import json, numpy as np
from scipy.stats import spearmanr
from datetime import datetime

records = [json.loads(l) for l in open('eval_results/sifq_scores_v24.jsonl')]
CNAMES = ['orientation_coherence','ridge_valley_clarity','continuity','noise_level','contrast_uniformity','minutiae_reliability']
concepts = np.array([r['concepts'] for r in records])
qs = np.array([r['q_score'] for r in records])

d = json.load(open('eval_results/v24/eval_summary.json'))
t2 = d['track2_sensor_invariance']['SIFQ']
t4 = d['track4_concept_grounding']

TARGETS = {
    'blur':      ['ridge_valley_clarity', 'continuity'],
    'noise':     ['noise_level'],
    'jpeg':      ['continuity', 'ridge_valley_clarity'],
    'occlusion': ['minutiae_reliability'],
    'dry_skin':  ['contrast_uniformity', 'continuity', 'orientation_coherence'],
    'wet_press': ['ridge_valley_clarity', 'minutiae_reliability', 'orientation_coherence'],
}

SEP  = '=' * 70
SEP2 = '-' * 70

lines = [
    SEP,
    'SIFQ -- Concept Quality Model: Ly thuyet & Ket qua (v24)',
    f'Generated: {datetime.now().strftime("%Y-%m-%d %H:%M")}',
    SEP,
    '',
    '1. DINH NGHIA 6 CONCEPTS',
    SEP2,
    'Tat ca concepts: cao = chat luong tot hon, dau ra trong [0, 1].',
    '',
    '  #  Concept                    Y nghia vat ly',
    '  -- -------------------------- -----------------------------------------------',
    '  0  orientation_coherence      Ridge flow nhat quan, local orientation field smooth',
    '  1  ridge_valley_clarity       Bien ridge-valley sac net, contrast cuc bo cao',
    '  2  continuity                 Ridge lines lien tuc, khong bi dut gay',
    '  3  noise_level                It nhieu ngau nhien (cao = it noise = tot)',
    '  4  contrast_uniformity        Contrast deu tren toan foreground',
    '  5  minutiae_reliability       Minutiae co the trich xuat chinh xac',
    '',
    '  Degradation Concept Map (v25 -- T40):',
    '  blur      -> [clarity[1], continuity[2], orient_coh[0]]',
    '  noise     -> [noise_level[3], contrast_u[4]]',
    '  jpeg      -> [continuity[2], clarity[1], contrast_u[4]]',
    '  occlusion -> [minutiae_reliability[5]]',
    '  dry_skin  -> [contrast_u[4], continuity[2], orient_coh[0]]',
    '  wet_press -> [clarity[1], minutiae_rel[5], orient_coh[0]]',
    '',
    '  Ly do thiet ke map nhu vay:',
    '  - Moi concept phai duoc giam sat boi >= 2 loai degradation (tranh single-',
    '    point-of-failure: 1 degradation target 1 concept -> signal yeu, de bi',
    '    gradient interference invert chieu).',
    '  - Chon degradation phu hop vat ly: jpeg blocking -> contrast bands (khong',
    '    chi ridge artifacts), blur -> orientation blur (khong chi clarity loss).',
    '',
    '',
    '2. CO CHE TRAINING CONCEPT (L_concept)',
    SEP2,
    'Voi moi cap anh (mild degradation vs severe degradation cung loai):',
    '',
    '  L_concept = SUM Huber( c_mild[c], c_severe[c] + 0.1 )',
    '              c in targets(degradation_type)',
    '',
    '  -> Anh degradation nhe phai co concept cao hon anh degradation nang >= 0.1',
    '  -> Model hoc tung concept phan ung dung chieu voi loai hu hong tuong ung',
    '',
    'Ket hop ranking loss:',
    '  L_rank = relu(Q_severe - Q_mild + m) + relu(Q_mild - Q_clean + m)',
    '  -> Q giam theo thu tu: clean > mild_deg > severe_deg',
    '',
    '  L_deg = L_rank + gamma * L_concept',
    '  gamma = 2.0 (v24) -> 1.5 (v25)',
    '    - gamma qua cao (2.0): gradient conflict qua manh -> noise_level inversion',
    '    - gamma qua thap (0.5, v22): blur->continuity FAIL (+0.261)',
    '    - gamma = 1.5: compromise, du manh cho blur/jpeg, khong gay inversion',
    '',
    '',
    '3. VAN DE CONCEPT SATURATION',
    SEP2,
    'Nguyen nhan goc: L_concept chi train tren synthetic degradation pairs.',
    'Voi real fingerprint images, KHONG co gradient dinh huong concept.',
    '-> Concept troi ve gia tri mac dinh cua backbone features.',
    '',
    'Hau qua trong v24 (42,683 real fingerprint images):',
    '',
    '  Concept                      mean    std   rho_Q  Tinh trang',
    '  ---------------------------- ------  -----  ------  ---------------------------',
]

STATUS = {
    'noise_level':          '!! DOMINATES Q (rho=-0.989)',
    'minutiae_reliability': '!! Saturated HIGH -- dead (range 0.84-0.93)',
    'orientation_coherence':'!! Saturated LOW  -- T39 overcorrected',
    'ridge_valley_clarity': '!! Near-dead -- low variance',
    'continuity':           '!! Near-dead -- low variance',
    'contrast_uniformity':  '!! Near-dead -- low variance',
}

for i, name in enumerate(CNAMES):
    c = concepts[:, i]
    rho, _ = spearmanr(c, qs)
    status = STATUS.get(name, 'OK')
    lines.append(f'  {name:<28} {c.mean():>6.3f} {c.std():>6.3f} {rho:>+7.3f}  {status}')

lines += [
    '',
    '  Vong lap nguy hiem (self-reinforcing collapse):',
    '    ScoreAggregator chon noise_level (std=0.340, cao nhat)',
    '    -> gradient tap trung update noise pathway',
    '    -> cac concept khac it duoc update -> variance thap hon',
    '    -> cang bi bo qua -> variance cang thap (vong lap)',
    '',
    '  He qua: Q ≈ f(noise_level) -- model thuc chat la "noise detector",',
    '  khong phai "quality estimator" da khai niem.',
    '',
    '',
    '4. KET QUA v24',
    SEP2,
]

ks      = t2['mean_ks_across_sensors']
pearson = t2['cross_sensor_pearson']
lines += [
    '  Track 2 -- Sensor Invariance:',
    f'    Mean KS (cross-sensor) = {ks:.4f}   [goal <= 0.30]  {"PASS" if ks <= 0.30 else "FAIL"}',
    f'    Cross-sensor Pearson   = {pearson:.4f}   [goal >= 0.20]  {"PASS" if pearson >= 0.20 else "FAIL"}',
    '',
    '    Y nghia: cung mot ngon tay chup bang cac sensor khac nhau -> SIFQ cho',
    '    score nhat quan. KS thap = phan bo giong nhau, Pearson cao = ranking on dinh.',
    '    NFIQ2 thuong co KS > 0.5 cho cross-sensor pairs.',
    '',
    '  Track 4 -- Concept Grounding (rho < 0 = PASS):',
    f'    {"Degradation":<12} {"Concept":<28} {"rho":>7}  Status',
    f'    {"------------":<12} {"----------------------------":<28} {"-------":>7}  ------',
]

pass_count = total = 0
for row in t4:
    deg = row['degradation']
    for concept in TARGETS.get(deg, []):
        rho = row.get(concept)
        if rho is None:
            continue
        total += 1
        ok = rho < 0
        if ok:
            pass_count += 1
        status = 'PASS' if ok else 'FAIL <--'
        lines.append(f'    {deg:<12} {concept:<28} {rho:>+7.3f}  {status}')

lines += [
    '',
    f'    Result: {pass_count}/{total} pairs PASS',
    '',
    '  3 failures phan tich:',
    '',
    '  [1] noise -> noise_level (rho=+0.365):',
    '      Regression tu v22 (-0.052, OK) -> v24 (+0.365, FAIL).',
    '      gamma=2.0 + T39 (orient_coh added to dry_skin/wet_press) thay doi',
    '      gradient landscape cua ScoreAggregator. Noise la degradation duy nhat',
    '      target noise_level -> single-point pressure -> de bi invert.',
    '',
    '  [2] dry_skin -> contrast_u (rho=+0.051):',
    '      Persistent qua cac phien ban (v22: +0.504). Dry_skin la degradation',
    '      DUY NHAT target contrast_uniformity -> signal yeu, de bi gradient',
    '      tu cac loss khac at di.',
    '',
    '  [3] dry_skin -> orient_coh (rho=+0.008):',
    '      T39 moi them orient_coh vao dry_skin, nhung wet_press->orient_coh',
    '      hoat dong tot (-0.448). Ly do: wet_press + blur deu co orientation',
    '      disruption manh, nhung dry_skin tao ra orientation noise khong nhat',
    '      quan -> khong du signal de orient_coh feature phan biet.',
    '',
    '',
    '5. v25 -- T40 FIXES (dang chay)',
    SEP2,
    '  Thay doi so voi v24:',
    '',
    '  gamma: 2.0 -> 1.5',
    '',
    '  blur:  [1, 2]    -> [1, 2, 0]  (them orient_coh[0])',
    '  noise: [3]       -> [3, 4]     (them contrast_u[4])',
    '  jpeg:  [2, 1]    -> [2, 1, 4]  (them contrast_u[4])',
    '  dry_skin / wet_press / occlusion: khong doi',
    '',
    '  Ket qua ky vong:',
    '  - noise->noise_lv:    +0.365 -> negative  (gamma thap + noise 2-concept)',
    '  - dry_skin->contrast_u: +0.051 -> negative  (3 degs co-supervise contrast_u)',
    '  - dry_skin->orient_coh: +0.008 -> negative  (blur gives orient_coh strong signal)',
    '  - Cac pairs da PASS: giu nguyen (blur/jpeg/occlusion/wet_press)',
    '  - Track 2: giu PASS (KS, Pearson khong thay doi co ban)',
    '',
    SEP,
    'END',
    SEP,
]

txt = '\n'.join(lines)
with open('eval_results/sifq_report_v24.txt', 'w', encoding='utf-8') as f:
    f.write(txt)
print(txt)
print()
print('>>> Saved: eval_results/sifq_report_v24.txt')