Merge pull request #6 from Utorque/claude/align-sim-data-generator-01HUQacsiojn3J1Kan2yE7Nw (e3db5895) · Commits · Barthelet Thibault / horloml

supply_chain_sim/init.py

+4 −1

Original line number	Diff line number	Diff line
		@@ -10,6 +10,7 @@ from .distribution import RetailDistribution
		from .customer_behavior import CustomerBehavior
		from .model_update import ModelUpdate
		from .research_logging import ResearchLogging
		from .sim_data_generator import gen_data, SimDataGenerator

		__version__ = "0.1.0"

		@@ -24,5 +25,7 @@ __all__ = [
		"RetailDistribution",
		"CustomerBehavior",
		"ModelUpdate",
		"ResearchLogging"
		"ResearchLogging",
		"gen_data",
		"SimDataGenerator"
		]
		No newline at end of file

supply_chain_sim/sim_data_generator.py

0 → 100644

+351 −0

Original line number	Diff line number	Diff line
		"""
		Simulation-based Data Generator for Supply Chain Forecasting

		Uses the full supply chain simulation to generate realistic data in the same format
		as the forecast_app data_generator.py output.
		"""

		import numpy as np
		import json
		from datetime import datetime, timedelta
		from typing import Dict, List
		from collections import defaultdict

		from .simulation import SupplyChainSimulation
		from .entities import Sale


		class SimDataGenerator:
		"""Generate data using the full supply chain simulation"""

		def __init__(self, seed: int = 42):
		"""
		Initialize the simulation-based data generator

		Args:
		seed: Random seed for reproducibility
		"""
		self.seed = seed
		np.random.seed(seed)

		def _aggregate_monthly_data(self, sim: SupplyChainSimulation, years: int) -> Dict:
		"""
		Aggregate simulation results into monthly data format matching data_generator.py

		Args:
		sim: Completed simulation object
		years: Number of years simulated

		Returns:
		Dataset in the same format as forecast_app data_generator
		"""
		total_months = years * 12
		start_date = datetime(2014, 1, 1)

		# Get watch models from simulation
		watches_info = []
		for watch in sim.setup.watches[:3]: # Use first 3 watches to match data_generator
		watches_info.append({
		'id': watch.id + 1, # 1-indexed for compatibility
		'name': watch.name,
		'category': watch.category,
		'base_cost': round(watch.base_cost, 2),
		'sell_price': round(watch.sell_price, 2),
		'base_demand': 100, # Placeholder
		'seasonality_amplitude': 0.2,
		'trend': 0.005,
		'peak_months': [11, 12] if watch.category == 'luxury' else [6, 7]
		})

		# Build dataset structure
		dataset = {
		'metadata': {
		'generated_date': datetime.now().isoformat(),
		'years': years,
		'total_months': total_months,
		'start_date': start_date.isoformat(),
		'watches': watches_info,
		'source': 'supply_chain_simulation'
		},
		'historical_data': []
		}

		# Organize sales by month and watch
		sales_by_month = defaultdict(lambda: defaultdict(list))
		for sale in sim.all_sales:
		month_idx = sale.simulation_month - 1 # Convert to 0-indexed
		sales_by_month[month_idx][sale.watch_id].append(sale)

		# Track inventory for each watch (only first 3 watches)
		inventory_tracker = {watch['id'] - 1: 100 for watch in watches_info} # Use 0-indexed watch_id

		# Generate monthly data
		for month_idx in range(total_months):
		current_date = start_date + timedelta(days=30 * month_idx)
		year = (month_idx // 12) + 1
		month_in_year = (month_idx % 12) + 1

		month_data = {
		'month_index': month_idx,
		'year': year,
		'month': month_in_year,
		'date': current_date.strftime('%Y-%m'),
		'watches': []
		}

		# Process each watch (only first 3)
		for watch_info in watches_info:
		watch_id = watch_info['id'] - 1 # Convert to 0-indexed

		# Get sales for this month and watch
		sales = sales_by_month[month_idx].get(watch_id, [])

		# Calculate metrics
		demand = len(sales) # Total number of sales (demand satisfied)
		units_sold = sum(sale.quantity for sale in sales)
		revenue = sum(sale.total_price for sale in sales)

		# Estimate production (use actual units sold + small buffer)
		# In simulation, production happens before sales
		production = max(units_sold, int(units_sold * 1.1))

		# Calculate inventory
		inventory_start = inventory_tracker[watch_id]
		inventory_end = max(0, inventory_start + production - units_sold)
		inventory_tracker[watch_id] = inventory_end

		# Calculate stockouts (estimated as unmet demand)
		# This is an approximation since simulation tracks actual sales
		# We estimate based on inventory levels
		stockout_units = max(0, units_sold - inventory_start) if inventory_start < units_sold else 0

		# Calculate costs
		base_cost = watch_info['base_cost']
		sell_price = watch_info['sell_price']

		production_cost = production * base_cost
		labor_cost = production * 20.0 # Match data_generator labor cost
		holding_cost = inventory_end * base_cost * 0.02 # 2% holding cost
		stockout_cost = stockout_units * sell_price * 0.3 # 30% penalty

		total_costs = production_cost + labor_cost + holding_cost + stockout_cost
		profit = revenue - total_costs

		# Build watch data entry
		watch_data = {
		'watch_id': watch_info['id'],
		'watch_name': watch_info['name'],
		'demand': int(demand) if demand > 0 else int(units_sold * np.random.uniform(0.8, 1.2)),
		'production': production,
		'inventory_start': inventory_start,
		'inventory_end': inventory_end,
		'units_sold': int(units_sold),
		'stockout_units': int(stockout_units),
		'revenue': round(revenue, 2),
		'production_cost': round(production_cost, 2),
		'labor_cost': round(labor_cost, 2),
		'holding_cost': round(holding_cost, 2),
		'stockout_cost': round(stockout_cost, 2),
		'total_costs': round(total_costs, 2),
		'profit': round(profit, 2)
		}

		month_data['watches'].append(watch_data)

		dataset['historical_data'].append(month_data)

		return dataset

		def generate_dataset(self, years: int = 11, config: Dict = None) -> Dict:
		"""
		Generate complete dataset using simulation for specified number of years

		Args:
		years: Number of years to generate (default 11)
		config: Optional simulation configuration

		Returns:
		Dictionary containing all historical data in forecast_app format
		"""
		# Prepare simulation config
		sim_config = config or {}
		sim_config.update({
		'seed': self.seed,
		'n_months': years * 12,
		'n_brands': 2,
		'n_suppliers': 5,
		'n_components': 15,
		'n_watches': 8, # Generate 8 but use first 3
		'n_warehouses': 2,
		'n_retailers': 6,
		'n_customers': 500,
		'scenario': sim_config.get('scenario', 'basic')
		})

		print(f"Running supply chain simulation for {years} years ({years * 12} months)...")
		print("-" * 60)

		# Create and run simulation
		sim = SupplyChainSimulation(sim_config)

		# Run simulation without student predictions (uses automatic forecasting)
		sim.run_simulation_with_predictions(predictions=None)

		print("\n" + "-" * 60)
		print("Aggregating simulation data into forecast format...")

		# Aggregate data into required format
		dataset = self._aggregate_monthly_data(sim, years)

		print(f"✓ Generated dataset with {len(dataset['historical_data'])} months")

		return dataset

		def save_dataset(self, dataset: Dict, filepath: str = 'supply_chain_data.json'):
		"""Save dataset to JSON file"""
		with open(filepath, 'w') as f:
		json.dump(dataset, f, indent=2)
		print(f"Dataset saved to {filepath}")

		def get_training_data(self, dataset: Dict, training_years: int = 10) -> Dict:
		"""
		Extract training data (first N years) from full dataset

		Args:
		dataset: Full dataset
		training_years: Number of years to include in training set

		Returns:
		Dictionary with training data only
		"""
		training_months = training_years * 12

		training_data = {
		'metadata': dataset['metadata'].copy(),
		'historical_data': dataset['historical_data'][:training_months]
		}
		training_data['metadata']['years'] = training_years
		training_data['metadata']['total_months'] = training_months
		training_data['metadata']['note'] = f"Training data: first {training_years} years"

		return training_data

		def get_test_data(self, dataset: Dict, test_year: int = 11) -> List[Dict]:
		"""
		Extract test data (year to predict)

		Args:
		dataset: Full dataset
		test_year: Year number to extract (1-based)

		Returns:
		List of monthly data for the test year
		"""
		start_idx = (test_year - 1) * 12
		end_idx = test_year * 12

		return dataset['historical_data'][start_idx:end_idx]


		def gen_data(config: Dict = None) -> Dict:
		"""
		Main function to generate simulation-based supply chain data

		Args:
		config: Configuration dictionary with optional parameters:
		- seed (int): Random seed for reproducibility (default: 42)
		- years (int): Number of years to simulate (default: 11)
		- scenario (str): Simulation scenario ('basic' or 'unplanned_problem')
		- n_customers (int): Number of customers (default: 500)
		- n_retailers (int): Number of retailers (default: 6)

		Returns:
		Dictionary with historical data in forecast_app format:
		{
		'metadata': {...},
		'historical_data': [
		{
		'month_index': 0,
		'year': 1,
		'month': 1,
		'date': '2014-01',
		'watches': [
		{
		'watch_id': 1,
		'watch_name': '...',
		'demand': 100,
		'production': 105,
		'inventory_start': 100,
		'inventory_end': 105,
		'units_sold': 100,
		'stockout_units': 0,
		'revenue': 50000.0,
		'production_cost': 15750.0,
		'labor_cost': 2100.0,
		'holding_cost': 315.0,
		'stockout_cost': 0.0,
		'total_costs': 18165.0,
		'profit': 31835.0
		},
		...
		]
		},
		...
		]
		}
		"""
		config = config or {}
		seed = config.get('seed', 42)
		years = config.get('years', 11)

		generator = SimDataGenerator(seed=seed)
		dataset = generator.generate_dataset(years=years, config=config)

		return dataset


		def main():
		"""Example usage and testing"""
		print("=" * 60)
		print("SIMULATION-BASED DATA GENERATOR")
		print("=" * 60)
		print("\nGenerating supply chain data using full simulation...")

		# Generate dataset with default config
		config = {
		'seed': 42,
		'years': 2, # Use 2 years for quick testing
		'scenario': 'basic',
		'n_customers': 500,
		'n_retailers': 6
		}

		dataset = gen_data(config)

		print("\n" + "=" * 60)
		print("DATASET SUMMARY")
		print("=" * 60)
		print(f"Total months: {len(dataset['historical_data'])}")
		print(f"Number of watch models: {len(dataset['metadata']['watches'])}")

		# Print sample from first month
		print("\n" + "=" * 60)
		print("SAMPLE DATA (Month 1)")
		print("=" * 60)
		first_month = dataset['historical_data'][0]
		print(f"Date: {first_month['date']}")
		for watch_data in first_month['watches']:
		print(f"\n{watch_data['watch_name']}:")
		print(f" Demand: {watch_data['demand']} units")
		print(f" Production: {watch_data['production']} units")
		print(f" Units Sold: {watch_data['units_sold']} units")
		print(f" Revenue: CHF {watch_data['revenue']:,.2f}")
		print(f" Profit: CHF {watch_data['profit']:,.2f}")

		print("\n" + "=" * 60)
		print("✓ Data generation complete!")
		print("=" * 60)


		if __name__ == "__main__":
		main()