validate_dataset.py

#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Childhood Immunisation Dataset."""

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

SCENARIOS = ['high_coverage', 'moderate_coverage', 'low_coverage']


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'immunisation_{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, 22))
    fig.suptitle('Childhood Immunisation Coverage — Validation Report',
                 fontsize=16, fontweight='bold', y=0.98)

    df = dfs.get('moderate_coverage', list(dfs.values())[0])
    elig = df[df['age_months'] >= 4]  # Old enough for primary series

    # Panel 1: Immunisation status
    ax = axes[0, 0]
    status_counts = df['immunisation_status'].value_counts()
    colors = {'fully_immunised': '#2ecc71', 'partially_immunised': '#f39c12',
              'zero_dose': '#e74c3c'}
    order = ['fully_immunised', 'partially_immunised', 'zero_dose']
    vals = [status_counts.get(s, 0) for s in order]
    ax.bar(range(3), vals, color=[colors[s] for s in order])
    ax.set_xticks(range(3))
    ax.set_xticklabels(['Fully\nImmunised', 'Partially\nImmunised', 'Zero\nDose'])
    for i, v in enumerate(vals):
        ax.text(i, v + 50, f'{v/len(df)*100:.1f}%', ha='center', fontsize=10)
    ax.set_ylabel('Count')
    ax.set_title('Immunisation Status (Moderate Coverage)')

    # Panel 2: Vaccine coverage cascade
    ax = axes[0, 1]
    vaccines = ['bcg', 'penta1', 'penta2', 'penta3', 'mcv1', 'mcv2']
    elig_ages = [1, 2, 3, 4, 10, 16]
    coverages = []
    for v, min_age in zip(vaccines, elig_ages):
        sub = df[df['age_months'] >= min_age]
        coverages.append(sub[v].mean() * 100 if len(sub) > 0 else 0)
    bars = ax.bar(range(len(vaccines)), coverages, color='#3498db', alpha=0.8)
    ax.set_xticks(range(len(vaccines)))
    ax.set_xticklabels([v.upper() for v in vaccines])
    for i, v in enumerate(coverages):
        ax.text(i, v + 1, f'{v:.0f}%', ha='center', fontsize=9)
    ax.set_ylabel('Coverage (%)')
    ax.set_title('Vaccine Coverage Cascade (age-eligible)')
    ax.set_ylim(0, 105)

    # Panel 3: Penta3 coverage by SES quintile across scenarios
    ax = axes[1, 0]
    x = np.arange(5)
    width = 0.25
    for i, sc in enumerate(SCENARIOS):
        if sc not in dfs:
            continue
        d = dfs[sc]
        elig_d = d[d['age_months'] >= 4]
        rates = []
        for q in range(1, 6):
            sub = elig_d[elig_d['ses_quintile'] == q]
            rates.append(sub['penta3'].mean() * 100 if len(sub) > 0 else 0)
        ax.bar(x + i * width, rates, width, label=sc.replace('_', ' ').title(),
               alpha=0.8)
    ax.set_xticks(x + width)
    ax.set_xticklabels([f'Q{q}' for q in range(1, 6)])
    ax.set_ylabel('Penta3 Coverage (%)')
    ax.set_title('Penta3 Coverage by Wealth Quintile')
    ax.legend(fontsize=8)

    # Panel 4: Coverage by urban/rural
    ax = axes[1, 1]
    for rt in ['urban', 'rural']:
        sub = elig[elig['region_type'] == rt]
        if len(sub) == 0:
            continue
        covs = [sub[v].mean() * 100 for v in ['bcg', 'penta1', 'penta3', 'mcv1']]
        ax.plot(['BCG', 'Penta1', 'Penta3', 'MCV1'], covs,
                'o-', label=rt.title(), linewidth=2, markersize=8)
    ax.set_ylabel('Coverage (%)')
    ax.set_title('Coverage by Urban/Rural')
    ax.legend(fontsize=10)
    ax.set_ylim(0, 100)

    # Panel 5: Distance vs total doses
    ax = axes[2, 0]
    sample = df.sample(min(3000, len(df)), random_state=42)
    ax.scatter(sample['distance_to_facility_km'], sample['total_basic_doses'],
              alpha=0.3, s=8, c='#3498db')
    ax.set_xlabel('Distance to Facility (km)')
    ax.set_ylabel('Total Basic Doses Received')
    ax.set_title('Distance vs Doses Received')

    # Panel 6: Cross-scenario zero-dose and fully immunised
    ax = axes[2, 1]
    metrics = ['zero_dose', 'fully_immunised']
    x = np.arange(len(SCENARIOS))
    width = 0.35
    for i, m in enumerate(metrics):
        rates = []
        for sc in SCENARIOS:
            if sc in dfs:
                d = dfs[sc]
                rates.append(d[d['age_months'] >= 1][m].mean() * 100)
            else:
                rates.append(0)
        color = '#e74c3c' if m == 'zero_dose' else '#2ecc71'
        ax.bar(x + i * width, rates, width, label=m.replace('_', ' ').title(),
               color=color, alpha=0.8)
    ax.set_xticks(x + width / 2)
    ax.set_xticklabels([s.replace('_', '\n').title() for s in SCENARIOS], fontsize=8)
    ax.set_ylabel('%')
    ax.set_title('Zero-Dose & Fully Immunised Across Scenarios')
    ax.legend(fontsize=9)

    # Panel 7: Dropout rates across scenarios
    ax = axes[3, 0]
    for sc in SCENARIOS:
        if sc not in dfs:
            continue
        d = dfs[sc]
        elig_d = d[d['age_months'] >= 4]
        if len(elig_d) == 0:
            continue
        p13 = elig_d['dropout_penta1_penta3'].mean() * 100
        elig_mcv = d[d['age_months'] >= 10]
        pm = elig_mcv['dropout_penta1_mcv1'].mean() * 100 if len(elig_mcv) > 0 else 0
        ax.bar([f'{sc.replace("_", chr(10)).title()}\nPenta1→3',
                f'{sc.replace("_", chr(10)).title()}\nPenta1→MCV1'],
               [p13, pm], alpha=0.7)
    ax.set_ylabel('Dropout Rate (%)')
    ax.set_title('Dropout Rates')

    # Panel 8: Coverage by maternal education
    ax = axes[3, 1]
    edu_order = ['none', 'primary', 'secondary', 'tertiary']
    for v, color in [('penta3', '#3498db'), ('mcv1', '#e74c3c')]:
        covs = []
        for edu in edu_order:
            sub = elig[elig['maternal_education'] == edu]
            covs.append(sub[v].mean() * 100 if len(sub) > 0 else 0)
        ax.plot(edu_order, covs, 'o-', label=v.upper(), linewidth=2,
                markersize=8, color=color)
    ax.set_xlabel('Maternal Education')
    ax.set_ylabel('Coverage (%)')
    ax.set_title('Coverage by Maternal Education')
    ax.legend(fontsize=10)

    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 not dfs:
        print('No data files found in data/')
    else:
        make_report(dfs)