#!/usr/bin/env python3 """ African Judicial Access Indicators — Validation & QA (Phase 6) + Visualization (Phase 7) ========================================================================================= Electric Sheep Africa """ import os import sys import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt import numpy as np import pandas as pd # ============================================================================ # VALIDATION TARGETS # ============================================================================ ACCESS_LEVEL_TARGETS = { "baseline": { "high": (0.02, 0.30), "moderate": (0.10, 0.50), "low": (0.15, 0.55), "very_low": (0.05, 0.35), }, "improved_access": { "high": (0.05, 0.40), "moderate": (0.15, 0.55), "low": (0.10, 0.45), "very_low": (0.02, 0.25), }, "constrained": { "high": (0.01, 0.20), "moderate": (0.05, 0.40), "low": (0.15, 0.55), "very_low": (0.10, 0.50), }, } EXPECTED_MOMENTS = { "baseline": { "lawyer_density_per_100k": {"mean": (3, 25), "sd": (5, 30)}, "distance_to_court_km": {"mean": (20, 60), "sd": (15, 55)}, "travel_time_hours": {"mean": (1, 8), "sd": (1, 12)}, "legal_aid_coverage_pct": {"mean": (2, 15), "sd": (2, 15)}, "cost_pct_monthly_income": {"mean": (50, 200), "sd": (30, 120)}, "bribery_rate_pct": {"mean": (20, 40), "sd": (5, 18)}, "trust_in_courts_pct": {"mean": (30, 65), "sd": (10, 30)}, "gender_access_ratio": {"mean": (0.50, 0.80), "sd": (0.05, 0.18)}, "court_contact_rate_pct": {"mean": (5, 18), "sd": (2, 8)}, "access_score": {"mean": (0.25, 0.55), "sd": (0.08, 0.22)}, }, "improved_access": { "lawyer_density_per_100k": {"mean": (5, 30), "sd": (5, 35)}, "distance_to_court_km": {"mean": (15, 55), "sd": (10, 50)}, "travel_time_hours": {"mean": (0.5, 7), "sd": (0.5, 10)}, "legal_aid_coverage_pct": {"mean": (3, 25), "sd": (3, 20)}, "cost_pct_monthly_income": {"mean": (30, 160), "sd": (25, 100)}, "bribery_rate_pct": {"mean": (20, 40), "sd": (5, 18)}, "trust_in_courts_pct": {"mean": (30, 65), "sd": (10, 30)}, "gender_access_ratio": {"mean": (0.50, 0.85), "sd": (0.05, 0.18)}, "court_contact_rate_pct": {"mean": (5, 20), "sd": (2, 8)}, "access_score": {"mean": (0.30, 0.60), "sd": (0.08, 0.22)}, }, "constrained": { "lawyer_density_per_100k": {"mean": (2, 20), "sd": (4, 25)}, "distance_to_court_km": {"mean": (25, 75), "sd": (15, 60)}, "travel_time_hours": {"mean": (1, 10), "sd": (1, 14)}, "legal_aid_coverage_pct": {"mean": (1, 10), "sd": (1, 10)}, "cost_pct_monthly_income": {"mean": (70, 300), "sd": (40, 160)}, "bribery_rate_pct": {"mean": (20, 40), "sd": (5, 18)}, "trust_in_courts_pct": {"mean": (30, 65), "sd": (10, 30)}, "gender_access_ratio": {"mean": (0.45, 0.75), "sd": (0.05, 0.18)}, "court_contact_rate_pct": {"mean": (4, 16), "sd": (2, 8)}, "access_score": {"mean": (0.20, 0.50), "sd": (0.08, 0.22)}, }, } CORRELATION_TARGETS = { ("lawyer_density_per_100k", "court_density_per_100k"): {"r": 0.55, "tol": 0.25}, ("distance_to_court_km", "court_contact_rate_pct"): {"r": -0.25, "tol": 0.25}, ("bribery_rate_pct", "trust_in_courts_pct"): {"r": -0.65, "tol": 0.20}, ("cost_pct_monthly_income", "court_contact_rate_pct"): {"r": -0.20, "tol": 0.25}, ("legal_aid_coverage_pct", "access_score"): {"r": 0.70, "tol": 0.25}, } PLAUSIBILITY_RULES = [ ("distance_to_court > 0", lambda df: (df["distance_to_court_km"] > 0).all()), ("travel_time > 0", lambda df: (df["travel_time_hours"] > 0).all()), ("lawyer_density > 0", lambda df: (df["lawyer_density_per_100k"] > 0).all()), ("legal_aid_coverage in [0,100]", lambda df: ((df["legal_aid_coverage_pct"] >= 0) & (df["legal_aid_coverage_pct"] <= 100)).all()), ("bribery_rate in [0,100]", lambda df: ((df["bribery_rate_pct"] >= 0) & (df["bribery_rate_pct"] <= 100)).all()), ("trust in [0,100]", lambda df: ((df["trust_in_courts_pct"] >= 0) & (df["trust_in_courts_pct"] <= 100)).all()), ("gender_ratio in [0,1]", lambda df: ((df["gender_access_ratio"] >= 0) & (df["gender_access_ratio"] <= 1)).all()), ("access_score in [0,1]", lambda df: ((df["access_score"] >= 0) & (df["access_score"] <= 1)).all()), ("contact_rate > 0", lambda df: (df["court_contact_rate_pct"] > 0).all()), ("wjp_score in [0.15,0.90]", lambda df: ((df["wjp_rule_of_law_score"] >= 0.15) & (df["wjp_rule_of_law_score"] <= 0.90)).all()), ("urban distance < rural distance (median)", lambda df: df.loc[df["region_type"] == "urban", "distance_to_court_km"].median() < df.loc[df["region_type"] == "rural", "distance_to_court_km"].median()), ("urban lawyer_density > rural (median)", lambda df: df.loc[df["region_type"] == "urban", "lawyer_density_per_100k"].median() > df.loc[df["region_type"] == "rural", "lawyer_density_per_100k"].median()), ] def load_all_scenario_csvs(data_dir): dfs = {} for fname in sorted(os.listdir(data_dir)): if fname.endswith(".csv"): scenario = fname.replace(".csv", "") dfs[scenario] = pd.read_csv(os.path.join(data_dir, fname)) print(f" Loaded {scenario}: {dfs[scenario].shape}") return dfs def test_prevalence(df, scenario_name): print(f"\n--- Access Level Distribution ({scenario_name}) ---") all_pass = True targets = ACCESS_LEVEL_TARGETS.get(scenario_name, ACCESS_LEVEL_TARGETS["baseline"]) al_dist = df["access_level"].value_counts(normalize=True) for level, (lo, hi) in targets.items(): observed = al_dist.get(level, 0) status = "PASS" if lo <= observed <= hi else "FAIL" if status == "FAIL": all_pass = False print(f" {level}: {observed:.3f} (target: {lo:.3f}-{hi:.3f}) [{status}]") return all_pass def test_distributions(df, scenario_name): print(f"\n--- Distribution Checks ({scenario_name}) ---") all_pass = True moments = EXPECTED_MOMENTS.get(scenario_name, EXPECTED_MOMENTS["baseline"]) for col, targets in moments.items(): obs_mean = df[col].mean() obs_sd = df[col].std() mean_lo, mean_hi = targets["mean"] sd_lo, sd_hi = targets["sd"] mean_ok = mean_lo <= obs_mean <= mean_hi sd_ok = sd_lo <= obs_sd <= sd_hi status = "PASS" if (mean_ok and sd_ok) else "FAIL" if status == "FAIL": all_pass = False print(f" {col}: mean={obs_mean:.3f} ({mean_lo}-{mean_hi}), " f"sd={obs_sd:.3f} ({sd_lo}-{sd_hi}) [{status}]") return all_pass def test_correlations(df, scenario_name): print(f"\n--- Correlation Verification ({scenario_name}) ---") all_pass = True for (v1, v2), target in CORRELATION_TARGETS.items(): obs_r = df[v1].corr(df[v2]) diff = abs(obs_r - target["r"]) status = "PASS" if diff < target["tol"] else "FAIL" if status == "FAIL": all_pass = False print(f" corr({v1}, {v2}): obs={obs_r:.3f}, target={target['r']:.2f}, " f"diff={diff:.3f} [{status}]") return all_pass def test_clinical_constraints(df, scenario_name): print(f"\n--- Plausibility Constraints ({scenario_name}) ---") all_pass = True for rule_name, check_fn in PLAUSIBILITY_RULES: try: passed = check_fn(df) except Exception as e: passed = False print(f" {rule_name}: ERROR ({e})") all_pass = False continue status = "PASS" if passed else "FAIL" if status == "FAIL": all_pass = False print(f" {rule_name} [{status}]") return all_pass def test_cross_scenario_monotonicity(dfs): print(f"\n--- Cross-Scenario Monotonicity ---") ordered = ["improved_access", "baseline", "constrained"] available = [s for s in ordered if s in dfs] if len(available) < 2: print(" SKIP: Need at least 2 scenarios") return True all_pass = True # Access score should decrease: improved > baseline > constrained means = {s: dfs[s]["access_score"].mean() for s in available} monotonic = all(means[available[i]] >= means[available[i+1]] - 0.02 for i in range(len(available) - 1)) status = "PASS" if monotonic else "FAIL" if status == "FAIL": all_pass = False vals = ", ".join(f"{s}={means[s]:.3f}" for s in available) print(f" access_score monotonicity: {vals} [{status}]") # Distance should increase means = {s: dfs[s]["distance_to_court_km"].mean() for s in available} monotonic = all(means[available[i]] <= means[available[i+1]] + 2 for i in range(len(available) - 1)) status = "PASS" if monotonic else "FAIL" if status == "FAIL": all_pass = False vals = ", ".join(f"{s}={means[s]:.1f}" for s in available) print(f" distance_to_court monotonicity: {vals} [{status}]") return all_pass def test_class_balance(df, scenario_name): print(f"\n--- Class Balance ({scenario_name}) ---") all_pass = True for col in ["access_level", "region_type"]: counts = df[col].value_counts() for label, count in counts.items(): status = "PASS" if count >= 50 else "WARN" if count == 0: status = "FAIL" all_pass = False print(f" {col}={label}: n={count} [{status}]") return all_pass # ============================================================================ # VISUALIZATION (Phase 7) # ============================================================================ def generate_diagnostic_plots(dfs, output_path): scenario = "baseline" if "baseline" in dfs else list(dfs.keys())[0] df = dfs[scenario] fig, axes = plt.subplots(4, 2, figsize=(16, 20), dpi=150) fig.suptitle( f"African Judicial Access Indicators — Diagnostic Report\n" f"Scenario: {scenario} | N={len(df):,} | Seed=42", fontsize=14, fontweight="bold", y=0.98 ) # Panel 1: Access level distribution ax = axes[0, 0] level_order = ["very_low", "low", "moderate", "high"] level_counts = df["access_level"].value_counts().reindex(level_order, fill_value=0) colors = ["#8e44ad", "#e74c3c", "#f39c12", "#2ecc71"] bars = ax.bar(level_order, level_counts.values, color=colors, edgecolor="black", linewidth=0.5) for bar, val in zip(bars, level_counts.values): ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 30, f"{val:,}\n({val/len(df)*100:.1f}%)", ha="center", fontsize=9) ax.set_title("Panel 1: Access Level Distribution", fontsize=11, fontweight="bold") ax.set_ylabel("Count") # Panel 2: Distance to court (histogram + KDE) ax = axes[0, 1] ax.hist(df["distance_to_court_km"], bins=60, density=True, alpha=0.7, color="#3498db", edgecolor="black", linewidth=0.3) from scipy.stats import gaussian_kde kde = gaussian_kde(df["distance_to_court_km"]) x_range = np.linspace(0, df["distance_to_court_km"].quantile(0.99), 200) ax.plot(x_range, kde(x_range), "r-", linewidth=2) ax.axvline(df["distance_to_court_km"].mean(), color="black", linestyle="--", label=f"Mean={df['distance_to_court_km'].mean():.1f}km") ax.set_title("Panel 2: Distance to Court (km)", fontsize=11, fontweight="bold") ax.legend(fontsize=8) ax.set_xlabel("km") # Panel 3: Lawyer density by region type (box plot) ax = axes[1, 0] region_order = ["urban", "peri_urban", "rural", "remote_rural"] data_by_region = [df.loc[df["region_type"] == r, "lawyer_density_per_100k"].values for r in region_order] bp = ax.boxplot(data_by_region, tick_labels=region_order, patch_artist=True) region_colors = ["#2ecc71", "#f39c12", "#e74c3c", "#8e44ad"] for patch, color in zip(bp["boxes"], region_colors): patch.set_facecolor(color) patch.set_alpha(0.6) ax.set_title("Panel 3: Lawyer Density by Region", fontsize=11, fontweight="bold") ax.set_ylabel("Lawyers per 100K") # Panel 4: Trust vs Bribery scatter ax = axes[1, 1] r_val = df["bribery_rate_pct"].corr(df["trust_in_courts_pct"]) ax.scatter(df["bribery_rate_pct"], df["trust_in_courts_pct"], alpha=0.15, s=5, c=df["access_score"], cmap="RdYlGn") ax.set_title(f"Panel 4: Bribery vs Trust (r={r_val:.3f})", fontsize=11, fontweight="bold") ax.set_xlabel("Bribery Rate (%)") ax.set_ylabel("Trust in Courts (%)") # Panel 5: Distance vs Court Contact Rate ax = axes[2, 0] r_val = df["distance_to_court_km"].corr(df["court_contact_rate_pct"]) ax.scatter(df["distance_to_court_km"], df["court_contact_rate_pct"], alpha=0.15, s=5, c="#e74c3c") ax.set_title(f"Panel 5: Distance vs Contact Rate (r={r_val:.3f})", fontsize=11, fontweight="bold") ax.set_xlabel("Distance to Court (km)") ax.set_ylabel("Court Contact Rate (%)") # Panel 6: Cross-scenario comparison ax = axes[2, 1] if len(dfs) > 1: scenario_names = list(dfs.keys()) access_means = [dfs[s]["access_score"].mean() for s in scenario_names] dist_means = [dfs[s]["distance_to_court_km"].mean() for s in scenario_names] x = np.arange(len(scenario_names)) width = 0.35 ax.bar(x - width/2, access_means, width, label="Access Score", color="#2ecc71", alpha=0.8) ax2 = ax.twinx() ax2.bar(x + width/2, dist_means, width, label="Avg Distance (km)", color="#e74c3c", alpha=0.8) ax.set_xticks(x) ax.set_xticklabels(scenario_names, rotation=15, fontsize=8) ax.set_ylabel("Access Score", color="#2ecc71") ax2.set_ylabel("Avg Distance (km)", color="#e74c3c") ax.legend(loc="upper left", fontsize=8) ax2.legend(loc="upper right", fontsize=8) ax.set_title("Panel 6: Cross-Scenario Comparison", fontsize=11, fontweight="bold") else: ct_counts = df["region_type"].value_counts() ax.barh(ct_counts.index, ct_counts.values, color="#3498db") ax.set_title("Panel 6: Region Distribution", fontsize=11, fontweight="bold") # Panel 7: Gender access ratio by region ax = axes[3, 0] data_by_region = [df.loc[df["region_type"] == r, "gender_access_ratio"].values for r in region_order] bp = ax.boxplot(data_by_region, tick_labels=region_order, patch_artist=True) for patch, color in zip(bp["boxes"], region_colors): patch.set_facecolor(color) patch.set_alpha(0.6) ax.set_title("Panel 7: Gender Access Ratio by Region", fontsize=11, fontweight="bold") ax.set_ylabel("Women/Men Ratio") ax.axhline(0.64, color="red", linestyle="--", alpha=0.5, label="Global avg (0.64)") ax.legend(fontsize=8) # Panel 8: Correlation heatmap ax = axes[3, 1] numeric_cols = ["lawyer_density_per_100k", "distance_to_court_km", "legal_aid_coverage_pct", "cost_pct_monthly_income", "bribery_rate_pct", "trust_in_courts_pct", "court_contact_rate_pct", "access_score"] corr_matrix = df[numeric_cols].corr() im = ax.imshow(corr_matrix.values, cmap="RdBu_r", vmin=-1, vmax=1, aspect="auto") short_names = ["lawyer", "distance", "legal_aid", "cost", "bribery", "trust", "contact", "access"] ax.set_xticks(range(len(numeric_cols))) ax.set_yticks(range(len(numeric_cols))) ax.set_xticklabels(short_names, rotation=45, ha="right", fontsize=7) ax.set_yticklabels(short_names, fontsize=7) for i in range(len(numeric_cols)): for j in range(len(numeric_cols)): ax.text(j, i, f"{corr_matrix.values[i, j]:.2f}", ha="center", va="center", fontsize=6) plt.colorbar(im, ax=ax, shrink=0.8) ax.set_title("Panel 8: Correlation Heatmap", fontsize=11, fontweight="bold") plt.tight_layout(rect=[0, 0, 1, 0.96]) fig.savefig(output_path, dpi=150, bbox_inches="tight") plt.close(fig) print(f"\nVisualization saved to: {output_path}") def print_validation_report(dfs): print("\n" + "=" * 70) print("VALIDATION REPORT — AFRICAN JUDICIAL ACCESS INDICATORS") print("=" * 70) results = {} for scenario_name, df in dfs.items(): print(f"\n{'~' * 50}") print(f"SCENARIO: {scenario_name} (N={len(df):,})") print(f"{'~' * 50}") r1 = test_prevalence(df, scenario_name) r2 = test_distributions(df, scenario_name) r3 = test_correlations(df, scenario_name) r4 = test_clinical_constraints(df, scenario_name) r5 = test_class_balance(df, scenario_name) results[scenario_name] = { "prevalence": r1, "distributions": r2, "correlations": r3, "constraints": r4, "class_balance": r5, } r6 = test_cross_scenario_monotonicity(dfs) print(f"\n{'=' * 70}") print("VALIDATION SUMMARY") print(f"{'=' * 70}") for scenario_name, res in results.items(): all_pass = all(res.values()) status = "ALL PASS" if all_pass else "SOME FAILURES" print(f" {scenario_name}: {status}") for test, passed in res.items(): print(f" {test}: {'PASS' if passed else 'FAIL'}") print(f" cross_scenario_monotonicity: {'PASS' if r6 else 'FAIL'}") print(f"{'=' * 70}\n") def main(): script_dir = os.path.dirname(os.path.abspath(__file__)) data_dir = os.path.join(script_dir, "data") if not os.path.isdir(data_dir): print(f"ERROR: Data directory not found: {data_dir}") sys.exit(1) print("Loading datasets...") dfs = load_all_scenario_csvs(data_dir) if not dfs: print("ERROR: No CSV files found.") sys.exit(1) print_validation_report(dfs) output_path = os.path.join(script_dir, "validation_report.png") generate_diagnostic_plots(dfs, output_path) print("Validation complete.") if __name__ == "__main__": main()