"""
Social Protection Coverage Africa Dataset Generator

Parameter Evidence Table:
| Parameter | Source | Value | Notes |
|-----------|--------|-------|-------|
| No coverage | World Bank 2025 | 2 billion | People without any coverage |
| Poorest countries | World Bank | 3/4 lack coverage | <25% coverage |
| SSA average | ILO/World Bank | 20-40% | Regional coverage rate |
| Pension coverage | ILO | 15-30% | Old-age pension |
| Health coverage | WHO | 30-50% | Universal health coverage |

Countries: 15 African nations
Years: 2018-2025
"""

import numpy as np
import pandas as pd
from pathlib import Path


COUNTRIES = [
    "Nigeria", "Ethiopia", "Tanzania", "Kenya", "Uganda", 
    "Mozambique", "Ghana", "Cote d'Ivoire", "Cameroon", "Senegal",
    "Zambia", "Malawi", "Rwanda", "Burkina Faso", "Mali"
]

YEARS = list(range(2018, 2026))

COUNTRY_COVERAGE = {
    "Nigeria": 0.12, "Ethiopia": 0.18, "Tanzania": 0.15, "Kenya": 0.28,
    "Uganda": 0.14, "Mozambique": 0.10, "Ghana": 0.35, "Cote d'Ivoire": 0.20,
    "Cameroon": 0.16, "Senegal": 0.22, "Zambia": 0.19, "Malawi": 0.11,
    "Rwanda": 0.32, "Burkina Faso": 0.13, "Mali": 0.09
}


def dag_sample(probabilities, n_samples, rng):
    """DAG-based sampling with probability weighting."""
    indices = rng.choice(len(probabilities), size=n_samples, p=probabilities, replace=True)
    unique, counts = np.unique(indices, return_counts=True)
    return dict(zip(unique, counts))


def generate_dataset(scenario: str, seed: int) -> pd.DataFrame:
    """Generate social protection coverage dataset for Africa."""
    np.random.seed(seed)
    rng = np.random.default_rng(seed)
    
    scenario_config = {
        "low_burden": {"n": 4000, "variance_scale": 0.8, "coverage_improvement": 1.15},
        "moderate": {"n": 5000, "variance_scale": 1.0, "coverage_improvement": 1.0},
        "high_burden": {"n": 6000, "variance_scale": 1.2, "coverage_improvement": 0.85}
    }
    
    config = scenario_config[scenario]
    n = config["n"]
    scale = config["variance_scale"]
    improvement = config["coverage_improvement"]
    
    records = []
    
    for year in YEARS:
        year_factor = 1.0 + (year - 2018) * 0.025 * improvement
        
        for country in COUNTRIES:
            base_coverage = COUNTRY_COVERAGE[country]
            coverage = base_coverage * year_factor
            coverage = min(0.65, max(0.05, coverage * scale))
            
            pension = coverage * 0.5 + rng.normal(0, 0.04)
            health = coverage * 0.85 + rng.normal(0, 0.05)
            unemployment = coverage * 0.25 + rng.normal(0, 0.02)
            disability = coverage * 0.3 + rng.normal(0, 0.025)
            
            n_country_year = n // (len(YEARS) * len(COUNTRIES))
            
            for _ in range(n_country_year):
                records.append({
                    "country": country,
                    "year": year,
                    "social_protection_coverage": max(0.02, min(0.75, coverage + rng.normal(0, 0.03 * scale))),
                    "pension_coverage": max(0.01, min(0.6, pension + rng.normal(0, 0.025))),
                    "health_protection_coverage": max(0.05, min(0.85, health + rng.normal(0, 0.03))),
                    "unemployment_benefits_coverage": max(0, min(0.4, unemployment + rng.normal(0, 0.015))),
                    "disability_benefits_coverage": max(0, min(0.45, disability + rng.normal(0, 0.02))),
                    "has_any_coverage": coverage > 0.1
                })
    
    df = pd.DataFrame(records)
    numeric_cols = df.select_dtypes(include=[float, int]).columns
    for col in numeric_cols:
        df[col] = df[col].clip(0.0, 1.0)
    
    return df


def main():
    output_dir = Path(__file__).parent
    output_dir.mkdir(parents=True, exist_ok=True)
    
    scenarios = {"low_burden": 42, "moderate": 43, "high_burden": 44}
    
    for scenario, seed in scenarios.items():
        df = generate_dataset(scenario, seed)
        df.to_csv(output_dir / f"social_protection_coverage_{scenario}.csv", index=False)
        print(f"Generated {scenario}: {len(df)} rows, seed={seed}")


if __name__ == "__main__":
    main()