validate_dataset.py

#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Oral Health Dataset."""

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

SCENARIOS = ['dental_clinic', 'district_hospital', 'rural_health_centre']


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'oral_{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('Oral Health & Dental Disease — Validation Report',
                 fontsize=16, fontweight='bold', y=0.98)
    df = dfs.get('district_hospital', list(dfs.values())[0])
    colors = ['#2ecc71', '#f39c12', '#e74c3c']

    ax = axes[0, 0]
    conditions = ['dental_caries', 'untreated_caries', 'periodontal_disease',
                   'dental_pain', 'dental_abscess', 'tooth_loss']
    c_labels = ['Caries', 'Untreated', 'Periodontal', 'Pain', 'Abscess', 'Tooth Loss']
    vals = [df[c].mean() * 100 if c != 'tooth_loss' else (df[c] > 0).mean() * 100 for c in conditions]
    ax.bar(range(6), vals, color='#e74c3c', alpha=0.7)
    ax.set_xticks(range(6))
    ax.set_xticklabels(c_labels, fontsize=8, rotation=15)
    for i, v in enumerate(vals):
        ax.text(i, v + 0.5, f'{v:.0f}%', ha='center', fontsize=8)
    ax.set_ylabel('Prevalence (%)')
    ax.set_title('Oral Disease Burden (caries most prevalent)')

    ax = axes[0, 1]
    x = np.arange(len(SCENARIOS))
    care = [dfs[sc]['sought_dental_care'].mean() * 100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, care, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Dental Clinic', 'District', 'Rural'], fontsize=9)
    for i, v in enumerate(care):
        ax.text(i, v + 0.5, f'{v:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('Care-Seeking Rate (%)')
    ax.set_title('Dental Care Access (<1 dentist/100K in SSA)')

    ax = axes[1, 0]
    caries_pts = df[df['dental_caries'] == 1]
    if len(caries_pts) > 0:
        ax.hist(caries_pts['dmft_score'], bins=20, color='#3498db', alpha=0.7, edgecolor='white')
    ax.set_xlabel('DMFT Score')
    ax.set_title('DMFT Distribution (mean ~4 in SSA)')

    ax = axes[1, 1]
    treatments = df[df['treatment_received'] != 'none']['treatment_received'].value_counts()
    if len(treatments) > 0:
        t_colors = ['#e74c3c', '#f39c12', '#3498db', '#2ecc71', '#9b59b6', '#e67e22']
        ax.pie(treatments.values,
               labels=[s.replace('_', ' ').title() for s in treatments.index],
               autopct='%1.0f%%', colors=t_colors[:len(treatments)],
               startangle=90, textprops={'fontsize': 8})
    ax.set_title('Treatment Type (extraction dominates)')

    ax = axes[2, 0]
    barriers = df[df['barrier_to_care'] != 'none']['barrier_to_care'].value_counts()
    if len(barriers) > 0:
        ax.barh(range(len(barriers)), barriers.values, color='#3498db', alpha=0.8)
        ax.set_yticks(range(len(barriers)))
        ax.set_yticklabels([s.replace('_', ' ').title() for s in barriers.index], fontsize=8)
    ax.set_xlabel('Count')
    ax.set_title('Barriers to Dental Care')

    ax = axes[2, 1]
    risks = ['sugary_diet', 'tobacco_use', 'fluoride_toothpaste', 'diabetes']
    r_labels = ['Sugary Diet', 'Tobacco', 'Fluoride Paste', 'Diabetes']
    caries_y = df[df['dental_caries'] == 1]
    caries_n = df[df['dental_caries'] == 0]
    if len(caries_y) > 0 and len(caries_n) > 0:
        vc = [caries_y[r].mean() * 100 for r in risks]
        vn = [caries_n[r].mean() * 100 for r in risks]
        w = 0.3
        ax.bar(np.arange(4) - w/2, vc, w, label='Caries', color='#e74c3c', alpha=0.8)
        ax.bar(np.arange(4) + w/2, vn, w, label='No Caries', color='#2ecc71', alpha=0.8)
        ax.set_xticks(np.arange(4))
        ax.set_xticklabels(r_labels, fontsize=8)
    ax.set_ylabel('Prevalence (%)')
    ax.set_title('Risk Factors vs Caries')
    ax.legend(fontsize=8)

    ax = axes[3, 0]
    children = df[df['child'] == 1]
    adults = df[df['child'] == 0]
    cats = ['Caries', 'Pain', 'Sought Care']
    if len(children) > 0 and len(adults) > 0:
        vc = [children['dental_caries'].mean()*100, children['dental_pain'].mean()*100,
              children['sought_dental_care'].mean()*100]
        va = [adults['dental_caries'].mean()*100, adults['dental_pain'].mean()*100,
              adults['sought_dental_care'].mean()*100]
        w = 0.3
        ax.bar(np.arange(3) - w/2, vc, w, label='Children', color='#3498db', alpha=0.8)
        ax.bar(np.arange(3) + w/2, va, w, label='Adults', color='#e74c3c', alpha=0.8)
        ax.set_xticks(np.arange(3))
        ax.set_xticklabels(cats, fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Children vs Adults')
    ax.legend(fontsize=8)

    ax = axes[3, 1]
    brush = df['brushing_frequency'].value_counts()
    b_order = ['never', 'occasional', 'once_daily', 'twice_daily']
    vals = [brush.get(b, 0) for b in b_order]
    ax.bar(range(4), vals, color=['#e74c3c', '#f39c12', '#3498db', '#2ecc71'], alpha=0.8)
    ax.set_xticks(range(4))
    ax.set_xticklabels(['Never', 'Occasional', 'Once/Day', 'Twice/Day'], fontsize=8)
    ax.set_ylabel('Count')
    ax.set_title('Brushing Frequency')

    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)