Merge pull request #7 from Utorque/claude/align-sim-data-generator-01HUQacsiojn3J1Kan2yE7Nw (c08c45ee) · Commits · Barthelet Thibault / horloml

forecast_app/data/sim_supply_chain_data_full.json

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Preview size limit exceeded, changes collapsed.

forecast_app/data/sim_supply_chain_data_test.json

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forecast_app/data/sim_supply_chain_data_training.json

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forecast_app/sim_data_gen.py

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		"""
		Simulation-based Data Generator for Supply Chain Forecasting Educational App

		Uses the full supply chain simulation to generate realistic data for 3 watch models.
		This generates more realistic data compared to the simple data_generator.py as it
		uses the complete simulation with all supply chain entities and dynamics.
		"""

		import sys
		import os
		import json
		import numpy as np
		from typing import Dict, List

		# Add parent directory to path to import supply_chain_sim
		sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

		from supply_chain_sim import gen_data


		def get_training_data(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(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 save_dataset(dataset: Dict, filepath: str):
		"""Save dataset to JSON file"""
		# Create directory if it doesn't exist
		os.makedirs(os.path.dirname(filepath), exist_ok=True)

		with open(filepath, 'w') as f:
		json.dump(dataset, f, indent=2)
		print(f"✓ Dataset saved to {filepath}")


		def main():
		"""Generate and save the simulation-based dataset"""
		print("=" * 60)
		print("SIMULATION-BASED DATA GENERATOR FOR FORECAST APP")
		print("=" * 60)
		print("\nGenerating Supply Chain Dataset using full simulation...")
		print("-" * 60)

		# Configure the simulation
		config = {
		'seed': 42,
		'years': 11,
		'scenario': 'basic',
		'n_customers': 500,
		'n_retailers': 6,
		'n_suppliers': 5,
		'n_warehouses': 2
		}

		print(f"\nConfiguration:")
		print(f" • Seed: {config['seed']}")
		print(f" • Years: {config['years']}")
		print(f" • Scenario: {config['scenario']}")
		print(f" • Customers: {config['n_customers']}")
		print(f" • Retailers: {config['n_retailers']}")
		print()

		# Generate full 11-year dataset using simulation
		full_dataset = gen_data(config)

		print("\n" + "-" * 60)
		print("Saving datasets...")
		print("-" * 60)

		# Save full dataset
		save_dataset(full_dataset, 'data/sim_supply_chain_data_full.json')

		# Save training data (10 years)
		training_data = get_training_data(full_dataset, training_years=10)
		save_dataset(training_data, 'data/sim_supply_chain_data_training.json')

		# Save test data (year 11)
		test_data = get_test_data(full_dataset, test_year=11)
		with open('data/sim_supply_chain_data_test.json', 'w') as f:
		json.dump(test_data, f, indent=2)
		print(f"✓ Dataset saved to data/sim_supply_chain_data_test.json")

		print("\n" + "-" * 60)
		print("Dataset Generation Complete!")
		print("-" * 60)
		print(f"Total months: {len(full_dataset['historical_data'])}")
		print(f"Training months: {len(training_data['historical_data'])}")
		print(f"Test months: {len(test_data)}")

		# Print sample statistics
		print("\n" + "=" * 60)
		print("Sample Statistics (Year 1):")
		print("=" * 60)

		year1_data = full_dataset['historical_data'][:12]
		for watch in full_dataset['metadata']['watches']:
		watch_id = watch['id']
		watch_name = watch['name']

		demands = [m['watches'][watch_id-1]['demand'] for m in year1_data]
		revenues = [m['watches'][watch_id-1]['revenue'] for m in year1_data]

		print(f"\n{watch_name}:")
		print(f" Avg Monthly Demand: {np.mean(demands):.1f} units")
		print(f" Avg Monthly Revenue: CHF {np.mean(revenues):,.2f}")
		print(f" Annual Revenue: CHF {np.sum(revenues):,.2f}")

		print("\n" + "=" * 60)
		print("✓ All files generated successfully!")
		print("=" * 60)
		print("\nGenerated files:")
		print(" • data/sim_supply_chain_data_full.json")
		print(" • data/sim_supply_chain_data_training.json")
		print(" • data/sim_supply_chain_data_test.json")
		print()


		if __name__ == "__main__":
		main()