Paper A v4.1: BCD-baseline reframe + screening positioning + trim

- Re-anchor inter-CPA coincidence-rate (ICCR) calibration on a normative non-Firm-A baseline (Firms B/C/D); Firm A held out as an out-of-sample target. Locked canonical numbers (codex-audited; Scripts 46/52/53): per-comparison HC 0.00014->0.000018, per-signature HC 0.0116, per-document HC+MC 0.34->0.1905; KDE crossover 0.837 retained corpus-wide. - Reposition as an operator-tunable, semi-automated screening/triage framework (title -> "Automated Screening..."): HC = high-specificity operating point; MC band demoted to low-specificity advisory; Firm A = demonstration that the screening surfaces a templated end, audit-quality implications deferred. - Apply codex prose-review fixes: triage-neutral five-way labels, soften mechanism/specificity wording, supersede MC claim-strength, update stale Appendix script references (40b/43/45 -> 46/52/53). - Trim pass: compress Sec. V discussion + Sec. III echoes (27.7k -> 26.8k words); no substantive content removed. - Add analysis scripts 45-53 (firm-year trends; BCD-only ICCR recompute; canonical-sampler locked numbers; Firm-A out-of-sample; BCD regression + cross-firm hit matrix). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-04 19:35:10 +08:00
parent becce857e1
commit 3c7fcc010f
11 changed files with 1225 additions and 184 deletions
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+#!/usr/bin/env python3
+"""Script 50: publication-grade scoped inter-CPA anchor recompute.
+Faithfully reproduces Script 45's any-pair five-way pool simulation
+(max_cos & min_dh over a random same-size inter-CPA pool, excl. same-CPA),
+then reports for scopes ABCD / BCD / BCD+nonBig4:
+  - per-signature HC (D1) and HC+MC (D2) any-pair FAR
+  - per-document HC (D1) and HC+MC (D2) any-pair FAR
+  - per-firm per-document D2
+ABCD is printed first to verify reproduction of published values
+(per-sig HC~0.1102, per-doc D2~0.3375, Firm A~0.62). Read-only.
+"""
+import sqlite3
+from collections import defaultdict
+import numpy as np
+
+DB = '/Volumes/NV2/PDF-Processing/signature-analysis/signature_analysis.db'
+FIRM_A = '勤業眾信聯合'
+BIG4 = ('勤業眾信聯合', '安侯建業聯合', '資誠聯合', '安永聯合')
+ALIAS = {'勤業眾信聯合': 'A', '安侯建業聯合': 'B', '資誠聯合': 'C', '安永聯合': 'D'}
+SEED = 42
+POP = np.array([bin(i).count('1') for i in range(256)], dtype=np.uint8)
+
+
+def wilson(k, n, z=1.96):
+    if n == 0:
+        return (None, None)
+    p = k/n; d = 1+z*z/n
+    c = (p+z*z/(2*n))/d
+    h = z*np.sqrt(p*(1-p)/n+z*z/(4*n*n))/d
+    return (max(0.0, c-h), min(1.0, c+h))
+
+
+def load():
+    conn = sqlite3.connect(f'file:{DB}?mode=ro', uri=True)
+    cur = conn.cursor()
+    cur.execute("""
+        SELECT s.assigned_accountant, a.firm, s.source_pdf,
+               s.feature_vector, s.dhash_vector
+        FROM signatures s JOIN accountants a ON s.assigned_accountant=a.name
+        WHERE s.assigned_accountant IS NOT NULL AND a.firm IS NOT NULL
+          AND s.feature_vector IS NOT NULL AND s.dhash_vector IS NOT NULL""")
+    rows = cur.fetchall()
+    conn.close()
+    return rows
+
+
+def run(rows, keep_fn, label):
+    keep = [r for r in rows if keep_fn(r[1])]
+    n = len(keep)
+    feats = np.stack([np.frombuffer(r[3], np.float32) for r in keep]).astype(np.float32)
+    feats /= np.clip(np.linalg.norm(feats, axis=1, keepdims=True), 1e-9, None)
+    dh = np.stack([np.frombuffer(r[4], np.uint8) for r in keep])
+    cpas = np.array([r[0] for r in keep])
+    firms = np.array([ALIAS.get(r[1], 'NonB4') for r in keep])
+    docs = np.array([r[2] for r in keep])
+    cpa_idx = defaultdict(list)
+    for i, c in enumerate(cpas):
+        cpa_idx[c].append(i)
+    cpa_idx = {c: np.array(v) for c, v in cpa_idx.items()}
+    pool_size = {c: len(v)-1 for c, v in cpa_idx.items()}
+    rng = np.random.default_rng(SEED)
+    max_cos = np.zeros(n, np.float32)
+    min_dh = np.full(n, 64, np.int32)
+    for si in range(n):
+        c = cpas[si]; npool = pool_size[c]
+        if npool <= 0:
+            continue
+        same = cpa_idx[c]
+        draw = rng.integers(0, n, size=npool + same.size + 20)
+        cand = draw[~np.isin(draw, same)][:npool]
+        cosv = feats[cand] @ feats[si]
+        dist = POP[dh[cand] ^ dh[si]].sum(axis=1)
+        max_cos[si] = cosv.max()
+        min_dh[si] = int(dist.min())
+    # any-pair classification
+    hc = (max_cos > 0.95) & (min_dh <= 5)
+    mc = (max_cos > 0.95) & (min_dh > 5) & (min_dh <= 15)
+    d1 = hc
+    d2 = hc | mc
+    print(f'\n===== {label} (n_sig={n:,}) =====')
+    for nm, arr in [('per-sig HC (D1)', d1), ('per-sig HC+MC (D2)', d2)]:
+        k = int(arr.sum()); lo, hi = wilson(k, n)
+        print(f'  {nm}: {k/n:.4f} ({k}/{n}) [{lo:.4f},{hi:.4f}]')
+    # per-document worst-case
+    doc_d1 = defaultdict(bool); doc_d2 = defaultdict(bool); doc_firm = {}
+    for i in range(n):
+        if d1[i]: doc_d1[docs[i]] = True
+        if d2[i]: doc_d2[docs[i]] = True
+        doc_firm.setdefault(docs[i], firms[i])
+        doc_d1.setdefault(docs[i], False); doc_d2.setdefault(docs[i], False)
+    dl = list(doc_d2.keys())
+    nd = len(dl)
+    k1 = sum(doc_d1[d] for d in dl); k2 = sum(doc_d2[d] for d in dl)
+    l1 = wilson(k1, nd); l2 = wilson(k2, nd)
+    print(f'  per-doc HC (D1):   {k1/nd:.4f} ({k1}/{nd}) [{l1[0]:.4f},{l1[1]:.4f}]')
+    print(f'  per-doc HC+MC (D2):{k2/nd:.4f} ({k2}/{nd}) [{l2[0]:.4f},{l2[1]:.4f}]')
+    df = np.array([doc_firm[d] for d in dl])
+    dv = np.array([doc_d2[d] for d in dl])
+    for f in sorted(set(df)):
+        m = df == f
+        print(f'      Firm {f} per-doc D2: {dv[m].sum()/m.sum():.4f} ({int(dv[m].sum())}/{int(m.sum())})')
+
+
+rows = load()
+run(rows, lambda fm: fm in BIG4, 'ABCD (verify vs published: HC~0.110 / D2~0.338 / A~0.62)')
+run(rows, lambda fm: fm in BIG4 and fm != FIRM_A, 'BCD-only')
+run(rows, lambda fm: fm != FIRM_A, 'BCD + non-Big4')