗"
+            print(f"  {status} {band_name:>22}: {synthetic_millions:>12.1f} vs {target_millions:>10.1f} ({accuracy:>6.1%})")
+        
+        print("-" * 75)
+        vat_liability_band_accuracy = self._print_accuracy_breakdown(vat_liability_band_accuracies, len(vat_liability_band_accuracies), "VAT LIABILITY BY BAND")
+        
+        # === FINAL SUMMARY ===
+        overall_accuracy = (hmrc_accuracy + ons_population_accuracy + employment_accuracy + sector_accuracy + vat_liability_sector_accuracy + vat_liability_band_accuracy) / 6
+        
+        self._print_validation_section("CALIBRATION SUMMARY", 80)
+        print(f"HMRC Turnover Bands:     {hmrc_accuracy:.1%}")
+        print(f"ONS Population:          {ons_population_accuracy:.1%}")
+        print(f"Employment Bands:        {employment_accuracy:.1%}")
+        print(f"Sector Distribution:     {sector_accuracy:.1%}")
+        print(f"VAT Liability by Sector: {vat_liability_sector_accuracy:.1%}")
+        print(f"VAT Liability by Band:   {vat_liability_band_accuracy:.1%}")
+        print(f"Overall Accuracy:        {overall_accuracy:.1%}")
+        print(f"Total Population: {total_synthetic_weighted:,.0f} firms")
+        
+        return hmrc_accuracy, ons_population_accuracy, employment_accuracy, sector_accuracy, vat_liability_sector_accuracy, vat_liability_band_accuracy
+    
+    
+
+    def generate_synthetic_firms(self) -> pd.DataFrame:
+        """Main function to generate comprehensive synthetic firms population.
+        
+        Creates complete firm dataset with VAT registration flags, calibrated
+        to match official ONS and HMRC data sources.
+        
+        Returns:
+            DataFrame with synthetic firms data including VAT registration flags
+        """
+        logger.info("Starting synthetic firm generation...")
+        
+        # Load data
+        ons_df, ons_employment_df, hmrc_turnover_df, hmrc_sector_df, vat_liability_df, vat_liability_band_df, ons_total = self.load_data()
+        
+        # Extract HMRC targets (VAT-registered firms only)
+        hmrc_latest = hmrc_turnover_df.iloc[-1]
+        hmrc_bands = {
+            'Negative_or_Zero': hmrc_latest['Negative_or_Zero'],
+            '£1_to_Threshold': hmrc_latest['£1_to_Threshold'],
+            '£Threshold_to_£150k': hmrc_latest['£Threshold_to_£150k'],
+            '£150k_to_£300k': hmrc_latest['£150k_to_£300k'],
+            '£300k_to_£500k': hmrc_latest['£300k_to_£500k'],
+            '£500k_to_£1m': hmrc_latest['£500k_to_£1m'],
+            '£1m_to_£10m': hmrc_latest['£1m_to_£10m'],
+            'Greater_than_£10m': hmrc_latest['Greater_than_£10m']
+        }
+        
+        logger.info(f"Target populations:")
+        logger.info(f"  ONS total firms: {ons_total:,} (includes all businesses)")
+        logger.info(f"  HMRC VAT firms: {sum(hmrc_bands.values()):,} (VAT-registered only)")
+        
+        # Generate base firms from ONS structure
+        base_sic_codes, base_turnover = self.generate_base_firms(ons_df)
+        
+        # Generate input values for firms
+        base_input = self.generate_input_values(base_turnover, base_sic_codes)
+        
+        # Create target matrix for multi-objective optimization
+        target_matrix, target_values = self.create_comprehensive_target_matrix(
+            base_turnover, base_sic_codes, base_input, hmrc_bands, hmrc_sector_df, ons_employment_df, vat_liability_df, vat_liability_band_df, ons_total
+        )
+        
+        # Optimize weights to match calibration targets
+        optimized_weights = self.optimize_weights(target_matrix, target_values)
+        
+        # Apply final calibration (add zero firms manually)
+        final_sic_codes, final_turnover, final_input, final_weights = self.apply_final_calibration(
+            base_sic_codes, base_turnover, base_input, optimized_weights, hmrc_bands
+        )
+        
+        # Assign employment to final firms
+        employment_values = self.assign_employment(len(final_sic_codes), ons_employment_df)
+        
+        # Assign VAT registration flags
+        vat_flags = self.assign_vat_registration_flags(final_turnover, hmrc_bands)
+        
+        # Convert to DataFrame
+        logger.info("Converting to final DataFrame...")
+        sic_codes_np = final_sic_codes.cpu().numpy().astype(int)
+        turnover_np = final_turnover.cpu().numpy()
+        input_np = final_input.cpu().numpy()
+        vat_liability_np = turnover_np - input_np  # Calculate VAT liability
+        
+        synthetic_df = pd.DataFrame({
+            'sic_code': [str(sic).zfill(5) for sic in sic_codes_np],
+            'annual_turnover_k': turnover_np,
+            'annual_input_k': input_np,
+            'vat_liability_k': vat_liability_np,
+            'employment': employment_values.cpu().numpy().astype(int),
+            'weight': final_weights.cpu().numpy(),
+            'vat_registered': vat_flags.cpu().numpy().astype(bool)
+        })
+        
+        logger.info(f"Generated firm dataset:")
+        logger.info(f"  Total firms: {len(synthetic_df):,}")
+        logger.info(f"  Weighted population: {synthetic_df['weight'].sum():,.0f}")
+        
+        # Validation against all data sources
+        self.validate_comprehensive_accuracy(synthetic_df, hmrc_bands, ons_total, ons_employment_df, hmrc_sector_df, vat_liability_df, vat_liability_band_df)
+        
+        return synthetic_df
+
+
+def main():
+    """Main execution function."""
+    logging.basicConfig(level=logging.INFO, 
+                       format='%(asctime)s - %(levelname)s - %(message)s')
+    
+    logger.info("UK BUSINESS POPULATION SYNTHETIC DATA GENERATION")
+    logger.info("Generating synthetic firm records using PyTorch for efficient processing")
+    
+    # Initialize generator
+    generator = SyntheticFirmGenerator(
+        device="cpu"  # Use CPU for compatibility
+    )
+    
+    # Generate synthetic data
+    synthetic_df = generator.generate_synthetic_firms()
+    
+    # Save results to STORAGE_FOLDER
+    output_path = STORAGE_FOLDER / 'synthetic_firms_turnover.csv'
+    synthetic_df.to_csv(output_path, index=False)
+    
+    file_size_mb = output_path.stat().st_size / 1024 / 1024
+    logger.info(f"Saved {len(synthetic_df):,} firms to {output_path}")
+    logger.info(f"File size: {file_size_mb:.1f} MB")
+    logger.info(f"Columns: {list(synthetic_df.columns)}")
+    
+    logger.info("Synthetic data generation complete!")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file

From be6ce1244d624c9823fa44dd1b351eee35d19040 Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 15:39:00 +0100
Subject: [PATCH 2/8] format

---
 .../datasets/firm/generate_synthetic_data.py  | 1713 +++++++++++------
 1 file changed, 1127 insertions(+), 586 deletions(-)

diff --git a/policyengine_uk_data/datasets/firm/generate_synthetic_data.py b/policyengine_uk_data/datasets/firm/generate_synthetic_data.py
index 88f93bc7..90bc909a 100644
--- a/policyengine_uk_data/datasets/firm/generate_synthetic_data.py
+++ b/policyengine_uk_data/datasets/firm/generate_synthetic_data.py
@@ -34,52 +34,76 @@
 class SyntheticFirmGenerator:
     """
     Synthetic firm data generator for UK business population analysis.
-    
+
     Generates complete UK firm population calibrated to official data sources:
     - ONS data for population structure and employment distribution
     - HMRC data for VAT registration and sector targets
     - Assigns VAT flags to identify HMRC-visible firms
     """
-    
-    def __init__(
-        self,
-        device: str = "cpu",
-        random_seed: int = 42
-    ):
+
+    def __init__(self, device: str = "cpu", random_seed: int = 42):
         """Initialize the synthetic firm generator.
-        
+
         Args:
             device: Computing device ('cpu', 'cuda', 'mps')
             random_seed: Random seed for reproducibility
         """
         self.device = device
         self.random_seed = random_seed
-        
+
         # Set random seeds
         torch.manual_seed(random_seed)
         np.random.seed(random_seed)
-        
+
         logger.info(f"Initialized firm generator on device: {device}")
-    
-    def load_data(self) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, int]:
+
+    def load_data(
+        self,
+    ) -> Tuple[
+        pd.DataFrame,
+        pd.DataFrame,
+        pd.DataFrame,
+        pd.DataFrame,
+        pd.DataFrame,
+        pd.DataFrame,
+        int,
+    ]:
         """Load ONS and HMRC data files from standardized CSV sources.
-        
+
         Returns:
-            Tuple containing ONS turnover data, ONS employment data, 
-            HMRC turnover bands, HMRC sector data, VAT liability by sector, 
+            Tuple containing ONS turnover data, ONS employment data,
+            HMRC turnover bands, HMRC sector data, VAT liability by sector,
             VAT liability by turnover band, and ONS total firm count
         """
         logger.info("Loading data files...")
-        
+
         # Define paths to data files
         project_root = Path(__file__).parent
-        ons_path = project_root / 'ONS_UK_business_data' / 'firm_turnover.csv'
-        ons_employment_path = project_root / 'ONS_UK_business_data' / 'firm_employment.csv'
-        hmrc_turnover_path = project_root / 'HMRC_VAT_annual_statistics' / 'vat_population_by_turnover_band.csv'
-        hmrc_sector_path = project_root / 'HMRC_VAT_annual_statistics' / 'vat_population_by_sector.csv'
-        vat_liability_sector_path = project_root / 'HMRC_VAT_annual_statistics' / 'vat_liability_by_sector.csv'
-        vat_liability_band_path = project_root / 'HMRC_VAT_annual_statistics' / 'vat_liability_by_turnover_band.csv'
-        
+        ons_path = project_root / "ONS_UK_business_data" / "firm_turnover.csv"
+        ons_employment_path = (
+            project_root / "ONS_UK_business_data" / "firm_employment.csv"
+        )
+        hmrc_turnover_path = (
+            project_root
+            / "HMRC_VAT_annual_statistics"
+            / "vat_population_by_turnover_band.csv"
+        )
+        hmrc_sector_path = (
+            project_root
+            / "HMRC_VAT_annual_statistics"
+            / "vat_population_by_sector.csv"
+        )
+        vat_liability_sector_path = (
+            project_root
+            / "HMRC_VAT_annual_statistics"
+            / "vat_liability_by_sector.csv"
+        )
+        vat_liability_band_path = (
+            project_root
+            / "HMRC_VAT_annual_statistics"
+            / "vat_liability_by_turnover_band.csv"
+        )
+
         # Load CSV files
         ons_df = pd.read_csv(ons_path)
         ons_employment_df = pd.read_csv(ons_employment_path)
@@ -87,176 +111,235 @@ def load_data(self) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFr
         hmrc_sector_df = pd.read_csv(hmrc_sector_path)
         vat_liability_sector_df = pd.read_csv(vat_liability_sector_path)
         vat_liability_band_df = pd.read_csv(vat_liability_band_path)
-        
+
         logger.info(f"Loaded ONS turnover data: {len(ons_df)} rows")
-        logger.info(f"Loaded ONS employment data: {len(ons_employment_df)} rows")
+        logger.info(
+            f"Loaded ONS employment data: {len(ons_employment_df)} rows"
+        )
         logger.info(f"Loaded HMRC turnover data: {len(hmrc_turnover_df)} rows")
         logger.info(f"Loaded HMRC sector data: {len(hmrc_sector_df)} rows")
-        logger.info(f"Loaded VAT liability by sector data: {len(vat_liability_sector_df)} rows")
-        logger.info(f"Loaded VAT liability by band data: {len(vat_liability_band_df)} rows")
-        
+        logger.info(
+            f"Loaded VAT liability by sector data: {len(vat_liability_sector_df)} rows"
+        )
+        logger.info(
+            f"Loaded VAT liability by band data: {len(vat_liability_band_df)} rows"
+        )
+
         # Extract ONS total
-        ons_total_row = ons_df[ons_df['SIC Code'].isna() | (ons_df['SIC Code'] == '')]
+        ons_total_row = ons_df[
+            ons_df["SIC Code"].isna() | (ons_df["SIC Code"] == "")
+        ]
         if len(ons_total_row) > 0:
-            ons_total = ons_total_row.iloc[0]['Total']
+            ons_total = ons_total_row.iloc[0]["Total"]
         else:
-            sector_rows = ons_df[~ons_df['Description'].str.contains('Total', na=False)]
-            ons_total = sector_rows['Total'].sum()
-        
+            sector_rows = ons_df[
+                ~ons_df["Description"].str.contains("Total", na=False)
+            ]
+            ons_total = sector_rows["Total"].sum()
+
         logger.info(f"ONS total firms: {ons_total:,}")
-        
-        return ons_df, ons_employment_df, hmrc_turnover_df, hmrc_sector_df, vat_liability_sector_df, vat_liability_band_df, ons_total
-    
-    def generate_base_firms(self, ons_df: pd.DataFrame) -> Tuple[Tensor, Tensor]:
+
+        return (
+            ons_df,
+            ons_employment_df,
+            hmrc_turnover_df,
+            hmrc_sector_df,
+            vat_liability_sector_df,
+            vat_liability_band_df,
+            ons_total,
+        )
+
+    def generate_base_firms(
+        self, ons_df: pd.DataFrame
+    ) -> Tuple[Tensor, Tensor]:
         """Generate base firm records using efficient batch processing.
-        
+
         Args:
             ons_df: ONS turnover data
-            
+
         Returns:
             Tuple of (sic_codes, turnover_values)
         """
-        logger.info("Generating base firms using efficient batch processing...")
-        
+        logger.info(
+            "Generating base firms using efficient batch processing..."
+        )
+
         # ONS turnover band parameters (min, max, midpoint in £k)
         band_params = {
-            '0-49': (0, 49, 24.5),
-            '50-99': (50, 99, 74.5),
-            '100-249': (100, 249, 174.5),
-            '250-499': (250, 499, 374.5),
-            '500-999': (500, 999, 749.5),
-            '1000-4999': (1000, 4999, 2999.5),
-            '5000+': (5000, 50000, 15000)
+            "0-49": (0, 49, 24.5),
+            "50-99": (50, 99, 74.5),
+            "100-249": (100, 249, 174.5),
+            "250-499": (250, 499, 374.5),
+            "500-999": (500, 999, 749.5),
+            "1000-4999": (1000, 4999, 2999.5),
+            "5000+": (5000, 50000, 15000),
         }
-        
+
         all_sic_codes = []
         all_turnovers = []
-        
+
         # Process each sector
         for _, row in ons_df.iterrows():
-            sic_code = row['SIC Code']
-            
+            sic_code = row["SIC Code"]
+
             # Skip summary rows
-            if pd.isna(sic_code) or sic_code == '' or sic_code == 'Total':
+            if pd.isna(sic_code) or sic_code == "" or sic_code == "Total":
                 continue
-            
+
             sic_formatted = str(int(sic_code)).zfill(5)
-            
+
             # Generate firms for each turnover band
             for band, (min_val, max_val, midpoint) in band_params.items():
                 if band in row and pd.notna(row[band]) and row[band] > 0:
                     count = int(row[band])
-                    
+
                     if count > 0:
                         # Generate turnover values with noise smoothing
                         turnovers = self._generate_turnover_values(
                             band, count, min_val, max_val, midpoint
                         )
-                        
+
                         # Store results
                         all_sic_codes.extend([sic_formatted] * count)
                         all_turnovers.extend(turnovers.cpu().numpy())
-        
+
         # Convert to efficient data structures
-        sic_codes_tensor = torch.tensor([int(sic) for sic in all_sic_codes], 
-                                       dtype=torch.int64, device=self.device)
-        turnover_tensor = torch.tensor(all_turnovers, 
-                                      dtype=torch.float32, device=self.device)
-        
+        sic_codes_tensor = torch.tensor(
+            [int(sic) for sic in all_sic_codes],
+            dtype=torch.int64,
+            device=self.device,
+        )
+        turnover_tensor = torch.tensor(
+            all_turnovers, dtype=torch.float32, device=self.device
+        )
+
         logger.info(f"Generated {len(all_sic_codes):,} base firms")
-        
+
         return sic_codes_tensor, turnover_tensor
-    
-    def _generate_turnover_values(self, band_name: str, firm_count: int, 
-                                        min_turnover: float, max_turnover: float, 
-                                        midpoint_turnover: float) -> Tensor:
+
+    def _generate_turnover_values(
+        self,
+        band_name: str,
+        firm_count: int,
+        min_turnover: float,
+        max_turnover: float,
+        midpoint_turnover: float,
+    ) -> Tensor:
         """Generate turnover values within a band with noise smoothing.
-        
+
         Args:
             band_name: ONS turnover band name (e.g., '0-49', '50-99')
             firm_count: Number of firms to generate in this band
             min_turnover: Minimum turnover value (£k)
-            max_turnover: Maximum turnover value (£k)  
+            max_turnover: Maximum turnover value (£k)
             midpoint_turnover: Midpoint turnover for distribution (£k)
-            
+
         Returns:
             Generated turnover values with noise smoothing applied
         """
         if firm_count == 0:
             return torch.empty(0, device=self.device)
-        
+
         # Generate turnover values with noise smoothing across the full band range
         band_width = max_turnover - min_turnover
-        
+
         # Use uniform noise for distribution smoothing
-        noise_std = max(25.0, band_width * 0.2)  # Uniform noise: 25k minimum or 20% of band width
-        
+        noise_std = max(
+            25.0, band_width * 0.2
+        )  # Uniform noise: 25k minimum or 20% of band width
+
         # Generate uniform base values across the full band range
         uniform_values = torch.rand(firm_count, device=self.device)
         base_turnover_values = min_turnover + uniform_values * band_width
-        
+
         # Add Gaussian noise for smoothing
         noise = torch.normal(0, noise_std, (firm_count,), device=self.device)
         turnover_values = base_turnover_values + noise
-        
+
         # Ensure all turnover values are positive
         turnover_values = torch.clamp(turnover_values, min=0.1)
-        
+
         return turnover_values
-    
-    def assign_vat_registration_flags(self, turnover_values: Tensor, hmrc_bands: Dict[str, int]) -> Tensor:
+
+    def assign_vat_registration_flags(
+        self, turnover_values: Tensor, hmrc_bands: Dict[str, int]
+    ) -> Tensor:
         """Assign VAT registration flags to identify HMRC-visible firms.
-        
+
         VAT registration triggers:
         1. Mandatory: Annual turnover > £85k
         2. Voluntary: Random subset of firms below threshold (calculated from HMRC data)
-        
+
         Args:
             turnover_values: Array of turnover values
             hmrc_bands: HMRC band data to calculate voluntary rate
-            
+
         Returns:
             Boolean array indicating VAT registration status
         """
         logger.info("Assigning VAT registration flags...")
-        
+
         # Calculate voluntary VAT rate from Target/Synthetic ratio for £1_to_Threshold
-        hmrc_target_1_to_threshold = hmrc_bands['£1_to_Threshold']  # HMRC target: 678,350
-        synthetic_1_to_threshold = ((turnover_values > 0) & (turnover_values <= 85.0)).sum().item()  # Current synthetic count
-        voluntary_rate = hmrc_target_1_to_threshold / synthetic_1_to_threshold if synthetic_1_to_threshold > 0 else 0.15
-        
-        logger.info(f"Calculated voluntary VAT rate: {voluntary_rate:.3f} (Target: {hmrc_target_1_to_threshold:,} / Synthetic: {synthetic_1_to_threshold:,})")
-        
+        hmrc_target_1_to_threshold = hmrc_bands[
+            "£1_to_Threshold"
+        ]  # HMRC target: 678,350
+        synthetic_1_to_threshold = (
+            ((turnover_values > 0) & (turnover_values <= 85.0)).sum().item()
+        )  # Current synthetic count
+        voluntary_rate = (
+            hmrc_target_1_to_threshold / synthetic_1_to_threshold
+            if synthetic_1_to_threshold > 0
+            else 0.15
+        )
+
+        logger.info(
+            f"Calculated voluntary VAT rate: {voluntary_rate:.3f} (Target: {hmrc_target_1_to_threshold:,} / Synthetic: {synthetic_1_to_threshold:,})"
+        )
+
         # Mandatory registration above threshold
         mandatory_vat = turnover_values > 85.0
-        
+
         # Voluntary registration below threshold (but above 0)
         below_threshold = (turnover_values > 0) & (turnover_values <= 85.0)
         n_below_threshold = below_threshold.sum().item()
-        
+
         if n_below_threshold > 0:
             # Random selection for voluntary VAT registration using calculated rate
-            voluntary_mask = torch.rand(len(turnover_values), device=self.device) < voluntary_rate
+            voluntary_mask = (
+                torch.rand(len(turnover_values), device=self.device)
+                < voluntary_rate
+            )
             voluntary_vat = below_threshold & voluntary_mask
         else:
             voluntary_vat = torch.zeros_like(below_threshold)
-        
+
         vat_registered = mandatory_vat | voluntary_vat
-        
-        logger.info(f"VAT registration: {mandatory_vat.sum():.0f} mandatory + {voluntary_vat.sum():.0f} voluntary = {vat_registered.sum():.0f} total")
+
+        logger.info(
+            f"VAT registration: {mandatory_vat.sum():.0f} mandatory + {voluntary_vat.sum():.0f} voluntary = {vat_registered.sum():.0f} total"
+        )
         logger.info(f"Non-VAT registered: {(~vat_registered).sum():.0f} firms")
-        
+
         return vat_registered
-    
-    def create_comprehensive_target_matrix(self, turnover_values: Tensor, sic_codes: Tensor, input_values: Tensor,
-                                      hmrc_bands: Dict[str, int], hmrc_sector_df: pd.DataFrame, ons_employment_df: pd.DataFrame, 
-                                      vat_liability_sector_df: pd.DataFrame, vat_liability_band_df: pd.DataFrame, ons_total: int) -> Tuple[Tensor, Tensor]:
+
+    def create_comprehensive_target_matrix(
+        self,
+        turnover_values: Tensor,
+        sic_codes: Tensor,
+        input_values: Tensor,
+        hmrc_bands: Dict[str, int],
+        hmrc_sector_df: pd.DataFrame,
+        ons_employment_df: pd.DataFrame,
+        vat_liability_sector_df: pd.DataFrame,
+        vat_liability_band_df: pd.DataFrame,
+        ons_total: int,
+    ) -> Tuple[Tensor, Tensor]:
         """Create comprehensive target matrix for calibration.
-        
+
         Creates targets for all HMRC turnover bands, sector targets, and VAT liability targets.
-        The optimization will determine which firms contribute to which targets 
+        The optimization will determine which firms contribute to which targets
         based on VAT registration flags.
-        
+
         Args:
             turnover_values: Array of turnover values
             sic_codes: Array of SIC codes
@@ -267,75 +350,99 @@ def create_comprehensive_target_matrix(self, turnover_values: Tensor, sic_codes:
             vat_liability_sector_df: VAT liability data by sector
             vat_liability_band_df: VAT liability data by turnover band
             ons_total: Total firm count target from ONS
-            
+
         Returns:
             Tuple of (target_matrix, target_values)
         """
         logger.info("Creating comprehensive target matrix for calibration...")
-        
+
         n_firms = len(turnover_values)
         # Get sector data and calculate ratios
-        sector_rows = hmrc_sector_df[hmrc_sector_df['Trade_Sector'] != 'Total'].copy()
-        hmrc_total = hmrc_sector_df[hmrc_sector_df['Trade_Sector'] == 'Total']['2023-24'].iloc[0]
+        sector_rows = hmrc_sector_df[
+            hmrc_sector_df["Trade_Sector"] != "Total"
+        ].copy()
+        hmrc_total = hmrc_sector_df[hmrc_sector_df["Trade_Sector"] == "Total"][
+            "2023-24"
+        ].iloc[0]
         n_sectors = len(sector_rows)
-        
+
         # Get VAT liability sector data (excluding total)
-        vat_liability_sector_rows = vat_liability_sector_df[vat_liability_sector_df['Trade_Sector'] != 'Total'].copy()
+        vat_liability_sector_rows = vat_liability_sector_df[
+            vat_liability_sector_df["Trade_Sector"] != "Total"
+        ].copy()
         n_vat_sectors = len(vat_liability_sector_rows)
-        
+
         # Get VAT liability by turnover band data
-        vat_liability_band_latest = vat_liability_band_df.iloc[-1]  # Get 2023-24 data
+        vat_liability_band_latest = vat_liability_band_df.iloc[
+            -1
+        ]  # Get 2023-24 data
         vat_liability_bands = {
-            'Negative_or_Zero': vat_liability_band_latest['Negative_or_Zero'],
-            '£1_to_Threshold': vat_liability_band_latest['£1_to_Threshold'],
-            '£Threshold_to_£150k': vat_liability_band_latest['£Threshold_to_£150k'],
-            '£150k_to_£300k': vat_liability_band_latest['£150k_to_£300k'],
-            '£300k_to_£500k': vat_liability_band_latest['£300k_to_£500k'],
-            '£500k_to_£1m': vat_liability_band_latest['£500k_to_£1m'],
-            '£1m_to_£10m': vat_liability_band_latest['£1m_to_£10m'],
-            'Greater_than_£10m': vat_liability_band_latest['Greater_than_£10m']
+            "Negative_or_Zero": vat_liability_band_latest["Negative_or_Zero"],
+            "£1_to_Threshold": vat_liability_band_latest["£1_to_Threshold"],
+            "£Threshold_to_£150k": vat_liability_band_latest[
+                "£Threshold_to_£150k"
+            ],
+            "£150k_to_£300k": vat_liability_band_latest["£150k_to_£300k"],
+            "£300k_to_£500k": vat_liability_band_latest["£300k_to_£500k"],
+            "£500k_to_£1m": vat_liability_band_latest["£500k_to_£1m"],
+            "£1m_to_£10m": vat_liability_band_latest["£1m_to_£10m"],
+            "Greater_than_£10m": vat_liability_band_latest[
+                "Greater_than_£10m"
+            ],
         }
         n_vat_bands = 7  # Number of VAT liability bands (excluding Total and Negative_or_Zero)
-        
+
         # Get employment data and calculate ratios
-        emp_bands = ['0-4', '5-9', '10-19', '20-49', '50-99', '100-249', '250+']
+        emp_bands = [
+            "0-4",
+            "5-9",
+            "10-19",
+            "20-49",
+            "50-99",
+            "100-249",
+            "250+",
+        ]
         n_employment_bands = len(emp_bands)
-        
-        n_targets = 7 + n_sectors + n_employment_bands + n_vat_sectors + n_vat_bands  # 7 turnover + sector + employment + VAT liability by sector + VAT liability by band
-        
-        # Initialize target matrix  
+
+        n_targets = (
+            7 + n_sectors + n_employment_bands + n_vat_sectors + n_vat_bands
+        )  # 7 turnover + sector + employment + VAT liability by sector + VAT liability by band
+
+        # Initialize target matrix
         target_matrix = torch.zeros(n_targets, n_firms, device=self.device)
-        
+
         # Map all firms to HMRC bands
         all_band_indices = self._map_to_hmrc_bands(turnover_values)
-        
+
         # Rows 0-6: Turnover band targets
         # Row 0: £1_to_Threshold - keep ONS structure
-        firms_in_threshold = (all_band_indices == 1)
+        firms_in_threshold = all_band_indices == 1
         target_matrix[0, firms_in_threshold] = 1.0
-        
-        # Row 1: £Threshold_to_£150k - calibrate to HMRC target  
-        firms_150k = (all_band_indices == 2)
+
+        # Row 1: £Threshold_to_£150k - calibrate to HMRC target
+        firms_150k = all_band_indices == 2
         target_matrix[1, firms_150k] = 1.0
-        
+
         # Rows 2-6: Individual HMRC bands above £150k
-        for i, band_idx in enumerate([3, 4, 5, 6, 7], start=2):  # £150k_to_£300k, etc.
-            firms_in_band = (all_band_indices == band_idx)
+        for i, band_idx in enumerate(
+            [3, 4, 5, 6, 7], start=2
+        ):  # £150k_to_£300k, etc.
+            firms_in_band = all_band_indices == band_idx
             target_matrix[i, firms_in_band] = 1.0
-        
+
         # Rows 7 to 7+n_sectors-1: Sector targets (for VAT-registered firms only)
         for i, (_, sector_row) in enumerate(sector_rows.iterrows(), start=7):
-            trade_sector = sector_row['Trade_Sector']
+            trade_sector = sector_row["Trade_Sector"]
             # Convert HMRC Trade_Sector to SIC code (00001 -> 1, 00002 -> 2, etc.)
             sic_code = int(trade_sector)
-            
+
             # Find firms in this sector
-            firms_in_sector = (sic_codes == sic_code)
+            firms_in_sector = sic_codes == sic_code
             target_matrix[i, firms_in_sector] = 1.0
-        
+
         # Generate employment assignments for target matrix (temporary assignments for optimization)
         employment_values = self.assign_employment(n_firms, ons_employment_df)
-        
+
         # Map employment to bands
         def map_employment_to_band_idx(emp_val):
             if emp_val <= 4:
@@ -352,813 +459,1197 @@ def map_employment_to_band_idx(emp_val):
                 return 5  # '100-249'
             else:
                 return 6  # '250+'
-        
-        employment_band_indices = torch.tensor([map_employment_to_band_idx(emp.item()) for emp in employment_values], 
-                                             dtype=torch.long, device=self.device)
-        
-        # Rows 7+n_sectors to 7+n_sectors+n_employment_bands-1: Employment ratio targets  
+
+        employment_band_indices = torch.tensor(
+            [
+                map_employment_to_band_idx(emp.item())
+                for emp in employment_values
+            ],
+            dtype=torch.long,
+            device=self.device,
+        )
+
+        # Rows 7+n_sectors to 7+n_sectors+n_employment_bands-1: Employment ratio targets
         emp_start_row = 7 + n_sectors
         for band_idx, _ in enumerate(emp_bands):
             row_idx = emp_start_row + band_idx
-            firms_in_emp_band = (employment_band_indices == band_idx)
+            firms_in_emp_band = employment_band_indices == band_idx
             target_matrix[row_idx, firms_in_emp_band] = 1.0
-        
+
         # Calculate VAT liability for each firm (output - input)
         vat_liability_values = turnover_values - input_values  # in £k
-        
+
         # Rows 7+n_sectors+n_employment_bands to 7+n_sectors+n_employment_bands+n_vat_sectors-1: VAT liability targets by sector
         vat_start_row = 7 + n_sectors + n_employment_bands
         for i, (_, vat_row) in enumerate(vat_liability_sector_rows.iterrows()):
             row_idx = vat_start_row + i
-            sic_code = int(vat_row['Trade_Sector'])
-            
+            sic_code = int(vat_row["Trade_Sector"])
+
             # Find firms in this sector and calculate their VAT liability contribution
-            firms_in_sector = (sic_codes == sic_code)
+            firms_in_sector = sic_codes == sic_code
             # Weight by VAT liability for this sector's target
-            target_matrix[row_idx, firms_in_sector] = vat_liability_values[firms_in_sector]
-        
+            target_matrix[row_idx, firms_in_sector] = vat_liability_values[
+                firms_in_sector
+            ]
+
         # Rows 7+n_sectors+n_employment_bands+n_vat_sectors to end: VAT liability targets by turnover band (excluding Negative_or_Zero)
         vat_band_start_row = 7 + n_sectors + n_employment_bands + n_vat_sectors
-        for i, band_name in enumerate(['£1_to_Threshold', '£Threshold_to_£150k', 
-                                       '£150k_to_£300k', '£300k_to_£500k', '£500k_to_£1m', 
-                                       '£1m_to_£10m', 'Greater_than_£10m']):
+        for i, band_name in enumerate(
+            [
+                "£1_to_Threshold",
+                "£Threshold_to_£150k",
+                "£150k_to_£300k",
+                "£300k_to_£500k",
+                "£500k_to_£1m",
+                "£1m_to_£10m",
+                "Greater_than_£10m",
+            ]
+        ):
             row_idx = vat_band_start_row + i
-            
+
             # Map firms to this turnover band
-            if band_name == '£1_to_Threshold':
+            if band_name == "£1_to_Threshold":
                 firms_in_band = (turnover_values > 0) & (turnover_values <= 85)
-            elif band_name == '£Threshold_to_£150k':
-                firms_in_band = (turnover_values > 85) & (turnover_values <= 150)
-            elif band_name == '£150k_to_£300k':
-                firms_in_band = (turnover_values > 150) & (turnover_values <= 300)
-            elif band_name == '£300k_to_£500k':
-                firms_in_band = (turnover_values > 300) & (turnover_values <= 500)
-            elif band_name == '£500k_to_£1m':
-                firms_in_band = (turnover_values > 500) & (turnover_values <= 1000)
-            elif band_name == '£1m_to_£10m':
-                firms_in_band = (turnover_values > 1000) & (turnover_values <= 10000)
+            elif band_name == "£Threshold_to_£150k":
+                firms_in_band = (turnover_values > 85) & (
+                    turnover_values <= 150
+                )
+            elif band_name == "£150k_to_£300k":
+                firms_in_band = (turnover_values > 150) & (
+                    turnover_values <= 300
+                )
+            elif band_name == "£300k_to_£500k":
+                firms_in_band = (turnover_values > 300) & (
+                    turnover_values <= 500
+                )
+            elif band_name == "£500k_to_£1m":
+                firms_in_band = (turnover_values > 500) & (
+                    turnover_values <= 1000
+                )
+            elif band_name == "£1m_to_£10m":
+                firms_in_band = (turnover_values > 1000) & (
+                    turnover_values <= 10000
+                )
             else:  # Greater_than_£10m
                 firms_in_band = turnover_values > 10000
-            
+
             # Weight by VAT liability for this band's target
-            target_matrix[row_idx, firms_in_band] = vat_liability_values[firms_in_band]
-        
+            target_matrix[row_idx, firms_in_band] = vat_liability_values[
+                firms_in_band
+            ]
+
         # Calculate targets
         # £1_to_Threshold: Use ONS structure (current count from generation)
         ons_threshold_count = firms_in_threshold.sum().item()
-        
+
         # Turnover band targets (absolute numbers)
         turnover_targets = [
-            ons_threshold_count,                     # £1_to_Threshold: Keep ONS structure
-            hmrc_bands['£Threshold_to_£150k'],       # £Threshold_to_£150k: HMRC
-            hmrc_bands['£150k_to_£300k'],            # £150k_to_£300k: HMRC
-            hmrc_bands['£300k_to_£500k'],            # £300k_to_£500k: HMRC
-            hmrc_bands['£500k_to_£1m'],              # £500k_to_£1m: HMRC
-            hmrc_bands['£1m_to_£10m'],               # £1m_to_£10m: HMRC
-            hmrc_bands['Greater_than_£10m']          # Greater_than_£10m: HMRC
+            ons_threshold_count,  # £1_to_Threshold: Keep ONS structure
+            hmrc_bands["£Threshold_to_£150k"],  # £Threshold_to_£150k: HMRC
+            hmrc_bands["£150k_to_£300k"],  # £150k_to_£300k: HMRC
+            hmrc_bands["£300k_to_£500k"],  # £300k_to_£500k: HMRC
+            hmrc_bands["£500k_to_£1m"],  # £500k_to_£1m: HMRC
+            hmrc_bands["£1m_to_£10m"],  # £1m_to_£10m: HMRC
+            hmrc_bands["Greater_than_£10m"],  # Greater_than_£10m: HMRC
         ]
-        
+
         # Sector targets (direct HMRC targets for VAT-registered firms)
         sector_targets = []
         for _, sector_row in sector_rows.iterrows():
-            sector_count = sector_row['2023-24']  # Direct HMRC target
+            sector_count = sector_row["2023-24"]  # Direct HMRC target
             sector_targets.append(sector_count)
-        
+
         # Employment count targets (direct ONS employment counts)
         employment_targets = []
         # Get ONS employment totals
         ons_emp_totals = {}
         for band in emp_bands:
             if band in ons_employment_df.columns:
-                sector_rows_emp = ons_employment_df[~ons_employment_df['Description'].str.contains('Total', na=False)]
+                sector_rows_emp = ons_employment_df[
+                    ~ons_employment_df["Description"].str.contains(
+                        "Total", na=False
+                    )
+                ]
                 ons_emp_totals[band] = sector_rows_emp[band].fillna(0).sum()
             else:
                 ons_emp_totals[band] = 0
-        
+
         # Use direct ONS employment counts as targets
         for band in emp_bands:
             emp_count = ons_emp_totals[band]
             employment_targets.append(emp_count)
-        
+
         # VAT liability targets by sector (in millions £, convert to £k)
         vat_liability_sector_targets = []
         for _, vat_row in vat_liability_sector_rows.iterrows():
-            vat_liability_millions = vat_row['2023-24']  # in millions £
+            vat_liability_millions = vat_row["2023-24"]  # in millions £
             vat_liability_k = vat_liability_millions * 1000  # convert to £k
             vat_liability_sector_targets.append(vat_liability_k)
-        
+
         # VAT liability targets by turnover band (in millions £, convert to £k) - excluding Negative_or_Zero
         vat_liability_band_targets = []
-        for band_name in ['£1_to_Threshold', '£Threshold_to_£150k', 
-                          '£150k_to_£300k', '£300k_to_£500k', '£500k_to_£1m', 
-                          '£1m_to_£10m', 'Greater_than_£10m']:
-            vat_liability_millions = vat_liability_bands[band_name]  # in millions £
+        for band_name in [
+            "£1_to_Threshold",
+            "£Threshold_to_£150k",
+            "£150k_to_£300k",
+            "£300k_to_£500k",
+            "£500k_to_£1m",
+            "£1m_to_£10m",
+            "Greater_than_£10m",
+        ]:
+            vat_liability_millions = vat_liability_bands[
+                band_name
+            ]  # in millions £
             vat_liability_k = vat_liability_millions * 1000  # convert to £k
             vat_liability_band_targets.append(vat_liability_k)
-        
-        target_values_list = turnover_targets + sector_targets + employment_targets + vat_liability_sector_targets + vat_liability_band_targets
-        
+
+        target_values_list = (
+            turnover_targets
+            + sector_targets
+            + employment_targets
+            + vat_liability_sector_targets
+            + vat_liability_band_targets
+        )
+
         target_values = torch.tensor(
-            target_values_list, 
-            dtype=torch.float32, device=self.device
+            target_values_list, dtype=torch.float32, device=self.device
         )
-        
+
         logger.info(f"Target matrix shape: {target_matrix.shape}")
         logger.info(f"Calibration targets (turnover + sector + employment):")
-        
+
         # Log turnover targets
-        turnover_names = ['£1_to_Threshold (ONS)', '£Threshold_to_£150k (HMRC)', 
-                         '£150k_to_£300k (HMRC)', '£300k_to_£500k (HMRC)', 
-                         '£500k_to_£1m (HMRC)', '£1m_to_£10m (HMRC)', 'Greater_than_£10m (HMRC)']
+        turnover_names = [
+            "£1_to_Threshold (ONS)",
+            "£Threshold_to_£150k (HMRC)",
+            "£150k_to_£300k (HMRC)",
+            "£300k_to_£500k (HMRC)",
+            "£500k_to_£1m (HMRC)",
+            "£1m_to_£10m (HMRC)",
+            "Greater_than_£10m (HMRC)",
+        ]
         for target_name, target_val in zip(turnover_names, turnover_targets):
             logger.info(f"  {target_name}: {target_val:,.0f}")
-        
+
         # Log sector and employment targets (summary)
-        logger.info(f"  Sector targets: {n_sectors} sectors from HMRC data (VAT-registered)")
-        logger.info(f"  Employment count targets: {n_employment_bands} bands from ONS data (direct counts)")
-        logger.info(f"  VAT liability sector targets: {n_vat_sectors} sectors from HMRC data (in £millions)")
-        logger.info(f"  VAT liability band targets: {n_vat_bands} bands from HMRC data (in £millions, 2x weight)")
-        logger.info(f"  Total targets: {len(target_values_list)} (7 turnover + {n_sectors} sector + {n_employment_bands} employment + {n_vat_sectors} VAT liability sector + {n_vat_bands} VAT liability band)")
+        logger.info(
+            f"  Sector targets: {n_sectors} sectors from HMRC data (VAT-registered)"
+        )
+        logger.info(
+            f"  Employment count targets: {n_employment_bands} bands from ONS data (direct counts)"
+        )
+        logger.info(
+            f"  VAT liability sector targets: {n_vat_sectors} sectors from HMRC data (in £millions)"
+        )
+        logger.info(
+            f"  VAT liability band targets: {n_vat_bands} bands from HMRC data (in £millions, 2x weight)"
+        )
+        logger.info(
+            f"  Total targets: {len(target_values_list)} (7 turnover + {n_sectors} sector + {n_employment_bands} employment + {n_vat_sectors} VAT liability sector + {n_vat_bands} VAT liability band)"
+        )
         logger.info(f"  Negative_or_Zero: MANUAL (ONS doesn't have them)")
-        
+
         return target_matrix, target_values
-    
-    def optimize_weights(self, target_matrix: Tensor, target_values: Tensor,
-                               n_iterations: int = 300, lr: float = 0.01) -> Tensor:
+
+    def optimize_weights(
+        self,
+        target_matrix: Tensor,
+        target_values: Tensor,
+        n_iterations: int = 300,
+        lr: float = 0.01,
+    ) -> Tensor:
         """Optimize weights to match multiple targets simultaneously.
-        
+
         Uses symmetric relative error loss for robust calibration.
-        
+
         Args:
             target_matrix: Matrix where A[i,j] = contribution of firm j to target i
             target_values: Vector of target values to match
             n_iterations: Number of optimization iterations
             lr: Learning rate
-            
+
         Returns:
             Optimized weights
         """
         logger.info("Starting multi-objective weight optimization...")
-        
+
         _, n_firms = target_matrix.shape
-        
+
         # Initialize log-weights (ensures positive weights)
-        log_weights = torch.zeros(n_firms, device=self.device, requires_grad=True)
+        log_weights = torch.zeros(
+            n_firms, device=self.device, requires_grad=True
+        )
         optimizer = torch.optim.Adam([log_weights], lr=lr)
-        
-        best_loss = float('inf')
+
+        best_loss = float("inf")
         patience = 100
         patience_counter = 0
-        
+
         for iteration in range(n_iterations):
             optimizer.zero_grad()
-            
+
             # Convert to positive weights
             weights = torch.exp(log_weights)
-            
+
             # Apply 15% dropout during training
             if log_weights.requires_grad:  # Only during training
-                dropout_mask = torch.rand_like(weights) > 0.05  # Keep 95%, drop 15%
+                dropout_mask = (
+                    torch.rand_like(weights) > 0.05
+                )  # Keep 95%, drop 15%
                 weights = weights * dropout_mask
-            
+
             # Calculate predictions: target_matrix @ weights
             predictions = torch.matmul(target_matrix, weights)
-            
+
             # Symmetric relative error loss (robust to different target scales)
             epsilon = 1e-6
             pred_adj = predictions + epsilon
             target_adj = target_values + epsilon
-            
+
             # Symmetric relative error: min(|pred/target - 1|^2, |target/pred - 1|^2)
             error_1 = ((pred_adj / target_adj) - 1) ** 2
             error_2 = ((target_adj / pred_adj) - 1) ** 2
             sre_loss = torch.minimum(error_1, error_2)
-            
+
             # Apply importance weights based on target type
             importance_weights = torch.ones_like(sre_loss)
             importance_weights[:7] = 5.0  # 5x weight for turnover targets
-            
+
             # Calculate indices for different target types
             # Structure: 7 turnover + n_sectors + n_employment_bands + n_vat_sectors + n_vat_bands
             n_total_targets = len(sre_loss)
-            
+
             # We need to dynamically calculate the section sizes from the actual target matrix
             # For now, use fixed estimates based on typical data structure
             n_employment = 7  # Fixed: 7 employment bands
-            
+
             # The remaining targets are split between sectors and VAT liability
-            n_remaining = n_total_targets - 7 - n_employment  # Remove turnover and employment
-            n_est_sectors = n_remaining // 3  # Rough estimate (sectors, vat_sectors, vat_bands)
+            n_remaining = (
+                n_total_targets - 7 - n_employment
+            )  # Remove turnover and employment
+            n_est_sectors = (
+                n_remaining // 3
+            )  # Rough estimate (sectors, vat_sectors, vat_bands)
             n_est_vat_sectors = n_est_sectors
             n_est_vat_bands = n_remaining - n_est_sectors - n_est_vat_sectors
-            
+
             sector_start_idx = 7
             emp_start_idx = 7 + n_est_sectors
             vat_sector_start_idx = emp_start_idx + n_employment
             vat_band_start_idx = vat_sector_start_idx + n_est_vat_sectors
-            
+
             # Set importance weights
-            importance_weights[sector_start_idx:emp_start_idx] = 1.0  # 1x weight for sector targets
-            importance_weights[emp_start_idx:vat_sector_start_idx] = 1.0  # 1x weight for employment targets
-            importance_weights[vat_sector_start_idx:vat_band_start_idx] = 1.0  # 1x weight for VAT liability sector targets
-            importance_weights[vat_band_start_idx:] = 2.0  # 2x weight for VAT liability band targets
-            
+            importance_weights[sector_start_idx:emp_start_idx] = (
+                1.0  # 1x weight for sector targets
+            )
+            importance_weights[emp_start_idx:vat_sector_start_idx] = (
+                1.0  # 1x weight for employment targets
+            )
+            importance_weights[vat_sector_start_idx:vat_band_start_idx] = (
+                1.0  # 1x weight for VAT liability sector targets
+            )
+            importance_weights[vat_band_start_idx:] = (
+                2.0  # 2x weight for VAT liability band targets
+            )
+
             weighted_loss = sre_loss * importance_weights
             total_loss = torch.mean(weighted_loss)
-            
+
             # Add regularization to prevent extreme weights
             reg_loss = 0.01 * torch.mean(torch.abs(log_weights))
             total_loss += reg_loss
-            
+
             total_loss.backward()
-            
+
             # Gradient clipping for stability
             torch.nn.utils.clip_grad_norm_([log_weights], max_norm=1.0)
-            
+
             optimizer.step()
-            
+
             # Early stopping
             if total_loss.item() < best_loss:
                 best_loss = total_loss.item()
                 patience_counter = 0
             else:
                 patience_counter += 1
-            
+
             if iteration % 100 == 0:
-                logger.info(f"Iteration {iteration}: Loss = {total_loss.item():.6f}")
-            
+                logger.info(
+                    f"Iteration {iteration}: Loss = {total_loss.item():.6f}"
+                )
+
             if patience_counter > patience:
                 logger.info(f"Early stopping at iteration {iteration}")
                 break
-        
+
         final_weights = torch.exp(log_weights)
         final_predictions = torch.matmul(target_matrix, final_weights)
-        
+
         logger.info("Optimization complete:")
-        target_names = ['Negative_or_Zero', '£1_to_Threshold', '£Threshold_to_£150k', '£150k_to_£300k', 
-                       '£300k_to_£500k', '£500k_to_£1m', '£1m_to_£10m', 'Greater_than_£10m']
-        for i, (pred, target, name) in enumerate(zip(final_predictions, target_values, target_names)):
+        target_names = [
+            "Negative_or_Zero",
+            "£1_to_Threshold",
+            "£Threshold_to_£150k",
+            "£150k_to_£300k",
+            "£300k_to_£500k",
+            "£500k_to_£1m",
+            "£1m_to_£10m",
+            "Greater_than_£10m",
+        ]
+        for i, (pred, target, name) in enumerate(
+            zip(final_predictions, target_values, target_names)
+        ):
             if target > 0:  # Only log targets we're actually trying to match
                 accuracy = 1 - abs(pred - target) / target
-                logger.info(f"  {name}: {pred:.0f} vs {target:.0f} ({accuracy:.1%})")
+                logger.info(
+                    f"  {name}: {pred:.0f} vs {target:.0f} ({accuracy:.1%})"
+                )
             else:
                 logger.info(f"  {name}: SKIPPED (pred: {pred:.0f})")
-        
+
         return final_weights.detach()
-    
-    def apply_final_calibration(self, base_sic_codes: Tensor, base_turnover: Tensor, base_input: Tensor,
-                                     weights_tensor: Tensor, hmrc_bands: Dict[str, int]) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
+
+    def apply_final_calibration(
+        self,
+        base_sic_codes: Tensor,
+        base_turnover: Tensor,
+        base_input: Tensor,
+        weights_tensor: Tensor,
+        hmrc_bands: Dict[str, int],
+    ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
         """Apply final calibration adjustments.
-        
+
         Key calibration steps:
         1. Keep ALL base firms (no sampling)
         2. Add zero/negative turnover firms manually
         3. Apply calibration weights to match targets
-        
+
         Args:
             base_sic_codes: Original base SIC codes
             base_turnover: Original base turnover values
             base_input: Original base input values
             weights_tensor: Calibration weights from optimization
             hmrc_bands: HMRC target bands
-            
+
         Returns:
             Tuple of final (sic_codes, turnover, input, weights) tensors
         """
         logger.info("Applying final calibration adjustments...")
-        
+
         all_final_firms = []
-        
+
         # Step 1: Add ALL base firms with their weights
         hmrc_band_indices = self._map_to_hmrc_bands(base_turnover)
-        
+
         logger.info(f"Adding {len(base_sic_codes):,} base firms...")
         for i in range(len(base_sic_codes)):
             band_idx = hmrc_band_indices[i].item()
-            
+
             # Use calibrated weights from optimization for all bands
             weight = weights_tensor[i].item()
-            
-            all_final_firms.append({
-                'sic_code': base_sic_codes[i].item(),
-                'annual_turnover_k': base_turnover[i].item(),
-                'annual_input_k': base_input[i].item(),
-                'weight': weight
-            })
-        
+
+            all_final_firms.append(
+                {
+                    "sic_code": base_sic_codes[i].item(),
+                    "annual_turnover_k": base_turnover[i].item(),
+                    "annual_input_k": base_input[i].item(),
+                    "weight": weight,
+                }
+            )
+
         # Step 2: Manually add zero/negative turnover firms
-        negative_zero_target = hmrc_bands['Negative_or_Zero']
-        
+        negative_zero_target = hmrc_bands["Negative_or_Zero"]
+
         if negative_zero_target > 0:
             # Get current sector distribution for proportional allocation
-            unique_sics, counts = torch.unique(base_sic_codes, return_counts=True)
+            unique_sics, counts = torch.unique(
+                base_sic_codes, return_counts=True
+            )
             total_current_firms = len(base_sic_codes)
-            
-            logger.info(f"Manually adding {negative_zero_target:,} zero turnover firms from HMRC...")
+
+            logger.info(
+                f"Manually adding {negative_zero_target:,} zero turnover firms from HMRC..."
+            )
             zero_firms_count = 0
-            
+
             for sic, count in zip(unique_sics, counts):
                 sector_weight = count.float() / total_current_firms
-                firms_for_sector = int((negative_zero_target * sector_weight).item())
+                firms_for_sector = int(
+                    (negative_zero_target * sector_weight).item()
+                )
                 if firms_for_sector > 0:
                     for _ in range(firms_for_sector):
-                        all_final_firms.append({
-                            'sic_code': sic.item(),
-                            'annual_turnover_k': 0.0,
-                            'annual_input_k': 0.0,
-                            'weight': 1.0
-                        })
+                        all_final_firms.append(
+                            {
+                                "sic_code": sic.item(),
+                                "annual_turnover_k": 0.0,
+                                "annual_input_k": 0.0,
+                                "weight": 1.0,
+                            }
+                        )
                         zero_firms_count += 1
-            
+
             logger.info(f"Added {zero_firms_count:,} firms with zero turnover")
-        
+
         # Convert back to tensors
         if all_final_firms:
-            final_sics = torch.tensor([f['sic_code'] for f in all_final_firms], 
-                                    dtype=torch.int64, device=self.device)
-            final_turnover = torch.tensor([f['annual_turnover_k'] for f in all_final_firms], 
-                                        dtype=torch.float32, device=self.device)
-            final_input = torch.tensor([f['annual_input_k'] for f in all_final_firms],
-                                     dtype=torch.float32, device=self.device)
-            final_weights = torch.tensor([f['weight'] for f in all_final_firms], 
-                                       dtype=torch.float32, device=self.device)
-            
+            final_sics = torch.tensor(
+                [f["sic_code"] for f in all_final_firms],
+                dtype=torch.int64,
+                device=self.device,
+            )
+            final_turnover = torch.tensor(
+                [f["annual_turnover_k"] for f in all_final_firms],
+                dtype=torch.float32,
+                device=self.device,
+            )
+            final_input = torch.tensor(
+                [f["annual_input_k"] for f in all_final_firms],
+                dtype=torch.float32,
+                device=self.device,
+            )
+            final_weights = torch.tensor(
+                [f["weight"] for f in all_final_firms],
+                dtype=torch.float32,
+                device=self.device,
+            )
+
             logger.info(f"Final dataset: {len(all_final_firms):,} firms")
-            logger.info(f"Total weighted population: {final_weights.sum():.0f}")
+            logger.info(
+                f"Total weighted population: {final_weights.sum():.0f}"
+            )
         else:
             # Empty tensors if no firms generated
             final_sics = torch.empty(0, dtype=torch.int64, device=self.device)
-            final_turnover = torch.empty(0, dtype=torch.float32, device=self.device)
-            final_input = torch.empty(0, dtype=torch.float32, device=self.device)
-            final_weights = torch.empty(0, dtype=torch.float32, device=self.device)
-        
+            final_turnover = torch.empty(
+                0, dtype=torch.float32, device=self.device
+            )
+            final_input = torch.empty(
+                0, dtype=torch.float32, device=self.device
+            )
+            final_weights = torch.empty(
+                0, dtype=torch.float32, device=self.device
+            )
+
         return final_sics, final_turnover, final_input, final_weights
-    
+
     def _map_to_hmrc_bands(self, turnover_values: Tensor) -> Tensor:
         """Map turnover values to HMRC band indices.
-        
+
         Args:
             turnover_values: Array of turnover values
-            
+
         Returns:
             HMRC band indices (0-7 for 8 HMRC bands)
         """
         # Initialize with highest band (Greater_than_£10m = 7)
         band_indices = torch.full_like(turnover_values, 7, dtype=torch.long)
-        
+
         # Assign bands based on turnover thresholds
-        band_indices = torch.where(turnover_values <= 0, 0, band_indices)  # Negative_or_Zero
-        band_indices = torch.where((turnover_values > 0) & (turnover_values <= 85), 1, band_indices)  # £1_to_Threshold
-        band_indices = torch.where((turnover_values > 85) & (turnover_values <= 150), 2, band_indices)  # £Threshold_to_£150k
-        band_indices = torch.where((turnover_values > 150) & (turnover_values <= 300), 3, band_indices)  # £150k_to_£300k
-        band_indices = torch.where((turnover_values > 300) & (turnover_values <= 500), 4, band_indices)  # £300k_to_£500k
-        band_indices = torch.where((turnover_values > 500) & (turnover_values <= 1000), 5, band_indices)  # £500k_to_£1m
-        band_indices = torch.where((turnover_values > 1000) & (turnover_values <= 10000), 6, band_indices)  # £1m_to_£10m
-        
+        band_indices = torch.where(
+            turnover_values <= 0, 0, band_indices
+        )  # Negative_or_Zero
+        band_indices = torch.where(
+            (turnover_values > 0) & (turnover_values <= 85), 1, band_indices
+        )  # £1_to_Threshold
+        band_indices = torch.where(
+            (turnover_values > 85) & (turnover_values <= 150), 2, band_indices
+        )  # £Threshold_to_£150k
+        band_indices = torch.where(
+            (turnover_values > 150) & (turnover_values <= 300), 3, band_indices
+        )  # £150k_to_£300k
+        band_indices = torch.where(
+            (turnover_values > 300) & (turnover_values <= 500), 4, band_indices
+        )  # £300k_to_£500k
+        band_indices = torch.where(
+            (turnover_values > 500) & (turnover_values <= 1000),
+            5,
+            band_indices,
+        )  # £500k_to_£1m
+        band_indices = torch.where(
+            (turnover_values > 1000) & (turnover_values <= 10000),
+            6,
+            band_indices,
+        )  # £1m_to_£10m
+
         return band_indices
-    
+
     def _print_validation_section(self, title: str, width: int = 65):
         """Print a formatted validation section header."""
         print(f"\n{title}:")
         print("-" * width)
-    
-    def _print_accuracy_breakdown(self, accuracies: list, n_items: int, label: str):
+
+    def _print_accuracy_breakdown(
+        self, accuracies: list, n_items: int, label: str
+    ):
         """Print standardized accuracy breakdown."""
         accuracy_95_plus = sum(1 for acc in accuracies if acc >= 0.95)
         accuracy_90_95 = sum(1 for acc in accuracies if 0.90 <= acc < 0.95)
         accuracy_80_90 = sum(1 for acc in accuracies if 0.80 <= acc < 0.90)
         accuracy_below_80 = sum(1 for acc in accuracies if acc < 0.80)
-        
+
         overall_accuracy = np.mean(accuracies) if accuracies else 0.0
         print(f"{label} OVERALL ACCURACY: {overall_accuracy:.1%}")
-        print(f"Accuracy breakdown: ≥95%: {accuracy_95_plus}/{n_items}, "
-              f"90-95%: {accuracy_90_95}/{n_items}, "
-              f"80-90%: {accuracy_80_90}/{n_items}, "
-              f"<80%: {accuracy_below_80}/{n_items}")
+        print(
+            f"Accuracy breakdown: ≥95%: {accuracy_95_plus}/{n_items}, "
+            f"90-95%: {accuracy_90_95}/{n_items}, "
+            f"80-90%: {accuracy_80_90}/{n_items}, "
+            f"<80%: {accuracy_below_80}/{n_items}"
+        )
         return overall_accuracy
-    
-    def generate_input_values(self, turnover_values: Tensor, sic_codes: Tensor) -> Tensor:
+
+    def generate_input_values(
+        self, turnover_values: Tensor, sic_codes: Tensor
+    ) -> Tensor:
         """Generate input values for firms directly from distributions.
-        
+
         Input values are drawn from beta distributions that allow for:
         - Most firms: inputs 60-95% of turnover (positive VAT liability)
         - Some firms: inputs > turnover (negative VAT liability)
-        
+
         Args:
             turnover_values: Array of turnover values (output)
             sic_codes: Array of SIC codes (used for sector-specific variations)
-            
+
         Returns:
             Array of input values in £k
         """
         logger.info("Generating input values for firms from distributions...")
-        
+
         n_firms = len(turnover_values)
         input_values = torch.zeros_like(turnover_values)
-        
+
         # Generate input/output ratios from a beta distribution
         # Beta(4, 2) gives a distribution centered around 0.67 with range [0, 1]
         # We'll scale and shift this to get our desired range
         alpha, beta = 4.0, 2.0
-        base_ratios = torch.distributions.Beta(alpha, beta).sample((n_firms,)).to(self.device)
-        
+        base_ratios = (
+            torch.distributions.Beta(alpha, beta)
+            .sample((n_firms,))
+            .to(self.device)
+        )
+
         # Scale to range [0.3, 1.3] to allow both positive and negative VAT liability
         # Most values will be in [0.6, 0.95] range
         scaled_ratios = 0.3 + base_ratios * 1.0  # Maps [0,1] to [0.3, 1.3]
-        
+
         # Add sector-specific noise
         sector_noise = torch.randn(n_firms, device=self.device) * 0.15
-        
+
         # For some sectors, shift the distribution to make negative liability more likely
         for i in range(n_firms):
             sic = sic_codes[i].item()
-            
+
             # Add extra variation for certain sectors that tend to have extreme values
-            if sic in [1, 3, 6, 7, 9, 10, 24, 30, 36, 37, 49, 50, 51, 60, 64, 79, 84]:
+            if sic in [
+                1,
+                3,
+                6,
+                7,
+                9,
+                10,
+                24,
+                30,
+                36,
+                37,
+                49,
+                50,
+                51,
+                60,
+                64,
+                79,
+                84,
+            ]:
                 # These sectors often have negative VAT liability
                 # Add positive bias to input ratio
-                scaled_ratios[i] += torch.rand(1, device=self.device).item() * 0.3
+                scaled_ratios[i] += (
+                    torch.rand(1, device=self.device).item() * 0.3
+                )
             elif sic in [11, 12, 69, 70, 78]:
                 # These sectors often have high VAT liability (low inputs)
                 # Add negative bias to input ratio
-                scaled_ratios[i] -= torch.rand(1, device=self.device).item() * 0.2
-        
+                scaled_ratios[i] -= (
+                    torch.rand(1, device=self.device).item() * 0.2
+                )
+
         # Apply ratios with noise
         final_ratios = torch.clamp(scaled_ratios + sector_noise, 0.1, 1.5)
-        
+
         # Generate input values
         for i in range(n_firms):
             if turnover_values[i] <= 0:
                 input_values[i] = 0
             else:
                 input_values[i] = turnover_values[i] * final_ratios[i]
-        
+
         logger.info(f"Generated input values for {n_firms:,} firms")
         logger.info(f"Input/output ratio statistics:")
         logger.info(f"  Mean: {final_ratios.mean():.2%}")
         logger.info(f"  Std: {final_ratios.std():.2%}")
         logger.info(f"  Min: {final_ratios.min():.2%}")
         logger.info(f"  Max: {final_ratios.max():.2%}")
-        logger.info(f"  Firms with negative VAT liability (input > output): {(final_ratios > 1.0).sum().item():,}")
-        
+        logger.info(
+            f"  Firms with negative VAT liability (input > output): {(final_ratios > 1.0).sum().item():,}"
+        )
+
         return input_values
-    
-    def assign_employment(self, num_firms: int, 
-                               ons_employment_df: pd.DataFrame) -> Tensor:
+
+    def assign_employment(
+        self, num_firms: int, ons_employment_df: pd.DataFrame
+    ) -> Tensor:
         """Assign employment using ONS distribution.
-        
+
         Args:
             num_firms: Number of firms to assign employment to
             ons_employment_df: ONS employment data
-            
+
         Returns:
             Array of employment values
         """
         logger.info("Assigning employment using ONS distribution...")
-        
+
         # Employment bands and parameters
-        emp_bands = ['0-4', '5-9', '10-19', '20-49', '50-99', '100-249', '250+']
+        emp_bands = [
+            "0-4",
+            "5-9",
+            "10-19",
+            "20-49",
+            "50-99",
+            "100-249",
+            "250+",
+        ]
         band_params = {
-            '0-4': (1, 4, 2.5),
-            '5-9': (5, 9, 7),
-            '10-19': (10, 19, 14.5),
-            '20-49': (20, 49, 34.5),
-            '50-99': (50, 99, 74.5),
-            '100-249': (100, 249, 174.5),
-            '250+': (250, 2000, 400)
+            "0-4": (1, 4, 2.5),
+            "5-9": (5, 9, 7),
+            "10-19": (10, 19, 14.5),
+            "20-49": (20, 49, 34.5),
+            "50-99": (50, 99, 74.5),
+            "100-249": (100, 249, 174.5),
+            "250+": (250, 2000, 400),
         }
-        
+
         # Calculate ONS employment band totals
         total_ons_counts = {}
         for band in emp_bands:
             if band in ons_employment_df.columns:
-                sector_rows = ons_employment_df[~ons_employment_df['Description'].str.contains('Total', na=False)]
+                sector_rows = ons_employment_df[
+                    ~ons_employment_df["Description"].str.contains(
+                        "Total", na=False
+                    )
+                ]
                 total_ons_counts[band] = int(sector_rows[band].fillna(0).sum())
             else:
                 total_ons_counts[band] = 0
-        
+
         total_ons_firms = sum(total_ons_counts.values())
-        
+
         # Calculate target counts for each band
         employment_values = []
         for band in emp_bands:
-            target_count = int(round(num_firms * total_ons_counts[band] / total_ons_firms))
+            target_count = int(
+                round(num_firms * total_ons_counts[band] / total_ons_firms)
+            )
             if target_count > 0:
                 min_val, max_val, midpoint = band_params[band]
-                
+
                 # Generate employment values
-                if band == '0-4':
+                if band == "0-4":
                     # Uniform for micro businesses
-                    values = torch.randint(1, 5, (target_count,), device=self.device)
-                elif band == '250+':
+                    values = torch.randint(
+                        1, 5, (target_count,), device=self.device
+                    )
+                elif band == "250+":
                     # Log-normal for large firms
-                    log_mean = torch.log(torch.tensor(midpoint, device=self.device))
-                    values = torch.normal(log_mean, 0.8, (target_count,), device=self.device).exp()
+                    log_mean = torch.log(
+                        torch.tensor(midpoint, device=self.device)
+                    )
+                    values = torch.normal(
+                        log_mean, 0.8, (target_count,), device=self.device
+                    ).exp()
                     values = torch.clamp(values, min_val, max_val).round()
                 else:
                     # Beta distribution for others
                     uniform = torch.rand(target_count, device=self.device)
-                    beta_values = uniform.pow(0.5) * (1 - uniform).pow(2.0)  # Beta approximation
+                    beta_values = uniform.pow(0.5) * (1 - uniform).pow(
+                        2.0
+                    )  # Beta approximation
                     values = min_val + beta_values * (max_val - min_val)
                     values = values.round()
-                
+
                 employment_values.extend(values.cpu().numpy())
-        
+
         # Shuffle and pad/trim to exact size
         np.random.shuffle(employment_values)
         if len(employment_values) < num_firms:
-            employment_values.extend([1] * (num_firms - len(employment_values)))
+            employment_values.extend(
+                [1] * (num_firms - len(employment_values))
+            )
         elif len(employment_values) > num_firms:
             employment_values = employment_values[:num_firms]
-        
-        employment_array = torch.tensor(employment_values, 
-                                       dtype=torch.float32, device=self.device)
-        
+
+        employment_array = torch.tensor(
+            employment_values, dtype=torch.float32, device=self.device
+        )
+
         logger.info(f"Assigned employment to {num_firms:,} firms")
-        
+
         return employment_array
-    
-    def validate_comprehensive_accuracy(self, synthetic_df: pd.DataFrame, hmrc_target_bands: Dict[str, int],
-                                       ons_total_target: int, ons_employment_df: pd.DataFrame, 
-                                       hmrc_sector_df: pd.DataFrame, vat_liability_df: pd.DataFrame, 
-                                       vat_liability_band_df: pd.DataFrame) -> Tuple[float, float, float, float, float, float]:
+
+    def validate_comprehensive_accuracy(
+        self,
+        synthetic_df: pd.DataFrame,
+        hmrc_target_bands: Dict[str, int],
+        ons_total_target: int,
+        ons_employment_df: pd.DataFrame,
+        hmrc_sector_df: pd.DataFrame,
+        vat_liability_df: pd.DataFrame,
+        vat_liability_band_df: pd.DataFrame,
+    ) -> Tuple[float, float, float, float, float, float]:
         """Validate synthetic data against official data sources.
-        
+
         Args:
             synthetic_df: Generated synthetic data with VAT flags
             hmrc_target_bands: HMRC VAT-registered firm targets by turnover band
-            ons_total_target: ONS total firm count target  
+            ons_total_target: ONS total firm count target
             ons_employment_df: ONS employment data for validation
             hmrc_sector_df: HMRC sector data for validation
             vat_liability_df: VAT liability data for validation
-            
+
         Returns:
             Tuple of (hmrc_accuracy, ons_accuracy, employment_accuracy, sector_accuracy, vat_liability_accuracy)
         """
-        logger.info("Validating synthetic data against official data sources...")
-        
+        logger.info(
+            "Validating synthetic data against official data sources..."
+        )
+
         # 1. HMRC VAT Firm Validation (VAT-registered firms only)
         def map_to_hmrc_band(turnover_k):
             if turnover_k <= 0:
-                return 'Negative_or_Zero'
+                return "Negative_or_Zero"
             elif turnover_k <= 85:
-                return '£1_to_Threshold'
+                return "£1_to_Threshold"
             elif turnover_k <= 150:
-                return '£Threshold_to_£150k'
+                return "£Threshold_to_£150k"
             elif turnover_k <= 300:
-                return '£150k_to_£300k'
+                return "£150k_to_£300k"
             elif turnover_k <= 500:
-                return '£300k_to_£500k'
+                return "£300k_to_£500k"
             elif turnover_k <= 1000:
-                return '£500k_to_£1m'
+                return "£500k_to_£1m"
             elif turnover_k <= 10000:
-                return '£1m_to_£10m'
+                return "£1m_to_£10m"
             else:
-                return 'Greater_than_£10m'
-        
+                return "Greater_than_£10m"
+
         # Map all firms to HMRC bands for validation
-        synthetic_df['hmrc_band'] = synthetic_df['annual_turnover_k'].apply(map_to_hmrc_band)
-        all_bands = synthetic_df.groupby('hmrc_band')['weight'].sum().round().astype(int)
-        
+        synthetic_df["hmrc_band"] = synthetic_df["annual_turnover_k"].apply(
+            map_to_hmrc_band
+        )
+        all_bands = (
+            synthetic_df.groupby("hmrc_band")["weight"]
+            .sum()
+            .round()
+            .astype(int)
+        )
+
         # === VAT REGISTRATION VALIDATION ===
-        vat_registered_count = synthetic_df[synthetic_df['vat_registered'] == True]['weight'].sum()
+        vat_registered_count = synthetic_df[
+            synthetic_df["vat_registered"] == True
+        ]["weight"].sum()
         hmrc_total_vat = sum(hmrc_target_bands.values())
-        vat_accuracy = 1 - abs(vat_registered_count - hmrc_total_vat) / hmrc_total_vat
-        
+        vat_accuracy = (
+            1 - abs(vat_registered_count - hmrc_total_vat) / hmrc_total_vat
+        )
+
         self._print_validation_section("VAT REGISTRATION VALIDATION", 80)
         print(f"VAT Registered (weighted): {vat_registered_count:,.0f}")
         print(f"HMRC Total VAT firms:      {hmrc_total_vat:,}")
-        print(f"Difference:                {vat_registered_count - hmrc_total_vat:+,.0f}")
+        print(
+            f"Difference:                {vat_registered_count - hmrc_total_vat:+,.0f}"
+        )
         print(f"VAT Registration Accuracy: {vat_accuracy:.1%}")
-        
+
         # === TURNOVER BAND VALIDATION ===
         self._print_validation_section("TURNOVER BAND VALIDATION", 80)
-        print(f"{'Band':>25} {'Synthetic':>12} {'Target':>12} {'Source':>8} {'Accuracy':>10}")
+        print(
+            f"{'Band':>25} {'Synthetic':>12} {'Target':>12} {'Source':>8} {'Accuracy':>10}"
+        )
         print("-" * 75)
-        
+
         hmrc_band_accuracies = []
-        
+
         # Validate each HMRC band
         for band_name, target_count in hmrc_target_bands.items():
             synthetic_count = all_bands.get(band_name, 0)
-            accuracy = 1 - abs(synthetic_count - target_count) / target_count if target_count > 0 else (1.0 if synthetic_count == 0 else 0.0)
-            
+            accuracy = (
+                1 - abs(synthetic_count - target_count) / target_count
+                if target_count > 0
+                else (1.0 if synthetic_count == 0 else 0.0)
+            )
+
             # Special handling for £1_to_Threshold (ONS-based, not HMRC target)
-            if band_name == '£1_to_Threshold':
+            if band_name == "£1_to_Threshold":
                 status = "○"
                 source = "ONS-based"
             else:
                 hmrc_band_accuracies.append(accuracy)
-                status = "✓" if accuracy > 0.90 else "⚠" if accuracy > 0.80 else "✗"
+                status = (
+                    "✓" if accuracy > 0.90 else "⚠" if accuracy > 0.80 else "✗"
+                )
                 source = "HMRC"
-            
-            print(f"  {status} {band_name:>22}: {synthetic_count:>10,} vs {target_count:>10,} {source:>8} ({accuracy:>6.1%})")
-        
-        hmrc_accuracy = np.mean(hmrc_band_accuracies) if hmrc_band_accuracies else 0.0
+
+            print(
+                f"  {status} {band_name:>22}: {synthetic_count:>10,} vs {target_count:>10,} {source:>8} ({accuracy:>6.1%})"
+            )
+
+        hmrc_accuracy = (
+            np.mean(hmrc_band_accuracies) if hmrc_band_accuracies else 0.0
+        )
         print("-" * 75)
         print(f"HMRC CALIBRATION ACCURACY: {hmrc_accuracy:.1%}")
-        
+
         # === ONS POPULATION VALIDATION ===
-        total_synthetic_weighted = synthetic_df['weight'].sum()
-        ons_population_accuracy = 1 - abs(total_synthetic_weighted - ons_total_target) / ons_total_target
-        
+        total_synthetic_weighted = synthetic_df["weight"].sum()
+        ons_population_accuracy = (
+            1
+            - abs(total_synthetic_weighted - ons_total_target)
+            / ons_total_target
+        )
+
         self._print_validation_section("ONS POPULATION VALIDATION")
         print(f"Synthetic Total: {total_synthetic_weighted:,.0f}")
         print(f"ONS Target:      {ons_total_target:,}")
-        print(f"Difference:      {total_synthetic_weighted - ons_total_target:+,.0f}")
+        print(
+            f"Difference:      {total_synthetic_weighted - ons_total_target:+,.0f}"
+        )
         print(f"ONS Accuracy:    {ons_population_accuracy:.1%}")
-        
+
         # === EMPLOYMENT BAND VALIDATION ===
         def _map_employment_to_band(employment: int) -> str:
             """Map employment count to ONS employment band."""
             if employment <= 4:
-                return '0-4'
+                return "0-4"
             elif employment <= 9:
-                return '5-9'
+                return "5-9"
             elif employment <= 19:
-                return '10-19'
+                return "10-19"
             elif employment <= 49:
-                return '20-49'
+                return "20-49"
             elif employment <= 99:
-                return '50-99'
+                return "50-99"
             elif employment <= 249:
-                return '100-249'
+                return "100-249"
             else:
-                return '250+'
-        
-        employment_bands = ['0-4', '5-9', '10-19', '20-49', '50-99', '100-249', '250+']
+                return "250+"
+
+        employment_bands = [
+            "0-4",
+            "5-9",
+            "10-19",
+            "20-49",
+            "50-99",
+            "100-249",
+            "250+",
+        ]
         ons_employment_targets = {}
-        
+
         # Get ONS employment targets
         for band in employment_bands:
             if band in ons_employment_df.columns:
-                sector_rows = ons_employment_df[~ons_employment_df['Description'].str.contains('Total', na=False)]
-                ons_employment_targets[band] = sector_rows[band].fillna(0).sum()
+                sector_rows = ons_employment_df[
+                    ~ons_employment_df["Description"].str.contains(
+                        "Total", na=False
+                    )
+                ]
+                ons_employment_targets[band] = (
+                    sector_rows[band].fillna(0).sum()
+                )
             else:
                 ons_employment_targets[band] = 0
-        
+
         # Calculate synthetic employment distribution
-        synthetic_df['employment_band'] = synthetic_df['employment'].apply(_map_employment_to_band)
-        synthetic_employment_counts = synthetic_df.groupby('employment_band')['weight'].sum().round().astype(int)
-        
+        synthetic_df["employment_band"] = synthetic_df["employment"].apply(
+            _map_employment_to_band
+        )
+        synthetic_employment_counts = (
+            synthetic_df.groupby("employment_band")["weight"]
+            .sum()
+            .round()
+            .astype(int)
+        )
+
         self._print_validation_section("EMPLOYMENT BAND VALIDATION")
-        print(f"{'Band':>8} {'Synthetic':>10} {'ONS Target':>11} {'Accuracy':>10}")
+        print(
+            f"{'Band':>8} {'Synthetic':>10} {'ONS Target':>11} {'Accuracy':>10}"
+        )
         print("-" * 65)
-        
+
         employment_accuracies = []
         for band in employment_bands:
             ons_target = ons_employment_targets.get(band, 0)
             synthetic_count = synthetic_employment_counts.get(band, 0)
-            accuracy = 1 - abs(synthetic_count - ons_target) / ons_target if ons_target > 0 else (1.0 if synthetic_count == 0 else 0.0)
+            accuracy = (
+                1 - abs(synthetic_count - ons_target) / ons_target
+                if ons_target > 0
+                else (1.0 if synthetic_count == 0 else 0.0)
+            )
             employment_accuracies.append(accuracy)
-            
-            status = "✓" if accuracy > 0.90 else "⚠" if accuracy > 0.80 else "✗"
-            print(f"  {status} {band:>6}: {synthetic_count:>8,} vs {ons_target:>9,} ({accuracy:>6.1%})")
-        
+
+            status = (
+                "✓" if accuracy > 0.90 else "⚠" if accuracy > 0.80 else "✗"
+            )
+            print(
+                f"  {status} {band:>6}: {synthetic_count:>8,} vs {ons_target:>9,} ({accuracy:>6.1%})"
+            )
+
         print("-" * 65)
-        employment_accuracy = self._print_accuracy_breakdown(employment_accuracies, len(employment_bands), "EMPLOYMENT")
-        
+        employment_accuracy = self._print_accuracy_breakdown(
+            employment_accuracies, len(employment_bands), "EMPLOYMENT"
+        )
+
         # === SECTOR VALIDATION (VAT-registered firms only) ===
-        sector_rows = hmrc_sector_df[hmrc_sector_df['Trade_Sector'] != 'Total'].copy()
-        
+        sector_rows = hmrc_sector_df[
+            hmrc_sector_df["Trade_Sector"] != "Total"
+        ].copy()
+
         # Calculate synthetic VAT-registered sector distribution
-        synthetic_df['sic_numeric'] = synthetic_df['sic_code'].astype(int)
-        vat_registered_firms = synthetic_df[synthetic_df['vat_registered'] == True]
-        synthetic_vat_sector_counts = vat_registered_firms.groupby('sic_numeric')['weight'].sum().round().astype(int)
-        
-        self._print_validation_section("SECTOR VALIDATION (VAT-registered firms)")
-        print(f"{'SIC':>5} {'Synthetic VAT':>12} {'HMRC Target':>12} {'Accuracy':>10}")
+        synthetic_df["sic_numeric"] = synthetic_df["sic_code"].astype(int)
+        vat_registered_firms = synthetic_df[
+            synthetic_df["vat_registered"] == True
+        ]
+        synthetic_vat_sector_counts = (
+            vat_registered_firms.groupby("sic_numeric")["weight"]
+            .sum()
+            .round()
+            .astype(int)
+        )
+
+        self._print_validation_section(
+            "SECTOR VALIDATION (VAT-registered firms)"
+        )
+        print(
+            f"{'SIC':>5} {'Synthetic VAT':>12} {'HMRC Target':>12} {'Accuracy':>10}"
+        )
         print("-" * 65)
-        
+
         sector_accuracies = []
         for _, sector_row in sector_rows.iterrows():
-            sic_code = int(sector_row['Trade_Sector'])
-            hmrc_target = sector_row['2023-24']
-            
+            sic_code = int(sector_row["Trade_Sector"])
+            hmrc_target = sector_row["2023-24"]
+
             synthetic_vat_count = synthetic_vat_sector_counts.get(sic_code, 0)
-            accuracy = 1 - abs(synthetic_vat_count - hmrc_target) / hmrc_target if hmrc_target > 0 else (1.0 if synthetic_vat_count == 0 else 0.0)
+            accuracy = (
+                1 - abs(synthetic_vat_count - hmrc_target) / hmrc_target
+                if hmrc_target > 0
+                else (1.0 if synthetic_vat_count == 0 else 0.0)
+            )
             sector_accuracies.append(accuracy)
-            
-            status = "✓" if accuracy > 0.90 else "⚠" if accuracy > 0.80 else "✗"
-            print(f"  {status} {sic_code:>3}: {synthetic_vat_count:>10,} vs {hmrc_target:>10,} ({accuracy:>6.1%})")
-        
+
+            status = (
+                "✓" if accuracy > 0.90 else "⚠" if accuracy > 0.80 else "✗"
+            )
+            print(
+                f"  {status} {sic_code:>3}: {synthetic_vat_count:>10,} vs {hmrc_target:>10,} ({accuracy:>6.1%})"
+            )
+
         print("-" * 65)
-        sector_accuracy = self._print_accuracy_breakdown(sector_accuracies, len(sector_accuracies), "SECTOR")
-        
+        sector_accuracy = self._print_accuracy_breakdown(
+            sector_accuracies, len(sector_accuracies), "SECTOR"
+        )
+
         # === VAT LIABILITY VALIDATION ===
         # Get VAT liability sector data (excluding total)
-        vat_liability_rows = vat_liability_df[vat_liability_df['Trade_Sector'] != 'Total'].copy()
-        
+        vat_liability_rows = vat_liability_df[
+            vat_liability_df["Trade_Sector"] != "Total"
+        ].copy()
+
         # Calculate synthetic VAT liability by sector
-        synthetic_df['vat_liability_k'] = synthetic_df['annual_turnover_k'] - synthetic_df['annual_input_k']
-        synthetic_df['sic_numeric'] = synthetic_df['sic_code'].astype(int)
-        
+        synthetic_df["vat_liability_k"] = (
+            synthetic_df["annual_turnover_k"] - synthetic_df["annual_input_k"]
+        )
+        synthetic_df["sic_numeric"] = synthetic_df["sic_code"].astype(int)
+
         # Group by sector and calculate weighted VAT liability
-        synthetic_vat_liability = synthetic_df.groupby('sic_numeric').apply(
-            lambda x: (x['vat_liability_k'] * x['weight']).sum()
-        ).reset_index(name='synthetic_vat_liability_k')
-        
+        synthetic_vat_liability = (
+            synthetic_df.groupby("sic_numeric")
+            .apply(lambda x: (x["vat_liability_k"] * x["weight"]).sum())
+            .reset_index(name="synthetic_vat_liability_k")
+        )
+
         self._print_validation_section("VAT LIABILITY VALIDATION (by sector)")
-        print(f"{'SIC':>5} {'Synthetic (£m)':>14} {'Target (£m)':>12} {'Accuracy':>10}")
+        print(
+            f"{'SIC':>5} {'Synthetic (£m)':>14} {'Target (£m)':>12} {'Accuracy':>10}"
+        )
         print("-" * 65)
-        
+
         vat_liability_accuracies = []
         for _, vat_row in vat_liability_rows.iterrows():
-            sic_code = int(vat_row['Trade_Sector'])
-            target_millions = vat_row['2023-24']  # in millions £
+            sic_code = int(vat_row["Trade_Sector"])
+            target_millions = vat_row["2023-24"]  # in millions £
             target_k = target_millions * 1000  # convert to £k
-            
+
             # Get synthetic VAT liability for this sector
-            sector_data = synthetic_vat_liability[synthetic_vat_liability['sic_numeric'] == sic_code]
+            sector_data = synthetic_vat_liability[
+                synthetic_vat_liability["sic_numeric"] == sic_code
+            ]
             if not sector_data.empty:
-                synthetic_k = sector_data['synthetic_vat_liability_k'].iloc[0]
+                synthetic_k = sector_data["synthetic_vat_liability_k"].iloc[0]
             else:
                 synthetic_k = 0
-            
-            synthetic_millions = synthetic_k / 1000  # convert back to millions for display
-            
+
+            synthetic_millions = (
+                synthetic_k / 1000
+            )  # convert back to millions for display
+
             # Calculate accuracy (handle negative targets)
             if abs(target_millions) > 0.1:  # Skip near-zero targets
                 # For negative targets, check if signs match and relative magnitude
                 if target_millions < 0 and synthetic_millions < 0:
                     # Both negative - check relative difference
-                    accuracy = 1 - min(abs(synthetic_millions - target_millions) / abs(target_millions), 1.0)
+                    accuracy = 1 - min(
+                        abs(synthetic_millions - target_millions)
+                        / abs(target_millions),
+                        1.0,
+                    )
                 elif target_millions > 0 and synthetic_millions > 0:
                     # Both positive - check relative difference
-                    accuracy = 1 - min(abs(synthetic_millions - target_millions) / target_millions, 1.0)
+                    accuracy = 1 - min(
+                        abs(synthetic_millions - target_millions)
+                        / target_millions,
+                        1.0,
+                    )
                 else:
                     # Sign mismatch - poor accuracy
-                    accuracy = max(0, 1 - abs(synthetic_millions - target_millions) / max(abs(target_millions), 1))
+                    accuracy = max(
+                        0,
+                        1
+                        - abs(synthetic_millions - target_millions)
+                        / max(abs(target_millions), 1),
+                    )
             else:
                 accuracy = 1.0 if abs(synthetic_millions) < 1 else 0.0
-            
+
             vat_liability_accuracies.append(accuracy)
-            
-            status = "✓" if accuracy > 0.70 else "⚠" if accuracy > 0.50 else "✗"
-            print(f"  {status} {sic_code:>3}: {synthetic_millions:>12.1f} vs {target_millions:>10.1f} ({accuracy:>6.1%})")
-        
+
+            status = (
+                "✓" if accuracy > 0.70 else "⚠" if accuracy > 0.50 else "✗"
+            )
+            print(
+                f"  {status} {sic_code:>3}: {synthetic_millions:>12.1f} vs {target_millions:>10.1f} ({accuracy:>6.1%})"
+            )
+
         print("-" * 65)
-        vat_liability_sector_accuracy = self._print_accuracy_breakdown(vat_liability_accuracies, len(vat_liability_accuracies), "VAT LIABILITY BY SECTOR")
-        
+        vat_liability_sector_accuracy = self._print_accuracy_breakdown(
+            vat_liability_accuracies,
+            len(vat_liability_accuracies),
+            "VAT LIABILITY BY SECTOR",
+        )
+
         # === VAT LIABILITY BY TURNOVER BAND VALIDATION ===
         # Get VAT liability by turnover band targets (excluding Negative_or_Zero)
-        vat_liability_band_latest = vat_liability_band_df.iloc[-1]  # Get 2023-24 data
+        vat_liability_band_latest = vat_liability_band_df.iloc[
+            -1
+        ]  # Get 2023-24 data
         vat_liability_band_targets = {
-            '£1_to_Threshold': vat_liability_band_latest['£1_to_Threshold'],
-            '£Threshold_to_£150k': vat_liability_band_latest['£Threshold_to_£150k'],
-            '£150k_to_£300k': vat_liability_band_latest['£150k_to_£300k'],
-            '£300k_to_£500k': vat_liability_band_latest['£300k_to_£500k'],
-            '£500k_to_£1m': vat_liability_band_latest['£500k_to_£1m'],
-            '£1m_to_£10m': vat_liability_band_latest['£1m_to_£10m'],
-            'Greater_than_£10m': vat_liability_band_latest['Greater_than_£10m']
+            "£1_to_Threshold": vat_liability_band_latest["£1_to_Threshold"],
+            "£Threshold_to_£150k": vat_liability_band_latest[
+                "£Threshold_to_£150k"
+            ],
+            "£150k_to_£300k": vat_liability_band_latest["£150k_to_£300k"],
+            "£300k_to_£500k": vat_liability_band_latest["£300k_to_£500k"],
+            "£500k_to_£1m": vat_liability_band_latest["£500k_to_£1m"],
+            "£1m_to_£10m": vat_liability_band_latest["£1m_to_£10m"],
+            "Greater_than_£10m": vat_liability_band_latest[
+                "Greater_than_£10m"
+            ],
         }
-        
+
         # Calculate synthetic VAT liability by turnover band (VAT-registered firms only)
-        vat_registered_firms = synthetic_df[synthetic_df['vat_registered'] == True]
-        synthetic_vat_band_liability = vat_registered_firms.groupby('hmrc_band').apply(
-            lambda x: (x['vat_liability_k'] * x['weight']).sum()
-        ).reset_index(name='synthetic_vat_liability_k')
-        
-        self._print_validation_section("VAT LIABILITY BY TURNOVER BAND VALIDATION")
-        print(f"{'Band':>25} {'Synthetic (£m)':>14} {'Target (£m)':>12} {'Accuracy':>10}")
+        vat_registered_firms = synthetic_df[
+            synthetic_df["vat_registered"] == True
+        ]
+        synthetic_vat_band_liability = (
+            vat_registered_firms.groupby("hmrc_band")
+            .apply(lambda x: (x["vat_liability_k"] * x["weight"]).sum())
+            .reset_index(name="synthetic_vat_liability_k")
+        )
+
+        self._print_validation_section(
+            "VAT LIABILITY BY TURNOVER BAND VALIDATION"
+        )
+        print(
+            f"{'Band':>25} {'Synthetic (£m)':>14} {'Target (£m)':>12} {'Accuracy':>10}"
+        )
         print("-" * 75)
-        
+
         vat_liability_band_accuracies = []
         for band_name, target_millions in vat_liability_band_targets.items():
             # Get synthetic VAT liability for this band
-            band_data = synthetic_vat_band_liability[synthetic_vat_band_liability['hmrc_band'] == band_name]
+            band_data = synthetic_vat_band_liability[
+                synthetic_vat_band_liability["hmrc_band"] == band_name
+            ]
             if not band_data.empty:
-                synthetic_k = band_data['synthetic_vat_liability_k'].iloc[0]
+                synthetic_k = band_data["synthetic_vat_liability_k"].iloc[0]
             else:
                 synthetic_k = 0
-            
-            synthetic_millions = synthetic_k / 1000  # convert back to millions for display
-            
+
+            synthetic_millions = (
+                synthetic_k / 1000
+            )  # convert back to millions for display
+
             # Calculate accuracy (handle negative targets)
             if abs(target_millions) > 0.1:  # Skip near-zero targets
                 if target_millions < 0 and synthetic_millions < 0:
                     # Both negative - check relative difference
-                    accuracy = 1 - min(abs(synthetic_millions - target_millions) / abs(target_millions), 1.0)
+                    accuracy = 1 - min(
+                        abs(synthetic_millions - target_millions)
+                        / abs(target_millions),
+                        1.0,
+                    )
                 elif target_millions > 0 and synthetic_millions > 0:
                     # Both positive - check relative difference
-                    accuracy = 1 - min(abs(synthetic_millions - target_millions) / target_millions, 1.0)
+                    accuracy = 1 - min(
+                        abs(synthetic_millions - target_millions)
+                        / target_millions,
+                        1.0,
+                    )
                 else:
                     # Sign mismatch - poor accuracy
-                    accuracy = max(0, 1 - abs(synthetic_millions - target_millions) / max(abs(target_millions), 1))
+                    accuracy = max(
+                        0,
+                        1
+                        - abs(synthetic_millions - target_millions)
+                        / max(abs(target_millions), 1),
+                    )
             else:
                 accuracy = 1.0 if abs(synthetic_millions) < 1 else 0.0
-            
+
             vat_liability_band_accuracies.append(accuracy)
-            
-            status = "✓" if accuracy > 0.70 else "⚠" if accuracy > 0.50 else "✗"
-            print(f"  {status} {band_name:>22}: {synthetic_millions:>12.1f} vs {target_millions:>10.1f} ({accuracy:>6.1%})")
-        
+
+            status = (
+                "✓" if accuracy > 0.70 else "⚠" if accuracy > 0.50 else "✗"
+            )
+            print(
+                f"  {status} {band_name:>22}: {synthetic_millions:>12.1f} vs {target_millions:>10.1f} ({accuracy:>6.1%})"
+            )
+
         print("-" * 75)
-        vat_liability_band_accuracy = self._print_accuracy_breakdown(vat_liability_band_accuracies, len(vat_liability_band_accuracies), "VAT LIABILITY BY BAND")
-        
+        vat_liability_band_accuracy = self._print_accuracy_breakdown(
+            vat_liability_band_accuracies,
+            len(vat_liability_band_accuracies),
+            "VAT LIABILITY BY BAND",
+        )
+
         # === FINAL SUMMARY ===
-        overall_accuracy = (hmrc_accuracy + ons_population_accuracy + employment_accuracy + sector_accuracy + vat_liability_sector_accuracy + vat_liability_band_accuracy) / 6
-        
+        overall_accuracy = (
+            hmrc_accuracy
+            + ons_population_accuracy
+            + employment_accuracy
+            + sector_accuracy
+            + vat_liability_sector_accuracy
+            + vat_liability_band_accuracy
+        ) / 6
+
         self._print_validation_section("CALIBRATION SUMMARY", 80)
         print(f"HMRC Turnover Bands:     {hmrc_accuracy:.1%}")
         print(f"ONS Population:          {ons_population_accuracy:.1%}")
@@ -1168,121 +1659,171 @@ def _map_employment_to_band(employment: int) -> str:
         print(f"VAT Liability by Band:   {vat_liability_band_accuracy:.1%}")
         print(f"Overall Accuracy:        {overall_accuracy:.1%}")
         print(f"Total Population: {total_synthetic_weighted:,.0f} firms")
-        
-        return hmrc_accuracy, ons_population_accuracy, employment_accuracy, sector_accuracy, vat_liability_sector_accuracy, vat_liability_band_accuracy
-    
-    
+
+        return (
+            hmrc_accuracy,
+            ons_population_accuracy,
+            employment_accuracy,
+            sector_accuracy,
+            vat_liability_sector_accuracy,
+            vat_liability_band_accuracy,
+        )
 
     def generate_synthetic_firms(self) -> pd.DataFrame:
         """Main function to generate comprehensive synthetic firms population.
-        
+
         Creates complete firm dataset with VAT registration flags, calibrated
         to match official ONS and HMRC data sources.
-        
+
         Returns:
             DataFrame with synthetic firms data including VAT registration flags
         """
         logger.info("Starting synthetic firm generation...")
-        
+
         # Load data
-        ons_df, ons_employment_df, hmrc_turnover_df, hmrc_sector_df, vat_liability_df, vat_liability_band_df, ons_total = self.load_data()
-        
+        (
+            ons_df,
+            ons_employment_df,
+            hmrc_turnover_df,
+            hmrc_sector_df,
+            vat_liability_df,
+            vat_liability_band_df,
+            ons_total,
+        ) = self.load_data()
+
         # Extract HMRC targets (VAT-registered firms only)
         hmrc_latest = hmrc_turnover_df.iloc[-1]
         hmrc_bands = {
-            'Negative_or_Zero': hmrc_latest['Negative_or_Zero'],
-            '£1_to_Threshold': hmrc_latest['£1_to_Threshold'],
-            '£Threshold_to_£150k': hmrc_latest['£Threshold_to_£150k'],
-            '£150k_to_£300k': hmrc_latest['£150k_to_£300k'],
-            '£300k_to_£500k': hmrc_latest['£300k_to_£500k'],
-            '£500k_to_£1m': hmrc_latest['£500k_to_£1m'],
-            '£1m_to_£10m': hmrc_latest['£1m_to_£10m'],
-            'Greater_than_£10m': hmrc_latest['Greater_than_£10m']
+            "Negative_or_Zero": hmrc_latest["Negative_or_Zero"],
+            "£1_to_Threshold": hmrc_latest["£1_to_Threshold"],
+            "£Threshold_to_£150k": hmrc_latest["£Threshold_to_£150k"],
+            "£150k_to_£300k": hmrc_latest["£150k_to_£300k"],
+            "£300k_to_£500k": hmrc_latest["£300k_to_£500k"],
+            "£500k_to_£1m": hmrc_latest["£500k_to_£1m"],
+            "£1m_to_£10m": hmrc_latest["£1m_to_£10m"],
+            "Greater_than_£10m": hmrc_latest["Greater_than_£10m"],
         }
-        
+
         logger.info(f"Target populations:")
-        logger.info(f"  ONS total firms: {ons_total:,} (includes all businesses)")
-        logger.info(f"  HMRC VAT firms: {sum(hmrc_bands.values()):,} (VAT-registered only)")
-        
+        logger.info(
+            f"  ONS total firms: {ons_total:,} (includes all businesses)"
+        )
+        logger.info(
+            f"  HMRC VAT firms: {sum(hmrc_bands.values()):,} (VAT-registered only)"
+        )
+
         # Generate base firms from ONS structure
         base_sic_codes, base_turnover = self.generate_base_firms(ons_df)
-        
+
         # Generate input values for firms
         base_input = self.generate_input_values(base_turnover, base_sic_codes)
-        
+
         # Create target matrix for multi-objective optimization
         target_matrix, target_values = self.create_comprehensive_target_matrix(
-            base_turnover, base_sic_codes, base_input, hmrc_bands, hmrc_sector_df, ons_employment_df, vat_liability_df, vat_liability_band_df, ons_total
+            base_turnover,
+            base_sic_codes,
+            base_input,
+            hmrc_bands,
+            hmrc_sector_df,
+            ons_employment_df,
+            vat_liability_df,
+            vat_liability_band_df,
+            ons_total,
         )
-        
+
         # Optimize weights to match calibration targets
         optimized_weights = self.optimize_weights(target_matrix, target_values)
-        
+
         # Apply final calibration (add zero firms manually)
-        final_sic_codes, final_turnover, final_input, final_weights = self.apply_final_calibration(
-            base_sic_codes, base_turnover, base_input, optimized_weights, hmrc_bands
+        final_sic_codes, final_turnover, final_input, final_weights = (
+            self.apply_final_calibration(
+                base_sic_codes,
+                base_turnover,
+                base_input,
+                optimized_weights,
+                hmrc_bands,
+            )
         )
-        
+
         # Assign employment to final firms
-        employment_values = self.assign_employment(len(final_sic_codes), ons_employment_df)
-        
+        employment_values = self.assign_employment(
+            len(final_sic_codes), ons_employment_df
+        )
+
         # Assign VAT registration flags
-        vat_flags = self.assign_vat_registration_flags(final_turnover, hmrc_bands)
-        
+        vat_flags = self.assign_vat_registration_flags(
+            final_turnover, hmrc_bands
+        )
+
         # Convert to DataFrame
         logger.info("Converting to final DataFrame...")
         sic_codes_np = final_sic_codes.cpu().numpy().astype(int)
         turnover_np = final_turnover.cpu().numpy()
         input_np = final_input.cpu().numpy()
         vat_liability_np = turnover_np - input_np  # Calculate VAT liability
-        
-        synthetic_df = pd.DataFrame({
-            'sic_code': [str(sic).zfill(5) for sic in sic_codes_np],
-            'annual_turnover_k': turnover_np,
-            'annual_input_k': input_np,
-            'vat_liability_k': vat_liability_np,
-            'employment': employment_values.cpu().numpy().astype(int),
-            'weight': final_weights.cpu().numpy(),
-            'vat_registered': vat_flags.cpu().numpy().astype(bool)
-        })
-        
+
+        synthetic_df = pd.DataFrame(
+            {
+                "sic_code": [str(sic).zfill(5) for sic in sic_codes_np],
+                "annual_turnover_k": turnover_np,
+                "annual_input_k": input_np,
+                "vat_liability_k": vat_liability_np,
+                "employment": employment_values.cpu().numpy().astype(int),
+                "weight": final_weights.cpu().numpy(),
+                "vat_registered": vat_flags.cpu().numpy().astype(bool),
+            }
+        )
+
         logger.info(f"Generated firm dataset:")
         logger.info(f"  Total firms: {len(synthetic_df):,}")
-        logger.info(f"  Weighted population: {synthetic_df['weight'].sum():,.0f}")
-        
+        logger.info(
+            f"  Weighted population: {synthetic_df['weight'].sum():,.0f}"
+        )
+
         # Validation against all data sources
-        self.validate_comprehensive_accuracy(synthetic_df, hmrc_bands, ons_total, ons_employment_df, hmrc_sector_df, vat_liability_df, vat_liability_band_df)
-        
+        self.validate_comprehensive_accuracy(
+            synthetic_df,
+            hmrc_bands,
+            ons_total,
+            ons_employment_df,
+            hmrc_sector_df,
+            vat_liability_df,
+            vat_liability_band_df,
+        )
+
         return synthetic_df
 
 
 def main():
     """Main execution function."""
-    logging.basicConfig(level=logging.INFO, 
-                       format='%(asctime)s - %(levelname)s - %(message)s')
-    
+    logging.basicConfig(
+        level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+    )
+
     logger.info("UK BUSINESS POPULATION SYNTHETIC DATA GENERATION")
-    logger.info("Generating synthetic firm records using PyTorch for efficient processing")
-    
+    logger.info(
+        "Generating synthetic firm records using PyTorch for efficient processing"
+    )
+
     # Initialize generator
     generator = SyntheticFirmGenerator(
         device="cpu"  # Use CPU for compatibility
     )
-    
+
     # Generate synthetic data
     synthetic_df = generator.generate_synthetic_firms()
-    
+
     # Save results to STORAGE_FOLDER
-    output_path = STORAGE_FOLDER / 'synthetic_firms_turnover.csv'
+    output_path = STORAGE_FOLDER / "synthetic_firms_turnover.csv"
     synthetic_df.to_csv(output_path, index=False)
-    
+
     file_size_mb = output_path.stat().st_size / 1024 / 1024
     logger.info(f"Saved {len(synthetic_df):,} firms to {output_path}")
     logger.info(f"File size: {file_size_mb:.1f} MB")
     logger.info(f"Columns: {list(synthetic_df.columns)}")
-    
+
     logger.info("Synthetic data generation complete!")
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()

From 0912dbdd52ac76953a41c22d6dd147985a6549e1 Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 15:43:03 +0100
Subject: [PATCH 3/8] changelog

---
 changelog_entry.yaml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/changelog_entry.yaml b/changelog_entry.yaml
index e69de29b..5455d5a3 100644
--- a/changelog_entry.yaml
+++ b/changelog_entry.yaml
@@ -0,0 +1,4 @@
+- bump: minor
+  changes:
+    added:
+    - Firm synthetic data generation script with ONS and HMRC data integration
\ No newline at end of file

From cea0b3324609bc4a3381348c6f36e7f94208d926 Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 15:43:42 +0100
Subject: [PATCH 4/8] edit

---
 changelog_entry.yaml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/changelog_entry.yaml b/changelog_entry.yaml
index 5455d5a3..ce154ba5 100644
--- a/changelog_entry.yaml
+++ b/changelog_entry.yaml
@@ -1,4 +1,3 @@
 - bump: minor
   changes:
-    added:
     - Firm synthetic data generation script with ONS and HMRC data integration
\ No newline at end of file

From 95dd14ed1f72913a749e6c4c8f92bfc616e1e0f3 Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 15:45:45 +0100
Subject: [PATCH 5/8] make csv files visible

---
 .gitignore                                    |  1 +
 .../vat_liability_by_sector.csv               | 89 ++++++++++++++++++
 .../vat_liability_by_turnover_band.csv        | 21 +++++
 .../vat_population_by_sector.csv              | 90 +++++++++++++++++++
 .../vat_population_by_turnover_band.csv       | 21 +++++
 .../ONS_UK_business_data/firm_employment.csv  | 90 +++++++++++++++++++
 .../ONS_UK_business_data/firm_turnover.csv    | 90 +++++++++++++++++++
 7 files changed, 402 insertions(+)
 create mode 100644 policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_liability_by_sector.csv
 create mode 100644 policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_liability_by_turnover_band.csv
 create mode 100644 policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_population_by_sector.csv
 create mode 100644 policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_population_by_turnover_band.csv
 create mode 100644 policyengine_uk_data/datasets/firm/ONS_UK_business_data/firm_employment.csv
 create mode 100644 policyengine_uk_data/datasets/firm/ONS_UK_business_data/firm_turnover.csv

diff --git a/.gitignore b/.gitignore
index 1e629c60..73f59117 100644
--- a/.gitignore
+++ b/.gitignore
@@ -13,6 +13,7 @@
 !demographics.csv
 !incomes_projection.csv
 !policyengine_uk_data/datasets/local_areas/**/*.csv
+!policyengine_uk_data/datasets/firm/**/*.csv
 **/_build
 !policyengine_uk_data/storage/*.csv
 **/version.json
diff --git a/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_liability_by_sector.csv b/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_liability_by_sector.csv
new file mode 100644
index 00000000..9d9aea39
--- /dev/null
+++ b/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_liability_by_sector.csv
@@ -0,0 +1,89 @@
+Trade_Sector,Trade_Sub_Sector,2023-24
+00001,"Crop and animal production, hunting and related service activities",-2330
+00002,Forestry and logging,100
+00003,Fishing and aquaculture,-100
+00005,Mining of coal and lignite,10
+00006,Extraction of crude petroleum and natural gas,-460
+00007,Mining of metal ores,-10
+00008,Other mining and quarrying,270
+00009,Mining and support service activities,-330
+00010,Manufacture of food products,-2150
+00011,Manufacture of beverages,1820
+00012,Manufacture of tobacco products,1700
+00013,Manufacture of textiles,450
+00014,Manufacture of wearing apparel,280
+00015,Manufacture of leather and related products,140
+00016,"Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials",650
+00017,Manufacture of paper and paper products,920
+00018,Printing and reproduction of recorded media,480
+00019,Manufacture of coke and refined petroleum products,7810
+00020,Manufacture of chemicals and chemical products,1500
+00021,Manufacture of basic pharmaceutical products and pharmaceutical preparations,570
+00022,Manufacture of rubber and rubber products,1440
+00023,Manufacture of other non-metallic mineral products,1130
+00024,Manufacture of basic metals,-830
+00025,"Manufacture of fabricated metal products, except machinery and equipment",2000
+00026,"Manufacture of computer, electronic and optical products",1050
+00027,Manufacture of electrical equipment,770
+00028,Manufacture of machinery and equipment n.e.c,1150
+00029,"Manufacture of motor vehicles, trailers and semi-trailers",2040
+00030,Manufacture of other transport equipment,-1240
+00031,Manufacture of furniture,730
+00032,Other manufacturing,1090
+00033,Repair and installation of machinery and equipment,810
+00035,"Electricity, gas, steam and air conditioning supply",6020
+00036,"Water collection, treatment and supply",-2250
+00037,Sewerage,-10
+00038,"Waste collection, treatment and disposal activities; materials recovery",680
+00039,Remediation activities and other waste management services,370
+00041,Construction of buildings,2780
+00042,Civil engineering,4130
+00043,Specialised construction activities,3950
+00045,Wholesale and retail trade and repair of motor vehicles and motorcycles,13040
+00046,"Wholesale trade, except of motor vehicles and motorcycles",25980
+00047,Retail trade except of motor vehicles and motorcycles,16440
+00049,Land transport and transport via pipelines,-420
+00050,Water transport,-100
+00051,Air transport,-1080
+00052,Warehousing and support activities for transportation,1930
+00053,Postal and courier activities,1140
+00055,Accommodation,2760
+00056,Food and beverage service activities,8450
+00058,Publishing activities,700
+00059,"Motion picture, video and television programme production, sound recording and music publishing activities",210
+00060,Programming and broadcasting activities,-20
+00061,Telecommunications,4320
+00062,"Computer programming, consultancy and related activities",13720
+00063,Information service activities,460
+00064,"Financial service activities, except insurance and pension funding",-190
+00065,"Insurance, reinsurance and pension funding, except compulsory social security",200
+00066,Activities auxiliary to financial services and insurance activities,1180
+00068,Real estate activities,4180
+00069,Legal and accounting services,9120
+00070,Activities of head offices; management consultancy services,9010
+00071,Architectural and engineering activities; technical testing and analysis,4830
+00072,Scientific research and development,160
+00073,Advertising and market research,1990
+00074,"Other professional, scientific and technical activities",1560
+00075,Veterinary activities,770
+00077,Rental and leasing activities,3410
+00078,Employment activities,6000
+00079,"Travel agency, tour operator and other reservation service and related activities",-30
+00080,Security and investigation activities,1050
+00081,Services to buildings and landscape activities,2150
+00082,Office administrative and support activities,6230
+00084,Public administration and defence; compulsory social security,-11080
+00085,Education,410
+00086,Human health activities,60
+00087,Residential care activities,260
+00088,Social work activities without accommodation,370
+00090,"Creative, arts and entertainment activities",1690
+00091,"Libraries, archives, museums and other cultural activities",40
+00092,Gambling and betting activities,50
+00093,Sports activities and amusement and recreation activities,1680
+00094,Activities of membership organisations,520
+00095,Repair of computers and personal and household goods,280
+00096,Other personal service activities,2780
+00097,Activities of households as employers of domestic personnel,0
+00098,Undifferentiated goods- and services-producing activities of private households for own use,0
+00099,Activities of extraterritorial organisations and bodies,40
diff --git a/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_liability_by_turnover_band.csv b/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_liability_by_turnover_band.csv
new file mode 100644
index 00000000..e8dd09f9
--- /dev/null
+++ b/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_liability_by_turnover_band.csv
@@ -0,0 +1,21 @@
+Financial_Year,Negative_or_Zero,£1_to_Threshold,£Threshold_to_£150k,£150k_to_£300k,£300k_to_£500k,£500k_to_£1m,£1m_to_£10m,Greater_than_£10m,Total
+2004-05,-980,150,2660,3020,2650,4100,14080,35270,60940
+2005-06,-1850,200,2630,2980,2640,3880,13060,32450,55980
+2006-07,-1490,170,2780,3150,2810,4250,14750,36330,62760
+2007-08,-1700,90,2680,3130,2720,4410,15900,40920,68140
+2008-09,-1540,120,2750,3060,2640,4150,15030,37590,63810
+2009-10,-960,470,2490,2770,2340,3550,14200,38780,63640
+2010-11,-1010,570,2880,3220,2630,4120,15750,41020,69200
+2011-12,-1300,680,3170,3760,2940,4630,17180,47370,78430
+2012-13,-1270,820,3190,3810,2980,4710,17700,50140,82070
+2013-14,-1490,800,3190,4000,3150,4800,19020,54700,88170
+2014-15,-1770,800,3300,4250,3220,5090,20210,57960,93060
+2015-16,-2230,720,3420,4370,3400,5300,21060,61120,97160
+2016-17,-2200,750,3510,4700,3460,5470,21460,62960,100110
+2017-18,-2250,770,3740,4830,3650,5640,22740,63820,102930
+2018-19,-2270,820,3760,4980,3650,5690,23360,65300,105300
+2019-20,-2270,770,3730,5170,3790,5870,23820,68270,109130
+2020-21,-2220,440,2530,3540,2770,4550,19620,67000,98230
+2021-22,-2500,1260,2290,3600,3060,5040,22720,105180,140650
+2022-23,-3050,-10,2300,4380,3690,5880,26340,117230,156740
+2023-24,-2640,1460,2430,4590,3900,6170,27520,129960,173380
diff --git a/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_population_by_sector.csv b/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_population_by_sector.csv
new file mode 100644
index 00000000..1f94fc1c
--- /dev/null
+++ b/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_population_by_sector.csv
@@ -0,0 +1,90 @@
+Trade_Sector,Trade_Sub_Sector,2023-24
+00001,"Crop and animal production, hunting and related service activities",117930
+00002,Forestry and logging,5150
+00003,Fishing and aquaculture,3790
+00005,Mining of coal and lignite,30
+00006,Extraction of crude petroleum and natural gas,230
+00007,Mining of metal ores,80
+00008,Other mining and quarrying,780
+00009,Mining and support service activities,1470
+00010,Manufacture of food products,8840
+00011,Manufacture of beverages,3190
+00012,Manufacture of tobacco products,70
+00013,Manufacture of textiles,3940
+00014,Manufacture of wearing apparel,2970
+00015,Manufacture of leather and related products,710
+00016,"Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials",8010
+00017,Manufacture of paper and paper products,1570
+00018,Printing and reproduction of recorded media,9560
+00019,Manufacture of coke and refined petroleum products,180
+00020,Manufacture of chemicals and chemical products,3430
+00021,Manufacture of basic pharmaceutical products and pharmaceutical preparations,790
+00022,Manufacture of rubber and rubber products,4590
+00023,Manufacture of other non-metallic mineral products,4060
+00024,Manufacture of basic metals,2320
+00025,"Manufacture of fabricated metal products, except machinery and equipment",19170
+00026,"Manufacture of computer, electronic and optical products",5630
+00027,Manufacture of electrical equipment,2690
+00028,Manufacture of machinery and equipment n.e.c,7440
+00029,"Manufacture of motor vehicles, trailers and semi-trailers",2970
+00030,Manufacture of other transport equipment,1970
+00031,Manufacture of furniture,5590
+00032,Other manufacturing,9760
+00033,Repair and installation of machinery and equipment,12640
+00035,"Electricity, gas, steam and air conditioning supply",7120
+00036,"Water collection, treatment and supply",630
+00037,Sewerage,840
+00038,"Waste collection, treatment and disposal activities; materials recovery",4970
+00039,Remediation activities and other waste management services,1180
+00041,Construction of buildings,108430
+00042,Civil engineering,20940
+00043,Specialised construction activities,195830
+00045,Wholesale and retail trade and repair of motor vehicles and motorcycles,67550
+00046,"Wholesale trade, except of motor vehicles and motorcycles",98080
+00047,Retail trade except of motor vehicles and motorcycles,290780
+00049,Land transport and transport via pipelines,51830
+00050,Water transport,1370
+00051,Air transport,1070
+00052,Warehousing and support activities for transportation,11610
+00053,Postal and courier activities,28550
+00055,Accommodation,17460
+00056,Food and beverage service activities,106570
+00058,Publishing activities,10980
+00059,"Motion picture, video and television programme production, sound recording and music publishing activities",29320
+00060,Programming and broadcasting activities,1410
+00061,Telecommunications,6750
+00062,"Computer programming, consultancy and related activities",116650
+00063,Information service activities,6640
+00064,"Financial service activities, except insurance and pension funding",12770
+00065,"Insurance, reinsurance and pension funding, except compulsory social security",2900
+00066,Activities auxiliary to financial services and insurance activities,6140
+00068,Real estate activities,107180
+00069,Legal and accounting services,56760
+00070,Activities of head offices; management consultancy services,131400
+00071,Architectural and engineering activities; technical testing and analysis,62270
+00072,Scientific research and development,5650
+00073,Advertising and market research,18930
+00074,"Other professional, scientific and technical activities",45160
+00075,Veterinary activities,3470
+00077,Rental and leasing activities,17530
+00078,Employment activities,24470
+00079,"Travel agency, tour operator and other reservation service and related activities",6550
+00080,Security and investigation activities,8090
+00081,Services to buildings and landscape activities,35200
+00082,Office administrative and support activities,47640
+00084,Public administration and defence; compulsory social security,3400
+00085,Education,20920
+00086,Human health activities,9160
+00087,Residential care activities,1550
+00088,Social work activities without accommodation,2990
+00090,"Creative, arts and entertainment activities",27010
+00091,"Libraries, archives, museums and other cultural activities",1080
+00092,Gambling and betting activities,370
+00093,Sports activities and amusement and recreation activities,30820
+00094,Activities of membership organisations,8320
+00095,Repair of computers and personal and household goods,4670
+00096,Other personal service activities,37910
+00097,Activities of households as employers of domestic personnel,130
+00098,Undifferentiated goods- and services-producing activities of private households for own use,140
+00099,Activities of extraterritorial organisations and bodies,60
+Total,,2178950
diff --git a/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_population_by_turnover_band.csv b/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_population_by_turnover_band.csv
new file mode 100644
index 00000000..c25922db
--- /dev/null
+++ b/policyengine_uk_data/datasets/firm/HMRC_VAT_annual_statistics/vat_population_by_turnover_band.csv
@@ -0,0 +1,21 @@
+Financial_Year,Negative_or_Zero,£1_to_Threshold,£Threshold_to_£150k,£150k_to_£300k,£300k_to_£500k,£500k_to_£1m,£1m_to_£10m,Greater_than_£10m,Unknown,Total
+2004-05,155820,559510,451390,257420,134860,121810,142720,23500,0,1847030
+2005-06,191450,602910,440700,254460,132240,118110,139290,23710,40050,1942920
+2006-07,269480,578240,446800,267030,141200,127650,154820,26160,0,2011380
+2007-08,270060,617920,433270,268310,142380,131830,160530,26780,0,2051080
+2008-09,263770,618080,417410,270400,141260,131860,158740,26830,42340,2070690
+2009-10,269450,625610,393750,258250,135420,124520,148210,25190,39800,2020180
+2010-11,233300,627540,387630,263380,137040,126160,151690,26350,44080,1997160
+2011-12,196770,641310,378070,270950,139110,129300,156260,27560,56110,1995440
+2012-13,197580,666670,358250,272340,138120,128940,156880,27800,54090,2000650
+2013-14,219850,680150,353640,280500,141150,132160,163040,29270,61470,2061220
+2014-15,226320,702100,352280,290160,144850,137210,170760,30810,73580,2128080
+2015-16,246320,740080,359750,297390,147780,139440,175940,31490,79670,2217840
+2016-17,265640,777330,362070,307320,151550,142730,181640,33170,87140,2308580
+2017-18,292510,752520,342910,306610,154870,146150,187480,34720,113980,2331730
+2018-19,310900,741550,346830,314620,157810,149410,191820,36130,103180,2352250
+2019-20,413970,732800,345250,318210,158960,150540,194580,36830,103750,2454910
+2020-21,462810,834440,297100,281300,147050,141160,180320,34100,103050,2481330
+2021-22,449120,773120,294410,312460,172520,166910,214490,40230,131220,2554470
+2022-23,402860,705960,307250,331490,181670,176740,231290,44090,11440,2392790
+2023-24,216500,678350,305320,334470,184080,180500,235060,44680,0,2178950
diff --git a/policyengine_uk_data/datasets/firm/ONS_UK_business_data/firm_employment.csv b/policyengine_uk_data/datasets/firm/ONS_UK_business_data/firm_employment.csv
new file mode 100644
index 00000000..1fd0a7fe
--- /dev/null
+++ b/policyengine_uk_data/datasets/firm/ONS_UK_business_data/firm_employment.csv
@@ -0,0 +1,90 @@
+SIC Code,Description,0-4,5-9,10-19,20-49,50-99,100-249,250+,Total
+01,Crop and animal production; hunting and related service activities,119405,11525,3095,1195,350,155,90,135815
+02,Forestry and logging,4180,530,250,105,25,5,0,5095
+03,Fishing and aquaculture,3640,345,125,25,10,0,0,4145
+05,Mining of coal and lignite,5,0,0,0,0,0,0,5
+06,Extraction of crude petroleum and natural gas,70,15,10,15,10,15,15,150
+07,Mining of metal ores,5,0,0,0,0,0,0,5
+08,Other mining and quarrying,725,300,220,150,45,20,5,1465
+09,Mining support service activities,260,40,25,35,20,15,15,410
+10,Manufacture of food products,5315,1870,1365,1050,495,440,405,10940
+11,Manufacture of beverages,1910,445,315,200,80,60,45,3055
+12,Manufacture of tobacco products,0,5,0,0,0,0,0,5
+13,Manufacture of textiles,2875,765,455,310,125,60,10,4600
+14,Manufacture of wearing apparel,2830,515,255,160,50,10,5,3825
+15,Manufacture of leather and related products,415,160,65,40,15,5,5,705
+16,Manufacture of wood and of products of wood and cork; except furniture; manufacture of articles of straw and plaiting materials,7955,1395,795,510,175,60,15,10905
+17,Manufacture of paper and paper products,705,190,195,235,135,130,30,1620
+18,Printing and reproduction of recorded media,7245,1535,725,470,150,90,20,10235
+19,Manufacture of coke and refined petroleum products,40,15,10,15,15,10,10,115
+20,Manufacture of chemicals and chemical products,2000,505,350,435,230,160,65,3745
+21,Manufacture of basic pharmaceutical products and pharmaceutical preparations,400,75,40,75,50,40,55,735
+22,Manufacture of rubber and plastic products,2460,1095,835,805,415,265,65,5940
+23,Manufacture of other non-metallic mineral products,2800,925,615,465,215,130,30,5180
+24,Manufacture of basic metals,1070,275,235,250,105,65,35,2035
+25,Manufacture of fabricated metal products; except machinery and equipment,13980,3870,2855,2145,715,280,70,23915
+26,Manufacture of computer; electronic and optical products,3335,850,560,610,265,165,55,5840
+27,Manufacture of electrical equipment,1640,480,420,430,195,120,40,3325
+28,Manufacture of machinery and equipment n.e.c.,4155,1205,865,845,410,225,105,7810
+29,Manufacture of motor vehicles; trailers and semi-trailers,2455,475,295,275,170,125,95,3890
+30,Manufacture of other transport equipment,1430,245,145,135,105,85,95,2240
+31,Manufacture of furniture,4450,1075,650,450,175,95,30,6925
+32,Other manufacturing,7045,1590,655,420,150,90,35,9985
+33,Repair and installation of machinery and equipment,11995,1695,985,580,180,130,50,15615
+35,Electricity; gas; steam and air conditioning supply,5200,730,390,275,145,155,95,6990
+36,Water collection; treatment and supply,375,170,145,165,65,45,40,1005
+37,Sewerage,975,260,175,180,45,35,5,1675
+38,Waste collection; treatment and disposal activities; materials recovery,4630,1480,1065,950,395,220,55,8795
+39,Remediation activities and other waste management services,845,210,135,85,15,5,0,1295
+41,Construction of buildings,110965,9690,4175,1665,625,395,80,127595
+42,Civil engineering,18605,2615,1330,955,415,205,95,24220
+43,Specialised construction activities,203530,21540,8795,4060,1010,375,70,239380
+45,Wholesale and retail trade and repair of motor vehicles and motorcycles,64535,16060,6460,4050,1130,330,50,92615
+46,Wholesale trade; except of motor vehicles and motorcycles,75485,21110,13315,7750,2300,1005,390,121355
+47,Retail trade; except of motor vehicles and motorcycles,185010,58960,35145,16905,3285,2255,1040,302600
+49,Land transport and transport via pipelines,52885,6295,3610,2665,1080,665,320,67520
+50,Water transport,1060,190,115,85,35,10,10,1505
+51,Air transport,950,115,55,70,35,30,50,1305
+52,Warehousing and support activities for transportation,16105,3795,2465,2145,950,680,440,26580
+53,Postal and courier activities,32395,1385,595,925,700,420,125,36545
+55,Accommodation,12930,3600,3375,3925,1435,630,170,26065
+56,Food and beverage service activities,91715,47075,32560,20325,2915,1345,135,196070
+58,Publishing activities,9985,1205,755,525,180,90,70,12810
+59,Motion picture; video and television programme production; sound recording and music publishing activities,30560,1500,900,745,270,95,30,34100
+60,Programming and broadcasting activities,1885,165,80,100,35,30,25,2320
+61,Telecommunications,7520,1440,1005,795,350,195,135,11440
+62,Computer programming; consultancy and related activities,107640,7760,5010,3550,1350,680,350,126340
+63,Information service activities,6740,830,575,325,115,70,55,8710
+64,Financial service activities; except insurance and pension funding,19050,3625,2290,1140,290,190,300,26885
+65,Insurance; reinsurance and pension funding; except compulsory social security,6970,170,130,100,50,60,105,7585
+66,Activities auxiliary to financial services and insurance activities,26920,4315,2270,1395,690,445,280,36315
+68,Real estate activities,107155,14675,6180,1825,475,335,195,130840
+69,Legal and accounting activities,59055,9660,5675,2980,960,585,355,79270
+70,Activities of head offices; management consultancy activities,142325,9000,5030,2955,1180,695,370,161555
+71,Architectural and engineering activities; technical testing and analysis,68340,7175,4330,2720,935,445,180,84125
+72,Scientific research and development,4070,790,660,535,250,165,135,6605
+73,Advertising and market research,19425,1960,1240,900,325,160,110,24120
+74,Other professional; scientific and technical activities,67880,5225,2245,955,220,75,25,76625
+75,Veterinary activities,2770,1460,1610,885,135,40,10,6910
+77,Rental and leasing activities,14710,3585,2190,1115,300,125,35,22060
+78,Employment activities,25925,4335,3385,2715,1715,1265,675,40015
+79,Travel agency; tour operator and other reservation service and related activities,7555,1875,695,350,140,80,30,10725
+80,Security and investigation activities,11060,1925,1175,980,365,205,130,15840
+81,Services to buildings and landscape activities,54515,9800,5185,2885,980,485,340,74190
+82,Office administrative; office support and other business support activities,89580,9640,4400,1950,760,420,295,107045
+84,Public administration and defence; compulsory social security,10920,3080,2885,3200,1615,1370,1325,24395
+85,Education,31990,8755,8850,14750,8445,4670,935,78395
+86,Human health activities,49340,11185,10020,7510,2355,1430,1050,82890
+87,Residential care activities,9530,5460,6725,7160,3885,870,85,33715
+88,Social work activities without accommodation,20760,12015,11720,8555,2270,845,255,56420
+90,Creative; arts and entertainment activities,29555,1665,820,405,170,100,20,32735
+91,Libraries; archives; museums and other cultural activities,2775,1205,835,625,255,125,40,5860
+92,Gambling and betting activities,4085,4160,500,320,95,40,10,9210
+93,Sports activities and amusement and recreation activities,27855,6455,4600,3650,1330,515,130,44535
+94,Activities of membership organisations,17845,4915,2240,1285,415,225,85,27010
+95,Repair of computers and personal and household goods,8545,875,345,210,55,40,20,10090
+96,Other personal service activities,69185,13545,4365,1170,195,95,25,88580
+97,Activities of households as employers of domestic personnel,0,0,0,0,0,0,0,0
+98,Undifferentiated goods- and services-producing activities of private households for own use,0,0,0,0,0,0,0,0
+99,Activities of extraterritorial organisations and bodies,0,0,0,0,5,0,0,5
+,Total,2269010,404720,242235,160880,54820,29115,12880,3173660
diff --git a/policyengine_uk_data/datasets/firm/ONS_UK_business_data/firm_turnover.csv b/policyengine_uk_data/datasets/firm/ONS_UK_business_data/firm_turnover.csv
new file mode 100644
index 00000000..f8bf05f7
--- /dev/null
+++ b/policyengine_uk_data/datasets/firm/ONS_UK_business_data/firm_turnover.csv
@@ -0,0 +1,90 @@
+SIC Code,Description,0-49,50-99,100-249,250-499,500-999,1000-4999,5000+,Total
+01,Crop and animal production; hunting and related service activities,42455,24050,29210,16385,11110,8190,1015,132415
+02,Forestry and logging,850,1035,1640,680,345,255,45,4850
+03,Fishing and aquaculture,1245,790,990,500,195,215,50,3985
+05,Mining of coal and lignite,0,0,0,0,0,0,5,5
+06,Extraction of crude petroleum and natural gas,10,10,10,15,10,15,40,110
+07,Mining of metal ores,0,0,0,0,0,0,0,0
+08,Other mining and quarrying,80,75,150,70,70,150,115,710
+09,Mining support service activities,45,50,120,25,20,40,70,370
+10,Manufacture of food products,1465,1360,2140,1205,870,1200,1170,9410
+11,Manufacture of beverages,645,465,665,330,260,275,135,2775
+12,Manufacture of tobacco products,0,0,0,0,0,0,0,0
+13,Manufacture of textiles,480,840,1260,625,415,505,215,4340
+14,Manufacture of wearing apparel,580,920,1145,460,290,255,85,3735
+15,Manufacture of leather and related products,125,135,140,55,45,65,30,595
+16,Manufacture of wood and of products of wood and cork; except furniture; manufacture of articles of straw and plaiting materials,720,2440,3630,1345,975,1025,380,10515
+17,Manufacture of paper and paper products,155,175,250,120,120,270,310,1400
+18,Printing and reproduction of recorded media,1740,1960,2850,1205,850,940,295,9840
+19,Manufacture of coke and refined petroleum products,5,5,10,5,5,10,45,85
+20,Manufacture of chemicals and chemical products,450,465,615,340,250,515,635,3270
+21,Manufacture of basic pharmaceutical products and pharmaceutical preparations,90,65,160,70,45,75,145,650
+22,Manufacture of rubber and plastic products,370,460,885,640,675,1310,890,5230
+23,Manufacture of other non-metallic mineral products,270,565,955,585,440,775,415,4005
+24,Manufacture of basic metals,170,230,415,220,205,325,280,1845
+25,Manufacture of fabricated metal products; except machinery and equipment,2000,4325,5060,3110,2615,4000,1475,22585
+26,Manufacture of computer; electronic and optical products,885,795,1120,570,465,905,655,5395
+27,Manufacture of electrical equipment,355,325,605,330,310,640,460,3025
+28,Manufacture of machinery and equipment n.e.c.,775,810,1515,870,760,1455,950,7135
+29,Manufacture of motor vehicles; trailers and semi-trailers,320,475,1230,405,310,455,380,3575
+30,Manufacture of other transport equipment,240,495,480,185,150,195,195,1940
+31,Manufacture of furniture,490,1155,2060,960,760,915,335,6675
+32,Other manufacturing,1470,2045,2625,1245,920,935,355,9595
+33,Repair and installation of machinery and equipment,1540,3755,4510,1845,1240,1360,440,14690
+35,Electricity; gas; steam and air conditioning supply,1210,640,1310,835,680,910,415,6000
+36,Water collection; treatment and supply,10,5,15,10,5,20,35,100
+37,Sewerage,95,200,350,215,150,165,50,1225
+38,Waste collection; treatment and disposal activities; materials recovery,480,605,1565,935,770,1170,685,6210
+39,Remediation activities and other waste management services,110,205,300,160,155,215,65,1210
+41,Construction of buildings,14640,14325,37080,25040,15385,12255,3100,121825
+42,Civil engineering,2165,3705,7230,3310,2160,2620,1215,22405
+43,Specialised construction activities,15930,56355,88660,34550,19270,17435,3770,235970
+45,Wholesale and retail trade and repair of motor vehicles and motorcycles,5695,12925,26270,15695,9030,8895,3115,81625
+46,Wholesale trade; except of motor vehicles and motorcycles,12565,10035,19705,14470,12770,20390,12030,101965
+47,Retail trade; except of motor vehicles and motorcycles,21735,30035,66940,42655,27695,21215,4345,214620
+49,Land transport and transport via pipelines,9325,15180,21980,6225,4100,4815,1550,63175
+50,Water transport,300,260,290,145,100,140,135,1370
+51,Air transport,155,130,105,300,155,85,120,1050
+52,Warehousing and support activities for transportation,1930,4245,3410,1845,2040,2550,1575,17595
+53,Postal and courier activities,20100,6175,4495,1315,710,690,200,33685
+55,Accommodation,2155,3080,6390,2950,2360,3095,1025,21055
+56,Food and beverage service activities,10165,27030,63160,29005,14375,7505,1215,152455
+58,Publishing activities,2580,2355,3765,1470,885,950,405,12410
+59,Motion picture; video and television programme production; sound recording and music publishing activities,5270,8610,11355,3890,1660,1805,720,33310
+60,Programming and broadcasting activities,315,430,835,245,105,105,65,2100
+61,Telecommunications,1085,1080,2750,1055,625,850,515,7960
+62,Computer programming; consultancy and related activities,20330,34310,44905,8900,5510,6865,2970,123790
+63,Information service activities,1455,2425,2210,685,570,585,265,8195
+64,Financial service activities; except insurance and pension funding,10135,975,3115,2365,1195,1000,900,19685
+65,Insurance; reinsurance and pension funding; except compulsory social security,6405,25,130,185,155,200,185,7285
+66,Activities auxiliary to financial services and insurance activities,8250,1190,10815,4240,2985,2730,1605,31815
+68,Real estate activities,24310,28790,33555,15220,7940,6280,1795,117890
+69,Legal and accounting activities,9640,16505,25585,9615,5330,4850,1480,73005
+70,Activities of head offices; management consultancy activities,25720,28145,66505,19080,7745,7230,2440,156865
+71,Architectural and engineering activities; technical testing and analysis,9245,22655,28290,7905,4615,5040,1775,79525
+72,Scientific research and development,1425,955,1425,595,425,625,400,5850
+73,Advertising and market research,3320,3665,8715,3225,1700,2020,905,23550
+74,Other professional; scientific and technical activities,9965,19125,32590,6620,3505,2890,675,75370
+75,Veterinary activities,275,740,750,415,505,665,100,3450
+77,Rental and leasing activities,2955,2625,4720,2525,1970,2530,965,18290
+78,Employment activities,2890,4120,9020,4840,3640,4915,2200,31625
+79,Travel agency; tour operator and other reservation service and related activities,1570,875,1925,1745,1085,1100,510,8810
+80,Security and investigation activities,920,1825,4195,1360,945,1115,315,10675
+81,Services to buildings and landscape activities,7060,10990,21090,7325,3295,2535,665,52960
+82,Office administrative; office support and other business support activities,9340,14280,33625,23885,13245,8060,1885,104320
+84,Public administration and defence; compulsory social security,325,4870,1515,400,160,185,390,7845
+85,Education,4255,10395,14345,6245,4840,5165,2750,47995
+86,Human health activities,3915,23290,16355,8070,5520,7115,1170,65435
+87,Residential care activities,1020,1265,1355,1350,1565,4180,1125,11860
+88,Social work activities without accommodation,1670,6145,9865,7150,5885,5000,780,36495
+90,Creative; arts and entertainment activities,5625,6715,12420,3545,1925,1760,375,32365
+91,Libraries; archives; museums and other cultural activities,250,315,465,280,230,270,110,1920
+92,Gambling and betting activities,55,100,210,180,115,130,90,880
+93,Sports activities and amusement and recreation activities,9080,7285,10975,5115,3175,2590,600,38820
+94,Activities of membership organisations,4830,4420,6820,3385,1600,1235,330,22620
+95,Repair of computers and personal and household goods,930,1885,4840,1010,440,380,120,9605
+96,Other personal service activities,13460,28750,27930,6390,2935,2050,390,81905
+97,Activities of households as employers of domestic personnel,0,0,0,0,0,0,0,0
+98,Undifferentiated goods- and services-producing activities of private households for own use,0,0,0,0,0,0,0,0
+99,Activities of extraterritorial organisations and bodies,0,0,0,0,0,0,0,0
+,Total,388665,537540,874665,384570,234660,226445,78230,2724775

From 7849b0fc0809c178ce64467af56b9aff0ed13247 Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 18:27:28 +0100
Subject: [PATCH 6/8] edit to be like frs

---
 policyengine_uk_data/datasets/firm.py         | 206 ++++++++++++++++++
 .../datasets/firm/generate_synthetic_data.py  |  10 +-
 2 files changed, 209 insertions(+), 7 deletions(-)
 create mode 100644 policyengine_uk_data/datasets/firm.py

diff --git a/policyengine_uk_data/datasets/firm.py b/policyengine_uk_data/datasets/firm.py
new file mode 100644
index 00000000..bd7713ea
--- /dev/null
+++ b/policyengine_uk_data/datasets/firm.py
@@ -0,0 +1,206 @@
+"""
+Firm dataset for PolicyEngine UK.
+
+This module processes synthetic firm data into PolicyEngine UK dataset format,
+handling firm demographics, turnover, VAT, employment, and other business variables.
+The synthetic firm data represents the UK business population for tax-benefit modelling.
+"""
+
+from policyengine_core.data import Dataset
+from pathlib import Path
+import pandas as pd
+import numpy as np
+from policyengine_uk_data.utils.datasets import STORAGE_FOLDER
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def create_firm(year: int = 2023):
+    """
+    Process synthetic firm data into PolicyEngine UK dataset format.
+    
+    Generates synthetic firm microdata and transforms it into a structured 
+    PolicyEngine UK dataset with firm, sector, and employment-level variables 
+    mapped to the appropriate tax-benefit system variables.
+    
+    Args:
+        year: Survey year for the dataset.
+        
+    Returns:
+        Dataset with processed firm data ready for policy simulation.
+    """
+    # Always generate fresh synthetic data using generate_synthetic_data.py
+    logger.info("Generating synthetic firm data...")
+    import sys
+    sys.path.append(str(Path(__file__).parent / "firm"))
+    from generate_synthetic_data import SyntheticFirmGenerator
+    generator = SyntheticFirmGenerator(device="cpu")
+    synthetic_df = generator.generate_synthetic_firms()
+    
+    # Create entity DataFrames for firm structure
+    pe_firm = pd.DataFrame()
+    pe_sector = pd.DataFrame()
+    pe_business_group = pd.DataFrame()
+    
+    # Add primary keys and identifiers
+    pe_firm["firm_id"] = range(len(synthetic_df))
+    pe_firm["firm_sector_id"] = synthetic_df["sic_code"].astype(int)
+    pe_firm["firm_business_group_id"] = pe_firm["firm_id"] // 100
+    
+    # Create unique sectors
+    unique_sectors = synthetic_df["sic_code"].astype(int).unique()
+    pe_sector["sector_id"] = unique_sectors
+    
+    # Create business groups
+    unique_groups = pe_firm["firm_business_group_id"].unique()
+    pe_business_group["business_group_id"] = unique_groups
+    
+    # Add grossing weights
+    pe_firm["firm_weight"] = synthetic_df["weight"].values
+    
+    # Add basic firm variables - exactly from synthetic data
+    pe_firm["sic_code"] = synthetic_df["sic_code"]
+    pe_firm["annual_turnover_k"] = synthetic_df["annual_turnover_k"].values
+    pe_firm["annual_input_k"] = synthetic_df["annual_input_k"].values
+    pe_firm["vat_liability_k"] = synthetic_df["vat_liability_k"].values
+    pe_firm["employment"] = synthetic_df["employment"].astype(int).values
+    pe_firm["vat_registered"] = synthetic_df["vat_registered"].astype(bool).values
+    
+    # Add derived variables that exist in generate_synthetic_data.py validation
+    def map_to_hmrc_band(turnover_k):
+        if turnover_k <= 0:
+            return "Negative_or_Zero"
+        elif turnover_k <= 85:
+            return "£1_to_Threshold"
+        elif turnover_k <= 150:
+            return "£Threshold_to_£150k"
+        elif turnover_k <= 300:
+            return "£150k_to_£300k"
+        elif turnover_k <= 500:
+            return "£300k_to_£500k"
+        elif turnover_k <= 1000:
+            return "£500k_to_£1m"
+        elif turnover_k <= 10000:
+            return "£1m_to_£10m"
+        else:
+            return "Greater_than_£10m"
+    
+    def _map_employment_to_band(employment):
+        if employment <= 4:
+            return "0-4"
+        elif employment <= 9:
+            return "5-9"
+        elif employment <= 19:
+            return "10-19"
+        elif employment <= 49:
+            return "20-49"
+        elif employment <= 99:
+            return "50-99"
+        elif employment <= 249:
+            return "100-249"
+        else:
+            return "250+"
+    
+    pe_firm["hmrc_band"] = pe_firm["annual_turnover_k"].apply(map_to_hmrc_band)
+    pe_firm["employment_band"] = pe_firm["employment"].apply(_map_employment_to_band)
+    pe_firm["sic_numeric"] = pe_firm["sic_code"].astype(int)
+    
+    # Add year field
+    pe_firm["year"] = year
+    pe_sector["year"] = year
+    pe_business_group["year"] = year
+    
+    # Create the dataset - use a simple object to hold the data
+    class FirmDataset:
+        def __init__(self):
+            self.firm = pe_firm
+            self.sector = pe_sector
+            self.business_group = pe_business_group
+            
+        def save(self, path):
+            # Save as HDF5 for compatibility
+            self.firm.to_hdf(path, key='firm', mode='w')
+            self.sector.to_hdf(path, key='sector', mode='a')
+            self.business_group.to_hdf(path, key='business_group', mode='a')
+    
+    dataset = FirmDataset()
+    
+    # Add metadata about the dataset
+    dataset.metadata = {
+        "source": "synthetic_firm_generator",
+        "year": year,
+        "n_firms": len(pe_firm),
+        "total_weighted_firms": pe_firm["firm_weight"].sum(),
+        "vat_registered_firms": pe_firm[pe_firm["vat_registered"]]["firm_weight"].sum(),
+        "total_employment": (pe_firm["employment"] * pe_firm["firm_weight"]).sum(),
+        "total_turnover_billions": (pe_firm["annual_turnover_k"] * pe_firm["firm_weight"]).sum() / 1e6,
+        "total_vat_liability_billions": (pe_firm["vat_liability_k"] * pe_firm["firm_weight"]).sum() / 1e6,
+    }
+    
+    logger.info(f"Created firm dataset with {len(pe_firm):,} firms")
+    logger.info(f"Total weighted population: {dataset.metadata['total_weighted_firms']:,.0f}")
+    logger.info(f"Total employment: {dataset.metadata['total_employment']:,.0f}")
+    logger.info(f"Total turnover: £{dataset.metadata['total_turnover_billions']:.1f}bn")
+    
+    return dataset
+
+
+# Dataset class for direct import like FRS
+class firm_2023_24:
+    """UK Firm dataset for 2023-24, following the FRS pattern."""
+    
+    def __init__(self):
+        # Load the dataset from storage or create if needed
+        dataset_path = STORAGE_FOLDER / "firm_2023_24.h5"
+        
+        if dataset_path.exists():
+            self.firm = pd.read_hdf(dataset_path, key='firm')
+            self.sector = pd.read_hdf(dataset_path, key='sector')
+            self.business_group = pd.read_hdf(dataset_path, key='business_group')
+        else:
+            # Create and save the dataset
+            dataset = create_firm(year=2023)
+            dataset.save(dataset_path)
+            self.firm = dataset.firm
+            self.sector = dataset.sector
+            self.business_group = dataset.business_group
+
+
+# Main execution for testing
+if __name__ == "__main__":
+    """Test the firm dataset creation."""
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s - %(levelname)s - %(message)s"
+    )
+    
+    logger.info("Creating firm dataset...")
+    
+    # Create the dataset
+    firm_dataset = create_firm(year=2023)
+    
+    # Save to storage
+    output_path = STORAGE_FOLDER / "firm_2023_24.h5"
+    firm_dataset.save(output_path)
+    
+    logger.info(f"Saved firm dataset to {output_path}")
+    
+    # Display summary statistics
+    print("\n" + "="*60)
+    print("FIRM DATASET SUMMARY")
+    print("="*60)
+    
+    for key, value in firm_dataset.metadata.items():
+        if isinstance(value, (int, float)):
+            if "billions" in key:
+                print(f"{key}: £{value:.2f}bn")
+            elif key in ["n_firms", "total_weighted_firms", "vat_registered_firms", "total_employment"]:
+                print(f"{key}: {value:,.0f}")
+            else:
+                print(f"{key}: {value}")
+        else:
+            print(f"{key}: {value}")
+    
+    print("="*60)
+    print("Dataset creation complete!")
\ No newline at end of file
diff --git a/policyengine_uk_data/datasets/firm/generate_synthetic_data.py b/policyengine_uk_data/datasets/firm/generate_synthetic_data.py
index 90bc909a..8c12bc7f 100644
--- a/policyengine_uk_data/datasets/firm/generate_synthetic_data.py
+++ b/policyengine_uk_data/datasets/firm/generate_synthetic_data.py
@@ -1813,13 +1813,9 @@ def main():
     # Generate synthetic data
     synthetic_df = generator.generate_synthetic_firms()
 
-    # Save results to STORAGE_FOLDER
-    output_path = STORAGE_FOLDER / "synthetic_firms_turnover.csv"
-    synthetic_df.to_csv(output_path, index=False)
-
-    file_size_mb = output_path.stat().st_size / 1024 / 1024
-    logger.info(f"Saved {len(synthetic_df):,} firms to {output_path}")
-    logger.info(f"File size: {file_size_mb:.1f} MB")
+    # Log results (CSV output now temporary, not saved to disk)
+    logger.info(f"Generated {len(synthetic_df):,} synthetic firms")
+    logger.info(f"Data size: {synthetic_df.memory_usage(deep=True).sum() / 1024 / 1024:.1f} MB")
     logger.info(f"Columns: {list(synthetic_df.columns)}")
 
     logger.info("Synthetic data generation complete!")

From 0a102cf1ace394c5e1556f35159a6cb1328fd533 Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 18:28:38 +0100
Subject: [PATCH 7/8] format

---
 policyengine_uk_data/datasets/firm.py         | 132 +++++++++++-------
 .../datasets/firm/generate_synthetic_data.py  |   4 +-
 2 files changed, 83 insertions(+), 53 deletions(-)

diff --git a/policyengine_uk_data/datasets/firm.py b/policyengine_uk_data/datasets/firm.py
index bd7713ea..5a5427bd 100644
--- a/policyengine_uk_data/datasets/firm.py
+++ b/policyengine_uk_data/datasets/firm.py
@@ -19,54 +19,58 @@
 def create_firm(year: int = 2023):
     """
     Process synthetic firm data into PolicyEngine UK dataset format.
-    
-    Generates synthetic firm microdata and transforms it into a structured 
-    PolicyEngine UK dataset with firm, sector, and employment-level variables 
+
+    Generates synthetic firm microdata and transforms it into a structured
+    PolicyEngine UK dataset with firm, sector, and employment-level variables
     mapped to the appropriate tax-benefit system variables.
-    
+
     Args:
         year: Survey year for the dataset.
-        
+
     Returns:
         Dataset with processed firm data ready for policy simulation.
     """
     # Always generate fresh synthetic data using generate_synthetic_data.py
     logger.info("Generating synthetic firm data...")
     import sys
+
     sys.path.append(str(Path(__file__).parent / "firm"))
     from generate_synthetic_data import SyntheticFirmGenerator
+
     generator = SyntheticFirmGenerator(device="cpu")
     synthetic_df = generator.generate_synthetic_firms()
-    
+
     # Create entity DataFrames for firm structure
     pe_firm = pd.DataFrame()
     pe_sector = pd.DataFrame()
     pe_business_group = pd.DataFrame()
-    
+
     # Add primary keys and identifiers
     pe_firm["firm_id"] = range(len(synthetic_df))
     pe_firm["firm_sector_id"] = synthetic_df["sic_code"].astype(int)
     pe_firm["firm_business_group_id"] = pe_firm["firm_id"] // 100
-    
+
     # Create unique sectors
     unique_sectors = synthetic_df["sic_code"].astype(int).unique()
     pe_sector["sector_id"] = unique_sectors
-    
+
     # Create business groups
     unique_groups = pe_firm["firm_business_group_id"].unique()
     pe_business_group["business_group_id"] = unique_groups
-    
+
     # Add grossing weights
     pe_firm["firm_weight"] = synthetic_df["weight"].values
-    
+
     # Add basic firm variables - exactly from synthetic data
     pe_firm["sic_code"] = synthetic_df["sic_code"]
     pe_firm["annual_turnover_k"] = synthetic_df["annual_turnover_k"].values
     pe_firm["annual_input_k"] = synthetic_df["annual_input_k"].values
     pe_firm["vat_liability_k"] = synthetic_df["vat_liability_k"].values
     pe_firm["employment"] = synthetic_df["employment"].astype(int).values
-    pe_firm["vat_registered"] = synthetic_df["vat_registered"].astype(bool).values
-    
+    pe_firm["vat_registered"] = (
+        synthetic_df["vat_registered"].astype(bool).values
+    )
+
     # Add derived variables that exist in generate_synthetic_data.py validation
     def map_to_hmrc_band(turnover_k):
         if turnover_k <= 0:
@@ -85,7 +89,7 @@ def map_to_hmrc_band(turnover_k):
             return "£1m_to_£10m"
         else:
             return "Greater_than_£10m"
-    
+
     def _map_employment_to_band(employment):
         if employment <= 4:
             return "0-4"
@@ -101,63 +105,83 @@ def _map_employment_to_band(employment):
             return "100-249"
         else:
             return "250+"
-    
+
     pe_firm["hmrc_band"] = pe_firm["annual_turnover_k"].apply(map_to_hmrc_band)
-    pe_firm["employment_band"] = pe_firm["employment"].apply(_map_employment_to_band)
+    pe_firm["employment_band"] = pe_firm["employment"].apply(
+        _map_employment_to_band
+    )
     pe_firm["sic_numeric"] = pe_firm["sic_code"].astype(int)
-    
+
     # Add year field
     pe_firm["year"] = year
     pe_sector["year"] = year
     pe_business_group["year"] = year
-    
+
     # Create the dataset - use a simple object to hold the data
     class FirmDataset:
         def __init__(self):
             self.firm = pe_firm
             self.sector = pe_sector
             self.business_group = pe_business_group
-            
+
         def save(self, path):
             # Save as HDF5 for compatibility
-            self.firm.to_hdf(path, key='firm', mode='w')
-            self.sector.to_hdf(path, key='sector', mode='a')
-            self.business_group.to_hdf(path, key='business_group', mode='a')
-    
+            self.firm.to_hdf(path, key="firm", mode="w")
+            self.sector.to_hdf(path, key="sector", mode="a")
+            self.business_group.to_hdf(path, key="business_group", mode="a")
+
     dataset = FirmDataset()
-    
+
     # Add metadata about the dataset
     dataset.metadata = {
         "source": "synthetic_firm_generator",
         "year": year,
         "n_firms": len(pe_firm),
         "total_weighted_firms": pe_firm["firm_weight"].sum(),
-        "vat_registered_firms": pe_firm[pe_firm["vat_registered"]]["firm_weight"].sum(),
-        "total_employment": (pe_firm["employment"] * pe_firm["firm_weight"]).sum(),
-        "total_turnover_billions": (pe_firm["annual_turnover_k"] * pe_firm["firm_weight"]).sum() / 1e6,
-        "total_vat_liability_billions": (pe_firm["vat_liability_k"] * pe_firm["firm_weight"]).sum() / 1e6,
+        "vat_registered_firms": pe_firm[pe_firm["vat_registered"]][
+            "firm_weight"
+        ].sum(),
+        "total_employment": (
+            pe_firm["employment"] * pe_firm["firm_weight"]
+        ).sum(),
+        "total_turnover_billions": (
+            pe_firm["annual_turnover_k"] * pe_firm["firm_weight"]
+        ).sum()
+        / 1e6,
+        "total_vat_liability_billions": (
+            pe_firm["vat_liability_k"] * pe_firm["firm_weight"]
+        ).sum()
+        / 1e6,
     }
-    
+
     logger.info(f"Created firm dataset with {len(pe_firm):,} firms")
-    logger.info(f"Total weighted population: {dataset.metadata['total_weighted_firms']:,.0f}")
-    logger.info(f"Total employment: {dataset.metadata['total_employment']:,.0f}")
-    logger.info(f"Total turnover: £{dataset.metadata['total_turnover_billions']:.1f}bn")
-    
+    logger.info(
+        f"Total weighted population: {dataset.metadata['total_weighted_firms']:,.0f}"
+    )
+    logger.info(
+        f"Total employment: {dataset.metadata['total_employment']:,.0f}"
+    )
+    logger.info(
+        f"Total turnover: £{dataset.metadata['total_turnover_billions']:.1f}bn"
+    )
+
     return dataset
 
 
 # Dataset class for direct import like FRS
 class firm_2023_24:
     """UK Firm dataset for 2023-24, following the FRS pattern."""
-    
+
     def __init__(self):
         # Load the dataset from storage or create if needed
         dataset_path = STORAGE_FOLDER / "firm_2023_24.h5"
-        
+
         if dataset_path.exists():
-            self.firm = pd.read_hdf(dataset_path, key='firm')
-            self.sector = pd.read_hdf(dataset_path, key='sector')
-            self.business_group = pd.read_hdf(dataset_path, key='business_group')
+            self.firm = pd.read_hdf(dataset_path, key="firm")
+            self.sector = pd.read_hdf(dataset_path, key="sector")
+            self.business_group = pd.read_hdf(
+                dataset_path, key="business_group"
+            )
         else:
             # Create and save the dataset
             dataset = create_firm(year=2023)
@@ -171,36 +195,40 @@ def __init__(self):
 if __name__ == "__main__":
     """Test the firm dataset creation."""
     logging.basicConfig(
-        level=logging.INFO,
-        format="%(asctime)s - %(levelname)s - %(message)s"
+        level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
     )
-    
+
     logger.info("Creating firm dataset...")
-    
+
     # Create the dataset
     firm_dataset = create_firm(year=2023)
-    
+
     # Save to storage
     output_path = STORAGE_FOLDER / "firm_2023_24.h5"
     firm_dataset.save(output_path)
-    
+
     logger.info(f"Saved firm dataset to {output_path}")
-    
+
     # Display summary statistics
-    print("\n" + "="*60)
+    print("\n" + "=" * 60)
     print("FIRM DATASET SUMMARY")
-    print("="*60)
-    
+    print("=" * 60)
+
     for key, value in firm_dataset.metadata.items():
         if isinstance(value, (int, float)):
             if "billions" in key:
                 print(f"{key}: £{value:.2f}bn")
-            elif key in ["n_firms", "total_weighted_firms", "vat_registered_firms", "total_employment"]:
+            elif key in [
+                "n_firms",
+                "total_weighted_firms",
+                "vat_registered_firms",
+                "total_employment",
+            ]:
                 print(f"{key}: {value:,.0f}")
             else:
                 print(f"{key}: {value}")
         else:
             print(f"{key}: {value}")
-    
-    print("="*60)
-    print("Dataset creation complete!")
\ No newline at end of file
+
+    print("=" * 60)
+    print("Dataset creation complete!")
diff --git a/policyengine_uk_data/datasets/firm/generate_synthetic_data.py b/policyengine_uk_data/datasets/firm/generate_synthetic_data.py
index 8c12bc7f..2ed49d9c 100644
--- a/policyengine_uk_data/datasets/firm/generate_synthetic_data.py
+++ b/policyengine_uk_data/datasets/firm/generate_synthetic_data.py
@@ -1815,7 +1815,9 @@ def main():
 
     # Log results (CSV output now temporary, not saved to disk)
     logger.info(f"Generated {len(synthetic_df):,} synthetic firms")
-    logger.info(f"Data size: {synthetic_df.memory_usage(deep=True).sum() / 1024 / 1024:.1f} MB")
+    logger.info(
+        f"Data size: {synthetic_df.memory_usage(deep=True).sum() / 1024 / 1024:.1f} MB"
+    )
     logger.info(f"Columns: {list(synthetic_df.columns)}")
 
     logger.info("Synthetic data generation complete!")

From 90dccdc145d1e2df6db0a41ef99d459add2e32cd Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 18:35:16 +0100
Subject: [PATCH 8/8] edit

---
 policyengine_uk_data/datasets/firm.py | 76 +++++++++++++--------------
 1 file changed, 38 insertions(+), 38 deletions(-)

diff --git a/policyengine_uk_data/datasets/firm.py b/policyengine_uk_data/datasets/firm.py
index 5a5427bd..2322066e 100644
--- a/policyengine_uk_data/datasets/firm.py
+++ b/policyengine_uk_data/datasets/firm.py
@@ -71,45 +71,45 @@ def create_firm(year: int = 2023):
         synthetic_df["vat_registered"].astype(bool).values
     )
 
-    # Add derived variables that exist in generate_synthetic_data.py validation
-    def map_to_hmrc_band(turnover_k):
-        if turnover_k <= 0:
-            return "Negative_or_Zero"
-        elif turnover_k <= 85:
-            return "£1_to_Threshold"
-        elif turnover_k <= 150:
-            return "£Threshold_to_£150k"
-        elif turnover_k <= 300:
-            return "£150k_to_£300k"
-        elif turnover_k <= 500:
-            return "£300k_to_£500k"
-        elif turnover_k <= 1000:
-            return "£500k_to_£1m"
-        elif turnover_k <= 10000:
-            return "£1m_to_£10m"
-        else:
-            return "Greater_than_£10m"
-
-    def _map_employment_to_band(employment):
-        if employment <= 4:
-            return "0-4"
-        elif employment <= 9:
-            return "5-9"
-        elif employment <= 19:
-            return "10-19"
-        elif employment <= 49:
-            return "20-49"
-        elif employment <= 99:
-            return "50-99"
-        elif employment <= 249:
-            return "100-249"
-        else:
-            return "250+"
-
-    pe_firm["hmrc_band"] = pe_firm["annual_turnover_k"].apply(map_to_hmrc_band)
-    pe_firm["employment_band"] = pe_firm["employment"].apply(
-        _map_employment_to_band
+    # Add derived variables using pd.cut for efficiency
+    turnover_bins = [0, 85, 150, 300, 500, 1000, 10000, float("inf")]
+    turnover_labels = [
+        "£1_to_Threshold",
+        "£Threshold_to_£150k",
+        "£150k_to_£300k",
+        "£300k_to_£500k",
+        "£500k_to_£1m",
+        "£1m_to_£10m",
+        "Greater_than_£10m",
+    ]
+    pe_firm["hmrc_band"] = (
+        pd.cut(
+            pe_firm["annual_turnover_k"],
+            bins=turnover_bins,
+            labels=turnover_labels,
+            include_lowest=True,
+        )
+        .astype(str)
+        .replace("nan", "Negative_or_Zero")
     )
+
+    employment_bins = [0, 4, 9, 19, 49, 99, 249, float("inf")]
+    employment_labels = [
+        "0-4",
+        "5-9",
+        "10-19",
+        "20-49",
+        "50-99",
+        "100-249",
+        "250+",
+    ]
+    pe_firm["employment_band"] = pd.cut(
+        pe_firm["employment"],
+        bins=employment_bins,
+        labels=employment_labels,
+        include_lowest=True,
+    ).astype(str)
+
     pe_firm["sic_numeric"] = pe_firm["sic_code"].astype(int)
 
     # Add year field