#!/usr/bin/env python3
"""
Literature-Informed Prostate Cancer Dataset
=============================================

Generates realistic synthetic records of prostate cancer patients
in sub-Saharan Africa, including presentation, diagnosis, staging,
treatment access, and survival outcomes.

References (web-searched):
-----------
[1]  PMC 2022. PCa screening/diagnosis/treatment in SSA.
     Inconsistent PSA screening. Late presentation.
[2]  PMC 2023. Public awareness PCa screening South Africa.
     Black men disproportionately aggressive disease.
[3]  BJUI 2024. Global viewpoints PCa in SSA. High
     incidence and mortality. PSA not readily available.
[4]  PMC 2021. PCa survival SSA by stage. Poor survival,
     high proportions late stage.
[5]  PubMed 2023. PCa management barriers in SSA.
     Screening, diagnosis, curative Tx not available.
[6]  PubMed 2018. Radiotherapy PCa Ghana. Emphasis on
     curative treatment at tertiary centres.
[7]  PubMed 2024. Radiotherapy access barriers SSA.
"""

import numpy as np
import pandas as pd
import argparse
import os

SCENARIOS = {
    'tertiary_oncology': {
        'description': 'Tertiary oncology centre with PSA, biopsy, '
                       'CT/MRI, radiotherapy, surgery, ADT '
                       '(e.g., Groote Schuur, LUTH, Kenyatta)',
        'psa_available': True,
        'biopsy_available': True,
        'imaging_available': True,
        'radiotherapy_available': True,
        'surgery_available': True,
        'early_stage_proportion': 0.35,
        'five_year_survival': 0.55,
    },
    'district_hospital': {
        'description': 'District hospital with PSA, DRE, limited '
                       'biopsy, no radiotherapy, ADT only '
                       '(e.g., district hospitals Uganda, Malawi)',
        'psa_available': True,
        'biopsy_available': False,
        'imaging_available': False,
        'radiotherapy_available': False,
        'surgery_available': False,
        'early_stage_proportion': 0.15,
        'five_year_survival': 0.30,
    },
    'rural_health_centre': {
        'description': 'Rural health centre, clinical diagnosis '
                       'only, no PSA, referral for all treatment '
                       '(e.g., rural DRC, Niger, CAR)',
        'psa_available': False,
        'biopsy_available': False,
        'imaging_available': False,
        'radiotherapy_available': False,
        'surgery_available': False,
        'early_stage_proportion': 0.05,
        'five_year_survival': 0.15,
    },
}


def generate_dataset(n=10000, seed=42, scenario='district_hospital'):
    rng = np.random.default_rng(seed)
    sc = SCENARIOS[scenario]

    records = []

    for idx in range(n):
        rec = {'id': idx + 1}

        # ── 1. Demographics ──
        rec['age'] = max(40, min(95, int(rng.normal(67, 9))))
        rec['education'] = rng.choice(
            ['none', 'primary', 'secondary', 'tertiary'],
            p=[0.25, 0.30, 0.30, 0.15])
        rec['urban'] = 1 if rng.random() < 0.40 else 0
        rec['family_history_pca'] = 1 if rng.random() < 0.10 else 0
        rec['bmi'] = round(max(15, min(40, rng.normal(24, 4))), 1)
        rec['hiv_positive'] = 1 if rng.random() < 0.06 else 0
        rec['diabetes'] = 1 if rng.random() < 0.08 else 0
        rec['hypertension'] = 1 if rng.random() < 0.30 else 0

        # ── 2. Presentation ──
        rec['presenting_symptom'] = rng.choice(
            ['luts', 'bone_pain', 'urinary_retention', 'haematuria',
             'weight_loss', 'incidental', 'screening'],
            p=[0.30, 0.20, 0.15, 0.10, 0.10, 0.05, 0.10])
        rec['symptom_duration_months'] = max(1, min(36, int(rng.exponential(6) + 2)))
        rec['traditional_medicine_first'] = 1 if rng.random() < 0.25 else 0

        # ── 3. Diagnosis [1][3] ──
        rec['dre_performed'] = 1 if rng.random() < 0.70 else 0
        rec['dre_suspicious'] = 0
        if rec['dre_performed']:
            rec['dre_suspicious'] = 1 if rng.random() < 0.65 else 0

        rec['psa_tested'] = 0
        if sc['psa_available']:
            rec['psa_tested'] = 1 if rng.random() < 0.80 else 0
        rec['psa_level'] = 0.0
        if rec['psa_tested']:
            rec['psa_level'] = round(max(0.5, min(5000,
                rng.lognormal(3.5, 1.5))), 1)
        rec['psa_elevated'] = 1 if rec['psa_level'] > 4.0 else 0

        rec['biopsy_performed'] = 0
        if sc['biopsy_available'] and (rec['psa_elevated'] or rec['dre_suspicious']):
            rec['biopsy_performed'] = 1 if rng.random() < 0.60 else 0

        rec['gleason_score'] = 0
        if rec['biopsy_performed']:
            rec['gleason_score'] = rng.choice(
                [6, 7, 8, 9, 10],
                p=[0.15, 0.30, 0.25, 0.20, 0.10])
        rec['gleason_group'] = 0
        if rec['gleason_score'] == 6:
            rec['gleason_group'] = 1
        elif rec['gleason_score'] == 7:
            rec['gleason_group'] = rng.choice([2, 3])
        elif rec['gleason_score'] == 8:
            rec['gleason_group'] = 4
        elif rec['gleason_score'] >= 9:
            rec['gleason_group'] = 5

        # ── 4. Staging [4] ──
        rec['stage'] = 'unknown'
        if rng.random() < sc['early_stage_proportion']:
            rec['stage'] = rng.choice(['I', 'II'], p=[0.30, 0.70])
        else:
            rec['stage'] = rng.choice(['III', 'IV'], p=[0.35, 0.65])

        rec['metastatic'] = 1 if rec['stage'] == 'IV' else 0
        rec['bone_metastases'] = 0
        if rec['metastatic']:
            rec['bone_metastases'] = 1 if rng.random() < 0.80 else 0
        rec['lymph_node_metastases'] = 0
        if rec['metastatic']:
            rec['lymph_node_metastases'] = 1 if rng.random() < 0.40 else 0

        rec['ct_scan_done'] = 0
        if sc['imaging_available']:
            rec['ct_scan_done'] = 1 if rng.random() < 0.50 else 0
        rec['bone_scan_done'] = 0
        if sc['imaging_available']:
            rec['bone_scan_done'] = 1 if rng.random() < 0.30 else 0

        # ── 5. Treatment [5][6][7] ──
        rec['treatment_received'] = 'none'
        if sc['surgery_available'] and rec['stage'] in ('I', 'II'):
            rec['treatment_received'] = rng.choice(
                ['radical_prostatectomy', 'radiotherapy', 'adt', 'watchful_waiting'],
                p=[0.25, 0.25, 0.30, 0.20])
        elif sc['radiotherapy_available'] and rec['stage'] == 'III':
            rec['treatment_received'] = rng.choice(
                ['radiotherapy_adt', 'adt_only', 'none'],
                p=[0.40, 0.45, 0.15])
        elif rec['stage'] == 'IV':
            if sc['psa_available']:
                rec['treatment_received'] = rng.choice(
                    ['adt_only', 'adt_chemo', 'palliative', 'none'],
                    p=[0.45, 0.10, 0.25, 0.20])
            else:
                rec['treatment_received'] = rng.choice(
                    ['adt_only', 'palliative', 'none'],
                    p=[0.30, 0.20, 0.50])
        else:
            if sc['psa_available']:
                rec['treatment_received'] = rng.choice(
                    ['adt_only', 'palliative', 'none'],
                    p=[0.40, 0.20, 0.40])
            else:
                rec['treatment_received'] = rng.choice(
                    ['palliative', 'none'], p=[0.30, 0.70])

        rec['adt_type'] = 'none'
        if 'adt' in rec['treatment_received']:
            rec['adt_type'] = rng.choice(
                ['surgical_castration', 'lhrh_agonist', 'antiandrogen'],
                p=[0.40, 0.35, 0.25])

        rec['pain_management'] = 0
        if rec['bone_metastases'] or rec['treatment_received'] == 'palliative':
            rec['pain_management'] = 1 if rng.random() < 0.50 else 0

        rec['treatment_delay_months'] = max(0, min(24,
            int(rng.exponential(3))))
        rec['referral_needed'] = 0
        if not sc['surgery_available'] and rec['stage'] in ('I', 'II', 'III'):
            rec['referral_needed'] = 1 if rng.random() < 0.70 else 0
        rec['referral_completed'] = 0
        if rec['referral_needed']:
            rec['referral_completed'] = 1 if rng.random() < 0.30 else 0

        # ── 6. Outcome ──
        base_mort = 1.0 - sc['five_year_survival']
        if rec['stage'] == 'IV':
            mort = base_mort * 1.3
        elif rec['stage'] == 'III':
            mort = base_mort * 0.8
        else:
            mort = base_mort * 0.3
        if rec['treatment_received'] == 'none':
            mort *= 1.5
        if rec['age'] > 75:
            mort *= 1.2

        rec['died_within_2_years'] = 1 if rng.random() < min(mort, 0.80) else 0

        rec['quality_of_life'] = rng.choice(
            ['good', 'moderate', 'poor'],
            p=[0.20, 0.40, 0.40] if rec['metastatic'] else [0.40, 0.40, 0.20])

        rec['castration_resistant'] = 0
        if 'adt' in rec['treatment_received']:
            rec['castration_resistant'] = 1 if rng.random() < 0.30 else 0

        records.append(rec)

    df = pd.DataFrame(records)

    print(f"\n{'='*65}")
    print(f"Prostate Cancer — {scenario} (n={n}, seed={seed})")
    print(f"{'='*65}")
    print(f"\n  Stage IV: {(df['stage']=='IV').mean()*100:.1f}%")
    print(f"  PSA tested: {df['psa_tested'].mean()*100:.1f}%")
    print(f"  No treatment: {(df['treatment_received']=='none').mean()*100:.1f}%")
    print(f"  2-yr mortality: {df['died_within_2_years'].mean()*100:.1f}%")

    return df


if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        description='Generate prostate cancer dataset')
    parser.add_argument('--scenario', type=str, default='district_hospital',
                        choices=list(SCENARIOS.keys()))
    parser.add_argument('--n', type=int, default=10000)
    parser.add_argument('--seed', type=int, default=42)
    parser.add_argument('--output', type=str, default=None)
    parser.add_argument('--all-scenarios', action='store_true')
    args = parser.parse_args()

    os.makedirs('data', exist_ok=True)

    if args.all_scenarios:
        for sc_name in SCENARIOS:
            df = generate_dataset(n=args.n, seed=args.seed, scenario=sc_name)
            out = os.path.join('data', f'pca_{sc_name}.csv')
            df.to_csv(out, index=False)
            print(f"  -> Saved to {out}\n")
    else:
        df = generate_dataset(n=args.n, seed=args.seed, scenario=args.scenario)
        out = args.output or os.path.join('data', f'pca_{args.scenario}.csv')
        df.to_csv(out, index=False)
        print(f"  -> Saved to {out}")