validate_dataset.py

#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Pharmaceutical Regulatory Capacity Dataset."""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os

SCENARIOS = ['ml3_ml4_advanced', 'ml2_developing', 'ml1_minimal']
REG_FUNCS = [
    'registration_marketing_authorization', 'pharmacovigilance',
    'market_surveillance_control', 'licensing_establishment',
    'regulatory_inspection', 'laboratory_access_testing',
    'clinical_trial_oversight', 'import_export_control', 'lot_release_biologicals',
]


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'reg_capacity_{sc}.csv')
        if os.path.exists(path):
            dfs[sc] = pd.read_csv(path)
    return dfs


def make_report(dfs, output='validation_report.png'):
    fig, axes = plt.subplots(4, 2, figsize=(16, 24))
    fig.suptitle(
        'Pharmaceutical Regulatory Capacity — Validation Report\n'
        '(ML3/ML4 Advanced → ML2 Developing → ML1 Minimal)',
        fontsize=15, fontweight='bold', y=0.99)
    colors = ['#2ecc71', '#f39c12', '#e74c3c']
    x = np.arange(len(SCENARIOS))
    labels = ['ML3/ML4', 'ML2', 'ML1']

    ax = axes[0, 0]
    scores = [dfs[sc]['overall_regulatory_score'].mean() for sc in SCENARIOS if sc in dfs]
    ax.bar(x, scores, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(scores):
        ax.text(i, v+1, f'{v:.0f}', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Score (0-100)'); ax.set_title('Overall Regulatory Score')

    ax = axes[0, 1]
    sf = [dfs[sc]['sf_prevalence_estimated_pct'].mean() for sc in SCENARIOS if sc in dfs]
    ax.bar(x, sf, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(sf):
        ax.text(i, v+0.5, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('SF Prevalence (%)'); ax.set_title('Estimated SF Prevalence')

    ax = axes[1, 0]
    staff = [dfs[sc]['staff_total'].mean() for sc in SCENARIOS if sc in dfs]
    ax.bar(x, staff, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(staff):
        ax.text(i, v+2, f'{v:.0f}', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Staff Count'); ax.set_title('Average NRA Staff')

    ax = axes[1, 1]
    budget = [dfs[sc]['budget_usd_millions'].mean() for sc in SCENARIOS if sc in dfs]
    ax.bar(x, budget, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(budget):
        ax.text(i, v+0.1, f'${v:.1f}M', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Budget (USD M)'); ax.set_title('Average NRA Budget')

    ax = axes[2, 0]
    df = dfs.get('ml2_developing', list(dfs.values())[1])
    func_scores = [df[f'score_{f}'].mean() for f in REG_FUNCS]
    func_labels = [f.replace('_', '\n').title()[:20] for f in REG_FUNCS]
    ax.barh(range(len(REG_FUNCS)), func_scores, color='#3498db', alpha=0.7)
    ax.set_yticks(range(len(REG_FUNCS)))
    ax.set_yticklabels(func_labels, fontsize=6)
    ax.set_xlabel('Score (0-100)'); ax.set_title('Regulatory Function Scores (ML2)')

    ax = axes[2, 1]
    insp = [dfs[sc]['inspection_coverage_pct'].mean() for sc in SCENARIOS if sc in dfs]
    ax.bar(x, insp, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(insp):
        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Coverage (%)'); ax.set_title('Inspection Coverage')

    ax = axes[3, 0]
    pv = [dfs[sc]['pharmacovigilance_centre'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, pv, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(pv):
        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Rate (%)'); ax.set_title('Has Pharmacovigilance Centre')

    ax = axes[3, 1]
    ax.scatter(df['overall_regulatory_score'], df['sf_prevalence_estimated_pct'],
               alpha=0.3, s=5, color='#e74c3c')
    ax.set_xlabel('Regulatory Score'); ax.set_ylabel('SF Prevalence (%)')
    ax.set_title('Regulatory Score vs SF Prevalence (ML2)')

    plt.tight_layout(rect=[0, 0, 1, 0.97])
    plt.savefig(output, dpi=150, bbox_inches='tight')
    print(f'Saved validation report to {output}')
    plt.close()


if __name__ == '__main__':
    dfs = load_scenarios()
    if dfs:
        make_report(dfs)