""" African Public Debt Management Dataset Validator Runs plausibility checks and generates 8-panel diagnostic plots. """ import os import sys import matplotlib.pyplot as plt import numpy as np import pandas as pd from matplotlib.gridspec import GridSpec OUTPUT_DIR = "data" PLOT_DIR = os.path.join(OUTPUT_DIR, "plots") # ── Plausibility thresholds ──────────────────────────────────────────────── CHECKS = { "debt_to_gdp_pct": (1, 300), "external_debt_pct": (0, 100), "domestic_debt_pct": (0, 100), "debt_service_ratio_pct": (0, 100), "concessional_debt_pct": (0, 100), "average_maturity_years": (0.5, 40), "average_interest_rate_pct": (0, 20), "debt_sustainability_rating": (1, 5), "refinancing_risk_score": (0, 100), "primary_balance_pct": (-20, 15), "fiscal_space_score": (0, 100), "gdp_growth_pct": (-15, 20), "inflation_pct": (0, 100), "reserves_months_imports": (0, 30), "revenue_to_gdp_pct": (1, 60), "expenditure_to_gdp_pct": (2, 70), } def run_checks(df: pd.DataFrame) -> list[str]: """Run plausibility checks; return list of failure messages.""" failures = [] n = len(df) # 1. Range checks for col, (lo, hi) in CHECKS.items(): if col not in df.columns: failures.append(f"MISSING COLUMN: {col}") continue below = (df[col] < lo).sum() above = (df[col] > hi).sum() if below > 0: failures.append(f"RANGE: {col} has {below} values below {lo}") if above > 0: failures.append(f"RANGE: {col} has {above} values above {hi}") # 2. External + domestic ≈ 100 if "external_debt_pct" in df.columns and "domestic_debt_pct" in df.columns: comp_sum = df["external_debt_pct"] + df["domestic_debt_pct"] bad = ((comp_sum - 100).abs() > 0.1).sum() if bad > 0: failures.append(f"COMPOSITION: {bad} rows where external+domestic ≠ 100%") # 3. Expenditure > revenue (fiscal deficit) if "expenditure_to_gdp_pct" in df.columns and "revenue_to_gdp_pct" in df.columns: exp_lt_rev = (df["expenditure_to_gdp_pct"] < df["revenue_to_gdp_pct"]).sum() if exp_lt_rev / n > 0.85: failures.append( f"FISCAL: {exp_lt_rev}/{n} ({100*exp_lt_rev/n:.1f}%) rows have expenditure < revenue (unusual for SSA)" ) # 4. No NaN/inf nan_counts = df.isna().sum() for col, cnt in nan_counts.items(): if cnt > 0: failures.append(f"NaN: {col} has {cnt} missing values") inf_cols = df.select_dtypes(include=[np.number]).columns for col in inf_cols: inf_cnt = np.isinf(df[col]).sum() if inf_cnt > 0: failures.append(f"INF: {col} has {inf_cnt} infinite values") # 5. Row count if len(df) < 25_000: failures.append(f"SIZE: Dataset has only {len(df)} rows (expected ≥25K)") # 6. All 12 countries present expected_countries = { "Nigeria", "Kenya", "Ghana", "South Africa", "DRC", "Ethiopia", "Tanzania", "Rwanda", "Mozambique", "Zambia", "Senegal", "Cote d'Ivoire", } actual = set(df["country"].unique()) missing = expected_countries - actual if missing: failures.append(f"COUNTRIES: Missing: {missing}") # 7. All 3 scenarios present expected_scenarios = {"baseline", "debt_consolidation", "debt_distress"} actual_scenarios = set(df["scenario"].unique()) missing_sc = expected_scenarios - actual_scenarios if missing_sc: failures.append(f"SCENARIOS: Missing: {missing_sc}") # 8. Scenario-specific plausibility for sc in ["baseline", "debt_consolidation", "debt_distress"]: sub = df[df["scenario"] == sc] if len(sub) == 0: continue mean_dsr = sub["debt_service_ratio_pct"].mean() if sc == "debt_distress" and mean_dsr < 15: failures.append(f"SCENARIO: {sc} mean DSR={mean_dsr:.1f}% seems too low") if sc == "debt_consolidation" and mean_dsr > 30: failures.append(f"SCENARIO: {sc} mean DSR={mean_dsr:.1f}% seems too high") return failures def generate_diagnostic_plots(df: pd.DataFrame): """Generate 8-panel diagnostic figure.""" os.makedirs(PLOT_DIR, exist_ok=True) plt.style.use("seaborn-v0_8-whitegrid") fig = plt.figure(figsize=(20, 16)) gs = GridSpec(3, 3, figure=fig, hspace=0.35, wspace=0.30) colors = {"baseline": "#2196F3", "debt_consolidation": "#4CAF50", "debt_distress": "#F44336"} # Panel 1: Debt-to-GDP distribution by scenario ax1 = fig.add_subplot(gs[0, 0]) for sc, c in colors.items(): sub = df[df["scenario"] == sc] ax1.hist(sub["debt_to_gdp_pct"], bins=50, alpha=0.5, label=sc.replace("_", " ").title(), color=c) ax1.set_xlabel("Debt-to-GDP (%)") ax1.set_ylabel("Count") ax1.set_title("A) Debt-to-GDP Distribution by Scenario") ax1.legend(fontsize=8) # Panel 2: Debt service ratio by country ax2 = fig.add_subplot(gs[0, 1]) country_order = df.groupby("country")["debt_service_ratio_pct"].median().sort_values(ascending=False).index bp = df.boxplot( column="debt_service_ratio_pct", by="country", ax=ax2, vert=True, patch_artist=True, showfliers=False, positions=range(len(country_order)), ) ax2.set_xticklabels(country_order, rotation=45, ha="right", fontsize=7) ax2.set_title("B) Debt Service Ratio by Country") ax2.set_xlabel("") plt.sca(ax2) plt.title("B) Debt Service Ratio by Country") # Panel 3: External vs Domestic composition ax3 = fig.add_subplot(gs[0, 2]) for sc, c in colors.items(): sub = df[df["scenario"] == sc] ax3.scatter(sub["external_debt_pct"], sub["domestic_debt_pct"], alpha=0.05, s=5, color=c, label=sc.replace("_", " ").title()) ax3.plot([0, 100], [100, 0], "k--", alpha=0.3, linewidth=1) ax3.set_xlabel("External Debt (%)") ax3.set_ylabel("Domestic Debt (%)") ax3.set_title("C) External vs Domestic Debt Composition") ax3.legend(fontsize=8, markerscale=10) # Panel 4: Debt sustainability rating distribution ax4 = fig.add_subplot(gs[1, 0]) for sc, c in colors.items(): sub = df[df["scenario"] == sc] counts = sub["debt_sustainability_rating"].value_counts().sort_index() ax4.bar(counts.index + (0.2 if sc == "debt_consolidation" else (-0.2 if sc == "baseline" else 0)), counts.values, width=0.2, alpha=0.7, color=c, label=sc.replace("_", " ").title()) ax4.set_xlabel("Debt Sustainability Rating (1=Low Risk, 5=Distress)") ax4.set_ylabel("Count") ax4.set_title("D) Debt Sustainability Rating Distribution") ax4.legend(fontsize=8) # Panel 5: Concessional vs interest rate ax5 = fig.add_subplot(gs[1, 1]) sample = df.sample(min(5000, len(df)), random_state=42) for sc, c in colors.items(): sub = sample[sample["scenario"] == sc] ax5.scatter(sub["concessional_debt_pct"], sub["average_interest_rate_pct"], alpha=0.3, s=8, color=c) ax5.set_xlabel("Concessional Debt (%)") ax5.set_ylabel("Average Interest Rate (%)") ax5.set_title("E) Concessional Debt vs Interest Rate") # Panel 6: Refinancing risk vs maturity ax6 = fig.add_subplot(gs[1, 2]) for sc, c in colors.items(): sub = sample[sample["scenario"] == sc] ax6.scatter(sub["average_maturity_years"], sub["refinancing_risk_score"], alpha=0.3, s=8, color=c, label=sc.replace("_", " ").title()) ax6.set_xlabel("Average Maturity (Years)") ax6.set_ylabel("Refinancing Risk Score") ax6.set_title("F) Refinancing Risk vs Debt Maturity") ax6.legend(fontsize=8, markerscale=5) # Panel 7: Primary balance vs fiscal space ax7 = fig.add_subplot(gs[2, 0]) for sc, c in colors.items(): sub = sample[sample["scenario"] == sc] ax7.scatter(sub["primary_balance_pct"], sub["fiscal_space_score"], alpha=0.3, s=8, color=c) ax7.set_xlabel("Primary Balance (% GDP)") ax7.set_ylabel("Fiscal Space Score") ax7.set_title("G) Primary Balance vs Fiscal Space") ax7.axvline(0, color="gray", linestyle="--", alpha=0.5) # Panel 8: Scenario comparison heatmap (mean values) ax8 = fig.add_subplot(gs[2, 1:]) metrics = [ "debt_to_gdp_pct", "debt_service_ratio_pct", "concessional_debt_pct", "average_interest_rate_pct", "refinancing_risk_score", "fiscal_space_score", "primary_balance_pct", "debt_sustainability_rating", ] scenario_means = df.groupby("scenario")[metrics].mean() # Normalize for heatmap normed = (scenario_means - scenario_means.min()) / (scenario_means.max() - scenario_means.min() + 1e-9) im = ax8.imshow(normed.values, aspect="auto", cmap="RdYlGn_r", vmin=0, vmax=1) ax8.set_xticks(range(len(metrics))) ax8.set_xticklabels([m.replace("_", "\n") for m in metrics], fontsize=7, rotation=0, ha="center") ax8.set_yticks(range(len(scenario_means.index))) ax8.set_yticklabels([s.replace("_", " ").title() for s in scenario_means.index]) ax8.set_title("H) Scenario Comparison (Normalized Mean Values)") # Add text annotations for i in range(len(scenario_means.index)): for j in range(len(metrics)): val = scenario_means.values[i, j] ax8.text(j, i, f"{val:.1f}", ha="center", va="center", fontsize=6, color="white" if normed.values[i, j] > 0.6 else "black") fig.suptitle("African Public Debt Management — Diagnostic Plots", fontsize=16, fontweight="bold", y=0.98) plot_path = os.path.join(PLOT_DIR, "diagnostic_plots.png") fig.savefig(plot_path, dpi=150, bbox_inches="tight") plt.close(fig) print(f"Diagnostic plots saved to {plot_path}") def main(): combined_path = os.path.join(OUTPUT_DIR, "all_scenarios.csv") if not os.path.exists(combined_path): print(f"ERROR: {combined_path} not found. Run generate_dataset.py first.") sys.exit(1) df = pd.read_csv(combined_path) print(f"Loaded {len(df)} records from {combined_path}") print(f" Countries: {df['country'].nunique()}") print(f" Scenarios: {df['scenario'].unique()}") print(f" Columns: {len(df.columns)}") # Run plausibility checks print("\n── Plausibility Checks ──") failures = run_checks(df) if failures: for f in failures: print(f" ✗ {f}") print(f"\n{len(failures)} check(s) failed.") else: print(" ✓ All checks passed.") # Summary statistics print("\n── Summary Statistics ──") numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist() summary = df.groupby("scenario")[numeric_cols].agg(["mean", "std"]).round(2) print(summary.to_string(max_cols=12)) # Generate plots print("\n── Generating Diagnostic Plots ──") generate_diagnostic_plots(df) return 0 if not failures else 1 if __name__ == "__main__": sys.exit(main())